Decoding aging clocks from a metabolomic perspective

Abstract

Biological age quantifies functional decline beyond chronological aging; however, current epigenetic clocks exhibit limitations in resolving dynamic metabolic fluctuations and tissue-specific aging trajectories. Metabolomics emerges as a pivotal solution, representing the endpoint cascade of biological events shaped by multifactorial interactions that capture real-time physiological status. This review delineates aging clocks through a metabolomic lens and proposes an executable research workflow comprising data preprocessing, feature selection, model construction, and model application. Furthermore, we design a three-phase causal strategy structured as global screening, local verification, and dynamic validation. This integrated methodology aims to enhance the predictive accuracy of aging clocks while strengthening the biological plausibility and causal inference potential of metabolite-derived aging biomarkers. Additionally, we evaluate the translational prospects and applied value of metabolomic aging clocks, providing actionable guidance for extending human healthspan and advancing prevention and treatment strategies for aging-related pathologies.

Introduction

With continuous advancements in medical technology, human life expectancy has progressively increased. It is projected that by 2050, the global population aged 60 years or older will reach two billion (Urbina-Varela et al., 2020). However, extended lifespan does not equate to extended healthspan. Age-related physiological decline and increased disease prevalence impose substantial burdens on both individuals and society.

Due to the continuously expanding geriatric population, interventions capable of slowing or even reversing aging processes are garnering increasing interest. Consequently, quantifying aging has become particularly crucial for evaluating the efficacy of such interventions. As individuals with identical chronological ages often exhibit divergent aging states and disease susceptibilities, chronological age alone is no longer a reliable indicator for assessing anti-aging efficacy. Consequently, studies suggest that biological aging precedes phenotypic aging and functional decline both temporally and mechanistically (Ferrucci et al., 2018). Through omics sciences—including genomics, transcriptomics, proteomics, and metabolomics—we can quantify aging and decipher its progression from genetic to metabolic levels.

Metabolomics has emerged as one of the most rapidly evolving disciplines within omics sciences, aiming to elucidate metabolic states associated with aging processes through comprehensive analysis of metabolite profiles in biological samples. Metabolites serve as both endpoints of environmental influences and final products of gene expression and enzymatic reactions. Consequently, metabolomics provides closer proximity to phenotypic manifestations of aging compared to other omics layers. Even prior to the formal proposal of the first metabolomic aging clock, studies had identified aging-associated metabolites (Lawton et al., 2008). Subsequent targeted metabolomic analyses further confirmed significant correlations between specific metabolites and chronological age (Yu et al., 2012).

Since the introduction of the first-generation aging clock, numerous models based on aging-related metabolites have been successively developed. However, due to variations in sample selection, analytical methodologies, and study designs, existing metabolomic aging clocks exhibit significant heterogeneity, and no universally accepted standardized model has yet been established within the scientific community. In light of this, our review systematically synthesizes the current research landscape and recent advancements in metabolomic aging clocks, critically examines methods to enhance the precision of metabolic biomarker selection, and summarizes both the potential opportunities and challenges in this evolving field.

This review is designed for a diverse academic audience. It provides researchers in metabolomics, aging biology, and multi-omics integration with methodological frameworks and recent advances in constructing metabolic clocks. For bioinformatics scholars, it offers an in-depth exploration of machine learning and causal inference applied to biological age prediction. Clinical and translational researchers will find insights into the potential of metabolic clocks for disease risk prediction, anti-aging interventions, and personalized health management. Furthermore, it equips public health and epidemiology experts with a population-level, metabolomics-based perspective on aging to inform policy-making. Through clear technical comparisons, case studies, and a well-structured research framework, this review also serves as a comprehensive resource for senior graduate students and scholars embarking on interdisciplinary studies in this field.

Survey methodology

To maximize coverage of relevant literature, our search encompassed major academic resources including PubMed, Web of Science, and Google Scholar. Search keywords underwent multiple optimization and combination tests, with the core strategy being: (“metabolic age” OR “biological age” OR “aging clock” OR aging OR “machine learning”) AND (metabolomics OR “metabolism”). Additionally, citation tracking of reference lists from included studies supplemented publications missed in the initial retrieval, particularly covering classical methodologies and cross-species investigations. Building upon this foundation, the following discussion will follow the inherent logic from “data” to “models” and then to “applications.” Through this structure, a systematic, in-depth interpretation of aging clocks from a metabolomics perspective will be achieved.

Metabolomics Data Bias Mitigation Strategies

Study cohort selection

In metabolomics research, establishing a well-defined and appropriate study cohort constitutes a fundamental prerequisite for scientific investigation (Fig. 1). This is particularly critical when developing metabolomic aging clocks for large-scale populations, where comprehensive consideration must be given to demographic heterogeneity (including sex, age, and ethnicity), lifestyle factors, and health status.

Figure 1. Data preprocessing.

The core workflow for metabolomics data preprocessing. The process is initiated with research cohort selection, followed by data acquisition primarily through two detection techniques: mass spectrometry (MS) and nuclear magnetic resonance (NMR). Ultimately, this workflow yields well-prepared datasets and identified metabolites for subsequent research applications.

Significant differences exist in metabolite profiles between males and females, and sexual dimorphism substantially influences metabolite identification and quantification. A sex-stratified analysis revealed that females exhibit approximately twice as many age-associated metabolites as males, with certain metabolites demonstrating opposing age-related trends—increasing in females while decreasing in males (Darst et al., 2019). Further investigations documented substantial alterations in metabolite composition and abundance in postmenopausal women (Rist et al., 2017). These findings underscore the critical importance of accounting for sex-specific factors in metabolomic aging clock research.

The age distribution within study populations significantly impacts the accuracy of aging clocks. Research demonstrates that when aging clock models are trained on data from younger cohorts, they systematically underestimate biological age in older individuals (Lassen et al., 2023). Concurrently, studies confirm that children exhibit twice the activity level of leucine metabolism pathways compared to adults, while older adults show significant accumulation of branched-chain amino acids (BCAAs) (Bunning et al., 2020). These findings collectively substantiate substantial metabolic disparities across distinct age stages.

Current research seldom incorporates race as an inclusion or exclusion criterion. However, racial disparities have become increasingly pronounced in large-scale studies. Investigations into racial differences in metabolic syndrome prevalence reveal a 9-percentage-point elevation among minority women (31.7%) compared to majority women (22.7%) (Adjei et al., 2024). This prevalence gap implies underlying metabolomic variations across racial groups. Nevertheless, the biological mechanisms mediating race-associated metabolic differences remain elusive, necessitating further validation of their causal effects on metabolic aging trajectories.

Lifestyle factors profoundly influence metabolic pathways, with established evidence demonstrating that smoking (Gu et al., 2016), alcohol consumption (Du et al., 2020), and physical activity levels (Kelly, Kelly & Kelly, 2020) significantly alter systemic metabolic processes. Consequently, the inclusion of individuals with severe comorbid conditions or those in specific physiological states (such as pregnancy) may confound the identification and interpretation of biological aging biomarkers (Liu et al., 2018; Wang et al., 2024).

It is critical to highlight that the lack of standardized cohort reporting represents a major limitation in current metabolomic aging research, contributing directly to the heterogeneity observed across studies and severely compromising reproducibility. Inconsistent documentation of cohort characteristics hinders cross-study comparison and undermines the reliability and generalizability of aging clock models (Konjevod et al., 2025). To address this issue, the establishment of field-specific minimal reporting standards is urgently needed, such as guidelines adapted from the STROBE framework tailored to metabolomics study design. These standards should unify the reporting of key dimensions including demographic profiles, lifestyle confounders, health conditions, sample processing, and metabolite quantification protocols. Implementing such standardized reporting will improve transparency, clarify cohort selection rationale, facilitate independent validation, reduce systematic bias, and ultimately strengthen the methodological rigor and translational potential of metabolomic aging clocks.

Considerations of sample size and statistical power assessment

In metabolomic aging clock studies, statistical power and sample size are central to model robustness. While no universally applicable minimum sample size threshold exists, empirical evidence shows that scale determines a model’s ability to capture heterogeneity. For example, high-dimensional metabolic clocks constructed from the UK Biobank’s ultra-large cohort have demonstrated excellent performance (You et al., 2026), whereas moderately sized cohorts can also build robust models through feature selection (Xu et al., 2025). Methodological research based on Monte Carlo simulations indicates that when using regularization methods such as elastic net, at least 70 samples are required to ensure calibration stability (Mayne, Berry & Jarman, 2021). Insufficient sample size directly leads to overfitting, low statistical power, and failure in external validation.

A careful balance is needed between the number of metabolites and sample size. High-dimensional models (e.g., using 249 metabolites) require very large samples to control for variation (You et al., 2026), whereas streamlined models (e.g., aging clocks comprising only nine key metabolites) achieve lower sample size demands by automatically selecting features through regularization (Zhang et al., 2024a). The use of composite metabolite indices, which are prone to causing multicollinearity, should be avoided; raw data analyzed via regularized regression are preferred (Anagnostakis et al., 2025).

Power assessment in the validation stage should be performed independently. It is generally recommended to use specialized software (such as G*Power) to calculate the required sample size based on preset effect size, α level, and target power (Kang, 2021). In practice, validation can be conducted in large-scale independent cohorts or via training/validation set splits. Monte Carlo simulation can serve as a supplementary approach to estimate the minimum sample size needed under different conditions (Mayne, Berry & Jarman, 2021).

In summary, large-scale cohorts support high-dimensional exploration, while streamlined models are more suitable for medium-scale studies. Validation should be based on rigorous power calculations, and composite indices should be avoided. Through feature selection, stratified design, and regularization methods, robust and translatable metabolic aging clocks can be constructed even with limited resources.

The critical role of circadian rhythms

Beyond the selection of research cohorts, the intrinsic metabolic variations driven by circadian rhythms present both a fundamental challenge and a developmental opportunity for metabolomic aging clocks. The mammalian circadian clock regulates the expression of numerous genes involved in core metabolic pathways, including glycolysis, lipid metabolism, and amino acid turnover (Lin et al., 2025; Zhi et al., 2024). Consequently, a significant portion of metabolites in the blood metabolome exhibit pronounced diurnal fluctuations (Sinturel et al., 2023). The magnitude of such fluctuations is comparable to that of metabolic changes associated with aging or disease. This implies that the common practice of single-timepoint sampling in most cross-sectional aging studies can confound biological age with the circadian phase of the subject at the time of sample collection.

The issue is compounded by circadian rhythm disruption or desynchrony, which itself is a hallmark of aging. With advancing age, circadian rhythms often exhibit dampened amplitude and phase shifts (Rahman et al., 2023; Shim, Fleisch & Barata, 2024). For instance, age-related alterations in sleep-wake cycles and feeding patterns may lead to desynchrony between the central pacemaker (e.g., the suprachiasmatic nucleus) and peripheral clocks (e.g., in the liver and muscle) (Li et al., 2023). This metabolic desynchrony can contribute to age-related pathologies such as insulin resistance and cardiovascular disease (Costello & Gumz, 2021; Speksnijder et al., 2024). Therefore, metabolomic signatures derived from cohorts with non-standardized sampling times may inadvertently capture variations linked to population-level circadian desynchrony rather than a pure metabolic aging trajectory. This scenario could inflate the apparent accuracy of an aging clock while undermining its biological specificity.

To enhance the validity of metabolomic aging clocks, future studies should rigorously control for and strategically leverage circadian biology. Standardizing sample collection within specific time windows is essential to minimize circadian-related confounding. By integrating chronobiology into the research design framework, we can advance metabolomic aging clocks from mere predictive tools toward causal diagnostic instruments capable of reflecting an individual’s dynamic physiological state.

Metabolomic analysis platforms

In addition to cohort selection and circadian rhythm considerations, the selection of an appropriate technological platform is equally critical for obtaining high-quality metabolite data. In metabolomic analysis, both nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are widely adopted platforms for metabolic profiling (Fig. 1). NMR investigates molecular structures by detecting nuclear energy level transitions within strong magnetic fields, offering advantages of high resolution, excellent reproducibility, and absolute quantification (Markley et al., 2017). Crucially, samples remain unconsumed during analysis, making this platform ideal for longitudinal studies. In contrast, MS operates by ionizing molecules followed by separation and detection based on mass-to-charge ratios. Its high sensitivity, throughput capacity, and ability to simultaneously detect multiple compounds facilitate the analysis of low-abundance metabolites in trace-level samples (Hajnajafi & Iqbal, 2025). These capabilities consolidate MS’s dominant position in the field, supporting over 70% of metabolomic studies (Wishart et al., 2022). However, as discussed in later sections, its practical applications face certain limitations.

Researchers delineated key technological differences in constructing metabolomic aging clocks within the ORCADES cohort (Wang et al., 2007). NMR spectroscopy detected 86 metabolites with a utilization rate of 94.2%. This highlights the “small yet refined” nature of NMR data, where its high reproducibility ensures that nearly all detected metabolites are reliable and suitable for modeling. In contrast, MS platforms identified substantially more metabolites, but demonstrated utilization rates of only 14.3% to 26.5%. While high-dimensional data generated by MS provides a wealth of information, it also introduces substantial noise and redundant signals, resulting in low modeling efficiency, as only a small number of the most predictive metabolites are ultimately retained in the final model. Conversely, NMR achieved comparable accuracy (r = 0.74) using fewer but higher-quality metabolites, yielding more parsimonious and robust models. (A detailed comparison of the metabolomic platforms is provided in Table S1).

To address such technological divergences, artificial intelligence (AI)-enhanced NMR methodologies—such as deep learning-powered spectral deconvolution and quantitative algorithms—effectively mitigate NMR’s limitations in detecting low-abundance metabolites (Maslov et al., 2019). For MS-inherent technical noise and high-dimensional challenges, noise correction algorithms (e.g., Combatting Batch Effects, Surrogate Variable Analysis) coupled with advanced dimensionality reduction and feature selection techniques provide robust solutions (Trifonova et al., 2018; Copes et al., 2015). Given the complementary strengths of both analytical platforms, integrated Nuclear Magnetic Resonance-Mass Spectrometry strategies have been successfully deployed in biomarker discovery for conditions including inflammatory bowel disease (Hertel et al., 2016) and hepatic fibrosis (Jia et al., 2024). Future research should prioritize multi-modal technological integration to enhance the precision and coverage of metabolic profiling.

Metabolomics data processing

The first step in building a robust aging clock is to perform rigorous quality control and filtering of raw features to remove low-quality or information-redundant variables. Common strategies include: (1) Filtering based on missing rate: For example, removing metabolite features with an excessively high missing rate (e.g., >50%) across all samples (Hwangbo et al., 2022), which can significantly reduce uncertainty in subsequent imputation steps. (2) Filtering based on variance: Removing features with minimal variation across individuals, as they contribute little to distinguishing aging states (Vishnyakova et al., 2026). (3) Filtering based on quality control samples: Quality Control (QC) samples are used to monitor experimental reproducibility. Typically, removing features with poor measurement precision (e.g., high relative standard deviation) in QC samples effectively ensures data reliability (González-Domínguez et al., 2024). Additionally, screening based on peak quality, removing non-informative spectral regions, and preliminary outlier detection via principal component analysis are common practices in the initial data screening stage (Sun & Xia, 2023). This preliminary screening forms the foundation for all subsequent analyses.

Appropriate handling of missing values is a critical decision point. Simple deletion or mean imputation may introduce bias. Therefore, methods should be selected based on the missingness mechanism: for missing at random, multiple imputation is considered a robust strategy, as it constructs multiple complete datasets to assess imputation uncertainty (Hwangbo et al., 2022); other methods such as k-Nearest Neighbors or random forest can also be used for imputation (Gromski et al., 2014). For systematic missingness due to values below the detection limit, approaches such as minimum/half-minimum imputation or models based on the detection limit may be applied (Sun & Xia, 2023). The work by Hwangbo et al. (2022) demonstrated that advanced imputation combined with drift-correction strategies can significantly improve the accuracy of clock models (reducing the root mean square error by 18%).

Addressing data scale is a necessary step before modeling. Metabolomics data often exhibit skewed distributions and scale variations. Data transformation aims to make the distribution closer to normal and stabilize variance; besides log-transformation, power transformations offer more flexible solutions (Feng et al., 2013; Van den Berg et al., 2006). Normalization is performed to eliminate systematic technical differences between samples. Common methods include Z-score normalization, Pareto scaling, range scaling, and variable stability scaling, each suitable for different analytical purposes (Sun & Xia, 2023). It is particularly important to emphasize that the choice of method must align with the subsequent machine learning algorithm: for instance, distance-or gradient-based models (e.g., support vector machines, neural networks) typically rely on normalized data, whereas tree-based ensemble models (e.g., random forest, eXtreme Gradient Boosting) often omit this step due to the scale-insensitivity of their splitting rules (Nam et al., 2020). Therefore, the selection of a normalization strategy should be considered within the overall modeling workflow.

Correcting batch effects is essential to ensure model generalizability. Correction strategies should span both experimental design and data analysis, encompassing proactive prevention and computational remediation. Proactive prevention at the experimental design stage is fundamental to reducing batch effects. Measures include sample randomization, insertion of QC samples or standard reference materials in each batch, and strict standardization of experimental procedures (Garrett et al., 2024; Yao et al., 2023; Yu, Chen & Huan, 2021). Internal Standard Normalization (e.g., creatinine correction for urine samples) can also effectively correct systematic errors in specific sample types (Jatlow, Mckee & O’Malley, 2003). Computational remediation refers to post-hoc processing of data that already exhibits technical variation. The core challenge lies in removing systematic errors while maximally preserving true biological variation. Commonly used methods include intensity correction based on QC samples and Concordance-Based Batch Effect Correction (Guo et al., 2023; Stanstrup & Dragsted, 2025). Furthermore, for chromatography-mass spectrometry data, employing peak alignment algorithms (e.g., icoshift) to correct retention-time drift, or using algorithms such as Local Asymmetric Gaussian Fitting to optimize peak detection and alignment, can further enhance the reliability of multi-batch data integration (Sun & Xia, 2023; Zou et al., 2025).

In summary, each preprocessing step in constructing a metabolomic aging clock involves critical methodological decisions. A systematic understanding and implementation of these preprocessing best practices are prerequisites for building the next generation of high-precision, highly interpretable metabolic age clocks. (A more detailed comparison of the data preprocessing workflows is provided in Table S2).

Feature Selection: a Causality-Oriented Triphasic Validation Framework

After obtaining high-quality, standardized metabolomic data, the next critical step is to screen for reliable biomarkers associated with aging from this dataset. Although changes in metabolites correlate with chronological age, this association does not imply causality. For instance, elevated levels of α-ketoglutarate in aged cohorts paradoxically demonstrate potential to ameliorate aging phenotypes (Bayliak & Lushchak, 2021). Therefore, the synergistic application of targeted and untargeted metabolomics is crucial for both the depth and robustness of biomarker discovery. Untargeted metabolomics, with its unbiased discovery capability, is suitable for large-scale, exploratory initial screening of biomarkers; whereas targeted metabolomics, with its high sensitivity, specificity, and absolute quantification capability, serves as a precise tool for subsequent validation (Odom & Sutton, 2021). Based on this, we propose a progressive triphasic causal validation framework comprising global screening (for discovery), localized verification (for targeted confirmation and biological annotation), and dynamic validation (for targeted longitudinal monitoring and causal inference) to enhance the reliability of aging biomarker discovery (Fig. 2).

Figure 2. Feature selection.

The core workflow of feature selection. The process initiates with global screening (left) to preliminarily identify aging-associated biomarkers, subsequently proceeds to pathway enrichment analysis (middle), and culminates in dynamic validation (right) using longitudinal cohort studies and Mendelian randomization (MR) approaches to confirm feature robustness and potential causal relationships.

Global screening serves as the initial screening process for aging biomarkers, aiming to narrow down the candidate pool from a large set of metabolites. This stage constitutes the core of untargeted metabolomics in exerting its advantages in unbiased discovery. The high-dimensional data generated in this stage can be effectively processed via sparse modeling with Least Absolute Shrinkage and Selection Operator (LASSO) regression, iterative optimization with Recursive Feature Elimination (RFE) (Staartjes et al., 2022), or feature importance evaluation using random forests (Li et al., 2022). Collectively, these approaches facilitate the preliminary screening of high-dimensional metabolites. In addition, dimensionality reduction techniques such as Principal Component Analysis contribute to further feature compression (Jolliffe & Cadima, 2016).

The Local Validation phase aims to assess the biological plausibility of candidate biomarkers obtained from global screening. In this stage, targeted metabolomics can be employed in an independent cohort to perform precise quantitative validation of the shortlisted core metabolites, confirming the robustness of their changes. Subsequently, a tripartite validation of candidate biomarkers is conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG)/Reactome maps—through hypergeometric enrichment tests, topological network hub analysis, and Gene Ontology functional annotation—to further strengthen the screening of metabolites. For example, in a study investigating the mechanism of propranolol treatment for infantile hemangiomas, researchers first accurately identified 34 differentially expressed metabolites in cell models using targeted metabolomics. Subsequent KEGG pathway enrichment analysis clearly showed that these metabolites were significantly enriched in glucose metabolism-related pathways such as the pentose phosphate pathway, glycolysis, and the citric acid cycle (Yang et al., 2023). The research trajectory from discovery to mechanistic elucidation indirectly demonstrates the effectiveness of this phase.

Dynamic validation serves as the cornerstone of causal inference, and its key lies in the longitudinal monitoring of metabolite trajectories. This enables the clarification of temporal sequence and directionality between metabolite changes over time and the aging process. Longitudinal monitoring is particularly well-suited for targeted metabolomics, as it can provide precise, comparable absolute quantitative data, thereby offering high-quality phenotypic time-course inputs for causal inference. For instance, one study statistically inferred 439 potential biomarker-disease causal associations through longitudinal analysis combined with multivariable Mendelian randomization (MVMR), offering an illustrative example for related research (Zhang et al., 2024b). It should be noted that the validity of Mendelian randomization methods strictly relies on the instrumental variables satisfying the three core assumptions (Lu et al., 2025). Moreover, in the context of complex phenotypes such as metabolites, identifying ideal instrumental variables that simultaneously meet all these conditions remains a significant challenge. Additionally, Mendelian randomization primarily estimates the lifelong cumulative effect of genetic variation on outcomes, which poses limitations in interpreting dynamic processes with temporal heterogeneity, such as aging. Therefore, the application of Mendelian randomization methods in the dynamic validation stage must be approached with caution.

The Aging Metabolomic Landscape: a Systematic Analysis of Consistency and Heterogeneity

Based on the findings from the initial screening of aging-related metabolic markers, it is necessary to further integrate and interpret their changing patterns at a systems level. This section aims to systematically dissect the multi-dimensional dynamic landscape of metabolites during aging according to functional categories, thereby providing a solid biological explanation for metabolic age (Table S3).

Remodeling of lipid metabolism during aging is characterized by a high degree of class-specificity and significant sexual dimorphism. The general increase in glycerolipids observed in both rhesus monkeys and humans is typically associated with decreased insulin sensitivity and an elevated risk of metabolic syndrome, indicating an imbalance in energy storage and metabolic homeostasis (Abou Elhassan et al., 2026). Sphingolipids exhibit complex interconversions and sex-specific differences: sphingomyelin levels decline with aging in non-human primate models, whereas its downstream product, ceramide, shows a general increase in humans. This conversion trend is likely closely linked to its roles in maintaining cell membrane integrity, regulating oxidative stress, and cellular signal transduction (Abou Elhassan et al., 2026; Mohammadzadeh Honarvar et al., 2021). Concurrently, in human models, sphingomyelin consistently decreases in males but shows an increase in most females, highlighting the profound influence of sex on metabolic trajectories of aging (Mohammadzadeh Honarvar et al., 2021). Changes in glycerophospholipids further corroborate the pervasiveness of this sexual dimorphism. For instance, phosphatidylcholine decreases with aging in males but shows inconsistent changes in females; phosphatidylethanolamine even exhibits opposite trends between sexes (decreasing in males, increasing in females) (Mohammadzadeh Honarvar et al., 2021). These alterations not only reflect age-related modifications in cell membrane composition but may also be associated with estrogen-regulated maintenance of membrane function and longevity mechanisms. Furthermore, the decrease in sterols such as cholesteryl esters, alongside the increase in monounsaturated fatty acids, adds to the complexity of the aging lipidomic landscape from the perspectives of reprogrammed cholesterol transport pathways and optimized lipid energy metabolism, respectively (Liu et al., 2023; Sol et al., 2025).

Amino acid metabolism demonstrates considerable heterogeneity during aging. In general population cohorts, levels of Branched-Chain Amino Acids (BCAAs) often increase with age (Sawicki et al., 2024), correlating with pathological states such as insulin resistance (Lynch & Adams, 2014). However, a significant decreasing trend associated with prospective favorable health outcomes is observed in cohorts of “healthy aging” (González-Beltrán et al., 2025). This suggests that elevated BCAA levels may be a marker of age-related metabolic dysfunction rather than an inevitable consequence of aging per se. Additionally, discrepancies in findings concerning citrulline across different studies underscore the profound impact of factors like tissue specificity, population health status, and technological platforms on research conclusions (Xie et al., 2025). This emphasizes the necessity of considering the underlying biological context and technical variables when interpreting the aging trajectories of metabolites.

A core manifestation of aging is the systemic decline in energy metabolism efficiency. The significant decrease in levels of Nicotinamide Adenine Dinucleotide (NAD+) and its precursors represents one of the most replicated findings in aging research (Fang et al., 2017). As the primary substrate for longevity-associated proteins like sirtuins, the depletion of NAD+ severely impairs cellular energy sensing, DNA repair, and stress resistance capabilities (Peng et al., 2024; Perico, Remuzzi & Benigni, 2024). Regarding carbohydrate metabolism, older adults often exhibit higher fasting blood glucose and a trend towards insulin resistance compared to younger individuals (Chen, Sun & Zhang, 2025; Shilo et al., 2024). Alterations in serum levels of glycolytic pathway intermediates reported in some studies may reflect a decreased efficiency in glucose uptake and utilization with advancing age (Jankowski et al., 2025).

Bile acids are not only essential for fat digestion but also crucial signaling molecules. During aging, the ratio and composition of primary to secondary bile acids undergo significant changes. This reflects alterations in hepatic synthetic function and is closely tied to age-related evolution of gut microbiota composition (Aliwa et al., 2023; Zheng et al., 2025). Gut microbiota-derived metabolites, such as decreased short-chain fatty acids (Chen et al., 2024) and abnormal levels of immunomodulatory molecules like indole derivatives (Da Silva et al., 2021), collectively exacerbate age-associated systemic chronic inflammation and immune system dysfunction.

The imbalance in cellular redox homeostasis is a hallmark of aging. This process is directly reflected by the decreased ratio of reduced glutathione to oxidized glutathione and declining levels of endogenous antioxidants like coenzyme Q10 (Diaz-Del Cerro et al., 2023; Rabanal-Ruiz, Llanos-González & Alcain, 2021). Such imbalance exacerbates oxidative damage to proteins, lipids, and DNA, thereby driving cellular senescence and organ functional decline.

In summary, metabolites exhibit complex yet orderly dynamic features during aging. Their consistency and heterogeneity collectively contribute to an integrated understanding of metabolic aging. Translating this level of systematic knowledge into practical biological tools requires the construction of computational models capable of capturing such temporal patterns. The next step, therefore, is to utilize these features to develop aging clocks that can accurately predict biological age.

Development of Aging Clocks

Multi-tiered modeling strategies from simple to complex

After identifying and selecting robust aging-related metabolites, the next step is to use these features to build models for accurate biological age prediction. Most aging clocks employ an incremental modeling strategy, initially adopting foundational models to explore data characteristics, then progressively incorporating complex algorithms to enhance model fitting performance (Fig. 3). This phased modeling approach not only captures deep-seated data patterns but also mitigates overfitting risks, thereby ensuring the scientific validity and reproducibility of predictive outcomes. (The performance comparison of the models is presented in Table S4).

Figure 3. Model construction.

The workflow for developing and evaluating metabolomic aging clocks, highlighting the application of machine learning techniques in their construction and assessment.

Multiple linear regression (MLR) serves as a fundamental statistical method for age prediction, where the estimated age is modeled as a weighted linear function of metabolic features. The coefficients quantify the direction and magnitude of each feature’s influence on predicted age (Vanden Akker et al., 2020). However, MLR faces challenges in capturing nonlinear associations between metabolites and chronological age (Huang et al., 2025). Particularly when high collinearity exists among metabolites, it interferes with variable significance testing, necessitating feature selection or collinearity elimination to maintain model accuracy.

Penalized linear models (e.g., LASSO, ridge regression, and elastic net) integrate regularization terms within the linear regression framework to balance model complexity with predictive performance, thereby enhancing stability (Rist et al., 2017). Mirzaei et al. (2024) utilized LASSO to identify metabolites associated with metabolic syndrome, delineating quantitative relationships between specific metabolites and the disease. Despite these advancements, linear models remain limited in their capacity to capture nonlinear dynamics between metabolites and chronological age.

To address these limitations, Klemera and Doubal introduced the K-D algorithm in 2006, designed to handle variable correlations and nonlinear relationships (Huang et al., 2025; Mitnitski, Howlett & Rockwood, 2017; Wei et al., 2022). This algorithm has been validated in a longitudinal cohort comprising 3,558 participants, demonstrating its utility as an effective tool for monitoring aging trajectories (Earls et al., 2019). However, Dimitri et al. (2022) observed significant performance degradation when applying the algorithm to metabolomic datasets, manifested through elevated prediction errors (root mean square error (RMSE) = 38.92; R² = 0.16), markedly underperforming alternative models. Given the constraints of traditional algorithms in capturing complex relationships within metabolomic data, exploring advanced methods capable of effectively modeling nonlinear associations between chronological age and metabolites becomes critically important.

Nonlinear associations between chronological age and metabolites may more accurately reflect biological reality (Bunning et al., 2020). Tree-based models (e.g., Random Forest, eXtreme Gradient Boosting, and Light Gradient Boosting Machine) and support vector machines excel at capturing nonlinear relationships while effectively handling high-dimensional omics data. These tree-based algorithms autonomously identify age-related biomarkers, eliminating the need for complex feature selection procedures (Huang et al., 2023). Building on this, deep learning architectures show significant promise in deciphering nonlinear relationships within large-scale datasets. Xiong et al. (2025) leveraged the multi-head attention mechanism of Transformers to predict hyperglycemic and hypoglycemic events, successfully capturing nonlinear interactions between glycemic fluctuations and metabolic determinants in a cohort of 17,000 participants.

However, deep learning models inherently possess a “black box” nature, which compromises the interpretability of results. To address this challenge, explainable artificial intelligence techniques (Auzine et al., 2024), particularly SHapley Additive exPlanations (SHAP) (Ladbury et al., 2022), have emerged as powerful tools for deciphering model logic and identifying critical metabolic features. In a neural network-based metabolomic clock study, Lassen et al. (2023) successfully employed SHAP analysis to not only reconfirm known aging-related metabolites but also discover novel biomarkers such as leucine-proline dipeptide. This further demonstrates that explainable artificial intelligence not only enhances the interpretability of complex models but also facilitates the extraction of biologically meaningful metabolites and pathways from high-dimensional data.

It is worth noting that SHAP, as a local interpretation method based on game theory, can effectively quantify the contribution of individual features to specific predictions, yet it is itself built upon certain assumptions (Hu et al., 2024) and involves relatively high computational complexity. Furthermore, a distinction should be made between local interpretability (explaining predictions for individual samples) and global interpretability (understanding the overall behavior of the model). To more comprehensively understand the decision-making mechanisms of deep learning models in metabolomic aging clocks, future studies could integrate emerging techniques such as concept-based explanations and prototype methods (Pinckaers et al., 2022) to complement analytical perspectives, thereby further enhancing model interpretability and the reliability of biomedical findings.

From classical linear models to complex non-linear deep learning architectures, the evolution of these modeling strategies collectively aims to effectively extract reliable signals that characterize the aging process. Therefore, after constructing a model, it is essential to objectively evaluate its performance and generalization ability through a rigorous assessment framework, thereby determining the practical utility of different modeling strategies.

Model performance evaluation

Predictive accuracy stands as the primary metric for aging clocks, with most models incorporating machine learning techniques to enhance performance. During model development, datasets are partitioned into training sets (for iterative algorithm optimization), validation sets (for identifying optimal architectures and hyperparameter configurations), and test sets (serving as benchmarks for assessing generalization capability) (Fig. 3). Common accuracy evaluation metrics include Pearson’s correlation coefficient, coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). However, disparities in metric reporting across studies complicate comparative analyses. As Horvath noted, Pearson’s correlation coefficient exhibits high sensitivity to the standard deviation of age, posing challenges for calibrating predicted ages in chronologically homogeneous cohorts (Horvath, 2013).

Research indicates that the correlation coefficient between metabolic age and chronological age (r = 0.74–0.81) is generally lower than that of epigenetic clocks (r = 0.96) and proteomic clocks (r = 0.93) (Macdonald-Dunlop et al., 2022; Mutz, Iniesta & Lewis, 2024). This discrepancy is rooted in their distinct biological foundations: epigenetic clocks, based on stable molecular imprints, are adept at reflecting long-term cumulative aging trends (Hamaya et al., 2025). In contrast, metabolites, as real-time outputs of physiological function, have levels susceptible to transient factors. Consequently, while they may be less effective at characterizing the mere passage of time, they are highly sensitive to short-term physiological fluctuations. For instance, although metabolic age shows a lower correlation with actual age, it is significantly associated with depressive states (Robinson et al., 2020). Furthermore, among various aging clocks, only the metabolomic clock can significantly indicate cardiometabolic disease risk (Jansen et al., 2021). This suggests that the core value of metabolomic aging clocks lies not in precise timekeeping, but in directly reflecting functional status and pathological trajectories. Compared to transcriptomes and proteomes, which are closer to upstream regulation, the metabolome—situated at the terminal end of the information flow—can more directly capture output signals of aging-related functional disturbances such as mitochondrial dysfunction and inflammation. Therefore, metabolomic aging clocks function as a “real-time monitoring system for organismal function,” providing dynamic insights into disease-specific risk warnings and short-term intervention efficacy assessment—a role uniquely complemented by other omics approaches.

In addition to accuracy metrics, the calibration performance of a model is crucial for assessing the predictive reliability of aging clocks. Calibration reflects the agreement between predicted and chronological age across the entire age spectrum, which can be evaluated using calibration curves and their quantitative metrics, such as the calibration slope and intercept. Ideally, the calibration slope should be 1 and the intercept 0. A slope that deviates from 1 suggests age-dependent systematic bias in predictions, while a non-zero intercept indicates an overall shift. For metabolomic aging clocks, it is recommended to perform age-stratified calibration or employ the Brier score to quantify calibration loss during the validation stage (Templeman et al., 2025), thereby correcting prediction bias in specific age intervals and enhancing the model’s robustness and biological interpretability for disease risk alert and intervention assessment.

Understanding aging acceleration

Aging acceleration serves as a key quantitative metric in various aging clock models. Derived from model residuals, it reflects the deviation between biological age and chronological age (Fransquet et al., 2019). This metric transforms model predictions into a directly interpretable value that measures an individual’s relative aging rate, providing key evidence for health risk assessment. However, metabolomic aging clocks systematically overestimate the age of younger individuals while underestimating that of older individuals (Mutz, Iniesta & Lewis, 2024). This computational bias has prompted extensive research into correction methodologies. Researchers now commonly characterize accelerated biological aging using residuals from the regression of biological age on chronological age—an approach validated in current metabolomic clock studies (Wang et al., 2024).

However, most current metrics of aging acceleration are derived from cross-sectional data. Given the fundamental differences between cross-sectional and longitudinal designs in revealing aging trajectories, the inherent limitations of each research paradigm thus become evident. The core strength of cross-sectional design lies in its efficiency in capturing inter-individual differences. It can effectively identify individuals whose aging rate is faster or slower than their peers at a specific time point and establish a macro-level association map between metabolite levels and age at the population level. However, its inherent limitation is the inability to track intra-individual dynamic trajectories. Consequently, it cannot distinguish whether the observed accelerated aging represents a stable individual trait or a temporary state fluctuation, nor can it infer causality or reveal non-linear individual aging pathways (Ala-Korpela et al., 2023; Nelson, Promislow & Masel, 2020).

Conversely, the value of longitudinal design is its ability to directly quantify the rate of change in aging within individuals, thereby providing the most direct dynamic evidence for aging acceleration. It can delineate heterogeneous patterns of aging trajectories, capture key life transition points, and offer stronger support for causal inference based on temporal sequence. Of course, longitudinal designs typically entail higher costs in terms of resources and time, making them less suitable for the initial, efficient screening of a vast number of candidate biomarkers.

Therefore, cross-sectional and longitudinal studies constitute complementary and progressive research paradigms. Discrepancies between cross-sectional estimates and longitudinal observations often suggest that a particular marker may primarily reflect stable inter-individual traits rather than a dynamic aging process, or that its changes occur over a timescale longer than the observation period. To effectively capture meaningful metabolic aging signals in future longitudinal studies, considering the dynamic nature of metabolites, it is recommended that the follow-up period be sufficiently long (e.g., no less than 4 years) to surpass short-term physiological fluctuations (Abugroun et al., 2025). Furthermore, different classes of metabolites vary in their applicability within this framework: metabolites predominantly determined by genetics or long-term lifestyle, characterized by high inter-individual variability, serve as excellent cross-sectional aging markers; whereas metabolites significantly influenced by recent environment, behavior, or circadian rhythms, with high intra-individual variability, require longitudinal designs for the accurate elucidation of their true dynamic aging information.

Despite the aforementioned challenges, substantial evidence indicates that aging acceleration metrics based on cross-sectional data remain significantly associated with various adverse health outcomes. In the future, hybrid study designs that combine cross-sectional screening with longitudinal validation will enable a more complete and precise unveiling of the dynamic evolution of the metabolome in human aging.

Applications of Metabolomic Aging Clocks

Disease risk prediction and clinical validation

Metabolomic aging clocks are increasingly recognized as novel tools for assessing cardiovascular disease (CVD) and all-cause mortality risks. Van den Akker et al. (2020) constructed a metabolomic clock (metaboAge) using 25,000 samples, demonstrating that individuals with metabolic age exceeding chronological age exhibit significantly elevated CVD incidence and mortality risk. Wang et al. (2024) computationally defined metabolic age acceleration through machine learning, revealing that the highest quintile (Q5) group had 2.13-fold higher CVD incidence (OR = 2.13), 2.01-fold increased 12-year CVD event risk (HR = 2.01), and 1.56-fold elevated 17-year all-cause mortality risk (HR = 1.56). Survival analysis further indicated Q5 males experienced median cardiovascular events 6 years earlier and all-cause death 4 years earlier than expected. Hertel et al. (2016) validated the robustness of metabolomic clocks across ethnicities and clinical contexts, demonstrating predictive utility for 13-year survival rates and metabolic improvement post-bariatric surgery.

In oncological prognostic assessment, metabolomic aging clocks demonstrate complementary value. Van Holstein et al. (2024) analyzed solid tumor patients aged ≥70 years using MetaboHealth (mortality prediction) and MetaboAge (biological age) models. Per 1-standard deviation increase in MetaboHealth, 1-year mortality risk rose 2.32-fold (HR = 2.32), whereas each additional MetaboAge year conferred a 4% mortality risk increment (HR = 1.04). The baseline clinical prediction model achieved an AUC of 0.76; integrating MetaboHealth increased AUC to 0.80 (p = 0.09), though MetaboAge provided no significant improvement. The multi-disease risk quantification capability of metabolomic aging clocks offers substantial public health advantages. By enabling early identification of accelerated aging individuals, public health systems can strategically allocate screening resources and optimize prevention strategies, providing scientific evidence at the metabolic level for public health policy formulation (Fig. 4).

Figure 4. Model application.

Three core application domains of metabolomic aging clocks: assessing and predicting disease risk, developing anti-aging therapies, and designing personalized health interventions.

Development of anti-aging therapies

The continuous evolution of aging clocks has significantly advanced the exploration of anti-aging therapies. Tomas-Loba et al. (2013) utilized metabolomic clocks to demonstrate that telomerase deficiency accelerates metabolic aging in murine models, while telomerase reverse transcriptase (TERT) gene therapy effectively restored the metabolic profiles of aged mice to a youthful state. Similarly, principles of metabolomic remodeling discovered in nematode models have been applied to screen compounds that delay aging (Copes et al., 2015). To further investigate the possibility of aging reversal, Yang Y’s team constructed the first high-precision primate aging clock by integrating multi-omics data. Based on this model, metformin intervention studies showed approximately 6 years of brain aging reversal in adult male monkeys, with neuroprotective effects linked to partial activation of the Nrf2 antioxidant pathway. The study simultaneously confirmed the drug’s ability to maintain neuronal homeostasis and improve cognitive function, providing critical primate experimental evidence for Nrf2-targeted anti-aging drug development (Yang et al., 2024).

Beyond pharmacological approaches, non-pharmacological interventions are gaining increasing attention. A randomized controlled trial conducted by Murukesu et al. (2024) demonstrated that participants in the WE-RISE™ intervention program showed significant improvements (p < 0.05) in cognitive function, physical performance, body composition, disability reduction, health-related quality of life, and self-perceived exercise self-efficacy. This integrated multi-domain intervention strategy for anti-aging health management effectively coordinates multiple physiological systems and is emerging as a novel paradigm in anti-aging therapy (Fig. 4).

Formulation of personalized health strategies

Personalized aging clocks based on metabolomic biomarkers are driving the clinical translation of precision medicine across multiple domains, enabling stratified intervention strategies. In nutritional interventions, the Mediterranean diet demonstrates significant predictive effects on cardiovascular disease risk through lipid metabolism modulation (Li et al., 2020). Research in exercise medicine confirms that the intensity and modality of physical activity profoundly reshape metabolic networks, with personalized exercise regimens optimizing metabolic outcomes (Kelly, Kelly & Kelly, 2020). Sleep deprivation—a common modern phenomenon—was shown to significantly alter 27 metabolite levels, potentially underlying its antidepressant effects and informing novel sleep-metabolism co-intervention strategies for depressed individuals (Davies et al., 2014). With advances in dynamic metabolic monitoring and machine learning algorithms, future interventions will evolve toward a “biomarker-guided real-time feedback regulation” paradigm, paving the methodological foundation for personalized medicine aimed at reversing metabolic aging (Fig. 4).

Emergence of Next-Generation Aging Clocks

The clinical utility of aging clocks does not always positively correlate with their predictive accuracy. Zhang et al. (2019) discovered that when traditional aging clock models excessively pursue accuracy in biological age prediction, their actual predictive power for mortality risk may paradoxically decrease. This critical paradox directly catalyzed the emergence of next-generation aging clocks, shifting their research focus from solely predicting chronological age to incorporating key aging phenotypes as the core of model construction.

The breakthrough progress in next-generation aging clocks heavily relies on large-scale longitudinal cohort studies. By conducting serial metabolomic tests at fixed intervals during critical life stages, researchers obtain core data revealing individualized aging trajectories. For instance, the MetaboAgeMort model developed using data from nearly 240,000 UK Biobank participants directly targets “mortality risk” as its ultimate prediction goal (Jia et al., 2024). This model not only overcomes the prediction disconnect identified by Zhang but also precisely quantifies the substantial metabolic aging heterogeneity among chronological age-matched individuals.

Longitudinal data inherently facilitates the mining of critical aging indicators. The reliability of these aging biomarkers—demonstrating consistent validation across diverse populations—fundamentally stems from long-term, repeated metabolomic profiling of the same individuals (Kuiper et al., 2023; Reel et al., 2021). As demonstrated by Fischer et al. (2014), utilizing multiple aging biomarkers to predict mortality risk significantly outperformed traditional methods. Through longitudinal analysis of large-scale population data, Zhang et al. (2024b) calculated personalized metabolic aging rates that directly reflect individual aging dynamics. Consequently, next-generation aging clocks exhibit dual advantages: maintaining stable performance across different populations while accurately predicting critical health outcomes like mortality risk, thereby addressing the limitation of traditional clocks that achieved precise measurement but poor prognostic prediction.

Challenges and Prospects

Dual constraints of sample sources and detection technologies on model accuracy

The predictive performance of aging clocks is dually influenced by sample sources and analytical technologies. Although aging models utilizing blood, urine, or cellular molecular signatures can estimate biological age, their reliability exhibits significant variations across sample types and detection methods. For instance, regarding sample selection, the KarMeN study demonstrated that plasma-based models outperformed urine-based models in predictive accuracy (Rist et al., 2017). At the technological level, researchers found that MS-based models achieved superior predictive accuracy compared to NMR-based approaches (Robinson et al., 2020). Currently, standardized criteria for quantifying “accuracy” remain elusive, and the integration of multi-platform and multi-sample data analysis, along with the development of composite biomarkers derived from multi-source data, continue to be critically unresolved challenges.

Challenges in multi-omics integration

In aging clock research, metabolomics captures dynamic aging alterations, whereas methylomics reflects long-term aging trajectories. Integrating multi-omics approaches is both imperative for deciphering aging complexity and a fundamental challenge (Solovev, Shaposhnikov & Moskalev, 2020). First, data integration is complicated by inherent heterogeneity across omics layers and variations among technical platforms. Second, elucidating intricate causal networks between metabolic functional strata and other regulatory tiers remains unresolved. Third, improvements in model prediction accuracy have not proportionally enhanced clinical utility. Consequently, developing multi-omics integration frameworks that harmonize technical feasibility with clinical applicability represents a critical direction for breakthrough.

Ethical considerations in biomarker application

While advancing the clinical application of metabolomic aging clocks, it is imperative to cautiously consider their accompanying ethical implications. As a quantifiable biological metric, biological age carries the risk of being misinterpreted or even misused if presented in isolation from individual and societal context. On a personal level, a standalone “rate of aging” value may be simplistically equated with health status or life expectancy, overlooking individual differences, environmental factors, and disease-specific pathological processes. Societally, aging biomarkers could be inappropriately utilized for workplace discrimination, insurance pricing, or social assessment, potentially reinforcing a tendency toward a ‘biological determinism’ of aging (Shen & Feldman, 2022) and imposing unnecessary psychological burdens on individuals. Therefore, the application and promotion of such biomarkers should be accompanied by the establishment of supplementary ethical guidelines. These guidelines must clarify their role as auxiliary tools, emphasize that their findings require integrated interpretation alongside other clinical indicators and the individual’s overall health status, and advocate for the refinement of relevant laws and regulations to prevent their misuse in non-medical contexts.

Technological accessibility and prospects for clinical translation

While metabolomic aging clocks have demonstrated significant potential, their translation from research tools to routine clinical application faces several challenges. The primary challenge is technological accessibility. Current platforms based on high-throughput mass spectrometry or nuclear magnetic resonance are costly and involve complex procedures, hindering widespread adoption in clinical settings. A viable translational path lies not in directly simplifying existing platforms, but in developing clinically-oriented derived metrics. Research indicates that simplified models, constructed by screening core biomarker combinations from hundreds of metabolites that best elucidate aging mechanisms, can achieve efficacy comparable to complex models in predicting endpoints such as mortality (Peng et al., 2026; Sebastiani et al., 2024).

Building upon accessibility, the second key challenge is interpretability of results. Model outputs are often highly integrated macroscopic indices, making it difficult for clinicians to formulate specific diagnosis or treatment plans based on them. Therefore, it is necessary to construct a multi-layered, interpretable explanatory framework. For instance, leveraging methods from explainable artificial intelligence can deconstruct the macroscopic predictive value, identifying key metabolites or pathways that contribute most significantly to an individual’s accelerated aging (Qiu et al., 2023). In doing so, clinical reports can evolve from stating “what” the aging status is to suggesting “why,” linking the abstract pace of aging to concrete biological processes. This provides a reliable biological foundation to inform clinical decision-making.

Conclusions

As an advanced assessment modality, metabolomics provides precise and dynamic perspectives for the development of aging clocks. In this review, we have comprehensively examined methodological approaches and modeling strategies for aging clocks from a metabolomic perspective, proposing a progressive triphasic causal validation framework to enhance the reliability of aging biomarker discovery. Although significant progress has been achieved in metabolomic aging clocks, opportunities for improvement remain in technical refinement, model enhancement, and clinical translation. Future research should continue to address existing limitations by rigorously validating causal relationships between metabolic alterations and aging phenotypes/health outcomes, improving model generalizability and cross-dataset comparability, and bridging the gap between cutting-edge research and clinical/public health applications. These efforts will provide scientific foundations for promoting healthy aging and extending healthspan.

Funding Statement

This work was supported by the Hubei Provincial Natural Science Foundation (Key Project of Joint Fund) (no. 2023AFD031). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.