Assessment of fatal cardiovascular disease risk using data-driven diabetes subgroups and SCORE2-Diabetes in 24,943 adults in Mexico City

BACKGROUNDCardiovascular disease (CVD) is a leading cause of diabetes-related mortality in Mexico. Although diabetes subgroups capture underlying disease heterogeneity, their association and utility for risk prediction for fatal CVD in Mexican adults remain unclear. METHODSWe analyzed 24,943 adults with diabetes from the Mexico City Prospective Study. Participants were classified into mild obesity-related (MOD), severe insulin-deficient (SIDD), severe insulin-resistant (SIRD), and mild age-related (MARD) diabetes using a self-normalizing neural network algorithm. Fatal CVD was defined as death from ischemic heart disease or stroke (ICD-10 I20-I25, I60-I69). SCORE2-Diabetes was recalibrated and validated overall and by diabetes subgroup. Cox proportional hazards models were used to estimate subgroup-specific risk, and sequential models evaluated the incremental predictive value of diabetes subgroups combined with SCORE2-Diabetes and traditional risk factors. RESULTSOver a median follow-up of 19.3 years (IQR 12.7-20.6), 2,223 fatal CVD events (8.9%) were recorded. SIDD was the most prevalent subgroup (50.6%), followed by SIRD (17.3%), MARD (16.8%), and MOD (15.4%). SIDD and MARD showed the highest adjusted risk of fatal CVD (RR 1.58 [95%CI 1.38-1.81] and 1.35 [1.13-1.60]), whereas MOD and SIRD had lower risk. Recalibrated SCORE2-Diabetes demonstrated adequate discrimination overall (c-statistic 0.759, 95%CI 0.745-0.773) and for most subgroups but underperformed in MARD, with recalibration improving risk assessment. The combination of diabetes subgroups and SCORE2-Diabetes improved prediction for fatal CVD outcomes. CONCLUSIONSDiabetes subgroups show heterogeneity in fatal CVD risk in Mexican adults. SIDD and MARD identify high-risk individuals and integration subgroup classification with SCORE2-Diabetes enhances risk prediction.