The metabolic association of different obesity phenotypes and their dietary intake with the Atherogenic Index of Plasma in the Azar Cohort population.

Abstract

ract Background: Obesity is a key risk factor for metabolic disorders, which is commonly assessed using the Body Mass Index (BMI). Current classifications define four obesity phenotypes based on BMI and metabolic syndrome (MetS) criteria: metabolically healthy normal weight, metabolically unhealthy normal weight, metabolically healthy obese, and metabolically unhealthy obese. The Atherogenic Index of Plasma (AIP), calculated as the logarithmic ratio of triglycerides (TG) to high-density lipoprotein cholesterol (HDL-c), is a strong predictor of metabolic and cardiovascular risks and can be an effective tool for assessing the metabolic risks associated with different obesity phenotypes and their dietary intake. This study was designed to investigate the relationship between metabolic phenotypes, dietary intake, and AIP in the Azar cohort population. Methods: This cross-sectional study utilized data from 9,515 participants aged 35 to 55 years from the Azar cohort study. Participants were classified into four different phenotypes based on BMI and MetS criteria: metabolically healthy normal weight (MHNW: BMI < 25 kg/m²), metabolically unhealthy normal weight (MUHNW: BMI < 25 kg/m²), metabolically healthy obese (MHO: BMI ≥ 25 kg/m²), and metabolically unhealthy obese (MUHO: BMI ≥ 25 kg/m²). Dietary intake was assessed using a semi-quantitative food frequency questionnaire (FFQ) containing 130 items. AIP was calculated as the logarithmic ratio of TG to HDL-c and categorized into three levels: low risk (AIP < 0.11), moderate risk (0.11 ≤ AIP < 0.21), and high risk (AIP > 0.21). To analyze the relationship between different obesity phenotypes and AIP, binary logistic regression was used, adjusting for age, sex, socioeconomic status, and physical activity. The correlation between dietary intake and AIP in different obesity phenotypes was assessed using partial correlation, adjusting for the same confounding factors. Results: The highest prevalence was observed in the MHO phenotype (50.4%) and the MUHO phenotype (28.2%). The prevalence of MHNW (20.3%) and MUHNW (1.1%) phenotypes was lower. The mean AIP index for the entire population was 0.12 ± 0.25, with the highest AIP found in the MUHNW phenotype (0.34 ± 0.20) and the lowest in the MHNW phenotype (0.042 ± 0.21). After adjusting for confounders, the odds of being classified in the high-risk AIP category (AIP > 0.21) were 103.46 times higher in the MUNHW, 2.64 times higher in the MHO phenotype, and 55.77 times higher in the MUHO phenotype. Significant differences in energy intake and percentage of energy from protein were observed between different phenotypes (p < 0.001). After adjusting for confounders, most correlations were significantly reduced or not significant, except for energy, carbohydrate, and protein intake with AIP, which showed weak but significant correlations in the MUHO phenotype (p = 0.01, r = 0.05; p = 0.03, r = 0.05; p = 0.01, r = 0.048). Similarly, carbohydrate and lipid intake showed weak but significant correlations with AIP in the MHO phenotype (p < 0.001, r = 0.55; p = 0.01, r = 0.34). Conclusion: This study found a significant association between AIP and different obesity phenotypes. A weak but significant relationship was observed between energy, carbohydrate, and protein intake and AIP in the MUHO phenotype and between carbohydrate and fat intake and AIP in the MHO phenotype, highlighting the role of energy and macronutrients in certain phenotypes with AIP. Longitudinal studies on the effects of energy and macronutrient composition with AIP in different obesity phenotypes are necessary. Keywords: Obesity phenotypes, Diet, atherogenic index of plasma.