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.

肥胖和心脏代谢疾病经常（但并非总是）同时发生。区分心脏代谢风险与给定体重指数 （BMI） 的预期风险不同的亚群可能有助于心脏代谢疾病的精确预防。因此，我们在四个基于欧洲人群的队列 （N ≈ 173,000） 中进行了无监督聚类。我们检测到五种不一致的特征，包括心脏代谢生物标志物高于或低于预期的个体，因为他们的 BMI 通常会增加疾病风险，总共占总人口的 ~20%。特征不一致的人与一致的人在主要不良心血管事件 （MACE） 和 2 型糖尿病的患病率和未来风险方面有所不同。生物标志物中细微的 BMI 不一致影响了疾病风险。例如，血脂水平不一致的概率高 10% 与 MACE 风险高 5% 相关（女性风险比 1.05,95% 置信区间 1.03、1.06，P = 4.19 × ^10-10;男性风险比 1.05,95% 置信区间 1.04、1.06，P = 9.33 × ^10-14）。MACE 和 2 型糖尿病的多变量预测模型在纳入不一致的概况信息时表现更好 （似然比检验 P < 0.001）。这种增强代表了每 10,000 名测试个体正确避免 4-15 次额外正确干预和 37-135 次额外不必要干预的额外净收益。