Advances in multi-omics and aging clock research for female reproductive health and aging

Abstract

With the global increase in life expectancy, the aging of the female reproductive system has become a critical area of focus due to its profound implications for overall health and quality of life. Advances in multi-omics technologies, encompassing epigenetics, transcriptomics, proteomics, metabolomics, and microbiomics, have provided transformative insights into the complex biology of female reproductive health and aging. Computational models called aging clocks, based on these approaches, enable the precise assessment of biological age, identification of tissue-specific vulnerabilities, and elucidation of systemic aging patterns. While multi-omics and aging clock research in the female reproductive tract remains an evolving field, the growing availability of high-quality studies and resources offers promising opportunities to advance our understanding of reproductive aging and other significant issues in female reproductive health, such as infertility and pregnancy complications.

Keywords

aging / multi-omics / aging clocks / female reproductive tract / female reproductive health

1 Introduction

Global population longevity continues to rise, yet the extension of healthy lifespan lags behind. Along with age, there is a growing awareness of the need to address age-related physical and psychological challenges. Among these challenges, reproductive aging has garnered increasing attention. Traditionally, reproductive aging has been viewed through the lens of chronological age, menopause, and childbirth history. However, the emerging concept of “biological age,” informed by multi-omics research, offers a novel perspective on aging.

The study of aging has been revolutionized by multi-omics approaches, which integrate data from genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics. These approaches enable a holistic understanding of molecular changes across biological systems, facilitating the identification of biomarkers—measurable indicators of biological processes, disease states, or therapeutic responses [1]. Aging clocks, as computational models, leverage these biomarkers to estimate biological age, which may deviate from chronological age, providing insights into the rate of aging and its impact on health outcomes [2,3]. By integrating biomarkers and multi-omics data, aging clocks serve as predictive models for understanding and potentially intervening in the aging process [4].

Several multi-omics and aging clock studies have been applied to the female reproductive tract, including aging studies in the ovary, uterus, cervix, and vagina, all providing deeper insights into its aging processes [5–11]. While chronological age remains the primary metric in clinical reproductive medicine, it is increasingly recognized as an imperfect proxy for physiologic decline due to inter-individual variability. Conceptually, “reproductive age” is defined as a distinct entity reflecting the functional reserve and cellular integrity of the reproductive system, suggesting that reproductive aging is not merely a sub-component of general organismal aging but a driver that influences systemic health. Therefore, it is essential to establish a multi-omics framework to measure this specific biological age. This review will comprehensively examine aging phenotypes in the female reproductive system, with a particular focus on research involving multi-omics studies and aging clocks (Fig. 1). Furthermore, it will propose future research directions to advance our understanding of female reproductive system aging.

2 An overview of female reproductive tract aging

Aging of the female reproductive system is a complex, multifactorial process involving shared mechanisms across various reproductive organs. Central to these changes are the depletion of estrogen, cellular senescence, oxidative stress, and inflammation. These factors contribute to structural and functional declines, including tissue atrophy, fibrosis, and impaired immune responses [12]. Ovarian aging, for example, manifests as a reduction in both oocyte quantity and quality, leading to fertility decline and increased susceptibility to age-related conditions such as osteoporosis and cardiovascular disease [13,14]. Similar processes of estrogen deficiency and immune senescence are observed in the lower reproductive tract, where aging results in structural changes such as uterine fibrosis [15], vaginal atrophy [16], and cervical vulnerability to infections and cancer [17]. These alterations across the entire reproductive system not only disrupt fertility but also impair the overall quality of life, with consequences that extend beyond the reproductive years [18].

Ovarian aging is driven by oxidative stress, mitochondrial dysfunction, genomic instability, telomere attrition, epigenetic alterations, and cellular senescence [19]. Beyond these, specific epigenetic changes, like altered DNA methylation patterns and non-coding RNA expression [20], alongside impaired protein homeostasis and dysregulated energy metabolism [21], also contribute to ovarian decline [13]. The accumulation of reactive oxygen species (ROS) and mitochondrial damage, along with defects in DNA repair mechanisms such as BRCA1 and ATM, accelerates follicular depletion and impairs oocyte quality, while telomere shortening further exacerbates this decline [22,23]. The dysfunction of the hypothalamic-pituitary-ovarian (HPO) axis [24], altered secretion of key neurotransmitters [25], and inflammation within the ovarian microenvironment including fibrosis [26] and vascular aging [27] also play central roles in ovarian aging [28]. Ovarian estrogen depletion exerts systemic endocrine effects that promote uterine fibrosis. Loss of estradiol disrupts HPO axis feedback and removes estrogen’s restraint on TGF-β signaling, leading to increased collagen deposition and extracellular matrix (ECM) stiffness [29]. These endocrine-paracrine effects explain why menopause and ovarian insufficiency are linked with progressive endometrial and myometrial fibrosis even without local injury. Clinically, ovarian aging manifests as menstrual irregularities, diminished ovarian reserve, poor response to IVF stimulation, and increased risks of menopause-related conditions, including osteoporosis, cardiovascular disease, and cognitive decline [30,31]. Pathologically, premature ovarian insufficiency (POI), often resulting from genetic mutations, environmental factors, or medical treatments, represents a significant form of early ovarian aging [32,33]. The early onset of ovarian aging accelerates health risks in postmenopausal women, with potential long-term systemic effects [34,35].

The aging process in the female lower reproductive tract involves complex structural, functional, and immunological alterations driven primarily by estrogen depletion. In the uterus, aging manifests as reduced volume and increased collagen deposition in the endometrium and myometrium [36,37]. Endometrial atrophy features thinning and diminished proliferative capacity, which is linked to mTOR pathway dysregulation and autophagy [38–40]. Concurrently, TGF-β pathway overactivation due to estrogen deficiency promotes fibrosis through increased collagen synthesis, reduced ECM degradation via TIMP upregulation and MMP suppression, and tissue stiffening [41,42]. Immune changes include upregulation of pro-inflammatory cytokines [43], NF-κB activation [44], decreased dendritic cells [45], and impaired macrophage M1-M2 polarization [46], all of which contribute to chronic inflammation and a senescence-associated secretory phenotype (SASP). These collectively reduce endometrial receptivity [47] and increase the risk of pelvic organ prolapse (POP) [48].

Cervical aging involves epithelial thinning, reduced cellular turnover, and stromal collagen/elastin loss [49], compounded by immune senescence featuring decreased cytotoxic cell activity [50], impaired antimicrobial peptide secretion, and altered dendritic cell function [51]. This environment facilitates persistent HPV infection and carcinogenesis, with SASP-mediated inflammation [52], genomic instability [53], and microbiome shifts [54]. Vaginal aging, central to genitourinary syndrome of menopause (GSM) affecting over half of postmenopausal women [55], exhibits epithelial thinning via mTORC1 dysregulation [56], ECM collagen/elastin reduction, oxidative stress-induced fibroblast apoptosis, and vascular impairment [57]. Microbiome disruption, including elevated pH, loss of Lactobacillus crispatus, and proliferation of Prevotella and Gardnerella, further heightens vulnerability to inflammation and infection [58].

Vaginal dysbiosis, characterized by the loss of Lactobacillus and increased anaerobes, triggers local inflammation and leakage of microbial metabolites that modulate systemic immune and metabolic pathways [59]. These perturbations act via the gut–ovary axis, which comprises several mechanistic routes: microbial β-glucuronidase activity deconjugates estrogens in the gut, microbial metabolites act on granulosa and theca cells to regulate inflammation, mitochondrial function, and gene expression, immune modulation via translocated LPS and microbial-associated molecular patterns induce low-grade systemic inflammation, insulin resistance, and oxidative stress in the ovary, and neuroendocrine signaling via the gut–brain axis influences GnRH release and downstream gonadotropin dynamics, indirectly affecting ovarian function [60,61]. Through these pathways, chronic genital or gut dysbiosis may accelerate ovarian aging, disrupt AMH/FSH dynamics, and contribute to POI-like phenotypes.

3 Multi-omics clocks in aging research: from molecular mechanisms to systemic insights

Different omics-based aging clocks exhibit distinct functional characteristics. Epigenetic clocks characterize cumulative, long-term molecular aging and are effective for predicting longitudinal outcomes such as ovarian reserve loss, as they remain largely unaffected by rapid hormonal fluctuations. Transcriptomic clocks focus on cell-type-specific functional decline and mechanistic pathways, providing detailed insights into processes like folliculogenesis but requiring high-quality tissue inputs. Proteomic and metabolomic clocks detect functional metabolic changes and the quality of the follicular microenvironment, serving as sensitive markers for short- to mid-term decline despite limited cellular resolution. Finally, microbiome-based clocks noninvasively assess host-environment interactions, capturing dysbiosis in the gut and vaginal compartments that influences estrogen metabolism and ovarian function.

3.1 Epigenetic clocks: DNA methylation-driven aging assessment

Epigenomic clocks are aging biomarkers based on DNA methylation patterns, offering precise estimates of biological age and showing strong correlations with healthspan, mortality, and age-related diseases. These clocks are essential for understanding the molecular underpinnings of aging and assessing aging-related risks [62,63]. The Horvath clock, introduced in 2013, was the first widely applicable epigenetic clock. It uses methylation data from over 350 CpG sites to estimate biological age across various tissues, providing a versatile and standardized tool for aging research [64]. The GrimAge clock, developed in 2019, advanced this field by integrating plasma protein markers and smoking history, significantly improving predictions of time-to-death and age-related diseases [65]. Other notable clocks include the Hannum clock—one of the earliest epigenetic clocks, which focuses on blood-specific methylation markers [66]—and the PhenoAge clock, which links DNA methylation to phenotypic aging traits, such as physical function and disease risk [67].

3.2 Transcriptomic clocks and single-cell atlases: decoding dynamic aging mechanisms

While transcriptomic aging clocks estimate biological age using gene expression data, single-cell transcriptomic atlases offer a comprehensive view of aging by integrating data across tissues, age groups, and physiologic states. Together, they enable both precise age prediction and a systemic understanding of aging processes [68,69]. Transcriptomic aging clocks, such as RAPToR [70] and MultiTIMER [71], are pivotal for aging research. RAPToR uses time-series gene expression data to estimate accurate biological age across tissues, single cells, and even species, effectively removing age as a confounding factor in gene expression studies. MultiTIMER extends this approach by integrating data across multiple tissues, highlighting tissue-specific aging dynamics and providing a holistic perspective on biological aging. Other clocks, like the BiT age clock, address variability in transcriptomic datasets to refine predictions, while cell-type-specific and spatial transcriptomic clocks reveal aging signatures at the cellular level and uncover the influence of cell interactions on tissue aging [72].

On the other hand, single-cell transcriptomic atlases provide a standardized reference for aging research by integrating data across tissues and physiologic states. These atlases enable cross-tissue comparisons and systemic analyses, revealing key age-related impairments such as reduced stem cell motility, angiogenesis, and macrophage activation, which contribute to regenerative decline [73,74]. For example, single-cell atlases have been used to uncover cell-type-specific vulnerabilities in the aging retinas [75] and skin [76], while other studies have highlighted immune aging patterns, such as inflammaging, which is characterized by chronic inflammation and immune dysfunction [77].

Spatial and temporal transcriptomics build upon single-cell analyses by adding critical dimensions to the study of aging. Spatial transcriptomics maps gene expression while preserving the physical context of tissues, enabling researchers to identify localized patterns of aging [78,79]. Temporal transcriptomics, on the other hand, examines gene expression changes over time, revealing dynamic trajectories of aging across tissues. For example, spatial transcriptomic studies have highlighted specific regions in tissues associated with aging markers, such as IgG accumulation, which may offer therapeutic targets [80]. Temporal analyses have uncovered that aging progresses asynchronously across tissues, with early changes observed in systems like adipose and immune tissues, which are linked to broader systemic factors such as plasma protein alterations [81].

3.3 Proteomic and metabolomic clocks: functional biomolecule tracking

Unlike epigenomic clocks and transcriptomic clocks, which capture dynamic changes, proteomic and metabolomic clocks provide direct links to physiologic and metabolic processes, making them particularly useful for tracking organ-specific aging and systemic health. Proteomic clocks utilize plasma protein profiles to estimate biological age and have demonstrated strong associations with physical and cognitive function, as well as health markers [82]. Studies have shown that plasma proteomic clocks can accurately predict chronological age and are closely associated with traits such as cognitive function, motor ability, and mortality risk [4]. Additionally, recent advancements have introduced organ-specific proteomic clocks, which can track aging trajectories in individual organs and predict disease risk [83]. A systematic review and meta-analysis identified 1128 proteins that change with age across multiple studies, highlighting 32 proteins consistently linked to aging and age-related diseases. A novel proteomic aging clock was thus developed, which demonstrated its ability to predict biological age in a cohort of 3301 individuals. Key proteins like GDF15 and APOE were found to play significant roles in aging [84].

Metabolomic clocks stand as one of the youngest and fastest-evolving disciplines within the realm of “omics” sciences and are closer to the aging phenotype in omics research, providing a wealth of biological information for quantifying aging [85]. Several metabolites have been consistently associated with aging and age-related outcomes, including lipid-related metabolites such as very low-density lipoprotein (VLDL) particles, triglycerides, and phosphatidylcholines, as well as amino acids like branched-chain amino acids (BCAAs) and aromatic amino acids—all of which show significant associations with aging processes [86]. Additionally, albumin, a key marker of systemic health, is often inversely correlated with biological aging [87], while energy metabolism-related metabolites such as glucose, lactate, and ketone bodies reflect shifts in metabolic pathways that occur with age [88].

Several metabolic aging clocks have been developed based on these findings, including the MileAge Clock, which integrates multiple machine learning algorithms to predict health outcomes and lifespan [89], and the LipidClock—a lipidomics-based aging clock that focuses on fatty acid and complex lipid profiles to enhance aging predictions [90]. A Metabolomic Aging Clock, based on data from 250 341 UK Biobank participants, identified 54 aging-related biomarkers and developed a score that predicts short-term mortality and aging acceleration, supported by 439 “biomarker-disease” causal pairs and longitudinal data from 13 263 individuals [91]. The MetaboAgeMort Clock, using data from 239 291 participants, showed strong associations with 10-year mortality, identified 99 modifiable factors and 99 genomic loci in metabolic aging [92]. In general, proteomic analyses in diverse populations have shown how aging-related biomarkers can vary based on geographical and genetic differences, providing insights into how global health trajectories can be modeled [93,94].

3.4 Microbiome-based clocks: host-environment interaction profiling

Microbiome research uniquely reflects the direct interaction between the host and its external environment, serving as a dynamic interface that mediates exposure to pollutants, dietary components, and toxins, influencing their metabolism and impact on health [95]. Additionally, the microbiome is shaped by the built environment, highlighting its role as a mediator between external factors and physiologic responses, a feature less emphasized in other omics fields [96].

Although the human body hosts diverse microbiomes across environments such as the skin, oral cavity, and respiratory tract, the gut microbiome remains the most extensively studied in microbiome-based research. A gut microbiome clock was developed using deep learning, achieving a mean absolute error (MAE) of 5.9 years in predicting chronological age and detecting accelerated microbiome aging in individuals with type 1 diabetes [97]. Additionally, the accuracy of aging clocks was further refined by integrating metatranscriptomic gut microbiome data from over 90 000 individuals with blood transcriptome data, explaining 46% and 53% of the variance in age using microbiome and transcriptome data, respectively [98].

There are also studies on microbiomes not only include the gut. For example, models using skin, oral, and gut microbiota predicted chronological age with MAEs of 3.8, 4.5, and 5.0 years, respectively, demonstrating their potential as aging biomarkers [99]. In the oral microbiome, microbial diversity was found to decrease with age and frailty, with pathogenic taxa becoming more abundant, linking oral health to aging [100]. Similarly, nasopharyngeal microbiota diversity declines with age, while taxa like Streptococcus pneumoniae increase, influenced by environmental and lifestyle factors [101]. The skin microbiome also reflects intrinsic aging and environmental exposures, with shifts in bacteria like Cutibacterium acnes correlating with age-related changes [107].

Different omics-based aging clocks offer complementary strengths for predicting reproductive aging phenotypes such as ovarian reserve decline and endometrial aging. Epigenetic clocks tend to reflect cumulative, long-term molecular aging and therefore are well suited to predict longitudinal outcomes like accelerated loss of ovarian reserve or earlier menopause; they can be applied to ovarian tissue or peripheral blood to estimate reproductive biological age but are relatively insensitive to rapid, cycle-linked hormonal fluctuations [102,108]. Transcriptomic clocks, especially when combined with single-cell or spatial transcriptomics, excel at revealing cell-type-specific functional decline. For example, they can detect the downregulation of granulosa cell genes involved in folliculogenesis or impaired stromal decidualization programs in aging endometrium, providing mechanistic insight but requiring high-quality tissue sampling and complex data integration [103]. Proteomic and metabolomic clocks capture functional metabolic changes that closely track follicular microenvironment quality [104] and endometrial receptivity [105], making them sensitive markers of short- to mid-term functional decline though with limited cellular resolution. Microbiome-based clocks can noninvasively reflect host–environment interactions influencing reproductive aging. Both the gut and vaginal microbiomes are implicated in this process: gut microbial dysbiosis alters estrogen metabolism and ovarian function through the gut–ovary axis [109], while age-related shifts in the vaginal microbiome—marked by the loss of Lactobacillus dominance and enrichment of anaerobes—are linked to declining ovarian reserve and adverse reproductive outcomes [106]. In the following sections, this article will explain in detail the application of each aging clock in the female reproductive system (Table 1).

4 Multi-omics insights into ovary aging and infertility research

4.1 Epigenetic mechanisms and aging clocks studies in ovarian aging

Epigenomics, the study of heritable changes in gene expression that do not involve alterations to the underlying DNA sequence, offers crucial insights into ovary aging, and the epigenetic clock is a commonly used methodology [108] (Table 2). For instance, a study employing Horvath algorithm and telomere length measurements in infertile women revealed that while the Horvath epigenetic clock accurately predicts chronological age in white blood cells, it yields a significantly “younger” predicted age for cumulus cells (CCs). This discrepancy, unique to CCs and stem cells, suggests a distinct epigenetic aging pattern in these ovarian somatic cells, potentially attributed to their quiescent state or interaction with oocytes. Furthermore, this research indicated that younger women experiencing poor ovarian response exhibited epigenetic age acceleration in their WBC samples, linking systemic epigenetic changes to ovarian function decline [110].

Age acceleration (AgeAccel), defined as biological age exceeding chronological age, correlates with decreased anti-Müllerian hormone (AMH) levels and reduced oocyte yield, thus serving as a potential epigenetic biomarker for ovarian reserve assessment [111]. AgeAccel is also associated with earlier menopause, suggesting a causal link between epigenetic aging and the timing of reproductive senescence. Lifestyle interventions and certain pharmacological agents show promise in mitigating AgeAccel, indicating potential strategies for delaying reproductive aging [108]. Research utilizing a bovine model demonstrated that epigenetic clocks can be developed for oocytes and blood. Oocytes exhibited fewer age-related CpG sites and a slower rate of epigenetic aging compared to blood, despite appearing epigenetically “older” at baseline, underscoring tissue-specific aging trajectories [112].

Furthermore, RNA methylation, particularly N⁶-methyladenosine (m⁶A), contributes to ovarian aging by impacting translational efficiency in oocytes—a mechanism that shows species-specific differences between mice and humans. Histone modifications and chromatin accessibility are also critical epigenetic factors in ovarian aging. Reductions in the translational levels of key DNA methylation regulators and histone modification regulators are observed in aged mouse oocytes, indicating a decline in epigenetic maintenance during aging [113]. Mitochondrial metabolites, such as acetyl-coenzyme A and alpha-ketoglutarate, serve as cofactors for epigenetic enzymes, directly linking cellular metabolism to epigenetic regulation in the context of aging [114].

The impact of reproductive efforts on epigenetic aging is also a growing area of study. A study investigating the relationship between reproductive history and cellular aging in young Filipino women, utilizing DNA methylation age (DNAmAge) measurements and Horvath clock, found that the number of pregnancies was associated with shorter TL and increased DNAmAge acceleration, suggesting a biological cost of reproduction on cellular aging [115]. Conversely, research on mothers demonstrated that increased reproductive efforts were significantly associated with decelerated aging, mediated by an increase in gray matter volume in the left precuneus, indicating a complex interplay between reproductive experiences and biological age [116]. The contradictory findings on whether reproduction accelerates or decelerates epigenetic aging likely stem from fundamental differences in study design. One study focused on the direct physiologic burden of pregnancy in a cohort of very young women, while the other study examined an older cohort of mothers and used broader indices of “motherhood,” potentially capturing the long-term, adaptive aspects of parenting. Meanwhile, the first study investigated the “costs of reproduction” by controlling for socioeconomic factors, and the second introduced a novel neurobiological dimension, finding that structural brain changes—specifically increased precuneus gray matter volume—mediated the relationship between parity and age deceleration. Therefore, the first study captures the immediate biological toll of pregnancy in early adulthood, while the other highlights a potential long-term, adaptive mechanism associated with active motherhood in a different life stage.

To delve deeper into the complex molecular mechanisms of ovarian aging, single-cell technologies have recently been widely applied to reveal cell-type-specific changes during the aging process. A single-nuclei multi-omics atlas of young and reproductively aged human ovaries, utilizing single-nuclei RNA sequencing (snRNA-seq) and single-nuclei ATAC sequencing (snATAC-seq), identified coordinated transcriptomic and chromatin accessibility changes across ovarian cell types during aging [117]. Beyond the ovary, other critical reproductive organs also undergo age-related changes. For instance, single-nuclei RNA sequencing and single-nuclei ATAC sequencing of human fallopian tubes across menstrual cycle phases and menopause revealed substantial shifts in cell type frequencies, gene expression, transcription factor activity, and chromatin accessibility during aging. Postmenopausal fallopian tubes exhibited increased chromatin accessibility in aging-associated TFs, reflecting molecular changes linked to reproductive aging [118].

4.2 Transcriptional omics in ovarian aging and related diseases

Transcriptomics provides a dynamic view of gene activity during ovary aging, with advanced techniques like scRNA-seq and spatial transcriptomics enabling high-resolution analysis. In aged mouse ovaries, scRNA-seq and scATAC-seq revealed cell-type-specific transcriptional changes, including altered TGF-β signaling in mesenchymal cells leading to fibrosis, and ER stress in granulosa cells linked to apoptosis [119]. Ovarian aging involves significant shifts in cellular composition and thousands of differentially expressed genes enriched in hallmarks of aging like nutrient sensing, cellular senescence, and mitochondrial function. mTOR signaling is a prominent ovary-specific aging pathway, and oxidative phosphorylation shows the highest activity change [117]. Markers of cellular senescence, such as CDKN1A/p21 expression, are also observed in various ovarian cell types [120].

Oocyte transcriptomics indicates age-related changes in genes affecting longevity, cell cycle, meiosis, and ubiquitination, all of which impact oocyte quality [121]. Single-cell RNA-seq shows aging profoundly affects metaphase II oocyte transcriptome, increasing genes for chromosome segregation and RNA splicing, while decreasing mitochondrial activity genes, with species-specific differences between mice and humans [113].

Spatial transcriptomics offers insights into ovarian tissue heterogeneity. In mice, it has characterized granulosa cell subtypes and distinguished healthy from atretic follicles, identifying Onecut2-positive luteal cells [122]. In human ovaries, it has identified FOXP1 as a key transcriptional regulator of ovarian cellular senescence, with its expression declining with age [120]. Research on ovarian blood vessel (oBV) aging in mice showed decreased oBV density and angiogenesis, leading to diminished ovarian blood supply, primarily due to vascular endothelium aging, which salidroside could reverse [123]. Transcriptomic profiling of individual cortical follicles revealed heterogeneity and chemotherapy-induced gene expression changes [124]. There is also temporal transcriptomic research that, according to GTExdata analysis, shows sex-dimorphic 12-h rhythmic gene expression in human ovaries with midlife as a critical reprogramming period for angiogenesis-related genes [125].

Multi-omics approaches are also extensively applied in ovarian cancer research, sharing biological processes with aging and aiding in identifying biomarkers and understanding molecular heterogeneity [126–129]. For example, integrated multi-omic analyses have identified super-enhancers driving oncogenesis in ovarian cancer [130] and characterized heterogeneity in high-grade serous ovarian carcinoma subtype evolution [131]. This can also apply to some special populations, such as post-chemotherapy cancer survivors and women undergoing oocyte cryopreservation. Cancer survivors often experience accelerated biological aging, primarily driven by therapy-induced cellular senescence and the associated pro-inflammatory SASP. This acceleration is quantified using epigenetic clocks, which reveal that survivors have a biological age exceeding their chronological age and a faster pace of aging, linked to increased mortality risk [132]. Despite these systemic aging markers, functional reproductive potential can persist, as successful oocyte collection and live births are achievable even in survivors with low ovarian reserve post-chemotherapy/radiotherapy [133]. Fertility preservation procedures themselves also have a molecular impact. Transcriptomic analysis reveals that the vitrification/warming of human ovarian tissue significantly dysregulates genes enriched in inflammatory and cellular stress pathways, such as cytokine and TNF signaling, identifying the process as a cellular stressor [134]. The underlying cancer may also influence oocyte quality, as patients with breast or hematologic cancers have shown lower oocyte maturation rates before treatment, indicating disease-specific effects on reproductive potential [135].

4.3 Proteomic, metabolomic omics studies and gut microbiota in ovarian aging

Proteomic studies have revealed distinct age-dependent changes in ovarian protein profiles. Research on mouse ovaries has shown that oocytes and ovaries contain exceptionally long-lived proteins, but many of these proteostasis network proteins decline with age, contributing to reduced fertility [136]. Quantitative proteomics in mouse ovaries has identified age-related upregulation of immune response and ECM remodeling pathways, alongside downregulation of DNA metabolism and translation pathways, suggesting a “fibroinflammatory milieu” in the aging ovary [137]. High-resolution N-glycoproteomics of aging mouse ovaries has uncovered significant age-related alterations in N-glycan structures, such as core-fucosylation, LacdiNAc glycans, and sialylated glycans, which are implicated in immune activation and fertility [138]. In human ovarian tissue, a proteome-wide and matrisome-specific analysis across different reproductive stages identified 26 differentially expressed matrisome proteins, providing age-specific molecular fingerprints linked to ovarian function [139]. Studies on ovarian granulosa cells from non-elderly women with diminished ovarian reserve have highlighted the significant involvement of oxidative phosphorylation dysfunction, with 4D label-free quantitative proteomics identifying 371 differentially expressed proteins [140]. Additionally, proteomic analysis of postovulatory aging mouse oocytes revealed 76 differentially expressed proteins enriched in gene expression, biosynthesis, RNA metabolism, and cell cycle regulation [141]. Broader reviews indicate that proteomic analyses of MII oocytes often show enrichment in RNA splicing pathways, while cumulus cell proteomics points to age-related upregulation of fatty acid metabolism and downregulation of oxidative phosphorylation [121].

Metabolomic approaches have identified significant age-related shifts in ovarian metabolic profiles. Analysis of follicular fluid exosomes revealed 17 differentially expressed metabolites between young and advanced-age women, closely linked to oocyte count and hormone levels [142]. Studies on FF and granulosa cell metabolomics have pinpointed GABA and succinic acid as potential therapeutic targets for ovarian aging, noting their reduced levels in advanced age [143]. Serum metabolomic profiling of women with poor ovarian response identified 538 differentially expressed metabolites, emphasizing the nicotinate and nicotinamide metabolism pathway as a potential predictive biomarker [144]. Age-dependent metabolomic changes in human FF include significantly higher levels of creatine, histidine, methionine, trans-4-hydroxyproline, choline, mevalonate, N2,N2-dimethylguanosine, and gamma-glutamylvaline in older women [145]. Reviews of metabolomics in infertility indicate that in aging and endometriosis, glycolysis and lipid metabolism are upregulated to compensate for mitochondrial dysfunction, whereas polycystic ovary syndrome (PCOS) and obesity show an opposite trend, and hypoxanthine and xanthine levels can signal ATP depletion [146]. Furthermore, metabolomic reviews on POR highlight variations across studies but consistently report downregulation of prostaglandin-related metabolites in POR/DOR groups, and note that growth hormone and dehydroepiandrosterone supplementation can alter FF metabolite profiles [147]. Broader omics reviews also emphasize the roles of NAD⁺ metabolism, branched-chain amino acids, spermidine, choline, and plasmalogens as key metabolic areas affected by ovarian aging [121].

Meanwhile, the gut microbiota profoundly influences ovarian health. In POI patients, specific bacterial genera correlate with serum hormone levels [148]. Beneficial bacteria can also enhance ovarian responsiveness during controlled ovarian stimulation [149]. Both natural ovarian aging and ovarian cancer are characterized by an increase in pro-inflammatory bacterial species and a decrease in beneficial ones. However, subtle differences exist in these bacterial trends between healthy aging and cancer [150]. The gut–ovary axis is bidirectional, and its modulation offers new therapeutic strategies. These approaches aim to prevent and alleviate ovarian aging by influencing follicular development, oocyte maturation, and ovulation [109,151]. The gut microbiota is also emerging as a biomarker and therapeutic target for POI and other ovarian conditions, such as PCOS and ovarian tumors. This is due to its effects on immune, hormonal, oxidative stress, and metabolic pathways [152,153].

4.4 Integrated omics approaches to aging-related infertility and PCOS

Aging significantly impacts female reproductive health, with ovarian aging being a major contributor. Infertility itself is a pervasive challenge, and PCOS stands as a common cause (Fig. 2).

Studies have extensively utilized blood-based DNA methylation profiles as biomarkers for biological aging in fertility. For instance, methylation sequencing techniques applied to blood samples establish correlations between epigenetic aging and infertility risk [154]. Similarly, analysis of follicular fluid samples from IVF procedures identifies localized markers of ovarian aging, linking epigenetic changes to ovarian response [155]. It is worth noting, however, that some studies on epigenetic age models and female infertility have found no statistically significant associations in specific cohorts [156]. Epigenetic clocks are also refined using machine learning models trained on multi-omics datasets, integrating methylation data with transcriptomics and proteomics for enhanced biological age predictions. These approaches show potential for predicting IVF success rates. Studies indicate that epigenetically younger women have higher live birth rates, a prediction independent of standard ovarian markers that slightly improves accuracy when combined. This is particularly promising for women aged 31–35 years [157]. Additionally, Mendelian randomization studies reveal causal links between women's reproductive traits and accelerated epigenetic aging. For instance, earlier age at first sexual intercourse and age at first birth are associated with faster epigenetic clock rates. These associations remain significant even when accounting for other factors like BMI, which, along with AFS and AFB, also act as a mediator in this process [158]. Other omics studies are also applied: for example, proteomic studies profile protein interactions, and metabolomic analyses identify metabolic shifts in follicular fluid and serum samples. These techniques reveal key pathways driving infertility, such as nutrient sensing and mitochondrial dysfunction [159]. Multi-omics profiling also applies to animal models, identifying molecular features associated with fertility in cattle, which could provide insights into human reproductive health [160].

Artificial intelligence (AI) has been integrated into these multi-omics frameworks to improve the prediction and management of infertility. Reviews highlight the role of AI in female infertility diagnosis, covering various algorithms, diagnostic tools, and associated challenges [161]. For instance, proposed protocols combining omics data and AI demonstrate potential for enhancing IVF success rates through optimized patient stratification and treatment [162]. Machine learning models trained on multi-omics datasets also predict pregnancy outcomes in intracytoplasmic sperm injection procedures, offering a novel approach to fertility treatment [163]. The broader application of multi-omics and machine learning for the prevention and management of female reproductive health is increasingly recognized [164].

Building on these multi-omics insights, PCOS, a prevalent cause of infertility, has seen significant progress in understanding its molecular underpinnings. Lipidomics analyses, for example, identify serum biomarkers associated with metabolic dysregulation in PCOS patients, offering novel diagnostic tools [165]. Transcriptomic profiling reveals dysregulation in inflammatory and protein synthesis pathways, with RNA sequencing uncovering differential expression patterns in ovarian tissues [166]. Single-cell transcriptomic studies further elucidate stage-specific changes in oocytes and CCs, highlighting the impact of PCOS on cellular development. These studies analyze transcriptomic profiles at different maturation stages, identifying molecular changes that impair oocyte competence [167]. Integrated multi-omics analyses pinpoint immune dysregulation as a central feature of PCOS, with complement component 3 identified as a key driver of these changes through proteomic and transcriptomic integration [168].

The gut microbiota has emerged as a critical factor in the pathophysiology of PCOS. Studies utilizing omics approaches, such as metagenomics for analyzing stool samples, consistently link alterations in microbial composition to metabolic and inflammatory dysfunction in PCOS patients. Cross-sectional analyses further demonstrate the intricate interplay between gut microbiota composition and serum metabolomics profiles, revealing specific microbial-metabolic interactions that contribute to the disorder's complexity [169]. Mechanistic reviews underscore the significant therapeutic potential of targeting these microbiome-related pathways, highlighting innovative strategies for managing this multifactorial condition by modulating the gut environment [170]. Compounding these challenges, aging exacerbates PCOS-related symptoms and metabolic complications. Research indicates significant shifts in both clinical features and molecular markers in older PCOS patients, emphasizing how the progression of age influences the disorder’s presentation and severity [171].

5 Multi-omics insights into molecular mechanisms and translational research of lower reproductive tract aging and pregnancy

5.1 Epigenetic regulation in lower reproductive tract aging

Epigenetic clocks estimate biological age by analyzing DNA methylation patterns at specific CpG sites, such as Horvath’s pan-tissue clock, Hannum’s clock and all these mentioned above, are widely used in aging research to assess biological age based on DNA methylation patterns [174]. These tools have proven effective in studying reproductive health, particularly in the uterus and endometrium, where accelerated epigenetic aging has been linked to infertility, recurrent implantation failure (RIF), and endometrial diseases like hyperplasia and cancer [7].

Pregnancy and parturition, as functions intimately tied to the lower reproductive tract, are worth discussing within the context of lower reproductive tract aging [175,176]. In pregnancy research, Horvath’s clock and GrimAge clock revealed that pregnancy accelerates maternal epigenetic aging by 0.5 to 2 years, with multiple pregnancies linked to increased biological age and reduced lifespan, likely due to metabolic and immune changes [6]. Studies on uterine aging showed that the myometrium’s epigenetic age exceeds blood age by 3 to 5 years, especially in women over 40, indicating heightened sensitivity to aging [9]. Similarly, infertile women’s endometrial epigenetic age was found to be 4 years older than their chronological age, correlating with a 30% reduction in embryo implantation success [7]. For placental aging, the placental epigenetic clock showed that placentas are biologically 2 to 4 weeks older than their gestational age, with maternal smoking accelerating aging by 1.5 weeks. Social and economic disadvantages also contributed to increased placental aging, emphasizing the influence of the maternal environment [177]. Omics approaches have also shown that placental epigenetic age acceleration, reflecting advanced placental maturation, is associated with early-onset preeclampsia [173].

In endometriosis, Horvath’s clock has revealed that ectopic lesions age 3 to 6 years faster than normal endometrial tissue, with greater aging linked to disease severity [178]. Finally, large-scale studies have shown that chromatin remodeling and epigenetic instability during aging accelerate epigenetic clocks and reactivate specific genes, such as placenta-specific genes, identifying chromatin changes as potential drivers of aging and therapeutic targets [179].

Epigenetic clocks have proven versatile beyond uterine applications, extending to vaginal secretions and integration with other omics methods. DNA methylation profiling in vaginal fluid has successfully identified cervical and endometrial cancers through cancer-specific methylation signatures, showcasing a non-invasive diagnostic approach [180]. Additionally, combining epigenetic analysis with microbiome profiling revealed how the epithelial epigenome shapes cervicovaginal microbial communities, with a CpG-based signature distinguishing Lactobacilli-dominant from non-Lactobacilli-dominant microbiomes [181].

5.2 Transcriptional omics in lower reproductive tract aging

In the uterus, spatial transcriptomics and scRNA-seq have provided insights into the molecular changes during aging (Table 3). Mouse models have shown that uterine aging is characterized by chronic inflammation, impaired ECM remodeling, and fibrosis. Fibroblasts, which play key roles in inflammation regulation and ECM remodeling, exhibit functional decline with age, contributing to tissue dysfunction [5].

In the endometrium, transcriptomic analyses have shown that aging disrupts endometrial cilia function, increases oxidative stress, and activates cellular senescence pathways, leading to reduced receptivity and regeneration [182]. In women over 35 years old, endometrial tissues exhibit altered expression of genes linked to cilia function and aging hallmarks, while aged endometrial cells show increased markers of senescence and reduced antioxidant activity, impairing their regenerative capacity [183]. Intrauterine adhesions, a fibrotic condition associated with infertility, are linked to increased stromal senescence in the endometrium. Transcriptomic analyses revealed elevated senescence markers and profibrotic factors, such as LGALS9, during the proliferative phase in IUA patients. These senescent cells create an immunosuppressive microenvironment, reducing endometrial receptivity and thickness [184]. There’s also a study focusing on the window of implantation (WOI) in fertile women and those with RIF. In RIF patients, a displaced WOI and hyper-inflammatory microenvironment were observed, contributing to implantation failure [185].

Additionally, perimenopausal stromal fibroblasts display distinct transcriptomic profiles, with upregulation of pro-inflammatory and ECM remodeling genes, further contributing to structural and functional decline [186]. In the myometrium, spatial transcriptomics has revealed that aging reduces smooth muscle contractility and alters intercellular communication, with chronic inflammation and fibrosis further impairing uterine contractility and structural integrity [8]. Furthermore, studies integrating spatial transcriptomics and single-nucleus RNA sequencing in uterine fibroid tissues have demonstrated that aging exacerbates ECM dysregulation and inflammation in fibroids [187].

In the vagina and associated structures, aging-related changes are closely linked to POP, a condition that significantly affects women’s health. scRNA-seq studies of vaginal wall tissues from elderly women with POP identified aging-related immune cell types, including macrophages and T cells, with altered inflammatory pathways that exacerbate chronic inflammation and ECM remodeling. These changes weaken the vaginal wall, contributing to tissue dysfunction [188]. Similarly, scRNA-seq profiling of the vaginal wall in women with severe anterior vaginal prolapse revealed fibroblast subtypes involved in ECM remodeling and inflammation, with aging leading to increased fibroblast senescence, altered ECM composition, and chronic inflammation [189]. In the uterosacral ligament, scRNA-seq uncovered cellular heterogeneity in women with and without POP, showing that aging-related changes in fibroblasts and smooth muscle cells, including reduced ECM production and contractility, contribute to POP progression [190]. Furthermore, studies on rhesus macaques with spontaneous POP demonstrated that aging-related ECM degradation, chronic inflammation, and fibroblast dysfunction closely mirror human POP, establishing this model as ideal for studying aging-related changes in pelvic tissues [191].

Meanwhile, endometrial cancer shares common mechanisms with aging, such as genomic instability, epigenetic drift, and immune dysregulation [192]. Multi-omics studies, which mainly focus on the transcriptome, have revealed aging-related changes, including altered chromatin accessibility, transcriptional rewiring, and loss of tumor suppressor function, which drive tumor heterogeneity and aggressive phenotypes [193,194]. Elevated expression of aging-associated genes like CDKN2A has been linked to inflammation, immune suppression, and poor therapeutic response, highlighting the impact of aging on tumor immunity [195].

5.3 Proteomic and metabolomic insights into lower reproductive tract aging and pregnancy

Research on proteins and metabolites related to lower reproductive tract aging is limited but promising. A study developed a multimodal aging clock for Chinese women using multi-omics data from 113 healthy individuals aged 20–66 years. It identified four key aging-related functions—chronic inflammation, lipid metabolism, hormone regulation, and tissue fitness—and highlighted significant biological changes in the 30s and 50s. Hormone replacement therapy (HRT) was shown to partially slow biological aging and reduce aging-related markers. This framework offers valuable tools for assessing biological age and evaluating rejuvenation strategies [196]. Yang et al. integrated proteomics, transcriptomics, and ubiquitylomics to highlight the role of ubiquitination in endometriosis fibrosis, demonstrating TRIM33’s inhibitory effect on fibrosis and offering new insights into potential therapeutic targets for endometriosis [197].

In the field of pregnancy, maternal obesity profoundly influences health across generations. Multi-omics studies, encompassing epigenomics, transcriptomics, proteomics, and metabolomics, have revealed that its impact can persist throughout the offspring’s life, disrupting placental function and increasing the offspring’s risk of cardiovascular and metabolic diseases [198,199]. Concurrently, recurrent pregnancy loss is significantly influenced by advancing maternal age, with genomics, epigenomics, transcriptomics, metabolomics, and proteomics studies uncovering its potential biomarkers and molecular mechanisms [200]. For gestational diabetes mellitus, omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and lipidomics, aid in understanding its pathophysiology, though a primary concern remains the long-term increased risk for mothers to develop type 2 diabetes and cardiovascular disease post-pregnancy [201].

5.4 Microbiome-inflammaging interplay in lower reproductive tract

The female reproductive tract microbiome, primarily dominated by Lactobacillus species, shows continuous distribution in different parts of the reproductive tract and plays a critical role in maintaining reproductive health through its metabolic products and immune-modulating functions [202–204]. The dominant metabolic product of Lactobacillus is lactic acid, which helps maintain an acidic environment in the vagina (pH < 4.5) and prevents the growth of harmful pathogens by inhibiting their adhesion and activity [205]. However, when Lactobacillus abundance decreases, the vaginal pH increases and allow pathogenic microorganisms such as Gardnerella and Atopobium to thrive, leading to conditions like bacterial vaginosis [206,207]. Additionally, short-chain fatty acids (SCFAs), produced by microbial fermentation in the reproductive tract, exhibit niche-specific immune effects. In the healthy vaginal microenvironment, low SCFA concentrations support anti-inflammatory signaling via HDAC inhibition and FFAR activation [208]. However, dysbiosis shifts metabolism toward elevated SCFAs, which degrade mucosal barriers, activate TLR4/NF-κB, and increase pro-inflammatory cytokines, including IL-6, IL-8, and TNF-α [209]. This chronic inflammation propagates conditions like endometriosis and preterm birth [210]. The microbiome also produces immune-inducing factors like lipopolysaccharides (LPS) from Gram-negative bacteria, which, when interacting with Toll-like receptors on immune cells, trigger inflammatory cytokine release [211]. This process can lead to the pathogenesis of diseases like endometrial cancer and recurrent pregnancy loss, as chronic inflammation impairs immune tolerance and disrupts reproductive health [212].

Pregnancy alters the vaginal microbiome, reducing its metabolic activity and complexity, which may increase susceptibility to dysbiosis and complications such as spontaneous abortion [213]. Dysbiosis has also been identified as a potential factor in infertility, with infertile women showing higher abundances of Lactobacillus iners, Lactobacillus gasseri, and Gardnerella vaginalis compared to fertile women, whose microbiomes exhibit greater diversity [214]. Furthermore, research suggests that vaginal dysbiosis may contribute to miscarriage through mechanisms such as inflammation and disruption of the vaginal ecosystem. Screening and targeted interventions to restore microbiome balance have been proposed as strategies to prevent miscarriage and improve reproductive outcomes [215].

The gut microbiota also plays a critical role in female reproductive health by regulating hormone levels, immune responses, and inflammation [216]. Dysbiosis, or imbalance in gut microbial composition, disrupts estrogen metabolism via the microbiome–estrogen axis, leading to altered hormone levels that affect ovarian and uterine function [217]. Additionally, gut microbial metabolites such as SCFAs influence systemic immune regulation and inflammatory responses, which can exacerbate conditions such as endometriosis and PCOS [218]. Dysbiosis also impacts the gut–vagina axis, increasing susceptibility to infections like bacterial vaginosis and adverse pregnancy outcomes [219].

In the field of lower reproductive tract microbiome research, recent multi-omics studies have shed light on the intricate relationships between microbial communities, host factors, and female reproductive health. Integrating microbiome, metabolome, and immunoproteome data from cervicovaginal samples, researchers revealed that metabolites, especially lipids and amino acid derivatives, are the strongest predictors of inflammation and microbiota composition in the cervicovaginal microenvironment [220]. Vaginal microbiota in perimenopausal and postmenopausal women with GSM were explored, finding that Lactobacillus abundance negatively correlated with GSM symptoms, while pathogenic bacteria were positively associated with symptom severity. Lactobacillus-based treatments were shown to alleviate GSM symptoms, highlighting vaginal microbiota dysbiosis as a key factor in GSM progression and a potential therapeutic target [221]. Analysis of vaginal microbiome profiles in early pregnancy demonstrated significant links between microbial taxa, lipid metabolites, and pro-inflammatory cytokines. Dysbiosis, particularly bacterial vaginosis, was associated with adverse pregnancy outcomes like preterm birth, while Lactobacilli were linked to positive outcomes [222].

Multi-omics-based aging clocks provide a practical and comprehensive tool to assess biological aging, enabling early risk identification and targeted prevention strategies. For instance, in the S-PRESTO study, a phenotypic aging clock was applied to Asian women of reproductive age. Integrated multi-omics analyses, including lipidomics, genomics, and gut microbiome data, identified various factors significantly associated with biological age acceleration, such as specific lipid species, genetic variants, and microbial taxa. The study also linked beneficial nutritional biomarkers to slower aging, while indicators of adiposity, insulin resistance, and certain metabolic pathway activities were associated with faster aging. Network analyses further revealed inflammation, metabolic dysregulation, and IGF signaling as key pathways mediating these effects, underscoring the utility of multi-omics clocks in identifying modifiable biological drivers of aging in women [223].

Although no studies directly address the microbiome’s role in lower reproductive tract aging, research shows a link between the vaginal microbiome and POI. One study found that POI patients had increased Actinobacteria, Atopobium, and Gardnerella, which correlated negatively with AMH and positively with FSH and LH, while Bifidobacterium was reduced [224]. Another study revealed decreased Lactobacillus and increased Streptococcus in POI patients, associated with altered hormone levels, including FSH, LH, estradiol, and AMH [225]. These findings suggest the vaginal microbiome’s potential role in ovarian function (Fig. 3).

6 Conclusions and perspectives

6.1 Decoding reproductive aging with multi-omics: from promise to clinical application

Reproductive aging in the female reproductive tract, encompassing ovarian aging and the aging of the lower reproductive tract, profoundly impacts women’s health, and translating research findings into effective therapies remains a significant challenge. Emerging multi-omics technologies—including epigenetic clocks, transcriptomics, proteomics, metabolomics, and microbiome analyses—are proving instrumental in decoding the complex mechanisms underlying reproductive aging. These tools enable precise biological age assessment while simultaneously revealing tissue-specific vulnerabilities and systemic aging patterns. Multi-omics technologies—including epigenetic clocks, transcriptomics, proteomics, metabolomics, and microbiome analyses—reveal accelerated tissue aging, inflammatory shifts, protein changes, metabolic shifts, and microbiome links to reproductive health issues. Integrating these approaches is crucial for comprehensive models, addressing gaps in robust aging clocks and broader multi-omics application. This will enable personalized interventions, as multi-omics has already identified key targets, some of which are currently in clinical trials.

Multi-omics aging clocks show strong predictive power in blood and somatic tissues, but their application to female reproductive organs faces key challenges. Tissue- and cell-type specificity is a major barrier, as ovaries, oocytes, cumulus, granulosa, and endometrial cells exhibit distinct methylation and transcriptomic patterns [226]. For example, Horvath’s and PhenoAge clocks show weak correlation with chronological age in granulosa cells [227]. Ovarian and endometrial cells exhibit distinct methylation and transcriptomic patterns, leading to inaccurate cross-tissue age estimates. Hormonal fluctuations during the menstrual cycle further complicate age estimation. The endometrium undergoes cyclical DNA methylome changes, initially reducing clock accuracy; however, accuracy improved when samples were timed to the mid-secretory phase, highlighting the need for cycle-phase control [228]. Conventional epigenetic age sources like blood and saliva fail to capture female reproductive system aging, consequently compromising the ability of commercial platforms such as TruAge and EpiAge to predict ovarian reserve, implantation success, or reproductive decline.

Another fundamental issue is that “reproductive age” is not equivalent to chronological age, and there is no universally accepted definition for it. Consequently, research is shifting from merely predicting chronological age to assessing functional reproductive decline. Studies now correlate epigenetic age acceleration in granulosa cells with established markers of ovarian reserve, such as AMH levels, antral follicle count (AFC), and oocyte yield [227]. Similarly, epigenetic changes are being linked to pathologies like POI, indicating that the epigenetic timeline of the ovarian compartment is distinct from that of somatic aging [229].

Recent reproductive-oriented omics initiatives exemplify both the promise and limitations of clinical translation in female reproductive medicine. The Endometrial Receptivity Analysis (ERA), developed by Igenomix, represents one of the earliest transcriptomic applications to clinical fertility practice. By characterizing endometrial gene expression profiles across the WOI, ERA aims to personalize embryo transfer timing [230]. Multiple studies, including large multicenter analyses, have reported improved implantation and live birth rates in selected patients with RIF or advanced maternal age when combined with euploid embryo transfer and preimplantation genetic testing for aneuploidy (PGT-A) [231]. This paradigm shift underscores the critical clinical value of precision phenotyping: rather than a “one-size-fits-all” approach, omics-driven tools enable stratified clinical management that optimizes time-to-pregnancy specifically for high-risk cohorts, while sparing low-risk patients from unnecessary interventions.

However, integrating multi-omic data into clinical practice is technically challenging due to platform variability, high dimensionality, and data heterogeneity. In reproductive tissues, this complexity is amplified by cyclic hormonal and cellular remodeling, which complicates longitudinal sampling and makes establishing a stable “healthy” baseline difficult. Therefore, establishing a systemic computational framework for integrating multi-omics data is not only a necessity for achieving mechanistic understanding of reproductive health and disease, but also for generating reliable clinical decision support systems. This framework is already designed to address the core challenges of data heterogeneity and high dimensionality by creating a unified, lower-dimensional space. A diverse toolkit has been developed ranging from statistical models like Multi-Omics Factor Analysis (MOFA), which identifies shared axes of variation across datasets [232], to AI-driven deep learning that captures complex nonlinear relationships [233]. The collective goal of this framework is to move beyond single biomarkers and identify entire modules of co-regulated molecules that define a functional state, thereby providing a more robust and systems-level view of health and disease. Crucially, this reduction in dimensionality translates directly into clinical value by filtering biological noise to reveal robust, patient-specific pathogenic signatures that can serve as reliable diagnostic classifiers.

As these powerful analytical methods mature, the focus naturally shifts toward overcoming the practical barriers to clinical deployment, which is the crucial next step in their evolution. Key areas of active development include addressing the “black-box” nature of some AI models to improve interpretability for clinicians, and creating methods to distill vast datasets into simple, actionable health indices [234]. Simultaneously, the field is working to tackle practical hurdles such as high costs and the lack of standardization by developing more efficient workflows and collaborative platforms [235].

6.2 Emerging targets and therapeutic strategies for mitigating reproductive aging

Current strategies for mitigating reproductive aging and associated pathologies can be categorized into four frontiers: metabolic reprogramming via conserved aging pathways, ecological restoration of the reproductive microbiome, targeted clearance of senescent cells, and precision molecular therapies for specific reproductive disorders.

Among the most promising metabolic targets, the mTOR signaling pathway stands out as a pivotal regulator of cell growth, metabolism, and aging, with its persistent activation driving the aging process. Rapamycin, an mTOR inhibitor, has shown effectiveness as an anti-aging compound in animal models, restoring ovarian function and extending reproductive lifespan in mice, with the human ovary considered a direct target [236]. The VIBRANT I clinical trial (NCT05836025) is currently evaluating low-dose rapamycin’s feasibility and safety in healthy reproductive-aged women to slow ovarian aging, with preliminary results suggesting good tolerability and potential for preserving ovarian reserve, marking a direct translation of multi-omics research into clinical intervention.

Microbiome modulation is another promising avenue, given its close link to fertility outcomes like implantation failure. Multi-omics research links altered microbial profiles to premature ovarian insufficiency, infertility, and miscarriage. Strategies include modulating the gut–ovary axis for follicular development [216], restoring vaginal microbiome balance to prevent infertility and miscarriage, and using Lactobacillus-based treatments for GSM [237]. Ongoing clinical trials, including NCT03843112, NCT05150639, and NCT05328999, are actively exploring these areas, from vaginal Lactobacillus supplementation post-implantation failure to the microbiome’s role in IVF outcomes and HPV-related infertility. Recent clinical trials have also increasingly explored microbiome-targeted interventions as adjunct strategies for improving reproductive and metabolic outcomes in women with infertility or PCOS. The majority of randomized controlled trials (e.g., NCT04593459, NCT04009603) generally suggest that probiotics may beneficially influence metabolic and hormonal profiles in PCOS. Several observational and interventional microbiome profiling trials (e.g., NCT03105453, NCT05150639) investigate how uterine, vaginal, and endometrial microbial communities affect IVF implantation and pregnancy success. These studies underscore the significant clinical value of microbiome modulation as a low-risk, cost-effective adjunct therapy that can be integrated into existing protocols to enhance implantation rates and manage metabolic comorbidities.

While cellular senescence and senolytics show promise [238,239], with markers like CDKN1A/p21 identified in ovarian cells, their direct application in reproductive aging is largely preclinical. A mouse study using dasatinib and quercetin (D + Q), for instance, failed to improve ovarian reserve or fertility and even showed detrimental effects, highlighting the need for more specific research on their reproductive system application [240].

Finally, multi-omics integration is driving the development of precision therapeutics for complex pathologies. For endometriosis, multi-omics analyses have identified key molecular targets like the MAP3K5 gene and its pathways [241], and the WNT signaling pathway [242], along with TRIM33’s inhibitory effect on fibrosis [197], providing a basis for new therapies. Based on these discoveries, novel JNK inhibitors and other drugs are being developed to treat endometriosis-related pain and inflammation [243]. Specific compounds like salidroside have also reversed ovarian vascular endothelium aging in animal models [123]. Translating these insights into personalized interventions—such as regenerative therapies [244], targeted drugs, gene therapy, and cell or stem cell therapy [245,246]—also shows promising future progress.

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