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J Affect Disord. 2008 Jul;109(1-2):75-82. Epub 2007 Dec 11.

Insulin resistance and depressive symptoms in middle-aged and elderly Chinese: findings from the Nutrition and Health of Aging Population in China Study.

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Key Laboratory of Systems Biology, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, China.



Depression is associated with an increased risk of incident diabetes, and insulin resistance is thought to be the underlying link between them. Nevertheless, only a few studies have explored the association between insulin resistance and depression with contradictory results, and none have been conducted in Chinese populations.


We aimed to determine the association between insulin resistance and depressive symptoms among middle-aged and elderly Chinese using data from the Nutrition and Health of Aging Population in China Study, a population-based cross-sectional study conducted in 2005 in China. Participants included 3285 community residents aged 50-70 years. Depressive symptoms were defined as a Center for Epidemiological Studies of Depression Scale (CES-D) score of 16 or higher. Insulin resistance was calculated using the updated homeostasis model assessment (HOMA2-IR) methods.


Value of HOMA2-IR was significantly higher in participants with depressive symptoms (0.284 vs. 0.261, P=0.008), even after adjustment for various confounding factors in the regression model (0.311 vs. 0.291, P=0.026). Logistical regression analyses showed that participants with depressive symptoms had a higher risk of having insulin resistance (defined as in the top quartile of HOMA2-IR values, odds ratio=1.54, 95% confidence intervals=1.17-2.04).


Due to the cross-sectional study design, causal relation remains unknown.


The present study found positive associations between depressive symptoms and insulin resistance among Chinese populations. Further prospective studies are needed to validate the results and find the temporal and causal relation between them.

[Indexed for MEDLINE]

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