PMID- 32835680
OWN - NLM
STAT- MEDLINE
DCOM- 20210111
LR  - 20210111
IS  - 1096-0953 (Electronic)
IS  - 0013-9351 (Linking)
VI  - 191
DP  - 2020 Dec
TI  - Metal mixtures and kidney function: An application of machine learning to NHANES 
      data.
PG  - 110126
LID - S0013-9351(20)31023-9 [pii]
LID - 10.1016/j.envres.2020.110126 [doi]
AB  - BACKGROUND: Exposure to heavy metals may increase risk of kidney disease, but 
      most studies have examined individual metals or two-way interactions. There is 
      increasing recognition of the importance of studying exposure to metal mixtures 
      and health outcomes. OBJECTIVES: We used Bayesian kernel machine regression 
      (BKMR) to examine associations between a mixture of four heavy metals and 
      indicators of kidney function. METHODS: We used NHANES 2015-16 data on 1435 
      adults aged 40 and over to study cross-sectional associations between blood 
      levels of four heavy metals (Co, Cr, Hg and Pb) and kidney function. Kidney 
      function was assessed by estimated glomerular filtration rate (eGFR) and by 
      albumin to creatinine ratio (ACR), measured continuously and dichotomized into 
      indicators of chronic kidney disease (CKD) and albuminuria, respectively. BKMR 
      tested for non-linearity in the exposure-specific responses to evaluate 
      dose-response relationships between mixtures and outcomes and possible 
      interaction effects among exposures. Interactions among continuous outcomes were 
      identified using the NLinteraction package in R. RESULTS: A higher metals mixture 
      was significantly associated with all four measures of kidney function in 
      dose-response patterns. Pb had the strongest association with eGFR, albuminuria 
      and ACR, and the second strongest association with CKD. We also observed an 
      interaction between Pb and Co for eGFR and an interaction between Pb and Cd for 
      ACR. CONCLUSIONS: Exposure to a co-occurring heavy metals mixture was associated 
      with indicators of poor kidney function. Within this mixture, Pb, Co and Cd 
      considered singly and jointly made the greatest contributions to the observed 
      effects. Future prospective study is needed to confirm the association between 
      metal mixtures and kidney function.
CI  - Copyright (c) 2020 Elsevier Inc. All rights reserved.
FAU - Luo, Juhua
AU  - Luo J
AD  - Department of Epidemiology and Biostatistics, School of Public Health, Indiana 
      University Bloomington, 1025 E 7th St. Bloomington, IN, 47405, USA.
FAU - Hendryx, Michael
AU  - Hendryx M
AD  - Department of Epidemiology and Biostatistics, School of Public Health, Indiana 
      University Bloomington, 1025 E 7th St. Bloomington, IN, 47405, USA. Electronic 
      address: hendryx@indiana.edu.
LA  - eng
PT  - Journal Article
DEP - 20200821
PL  - Netherlands
TA  - Environ Res
JT  - Environmental research
JID - 0147621
SB  - IM
MH  - Bayes Theorem
MH  - Cross-Sectional Studies
MH  - *Kidney
MH  - Machine Learning
MH  - *Nutrition Surveys
MH  - Prospective Studies
EDAT- 2020/08/25 06:00
MHDA- 2021/01/12 06:00
CRDT- 2020/08/25 06:00
PHST- 2020/05/21 00:00 [received]
PHST- 2020/08/15 00:00 [revised]
PHST- 2020/08/16 00:00 [accepted]
PHST- 2020/08/25 06:00 [pubmed]
PHST- 2021/01/12 06:00 [medline]
PHST- 2020/08/25 06:00 [entrez]
AID - S0013-9351(20)31023-9 [pii]
AID - 10.1016/j.envres.2020.110126 [doi]
PST - ppublish
SO  - Environ Res. 2020 Dec;191:110126. doi: 10.1016/j.envres.2020.110126. Epub 2020 
      Aug 21.