Abstract
Metabolic syndrome (MetS) has been associated with chronic damage to the cardiovascular system. This study aimed to evaluate early stage cardiac autonomic dysfunction with electrocardiography (ECG)-based measures in MetS subjects. During 2012–2013, 175 subjects with MetS and 226 healthy controls underwent ECG recordings of at least 4 hours starting in the morning with ambulatory one-lead ECG monitors. MetS was diagnosed using the criteria defined in the Adult Treatment Panel III, with a modification of waist circumference for Asians. Conventional heart rate variability (HRV) analysis, and complexity index (CI1–20) calculated from 20 scales of entropy (multiscale entropy, MSE), were compared between subjects with MetS and controls. Compared with the healthy controls, subjects with MetS had significantly reduced HRV, including SDNN and pNN20 in time domain, VLF, LF and HF in frequency domain, as well as SD2 in Poincaré analysis. MetS subjects have significantly lower complexity index (CI1–20) than healthy subjects (1.69 ± 0.18 vs. 1.77 ± 0.12, p < 0.001). MetS severity was inversely associated with the CI1–20 (r = −0.27, p < 0.001). MetS is associated with significant alterations in heart rate dynamics, including HRV and complexity.
Similar content being viewed by others
Introduction
Metabolic syndrome (MetS), also known as the cardiometabolic syndrome, is a medical disorder that consists of a complex combination of abdominal obesity, hypertension, impaired glucose tolerance and dyslipidemia1,2. With those interrelated risk factors, MetS causes chronic damage to the cardiovascular system and thus is strongly linked with incident cardiovascular diseases (CVD), diabetes, and related mortality and morbidity. It is estimated that MetS affects 20–25% of adults in general population3. It has also been predicted that the incidence and prevalence of MetS will keep increasing4,5,6,7,8, imposing an inevitable and profound impact on global healthcare systems9. Compared with healthy population, people with MetS have a five-fold greater risk of developing type 2 diabetes10, twice as likely to develop CVD11, three times as likely to have a heart attack or stroke9,12. The more components of the MetS that are evident, the higher is the cardiovascular mortality rate13.
Reciprocal reinforcement of insulin resistance14,15,16 and sympathetic activity17,18,19 plays an important role in the pathophysiology of cardiac dysfunction. Cardiac autonomic function can be evaluated non-invasively with ECG-based measurements. Heart rate variability (HRV), including time domain, frequency domain and non-linear analysis, is one of the most frequently studied measurements with its predictive power20,21,22. In recent years, system complexity gradually becomes a more established theory to evaluate health. Decrease of complexity has shown to be a common sign of pathological conditions or aging23,24,25. Healthy physiologic function represents the body’s capacity to adapt to ever changing stresses by complex interactions between multiple control systems, feedback loops, and regulatory processes that operate over multiple scales of time and space25. Entropy measurement is considered an important index for complexity. Therefore, multiscale entropy (MSE)26,27,28 has been widely used to evaluate human health conditions on a system level, but has not yet been applied in subjects with MetS.
We hypothesized that subjects with MetS may have early cardiac autonomic dysfunction in terms of alterations of heart rate dynamics, which may further lead to significant cardiovascular comorbidities and complications. To test the hypothesis, we prospectively recruited subjects from a Chinese general population, who attended periodic health check-ups in our institute. All subjects have undergone a comprehensive evaluation for components of MetS, as well as an ambulatory ECG monitoring. We compared various ECG-derived measurements of cardiac autonomic functions between subjects with and without MetS. Furthermore, we tested the clinical utility of complexity index in the evaluation of the presence and the severity of MetS in the general population.
Results
Subjects and demographics
From Jan 2012 to June 2013, 175 subjects with MetS and 226 healthy subjects were identified and included in the final analysis (Table 1). Since age and gender were different in these two groups, comparisons of various outcome measures were adjusted for age and gender. Comparing with healthy subjects, subjects with MetS had significantly higher body mass index (BMI), waist circumference (WC), body fat percentage and blood pressure. In addition, more subjects with MetS had the comorbidities of sleep apnea. No significant difference was found in terms of mental status for the two groups. In terms of biometric parameters, significant difference presented on triglycerides, high density lipoprotein, both fasting and 2-hr postprandial blood glucose, hemoglobin A1c, high-sensitivity C-reactive protein and hemoglobin, while total cholesterol, low density lipoprotein and total protein showed no significant difference between groups.
Time and frequency domain heart rate variability analyses
In time domain analysis of HRV (Table 2), subjects with MetS have faster heart rates and shorter heart beat intervals, after controlling for age and gender. Significant differences were also seen in SDNN and pNN20 before or after adjustment. No difference was seen in rMSSD and pNN50 even after adjustment. In frequency domain analysis (Table 2), decreased HRV was seen in MetS subjects, with significantly lower power in VLF, LF and HF bands. Poincaré analysis showed a significant difference in SD2 with or without adjustments. Further regression analysis adjusting for various confounding variables, including medication, mental status and sleep apnea, still confirmed the impact of MetS on various HRV parameters (Table 2).
Multiscale entropy for complexity analysis
Complexity index (CI1–20) in MetS subjects was significantly lower than those in healthy subjects (1.69 ± 0.18 vs 1.77 ± 0.12, p < 0.001), and the difference remained significant even after adjustment for age and gender (Table 2). When investigated in different scales, MetS subjects had significantly lower entropies than healthy subjects in all scales (Fig. 1).
MetS severity and heart rate dynamics
Following the definition of metabolic syndrome, all subjects were classified according to the numbers of metabolic derangement, as having 0, 1–2, 3–4, and 5 MetS components, which can be considered as a spectrum from healthy to severe MetS. As shown in Fig. 2, for daytime heart rate dynamics in time and frequency domain, mean NN, nLF, nHF and LF/HF did not show any difference with the severity of MetS, but SDNN and pNN20 showed significant difference in subjects with 3 or more MetS components. For SD1 in Poincaré analysis, only subjects with 5 MetS components showed significant difference, but not between other subgroups. SD2 is comparatively more sensitive than SD1. For complexity, subjects with 3 or more MetS components had significantly lower complexity index (CI1–20) than subjects with no or 1–2 MetS components.
MetS severity and Complexity
As shown in Fig. 3, complexity index (CI1–20) was negatively correlated to MetS Score (Pearson’s correlation, r = −0.27, p < 0.001), suggesting that as the severity of metabolic derangement went up, the health level, as indicated by the complexity index, decreased.
Discussion
This prospective study included healthy individuals and subjects with MetS from periodic health examinations and obtained comprehensive biometric measurements and metabolic profiles for advanced analysis. From daytime ECG characteristics, subjects with Mets had significantly reduced SDNN and pNN20 for HRV time domain, VLF, LF and HF in frequency domain, as well as SD2 in Poincaré analysis. Complexity index (CI1–20) in subjects with MetS was significantly lower than healthy subjects. These findings indicate reduced heart rate variability and lower complexity in MetS group than healthy group. Additional analysis also showed that when subjects had more MetS components, ECG-based heart rate dynamic characters changed significantly in Poincaré SD2 and nonlinear analysis by MSE complexity index, in addition to conventional linear parameters.
Heart rate is intricately regulated by complex interactions of multiple mechanisms, including sympathetic and parasympathetic nervous system, as well as hormonal homeostasis. Cardiac autonomic function is commonly measured non-invasively with HRV, and altered sympathovagal balance can be inferred by both short-term and long-term HRV29,30. Given the potential mechanism underlying the development of MetS and its major cardiovascular complications, HRV is well recognized for its predictive power. Several HRV parameters have been developed, among them, time and frequency domain measures of HRV were the most commonly used31. In a systemic review of 14 studies examining the associations between HRV parameters and MetS, SDNN was the only conventional HRV parameter that was consistently reduced in all 14 studies when one or more risk factors were present compared to zero MetS components31. However, in the present study, several conventional parameters (pNN20, LnLF, LnHF) were able to distinguish these populations. Such discrepancy may be due to the heterogeneity in ECG recording duration, study population, accountable variables, body position during ECG recording, and HRV analysis methods among different studies. Methods from nonlinear dynamics have shed new insights into HRV changes under various physiological and pathological conditions, providing additional prognostic information and complementing conventional time and frequency domain analyses. Reduced entropy values have been observed in diabetic patients in comparison with control group32,33. Khandoker et al. further demonstrated that, as compared to conventional HRV indices and Poincaré plot parameters, entropy measure was able to better distinguish diabetic patients with cardiac autonomic neuropathy from the diabetic patient without cardiac autonomic neuropathy34. Our present study confirmed the altered cardiac autonomic function in the MetS group with conventional HRV time and frequency domain measures. Complexity index, derived from the MSE analysis, and pNN20 provided the best discrimination between the groups, followed by SDNN, LnLF, LnHF and SD2.
The clustering of various cardiovascular risks referred to as the metabolic syndrome have led to the fact that patients with cardiovascular diseases often have one or more MetS components or undetected diabetes mellitus9. Cardiac dynamic alterations are associated with increase cardiovascular risk profile such as insulin-resistance, endothelial dysfunction, arterial stiffening, cardiac hypertrophy, and sympathetic activation35. Results from previous studies have shown that metabolic syndrome factors by themselves, or in any combination, portend cardiovascular disease and many other adverse outcomes36. The underlying mechanism of MetS and associated cardiac alternation remains unclear. Our previous studies have identified overactivated sympathetic nervous system, as assessed by HRV analysis, in patients with nonalcoholic fatty liver disease (NAFLD), which was also commonly observed in patients with MetS37. This association was independent of leptin or subclinical inflammation.
Evidence suggests that both lifestyle and pharmacological interventions can reverse MetS38. Metabolic syndrome is conventionally managed by both pharmacological and non-pharmacological approaches39, targeting specific core disorders such as obesity, hypertension40,41,42 and hyperlipidaemia. Therefore, since the prevalence of MetS has increased remarkably worldwide, early detection of minute cardiac alternations and early intervention may help to prevent or alleviate the late and more severe cardiovascular complications as a result of MetS in general population. Ambulatory ECG monitoring is easy, accessible, non-invasive, and heart rate based methods are relatively mature techniques for this purpose. In addition, such approach is ideal for the dynamic monitoring of intervention response at multiple times. Conventional or nonlinear methods for heart rate dynamics are feasible as cost-effective approaches for metabolic syndrome. Further interventional studies with exercise or weight loss to modify the severity of metabolic syndrome and/or cardiac autonomic dysfunction may help to elucidate the temporal relationship between the metabolic syndrome and cardiovascular complications.
Limitations
There are limitations in this study. First, this study was cross-sectional in design, the actual causality between MetS and cardiac autonomic dysfunction could be questioned. In addition, since this study was not specifically designed to investigate cardiovascular damage or cardiac dysfunctions in people with metabolic syndrome, we do not have long-term follow up data yet available. Further longitudinal study is warranted to investigate the impact of heart rate dynamic alterations and long-term health outcomes. Second, we collected at least 4-h ECG recordings during daytime for the present HRV analysis. However, differences in data collection, including the body position, leisure activity, and length of ECG recordings, could affect the HRV analysis and interpretation31. Further studies to compare measures during day time wakefulness with the same parameters acquired during sleep, when external influences are minimized, may help to address this important issue. Third, since insulin resistance has been regarded as the underlying pathophysiology of metabolic syndrome, some factors including plasma norepinephrine levels, various adipocytokine levels, fasting insulin, homeostatic model assessment – insulin resistance (HOMA-IR), and unreported medication use were not measured in this study. The possible confounding effects of these factors cannot be totally excluded.
Conclusion
MetS is significantly associated with alterations in heart rate dynamics. Compared with conventional time and frequency domain HRV measures, Poincaré SD2 analysis and complexity index (derived from MSE) are more sensitive in distinguishing the alterations caused by MetS. Since ambulatory ECG monitoring is readily available and feasible in our clinical practice, large-scale screening to detect early stage cardiac dysfunction may help to prevent or alleviate various late cardiovascular complications.
Methods
Materials and Study Design
This prospective study recruited subjects aged equal to or greater than 20 years from a routine health check-ups program in the Health Management Center of National Taiwan University Hospital, starting from January 2012. Attendees of the health check-up in our institute were recruited through advertising messages for health-promotion purposes from the general population and therefore the participants did not belong to any particular socio-economic class or share a unifying form of employment. Subjects with atrial fibrillation, use of ventricular pacing, severe comorbidities, such as congestive heart failure, symptomatic coronary heart disease, uncontrolled pulmonary disease, chronic renal failure, or pregnancy were excluded from the study. Data of medical history, including sleep apnea, was recorded Mental status was evaluated with a validated questionnaire, the five-item Brief Symptom Rating Scale (BSRS-5)43 and interviewed by clinicians to approve the eligibility. Information of current use of important medications, including anti-hypertensive agents, hypoglycemic agents and anxiolytics/hypnotics was also comprehensively collected. This study was approved by the ethical committee of National Taiwan University Hospital (No. 201006037R), and we confirm that all experiments were performed in accordance with relevant guidelines and regulations. All subjects have provided written informed consent prior to participating in the study.
The standard protocol of our health check-up program consisted of a self-administered questionnaire, face-to-face interview by an internal medicine physician, physical examination, blood biochemical analysis, and various radiology and gastrointestinal endoscopy exams44,45. Therefore, analytical data were obtained from this health examination database, with recordings of demography/anthropometry, medical history, medication use, dieting, smoking, alcohol, and level of physical activities. BMI was calculated as weight (kg) divided by height squared (m2). Waist circumference was measured at the level of the umbilicus at minimal respiration. Blood pressures were measured at 8 am before taking any medication, and subjects were in the sitting position after sat quietly for 10 min. Systolic and diastolic blood pressures (SBP and DBP) were measured at bilateral upper arms and bilateral thighs, and the reported SBP and DBP in this article were both from upper right arm. Subjects were instructed to fast for at least 10 hours and avoid smoking, alcohol, coffee, and tea on the day of examinations. Comprehensive biometric tests included 110 biomarkers or parameters (i.e., white blood cell count, hemoglobin concentration, fasting blood glucose, high-density lipoprotein cholesterol, triglycerides, uric acid, creatinine, aspartate aminotransferase, and alanine aminotransferase, etc.). The laboratory tests have both internal and external quality control procedures accredited by the Taiwan Society of Laboratory Medicine twice a year.
ECG recordings were collected by an FDA approved ambulatory electrocardiogram monitor (DynaDx Corporation, Taipei, Taiwan) with a computer-based data-acquisition system. The ECG recording equipment was an one-lead Holter device that could record ECG for over 24 hours. All subjects were monitored at home one week after they finished their routine health check-ups to avoid interference. Two long-term ECG recordings were collected during daytime and sleep respectively. All sleep related analysis will be elaborated in another paper46. During daytime, all subjects were instructed to wear the device for at least 4 hours and to avoid exercise and naps during recordings. Sampling rate of ECG monitoring was 200 Hz. All ECG recordings were carefully checked with noise level, artifacts, R peak detection and ectopic beats. Data was discarded if less than 4 hours or low quality, or cut if longer than 4 hours.
Definition of metabolic syndrome
Subjects with MetS were defined by the criteria defined in the Adult Treatment Panel III, with a modification of waist circumference as appropriate for Asians47, and was also proposed by the Taiwan National Health Bureau. The five metabolic syndrome characteristic components are: 1) abdominal obesity, defined as WC ≥ 90 cm (in male) or ≥80 cm (in female); 2) elevated blood pressure, measured as SBP ≥ 130 mmHg and/or DBP ≥ 85 mmHg or taking blood pressure-lowering medications; 3) hyperglycemia, fasting blood glucose ≥100 mg/dL (5.6 mmol/L) or taking hypoglycemic medications; 4) hypertriglyceridemia: fasting Triglycerides (TG) ≥150 mg/dL (1.69 mmol/L); and 5) high density lipoprotein (HDL) < 40 mg/dl (in male) or <50 mg/dl (in female). Individuals who were using antidiabetic or antihypertensive therapy were treated as those who met the criteria for high fasting glucose level or high blood pressure. When three of the five listed characteristics were present, a diagnosis of metabolic syndrome was made. Healthy subjects were screened by all past history and were determined as absence of any abnormality of biometric markers, or if they have less than three metabolic syndrome components.
Heart rate variability (HRV)
Based on non-invasive ECG recordings, heart rate variability (HRV) is a widely used method for assessing activity of the cardiac autonomic nervous system. R-peaks were detected from ECG, and the RR intervals (RRI) were defined as the time intervals between consecutive R peaks. Normal heart beat from the ECG recordings were automatically detected by commercial software (DynaDx Corporation, Taipei, Taiwan) and verified by visual inspections. Ectopic beats were identified and excluded from calculations. Recordings with artifacts or arrhythmias comprising more than 5% of the total epoch were discarded. Thus, normal-to-normal (NN) intervals were extracted for complete HRV analysis by time domain, frequency domain and non-linear analysis. In time domain, mean heart rate (HR), mean of NN intervals (mean NN), standard deviation of NN (SDNN), square root of the mean of the squares of successive N-N interval differences (rMSSD), percentage of heart period differences >20 ms (pNN20) and >50 ms (pNN50) were included. All time domain HRV measurements were analyzed based on the 4-hours ECG recordings. In frequency domain, NN intervals were interpolated and resampled to 4 Hz for HRV frequency domain analysis. The Welch protocol (with a Hamming window applied to each 5 minute segment) was used for spectral analysis. HRV power spectrum measurements were log-transformed to normalize their distribution for analysis. Normalized percentage of LF and HF was defined as nLF = LF/(LF + HF) and nHF = HF/(LF + HF) respectively. Ratio of low frequency over high frequency (LF/HF) was selected to indicate autonomic balance. For non-linear dynamics, Poincaré plot as the two-dimensional reconstructed RR interval phase-spaces was chosen to describe the dynamics of the cardiac system, and multiscale entropy (MSE) was used to analyze the heart rate dynamic complexity.
Multiscale Entropy (MSE)
MSE analysis was first proposed to evaluate the complexity of physiologic time series, and was well-recognized as a way to assess human health conditions in many studies26,27,48,49,50,51. In human health, decrease of complexity is a common sign of pathological conditions or aging23. The MSE applies SampEn (sample entropy) analysis to measure the degree of irregularity of the time series, and SampEn requires the time series being studied to be stationary. Therefore, the retrieved NN intervals were first detrended by Ensemble Empirical Mode Decomposition (EEMD)52,53,54, and the long-term overall trend was removed to improve the stationarity of the time series, and thus the accuracy of entropy calculation. In this study, MSE analysis included 20 scales, and the mean of entropies on all 20 scales was calculated as a complexity index (CI1–20).
MetS Score
Since MetS is characterized by concomitant derangements in multiple factors, MetS Score was proposed by a multiethnic cohort study (6780 subjects)55. MetS Score = −11.8769 + (1.5432298 * Log Glucose) + (0.7872732 * Log Triglyceride) − (1.588791 * Log HDL) + (0.0277125 * WC) + (0.0232299 * SBP) + (0.0420722 * DBP) − (0.016408 * Age) − (0.73821 * Gender). For gender in the formula, male = 1 and female = 0. MetS Score is a continuous measure of MetS severity and is proposed as a better predictor of cardiovascular events overall and in individual ethnicities55.
Softwares and Statistical Analyses
MATLAB R2012a (The MathWorks, Inc.) was used for data processing and analysis programming. SPSS 19.0 (IBM SPSS Statistics) was used for statistical analyses. Descriptive statistics were reported as mean ± standard deviation for continuous data, and number (percentage) for categorical data. Comparisons of categorical variables were made using the chi-squared or Fisher’s exact test, where appropriate. Comparisons of continues variables were assessed by t-test or non-parametric test (Mann-Whitney U), where appropriate. Linear and logistic regression models were constructed in sequential models, adjusted for age and gender. A p value < 0.05 was considered statistically significant.
Additional Information
How to cite this article: Ma, Y. et al. Cardiac Autonomic Alteration and Metabolic Syndrome: An Ambulatory ECG-based Study in A General Population. Sci. Rep. 7, 44363; doi: 10.1038/srep44363 (2017).
Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
Alberti, K. G. et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 120, 1640–1645 (2009).
Grundy, S. M., Brewer, H. B. Jr., Cleeman, J. I., Smith, S. C. Jr. & Lenfant, C. Definition of metabolic syndrome: Report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Circulation 109, 433–438 (2004).
Alberti, G., Zimmet, P., Shaw, J. & Grundy, S. M. The IDF Consensus Worldwide Definition of the Metabolic Syndrome. Brussels: International Diabetes Federation. 1–23 (2006).
de Ferranti, S. D. et al. Prevalence of the metabolic syndrome in American adolescents: findings from the Third National Health and Nutrition Examination Survey. Circulation 110, 2494–2497 (2004).
Duncan, G. E., Li, S. M. & Zhou, X. H. Prevalence and trends of a metabolic syndrome phenotype among u.s. Adolescents, 1999-2000. Diabetes care 27, 2438–2443 (2004).
Ford, E. S. & Giles, W. H. A comparison of the prevalence of the metabolic syndrome using two proposed definitions. Diabetes care 26, 575–581 (2003).
Ford, E. S., Giles, W. H. & Dietz, W. H. Prevalence of the metabolic syndrome among US adults: findings from the third National Health and Nutrition Examination Survey. Jama 287, 356–359 (2002).
Meigs, J. B. et al. Prevalence and characteristics of the metabolic syndrome in the San Antonio Heart and Framingham Offspring Studies. Diabetes 52, 2160–2167 (2003).
Ryden, L. et al. ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD: the Task Force on diabetes, pre-diabetes, and cardiovascular diseases of the European Society of Cardiology (ESC) and developed in collaboration with the European Association for the Study of Diabetes (EASD). European heart journal 34, 3035–3087 (2013).
Lorenzo, C., Okoloise, M., Williams, K., Stern, M. P. & Haffner, S. M. The metabolic syndrome as predictor of type 2 diabetes: the San Antonio heart study. Diabetes care 26, 3153–3159 (2003).
Mottillo, S. et al. The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis. Journal of the American College of Cardiology 56, 1113–1132 (2010).
Lakka, H. M. et al. The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. Jama 288, 2709–2716 (2002).
Hu, G. et al. Prevalence of the metabolic syndrome and its relation to all-cause and cardiovascular mortality in nondiabetic European men and women. Archives of internal medicine 164, 1066–1076 (2004).
de Kreutzenberg, S. V. et al. Downregulation of the longevity-associated protein sirtuin 1 in insulin resistance and metabolic syndrome: potential biochemical mechanisms. Diabetes 59, 1006–1015 (2010).
Gallagher, E. J., Leroith, D. & Karnieli, E. The metabolic syndrome–from insulin resistance to obesity and diabetes. The Medical clinics of North America 95, 855–873 (2011).
Rutter, M. K., Meigs, J. B., Sullivan, L. M., D’Agostino, R. B. Sr. & Wilson, P. W. Insulin resistance, the metabolic syndrome, and incident cardiovascular events in the Framingham Offspring Study. Diabetes 54, 3252–3257 (2005).
Canale, M. P. et al. Obesity-related metabolic syndrome: mechanisms of sympathetic overactivity. International journal of endocrinology 2013 (2013).
Limberg, J., Morgan, B. & Schrage, W. Mechanical and metabolic reflex activation of the sympathetic nervous system in younger adults with metabolic syndrome. Autonomic neuroscience: basic & clinical 183, 100–105 (2014).
Schlaich, M., Straznicky, N., Lambert, E. & Lambert, G. Metabolic syndrome: a sympathetic disease? The lancet. Diabetes & endocrinology 3, 148–157 (2015).
May, O. & Arildsen, H. Long-term predictive power of heart rate variability on all-cause mortality in the diabetic population. Acta diabetologica 48, 55–59 (2011).
Piccirillo, G. et al. Power spectral analysis of heart rate variability as a predictive test in choosing the most effective length for tilt-training. International journal of cardiology 111, 59–66 (2006).
Wichterle, D. et al. Prevalent low-frequency oscillation of heart rate: novel predictor of mortality after myocardial infarction. Circulation 110, 1183–1190 (2004).
Lipsitz, L. A. & Goldberger, A. L. Loss of ‘complexity’ and aging. Potential applications of fractals and chaos theory to senescence. Jama 267, 1806–1809 (1992).
Sturmberg, J. P., Topolski, S. & Lewis, S. In Handbook of Systems and Complexity in Health 251–253 (Springer, 2013).
Manor, B. & Lipsitz, L. A. Physiologic complexity and aging: implications for physical function and rehabilitation. Progress in neuro-psychopharmacology & biological psychiatry 45, 287–293 (2013).
Costa, M., Goldberger, A. L. & Peng, C. K. Multiscale entropy analysis of complex physiologic time series. Physical review letters 89, 068102 (2002).
Costa, M., Goldberger, A. L. & Peng, C. K. Multiscale entropy analysis of biological signals. Physical review. E, Statistical, nonlinear, and soft matter physics 71, 021906 (2005).
Peng, C. K., Costa, M. & Goldberger, A. L. Adaptive data analysis of complex fluctuations in physiologic time series. Advances in adaptive data analysis 1, 61–70 (2009).
Thayer, J. F., Yamamoto, S. S. & Brosschot, J. F. The relationship of autonomic imbalance, heart rate variability and cardiovascular disease risk factors. International journal of cardiology 141, 122–131 (2010).
Ma, Y. & McCraty, R. Heart rate variability in mind-body interventions. Complementary therapies in medicine 29, A1–A2 (2016).
Stuckey, M. I., Tulppo, M. P., Kiviniemi, A. M. & Petrella, R. J. Heart rate variability and the metabolic syndrome: a systematic review of the literature. Diabetes/metabolism research and reviews 30, 784–793 (2014).
Javorka, M., Javorkova, J., Tonhajzerova, I., Calkovska, A. & Javorka, K. Heart rate variability in young patients with diabetes mellitus and healthy subjects explored by Poincare and sequence plots. Clinical physiology and functional imaging 25, 119–127 (2005).
Javorka, M., Javorkova, J., Tonhajzerova, I. & Javorka, K. Parasympathetic versus sympathetic control of the cardiovascular system in young patients with type 1 diabetes mellitus. Clinical physiology and functional imaging 25, 270–274 (2005).
Khandoker, A. H., Jelinek, H. F. & Palaniswami, M. Identifying diabetic patients with cardiac autonomic neuropathy by heart rate complexity analysis. Biomedical engineering online 8, 3 (2009).
Grassi, G., Arenare, F., Quarti-Trevano, F., Seravalle, G. & Mancia, G. Heart rate, sympathetic cardiovascular influences, and the metabolic syndrome. Progress in cardiovascular diseases 52, 31–37 (2009).
Kahn, R. Metabolic syndrome–what is the clinical usefulness? Lancet 371, 1892–1893 (2008).
Liu, Y. C. et al. Influence of non-alcoholic fatty liver disease on autonomic changes evaluated by the time domain, frequency domain, and symbolic dynamics of heart rate variability. PloS one 8, e61803 (2013).
Dunkley, A. J. et al. Effectiveness of interventions for reducing diabetes and cardiovascular disease risk in people with metabolic syndrome: systematic review and mixed treatment comparison meta-analysis. Diabetes, obesity & metabolism 14, 616–625 (2012).
Ma, Y. et al. Publication trends in acupuncture research: a 20-year bibliometric analysis based on PubMed. PloS one 11, e0168123 (2016).
Ma, Y., Sun, S., Peng, C. K., Fang, Y. & Thomas, R. J. Ambulatory Blood Pressure Monitoring in Chinese Obstructive Sleep Apnea Patients. Journal of clinical sleep medicine: JCSM: official publication of the American Academy of Sleep Medicine(2016).
Chang, Y., Ma, Y. & Sun, S. Clinical observation on effect of auto-CPAP on blood pressure in OSAHS patients. Sleep medicine 16, S212 (2015).
Ma, Y. et al. BP variation study on OSA patients with and without hypertension. Sleep medicine 12, S105 (2011).
Lung, F. W. & Lee, M. B. The five-item Brief-Symptom Rating Scale as a suicide ideation screening instrument for psychiatric inpatients and community residents. BMC psychiatry 8, 53 (2008).
Tseng, P. H. et al. Association of diabetes and HbA1c levels with gastrointestinal manifestations. Diabetes care 35, 1053–1060 (2012).
Wu, Y. W. et al. Association of esophageal inflammation, obesity and gastroesophageal reflux disease: from FDG PET/CT perspective. PloS one 9, e92001 (2014).
Tseng, P. H. et al. A higher proportion of metabolic syndrome in Chinese subjects with sleep-disordered breathing: a case-control study based on electrocardiogram-derived sleep analysis. PloS one 12, e0169394 (2017).
Diagnosis and classification of diabetes mellitus. Diabetes care 37 Suppl 1, S81–90 (2014).
Ma, Y., Sun, S. & Peng, C. K. Applications of dynamical complexity theory in traditional Chinese medicine. Frontiers of medicine 8, 279–284 (2014).
Ma, Y., Zhou, K., Fan, J. & Sun, S. Traditional Chinese medicine: potential approaches from modern dynamical complexity theories. Frontiers of medicine 10, 28–32 (2016).
Shi, W., Shang, P., Ma, Y., Sun, S. & Yeh, C.-H. A comparison study on stages of sleep: Quantifying multiscale complexity using higher moments on coarse-graining. Communications in Nonlinear Science and Numerical Simulation 44, 292–303 (2017).
Ma, Y., Shi, W., Peng, C.-K. & Yang, A. C. Nonlinear dynamical analysis of sleep electroencephalography using fractal and entropy approaches. Sleep Medicine Reviews(2017).
Huang, N. E. et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences 454, 903–995 (1998).
Huang, Z. W. & E., N. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in adaptive data analysis 1, 1–41 (2009).
Yeh, J.-R., Shieh, J.-S. & Huang, N. E. Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method. Advances in adaptive data analysis 2, 135–156 (2010).
Agarwal, S. et al. Metabolic Syndrome Derived from Principal Component Analysis and Incident Cardiovascular Events: The Multi Ethnic Study of Atherosclerosis (MESA) and Health, Aging, and Body Composition (Health ABC). Cardiology research and practice 2012, 919425 (2012).
Acknowledgements
This study was supported by research grants funded by National Taiwan University Hospital (NTUH.104-M2854, NTUH.105-003024) and DynaDx Corporation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Y.M. acknowledges the Fellowship support from Delta Environmental & Educational Foundation. None of the authors has received compensation for the work on this manuscript. The authors thank the staff of the Health Management Center and the Sleep Center at the National Taiwan University Hospital for their assistance.
Author information
Authors and Affiliations
Contributions
Y.M. and P.H.T. analyzed data, drafted and edited the manuscript and contributed to the discussion. C.K.P. Y.L.H. and M.S.W. conceived and designed the study, supervised the analysis, contributed to the discussion, and reviewed the manuscript. A.A. and M.F.C. contributed to the discussion and reviewed the manuscript.
Corresponding author
Ethics declarations
Competing interests
C.K. Peng is a co-inventor of the sleep spectrogram method (licensed by BIDMC to Embla), and share patent rights and royalties. All the other authors declare no conflict of interest. No financial disclosures were reported by all authors of this paper.
Rights and permissions
This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
About this article
Cite this article
Ma, Y., Tseng, PH., Ahn, A. et al. Cardiac Autonomic Alteration and Metabolic Syndrome: An Ambulatory ECG-based Study in A General Population. Sci Rep 7, 44363 (2017). https://doi.org/10.1038/srep44363
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/srep44363
- Springer Nature Limited
This article is cited by
-
Exploring heart rate variability in polycystic ovary syndrome: implications for cardiovascular health: a systematic review and meta-analysis
Systematic Reviews (2024)
-
Electrocardiogram-based sleep analysis for sleep apnea screening and diagnosis
Sleep and Breathing (2020)
-
Complexity-Based Measures of Heart Rate Dynamics in Older Adults Following Long- and Short-Term Tai Chi Training: Cross-sectional and Randomized Trial Studies
Scientific Reports (2019)
-
Does resistance training modulate cardiac autonomic control? A systematic review and meta-analysis
Clinical Autonomic Research (2019)
-
Modifications of short-term intrinsic pacemaker variability in diet-induced metabolic syndrome: a study on isolated rabbit heart
Journal of Physiology and Biochemistry (2019)