PMID- 38091680
OWN - NLM
STAT- MEDLINE
DCOM- 20240112
LR  - 20240116
IS  - 1090-2414 (Electronic)
IS  - 0147-6513 (Linking)
VI  - 269
DP  - 2024 Jan 1
TI  - Exploring the association between two groups of metals with potentially opposing 
      renal effects and renal function in middle-aged and older adults: Evidence from 
      an explainable machine learning method.
PG  - 115812
LID - S0147-6513(23)01316-7 [pii]
LID - 10.1016/j.ecoenv.2023.115812 [doi]
AB  - BACKGROUND: Machine learning models have promising applications in capturing the 
      complex relationship between mixtures of exposures and outcomes. OBJECTIVE: Our 
      study aimed at introducing an explainable machine learning (EML) model to assess 
      the association between metal mixtures with potentially opposing renal effects 
      and renal function in middle-aged and older adults. METHODS: This study extracted 
      data from two cycle years of the National Health and Nutrition Examination Survey 
      (NHANES). Participants aged 45 years or older with complete data on six metals 
      (lead, cadmium, manganese, mercury, and selenium) and related covariates were 
      enrolled. The EML model was developed by the optimized machine learning model 
      together with Shapley Additive exPlanations (SHAP) to assess the chronic kidney 
      disease (CKD) risk with metal mixtures. The results from EML were further 
      compared in detail with multiple logistic regression (MLR) and Bayesian kernel 
      machine regression (BKMR). RESULTS: After adjusting for included covariates, MLR 
      pointed out the lead and arsenic were generally positively associated with CKD, 
      but manganese had a negative association. In the BKMR analysis, each metal was 
      found to have a non-linear association with the risk of CKD, and interactions can 
      exist between metals, especially for arsenic and lead. The EML ranked the feature 
      importance: lead, manganese, arsenic and selenium were close behind in importance 
      after gender, age or BMI for participants with CKD. Strong interactions between 
      mercury and lead, manganese and cadmium and arsenic and manganese were identified 
      by partial dependence plot (PDP) of SHAP and bivariate exposure-response effect 
      plots of BKMR. The EML model determined the "trigger point" at which the risk of 
      CKD abruptly changed. CONCLUSION: Co-exposure to metals with different 
      nephrotoxicity could have different joint association with renal function, and 
      EML can be a powerful method for studying complex exposure mixtures.
CI  - Copyright (c) 2023 The Authors. Published by Elsevier Inc. All rights reserved.
FAU - Chen, Haoran
AU  - Chen H
AD  - Institute of Medical Information/Library, Chinese Academy of Medical Sciences & 
      Peking Union Medical College, Beijing 100020, China.
FAU - Wang, Min
AU  - Wang M
AD  - Institute of Medical Information/Library, Chinese Academy of Medical Sciences & 
      Peking Union Medical College, Beijing 100020, China.
FAU - Li, Jiao
AU  - Li J
AD  - Institute of Medical Information/Library, Chinese Academy of Medical Sciences & 
      Peking Union Medical College, Beijing 100020, China. Electronic address: 
      li.jiao@imicams.ac.cn.
LA  - eng
PT  - Journal Article
DEP - 20231212
PL  - Netherlands
TA  - Ecotoxicol Environ Saf
JT  - Ecotoxicology and environmental safety
JID - 7805381
RN  - N712M78A8G (Arsenic)
RN  - 00BH33GNGH (Cadmium)
RN  - 42Z2K6ZL8P (Manganese)
RN  - H6241UJ22B (Selenium)
RN  - 0 (Metals)
RN  - FXS1BY2PGL (Mercury)
RN  - 0 (Metals, Heavy)
SB  - IM
MH  - Middle Aged
MH  - Humans
MH  - Aged
MH  - *Arsenic/analysis
MH  - Nutrition Surveys
MH  - Cadmium/toxicity/analysis
MH  - Manganese/toxicity/analysis
MH  - *Selenium/analysis
MH  - Environmental Exposure/analysis
MH  - Bayes Theorem
MH  - Metals
MH  - Kidney/chemistry
MH  - Machine Learning
MH  - *Mercury/toxicity/analysis
MH  - *Renal Insufficiency, Chronic/chemically induced/epidemiology
MH  - *Metals, Heavy/toxicity/analysis
OTO - NOTNLM
OT  - Chronic kidney disease
OT  - Explainable machine learning
OT  - Metal mixtures
OT  - Multiply statistical methods
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/12/14 00:42
MHDA- 2024/01/12 06:43
CRDT- 2023/12/13 18:02
PHST- 2023/03/27 00:00 [received]
PHST- 2023/11/12 00:00 [revised]
PHST- 2023/12/08 00:00 [accepted]
PHST- 2024/01/12 06:43 [medline]
PHST- 2023/12/14 00:42 [pubmed]
PHST- 2023/12/13 18:02 [entrez]
AID - S0147-6513(23)01316-7 [pii]
AID - 10.1016/j.ecoenv.2023.115812 [doi]
PST - ppublish
SO  - Ecotoxicol Environ Saf. 2024 Jan 1;269:115812. doi: 10.1016/j.ecoenv.2023.115812. 
      Epub 2023 Dec 12.