Dataset Information

Metabolic Syndrome and Its Components Predict the Risk of Type 2 Diabetes Mellitus in the Mainland Chinese: A 3-Year Cohort Study.

ABSTRACT: Introduction:It has well established that metabolic syndrome (MetS) can predict the risk of type 2 diabetes mellitus (T2DM) in some population groups. However, limited evidence is available regarding the predictive effect of MetS for incident T2DM in mainland Chinese population. Methods:A 3-year cohort study was performed for 9735 Chinese without diabetes at baseline. MetS and its components were assessed by multivariable analysis using Cox regression. Prediction models were developed. Discrimination was assessed with area under the receiver operating characteristic curves (AUCs), and performance was assessed by a calibration curve. Results:The 3-year cumulative incidence of T2DM was 11.29%. Baseline MetS was associated with an increased risk of T2DM after adjusting for age (HR?=?2.68, 95% CI, 2.27-3.17 in males; HR?=?2.59, 95% CI, 1.83-3.65 in females). Baseline MetS exhibited relatively high specificity (88% in males, 94% in females) and high negative predictive value (90% in males, 94% in females) but low sensitivity (36% in males, 23% in females) and low positive predictive value (31% in males and females) for predicting the 3-year risk of T2DM. AUCs, including age and components of MetS, for the prediction model were 0.779 (95% CI: 0.759-0.799) in males and 0.860 (95% CI: 0.836-0.883) in females. Calibration curves revealed good agreement between prediction and observation results in males; however, the model could overestimate the risk when the predicted probability is >40% in females. Conclusions:MetS predicts the risk of T2DM. The quantitative MetS-based prediction model for T2DM risk may improve preventive strategies for T2DM and present considerable public health benefits for the people in mainland China.

SUBMITTER: Wang K

PROVIDER: S-EPMC6311882 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Metabolic Syndrome and Its Components Predict the Risk of Type 2 Diabetes Mellitus in the Mainland Chinese: A 3-Year Cohort Study.

Wang Kun K Yang Qun-Fang QF Chen Xing-Lin XL Liu Yu-Wei YW Shan Sheng-Shuai SS Zheng Hua-Bo HB Zhao Xiao-Fang XF Chen Chang-Zhong CZ Liu Cheng-Yun CY

International journal of endocrinology 20181213

<h4>Introduction</h4>It has well established that metabolic syndrome (MetS) can predict the risk of type 2 diabetes mellitus (T2DM) in some population groups. However, limited evidence is available regarding the predictive effect of MetS for incident T2DM in mainland Chinese population.<h4>Methods</h4>A 3-year cohort study was performed for 9735 Chinese without diabetes at baseline. MetS and its components were assessed by multivariable analysis using Cox regression. Prediction models were devel ...[more]

PMID: 30647739

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Project description:BackgroundThe associations of metabolic syndrome (MetS) or diabetes mellitus (DM) on long-term survival after coronary artery bypass grafting (CABG) have not been extensively evaluated. The aim of the present study was to assess the impact of MetS and DM on the 16-year survival after CABG.MethodsDiabetic and metabolic status together with relevant cardiovascular data was established in 910 CABG patients operated in 1993-94. They were divided in three groups as follows: neither DM nor MetS (375 patients), MetS alone (279 patients) and DM with or without MetS (256 patients). The 16-year follow-up of patient survival was carried out using national health databases. The relative survival rates were analyzed using the Life Table method comparing the observed survival rates of three patient groups to the rates based on age-, sex- and time-specific life tables for the whole population in Finland. To study the independent significance of MetS and DM for clinical outcome, multivariate analysis was made using an optimizing stepwise procedure based on the Bayesian approach.ResultsBayesian multivariate analysis revealed together six variables to predict clinical outcome (2 months to 16 years) in relation to the national background population, i.e. age, diabetes, left ventricular ejection fraction, BMI, perfusion time during the CABG and peripheral arterial disease. Our principal finding was that after postoperative period the 16-year prognosis of patients with neither DM nor MetS was better than that of the age-, sex-and time-matched background population (relative survival against background population 1.037, p < 0.0001). The overall survival of MetS patients resembled that of the matched background population (relative survival 0.998, NS). DM was associated with significantly increased mortality (relative survival 0.86, p < 0.0001). Additionally, mortality was even higher in patients receiving insulin treatment than in those without. Excess death rate of DM patients was predominantly caused by cardiovascular causes.ConclusionIn this long-term follow-up study patient groups without diabetes had at least equal 16 years' survival after CABG than their matched background populations. Survival of DM patients started to deteriorate already few years after the operation.

Project description:Background:Metabolic syndrome is associated with an increased risk of diabetes. This study investigated the associations between the number of metabolic syndrome components and diabetes risk by age, sex and BMI. Methods:Data for 19,475,643 participants ??20 years old with no history of diabetes were obtained between 2009 and 2012 and were accessed using the South Korean National Health Insurance Service. Metabolic syndrome was defined according to the modified criteria of the National Cholesterol Education Program Adult Treatment Panel III. We assessed the risk of diabetes according to the number of metabolic syndrome components after stratifying the study participants into groups by age (20-39, 46-64, ??65 years), sex, and BMI (below or above 25). Results:During an average of 5.13 years of follow-up, the incidence rates of diabetes increased with the number of metabolic syndrome components. Age and BMI gradually increased with the number of metabolic syndrome components. The multivariable-adjusted hazard ratios (HRs) for incident diabetes were 1.401, 1.862, 2.47, 3.164 and 4.501 for participants with one through five components, respectively, compared with those without metabolic syndrome components. The risk of diabetes was 1.79-, 2.18-, and 3.05-times higher for participants ??65 years; 2.57-, 3.45-, and 5.18-times higher for participants 40-64 years; and 2.55-, 3.89-, and 6.31-times higher for participants 20-39 years of age with three through five components, respectively, compared to those with no components. There was no difference in the risk of diabetes between men and women. The HRs were 5.63 for participants with a BMI ??25 and 3.98 for those with a BMI <?25 among individuals with five components. Conclusions:The risk of diabetes was more strongly associated with the number of metabolic syndrome components among younger adults. In addition, the risk of diabetes across the number of metabolic syndrome components was greater in participants with a BMI ??25.

Project description:BackgroundGestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both the mother and the infant. Metabolomics has provided new insights into the pathology of GDM and systemic analysis of GDM with metabolites is required for providing more clues for GDM diagnosis and mechanism research. This study aims to reveal metabolic differences between normal pregnant women and GDM patients in the second- and third-trimester stages and to confirm the clinical relevance of these new findings.MethodsMetabolites were quantitated with the serum samples of 200 healthy pregnant women and 200 GDM women in the second trimester, 199 normal controls, and 199 GDM patients in the third trimester. Both function and pathway analyses were applied to explore biological roles involved in the two sets of metabolites. Then the trimester stage-specific GDM metabolite biomarkers were identified by combining machine learning approaches, and the logistic regression models were constructed to evaluate predictive efficiency. Finally, the weighted gene co-expression network analysis method was used to further capture the associations between metabolite modules with biomarkers and clinical indices.ResultsThis study revealed that 57 differentially expressed metabolites (DEMs) were discovered in the second-trimester group, among which the most significant one was 3-methyl-2-oxovaleric acid. Similarly, 72 DEMs were found in the third-trimester group, and the most significant metabolites were ketoleucine and alpha-ketoisovaleric acid. These DEMs were mainly involved in the metabolism pathway of amino acids, fatty acids and bile acids. The logistic regression models for selected metabolite biomarkers achieved the area under the curve values of 0.807 and 0.81 for the second- and third-trimester groups. Furthermore, significant associations were found between DEMs/biomarkers and GDM-related indices.ConclusionsMetabolic differences between healthy pregnant women and GDM patients were found. Associations between biomarkers and clinical indices were also investigated, which may provide insights into pathology of GDM.

Dataset Information

Metabolic Syndrome and Its Components Predict the Risk of Type 2 Diabetes Mellitus in the Mainland Chinese: A 3-Year Cohort Study.

Publications

Metabolic Syndrome and Its Components Predict the Risk of Type 2 Diabetes Mellitus in the Mainland Chinese: A 3-Year Cohort Study.

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