Association between occupational heat exposure and early renal dysfunction among Chinese petrochemical workers: a combined machine learning and WQS modeling study

TL;DR

This study finds that occupational heat exposure in Chinese petrochemical workers is linked to higher risk of hyperuricemia, suggesting early kidney issues.

Contribution

The study combines machine learning and WQS modeling to assess the combined effects of occupational heat exposure and other hazards on hyperuricemia in petrochemical workers.

Findings

Occupational heat exposure was significantly associated with increased hyperuricemia risk (OR = 1.68).

Heat exposure contributed nearly half (49.2%) of the overall effect in mixed occupational hazards.

Machine learning identified heat exposure, length of service, age, BMI, and gender as key predictors of hyperuricemia.

Abstract

To investigate the association between occupational heat exposure and hyperuricemia among petrochemical workers.

We retrospectively analyzed the association between workplace heat exposure and hyperuricemia by using 10 years of occupational health examination records from 2,312 petrochemical workers in Fujian Province, China. Generalized linear models (GLMs) were employed to estimate the effects of individual exposures. Weighted quantile sum (WQS) regression model was used to evaluate the combined effects of multiple occupational exposures and to identify the relative contribution of each exposure factor. A hyperuricemia risk prediction model was developed using the LightGBM machine-learning algorithm, with feature importance assessed using SHAP (SHapley Additive exPlanations) values.

Occupational heat exposure was significantly associated with an increased risk of hyperuricemia (OR = 1.68, 95% CI: 1.28–2.20). In the GLM analysis, co-exposure to heat with benzene (OR = 1.93, 95% CI 1.05–3.55), H2S (OR = 3.38, 95% CI 1.94–5.88), gasoline (OR = 2.58, 95% CI 1.49–4.48), acid anhydride (OR = 2.21, 95% CI 1.09–4.48) and CO (OR = 2.14, 95% CI 1.16–3.97) further increased the risk (all p < 0.05), suggesting synergistic effects. The WQS analysis indicated that in the mixed occupational hazards exposure, heat exposure (49.2%) contributing nearly half the effect to the overall effect. The LightGBM machine learning model identified length of service, age, BMI, gender, and heat exposure as the main predictors of hyperuricemia. The SHAP analysis confirmed heat exposure as a key independent contributor alongside length of service.

Occupational heat exposure in petrochemical settings is significantly associated with hyperuricemia, suggesting potential early renal dysfunction risk. Integrating machine learning–based predictive models into workplace health surveillance may facilitate the early identification and management of high-risk workers. However, causal inference remains limited by the retrospective design and potential residual confounding, underscoring the need for prospective studies to validate and extend these findings.

Flowchart illustrating the relationship between occupational heat exposure, hyperuricemia, and early renal dysfunction among petrochemical workers. It includes factors like inflammation and oxidative stress leading to renal issues. Analysis involved 2312 workers using GLM and WQS models. Machine learning methods, including LASSO and SHAP values, were used for feature selection and performance evaluation.

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