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Heart Rate Variability as a Nervous System Vital Sign for Stress Resilience, Metabolic Risk, and Biological Aging

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Introduction

Heart rate variability (HRV) has emerged as a systems biomarker that captures the dynamic interplay between the heart, brain, and autonomic nervous system (ANS). It is derived from beat-to beat fluctuations in sinus rhythm and provides a non-invasive, continuous, and responsive measure of autonomic regulation across multiple timescales. Rather than reflecting cardiac function in isolation, HRV encodes the capacity of the organism to flexibly shift between sympathetic mobilization and parasympathetic recovery in response to internal and external demands, thereby indexing stress resilience, adaptability, and overall neurovisceral health. In this context, HRV can be conceptualized as a real-time output of heart-brain communication, integrating inputs from cortical, limbic, and brainstem circuits into a measurable physiological signal.

Epidemiological and clinical studies consistently show that reduced HRV is associated with increased all-cause and cardiovascular mortality, even after adjustment for traditional risk factors. Depressed HRV predicts adverse outcomes in populations with myocardial infarction, heart failure, and other cardiometabolic conditions, supporting its role as an independent prognostic marker. These findings underscore that HRV should not be viewed as a niche cardiology metric, but as a powerful integrative indicator of systemic health, autonomic capacity, and long-term outcome risk.

From a preventive medicine perspective, autonomic imbalance and chronic sympathetic dominance are increasingly recognized as upstream drivers of hypertension, insulin resistance, visceral adiposity, systemic inflammation, and accelerated biological aging. low HRV and blunted vagal modulation frequently accompany these states, indicating reduced capacity to terminate stress responses and restore homeostasis, which in turn amplifies cardiometabolic and neuropsychiatric vulnerability. HRV thus offers a practical window into the hidden autonomic component of metabolic and aging trajectories, complementing conventional biomarkers such as blood pressure, lipids and glycemic indices.

The rapid proliferation of wearable sensors and continuous monitoring platform creates an opportunity to embed HRV into AI-enabled health tech ecosystem as a core nervous system vital sign. Longitudinal HRV data, combined with contextual information on sleep, activity, mood, and glycemic variability, can be leveraged by machine learning models to detect early patterns of autonomic dysregulation long before overt metabolic disease manifests. Such integration could enable personalized feedback loops, where real-time changes in HRV guide adaptive interventions in stress management, training load, recovery, and lifestyle modification, repositioning HRV from a descriptive marker to a central tool in proactive, nervous system-centric preventive care.

What HRV Measures in the Nervous System

Heart rate variability (HRV) is classically defined as the beat-to beat variation in the R-R intervals of sinus rhythm, arising from the continuous interaction between parasympathetic (vagal) and sympathetic efferents innervating the sinoatrial node. Under resting conditions, vagal influences dominate moment-to-moment heart rate dynamics, such that higher HRV-particularly in vagally mediated indices such as high-frequency HRV and time-domain measures like RMSSD generally reflects stronger vagal modulation and more flexible autonomic control. Conversely, states of sympathetic predominance, high catecholamine tone, or sinoatrial “ceiling effects” under intense arousal are typically associated with reduced HRV and diminished total power, indicating constrained capacity of the cardiac pacemaker to respond to neural inputs. In this sense, HRV functions as a non-invasive index of sympatho-vagal balance at the sinoatrial level, translating complex autonomic signalling into a quantifiable marker of nervous system state [1,2,3].

Beyond this peripheral description, contemporary frameworks such as the neurovisceral integration model and polyvagal theory situate HRV within a broader brain-body regulatory network that links autonomic function to emotion, cognition, and behaviour. The neurovisceral integration model proposes that prefrontal and anterior cingulate cortices exert tonic inhibitory control over limbic and brainstem structures (e.g., amygdala, hypothalamus), thereby modulating vagal outflow to the heart; higher resting HRV is associated with more efficient prefrontal regulation, better executive functioning, and greater cognitive flexibility. Polyvagal theory further differentiates phylogenetically older dorsal vagal pathways from the myelinated ventral vagal system originating in the nucleus ambiguous, positing that robust ventral vagal tone indexed by respiratory sinus arrhythmia and related HRV measures is linked to adaptive emotion regulation, social engagement, and rapid recovery from stress. Together, these models support the view that HRV is not merely a cardiac signal but a systems-level indicator of central autonomic network integrity, emotional regulation capacity, and stress resilience [4,5,6,7,8].

From a measurement perspective, HRV can be decomposed into time-domain, frequency-domain, and non-linear metrics, each capturing different aspects of autonomic regulation. Time-domain indices such as the standard deviation of normal-to-normal intervals (SDNN) and the root mean square of successive differences (RMSSD) are derived directly from the variability of R-R intervals over a recording period; SDNN reflects overall variability influenced by both sympathetic and parasympathetic inputs, whereas RMSSD primarily indexes short-term, beat-to-beat changes driven by vagal activity. Frequency-domain analyses partition HRV power into spectral bands, with high-frequency (HF) power closely related to respiratory sinus arrhythmia and parasympathetic modulation, low-frequency (LF) power reflecting a more complex mix of baroreflex, sympathetic, and parasympathetic influences, and the LF/HF ratio historically used albeit controversially as a surrogate for sympatho-vagal balance. Non-linear measures, including Poincaré plot indices, approximate entropy, and detrended fluctuation analysis, characterize the fractal and complex dynamics of heart rate signals and may better capture subtle alterations in autonomic regulation under pathological or high-stress conditions, across both research and consumer-grade wearables, RMSSD and HF-HRV have emerged as relatively robust and interpretable markers of parasympathetic activity, partly due to their relative insensitivity to slow drifts and their feasibility in short-term recordings [1,3,9,10].

However, HRV is highly context-dependent, and its interperetation requires attention to key sources of physiological and methodological variance. Normative HRV values decline with age and tend to be higher in females during much of the reproductive lifespan, while posture (supine versus seated or standing), spontaneous versus paced breathing, circadian timing, physical fitness, and acute behaviours such as caffeine, nicotine, and alcohol intake all exert measurable effects on HRV metrics. Medications including beta-blockers, antiarrhythmics, antidepressants, and anticholinergic agents can markedly alter autonomic tone and thereby confound HRV-based inferences, particularly in cardiometabolic or psychiatric populations. For these reasons, standardized protocols, defining minimum recording duration, body position, breathing instructions, time of day, pre-measurement abstinence from stimulants and documentation of medication use are essential to ensure reliability, comparability, and valid longitudinal interpretation of HRV in both clinical and digital-health applications. In AI-enabled health tech settings, incorporating such contextual metadata into model design is critical to avoid spurious associations and to translate HRV from a noisy signal into a stable, clinically meaningful nervous system biomarker [1,3,9,10,11].

HRV as a Marker of Health, Disease Risk, and Aging

Extensive prospective and meta-analytic evidence demonstrates that reduced HRV independently predicts all-cause and cardiovascular mortality across diverse clinical populations, with the strongest and most consistent association observed for time-domain parameters such as SDNN below 70ms, identifies a subset of patients at two- to three-fold higher risk of sudden death and total mortality, even after adjustment for left ventricular ejection fraction, New York Heart Association class, and contemporary pharmacotherapy. A recent meta-analysis synthesizing data from over 10,000 heart failure patients confirmed a pooled effect size of 1.99 (95% CI: 1/36-2.61) for the association between impaired HRV and mortality risk, with time-domain measures showing the most reliable stratification. Importantly, HRV retains prognostic value in optimally treated contemporary cohorts, including landmark trials such as VICTORIA, underscoring that it captures residual autonomic and neurohumoral risk not fully addressed by standard care. Beyond heart failure, depressed HRV following myocardial infarction, in chronic kidney disease, and in general-population cohorts consistently predicts adverse cardiovascular events and mortality, reinforcing HRV’s role as a complementary biomarker in clinical risk stratification that extends beyond traditional risk factors [12,13,14].

From a mechanistic perspective, low HRV is increasingly recognized as a marker of impaired vagal cholinergic anti-inflammatory signalling, a pathway through which the brain modulates peripheral immune and inflammatory responses. The vagus nerve exerts tonic inhibitory control over proinflammatory cytokine release, particularly tumor necrosis factor-a, interleukin-1b, and interleukin-6 through acetylcholine binding to the a7 nicotinic acetylcholine receptor on macrophages and monocytes; diminished vagal tone, indexed by reduced HRV, corresponds to disinhibition of this pathway and elevated systemic inflammation. Cross-sectional and longitudinal studies confirm roust inverse associations between HRV (especially high-frequency and RMSSD components) and circulating inflammatory markers including C-reactive protein, interleukin-6, and fibrinogen, with higher HRV predicting lower inflammatory burden independent of age, sex, and body mass index. Clinically, reduced HRV is associated with increased susceptibility to infection, worse outcomes in sepsis and higher 30-day mortality in critical illness, consistent with impaired neural control over innate immune activation. These findings position HRV not only as a cardiac autonomic metric but as a system-level indicator of neuroimmunomodulatory function and inflammatory homeostasis [15,16,17,18,19].

Age-related decline in HRV is well documented, with marked reductions in both time- and frequency-domain parameters after age 40, reflecting progressive autonomic dysregulation and diminished parasympathetic reserve as part of the aging process. Recent evidence links this age, dependent HRV decline to cellular aging markers such as leukocyte telomere length, suggesting that HRV may serve as a feasible proxy for biological rather than merely chronological age and autonomic nervous system integrity. In the context of cardiometabolic disease, lower HRV is associated with insulin resistance, hypertension, obesity, and metabolic syndrome, and prospective data indicate that early autonomic dysfunction detectable via HRV precedes overt metabolic and cardiovascular morbidity by years and decades. Consequently, HRV is being explored as a dynamic biomarker of “physiological age” that integrates cardiovascular, autonomic, inflammatory, and metabolic risk into a single accessible metric, with higher HRV values indicating a younger biological profile and lower chronic disease vulnerability. This convergence of aging biology, autonomic function , and cardiometabolic risk positions HRV as a promising target for preventive intervention aimed at slowing autonomic and systemic aging trajectories [11,20,21,22,23].

HRV, Stress, Emotion, and Neurovisceral Health

Acute and chronic psychological stress exert robust, bidirectional effects on HRV, primarily through downregulation of parasympathetic tone and upregulation of sympathetic drive, as evidenced by decreased high-frequency HRV (HF-HRV), RMSSD, and other vagally mediated indices across experimental and naturalistic stress paradigms. Meta-analytic evidence demonstrates that individuals with anxiety disorders, major depressive disorder, and posttraumatic stress disorder (PTSD) consistently exhibit lower resting HRV compared to healthy controls, with the most robust reduction observed in HF-HRV and RMSSD, metrics that index parasympathetic modulation suggesting impaired capacity for flexible autonomic regulation and recovery from stress. In PTSD specifically, the effect is substantial (Hedges’g = -.158 for HF-HRV), and critically, longitudinal data indicate that lower baseline HRV prospectively predicts the development of PTSD following trauma exposure, supporting the hypothesis that HRV reflects a pre-existing vulnerability or endophenotype related to stress resilience and emotional regulation capacity. Mechanistically, this association is explained by the neurovisceral integration model: reduced prefrontal inhibitory control over subcortical threat circuits (amygdala, hypothalamus) leads to disinhibition of sympathetic tone, chronic HPA axis activation, and withdrawal of vagal cardioprotective signalling, all of which converge to lower HRV while simultaneously amplifying subjective distress, rumination, and emotional dysregulation [5,24,25,26,27,28,29].

Importantly, HRV functions as an objective, quantifiable correlate of subjective stress and emotion regulation capacity, bridging domains of psychiatry, neurology and cardiometabolic prevention in trandiagnostic manner. Higher resting HRV is associated with greater cognitive flexibility, more adaptive use of emotion regulation strategies such as cognitive reappraisal and resilience to adversity, whereas lower HRV corresponds to maladaptive strategies such as expressive suppression and poorer emotional recovery following challenge. Evidence from real-world ambulatory monitoring further confirms that higher HRV buffers the relationship between daily stressors and negative affect, stress, and mental exhaustion, such that individuals with high-HRV experience fewer psychological symptoms despite similar external demands. Conversely, sustained reductions in HRV over multiple days signals decreased resilience and increased vulnerability to psychological decompensation, positioning HRV as a dynamic, within-person marker of nervous system “reserve” that can be tracked continuously in naturalistic settings. This convergence of neurophysiological, affective, and health outcomes positions HRV not merely as a cardiac metric but as a system-level biomarker integrating brain-heart-immune communication and representing the organism’s capacity for self-regulation under stress [24,26,27,28,30,31,32].

From a preventive and wellness perspective, HRV can be conceptualized as a proxy for nervous system “fitness”, the organism’s ability to rapidly and efficiently shift between states of sympathetic mobilization and parasympathetic recovery in response to internal and external demands. Just as cardiovascular fitness reflects the heart’s capacity to meet physical challenges, HRV reflects the autonomic system’s adaptability and readiness to respond to cognitive, emotional and metabolic stressors, making it a valuable indicator of day-to-day resilience and readiness for performance. Emerging evidence links higher HRV to improved sleep quality, faster cognitive processing, better attentional control, and subjective feelings of mental and physical readiness the following day, whereas reduced HRV during sleep predicts lower perceived fitness, increased fatigue, and heightened stress reactivity in the subsequent 24-48 hours. These associations are now routinely captured by consumers wearables and health apps, which track overnight or resting HRV as a “recovery” score and provide personalized feedback on readiness to train, work, or engage in high-demand activities. While the predictive strength of HRV for within-person changes in subjective fitness is modest, the accumulation of multi-day trends and context-aware interpretation of HRV alongside sleep, activity, and self-reported mood may enhance its practical utility as a dynamic biomarker of nervous system state and resilience trajectory. Integrating HRV monitoring into preventive health tech ecosystems thus offers a scalable, non‑invasive strategy for early detection of autonomic imbalance and real‑time guidance for stress management, recovery optimization, and metabolic health preservation [10,30,33].

Modulating HRV: Lifestyle, Therapeutics, and Biofeedback

Regular physical exercise represents one of the most robust modifiable determinants of HRV, with meta‑analytic evidence demonstrating that exercise training significantly improves vagally mediated parameters (RMSSD, HF‑HRV) as well as global HRV indices (SDNN) in both healthy adults and individuals with cardiovascular disease. The magnitude and direction of these effects depend on exercise modality, duration, and population characteristics: long‑term aerobic training consistently enhances resting HRV in previously sedentary individuals, reflecting improved parasympathetic modulation and reduced sympathetic dominance, whereas high‑intensity interval training (HIIT) may be particularly effective for improving SDNN, RMSSD, and the LF/HF ratio, potentially via enhanced baroreflex sensitivity and autonomic flexibility. Resistance training and combined aerobic‑resistance protocols also demonstrate favourable effects on HRV, particularly in populations with metabolic dysfunction or heart failure, where exercise‑induced improvements in HRV correlate with reductions in clinical symptoms, improved exercise capacity, and better long‑term outcomes. Sleep optimization further modulates HRV through restoration of autonomic balance, with adequate sleep duration and quality associated with higher nocturnal HRV, whereas sleep deprivation and fragmentation lead to suppression of parasympathetic activity and elevated sympathetic tone. Stress‑reduction practices including mindfulness meditation, slow breathing, and yoga consistently increase resting HRV across multiple trials, likely via enhancement of prefrontal inhibitory control over limbic circuits and direct vagal activation through respiratory‑cardiac coupling [30,31,34,35,36,37,38,39].

From a nutritional perspective, adherence to the Mediterranean dietary pattern, characterized by high intake of fruits, vegetables, whole grains, legumes, fish, nuts, and olive oil s associated with significantly higher HRV across time‑ and frequency‑domain parameters, even after adjusting for shared genetic and environmental factors. In a twin study controlling for familial confounding, each one‑unit increase in Mediterranean diet score was associated with 3.5% to 13% higher HRV indices, including SDNN, RMSSD, and HF‑HRV, independent of energy intake, cardiovascular risk factors, and medication use. Mechanistic pathways likely involve reduction of systemic inflammation, improved endothelial function, enhanced metabolic flexibility, and favourable modulation of the gut microbiome, all of which support autonomic balance and vagal tone. Specific dietary components such as omega‑3 fatty acids, polyphenol‑rich foods, and dietary fiber have been independently linked to higher HRV and lower inflammatory burden, though the synergistic, whole‑diet effect appears stronger than isolated nutrient supplementation. While the evidence base remains smaller and more heterogeneous than for exercise, emerging data suggest that dietary patterns optimizing metabolic flexibility and minimizing chronic inflammation, whether Mediterranean, plant-forward, or low-glycemic may support higher HRV, though causal pathways and optimal intervention strategies continue to be elucidated [40,41,42,43].

HRV biofeedback (HRVB) represents a non‑pharmacologic, vagal‑focused intervention in which individuals learn to breathe at their resonance frequency, typically around 0.1 Hz or approximately five to six breaths per minute while receiving real-time visual or auditory feedback on their heart rate patterns. Breathing at resonance frequency synchronizes respiration, heart rate oscillations, blood pressure fluctuations, and baroreflex activity, producing high‑amplitude oscillations in HRV and maximal stimulation of the baroreflex mechanism, thereby “exercising” cardiovascular and autonomic regulatory circuits. Systematic reviews and meta‑analyses confirm that HRVB training, typically delivered over 4 to 10 sessions with home practice, leads to clinically meaningful reductions in symptoms of anxiety, depression, and PTSD, with effect sizes comparable to or exceeding first‑line psychotherapies, alongside measurable increases in resting HRV that persist beyond the training period. Importantly, the magnitude of symptom improvement correlates with the degree of HRV increase, supporting a dose‑response relationship and suggesting that improved autonomic regulation mediates therapeutic benefit. HRVB has also demonstrated efficacy in cardiac populations, including patients with heart failure and coronary artery disease, where improvements in HRV and baroreflex sensitivity translate into enhanced exercise tolerance, reduced arrhythmia burden, and better quality of life [37,38,39,44].

Beyond HRVB, emerging vagal‑enhancing strategies include non‑invasive vagal nerve stimulation (VNS) via auricular, transcutaneous, or respiratory‑gated protocols, which deliver targeted electrical or mechanical stimulation to vagal afferent pathways and have shown preliminary efficacy in modulating HRV, reducing inflammatory markers, and improving outcomes in conditions ranging from atrial fibrillation to treatment‑resistant depression. Targeted breathing therapies incorporating slow, diaphragmatic, or coherence‑focused breathing patterns, often integrated into digital health apps and wearables offer accessible, scalable means of enhancing vagal tone without specialized equipment or clinical supervision. Collectively, these interventions leverage the bidirectional heart–brain axis: by consciously modulating respiratory and cardiovascular rhythms, individuals can upregulate vagal efferent activity, dampen sympathetic outflow, reduce systemic inflammation via the cholinergic anti‑inflammatory pathway, and improve both autonomic balance and subjective well‑being, positioning HRV biofeedback and related vagal therapies as promising tools for preventive and integrative medicine [17,38,44].

HRV in AI-Enabled Preventive Health and Health tech

The convergence of wearable technology, smartphone sensing, and cloud‑based analytics has enabled continuous, high‑frequency HRV assessment in real‑world environments, transforming it from a laboratory tool into a pervasive signal for digital health and preventive medicine. Consumer devices using optical (PPG) or ECG‑based sensors can now capture inter‑beat intervals during sleep, rest, and daily activities, generating longitudinal HRV time series that reflect day‑to‑day fluctuations in autonomic function, stress load, and recovery. These data streams are increasingly integrated into digital phenotyping platforms that combine HRV with passive measures of activity, geolocation, phone use, and environmental context to infer mental health status, stress exposure, and health‑related behaviours at scale. Within such ecosystems, HRV‑based “nervous system scores” can be conceptualized as composite indices summarizing vagal tone, stress reactivity, and recovery capacity, which are then combined with glucose dynamics, sleep architecture, physical activity, and subjective mood reports to define dynamic phenotypes of stress load and metabolic resilience. These phenotypes, updated continuously, could support individualized baselines, early deviation alerts, and tailored recommendations, positioning HRV as a central pillar of nervous system–centric preventive care [45,46,47,48].

Machine‑learning approaches are well suited to exploit the multidimensional structure of HRV data for early detection and prediction tasks. Models trained on time‑, frequency‑, and non‑linear HRV features have already demonstrated high accuracy in differentiating healthy individuals from those with congestive heart failure, with reported classification accuracies exceeding 90–95% for binary normal vs. diseased discrimination. Extending these methods to continuous monitoring, HRV‑derived features such as nocturnal RMSSD trajectories, short‑term declines in HF‑HRV, or changes in circadian HRV patterns an be incorporated into algorithms designed to detect early decompensation in heart failure, impending infection, overtraining in athletes, or burnout in high‑stress professions, triggering proactive interventions before clinical deterioration becomes overt. When combined with contextual signals (e.g., step counts, sleep disruption, temperature, self‑reported symptoms), HRV features can also support personalized intervention timing, for example, suggesting recovery days, targeted breathing exercises, or medical review when autonomic patterns deviate from an individual’s historical baseline. However, the explosion of consumer‑grade HRV sensing also introduces significant challenges. Most commercial wearables rely on wrist‑based PPG, which is inherently less accurate than ECG for HRV analysis and is highly susceptible to motion artifacts, skin tone variability, tattoos, and device fit, leading to noisy inter‑beat interval estimates and potential bias across demographic groups. Additional confounding arises from medications, comorbidities, hydration status, and ambient temperature, which can alter HRV independently of stress or disease progression but are often unmeasured in consumer datasets. To avoid overinterpretation and spurious inferences, robust standardized preprocessing pipelines (artifact detection, interpolation rules, minimum data‑quality thresholds), explicit modelling of contextual covariates, and algorithm validation against gold‑standard ECG and clinically adjudicated outcomes are essential. Ultimately, AI models using HRV must be designed as context‑aware decision‑support tools, augmenting, rather than replacing, clinical judgment, if HRV is to fulfil its promise as a scalable nervous system biomarker in preventive health and health tech applications [9,45,46,47,48,49,50,51].

Future Directions

Future work on HRV should prioritize large, harmonized normative datasets stratified by age, sex, ethnicity, and fitness to enable robust reference ranges and individualized thresholds for risk stratification. Mechanistic studies combining HRV with direct or proxy sympathetic measures and baroreflex sensitivity are needed to better separate parasympathetic from sympathetic contributions and to define clinically meaningful cut‑offs and minimal important changes across cardiology, metabolic, and psychiatric contexts. Integrating HRV with multi‑omic, inflammatory, and metabolic markers, including cytokine profiles, metabolomics, microbiome data, and continuous glucose monitoring will help position HRV as a dynamic “systems biomarker” of aging and disease risk rather than an isolated autonomic metric [3,9,11,13,21,22,23,52].

A key next step is to move from passive observation to HRV‑guided intervention trials, in which predefined HRV changes inform the dose and timing of lifestyle, pharmacologic, and neuromodulation therapies. Examples include adjusting exercise intensity and recovery, tailoring stress‑reduction programs, or titrating non‑invasive vagal stimulation and HRV biofeedback based on individual autonomic responses. Embedding HRV into adaptive, closed‑loop digital therapeutics, where continuous HRV, combined with sleep, activity, glucose, and mood data, triggers context‑aware recommendations could transform HRV into an active control signal for personalized preventive care, provided that AI models are rigorously validated, data quality is standardized, and integration with clinical workflows ensures that HRV‑driven insights complement clinical judgment [34,37,38,45,48,50].

Conclusion

Heart rate variability synthesizes information about autonomic balance, cumulative stress load, inflammatory tone, and cardiometabolic risk into a single, non‑invasive signal, making it one of the most informative accessible indicators of nervous system functioning in routine practice. By capturing the dynamic interplay between sympathetic and parasympathetic activity, HRV reflects not only cardiac regulation but also the integrity of heart–brain–immune communication and the organism’s capacity to adapt to internal and external demands. In this sense, HRV can be reframed as a nervous system “vital sign” that complements traditional metrics such as heart rate, blood pressure, and glycemic indices, while adding a temporal, resilience‑oriented dimension to risk assessment.

As sensing technologies, continuous monitoring platforms, and AI‑driven analytics mature, HRV is poised to become a core pillar of preventive strategies in aging and metabolic medicine. High‑resolution HRV data integrated with sleep, activity, glucose, and mood streams can support earlier detection of autonomic dysregulation, more precise phenotyping of stress and metabolic resilience, and personalized timing and dosing of lifestyle, pharmacologic, and neuromodulation interventions. When embedded into context‑aware, clinically validated decision‑support systems, HRV‑guided care has the potential to shift practice from episodic, symptom‑driven management toward continuous, nervous system–centric prevention, aligning cardiovascular, metabolic, mental health, and longevity goals within a single, integrative framework.

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