Principal Components of Type 2 Diabetes Risk: An Exploratory Factor Analysis in an Iranian Cohort Publisher Pubmed

Summary: What drives T2D risk? PCA study finds lipids, BMI & chronic diseases increase T2D odds. #Type2Diabetes #RiskFactors

Geravandi S¹ ; Emamgholipour S^{1, 2} ; Pakdaman M³ ; Sari AA¹ ; Esmaeili A⁴
Source: BMC Public Health Published:2025

Abstract

Background: The significance of interrelated risk factors for Type 2 diabetes (T2D) is not easily demonstrated by conventional statistical methods. This study aims to investigate the principal components of T2D risk by employing exploratory factor analysis in Iranian cohort. The analysis encompasses a range of sociodemographic, lifestyle, and health-related variables to uncover clusters of factors associated with the risk of T2D. Methods: Cross-sectional data of 1200 diabetic and 1200 nondiabetic Iranian adults was analyzed using STATA 14.2 (p < 0.05). Pearson’s Chi-squared test was used to assess the difference between the two groups. Spearman correlation explored the relationships between variables. Separate factor analyses were conducted for diabetic, non-diabetic, and combined groups. Principal component analysis (PCA) identified the initial components. Crude and adjusted logistic regressions examined the associations between derived factors and T2D risk. Results: PCA identified eleven components with eigenvalues ≥ 1, accounting for 65.09% of the variance. Logistic regression analysis highlighted several significant associations with T2D risk. Positive associations were observed for PC1 (“drugs, smoking, and alcohol”), PC2 (“chronic diseases”, including age, hypertension, dyslipidemia, and coronary heart disease), PC3 (“lipids”, such as triglycerides, cholesterol, and low-density lipoprotein), PC4 (“body mass”, including BMI, waist circumference, and waist-to-hip ratio), PC5 (“gestational-related risks”, such as gestational diabetes and gestational hypertension), and PC6 (“glucose/lipid factors”, including fasting glucose, triglycerides, and an inverse relationship with high-density lipoprotein). Conversely, negative associations with T2D risk were found for PC7 (“socioeconomic factors”, such as socioeconomic status and education), PC8 (inverse association with age, along with fatty liver, thyroid disorders, and low waist-to-hip ratio), and PC10 (marital status, sleep duration, low fasting glucose, lower age, and an inverse association with fatty liver). Conclusions: Key metabolic clusters, including “Lipids”, “Body Mass”, “Chronic Diseases”, and “Glucose/Lipid” align with previous findings. These results underscore the multifactorial and interconnected nature of T2D risk, highlighting underlying physiological processes. © The Author(s) 2025.