Duan Jiayi, Chen Jianhui, Xiang Zhongtian
School of Clinical Medicine, Ningxia Medical University, Yinchuan 750004, China.
Department of Cardiothoracic Surgery, Xiaogan Hospital Affiliated to Wuhan University of Science and Technology, Xiaogan Central Hospital, Xiaogan, China.
J Affect Disord. 2025 Jul 1;380:270-278. doi: 10.1016/j.jad.2025.03.139. Epub 2025 Mar 25.
This investigation aims to examine the connection between the aggregate index of systemic inflammation (AISI) and depression, using data from the National Health and Nutrition Examination Survey (NHANES) database.
We conducted a cross-sectional study using data from the NHANES collected between 2005 and 2018. Depression was assessed via the patient health questionnaire-9. To investigate the connection between AISI and the prevalence of depression, we employed weighted multivariable logistic regression models as well as restricted cubic spline (RCS) models. This study also performed subgroup and interaction analyses to further explore these associations. Additionally, threshold effect and saturation effect analyses were conducted to identify potential inflection points for AISI and depression. Finally, we compared the area under the curve (AUC) values from receiver operating characteristic (ROC) analyses to assess the diagnostic capability of the optimal model for depression.
This study initially recruited 29,092 individuals, of whom 2596 had depression. After adjusting for potential confounders, we discovered a higher AISI was significantly linked with an higher risk of depression when comparing the highest to the lowest quantile of AISI (odds ratio: 1.205; 95 % confidence interval: 1.019-1.424; P = 0.032). Marital status interacted with AISI to influence the prevalence of depression (P for trend = 0.0275). The curve for participants was U-shaped, with an optimal AISI value of 828.8, and a non-linear relationship was found between AISI and depression (P for log-likelihood ratio test <0.001). ROC analysis indicated that AISI had a stronger discriminative ability and accuracy in predicting depression compared to other inflammatory biomarkers.
The AISI level exhibited a U-shaped association with depression, indicating that maintaining AISI within a reasonable range may help reduce the prevalence of depression.
本研究旨在利用美国国家健康与营养检查调查（NHANES）数据库中的数据，探讨全身炎症综合指数（AISI）与抑郁症之间的联系。
我们采用横断面研究方法，使用2005年至2018年期间收集的NHANES数据。通过患者健康问卷-9评估抑郁症。为了研究AISI与抑郁症患病率之间的联系，我们采用了加权多变量逻辑回归模型以及受限立方样条（RCS）模型。本研究还进行了亚组分析和交互作用分析，以进一步探讨这些关联。此外，进行了阈值效应和饱和效应分析，以确定AISI与抑郁症的潜在拐点。最后，我们比较了来自受试者工作特征（ROC）分析的曲线下面积（AUC）值，以评估抑郁症最佳模型的诊断能力。
本研究最初招募了29092名个体，其中2596人患有抑郁症。在调整潜在混杂因素后，我们发现，将AISI的最高四分位数与最低四分位数进行比较时，较高的AISI与较高的抑郁症风险显著相关（比值比：1.205；95%置信区间：1.019-1.424；P = 0.032）。婚姻状况与AISI相互作用，影响抑郁症的患病率（趋势P = 0.0275）。参与者的曲线呈U形，最佳AISI值为828.8，并且发现AISI与抑郁症之间存在非线性关系（对数似然比检验P<0.001）。ROC分析表明，与其他炎症生物标志物相比，AISI在预测抑郁症方面具有更强的判别能力和准确性。
AISI水平与抑郁症呈U形关联，表明将AISI维持在合理范围内可能有助于降低抑郁症的患病率。
