The Association Between Beverages Consumption Patterns, Metabolic Risk Factors, and Anthropometric Features Among Overweight and Obese Adults: A Cross-Sectional Study.

Background: Obesity, as one of the main causes of metabolic diseases, is a global public health concern owing to its prevalence. While some consumed beverages have shown favorable effects on obesity and components of metabolic syndrome (MetS), others have not. Additionally, most studies have investigated the effect of specific beverage consumption on obesity and MetS components, rather than overall beverage consumption patterns. Therefore, this cross-sectional study aims to investigate the association between beverage consumption patterns and cardiometabolic risk factors in individuals with obesity. Method: This cross-sectional study was conducted on 150 obese and overweight participants (44.0% males and 56.0% females) aged 18 to 65. Participants' general characteristics, anthropometric, dietary, and biochemical assessments were obtained. The principal component analysis (PCA) was used to determine beverage consumption patterns, with varimax rotation. Three beverage consumption patterns were obtained based on eigenvalues >0.3, scree plot, and interpretability of factors. Multivariate multinomial logistic regression was conducted to estimate the odds ratio (OR) and 95% confidence interval of the association between beverage consumption patterns and cardio-metabolic risk factors for each beverage pattern after controlling for covariates. Results: Three major beverage consumption patterns were identified using principal component analysis: the "fruit juice" pattern (high loadings of apple juice, fruit juice, sugar-sweetened beverages [SSBs], soft drinks, cantaloupe juice, and orange juice); the "milk and milk product" pattern (high loadings for milk alternatives, chocolate milk, unsweetened coffee, packaged fruit juice, milk, low-fat milk, dough, and khakshir); and the "tea and water" pattern (high consumption of sweetened tea, unsweetened tea, and water). The ORs (95% CIs) of associations of the "fruit juice" pattern with risks of elevated FBG were 1.031 (1.002-1.061), 1.033 (1.002-1.065), and 1.034 (1.002-1.066), respectively for model I, II, and III. Participants with higher intake of "fruit juice" pattern were more likely to have lower LDL-C level, in all three models (0.986 [0.973-0.999], 0.987 [0.973-0.999], and 0.983 [0.96-0.997], respectively). Also, there was a positive association between "fruit juice" pattern and triglyceride (TG) level (1.007 [1.001-1.015]), only in the crude model. There was no significant association between the tertiles of the "milk and milk product" pattern and the odds of cardiometabolic risk factors. Upon adjusting for potential confounders, we found that individuals in the second tertile of the "tea and water" pattern showed lower HDL-C compare to the first tertile (0.950 [0.903-0.999]). The study analyzed the association between beverage consumption pattern and anthropometric factors. Participants in the second tertile of "fruit juice" pattern had lower body mass index (BMI; 0.878 [0.792-0.972], 0.895 [0.805-0.995], and 0.891 [0.800-0.992], respectively) and hip circumference (HC; 0.938 [0.893-0.984], 0.947 [0.901-0.996], and 0.947 [0.901-0.996], respectively). Conclusion: In this study, the "fruit juice" pattern was associated with lower serum LDL-C levels, but higher FBS and TG levels. Understanding the effect of beverage consumption patterns on cardiometabolic health in obesity is important for developing risk management strategies, but further research is needed for more conclusive results.