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      Prevalence of Insulin Resistance in the Hungarian General and Roma Populations as Defined by Using Data Generated in a Complex Health (Interview and Examination) Survey

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          Abstract

          Data mainly from one-off surveys clearly show that the health of Roma, the largest ethnic minority of Europe, is much worse than that of the general population. However, results from comprehensive exploratory studies are missing. The aim of our study was to create a complex database for comparative and association studies to better understand the background of the very unfavourable health of Roma, especially the high burden of cardiometabolic diseases. A three-pillar (questionnaire-based, physical and laboratory examinations) health survey was carried out on randomly selected samples of the Hungarian general (HG, n = 417) and Roma (HR, n = 415) populations, and a database consisting of more than half a million datapoints was created. Using selected data, the prevalence rates of metabolic syndrome (MetS) and of its components were determined, and to estimate the risk of insulin resistance (IR), surrogate measures (the homeostasis model assessment of insulin resistance index, quantitative insulin sensitivity check index, McAuley and TyG indices and the TG/HDL-C ratio) were calculated. Receiver operating characteristic curve analysis and Youden’s method were used to define the optimal cut-off values of each IR index. The prevalence of MetS was very high in both study populations (HG: 39.8%, HR: 44.0%) with no statistically significant difference between the two groups in females or males. The prevalence of MetS showed a very marked increase in the HR 35–49 years age group. Among surrogate measures, the TyG index showed the greatest power for predicting IR/MetS at a cut-off value of 4.69 (77% sensitivity, 84% specificity) and indicated a 42.3% (HG) and 40.5% (HR) prevalence of IR. The prevalence of MetS and IR is almost equally very unfavourable in both groups; thus, the factors underlying the high premature mortality burden of Roma should be further clarified by investigating the full spectrum of risk factors available in the database, with a special focus on the access of Roma people to preventive and curative health services.

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          Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans.

          Insulin resistance plays an important role in the pathophysiology of diabetes and is associated with obesity and other cardiovascular risk factors. The "gold standard" glucose clamp and minimal model analysis are two established methods for determining insulin sensitivity in vivo, but neither is easily implemented in large studies. Thus, it is of interest to develop a simple, accurate method for assessing insulin sensitivity that is useful for clinical investigations. We performed both hyperinsulinemic isoglycemic glucose clamp and insulin-modified frequently sampled iv glucose tolerance tests on 28 nonobese, 13 obese, and 15 type 2 diabetic subjects. We obtained correlations between indexes of insulin sensitivity from glucose clamp studies (SI(Clamp)) and minimal model analysis (SI(MM)) that were comparable to previous reports (r = 0.57). We performed a sensitivity analysis on our data and discovered that physiological steady state values [i.e. fasting insulin (I(0)) and glucose (G(0))] contain critical information about insulin sensitivity. We defined a quantitative insulin sensitivity check index (QUICKI = 1/[log(I(0)) + log(G(0))]) that has substantially better correlation with SI(Clamp) (r = 0.78) than the correlation we observed between SI(MM) and SI(Clamp). Moreover, we observed a comparable overall correlation between QUICKI and SI(Clamp) in a totally independent group of 21 obese and 14 nonobese subjects from another institution. We conclude that QUICKI is an index of insulin sensitivity obtained from a fasting blood sample that may be useful for clinical research.
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            Risks for all-cause mortality, cardiovascular disease, and diabetes associated with the metabolic syndrome: a summary of the evidence.

            E. Ford (2005)
            In recent years, several major organizations have endorsed the concept of the metabolic syndrome and developed working definitions for it. How well these definitions predict the risk for adverse events in people with the metabolic syndrome is only now being learned. The purpose of this study was to summarize the estimates of relative risk for all-cause mortality, cardiovascular disease, and diabetes reported from prospective studies in samples from the general population using definitions of the metabolic syndrome developed by the National Cholesterol Education Program (NCEP) and World Health Organization (WHO). The author reviewed prospective studies from July 1998 through August 2004. For studies that used the exact NCEP definition of the metabolic syndrome, random-effects estimates of combined relative risk were 1.27 (95% CI 0.90-1.78) for all-cause mortality, 1.65 (1.38-1.99) for cardiovascular disease, and 2.99 (1.96-4.57) for diabetes. For studies that used the most exact WHO definition of the metabolic syndrome, the fixed-effects estimates of relative risk were 1.37 (1.09-1.74) for all-cause mortality and 1.93 (1.39-2.67) for cardiovascular disease; the fixed-effects estimate was 2.60 (1.55-4.38) for coronary heart disease. These estimates suggest that the population-attributable fraction for the metabolic syndrome, as it is currently conceived, is approximately 6-7% for all-cause mortality, 12-17% for cardiovascular disease, and 30-52% for diabetes. Further research is needed to establish the use of the metabolic syndrome in predicting risk for death, cardiovascular disease, and diabetes in various population subgroups.
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              Glucose clamp technique: a method for quantifying insulin secretion and resistance.

              Methods for the quantification of beta-cell sensitivity to glucose (hyperglycemic clamp technique) and of tissue sensitivity to insulin (euglycemic insulin clamp technique) are described. Hyperglycemic clamp technique. The plasma glucose concentration is acutely raised to 125 mg/dl above basal levels by a priming infusion of glucose. The desired hyperglycemic plateau is subsequently maintained by adjustment of a variable glucose infusion, based on the negative feedback principle. Because the plasma glucose concentration is held constant, the glucose infusion rate is an index of glucose metabolism. Under these conditions of constant hyperglycemia, the plasma insulin response is biphasic with an early burst of insulin release during the first 6 min followed by a gradually progressive increase in plasma insulin concentration. Euglycemic insulin clamp technique. The plasma insulin concentration is acutely raised and maintained at approximately 100 muU/ml by a prime-continuous infusion of insulin. The plasma glucose concentration is held constant at basal levels by a variable glucose infusion using the negative feedback principle. Under these steady-state conditions of euglycemia, the glucose infusion rate equals glucose uptake by all the tissues in the body and is therefore a measure of tissue sensitivity to exogenous insulin.
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                Author and article information

                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                04 July 2020
                July 2020
                : 17
                : 13
                : 4833
                Affiliations
                [1 ]MTA-DE Public Health Research Group, Public Health Research Institute, University of Debrecen, 4032 Debrecen, Hungary; piko.peter@ 123456sph.unideb.hu (P.P.); balazs.margit@ 123456sph.unideb.hu (M.B.)
                [2 ]Department of Preventive Medicine, Faculty of Public Health, University of Debrecen, 4032 Debrecen, Hungary; fiatal.szilvia@ 123456sph.unideb.hu (S.F.); sandor.janos@ 123456sph.unideb.hu (J.S.); biro.eva@ 123456sph.unideb.hu (E.B.); varga.orsolya@ 123456sph.unideb.hu (O.V.)
                [3 ]Department of Health Methodology and Public Health, Faculty of Health, University of Debrecen, 4400 Nyíregyháza, Hungary; kosa.zsigmond@ 123456foh.unideb.hu
                [4 ]Institute of Behavioural Sciences, Faculty of Public Health, University of Debrecen, 4032 Debrecen, Hungary; kosa.karolina@ 123456sph.unideb.hu
                [5 ]Institute of Internal Medicine, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary; paragh.gyorgy@ 123456med.unideb.hu
                [6 ]Institute of Sport Management, University of Debrecen, 4032 Debrecen, Hungary; bacsne.baba.eva@ 123456econ.unideb.hu
                [7 ]Department of Physiotherapy, Faculty of Public Health, University of Debrecen, 4032 Debrecen, Hungary; balajti.ilona@ 123456sph.unideb.hu
                [8 ]Department of Health Systems Management and Quality Management in Health Care, Faculty of Public Health, University of Debrecen, 4032 Debrecen, Hungary; kbiro@ 123456med.unideb.hu
                Author notes
                [* ]Correspondence: adany.roza@ 123456sph.unideb.hu ; Tel.: +36-52-512-765/77408
                Author information
                https://orcid.org/0000-0001-5539-907X
                https://orcid.org/0000-0002-0131-8147
                https://orcid.org/0000-0001-7867-2403
                https://orcid.org/0000-0001-5108-9154
                Article
                ijerph-17-04833
                10.3390/ijerph17134833
                7370128
                32635565
                f2426150-94d8-4c1c-b2d8-3a6aedb13fca
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 21 May 2020
                : 02 July 2020
                Categories
                Article

                Public health
                roma population,health survey,metabolic syndrome,insulin resistance,cut-off values for surrogate indices,homa-ir,quicki,mcauley index,tg/hdl-c ratio,tyg index

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