机器学习辅助的重金属暴露与糖尿病肾病的关联：一项横断面调查和孟德尔随机化分析。
Association between machine learning-assisted heavy metal exposures and diabetic kidney disease: a cross-sectional survey and Mendelian randomization analysis.
机构信息
The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, Guangdong, China.
The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
出版信息
Front Public Health. 2024 Jun 14;12:1367061. doi: 10.3389/fpubh.2024.1367061. eCollection 2024.
BACKGROUND AND OBJECTIVE
Heavy metals, ubiquitous in the environment, pose a global public health concern. The correlation between these and diabetic kidney disease (DKD) remains unclear. Our objective was to explore the correlation between heavy metal exposures and the incidence of DKD.
METHODS
We analyzed data from the NHANES (2005-2020), using machine learning, and cross-sectional survey. Our study also involved a bidirectional two-sample Mendelian randomization (MR) analysis.
RESULTS
Machine learning reveals correlation coefficients of -0.5059 and - 0.6510 for urinary Ba and urinary Tl with DKD, respectively. Multifactorial logistic regression implicates urinary Ba, urinary Pb, blood Cd, and blood Pb as potential associates of DKD. When adjusted for all covariates, the odds ratios and 95% confidence intervals are 0.87 (0.78, 0.98) ( = 0.023), 0.70 (0.53, 0.92) ( = 0.012), 0.53 (0.34, 0.82) ( = 0.005), and 0.76 (0.64, 0.90) ( = 0.002) in order. Furthermore, multiplicative interactions between urinary Ba and urinary Sb, urinary Cd and urinary Co, urinary Cd and urinary Pb, and blood Cd and blood Hg might be present. Among the diabetic population, the OR of urinary Tl with DKD is a mere 0.10, with a 95%CI of (0.01, 0.74), urinary Co 0.73 (0.54, 0.98) in Model 3, and urinary Pb 0.72 (0.55, 0.95) in Model 2. Restricted Cubic Splines (RCS) indicate a linear linkage between blood Cd in the general population and urinary Co, urinary Pb, and urinary Tl with DKD among diabetics. An observable trend effect is present between urinary Pb and urinary Tl with DKD. MR analysis reveals odds ratios and 95% confidence intervals of 1.16 (1.03, 1.32) ( = 0.018) and 1.17 (1.00, 1.36) ( = 0.044) for blood Cd and blood Mn, respectively.
CONCLUSION
In the general population, urinary Ba demonstrates a nonlinear inverse association with DKD, whereas in the diabetic population, urinary Tl displays a linear inverse relationship with DKD.
背景与目的
重金属在环境中普遍存在，对全球公共健康构成了威胁。这些重金属与糖尿病肾病（DKD）之间的相关性尚不清楚。本研究旨在探讨重金属暴露与 DKD 发病之间的相关性。
方法
我们使用机器学习和横断面调查分析了 NHANES（2005-2020 年）的数据。我们还进行了双向两样本孟德尔随机化（MR）分析。
结果
机器学习显示，尿中钡（Ba）和尿中铊（Tl）与 DKD 的相关系数分别为-0.5059 和-0.6510。多因素逻辑回归提示尿中钡、尿中铅、血镉和血铅可能与 DKD 相关。在校正所有协变量后，比值比（OR）及其 95%置信区间（CI）分别为 0.87（0.78，0.98）（=0.023）、0.70（0.53，0.92）（=0.012）、0.53（0.34，0.82）（=0.005）和 0.76（0.64，0.90）（=0.002）。此外，尿中钡与尿中锑、尿中镉与尿中钴、尿中镉与尿中铅以及血中镉与血中汞之间可能存在乘法交互作用。在糖尿病患者中，尿中 Tl 与 DKD 的 OR 仅为 0.10，95%CI 为（0.01，0.74），模型 3 中尿中钴为 0.73（0.54，0.98），模型 2 中尿中铅为 0.72（0.55，0.95）。限制性三次样条（RCS）显示，在普通人群中，血镉与糖尿病患者的尿中钴、尿中铅和尿中 Tl 之间呈线性关联。在一般人群中，尿中 Pb 与尿中 Tl 与 DKD 之间存在明显的趋势效应。MR 分析显示，血镉和血锰的 OR 及其 95%CI 分别为 1.16（1.03，1.32）（=0.018）和 1.17（1.00，1.36）（=0.044）。
结论
在一般人群中，尿中 Ba 与 DKD 呈非线性负相关，而在糖尿病患者中，尿中 Tl 与 DKD 呈线性负相关。
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