A latent class analysis of cardiometabolic risk factors and the predicted prevalence of subclinical atherosclerosis in middle-aged Swedish adults

Previous research on cardiometabolic risk has mostly used a variable-centred approach, assessing risk factors separately or in predefined combinations. This study used a probabilistic modelling approach to identify distinct cardiometabolic risk classes and estimate the predicted prevalence of subclinical atherosclerosis. The analysis included 28,307 middle-aged adults from the Swedish CArdioPulmonary bioImage Study (2013–2018), linked to national registers. Eleven risk factors were assessed: smoking, alcohol consumption, sodium and fibre intake, physical activity, stress, waist circumference, triglycerides, HDL-cholesterol, blood pressure, and fasting glucose. Subclinical atherosclerosis was defined using coronary artery calcium (CAC) scores and the presence of carotid plaque. A three-step latent class analysis identified four cardiometabolic risk classes: “low fibre intake and normolipidemia” (55.2%, Class 1), “high sodium intake and normolipidemia” (12.8%, Class 2), “unhealthy lifestyle and heightened metabolic risk” (10.1%, Class 3), and “unhealthy lifestyle and high metabolic risk” (21.9%, Class 4). Predicted mean CAC scores ranged from 42.6 (Class 2, 95% CI 39.0–46.3) to 92.1 (Class 4, 95% CI 86.2–98.0). Predicted carotid plaque prevalence ranged from 51.6% (Class 2, 95% CI 50.6–52.6) to 60.8% (Class 4, 95% CI 59.8–61.9). Latent classes offered a complementary descriptive framework beyond single risk factors, supporting more tailored prevention according to risk profiles.