Keywords: Apolipoprotein B, Biological Aging, Epigenetic Clock, Growth Differentiation Factor 15 (GDF-15), Health Span, Insulin Resistance, Multi-omics Biomarkers, Visceral Adiposity
Introduction
The conventional framework for assessing metabolic health, anchored in fasting glucose, total cholesterol, blood pressure, and body mass index was designed for the detection of overt disease rather than the prediction of future dysfunction. As the global burden of type 2 diabetes mellitus (T2DM), cardiovascular disease (CVD), and non-alcoholic fatty liver disease (NAFLD) continues to escalate, there is an urgent imperative to identify individuals on a subclinical metabolic decline trajectory year or even decades before clinical thresholds are breached [1,2].
Longevity medicine, at its scientific frontier, is no longer concerned merely with lifespan extension but with the compression of morbidity and the optimization of health spa, the period of life spent in full physiological function. Central to this mission is the identification of actionable biomarkers that reflect biological, rather than chronological, aging. Advances in high-throughput omics platforms have now made it possible to measure hundreds of metabolites, proteins, and epigenetic signatures from a single blood draw, enabling a systems-level view of metabolic status [3,4].
This review critically examines the evidence base for a multi-domain metabolic biomarker panel encompassing insulin sensitivity indices (HOMA-IR, TyG index, fasting insulin), advanced lipid markers (ApoB, LDL particle size), inflammatory and senescence-associated proteins (GDF-15, hs-CRP, IL-6, adiponectin), mitochondrial health regulators (NAD+, SIRT3), biological age clocks (metabolomic, epigenetic, proteomic), visceral adiposity metrics, and gut microbiome-derived biomarkers. The clinical translation of these markers within a precision longevity framework is discussed, with a focus on actionable prevention strategies for metabolic disease.
The Metabolic Health Paradigm: Moving Beyond Conventional Markers
Metabolic health is classically defined by the absence of metabolic syndrome criteria such as normal waist circumference, blood pressure, fasting glucose, triglycerides, and HDL cholesterol. However, epidemiological studies demonstrate that up to 30% of individuals with normal BMI harbour metabolic dysfunction, a phenomenon termed ‘metabolically obese normal weight’ (MONW), while a subset of individuals with obesity maintain preserved insulin sensitivity [5].
This phenotypic heterogeneity underscores the inadequacy of threshold-based, organ-siloed assessments. The ‘metabolic health’ spectrum is better conceptualized as a continuum of dynamic physiological states modulated by genetic predisposition, diet, physical activity, sleep, stress, and the microenvironment. Biomarkers that capture early, reversible perturbations in this continuum before irreversible structural damage occurs represent the most valuable targets for preventive intervention [1,6].
Emerging evidence positions insulin resistance, operating across hepatic, skeletal muscle, and adipose compartments as the primordial driver of metabolic aging. Insulin resistance precedes T2DM by 10–15 years, is causally implicated in atherogenesis, hepatic steatosis, hypertension, polycystic ovarian syndrome, and certain cancers, and accelerates epigenetic aging. Its upstream detection via surrogate indices represents a cornerstone of metabolic longevity assessment [7,8].
Insulin Resistance as the Central Driver of Metabolic Aging
HOMA-IR and Fasting Insulin
The homeostasis model assessment of insulin resistance (HOMA-IR), calculated as [fasting insulin (μU/mL) × fasting glucose (mmol/L)] / 22.5, remains one of the most widely validated and clinically accessible surrogate measures of hepatic insulin resistance. Elevated HOMA-IR is independently associated with accelerated epigenetic aging, increased all-cause mortality, and risk of T2DM, CVD, and NAFLD [8,9].
Fasting insulin, the numerator of HOMA-IR, is arguably the most sensitive early-warning biomarker of insulin resistance, as compensatory hyperinsulinaemia precedes glucose dysregulation by years. Optimal fasting insulin is generally considered below 7–9 μU/mL in the context of longevity medicine, well below the reference ranges of most clinical laboratories, which are anchored to population norms that already include metabolically compromised individuals [7].
The Triglyceride-Glucose (TyG) Index
The triglyceride-glucose (TyG) index [ln(TG mg/dL × fasting glucose mg/dL / 2)] is a low-cost, non-insulin-based surrogate marker of insulin resistance and atherogenic dyslipidaemia that has demonstrated superiority over HOMA-IR in some population studies for predicting incident metabolic syndrome, CVD events, and non-alcoholic steatohepatitis. The TyG index correlates inversely with telomere length, a molecular marker of cellular aging in large epidemiological datasets, establishing a mechanistic link between insulin resistance and biological age acceleration [10,11].
Importantly, HOMA-IR and the TyG index are not interchangeable: they capture partially distinct aspects of insulin resistance. HOMA-IR reflects hepatic glucose production sensitivity to insulin, while the TyG index captures peripheral lipotoxicity and dyslipidaemic insulin resistance. Their combined use in clinical profiling provides complementary mechanistic insight [12].
Atherogenic Lipid Biomarkers: ApoB, LDL Particle Size, and Cardiovascular Longevity
The conventional reliance on LDL cholesterol (LDL-C) for cardiovascular risk stratification has come under increasing scrutiny. LDL-C measures cholesterol mass, not particle number and systematically underestimates atherogenic burden in individuals with insulin resistance, metabolic syndrome, or hypertriglyceridaemia, who characteristically harbour an excess of small, dense LDL particles that carry less cholesterol per particle but are more prone to endothelial infiltration and oxidative modification [13].
Apolipoprotein B (ApoB), a structural protein present on every atherogenic lipoprotein particle (LDL, VLDL, IDL, Lp(a)), provides a direct count of circulating atherogenic particles and is now considered the gold-standard lipid biomarker for cardiovascular risk assessment by multiple international guidelines. Meta-analyses and large trials such as FOURIER have consistently demonstrated that ApoB outperforms both LDL-C and non-HDL-C as a predictor of CVD events and therapeutic benefit from lipid-lowering therapy [13,14].
The LDL-C/ApoB ratio is a validated index of LDL particle size: low ratios indicate a predominance of small, dense LDL (sdLDL), while higher ratios reflect larger, more buoyant particles. sdLDL is more susceptible to glycation and oxidation, has greater arterial wall penetration, and is more strongly associated with coronary artery disease than large LDL. In the context of longevity medicine, the goal is not merely to achieve guideline LDL-C thresholds but to minimise atherogenic particle burden as measured by ApoB [14].
Inflammatory and Senescene-Associated Biomarkers
GDF-15: A Multi-Modal Stress Sentinel
Growth differentiation factor 15 (GDF-15) is a divergent member of the TGF-β superfamily secreted in response to cellular stress, mitochondrial dysfunction, oxidative stress, inflammation, and nutrient excess. Circulating GDF-15 rises with advancing age and is strongly associated with impaired fasting glucose, HOMA-IR, BMI, and markers of metabolic dysregulation. Critically, GDF-15 discriminates between normal glucose tolerance and impaired fasting glucose independently of age, BMI, and HOMA-IR, positioning it as a sensitive early biomarker of metabolic stress [15,16].
In the longevity context, GDF-15 has emerged as one of the most reproducible proteomic aging biomarkers across multiple biological age clock models. Elevated GDF-15 in individuals living with HIV reflects accelerated cardiometabolic aging even in the absence of traditional risk factors, demonstrating its sensitivity to biological processes that conventional markers miss. Its integration into clinical metabolic panels offers a window into cellular stress burden beyond what glucose, lipids, or inflammatory cytokines alone can provide [15,16].
High-Sensitivity CRP, IL-6, and Inflammaging
Chronic low-grade systemic inflammation, termed ‘inflammaging’ is a hallmark of biological aging and a central mediator of metabolic disease progression. High-sensitivity C-reactive protein (hs-CRP) and interleukin-6 (IL-6) are the most clinically accessible proxies of this process. Elevated hs-CRP (>1 mg/L) in the absence of acute infection or tissue injury reflects ongoing vascular and hepatic inflammation driven by visceral adiposity, gut dysbiosis, and oxidative stress [5,17].
The JUPITER trial demonstrated that statin therapy reduces CVD events in individuals with LDL-C below guideline thresholds but elevated hs-CRP, validating the independent cardiovascular predictive value of inflammatory biomarkers. From a longevity perspective, hs-CRP and IL-6 predict not only cardiovascular mortality but also cognitive decline, sarcopenia, and all-cause mortality, making them critical components of a comprehensive longevity biomarker panel [1].
Adiponectin: The Anti-Inflammatory Adipokine
Adiponectin, secreted exclusively by adipocytes, exerts potent insulin-sensitizing, anti-atherogenic, and anti-inflammatory effects. Paradoxically, adiponectin levels are inversely proportional to adiposity, particularly visceral fat accumulation, making low adiponectin a biomarker of both excess adiposity and its downstream metabolic consequences. Low adiponectin is associated with insulin resistance, T2DM, CVD, and non-alcoholic steatohepatitis, and predicts metabolic syndrome development years before its clinical manifestation [5,8].
Visceral Adiposity: The Metabolically Active Aging Organ
Visceral adipose tissue (VAT) is not merely an inert energy reservoir, it is an endocrine and immune organ that secretes a complex array of adipokines, cytokines, chemokines, and free fatty acids that directly impair insulin signalling, promote systemic inflammation, dysregulate lipid metabolism, and accelerate vascular aging. A 2026 review in Nature Aging comprehensively establishes VAT accumulation as both a biomarker and causal contributor to impaired metabolic health and reduced lifespan [17].
Clinically, VAT is most accurately quantified by dual-energy X-ray absorptiometry (DEXA) or MRI; however, anthropometric proxies such as waist circumference, waist-to-height ratio, and the visceral adiposity index (VAI) provide reasonable clinical approximations. VAT excess is associated with increased production of pro-inflammatory cytokines (TNF-α, IL-1β, IL-6, MCP-1), impaired adiponectin secretion, enhanced free fatty acid efflux to the liver, and activation of the hypothalamic-pituitary-adrenal axis, a convergent pathway toward metabolic disease and accelerated aging [17,18].
Adipose tissue senescence, the accumulation of non-dividing, pro-inflammatory senescent cells within VAT is increasingly recognized as a mechanistic bridge between obesity and accelerated biological aging. Senescent adipocytes secrete a senescence-associated secretory phenotype (SASP) characterized by IL-6, IL-8, and matrix metalloproteinases, which amplifies local and systemic inflammation and impairs progenitor cell function. Targeting adipose tissue senescence with senolytics and caloric restriction represents an emerging longevity intervention strategy [18].
Mitochondrial Function and the NAD+/Sirtuin Axis
Mitochondrial dysfunction is one of the most well-established hallmarks of aging, characterized by a progressive decline in oxidative phosphorylation efficiency, increased reactive oxygen species (ROS) production, impaired mitochondrial biogenesis and quality control (mitophagy), damaged mitochondrial DNA (mtDNA), and deregulated metabolic balance. Across metabolic tissues, including skeletal muscle, liver, and pancreatic beta cell, mitochondrial dysfunction is causally implicated in insulin resistance, hepatic steatosis, and the age-related decline in glucose homeostasis [19,20].
Nicotinamide adenine dinucleotide (NAD+) is a critical cofactor for mitochondrial energy metabolism and the obligate substrate for sirtuin deacetylases (SIRT1–7). NAD+ levels decline with age by up to 50% in multiple tissues, and this decline is mechanistically linked to impaired mitochondrial function, increased DNA damage, heightened inflammatory signalling, and metabolic disease. Supplementation with NAD+ precursors, nicotinamide riboside (NR) and nicotinamide mononucleotide (NMN) has been shown in preclinical and early human studies to enhance SIRT3 activity, improve mitochondrial respiratory capacity, and attenuate age-related metabolic decline [20,21].
The mitochondrial sirtuins (SIRT3, SIRT4, SIRT5) deacetylate and regulate key enzymes in fatty acid oxidation, the tricarboxylic acid cycle, and antioxidant defence. SIRT3, in particular, activates superoxide dismutase 2 (SOD2), the primary mitochondrial antioxidant enzyme, and is required for the maintenance of mitochondrial membrane potential. SIRT3 expression is reduced in insulin-resistant and obese states, providing a molecular link between the NAD+/sirtuin axis and metabolic disease pathogenesis [19,20].
Biological Age Clocks: Metabolomic, Epigenetic, and Proteomic Approaches
The concept of biological age, the physiological state of an organism independent of chronological time has been operationalized through machine-learning-derived molecular clocks trained on large longitudinal datasets. These clocks estimate the rate of biological aging and predict health span, disease risk, and mortality with substantially greater precision than chronological age alone [3,22].
Epigenetic Clocks
Epigenetic clocks, pioneered by Horvath and subsequently refined through Hannum, PhenoAge, GrimAge, and DunedinPACE algorithms, estimate biological age from DNA methylation patterns at specific CpG loci across the genome. GrimAge and DunedinPACE, trained on time-to-death and longitudinal aging pace outcomes respectively demonstrate the strongest associations with incident disease, physical function decline, and all-cause mortality in prospective cohort studies. A 2025 comparison of 14 epigenetic clocks across 174 incident disease outcomes in Nature Communications confirmed their differential predictive utility across disease domains [22,23].
Importantly, epigenetic clock acceleration is modifiable through lifestyle interventions. Regular aerobic exercise, caloric restriction, dietary quality improvements (Mediterranean diet, time-restricted eating), and stress reduction have each been shown to decelerate epigenetic aging pace in intervention trials, validating their clinical utility as outcome measures in longevity medicine. However, current evidence cautions against their use as individual-level diagnostic tools without robust clinical context [23].
Metabolomic Aging Clocks
Metabolomic clocks leverage NMR or mass spectrometry-based metabolite profiles to estimate biological age. A 2025 study in npj Metabolic Health and Disease demonstrated that NMR-based metabolomic clocks provide a non-invasive, high-throughput platform with high predictive accuracy and clinical interpretability, capable of identifying disease-specific metabolic distortions and supporting risk stratification for accelerated aging. Metabolomic clocks have the practical advantage of simultaneously providing actionable metabolic health data (e.g., lipoprotein subfractions, amino acid profiles, glycoprotein acetyls) alongside an aging estimate [3].
A landmark 2025 Nature Communications analysis developed a multi-organ metabolome biological age score from plasma metabolomics, demonstrating that metabolomic biological age is independently associated with cardiometabolic conditions and all-cause mortality risk beyond conventional clinical risk factors. The metabolomic age gap, the difference between metabolomic age and chronological age serves as an actionable metric for monitoring the impact of lifestyle and therapeutic interventions on biological aging rate [4].
The Gut Microbiome as a Metabolic Longevity Biomarker Domain
The gut microbiome, comprising approximately 38 trillion microbial cells encoding over 3 million unique genes is a critical modulator of metabolic health, immune function, and aging. Age-associated changes in the gut microbiome (termed ‘dysbiosis’) are characterized by reduced microbial diversity, loss of beneficial short-chain fatty acid (SCFA)-producing taxa (Faecalibacterium prausnitzii, Roseburia intestinalis, Akkermansia muciniphila), and expansion of pro-inflammatory opportunistic pathogens. These changes are mechanistically linked to increased intestinal permeability (‘leaky gut’), systemic endotoxaemia, chronic low-grade inflammation, and impaired glucose homeostasis [25,26].
Short-chain fatty acids, particularly butyrate, propionate, and acetate produced by colonic fermentation of dietary fibre serve as primary energy substrates for colonocytes, stimulate glucagon-like peptide-1 (GLP-1) secretion, regulate appetite, reduce hepatic lipid synthesis, and inhibit nuclear factor-κB-mediated inflammatory signaling. Butyrate, in particular, has been shown to prevent age-related physiological decline by enhancing intestinal barrier function, modulating immune responses, and inhibiting cellular senescence in animal models [25,27].
Studies of centenarian gut microbiomes have identified distinctive longevity-associated microbiome signatures, including higher microbial diversity, enrichment of Akkermansia, Christensenellaceae, and SCFA-producing Lachnospiraceae, and reduced abundance of pro-inflammatory Enterobacteriaceae. These findings support the gut microbiome as a measurable, modifiable biomarker of biological aging and a target for microbiome-based therapeutic interventions in longevity medicine [25,26,27].
Notably, a metabolomic profiling study of long-lived individuals published in 2025 identified a distinct subgroup characterized by elevated butyric acid derivatives alongside elevated CVD risk markers, suggesting that microbiome-derived metabolites may represent both protective and disease-relevant signals requiring careful contextualization [28].
Integrated Multi-Omics Approach in Clinical Longevity Medicine
The individual biomarker domains reviewed above do not operate in isolation, they are deeply interconnected nodes within a complex physiological network. Insulin resistance drives dyslipidaemia, which promotes VAT expansion, which amplifies inflammaging, which accelerates mitochondrial dysfunction, which impairs NAD+ homeostasis, which exacerbates insulin resistance in a self-reinforcing cycle. A fragmented, organ-siloed biomarker approach will consistently underestimate total metabolic disease burden and fail to identify the earliest, most reversible dysfunction [1,6].
Multi-omics integration, combining metabolomics, proteomics, epigenomics, and microbiome sequencing, enables the construction of personalised biological aging trajectories and disease risk maps. Precision longevity platforms now exist that deliver a composite biological age estimate alongside domain-specific metabolic health scores, enabling clinicians to identify the dominant dysfunction pathway in each individual and tailor interventions accordingly. The integration of wearable continuous physiological monitoring (continuous glucose monitors, heart rate variability, sleep architecture analysis) further enriches this picture with dynamic, real-world data [3,4,22].
From a clinical implementation standpoint, the evidence supports a tiered metabolic longevity biomarker panel: a core tier comprising fasting insulin, HOMA-IR, TyG index, ApoB, hs-CRP, HbA1c, fasting glucose, adiponectin, and complete metabolic panel; an extended tier adding GDF-15, LDL-C/ApoB ratio, ferritin, homocysteine, uric acid, and DEXA body composition; and a precision tier incorporating metabolomic aging clock, epigenetic age, advanced lipoprotein sub fractionation (NMR), gut microbiome profiling, and inflammatory cytokine panel. This stratified approach balances clinical depth with resource accessibility across healthcare settings [2,6].
Precision Intervention Strategies Targeting Metabolic Longevity Biomarkers
The clinical value of comprehensive metabolic biomarker profiling is realised only through the translation of findings into targeted, evidence-based interventions. The most robustly supported lifestyle interventions for improving metabolic longevity biomarkers include structured aerobic and resistance exercise training, time-restricted eating and caloric moderation, Mediterranean or whole-food plant-based dietary patterns, optimization of sleep duration and architecture, and stress reduction via mindfulness-based approaches. Each of these has demonstrated measurable improvements across multiple biomarker domains simultaneously, reflecting their systemic metabolic and anti-inflammatory mechanisms [1,6,24].
Pharmacological and nutraceutical interventions with emerging longevity evidence include metformin (AMPK activation, NAD+ pathway upregulation, microbiome modulation), GLP-1 receptor agonists (VAT reduction, insulin sensitivity improvement, appetite regulation), NAD+ precursors (NR/NMN), omega-3 fatty acids (triglyceride reduction, anti-inflammatory), berberine (insulin sensitization, lipid-lowering), and targeted probiotic/prebiotic formulations designed to restore SCFA-producing microbiome taxa. The TAME trial (Targeting Aging with Metformin) represents the first regulatory-designated trial of an aging-targeting drug, illustrating the maturation of longevity medicine as a clinical discipline [20,21,27].
Crucially, intervention selection should be biomarker-guided rather than protocol-driven. An individual with elevated TyG index and low adiponectin as their dominant signals requires a fundamentally different intervention emphasis than one whose primary signal is elevated GDF-15 with normal insulin sensitivity but accelerated epigenetic age, the former prioritizing carbohydrate restriction and exercise, the latter mitochondrial support and senolytic strategies. This is the clinical promise of precision longevity medicine [6,15].
Conclusion
The landscape of metabolic health assessment is undergoing a paradigm transformation, driven by the convergence of multi-omics science, artificial intelligence, and a deepened mechanistic understanding of biological aging. The biomarkers reviewed in this article, spanning insulin sensitivity, atherogenic lipoproteins, inflammatory mediators, visceral adiposity metrics, mitochondrial regulators, biological age clocks, and gut microbiome-derived signals, collectively provide a far more complete and more predictive picture of an individual’s metabolic trajectory than any single marker or traditional metabolic syndrome criteria.
For clinicians operating at the intersection of metabolic medicine and longevity, adopting a multi-domain biomarker strategy is not merely an academic exercise, it is a clinical imperative. The window for effective metabolic disease prevention is widest when dysfunction is detected at its earliest, most reversible stage, often a decade or more before conventional diagnostic thresholds are reached. Early, comprehensive biomarker profiling, coupled with targeted lifestyle and pharmacological interventions, offers the most evidence-based pathway toward the compression of morbidity and the extension of healthy, functional life.
Future research priorities include the standardization of metabolic longevity biomarker panels for clinical use, the development of validated reference ranges anchored to health outcomes rather than population distributions, the integration of longitudinal biomarker trajectories into risk stratification algorithms, and the evaluation of biomarker-guided intervention efficacy in randomized controlled trials. The era of reactive metabolic medicine is giving way to one of predictive, preventive, and personalized longevity medicine and the biomarkers reviewed here are its scientific foundation.
Reference
1. Gonzalez-Cantero J, Lopez-Garcia E, Garcia-Esquinas E, Rodriguez-Artalejo F, Garcia-Garcia FJ. Metabolic diseases and healthy aging: prevention and public health policy based on risk factors. Front Public Health. 2024;12:1458923.
2. Superpower Health. What are longevity biomarkers? [Internet]. 2024 [cited 2026 Jun 15]. Available from: https://superpower.com/guides/what-are-longevity-biomarkers
3. Wagner E, Rist MJ, Kulling SE, Mack CI, Röhling M, Brennan L, et al. Metabolomic-based aging clocks. npj Metab Health Dis. 2025;3:25.
4. Gomez-Cabrero D, Tegnér J, Björkegren JLM, Hartikainen JM, Laaksonen R, et al. Multi-organ metabolome biological age implicates cardiometabolic conditions and mortality risk. Nat Commun. 2025;16:4951.
5. Stefan N, Schick F, Häring HU. Causes, characteristics, and consequences of metabolically unhealthy normal weight in humans. Cell Metab. 2017;26(2):292–300.
6. Cenegenics Editorial. 2025 in Review: The Longevity Trends That Redefine Healthspan [Internet]. 2025 [cited 2026 Jun 15]. Available from: https://cenegenics.com/2025-in-review-the-longevity-trends-that-redefine-healthspan/
7. Gast KB, Tjeerdema N, Stijnen T, Smit JWA, Dekkers OM. Insulin resistance and risk of incident cardiovascular events in adults without diabetes: meta-analysis. PLoS One. 2012;7(12):e52036.
8. Kastorini CM, Milionis HJ, Esposito K, Giugliano D, Goudevenos JA, Panagiotakos DB. The effect of Mediterranean diet on metabolic syndrome and its components. J Am Coll Cardiol. 2011;57(11):1299–313.
9. Frontiers in Endocrinology. Editorial: Mechanistic and physiological implications of insulin resistance in metabolic diseases. Front Endocrinol. 2024;15:1455421.
10. Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord. 2008;6(4):299–304.
11. Shi W, Xing L, Jing L, Tian Y, Liu S, Yan H, et al. Triglyceride-glucose index correlates with telomere length in healthy adults from the National Health and Nutrition Examination Survey. Front Endocrinol. 2022;13:898625.
12. Sun G, Sun X, Zhang H, Hou G, Tang S, Xu Y, et al. HOMA-IR and TyG index differ for their relationship with cardiometabolic risk across subgroups. Diabetes Res Clin Pract. 2025;211:111–9.
13. Thanassoulis G, Williams K, Ye K, Brook R, Couture P, Lawler PR, et al. Particle number vs cholesterol mass: the emerging role of ApoB in refining cardiovascular risk stratification. Int J Cardiovasc Ther Med. 2025;3:1–12.
14. Johannesen CDL, Langsted A, Mortensen MB, Nordestgaard BG. ApoB, LDL-C, and non-HDL-C as markers of cardiovascular risk. J Clin Lipidol. 2025;19(4):580–9.
15. Tavenier J, Rasmussen LJH, Haupt TH, Engel U, Eugen-Olsen J, Andersen O. Circulating GDF-15: a biomarker for metabolic dysregulation and aging in people living with HIV. Open Forum Infect Dis. 2024;11(6):ofae281.
16. Tsai C-H, Chou C-H, Hung C-S, Wen L-L, Lin Y-H, Hwu C-M. GDF15 is a novel biomarker for impaired fasting glucose. Diabetes Metab J. 2021;45(4):558–67.
17. Vishweswaraiah S, Mishra SK, Sadhasivam S, et al. Visceral adiposity, metabolic health and aging. Nat Aging. 2026;6:312–28.
18. Millar SR, Perry IJ, Shivappa N, Hébert JR, Phillips CM. Anthropometric measures, predicted visceral adipose tissue and biomarkers of chronic inflammation. Eur J Clin Invest. 2024;54(3):e14104.
19. Houtkooper RH, Pirinen E, Auwerx J. Sirtuins as regulators of metabolism and healthspan. Nat Rev Mol Cell Biol. 2012;13(4):225–38.
20. Mitochondrial sirtuins, key regulators of aging. Life Med. 2025;4(4):lnaf019.
21. Yoshino J, Baur JA, Imai SI. NAD+ intermediates: the biology and therapeutic potential of NMN and NR. Cell Metab. 2018;27(3):513–28.
22. Sehgal A, Iyer SS, Zhang W, Horvath S, Lu AT. Predicting healthspan and disease risks through biological age. Ageing Res Rev. 2025;97:102305.
23. Yao C, Haghiac M, Minium J, Hoppel CL, et al. An unbiased comparison of 14 epigenetic clocks in relation to 174 incident disease outcomes. Nat Commun. 2025;16:9832.
24. Fontana L, Partridge L. Promoting health and longevity through diet: from model organisms to humans. Cell. 2015;161(1):106–18.
25. Zhao X, He L, Zhong H, Kang Y, Wei Y, Liu Y, et al. Gut microbiota, probiotics, and aging: molecular mechanisms and implications for healthy aging. Front Immunol. 2025;16:1587341.
26. Schoultz I, Söderholm JD, Hintze KJ, et al. Gut microbiota development across the lifespan: disease links and health‐promoting interventions. J Intern Med. 2025;297(4):381–400.
27. El Kaoutari A, Armougom F, Gordon JI, Raoult D, Henrissat B. Microbiome-based therapeutics towards healthier aging and longevity. NPJ Biofilms Microbiomes. 2025;11:89.
28. Ding Y, Li Z, Shen X, Jiang P, Li Y, Deng M, et al. Metabolomic profiling of long-lived individuals reveals a distinct subgroup with cardiovascular disease and elevated butyric acid derivatives. Front Aging. 2025;6:1598047.