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Building a predictive model for hypertension related to environmental chemicals using machine learning

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Abstract

Hypertension is a chronic cardiovascular disease characterized by elevated blood pressure that can lead to a number of complications. There is evidence that the numerous environmental substances to which humans are exposed facilitate the emergence of diseases. In this work, we sought to investigate the relationship between exposure to environmental contaminants and hypertension as well as the predictive value of such exposures. The National Health and Nutrition Survey (NHANES) provided us with the information we needed (2005–2012). A total of 4492 participants were included in our study, and we incorporated more common environmental chemicals and covariates by feature selection followed by regularized network analysis. Then, we applied various machine learning (ML) methods, such as extreme gradient boosting (XGBoost), random forest classifier (RF), logistic regression (LR), multilayer perceptron (MLP), and support vector machine (SVM), to predict hypertension by chemical exposure. Finally, SHapley Additive exPlanations (SHAP) were further applied to interpret the features. After the initial feature screening, we included a total of 29 variables (including 21 chemicals) for ML. The areas under the curve (AUCs) of the five ML models XGBoost, RF, LR, MLP, and SVM were 0.729, 0.723, 0.721, 0.730, and 0.731, respectively. Butylparaben (BUP), propylparaben (PPB), and 9-hydroxyfluorene (P17) were the three factors in the prediction model with the highest SHAP values. Comparing five ML models, we found that environmental exposure may play an important role in hypertension. The assessment of important chemical exposure parameters lays the groundwork for more targeted therapies, and the optimized ML models are likely to predict hypertension.

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Data Availability

The datasets analyzed during the current study are available in the NHANES repository (https://wwwn.cdc.gov/nchs/nhanes/Default.aspx).

Abbreviations

AUC :

Area under the curve

BMI :

Body mass index

BP3 :

Benzophenone-3

BPA :

Bisphenol A

BUP :

Butylparaben

Cd :

Cadmium

COT :

Cotinine

DBP :

Diastolic blood pressure

EPB :

Ethylparaben

Hg :

Mercury

LOD :

The limit of detection

LR :

Logistic regression

MBzP :

Monobenzyl phthalate

MCNP :

Mono-carboxynonyl phthalate

MCOP :

Mono-carboxyoctyl phthalate

MCPP :

Mono-(3-carboxypropyl) phthalate

MECPP :

Mono-2-ethyl-5-carboxypentyl phthalate

MEHHP :

Mono-(2-ethl-5-hydrxhxyl) phthalate

MEHP :

Mono-(2-ethyl)-hexyl phthalate

MEOHP :

Mono-(2-ethyl-5-oxohexyl) phthalate

MEP :

Monoethyl phthalate

MiBP :

Monoisobutyl phthalate

MiNP :

Monoisononyl phthalate

ML :

Machine learning

MLP :

Multilayer perceptron

MMP :

Mono-n-methyl phthalate

MnBP :

Phthalates mono-n-butyl phthalate

MPB :

Methylparaben

MVPA :

Moderate-to-vigorous physical activity

NCHS :

The National Center for Health Statistics

NHANES :

The National Health and Nutrition Survey

P01 :

1-Hydroxynaphthalene

P02 :

2-Hydroxynaphthalene

P03 :

3-Hydroxyfluorene

P04 :

2-Hydroxyfluorene

P05 :

3-Hydroxyphenanthrene

P06 :

1-Hydroxyphenanthrene

P07 :

2-Hydroxyphenanthrene

P10 :

1-Hydroxypyrene

P17 :

9-Hydroxyfluorene

PAH :

Polyaromatic hydrocarbons

Pb :

Lead

PIR :

Poverty income ratio

PPB :

Propylparaben

RF :

Random forest classifier

SBP :

Systolic blood pressure

SHAP :

SHapley Additive exPlanations

SVM :

Support vector machine

TCS :

Triclosan

XGBoost :

Extreme gradient boosting

2,4-DCP :

2,4-Dichlorophenol

2,5-DCP :

2,5-Dichlorophenol

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Acknowledgements

This work was supported by the Heilongjiang Provincial Science and Technology Department (2022ZXJ03C05), the Military Healthcare Program (19BJZ24), and the National Natural Science Foundation of China (82271770).

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Author notes

  1. Shanshan Liu, Lin Lu, and Fei Wang have contributed equally to this work.

Authors and Affiliations

  1. Health Management Institute, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, No. 28, Fuxing Road, Haidian District, Beijing, 100853, China

    Shanshan Liu, Fei Wang, Bingqing Han, Lei Ou, Xiangyang Gao, Yi Luo & Qiang Zeng

  2. Medical School of Chinese PLA, Chinese PLA General Hospital, Beijing, 100853, China

    Shanshan Liu

  3. Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China

    Lin Lu

  4. School of Medicine, Nankai University, Tianjin, 300071, China

    Bingqing Han

  5. Medical Department, 305 Hospital of PLA, Beijing, 100034, China

    Wenjing Huo

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  1. Shanshan Liu
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Contributions

Shanshan Liu: formal analysis and writing—original draft; Lin Lu: methodology and formal analysis; Fei Wang: formal analysis; Bingqing Han: data curation; Lei Ou: writing—review and editing. Xiangyang Gao: data curation; Yi Luo: formal analysis; Wenjing Huo: data curation and methodology; Qiang Zeng: project administration and writing—review and editing.

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Correspondence to Qiang Zeng.

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Liu, S., Lu, L., Wang, F. et al. Building a predictive model for hypertension related to environmental chemicals using machine learning. Environ Sci Pollut Res 31, 4595–4605 (2024). https://doi.org/10.1007/s11356-023-31384-w

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