Huang Jinghong, Chen Le, Chen Huiyu, Liu Qiaodan, Tu Chuandeng, Dai Yue, Li Yueping, Tu Raoping
School of Health Management, Fujian Medical University, Fuzhou, China; School of Public Health, Lanzhou University, Lanzhou, China.
The Third Xiangya Hospital of Central South University, Changsha, China.
J Psychosom Res. 2025 Apr;191:112052. doi: 10.1016/j.jpsychores.2025.112052. Epub 2025 Feb 8.
Although previous studies have demonstrated allostatic load (AL) predicts depressive symptoms, few studies have considered the association between AL and trajectories of depressive symptoms. This study aims to systematically examine the associations of abnormal blood biomarkers in the three biological systems with trajectories of depressive symptoms.
A total of 6251 participants aged 45+ from the China Health and Retirement Longitudinal Study (CHARLS). Depressive symptoms were assessed using the 10-item Center for Epidemiological Studies Depression Scale (CESD-10) in five visits (waves 2011, 2013, 2015, 2018, and 2020). Biomarkers in three biological systems were evaluated based on standard criteria, including C-reactive protein in the inflammation system; systolic and diastolic blood pressures in the cardiovascular system; and high-density lipoprotein cholesterol (HDLC), total cholesterol/HDL-C ratio, and glycosylated hemoglobin (HbA1c) in the metabolic system. The trajectories of depressive symptoms were measured using group-based trajectory modelling (GBTM). Multinomial logistic regression models were conducted to examine the association between the number of abnormal biological systems and trajectories of depressive symptoms.
Four different trajectories of depressive symptoms were identified: mild (44.22 %), moderate (42.09 %), increasing (9.39 %), and severe (4.30 %). Compared to participants with normal values of biomarkers in all three systems, those with abnormal values of biomarkers in three systems had a 2.26-fold risk of developing the severe depressive symptoms trajectory.
Our findings highlight the importance of monitoring multiple biological systems to prevent long-term accelerated severe depressive symptoms trajectory.
尽管先前的研究表明，累积负荷（AL）可预测抑郁症状，但很少有研究考虑AL与抑郁症状轨迹之间的关联。本研究旨在系统地研究三个生物系统中血液生物标志物异常与抑郁症状轨迹之间的关联。
来自中国健康与养老追踪调查（CHARLS）的6251名45岁及以上参与者。使用10项流行病学研究中心抑郁量表（CESD-10）在五次访视（2011年、2013年、2015年、2018年和2020年）中评估抑郁症状。根据标准对三个生物系统中的生物标志物进行评估，包括炎症系统中的C反应蛋白；心血管系统中的收缩压和舒张压；代谢系统中的高密度脂蛋白胆固醇（HDLC）、总胆固醇/HDL-C比值和糖化血红蛋白（HbA1c）。使用基于组的轨迹模型（GBTM）测量抑郁症状轨迹。采用多项逻辑回归模型检验异常生物系统数量与抑郁症状轨迹之间的关联。
确定了四种不同的抑郁症状轨迹：轻度（44.22%）、中度（42.09%）、上升（9.39%）和重度（4.30%）。与三个系统中生物标志物值均正常的参与者相比，三个系统中生物标志物值异常的参与者出现重度抑郁症状轨迹的风险高2.26倍。
我们的研究结果强调了监测多个生物系统以预防长期加速的重度抑郁症状轨迹的重要性。
