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Published on: February 22, 2018
Association between dietary patterns and depressive symptoms over time: a 10-year follow-up study of the GAZEL cohort
Agnès Le Port1, Alice Gueguen, Emmanuelle Kesse-Guyot
1INSERM U1018, University of Versailles St Quentin. Centre for research in Epidemiology and Population Health, Villejuif, France. agnesleport@yahoo.fr
Background:
Data on the association between dietary patterns and depression are scarce. The objective of this study was to examine the longitudinal association between dietary patterns and depressive symptoms assessed repeatedly over 10 years in the French occupational GAZEL cohort.
Methods:
A total of 9,272 men and 3,132 women, aged 45-60 years in 1998, completed a 35-item Food Frequency Questionnaire (FFQ) at baseline. Dietary patterns were derived by Principal Component Analysis. Depressive symptoms were assessed by the Center for Epidemiologic Studies Depression scale (CES-D) in 1999, 2002, 2005 and 2008. The main outcome measure was the repeated measures of CES-D. Longitudinal analyses were performed with logistic regression based on generalized estimating equations.
Principal Findings:
The highest quartile of low-fat, western, high snack and high fat-sweet diets in men and low-fat and high snack diets in women were associated with higher likelihood of depressive symptoms at the start of the follow-up compared to the lowest quartile (OR between 1.16 and 1.50). Conversely, the highest quartile of traditional diet (characterized by fish and fruit consumption) was associated with a lower likelihood of depressive symptoms in women compared to the lowest quartile, with OR = 0.63 [95%CI, 0.50 to 0.80], as the healthy pattern (characterized by vegetables consumption) with OR = 0.72 [95%CI, 0.63 to 0.83] and OR = 0.75 [95%CI, 0.61 to 0.93] in men and women, respectively. However, there was probably a reverse causality effect for the healthy pattern.
Conclusion:
This longitudinal study shows that several dietary patterns are associated with depressive symptoms and these associations track over time.
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