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      The correlations of glycated hemoglobin and carbohydrate metabolism parameters with heart rate variability in apparently healthy sedentary young male subjects

      research-article
      a , b , c , * , c , d , e , e , b , e , f , g
      Redox Biology
      Elsevier
      Sedentary lifestyle, Glycated hemoglobin, Heart rate variability, Insulin sensitivity, Correlations, ANS, autonomous nervous system, BMI, body mass index, CVS, cardiovascular system, ECG, electrocardiogram, FBG, fasting blood glucose, HBA1c, glycated hemoglobin, HF, the power of high frequency oscillations, HOMA, homeostatic model assessment, HR, heart rate, HRV, heart rate variability, HSS, healthy sedentary subjects, IR, insulin resistance, LF, the power of low frequency oscillations, OT, orthostatic test, pNN50, percentage of differences between adjacent normal RR intervals exceeding 50 milliseconds , RMSSD, square root of the mean squared differences of successive RR intervals, SAN, sinoatrial node, SDNN, standard deviation of normal RR intervals, TP, total power of RR-intervals oscillations, VLF, the power of very low frequency oscillations

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          Abstract

          Introduction

          Sedentary lifestyle is a major risk factor for diabetes, cardiovascular and many other age-related diseases. Heart rate variability (HRV) reflects the function of regulatory systems of internal organs and may sensitively indicate early metabolic disturbances. We hypothesize that quantitative and qualitative changes of HRV in young subjects may reflect early metabolic derangements responsible for further development of clinically significant disease.

          Aim

          The aim of our study was to determine whether the parameters of carbohydrate metabolism (fasting blood glucose, HBA 1c and surrogate insulin sensitivity/resistance indices) correlate with anthropometric data and HRV.

          Methods

          The study group consisted of 30 healthy sedentary male subjects aged 20–40, nonsmokers, mainly office and research employees, medical staff and students. Athletes, actively training more than one hour per week, severely obese and men of physical work were excluded from the study. HRV parameters were derived from short term ECG records (five minutes intervals) in supine position and during orthostatic test. Anthropometric data included height, weight, body mass index (BMI), age and body composition (estimation by bioelectric impedance method). The fasting blood glucose, insulin and C-peptide, homeostatic model assessment (HOMA-IR) index and glycated hemoglobin (HbA 1c) were evaluated. Linear correlation coefficient ( r) was calculated using Statistica 10.0 software.

          Results and discussion

          HOMA-IR index correlated positively with body weight, visceral fat and BMI ( p=0.047, 0.027 and 0.017 respectively). In supine position pNN50 positively correlated with glucose/insulin ratio ( p=0.011) and heart rate with HOMA-IR ( p=0.006). In orthostatic test negative correlations of HBA 1c with standard deviation, total and low frequency power were determined ( p=0.034, 0.400 and 0.403 respectively), which indicates a gradual worsening of functional capacity of cardiovascular system with low-grade increase (under the conventional threshold) of HBA 1c.

          Conclusions

          In apparently healthy sedentary subjects HRV reduction correlates with the age advancement, subclinical deteriorations of carbohydrate metabolism and excessive fat accumulation.

          Graphical abstract

          Highlights

          • Apparently healthy sedentary young male subjects were enrolled in the study.

          • HRV negatively correlates with age, BMI, visceral fat and insulin resistance.

          • Glycated hemoglobin negatively correlates with HRV parameters in orthostatic test.

          • Changes of HRV may reflect subclinical metabolic deteriorations in sedentary subjects.

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          Most cited references17

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          Metabolically healthy but obese, a matter of time? Findings from the prospective Pizarra study.

          Prospective longitudinal studies evaluating the relevance of "Metabolically Healthy but Obese" (MHO) phenotype at risk for type 2 diabetes mellitus (T2D) and cardiovascular diseases are few and results are contradictory. As a representative of the general population, 1051 individuals were evaluated in 1997-1998 and re-evaluated after 6 years and 11 years. Subjects without known T2D were given an oral glucose tolerance test. Anthropometric and biochemical variables were measured. Four sets of criteria were considered to define MHO subjects besides body mass index ≥30 kg/m(2): A: Homeostatic Model of Assessment-Insulin Resistance Index (HOMA-IR) 40 mg/dL in men and high-density lipoprotein cholesterol >50 mg/dL in women, triglycerides <150 mg/dL, fasting glucose <110 mg/dL, and blood pressure ≤140/90 mm Hg; C: HOMA-IR <90th percentile, triglycerides <150 mg/dL, fasting glucose <110 mg/dL, and blood pressure ≤140/90 mm Hg; D: HOMA-IR <90th percentile, triglycerides <150 mg/dL, and fasting glucose <110 mg/dL. Subjects with T2D at baseline were excluded from the calculations of incidence of T2D. The baseline prevalence of MHO phenotype varied between 3.0% and 16.9%, depending on the set of criteria chosen. Metabolically nonhealthy obese subjects were at highest risk for becoming diabetic after 11 years of follow-up (odds ratio = 8.20; 95% confidence interval = 2.72-24.72; P < .0001). In MHO subjects the risk for becoming diabetic was lower than in metabolically nonhealthy obese subjects, but this risk remained significant (odds ratio = 3.13; 95% confidence interval = 1.07-9.17; P = .02). In subjects who lost weight during the study, the association between MHO phenotype and T2D incidence disappeared, even after adjusting for HOMA-IR. The results suggest that MHO is a dynamic concept that should be taken into account over time. As a clinical entity, it may be questionable.
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            Reactive aldehydes--second messengers of free radicals in diabetes mellitus.

            Elevated levels of pro-oxidants and various markers of oxidative tissue damage were found in diabetic patients, indicating involvement of oxidative stress in the pathogenesis of diabetes mellitus (DM). On one side, physiological levels of reactive oxygen species (ROS) play an important role in redox signaling of various cells, while on the other, excessive ROS production can jeopardize the integrity and physiological functions of cellular macromolecules, in particular proteins, thus contributing to the pathogenesis of DM. Reactive aldehydes, especially 4-hydroxynonenal (HNE), are considered as second messengers of free radicals that act both as signaling molecules and as cytotoxic products of lipid peroxidation causing long-lasting biological consequences, in particular by covalent modification of macromolecules. Accordingly, the HNE and related reactive aldehydes may play important roles in the pathophysiology of DM, both in the development of the disease and in its progression and complications due to the following: (i) exposure of cells to supraphysiological levels of 4-hydroxyalkenals, (ii) persistent and sustained generation of 4-hydroxyalkenals that progressively affect vulnerable cells that lack an efficient bioactive aldehyde neutralization system, (iii) altered redox signaling influenced by reactive aldehydes, in particular by HNE, and (iv) induction of extracellular generation of similar aldehydes under secondary pathological conditions, such as low-grade inflammation.
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              A review of population-based prevalence studies of physical activity in adults in the Asia-Pacific region

              Background Physical activity (PA) surveillance is an important component of non-communicable disease risk factor monitoring, and occurs through national and international surveillance systems. This review identifies population PA estimates for adults in the Asia-Pacific region, and examines variation in trends and prevalence rates obtained using different PA measures. Methods Data were obtained from a MEDLINE search; World Health Organization's Global Health Infobase; Government websites and reference lists of relevant papers. Inclusion criteria included: national studies or those reporting large scale population-level data; data published from 2000 to 2010 and trend data prior; sample sizes over n = 1000, or fewer subjects in small nations. Results In total, 56 population surveys from 29 Asia-Pacific countries were identified. Data on 'sufficient physical activity' amongst adults were available from 45 studies (80%), with estimates ranging from 7% to 93% (median 62%, inter-quartile range 40%-85%). For 14 countries, estimates of 'sufficient activity' were documented in multiple surveys using different methods, with the largest variation from 18% to 92% in Nepal. Median or mean MET-minutes/day, reported in 20 studies, ranged from 6 to 1356. Serial trend data were available for 11 countries (22%), for periods spanning 2-10 years. Of these, five countries demonstrated increases in physical activity over time, four demonstrated decreases and three showed no changes. Conclusions Many countries in the Asia-Pacific region collect population-level PA data. This review highlights differences in estimates within and between countries. Some differences may be real, others due to variation in the PA questions asked and survey methods used. Use of standardized protocols and measures, and combined reporting of data are essential goals of improved international PA surveillance.
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                Author and article information

                Contributors
                Journal
                Redox Biol
                Redox Biol
                Redox Biology
                Elsevier
                2213-2317
                03 June 2015
                August 2015
                03 June 2015
                : 5
                : 301-307
                Affiliations
                [a ]Department of Medicine, Lviv College of Physical Culture, Lviv, Ukraine
                [b ]Department of Endocrinology, Danylo Halytskyi Lviv National Medical University, Lviv, Ukraine
                [c ]Department of Internal Medicine #1, Danylo Halytskyi Lviv National Medical University, Lviv, Ukraine
                [d ]Department of Pharmaceutical, Organic and Bioorganic Chemistry, Danylo Halytskyi Lviv National Medical University, Lviv, Ukraine
                [e ]Institute of Cancer Research, Internal Medicine I, Medical University of Vienna, Vienna, Austria
                [f ]Laboratory for Oxidative Stress, Rudjer Boskovic Institute, Zagreb, Croatia
                [g ]Department of Cell Biology, University of Salzburg, Salzburg, Austria
                Author notes
                [* ]Corresponding author at: Knyagyni Olgy St. 1, Lviv, 79044, Ukraine. Tel.: +380322382794; fax: +380322382793. cherkasandriy@ 123456yahoo.com
                Article
                S2213-2317(15)00050-6
                10.1016/j.redox.2015.05.007
                4484544
                26092779
                0f0ac1fb-84f8-4158-b0f6-1a10a476a6a7
                © 2015 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 27 April 2015
                : 28 May 2015
                : 29 May 2015
                Categories
                Research Paper

                sedentary lifestyle,glycated hemoglobin,heart rate variability,insulin sensitivity,correlations,ans, autonomous nervous system,bmi, body mass index,cvs, cardiovascular system,ecg, electrocardiogram,fbg, fasting blood glucose,hba1c, glycated hemoglobin,hf, the power of high frequency oscillations,homa, homeostatic model assessment,hr, heart rate,hrv, heart rate variability,hss, healthy sedentary subjects,ir, insulin resistance,lf, the power of low frequency oscillations,ot, orthostatic test,pnn50, percentage of differences between adjacent normal rr intervals exceeding 50 milliseconds,rmssd, square root of the mean squared differences of successive rr intervals,san, sinoatrial node,sdnn, standard deviation of normal rr intervals,tp, total power of rr-intervals oscillations,vlf, the power of very low frequency oscillations

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