基于老年人重金属的抑郁机器学习模型：一项基于 2017-2018 年全国健康与营养调查的研究。

Machine learning model for depression based on heavy metals among aging people: A study with National Health and Nutrition Examination Survey 2017-2018.

机构信息

Department of Public Health, Wuhan Fourth Hospital, Wuhan, China.

出版信息

Front Public Health. 2022 Aug 4;10:939758. doi: 10.3389/fpubh.2022.939758. eCollection 2022.

DOI:10.3389/fpubh.2022.939758

PMID:35991018

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9386350/

Abstract

OBJECTIVE

To explore the association between depression and blood metal elements, we conducted this machine learning model fitting research.

METHODS

Datasets from the National Health and Nutrition Examination Survey (NHANES) in 2017-2018 were downloaded (https://www.cdc.gov/nchs/nhanes). After screening, 3,247 aging samples with 10 different metals [lead (Pb), mercury (Hg), cadmium (Cd), manganese (Mn), selenium (Se), chromium (Cr), cobalt (Co), inorganic mercury (InHg), methylmercury (MeHg) and ethyl mercury (EtHg)] were included. Eight machine learning algorithms were compared for analyzing metal and depression. After comparison, XGBoost showed optimal effects. Poisson regression and XGBoost model (a kind of decision tree algorithm) were conducted to find the risk factors and prediction for depression.

RESULTS

A total of 344 individuals out of 3247 participants were diagnosed with depression. In the Poisson model, we found Cd (β = 0.22, = 0.00000941), EtHg (β = 3.43, = 0.003216), and Hg (β=-0.15, = 0.001524) were related with depression. XGBoost model was the suitable algorithm for the evaluation of depression, the accuracy was 0.89 with 95%CI (0.87, 0.92) and Kappa value was 0.006. Area under the curve (AUC) was 0.88. After that, an online XGBoost application for depression prediction was developed.

CONCLUSION

Blood heavy metals, especially Cd, EtHg, and Hg were significantly associated with depression and the prediction of depression was imperative.

摘要

目的

为了探究抑郁与血液金属元素之间的关系，我们进行了这项机器学习模型拟合研究。

方法

下载了 2017-2018 年国家健康与营养检查调查（NHANES）的数据（https://www.cdc.gov/nchs/nhanes）。经过筛选，纳入了 3247 个年龄在 10 种不同金属[铅（Pb）、汞（Hg）、镉（Cd）、锰（Mn）、硒（Se）、铬（Cr）、钴（Co）、无机汞（InHg）、甲基汞（MeHg）和乙基汞（EtHg）]的老化样本。比较了 8 种机器学习算法对金属和抑郁的分析效果。比较后，XGBoost 算法效果最佳。采用泊松回归和 XGBoost 模型（一种决策树算法）来寻找抑郁的风险因素和预测因子。

结果

在 3247 名参与者中，共有 344 人被诊断患有抑郁症。在泊松模型中，我们发现 Cd（β=0.22， = 0.00000941）、EtHg（β=3.43， = 0.003216）和 Hg（β=-0.15， = 0.001524）与抑郁有关。XGBoost 模型是评估抑郁的合适算法，准确率为 0.89，95%CI（0.87，0.92），Kappa 值为 0.006。曲线下面积（AUC）为 0.88。之后，我们开发了一个在线 XGBoost 抑郁预测应用程序。