Cardiac Autonomic Alteration and Metabolic Syndrome: An Ambulatory ECG-based Study in A General Population

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.

[1]  E. Ford,et al.  A comparison of the prevalence of the metabolic syndrome using two proposed definitions. , 2003, Diabetes care.

[2]  V. Rovella,et al.  Obesity-Related Metabolic Syndrome: Mechanisms of Sympathetic Overactivity , 2013, International journal of endocrinology.

[3]  Madalena Costa,et al.  Multiscale entropy analysis of complex physiologic time series. , 2002, Physical review letters.

[4]  P. O S I T I O N S T A T E M E N T,et al.  Diagnosis and Classification of Diabetes Mellitus , 2011, Diabetes Care.

[5]  Marek Malik,et al.  Prevalent Low-Frequency Oscillation of Heart Rate: Novel Predictor of Mortality After Myocardial Infarction , 2004, Circulation.

[6]  Marimuthu Palaniswami,et al.  Identifying diabetic patients with cardiac autonomic neuropathy by heart rate complexity analysis. , 2009, Biomedical engineering online.

[7]  Antti M Kiviniemi,et al.  Heart rate variability and the metabolic syndrome: a systematic review of the literature , 2014, Diabetes/metabolism research and reviews.

[8]  S. Grundy,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 As , 2009, Circulation.

[9]  Jukka T Salonen,et al.  The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. , 2002, JAMA.

[10]  G. Duncan,et al.  Prevalence and trends of a metabolic syndrome phenotype among u.s. Adolescents, 1999-2000. , 2004, Diabetes care.

[11]  C. Peng,et al.  Applications of dynamical complexity theory in traditional Chinese medicine , 2014, Frontiers of Medicine.

[12]  A. Goldberger,et al.  Loss of 'complexity' and aging. Potential applications of fractals and chaos theory to senescence. , 1992, JAMA.

[13]  C. Hung,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 , 2013, PloS one.

[14]  Wei-Chih Liao,et al.  Association of Diabetes and HbA1c Levels With Gastrointestinal Manifestations , 2012, Diabetes Care.

[15]  Yen-Wen Wu,et al.  Association of Esophageal Inflammation, Obesity and Gastroesophageal Reflux Disease: From FDG PET/CT Perspective , 2014, PloS one.

[16]  B. Morgan,et al.  Mechanical and metabolic reflex activation of the sympathetic nervous system in younger adults with metabolic syndrome , 2014, Autonomic Neuroscience.

[17]  Giuseppe Mancia,et al.  Heart rate, sympathetic cardiovascular influences, and the metabolic syndrome. , 2009, Progress in cardiovascular diseases.

[18]  Jianpin Liu,et al.  Publication Trends in Acupuncture Research: A 20-Year Bibliometric Analysis Based on PubMed , 2016, PloS one.

[19]  J. Shaw,et al.  Metabolic syndrome—a new world‐wide definition. A Consensus Statement from the International Diabetes Federation , 2006, Diabetic medicine : a journal of the British Diabetic Association.

[20]  Chung-Kang Peng,et al.  Adaptive Data Analysis of Complex Fluctuations in physiologic Time Series , 2009, Adv. Data Sci. Adapt. Anal..

[21]  R. Thomas,et al.  Ambulatory Blood Pressure Monitoring in Chinese Patients with Obstructive Sleep Apnea. , 2017, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[22]  D. Leroith,et al.  The metabolic syndrome--from insulin resistance to obesity and diabetes. , 2008, Endocrinology and metabolism clinics of North America.

[23]  P. Allhoff,et al.  San Antonio Heart Study , 1991 .

[24]  Lawrence Joseph,et al.  The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis. , 2010, Journal of the American College of Cardiology.

[25]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[26]  John Beilby,et al.  Definition of Metabolic Syndrome: Report of the National Heart, Lung, and Blood Institute/American Heart Association Conference on Scientific Issues Related to Definition , 2004 .

[27]  Madalena Costa,et al.  Multiscale entropy analysis of biological signals. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  G. Lambert,et al.  Metabolic syndrome: a sympathetic disease? , 2015, The lancet. Diabetes & endocrinology.

[29]  Jeanette S. Andrews,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) , 2012, Cardiology research and practice.

[30]  L. Lipsitz,et al.  Physiologic complexity and aging: Implications for physical function and rehabilitation , 2013, Progress in Neuro-Psychopharmacology and Biological Psychiatry.

[31]  L J Gray,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 , 2012, Diabetes, obesity & metabolism.

[32]  Julie A. E. Christensen,et al.  A Noise-Assisted Data Analysis Method for Automatic EOG-Based Sleep Stage Classification Using Ensemble Learning , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[33]  F. Cosentino,et al.  The 2019 ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD. , 2019, European heart journal.

[34]  F. Lung,et al.  The five-item Brief-Symptom Rating Scale as a suicide ideation screening instrument for psychiatric inpatients and community residents , 2008, BMC psychiatry.

[35]  Tim Nolan,et al.  International Diabetes Federation. , 2013, Diabetes research and clinical practice.

[36]  J. Tuomilehto,et al.  Prevalence of the metabolic syndrome and its relation to all-cause and cardiovascular mortality in nondiabetic European men and women. , 2004, Archives of internal medicine.

[37]  C. Peng,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 , 2017, PloS one.

[38]  R. D'Agostino,et al.  Prevalence and characteristics of the metabolic syndrome in the San Antonio Heart and Framingham Offspring Studies. , 2003, Diabetes.

[39]  O. May,et al.  Long-term predictive power of heart rate variability on all-cause mortality in the diabetic population , 2011, Acta Diabetologica.

[40]  Ralph B D'Agostino,et al.  Insulin resistance, the metabolic syndrome, and incident cardiovascular events in the Framingham Offspring Study. , 2005, Diabetes.

[41]  S. Gabriel,et al.  Systematic Review of the Literature , 2021, Adherence to Antiretroviral Therapy among Perinatal Women in Guyana.

[42]  Wenbin Shi,et al.  Nonlinear dynamical analysis of sleep electroencephalography using fractal and entropy approaches. , 2018, Sleep medicine reviews.

[43]  J. Thayer,et al.  The relationship of autonomic imbalance, heart rate variability and cardiovascular disease risk factors. , 2010, International journal of cardiology.

[44]  Norden E. Huang,et al.  Complementary Ensemble Empirical Mode Decomposition: a Novel Noise Enhanced Data Analysis Method , 2010, Adv. Data Sci. Adapt. Anal..

[45]  Bengt Jönsson,et al.  [Guidelines on diabetes, pre-diabetes, and cardiovascular diseases]. , 2007, Revista espanola de cardiologia.

[46]  R. Kahn Metabolic syndrome—what is the clinical usefulness? , 2008, The Lancet.

[47]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[48]  K. Gauvreau,et al.  Prevalence of the Metabolic Syndrome in American Adolescents: Findings From the Third National Health and Nutrition Examination Survey , 2004, Circulation.

[49]  W. Dietz,et al.  Prevalence of the metabolic syndrome among US adults: findings from the third National Health and Nutrition Examination Survey. , 2002, JAMA.

[50]  Joachim P. Sturmberg,et al.  Handbook of Systems and Complexity in Health , 2013 .

[51]  Jing Fan,et al.  Traditional Chinese medicine: potential approaches from modern dynamical complexity theories , 2016, Frontiers of Medicine.

[52]  Michal Javorka,et al.  Parasympathetic versus sympathetic control of the cardiovascular system in young patients with type 1 diabetes mellitus , 2005, Clinical physiology and functional imaging.

[53]  S. Haffner,et al.  The metabolic syndrome as predictor of type 2 diabetes: the San Antonio heart study. , 2003, Diabetes care.

[54]  Pengjian Shang,et al.  A comparison study on stages of sleep: Quantifying multiscale complexity using higher moments on coarse-graining , 2017, Commun. Nonlinear Sci. Numer. Simul..

[55]  S. Ferrari,et al.  Author contributions , 2021 .

[56]  Michal Javorka,et al.  Heart rate variability in young patients with diabetes mellitus and healthy subjects explored by Poincaré and sequence plots , 2005, Clinical physiology and functional imaging.

[57]  Yan Ma,et al.  Heart rate variability in mind-body interventions. , 2016, Complementary therapies in medicine.

[58]  C. Cobelli,et al.  Downregulation of the Longevity-Associated Protein Sirtuin 1 in Insulin Resistance and Metabolic Syndrome: Potential Biochemical Mechanisms , 2010, Diabetes.

[59]  G. Piccirillo,et al.  Power spectral analysis of heart rate variability as a predictive test in choosing the most effective length for tilt-training. , 2006, International journal of cardiology.