一种使用美国国家健康与营养检查调查（NHANES）数据并经孟德尔随机化验证的新型抑郁症风险预测模型。

A Novel Depression Risk Prediction Model Using NHANES Data With Mendelian Randomization Validation.

作者信息

Lin Lin, Zhang Liqun, Zhang Jingdong, Ding Dapeng

机构信息

Department of Clinical Laboratory Medicine, First Affiliated Hospital of Dalian Medical University, Zhongshan Road, Xigang District, Dalian, Liaoning Province, China.

School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, No. 2 Linggong Road, Ganjingzi District, Dalian, Liaoning Province, China.

出版信息

Brain Behav. 2025 Jul;15(7):e70674. doi: 10.1002/brb3.70674.

DOI:10.1002/brb3.70674

PMID:40714919

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12409830/

Abstract

BACKGROUND

Despite depression's significant public health impact, efficient and accessible screening tools utilizing routine clinical indicators remain limited. This study aimed to develop and validate a practical depression risk prediction model based on commonly available biochemical markers, facilitating widespread early screening and timely intervention in general clinical settings.

METHODS

We formulated a model for depression, scrutinizing an assortment of biochemical indicators and their bidirectional interrelationships with depression, employing data derived from the National Health and Nutrition Examination Survey (NHANES) and leveraging the Mendelian randomization (MR) approach, a method that utilizes genetic variants as instrumental proxies to ascertain causal nexus between risk determinants and diseases.

RESULTS

Using NHANES data (training cohort: n = 27,327; validation cohort: n = 4383), we developed two prediction models through LASSO and multivariate logistic regression. Both models demonstrated comparable performance in terms of discrimination (ROC curves), calibration (slope and Hosmer-Lemeshow test), Brier score, decision curve analysis, net reclassification improvement, and integrated discrimination improvement. Given the similar performance metrics and more parsimonious nature, Model 2, with 14 variables, was selected as the final model. MR analysis revealed bidirectional relationships between biomarkers and depression. Higher body mass index level was associated with increased depression risk (odds ratio [OR]: 1.061, p = 0.008). Depression itself showed significant associations with increased ALP (OR: 1.048, p = 0.010), decreased BUN (OR: 0.966, p = 0.032), and TB (OR: 0.963, p = 0.044) levels.

CONCLUSIONS

Model 2, selected for its predictive accuracy and streamlined complexity, presents a pragmatic instrument for large-scale population screenings, facilitating timely intervention and therapeutic strategies.

摘要

背景

尽管抑郁症对公众健康有重大影响，但利用常规临床指标的高效且可及的筛查工具仍然有限。本研究旨在基于常用生化标志物开发并验证一个实用的抑郁症风险预测模型，以促进在一般临床环境中广泛开展早期筛查和及时干预。

方法

我们构建了一个抑郁症模型，通过审视一系列生化指标及其与抑郁症的双向相互关系，利用来自美国国家健康与营养检查调查（NHANES）的数据，并采用孟德尔随机化（MR）方法，该方法利用基因变异作为工具代理来确定风险决定因素与疾病之间的因果关系。

结果

利用NHANES数据（训练队列：n = 27327；验证队列：n = 4383），我们通过LASSO和多变量逻辑回归开发了两个预测模型。两个模型在区分度（ROC曲线）、校准（斜率和Hosmer-Lemeshow检验）、Brier评分、决策曲线分析、净重新分类改善和综合区分改善方面表现相当。鉴于相似的性能指标和更简洁的性质，选择包含14个变量的模型2作为最终模型。MR分析揭示了生物标志物与抑郁症之间的双向关系。较高的体重指数水平与抑郁症风险增加相关（优势比[OR]：1.061，p = 0.008）。抑郁症本身与碱性磷酸酶升高（OR：1.048，p = 0.010）、尿素氮降低（OR：0.966，p = 0.032）和总胆红素降低（OR：0.963，p = 0.044）水平显著相关。