Metabolic syndrome (MetS) is a complex clinical condition characterized by a cluster of interrelated metabolic abnormalities, including central obesity, hypertension, dyslipidemia, and insulin resistance, all of which contribute to an increased risk of cardiovascular disease (CVD) and type 2 diabetes mellitus (1,2). The global prevalence of MetS has risen significantly due to sedentary lifestyles and poor dietary habits, making it a major public health concern (3). Patients with MetS are at a substantially higher risk of developing cardiovascular events (CVEs) such as myocardial infarction, stroke, and heart failure compared to the general population (4).

Heart rate variability (HRV), a non-invasive measure of autonomic nervous system (ANS) function, has gained attention as a potential prognostic marker for cardiovascular outcomes (5). HRV reflects the balance between sympathetic and parasympathetic modulation of heart rate, and reduced HRV is indicative of autonomic dysfunction, which has been associated with adverse cardiovascular events and increased mortality (6,7). Several studies have demonstrated that impaired HRV parameters, such as reduced standard deviation of normal-to-normal intervals (SDNN) and altered low-frequency to high-frequency (LF/HF) ratio, are linked to higher cardiovascular risk in various patient populations (8,9).

In individuals with MetS, autonomic imbalance is commonly observed due to the cumulative effect of metabolic disturbances, which further exacerbates cardiovascular risk (10). Despite this association, HRV assessment is not yet widely integrated into routine clinical practice for risk stratification in MetS patients. Early identification of individuals at higher risk through HRV monitoring could provide an opportunity for timely intervention and prevention of major cardiovascular events (11).

This study aims to evaluate HRV as a predictive tool for cardiovascular events in patients with metabolic syndrome, focusing on identifying specific HRV parameters that could serve as independent risk indicators.

A prospective cohort study was conducted at a tertiary care centre for one year. A total of 150 patients diagnosed with metabolic syndrome (MetS) were enrolled based on the International Diabetes Federation (IDF) criteria, which include central obesity along with any two of the following: elevated blood pressure, raised fasting plasma glucose, high triglyceride levels, or reduced high-density lipoprotein cholesterol (HDL-C) levels.

Inclusion Criteria:

Patients aged 30–65 years with a confirmed diagnosis of MetS were included. All participants provided written informed consent prior to enrollment.

Exclusion Criteria:

Patients with known arrhythmias, existing cardiovascular disease, chronic kidney disease, thyroid dysfunction, or those on medications affecting heart rate variability (such as beta-blockers) were excluded from the study.

Heart Rate Variability Assessment:

HRV was recorded using a 24-hour ambulatory Holter ECG monitoring system (Model XYZ, Company Name). Participants were instructed to maintain their usual daily activities during the monitoring period. Data were analyzed according to the standards set by the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Both time-domain parameters (SDNN, RMSSD) and frequency-domain parameters (LF, HF, LF/HF ratio) were extracted for analysis.

Follow-Up and Outcome Measures:

Patients were followed for a period of 12 months through regular outpatient visits and telephonic interviews. The primary outcome was the occurrence of cardiovascular events, defined as non-fatal myocardial infarction, stroke, hospitalization due to heart failure, or cardiovascular-related death.

Statistical Analysis:

Data were analyzed using SPSS software version 25.0 (IBM Corp., Armonk, NY, USA). Continuous variables were expressed as mean ± standard deviation (SD), while categorical variables were presented as frequencies and percentages. Comparisons between groups (those with and without cardiovascular events) were performed using the independent t-test for continuous variables and the chi-square test for categorical variables. Cox proportional hazards regression analysis was used to determine the predictive value of HRV parameters for cardiovascular events. A p-value of <0.05 was considered statistically significant

A total of 150 patients diagnosed with metabolic syndrome were included in the study, with a mean age of 52.4 ± 8.6 years. Among these, 90 (60%) were male and 60 (40%) were female. During the 12-month follow-up period, 28 patients (18.7%) experienced cardiovascular events, including 15 cases of myocardial infarction, 8 strokes, and 5 hospitalizations due to heart failure.

Patients who experienced cardiovascular events showed significantly lower HRV values compared to those without events. The mean SDNN in the event group was 85.4 ± 12.3 ms, whereas it was 113.2 ± 14.8 ms in the non-event group (p < 0.001). Similarly, the LF/HF ratio was markedly higher in patients with cardiovascular events (3.1 ± 0.7) compared to those without events (2.0 ± 0.5), which was statistically significant (p = 0.002) (Table 1).

Cox proportional hazards regression analysis identified reduced SDNN (<100 ms) and elevated LF/HF ratio (>2.5) as independent predictors of cardiovascular events after adjusting for confounding factors such as age, gender, blood pressure, and fasting glucose levels (Table 2). The hazard ratio for SDNN was 2.9 (95% CI: 1.7–5.0; p = 0.001), and for LF/HF ratio, it was 2.5 (95% CI: 1.4–4.3; p = 0.003).

Table 1: Comparison of HRV Parameters between Patients with and Without Cardiovascular Events

Table 1 shows a significant reduction in both time-domain and frequency-domain HRV parameters among patients who experienced cardiovascular events.

Table 2: Cox Regression Analysis for Predictors of Cardiovascular Events

Table 2 indicates that reduced SDNN and elevated LF/HF ratio were significant independent predictors of cardiovascular events.

As shown in Table 1, patients with adverse cardiovascular outcomes had significantly impaired HRV profiles. Furthermore, multivariate analysis in Table 2 confirmed that autonomic dysfunction markers, particularly SDNN and LF/HF ratio, were strongly associated with increased cardiovascular risk in this population. Other metabolic factors did not show significant predictive value after adjustment.

This study investigated the prognostic value of heart rate variability (HRV) in predicting cardiovascular events among patients diagnosed with metabolic syndrome (MetS). The findings demonstrate a significant association between reduced HRV—particularly lower SDNN and higher LF/HF ratio—and increased incidence of cardiovascular events, supporting previous evidence that autonomic dysfunction plays a central role in cardiovascular risk stratification (1–3).

Metabolic syndrome, characterized by a combination of central obesity, hypertension, dyslipidemia, and insulin resistance, is a known precursor for cardiovascular disease and type 2 diabetes (4,5). These metabolic disturbances contribute to autonomic imbalance, manifesting as decreased parasympathetic and increased sympathetic activity, both of which negatively affect cardiac function and increase arrhythmic susceptibility (6,7). HRV analysis offers a non-invasive means of assessing autonomic nervous system function, and studies have shown that reduced HRV is associated with adverse cardiac outcomes in various populations (8,9).

In the current study, the mean SDNN value in patients who experienced cardiovascular events was significantly lower than in those without such events. SDNN is considered a robust time-domain index that reflects overall HRV, and its reduction has been linked to a higher risk of sudden cardiac death and arrhythmias (10). Likewise, a high LF/HF ratio—reflecting sympathetic dominance—was found to be a significant predictor of cardiovascular events. Similar trends have been observed in earlier studies involving patients with myocardial infarction, heart failure, and diabetes (11,12).

Interestingly, other conventional risk factors such as systolic blood pressure and fasting glucose levels did not retain statistical significance in multivariate analysis, suggesting that HRV may offer additional predictive value beyond traditional risk markers. This observation aligns with earlier reports highlighting the independent predictive utility of HRV for cardiovascular mortality and morbidity (13,14).

Moreover, the clinical relevance of incorporating HRV assessment into routine evaluation of MetS patients is underscored by its simplicity and non-invasive nature. The ability to identify high-risk individuals based on HRV allows for the implementation of early preventive strategies such as lifestyle modifications, stress management, and pharmacologic interventions to restore autonomic balance (15).

Despite the strengths of this study, including a prospective design and standardized HRV measurement, some limitations must be acknowledged. The sample size, while adequate, limits broader generalizability. In addition, HRV can be influenced by factors such as sleep quality, physical activity, and emotional stress, which were not fully controlled. Future studies with larger, more diverse populations and longer follow-up periods are warranted to validate these findings.

In conclusion, HRV parameters—especially SDNN and LF/HF ratio—serve as independent predictors of cardiovascular events in patients with metabolic syndrome. Incorporating HRV assessment into clinical practice may enhance early identification of at-risk individuals and improve preventive cardiovascular care.

1. Palatini P. Elevated heart rate as a predictor of increased cardiovascular morbidity. J Hypertens Suppl. 1999 Aug;17(3):S3-10. PMID: 10489092.
2. Palatini P, Julius S. Association of tachycardia with morbidity and mortality: pathophysiological considerations. J Hum Hypertens. 1997 Aug;11 Suppl 1:S19-27. PMID: 9321736.
3. Palatini P, Julius S. The physiological determinants and risk correlations of elevated heart rate. Am J Hypertens. 1999 Jan;12(1 Pt 2):3S-8S. PMID: 10077413.
4. Palatini P. Heart rate as a risk factor for atherosclerosis and cardiovascular mortality: the effect of antihypertensive drugs. Drugs. 1999 May;57(5):713-24. doi: 10.2165/00003495-199957050-00004. PMID: 10353296.
5. Sclavo M. [Cardiovascular risk factors and prevention in women: similarities and differences]. Ital Heart J Suppl. 2001 Feb;2(2):125-41. PMID: 11255880. Italian.
6. Julius S, Palatini P, Nesbitt SD. Tachycardia: an important determinant of coronary risk in hypertension. J Hypertens Suppl. 1998 Jan;16(1):S9-15. PMID: 9534091.
7. Kjeldsen SE, Rostrup M, Moan A, Mundal HH, Gjesdal K, Eide IK. The sympathetic nervous system may modulate the metabolic cardiovascular syndrome in essential hypertension. J Cardiovasc Pharmacol. 1992;20 Suppl 8:S32-9. PMID: 1283768.
8. Julius S. Effect of sympathetic overactivity on cardiovascular prognosis in hypertension. Eur Heart J. 1998 Jun;19 Suppl F:F14-8. PMID: 9651730.
9. Bounhoure JP, Galinier M, Didier A, Leophonte P. [Sleep apnea syndromes and cardiovascular disease]. Bull Acad Natl Med. 2005 Mar;189(3):445-59; discussion 460-4. PMID: 16149210. French.
10. Tanchoco CC, Cruz AJ, Duante CA, Litonjua AD. Prevalence of metabolic syndrome among Filipino adults aged 20 years and over. Asia Pac J Clin Nutr. 2003;12(3):271-6. PMID: 14505989.
11. Palatini P, Julius S. Relevance of heart rate as a risk factor in hypertension. Curr Hypertens Rep. 1999 Jun;1(3):219-24. doi: 10.1007/s11906-999-0024-7. PMID: 10981069.
12. Bigazzi R, Bianchi S, Batini V, Guzzo D, Campese VM. Metabolic risk factors and markers of cardiovascular and renal damage in overweight subjects. Am J Hypertens. 2006 Apr;19(4):426-31. doi: 10.1016/j.amjhyper.2005.10.002. PMID: 16580581.
13. Fogari R, Zoppi A, Marasi G, Preti P, Mugellini A, Vanasia A, et al. The epidemiology of resting heart rate in a male working population: association with blood pressure, age, smoking habits and other cardiovascular risk factors. J Cardiovasc Risk. 1997 Jun;4(3):209-13. PMID: 9475676.
14. Gupta R, Sarna M, Thanvi J, Rastogi P, Kaul V, Gupta VP. High prevalence of multiple coronary risk factors in Punjabi Bhatia community: Jaipur Heart Watch-3. Indian Heart J. 2004 Nov-Dec;56(6):646-52. PMID: 15751521.
15. Fagard RH. Exercise is good for your blood pressure: effects of endurance training and resistance training. Clin Exp Pharmacol Physiol. 2006 Sep;33(9):853-6. doi: 10.1111/j.1440-1681.2006.04453.x. PMID: 16922820.