Cardiovascular Risk Assessment Across Different Obesity Phenotypes

Background/Objectives: Obesity is a major risk factor for cardiovascular disease (CVD), but traditional risk calculators such as Systematic COronary Risk Evaluation (SCORE2) may not fully capture the elevated risks in individuals with obesity, especially when metabolic health is considered. This study aimed to evaluate the effectiveness of QRESEARCH risk estimator version 3 (QRISK3) in estimating 10-year cardiovascular risk in individuals with varying obesity phenotypes compared to SCORE2. Methods: A total of 88 participants (25 men, 63 women; mean age 37.4 ± 11.8 years) were categorized into four obesity phenotypes according to metabolic and anthropometric criteria. The 10-year CVD risk was calculated using SCORE2 and QRISK3 algorithms. Functional cardiovascular assessment included blood pressure (BP) measurement and electrocardiogram (ECG) interpretation for conduction abnormalities and left ventricular hypertrophy (LVH). Biochemical analysis included carbohydrate metabolism (fasting glucose, postprandial glucose, HbA1c) and lipid profile (total cholesterol, LDL-C, HDL-C, triglycerides, atherogenic index). Results: SCORE2 underestimated CVD risk (3–8%), whereas QRISK3 predicted higher values (6–16%), particularly in metabolically unhealthy phenotypes. LVH occurred in 26–45% of participants, with elevated BP and early subclinical ECG changes even in metabolically healthy obesity individuals. Carbohydrate metabolism disturbances were observed in metabolically unhealthy participants with normal or elevated BMI, while lipid abnormalities—including elevated total cholesterol, LDL-C, triglycerides, and atherogenic index—were prominent in these metabolically unhealthy phenotypes. Insulin resistance, assessed via the triglyceride–glucose index, exceeded reference ranges in all obesity phenotypes, with the highest values seen in metabolically unhealthy individuals. Conclusions: QRISK3 provides a more precise and thorough assessment of 10-year cardiovascular risk in individuals with obesity than SCORE2. These findings highlight the importance of incorporating anthropometric and metabolic data into cardiovascular risk assessments and support the clinical use of QRISK3 for more personalized risk stratification, especially in populations with obesity and metabolic disturbances. Early identification of high-risk individuals using QRISK3 could lead to more timely and targeted preventive interventions, improving long-term cardiovascular outcomes.