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      The triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio as a predictor of insulin resistance but not of β cell function in a Chinese population with different glucose tolerance status

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          Abstract

          Background

          Triglyceride/high-density lipoprotein-cholesterol (TG/HDL-C) ratio was a surrogate marker of IR; however, the relationship of TG/HDL-C with IR might vary by ethnicity. This study aims to investigate whether lipid ratios-TG/HDL-C, cholesterol/high-density lipoprotein-cholesterol (TC/HDL-C) ratio, low-density lipoprotein-cholesterol/high-density lipoprotein-cholesterol (LDL-C/HDL-C)) could be potential clinical markers of insulin resistance (IR) and β cell function and further to explore the optimal cut-offs in a Chinese population with different levels of glucose tolerance.

          Methods

          Four hundred seventy-nine subjects without a history of diabetes underwent a 75 g 2 h Oral Glucose Tolerance Test (OGTT). New-onset diabetes ( n = 101), pre-diabetes ( n = 186), and normal glucose tolerance ( n = 192) were screened. IR was defined by HOMA-IR > 2.69. Based on indices (HOMA-β, early-phase disposition index [DI 30], (ΔIns30/ΔGlu30)/HOMA-IR and total-phase index [DI 120]) that indicated different phases of insulin secretion, the subjects were divided into two groups, and the lower group was defined as having inadequate β cell compensation. Logistic regression models and accurate estimates of the areas under receiver operating characteristic curves (AUROC) were obtained.

          Results

          In all of the subjects, TG/HDL, TC/HDL-C, LDL-C/HDL-C, and TG were significantly associated with IR. The AUROCs of TG/HDL-C and TG were 0.71 (95 % CI: 0.66–0.75) and 0.71 (95 % CI: 0.65–0.75), respectively. The optimal cut-offs of TG/HDL-C and TG for IR diagnosis were 1.11 and 1.33 mmol/L, respectively. The AUROCs of TC/HDL-C and LDL-C/HDL-C were 0.66 and 0.65, respectively, but they were not acceptable for IR diagnosis. TG/HDL-C,LDL-C/HDL-C and TG were significantly associated with HOMA-β, but AUROCs were less than 0.50; therefore, the lipid ratios could not be predictors of basal β cell dysfunction. None of the lipid ratios was associated with early-phase insulin secretion. Only TG/HDL-C and TG were significantly correlated with total-phase insulin secretion, but they also were not acceptable predictors of total-phase insulin secretion (0.60 < AUROC < 0.70).

          Conclusions

          In a Chinese population with different levels of glucose tolerance, TG/HDL-C and TG could be the predictors of IR. The lipid ratios could not be reliable makers of β cell function in the population.

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          Applied Logistic Regression

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            Use of metabolic markers to identify overweight individuals who are insulin resistant.

            Insulin resistance is more common in overweight individuals and is associated with increased risk for type 2 diabetes mellitus and cardiovascular disease. Given the current epidemic of obesity and the fact that lifestyle interventions, such as weight loss and exercise, decrease insulin resistance, a relatively simple means to identify overweight individuals who are insulin resistant would be clinically useful. To evaluate the ability of metabolic markers associated with insulin resistance and increased risk for cardiovascular disease to identify the subset of overweight individuals who are insulin resistant. Cross-sectional study. General clinical research center. 258 nondiabetic, overweight volunteers. Body mass index; fasting glucose, insulin, lipid and lipoprotein concentrations; and insulin-mediated glucose disposal as quantified by the steady-state plasma glucose concentration during the insulin suppression test. Overweight was defined as body mass index of 25 kg/m2 or greater, and insulin resistance was defined as being in the top tertile of steady-state plasma glucose concentrations. Receiver-operating characteristic curve analysis was used to identify the best markers of insulin resistance; optimal cut-points were identified and analyzed for predictive power. Plasma triglyceride concentration, ratio of triglyceride to high-density lipoprotein cholesterol concentrations, and insulin concentration were the most useful metabolic markers in identifying insulin-resistant individuals. The optimal cut-points were 1.47 mmol/L (130 mg/dL) for triglyceride, 1.8 in SI units (3.0 in traditional units) for the triglyceride-high-density lipoprotein cholesterol ratio, and 109 pmol/L for insulin. Respective sensitivity and specificity for these cut-points were 67%, 64%, and 57% and 71%, 68%, and 85%. Their ability to identify insulin-resistant individuals was similar to the ability of the criteria proposed by the Adult Treatment Panel III to diagnose the metabolic syndrome (sensitivity, 52%, and specificity, 85%). Three relatively simple metabolic markers can help identify overweight individuals who are sufficiently insulin resistant to be at increased risk for various adverse outcomes. In the absence of a standardized insulin assay, we suggest that the most practical approach to identify overweight individuals who are insulin resistant is to use the cut-points for either triglyceride concentration or the triglyceride-high-density lipoprotein cholesterol concentration ratio.
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              Assessment of Pancreatic β-Cell Function: Review of Methods and Clinical Applications

              Type 2 diabetes mellitus (T2DM) is characterized by a progressive failure of pancreatic β-cell function (BCF) with insulin resistance. Once insulin over-secretion can no longer compensate for the degree of insulin resistance, hyperglycemia becomes clinically significant and deterioration of residual β-cell reserve accelerates. This pathophysiology has important therapeutic implications. Ideally, therapy should address the underlying pathology and should be started early along the spectrum of decreasing glucose tolerance in order to prevent or slow β-cell failure and reverse insulin resistance. The development of an optimal treatment strategy for each patient requires accurate diagnostic tools for evaluating the underlying state of glucose tolerance. This review focuses on the most widely used methods for measuring BCF within the context of insulin resistance and includes examples of their use in prediabetes and T2DM, with an emphasis on the most recent therapeutic options (dipeptidyl peptidase-4 inhibitors and glucagon-like peptide-1 receptor agonists). Methods of BCF measurement include the homeostasis model assessment (HOMA); oral glucose tolerance tests, intravenous glucose tolerance tests (IVGTT), and meal tolerance tests; and the hyperglycemic clamp procedure. To provide a meaningful evaluation of BCF, it is necessary to interpret all observations within the context of insulin resistance. Therefore, this review also discusses methods utilized to quantitate insulin-dependent glucose metabolism, such as the IVGTT and the euglycemic-hyperinsulinemic clamp procedures. In addition, an example is presented of a mathematical modeling approach that can use data from BCF measurements to develop a better understanding of BCF behavior and the overall status of glucose tolerance.
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                Author and article information

                Contributors
                +86-10-69155088 , +86-10-69155088 , liyuxiu@medmail.com.cn
                Journal
                Lipids Health Dis
                Lipids Health Dis
                Lipids in Health and Disease
                BioMed Central (London )
                1476-511X
                7 June 2016
                7 June 2016
                2016
                : 15
                : 104
                Affiliations
                [ ]Department of Endocrinology, Key Laboratory of Endocrinology, Ministry of Health, Peking Union Medical College Hospital, Beijing, 100730 China
                [ ]Nankou Community Health Service Centers, Changping District, Beijing, 102200 China
                [ ]Nankou Railway Hospital, Changping District, Beijing, 102200 China
                [ ]Department of Nutrition, Peking Union Medical College Hospital, Beijing, 100730 China
                Article
                270
                10.1186/s12944-016-0270-z
                4895977
                27267043
                bf179fad-6d6b-4f5d-b171-795d7ae6f9cf
                © The Author(s). 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 23 December 2015
                : 23 May 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 81270878
                Award Recipient :
                Funded by: National Key Program of Clinical Science of China
                Award ID: WBYZ2011-873
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2016

                Biochemistry
                lipid ratios,glucose tolerance status,insulin resistance,β cell function
                Biochemistry
                lipid ratios, glucose tolerance status, insulin resistance, β cell function

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