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Abstract Background According to epidemiological studies, unhealthy dietary patterns and lifestyle lead to rising obesity and cardiometabolic diseases in Iran. Hybrid techniques were used to identify a dietary pattern characterized by fiber, folic acid, and carotenoid intake due to their association with cardiometabolic risk factors such as anthropometric measurements, blood pressure, lipid profile, C-Reactive Protein (CRP), Plasminogen Activator Inhibitor (PAI), Homeostatic Model Assessment Index (HOMA Index), cardiometabolic index (CMI), and monocyte chemoattractant protein (MCP-1). So, the objective of the recent study is to compare the reduced-rank regression (RRR) and partial least–squares (PLS) approaches to principal component analysis (PCA) for estimating diet-cardiometabolic risk factor correlations in Iranian obese women. Methods Data on dietary intake was gathered from 376 healthy overweight and obese females aged 18 to 65 years using a 147-item food frequency questionnaire (FFQ). In this cross-sectional study, participants were referred to health centers of Tehran. Dietary patterns were developed using PCA, PLS, and RRR, and their outputs were assessed to identify reasonable patterns connected to cardiometabolic risk factors. The response variables for PLS and RRR were fiber, folic acid, and carotenoid intake. Results In this study, 3 dietary patterns were identified by the PCA method, 2 dietary patterns by the PLS method, and one dietary pattern by the RRR method. High adherence to the plant-based dietary pattern identified by all methods were associated with higher fat free mass index (FFMI) (P

Abstract General and electronic health literacy are important factors engaging in healthy behaviors and maintaining good health. The present study explored demographic factors associated with general and electronic health literacy in the Iranian adult population. Via stratified cluster sampling, trained interviewers visited adult residents in Qazvin Province, Iran between January, and April 2022. The participants (N = 9775; mean age = 36.44 years; 6576 [67.3%] females) completed the Health Literacy Instrument for Adults (HELIA) assessing health literacy and the eHealth Literacy Scale (eHEALS) assessing electronic health literacy. Demographic data, including age, gender, educational level, marital status, and living location (city or rural), were collected. Latent class analysis (LCA) was used to classify the participants into different health literacy/electronic health literacy levels. The relationships between health literacy/electronic health literacy levels and demographic factors were examined using χ2 or analysis of variance. The LCA used HELIA scores to suggest five classes of health literacy and eHEALS scores to suggest three classes of electronic health literacy. For general and electronic health literacy, similar relationships were with demographic factors: females as compared with males had better general/electronic health literacy; younger people as compared with older people had better general/electronic health literacy; higher educational level was associated with better general/electronic health literacy; and city residents as compared with rural residents had better general/electronic health literacy. In conclusion, Iranian governmental agencies may wish to target on males, older adults, people with low educational level, and rural residents to improve their health literacy.