Open AccessA structural equation modeling approach for the association of a healthy eating index with metabolic syndrome and cardio-metabolic risk factors among obese individuals
Author(s) -
Mahdieh Khodarahmi,
Mohammad AsghariJafarabadi,
Mahdieh Abbasalizad Farhangi
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0219193
Subject(s) - structural equation modeling , metabolic syndrome , body mass index , obesity , association (psychology) , medicine , psychology , mathematics , statistics , psychotherapist
Background Numerous studies have evaluated the association between dietary factors and cardiovascular risk among patients with chronic disease. It is worthwhile to assess these associations in a combination model rather than in an isolated form. In the current study, we aimed to use structural equation modeling (SEM) to assess the association of adherence to a healthy eating index (HEI)-2015 with socio-demographic factors, psychological characteristics, metabolic syndrome (MetS) and other cardio-metabolic risk factors among obese individuals. Methods This cross-sectional study was conducted among 188 healthy obese adults (96 males and 92 females) aged 20–50 years in Tabriz. A validated semi-quantitative food frequency questionnaire (FFQ) was used to record dietary intake and to estimate HEI-2015. Anthropometric parameters, blood pressure and biochemical measurements were evaluated according to standard protocols. Interrelationships among socio-demographic parameters and HEI with cardio-metabolic risk factors were analyzed using SEM. Results The results of SEM analysis revealed that HEI mediated the association between age and several cardio-metabolic risk factors including fat mass (FM), fat free mass (FFM), systolic blood pressure (SBP) and high-density lipoprotein (HDL) (p < 0.05). Moreover, adherence to Dietary Guidelines for Americans (DGA) appears to mediate association between gender and waist circumference (B = -9.78), SBP (B = -4.83), triglyceride (B = -13.01) and HDL (B = 4.31). HEI also mediated indirect negative effects of socioeconomic status on FM (B = -0.56), FFM (B = -0.25), SBP (B = -0.55) and diastolic blood pressure (DBP) (B = -0.3). Additionally, depression and age had indirect unfavorable effects on some insulin resistance indices such as homeostasis model assessment of insulin resistance (B = 0.07; p<0.05, for age) and quantitative insulin sensitivity check index (p<0.05, for age and depression) via HEI. High adherence to HEI was found to be inversely associated with MetS risk (p<0.05). Conclusion Adherence to HEI-2015 seems to mediate the effect of socio-demographic parameters and mental health on cardio-metabolic risk factors as well as MetS risk. Further studies are needed to confirm these findings.