Development and internal validation of a routine metabolic screening model for current hyperuricemia among gas station workers in Guangxi, China: a cross-sectional study
Abstract
Background: Serum uric acid (SUA) measurement is the direct basis for identifying current hyperuricemia (HUA). We developed a non-SUA screening model for situations in which routine occupational health information is already available but a contemporaneous SUA result is temporarily unavailable, delayed, or requires separate testing.
Methods: This cross-sectional study included 894 workers from 11 gas stations and related operational sites in Nanning, Guangxi, China. Current HUA was defined as same-examination SUA > 420 µmol/L in both sexes. Twelve prespecified non-SUA candidate variables were considered; total cholesterol was excluded because of high redundancy and multicollinearity with LDL-C, leaving 11 candidates for least absolute shrinkage and selection operator (LASSO) screening. Internal validation used 100 repetitions of site-grouped five-fold cross-validation. Model performance was assessed using out-of-fold (OOF) predictions, discrimination, calibration, decision curve analysis, candidate screening thresholds, and bootstrap optimism correction.
Results: Among 894 participants, 169 (18.9%) had current HUA. Sex, waist circumference, triglycerides (TG), systolic blood pressure (SBP), and fasting blood glucose (FBG) met the prespecified selection-stability threshold and comprised the final model. The mean area under the receiver operating characteristic curve (AUC) across repeated site-grouped cross-validation was 0.858 (SD 0.004), and the pooled OOF AUC was 0.866 (95% CI, 0.838–0.893). The pooled OOF Brier score was 0.108, the calibration intercept was − 0.064 (95% CI, -0.322 to 0.192), and the calibration slope was 0.920 (95% CI, 0.791–1.077). At a 10% candidate probability threshold, sensitivity was 92.9%, specificity was 64.6%, and the negative predictive value was 97.5%.
Conclusions: The five-variable model estimates the probability of current HUA and may support prioritization of confirmatory SUA testing when a contemporaneous SUA result is unavailable. It does not replace direct SUA measurement or predict future incident HUA. Independent external validation is required before broader implementation.
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BibTeX
@article{wang2026development,
title = {{Development and internal validation of a routine metabolic screening model for current hyperuricemia among gas station workers in Guangxi, China: a cross-sectional study}},
author = {Jinbo Wang and Xinna He and Yinxia Lin and Zhuoheng Chen and Jianing Cong and Yaqin Pang and Guangzi Qi},
journal = {BMC Public Health},
year = {2026},
month = oct,
doi = {10.1186/s12889-026-29953-y},
url = {https://link.springer.com/article/10.1186/s12889-026-29953-y},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Development and internal validation of a routine metabolic screening model for current hyperuricemia among gas station workers in Guangxi, China: a cross-sectional study
AU - Jinbo Wang
AU - Xinna He
AU - Yinxia Lin
AU - Zhuoheng Chen
AU - Jianing Cong
AU - Yaqin Pang
AU - Guangzi Qi
PY - 2026
DA - 2026/10/28
JO - BMC Public Health
DO - 10.1186/s12889-026-29953-y
UR - https://link.springer.com/article/10.1186/s12889-026-29953-y
AB - Background: Serum uric acid (SUA) measurement is the direct basis for identifying current hyperuricemia (HUA). We developed a non-SUA screening model for situations in which routine occupational health information is already available but a contemporaneous SUA result is temporarily unavailable, delayed, or requires separate testing. Methods: This cross-sectional study included 894 workers from 11 gas stations and related operational sites in Nanning, Guangxi, China. Current HUA was defined as same-examination SUA > 420 µmol/L in both sexes. Twelve prespecified non-SUA candidate variables were considered; total cholesterol was excluded because of high redundancy and multicollinearity with LDL-C, leaving 11 candidates for least absolute shrinkage and selection operator (LASSO) screening. Internal validation used 100 repetitions of site-grouped five-fold cross-validation. Model performance was assessed using out-of-fold (OOF) predictions, discrimination, calibration, decision curve analysis, candidate screening thresholds, and bootstrap optimism correction. Results: Among 894 participants, 169 (18.9%) had current HUA. Sex, waist circumference, triglycerides (TG), systolic blood pressure (SBP), and fasting blood glucose (FBG) met the prespecified selection-stability threshold and comprised the final model. The mean area under the receiver operating characteristic curve (AUC) across repeated site-grouped cross-validation was 0.858 (SD 0.004), and the pooled OOF AUC was 0.866 (95% CI, 0.838–0.893). The pooled OOF Brier score was 0.108, the calibration intercept was − 0.064 (95% CI, -0.322 to 0.192), and the calibration slope was 0.920 (95% CI, 0.791–1.077). At a 10% candidate probability threshold, sensitivity was 92.9%, specificity was 64.6%, and the negative predictive value was 97.5%. Conclusions: The five-variable model estimates the probability of current HUA and may support prioritization of confirmatory SUA testing when a contemporaneous SUA result is unavailable. It does not replace direct SUA measurement or predict future incident HUA. Independent external validation is required before broader implementation.
SN - 1471-2458
ER -