We performed a cross-sectional observational study in the Department of Physiology, Puducherry Teaching Hospital, India. The Institutional Ethical Committee approved the study protocol for human studies at JIPMER, Puducherry (JIP/IEC/2018/0301). A power analysis was performed before the study using OpenEpi, version 3 (www.OpenEpi.com). Accordingly, when the effect size was considered moderate based on the previous report with a difference of 0.54 between two independent means of the log-transformed ratio of low-frequency to high-frequency power (LF/HF) of HRV, the sample size obtained was 156 subjects with 78 in each group, which have resulted from 95% confidence and 90% power [20]. However, we enrolled 182 subjects, with 89 subjects in the control group and 93 subjects in the study group. Before collecting the data, we obtained written informed consent from all the participants. The subjects aged 18 to 65 years of either gender volunteered to participate in this study.

The study group comprises pre-MetS subjects recruited based on predetermined eligibility criteria set by National Cholesterol Education Program Adult Treatment Panel III guidelines. Pre-MetS was detected using two of the following five conditions [21]. Elevated waist circumference (WC) ≥ 90 cm in men and 80 cm in women (South-Asian criteria); hyperglycemia with fasting plasma glucose (FPG) ≥ 100 mg/dl (5.6 mM) or previously diagnosed with T2D or on medication for T2D; atherogenic dyslipidemia with serum triglyceride (TG) ≥ 150 mg/dl (1.7 mM) and high-density-lipoprotein cholesterol (HDL-c) ≤ 40 mg/dl (1.03 mM) in men, ≤ 50 mg/dl (1.29 mM) in women or on specific treatment for dyslipidemia; raised BP with systolic BP ≥ 130 mmHg or diastolic BP ≥ 85 mmHg or receiving antihypertensive medication. The control group comprises age-matched healthy participants having normal clinical and biochemical (blood glucose and lipid) profiles. Patients with cardiovascular, neurological, psychiatric, endocrine, and other significant co-morbidities or using medication for a chronic condition were excluded from the study during the screening procedure.

Subjects were instructed to report to the Department of Physiology on their scheduled appointment date fixed during the screening process. A standard questionnaire was directed to gather data on health status, personal and family history of chronic diseases (hypertension, T2D, and CVD), smoking and alcohol intake, medication, and lifestyle details. Anthropometric measurements were performed using the World Health Organisation (WHO) guidelines [22]. The body mass (weight in kg) was assessed using a digital weighing machine. Height was measured with a wall-mounted stadiometer. Body mass index (BMI) was calculated using Quetelet’s index [23].

We used non-stretchable tape to measure WC (cm) around the abdomen at the level of the umbilicus (belly button) between the lower edge of the last palpable rib and the top of the iliac crest. In contrast, hip circumference (HC in cm) was measured as the largest diameter around the buttocks in a standing position. The ratio of waist-hip (WHR) and waist-height (WHtR) were calculated. The percentage of body fat (BF%) was assessed using a body fat analyzing device (Quadscan 4000; Bodystat, Isle of Man, UK) with a bioelectrical impedance analysis approach. Participants were asked to remove any metals and lie supine on the couch. Then electrodes were connected to the hand and foot in a tetrapolar pattern, which operates because the electrical conductivity of fat-free tissue is far greater than the fat mass.

BP was measured in the right arm using an Omron automated BP device after 10 min of rest in a sitting posture (SEM-1; Omron, Kyoto, Japan). The same observer obtained a minimum of three recordings at 1-min intervals. An average of three BP readings was taken. Fasting blood samples were drawn by vein puncture and processed for biochemical analysis of FPG, total cholesterol (TC), HDL-c, and TG using commercial kits adapted to clinical chemistry autoanalyzer (Olympus 400; Beckman Coulter, Orlando, FL, USA). Friedwalds’s formula assessed low-density lipoprotein cholesterol (LDL-c) and very low-density lipoproteins (VLDL) [24]. Homeostatic model assessment of IR (HOMA-IR) and other lipid profile-derived parameters were calculated (TC/HDL-c, TG/HDL-c, LDL/HDL-c, non-HDL-c/HDL-c, and atherogenic index of plasma (AIP: log10[TG/HDL-c])). We used commercially available enzyme-linked immunosorbent assay (ELISA) kits and followed the manufacturer’s instructions to measure insulin, high-sensitive C reactive protein (hs-CRP), and adiponectin (Calbiotech, El Cajon, CA, USA).

The SPSS software program (version 22) was used for all statistical analyses (IBM Corp., Armonk, NY, USA). The Kolmogorov–Smirnov test was used to determine the normality of data. Non-normal distribution data were reported as median (interquartile range), whereas normal distribution data were reported as mean ± SD. Student’s t-test for continuous parametric data and Mann–Whitney U-test for nonparametric data were used to compare the control and test groups. We provided percentages and frequencies for categorical data, and the chi-square test (χ²) determined the difference between groups. A point-biserial correlation was used to measure the strength and direction of the association between continuous variables (cardiac AFT parameters) and dichotomous variables (presence and absence of each MetS component). We used Pearson’s correlation coefficient analysis to assess the degree of correlation between cardiometabolic variables. The correlation heatmaps were created using the “ggcorrplot” R package (R Studio windows version 4.2.1). Multiple linear regression analysis was used to investigate the independent contribution of WhtR, FPG, TG, HDL-c, RPP, and BF% to BRS, HRV, and AFT parameters. All analyses were two-tailed, and the study employed p < 0.05 as a level of statistical significance.

In addition to significantly elevated WC, which is already regarded as a component of MetS, the study found weight, BMI, HC, WHR, WHtR, and BF% were significantly higher in pre-MetS subjects signifying the presence of abdominal obesity, predisposing to CVD risk. Among the anthropometric parameters, WHR, particularly WHtR, has been reported to be linked with CVD deaths [31]. Notably, the significant association of WHtR with BRS, SDNN, RMSSD, TP, and E:I ratio in pre-MetS subjects suggests an early CAD (Fig. 2). Moreover, a substantial rise in BF% with a concomitant reduction in body lean% in pre-MetS subjects might suggest a dysfunctional energy metabolism involving glucose and lipids due to increased adiposity. The substantial relationship between BF% and BRS, SDNN, RMSSD, TP, and E:I ratio might imply that excess adiposity contributes to early CAD (Fig. 2).

The altered glycemic profile in pre-MetS subjects indicated an insulin-resistant condition with significantly higher FPG, insulin, and HOMA-IR (Table 2). Lipid profile abnormalities were observed in the pre-MetS group with markedly elevated TG, lipid to lipoprotein ratios, and lower HDL-c. The significant associations of FPG and HDL-c with the examined parameters of BRS, HRV, and AFTs and the independent contribution of FPG and HDL-c to altered BRS, HRV, and AFT parameters imply that the subjects in the pre-MetS group are at an increased CVD risk associated with CAD due to IR, and atherogenic dyslipidemia. Our recent findings support the theory that persistent low-grade inflammation and hypoadiponectinemia are vital in developing metabolic abnormalities in MetS patients [12]. Similarly, significantly higher hs-CRP and lower adiponectin levels in pre-MetS, as well as the association of hs-CRP with a few studied BRS, HRV, and AFT parameters, suggest an association of chronic inflammation with CAD in the current investigation.

Among the BPV parameters, BHR is the most direct and straightforward predictor of cardiac health. Low vagal potency and autonomic imbalance have been linked to a high resting HR [32]. Higher resting HR could indicate an imbalance in the heart’s sympathovagal control. Consequently, myocardial oxygenation and workload are determined by the RPP, and an increase in RPP has been identified as a CVD risk factor [26]. The pre-MetS group had a significant increase in BHR and RPP, along with systolic, diastolic, and mean arterial BP (SBP, DBP, and MAP), when compared to controls, indicating poor CV health and increased susceptibility to cardiac arrhythmias and other cardiovascular complications. Additionally, BRS, a strong determinant of cardiac mortality, was significantly lower in the pre-MetS group than in controls, confirming poor CV health and cardiac autonomic imbalance in these subjects. Pikkujämsä et al. [33] found impaired BRS and respiratory modulation of HR in hypertensive subjects with or without IR syndrome. These findings were backed up by our study findings in the pre-MetS group. In another study, Lindgren et al. [34] mentioned that BRS could precisely detect autonomic complications and disease prognosis and found a cardiovascular autonomic imbalance in MetS and IR subjects as a low BRS.

The spectral analysis of HRV is a robust approach for evaluating cardio-vagal modulation and the type and severity of CAD in both health and illness. To date, no data on cardio-vagal modulation in pre-MetS has been penned. In the current investigation, we detected a strong and consistent link between pre-MetS and reduced HRV in the form of changes in the time and frequency domain measurements. The TP of HRV represents entire autonomic activity and cardio-vagal regulation. Reduced TP has recently been linked with sudden CVD morbidity and mortality [15,35]. Consequently, the pre-MetS group in this study demonstrated a significant drop in the TP, presumably increasing the risk of unfavorable CVD events. The lower TP was complemented by a lower SDNN, which indicates a lower overall HRV. The LF power of HRV represents both sympathetic and parasympathetic modulation, with the cardiac sympathetic drive being the most prominent. The pre-MetS group had considerably higher LFnu than the controls, indicating sympathetic dominance (Table 3). Inhibition of vagal activity was also observed in the pre-MetS group by a substantial drop in the parasympathetic drive indicators, including HF power and HFnu. In the pre-MetS group, lower HF power and HFnu were accompanied by lower RMSSD (an indicator of parasympathetic activity), confirming inadequate cardiac vagal regulation. The LF/HF ratio shows the sympathovagal balance, and an increased LF/HF ratio suggests a sympathovagal imbalance. In the present study, the pre-MetS group had a much higher LF/HF ratio, indicating a considerable sympathovagal imbalance in these subjects. These findings are also consistent with the previous cross-sectional studies by Liao et al. [36], Min et al. [37], and Koskinen et al. [38]. They revealed that MetS and its components were linked to CAD with reduced parasympathetic and increased sympathetic activity [36-38].

Classical AFTs employ physiological exercises and noninvasively determine fast and dynamic changes in autonomic nervous activity. Parasympathetic function tests include HR response to deep breathing (E:I ratio) and standing (30:15 ratio), whereas sympathetic functions are assessed using the isometric handgrip (ΔDBP_IHG) [27,28]. Reduced parasympathetic reactivity was observed as a considerable drop in the E:I ratio and the 30:15 ratio in pre-MetS subjects. Furthermore, the higher ΔDBP_IHG in pre-MetS subjects suggests increased sympathetic reactivity. As a result of elevated sympathetic tone and reactivity and reduced vagal tone and reactivity in pre-MetS patients, the current study’s results may suggest CAD in these subjects. Endukuru et al. [12] and Keerthi et al. [39] reported similar findings and demonstrated impaired AFT parameters in MetS and prediabetes, respectively. Several multifaceted metabolic pathways involving glucotoxicity, lipotoxicity, altered insulin signaling, increased cytokine activity, and interstitial deposition of triacylglycerol result in increased glucose and free fatty acid circulation, increasing pancreatic insulin secretion, leading to increased sodium reabsorption, and enhanced sympathetic nervous system activity, which could underlie reduced BRS and lower HRV in pre-Met subjects [40]. Furthermore, pre-MetS subjects exhibit excess adiposity, atherogenic dyslipidemia, elevated BP, hypoadiponectinemia, and pro-inflammatory events, which may be linked to cardiac autonomic imbalance.

Despite the well-recognized link between CAD and pre-MetS, we assessed BRS, HRV, and AFT parameters in pre-MetS subjects stratified by the presence and absence of concurrent MetS components. In the present study, we observed a significant correlation of hyperglycemia with lower BRS, SDNN, RMSSD, E:I ratio, 30:15 ratio, and higher LF/HF ratio and ΔDBP_IHG in the pre-MetS subjects. We also observed significant differences in a few parameters of HRV in pre-MetS with abdominal obesity (Fig. 1). Surprisingly, hypertriglyceridemia was linked positively to TP of HRV and 30:15 ratio. However, no differences were observed in the other two MetS components (low HDL-c and elevated BP). Moreover, FPG levels could remain an independent predictor of reduced BRS, low HRV, and abnormal AFT parameters in pre-MetS subjects. Similar results were reported by Rasic et al. [41] and Balcioğlu et al. [42]. They investigated CAD in MetS and its components and found hyperglycemia as a reliable predictor for HRV [41,42]. Recent research suggested that hyperglycemia may cause CAD via increased formation of advanced glycation end products, endothelial dysfunction, and oxidative stress, all of which may lead to neuronal damage and subsequent autonomic impairment [43]. In addition, the association between hyperglycemia and CAD may be bidirectional. It has been suggested that CAD causes hyperglycemia via impaired insulin release by the pancreas, increased glucose production by the liver, impaired glucose uptake, and IR in skeletal muscles [43]. Hence, CAD might be a result of, as well as a precursor to hyperglycemia, and a vicious cycle of hyperglycemia and CAD may exist.

The noteworthy observation of the current study is that pre-MetS is asymptomatic and persists for a long time, exposing subjects to premature CVD risks before they fully develop MetS and T2DM. Thus, early diagnosis of pre-MetS and its associated CAD is of great clinical importance, leading to more intense monitoring of at-risk people. Our study has other strengths: we enrolled pre-MetS subjects with no major co-morbidities. We assessed numerous measures of cardiac autonomic modulation to evaluate CAD in pre-MetS. Additionally, we analyzed the impact of individual components of MetS on BRS, HRV, and AFT parameters in the pre-MetS group to find out which MetS component contributes more to CAD. However, there are several limitations in the study that must be addressed. First, the cross-sectional research design may not demonstrate the cause-effect relationship and bidirectional association between autonomic impairments and metabolic disturbances of MetS. In the future, well-designed longitudinal studies with robust measures are needed to examine these relationships over time. The sample size was modest, and we have not involved other inflammatory markers and adipokines contributing to CAD and CVD risks in pre-MetS.

In conclusion, reduced BRS, lower HRV, and altered AFT parameters in the pre-MetS group revealed an early CAD compared to controls. Thus, it appears that CAD begins early in the pre-MetS stage. These findings confirmed the hypothesis that early cardiac autonomic functional derangements are present in the initial stages of pre-MetS and associated with progressive deterioration before the development of MetS and T2DM. Hyperglycemia potentiates CAD with a sympathovagal imbalance in pre-MetS subjects. These findings may reveal that pre-MetS subjects with hyperglycemia are more predisposed to CAD and CVD risk. Consequently, early diagnosis of CAD and screening for the presence of MetS components should be equally important. Further research should demonstrate whether early treatment of CAD and MetS components can improve CVD outcomes.