Subclassification of obesity for precision prediction of cardiometabolic diseases

Obesity and cardiometabolic disease often, but not always, coincide. Distinguishing subpopulations within which cardiometabolic risk diverges from the risk expected for a given body mass index (BMI) may facilitate precision prevention of cardiometabolic diseases. Accordingly, we performed unsupervised clustering in four European population-based cohorts (N ≈ 173,000). We detected five discordant profiles consisting of individuals with cardiometabolic biomarkers higher or lower than expected given their BMI, which generally increases disease risk, in total representing ~20% of the total population. Persons with discordant profiles differed from concordant individuals in prevalence and future risk of major adverse cardiovascular events (MACE) and type 2 diabetes. Subtle BMI-discordances in biomarkers affected disease risk. For instance, a 10% higher probability of having a discordant lipid profile was associated with a 5% higher risk of MACE (hazard ratio in women 1.05, 95% confidence interval 1.03, 1.06, P = 4.19 × 10−10; hazard ratio in men 1.05, 95% confidence interval 1.04, 1.06, P = 9.33 × 10−14). Multivariate prediction models for MACE and type 2 diabetes performed better when incorporating discordant profile information (likelihood ratio test P < 0.001). This enhancement represents an additional net benefit of 4−15 additional correct interventions and 37−135 additional unnecessary interventions correctly avoided for every 10,000 individuals tested. A precision medicine approach used unsupervised clustering to identify five distinct phenotypic profiles that can better predict risks of cardiometabolic disease compared with those ascertained based on the additive value of body mass index and other biomarkers, and validated these findings across four independent cohorts.