Cardiometabolic multimorbidity aggregation patterns, network mechanisms, and disease burden projections to 2040

Cardiometabolic multimorbidity is a growing public health challenge. Existing studies examine single diseases or isolated risk pathways, limiting understanding of how cardiometabolic diseases and risk factors cluster at the population level. We developed a national scale framework to characterize co-occurrence patterns, risk networks, and future burden. We used Global Burden of Disease data from 2010 to 2021 for adults aged 25 years and older in 204 countries and territories. We analyzed incidence rates of ischemic heart disease, stroke, and type 2 diabetes mellitus, together with 17 summary exposure values. Spatial analysis, machine learning, an adjusted Bayesian network model, and trend projection were used. Here we show five global patterns of cardiometabolic multimorbidity, with 55.4 percent of countries showing high burden for at least two diseases. The adjusted Bayesian network identified macro-level associations linking metabolic, behavioral, and environmental risk factors with disease co-occurrence. Compared with the classical Bayesian network, the adjusted model performed better when only partial risk information was available. However, when all risk factor layers were included, its performance converged with the classical model. This indicates that its main value lies in early risk stratification rather than full information prediction. Projections to 2040 suggest that type 2 diabetes mellitus will increase faster than ischemic heart disease and stroke. This country-level ecological analysis identifies global heterogeneity in cardiometabolic multimorbidity and highlights upstream risk factor clustering as a prevention target. The framework may support earlier public health planning but should not be interpreted as individual level causality. Ischemic heart disease, stroke, and type 2 diabetes mellitus often occur together and create a growing challenge for health systems worldwide. This study used public Global Burden of Disease data from 204 countries and territories to examine where these diseases cluster, which risk factors are linked to them, and how their burden may change by 2040. We found clear geographic differences in disease patterns and identified several shared risk factors, especially high fasting plasma glucose, high body mass index, high blood pressure, and unhealthy diet. Type 2 diabetes mellitus is projected to grow faster than ischemic heart disease and stroke. These findings may help countries identify high risk areas earlier and design prevention strategies that target several related diseases at the same time. Han, Hu, An, Zhang et al. analyze Global Burden of Disease data from 204 countries and territories using spatial analysis, machine learning, and Bayesian network modelling to map global patterns of cardiometabolic multimorbidity and associated risk factors. We identify five distinct global patterns, show that over half of countries have a high burden of at least two cardiometabolic diseases, and project that type 2 diabetes mellitus will rise faster than ischemic heart disease and stroke by 2040.