Back

Does the metabolic syndrome exist? The identification of cardio-metabolic profiles in a South Asian population study

Harber-Aschan, L.; Bakolis, I.; Glozier, N.; Ismail, K.; Jayaweera, K.; Pannala, G.; Pariante, C.; Rijsdijk, F.; Siribaddana, S.; Sumathipala, A.; Zavos, H.; Zunszain, P.; Hotopf, M.

2019-11-29 endocrinology
10.1101/19012195 medRxiv
Show abstract

OBJECTIVEResearch testing the validity of the metabolic syndrome (MetS) as a clinical construct associated with cardiovascular disease risk has produced inconsistent results. This study tested the existence of the MetS, explored alternative cardiometabolic risk characterisations, and examined the relative influence of genetic and environmental factors in a South Asian sample. RESEARCH DESIGN AND METHODSData came from the Colombo Twin and Singleton follow-up Study, CoTaSS-2 (N=3969). Latent class analysis tested the clustering of MetS indicators (waist circumference, high-density-lipoprotein cholesterol (HDL-C), triglycerides (TG), blood pressure, fasting plasma glucose (FPG), medications and diabetes). Regression analyses tested cross-sectional associations between identified latent classes and covariates. Structural equation modelling estimated genetic and environmental influences on these classes. Analyses were stratified by gender (n=1681 men, n=2288 women). RESULTSThree classes were identified in men: 1) "Healthy" (52.3%), 2) "Central obesity, high TG, high FPG" (40.2%), and 3) "Central obesity, high TG, diabetes" (7.6%). Four classes were identified in women: 1) "Healthy" (53.2%), 2) "Very high central obesity, low HDL-C, raised FPG" (32.8%), 3) "Very high central obesity, diabetes" (7.2%) and 4) "Central obesity, hypertension, raised FPG" (6.8%). Older age in men and women, and high socioeconomic status in men, was associated with cardiometabolic risk categories, compared to the "Healthy" classes. In men, individual differences in cardiometabolic class membership were due to environmental effects. In females, genetic differences significantly predicted class membership. CONCLUSIONSThe findings did not support the MetS construct. Instead, distinct clinical profiles were identified in men and women, suggesting different aetiological pathways.

Matching journals

The top 12 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.