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The Metabolic Vulnerability Index Bridging Biomarkers, Biological Aging, and Digital Phenotyping for Preventive Health

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dr. Karunia Valeriani Japar

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Introduction

Chronically elevated metabolic risk now begins decades before the clinical onset of diabetes, atherosclerotic cardiovascular disease, or other cardiometabolic conditions, driven by interacting perturbations in adiposity, insulin sensitivity, lipid metabolism, inflammation, and lifestyle patterns that are not adequately captured by single biomarkers or binary diagnostic thresholds. In response, an expanding family of continuous metabolic and cardiometabolic scores ranging from metabolic syndrome severity scores and insulin-resistance indices to cardiometabolic indices and biological age clocks has been developed to summarize clustered risk into a single, quantitative measure that tracks with incident events and long-term health span. Within this landscape, apolipoprotein B (ApoB) and low-density lipoprotein cholesterol (LDL-C) occupy a central position: ApoB, as a direct count of circulating atherogenic lipoprotein particles, consistently outperforms LDL-C and non-HDL-C in predicting atherosclerotic cardiovascular disease across populations and therapeutic states, while LDL-C and non-HDL-C remain entrenched in guidelines and risk engines used in routine care.

These converging lines of evidence motivate a broader, integrative construct that can be termed the metabolic vulnerability index, a multidimensional, continuous framework that quantifies the degree to which an individual’s metabolic network is predisposed to near-term cardiometabolic events and accelerated biological aging, rather than merely crossing disease thresholds. In this framework, traditional risk factors (waist circumference, blood pressure, fasting and postprandial glycemia, triglycerides, HDL-C) are complemented by particle-based lipid measures such as ApoB, discordance patterns between ApoB and LDL-C, insulin-resistance and visceral adiposity scores, inflammatory markers, lifestyle and behavioral composites, and, where available, omics-based and digital phenotypes including biological age clocks, wearable-derived metrics, and continuous glucose monitoring profiles. By embedding LDL-C and ApoB explicitly within this broader metabolic vulnerability index, the article aims to bridge guideline-driven lipid management with next-generation composite risk stratification, providing a conceptual and practical scaffold for precision prevention, metabolic wellness and aging-focused care both in clinical and AI-enabled health tech settings.

Conceptual Framework

At its broadest, a metabolic vulnerability index can be defined as a continuous, composite measure that quantifies the degree to which an individual’s metabolic network is predisposed to near-term events such as cardiovascular disease, stroke and type 2 diabetes and to long-term trajectories including cardiometabolic multimorbidity and accelerated biological aging. unlike binary diagnostic thresholds that dichotomize risk and lose critical information, continuous metabolic scores retain the full spectrum of physiological variation, enabling more sensitive detection of metabolic fragility at earlier stages and allowing granular tracking of response to interventions. Traditional continuous metabolic syndrome scores, constructed by computing standardized residuals (z-scores) or principal component analysis (PCA) weights from the five canonical metabolic syndrome components which are waist circumference, blood pressure, fasting glucose, triglycerides, and HDL cholesterol already embody this logic and have been robustly validated across diverse populations. these scores demonstrate graded, dose-dependent associations with incident cardiovascular events, type 2 diabetes, and all-cause mortality, even in younger adults, with continuous metabolic syndrome scores derived via PCA or confirmatory factor analysis (CFA) consistently outperforming dichotomous National Cholesterol Education Program (NCEP) definitions in prediction of five-year cardiovascular disease and ten-year cardiometabolic risk [1,2,3,4,5,6,7,8,9,10].

Building upon this foundation, newer indices refine the metabolic vulnerability construct by targeting specific, mechanistic drivers of metabolic disease and aging. the metabolic score for insulin resistance (METS-IR), calculated as Ln ((2 X glucose0 + triglycerides0) X BMI) / Ln (HDL-C), was developed and validated against gold-standard euglycemic-hyperinsulinemic clamp-derived measures of whole-body insulin sensitivity, demonstrating strong inverse correlations with adjusted glucose disposal rates and superior predictive performance for incident type 2 diabetes and all-cause mortality compared to other non-insulin-based insulin resistance indices such as triglyceride-glucose (TyG) index, TG/HDL-C ratio, and homeostatic model assessment of insulin resistance (HOMA-IR). METS-IR values above threshold ranging from 39 to 50, depending on population predict two-to threefold increases in diabetes risk and major adverse cardiovascular events over three to six years, even after adjustment for traditional cardiovascular risk factors, underscoring its utility in both primary prevention and prognostic stratification. Similarly, the metabolic score for visceral fat (METS-VF( and related indices combine anthropometric and metabolic components to estimate visceral adiposity, a core driver of insulin resistance, chronic inflammation, and cardiometabolic complications with validation studies across Mexican, Indian, and Turkish populations consistently showing strong correlations (r = 0.75-0.77) with dual-energy X-ray absorptiometry (DXA)-measured visceral fat area and high discrimination (area under the curve, AUC > 0.84-0.92) for identifying elevated visceral adipose tissue. The cardiometabolic index (CMI), defined as the product of waist-to-height ratio and triglyceride-to-HDL cholesterol ratio, has likewise shown strong associations with hyperglycemia, diabetes, and stroke risk, encapsulating both central adiposity and atherogenic dyslipidemia in a simple, easily calculated score [11,12,13,14,15,16,17,18,19,20,21].

Beyond traditional metabolic risk components, biological aging clocks extend the metabolic vulnerability framework by capturing cumulative inflammatory and metabolic burden as a measurable divergence from chronological age. The IgG glycan clock, derived from N-glycosylation patterns attached to immunoglobulin G, tracks age-related increases in pro-inflammatory agalactosylated (G0) glycoforms and decreases in anti-inflammatory, younger-associated digalactosylated (G2) and sialylated glycans with these shifts strongly correlating with obesity, hypertension, and cardiovascular disease risk scores. Weight loss via low-calorie diet and bariatric surgery has been shown to reverse IgG N-glycome profiles from “old-like” to “young-like,” with progressive decreases in body mass index associated with increased galactosylation and reduced biological age, implying that metabolic interventions can decelerate or even partially reverse glycan-based biological aging. metabolomic aging clocks, which aggregate plasma metabolite profiles across immune, endocrine, hepatic, digestive, and metabolic organ systems, likewise demonstrate moderate SNP-based heritability and strong genetic and causal correlations with cardiometabolic diseases, particularly hypertension, disorders of lipoprotein metabolism and metabolic syndrome and predict all-cause and cardiovascular mortality with effect sizes comparable to or exceeding traditional risk scores. Analyses of multi-state disease trajectories further reinforce the central role of metabolic factors in biological aging: biological age acceleration measured by PhenoAge and Klemera-Doubal method age (KDMAge) is associated with 18-25% higher hazard ratios for progression from health to first cardiometabolic disease, from single disease to multimorbidity, and form multimorbidity to death, with each standard deviation increase in PhenoAge acceleration associated with reductions in both disease-free and total life expectancy [5,7,22,23,24,25,26].

Importantly, apolipoprotein B (ApoB) and its ratio to apolipoprotein A1 (ApoB/A1) emerge as critical, underutilized components of a broad metabolic vulnerability index. The ApoB/A1 ratio is a composite measure that integrates atherogenic lipoprotein particle number (ApoB) and antiatherogenic capacity (ApoA1), reflecting both lipid metabolism balance and inflammatory status, and has been shown to correlate positively with disease severity scores (Ranson, BISAP, APACHE II) in acute pancreatitis and to outperform traditional lipid ratios (TC/HDL‑C, LDL‑C/HDL‑C, non‑HDL‑C/HDL‑C) in predicting cardiovascular disease and metabolic syndrome. In statin‑treated patients post–acute coronary syndrome, discordantly high ApoB (above median) despite LDL‑C below median conferred approximately 1.5‑fold higher risk of myocardial infarction, whereas isolated high LDL‑C with normal ApoB carried no significant residual risk, confirming ApoB’s superiority as a marker of lipoprotein‑attributable risk and supporting its integration into composite metabolic vulnerability scores. Taken together, these findings underscore that a metabolic vulnerability index framework can coherently integrate traditional metabolic syndrome components, insulin resistance and visceral adiposity metrics, apolipoprotein ratios, biological aging clocks, and multi‑state disease progression models into a unified, continuous risk axis that captures the multidimensional fragility of metabolic networks before, during, and after the onset of overt disease [2,4,5,7,28,29,30,31].

Dimensions and Building Blocks

A broad metabolic vulnerability index naturally spans multiple domains that collectively capture the multifactorial origins of cardiometabolic risk and biological aging. the first core dimension encompasses traditional core metabolic risk components which are anthropometrics including body mass index (BMI) and waist circumference, blood pressure, fasting and postprandial glycemia, and lipid fractions including LDL-C, non-HDL-C and apolipoprotein B (ApoB), as well as established composite metabolic scores that standardize and integrate these variables into continuous risk indices. As discussed previously, continuous metabolic syndrome score built via z-score transformations or principal component analysis (PCA) of the canonical metabolic syndrome components consistently outperform binary diagnostic definitions in predicting incident cardiovascular disease and type 2 diabetes, highlighting the value of preserving the full spectrum of physiological variation within this domain. The inclusion of ApoB alongside or in place of LDL-C further refines this dimension, given ApoB’s superior discrimination of residual atherogenic risk and its utility in identifying discordant high-risk states where particle number exceeds cholesterol mass [3,4,6,8,10,27,31,32,33].

The second-dimension targets insulin resistance and adiposity, employing surrogate insulin resistance indices and visceral adiposity metrics that capture mechanistic drivers of metabolic disease beyond gross weight or waist measures. The metabolic score for insulin resistance (METS‑IR), calculated from fasting glucose, triglycerides, BMI, and HDL‑C, has been validated against euglycemic‑hyperinsulinemic clamp and demonstrates strong, graded associations with incident type 2 diabetes, all‑cause mortality, and major adverse cardiovascular events, outperforming older indices such as HOMA‑IR and TyG in multiple populations. The metabolic score for visceral fat (METS‑VF) and cardiometabolic index (CMI) similarly integrate anthropometric and lipid parameters to estimate visceral adipose tissue burden, a key driver of insulin resistance, chronic inflammation, and cardiometabolic complications with validation studies showing strong correlations (r = 0.75–0.77) with DXA‑measured visceral fat and high discrimination (AUC > 0.84–0.92) for elevated visceral adiposity. Body composition measures that distinguish visceral from subcutaneous fat distribution, such as DXA‑derived trunk‑to‑peripheral fat ratios or imaging‑based visceral adipose tissue quantification, further enrich this dimension and may inform targeted lifestyle or pharmacologic interventions [11,14,15,16,17,18,19,20,21].

The third dimension addresses inflammation and aging biology, integrating both traditional inflammatory markers and emerging biological age constructs that capture cumulative metabolic and immune burden. High‑sensitivity C‑reactive protein (hs‑CRP) and related acute‑phase reactants reflect systemic low‑grade inflammation and consistently associate with metabolic syndrome severity, cardiovascular events, and multimorbidity progression. Biological aging clocks extend this dimension by translating molecular signatures such as IgG N-glycosylation patterns or plasma metabolite profiles into “biological age” estimates that diverge from chronological age in ways that correlate with obesity, hypertension, atherogenic dyslipidemia, and cardiovascular disease risk. The IgG glycan clock, for instance, tracks age‑related shifts in pro‑inflammatory agalactosylated glycoforms, and these shifts are reversible with weight loss and metabolic improvement, implying that interventions targeting metabolic vulnerability can decelerate or partially reverse glycan‑based biological aging. Similarly, metabolomic aging clocks aggregate organ‑system‑level metabolite profiles and show moderate heritability, strong genetic and causal correlations with cardiometabolic diseases, and effect sizes comparable to traditional risk scores for predicting all‑cause and cardiovascular mortality [22,23,24,25,26,30,34,35,36,37,38].

The fourth dimension incorporates lifestyle and behavioural traits, recognizing that modifiable behaviours are both determinants and therapeutic targets within a metabolic vulnerability framework. The American Heart Association’s Life’s Essential 8 (LE8), a composite score spanning diet quality, physical activity, nicotine exposure, sleep health, BMI, blood lipids, blood glucose, and blood pressure demonstrates robust, dose‑dependent inverse associations with metabolic syndrome prevalence, with each 10‑point LE8 increment reducing metabolic syndrome risk by approximately 15% and high cardiovascular health scores (80–100) conferring up to 98% lower odds of metabolic syndrome compared with low scores (0–49). Within LE8, diet quality indices such as the Dietary Approaches to Stop Hypertension (DASH) score, Mediterranean Diet Score, Mediterranean‑DASH Intervention for Neurodegenerative Delay (MIND) score, and Dietary Inflammatory Index (DII) each show independent, inverse associations with metabolic syndrome and cardiovascular events, with adherence to DASH, Mediterranean, and MIND patterns reducing metabolic syndrome odds by 5–16% per unit increase in score. Physical activity volume, intensity, and modality, particularly moderate‑to‑vigorous leisure‑time physical activity (≥3.5 METs) and resistance training at frequencies of once per week or greater reduce metabolic syndrome incidence and improve individual components including hyperglycemia, dyslipidemia, and central adiposity, with graded benefits observed across activity dose and fitness levels. Sleep duration, quality, and timing likewise shape metabolic syndrome risk, with both short and long sleep durations and circadian misalignment associated with elevated metabolic syndrome prevalence and adverse cardiometabolic outcomes [39,40,41,42,43,44,45,46,47].

The fifth and emerging dimension encompasses digital phenotypes, leveraging continuous glucose monitoring (CGM), wearable‑derived activity and sleep metrics, and app‑captured dietary and behavioral data to create high‑resolution, longitudinal profiles of metabolic function and lifestyle patterns. Time in range (TIR), the percentage of time spent with glucose between 70 and 180 mg/dL as assessed by CGM has been validated as a surrogate marker for long‑term adverse clinical outcomes, with each 10% decrease in TIR associated with increased risks of all‑cause and cardiovascular mortality, diabetic retinopathy, albuminuria, and arterial stiffness, independent of HbA1c and traditional risk factors. CGM‑derived metrics including glycemic variability (coefficient of variation), time above range, and time in tight range (70–140 mg/dL) provide complementary information on postprandial excursions, nocturnal hypoglycemia, and day‑to‑day stability that is not captured by fasting glucose or HbA1c alone and may better reflect real‑world glycemic stress. Wearable devices utilizing photoplethysmography (PPG), accelerometry, and heart rate sensors enable continuous tracking of physical activity, sedentary time, sleep duration and efficiency, circadian rhythm biomarkers (midline estimating statistic of rhythm, amplitude, interdaily stability), and novel metrics such as continuous wavelet circadian rhythm energy (CCE), with studies identifying CCE and heart rate relative amplitude as key circadian biomarkers for metabolic syndrome identification, outperforming traditional sleep markers in explainable AI models. Machine‑learning algorithms applied to integrated wearable, dietary, and phenotypic data have successfully clustered individuals into distinct “lifemetabotypes”, phenotypic groups with shared metabolic, lifestyle, and health-related quality of life profiles enabling objective, data-driven stratification of metabolic vulnerability and tailored prevention strategies [40,48,49,50,51,52,53,54,55,56].

Clinical, Public Health, and AI Applications

In clinical practice, continuous cardiometabolic and metabolic scores substantially improve discrimination of cardiovascular disease, stroke, type 2 diabetes, and metabolic syndrome beyond binary cut‑offs, with areas under the curve frequently exceeding 0.90 and significant reclassification improvement over traditional categorical approaches. These findings support the use of continuous, composite metabolic vulnerability indices for routine risk stratification and monitoring response to interventions, particularly when integrated with advanced lipid markers such as ApoB and biological age clocks [8,27,57,58,59,60,61].

Lifestyle‑focused metrics such as Life’s Essential 8 show strong, inverse dose‑response relationships with metabolic syndrome, reinforcing that a high metabolic vulnerability index is modifiable through behaviour change. Machine‑learning algorithms applied to biochemical, anthropometric, and lifestyle data have enabled classification of distinct “lifemetabotypes” associated with differing cardiometabolic profiles and health behaviours, highlighting the feasibility of data‑driven metabolic stratification at the population level [39,43,47,48,52,62].

For AI‑enabled health tech platforms, a metabolic vulnerability index integrates wearable metrics, continuous glucose monitoring, dietary data, and biomarkers into a central outcome that supports personalized risk communication and guides intervention intensity. Precision nutrition and AI‑driven behavioural coaching have demonstrated clinically meaningful improvements in HbA1c, lipids, and weight loss, while explainable AI frameworks enhance trust by revealing actionable, personalized risk drivers. Integration of large language models further enables natural language interaction and real‑time educational support, bridging mechanistic science and scalable, consumer‑facing preventive care [63,64,65,66,67].

Future Directions

Future research on the metabolic vulnerability index should prioritize systematic standardization of its components, moving from heterogeneous, study‑specific scores toward a harmonized, preventive‑focused framework that can be deployed across populations and care settings. This will require rigorous selection and weighting of biomarkers, spanning anthropometrics, blood pressure, glycemia, lipid fractions including ApoB, inflammatory markers, and biological age measures, using methods such as penalized regression, machine learning, and causal modelling to balance parsimony, interpretability, and predictive performance. Equally important is calibration and validation of index cut‑offs across age, sex, ethnic, and socioeconomic strata, as continuous metabolic syndrome scores and biological age accelerations have shown differential distributions and risk gradients in younger versus older adults and across diverse ancestry groups. Head‑to‑head comparisons of a standardized metabolic vulnerability index with existing risk engines (e.g., pooled cohort equations), single‑domain scores (e.g., METS‑IR, METS‑VF, CMI), and multiple biological clocks (glycan, metabolomic, epigenetic) will be needed to determine incremental value for predicting incident cardiometabolic disease, multimorbidity trajectories, and health span outcomes [2,3,4,5,7,10,22,23,26,30,57,60,68].

A second key direction is the deeper integration of digital phenotyping and longitudinal data streams to transform the metabolic vulnerability index from a static baseline score into a dynamic measure of metabolic resilience. Continuous glucose monitoring can capture postprandial excursions, glycemic variability, and time‑in‑range metrics that correlate with microvascular and macrovascular complications and may better reflect real‑world glycemic stress than fasting glucose alone. Wearable‑derived activity, heart rate, and sleep metrics provide high‑resolution data on circadian alignment, physical fitness, and recovery, while serial assessments of body composition (e.g., appendicular lean mass, visceral fat) and repeated measurement of biological age clocks offer longitudinal insight into how lifestyle and pharmacologic interventions reshape metabolic and aging trajectories. Embedding these signals into adaptive, AI‑driven models could enable real‑time updating of an individual’s metabolic vulnerability score, closed‑loop feedback on behavioural targets, and early alerts when trajectories deviate toward higher‑risk patterns, particularly valuable for intermediate-risk individuals and those in midlife undergoing rapid transitions in weight, glycemia, or blood pressure [2,22,26,30,52,68,69,70].

From an implementation standpoint, future work should examine how index‑guided care pathways can be integrated into primary care, cardiometabolic clinics, and consumer health tech platforms without increasing clinician burden or digital inequities. Prospective interventional trials will be essential to test whether targeting specific index thresholds or trajectories with stepped interventions yields greater reductions in incident cardiometabolic disease, slows biological age acceleration, or alters multimorbidity patterns compared with standard guideline‑based care. Parallel qualitative and behavioural research is needed to refine communication strategies around composite risk constructs such as “metabolic age” and “vulnerability,” ensuring that these metrics enhance motivation, self‑efficacy, and health literacy rather than generating anxiety or fatalism, especially in younger, asymptomatic individuals [2,5,7,30,39,71].

Conclusion

The metabolic vulnerability index represents a conceptual and practical evolution from binary diagnostic labels toward continuous, multidimensional quantification of cardiometabolic and aging risk across the life course. By integrating traditional risk factors, insulin resistance and visceral adiposity scores, advanced lipid markers such as ApoB, inflammatory and biological aging biomarkers, and increasingly rich digital phenotypes, such an index can capture the fragility of metabolic networks well before overt disease emerges. In doing so, it offers a unifying framework that bridges guideline-driven lipid and glucose management with next-generation biological clocks and AI-enabled risk stratification, making it possible to operationalize concepts like metabolic age and resilience in both clinical and consumer environments.

If rigorously standardized, validated, an implemented with attention to equity and interpretability, a metabolic vulnerability index could help shift the center of gravity in cardiometabolic medicine from late-stage disease management to proactive prevention, rather than waiting for dysglycemia, dyslipidemia, or organ damage to cross diagnostic thresholds, clinicians, health systems, and health tech platform could use dynamic composite scores to identify high-risk architecture, and monitor the impact of lifestyle, pharmacologic, and digital therapies on both metabolic and biological aging trajectories. In this vision, the metabolic vulnerability index becomes a central organizing tool for metabolic wellness and longevity-oriented practice, aligning mechanistic insights, clinical workflows, and personalized digital health into a coherent, prevention-first paradigm.

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