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Assessing Baseline and Temporal Changes in Cardiometabolic Risk Using Metabolic Syndrome Severity and Common Risk Scores.

ABSTRACT: Background Type 2 diabetes mellitus (T2 DM ) is considered a cardiovascular disease ( CVD ) risk equivalent, thereby linking assessment of cardiometabolic risk with that of CVD risk over time. Our goal was to determine how commonly used CVD risk scores and metabolic syndrome (MetS) severity performed in predicting T2 DM with and without ultimate CVD . Methods and Results We assessed data from 8273 participants of the ARIC (Atherosclerosis Risk in Communities) Study, using the pooled cohort atherosclerotic CVD risk score, the Framingham Risk Score, and a MetS severity Z score to assess their association with future risk for CVD alone, T2 DM alone, or both over 20 years of follow-up. Baseline levels of all scores were significantly associated with isolated incident T2 DM (odds ratios [ OR s] for each 1- SD increase: atherosclerotic CVD =1.7, Framingham risk score=1.7, MetS Z score=5.1). All 3 baseline scores were also significantly associated with isolated incident CVD (atherosclerotic CVD OR =2.4, Framingham risk score OR =2.3, MetS Z-score OR =1.8), with the 2 CVD scores remaining significant independent of MetS severity. MetS severity was strongly associated with future T2 DM leading to CVD (MetS Z-score OR =7.0, atherosclerotic CVD OR =3.9, Framingham risk score OR =3.5). Furthermore, changes in MetS severity were independently associated with future T2 DM - CVD progression. Conclusions CVD risk scores are associated with risk for future isolated T2 DM in addition to isolated CVD . However, MetS severity (both baseline and changes over time) was more strongly associated with T2 DM , including T2 DM ultimately leading to CVD . Following MetS severity within patients over time may identify those at greatest risk of combined cardiometabolic disease.

SUBMITTER: Gurka MJ

PROVIDER: S-EPMC6201393 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Assessing Baseline and Temporal Changes in Cardiometabolic Risk Using Metabolic Syndrome Severity and Common Risk Scores.

Gurka Matthew J MJ Filipp Stephanie L SL Pearson Thomas A TA DeBoer Mark D MD

Journal of the American Heart Association 20180801 16

Background Type 2 diabetes mellitus (T2 DM ) is considered a cardiovascular disease ( CVD ) risk equivalent, thereby linking assessment of cardiometabolic risk with that of CVD risk over time. Our goal was to determine how commonly used CVD risk scores and metabolic syndrome (MetS) severity performed in predicting T2 DM with and without ultimate CVD . Methods and Results We assessed data from 8273 participants of the ARIC (Atherosclerosis Risk in Communities) Study, using the pooled cohort ather ...[more]

PMID: 30369320

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Project description:Objective: Adiposity induces the clustering of cardiometabolic risk factors, and pediatric adiposity is a better indicator for adulthood cardiometabolic diseases than pediatric metabolic syndrome. However, the observed prevalence of pediatric adiposity depends on the methods and cut-points used. Therefore, we aimed to define diagnostic criteria for adiposity which enable more valid identification of prepubertal children at increased cardiometabolic risk. Methods: The participants were 470 prepubertal children (249 boys) aged 6-8 years. The measures of adiposity included body mass index-standard deviation score (BMI-SDS), waist-to-height ratio (WHtR) and body fat percentage (BF%) assessed by bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA). Criteria for adiposity were determined by increased cardiometabolic risk. Cardiometabolic risk factors which correlated with BF% assessed by DXA in the upper but not lower half of BF% (serum insulin and plasma high-density lipoprotein cholesterol, triglycerides, gamma-glutamyl transferase, high-sensitivity C-reactive protein and uric acid) were included in the cardiometabolic risk score (CMS). We computed receiver operating characteristics curves for the measures of adiposity using the ≥90th percentile of CMS as a measure of increased cardiometabolic risk, and local regression curves were graphed to demonstrate the associations of the measures of adiposity with CMS. Results: In girls, WHtR of 0.445 (area under curve 0.778, its 95% confidence interval 0.65-0.91, sensitivity and specificity 0.73) and BF% of 19.5% assessed by BIA (0.801, 0.70-0.90, 0.73) were the best overall criteria for increased cardiometabolic risk. In boys, BMI-SDS of 0.48 (0.833, 0.75-0.92, 0.76) was the best overall criterion for increased cardiometabolic risk. While local regression curves in girls showed that WHtR of 0.445 corresponds well to a point where CMS began to increase, in boys local regression curves suggest that CMS began to increase even at a lower level of BMI-SDS than 0.48. Moreover, the diagnostic ability of the measures of adiposity to exclude increased cardiometabolic risk was poorer than the ability to detect it. Conclusions: In general, the measures of adiposity have sufficient diagnostic accuracy to be utilized as the screening tool for increased cardiometabolic risk. The observed cut-points for adiposity were lower than the traditional cut-points for adiposity.

Project description:Polygenic risk scores (PRS) have predominantly been derived from genome-wide association studies (GWAS) conducted in European ancestry (EUR) individuals. In this study, we present an in-depth evaluation of PRS based on multi-ancestry GWAS for five cardiometabolic phenotypes in the Penn Medicine BioBank (PMBB) followed by a phenome-wide association study (PheWAS). We examine the PRS performance across all individuals and separately in African ancestry (AFR) and EUR ancestry groups. For AFR individuals, PRS derived using the multi-ancestry LD panel showed a higher effect size for four out of five PRSs (DBP, SBP, T2D, and BMI) than those derived from the AFR LD panel. In contrast, for EUR individuals, the multi-ancestry LD panel PRS demonstrated a higher effect size for two out of five PRSs (SBP and T2D) compared to the EUR LD panel. These findings underscore the potential benefits of utilizing a multi-ancestry LD panel for PRS derivation in diverse genetic backgrounds and demonstrate overall robustness in all individuals. Our results also revealed significant associations between PRS and various phenotypic categories. For instance, CAD PRS was linked with 18 phenotypes in AFR and 82 in EUR, while T2D PRS correlated with 84 phenotypes in AFR and 78 in EUR. Notably, associations like hyperlipidemia, renal failure, atrial fibrillation, coronary atherosclerosis, obesity, and hypertension were observed across different PRSs in both AFR and EUR groups, with varying effect sizes and significance levels. However, in AFR individuals, the strength and number of PRS associations with other phenotypes were generally reduced compared to EUR individuals. Our study underscores the need for future research to prioritize 1) conducting GWAS in diverse ancestry groups and 2) creating a cosmopolitan PRS methodology that is universally applicable across all genetic backgrounds. Such advances will foster a more equitable and personalized approach to precision medicine.

Dataset Information

Assessing Baseline and Temporal Changes in Cardiometabolic Risk Using Metabolic Syndrome Severity and Common Risk Scores.

Publications

Assessing Baseline and Temporal Changes in Cardiometabolic Risk Using Metabolic Syndrome Severity and Common Risk Scores.

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