Body mass index, waist circumference and waist–hip ratio are associated with depressive symptoms in older Chinese women: results from the Rugao Longevity and Ageing Study (RuLAS)

ABSTRACT

Objective: The objective was to determine whether obesity is associated with depressive symptoms among older Chinese.

Methods: Data from the cross-sectional Rugao Longevity and Ageing Study were used including anthropometric measurements (body mass index (BMI), waist circumference (WC) and waist–hip ratio (WHR)), socio-demographic characteristics, living habits, physical health and cognitive impairment. Depressive symptoms were assessed by the 15-item Geriatric Depression Scale (GDS-15). Chi-square tests and multivariate logistic regression analyses were performed to investigate the association between obesity and depressive symptoms.

Results: Among 1732 elderly Chinese aged 70–84 years, the prevalence of depressive symptoms was 6.7% (5.0%–8.5%) in men and 12.5% (10.4%–14.6%) in women. A negative linear trend was found between depressive symptoms and BMI in women (P_{for trend} < 0.05). Women with BMI ≥ 28.0 kg/m² had lower chances (OR = 0.41 (0.20–0.84), p = 0.01) to have elevated depressive symptoms compared with their normal weight counterparts. Furthermore, consistent trends were observed with lower depression prevalence rates in higher WC and WHR categories in women. However, no such associations were apparent in men.

Conclusion: Higher BMI, WC and WHR categories were all associated with a lower risk of depressive symptoms in older women.

KEYWORDS:

• Elderly
• depressive symptoms
• body mass index
• waist circumference
• waist–hip ratio

Acknowledgements

We acknowledge all people involved in this study.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by grants from the National Natural Science Foundation [grant number 81100551], [grant number 31171216], [grant number 81571372], [grant number 81270259]; and the grant from Shanghai Municipal Natural Science Foundation [grant number 11ZR1438600].