The Relationship between Dietary Patterns and Metabolic Phenotypes: A Cross-Sectional Study in a Representative Sample of Iranian Industrial Employees.
Hamidreza RoohafzaAwat FeiziShahnaz Amani TiraniSahar Golpour-HamedaniSaeede Jafari NasabNizal SarrafzadeganPublished in: Metabolic syndrome and related disorders (2024)
Background and Aim: There is limited evidence to support the relationship between dietary patterns and metabolic phenotypes. Therefore, this study aimed to assess the association of dietary patterns with metabolic phenotypes among a large sample of Iranian industrial employees. Methods: This cross-sectional study was conducted among 3,063 employees of Esfahan Steel Company, Iran. Using exploratory factor analysis, major dietary patterns were obtained from a validated short form of food frequency questionnaire. The metabolic phenotypes were defined according to Adult Treatment Panel III guidelines. The independent-sample t -test, one-way analysis of variance, χ 2 test, and multivariable logistic regression were applied to analyze data. Results: Three major dietary patterns were identified by factor analysis: the Western dietary pattern, the healthy dietary pattern, and the traditional dietary pattern. After controlling for potential confounders, subjects in the highest tertile of Western dietary pattern score had a higher odds ratio (OR) for metabolically healthy obese (MHO; OR 1.58, 95% confidence interval [CI]: 1.29-1.94), metabolically unhealthy normal weight (OR 1.93, 95% CI 1.08-3.45), and metabolically unhealthy obese (MUHO) phenotypes (OR 2.87, 95% CI 2.05-4.03) than those in the lowest tertile. Also, higher adherence to traditional dietary pattern was positively associated with a higher risk of MHO (OR 1.91, 95% CI 1.56-2.34) and MUHO phenotypes (OR 2.33, 95% CI 1.69-3.22) in the final model. Conclusion: There were significant associations between dietary patterns and metabolic phenotypes, suggesting the necessity of nutritional interventions in industrial employees to improve metabolic phenotype, health outcomes, and, therefore, job productivity in the workforce population.
Keyphrases
- weight loss
- wastewater treatment
- heavy metals
- metabolic syndrome
- physical activity
- adipose tissue
- type diabetes
- south africa
- body mass index
- public health
- electronic health record
- insulin resistance
- cross sectional
- skeletal muscle
- climate change
- bariatric surgery
- risk factors
- weight gain
- risk assessment
- artificial intelligence
- data analysis
- big data