Association between cardiometabolic phenotypes with atherogenic index of plasma: a cross-sectional study from the Azar cohort population.

Background: Cardiometabolic phenotypes combine metabolic health and obesity measures for a more accurate cardiovascular risk assessment than body mass index (BMI) alone. The Atherogenic Index of Plasma (AIP), based on triglycerides and high-density lipoprotein cholesterol (HDL-C), is a promising cardiovascular disease (CVD) risk marker. This study explores the relationship between cardiometabolic phenotypes and AIP to improve understanding of their combined predictive value for CVD risk.

Methods: This cross-sectional study analyzed data from 9,515 participants aged 35-55 in the Azar Cohort Study. Metabolic syndrome (MetS) was defined using ATP III criteria. Participants were classified into four phenotypes: metabolically healthy normal weight (MHNW, BMI < 25 kg/m), metabolically unhealthy normal weight (MUHNW, BMI < 25 kg/m), metabolically healthy obese (MHO, BMI ≥ 25 kg/m), and metabolically unhealthy obese (MUHO, BMI ≥ 25 kg/m). AIP was calculated as the logarithm of triglycerides to HDL. Multinomial regression was used to analyze the relationship between AIP tertiles and phenotypes using both unadjusted and adjusted models. Confounders were controlled for across the three groups.

Results: Among participants, 50.4% were classified as MHO and 28.2% as MUHO. High-risk AIP levels (> 0.21) were found in 79.6% of MUHNW and 64.6% of MUHO, compared to 18.6% of MHO and 13.5% of MHNW. After adjusting for age, physical activity (METs), the wealth score index (WSI), and smoking status, the odds ratios (ORs) for cardiometabolic phenotypes in the high-risk AIP group remained significant. At the high-risk level, MUHNW (OR = 63.49, p < 0.001), MHO (OR = 1.96, p < 0.001), and MUHO (OR = 32.15, p < 0.001) showed significant associations.

Conclusion: In this study, significant associations were found between cardiometabolic phenotypes and AIP. Findings emphasize integrating metabolic health assessments with AIP to improve CVD risk identification. Prospective longitudinal studies are also needed to confirm these associations.