Cardiometabolic disease burden is rising rapidly in India and other South Asian populations, with escalating prevalence of obesity, type 2 diabetes mellitus, dyslipidaemia, hypertension and cardiovascular disease (CVD). In parallel, increasing mechanistic evidence has emerged for a role of the intestinal microbiome and its metabolites in the regulation of host metabolic, inflammatory and cardiovascular homeostasis. The gut-microbiome-derived metabolites (such as short‐chain fatty acids [SCFAs], trimethylamine‐N-oxide [TMAO], bile acid derivatives and others) have been implicated in insulin resistance, adiposity, hypertension, endothelial dysfunction and atherosclerosis [1-4]. Moreover, autonomic nervous system (ANS) dysfunction — commonly manifesting as altered heart rate variability (HRV) and impaired sympatho‐vagal balance — is recognised as an early marker of cardiometabolic risk and predictor of adverse cardiovascular outcomes [5-7]. The interplay between gut-microbiome metabolites and autonomic regulation presents a compelling integrative axis: metabolites may modulate neural reflexes (via vagal afferents, enteric nervous system, immune–neuro circuits), while autonomic dysfunction may influence intestinal milieu, barrier integrity, and microbial metabolism. Although animal and small human studies suggest a gut-brain–heart axis, comprehensive data linking gut microbial metabolome, autonomic functional indices and traditional cardiometabolic risk in adult populations are scarce — especially in Indian settings, where unique ethnic, dietary and environmental factors may modulate microbiome–metabolite–host interactions [2,8].

Study design and population This community-based, cross-sectional study was conducted at the Department of Physiology and Biochemistry, India. A total of 210 apparently healthy Indian adults (men = 112, women = 98) aged 25–60 years were recruited through public advertisement and health camps. Participants were classified into low (n = 70), moderate (n = 70), and high (n = 70) cardiometabolic-risk groups based on a composite z-score derived from waist circumference, systolic blood pressure, fasting glucose, triglycerides, and HDL-cholesterol. Exclusion criteria included prior myocardial infarction, chronic kidney disease (eGFR < 60 mL/min/1.73 m²), gastrointestinal surgery, autoimmune or inflammatory bowel disease, antibiotic/probiotic use in the past 3 months, and medication affecting autonomic function. The study was approved by the Institutional Ethics Committee and written informed consent was obtainned from all participants. Clinical and anthropometric assessment Participants attended the laboratory after a 12-hour overnight fast. Height and weight were recorded with a stadiometer and digital scale (Seca 213 and 813; Seca GmbH, Germany), and BMI = weight (kg)/height² (m²) was computed. Waist circumference was measured midway between the lower costal margin and iliac crest using a non-stretch tape. Mean ± SD BMI was 26.8 ± 4.1 kg/m², and mean waist circumference was 90.4 ± 9.2 cm. Blood pressure was measured thrice (Omron HEM-7130, Japan) after 5 min seated rest; mean systolic/diastolic values were 126.7 ± 12.5 / 82.3 ± 8.4 mmHg. Physical activity was estimated by the Global Physical Activity Questionnaire (GPAQ), and dietary intake using a validated semi-quantitative food frequency questionnaire emphasising fibre and choline intake. Laboratory investigations Venous blood (10 mL) was collected between 07:00 and 09:00 h. Fasting glucose, lipid profile and high-sensitivity C-reactive protein (hs-CRP) were analysed on an AU480 Chemistry Analyzer (Beckman Coulter, USA) using enzymatic methods. Mean ± SD fasting glucose was 94.8 ± 10.6 mg/dL, total cholesterol 189.5 ± 32.1 mg/dL, HDL-C 46.9 ± 10.3 mg/dL, LDL-C 115.8 ± 27.6 mg/dL, triglycerides 138.7 ± 55.2 mg/dL, and hs-CRP median [IQR] = 1.8 [0.9–3.6] mg/L. Serum insulin was measured by electrochemiluminescence (Roche e411), and HOMA-IR calculated as [fasting insulin (µU/mL) × fasting glucose (mg/dL)] / 405, yielding median [IQR] = 2.1 [1.3–3.4]. Gut-microbiome-derived metabolite assay Fasting plasma and morning stool samples were collected and stored at −80 °C. Plasma TMAO, choline, betaine, acetate, propionate, and butyrate were quantified using UPLC–MS/MS (Waters Xevo TQ-S) with internal standards and calibration curves. The intra-assay coefficient of variation was < 8 %. Median (IQR) plasma concentrations were: • TMAO = 4.3 (3.1–6.7) µmol/L • Acetate = 52.6 (41.3–66.9) µmol/L • Propionate = 16.8 (11.9–22.5) µmol/L • Butyrate = 12.1 (8.2–17.3) µmol/L. Ratios of SCFAs to TMAO were computed as indices of microbial metabolic balance. Autonomic function testing Autonomic function was assessed in a quiet, temperature-controlled room (24 ± 1 °C) between 09:00 and 11:00 h using the PowerLab 15T System (ADInstruments, Australia). Participants rested supine for 10 min before a 5-minute ECG recording (sampling = 1000 Hz). HRV analysis used Kubios HRV Premium v3.5. Time-domain indices: SDNN = 38.5 ± 14.2 ms; RMSSD = 31.8 ± 13.5 ms. Frequency-domain indices: LF = 412 ± 218 ms², HF = 296 ± 187 ms², LF/HF = 1.45 ± 0.63. Autonomic reflex tests included: • Deep breathing test (E/I ratio = 1.26 ± 0.15) • Valsalva ratio = 1.52 ± 0.25 • 30/15 ratio = 1.13 ± 0.09. • Lower values indicated reduced parasympathetic tone or sympathetic predominance. Statistical analysis Data normality was tested by Shapiro–Wilk. Continuous variables are expressed as mean ± SD or median [IQR]. Correlations between plasma metabolites and HRV parameters were assessed using Pearson or Spearman coefficients, adjusted for age, sex, BMI, physical activity, and dietary fibre. Multivariable linear regression determined independent predictors of HRV and cardiometabolic-risk score. Mediation was examined using PROCESS macro (Model 4, SPSS v25) with 5,000 bootstrap samples. Significance was defined as p < 0.05. Power analysis Assuming an effect size = 0.25 and α = 0.05, a sample of 200 provided 85 % power to detect correlations r ≥ 0.25 between metabolites and HRV indices.

Participant Characteristics A total of 210 adults (mean age = 42.7 ± 9.6 years; 53 % men) were included after exclusions. Mean BMI was 26.8 ± 4.1 kg/m², and 56 % of participants were overweight or obese. The prevalence of metabolic syndrome was 28.6 % by IDF criteria. Table 1 summarises baseline characteristics. Key finding: Indian adults demonstrated a moderate cardiometabolic burden even in apparently healthy individuals, highlighting early metabolic risk patterns typical of South Asian cohorts. Gut-Microbiome–Derived Metabolites Median (IQR) plasma concentrations were TMAO 4.3 (3.1–6.7) µmol/L, acetate 52.6 (41.3–66.9) µmol/L, propionate 16.8 (11.9–22.5) µmol/L, and butyrate 12.1 (8.2–17.3) µmol/L (Table 2). Higher TMAO levels correlated positively with fasting glucose (r = 0.29, p < 0.01) and HOMA-IR (r = 0.32, p < 0.001). In contrast, butyrate correlated inversely with triglycerides (r = −0.27, p = 0.002) and positively with HDL-C (r = 0.22, p = 0.006). Key finding: An adverse microbial metabolite pattern (↑ TMAO, ↓ SCFA) was associated with higher insulin resistance and lipid derangements. Autonomic Function Indices Time- and frequency-domain heart rate variability (HRV) indices indicated mild sympathetic predominance (LF/HF > 1). Mean RMSSD was 31.8 ± 13.5 ms, HF = 296 ± 187 ms², and LF/HF = 1.45 ± 0.63 (Table 3). Participants in the highest TMAO tertile had lower RMSSD (26.1 ± 10.8 ms) and lower HF power (228 ± 144 ms²) compared to the lowest tertile (RMSSD 36.9 ± 14.5 ms; p = 0.002). Key finding: Elevated TMAO was consistently linked with impaired vagal tone, whereas higher SCFA concentrations reflected better autonomic balance. Multivariable Regression and Mediation Analyses Regression models adjusted for age, sex, BMI, and physical activity demonstrated that TMAO independently predicted reduced RMSSD (β = −0.27, p = 0.001) and higher cardiometabolic risk score (β = 0.31, p < 0.001). Butyrate remained a positive predictor of RMSSD (β = 0.23, p = 0.004) and an inverse predictor of systolic BP (β = −0.21, p = 0.006). Mediation analysis indicated that RMSSD mediated 26 % of the TMAO–cardiometabolic risk relationship (bootstrapped 95 % CI 0.08–0.41; p = 0.01) (Table 4). Key finding: Autonomic function partially mediated the biochemical link between microbial metabolites and cardiometabolic risk, suggesting a gut–brain–heart pathway. Tables Table 1. Baseline Characteristics of Study Participants (n = 210) Parameter Mean ± SD / Median (IQR) Age (years) 42.7 ± 9.6 Male : Female 112 : 98 BMI (kg/m²) 26.8 ± 4.1 Waist circumference (cm) 90.4 ± 9.2 Systolic BP (mmHg) 126.7 ± 12.5 Diastolic BP (mmHg) 82.3 ± 8.4 Fasting glucose (mg/dL) 94.8 ± 10.6 Triglycerides (mg/dL) 138.7 ± 55.2 HDL-C (mg/dL) 46.9 ± 10.3 LDL-C (mg/dL) 115.8 ± 27.6 hs-CRP (mg/L) 1.8 (0.9–3.6) HOMA-IR 2.1 (1.3–3.4) Table 2. Gut-Microbiome–Derived Metabolites and Correlations with Metabolic Markers Metabolite Median (IQR) µmol/L r (Glucose) r (Triglycerides) r (HOMA-IR) p-Value (trend) TMAO 4.3 (3.1–6.7) 0.29 0.21 0.32 < 0.01 Acetate 52.6 (41.3–66.9) −0.12 −0.10 −0.09 0.18 Propionate 16.8 (11.9–22.5) −0.18 −0.22 −0.20 0.04 Butyrate 12.1 (8.2–17.3) −0.20 −0.27 −0.19 0.002 SCFA : TMAO ratio 18.7 (12.6–25.1) −0.30 −0.28 −0.32 0.001 Table 3. Autonomic Function and HRV Indices Across TMAO Tertiles Variable TMAO Low (< 3.5 µmol/L) n = 70 TMAO Mid (3.5–6.5 µmol/L) n = 70 TMAO High (> 6.5 µmol/L) n = 70 p-Value HR (bpm) 72.8 ± 8.3 75.1 ± 9.5 78.2 ± 8.8 0.02 SDNN (ms) 41.9 ± 13.9 37.4 ± 12.6 34.2 ± 13.1 0.03 RMSSD (ms) 36.9 ± 14.5 30.8 ± 11.9 26.1 ± 10.8 0.002 HF (ms²) 371 ± 205 295 ± 182 228 ± 144 0.004 LF/HF ratio 1.22 ± 0.49 1.42 ± 0.58 1.68 ± 0.71 0.01 Table 4. Multivariable Regression of Metabolites Predicting HRV and Cardiometabolic Risk Predictor Dependent Variable β (Standardised) 95 % CI p-Value TMAO RMSSD −0.27 −0.43 to −0.11 0.001 TMAO Cardiometabolic z-score 0.31 0.16 to 0.47 < 0.001 Butyrate RMSSD 0.23 0.07 to 0.38 0.004 Butyrate Systolic BP −0.21 −0.36 to −0.06 0.006 RMSSD (mediator) CMR z-score −0.25 −0.38 to −0.10 0.002

This integrative cohort study demonstrates that higher plasma concentrations of trimethylamine-N-oxide (TMAO) and lower levels of short-chain fatty acids (SCFAs) were significantly associated with autonomic imbalance and elevated cardiometabolic risk in Indian adults. Importantly, autonomic function—particularly reduced parasympathetic activity measured by RMSSD—partially mediated the relationship between TMAO and cardiometabolic risk. These findings highlight a gut-microbiome–autonomic–metabolic axis that may play an under-recognized role in the early pathogenesis of cardiometabolic disease within the South Asian population. Microbial Metabolites and Cardiometabolic Health The observed association between elevated TMAO and higher fasting glucose, triglycerides, and HOMA-IR aligns with prior evidence linking TMAO to insulin resistance, endothelial dysfunction, and cardiovascular events [11,12]. Indian adults are predisposed to cardiometabolic disorders even at lower BMI thresholds due to increased visceral adiposity and early insulin resistance [11]. In our cohort, the mean BMI (26.8 kg/m²) and waist circumference (90 cm) fall within this risk zone. Similar to global findings from the Global Burden of Cardiovascular Diseases (GBD 2019) analysis, metabolic dysregulation continues to drive the cardiovascular epidemic in South Asia [12]. TMAO, generated via hepatic oxidation of microbially derived trimethylamine from dietary choline and carnitine, enhances atherogenic and pro-inflammatory pathways [14,15]. Elevated TMAO levels have been linked with vascular inflammation, platelet activation, and reduced nitric oxide bioavailability. Our results reinforce this biochemical risk profile and extend it by demonstrating autonomic involvement—specifically, an inverse correlation between TMAO and RMSSD, indicating reduced vagal modulation. Conversely, SCFAs—acetate, propionate, and butyrate—exert protective effects through anti-inflammatory signaling, improvement in insulin sensitivity, and maintenance of gut barrier integrity [18]. Butyrate, in particular, enhances vagal afferent signaling and activates G-protein–coupled receptors that influence autonomic and metabolic regulation. Our findings of higher butyrate levels correlating with improved HRV and lower blood pressure support this mechanism. Autonomic Dysfunction as a Mediator Heart rate variability (HRV) reflects dynamic autonomic control of the heart, and reduced parasympathetic indices (RMSSD, HF power) are well-established markers of cardiometabolic stress [16,17]. The Task Force of the European Society of Cardiology (ESC) and North American Society of Pacing and Electrophysiology has defined HRV as a sensitive early biomarker of autonomic imbalance preceding overt disease [9]. In our cohort, higher TMAO tertiles exhibited a 25–30 % reduction in RMSSD and HF power compared to lower tertiles, suggesting that microbial metabolites may influence cardiac autonomic regulation, either via systemic inflammation or neural gut–vagal signaling. Our mediation analysis demonstrated that RMSSD accounted for approximately 26 % of the total association between TMAO and the cardiometabolic risk z-score. This partial mediation implies that autonomic imbalance is one, but not the only, pathway linking gut-microbial metabolism to metabolic dysfunction. Other mechanisms, including low-grade inflammation, oxidative stress, or endothelial injury, likely operate in parallel. These findings resonate with population data where lower HRV predicts future type 2 diabetes and hypertension, independent of conventional risk factors [16,17]. South Asian Context and Dietary Patterns The Indian dietary pattern, characterized by high refined carbohydrate intake and relatively low fibre and fermented food consumption, may favor microbial taxa that generate higher TMAO and lower SCFAs [11]. Moreover, habitual low physical activity—as reflected by GPAQ metrics—further compounds autonomic imbalance and metabolic risk [10]. The ICMR-INDIAB study identified over 100 million Indians with diabetes and 136 million with prediabetes, underscoring the need for novel mechanistic insights for prevention [11]. Our findings position microbial-metabolite and autonomic markers as early, modifiable targets. Methodological Strengths and Analytical Rigor Strengths of this study include the integrative design, combining validated biochemical assays, standardized HRV measurements using Kubios HRV Premium software [13], and robust statistical adjustment for confounders such as age, BMI, and dietary fibre. The use of UPLC–MS/MS–based quantification for metabolites ensured analytical precision [14,15]. Mediation testing with the PROCESS macro for SPSS further strengthened causal inference within cross-sectional limitations [19]. Limitations Despite these strengths, several limitations must be acknowledged. First, the cross-sectional design restricts causal inference; it is plausible that autonomic dysfunction could itself alter gut physiology and microbial composition. Second, while we measured key metabolites (TMAO and SCFAs), we did not perform metagenomic profiling to identify specific microbial taxa. Third, residual dietary confounding is possible, as detailed nutrient data (choline, carnitine) were not captured quantitatively. Fourth, renal function, a determinant of TMAO clearance, though normal on average, may subtly modulate plasma levels [20]. Lastly, the modest sample size limits subgroup analyses by sex, BMI, or age strata. Clinical and Public Health Implications The convergence of microbial and autonomic pathways offers potential for new diagnostic and therapeutic strategies. Interventions enhancing SCFA production—via high-fibre diets, prebiotics, and probiotics—could simultaneously improve vagal activity and metabolic profiles [18]. Pharmacologic modulation of TMAO synthesis or hepatic oxidation may also have downstream benefits. Importantly, HRV monitoring provides a non-invasive window into systemic autonomic and metabolic health, suitable for early community screening in high-risk Indian adults. Future Directions Longitudinal and interventional studies are warranted to establish temporal relationships and causality. Integration of metagenomics, metabolomics, and neurophysiology could illuminate specific microbial species or metabolites influencing ANS pathways. Randomized dietary or probiotic trials assessing changes in SCFAs, HRV, and metabolic outcomes would validate the clinical relevance of this gut–autonomic–cardiometabolic axis.

In this cohort of Indian adults, elevated levels of the gut-microbiome-derived metabolite TMAO, along with lower concentrations of beneficial SCFAs, were significantly associated with autonomic imbalance (reduced vagal tone) and higher cardiometabolic risk. Autonomic function partially mediated the association between microbial metabolites and cardiometabolic burden, supporting a novel integrative axis of gut-microbiome → autonomic nervous system → cardiometabolic risk. These results underscore the importance of considering microbial-metabolic and neural networks in the early detection and prevention of cardiometabolic diseases in Indian populations. Future longitudinal and interventional studies should aim to confirm causality and explore therapies modulating microbial-metabolite profiles and autonomic regulation.

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