Investigating Associations Between Depressive Symptoms and Anti-/Pro-Inflammatory Nutrients in an Elderly Population in Northern China: A Bayesian Kernel Machine Regression Approach

Ruiqiang Li; Wenqiang Zhan; Xin Huang; Limin Zhang; Yan Sun; Zechen Zhang; Wei Bao; Yuxia Ma

doi:10.2147/JIR.S330300

Investigating Associations Between Depressive Symptoms and Anti-/Pro-Inflammatory Nutrients in an Elderly Population in Northern China: A Bayesian Kernel Machine Regression Approach

J Inflamm Res. 2021 Oct 9:14:5201-5213. doi: 10.2147/JIR.S330300. eCollection 2021.

Authors

Ruiqiang Li^#¹, Wenqiang Zhan^#², Xin Huang¹, Limin Zhang¹, Yan Sun¹, Zechen Zhang¹, Wei Bao¹, Yuxia Ma¹

Affiliations

¹ Department of Nutrition and Food Hygiene, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, Shijiazhuang, People's Republic of China.
² School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, People's Republic of China.

^# Contributed equally.

Abstract

Backgroud: The potential for dietary inflammation has been shown to be associated with a variety of chronic diseases. The relationship between the potential for dietary inflammation and depression in the elderly is unclear.

Objective: This study aimed to exam the relationship between different nutrients and the risk of depression symptoms in the elderly.

Methods: In total, 1865 elderly in northern China were investigated at baseline from 2018 to 2019 and followed up in 2020. We measured the baseline intake of 22 nutrients and used Least Absolute Shrinkage and Selection Operator(LASSO) regression analysis and Bayesian Kernel Machine Regression (BKMR) to explore the association between exposure to a variety of nutrients with different inflammatory potentials and the risk of depressive symptoms.

Results: A total of 447 individuals (24.0%) were diagnosed with depressive symptoms. Through the lasso regression model, it was found that 11 nutrients are significantly related to the risk of depressive symptoms, of which 6 nutrients are pro-inflammatory nutrients (inflammation effect score>0), and 5 are anti-inflammatory nutrients (inflammation effect score<0). We incorporated the inflammatory effect scores of 11 nutrients into the BKMR model at the same time, and found that the overall inflammatory effect of 11 nutrients increased with the increase of total inflammatory scores, suggesting that the overall effect was pro-inflammatory. BKMR subgroup analysis shows that whether in the pro-inflammatory nutrient group or the anti-inflammatory nutrient group, multiple nutrients have a significant combined effect on depressive symptoms. By comparing the overall and group effects, we found that the inflammatory effects of the pro-inflammatory diet and the anti-inflammatory diet in the study's diet are offset by each other (P<0.005).

Conclusion: We determined the combined effect of multiple nutrients of different inflammatory potential classifications on depressive symptoms in the elderly.

Keywords: Bayesian kernel machine regression approach; anti-inflammatory; depression; elderly; pro-inflammatory.

Grants and funding

Community Cohort Study on Specialized Nervous System Diseases (No.2017YFC0907701).