Ogundare Olamide, Obeng-Gyasi Emmanuel
Department of Built Environment, North Carolina A&T State University, Greensboro, NC 27411, USA.
Environmental Health and Disease Laboratory, North Carolina A&T State University, Greensboro, NC 27411, USA.
Toxics. 2024 Dec 2;12(12):879. doi: 10.3390/toxics12120879.
This study investigates the combined effects of environmental pollutants (lead, cadmium, total mercury) and behavioral factors (alcohol consumption, smoking) on depressive symptoms in women. Data from the National Health and Nutrition Examination Survey (NHANES) 2017-2018 cycle, specifically exposure levels of heavy metals in blood samples, were used in this study. The analysis of these data included the application of descriptive statistics, linear regression, and Bayesian Kernel Machine Regression (BKMR) to explore associations between environmental exposures, behavioral factors, and depression. The PHQ-9, a well-validated tool that assesses nine items for depressive symptoms, was used to evaluate depression severity over the prior two weeks on a 0-3 scale, with total scores ranging from 0 to 27. Exposure levels of heavy metals were measured in blood samples. BKMR was used to estimate the exposure-response relationship, while posterior inclusion probability (PIP) in BKMR was used to quantify the likelihood that a given exposure was included in the model, reflecting its relative importance in explaining the outcome (depression) within the context of other predictors in the mixture. A descriptive analysis showed mean total levels of lead, cadmium, and total mercury at 1.21 µg/dL, 1.47 µg/L, and 0.80 µg/L, respectively, with a mean PHQ-9 score of 5.94, which corresponds to mild depressive symptoms based on the PHQ-9 scoring. Linear regression indicated positive associations between depression and lead as well as cadmium, while total mercury had a negative association. Alcohol and smoking were also positively associated with depression. These findings were not significant, but limitations in linear regression prompted a BKMR analysis. BKMR posterior inclusion probability (PIP) analysis revealed alcohol and cadmium as significant contributors to depressive symptoms, with cadmium (PIP = 0.447) and alcohol (PIP = 0.565) showing notable effects. Univariate and bivariate analyses revealed lead and total mercury's strong relationship with depression, with cadmium showing a complex pattern in the bivariate analysis. A cumulative exposure analysis of all metals and behavioral factors concurrently demonstrated that higher quantile levels of combined exposures were associated with an increased risk of depression. Finally, a single variable-effects analysis in BKMR revealed lead, cadmium, and alcohol had a stronger impact on depression. Overall, the study findings suggest that from exposure to lead, cadmium, mercury, alcohol, and smoking, cadmium and alcohol consumption emerge as key contributors to depressive symptoms. These results highlight the need to address both environmental and lifestyle choices in efforts to mitigate depression.
本研究调查了环境污染物（铅、镉、总汞）和行为因素（饮酒、吸烟）对女性抑郁症状的综合影响。本研究使用了2017 - 2018年国家健康与营养检查调查（NHANES）的数据，特别是血样中重金属的暴露水平。这些数据的分析包括应用描述性统计、线性回归和贝叶斯核机器回归（BKMR），以探索环境暴露、行为因素与抑郁症之间的关联。PHQ - 9是一种经过充分验证的工具，用于评估九个抑郁症状项目，用于评估前两周内抑郁症状的严重程度，评分为0 - 3分，总分范围为0至27分。通过血样测量重金属的暴露水平。BKMR用于估计暴露 - 反应关系，而BKMR中的后验包含概率（PIP）用于量化给定暴露被纳入模型的可能性，反映其在混合模型中其他预测因素背景下解释结果（抑郁症）的相对重要性。描述性分析显示，铅、镉和总汞的平均总水平分别为1.21μg/dL、1.47μg/L和0.80μg/L，PHQ - 9平均得分为5.94，根据PHQ - 9评分，这对应于轻度抑郁症状。线性回归表明抑郁症与铅和镉呈正相关，而总汞呈负相关。饮酒和吸烟也与抑郁症呈正相关。这些发现并不显著，但线性回归的局限性促使进行BKMR分析。BKMR后验包含概率（PIP）分析显示，饮酒和镉是抑郁症状的重要促成因素，镉（PIP = 0.447）和饮酒（PIP = 0.565）显示出显著影响。单变量和双变量分析显示铅和总汞与抑郁症有很强的关系，镉在双变量分析中显示出复杂的模式。对所有金属和行为因素同时进行的累积暴露分析表明，更高分位数水平的综合暴露与抑郁症风险增加有关。最后，BKMR中的单变量效应分析显示铅、镉和饮酒对抑郁症有更强的影响。总体而言，研究结果表明，从铅、镉、汞、饮酒和吸烟暴露来看，镉和饮酒是抑郁症状的关键促成因素。这些结果凸显了在努力减轻抑郁症时，需要同时关注环境和生活方式选择。
