PMID- 38219999
OWN - NLM
STAT- MEDLINE
DCOM- 20240208
LR  - 20240208
IS  - 1879-1026 (Electronic)
IS  - 0048-9697 (Linking)
VI  - 915
DP  - 2024 Mar 10
TI  - Risk of papillary thyroid carcinoma and nodular goiter associated with exposure 
      to semi-volatile organic compounds: A multi-pollutant assessment based on machine 
      learning algorithms.
PG  - 169962
LID - S0048-9697(24)00096-2 [pii]
LID - 10.1016/j.scitotenv.2024.169962 [doi]
AB  - BACKGROUND: Exposure to semi-volatile organic compounds (SVOCs) may link to 
      thyroid nodule risk, but studies of mixed-SVOCs exposure effects are lacking. 
      Traditional analytical methods are inadequate for dealing with mixed exposures, 
      while machine learning (ML) seems to be a good way to fill the gaps in the field 
      of environmental epidemiology research. OBJECTIVES: Different ML algorithms were 
      used to explore the relationship between mixed-SVOCs exposure and thyroid nodule. 
      METHODS: A 1:1:1 age- and gender-matched case-control study was conducted in 
      which 96 serum SVOCs were measured in 50 papillary thyroid carcinoma (PTC), 50 
      nodular goiters (NG), and 50 controls. Different ML techniques such as Random 
      Forest, AdaBoost were selected based on their predictive power, and variables 
      were selected based on their weights in the models. Weighted quantile sum (WQS) 
      regression and Bayesian kernel machine regression (BKMR) were used to assess the 
      mixed effects of the SVOCs exposure on thyroid nodule. RESULTS: Forty-three of 96 
      SVOCs with detection rate >80 % were included in the analysis. ML algorithms 
      showed a consistent selection of SVOCs associated with thyroid nodule. 
      Fluazifop-butyl and fenpropathrin are positively associated with PTC and NG in 
      single compound models (all P < 0.05). WQS model shows that exposure to 
      mixed-SVOCs was associated with an increased risk of PTC and NG, with the mixture 
      dominated by fenpropathrin, followed by fluazifop-butyl and propham. In the BKMR 
      model, mixtures showed a significant positive association with thyroid nodule 
      risk at high exposure levels, and fluazifop-butyl showed positive effects 
      associated with PTC and NG. CONCLUSION: This study confirms the feasibility of ML 
      methods for variable selection in high-dimensional complex data and showed that 
      mixed exposure to SVOCs was associated with increased risk of PTC and NG. The 
      observed association was primarily driven by fluazifop-butyl and fenpropathrin. 
      The findings warranted further investigation.
CI  - Copyright (c) 2024 Elsevier B.V. All rights reserved.
FAU - Wang, Fei
AU  - Wang F
AD  - Department of Occupational Health and Environmental Health, School of Public 
      Health, Guangxi Medical University, Nanning, Guangxi, China; Guangxi Key 
      Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second 
      Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, 
      Guangxi, China.
FAU - Lin, Yuanxin
AU  - Lin Y
AD  - Department of Occupational Health and Environmental Health, School of Public 
      Health, Guangxi Medical University, Nanning, Guangxi, China; Guangxi Key 
      Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second 
      Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, 
      Guangxi, China.
FAU - Xu, Jianing
AU  - Xu J
AD  - Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The 
      Second Affiliated Hospital, Guangxi University of Science and Technology, 
      Liuzhou, Guangxi, China; School of Electronic Engineering, Guangxi University of 
      Science and Technology, Liuzhou, Guangxi, China.
FAU - Wei, Fugui
AU  - Wei F
AD  - Department of Head and Neck Surgery, The Second Affiliated Hospital of Guangxi 
      University of Science and Technology, Liuzhou, Guangxi, China.
FAU - Huang, Simei
AU  - Huang S
AD  - School of Science, Guangxi University of Science and Technology, Liuzhou, 
      Guangxi, China.
FAU - Wen, Shifeng
AU  - Wen S
AD  - Department of Occupational Health and Environmental Health, School of Public 
      Health, Guangxi Medical University, Nanning, Guangxi, China; Guangxi Key 
      Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second 
      Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, 
      Guangxi, China.
FAU - Zhou, Huijiao
AU  - Zhou H
AD  - Department of Occupational Health and Environmental Health, School of Public 
      Health, Guangxi Medical University, Nanning, Guangxi, China; Guangxi Key 
      Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second 
      Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, 
      Guangxi, China.
FAU - Jiang, Yuwei
AU  - Jiang Y
AD  - Department of Occupational Health and Environmental Health, School of Public 
      Health, Guangxi Medical University, Nanning, Guangxi, China; Guangxi Key 
      Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second 
      Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, 
      Guangxi, China.
FAU - Wang, Haoyu
AU  - Wang H
AD  - Department of Occupational Health and Environmental Health, School of Public 
      Health, Guangxi Medical University, Nanning, Guangxi, China; Guangxi Key 
      Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second 
      Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, 
      Guangxi, China.
FAU - Ling, Wenlong
AU  - Ling W
AD  - Department of Thyroid Surgery, The Second Affiliated Hospital of Guangxi 
      University of Science and Technology, Liuzhou, Guangxi, China.
FAU - Li, Xiangzhi
AU  - Li X
AD  - Guangxi Key Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The 
      Second Affiliated Hospital, Guangxi University of Science and Technology, 
      Liuzhou, Guangxi, China; Department of Public Health, School of Medicine, Guangxi 
      University of Science and Technology, Liuzhou, Guangxi, China.
FAU - Yang, Xiaobo
AU  - Yang X
AD  - Department of Occupational Health and Environmental Health, School of Public 
      Health, Guangxi Medical University, Nanning, Guangxi, China; Guangxi Key 
      Laboratory on Precise Prevention and Treatment for Thyroid Tumor, The Second 
      Affiliated Hospital, Guangxi University of Science and Technology, Liuzhou, 
      Guangxi, China. Electronic address: yangx@gxmu.edu.cn.
LA  - eng
PT  - Journal Article
DEP - 20240114
PL  - Netherlands
TA  - Sci Total Environ
JT  - The Science of the total environment
JID - 0330500
RN  - 0 (Environmental Pollutants)
RN  - 87BH96P0MX (fenpropathrin)
RN  - 0 (Volatile Organic Compounds)
RN  - 0 (Pyrethrins)
SB  - IM
MH  - Humans
MH  - Thyroid Cancer, Papillary
MH  - *Environmental Pollutants
MH  - *Goiter, Nodular/pathology
MH  - *Thyroid Nodule
MH  - *Thyroid Neoplasms
MH  - *Volatile Organic Compounds
MH  - Case-Control Studies
MH  - Bayes Theorem
MH  - Algorithms
MH  - Machine Learning
MH  - *Pyrethrins
OTO - NOTNLM
OT  - Case-control study
OT  - Machine learning (ML)
OT  - Mixed exposure
OT  - Multipollutant modeling
OT  - Papillary thyroid cancer (PTC)
OT  - Semi-volatile organic compounds (SVOCs)
COIS- Declaration of competing interest The authors state that they do not have any 
      competing financial interests or personal ties that could appear to have 
      influenced the work disclosed in this study.
EDAT- 2024/01/15 00:42
MHDA- 2024/02/08 06:43
CRDT- 2024/01/14 19:38
PHST- 2023/08/31 00:00 [received]
PHST- 2023/12/30 00:00 [revised]
PHST- 2024/01/04 00:00 [accepted]
PHST- 2024/02/08 06:43 [medline]
PHST- 2024/01/15 00:42 [pubmed]
PHST- 2024/01/14 19:38 [entrez]
AID - S0048-9697(24)00096-2 [pii]
AID - 10.1016/j.scitotenv.2024.169962 [doi]
PST - ppublish
SO  - Sci Total Environ. 2024 Mar 10;915:169962. doi: 10.1016/j.scitotenv.2024.169962. 
      Epub 2024 Jan 14.