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When One Organ Leads the Decline

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dr Karunia Valeriani Japar

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Keywords: Organ Specific Aging Clocks, Plasma Proteomics, Biological Aging, AI-Driven Longevity Medicine, Multi-omics Biomarkers

From One Biological Age to Eleven Organ Clocks

For decades, clinicians and researchers have spoken of “biological age” as if it were a single scalar value that captures how fast a person is aging compared with their chronological years. This view has been reinforced by composite biomarkers such as phenotypic age scores and methylation clocks, which summarize systemic aging into one number that is easy to communicate but difficult to translate into organ-specific action. However, accumulating evidence from large proteomic and imaging studies shows that human organs do not age in lockstep. Instead, each organ follows a partially independent trajectory, so that an individual may have a “young” liver but an “old” heart or lung, with distinct implications for disease risk and clinical decision-making.

Recent advances in plasma proteomics have made it possible to non-invasively capture these heterogeneous trajectories at scale. By quantifying thousands of circulating proteins in a single blood sample, researchers can identify sets of proteins that are enriched for specific organs and use them as molecular fingerprints of organ state and damage. Machine-learning models trained on these organ-enriched signatures can then estimate an organ-specific biological age, often termed an “organ clock” for multiple systems simultaneously, including the heart, liver, lungs, kidneys, brain, adipose tissue, muscle, immune system, and vasculature.

Across tens of thousands of adults, such proteomic organ clocks have been validated in diverse cohorts, demonstrating high cross-cohort reproducibility and robust associations with incident disease, multimorbidity, and mortality. These studies consistently reveal that different organs within the same person can age at markedly different rates and that accelerated aging of a single organ often predicts future organ-specific diseases many years before clinical onset. In other words, biological age is better conceptualized as a constellation of organ clocks rather than a single composite index.

In the context of preventive, AI-driven wellness and longevity medicine, this reconceptualization is more than semantic. Organ-specific aging information enables clinicians and digital health platforms to identify which organ is “leading the decline,” refine risk stratification beyond traditional scores, and align interventions with the most vulnerable systems in each individual. The aim of this article is to synthesize emerging evidence on proteomic organ clocks and to discuss how integrating these tools into AI health-tech ecosystems could transform early risk detection, personalize lifestyle and pharmacologic interventions, and ultimately extend health span in routine practice.

Proteomics, Machine Learning, and the Biology of Organ Aging

Aging at the tissue level is accompanied by continuous remodelling of the extracellular matrix, cell turnover, inflammation, and micro-injury, all of which alter the proteins that organs release into the circulation. Each organ therefore “leaks” a characteristic constellation of proteins into blood, creating a molecular fingerprint that reflects its current structural integrity, metabolic activity, and cumulative damage. By mapping circulating proteins back to their most probable tissue of origin using existing transcriptomic and proteomic atlases, researchers can infer organ-specific biology from a single plasma sample, turning the blood proteome into a minimally invasive window on multi-organ aging [1].

High-throughput plasma proteomics platforms, such as affinity-based arrays and mass-spectrometry–based assays now allow simultaneous quantification of thousands of proteins at scale in large epidemiologic cohorts. This deep molecular coverage captures subtle, multivariate shifts that are invisible to traditional single-analyte biomarkers, providing an exceptionally rich feature space for data-driven modelling of aging processes. When combined with detailed phenotyping, longitudinal follow-up, and clinical outcome data, these proteomic datasets become ideal substrates for machine-learning approaches that can detect complex, non-linear patterns linking protein signatures to age and disease [1-3].

Using these data, investigators have trained separate machine-learning clocks for individual organs by selecting organ-enriched proteins and modelling their relationship with chronological age and, in some cases, organ-specific functional measures. The output of each model is an “organ age,” typically expressed as the predicted age of the organ based on its proteomic profile; the difference between this value and the person’s chronological age (the “age gap”) quantifies accelerated or decelerated aging relative to peers. Distinct clocks have been developed for organs and systems such as the heart, vasculature, brain, liver, kidneys, lungs, pancreas, intestine, adipose tissue, skeletal muscle, immune system, and blood, enabling simultaneous estimation of aging trajectories across the body [1-3].

Validation across multiple, independent cohorts has shown that these organ-specific clocks are both reproducible and clinically meaningful. The original Nature study by Oh et al. estimated organ ages in 11 systems in 5,676 adults from five cohorts and reported high cross-cohort correlations in predicted ages. Subsequent work in the UK Biobank and other populations confirmed that organ age gaps predict all-cause mortality and organ-specific disease risk beyond traditional clinical and genetic risk factors. A consistent finding across studies is that approximately 20% of individuals exhibit markedly accelerated aging in at least one organ, and a smaller subset show multi-organ acceleration, highlighting the prevalence of organ-specific vulnerability within ostensibly healthy populations [1-4].

Methods in Brief: Building and Validating Organ-Specific Aging Clocks

Development of organ-specific proteomic aging clocks typically begins with large, well-phenotyped cohorts in which plasma samples, clinical data, and long-term outcomes are available. Blood is drawn under standardized conditions, processed to obtain plasma, and stored at low temperature to preserve protein integrity before batch analysis. High-throughput proteomic platforms, such as aptamer-based or proximity extension assays—are then used to quantify thousands of circulating proteins per sample, generating a dense matrix of protein abundances that can be linked to demographic variables, organ-specific diagnoses, imaging, and mortality over years of follow-up [1,2].

To construct organ-specific models, investigators first attribute proteins to their most likely tissue of origin by integrating information from prior transcriptomic and proteomic atlases that catalogue tissue-enriched expression patterns across major organs. Proteins that show preferential expression in the heart, liver, lung, kidney, brain, vasculature, adipose tissue, skeletal muscle, pancreas, intestine, or immune system are grouped into organ-enriched sets, which serve as candidate features for each organ clock. Feature selection methods, such as correlation filtering, Boruta, or regularized regression are then applied to identify the subset of organ-enriched proteins that best predict chronological age, reducing dimensionality and improving interpretability [1,2,5].‌

Separate machine-learning models are trained for each organ using these protein subsets as inputs and chronological age as the primary label. Approaches such as penalized linear models (for example, LASSO), gradient-boosted decision trees, or ensemble methods are commonly used and tuned using k-fold cross-validation within the derivation cohort. The resulting models output a predicted age for each organ; subtracting the individual’s chronological age from this predicted age yields an “organ age gap” that quantifies accelerated or decelerated aging at the organ level. Some studies additionally incorporate organ-specific functional measures or imaging markers as auxiliary labels, further anchoring the clocks in clinically meaningful physiology [1,2,6].

Validation proceeds in several stages. Internally, cross-validation and held-out test sets are used to assess the correlation between predicted and chronological age and to evaluate model stability across resampling schemes. Externally, the clocks are applied to independent cohorts with different ancestry and environmental backgrounds such as Chinese, European, and US samples to confirm that organ age estimates generalize across populations, often achieving high cross-cohort correlations. Crucially, accelerated organ age gaps are then related to incident disease and mortality using survival models; for example, higher brain or vascular age gaps have been linked to increased risk of dementia and stroke, while elevated cardiac, hepatic, or pulmonary age gaps predict heart failure, liver failure, and lung cancer with hazard ratios typically in the range of 1.2–2.0 per standard-deviation increase [2,7].

Despite this rich organ-level information, most current commercial “biological age” offerings aggregate molecular signals from multiple tissues into a single composite age, whether based on DNA methylation, multi-protein panels, or mixed biomarkers. While such summaries are easy to communicate, they can obscure which specific organ is driving risk, particularly in individuals who exhibit accelerated aging in only one or two systems while others remain relatively preserved. Organ-specific proteomic clocks therefore represent an important methodological and conceptual advance: they retain the granularity needed to identify the first failing organ and enable targeted prevention, even though translating this complexity into simple, clinically usable tools remains an ongoing challenge [1,2,5,7,8].

Clinical Implications: When One Organ Ages Faster Than the Best

Across several large cohorts, accelerated organ-specific ages derived from plasma proteomic clocks have been consistently associated with higher risk of future disease in the corresponding organ. Accelerated liver age predicts incident liver failure, while advanced heart age is linked to dilated cardiomyopathy and chronic heart failure, and an older-than-expected lung age confers increased risk of lung cancer and other respiratory diseases. These associations typically remain robust after adjustment for chronological age, conventional risk factors, and, in some studies, global organismal proteomic age, underscoring that organ clocks capture risk information that is not fully reflected in traditional metrics [2,7,9-11].

Importantly, organ-specific age gaps can be clinically informative even when a person’s overall biological age or composite proteomic age appears close to their chronological age. Individuals with a single fast-aging organ often have substantially elevated hazard for diseases of that organ compared with peers whose organ ages are all near expected values. For example, in long-term follow-up studies, a rapidly aging heart predicted markedly higher risks of cardiovascular disease and heart failure, while accelerated lung aging was associated with increased incidence of chronic obstructive pulmonary disease, respiratory infections, and lung cancer, despite average composite biological age in some cases. These findings suggest that the clinically meaningful signal lies less in the global age estimate and more in the pattern of relative organ ages, the identity of the first organ to diverge from the rest [2,9,11-13].

Conceptually, this shifts prevention away from undifferentiated “anti-aging” strategies toward a focus on “Which organ is leading the decline, and why?” When a specific organ clock is substantially older than the others, that organ becomes the primary target for early investigation and intervention, even if symptoms are absent and standard laboratory tests remain within reference ranges. Observational data further indicate that individuals with multiple extremely aged organs have a stepwise increase in multimorbidity and mortality, supporting the notion of organ ageotypes that define distinct trajectories of aging and disease across the lifespan [2,10,12].

In practice, organ-based aging information can be used to personalize screening, lifestyle modification, and pharmacologic management. A patient with an accelerated cardiac or arterial age may benefit from intensified cardiometabolic risk reduction, aggressive blood-pressure and lipid control, structured exercise programs, and closer surveillance for subclinical cardiomyopathy whereas someone with markedly elevated liver age might prompt detailed review of alcohol use, hepatotoxic medications, metabolic dysfunction, and consideration of targeted imaging or fibrosis assessment. Similarly, accelerated lung age could justify earlier smoking cessation interventions, low-dose CT screening in high-risk individuals, and proactive vaccination and pulmonary rehabilitation strategies. As organ clocks mature and become integrated into AI-driven decision-support systems, they have the potential to refine risk stratification and guide organ-specific prevention in routine care, provided their use is coupled with careful clinical interpretation and evidence-based follow-up pathways [2, 9,11,12,14].

From Lab to Lifestyle: Integrating Organ Clocks into Preventive and Longevity Care

Translating organ-specific aging clocks from research settings into everyday practice will depend heavily on AI-enabled health-tech platforms that can turn complex proteomic outputs into simple, actionable insights for clinicians and patients. Multi-organ age results can be integrated into personalized wellness dashboards that display each organ’s biological age, highlight the “leading” (most accelerated) organ, and surface modifiable risk factors linked to that organ’s age gap, such as smoking, adiposity, inactivity, or specific comorbidities. Combining organ ages with wearable data, routine laboratory results, and patient-reported outcomes allows AI systems to generate tailored recommendations and risk trajectories rather than static scores, making the information usable in time-constrained clinical encounters and digital wellness programs [2,10,15,16].

Within clinical workflows, organ clocks could serve as a triage layer that directs individuals toward the most relevant preventive pathways. In primary-care or wellness-clinic settings, an older-than-expected cardiac or arterial age might trigger referral to cardiometabolic programs focusing on hypertension, dyslipidemia, obesity, and fitness, whereas pronounced hepatic aging could route patients into liver health clinics emphasizing metabolic dysfunction–associated steatotic liver disease, alcohol risk management, and hepatotoxic medication review. Accelerated lung age could justify intensified smoking-cessation support, consideration of low-dose CT screening in high-risk individuals, and early evaluation for obstructive airway disease, while an advanced immune or inflammatory age might motivate assessment for chronic inflammatory conditions, immunizations, and infection-prevention strategies. By embedding organ-specific prompts into electronic health records and telehealth platforms, AI can help clinicians systematically align preventive resources with each patient’s dominant organ vulnerabilities [2,10-12,16]. 

Critically, these organ-based risk signals must be tied to concrete, evidence-informed interventions rather than remaining abstract labels. For cardiovascular and metabolic age acceleration, this includes structured aerobic and resistance exercise prescriptions, dietary patterns that improve insulin sensitivity and lipid profiles (such as Mediterranean-style diets), aggressive control of blood pressure and LDL cholesterol with agents like ACE inhibitors, ARBs, and statins, and, where appropriate, metabolic therapies including GLP-1 receptor agonists or SGLT2 inhibitors. Elevated liver age could prompt targeted counselling on alcohol and fructose intake, weight reduction for steatotic liver disease, careful review of medications and herbal supplements with hepatotoxic potential, and periodic imaging or elastography when indicated. In patients with accelerated lung age, smoking cessation, air-quality and occupational exposure interventions, pulmonary rehabilitation, and guideline-concordant screening and vaccination become priorities, while brain or immune aging signals may guide programs focused on sleep and circadian hygiene, cognitive engagement, stress reduction, and treatment of chronic inflammatory drivers [2,10-12,17,18].

Longitudinal use of organ clocks offers an additional layer of value by creating closed feedback loops between interventions and measurable changes in organ-specific aging trajectories. Serial testing over months to years can show whether lifestyle modifications, pharmacologic treatments, or procedural interventions are stabilizing, accelerating, or reversing organ age gaps, helping patients see tangible effects of their efforts and allowing clinicians to adapt care plans dynamically. Early longitudinal analyses suggest that organ ages are partially modifiable: changes in smoking status, weight, fitness, and cardiometabolic control track with improvements in heart, lung, adipose, and metabolic ages, while medication initiation alters specific organ clocks through drug-targeted pathways. As AI platforms learn from these repeated measurements across large populations, they may eventually support n-of-1 experimentation, testing which combinations of behavioural and pharmacologic interventions most effectively slow the leading organ’s decline in each individual, thus operationalizing organ clocks as practical tools for personalized longevity medicine rather than purely prognostic markers [12,14-17,19,20].

AI-Driven Organ Aging: Opportunities, Limitations, and Ethics

Advanced machine-learning methods are central to translating organ-specific proteomic data into clinically useful aging clocks. Large, longitudinal resources such as the UK Biobank and other population cohorts provide tens of thousands of plasma proteomes linked to detailed phenotypes and long-term outcomes, allowing non-linear models, such as gradient-boosted trees and related algorithms to learn complex relationships between organ-enriched protein signatures, chronological age, and future disease risk. These models can then be iteratively refined as additional longitudinal datasets become available, improving calibration, reducing overfitting, and enabling parsimonious protein panels that retain predictive accuracy while becoming more feasible for routine clinical implementation. In principle, integration with other digital biomarkers (for example, imaging-derived phenotypes, wearables, and electronic health records) could further enhance organ-age estimation and support dynamic, AI-driven risk prediction across the life course [1,2,14,16,21].

Despite their promise, organ-specific aging clocks face substantial practical limitations at present. Most proteomic clocks remain research tools without formal regulatory approval for diagnostic use, and regulatory agencies have emphasized the need for rigorous validation, standardized protocols, and clear demonstration of clinical utility before adoption in routine care. Assay variability between platforms, batch effects, and differences in protein panels can lead to non-comparable results across laboratories and commercial offerings, complicating interpretation for clinicians and patients. There is also a risk of overinterpreting early-stage biomarkers, treating modest age gaps or unvalidated extreme “ageotypes” as deterministic forecasts of disease, particularly in direct-to-consumer contexts where medical oversight is limited and access may be restricted to affluent, health-literate users, thereby exacerbating existing health inequities [2,22-24].

The generation and use of large-scale proteomic aging datasets raise several ethical concerns that parallel, but also extend, those seen in genomics and epigenetic clock research. Proteomic profiles are increasingly recognized as rich, re-identifiable sources of sensitive information, revealing not only disease risk but potentially pregnancy status, substance exposures, and other traits, which heightens the importance of robust data-protection, consent, and governance frameworks. Algorithmic bias is another major concern: if training datasets under-represent certain ethnic, socioeconomic, or geographic groups, organ-age predictions and associated risk estimates may systematically misclassify these populations, entrenching disparities in access to preventive care and insurance. In addition, labelling individuals as having “old” hearts, livers, or brains may carry psychological burdens, influence self-identity, or facilitate discriminatory uses by third parties such as employers or insurers, echoing worries already raised for epigenetic age estimators and other biological age tests [2,24-26]. 

Addressing these challenges will require a deliberate, multi-stakeholder approach to the development and deployment of AI-driven organ clocks. Transparent model reporting, including documentation of training data composition, feature selection, performance across subgroups, and limitations is essential so that clinicians and regulators can critically appraise claims and avoid “black-box” adoption. Diverse, globally representative training cohorts and ongoing bias audits are needed to ensure equitable performance, while clinician education and clear interpretation guidelines can help prevent overreliance on clock outputs at the expense of conventional clinical reasoning. Rather than replacing nuanced judgment, organ-specific aging tools should be positioned as decision-support instruments that complement history-taking, examination, and established risk scores, with professional societies, regulators, industry, and patient groups collaborating to define standards for responsible use, communication of results, and safeguards against misuse [2,10,16,21,24,27]. 

Future Directions: Towards Multi-Omics and Organ-Specific Longevity Therapies

The next generation of organ-specific aging clocks will likely move beyond single-layer plasma proteomics toward integrated, multi-omic organ health scores. Combining organ-enriched proteomic signatures with genomics, metabolomics, microbiome profiles, imaging phenotypes, and digital biomarkers from wearables can capture complementary aspects of organ biology, genetic susceptibility, metabolic state, environmental exposures, structural change, and functional reserve within a unified framework. Early multi-omics studies already show that metabolomic and microbiome-based clocks provide additional information about aging trajectories and multimorbidity risk that is not fully captured by traditional biomarkers, suggesting that integrated models could offer a more granular and robust picture of organ-level resilience and decline. In clinical AI platforms, such multi-omic organ scores could underpin richer dashboards that distinguish, for example, a heart that is genetically vulnerable but metabolically well-controlled from one that is structurally and metabolically failing despite low traditional risk [28].

As our ability to quantify organ-level aging improves, these measures could guide the development and testing of targeted geroprotective therapies. Preclinical work has already demonstrated that interventions such as vascular endothelial growth factor (VEGF) augmentation, senolytic agents, mitochondrial protectors, and mTOR/AMPK-modulating drugs can selectively rejuvenate cardiovascular, metabolic, or musculoskeletal systems and extend healthspan in animal models. Organ clocks offer a way to translate this concept into humans by providing quantitative readouts of how specific organs respond to candidate interventions, both in early-phase clinical trials and in real-world practice. In principle, one could stratify participants by baseline organ ageotypes (for example, vascular-dominant versus hepatic-dominant aging) and test whether organ-targeted therapies preferentially benefit those whose accelerated clocks align with the therapy’s mechanistic focus. Over time, this could support a shift from generic “anti-aging” drugs to portfolios of organ-specific longevity therapies tailored to each person’s leading failing systems [29-33].

AI-enabled platforms are also well positioned to enable near real-time risk updating based on longitudinal organ-age measurements. With repeated sampling, potentially facilitated by microsampling technologies and home-based collection, organ clocks could be recalculated at regular intervals, allowing models to track how fast each organ’s age gap is changing in response to lifestyle or pharmacologic interventions. In clinical trials, these dynamic organ-age trajectories might serve as early surrogate endpoints that change more rapidly than hard outcomes, helping to identify promising geroprotective strategies sooner and to de-risk large, long-duration studies. In individualized care, similar feedback loops could underpin adaptive treatment algorithms: when an intervention slows or reverses a patient’s accelerated liver or heart age, the platform can reinforce that strategy; if organ ages continue to worsen, AI systems can suggest intensification, switching, or combination approaches based on patterns learned across many users [14,28,33,34]. 

Ultimately, however, the clinical value of organ-specific and multi-omic aging clocks will depend on rigorous prospective evaluation. It remains an open question whether acting on organ-age information, over and above established risk factors and guidelines actually reduces incident disease, disability, and mortality or meaningfully extends health span. Well-designed randomized and pragmatic trials are needed to compare organ-clock–guided care versus standard risk-factor–based management, to determine which patient groups benefit most, and to assess potential harms such as overdiagnosis, overtreatment, or psychological distress. As these studies unfold, collaboration between clinicians, trialists, data scientists, and regulators will be essential to define clinically meaningful changes in organ age, validate them as surrogate endpoints where appropriate, and ensure that emerging organ-specific longevity therapies are deployed in ways that genuinely improve population health rather than simply expanding a wellness market [27,28,30,33,].

Redefining Biological Age Around the First Failing Organ

Biological age is increasingly understood not as a single scalar value but as a composite of multiple organ-specific clocks, each following its own trajectory and each carrying distinct implications for future disease and mortality risk. Large plasma-proteomic studies have demonstrated that accelerated aging of individual organs, such as the heart, liver, lungs, kidneys, brain, or immune system can be quantified from circulating protein signatures and often diverges substantially within the same individual. These organ ages predict organ-specific and systemic outcomes over many years, indicating that it is the pattern of relative organ decline, rather than a single averaged “biological age,” that best captures vulnerability across the lifespan.

Recognizing this heterogeneity enables a shift away from generic, one-size-fits-all “anti-aging” approaches toward precise, organ-targeted prevention and longevity strategies guided by the earliest failing systems. When proteomic organ clocks identify an accelerated cardiovascular age, for example, clinicians can intensify cardiometabolic risk management and lifestyle interventions, while a disproportionately old hepatic or pulmonary age may prompt focused evaluation for hepatotoxic exposures, metabolic dysfunction, or smoking-related injury. The central clinical question therefore becomes: which organ is leading the decline in this patient, and which modifiable levers, behavioural, pharmacologic, or environmental, can we act on now to slow or reverse its trajectory?

AI-enabled organ clocks represent a promising but still maturing class of tools that should augment, not replace, careful clinical assessment, established risk scores, and shared decision-making. Their responsible deployment will require rigorous external validation, prospective trials testing organ-clock–guided interventions, and transparent, fair algorithms that perform robustly across populations. Close collaboration between clinicians, data scientists, and industry partners will be essential to refine assay design, integrate multi-omics and digital biomarkers, and embed organ-specific aging information into workflows in ways that genuinely improve patient outcomes and extend health span.

Reference

  1. Oh HSH, Rutledge J, Nachun D, Pálovics R, Abiose O, Moran-Losada P, et al. Organ aging signatures in the plasma proteome track health and disease. Nature [Internet]. 2023 Dec 1;624(7990):164–72. Available from: https://www.nature.com/articles/s41586-023-06802-1
  2. Wang Y, Xiao S, Liu B, Jiang R, Liu Y, Hang Y, et al. Organ-specific proteomic aging clocks predict disease and longevity across diverse populations. Nature Aging [Internet]. 2025 Nov 26 [cited 2026 Feb 14];6(1):162–80. Available from: https://www.nature.com/articles/s43587-025-01016-8
  3. Zhao R, Lu H, Yuan H, Chen S, Xu K, Zhang T, et al. Plasma proteomics-based organ-specific aging for all-cause mortality and cause-specific mortality: a prospective cohort study. GeroScience. 2024 Oct 31;
  4. Organ aging signatures in the plasma proteome track health and disease – ADSP – FGC [Internet]. Niagads.org. FunGen; 2023 [cited 2026 Apr 29]. Available from: https://adsp-fgc.niagads.org/publications/organ-aging-signatures-in-the-plasma-proteome-track-health-and-disease/
  5. (11A4425N) Organ-specific proteomics clocks to monitor and unravel the dynamics of age-related disease [Internet]. Ugent.be. 2024 [cited 2026 Apr 29]. Available from: https://research.ugent.be/web/result/project/95c7414c-8d48-11ef-980e-fb447b629353/details/en
  6. Jalal W, Musarrat M, Samee MAH, Rahman MS. ORANGE: a machine learning approach for modeling tissue-specific aging from transcriptomic data. Briefings in Bioinformatics [Internet]. 2026 Mar 1 [cited 2026 Apr 29];27(2). Available from: https://academic.oup.com/bib/article/27/2/bbag093/8503316?login=false
  7. Kivimäki M, Frank P, Pentti J, Jokela M, Nyberg ST, Blake A, et al. Proteomic organ-specific ageing signatures and 20-year risk of age-related diseases: the Whitehall II observational cohort study. The Lancet Digital Health. 2025 Mar;7(3):e195–204.
  8. the A. Applying the Organage Proteomic Clock to Old Blood Samples to Assess Predictive Ability [Internet]. Fight Aging! 2025 [cited 2026 Apr 29]. Available from: https://www.fightaging.org/archives/2025/03/applying-the-organage-proteomic-clock-to-old-blood-samples-to-assess-predictive-ability/mito-health. Biological Age Test: Guide to Longevity Testing | Mito Health Blog [Internet]. Mito Health. 2026 [cited 2026 Apr 29]. Available from: https://mitohealth.com/blog/biological-age-testing-guide-everything-you-need-to-know
  9. Biological organ ages predict disease risk decades in advance [Internet]. ScienceDaily. 2025. Available from: https://www.sciencedaily.com/releases/2025/02/250225201300.htm
  10. Liu CS, Yeo WJ, Surapaneni A, Windham BG, Oh HSH ., Prizment A, et al. Biological organ ages associate with risk of chronic diseases in a community-based population. JCI Insight [Internet]. 2025 Dec 8 [cited 2026 Apr 29];10(23). Available from: https://insight.jci.org/articles/view/197304
  11. UCL. Biological organ ages predict disease risk decades in advance [Internet]. UCL News. 2025. Available from: https://www.ucl.ac.uk/news/2025/feb/biological-organ-ages-predict-disease-risk-decades-advance
  12. Wang Q, Huang J, He Q, Sun M, Snyder MP, Li L. Patterns of Organ‐Specific Proteomic Aging in Relation to Lifestyle, Diseases, and Mortality. Aging Cell [Internet]. 2025 Oct 8;24(12). Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC12686593/
  13. Lang K. Individual organ age could predict dementia, heart disease [Internet]. Medicalnewstoday.com. Medical News Today; 2025. Available from: https://www.medicalnewstoday.com/articles/individual-organ-age-could-predict-dementia-heart-disease
  14. Neirynck RE, Chirinos JA, Van Damme M, Coussement L, Segers P, De Buyzere ML, et al. Longitudinal dynamics of organ-specific proteomic aging clocks over a decade of midlife. 2026 Feb 18;
  15. News Medical. News-Medical [Internet]. News-Medical. 2025 [cited 2026 Apr 29]. Available from: https://www.news-medical.net/news/20250506/New-tool-uses-eight-health-metrics-to-track-biological-aging.aspx
  16. Kushner JA, Pandey M, Kohli SSS. Biomarker integration and biosensor technologies enabling AI-driven insights into biological aging. Frontiers in Aging. 2025 Nov 7;6.
  17. Filippos Anagnostakis, Ko S, Mehrshad Saadatinia, Wang J, Christos Davatzikos, Wen J. Multi-organ metabolome biological age implicates cardiometabolic conditions and mortality risk. Nature Communications [Internet]. 2025 May 26;16(1). Available from: https://www.nature.com/articles/s41467-025-59964-z
  18. Backman I. The biology of aging [Internet]. Yale School of Medicine. 2024. Available from: https://medicine.yale.edu/news/yale-medicine-magazine/article/the-biology-of-aging/
  19. Superpower [Internet]. Superpower.com. 2025 [cited 2026 Apr 29]. Available from: https://superpower.com/guides/organ-age-testing-what-it-means-when-your-heart-brain-or-liver-ages-faster
  20. Backman I. The biology of aging [Internet]. Yale School of Medicine. 2024. Available from: https://medicine.yale.edu/news/yale-medicine-magazine/article/the-biology-of-aging/
  21. Bortz J, Guariglia A, Klaric L, Tang D, Ward P, Geer M, et al. Biological age estimation using circulating blood biomarkers. Communications Biology [Internet]. 2023 Oct 26;6(1):1–10. Available from: https://www.nature.com/articles/s42003-023-05456-z
  22. Dataintelo, Sharma R. Biological Age Testing Market Research Report 2033 [Internet]. Dataintelo.com. 2025 [cited 2026 Apr 29]. Available from: https://dataintelo.com/report/biological-age-testing-market
  23. Commercialization of Biological Data FORESIGHT BRIEF [Internet]. Available from: https://horizons.service.canada.ca/en/2024/biological-data/Commercialization_of_biological_data-FINAL-April_12_2024.pdf
  24. Mann SP, Treit PV, Geyer PE, Omenn GS, Mann M. Ethical Principles, Constraints, and Opportunities in Clinical Proteomics. Molecular & Cellular Proteomics. 2021;20(1):100046.
  25. Organ-specific proteomic aging clocks predict disease and longevity across diverse populations [Internet]. UK Biobank. 2026 [cited 2026 Apr 29]. Available from: https://www.ukbiobank.ac.uk/publications/organ-specific-proteomic-aging-clocks-predict-disease-and-longevity-across-diverse-populations/
  26. Martinez-Martin N, Magnus D. Privacy and ethical challenges in next-generation sequencing. Expert Review of Precision Medicine and Drug Development [Internet]. 2019 Mar 4;4(2):95–104. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7413244/
  27. Prattichizzo F, Chiara Frigé, Pellegrini V, Scisciola L, Santoro A, Monti D, et al. Organ-specific biological clocks: Ageotyping for personalized anti-aging medicine. Ageing research reviews. 2024 Apr 1;96:102253–3.
  28. Shen X, Kellogg R, Panyard DJ, Bararpour N, Castillo KE, Lee-McMullen B, et al. Multi-omics microsampling for the profiling of lifestyle-associated changes in health. Nature Biomedical Engineering [Internet]. 2023 Jan 19 [cited 2023 Jun 28];1–19. Available from: https://www.nature.com/articles/s41551-022-00999-8
  29. Phenome-Wide Multi-Omics Integration Uncovers Distinct Archetypes of Human Aging [Internet]. Arxiv.org. 2018 [cited 2026 Apr 29]. Available from: https://arxiv.org/html/2510.12384v1
  30. Kassab A, Rizk N, Prakash S. The Role of Systemic Filtrating Organs in Aging and Their Potential in Rejuvenation Strategies. International Journal of Molecular Sciences [Internet]. 2022 Apr 14;23(8):4338–8. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC9025381/
  31. Grunewald M, Kumar S, Sharife H, Volinsky E, Gileles-Hillel A, Licht T, et al. Counteracting age-related VEGF signaling insufficiency promotes healthy aging and extends life span. Science (New York, NY) [Internet]. 2021 Jul 30;373(6554):eabc8479. Available from: https://pubmed.ncbi.nlm.nih.gov/34326210/
  32. Sanada F, Hayashi S, Morishita R. Targeting the hallmarks of aging: mechanisms and therapeutic opportunities. Frontiers in Cardiovascular Medicine [Internet]. 2025 Jul 1;12. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC12259695/
  33. Abdellatif M, Schmid ST, Fuerlinger A, Kroemer G. Anti-ageing interventions for the treatment of cardiovascular disease. Cardiovascular Research [Internet]. 2024 Aug 22 [cited 2025 Jan 7]; Available from: https://doi.org/10.1093/cvr/cvae177
  34. Marabita F, James T, Karhu A, Virtanen H, Kettunen K, Stenlund H, et al. Multiomics and digital monitoring during lifestyle changes reveal independent dimensions of human biology and health. Cell Systems [Internet]. 2022 Mar 16;13(3):241-255.e7. Available from: https://www.sciencedirect.com/science/article/pii/S2405471221004518

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