Korean J Physiol Pharmacol.  2023 Jan;27(1):49-59. 10.4196/kjpp.2023.27.1.49.

Association between metabolic syndrome components and cardiac autonomic modulation in southern Indian adults with pre-metabolic syndrome: hyperglycemia is the major contributing factor

  • 1Department of Physiology, Sri Siddhartha Institute of Medical Sciences & Research Centre, Bangalore 562123, India
  • 2Department of Physiology, Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Puducherry 605008, India
  • 3Department of Endocrinology, Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Puducherry 605008, India
  • 4Department of Biochemistry, Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Puducherry 605008, India
  • 5Department of Dermatology, Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Puducherry 605008, India


Metabolic syndrome (MetS) involves multi-factorial conditions linked to an elevated risk of type 2 diabetes mellitus and cardiovascular disease. Pre-metabolic syndrome (pre-MetS) possesses two MetS components but does not meet the MetS diagnostic criteria. Although cardiac autonomic derangements are evident in MetS, there is little information on their status in pre-MetS subjects. In this study, we sought to examine cardiac autonomic functions in pre-MetS and to determine which MetS component is more responsible for impaired cardiac autonomic functions. A total of 182 subjects were recruited and divided into healthy controls (n=89) and pre-MetS subjects (n=93) based on inclusion and exclusion criteria. We performed biochemical profiles on fasting blood samples to detect pre-MetS. Using standardized protocols, we evaluated anthropometric data, body composition, baroreflex sensitivity (BRS), heart rate variability (HRV), and autonomic function tests (AFTs). We further examined these parameters in pre-MetS subjects for each MetS component. Compared to healthy controls, we observed a significant cardiac autonomic dysfunction (CAD) through reduced BRS, lower overall HRV, and altered AFT parameters in pre-MetS subjects, accompanied by markedly varied anthropometric, clinical and biochemical parameters. Furthermore, all examined BRS, HRV, and AFT parameters exhibited an abnormal trend and significant correlation toward hyperglycemia. This study demonstrates CAD in pre-MetS subjects with reduced BRS, lower overall HRV, and altered AFT parameters. Hyperglycemia was considered an independent determinant of alterations in all the examined BRS, HRV, and AFT parameters. Thus, hyperglycemia may contribute to CAD in pre-MetS subjects before progressing to MetS.


Autonomic nervous system; Cardiovascular diseases; Heart rate physiology; Hyperglycemia; Metabolic diseases; Metabolic syndrome


  • Fig. 1 Graph illustrating the correlation between cardiac autonomic function test (CAFT) parameters and each component of metabolic syndrome (MetS) in pre-MetS subjects (n = 93). We created the correlation heatmap using the R package “ggcorrplot”. The color gradients display pairwise correlation measured by Pearson’s correlation coefficient (r values). In a total of 93 pre-MetS subjects, there were 31 subjects with abdominal obesity, 56 subjects with hyperglycemia, 26 subjects with hypertriglyceridemia, 36 subjects with low HDL-c, and 24 subjects with elevated BP. A cross represents an absence of statistical significance (p-value > 0.05). The correlation is significant at 0.05, 0.01, and 0.001. For instance, hyperglycemia was statistically correlated with BRS, SDNN, RMSSD, TP, LF:HF, E:I ratio, 30:15 ratio, and DBPIHG. HDL-c, high-density-lipoprotein cholesterol; BP, blood pressure; BRS, baroreflex sensitivity; SDNN, standard deviation of normal to normal interval; RMSSD, square root of the mean squared differences of successive normal to normal intervals; TP, total power; LF, low-frequency power; HF, high-frequency power; E:I ratio, a ratio of maximum RR interval during expiration to minimum RR interval during inspiration following deep breathing; DBPIHG, diastolic BP above baseline following sustained handgrip.

  • Fig. 2 Correlation matrix visualization depicting the correlation between cardiometabolic parameters in pre-MetS subjects (n = 93). We created the correlation heatmap using the R package “ggcorrplot”. The color gradient indicates Pearson’s correlation coefficients (r values) based on pairwise correlations. Crosses indicate a lack of statistical significance (p-value > 0.05). Correlation is significant at 0.05, 0.01, and 0.001. A statistically significant correlation was seen, for instance, between BRS, SDNN, RMSSD, TP, LF:HF, E:I ratio, 30:15 ratio, DBPIHG, insulin, HOMA-IR, BF%, hs-CRP, and FPG. MetS, metabolic syndrome; BRS, baroreflex sensitivity; SDNN, standard deviation of normal to normal interval; RMSSD, square root of the mean squared differences of successive normal to normal intervals; TP, total power; LF, low-frequency power; HF, high-frequency power; E:I ratio, a ratio of maximum RR interval during expiration to minimum RR interval during inspiration following deep breathing; DBPIHG, diastolic BP above baseline following sustained handgrip; BMI, body mass index; WHR, ratio of waist-hip; WHtR, ratio of waist-height; FPG, fasting plasma glucose; HOMA-IR, homeostatic model assessment of insulin resistance; TG, triglyceride; HDL-c, high-density-lipoprotein cholesterol; AIP, atherogenic index of plasma; MAP, mean arterial pressure; RPP, rate pressure product; BF%, percentage of body fat; hs-CRP, high-sensitive C reactive protein.


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