PDF(Pubmed)
Abstract：
UNASSIGNED: Previous studies have identified several genetic and environmental risk factors for chronic kidney disease (CKD). However, little is known about the relationship between serum metals and CKD risk.
UNASSIGNED: We investigated associations between serum metals levels and CKD risk among 100 medical examiners and 443 CKD patients in the medical center of the First Hospital Affiliated to China Medical University. Serum metal concentrations were measured using inductively coupled plasma mass spectrometry (ICP-MS). We analyzed factors influencing CKD, including abnormalities in Creatine and Cystatin C, using univariate and multiple analysis such as Lasso and Logistic regression. Metal levels among CKD patients at different stages were also explored. The study utilized machine learning and Bayesian Kernel Machine Regression (BKMR) to assess associations and predict CKD risk based on serum metals. A chained mediation model was applied to investigate how interventions with different heavy metals influence renal function indicators (creatinine and cystatin C) and their impact on diagnosing and treating renal impairment.
UNASSIGNED: Serum potassium (K), sodium (Na), and calcium (Ca) showed positive trends with CKD, while selenium (Se) and molybdenum (Mo) showed negative trends. Metal mixtures had a significant negative effect on CKD when concentrations were all from 30th to 45th percentiles compared to the median, but the opposite was observed for the 55th to 60th percentiles. For example, a change in serum K concentration from the 25th to the 75th percentile was associated with a significant increase in CKD risk of 5.15(1.77,8.53), 13.62(8.91,18.33) and 31.81(14.03,49.58) when other metals were fixed at the 25th, 50th and 75th percentiles, respectively.
UNASSIGNED: Cumulative metal exposures, especially double-exposure to serum K and Se may impact CKD risk. Machine learning methods validated the external relevance of the metal factors. Our study highlights the importance of employing diverse methodologies to evaluate health effects of metal mixtures.
UNASSIGNED: We investigated associations between serum metals levels and CKD risk among 100 medical examiners and 443 CKD patients in the medical center of the First Hospital Affiliated to China Medical University. Serum metal concentrations were measured using inductively coupled plasma mass spectrometry (ICP-MS). We analyzed factors influencing CKD, including abnormalities in Creatine and Cystatin C, using univariate and multiple analysis such as Lasso and Logistic regression. Metal levels among CKD patients at different stages were also explored. The study utilized machine learning and Bayesian Kernel Machine Regression (BKMR) to assess associations and predict CKD risk based on serum metals. A chained mediation model was applied to investigate how interventions with different heavy metals influence renal function indicators (creatinine and cystatin C) and their impact on diagnosing and treating renal impairment.
UNASSIGNED: Serum potassium (K), sodium (Na), and calcium (Ca) showed positive trends with CKD, while selenium (Se) and molybdenum (Mo) showed negative trends. Metal mixtures had a significant negative effect on CKD when concentrations were all from 30th to 45th percentiles compared to the median, but the opposite was observed for the 55th to 60th percentiles. For example, a change in serum K concentration from the 25th to the 75th percentile was associated with a significant increase in CKD risk of 5.15(1.77,8.53), 13.62(8.91,18.33) and 31.81(14.03,49.58) when other metals were fixed at the 25th, 50th and 75th percentiles, respectively.
UNASSIGNED: Cumulative metal exposures, especially double-exposure to serum K and Se may impact CKD risk. Machine learning methods validated the external relevance of the metal factors. Our study highlights the importance of employing diverse methodologies to evaluate health effects of metal mixtures.
摘要：
■先前的研究已经确定了慢性肾脏疾病（CKD）的几种遗传和环境危险因素。然而,对血清金属与CKD风险之间的关系知之甚少。
■我们在中国医科大学附属第一医院的医疗中心调查了100名体检医师和443名CKD患者的血清金属水平与CKD风险之间的关系。使用电感耦合等离子体质谱法(ICP-MS)测量血清金属浓度。我们分析了影响CKD的因素，包括肌酸和胱抑素C异常，使用单变量和多元分析，如Lasso和Logistic回归。还探讨了CKD患者在不同阶段的金属水平。该研究利用机器学习和贝叶斯内核机器回归（BKMR）来评估相关性并基于血清金属预测CKD风险。应用链式调解模型来研究不同重金属的干预措施如何影响肾功能指标（肌酐和胱抑素C）及其对诊断和治疗肾功能损害的影响。
■血清钾（K），钠（Na），钙（Ca）与CKD呈正趋势，硒（Se）和钼（Mo）呈负趋势。与中位数相比，当浓度均为30至45百分位数时，金属混合物对CKD具有显着的负面影响，但在第55至第60百分位数观察到相反的情况。例如,血清钾浓度从第25百分位数到第75百分位数的变化与CKD风险5.15（1.77,8.53）的显着增加有关，在25日固定其他金属时，13.62（8.91,18.33）和31.81（14.03,49.58），第50和第75百分位数，分别。
■累积金属暴露量，特别是血清钾和硒的双重暴露可能会影响CKD的风险。机器学习方法验证了金属因素的外部相关性。我们的研究强调了采用多种方法评估金属混合物对健康影响的重要性。
■我们在中国医科大学附属第一医院的医疗中心调查了100名体检医师和443名CKD患者的血清金属水平与CKD风险之间的关系。使用电感耦合等离子体质谱法(ICP-MS)测量血清金属浓度。我们分析了影响CKD的因素，包括肌酸和胱抑素C异常，使用单变量和多元分析，如Lasso和Logistic回归。还探讨了CKD患者在不同阶段的金属水平。该研究利用机器学习和贝叶斯内核机器回归（BKMR）来评估相关性并基于血清金属预测CKD风险。应用链式调解模型来研究不同重金属的干预措施如何影响肾功能指标（肌酐和胱抑素C）及其对诊断和治疗肾功能损害的影响。
■血清钾（K），钠（Na），钙（Ca）与CKD呈正趋势，硒（Se）和钼（Mo）呈负趋势。与中位数相比，当浓度均为30至45百分位数时，金属混合物对CKD具有显着的负面影响，但在第55至第60百分位数观察到相反的情况。例如,血清钾浓度从第25百分位数到第75百分位数的变化与CKD风险5.15（1.77,8.53）的显着增加有关，在25日固定其他金属时，13.62（8.91,18.33）和31.81（14.03,49.58），第50和第75百分位数，分别。
■累积金属暴露量，特别是血清钾和硒的双重暴露可能会影响CKD的风险。机器学习方法验证了金属因素的外部相关性。我们的研究强调了采用多种方法评估金属混合物对健康影响的重要性。
