Ovarian Aging [1st ed. 2023] 9811988471, 9789811988479

Table of contents :
Preface
Contents
Editors and Contributors
Contributors
Part I: The Basic Issues of Ovarian Aging
1: Overview of Ovarian Aging: Why Do We Need to Discuss
1.1 Aging and Organ Aging
1.2 The History of Ovarian Aging Research
References
2: The Life Cycle of the Ovary
2.1 The Genesis of the Ovary
2.1.1 Sexual Undifferentiation: Primitive Gonad Formation
2.1.1.1 Genital Crest Formation
The Gonadal Primordium
PCG Formation
The Source
PGC Specialization
PGC Migration
2.1.2 Sexual Differentiation: Gonad Formation
2.1.2.1 Ovary Differentiation
Determining Factors
The Forkhead Box L2 (FOXL2) Gene
WNT4, R-Spondin 1 (RSPO1), and β-Catenin Genes
GATA Binding Proteins 4 and 6 (GATA4, GATA6) and FOG Family Member 2 (FOG2)
2.1.2.2 Oocyte Development
Primordial Follicular Pool Formation
2.1.2.3 Ovarian Development Summary
2.2 Growth and Maturation of the Ovarian Function
2.2.1 Follicles and Their Surroundings
2.2.1.1 The Follicle
Oocytes
Granulosa Cells
Theca Cells
2.2.1.2 The Follicle Surroundings
Steroid-Producing Cells in the Ovary
Ovarian Stroma
Ovarian Surface Epithelium
Ovarian Leukocytes
Ovarian Innervation, Neurotrophic Factors, and Tachykinin
Ovarian Stem Cells
2.2.2 Follicular Growth
2.2.2.1 Oocyte Growth
2.2.2.2 Factors Affecting Follicular Growth
2.2.3 Ovarian Maturation
2.2.3.1 Follicle Maturation
Antral Follicle Formation
Dominant Follicle Recruitment and Selection
Ovulation
Oocyte Maturation
Meiosis Restoration
Cytoplasmic Maturation
2.2.3.2 Follicular Atresia
2.2.3.3 The Formation, Function, and Dissolution of the Corpus Luteum
Formation
Luteolysis
2.3 Ovarian Function Prosperity
2.3.1 Ovarian Hormone Secretions and Pathway Regulations
2.3.1.1 Steroid Hormones
Cytochrome P450
Cholesterol Side-Chain Cleavage Enzyme (scc; P450scc, Encoded by CYP1A1)
17α-Hydroxylase/17,20-Lyase (P450c17, Encoded by CYP17A1)
Aromatase (p450Aro, Encoded by CYP19A1)
11β-Hydroxylase and 11β-Hydroxysteroid Dehydrogenase (p450c11β and p450c11AS, Encoded by CYP11B1 and CYP11B2, Respectively)
Hydroxysteroid Dehydrogenases (HSDs)
3β-Hydroxysteroid Dehydrogenase/Δ5-4-Isomerase/3β-Hydroxysteroid Dehydrogenase (i.e., 3β-HSD/Δ5-4-Isomerase)
17β-Hydroxysteroid Dehydrogenases (17β-HSDs)
17β-HSD1
17β-HSD2
Estrogen
The Reproductive System
Mammary Glands and Secondary Sexual Characteristics
The Hypothalamus and Pituitary Gland
The Metabolic System
Bones
The Cardiovascular System
The Central Nervous System
Skin
The Blood System
Androgen
The Reproductive System
Metabolism
The Vascular System
Progesterone
The Reproductive System
The Hypothalamus and Pituitary Gland
Mammary Glands
Metabolism
The Nervous System
The Respiratory System
The Skin
2.3.1.2 Protein Hormones
IHN
Act
AMH
GnRH
Relaxin and Relaxin-Like Factors
2.3.2 Ovarian Hormone Secretion and Regulation
2.3.2.1 Ovarian Function Regulation
Hypothalamus and Pituitary Gland Regulation of Ovarian Function
2.3.2.2 Gonadotropin Synthesis and Regulation in the Pituitary Gland
LH and FSH
Differential LH and FSH Regulation
Differential Regulation of GnRH on LH and FSH
Autocrine and Paracrine Regulation of Gonadotropins
2.3.2.3 The Feedback System Between the Ovaries and the Hypothalamus and Pituitary Gland
Negative Feedback
Estrogen
Progesterone
IHN A and IHN B
Act and FST
Gonadotropin Release Inhibitor
Positive Feedback
Estrogen
Progesterone
IHN A
Kisspeptin
2.3.2.4 The Influence of Other Endocrine Hormones on Ovarian Function
Adrenal Cortex Hormones
Thyroid Hormones and Ovarian Function
Insulin, Opioid Peptides, and Oxytocin
Insulin
Opioid Peptides
Oxytocin
2.3.2.5 Conclusions
2.4 Ovarian Function Decline
2.5 Ovarian Activity Loss
References
3: The Systemic Effects of Ovarian Aging
3.1 The Reproductive System
3.1.1 Reproductive Function
3.1.1.1 Fertility Decline
Pregnancy Rate Decrease
Miscarriage Rate Increase
Live Birth Rate Decrease
3.1.1.2 Fertility Quality Decline
3.1.2 Endocrine Function
3.1.2.1 Ovarian Hormone Secretion
Estrogen
A. E2
E1
Androgens
Androstenedione
Testosterone
Dehydroepiandrosterone and its Sulfate
Progesterone
AMH
INH
Activin (Act)
3.1.2.2 Local Ovarian Secretion Factors
Insulin-like Growth Factor
Interleukin 33
3.1.3 Reproductive Organ Morphology
3.1.3.1 The Ovaries
Histology
Pathology
Ovarian Interstitial Hyperplasia
Follicular Hyperplasia
Leydig Cell Proliferation in the Hilum of the Ovary
3.1.3.2 The Uterus
The Cervix
The Uterine Body
The Endometrium
3.1.3.3 The Fallopian Tubes
3.1.3.4 The Vagina
3.1.3.5 The Vulva
3.1.4 Menstruation Changes
3.1.4.1 The Menstrual Cycle
A Shortened Menstrual Cycle
A Prolonged Menstrual Cycle
An Irregular Menstrual Cycle
3.1.5 Sexual Function
3.1.6 Conclusions
3.2 The Nervous System
3.2.1 The Pathophysiological Processes of Ovarian Aging on the Nervous System
3.2.1.1 Interactions Between Ovarian Aging and the Nervous System
3.2.1.2 The Effects of Estrogen on the Nervous System
The Expression and Function of Estrogen Receptors (ERs) in the Brain
Neuron Protective Mechanisms
The Effects of Estrogen on Classical Neurotransmitters
Cholinergic System
Dopaminergic System
Serotonin System
The Critical Period Hypothesis: The Effects of Estrogen on Cognition
3.2.1.3 The Effects of an Aged Nervous System on GnRH
Glutamate
Gaba
Kisspeptin
Glial Cell Morphology
3.3 The Motor System
3.3.1 Bone
3.3.1.1 Bone Metabolism
3.3.1.2 Estrogen and Bone Metabolism
The History of Estrogen’s Effect on Bone Metabolism
Estrogen and Bone Cells
Osteoclasts
Osteoblasts
Indirect Regulation of Estrogen on Bone Metabolism
FSH and Bone Metabolism
3.3.1.3 Muscle
3.3.1.4 The Bones and Joints
3.3.2 Conclusions
3.4 The Cardiovascular System
3.4.1 The Cardiovascular System
3.4.1.1 Epidemiology
3.4.1.2 Ovarian Function and CVD
E2
Direct Action on Blood Vessels
Decrease of the Blood Vessel Tension
Protection of the Endothelial Cells
Inhibition of the Vascular Calcification
Effects on the Myocardium
Androgens
Progesterone
3.4.1.3 Ovarian Aging and CVD
Diminished Ovarian Reserve
Premature Ovarian Insufficiency
Turner Syndrome
Menopause
3.4.2 Conclusions
3.5 The Urinary System
3.5.1 Epidemiology
3.5.2 Ovarian Aging and the Gynecological Urinary System
3.5.2.1 The Pathophysiological Processes and Mechanisms of ER, PR, and AR on the Urogenital System
Estrogen and Androgens
ER and UI
ER and POP
PR and Urethral Function
The Effects of Estrogen and Androgens on the Pelvic Floor
3.5.2.2 Ovarian-Related Hormones and Chronic Pain Syndrome
3.5.3 The Clinical Manifestations of Urinary and Pelvic Floor Function with Ovarian Age
3.5.3.1 The Urinary System
3.5.3.2 The Pelvic Floor Function
3.5.4 Summary and Prospects
3.6 The Skin and Microecology
3.6.1 Ovarian Aging and the Skin
3.6.1.1 Introduction
3.6.1.2 Structure and Function Loss
Structural Changes
Functional Changes
Wound Healing
Thermoregulation
Other Functional Changes
3.6.1.3 The Effects of Estrogen
Skin Thinning
Skin Wrinkling
Skin Dryness
Wound Healing
The Inflammatory Phase
The Proliferative Phase
The Remodeling Phase
3.6.1.4 Conclusions
3.6.2 Ovarian Aging and Female Microbiota
3.6.2.1 Vaginal Flora
3.6.2.2 Intestinal Flora
3.6.2.3 Oral Flora
3.6.2.4 Conclusions
References
4: Ovarian Aging Etiology and Risk Factors
4.1 Age-Related Factors
4.2 Genetics
4.2.1 I. Heritability
4.2.2 Nuclear Genome
4.2.2.1 Chromosomal Abnormalities
45,X and 45,X/46,XX Mosaicism
Trisomy X Syndrome (47,XXX)
Structurally Abnormal X or X-Autosome Translocations
4.2.2.2 Telomere Shortening and Decreased Telomerase Activity
4.2.2.3 Submicroscopic Structural Changes: Copy Number Variations (CNV)
4.2.2.4 Genes and Mutations
The Pathogenic Genes Associated with a POF-Inducing Inherited Syndrome
POF Candidate Genes
4.2.2.5 Genetic Polymorphisms
4.3 The Hypothalamus and Pituitary Factors
4.3.1 Neuroendocrine System-Driven Aging
4.3.1.1 Questions
What Defines the Starting Point of Ovarian and Neuroendocrine Aging?
How Are the Relationships at the Molecular, Cellular, and Organ Levels Coordinated?
Would the Ovaries and Other Organs Be an Ideal Experimental Model If They Could Be Independently Studied?
How Do the Thymus and Skin Initiate Ovarian Aging Outside the HPO Axis?
What Other Elements Are Critical Regulators of the Body?
4.3.2 An Introduction to the Neuroendocrine System
4.3.2.1 The Hypothalamus
4.3.2.2 The Pituitary Gland
4.3.2.3 Adenohypophyseal Hormone Regulation
4.3.3 Neuroendocrine System Aging
4.3.3.1 GnRH Regulates Ovarian Aging
4.3.3.2 The Circadian Clock Regulates the Hypothalamic-Pituitary System
The Biological Clock (i.e., Circadian Rhythm)
Influences on the HPO Axis
4.3.3.3 The Extragonadal Effects of FSH
The FSH Mechanism in Osteoporosis
The FSH Mechanism in Cardiovascular Disease
4.3.4 Conclusions
4.4 Social and Psychological Factors
4.4.1 Stress
4.4.1.1 Stress and Ovarian Function
Ovarian Function Decline
Stress Mechanisms
The Hypothalamic Level
The Pituitary Level
The Ovarian Level
4.4.1.2 Glucocorticoids and Ovarian Function
Stress Mechanisms
The Hypothalamic Level
The Pituitary Level
The Ovarian Level
4.4.1.3 Conclusions
4.4.2 Adverse Psychological Factors
4.4.3 Educational Level and Social Status
4.4.4 Conclusions
4.5 Environmental Factors
4.5.1 Air Pollution
4.5.2 EDCs
4.5.2.1 Heavy Metals
Lead
Mercury
Cadmium
4.5.2.2 PAHs
4.5.2.3 PCBs
4.5.2.4 Pesticides
4.5.2.5 BPA
4.5.2.6 PAEs
4.5.2.7 PBs
4.5.2.8 Others
Perfluorinated Compounds (PFCs)
4-Vinylcyclohexene (VCH) and 4-Vinylcyclohexene Double-Loop Oxide (VCD)
Triclosan
4.5.2.9 Conclusions
4.5.3 Occupational Exposure
4.5.3.1 Anesthetic Gas
4.5.3.2 Electromagnetic Radiation
4.5.3.3 Noise
4.5.4 Conclusions
4.6 Behavioral Factors
4.6.1 Smoking
4.6.1.1 Smoking and Menopausal Age
4.6.1.2 Smoking and Ovarian Reserve
Smoking and the Follicle Number
Smoking and AMH
Smoking and Hormone Secretion
4.6.1.3 Conclusions
4.6.2 Alcohol Consumption
4.6.2.1 Alcohol Consumption and Menopausal Age
4.6.2.2 Alcohol Consumption and Ovarian Reserve
Alcohol Consumption and Follicle Number
Alcohol Consumption and AMH
Alcohol Consumption and Hormone Secretion
4.6.2.3 Conclusions
4.6.3 High-Fat Diets and Obesity
4.6.3.1 Obesity and Ovarian Reserve
Decreased Ovarian Reserve
Mechanisms of Diet-Induced Ovarian Reserve Decline
HFD, Obesity, and Ovarian Endocrine Function
4.6.4 Oral Contraceptives
4.6.4.1 Oral Contraceptives and the Biological Menopausal Age
4.6.4.2 Oral Contraceptives and the Ovarian Reserve
4.6.5 Sleep
4.6.6 Conclusions
4.7 Iatrogenic Factors
4.7.1 Chemotherapy
4.7.1.1 High-Risk Chemotherapy Drugs
4.7.1.2 Medium-Risk Chemotherapy Drugs
Platinum Drugs
Anthracyclines
4.7.1.3 Low-Risk Chemotherapy Drugs
Antimetabolites
Vinblastine
4.7.1.4 Other Chemotherapy Drugs
Irinotecan and Etoposide (Topoisomerase Inhibitor)
Arsenic Trioxide (ATO)
4.7.2 Radiotherapy
4.7.3 Surgical Interventions
4.7.3.1 Ovariectomy
Ovarian Mass Removal Surgery
Ovarian Puncture
Hysterectomy
Internal Iliac Artery or Uterine Artery Embolization
Salpingectomy (Fallopian Tube Resection)
4.7.4 Other Iatrogenic Factors
4.7.4.1 The Human Papillomavirus (HPV) Vaccine
4.7.4.2 ART
4.7.4.3 Other Drugs
4.7.5 Conclusions
4.8 Immunological Factors
4.8.1 Abnormal Humoral Immunity
4.8.1.1 AOAs
4.8.1.2 Other Antibodies
4.8.2 Abnormal Cellular Immunity
4.8.2.1 CD4 Cells
4.8.2.2 CD8 Cells
4.8.2.3 The Ratio of CD4 to CD8 Cells
4.8.2.4 Natural Killer (NK) Cells
4.8.2.5 B Cells
4.8.3 Cytokines
4.8.3.1 IFN
4.8.3.2 IL-1
4.8.3.3 IL-6
4.8.3.4 Others
4.8.4 MHC Antigens
4.8.5 The Complement System
4.8.6 Other Autoimmune Diseases
4.8.7 Conclusions
4.9 Infectious Factors
4.9.1 I. MuV
4.9.2 Human Immunodeficiency Virus (HIV)
4.9.3 M. tuberculosis
4.9.4 Pelvic Inflammatory Disease (PID)
4.9.5 Other Risk Factors
4.9.6 Conclusions
4.10 Endocrine Factors
4.10.1 Thyroid Disease
4.10.1.1 Thyroid Hormone and Follicular Development
4.10.1.2 Hyperthyroidism and Ovarian Function
4.10.1.3 Hypothyroidism and Ovarian Function
4.10.2 Metabolic Diseases and Ovarian Aging
4.10.2.1 Diabetes and Ovarian Aging
The Effects of Diabetes on the HPO Axis
Diabetes, Ovarian Cells, and Follicular Development Disorder
Abnormal Insulin Levels and Follicular Development
The Effect of Hyperglycemia on Granulosa Cell Apoptosis
The Effect of Hyperglycemia on the Mitochondrial Function of Oocytes
The Effects of AGEs on Follicular Development
The Effects of Hyperglycemia on the Adenosine Monophosphate (AMP)-Activated Protein Kinase (AMPK) Pathway
4.10.2.2 The Effects of the IGF and Insulin-Like Growth Factor Binding Protein (IGFBP) Systems on Ovarian Aging
The Effects of IGFs on Ovarian Aging
The Role of IGFBP in Ovarian Follicular Dominance, Follicular Atresia, and Granulosa Cell Apoptosis11
4.10.2.3 Metabolic Syndrome (MS)
4.10.3 Abnormal Enzymes and Ovarian Aging
4.10.4 Nutrition and Ovarian Aging
4.10.5 Conclusions
References
5: The Cellular and Molecular Mechanisms of Ovarian Aging
5.1 Primordial Follicle Activation and Follicular Atresia
5.1.1 I. PFA Molecular Mechanisms
5.1.1.1 Inhibitory Factors
Anti-Müllerian Hormone (AMH)
Phosphatase and Tensin Homolog (PTEN)
Forkhead Box O3 (FOXO3a)
Forkhead Box Protein L2 (FOXL2)
Tuberous Sclerosis Proteins (TSC)
Yes-Associated Protein (YAP)
5.1.1.2 Irritating Factors
Kit
PI3K
mTORC1
Insulin
5.1.1.3 Conclusions
5.1.2 Follicular Atresia
5.1.2.1 Apoptosis
Apoptotic Stages
Stage One: Signal Transduction
Stage Two: Gene Activation
Stage Three: Execution
Stage Four: Clearance
5.1.2.2 Apoptosis in Follicular Atresia
The Primordial Follicle Stage
The Preantral Follicle Stage
The Early Antral Follicle Stage
The Pre-ovulation Follicle Stage
The Periovulatory Follicle Stage
5.1.2.3 Autophagy and Follicular Atresia
5.1.3 Conclusions
5.2 Gene Mutations, DNA Damage, and Ovarian Aging
5.2.1 DNA Damage and Ovarian Aging
5.2.1.1 DNA Damage and Oocyte Function
DNA Damage and Oocyte Apoptosis
DNA Damage and the Cell Cycle in Germinal Vesicle (GV) Oocytes
DNA Damage and Primordial Follicle Development
5.2.1.2 DNA Damage and Granulosa Cell Function
5.2.1.3 Factors Influencing DNA Damage
Environmental Factors
Heavy Metals
Environmental Endocrine Disruptors
Medical Factors
A. Chemotherapy Drugs
Radiotherapy
Behavioral Factors
5.2.2 Conclusions
5.3 Epigenetic Modification and Ovarian Aging
5.3.1 DNA Methylation
5.3.1.1 DNA Methylation
5.3.1.2 DNA Methylation and Aging
5.3.1.3 DNA Methylation and Ovarian Aging
5.3.2 Histone Modification and Ovarian Aging
5.3.2.1 An Introduction to Histone Modifications
Histone Modifications
Histone Methylation
Other Histone Modifications
5.3.2.2 Histone Modification and Aging
Histone Acetylation
Histone Methylation
Histone Modifications and Ovarian Aging
Histone Modifications and Genes Related to Ovarian Hormone Synthesis
StAR
LHR
5.3.2.3 ncRNA and Ovarian Aging
miRNA
Follicle Development
Primordial Follicle Activation and Maintenance
Growing Follicle Development and Maturation
Follicular Quality
Granulosa Cell Function
5.3.2.4 LncRNA and Ovarian Aging
5.3.2.5 circRNA and Ovarian Aging
5.3.3 Conclusions
5.4 Telomerases
5.4.1 Introduction
5.4.2 Follicular Development
5.4.3 Ovarian Aging
5.4.3.1 Natural Ovarian Aging
5.4.3.2 POF
5.4.3.3 Telomerase and Sex Hormone Regulation
5.4.4 Influencing Factors
5.4.5 Conclusions
5.4.6 Attachments
5.4.6.1 Milestone Events Related to Telomere and Telomerase [192]
Telomeres
Telomeres and Chromosome Integrity
Immortalization of Cultured Cells
Challenging the Concept of Immortal Cells
The End Replication Problem
Cellular Senescence Hypothesis
Protozoa Telomeres Are Composed of Tandem Repeats
Telomerase
Human Telomeres Shorten with Age
Telomere Binding Protein
Telomere Shortening
Human Telomerase
Telomeres and HeLa Cells
Cancer
hTERT
5.5 Mitochondrial Dysfunction and Ovarian Aging
5.5.1 Mitochondrial Structure and Function
5.5.1.1 Mitochondrial Structure
5.5.1.2 Mitochondrial Function
Energy Transformation: Mitochondrial OXPHOS
Calcium Ion Storage and Maintaining Calcium Homeostasis
OS
Regulating Cell Apoptosis
5.5.2 Mitochondrial Dysfunction and Aging
5.5.2.1 Mitochondrial Function and Aging
5.5.2.2 Mitochondrial Function and Stem Cell Senescence
5.5.3 Mitochondrial Dysfunction and Ovarian Aging
5.5.3.1 Mitochondrial Numbers
5.5.3.2 Mitochondrial Morphology
5.5.3.3 Mitochondrial DNA Copy Number
5.5.3.4 Mitochondrial DNA Deletions and Mutations
5.5.3.5 Cell-Free DNA (cfDNA)
5.5.3.6 Key Mitochondrial Function Genes
5.5.4 Conclusions
5.6 Free Radicals and Ovarian Aging
5.6.1 Free Radicals and the Antioxidant System
5.6.1.1 Free Radicals
5.6.1.2 The Antioxidant System
5.6.2 Free Radicals and Aging
5.6.2.1 Free Radicals and Lifespan
5.6.2.2 Free Radicals Aging Theories
5.6.3 Free Radicals and Ovarian Aging
5.6.3.1 The Physiological Functions of ROS in the Ovaries
ROS Participates in Follicular Development and Oocyte Maturation
ROS Participates in Hormone Synthesis Regulation
ROS Participates in Ovulation
ROS Regulates Luteal Degeneration
5.6.3.2 Free Radical Damage Advances Ovarian Aging
5.6.4 Conclusions
5.7 The Ovarian Microenvironment and Aging
5.7.1 The Ovarian Microenvironment
5.7.1.1 The Ovarian Immune Microenvironment
5.7.1.2 The Ovarian ECM
5.7.1.3 The Ovarian Vascular System
5.7.2 The Ovarian Microenvironment and Ovarian Aging
5.7.2.1 The Immune Microenvironment
Macrophages
Lymphocytes
Immune Cytokines
Age-related secretory phenotypes
5.7.2.2 Ovarian ECM and Ovarian Aging
5.7.2.3 The Ovarian Vascular System and Ovarian Aging
Ovarian Blood Vessels
Ovarian Lymphatic Vessels
5.7.3 Conclusions
5.8 Other Mechanisms of Ovarian Aging
5.8.1 FGSCs and Ovarian Aging
5.8.1.1 FGSCs
5.8.1.2 FGSC Differentiation and Self-Renewal
5.8.1.3 FGSCs and Ovarian Aging
5.8.2 Cellular Senescence and Ovarian Aging
5.8.2.1 Senescent Cells
5.8.2.2 Cellular Senescence and Ovarian Aging
Chemotherapy-Induced Ovarian Injury Protection
Improving the Ovarian Function of Patients with Diminished Ovarian Reserve (DOR)
References
Part II: The Clinical Management of Ovarian Aging
6: Evaluation and Early Warning Systems of Ovarian Aging
6.1 Markers of Ovarian Aging
6.1.1 Age
6.1.2 Menstrual Patterns
6.1.3 Endocrine Markers
6.1.3.1 Static Markers
FSH, FSH/LH Ratio, and E2
FSH
The FSH/LH Ratio
E2
AMH
Inhibin B
Activin
6.1.3.2 Dynamic Markers
CCCT
GAST
EFORT
6.1.4 Imaging Examination
6.1.4.1 Ultrasonic Markers
AFC
OV
Ovarian Stromal Blood Flow
Spectral Doppler Blood Flow Parameters
3D Power Doppler Blood Flow Parameters
6.1.4.2 Other Image Examinations
CT
MRI
6.1.5 Histological Examination
6.1.6 Other Potential Markers
6.1.6.1 Metabolic Markers
6.1.6.2 Dehydroepiandrosterone (DHEA)
6.1.6.3 Insulin-Like Growth Factor-1 (IGF-1)
6.1.7 Summary
6.2 Evaluation of Ovarian Aging
6.2.1 Evaluation of Ovarian Aging
6.2.1.1 DOR
6.2.1.2 POR
6.2.1.3 POI
6.2.1.4 Early Menopause
6.2.2 Individualized Functional Assessment and Ovarian Age
6.2.3 Summary
6.3 Early Warning Systems of Ovarian Aging and Related Diseases
6.3.1 Prediction of Age at Menopause
6.3.1.1 Endocrine Markers
FSH
AMH
6.3.1.2 Ultrasound Markers
6.3.1.3 Other Markers: Genetic Markers
6.3.2 Prediction of Reproductive Potential
6.3.2.1 Age
6.3.2.2 Menstrual Changes
6.3.2.3 Endocrine Markers
FSH
AMH
Inhibin B
E2
The FSH/LH Ratio
6.3.2.4 Ultrasound Markers
6.3.2.5 Other Predictive Indicators
CCCT
GAST
6.3.3 Risk Prediction of Diseases Related to Ovarian Aging
6.3.3.1 Prediction of the Risk of CVD Associated with Ovarian Aging
Age at Menarche
Age at Menopause
Duration of Reproductive Life Span
AMH
E2
Vasomotor Symptoms
6.3.4 Susceptibility of Ovarian Aging (SOA) Test
References
7: Prevention and Management of Ovarian Aging
7.1 Prevention and Treatment Strategies
7.1.1 Popular Science Education
7.1.2 Sociopsychological Support
7.1.3 Lifestyle Interventions
7.1.4 Genetic Counseling
7.1.5 Ovarian Protection against Medical Injuries
7.1.6 Management of Fertility-Related Issues
7.1.7 Hormone Replacement Therapy
7.1.8 The Frontier of Exploration
7.2 Hospital Education and Management of Ovarian Aging
7.2.1 Health Education for Patients
7.2.2 Continuing Medical Education
7.2.3 Ovarian Aging Characteristic Professional Subjects
7.3 Menopause Hormone Therapy
7.3.1 Historical Overview of MHT Applications
7.3.2 Clinical Applications of MHT
7.3.2.1 General Principles for Using MHT
7.3.2.2 MHT Risk-Benefit Assessment
7.3.2.3 Therapeutic Options
7.3.2.4 Implications for Practice and Patient Education
7.3.3 Problems and Future Directions of MHT
7.4 Clinical Management of POI and DOR
7.4.1 Clinical Management of POI
7.4.1.1 General Management of POI
POI and Life Expectancy
POI and Bone Health
POI and Cardiovascular Health
POI and Quality of Life
POI, Sexual Desire, and Genitourinary System Function
Transvaginal Estrogen
Drugs to Improve Libido
Tiburon
Testosterone
Vaginal Lubricants
POI and Nervous System Function
POI and Endocrine System Health
Thyroid Function
Adrenal Function
Diabetes
Management of the Relatives of Patients with POI
7.4.2 Clinical Management of Special Problems with POI
7.4.2.1 Clinical Management of Fertility-Related Problems and POI
POI and Fertility
Oocyte Donation
Fertility Preservation
Pregnancy Management of Patients with POI
7.4.2.2 Particular Problems of Hormone Therapy in Women with POI and Puberty Induction
Particular Problems of Hormone Therapy in Women with POI
Puberty Induction in Patients with POI
Commonly Used Drugs and Programs for Puberty Induction
Dose
Monitoring during Treatment
7.4.3 Clinical Management of DOR
7.4.3.1 General Management of DOR
Management of the Environment, Social Psychology, and Lifestyle
Ovarian Destructive Factors and Management
Ovarian Destructive Factors
Ovarian Surgery
Chemotherapy
Radiotherapy
Management of Destructive Ovarian Factors
Treatment of Related Chronic Diseases
Infectious Factors
Drug Treatment for DOR
Western Medicine
Dehydroepiandrosterone (DHEA)
Growth Hormone (GH)
Aspirin
Heparin
Melatonin
Coenzyme Q10 (CoQ10)
Traditional Chinese Medicine (TCM) Treatment
Physical Therapy
Hormone Therapy
7.4.3.2 Clinical Management of Special Problems with DOR
Clinical Management of DOR Patients with Fertility Requirements
Clinical Management of the Fertility-Related Problems of DOR
Pregnancy Success Rates in Patients with DOR
Natural Pregnancy Success Rate
In Vitro Fertilization Pregnancy Success Rate
Pregnancy Outcomes in Patients with DOR
Abortion Rate
Ectopic Pregnancy
7.5 Other Treatment Strategies
7.5.1 Mitochondrial Transplantation and Ovarian Aging
7.5.1.1 Introduction
7.5.1.2 Development of Mitochondrial Transplantation
7.5.1.3 Classification of Mitochondrial Transplantation
Heterologous Transplantation
Cytoplasmic/Isolated Mitochondrial Transfer
Nuclear Transfer
GVT
ST
PNT
PBT
Autologous Mitochondrial Transplantation
7.5.1.4 Conclusion
7.5.2 In Vitro Activation of Ovaries
7.5.2.1 Background of IVA Development
7.5.2.2 Clinical Application of IVA
7.5.2.3 Potential Application Range of IVA
7.5.3 Ovarian Tissue Transplantation
7.5.3.1 The History of Ovarian Transplantation
7.5.3.2 Ovarian Allogeneic and Autologous Transplantation
Allotransplantation and Autotransplantation
Ovarian Autotransplantation
7.5.3.3 Ovarian Cortical Strips and Whole Ovarian Transplantation
Transplantation of Ovarian Cortical Strips
Whole Ovarian Transplantation
7.5.3.4 Orthotopic and Heterotopic Ovarian Transplantation
Orthotopic Ovarian Transplantation
Heterotopic Ovarian Transplantation
7.5.3.5 Frozen OTT and Fresh OTT
Frozen OTT
Screening Criteria for OTC
Indications for OTC
OTC Process
Fresh Ovarian Transplantation
7.5.3.6 OTT and Follow-Up
Follow-Up after Transplantation
Patient Management before and after Cryopreserved OTT
Safety Concerns
Follicle Loss
Limited and Inaccurate Reports
Heterogeneity Between Reproduction Centers
Summary and Outlook
7.5.4 Stem Cell Therapy
7.5.4.1 Overview
7.5.4.2 History of Research with Stem Cells for the Prevention and Treatment of Aging
7.5.4.3 Status of Research on Various Stem Cells Treating Ovarian Aging/Injury
Mesenchymal Stem Cells
Adult Germ Stem Cells
Embryonic Stem Cells and Induced Pluripotent Stem Cells
Other Types of Stem Cells
7.5.4.4 Opportunities and Challenges of Stem Cell Therapy
Heterogeneity of Stem Cells
Detection Method of the Biological Efficacy of Stem Cells
Safety of Stem Cells
Ethical Issues in Stem Cell Transplantation Therapy
Standardized Management of Stem Cell Therapy
Other Issues
7.5.4.5 Summary and Prospects
References
8: Therapy-Associated Ovarian Damage and its Management Strategies
8.1 Chemotherapy-Associated Ovarian Damage
8.1.1 Cryopreservation and Transplantation of Embryos, Eggs, and the Ovarian Cortex before Chemotherapy
8.1.2 Formulation of Chemotherapy Regimens
8.1.3 Protective Drugs for Ovarian Function Damage Caused by Chemotherapy
8.1.3.1 Gonadotropin-Releasing Hormone Agonist (GNRH-A)
Mechanism of Ovarian Function Protection during Chemotherapy
Clinical Studies on GNRH-A Protection of Ovarian Function during Chemotherapy
GNRH-A Protects Ovarian Function from Chemotherapy
8.1.3.2 Oral Contraceptives
8.1.3.3 Other Small-Molecule Drugs and Plant Extracts
8.1.4 Summary
8.2 Surgery-Associated Ovarian Damage
8.2.1 Preoperative Evaluation of Ovarian Function and Selection of Surgical Methods
8.2.1.1 Preoperative Evaluation of Ovarian Function
8.2.1.2 Selection of Surgical Methods
8.2.1.3 Selection of Fertility Protection Measures
8.2.2 Intraoperative Protection of Ovarian Function
8.2.2.1 Reducing the Loss of Normal Ovarian Tissue
8.2.2.2 Ensuring a Good Ovarian Blood Supply
8.2.2.3 Selecting Reasonable Homeostasis Methods
8.2.3 Postoperative Monitoring, Evaluation, and Measures of Ovarian Function
8.2.3.1 Monitoring Ovarian Reserve/Function
8.2.3.2 Assessment of Fertility
8.2.3.3 Hormone Replacement Therapy
8.2.4 Psychological Support
8.3 Radiotherapy-Associated Ovarian Damage
8.3.1 Ovarian Protection from Pelvic Radiotherapy
8.3.1.1 Ovarian Transposition
Transposition Site
Ovarian Function after Transposition and its Influencing Factors
Complications after Ovarian Transposition
Risk for Tumor Recurrence, Metastasis, and Transplanted Ovarian Cancer after Ovarian Transposition
8.3.1.2 Cryopreservation and Transplantation of Ovarian Tissue
8.3.1.3 Protective Treatment with Drugs
GNRH-A
Gonadotropin-Releasing Hormone Antagonist (GNRH-ANT)
8.3.1.4 Improvement of Radiotherapy Technology
8.3.2 Monitoring Ovarian Function after Radiotherapy and Corresponding Treatment Methods
8.3.2.1 Evaluation of Ovarian Function
8.3.2.2 Clinical Management of Ovarian Function Decline
References
9: Strategies to Maintain Ovarian Function
9.1 Maintaining a Healthy Lifestyle
9.1.1 Sufficient Sleep and Ovarian Aging
9.1.1.1 Sleep Rhythm and Quality Assessment
9.1.1.2 The Influence of Sleep on Health and Ovarian Function
9.1.1.3 Sleep Improvement Methods
9.1.2 Maintaining Moderate Exercise
9.1.2.1 Moderate Exercise and Health
9.1.2.2 Moderate Exercise and Ovarian Function
9.1.3 Drinking Tea
9.1.3.1 Introduction of Tea
9.1.3.2 The Health Benefits of Tea
9.1.3.3 Protection of Female Fertility by Tea
9.1.4 Alcohol Restriction
9.1.4.1 Drinking and Health
9.1.4.2 Moderate Drinking May Delay Menopausal Age
9.1.4.3 Alcohol Consumption and Ovarian Aging
Alcohol Consumption and Follicle Number
Alcohol Consumption and AMH Level
Recommended Alcohol Intake
9.2 Calorie Restriction and a Balanced Diet
9.2.1 Calorie Restriction and Aging
9.2.1.1 Caloric Restriction Models
9.2.1.2 Calorie Restriction and Body Aging
9.2.2 Calorie Restriction and Ovarian Aging
9.2.2.1 CR and Ovarian Reserve
9.2.2.2 CR and Oocyte Quality and Quantity
9.2.2.3 CR
9.2.3 Balanced Diet
9.2.3.1 Carbohydrates
9.2.3.2 Fat
9.2.3.3 Protein
9.2.3.4 Vitamins
Vitamin D and 25-Hydroxyvitamin D [25-(OH)D]
Vitamin A
Vitamin A
β-Cryptoxanthin
Antioxidant Vitamins
9.2.3.5 Microelements
Zinc
Copper
9.2.3.6 Vegetables and Fruits
9.3 Potential Drug Therapies
9.3.1 Antioxidants
9.3.1.1 Vitamins C and E
9.3.1.2 Melatonin
9.3.1.3 N-Acetyl-L-Cysteine
9.3.1.4 Coenzyme Q10
9.3.1.5 Other Small-Molecule Compounds and Plant Extracts
9.3.2 Small-Molecule Compounds and Plant Extracts
9.3.2.1 Small-Molecule Compounds
AS101
Sphingosine-1-Phosphate
The SIRT1 Agonist, SRT1720
Erythropoietin
Selenium Compounds
9.3.2.2 Plant Extracts
Ginsenoside
Lycium Barbarum Polysaccharide
Tanshinone
Puerarin
Allicin
Proanthocyanidin
Quercetin and Other Plant Polyphenols
Curcumin
9.3.3 Hormone Drugs
9.3.3.1 Androgens for the Prevention and Treatment of Ovarian Aging
9.3.3.2 Use of Other Hormone Drugs in Ovarian Aging
Leptin
Growth Hormone
9.3.4 Immunomodulatory Drugs
9.3.4.1 The Role of Immunomodulators in Organ Aging
9.3.4.2 Immunomodulators and Ovarian Aging
9.3.5 Caloric Restriction Mimetic
9.3.5.1 Metformin
9.3.5.2 ω-3 Fatty Acids
9.3.5.3 Resveratrol
9.4 Other Explorations
9.4.1 Artificial Ovaries
9.4.1.1 Development of the Artificial Ovary
Follicles
Ovarian Stromal Cells
Biological Materials
Natural Polymers
Synthetic Polymers
Alternative Sources of Follicles and Oocytes
9.4.1.2 Clinical Applications of Artificial Ovaries
Restoration of Reproductive Function
Restoration of Endocrine Function
9.4.2 Probiotics
9.4.2.1 Probiotics Improve Osteoporosis in Postmenopausal Women
9.4.2.2 Probiotics Improve Metabolic Syndrome in Postmenopausal Women
9.4.2.3 Probiotics Improve Cardiovascular Function in Postmenopausal Women
9.4.3 Telomere Protection
9.4.3.1 Telomere System and Aging
9.4.3.2 Telomere Protection and Ovarian Reserve and Function
9.4.3.3 Summary and Prospects
9.4.4 Gene Therapy
References

Citation preview

Shixuan Wang Editor

Ovarian Aging

123

Ovarian Aging

Shixuan Wang Editor

Ovarian Aging

Editor Shixuan Wang Department of Obstetrics and Gynecology Tongji Hospital, Tongji Medical College Huazhong University of Science and Technology Wuhan, China

ISBN 978-981-19-8847-9    ISBN 978-981-19-8848-6 (eBook) https://doi.org/10.1007/978-981-19-8848-6 Jointly published with People’s Medical Publishing House, PR of China © People’s Medical Publishing House, PR of China 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publishers, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publishers nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publishers remain neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

Humans have entered the era of aging. Aging drives the onset and development of multiple disorders. Ovarian aging is the pacemaker of systemic aging. Why does ovarian function decline? What are the causes and risk factors? How does it happen? Can ovarian aging be delayed or prevented?. Our team has been working on these issues for 17 years. The present book is a synthesis of our research achievements and the latest advances regarding ovarian aging, and to the best of my knowledge, is the first monograph to systematically explain “the rise and fall” of ovarian function. My understanding of aging and ovarian aging stems from concerns about the aging of the population. From April 2002 to August 2004, I had been working on tumor immunology as a postdoctoral fellow under the guidance of Professor Jerry Y. Niederkorn at the University of Texas Southwestern Medical Center at Dallas, Texas, the United States of America. During that period, I often participated in various academic lectures or seminars on campus and learned a thing or two about aging research, for which I gradually developed a strong interest. Since 2006, my research focus has gradually shifted to ovarian aging. A clinical and basic research background in gynecological cancer and tumor immunology enabled me to understand and study ovarian aging from a different perspective. Our research focused on the etiology and mechanism of ovarian aging, as well as early warnings and interventions. One of the most confusing aspects was the lack of reference books on aging and ovarian aging at that time. Therefore, the initial stages of both basic and clinical research were difficult. Through the concerted and persistent efforts of the research team members, we have made a series of interesting and exciting discoveries. We have systematically developed a new theoretical understanding and conception of ovarian aging. Therefore, we compiled the present book. The book is divided into two parts, basic and clinical, with a total of nine chapters. It is written in an order similar to that of a medical textbook and in an interesting, vivid, and easyto-understand style, allowing the readers to freely explore the text. At the beginning of the book, we redefine ovarian aging and its major concepts. The core concept of ovarian aging is that ovarian function diminishes from being sufficient to insufficient, and eventually failed. Ovarian aging is a pathological process that can lead to systemic disorders. The life cycle of the ovary, which is a new term proposed by our team, includes ovarian genesis, development, maturity, recession, and failure. This book systematically interprets the connotations and characteristics of the ovarian life cycle. The ovaries are among the most important organs that secrete hormones that affect the whole body. Therefore, the harmful effects of ovarian aging are systemic. In the past, menopause was the main research interest in this field. We believe that more attention should be paid to the nature and cause of menopause and the decline in ovarian function. Ovarian aging is a central factor in female aging, not only leading to a recession of the ability and quality of reproduction, including infertility, repeated pregnancy loss, and congenital developmental disorders, but also causing menopausal syndrome (known as climacteric synv

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Preface

drome), genitourinary syndrome of menopause, and related diseases (e.g., osteoporosis, cognitive disorders, and cardiovascular diseases). Ovarian aging is an extremely complex process with many possible etiologies and influencing factors that have not been clarified, and there is no universally accepted etiological classification. We summarize the causes of ovarian aging into two broad categories: internal and external. Furthermore, based on the concept of precision medicine, we subclassify the influencing factors into ten categories, named the Tongji classification. The pathophysiological mechanisms, especially the cellular and molecular mechanisms of ovarian aging, have been systematically reviewed and clarified. Proper diagnosis is the cornerstone of management. Chapter 6 focuses on identifying the key parameters and establishing accurate evaluation and early warning systems for ovarian aging. The prediction of two crucial endpoints of ovarian function, menopause and sterilization, was also elucidated in the book. As early as more than 2000  years ago, an ancient Chinese proverb taught us that, “The superior doctor prevents illness. The mediocre doctor attends to impending sickness. The inferior doctor treats actual diseases.” In recent years, the tertiary prevention of chronic diseases has gradually gained popularity. We innovatively propose the philosophy of the tertiary prevention of ovarian aging and put forward our detailed suggestions of its concept, strategies, and methods. Surgery, chemotherapy, or radiotherapy can cause a certain degree of ovarian injury in women with gynecological diseases or malignant tumors, an effect which is called therapyassociated ovarian damage. We have systematically sorted the strategies and methods for avoiding and reducing such damage and proposed many new constructive suggestions. The aim of understanding ovarian aging is to prevent it. Although no effective drugs have yet been used in clinical practice, many methods and drugs have shown encouraging potential and prospects. This is discussed in the last chapter. Over the past 17 years, our team has been supported by research funds from the Ministry of Science and Technology, Ministry of Education, Health Commission of China, Huazhong University of Science and Technology, and Tongji Hospital. We would like to express our deepest gratitude to them. At the same time, we would like to pay special tribute to Professor Ding Ma, Academician of the Chinese Academy of Engineering and Chairman of the Department of Obstetrics and Gynecology of Tongji Hospital. Without his help and support, we could not have achieved what we have done. We would also like to thank Ms. Yuhang Yan, the editor of the Chinese version of Ovarian Aging, for her great efforts and Ms. Winne, our present editor, for her help. The Chinese version of the book, which was published in January 2021 and has been widely welcomed, was the second best seller for the publisher and won first place in the “Good Books in the Medical Field” nationwide. We want to share what we DO and what we THINK with more colleagues and friends, so we have rewritten Ovarian Aging in English. We dedicate this book to all women and to those who strive for women’s health. Wuhan, China

Shixuan Wang

Contents

Part I The Basic Issues of Ovarian Aging 1 Overview  of Ovarian Aging: Why Do We Need to Discuss�������������������������������������   3 Jinjin Zhang and Aiyue Luo 2 The  Life Cycle of the Ovary���������������������������������������������������������������������������������������   7 Lingwei Ma, Wei Shen, and Jinjin Zhang 3 The  Systemic Effects of Ovarian Aging �������������������������������������������������������������������  35 Shuhong Yang, Suzhen Yuan, Xiaofan Zhang, Sheng Wang, Jingyi Wen, Mingfu Wu, and Lu Shen 4 Ovarian  Aging Etiology and Risk Factors ���������������������������������������������������������������  67 Shuhong Yang, Ting Ding, Wenqing Ma, Tong Wu, Milu Li, Wei Yan, Su Zhou, Ya Li, Li Tian, Wenwen Wang, and Yong Tian 5 The  Cellular and Molecular Mechanisms of Ovarian Aging ��������������������������������� 119 Tong Wu, Fangfang Fu, Jing Cheng, Xiang Li, Su Zhou, Yueyue Xi, Meng Wu, and Dingfu Du Part II The Clinical Management of Ovarian Aging 6 Evaluation  and Early Warning Systems of Ovarian Aging������������������������������������� 173 Ting Ding, Jingjing Jiang, Yan Zhang, Li Fang, Jun Dai, Yueyue Gao, Xiaofang Du, Jingyi Wen, and Yan Li 7 Prevention  and Management of Ovarian Aging������������������������������������������������������� 199 Jinjin Zhang, Minli Zhang, Aiyue Luo, Shuhong Yang, Lu Shen, Man Wang, Tong Wu, and Zhiyong Lu 8 Therapy-Associated  Ovarian Damage and its Management Strategies ��������������� 239 Jia Wei, Ronghua Liu, Rong Liu, and Tao Xiang 9 Strategies  to Maintain Ovarian Function����������������������������������������������������������������� 253 Jia Wei, Shuangmei Ye, Qian Chen, Milu Li, Weicheng Tang, Jinjin Zhang, Huan Lu, Yueyue Xi, Mingfu Wu, Ming Yuan, Dingfu Du, Jingyi Wen, and Yan Zhang

vii

Editors and Contributors

Associate Editors Aiyue  Luo Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Jia Wei  Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Shuhong Yang  Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Jinjin  Zhang  Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

Contributors Qian  Chen Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Jing  Cheng Department of Obstetrics and Gynecology, Zhongnan hospital of Wuhan University, Wuhan, China Jun Dai  Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Ting  Ding Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Dingfu  Du Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Xiaofang  Du  Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Li Fang  Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Fangfang  Fu  Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Jingjing Jiang  Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Milu Li  Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

ix

x

Xiang Li  Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Ya Li  Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Rong  Liu Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Ronghua  Liu  Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Huan Lu  Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Zhiyong  Lu Center for Reproductive Medicine, Hainan women and Children’s Medical Center, Hainan, China Lingwei  Ma Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Wenqing  Ma  Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Lu Shen  Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Wei  Shen Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Weicheng Tang  Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Li Tian  Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Yong Tian  Department of Obstetrics and Gynecology, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi, China Man  Wang Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Sheng  Wang Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Wenwen Wang  Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Jingyi  Wen Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Meng  Wu Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Mingfu  Wu Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Tong Wu  Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Yueyue  Xi Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

Editors and Contributors

Editors and Contributors

xi

Yueyue  Gao Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Tao  Xiang Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Yan Li  Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Wei Yan  Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Shuangmei Ye  Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Ming  Yuan Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Suzhen  Yuan  Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Minli  Zhang Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Xiaofan  Zhang Department of Psychiatry, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Yan  Zhang Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China Su Zhou  Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

Part I The Basic Issues of Ovarian Aging

1

Overview of Ovarian Aging: Why Do We Need to Discuss Jinjin Zhang and Aiyue Luo

1.1 Aging and Organ Aging

This paradigm shift began in 1939 when a study showed that caloric restriction can extend the lives of rodents [5]. Jinjin Zhang Remarkably, a caloric restriction not only extended the overall lifespan but also delayed the onset of age-related diseases. Globally, over the past 100  years, the use of vaccinations, Thus, the concept of a “healthy lifestyle” was proposed. For disinfectants, and antibiotics has dramatically reduced the some time, the possibility of aging interventions encouraged number of deaths due to infectious diseases, increasing life a flurry of related studies, sparking the era of research on expectancy. As the quality of life improved over time, pre- aging. ventive health measures, such as balanced nutrition, moderResearchers attempting to unveil the mystery of aging ate exercise, and less smoking, gradually became the primary have identified many genes and essential regulatory pathreasons for extended life expectancies [1]. New data pub- ways. In 1952, a study on aging and reproduction in fruit lished in The Lancet indicates that life expectancy has flies demonstrated that lifespan was related to reproduction increased dramatically worldwide [2] and population aging timing and that this difference was inheritable [6]. With time, is now a feature of modern society. The incidence of age-­ people have realized that genes can determine longevity. A related diseases, such as cancer, stroke, heart failure, 1988 study on nematodes showed that a single gene, AGE-1, Alzheimer’s disease, and metabolic diseases, has increased determined an individual’s lifespan [7]. Since then, scientists significantly and has become a major social, health, and eco- have discovered other genes that help determine lifespan, nomic challenge [3, 4]. subsequently establishing the Ageing Gene Database Age-related diseases are the biggest threat to human soci- (GenAge, https://genomics.senescence.info/genes/index. ety in the twenty-first century. With progressions in modern html). However, people were not satisfied with simply findmedicine and the accumulation of aging research, our under- ing longevity genes and identifying aging phenotypes. There standing of nature and ourselves is also changing. Aging, was also a desire to understand the genetic pathways that sickness, and death are considered powers of nature. determine aging phenotypes; clarifying the mechanisms by However, as human civilization evolved, the relationship which these genes regulate aging lays a solid foundation for between human beings and aging has also evolved from pas- subsequent aging interventions. In 1993, researchers discovsive acceptance to active engagement. There have been con- ered that the daf-2 gene, encoding insulin-like receptors, siderable changes to our understanding of aging; we initially nearly doubled the lifespan of Caenorhabditis elegans [8]. thought of aging as a natural physiological phenomenon. Other studies showed that inhibiting the insulin signaling Today, we know that genes producing specific genetic traits pathway prolonged the lifespan of nematodes, fruit flies, and control life processes and regulating the expression of genes mice [9]. The above studies suggest the conservatism of and pathways related to aging can slow the aging process and heredity, which makes it feasible to intervene in human prolong life. Presently, people are more inclined to think that aging, thereby informing the link between humans and aging. aging is a pathological process susceptible to interventions. At the same time, the genes encoding TOR1 and TOR2, rapamycin targets identified from yeast mutants with cell cycle-blocking properties, also have a mammalian homologous gene, MTOR [10]. The conserved nutrient-sensing TOR J. Zhang · A. Luo (*) and insulin signaling pathways also provide sufficient eviDepartment of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, dence supporting the hypothesis that caloric restriction Wuhan, China extends human lifespan [11]. The key protein for caloric e-mail: [email protected]

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restriction to extend the lifespan of yeast, Sir2, was also found in humans and mice. Follow-up studies demonstrated that seven sirtuin proteins expressed in humans protect against age-related diseases, increasing healthy life expectancy. The possibility of extending a healthy life by preventing age-related diseases is exciting. Aging is an extremely complex process influenced and regulated by many factors, links, and mechanisms, and investigations into aging mechanisms and strategies to delay aging and extend healthy life have been of continued interest. For example, one study found that patients with a longer overall lifespan and healthy longevity had increased nicotinamide adenine dinucleotide (NAD+) levels, suggesting that NAD+ plays a protective role in aging [12]. The theory of free radical aging based on mitochondria and oxidative stress has also been significantly related to lifespan and aging but remains only partially verified. Studies regarding telomerase shortening and senescent cell accumulation during the aging process are being performed in full swing; animal research and clinical trials are currently investigating senolytics, which is the process of clearing senescent cells to prevent organ aging and prolong life [13]. Simultaneously, research on various immune and inflammatory factors associated with age-related diseases has also prompted the exploration of immune and inflammatory aging [14]. The accumulation of unstable proteins during aging and the consequent protein homeostasis imbalance have also become a hot topic. Overall, aging is an inherent, universal, multifactorial, and gradual process characterized by the degradation and gradual loss of organ functions, ultimately leading to an increased risk of death. The complex network of senescence is regulated by genomic stability, nutrient perception, epigenetic changes, telomere depletion, protein homeostasis, cell senescence, mitochondrial function, and free radicals [15]. These regulatory modes can damage cells and tissues through their respective molecular pathways, leading to the senescence of multiple organs and shortening the overall lifespan. For decades, geriatrics research strategies have aimed to reduce the incidence and mortality of diseases related to organ aging, and this view has also been adopted in the healthcare sector and health-related research. Healthy life expectancy refers to the number of years at a given age that a person can expect to live with good health without being affected by disease, death, or dysfunction. Extending a healthy lifespan is important for achieving an “optimal” lifespan, defined as a long life with a satisfactory level of health and quality of life [16]. Previous research aimed at extending the human lifespan raised concerns that such efforts could lead to a further increase in the elderly population, thus resulting in a high prevalence of chronic age-­ related organ diseases, which may have the opposite effect. However, experimental data consistently shows associations

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between increased longevity and delayed onset or a reduced incidence of age-related diseases [17]. Animal studies have shown that centenarians have an extremely long lifespan and are often free of chronic diseases and disabilities until a very advanced age [18]. An increasing number of people hypothesize that aging is a pathological process. Therefore, the ideal way to prevent and treat aging organs and related diseases is by prolonging a healthy lifespan, in other words, delaying aging. Organ senescence has become increasingly important as patients with organ senescence are living longer and requiring treatment. By regulating the pathways related to aging, researchers can discover or design drugs and measures to intervene in aging and prevent organ aging, thereby prolonging healthy life. A series of drugs, including metformin, rapamycin analogs, senescent cell scavenging agents, sirtuin activators, and NAD+ precursors, have been used in preclinical studies and clinical trials. Calorie restriction and moderate exercise are also important measures to prevent aging of the body and organs and combat agerelated diseases [6]. The ovary is the female reproductive organ and concurrently maintains endocrine homeostasis. In ovarian aging, the number and quality of ovarian follicles decrease, leading to a decline in female fertility and reproductive quality, which is an important concern for the health of oneself and the next generation. Ovarian senescence occurs more rapidly than other major organs, meaning that constant yet gradual sex and stage characteristic changes take place. Fertility in women gradually decreases with age, and the decline becomes more pronounced after the age of 35 years old. The average age of initiation of menopause is 51 years old [19]. Thus, as women live longer, an increasing number live nearly one-third of their lives after menopause [20]. The current statistics indicate there are 130 million perimenopausal women in China and this number will exceed 280 million in China and 1.2 billion worldwide by 2030. Moreover, some women will experience ovarian function failure before age 40 for several reasons, such as genetic, behavioral, psychological, and immunological reasons, or because of other diseases, especially malignant tumors and subsequent treatment. These unique scenarios cause different degrees of ovarian damage and decreased function, inducing early-onset ovarian dysfunction and premature ovarian function (hereafter referred to as ovarian premature aging). The ability to delay ovarian aging and prevent subsequent multi-organ aging has become an urgent need in modern society. Menopause is characterized by ovarian senescence. After menopause, ovarian function loss and endocrine imbalances lead to or aggravate multi-organ and multi-system dysfunction [21, 22]. Specifically, the incidence of cardiovascular disease, osteoporosis, senile dementia, obesity, cancer, diabetes, and other diseases increases with the onset of menopause. These results indicate that ovarian aging is not a

1  Overview of Ovarian Aging: Why Do We Need to Discuss

natural physiological process as one gets older but a pathological process and disease state with a wide range of effects on other organs. As such, ovarian aging has been referred to as the pacemaker of female body aging and is a crucial factor influencing the aging of multiple organs and systems of the human body [22]. Ovarian aging initiates a series of events that seriously affect a woman’s quality of life, health level, life expectancy, family happiness index, and social stability. Because the number of perimenopausal women is expected to increase worldwide, delaying ovarian aging and preventing subsequent multi-organ aging are paramount in an aging society. This book covers all research ranges from bench to bedside of ovarian aging, from the level of systems and organs to tissues, cells, and molecules. Furthermore, we have systematically elaborated on the causes and the factors that influence ovarian aging, the processes related to the occurrence and development of ovarian aging, and the internal mechanisms. We also introduce an ovarian function assessment system as well as current and novel early warning, prevention, and treatment strategies. This book discusses concepts and standards related to ovarian aging, focusing on current research advances. We hope this book lays a theoretical foundation for readers to become familiar with and understand ovarian aging, serves as a practical guide, and inspires new research directions.

1.2 The History of Ovarian Aging Research Aiyue Luo Ovarian aging occurs early and progresses rapidly. Generally, ovarian function declines sharply starting at 35 years of age. A substantial amount of research has focused on ovarian aging, although some studies did not mention the concept directly, discussing menopause, menopausal syndrome, and premature ovarian failure instead. However, all these conditions are related to ovarian aging. More than 2000 years ago, traditional medicine in China discussed periodic changes in female reproductive function using seven interval periods. Usually, menarche, the second of the seven periods, occurred at approximately 14 years old and indicated reproductive system development. Menopause, the seventh period, occurred at approximately 49 years old, indicating that the female body had aged and lost fertility. Statistical research on the relationship between a woman’s age and fertility was performed as early as the seventeenth century. French demographic studies in 1670 and 1789 showed that, on average, women who married between the ages of 20 and 24 years had seven children and 3.7% of them had no births. Women who married between the ages of

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25 and 29 had 5.7 children, and 5.0% had no births. Finally, women who married between the ages of 30 and 34 had four children, and 8.2% had no births, and the average age of a woman’s last birth was 40  years old. In the middle of the twentieth century, the United States conducted a large-scale population survey of people not using birth control, analyzing the age of women and the infertility incidence. At age 30, 7% of females reported infertility, and 11% reported infertility at age 35, 33% at age 40, and 87% at age 45. Although fertility is affected by many factors, the reproductive function of the ovary is the core factor, and ovarian aging leads to fertility decline and infertility (Age and female fertility. https://en.wikipedia.org/wiki/Age_and_female_fertility). High gonadotropin and low estrogen levels in women with premature ovarian failure (POF) started receiving attention as early as 1939 [23]. In 1942, Fuller Albright proposed the concept of primary ovarian insufficiency. In 1946, Korenchevsky and Jones explored the effects of androsterone, estradiol, and thyroid hormone on the artificial premature climacteric of pure gonadal origin produced by ovariectomy in rats and studied the effects on organ weight and the histological structure of the liver and kidneys. In the 1950s, clinicians first documented early menopause or premature climacterium, induced naturally or surgically, and discussed the syndrome and its effects on other organs. In 1950, Atria proposed the concept and presented detailed clinical characteristics of POF, and in 1964, Keettel et  al. reported that permanent ovarian failure could spontaneously occur as early as age 16. In the mid-twentieth century, scientists began studying ovarian histology and blood supply changes during aging, proposing the concept of ovarian senescence, and other studies reported on early menopause and hormone therapy. For example, in the 1980s, some scholars initially explored hormone secretion during ovarian aging and the hypothalamus-­ pituitary regulation. In the following 30 years, many related studies were conducted, such as the 1981–1991 Massachusetts Women’s Health Research, which investigated the subjective feelings of nearly 10,000 perimenopausal women over 10 years. Menopause was defined at the first International Menopause Congress held in France in 1976, and the World Health Organization Expert Group defined the terminology related to menopause in 1994. In 1999, the International Menopause Society proposed that menopause should include perimenopause, and the Stages of Reproductive Aging Workshop (STRAW) in the United States in 2001 proposed a STRAW staging system [24]. Over the next 10  years, the STRAW staging system became the gold standard for describing reproductive aging throughout menopause, and in 2011, the STRAW +10 Staging System was proposed [25]. Ovarian aging directly affects the health of multiple systems in females and initiates the aging process of multiple

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organs. This book explores the risk factors and internal mechanisms of ovarian aging and discusses prevention and treatment methods, ovarian function maintenance, and standardized clinical treatment.

References 1. De Magalhães JP. The scientific quest for lasting youth: prospects for curing aging. Rejuvenation Res. 2014;17(5):458–67. 2. Steel N, Ford J, Newton JN, et al. Changes in health in the countries of the UK and 150 English local authority areas 1990-2016: a systematic analysis for the global burden of disease study 2016. Lancet. 2018;392(10158):1647–61. 3. Beard JR, Bloom DE.  Towards a comprehensive public health response to population ageing. Lancet. 2015;385(9968):658–61. 4. Harper S.  Economic and social implications of aging societies. Science. 2014 Oct;346(6209):587–91. 5. McCay CM, et al. Retarded growth, life span, ultimate body size and age changes in the albino rat after feeding diets restricted in calories. Nutrition Rev. 1975;33(8):241–3. The Journal of Nutrition. Volume 18 July--December, 1939. Pages 1--13 6. Campisi J, et al. From discoveries in ageing research to therapeutics for healthy ageing. Nature. 2019;571(7764):183–92. 7. Friedman DB, Johnson TE.  A mutation in the age-1 gene in Caenorhabditis elegans lengthens life and reduces hermaphrodite fertility. Genetics. 1988;118(1):75–86. 8. Kenyon C, et al. A C. elegans mutant that lives twice as long as wild type. Nature. 1993;366(6454):461–4. 9. Bartke A.  Impact of reduced insulin-like growth factor-1/insulin signaling on aging in mammals: novel findings. Aging Cell. 2008;7(3):285–90. 10. Heitman J, Movva NR, Hall MN. Targets for cell cycle arrest by the immunosuppressant rapamycin in yeast. Science (New York, N.Y.). 1991;253(5022):905–9.

J. Zhang and A. Luo 11. Kaeberlein M, McVey M, Guarente L. The SIR2/3/4 complex and SIR2 alone promote longevity in Saccharomyces cerevisiae by two different mechanisms. Genes Dev. 1999;13(19):2570–80. 12. Belenky P, Bogan KL, Brenner C. NAD+ metabolism in health and disease. Trends Biochem Sci. 2007;32(1):12–9. 13. Kirkland JL, et al. The clinical potential of Senolytic drugs. J Am Geriatr Soc. 2017;65(10):2297–301. 14. Franceschi C, Campisi J.  Chronic inflammation (inflammaging) and its potential contribution to age-associated diseases. J Gerontol A Biol Sci Med Sci. 2014;69(Suppl 1):S4–9. 15. López-Otín C, et  al. The hallmarks of aging. Cell. 2013;153(6):1194–217. 16. Seals DR, Justice JN, LaRocca TJ. Physiological geroscience: targeting function to increase healthspan and achieve optimal longevity. J Physiol. 2016;594(8):2001–24. 17. Fontana L, Partridge L, Longo VD. Extending healthy life span-from yeast to humans. Science. 2010 Apr.;328(5976):321–6. 18. Willcox BJ, Willcox DC, Ferrucci L. Secrets of healthy aging and longevity from exceptional survivors around the globe: lessons from octogenarians to supercentenarians. J Gerontol A Biol Sci Med Sci. 2008;63(11):1181–5. 19. Bernard Rosner GAC. Age at menopause: imputing age at menopause for women with a hysterectomy with application to risk of postmenopausal breast cancer. Ann Epidemiol. 2011;21(6):450–60. 20. Daayana S, Holland CM.  Hormone replacement therapy and the endometrium. Menopause Int. 2009;15(3):134–8. 21. Nelson HD. Menopause. Lancet. 2008;371(9614):760–70. 22. Couzin-Frankel J. Reproductive biology. Faulty DNA repair linked to ovarian aging in mice and humans. Science. 2013;339(6121):749. 23. Heller CG, Heller EJ. Gonadotropic hormone: urine assays of normally cycling, menopausal, castrated, and estrin treated human females. J Clin Invest. 1939;18(2):171–8. 24. Soules MR, et al. Executive summary: stages of reproductive aging workshop (STRAW). Climacteric. 2001;4(4):267–72. 25. Harlow SD, et al. Executive summary of the stages of reproductive aging workshop + 10: addressing the unfinished agenda of staging reproductive aging. Menopause. 2012;19(4):387–95.

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The Life Cycle of the Ovary Lingwei Ma, Wei Shen, and Jinjin Zhang

The ovary is the primary female reproductive endocrine organ, providing gametes for human reproduction and secreting several hormones that support and maintain multiple system functions. The life cycle of the ovary begins with genesis and progresses through growth and maturity, prosperity, decline, and exhaustion, reflected in the reproductive capacity and hormone-level changes. Physiological changes are closely related to the quantity and quality of follicles in the ovary. Starting at the embryonic stage, primordial germ cells (PGCs) migrate to the genital ridge to form the primordial follicle pool, peaking at 6–7 million primordial follicles in the ovary. However, the number of primordial follicles decreases due to spontaneous activation and atresia, and only 1–4 million remain at birth. After birth, primordial follicles continue to be recruited, and some of these continue developing during the first 4 weeks of life. However, the ovaries have almost no physiological function at this stage. After the first 4 weeks, ovarian development continues, albeit slowly. Between 4  weeks of age and early puberty, most follicles (50–70%) degenerate and go through atresia at various stages, but the ovaries gradually mature. At puberty, with the aid of the hypothalamic and pituitary hormones, the ovaries begin to produce mature eggs and secrete hormones and cytokines that support the physiological functions of the mature ovaries and maintain fertility and normal organ functions in the female. However, ovarian function declines with the continuous decrease in the number and quality of follicles, thereby initiating the perimenopausal period, characterized by a rapid decline in the follicle pool. Eventually, the follicle pool is exhausted, followed by loss of function, resulting in menopausal syndrome and systemic multiple organ function damage, thus seriously endangering a woman’s health.

L. Ma · W. Shen (*) · J. Zhang Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China e-mail: [email protected]

2.1 The Genesis of the Ovary Lingwei Ma The genesis of ovarian tissue can be divided into sexually undifferentiated and differentiated periods. In the early stages of embryonic development, there are no distinguishing characteristics of the gonads of either sex. However, female embryos are affected by a series of genes and transcription factors during development, gradually differentiating into ovaries.

2.1.1 Sexual Undifferentiation: Primitive Gonad Formation Sperms fertilize oocytes with type X or Y sex chromosomes, which determine the genetic sex of human embryos. However, there are no morphological or histological male or female gonadal characteristics until week 7 of embryonic development. Moreover, during this period, the reproductive systems are similar, allowing the embryo to differentiate into one of the sexes. Thus, the early stage of reproductive system development is undifferentiated in terms of gender development.

2.1.1.1 Genital Crest Formation Primitive gonads begin with genital ridge formation. The genital ridge comprises a pair of longitudinal ridges derived from the middle zone of the mesoderm and the epithelium and is initially without germ cells. During week 4 of human embryo development, germ cells begin migrating from the endoderm of the yolk sac to the genital crest through the dorsal mesangial of the hindgut, reaching the genital crest during week 6. Simultaneously, the epithelium of the genital ridge proliferates and penetrates the middle mesoderm to form primordial sex cords. Together, germ cells and the primordial gonads become undifferentiated gonads, which develop into either testes or ovaries.

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The Gonadal Primordium During week 4 of embryonic development, the ventral mesoderm gradually moves and separates from the body section, forming a cord-like structure around two longitudinal rows, called the nephrogenic cord. Moreover, the raw kidney cord volume continues to increase and protrudes from the posterior wall of the embryo body into the body cavity, forming a pair of symmetrical longitudinal ridges on both sides of the dorsal aorta, called the urogenital ridge. The urogenital crest develops as a longitudinal groove that appears in the middle of the ridge, dividing it into two parts: a short inner portion that forms the gonadal ridge and a long and thick outer portion that forms the mesonephric ridge. During week 5, the PGCs use amoeboid movement to move along the dorsal mesentery of the hindgut and reach the waist area of the developing embryo, i.e., the area where the genital ridge is to be formed. The epithelium of the body cavity, arranged on the anterior medial side of the Wolff body (i.e., Wolffian body), thickens to form the gonads and provide nutrition for the supporting gonadal cells. If the PGCs are unable to reach the gonads, the gonads fail to develop, leading to common gonadal dysgenesis syndrome. During week 6 of embryonic development, PGCs invade the reproductive ridge and are absorbed into the primary sex cords; thereafter, they proliferate from the epithelium of the body cavity and grow into the underlying mesenchyme, forming the main parts of the sex cords. During this period, the genital ridge is considered an undifferentiated gonad because male and female fetuses have the same gonadal appearance. Undifferentiated gonads are composed of an outer cortex and an inner medulla. For embryos with an XX chromosome complex, the cortex becomes an ovary, and the medulla degenerates. For embryos with an XY chromosome complex, the medulla becomes a testicle, and the cortex degenerates. The sex cords eventually form the male seminiferous tubule and the female medullary cord. The primary sex cords continue to proliferate actively, coinciding in the depths of the mesenchyme and forming a complex mesh, identified as a subepithelial bulge in the body cavity, on the anterior medial body of the Wolff (i.e., Wolffian) body. Gonadal development starts with bipotent gonads, which differentiate into mature testes or ovaries. This process relies on the activation of testis- or ovary-specific pathways with simultaneous and continuous inhibition of the opposite pathways. The transcription factor regulatory network strictly regulates the initiation and maintenance of different pathways. Disrupting these networks can lead to gonadal developmental disorders. For example, in mice, network disruptions cause reversal of the differentiation into male and female sexes. Genes related to gonadal development are roughly divided into three categories: 1) genes that form undifferentiated gonads, such as steroidogenic factor 1 (SF1) and Wilms tumor 1 (WT1); 2) genes that determine male or female dif-

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ferentiation of the sex glands, such as sex-determining region Y (SRY), SRY-box transcription factor 9 (i.e., SOX9), and nuclear receptor subfamily 0 group B member 1 (i.e., DAX1); and 3) genes that promote differentiation of female or male structures, such as SF1, WT1, and Wnt family member 4 (WNT4). SRY is expressed only in the developing gonads, but the expression of the others is not limited to the gonads during development. Bipotential gonad formation is initiated in mice on embryonic day 10.5 (E10.5). Several transcription factors play crucial roles in the formation of undifferentiated bipotent gonads, such as empty spiracles homeobox 2 (i.e., EMX2); chromobox homolog 2 (i.e., CBX2); LIM homeobox 9 (i.e., LHX9); nuclear receptor subfamily 5, group A, member 1 (i.e., NR5A1); SF1; and WT1. Mutations in the genes encoding these transcription factors lead to loss of function in gonadal development, and fibrous tissue replaces the gonadal structures. PCG Formation The Source

The gonads, testes, and ovaries are comprised of PGCs and nutritional support cells (i.e., ovarian and testicular follicular cells). PGCs are the first germ cell populations established during development and are the direct precursors of oocytes and spermatogonia. During the mammalian embryonic gastrulation, complex signals induce the transformation of ectoderm cells to PGCs. PGCs are large, spherical cells (25 to 30  mm in diameter) containing granular cytoplasm. Germ cell development produces pluripotent cells through genetic and epigenetic regulation of genomic functions. Reprogramming occurs during PGC proliferation, including, most importantly, genome-wide demethylation, which erases genomic imprinting [1]. PGC Specialization

Dynamic changes in gene expression occur during PGC specialization. Single-cell PGC analyses identified two highly expressed PGC-specific genes on embryonic day 7.25, Fragilis and Stella. Moreover, PGC transcriptome screening and identification found two key factors related to specialization, B lymphocyte-induced maturation protein 1 (i.e., BLIMP1) and PR/SET domain 14 (i.e., PRDM14) [2]. In summary, PGCs undertake three key biological events: (1) programmable inhibition of the somatic mesoderm, (2) regaining potential pluripotency, and (3) reprogramming. In mice (and all mammals), a series of signal molecules during the early gastrulation period stimulates the germ cell line, prompting differentiation into PGCs. PGC Migration

Human PGCs are available for identification, and eggs near the origin of the allantoic endoderm cells are visible in the

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yellow wall until embryonic day 21. Therefore, the PGCs are a certain distance away from their final position at the genital ridge, thus requiring precise regulation to induce migration to this location. The factors and mechanisms driving PGC migration are current research topics. In recent years, newly developed molecular biology methods and high-throughput sequencing technologies have allowed for better elucidation of these mechanisms.

2.1.2 Sexual Differentiation: Gonad Formation 2.1.2.1 Ovary Differentiation Although the chromosomal sex of an embryo is determined at the time of fertilization, the gonads do not begin to differentiate into testes or ovaries until weeks 6 to 7 of embryo development. The genital ridge region develops slowly in embryos lacking the Y chromosome, and the initially undifferentiated female gonads become recognizable ovaries at approximately week 10 when the characteristics of the cortex become apparent. The X chromosome has genes that promote ovarian development, as do the autosomal genes. During embryonic development, the germ epithelial cells of the body cavity proliferate into the underlying mesenchyme. The gonadal cord extends to the center of the ovary, forming the basic ovarian network (i.e., the rete ovarii). After week 10, the primary sex cords degenerate and are replaced by stroma and blood vessels to become the medulla of the ovary. Again, the surface epithelium proliferates into the deep layer to form new cell cords, which become secondary sex cords or cortex. The cords (i.e., the cortical cords) eventually form the cortex of the ovary. The cortex gradually absorbs PGCs as its volume increases. At approximately week 16, the cortex begins dividing into independent cell clusters, with the oocyte at the center of the cortex, differentiated from PGCs. Oocytes are large cells with clear cytoplasm surrounded by a layer of small and flat follicular cells that differentiate from secondary cortices or sex cord cells. Together, they constitute a primordial follicle; these are distributed in the connective tissue matrix and limited in number. At birth, there are between 0.3 and 2 million primordial follicles. Of the initial primordial follicles, between 400 and 500 mature during puberty and before menopause to produce fertile eggs. Oocytes undergo active mitosis during the embryonic period, and current evidence suggests that oocyte production ceases after birth in humans. Primary oocytes cannot replicate themselves, so the number of primary oocytes in the ovaries remains the same after birth. Primary follicles are primary oocytes surrounded by one to two layers of cuboidal or low columnar follicular cells. Most follicles remain static until puberty. After birth, the surface epithelium of the ovary and the monolayer of peritoneal mesothe-

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lial cells are continuous. Also, the surface epithelial cells are separated from the follicles in the cortex by a thin fibrous capsule called the white membrane. In female embryonic gonads, the primordial gonadal cord is not prominent and extends into the medulla to form the primitive ovarian network. Usually, the ovarian network and primary sex cords degenerate and disappear. During the undifferentiated gonadal period, the sex cords proliferate for the first time and relocate to the central area of the ​​ glands to form medullary cords. Eventually, the connection between the medullary cord, the ovarian network, and the middle kidney degenerates and becomes a Rosenmüller’s body or an accessory ovary. Determining Factors There are far fewer theories and studies on ovarian differentiation and development than the testis. As such, ovary differentiation is not yet fully understood. However, scientists have identified some genes that provide insight into the regulation of ovarian development. The Forkhead Box L2 (FOXL2) Gene

FOXL2 belongs to the forkhead box gene family and encodes an evolutionarily conserved transcription factor. In mice, FOXL2 is one of the earliest upregulated genes during female-specific gonads (i.e., the ovary) development, indicating that this gene plays an essential role in early ovarian differentiation. FOXL2 inhibits the expression of genes related to testicular differentiation from the early period of embryonic gonadal differentiation until adulthood and contributes to maintaining the ovaries after birth. WNT4, R-Spondin 1 (RSPO1), and β-Catenin Genes

Several genes are related to ovarian-specific activation. WNT4 and RSPO1 are important components of the Wnt signaling pathway that contribute to ovarian differentiation and development. WNT4 and RSPO1 activate β-catenin, which in turn regulates the transcription of several types of genes, including genes that contribute to ovarian components, such as WNT4 and follistatin (FST). Regarding RSPO1 and WNT4 expression, on embryonic day 12.5 in gonads with the genotype XX, the WNT/β-catenin signaling pathway activates genes related to sex-specific somatic and germ cells of the ovary, such as Axin 2 (i.e., AXIN2). In gonads with the genotype XY, the WNT/β-catenin signaling pathway gradually downregulates via SRY expression. In vitro studies also demonstrated that SRY interacts at the protein level, antagonizing catenin beta 1 (CTNNB1) targeted to nucleosomes, triggering the degradation and inhibition of CTNNB1-mediated transcriptional activity. The ectopic activation of CTNNB1 in XY somatic cells promotes ovarian development by disrupting the fate of the testis, leading to male-female sex reversal. Thus, the β-catenin signaling pathway is a female sex-­ determining pathway.

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Primordial Follicular Pool Formation The primordial follicle is a single layer of flattened pre-­ Mammalian sex determination requires the interaction granular cells in the ovary surrounding the primary oocyte. between GATA family transcription factors (GATA4 and Moreover, it is the only ovarian reserve and the basic unit of GATA6) and their cofactor, FOG2, and this relationship is a female reproduction. The primordial follicles gradually form driving factor in several developmental processes. Individual between week 16 of embryonic development and 6 months sex determination requires the GATA4-FOG2 complex, but after birth. After the primordial follicle pool has been estabovarian differentiation only requires GATA4, not FOG2. lished, the primordial follicles begin initiation and apoptosis. Losing GATA4 expression in the ovary inhibits granulosa There are two categories of primordial follicle recruitment: cell proliferation and mesenchymal cell recruitment. The initial and periodic. Initial recruitment is gonadotropin-­ number of primordial follicles in the ovarian cortex also independent, and the primordial follicles break away from decreases; thus, the follicles do not develop. the primordial follicle pool influenced by ovarian and other The GATA4-FOG2 complex also inhibits the Dickkopf regulatory factors to transform into primary follicles. Initial Wnt signaling pathway inhibitor 1 (i.e., DKK1) gene, which recruitment has two stages. First, pre-granulosa cells transencodes a secreted protein that inhibits the β-catenin signal- form from flat to cubic shaped, and the granulosa cells proing pathway and is the target of GATA4-FOG2 inhibition in liferate. Second, the oocyte volume increases, and granulosa ovarian development. Studies have demonstrated that tissue-­ cells continue proliferating. Periodic recruitment depends on specific knockout of CTNNB1 (which encodes β-catenin) in cyclical gonadotropin changes during each menstrual cycle, the gonad disrupted female development. Thus, the role of and the follicles that respond during periodic recruitment the GATA family of proteins in sex determination and gradually become secondary follicles. Primordial follicle gonadal differentiation has become an interesting research recruitment is crucial in ovarian biology, directly affecting topic for elucidating the regulation of embryonic sex devel- the number of eggs a woman produces in her lifetime. opment. Notably, several factors have also been identified as antagonistic between male and female pathways during 2.1.2.3 Ovarian Development Summary gonadal differentiation and even after full testicular or ovar- During week 4 of development, the allantois contains many ian development. round PGCs originating from the endoderm, which gradually migrate to the genital crest via the dorsal mesenteric and 2.1.2.2 Oocyte Development enter the primary sex cord around week 6. During week 5, Oocyte formation is called oogenesis. Primary oocytes form the mesonephric ridge forms the gonadal ridge and gradually during the embryonic period, and secondary oocytes form becomes cellular cords, called primary sex cords. If there is during ovulation. In mammals, oogenesis originates from no SRY expression in the somatic cells or PGCs, the undifthe differentiation of PGCs into oocytes in the embryonic ferentiated gonads differentiate toward the ovary. After ovary, which includes several sub-processes. First, there are approximately week 10, the primary sex cord grows into the oocytogenesis (oocyte formation) and ootidogenesis (final deep layer, forming an imperfect ovarian network. oocyte maturation). Folliculogenesis is an independent sub- Subsequently, the primary sex cords and ovarian network process that supports oogenesis, oocytogenesis, and ootido- degenerate and are replaced by blood vessels and stroma, genesis. PGCs undergo mitosis followed by oogonia forming the ovarian medulla. Thereafter, new cell cords form formation; then the oogonia remain in a mitosis state and on the surface of the gonads, called cortical cords, which are enter the first meiotic division. After the leptotene, zygo- shorter and dispersed throughout the cortex. At approxitene, pachytene, and diplotene phases, primary oocytes are mately week 16, the cortical cord breaks to form many isoformed. After entering puberty, during each menstrual cycle, lated cell clusters, called primordial follicles. Embryonic only a small number of primary oocytes are recruited, and germ cell and reproductive organ development are precisely only one mature egg is formed. The primary oocyte com- regulated by several genes, transcription factors, and signalpletes the first meiotic division before ovulation, produces ing pathways, playing a key role in normal ovary develophaploid secondary oocytes, and discharges the first polar ment. Over the past 10  years, with the discovery of new body. The secondary oocyte enters the second meiotic divi- genes that regulate SRY gene expression, scientists have sion and stagnates in the middle stage. Only if fertilization deepened their understanding of gonadal regulation and suboccurs, the meiotic process is completed, thereby discharg- sequent sex differentiation. New levels of regulation that ing the second polar body. The follicle develops from the support gonadal development, such as epigenetic modificaprimordial follicle to the pre-­ovulatory follicle in synchrony tions and non-coding RNAs, have also been discovered. with the occurrence of the egg. However, a comprehensive understanding of the regulatory GATA Binding Proteins 4 and 6 (GATA4, GATA6) and FOG Family Member 2 (FOG2)

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network required to differentiate a fully functional ovary is still lacking. The latest advances in science and technology (e.g., epigenetics and non-coding RNAs) may help develop a more complete understanding of the complex network regulating gonadal differentiation.

2.2 Growth and Maturation of the Ovarian Function Wei Shen Once the ovary forms, it starts slowly growing and migrating from the lower edge of the iliac fossa to the pelvic cavity, gradually increasing in volume. Follicle development accompanies ovarian growth. Primordial follicles are continuously activated, decreasing the total follicle number. However, due to insufficient hypothalamic-pituitary hormone in the early stage, the follicles only develop to the preantral follicle stage. Some may become pre-ovulatory follicles, but they eventually develop atresia. During this period, the ovary has a low level of hormone secretions. Many cellular components make up the ovary, and interactions between these components play important roles in follicle development and maturation and ovarian growth.

2.2.1 Follicles and Their Surroundings 2.2.1.1 The Follicle The follicle is a multicellular functional unit composed of oocytes, granulosa cells, and theca cells and is the life source of the ovary. The quantity and quality of follicles directly affect ovary vitality. The oocytes within the follicle and the surrounding cells transport metabolites through intercellular junctions and regulate one another through a series of cytokines comprised of paracrine and autocrine factors, primarily transforming growth factor-beta (TGF-β) family members. Other ovarian components are equally important for the survival and growth of follicles. The ovarian matrix provides a barrier for growth and regulatory factors produced in growing follicles, and ovarian epithelial cells, immune cells, and neural regulatory factors also contribute. Oocytes Oocytes are rare giant cells with a large nucleus and high transcriptional activity, also known as germinal vesicles. The cytoplasm is rich in ribosomes, maternal messenger RNA (mRNA), and mitochondria that accumulate during follicular development [3]. The structure and quantity between oocyte and somatic cell mitochondria significantly differ. Somatic

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cell mitochondria are spherical, almost without cristae, and each cell contains only one to two mitochondria. However, oocyte mitochondria are short rods with abundant cristae, and the number of mitochondria is much greater than that of somatic cells. Each primordial germ cell contains approximately 10 mitochondria, while each primordial oocyte contains up to 6000. As oocytes grow, the number of mitochondria increases. In fully mature human oocytes, there are between 300,000 and 400,000 mitochondria. During follicular growth, the primary task of the oocyte is to produce and accumulate all the required components for ovulation, fertilization, and preimplantation embryo formation [4]. Oocyte-specific genes play an essential role in follicular maturation, fertilization, and preimplantation development. For example, folliculogenesis-specific BHLH transcription factor (FIGLA), spermatogenesis- and oogenesis-specific basic helix-loop-helix 1 (SOHLH1), and LIM homeobox 8 (i.e., LXH8) expression are essential for follicle formation in nude oocytes, and NOBOX oogenesis homeobox (NOBOX) is the critical gene for primordial follicle recruitment. Deleted in azoospermia-like (DAZL), cytoplasmic polyadenylation element-binding protein 1 (CPEB1), and Y-box binding protein 2 (i.e., YBX2, formerlyMYS2) are DNA or RNA binding proteins regulating mRNA expression in oocytes [5]. Additionally, extracellular matrix and zona pellucida (ZP) formations accompany oocyte development. FIGLA regulates the coordinated expression of these genes during oocyte growth [6–8]. Oocytes also express specific maternal-effect genes that only contribute to preimplantation embryonic development. The proteins encoded by the NLR family pyrin domain-­ containing 5 (i.e., NLRP5), KH domain-containing protein 3 (i.e., KHDC3), oocyte-expressed protein (i.e., OOEP), and TLE family member 6 (i.e., TLE6) genes form the subcortical maternal complex in oocytes [9, 10]. Peptidyl arginine deiminase 6 (i.e., PADI6) also regulate protein synthesis and cell division of preimplantation embryos. Other maternal-­ effect genes, such as the cytoplasmic protein encoded by zygote arrest 1 (i.e., ZAR1), facilitate the transformation from a fertilized egg to a cleaved embryo, and glutamate-­ cysteine ligase modifier subunit (i.e., GCLM) encodes a protein that regulates glutathione synthesis and the redox state during vesicle development. Moreover, nucleoplasmin 2 (i.e., NPM2) affects heterochromosome combination, and histone deacetylation developmental pluripotency-­associated 3 (i.e., DPPA3) plays a role in PGCs and is a maternal-effect gene required for the normal development of preimplantation embryos [3, 10–18]. Selective and specific transformation growth factors regulate follicle development through oocyte-related beta-­ superfamily members, such as growth differentiation factor 9

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(GDF9) and bone morphogenetic protein 15 (BMP15) [19– 21]. GDF9 and BMP15 are synthesized by dimer precursor proteins and then hydrolyzed by other proteins to produce bioactive molecules. The homologous dimers of GDF9 and BMP15 have biological activity, and the heterodimers of these proteins have strong biological activity for regulating the survival of granulocytes, supporting oocyte metabolism by granulocytes, and expanding cumulus cells during maturation [22, 23]. Therefore, a GDF9-BMP15 heterodimer may be the necessary functional ligand for oocyte secretion [24–26]. Granulosa Cells Granulosa cells originate from epithelial cells on the surface of the ovary and are produced in two waves corresponding to the formation of ovarian medullary follicles (wave 1) and ovarian cortical follicles (wave 2). GATA4-expressing cells drive granulosa cell formation, and GATA4, WNT4, RSPO1, β-catenin, and FOXL2 synergistically promote fetal granulosa cell development and regulate follicular formation. The granulosa cells surrounding each oocyte are oligoclonal in origin, and the granulosa cells in mature follicles are derived from the first three to five cells with granulosa cell potential. Granulosa cells are the main sources of estradiol (E2), inhibin (IHN), and activin (Act) in the ovary and provide essential components for oocyte development and maturation. However, they do not directly receive blood supply. The basal layer of the follicle separates the granulosa cells from the blood vessels of the follicle membrane, forming a relative blood barrier and limiting the entry of leukocytes and high molecular substances (such as low-density lipoprotein [LDL]). Therefore, the connection between adjacent granulosa cells and follicular cells is particularly important. Granulosa cells connect through a wide gap junction network, which provides a path for metabolic exchange and the transmission of small molecules between adjacent cells. The number of gap junctions in each granulosa cell increases as the follicles develop, creating a functional network. Granulosa cells also cross the ZP through cytoplasmic synapses, forming gap junctions with oocyte membranes. In antral follicles, granulosa cells transport cyclic guanosine monophosphate (cGMP) through gap junctions to inhibit oocyte meiosis [27]. At the ovulation peak, luteinizing hormone (LH) initiates gap junction closures and interrupts intercellular metabolic coupling. Granulosa cell regulation involves a series of cytokines, including oocyte-derived factors, autocrine and paracrine factors produced by the granulosa cells, theca cell products, and circulating factors from the pituitary gland and other tissues (e.g., fat). Regulation also involves follicle-stimulating hormone (FSH), LH, hypothalamic factors (e.g., gonadotropin-­releasing hormone [GnRH] and kisspeptin), other pituitary hormones (e.g., growth hormone and prolac-

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tin), growth factors (e.g., epidermal growth factor [EGF]),TGF-β family members, insulin-like growth factors (IGFs), metabolic hormones (e.g., insulin), angiotensin, angiogenic factors, cytokines (e.g., tumor necrosis factor-­ alpha [TNF-α]), and fat metabolism-related factors (e.g., leptin and adiponectin) [28, 29]. Theca Cells Theca cells originate from fibroblast-like stromal cells in the ovarian stroma and first appear around follicles with two or more layers of granulosa cells. The kit ligand system of GDF9 granulosa cells secreted by oocytes is important for the early development and differentiation of theca cells [30]. Influenced by GDF9, the desert hedgehog signaling molecule (i.e., DHH) and Indian hedgehog signaling molecule (i.e., IHH) proteins secreted by granulosa cells jointly promote theca cell recruitment and differentiation [31]. Like FSH, keratinocyte growth factor (KGF) and hepatocyte growth factor (HGF) stimulate granulosa cells to produce kit ligand, which acts on theca cells to promote the positive feedback expression of KGF and HGF. Oocytes express kit receptors, which are beneficial for providing nutrients for oocyte growth and development. Insulin-like factor 3 (INSL3) produced by theca cells also promotes oocyte maturation. TGF-β family members, produced by granulosa cells, play a key role in the local regulation of follicular androgen synthesis. After ovulation, some theca cells participate in luteal formation; these cells are called small luteal cells and express steroidogenic activity to produce androgen precursors. Granulosa cells transform into larger luteal cells, express genes related to aromatase activity, and transform androgen precursors.

2.2.1.2 The Follicle Surroundings Steroid-Producing Cells in the Ovary Noradrenergic nerves target hypertrophic inner-layer cells in atresia follicles to regulate ovarian steroidogenic activity. Ovarian hilum stromal cells are large, flavin-like cells with a similar structure and function to testicular stromal cells. Like testicular stromal cells, they contain hexagonal stromal cell crystals. Ovarian hilum stromal cells are also closely related to non-myelinated sympathetic nerve fibers, and their endocrine activity is significantly related to steroid hormone secretion during puberty, pregnancy, and perimenopause. Ovarian Stroma The ovarian stroma contains fibroblasts expressing some steroidogenic enzymes. The matrix also contains precursors of androgen-producing cells. Ovarian stromal cells express androgen receptors and can proliferate under androgen stimulation. Therefore, ovarian-derived hyperandrogenemia, for example, polycystic ovary syndrome (PCOS) and androgen-­

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producing ovarian tumors, leads to increased ovarian stromal density. Stromal cells physically separate follicles and the corpus luteum from adjacent structures and secrete several growth factors and growth factor-binding proteins, creating a biological barrier. For example, ovarian stromal cells highly express gremlin and secrete FST, binding and inactivating BMP and Act, respectively. Moreover, ovarian stromal cells secrete IGF-binding proteins and curl-related proteins that, in turn, bind Wnt signaling pathway family members. Ovarian Surface Epithelium The ovarian surface epithelium is a flat cuboidal epithelium derived from mesoderm cells, also called the ovarian mesothelium, mistakenly thought to produce germ cells. Thus, for a period, it was incorrectly named the germinal epithelium. The ovarian surface epithelium in adults is characterized by mucin 1 (i.e., MUC1) gene expression, surface cilia, and apical microvilli. These cells are on the basement membrane, which covers the dense connective tissue layer. The ovarian surface epithelium helps facilitate material exchange with the abdominal cavity and repair ovulation-induced surface defects. During ovulation, the epithelial cells covering the follicles undergo apoptosis, activating the repair process, which includes increasing cell proliferation immediately after ovulation and resynthesizing extracellular matrix components. Pro-inflammatory cytokines may also be involved in the regulation of these processes. Invagination of the ovarian surface epithelium at the ovulation site leads to inclusion body cyst formation. Metaplasia of ovarian surface epithelium in inclusion body cysts and tumorigenesis is possible and closely related to PCOS. Ovarian Leukocytes Macrophages, lymphocytes, and polymorphonuclear granulocytes are involved in different stages of the ovarian life cycle. These cells help maintain normal ovarian function and pathology. For example, lymphocyte infiltration, especially the membranous stromal cell infiltration, is the primary manifestation of autoimmune ovarian dysfunction. Macrophages mainly comprise ovarian stroma, usually appearing near the follicle capillaries. There are few other types of leukocytes in the ovary during the early stages of follicular development, but many leukocytes infiltrate in the early stage of ovulation, which is related to follicular atresia. The number of mast cells gradually increases in the late follicular phase, and histamine release may lead to ovarian hyperemia during ovulation. After ovulation, chemokines, eosinophils, and T lymphocytes are recruited and migrate to the corpus luteum. Invasion and subsequent activation of these cells occur before luteal degeneration. Activated T cells produce chemokines that attract and activate macrophages. Dark stellate K lymphocytes scattered between the luteal cells are considered macrophages, as they regulate luteal cell function

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through direct cell-to-cell contact and the production of growth factors and cytokines. Regulatory T cells, the population of lymphocytes that maintains the tolerance of antigen-­ specific T cells, also play a key role in regulating the corpus luteum. These cells are abundant in the metaphase corpus luteum and work to reduce inflammation and maintain homeostasis. Regulatory T-cell loss occurs during an inflammatory reaction and promotes luteolysis. Thus, T lymphocyte subtypes and macrophage infiltration are essential for preventing luteolysis, which is delayed during pregnancy. Neutrophils, eosinophils, lymphocytes, monocytes, macrophages, and mast cells in the ovary produce a series of cytokines that assist in follicular formation and luteal function, including several members of the interleukin family, TNF-α, interferon-gamma (i.e., IFN-γ), granulocyte-macrophage colony-stimulating factor (i.e., GM-CSF), and macrophage inflammatory protein 1 alpha (i.e., MIP-1α) [32]. Ovarian Innervation, Neurotrophic Factors, and Tachykinin Endogenous and exogenous nerves regulate the ovary. Exogenous nerves primarily consist of sympathetic, sensory, and a few parasympathetic nerves, entering the ovary through the perivascular plexus. Exogenous nerves mainly regulate the ovarian blood supply and the pathological state of pain, such as in ovarian endometriosis and the endocrine function of ovarian cells. Some studies also found significantly enhanced innervations between ovarian membranous stromal cells in patients with PCOS and adrenocortical receptor over activation and the pathogenesis of ovarian hyperandrogenism. Catecholaminergic neurons are endogenous nerves in the human ovary and express tyrosine hydroxylase, the rate-­ limiting enzyme in catecholamine synthesis. Most of these neurons express neurotrophin (NT) receptors. NTs are a class of cytokines that help regulate nerve survival and differentiation. They primarily act on the high-affinity receptor and low-affinity p75 nerve growth factor (NGF) receptor of the neurotrophic receptor tyrosine kinase 1 (Trk) proto-­ oncogene family, regulating early follicle development. The neurotrophin-4 (i.e., NTN4) receptor is also known as NT5 or the brain-derived neurotrophic factor (BDNF), and its lack of expression is related to decreased primordial follicle formation [33, 34]. Current studies suggest that the ovarian neurotrophic factor system may play an important role in early follicular development and oocyte maturation. Human fetal ovaries also express NTs and their receptors, and TrkB receptors are present in the germ cell matrix. The p75NGF receptor, BDNF, and NTs 3, 4, and 5 were detected in the follicular fluid obtained from women receiving ovulation induction. Human granulosa cells also express tachykinins, including substance P, heme-1, and the truncated receptors NTK1R-Tr and NTK2R [35].

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Ovarian Stem Cells Embryonic stem cells are pluripotent and can produce oocyte-like structures in vitro, suggesting that ovarian stem cells may produce germ cells in adulthood. Although still controversial, studies demonstrated that isolating a small number of ovarian germ stem cells from mouse and human ovaries by flow cytometry was possible with the DEAD-box helicase 4 (DDX4) N-terminal antibody [36]. This result highlights the potential to divide and form oocyte-like structures based on morphology and oocyte-specific marker expression (including DDX4 and LIM homeobox 8 [i.e., LHX8]). These oocyte-like cells isolated from mouse ovaries can develop into oocytes capable of undergoing fertilization and embryo development in  vitro and in  vivo, producing viable offspring. Human ovarian cortical stem cells isolated from the human ovarian cortex have similar phenotypes. Existing studies investigated the effect of ovarian stem cells on the number of follicles in women with healthy ovaries and women with ovarian dysfunction, such as primary ovarian insufficiency or PCOS with different follicle pool sizes, and the results were inconclusive. Different stem cell types can also be induced to develop into germ cell precursors. For example, with the aid of appropriate transcription and growth factors, human embryonic stem cells and induced pluripotent stem cells can be differentiated into PGC-like cells in culture. These cells are currently being used for basic research on the mechanisms of specific differentiation and human germ cell development. Generally, the number of pluripotent stem cells in the adult ovary is very small, but transplantation into ovarian tissue can produce functional oocytes after treatment in vitro. These rare stem cells may not considerably supplement the follicular pool under normal physiological conditions. However, in  vitro, different human stem cell types can be induced to develop into cells very similar to PGCs, which has enormous clinical application potential.

2.2.2 Follicular Growth Follicle growth begins when the follicle leaves the resting state between months 4 and 6 of embryonic development and lasts until ovarian function ceases. During follicle growth, the granular cell shape changes from flat to cubic, granular cells proliferate, the number of oocytes increases, and transparent bands form. The morphological shift in the granular cell shape (from flat to cubic) initiates functional transformations, including some functional mRNA expression (e.g., FST mRNA), and shape changes together with increased proliferation, prompting the oocyte diameter to increase. The first considerable increase in the diameter of human oocytes occurs when 15 granular cells are present on the largest follicle cross-section. The formation of the transparent zone

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accompanies oocyte growth, and oocyte growth positively correlates with an increased follicle diameter until the average diameter of the oocyte reaches 80 μm; then it becomes a secondary follicle. At this point, the secondary follicle has a diameter between 110 and 120 μm and approximately 600 granular cells. At this follicle size, the germinal vesicle of the oocytes reaches its average maximum diameter of 26 to 27  μm. The follicles continue expanding and compressing the surrounding matrix to form the outer membrane, and the former follicle membrane cells migrate to the extracellular matrix prompted by secretion signals produced by the granulocyte, including kit ligand and IGF1. The oocytes also secrete signals promoting the migration and proliferation of theca cells, including fibroblast growth factor 2 (i.e., FGF2), platelet-derived growth factor, GDF9, and BMP15 [37, 38]. Granulocytes express FSH, estrogen, and androgen receptors with the formation of secondary follicles, which are closely linked through gap connections. The follicle membrane formation correlates with the development of the follicles’ blood supply, derived from arteries that form a ring capillary network near the basement membrane of the follicle. Meanwhile, the follicle membrane cells express LH receptor (LHR) and acquire the ability to synthesize steroid hormone. After, the secondary follicles become the anterior antral follicle pool, and FSH-dependent follicles are selected from the anterior antral follicle pool and enter the next development stage. However, most follicles are lost through atresia.

2.2.2.1 Oocyte Growth Active synthesis, secretion, and ZP protein assembly gradually create the mature ZP during oocyte growth. Some organelles gradually increase in number, especially mitochondria; fully mature human oocytes can contain up to approximately 400,000 mitochondria. Conversely, some organelles are gradually lost during oocyte maturation, such as centrioles. Organelle distributions change with the oocyte development, for example, mitochondrial accumulations, the endoplasmic reticulum, and the Golgi complex. Gradually, oocytes begin proceeding through meiosis as part of the growth process, although the specific mechanism is unclear. Oocytes are able to begin meiosis only after the cell attained critical volume. Meiotic oocytes have increased levels of cyclin-dependent kinase 1 (CDK1), cyclin B, and cell division cycle 25 (i.e., CDC25) [39]. The regulatory threshold of these proteins may be a prerequisite for cell cycle recovery. In addition, oocyte development and maturation require a lot of energy, primarily sourced from ATP and energy substrates transported by free granulosa cells through gap junctions and oxidative phosphorylation of pyruvate in the oocytes. Abnormal oxidative phosphorylation of pyruvate in the ovaries of postmenopausal women may lead to non-segregation, false segregation, and aneuploidy of fetal chromosomes [40]. Genomic imprinting reconstruction begins at the growth stage of the

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oocyte, which does not occur until preimplantation embryo development and is regulated by multiple mechanisms, including DNA methylation [41, 42]. Histone methylation also contributes to maternal imprinting. Abnormal expression of maternal or paternal alleles can lead to imprinting reconstruction failure in oocytes or male germ cells. These gene expression changes are associated with several human genetic diseases, such as Beckwith-Wiedemann, Prader-­ Willi, and Angelman syndrome. Moreover, a complete hydatidiform mole can result from complete maternal imprint loss in oocytes [43, 44]. Growing oocytes not only restore meiosis but also gradually acquire embryonic developmental potential by supporting preimplantation embryo development and maturation and by producing a highly stable maternal mRNA library. At the end of oocyte growth and until oocyte maturation or fertilization, the transcription of maternal mRNA library is silenced, and protein translation is inhibited. Transcription silencing requires a special type of communication between oocytes and cumulus granulosa cells through gap junctions and large-scale chromatin structure changes, which are crucial for growing oocytes to resume meiosis and maturation. After transcriptional silencing, storing maternal proteins and mRNAs in pre-ovulatory oocytes supports meiosis recovery and the first division after fertilization. Moreover, it allows the oocyte cytoplasm to remodel sperm DNA to produce enhanced calcium oscillations.

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mature ovarian failure after in vitro activation of ovarian tissue and assisted reproduction. Mice functional genomics indicates that other transcription factors, such as Nobox, Sohlh1, and Sohlh2, contribute to the primordial to primary follicle transition. Cyclin-­ dependent kinase inhibitor 1B (CDKN1B) helps inhibit the early activation of follicles in the primordial oocyte nucleus. Although the preantral follicle development does not depend on gonadotropin, FSH may promote follicle growth [47]. Follicle development and maturation are the basis of ovarian function. It takes approximately 1 year for primordial follicles to mature into dominant follicles. Follicle growth is independent of gonadotropin for approximately 300  days. During this time, somatic and germ cell paracrine and autocrine factors regulate development. Follicles produced during the same period continue to atresia, decreasing until puberty. Gene transcription, post-transcriptional mechanisms (including microRNAs), and post-translational protein modification signaling pathways promote gonadotropin-­ dependent follicle maturation.

2.2.3 Ovarian Maturation

Ovarian function matures with follicle development and regular discharge of mature oocytes. Multiple cellular follicular components are closely linked during this process, interacting through a series of highly complex pathways to regulate 2.2.2.2 Factors Affecting Follicular Growth steroid and protein hormone secretion from the ovaries and A series of activating and inhibiting ovarian cytokines are control oocyte development and maturation. In the lifetime essential for regulating the early stages of follicular growth. of the ovary, only 400 to 500 follicles (less than one-­ Follicular growth and development activators include leuke- thousandth of the recruited follicles) eventually mature and mia inhibitory factor, basic fibroblast growth factor (i.e., ovulate. Atresia occurs for most follicles during maturation. FGF), and kit ligand. Follicular granulosa cells produce kit As a woman’s physiological age increases, the favorable facligand, and kit receptors acting on oocytes and theca cells are tors regulating follicle consumption rate gradually decrease, necessary for initiating follicular and oocyte growth. Anti-­ accelerating follicle depletion. Müllerian hormone (AMH), Activin A, and the chemokine stromal cell-derived factor 1/C-X-C motif chemokine 12 2.2.3.1 Follicle Maturation (i.e., SDF-1/CXCL12) inhibit the early follicular growth through their CXCR4 receptor. The phosphatidylinositol-­3-­ Antral Follicle Formation kinase (PI3K)/protein kinase B (Akt) and mammalian target Influenced by FSH, the follicles develop into antral follicles. of rapamycin (mTOR) signaling pathways are crucial for the Antrum and follicular fluid act as a carrier for nutrient activation of primordial follicles [45, 46]. The Hippo signal- exchange and waste removal in follicles and form a unique ing pathway also plays an essential role in inhibiting growth, environment for the cumulus-oocyte complex to complete determining organ size, and maintaining tissue homeostasis. growth and maturation [48]. Moreover, it contributes to the Some scholars suggest that phosphatase and tensin homo- release of the cumulus-oocyte complex during ovulation. log (i.e., PTEN) inhibitors as well as PI3K and mTOR acti- Antral cavity development is mediated by aquaporins 7, 8, vators should be used to intervene in the Akt, mTOR, and and 9 [49]. Granulosa cells actively transport ions to produce Hippo pathways for treating premature ovarian failure. an osmotic gradient. Additionally, glycosaminoglycan However, existing studies have verified the in vitro activation hydrolysis in the antral cavity increases the osmotic pressure of primordial follicles in human and mouse ovaries using of follicles, supporting water infiltration. Five to 6  days drug interventions for the above pathways. Other studies before ovulation, the follicle expands rapidly and moves to have also documented healthy fetuses from women with pre- the ovary surface owing to granulosa cell proliferation and

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antral cavity accumulation. Accelerated follicle expansion before ovulation can cause pelvic pain in the middle of menstruation. Dominant Follicle Recruitment and Selection The term recruitment refers to when follicles begin to grow separately from the rest of the pool. However, others also use this term to describe a group of antral follicles involved in further growth. Some have suggested calling the first scenario initial recruitment and the latter cyclical recruitment. Selection refers to the process of reducing the number of mature follicles to ovulation quotas suitable for a specific species. This process requires negative selection of subordinate follicles and positive selection of dominant follicles. In the early follicular stage, the morphologies of the dominant follicles and other healthy follicles do not differ. However, dominant follicles are larger, and the granulosa cells have a higher mitotic index than the other healthy follicles. Dominant follicle markers include FSH and significantly high E2 levels in the follicular fluid. FSH is necessary for the anterior antral to antral follicle transition and antral follicle survival [50–52]. Local factors sensitize FSH to the follicles; without this, a decrease in the FSH level would induce apoptosis. At the start of a new menstrual cycle, the FSH level in the late luteal stage increases due to decreased progesterone, E2, and IHN A concentrations, inducing the maturation of follicles to antral follicles. This maturation step requires a threshold FSH concentration to maintain growth, reaching the threshold level during the late luteal stage. Notably, FSH only increases by 10–30% to cross the threshold, indicating that granular cells are highly sensitive for detecting the FSH level in circulation. Without LH, FSH induces follicle growth up to the pre-ovulation size (at least 17  mm). Although the E2 concentration decreases during this period, IHN production induces and restores the normal response of the granulocytes to FSH. FSH may indirectly promote granulocyte proliferation through growth factors produced by somatic cells or oocytes. Aromatase, cytochrome P450 reductase, and 17β-hydroxysteroid dehydrogenase 1 (HSD17B1) mainly affect the granulosa cells. Therefore, without FSH activation, even with aromatic androgen precursors, granular cells cannot produce estrogen. FSH induces LHR expression in the granulosa cells of follicles before ovulation. LHR mRNA has been detected in antrum follicles with a diameter of 3–10 mm, but the highest mRNA level is in the granulosa cells of follicles before ovulation. Conversely, the FSH receptor mRNA level in granulosa cells decreases as the follicle diameter increases. LH and FSH promote follicular maturation together during the late stage. Thus, the dominant follicle completes its maturation cycle when the FSH level drops, and it is prepared to respond to the LH peak before ovulation.

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The term dominate refers to the state of the follicle destined to ovulate and its role in regulating the ovulation quota. Follicle selection for ovulation occurs 5–7  days after the luteal body disappears in the previous cycle. Moreover, the E2 level in the ovarian vein significantly changes on days 5–7 of the menstrual cycle, supporting the appearance of the dominant follicle [40]. Even if the two ovaries are conducive to competitive follicle growth, the dominant follicle makes the internal environments of both ovaries unfavorable, so that the dominant follicle itself might continue to develop. The destruction of dominant follicles occurring in the ovaries of primates on days 8–12 of the menstrual cycle may delay peak gonadotropin secretion by the pituitary gland before ovulation. In contrast, lutectomy (days 16–19 of the menstrual cycle) in the middle of the luteal phase can prompt an early gonadotropin peak. The interval from the ablation of dominant follicles or the corpus luteum to the next ovulation is generally 14 days. Therefore, the 28-day menstrual cycle is the inherent life of the dominant follicle (i.e., the follicular phase) and the corpus luteum (i.e., the luteal phase) rather than the time determined by the brain or pituitary gland. Follicle selection for ovulation occurs in the early stages of the menstrual cycle. No other member of the follicle group has the ability to act as its substitute, and the destruction of the dominant follicle does not induce the gonadotropin peak in the middle of the cycle in time. In the luteal phase, the growth of the next follicle occurs only after the corpus luteum interference is removed by natural (i.e., luteal dissolution) or artificial (i.e., lutectomy) means. Progesterone is the main inhibitor of luteal follicles growth. IHN A, secreted by the corpus luteum, also inhibits FSH, thus inhibiting follicle development and maturation. Additionally, vascular endothelial growth factor inhibition slows follicular maturation by inhibiting the follicle vessel formation, reducing vascular permeability, and limiting the acquisition of key growth factors or hormones necessary for follicular growth. Dominant follicles have significant endocrine characteristics. Follicles less than 8 mm in diameter have a relatively low intra-follicular estrogen-to-androgen ratio, but gradually, this reverses in the middle stage of follicular growth. The selected dominant follicle synthesizes enough E2 for release into blood circulation, with significant asymmetry in the amount of secreted estrogen between the ovaries as early as days 5 to 7 of the menstrual cycle. The local E2 concentration is directly related to the follicle size. When the circulating E2 level reaches the peak concentration (~1 μg/mL), the androstenedione concentration decreases accordingly. Meanwhile, the progesterone and 17α-hydroxyprogesterone concentration increase, resulting in the early luteinization of granulosa cells. The follicular fluid IHN A concentration increases with follicular maturation, while IHN B, Act A, and free FST do not change with follicular size. Therefore, the environment changes from Act-dominated to IHN A-dominated as the follicle matures.

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Although many cells in the human ovary express E2 receptors, the physiological role of estrogen in follicular health, maturation, and luteal function remains unclear. Androgen has many effects on the ovary of primates and may positively and negatively affect the growth and function of follicles in a stage-dependent manner through androgen receptor- and non-receptor-mediated mechanisms. Ovulation The LH concentration peaks and the FSH concentration slightly increases when estrogen is released from the dominant follicle, triggering meiotic recovery in the oocytes, ovulation, and luteal formation. Ovarian stromal cells, leukocytes, and macrophages release matrix-degrading enzymes and cytokines, including interleukin-1β and TNF-α. During a normal menstrual cycle, FSH induces LHR expression on the granulosa cells of follicles before ovulation, enabling the granulosa cells to respond to the LH concentration peak. The LH peak initiates the recovery of oocyte meiosis, ovulation, and subsequent luteinization of granulosa and theca cells. However, this series of biological processes only commences when the LH reaches the threshold concentration. A certain amount of LH stimulates the follicle membrane and androgen production and, together with FSH, promotes follicle maturation. High LH levels promote premature luteinization of follicles that have not yet reached the Graafian stage, further inducing follicular atresia, known as the “LH window” during the follicle maturation process. The LH window provides a theoretical basis for ovulation induction; the LH concentration that stimulates dominant follicle maturation also limits the small follicle growth and inhibits aromatase activity [53]. Therefore, theoretically, LH or human chorionic gonadotropin (hCG) may drive terminal stage follicle maturation and limit multiple ovulations. Granulosa and theca cell responses to signal intensities (i.e., an increase in the amplitude of cyclic adenosine monophosphate [cAMP]) and the activation of supplementary auxiliary signals for cAMP initiate ovulation luteinization. LHR activates cAMP and inositol trisphosphate signaling in a dose-dependent manner, requiring levels 10–100 times greater than the LH level necessary for phospholipase C activation. These signaling pathways affect the expression of non-coding RNAs and are important for coordinating gene expression and the response to the LH peak during ovulation. The mitogen-activated protein kinase (MAPK) signaling pathway partly mediates a series of ovulation-related events. Mice with Mapk1 and Mapk3 double mutations were infertile due to dysfunction related to cumulus expansion, ovulation, luteinization, and meiotic maturation. LHR gene mutation studies found that the normal estrogen secretion, follicle development and maturation, and corpus luteum formation required LH, but theca cell formation did not.

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The LH peak appears in the follicle rupture 38  hours before ovulation. There are many significant changes to the granulosa cells and oocytes before follicle rupture, such as the inhibition of granulosa cell proliferation. The gap junction function is also lost, which decouples the electrophysiological syncytia between granulosa cells and oocytes, as is the expression of genes necessary for granulosa cells to mediate ovulation, such as EGF-like factors, amphoteric regulatory proteins, epigenetic regulatory proteins, progesterone, and β-fibrin. Prostaglandin E2 (i.e., PGE2) and EGF-­ like factors synergistically trigger cumulus cells to form the hyaluronic acid-rich matrix, leading to cumulus expansion. Cumulus expansion is a critical ovulation process mediated by genes required to produce the hyaluronic acid skeleton, hyaluronan synthase 2 (i.e., HAS2), hyaluronan-binding proteoglycan versican, TNF-stimulated gene-6 (TSG-6), and pentraxin 3 (i.e., PTX3) [54]. The matrix supporting cumulus expansion is a meshlike network of hyaluronan chains that bind TSG-6, which transfers to hyaluronan the heavy chains of serum-derived inter-α-trypsin inhibitor, a complex macromolecule consisting of two heavy chains that are covalently bound to chondroitin sulfate and bikunin, a trypsin inhibitor. Pentraxin 3, a pentamer protein, is another component of the tissue hyaluronic acid matrix. Finally, the cumulus forms a cylindrical columnar structure and rises to the surface of the follicle to prepare for the follicle rupture. Follicle rupture is accompanied by a mild (rather than explosive) discharge of the egg and follicular fluid, indicating that the follicular fluid is not under high pressure. In general, ovulation requires the following: (1) the induction of progesterone receptor (PR) expression and progesterone synthesis in the granulosa cells by LH within hours of the ovulation peak; (2) the induction of PTGS2 expression in the granulosa cells by the LH peak before ovulation and synthesis of prostaglandins in the follicle [55, 56]; (3) the induction of the sequential expression of proteins such as EGF-like growth factors, amphiregulin, epithelial regulatory protein, and β-cellular proteins by LH [57]; and (4) follicular rupture [58]. Oocyte Maturation Oocyte development and maturation accompany ovulation, and oocyte maturation includes meiosis recovery and cytoplasm maturation. Once follicle formation occurs, the follicles produce oocyte maturation inhibitors to maintain meiotic arrest, which has been demonstrated in culture; oocytes removed from the internal environment of the follicles spontaneously resumed meiosis. Meiosis inhibition must be mediated by peripheral granulosa cells. Like somatic cells, changes in protein levels and cyclins and cyclin-dependent kinase activities control the oocyte cell cycle. ­Maturation-­promoting factor (MPF) is one protein that induces meiotic recovery and is a heterodimer of two

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proteins: cyclin B and CDK1 [59–61]. MPF exists in mature oocytes, but cAMP and cGMP inhibit its activity before the LH peak during ovulation. Cumulus cells produce cGMP by binding a transmembrane guanylyl cyclase with a C-type natriuretic peptide, natriuretic peptide receptor 2 (i.e., Npr2), which is a ligand from parietal granulosa cells. cGMP continuously enters the oocytes from the cumulus cells through gap junctions, inhibiting oocyte cAMP phosphodiesterase 3A (PDE3A) and preventing a decrease in the oocyte-derived cAMP level. The active receptor, G protein-coupled receptor 3 (GPR3), on the cytoplasm membrane is primarily responsible for producing cAMP in oocytes. GPR3 couples with stimulatory G-protein (GS) to activate adenylate cyclase, resulting in continuous cAMP production [62]. A stable cAMP level in oocytes results from the balance between cAMP production by adenylate cyclase on the cell membrane and cAMP degradation by PDE3A in the cytoplasm. Oocyte-derived cAMP maintains meiotic arrest by stimulating cAMP-dependent protein kinase A (PKA) activity. PKA phosphorylates at least three proteins (WEE1 homolog 2 [i.e., WEE1B], myelin transcription factor 1 [i.e., MYT1], and cell division cycle 25B [i.e., CDC25B]), thereby inhibiting MPF activity and preventing meiotic recovery. However, studies have shown that even if cGMP produced by granulosa cells blocked the PDE3A activity of the oocytes and cAMP (produced by the GS complex) activated PKA rendering MPF inactive, oocyte meiosis could not be restored. Meiosis Restoration The LH peak in the middle of menstruation initiates a series of events in the mature antral follicles, leading to MPF activation and the recovery of the meiotic cell cycle. When the germinal vesicle ruptures from the destruction of the nuclear layer, the oocyte nucleus gradually matures and enters stage I of meiosis. With the cytoplasm exposed, chromatin and the microtubule centers condense and then accumulate at each spindle pole. Stage I is complete after discharge of the first polar body containing half of the oocyte chromosomes. The meiotic cell cycle immediately enters stage II and then arrests. Oocytes are now called secondary oocytes or metaphase II (MII) arrested eggs. During ovulation, meiosis stops at stage MII until the follicle releases the oocyte, fertilization occurs, and the second polar body is discharged. Cytoplasmic Maturation Cytoplasmic maturation also occurs after the LH peak. The morphological changes are less obvious than nuclear maturation but are critical for egg activation and preimplantation embryo development. At the ultrastructural level, the endoplasmic reticulum, mitochondria, and cortical granules move to the oocyte cortex, shifting the organelle distribution. Microtubules and microfilaments mediate organelle move-

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ment and depend on the existence of the cytoplasmic grid. When the mitochondria move to the cortex, they gather around the spindle, but during the polar body release, they move to the spindle poles of the oocyte. Thus, most remain in the oocyte. Golgi complex rupturing may also explain the extensive decline in the ability of mature oocytes to synthesize new proteins. As chromatin moves to the cortex, the oocytes become highly asymmetric. The actin skeleton is altered, with a thickening of cortical actin overlying the metaphase II spindle. This region of the plasma membrane is devoid of microvilli—unlike the rest of the oocyte plasma membrane, which is enriched with microvilli. The absence of microvilli may reduce the possibility of sperm entering the MII spindle region, potentially interfering with normal meiosis progression. Plasma membrane zinc is a basic element that plays a structural and catalytic role in many cellular proteins, such as zinc-finger transcription factors and metalloenzymes. During meiotic maturation, plasma membrane zinc transporters absorb and extensively accumulate zinc; the total zinc content of oocytes increases by about 50%. In MII arrest, the intact germinal vesicle oocytes interfere with zinc-binding protein and F-box protein 43 (i.e., FBXO43, formerly EMI2) functions, resulting in less available zinc in the cells and meiosis recovery, abnormal cortical reorganization, and decreased cell polarity. The mechanism of zinc storage in oocytes has not been elucidated, but it may involve isolation mechanisms in subcellular organelles. At the molecular level, cytoplasmic maturation accompanies the collection of specific resting maternal mRNA, which are translated into proteins. The maternal mRNA molecular mechanism is cytoplasm polyadenylation. The specific nucleotide sequence in the untranslated region of the 3' end of the maternal mRNAs, facilitates the addition of these mRNAs deploy polymerases to facilitate the addition of these mRNAs to the poly(A) tail; CPEB1 and DAZL proteins regulate this process [63]. Polyadenylation leads to the binding of specific maternal mRNA to the polymer and mRNA translation, increasing the protein coding amount. Post-translational modifications of cytoplasmic proteins also occur during oocyte maturation. For example, microtubules acetylate during the transition from meiosis to MII, and interactions between microtubules and the cytoplasmic grid regulate the location and movement of organelles. Phosphorylation and dephosphorylation of cytoplasmic proteins, especially cell cycle regulation proteins, are necessary conditions for cytoplasm maturation.

2.2.3.2 Follicular Atresia Follicular atresia can occur in all stages of follicular development spontaneously or in response to environmental factors or drugs. The absence of essential nutrients (e.g., FSH and IGFs) at critical periods of follicular formation or maturation

2  The Life Cycle of the Ovary

characterizes spontaneous follicular atresia. In human embryonic ovaries, apoptosis is primarily responsible for germ cell clearance. In resting follicles, oocytes and granulosa cells undergo apoptosis, but apoptotic cells do not accumulate. Conversely, in growing follicles, granulosa cells are the first to undergo apoptosis and gradually accumulate apoptotic cells, leading to follicular atresia [64]. Apoptosis plays an important role in controlling follicular dynamics. TNF-α can also regulate the apoptosis signaling pathway and significantly affect the ovarian reserve and follicular atresia. Moreover, the TNF-related apoptosis-­inducing ligand (i.e., TRAIL) is associated with granulosa cell apoptosis and follicular atresia. PI3K and Aktare, the primary anti-apoptotic factors, are functioning by phosphorylating forkhead box O1 (FOXO1) and FOXO3. When phosphorylated, the nucleus excretes FOXO1 and FOXO3. When dephosphorylated, these factors activate pro-apoptotic gene transcription, including Fas ligands and pro-apoptotic proteins of the B-cell lymphoma 2 (i.e., BCL2) family, resulting in the caspase 3, 8, and 9 activations.

2.2.3.3 The Formation, Function, and Dissolution of the Corpus Luteum Formation After ovulation, the ruptured follicles recombine to form the corpus luteum. Immense gene expression changes accompany luteinization, including hundreds of genes in the granulosa cells alone. A prominent feature of reconstituted ruptured follicles is the establishment of an abundant vascular network. Infiltration and proliferation of capillaries and fibroblasts from the surrounding stroma accompany ovulatory hemorrhage associated with follicle rupture. The resulting neovascularization enables large molecular weight molecules circulating in the blood to reach the granulosa and membranous luteal cells. Moreover, these cells secrete products into circulation, such as LDL, which provides the cholesterol substrate for progesterone production. Luteal neovascularization development and progesterone production promote each other. Once the corpus luteum has completely formed, vascular endothelial cells comprise about 50% of all corpus luteum cells. Estrogen metabolites have two-way regulation on angiogenesis in the corpus luteum. These metabolites have angiogenic (e.g., 16-ketoestradiol and 4-hydroxyestrone) and antiangiogenic (e.g., 2-methoxyestrone and 2-­methoxyestradiol) activities. The concentrations of these metabolites are higher in the early and middle luteal phases than in the late luteal phase. Parietal granulosa cells undergo significant morphological changes influenced by the LH peak, called luteinization. Parietal granulosa cells lose their mitotic potential as gene

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expression related to granulosa cell proliferation changes. Specifically, cyclin D2 expression ceases, and cyclin-­ dependent kinase inhibitors 1A and 1B (i.e., CDKN1A, CDKN1B, cell cycle inhibitors) expression increases. Simultaneously, the expression of protein-coding genes involved in progesterone synthesis (including steroidogenic acute regulatory protein [STARD1] and 3b-hydroxysteroid dehydrogenase type 23 [i.e., 3bHSD2]) significantly increases. Human luteal steroidogenic cells, including luteinized granules and membrane cells, are heterogeneous in size and function [65]. Granulosa luteal cells have a higher level of basal progesterone production, which is the site of luteal estrogen synthesis. The luteal cells of the follicle membrane have 17α-hydroxylase/17,20-lyase activity, producing an androgen precursor aromatized by granulosa luteal cells. The luteal cells are also the main hydroxyprogesterone production site. Therefore, the corpus luteum has a two-cell system for estrogen synthesis. Luteal cells in the corpus luteum produce relaxin, which is a hormone hypothesized to promote decidualization of the endometrium, inhibit the contractile activity of the uterine muscle, and assist maternal adaptation to pregnancy. In addition to ovulation induction and luteinization, LH helps maintain luteal function. Long-term withdrawal of LH in various experimental environments almost always leads to luteal degeneration. In the middle and late stages of the human corpus luteum, the secretion pattern of progesterone is consistent with the pulse release pattern of LH, indicating that LH is important for regulating progesterone secretion. Notably, the endocrine function of the corpus luteum can be restored if the normal LH level is reinstated after short-term inhibition. The corpus luteum can survive without dysfunction for 14 days, indicating that the luteinization process triggers a predetermined life cycle that ends according to a predetermined cycle without conception [66, 67]. The human corpus luteum produces 25–50 mg of progesterone daily. The luteal cells respond to progesterone, and as such, they have endocrine and autocrine reproductive functions. PR types A and B exist in the corpus luteum of rhesus monkeys and humans, and PR mRNA expression gradually increases from the early to the middle luteal phases and then decreases as the corpus luteum ages. As the luteal age increases, the ratio of PR A to PR B gradually decreases. Luteolysis The functional lifespan of the corpus luteum in a non-­ fertilization cycle is 14 ± 2 days. The corpus luteum in the non-pregnant state transforms into nonvascular scars, known as the white body. Luteal degeneration, or luteolysis, includes a series of functional (i.e., endocrine changes and decreased progesterone production) and structural (i.e., apoptosis, autophagy, and tissue degeneration) changes. LH withdrawal and the decrease of LHRs do not indicate luteolysis in pri-

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mates. However, the primate corpus luteum response to hCG weakens with decreased receptor expression secondary to LH, and the hCG level decreases. The progesterone production decrease, caused by decreases in signal transduction efficiency in the late luteal phase, is related to protein and mRNA expression decreases of the STARD1 gene. An important feature of human functional luteolysis is a decrease in STARD1 expression. High hCG levels can prevent this decline, maintaining the progesterone production capacity. Finally, corpus luteum degeneration results from apoptosis and autophagy [68].

2.3 Ovarian Function Prosperity Wei Shen and Lingwei Ma Gradually with ovarian function maturation, the ovary periodically ovulates and secretes related hormones that help regulate and maintain systemic multi-organ function during the reproductive stage of life. Hormones secreted by follicles are essential for ovarian function. For example, there are two modes of gonadotropin regulation. First, local autocrine and paracrine factors control the effects of gonadotropin on the ovary. Second, modified gonadotropin secretion (as an endocrine signal) promotes follicular development, induces the pre-ovulation LH peak after follicular maturation, regulates the female reproductive tract, and promotes fertilization, embryo implantation, and early pregnancy. Concurrently, various hormones participate in establishing and maintaining the function of multiple organs. The establishment and regulation of ovarian function involve multiple systems within the ovary and the entire body.

2.3.1 Ovarian Hormone Secretions and Pathway Regulations In addition to producing mature gametes for human reproduction, the ovaries also secrete steroid and protein hormones. Ovarian hormones and cytokines regulate follicle development and maturation through autocrine and paracrine factors and participate in the regulation and maintenance of multiple organ functions.

2.3.1.1 Steroid Hormones Steroids are lipid compounds formed by the collective action of several enzymes to modify cholesterol by removing the side chain, adjusting the position of the alkene bond, and adding hydroxyl. Steroid hormones, named for their similar structure with cholesterol, have a basic cyclopentane structure. Ovarian steroids mainly include estrogen, progesterone, and androgen. In brief, acute regulatory proteins (e.g., ste-

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roidogenic acute regulatory protein [i.e., STAR] or STARD1) transfer cholesterol absorbed by the cells from the outer mitochondrial membrane to the relatively deficient inner membrane by cytochrome P450, heme protein mixed-­ function oxidase, and hydroxysteroid dehydrogenase to synthesize steroid hormones. A series of complex chemical modifications by reductase form cholesterol. Cytochrome P450 Cytochrome P450 catalyzes change in the steroidal framework, side-chain cleavage, hydroxylation, and aromatization, completing the catalytic process in the presence of oxygen molecules and equivalent reducing substances. The abbreviation CYP represents each cytochrome P450 gene family member that produces steroids, followed by the position of the carbon atom at the site of action. Cholesterol Side-Chain Cleavage Enzyme (scc; P450scc, Encoded by CYP1A1)

Cholesterol side-chain cleavage occurs via three catalytic cycles; the first two cycles introduce hydroxyl groups at positions C-22 and C-20, respectively, and the third cycle cleaves the side chain between the carbons. 17α-Hydroxylase/17,20-Lyase (P450c17, Encoded by CYP17A1)

This enzyme, identified by enzyme digestion, primarily catalyzes the hydroxylation of pregnenolone and progesterone at C-17 and converts pregnenolone into a C-19 steroid. Aromatase (p450Aro, Encoded by CYP19A1)

Aromatase is an endoplasmic reticulum enzyme that catalyzes three consecutive hydroxylation reactions of the C-19 substrate by three nicotinamide adenine dinucleotide phosphate (NADPH) and three oxygen molecules to form a C-18 steroid molecule containing phenol a ring. 11β-Hydroxylase and 11β-Hydroxysteroid Dehydrogenase (p450c11β and p450c11AS, Encoded by CYP11B1 and CYP11B2, Respectively)

The proteins encoding these isoenzymes differ by 33 amino acid residues. Both have 11β, but p450c11AS can catalyze the aldosterone production from C-18 with assistance from redox substrates. CYP11B is primarily expressed in the ovary and participates in11-ketotestosterone production in theca cells. Hydroxysteroid Dehydrogenases (HSDs) Pyrimidine nucleotides are used as cofactors to reduce ketone or hydroxyl groups. HSDs not only participate in steroid synthesis in steroid-producing cells but regulate ­bioactive hormone levels in the target tissues together with reductase, steroid sulfotransferase, and steroid sulfatase.

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They are also the main determinant of cellular responses to endogenous steroids and steroidal drugs.

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to the same steroid hormone in similar ways. For example, estrogen and progesterone enter cells through the cell membrane by diffusion and bind with receptor proteins to form 3β-Hydroxysteroid Dehydrogenase/Δ5-4-Isomerase/3β-­ hormone-receptor complexes. The receptor conformation Hydroxysteroid Dehydrogenase (i.e., 3β-HSD/ changes, and the activated hormone receptor complex forms Δ5-4-Isomerase) dimers and enters the nucleus and then combines with a speNicotinamide adenine dinucleotide (NAD+) is a membrane-­ cific DNA site upstream of the target gene, called hormone binding enzyme located in the endoplasmic reticulum and response elements. This action triggers RNA polymerase mitochondria. First, it catalyzes 3β-hydroxyl dehydrogena- activation, gene transcription, and specific mRNA generation, Δ5-alkene bond isomerization, and the Δ4-keto struc- tion, which are released into the cytoplasm and translated ture. Thereafter, pregnenolone is converted into progesterone, into proteins. Finally, shearing transforms the gene-encoded 17α-hydroxypregnenolone is converted into proteins into functional proteins with biological effects. 17α-hydroxyprogesterone, and dehydroepiandrosterone In women, steroid metabolism mainly occurs in the liver, (i.e., DHEA) is converted into androstenedione. and the metabolic rate is inversely proportional to the binding capacity of sex hormone-binding globulin (SHBG). Steroid 17β-Hydroxysteroid Dehydrogenases (17β-HSDs) uptake by the liver and other organs is affected by the affinity 17β-HSDs are a class of enzymes with specific biosynthesis of hormones with steroid-binding proteins and albumin in and catabolism important for the synthesis and metabolism plasma. Combining steroids with SHBG and cortisol binding of steroids. To date, there are 14 17β-HSDs, named 1 to 14 globin reduces their peripheral metabolism. The affinity of based on their discovery order. 17β-HSDs are classified as free steroids and albumin is relatively reduced, and sulfateNAD+-dependent oxidases (types 2, 4, 6, 8, 9, 10, 11, and bound steroids and albumin are closely bound. Thus, removal 14) and NADPH-related reductases (types 1, 3, 5, and 7) from the blood is very slow. As such, the steroid sulfate conbased on their function [69]. centration in blood is generally several times higher than that of their corresponding non-binding forms. Conversely, the 17β-HSD1 binding ability of steroid glucuronide and albumin is weak 17β-HSD1, also known as estrogen 17β-HSD, produces E2 and quickly removed. The steroids secreted by the ovary more efficiently by reducing the weak estrogen 17β-estradiol mainly include estrogen, progesterone, and androgen. activity, which is the last step in estrogen biosynthesis. This enzyme is a cytoplasmic protein that uses nicotinamide ade- Estrogen nine dinucleotide (i.e., NADH) or NADPH as an auxiliary Estrogen is a steroid hormone composed of a C-18 estrane factor. 17β-HSD1 has 100 times more affinity for C-18 ste- skeleton primarily secreted by the dominant ovarian follicle. roids than C-19 steroids and can also convert 16α-hydroxyl The E2 secreted by the ovaries accounts for 95% of the total ester alcohol to estriol. E2 production in childbearing-age women, producing 90–250  μg per day. The biological efficiency of E1 is one-­ 17β-HSD2 third of that of E2, accounting for 50% of the total E1 produc17β-HSD2 is located in the endoplasmic reticulum and inac- tion in childbearing-age women, producing 110–260 μg per tivates hormones. NAD+ is an auxiliary factor for oxidizing day. The serum estrogen level in childbearing-age women testosterone to androstenedione and E2 to estrone (E1). The periodically changes. Generally, the estrogen concentration is liver, secretory endometrium, fetal capillary endothelial very low during the first week of the menstrual cycle, reachcells, breast tissue, and large vascular endothelial cells ing the first peak 1 day before ovulation. The estrogen level express 17β-HSD2. decreases after ovulation but peaks again approximately NADPH-hemoprotein reductase saturates the A-ring of a 21  days after the menstrual cycle begins. When the corpus steroid hormone. Abnormalities in key enzymes involved in luteum atrophies, the estrogen level drops rapidly, reaching steroid hormone production are related to a series of patho- the lowest concentration in the premenstrual period. Estrogen phenotypes. The embryonic ovary is resting regarding ste- synthesis is based on androstenedione secreted by follicular roid production, unlike the embryonic testis. Although membrane cells, which are transformed after catalyzation cholesterol side-chain cleavage and CYP17A1 activity are with aromatase (produced by FSH-activated granulosa cells). detectable, steroidal synthesis is not obvious until adoles- This describes the two-cell/two-gonadotropin theory; luteincence [70, 71]. ized granulosa cells also secrete E1 and E2. E1 is the main Steroids are usually small molecules that enter cells by source of estrogen in postmenopausal women, but the adrenal diffusion and bind with specific nuclear receptors eliciting a cortex also secretes a small amount of estrogen. biological effect. Some steroids have membrane receptor The E2 metabolites are estrone, sulfate, and estriol. A expression, such as estrogen [72]. Different tissues respond small portion of sulfate and estriol are converted into E2, but

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estriol is an irreversible metabolite. Estriol is primarily excreted through the kidneys, but a portion is excreted into the intestine through bile and absorbed into the liver, called enterohepatic circulation. Overall, estrogen has a wide range of effects on the body, as outlined below.

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pigmentation. Moreover, pubertal estrogen induces the formation of female secondary sexual characteristics such as posture, fat distribution (e.g., buttocks, thighs, and breasts), pelvis and bone width, pitch of voice, and hair growth and distribution, especially pubic and armpit hair. The Hypothalamus and Pituitary Gland

The Reproductive System

Estrogen participates in the regulation of follicular growth and development and promotes the proliferation and differentiation of granulosa cells. The feedback regulation of estrogen is related to follicular dominance. The dominant follicle has a strong ability to secrete estrogen, and the estrogen level in follicular fluid is high. Estrogen cooperates with FSH to promote LHR synthesis in the endometrial and granulosa cells of follicles, enhance the effect of FSH on granulosa cells, and improve the sensitivity of follicles to FSH.  However, estrogen has a negative feedback effect on FSH secretion in the pituitary gland, causing a decrease in the level of circulating FSH. Therefore, with follicle development and increased estrogen secretion, FSH secreted by the pituitary gland decreases. During this period, FSH-­ sensitive follicles dominate, and the others enter atresia. Estrogen also contributes to the proliferation and repair of endometrial glands and stroma. Long-term estrogen without progesterone antagonism can cause endometrial hyperplasia or endometrial cancer. It also promotes the proliferation and hypertrophy of myocytes, thickens the myometrium, increases the blood supply of the myometrium, promotes and maintains uterine development, and increases smooth muscle sensitivity to oxytocin. Estrogen can also relax and expand the cervix, increase cervical gland secretions, and increase the water, salt, and glycoprotein levels, causing the secreta to become thin and elastic to stretch into filaments and facilitate the survival and penetration of sperm. Moreover, estrogen promotes the development and contraction of the oviduct muscle layer, increases epithelial cell secretion and cilia growth in the oviduct intima, and enhances peristalsis of the oviduct, which are all conducive for egg delivery. Estrogen also promotes the proliferation and keratinization of vaginal epithelial cells, thickens the mucosa, increases glycogen storage in the cells, and creates an acidic environment in the vagina with the assistance of Lactobacillus, which are unfavorable for bacterial reproduction. Finally, estrogen induces the labia to increase the size and prompts fat deposits and pigmentation. Mammary Glands and Secondary Sexual Characteristics

Pubertal estrogen triggers prolactin synthesis and release in the anterior pituitary gland, stimulates hyperplasia of the mammary duct and connective tissue, promotes mammary gland growth and development, and cooperates with progesterone, prolactin, and adrenocortical hormones to promote mammary gland development and increase nipple and areola

Estrogen has negative and positive feedback regulations on the hypothalamus and pituitary gland, indirectly regulating ovarian function. The Metabolic System

Estrogen enhances the glucose response to stimulate insulin secretion, increases plasma insulin levels, and reduces glucose tolerance. It also promotes the degradation and excretion of plasma cholesterol, thereby reducing plasma cholesterol and β -lipoprotein levels, increasing blood Apo lipoprotein AI, increasing serum phospholipid and α -lipoprotein levels, and lowering blood cholesterol levels. Regarding proteins, estrogen promotes cell proliferation and differentiation of reproductive organs by enhancing transcription and accelerating protein synthesis. Peripheral tissues can cause nitrogen retention, which affects metabolism. For example, estrogen deficiency often causes a negative nitrogen balance. Finally, estrogen participates in the progesterone and aldosterone competition, causing water and sodium retention, and estrogen and the parathyroid hormone maintain the calcium and phosphorus balance in the blood. Bones

Estrogen promotes long bone growth and accelerates epiphyseal closure in children. It also directly promotes osteoblast function, inhibits osteoclast differentiation and function, and inhibits bone resorption and transformation. In addition, estrogen promotes 1,25-(OH)2D3 synthesis to increase intestinal calcium absorption, promote calcitonin synthesis, and resist the effects of the parathyroid hormone to maintain bone mass. E2 also inhibits osteoblast apoptosis. During the perimenopausal period, osteoporosis occurs due to lacking estrogen secretion, bone matrix formation, and calcium deposition. Tooth loss is also possible from the same mechanisms; some studies reported a significantly higher rate of tooth loss in women with estrogen deficiency than in women supplementing with estrogen. The Cardiovascular System

Estrogen dilates the blood vessels, improves blood supply, maintains vascular tension, and keeps the blood flow stable. Improving lipid metabolism, reducing total cholesterol and LDL levels, and increasing the high-density lipoprotein level can change the blood lipid profile and inhibit atherosclerotic plaque formation. Postmenopausal estrogen deficiency studies have also demonstrated that estrogen protects the cardiovascular system by directly affecting the vascular endothelial

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function, estrogen metabolites, and the renin-angiotensin system. Moreover, estrogen antagonizes oxidative stress and inhibits vascular remodeling to elicit antihypertensive effects and maintain vascular tension and blood flow stability. The Central Nervous System

Estrogen affects the neural circuits related to reproduction in the brain and the neural circuits related to learning, memory, emotion, and motor coordination. In recent years, studies identified a neuroprotective effect of estrogen in adults and animals, as estrogen contributes to nervous system development and functional activities of the nervous system. There is a close relationship between changes in the circulating estrogen level and female neurodegenerative diseases, as estrogen is closely related to the structure and function of the central nervous system. Physiological estrogen promotes neonatal brain development, neuron growth, and neurotransmitter function in adults, and prevents neuronal cell atrophy, and regulates synaptic plasticity. Moreover, it affects several human psychological activities, including cognition, mood, sexual desire, and aggressive behavior. Therefore, some refer to estrogen as a natural mental protective factor regulating emotion-related brain function. In the early stage of fetal development, the brain is the only organ that produces estrogen. During nervous system development, most neurons in the brain express the required aromatase for estrogen synthesis (mainly concentrated in the preoptic area of the hypothalamus, cortex, hippocampus, midbrain, and amygdala). Aromatase in these neurons catalyzes estrogen synthesis. During development, estrogen affects cortical neuron development and is important for hippocampus development and neural stem cell proliferation and differentiation. During the reproductive period, estrogen receptors (ER) in the central nervous system participate in several activities, including endocrine and emotional balance, reproductive behavior, and cognitive function. Estrogen, neurotransmitters, and affective disorders are very complex and mainly mediated by the serotonin system, which affects mood and can lead to aggressive behavior. Higher estrogen levels during the menstrual period can reduce the incidence of mental illness. Researchers discovered that mental symptoms worsened before and during menstruation when the estrogen level was low. During pregnancy, the serum estrogen level is 20–100 times higher than that in non-pregnant women, and the estrogen level rapidly drops to the normal range within 3 days of delivery. When this occurs, mood disorders become obvious. The incidence of mental disorders in postmenopausal women, such as Alzheimer’s disease, depression, and Parkinson’s disease, increases daily. However, estrogen replacement therapy is trending downward, emphasizing that estrogen and ERs are crucial in brain development, neurotransmitter release, neuron differentiation, neural function maintenance, and nerve injury repair.

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Skin

Estrogen thickens the dermis, slows collagen decomposition in connective tissues, and improves the proliferation, elasticity, and blood supply of the epidermis. Estrogen also affects keratin and collagen metabolism by reducing the copper ion level. In postmenopausal women, decreased estrogen and water loss cause collagen decomposition, making the dermis thinner and causing the secretory bodies of the skin to atrophy. The bladder and urethra are also more susceptible to trauma when the estrogen levels are low. The Blood System

E2 is a key for activating platelet formation, and abnormal platelet counts may lead to thrombosis. Estrogen binds to specific receptors, including ERα, ERβ, membrane receptors, and G protein-coupled ER, to initiate its various functions. Different genes encode ERα and ERβ, but they are highly homologous in structure. There are 83 and 80 amino acids in the DNA binding regions of ERα and Erβ, respectively, with 96% homology. Moreover, there are 250 and 243 amino acids in the hormone-binding regions of ERα and ERβ, respectively, with 53% homology. ER distribution differs among tissues, as does the affinity between the two ER types and different estrogens. Estrogen is activated primarily using the classical gene pathway; estrogen enters the nucleus of the cell and then binds to the receptor, activating estrogen-related gene transcription, a new signal transduction model through the gene pathway, and membrane receptor-related signaling pathways. Androgen Follicle membrane cells in the ovary synthesize and secrete the most androgen (mainly androstenedione). Ovarian-­ secreted androstenedione accounts for approximately 50% of the total androstenedione production in childbearing-age women. Ovarian stromal and portal cells synthesize and secrete the most testosterone, accounting for approximately 25% of the total testosterone production in childbearing-age women. Testosterone is mainly excreted in urine as glucuronide. Dihydrotestosterone (DHT) is converted into 3α- and 3β-androstanediol, and the resulting glucuronide is excreted by the kidney. The primary androgen functions in women are outlined below. The Reproductive System

Androgen is the precursor of estrogen synthesis and an important hormone for maintaining female reproductive function. It promotes the growth of pubic and axillary hair and the development of the clitoris, pubic caruncle, and labia. Too much androgen antagonizes the effects of estrogen, slows uterus and endometrium growth and proliferation, and inhibits vaginal epithelium proliferation and keratiniza-

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tion, which are important for maintaining sexual desire. Moreover, excessive androgen may affect follicular growth and ovulation, leading to menstrual disorders. Metabolism

Androgen promotes protein synthesis and muscle growth. Moreover, it stimulates hematopoiesis and red blood cell proliferation in the bone marrow. Androgen also promotes muscle cell and bone growth in adolescence, closes the epiphysis, and stops growth in late adolescence. The Vascular System

DHT regulates the E2 level, and androgen is important for cerebrovascular tension, vascular endothelial function, oxidative stress, and inflammatory response changes. Studies have reported that an increased serum testosterone concentration in pregnant women was related to abnormal clinical manifestations and pregnancy-induced hypertension was related to non-mediated vasodilation. A higher serum testosterone concentration may cause an increase in blood resistance, which is related to pregnancy-induced hypertension. In cells, androgen converts testosterone into dihydrotestosterone and E2. Testosterone targets the derived structures of the mesonephric duct, yet hair follicles and derived structures of the urogenital sinuses and urogenital nodules are required to convert testosterone into dihydrotestosterone. Androgen receptors are similar to PRs, with short type A and full-length type B receptors. Androgen receptor- and non-receptor-mediated mechanisms may regulate follicular growth and function stage-dependently.

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The Reproductive System

Progesterone inhibits uterine myometrium contraction and reduces the excitability and sensitivity of uterine smooth muscle to oxytocin, which is conducive to embryonic and fetal growth and development. Moreover, it counteracts the endometrial proliferation signals from estrogen and transforms the proliferative endometrium into secretory endometrium. Progesterone also regulates capillary relaxation and contraction, improves endometrial blood flow, increases stromal edema and interstitial decidua-like degeneration (conducive for egg implantation and development), inhibits the maternal immune response to the fetus (for maintaining the pregnancy), and promotes and maintains luteal function. However, progesterone closes the cervix, inhibits cervical gland secretion, reduces mucosal secretions, and thickens the cervix, which are all unfavorable for sperm penetration. Progesterone inhibits the frequency and amplitude of tubal smooth muscle contractions, inhibits tubal epithelial cilia growth, and helps regulate egg production. Moreover, it reduces vaginal epithelium keratinization, increases the number of middle layer cells, and accelerates the shedding of vaginal epithelial cells. The Hypothalamus and Pituitary Gland

Progesterone enhances estrogen’s positive feedback on LH release during the ovulation peak in the middle of the menstruation cycle. However, during the luteal phase, progesterone has a negative feedback effect on the hypothalamus and pituitary gland, inhibiting gonadotropin secretion. Mammary Glands

Progesterone

The ovaries secrete progesterone, and granulosa and theca luteal cells are the primary producers of 17α-hydroxyprogesterone. Follicles have no blood supply. Thus, granulosa cells lack LDL-C, which is necessary for progesterone synthesis. Only after luteinization, when there is a direct blood supply, can granulosa cells obtain LDL-C for progesterone synthesis and secretion. Childbearing-age women in the follicular phase produce approximately 2 mg of progesterone per day. Generally, 1  week after ovulation (menstrual cycle day ~20), the luteal body matures, and progesterone secretion peaks at approximately 25  mg per day. As the corpus luteum atrophies, the secretion level gradually decreases, returning to pre-ovulation levels when menstruation begins. The urine concentration of the progesterone metabolite, pregnanediol glucuronide, is often used as an ovulation indicator. Pregnanediol is a metabolite of progesterone and excreted by the kidney. Progesterone usually inhibits ER supplementation and promotes E2 metabolism based on the estrogen level. Thus, progesterone has an anti-estrogen effect. Progesterone also inhibits the production of its own receptors.

Progesterone promotes the development of acinus based on estrogen levels. A high progesterone concentration during pregnancy promotes mammary gland development, preparing the body for lactation. However, too much progesterone inhibits milk secretion. Mammary gland hyperplasia in childbearing-age women changes periodically with the menstrual cycle. Hyperplasia of the mammary gland epithelium significantly correlates with the progesterone level, and luteal phase hyperplasia is obvious. Metabolism

Progesterone affects the metabolism of protein, fat, and carbohydrate, promoting protein decomposition. It also competes with aldosterone receptors in the renal tubules and promotes water and sodium excretion. The Nervous System

Progesterone excites the hypothalamic thermoregulatory center, which causes the basal body temperature to increase by 0.–0.5 °C after ovulation. Clinically, this is an indicator of ovulation and is used to estimate the ovulation date. In addi-

2  The Life Cycle of the Ovary

tion, progesterone positively and significantly affects nerve regeneration and repair and myelin repair in brain injuries. The Respiratory System

Progesterone stimulates breathing. In the luteal phase, the partial pressure of carbon dioxide (pCO2) of the alveoli in females is lower than in males owing to progesterone. Progesterone also improves lung ventilation, increases the partial pressure of oxygen, and decreases pCO2. The Skin

Progesterone can cause a rare type of dermatitis called autoimmune progesterone dermatitis. The clinical symptoms present when the progesterone level increases in the luteal phase of the menstrual cycle. The symptoms include rash, erythema multiforme, eczema, urticaria, angioneurotic edema, and ketone-induced anaphylactic shock. PR is a single polypeptide chain in the nucleus with two subtypes (PRA and PRB) and is similar to ER regarding the structure and transcriptional activation. PR is activated through a ligand-independent pathway, and growth factors and dopamine improve the intracellular kinase activity and activate the intracellular phosphorylation pathway and PR phosphorylation.

2.3.1.2 Protein Hormones IHN IHN is a32 kDa heterodimeric glycoprotein belonging to the TGF-β superfamily and comprised of α (18  kDa) and β (12 kDa) subunits connected by a disulfide bond. There are two types of β subunits, βA and βB. Thus, αβA and αβB are named IHN A and IHNB, respectively. The gonads primarily produce IHN, more specifically, in ovarian granulosa cells. IHN slows pituitary gonadotropin production and helps regulate several biological functions from the early stages of embryonic development to the final differentiation of cells and tissues [73]. In vitro, IHN synergistically affects follicle stem cell androgen production by stimulating LH and IGFs. Although the IHN subtypes have similar biological characteristics, they form at different stages of follicle development. IHN B is mainly secreted during the early stage; the concentration decreases in the middle stage, becoming undetectable after the LH peak. Conversely, the IHN A concentration is lower during the first half of follicular development and increases during the middle stage and luteal phase. Gonadotropin regulates IHN A secretion, but IHN B production is unregulated. IHN A and IHN B expression significantly differ in different-sized follicles; IHN A expression increases with size for follicles 10–15  IU/L reserve function decline, the increase of the FSH level is more indicate decreased ovarian reserve function. Early onset significant than that of the LH level, leading to an increase of ovarian insufficiency can be diagnosed in women who pres- the FSH/LH ratio. Therefore, the basal FSH/LH ratio can be ent with amenorrhea or sparse menstruation lasting 4 months used to predict the change of ovarian reserve function [6]. An before the age of 40, with or without hypoestrogenic symp- increased FSH/LH ratio indicates a decreased ovarian reserve toms, and with FSH levels exceeding 25 IU/L detected twice and low ovarian response and may be more sensitive than a at an interval of more than 4  weeks. When the basal FSH single basal hormone value; a ratio of >3 indicates decreased level of >40  IU/L is associated with hypergonadotropic ovarian reserve function and reactivity.

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E2

Estrogens can be divided into estrone (E1), estradiol (E2), and estriol (E3), of which E2 is the most active ingredient that is crucial for maintaining female secondary sexual characteristics and reproductive function. Ovaries are the most important organs for female estrogen secretion, although the placenta also plays an important role in estrogen synthesis during pregnancy. In addition, the adrenal cortex’s reticulate zone and the testes also produce small amounts of estrogen. The physiological effects of estrogen on the ovaries include: (1) a direct effect, wherein estrogen stimulates the development of follicles and (2) an indirect effect, wherein changes in the serum estrogen concentration regulate the hypothalamus and pituitary gland through positive and negative feedback, control the release of gonadotropins, and indirectly affect ovarian function. Throughout a woman’s life, estrogen level fluctuations accompany changes in age. Before puberty, the level of estrogen is low, with the level of E2 gradually increasing from puberty to sexual maturity. During the menopausal transition period, estrogen levels fluctuate and may be increased or decreased. In postmenopausal women, ovarian function declines or even fails; E2 levels are consistently low, and the circulating estrogen is mainly E1 converted from androstenedione. Similarly, estrogen levels change over the course of a normal menstrual cycle. In the early follicular stage, estrogen production is low. On the seventh day of menstruation, the amount of estrogen secreted by the follicles increases rapidly, peaking before ovulation. After ovulation, circulating estrogen levels drop to a low level due to the release of estrogen from the follicular fluid into the abdominal cavity. At 1–2 days after ovulation, the corpus luteum begins to secrete estrogen, causing the circulating estrogen levels to gradually rise again. At 7–8 days after ovulation, the corpus luteum has matured, and the circulating estrogen levels have reached a second peak. Afterwards, the luteum begins to shrink, and the estrogen levels drop sharply, reaching their lowest level during menstruation. Serum E2 levels are usually less than 50 pg/mL during the second to fourth day of the menstrual cycle, and an increase of the E2 level above 80 pg/mL during this period may indicate ovarian dysfunction. However, E2 alone is not recommended for the evaluation of ovarian reserve function because intra- and pericycle variations in the determination of early follicular E2 levels cause poor reliability. As previously mentioned, an early increase in the serum E2 concentration can reduce an elevated basal FSH level to the normal range, leading to the misinterpretation of the basal FSH value. Therefore, the importance of detecting the early E2 concentration is to allow a correct interpretation of the “normal” basal serum FSH value.

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AMH AMH, also known as Müller tube inhibitor, is a homodimer glycoprotein hormone belonging to the transforming growth factor β (TGF-β) superfamily that was first identified by Professor Alfred Jost in 1947. AMH is specifically produced by granulosa cells in early-growing follicles. It is mainly synthesized and secreted by preantral and small sinusoid follicles (2–6 mm in size) and released into the follicular fluid, where it enters the circulation through the vascular network surrounding the follicles, allowing serum detection. AMH synthesis continues from the preantral follicles until the follicles are approximately 8  mm in diameter, with very low expression in large sinusoid follicles and no production in FSH-dependent dominant follicles or follicular atresia. Histological studies of human ovaries have shown that since the AMH level is well correlated with the number of primordial follicles in the ovaries, its serum level reflects ovarian reserve function [7]. AMH is a regulatory factor of follicle growth and development. Its physiological functions in the ovaries mainly include: (1) negative regulation of the initiation and recruitment of follicles, inhibition of the recruitment of primordial follicles into primary follicles, and a slowing down of the depletion of the primordial follicle pool; (2) a reduction in the sensitivity of the preantral follicles to FSH, inhibition of follicular growth, prevention of rapid or early follicle consumption, and preservation of ovarian reserve function; and (3) a reduction in the aromatase activity and inhibition of estrogen synthesis. After 36  weeks of gestation, the ovaries of the female fetus begin to synthesize AMH while inside the mother. At birth, AMH is almost undetectable in the female infant’s umbilical cord blood. However, AMH concentrations are detectable and gradually increase during infancy (mainly in the first 3  months). AMH levels also rise before puberty, remaining relatively stable until the age of 25 years. From 25 to 30  years old, the serum AMH levels gradually decrease with age and fall below the detection limit of 5 years before menopause [8, 9]. Based on the changing levels of AMH, this indicator is consistent with the trends of ovarian aging, and a large number of studies have demonstrated its predictive value for ovarian response, in  vitro fertilization (IVF) outcome, and menopausal age in assisted reproductive technology (ART) [10]. The main advantages of using the AMH level as an indicator are as follows: (1) The secretion of AMH is independent of gonadotropins and is not affected by the ­menstrual cycle. Being relatively stable and detectable on any day of the menstrual cycle, it is more convenient to use. (2) AMH level changes appear first in the process of ovarian aging, with a high sensitivity and specificity. Even in women with normal FSH and E2 levels and regular menstrual cycles, a decrease in AMH indicates decreased ovarian reserve func-

6  Evaluation and Early Warning Systems of Ovarian Aging

tion. However, there are differences in the definition of the AMH threshold in various studies. For instance, in China, it is generally believed that an AMH level of less than 1.1 ng/ mL indicates decreased ovarian reserve. Although AMH has performed well in the assessment and prediction of ovarian function, there are still some limitations, including: (1) a lack of internationally standardized detection methods and difficulty comparing AMH values obtained by different detection methods; (2) an ability to predict the number of follicles but not the quality of the ovum; and (3) individual differences in the AMH concentration, including a susceptibility to influences by race, heredity, birth, smoking, and other factors. Therefore, analysis of a single AMH level is not completely reliable, and a comprehensive evaluation should be combined with age and other indicators. Further studies are needed to fully explore the value of AMH levels in the field of ovarian aging. Inhibin B Inhibin B, a member of the TGF-β superfamily, is a heterodimeric glycoprotein hormone composed of α- and β-subunits connected by disulfide bonds. It was first discovered in bovine follicular fluid in 1985 [11]. Inhibin can be divided into inhibin A and inhibin B based on differences in the β-subunits (i.e., βA and βB). In women, statins are secreted by ovarian granulosa and follicular membrane cells. Granulosa cells begin to secrete inhibin (INH) early in follicle development, and larger follicles secrete additional INH after recruitment. Inhibin B is mainly produced by granulosa cells of medium and small sinusoid follicles in the ovary and acts specifically on the adenohypophysis to negatively inhibit the secretion of FSH. Meanwhile, the secretion of inhibin B is also regulated by FSH, which can promote the secretion of INH by granulosa cells. As a secreted product of granulosa cells, the inhibin B level has been used as a biomarker reflecting ovarian reserve. However, some studies have shown that inhibin B levels can significantly change between menstrual cycles and cannot reliably predict ovarian reactivity; thus, routine use as an ovarian reserve test is not recommended [12]. In addition, although the serum inhibin B concentration decreases with age and ovarian senescence, it is largely considered a marker of ovarian activity rather than ovarian reserve. Thus, the determination of inhibin B levels cannot accurately predict the occurrence of ovarian failure, and compared with other indicators (such as AMH levels), inhibin B levels have a low predictive ability for menopause [13]. Activin Activin is comprised of two statin β-subunits, forming activin A (βAβA), activin AB (βAβB), and activin B (βBβB). It belongs to the TGF-β superfamily and acts through the classical TGF-β signaling pathway. Activin A stimulates the devel-

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opment of primary follicles into sinus follicles but can also cause follicular atresia and stimulate granulosa cell proliferation. Activin mainly increases the number of gonadotropin-­ releasing hormone (GnRH) receptors in pituitary cells through autocrine action and improves the pituitary’s reactivity to GnRH, thus stimulating the production of FSH. INH inhibits the secretion of FSH. Meanwhile, some studies have shown that activin can regulate the expression of murine estrogen receptors [14]. Activin’s biological activity is regulated by two endogenous inhibitors, INH and follistatin, and dysregulation of the activin-follistatin system can lead to female reproductive dysfunction. Compared with young women, the serum activin A levels in middle-aged and perimenopausal women were significantly higher. However, the concentration range of activin A for the diagnosis of premature ovarian failure (POF) has not been determined. In the literature, the serum activin A levels of patients with hypergonadotropic amenorrhea are increased to more than 1 ng/mL [15].

6.1.3.2 Dynamic Markers Li Fang Dynamic tests of ovarian reserve determine the responsiveness of the ovaries after exogenous stimulation by measuring changes in hormone levels. The main methods include the clomiphenecitrate challenge test (CCCT), the GnRH agonist stimulation test (GAST), and the exogenous FSH ovarian reserve test (EFORT). CCCT In CCCT, the serum FSH levels are assessed on the third and tenth day of the menstrual cycle, with continuous daily oral administration of 100  mg clomiphene on days 5–9 of the menstrual cycle. On the tenth day of the menstrual cycle, the FSH levels increased by the clomiphene stimulus are reduced by the negative ovarian feedback. However, if the FSH concentration does not decrease but increases, abnormal ovarian function is indicated. Thus, on the tenth day of the menstrual cycle after oral clomiphene administration, serum FSH levels of >10 IU/L indicate a decreased ovarian reserve, while FSH levels of ≤10 IU/L indicate a good ovarian reserve [16, 17]. CCCT was first used to predict potential female fertility by Navot in 1987. It is the most common dynamic test for predicting ovarian reserve and detects the ovarian response after clomiphene stimulation [18]. Clomiphene citrate (CC) is a synthetic nonsteroidal medication with a chemical structure similar to diethylstilbestrol. The physiological basis of the CCCT is the INH concentration, although its precise mechanism for predicting ovarian reserve is still unclear [19]. CC, a nonsteroidal estrogen antagonist with a weak estrogenic effect, blocks the negative effects of sex hormones on the hypothalamus and/or the pituitary gonadotropic cells

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by binding to estrogen and androgen receptors in the hypothalamus. As a result, GnRH, FSH, and LH are released and induce ovulation. In this process, INH becomes the only inhibitory factor of FSH synthesis and secretion. Women with a normal ovarian reserve can produce enough INH and E2 to resist the effect of clomiphene on the HPO axis, preventing FSH from rising above a certain level; women with decreased ovarian reserve have increased FSH levels after clomiphene stimulation. Therefore, in CCCT, women with good ovarian reserve may have slight increases in or maintain the original FSH level on the tenth day of menstruation, and E2 will increase exponentially; women with decreased ovarian reserve may have a normal FSH level on the third day of the menstrual cycle that rises sharply on the tenth day, while the E2 level rises slightly. As one of the methods to predict ovarian reserve, CCCT more accurately predicts low ovarian response and is less useful at predicting high ovarian response. It has a wide range of applications and can be used for both infertile women and those receiving ART. However, CCCT has certain limitations. Its clinical application and accuracy have no obvious advantages compared to using AFC combined with basal FSH levels, so CCCT has not been widely developed for clinical practice. Although CCCT is a good indicator for predicting ovarian responsiveness in natural menstrual cycles and in induced ovulation, its reliability is limited due to large variations in the test results between different menstrual cycles. In addition, CCCT may have side effects, including multiple pregnancies, vasomotor symptoms, nausea and vomiting, breast discomfort, and pelvic and abdominal discomfort. With the emergence of new indicators and technologies, CCCT has gradually been abandoned in clinical practice.

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the case of normal ovarian reserve, a transient increase in FSH and LH levels stimulates the development of a group of follicles, and E2 and INH increase accordingly, which counteracts the excessive increase in FSH levels. As ovarian reserve decreases, the number of follicles in the ovaries decreases, and the synthesis and secretion of E2 decreases. Therefore, GAST can be used to predict ovarian reserve. Although GAST relies on the mutual feedback effect between the HPO axis and hormones, the FSH threshold and the amplitude of the E2 rise are different due to individual differences in ovarian response, and the secretion of E2 depends more on the ovary itself and ovarian responsiveness. The research by Sills showed that GAST can predict ovarian hyporesponsiveness, but its clinical value and prediction accuracy are no better than basal FSH, basal AFC, and inhibin B levels, and it cannot predict pregnancy outcomes [22]. Performing GAST is complicated and expensive, and its clinical application is limited to testing ovarian reserve, guiding the adjustment of ovulation induction programs, reducing the occurrence of ovarian hyperstimulation syndrome, and increasing the pregnancy rate of the patients undergoing fertility adjuvant therapy. It is not suitable for predicting the fertility potential of the general infertile population.

EFORT EFORT is also called the FSH challenge test (FCT). Exogenous FSH (300  U) is administered on day 3 of the menstrual cycle, and the E2 and/or inhibin B levels are measured 24 hours before and after administration. Increases in E2 levels of 26 IU/L are abnormal, and can accurately predict ovarian responsiveness [23]. suggesting decreased ovarian reserve and low ovarian Studies have shown that the ability of EFORT to accurately response [20]. predict ovarian reserve is better than that of the basal FSH, GAST was first reported by Padilla et al. in 1990. Changes E2, and inhibin B levels. EFORT is better than CCCT in prein E2 levels have shown a strong correlation with the results dicting ovarian hyper responsiveness, while CCCT is better of IVF [21]. The basis of GAST is that the GnRH-agonist than EFORT in predicting ovarian hyporesponsiveness [24]. (GnRH-a) stimulates the pituitary to produce gonadotropins. Because EFORT is difficult to use, expensive, and may cause The biological activity of GnRH-a is 50–300 times that of serious adverse reactions, such as ovarian hyperstimulation natural GnRH.  GnRH-a specifically binds to GnRH recep- syndrome, it has a low clinical application value and is not tors in the pituitary, stimulating the pituitary to release large routinely used. However, with IVF, EFORT can be used to amounts of gonadotropins and causing a sharp increase in predict ovarian responsiveness and to adjust the dose of conthe concentration of FSH and LH in the peripheral blood. In trolled ovarian hyperstimulation (COH).

6  Evaluation and Early Warning Systems of Ovarian Aging

In summary, ovarian stimulation tests can evaluate ovarian reserve, predict the responsiveness to COH, and guide the choice of ovulation stimulation programs. However, these tests are very inconvenient, requiring repeated administration and multiple blood samples. In particular, EFORT and GAST are expensive and cannot effectively predict the outcome of pregnancy, which may be related to the influence of many factors. By directly reflecting the ovarian responsiveness to gonadotropins, EFORT has the highest value, followed by CCCT, which has a high predictive accuracy for ovarian hyporesponsiveness. GAST has less predictive value but can help with COH program adjustments. Clinically, cheaper, simpler, more specific, and widely applicable prediction methods are necessary.

6.1.4 Imaging Examination 6.1.4.1 Ultrasonic Markers Jingjing Jiang Ultrasound examination has the advantages of being noninvasive, economical, and convenient, allowing real-time observations and repeatability. The development and application of three-dimensional (3D) ultrasound has shortened the examination time and reduced the errors caused by the examiner’s operative technique so that the measurement results are more repeatable and reliable. Therefore, as an examination method to evaluate ovarian reserve, ultrasound has been widely used in clinical practice. The indicators of ovarian reserve function tested by ultrasound mainly included AFC, the ovarian volume (OV), and ovarian stromal blood flow. AFC AFC is the total number of bilateral antral follicles observed by transvaginal ultrasonography at the early stage of folliculogenesis (the first to fourth day of the menstrual cycle, with the third day being the most common) [25].Clinically, the definition of the size of antral follicles is controversial. Most are 2–10 mm in diameter; those 2–6 mm in diameter are considered small antral follicles, while those 7–10 mm in diameter are considered large antral follicles. Studies have shown that the number of small antral follicles with a diameter of 2–6 mm better reflects ovarian reserve function [26]. Measuring the follicle diameter and counting the number of antral follicles by two-dimensional (2D) ultrasound are subjective methods, which may lead to errors in the AFC.  With the development of 3D ultrasound technology, the methods to count the number of antral follicles have been improved. Sonography-based automated volume count (SonoAVC) can automatically measure the number, diameter, and volume of antral follicles. Some studies have shown that SonoAVC can reliably measure the total number of

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antral follicles and the number of antral follicles in each diameter group, while taking less time than 2D ultrasound. SonoAVC can automatically and more effectively measure the diameter and volume of follicles in follicular monitoring during COH induction compared to 2D ultrasound [27]. The most direct data on follicle counts come from autopsy and postoperative pathology results, but these methods are not routinely used due to the limitations of the sampling methods. AFC decreases with a woman’s age. AFC has a high specificity in predicting ovarian reactivity and IVF outcomes, but its sensitivity is insufficient, and different studies have inconsistent definitions of a low AFC. Therefore, AFC should not be used alone to evaluate the status of ovarian aging. OV OV gradually increases with the development of female puberty, reaches a maximum at sexual maturity, and remains until the menopausal transition period; after which, the ovary gradually becomes smaller. OV should be measured in the early follicular stage (the first to fourth day of the menstrual cycle, usually on the third day) to avoid the influence of dominant follicles or the luteum. There are two methods for measuring OV by 2D ultrasound: (1) replacing OV with the maximum or average ovarian diameter line or (2) calculating the OV with the elliptic formula, D1  ×  D2  ×  D3  ×  0.523, where the maximum diameter lines in the three planes of the same ovary, D1, D2, and D3, refer to the long, anteroposterior, and transverse diameters, respectively [28, 29]. In recent years, the technique of transvaginal 3D ultrasound for measuring OV has been gradually developed with superior results but requires that anatomical structures are fully understood and the quality of the 2D images can be guaranteed. At the same time, the post-processing of 3D ultrasonic images requires time, experience, and skills. There are great individual differences in OV in women of childbearing age. Some studies suggest that OV is related to the size of the original follicle pool and the number of growing follicles but not to the quality of the egg. In addition, studies of OV tend to exclude patients with ovarian lesions, including patients with polycystic ovary syndrome, endometriomas, and large cysts, limiting its usefulness. In summary, there are conflicting opinions on the value of OV in predicting ovarian reactivity and IVF outcomes, so the use of AFC or OV in combination with other indicators should be prioritized in evaluating ovarian reserve function. Ovarian Stromal Blood Flow The growth and development of follicles are not only regulated by the HPO axis but also by paracrine and autocrine factors. Primordial follicles have no separate blood supply and mainly rely on the stromal blood vessels to deliver materials. Therefore, the growth and development of follicles and ovarian function are closely related to the stromal blood supply.

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Spectral Doppler Blood Flow Parameters

Spectral Doppler blood flow parameters mainly include the peak systolic velocity (PSV), resistance index (RI), pulsatility index (PI), and systolic/diastolic velocity ratio (S/D). The blood flow resistance of the ovarian artery is closely related to the development of follicles, and the RI value reflects the resistance of the ovarian artery and the diastolic blood flow velocity. The RI value of the ovarian artery is negatively correlated with the blood flow to the ovarian parenchyma. RI values affect follicular development and maturation. There are limitations to 2D spectral Doppler measurement of ovarian stromal blood flow. For example, the measurement of a single section of the ovary cannot reflect the blood supply of the entire organ, and the measurement is angle-dependent, which may lead to operational errors. In addition, blood flow in the ovarian parenchyma of patients with early follicular and premature ovarian failure is rare, and it is sometimes difficult to detect the blood flow spectrum, which limits the application of spectral Doppler blood flow parameters in the evaluation of ovarian reserve function. 3D Power Doppler Blood Flow Parameters

3D power Doppler blood flow parameters mainly include the ovarian vascularization index (VI), blood flow index (FI), and vascularization blood flow index (VFI). There are many kinds of computer models for the quantitative analysis of power Doppler signals in clinical practice, but the histogram of the Virtual Organ Computer-Aided Analysis (VOCAL) software is the most widely used. VOCAL obtains the VI, FI, and VFI representing the density of blood vessels in the tissue, the number of blood cells at the moment of 3D scanning, and the combination of the existing blood vessel and blood flow information, respectively. The three parameters can be used to quantitatively evaluate the blood perfusion of the target tissues and organs. Compared with 2D ultrasound and 3D color Doppler, 3D power Doppler has the advantages of being able to detect tiny blood vessels and low-speed blood flow and to display the 3D vascular distribution of parenchymal organs with high sensitivity and repeatability. Although the value of using ovarian stromal blood perfusion parameters to predict ovarian reactivity and IVF outcomes remains controversial, compared to AFC and OV, ovarian stromal blood flow is not only associated with the number of developed follicles but also with the quality of the follicular development, so there are still important research prospects and potential application value.

6.1.4.2 Other Image Examinations Li Fang The image methods used for the examination of female pelvic diseases mainly include ultrasound, computed tomogra-

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phy (CT), and magnetic resonance imaging (MRI). Ultrasound is widely used in the field of ovarian aging, while CT and MRI are less used. CT The basic principle of CT is to use X-ray beams to scan a certain thickness of the bodily region being inspected. Because X-rays have different penetration capabilities for tissues with different densities, the X-rays received by the detector after passing through the tissue layers are also different. The information is converted into digital signals, which are processed by the computer to display the image. CT has the advantages of a fast-scanning speed and high spatial resolution. With a variety of scanning and image post-­ processing techniques, tumors can be comprehensively analyzed, including an accurate display of the tumor’s location, nature, structural characteristics, lesion area, and relationship with surrounding tissues. The accuracy of the CT diagnosis of gynecological tumors reaches more than 90%, which provides a scientific basis for the formulation of various gynecological tumor treatment plans and prognostic evaluations. CT has become an important imaging method for gynecological pelvic tumors, but there are few studies or applications of CT in ovarian aging. Computed tomography angiography (CTA) combines enhanced CT technology with the ability to quickly scan thin layers on a large scale. It can display an accurate vascular image through 3D reconstruction. As a noninvasive and easy-to-operate blood vessel examination technology, CTA has advantages over traditional autopsy and angiography and has been widely used in clinical practice. Yi found that compared with normal women of childbearing age, patients with premature ovarian failure have a decreased visualization rate and narrower diameter of the ovarian branches of the uterine artery through 64-slice spiral 3D CTA examination. Identifying the characteristics of the ovarian blood vessels in such patients provides a potential method and parameters to diagnose premature ovarian failure and has clinical value. MRI MRI is an imaging technology in which the hydrogen nuclei in the tissues are excited by radio frequency pulses in a magnetic field, generating a magnetic resonance signal that is processed by computers to reconstruct images of regions of the body. MRI allows multi-directional imaging with no radiation damage or bone artifacts and produces high-­ resolution images of soft tissues. It is especially suitable for pelvic lesions and the determination of the relationship between lesions and the adjacent structures. It is widely used in the diagnosis and preoperative evaluation of gynecological tumors and endometriosis. In recent years, the value of MRI in the assessment of ovarian aging has received increased attention, especially since it clearly and efficiently displays morphological changes such as OV and follicle

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number. OV, AFC, and serological indicators, such as the FSH, LH, and AMH levels, are indicators of ovarian reserve function, so MRI may play a role in the research and diagnosis of ovarian aging. Compared with other imaging techniques such as ultrasound, the main advantages of MRI to detect ovarian reserve are as follows: (1) MRI enables a higher resolution of soft tissues and multi-directional, wide-field imaging with which to observe the morphology of the ovary from multiple tomographic planes; (2) Germ cells in ovarian tissues are very sensitive to radiation, while MRI has no ionizing radiation; (3) MRI is a noninvasive method that avoids the discomfort caused by transvaginal or transrectal ultrasound; (4) Compared with transabdominal ultrasound, MRI is less affected by factors such as intestinal gas, subcutaneous fat layers, abdominal scars, and the doctor’s operating skill; and (5) The detection rate of MRI for smaller follicles is higher than that of ultrasound. MRI can distinguish small follicles with diameters of 1 mm. In contrast, ultrasound has a lower resolution and has difficulties distinguishing between follicles with a diameter of less than 3 mm and noise. Based on the above advantages, MRI has significance for the evaluation of ovarian reserve function and is worthy of further research.

6.1.5 Histological Examination Jun Dai Biopsy is the use of local excision, needle puncture aspiration, and other surgical methods to remove tissue from the site of lesions for pathological examination. This is a widely used diagnostic method, especially for cancer. The advantage of biopsy is that it retains the original appearance of the lesion, which is conducive to histological, cytological, and ultrastructural examination and enables confirmation of the diagnosis. Aging ovaries not only have changes in their general shape, appearance, and size but also have a series of histological changes, involving: (1) thinning of the ovarian cortex; (2) a decrease in follicle number; (3) follicular dysplasia; (4) fragmentation of the corpus albicans; (5) ovarian interstitial fibrosis; and (6) changes in the structure and number of blood vessels. On average, the number of primordial follicles in perimenopausal women is only one-tenth that of women of reproductive age, and it is difficult to find primordial follicles in the ovaries of postmenopausal women. The number of primordial follicles in the biopsy tissue can be used to estimate the size of the primordial follicle pool throughout the entire ovary, reflecting ovarian function and the possible menopausal age. In theory, a histological examination of the ovarian follicles is the most direct and accurate method to evaluate ovarian reserve. However, studies have shown that the distribution of follicles is often clustered, and the clustered distribution is

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more evident with age. Even in different parts of the same ovary, the density of follicles in the cortex varies greatly. The number of follicles in a biopsy sample cannot represent the number of remaining follicles in the ovary; this detection requires risky and invasive surgery. Therefore, local ovarian biopsy is not an accurate indicator of ovarian reserve and is not recommended in clinical practice.

6.1.6 Other Potential Markers Ting Ding There are many markers for ovarian reserve, but their specificity, sensitivity, and accuracy are still to be determined. Since ovarian function is affected by a variety of factors, there may be many potential markers for the evaluation of ovarian function that are not limited to the ovary itself or the HPO axis. These potential markers are expected to improve the accuracy of ovarian function evaluation.

6.1.6.1 Metabolic Markers Obesity has been recognized worldwide as a chronic metabolic disease that is harmful to the health of women of childbearing age. Obese women are more prone to anovulation, abnormal uterine bleeding, endometrial lesions, infertility, and abortion- and pregnancy-related complications than women with a normal weight. Studies have shown that obesity, metabolic syndrome, and diabetes have harmful effects on ovarian reproductive and endocrine function. Examination of the BMI and the levels of metabolism-related markers, such as the fasting plasma glucose (FPG) and triglycerides (TG), is expected to augment the analysis of AMH levels to improve the accuracy of ovarian function evaluation [30]. 6.1.6.2 Dehydroepiandrosterone (DHEA) DHEA is an androgen precursor and has been widely used in the field of assisted reproduction. DHEA improves ovarian function by promoting the production of E2, improving the ovarian response to gonadotropic stimulation, and promoting the development of follicles, among other mechanisms; its level decreases with age. Studies have shown that DHEA can improve oocyte and embryo quality, increase pregnancy rates, and reduce fetal aneuploidy and abortion rates, ­suggesting that the DHEA level may be one of the factors affecting ovarian function [31]. However, its use as a marker to evaluate ovarian function needs more research. 6.1.6.3 Insulin-Like Growth Factor-1 (IGF-1) The insulin-like growth factor (IGF) family, a superfamily composed of IGFs, IGF receptors (IGFRs), IGF binding proteins (IGFBPs), and related proteases, is highly conserved among species. It has multiple functions, including promoting bodily growth, regulating reproductive and immune function, nourish-

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ing nerves, and having an antiapoptotic effect. In ovarian tissue, it not only regulates the growth and development of follicles but also regulates the endocrine function of the ovary. IGF-I is a gonadotropin cofactor; together with FSH, it stimulates the production of estrogen in granulosa cells, and together with LH, it stimulates the production of androgens in theca cells and increases the expression of their LH receptors. IGF-I plays an important role in preventing follicular atresia and is a key factor in oocyte maturation. Since the level of serum IGF-I increases with age, peaks at puberty, and then decreases gradually, it may be an auxiliary indicator for evaluating ovarian function.

6.1.7 Summary In view of the various markers mentioned in this section, age plays an irreplaceable role in the evaluation of ovarian function and is the best predictor of oocyte quality. However, age alone does not reflect the true functional status of the ovaries. Changes of menstrual cycle are easily identified but may be influenced by variety of factors and appear late in life. Among endocrinological markers, an abnormally elevated FSH level has a high positive predictive value for decreased ovarian reserve function and has been widely used in clinical practice. However, due to the lack of reliability of a single FSH measurement, combination with E2 levels or use of the FSH/LH ratio with a high sensitivity is recommended to reduce false negatives. The AMH level, which is relatively stable, convenient, and unaffected by the menstrual cycle, is the most sensitive and highly specific marker for ovarian reserve and menopause prediction. However, there is no unified international testing standard, and more attention should be paid to the clinical interpretation of AMH values according to the testing methods and standards used. Inhibin B cannot reliably predict the ovarian response to stimulation and is not recommended for routine use in ovarian reserve tests. Most stimulation tests require repeated drug administration and blood collection, making them inconvenient and non-­ ideal for evaluating ovarian aging. The most promising ultrasound marker is AFC, which has a high specificity in predicting ovarian response. However, its sensitivity is insufficient, and specific requirements for the ultrasound technology and the skill level of the examiners are needed. Molecular biological, metabolic, and other potential new markers have broad research prospects but are still undergoing scientific research and exploration and cannot be clinically applied. The most widely used markers for evaluating and predicting ovarian aging in clinical practice include age, FSH, and the E2 levels. Although the AMH level and AFC are currently the best markers to reflect ovarian reserve, they still cannot obtain satisfactory specificity and sensitivity, especially in predicting the pregnancy outcomes of IVF. Due to the wide and inconsistent ranges of the AMH level and AFC

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reference values used for prediction, there are deficiencies in the assessment of individualized ovarian function and menopause prediction that need to be optimized. No single marker can fully and accurately reflect the state of ovarian function. A comprehensive evaluation of ovarian function and related clinical diagnostic criteria and evaluation systems based on these markers will be introduced in the next section. In general, these existing markers cannot fully meet the requirements for clinical use. The following directions would be better for the assessment and prediction of ovarian function: one is to seek new markers with better sensitivity, specificity, convenience, accuracy, and other characteristics; the second is to use the existing markers to create better evaluation and prediction models. It is difficult to avoid the instability of a single evaluation marker. To establish an ideal model incorporating multiple existing markers, both the selection of the markers and the application of the statistical methods are critical. In the future, clinicians, researchers, and experts in epidemiology, statistics, and other fields need to achieve real-time assessments and accurate predictions of ovarian aging in order to benefit women.

6.2 Evaluation of Ovarian Aging Ting Ding and Yueyue Gao Ovarian aging refers to the progressive loss of fertility and ovarian endocrine function that ultimately results in menopause, which has a profound impact on the health of women and even their offspring. An accurate assessment of women’s ovarian status and the degree of ovarian aging can help women understand their current ovarian function and reproductive potential, make reasonable fertility and career plans, and prevent menopause or related diseases. As described in Sect. 6.1, although many markers and methods for evaluating ovarian function have been published, they all have certain drawbacks. Until now, there is no unified diagnostic criteria or evaluation system for ovarian aging. With female aging and the decline of ovarian function, some common evaluation systems for ovarian aging have been used, such as the diminished ovarian reserve (DOR), poor ovarian response (POR) in the ART field, premature ovarian insufficiency (POI), POF, early menopause, and finally menopause. In addition, the perimenopausal theory and the STRAW system are still widely used in clinical practice. These evaluation systems are based on age, menstrual changes, and various markers related to ovarian aging, of which age plays an important role. With the search for novel markers and the development of evaluation methods, the accuracy of diagnosis and evaluation on ovarian aging has been strengthened. With more attention given to the idea of functional diagnosis, the evaluation of ovarian reserve function is the embodiment

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of this ideal. Scholars estimate the mathematical model of the “ovarian age” using a multifactor model, which provides individualized diagnoses using new ideas and methods. Commonly used diagnosis and evaluation systems and the latest progress in ovarian aging will be summarized in this section.

6.2.1 Evaluation of Ovarian Aging 6.2.1.1 DOR Ovarian function is mainly divided into reproductive and endocrine functions. Ovarian reserve reflects the fertility potential and represents the quantity and quality of remaining oocytes. DOR means the potential decreased fertility ability and is gaining more attention from women who expect childbearing delay. The definition of DOR in the Federal Register Notice is “A condition of reduced fecundity related to diminished ovarian function based on clinical assessment; often indicated by FSH  >  10 mIU/mL or AMH  4  weeks apart; and (4) decreased fluctuations in estrogen levels [39, 40]. The diagnostic threshold of POI changed from a FSH level of >40 U/L to a level of >25 U/L for the early detection of ovarian dysfunction to enable early treatment. POI can be divided into secondary and primary POI based on whether spontaneous menstruation has ever occurred. The academic community generally believes that POF cannot reflect the development process of the disease, so the concept of POI is preferred. Based on the FSH levels, fertility ability, and menstrual condition, the POI process represents a continuum of ovarian conditions from a normal state to an “occult” clinical state (reduced fertility but normal FSH levels and regular menses) to a “biochemical” state (reduced fecundity, elevated FSH levels, and regular periods) to an “overt” state (approximately corresponding to POF, though perhaps with irregular menses). However, this four-­ state nomenclature is not widely used and has not been validated. The diagnosis and evaluation process of POI is as follows [43]: In women younger than 40  years of age, when menstrual abnormalities occur, especially secondary amenorrhea, it is necessary to exclude pregnancy by detecting serum β-human chorionic gonadotropin (β-HCG) levels, as well as thyroid stimulating hormone (TSH) and (prolactin) PRL levels, to exclude menstrual disorders caused by thyroid disease, hyperprolactinemia, and other endocrine diseases. If the patient meets the above diagnostic criteria, POI is diagnosed. Karyotyping is recommended for all patients with non-iatrogenic POI, and fragile-X premutation testing is indicated, when available, which interests all women with POI and their relatives. Screening for 21-hydroxylase antibodies (21OH-Ab), adrenocortical antibodies (ACA), or thyroid antibodies (TPO-Ab) should be considered in women with POI of unknown cause or if an immune disorder is suspected. In patients with a positive TPO-Ab test, the TSH level should be measured yearly.

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A typical strategy for the molecular diagnosis of patients with POI is as follows: Patients are initially evaluated by repeated blood tests, and a diagnosis of POI is confirmed by consistently elevated FSH levels. Current guidelines recommend FMR1 premutation analysis and chromosomal analysis by karyotyping and/or array. Analysis of autoantibodies is performed to distinguish cases of autoimmune POI. The dotted line indicates where investigations usually end in current diagnostic practice. As NGS becomes increasingly available, genetic analyses should follow a diagnosis of POI. When a monogenic syndrome is suspected, candidate gene sequencing may be performed. In other cases, NGS is more appropriate. After a genetic diagnosis, patients should be managed by a multidisciplinary team of specialists, the constitution of which should be appropriate for the specific cause [43].

6.2.1.4 Early Menopause Natural menopause occurs at the age of 40–60 years, and the average age is 52  years. Different from premature menopause or POF before the age of 40 years, about 5% of women will go through menopause before the age of 45 years, and menopause occurring between 40 and 45  years of age is called early menopause [42]. Based on the age at menopause, early menopause is a relatively simple concept that does not require measuring hormone levels or ultrasound tests.

6.2.2 Individualized Functional Assessment and Ovarian Age Most of the ovarian aging evaluation systems are based on the diagnostic criteria of “with or without” to determine

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whether a woman is in a certain state of ovarian aging. STRAW+10 is a staging system for female reproductive aging, but it fails to make full use of AMH levels, the AFC, and other markers of ovarian reserve function. With the emergence of various ovarian function evaluation markers, the concept of a diagnosis based on function is increasingly recognized and reflects the concept of “diminished ovarian reserve.” However, the ambiguous definition of DOR currently limits its clinical application. Using these markers reflecting ovarian function to evaluate and predict female ovarian aging individually will be an important direction in the future. Considering that ovarian function is affected by many factors such as genetics, environment, immunity, and infection, the accuracy of using a single marker to evaluate ovarian aging is limited. Even the best marker currently recognized, AMH, has its limitations and is not ideal. However, there is no clear and practical method for comprehensive evaluation with multiple factors. Therefore, some scholars proposed a mathematical model incorporating multiple ovarian function markers and related influential factors and combining all relevant information to individually and accurately evaluate the status of ovarian aging. The concept of “ovarian age,” which is supposed to be the “biological” age of the ovary and is estimated with a combination of multiple markers, reflects the true ovarian function better than the chronological age. Roberta et  al. [44] measured the basal values of AMH, FSH, E2, AFC, OV, VI, FI, and VFI on the first to fourth days of the menstrual cycles of 652 healthy women of childbearing age. Generalized Linear Models (GZLM) were used to construct the formula for ovarian age (OVAge) as follows:

OvAge = 48.05 - 3.14 ´ AMH + 0.07 ´ FSH - 0.77 ´ AFC - 0.11´ FI + 0.25 ´ VI + 0.1´ AMH ´ AFC + 0.02 ´ FSH ´ AFC The OVage (50.63 ± 3.80 years) was significantly higher than the chronologic age (37.90 ± 3.31 years) in 29 patients with POI. In 29 polycystic ovary syndrome (PCOS) patients, the OVage (24.98 ± 0.91 years) was significantly lower than the actual age (29 ± 2.75 years), indicating that the OVage formula can identify pathological changes in ovarian function. This study is the first to use a multivariate model to assess ovarian age; the model was validated in healthy women and in patients with POI and PCOS, but its ability to predict reproductive function and age at menopause is unclear. In addition, the model involves many ultrasonic measurement indexes, and its reliability needs further clinical verification due to the subjective judgment of the sonographers and the different degrees of error produced by the hardware facilities. Younis et  al. established a multivariate scoring system using the BMI, infertility years, mean OV, basal FSH levels, FSH/LH ratio, AFC, age, and other indicators from 168 women who are about to go through IVF-embryo transfer

(IVF-ET) [45]. In their model with an area under the receiver-­ operating characteristic curve of 0.90, a score greater than 14 points predicts ovarian function with low sensitivity and specificity (88% and 69%, respectively). The model is simple and easy to use, but it was built using a small sample size, only used for infertile patients with fertility requirements, and only assessed IVF cycles; therefore, its scope of application is limited. With the wide application of artificial intelligence (AI), building a multifactor model to evaluate ovarian function may be more effective with the help of AI algorithms. The successful model can accurately assess and predict the female reproductive function and the estimated age of menopause, providing individualized assessment of ovarian aging and giving reasonable guidance to allow planning. Meanwhile, the model can help infertile patients develop personalized treatment programs, providing strong support for women’s personal health, family happiness, and social stability.

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6.2.3 Summary The various diagnostic criteria or evaluation systems for ovarian aging used in clinical practice are the embodiment of comprehensive evaluations using various markers, each of which has its applicable area and limitations. Perimenopausal syndrome is the most common clinical diagnosis. Women with the typical symptoms of perimenopause, combined with age, medical history, and auxiliary examination, can be diagnosed regardless of natural or surgical menopause. However, this is a late sign of ovarian aging. Serum FSH and E2 levels should be tested to determine the presence of POI in women under 40 years of age who experience abnormal menstrual changes after excluding pregnancy and other endocrine disorders. POR and DOR are commonly used in the field of assisted reproduction to evaluate ovarian reserve function and to predict ovarian response and the outcome of ART. The definition of DOR is not exact, and the diagnostic criteria need to be unified. The diagnostic criteria can be applied in clinical practice, but they should be improved in combination with the latest research on ovarian function indicators. The STRAW+10 system is a reproductive age staging system suitable for most women, but it requires further optimization. If the potential value of various ovarian aging markers can be fully explored, including the data from patients with POI, PCOS, and other conditions, the system will have better significance for guiding clinical decisions and scientific research. In addition, ovarian aging assessments and early warning models are some of the most important topics for future development. Even though the models are at the early stages of development and have not yet been fully evaluated, the discovery of additional ovarian function markers and the assistance of AI will soon enable the establishment of an accurate model to assess and predict reproductive function and the age of menopause. Of course, the ideal ovarian aging evaluation system must be based on good ovarian function makers, and the existing markers need to be continuously improved. In particular, international unified standards need to be established for AMH detection, and potential or unknown markers need to be explored.

6.3 Early Warning Systems of Ovarian Aging and Related Diseases

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population. With delays in childbearing, declines in ovarian function increasingly influence women’s fertility. However, female fertility could be affected by multiple factors, such as age, menstrual cycle, BMI, behavioral factors, psychological state, and tubal and uterine conditions. Can traditional or new ovarian function markers predict natural fertility or ovarian responsiveness to hyperstimulation during ART for infertile women? In particular, whether poor ovarian reserve results in low clinical PR is still controversial and needs to be verified by larger clinical trials. Although the process is certainly not new, events over the last few decades have brought renewed interest in developing a better understanding of the reproductive aging process and the prediction of clinical outcomes. Menopause is the end point of ovarian aging. Since the time interval between menopause and the age of fertility loss is relatively fixed and reflects the reproductive life of women, the prediction of the age of menopause has attracted many researchers’ attention. Generally speaking, for a given age, higher AMH levels, greater AFC values, and lower FSH levels correspond to a later menopausal age. Conversely, lower AMH levels, lower AFC values, and higher FSH levels correspond to an earlier menopausal age. For an individualized and accurate prediction of the menopausal age, identifying more influencing factors to establish an ideal prediction model represents a new direction for future research on menopausal age prediction. In the past, ovarian aging was regarded as the main reason for menopause; ovarian aging is the pacemaker of body aging and the initiating factor in the aging of multiple organs. With the extension of the human life span, the time women spend in the stage of ovarian aging is also extended. As a result, risks of ovarian aging-related diseases are also increased. Cardiovascular, cerebrovascular diseases, and osteoporosis are the most common long-term complications related to ovarian aging. Therefore, changes in ovarian aging markers could also help predict the risk of ovarian aging-­ related diseases, providing a suggestion for fertility and career planning, as well as the prevention of related long-­ term complications.

6.3.1 Prediction of Age at Menopause Ting Ding

Ting Ding Loss of fertility and menopause are two key points of ovarian aging and are important representative events of ovarian life. Fertility loss indicates total failure of ovarian reproductive function, which occurs at an average age of 41 years. Later, menopause occurs and indicates the end of ovarian endocrine function, at an average age of 52 years in the Chinese

Menopause refers to the permanent cessation of menstruation and is the only reproductive event that can be clearly confirmed after menarche, requiring retrospective diagnosis. It marks the failure of ovarian endocrine function and the end point of ovarian life. Menopause is an irreversible process triggered by the number of follicles or the corresponding endocrine level reaching a critical threshold; this process is

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irreversible. The follicular pools cannot regenerate after birth [3, 46]. Since menopause is caused by the programmed disappearance of a limited number of follicles and the corresponding changes in endocrine levels, the age at menopause can be predicted by mathematical methods based on the basal number of follicles at different ages or hormone levels and other markers that reflect ovarian function. There is an appreciable relationship between age at menopause and loss of fertility. Studies have demonstrated a relatively fixed interval between these two reproductive events, and the average duration between these events, namely the ages between 37.5 and 51 years, is 13 years. These 13 years are the period when the number of follicles decreases from 25,000 to 1000. Therefore, the time period of fertility loss can also be estimated by the predicted age of menopause. Although menopause is a normal physiological event for women, each woman’s experience varies greatly, including not only the age at menopause but also the severity of symptoms during perimenopause [47]. Early menopause increases the risk of osteoporosis, cardiovascular disease (CVD), and a relatively shorter fertile period. On the other hand, late menopause has been shown to be an important risk factor for breast, endometrial, and ovarian cancer [48, 49]. Therefore, accurately predicting the age at menopause can help women plan their fertility and provide early prevention of potential diseases. Menopause can be divided into natural and artificial menopause. The age at menopause is affected by multiple factors, including genetic, environmental, and iatrogenic factors and endocrine, ultrasound, and genetic markers.

6.3.1.1 Endocrine Markers Studies on the prediction of age at menopause have focused on endocrine markers. Besides the traditional FSH levels, markers of early changes in ovarian function, such as the AMH level, have received increased attention. FSH The serum FSH level is a traditional endocrine marker of ovarian aging that increases later in the life of women in the process of ovarian aging. When fertility begins to decline, elevated FSH levels can maintain relatively normal follicular development and ovulation. Although the ability of the FSH levels to predict the last menstrual period is limited, it is still considered the most appropriate and available biochemical marker of the beginning of the late reproductive period, when ovarian aging is irreversible [50, 51]. Jiang B et al. conducted a 14-year cohort study wherein a Bayesian model identified that different groups of FSH level changes were closely related to menopausal age. The group with early FSH level changes (an increased FSH level after 40 years of age) had earlier menopause, while the group with late FSH level changes (an increased FSH level after 45 years of age) had later menopause [52].

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AMH AMH is an endocrine marker that changes early in the process of ovarian aging. The serum level of AMH begins to decline at an average age of30 years, much earlier than the increase in the FSH level, and has small fluctuations within the menstrual cycle [51, 53]. It reflects the transformation process from static primordial follicles to growing follicles and is regarded as the best marker to reflect ovarian reserve. Some researchers have demonstrated that AMH levels decline to a low or undetectable level 5 years before menopause [54]. Therefore, the AMH level can be a good candidate marker to accurately predict the age at menopause. The AMH level has been used to predict the menopausal age for decades, and most prediction models are based on AMH levels. Early in 2008, Broekmans’s team in the Netherlands examined AMH levels in a cross-sectional group of 144 normal and fertile female volunteers. This study combined the age and AMH level to predict the age of menopause and compared the results with the actual age of menopause in the postmenopausal population. There was good agreement between the observed distribution of the ages at menopause and that predicted from declining AMH levels, supporting the hypothesis that AMH levels are related to the onset of menopause [53]. Subsequently, the Iranian scholars, Tehrani et al., conducted a 3-year prospective study that also showed that the AMH level was a good predictor of menopause and revealed that only 10% of women with an AMH level of >0.39 ng/mL in the late reproductive period would reach menopause within 6  years [55]. In 2011, Broekmans’ steam compared the predictive power of the AMH and FSH levels and the AFC value in 257 norm ovulatory women aged 21–46 years and found that chronological age, AMH levels, and AFC values were significantly associated with the remaining ovarian life (i.e., time to menopause). After adjusting for age, only the AMH level contributed to the prediction of the time to menopause. Using age and AMH levels, the age range in which menopause will subsequently occur can be individually calculated [56]. Similarly, data from Penn’s 14-year cohort study of ovarian aging showed that AMH levels combined with age strongly improved the predictions. Among women with a baseline AMH level below 0.20  ng/mL, the median time to menopause was 5.99  years in the 45- to 48-year age group and 9.94  years (95% CI, 3.31–12.73) in the 35-to 39-year age group. With higher baseline AMH levels above 1.50 ng/mL, the median time to menopause was 6.23 years in the oldest age group and more than 13.01  years in the youngest age group. This study also suggested that AMH levels strongly predicted the time to menopause; age further improved the prediction accuracy. In 2013, Tehrani’s team expanded the sample size to 1015 patients on the basis of their previous study, the Tehran Lipid and Glucose Study, and constructed an accelerated aging

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model, which indicated that AMH levels combined with age can predict the age of menopause many years earlier [57]. They measured AMH levels in healthy women, aged 20 to 50  years, and prospectively followed them to establish a model for predicting menopausal age. From their model, the age was at menopause calculated as follows:

{

Menopause age = éë - ln ( 0.5 ) ùû

0.060388

}´ e

( 3.18019+0.1608897 AMH +0.016068 age )

Meanwhile, for convenient clinical application, a table of the estimated ages at menopause and their 95% confidence intervals for a range of AMH levels and ages was prepared, with a model adequacy of 92%. In 2016, Broekmans’s team followed up their previous cohort and confirmed the accuracy of using AMH levels to predict natural menopause in a cohort of healthy women with regular menstrual cycles, although they questioned the accuracy of individual predictions. This study found that the predictive value of the AMH level decreased with increasing age, the prediction intervals were broad, and extreme ages at menopause could not be predicted, making this marker currently unsuitable for use in clinical practice [58]. To solve this problem, Broekmans’s team assessed 2434 premenopausal women from the population-­ based Doetinchem Cohort Study. Participants were followed up every 5 years in the span of 20 years, and AMH levels were measured in 6699 plasma samples to investigate whether using individual AMH level decline patterns improved the prediction of menopause compared with using a single measurement. However, assessing the individual AMH level changes did not improve the prediction of the menopausal age. Based on a low discriminative ability and an underestimation of the risk of early menopause, the use of AMH levels as a screening method for the timing of menopause cannot be advocated [59]. Alternatively, the Tehrani team improved the existing prediction models for age at menopause. The flexible parametric survival model and spline-based proportional odds model provided the most clinically relevant and realistic predictions [60]. Furthermore, the Tehrani team also used individual AMH level trajectories to predict each woman’s age at menopause. Different from Broekmans’s conclusion, they proposed that longitudinal measurements of AMH levels be used to individualize the prediction of menopause [61]. Besides the prediction of natural menopause, some researchers have studied the relationship of ovarian reserve markers and early menopause. A nested case-control study within the prospective Nurses’ Health Study II cohort demonstrated that a 0.10 ng/mL decrease in AMH level was associated with a 14% higher risk of early menopause, after adjusting for matching factors, BMI, smoking, parity, oral contraceptive use, and other factors [62]. In addition, other scholars compared the relationship between AMH levels, FSH levels, and AFC values with the 5-year menopause rate

and found that the ability to predict the onset of menopause was improved with consideration of any of the three menopausal markers in combination with age. AMH concentrations were more closely associated with menopause than AFC values or FSH concentrations [63]. After decades of research, AMH is the best indicator for predicting menopausal age. For most women, the higher the AMH level, the later the menopausal age; conversely, the lower the AMH level, the earlier the age of menopause. However, for individualized predictions, AMH concentrations still cannot accurately predict menopause, even with multiple measurements, which poses a challenge for future research on accurate individualized prediction of menopausal age.

6.3.1.2 Ultrasound Markers The size of the follicular pool decreases gradually with age, a process that begins in the fetus. Gougeon et  al. demonstrated that transvaginal ultrasonography showed a correlation between the total number of antral follicles in bilateral ovaries with diameters of 2–10 mm and the size of the primordial follicular pool. Therefore, AFC can be used as an appropriate ultrasound marker for quantitative evaluation of ovarian reserve and can also be used to predict menopausal age. Broekmans et al. revealed the relationship between AFC loss and key reproductive events, such as loss of natural fertility and menopause, through a nonlinear model [64]. Due to the obvious advantages of using the AMH level to predict the age of menopause, although there are few studies on AFC alone in predicting the age of menopause, AFC still has potential value in predicting the age of menopause. 6.3.1.3 Other Markers: Genetic Markers Genome-wide association studies (GWAS) succeeded in revealing the genetic determinants of natural menopausal age. A 2014 GWAS identified 17 new gene loci associated with natural menopausal age. However, the functional ­characterization of these new loci, genes, and their application in menopause prediction need further study [65]. In a GWAS study of menopausal age in the Asian population, 8073 women from Shanghai, China were included in Stage I in order to discover promising novel loci. Then, the Stage II replication study used the data from another Chinese GWAS (n = 1458), a Korean GWAS (n = 1739), and de novo genotyping of an additional 2877 Chinese women. Previous GWAS-identified loci for age at menopause were also evaluated. Of the 20 menopause loci previously identified in women of European ancestry, five were identified in East Asian women, suggesting a shared genetic architecture for these two traits across populations [66]. In order to discover and verify specific menopausal age-related loci in the Chinese population, 22 menopausal age-related single nucleotide polymorphism (SNP) loci confirmed in the GWAS study were examined in 3533 Chinese women with natural

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menopause in 2016. As a result, rs4246511 (rhBDL2), rs12461110 (NLRP11), rs2307449 (POLG), rs12611091 (BRSK1), rs1172822 (BRSK1), eight SNPs, rs365132 (UIMC1), rs2720044 (ASH2L), and rs7246479 (TMEM150B) were associated with menopausal age in the Chinese population [67]. In the process of ovarian aging, the decline of ovarian reproductive and endocrine function shows a certain regularity, which could be used by a mathematical model to predict the endpoint menopausal age. However, this process is affected by many other factors that could delay or accelerate ovarian aging. Great progress has been made on menopause prediction, from the initial cross-sectional study to long-term prospective studies, from the cohort study to individualized clinical predictions, and from a single marker to multifactor modeling. The most valuable markers are the serum AMH level and AFC. Genetic markers also have potential predictive value. Although these studies have great significance for predicting a woman’s menopausal age and providing guidance and suggestions for clinical diagnosis, treatment, and daily life, they still have limitations. Factors that affect ovarian function, such as age, race, genetic background, maternal age at menopause, BMI, smoking, radiotherapy and chemotherapy, and ovarian surgery, should be taken into account to adjust the final prediction. Large numbers of high-quality prospective cohort studies and analyses are still needed to achieve a more accurate prediction of menopausal age and to allow comprehensive application in clinical practice.

6.3.2 Prediction of Reproductive Potential Xiaofang Du With social progress and economic development, the overall status of women in society has improved, allowing a worldwide trend for women, especially working women, to delay motherhood. When women enter their middle and late reproductive age, their fertility gradually decreases, generally beginning after the age of 30 years, with the decline accelerating after the age of 37.5  years. Complete fertility loss occurs several years before menopause, and some women may experience a natural decline of fertility even before the average age of fertility decline [68]. Therefore, some women with late family planning often face difficulties in getting pregnant. If the natural ovarian fertility can be predicted accurately, it can provide a reference for women to carry out timely fertility planning and to seek the necessary medical assistance. In recent years, the rapid development of ART has benefited infertile patients, and one of the key factors affecting the outcome of IVF-ET is the ovarian response of infertile women during COH. Ovarian reactivity, the response of the ovary to

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gonadotropins in the process of controlled hyperovulation induction, can be roughly divided into three categories: overresponse, normal response, and low response. In 1983, Garcia put forward the concept of POR. It is generally believed that ovarian dysfunction is the main cause of low response. POR is a challenge in ART.  Predicting ovarian reactivity before COH can help with the selection of a reasonable treatment plan; allow adjustments to the dosage of medications; enable treatment in advance, if necessary; improve the success rate of assisted reproduction; and reduce the economic burden of patients. Therefore, the assessment and prediction of natural fertility, as well as the prediction of ovarian reactivity to COH in infertile patients, can provide direct guidance for female fertility planning, which is of great significance. Next, the current research on predicting ovarian reproductive function by various indexes will be discussed.

6.3.2.1 Age Age is a simple predictor of ovarian reproductive function. Studies have shown that women’s natural fertility declines with age after they reach their 30  s. Wesselink et  al. conducted a prospective cohort study to investigate the relationship between female age and natural fertility. North American women, aged 21 to 45 years, who were planning to become pregnant were followed. After analyzing the data of 2962 couples, they found that the cumulative PR of six trial cycles in the older group (40–45 years old) was 27.6%, while that in the younger group (28–30 years old) was 62.0%. The cumulative PR of 12 trial cycles in the older and younger groups were 55.5% and 79.3%, respectively. That is, with an increase in female age, fertility shows an almost linear decline [69]. In addition, with increased age, ovarian reserve function decreases, which also leads to a decrease in ovarian responsiveness in IVF and a decrease in the success rate of ART.  Although older women still have a small number of follicles sufficient to sustain menstruation, reduced egg ­quality reduces the chances of fertilization, implantation, and early embryo development. Women aged ≥40 years are recognized as a group with high incidence of adverse ovarian reactions, and the success rate of assisted pregnancy is very low. The research of Zhang Lizhu showed that for a given dose of superovulation induction drugs, the ovarian response gradually weakened with an increase in age, and this weakening trend was more obvious after the age of 35 years. 6.3.2.2 Menstrual Changes Research has shown that menstrual patterns are linked to a woman’s fertility. Zhang et al. designed a prospective cohort study among rural women in China to investigate the relationship between the age of menarche, length of menstrual cycle, duration of menstrual period, and time to pregnancy (TTP). A total of 391,320 rural fertile women were followed up, and women with menarche in the ages between 13 and

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14 years were more likely to become pregnant than women with menarche after the age of 14 years. Women with a menstrual cycle lasting more than 29  days were less likely to become pregnant than women with a menstrual cycle of 27 to 29 days. Those with a period lasting less than 4 days or more than 5 days had a lower pregnancy rate than those with a period lasting 4–5 days. In Chinese rural women, late onset of menorrhea, a short menstrual period (5 days) are associated with lower natural fertility and longer conception time [70]. In addition, Lum confirmed that changes in the menstrual cycle predict an increased risk of decline in fertility in a prospective cohort study in 2016. A Bayesian joint model between the menstrual cycle length and natural fertility revealed that fertile women had already started ovarian aging before a clinically visible menstrual cycle [71].

6.3.2.3 Endocrine Markers In addition to age and menstrual status, which are easy data to obtain, many endocrine indicators reflecting ovarian function and reserve are also widely used in clinical practice, including FSH, E2, AMH, and inhibin B.  These indicators have predictive value for female fertility, including natural fertility, IVF, ovarian reactivity, and pregnancy outcomes. FSH Van der Steeg et al. followed up and observed 3159 infertile couples with unknown causes to study the relationship between the serum FSH level at the early stage of follicular development and the possibility of spontaneous pregnancy within 12 months; women with serum FSH values over 8 mIU/ mL had a lower possibility of pregnancy [72]. Steiner et al. studied the relationship between urinary FSH levels and natural fertility by following up 209 women aged 30 to 44 years. Low urinary FSH levels (12 mU/mg Cr) at the early stage of follicular development corresponded to a slight decrease in fertility, but the difference was not statistically significant compared with those with FSH levels within the normal range [73]. Basal FSH levels can predict not only natural fertility but also ovarian reactivity and pregnancy outcomes during IVF.  In 1988, Muasher et  al. from the Norfolk IVF project group first reported that the basal FSH level could predict ovarian responsiveness and pregnancy outcome during the IVF cycle and showed a negative correlation between the FSH level and IVF pregnancy rate [74]. Since then, many scholars have also successively confirmed that an increase in FSH indicates the decline of ovarian reserve, and those with high FSH levels have poor ovarian reactivity, fewer developing follicles and eggs, fewer high-quality embryos available for transplantation, a higher IVF cycle cancelation rate, and a lower pregnancy rate [75]. When ovarian function began to decline, FSH tended to rise, but was not stable. This is because

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the transient increase in the FSH level can accelerate the development of residual follicles, increase the secretion of estrogen, and support feedback inhibition of FSH secretion. In women with basal FSH levels within the normal range, the variation in FSH values measured during different natural cycles was small (mean 2.6  ±  0.2  IU/L) and was larger in women with high basal FSH levels (mean 7.4  ±  0.7  IU/L). The study found that patients with fluctuating FSH levels were generally in a declining stage of ovarian reserve function, so the response to COH was poor and the cycle cancelation rate was high. Once the ovum collection stage has been reached, these patients, especially those that are younger (