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      Trends in baseline triglyceride-glucose index and association with predicted 10-year cardiovascular disease risk among type 2 diabetes patients in Thailand

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          Abstract

          Triglyceride-glucose (TyG) index is an independent risk factor for cardiovascular diseases (CVD). Our study determined the trends of the TyG index and its relationship to predicted CVD risk among patients with type 2 diabetes (T2D). A serial cross-sectional study was conducted including 63,815 participants with T2D aged 30–74 years without a history of CVD. The predicted CVD risk was based on the Framingham Heart Study (FHS). The receiver operating characteristic (ROC) curve was utilized for identifying the cutoff point of TyG index to predict intermediate-to-high CVD risk. The relationship between TyG index and predicted CVD risk was tested using linear and logistic regression. Decreasing trends of TyG index were observed between 2014 and 2018 ( p < 0.001). ROC curve analysis of the TyG index indicated an AUC of 0.57 (95% CI 0.56–0.57, p < 0.001) in predicting intermediate-to-high predicted CVD risk, with a cutoff value of TyG index > 9.2 (sensitivity of 55.7%, specificity of 46.8%). An independent relationship between the TyG index and predicted CVD risk was observed. High TyG index was independently associated with intermediate-to-high predicted CVD risk. From our study, the TyG index was positively related to predicted 10-year CVD risk. However, the predictive ability of the TyG index in predicting the intermediate-to-high predicted 10-year CVD risk among patients with T2D remained questionable.

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          General cardiovascular risk profile for use in primary care: the Framingham Heart Study.

          Separate multivariable risk algorithms are commonly used to assess risk of specific atherosclerotic cardiovascular disease (CVD) events, ie, coronary heart disease, cerebrovascular disease, peripheral vascular disease, and heart failure. The present report presents a single multivariable risk function that predicts risk of developing all CVD and of its constituents. We used Cox proportional-hazards regression to evaluate the risk of developing a first CVD event in 8491 Framingham study participants (mean age, 49 years; 4522 women) who attended a routine examination between 30 and 74 years of age and were free of CVD. Sex-specific multivariable risk functions ("general CVD" algorithms) were derived that incorporated age, total and high-density lipoprotein cholesterol, systolic blood pressure, treatment for hypertension, smoking, and diabetes status. We assessed the performance of the general CVD algorithms for predicting individual CVD events (coronary heart disease, stroke, peripheral artery disease, or heart failure). Over 12 years of follow-up, 1174 participants (456 women) developed a first CVD event. All traditional risk factors evaluated predicted CVD risk (multivariable-adjusted P<0.0001). The general CVD algorithm demonstrated good discrimination (C statistic, 0.763 [men] and 0.793 [women]) and calibration. Simple adjustments to the general CVD risk algorithms allowed estimation of the risks of each CVD component. Two simple risk scores are presented, 1 based on all traditional risk factors and the other based on non-laboratory-based predictors. A sex-specific multivariable risk factor algorithm can be conveniently used to assess general CVD risk and risk of individual CVD events (coronary, cerebrovascular, and peripheral arterial disease and heart failure). The estimated absolute CVD event rates can be used to quantify risk and to guide preventive care.
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            The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects.

            Because the insulin test is expensive and is not available in most laboratories in the cities of undeveloped countries, we tested whether the product of fasting triglycerides and glucose levels (TyG) is a surrogate for estimating insulin resistance compared with the homeostasis model assessment of insulin resistance (HOMA-IR) index. We performed a population-based cross-sectional study. Sampling strategy was based on a randomized two-stage cluster sampling procedure. Only apparently healthy subjects, men and nonpregnant women aged 18-65 years, with newly diagnosed impaired fasting glucose (IFG), impaired glucose tolerance (IGT), or IFG + IGT were enrolled. Renal disease, malignancy, and diabetes were exclusion criteria. Sensitivity, specificity, predictive values, and the probability of disease given a positive test were calculated. The optimal TyG index for estimating insulin resistance was established using a receiver operating characteristic scatter plot analysis. A total of 748 apparently healthy subjects aged 41.4 +/- 11.2 years were enrolled. Insulin resistance was identified in 241 (32.2%) subjects (HOMA-IR index 4.4 +/- 1.6). New diagnoses of IFG, IGT, and IFG + IGT were established in 145 (19.4%), 54 (7.2%), and 75 (10.0%) individuals. respectively. The best TyG index for diagnosis of insulin resistance was Ln 4.65, which showed the highest sensitivity (84.0%) and specificity (45.0%) values. The positive and negative predictive values were 81.1% and 84.8%, and the probability of disease, given a positive test, was 60.5%. The TyG index could be useful as surrogate to identify insulin resistance in apparently healthy subjects.
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              World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions

              Summary Background To help adapt cardiovascular disease risk prediction approaches to low-income and middle-income countries, WHO has convened an effort to develop, evaluate, and illustrate revised risk models. Here, we report the derivation, validation, and illustration of the revised WHO cardiovascular disease risk prediction charts that have been adapted to the circumstances of 21 global regions. Methods In this model revision initiative, we derived 10-year risk prediction models for fatal and non-fatal cardiovascular disease (ie, myocardial infarction and stroke) using individual participant data from the Emerging Risk Factors Collaboration. Models included information on age, smoking status, systolic blood pressure, history of diabetes, and total cholesterol. For derivation, we included participants aged 40–80 years without a known baseline history of cardiovascular disease, who were followed up until the first myocardial infarction, fatal coronary heart disease, or stroke event. We recalibrated models using age-specific and sex-specific incidences and risk factor values available from 21 global regions. For external validation, we analysed individual participant data from studies distinct from those used in model derivation. We illustrated models by analysing data on a further 123 743 individuals from surveys in 79 countries collected with the WHO STEPwise Approach to Surveillance. Findings Our risk model derivation involved 376 177 individuals from 85 cohorts, and 19 333 incident cardiovascular events recorded during 10 years of follow-up. The derived risk prediction models discriminated well in external validation cohorts (19 cohorts, 1 096 061 individuals, 25 950 cardiovascular disease events), with Harrell's C indices ranging from 0·685 (95% CI 0·629–0·741) to 0·833 (0·783–0·882). For a given risk factor profile, we found substantial variation across global regions in the estimated 10-year predicted risk. For example, estimated cardiovascular disease risk for a 60-year-old male smoker without diabetes and with systolic blood pressure of 140 mm Hg and total cholesterol of 5 mmol/L ranged from 11% in Andean Latin America to 30% in central Asia. When applied to data from 79 countries (mostly low-income and middle-income countries), the proportion of individuals aged 40–64 years estimated to be at greater than 20% risk ranged from less than 1% in Uganda to more than 16% in Egypt. Interpretation We have derived, calibrated, and validated new WHO risk prediction models to estimate cardiovascular disease risk in 21 Global Burden of Disease regions. The widespread use of these models could enhance the accuracy, practicability, and sustainability of efforts to reduce the burden of cardiovascular disease worldwide. Funding World Health Organization, British Heart Foundation (BHF), BHF Cambridge Centre for Research Excellence, UK Medical Research Council, and National Institute for Health Research.
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                Author and article information

                Contributors
                boonsub1991@pcm.ac.th
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                10 August 2023
                10 August 2023
                2023
                : 13
                : 12960
                Affiliations
                [1 ]GRID grid.10223.32, ISNI 0000 0004 1937 0490, Department of Pharmacology, , Phramongkutklao College of Medicine, ; Bangkok, 10400 Thailand
                [2 ]GRID grid.10223.32, ISNI 0000 0004 1937 0490, Department of Parasitology, , Phramongkutklao College of Medicine, ; Bangkok, 10400 Thailand
                [3 ]GRID grid.10223.32, ISNI 0000 0004 1937 0490, Department of Military and Community Medicine, , Phramongkutklao College of Medicine, ; Bangkok, 10400 Thailand
                Article
                40299
                10.1038/s41598-023-40299-y
                10415402
                37563268
                e9b3f29a-581a-43d2-b566-f6e1adacb304
                © Springer Nature Limited 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 27 December 2022
                : 8 August 2023
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                © Springer Nature Limited 2023

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                epidemiology,endocrine system and metabolic diseases,public health
                Uncategorized
                epidemiology, endocrine system and metabolic diseases, public health

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