Dietary patterns derived by RRR and PCA in relation to insulin resistance and adiposity: a cross-sectional study

Insulin resistance is a key contributor to metabolic and cardiovascular diseases. This study compares dietary patterns identified using reduced rank regression (RRR) and principal component analysis (PCA) in relation to insulin resistance and adiposity indices among Iranian adults. This cross-sectional study recruited 850 adults aged 20–59 from health centers in Tehran in 2020. Dietary intake was assessed using a validated 168-item semi-quantitative food frequency questionnaire (sq-FFQ). The triglyceride glucose (TyG) index and lipid accumulation product (LAP) were used as insulin resistance markers, while the body roundness index (BRI), visceral adiposity index (VAI), and body adiposity index (BAI) were used as adiposity indices. PCA-derived dietary patterns were labeled as “Healthy” and “Mixed,” while a single RRR-derived pattern was extracted. Logistic regression models estimated odds ratios (ORs) and 95% confidence intervals (CIs) across tertiles of dietary patterns, adjusting for confounders. Participants had a mean age of 44.76 years and mean BMI of 27.77 kg/m². The RRR pattern, characterized by high intake of fruits, organ meat, eggs, sweets, nuts, and refined grains, was significantly associated with a lower OR for VAI (OR: 0.53, CI: 0.28–0.97, p-trend: 0.041). PCA-derived patterns were not significantly associated with insulin resistance biomarkers or adiposity indices. These findings highlight the potential of RRR in capturing dietary patterns in Iranian adults that may be relevant to metabolic health.