Experience in using predictive mathematical models for assessing risks of cardiometabolic disorders in metallurgists

UDC: 
616.12-008: 616-057: 669 (470.54)
DOI: 
10.21668/health.risk/2026.1.09.eng
Authors: 

E.D. Konstantinova1, V.G. Gazimova2, S.Yu. Ogorodnikova1, T.A. Maslakova1, D.V. Chaurina2, A.S. Shastin2

Organization: 

1Institute of Industrial Ecology, Ural Branch of the Russian Academy of Sciences, 20 Sofia Kovalevskaya Str., Ekaterinburg, 620990, Russian Federation
2Yekaterinburg Medical Research Center for Prophylaxis and Health Protection in Industrial Workers, 30 Popova Str., Ekaterinburg, 620014, Russian Federation

Abstract: 

The increasing prevalence of metabolic syndrome is driving the search for simple, non-invasive, and cost-effective methods for identifying individuals at risk for its development that allow for the implementation of preventive and personalized medicine principles in managing the risk of cardiometabolic disorders among the working population at minimal cost.

The aim of the study is to develop risk predictive models for cardiometabolic disorders in metallurgists.

The study focused on the results of periodic medical examinations of workers at a metallurgical plant over the previous five years. Metabolic syndrome was diagnosed in compliance with the criteria of the International Diabetes Federation. The body roundness index (BRI) was calculated. The Shapiro-Wilk test was used to assess the normality of the distribution of the studied parameters. Either the paired Student's t-test or the Wilcoxon signed-rank test (with Bonferroni correction) was applied to determine the statistical significance of differences between means and medians for dependent samples. One-way repeated measures analysis of variance (ANOVA) was used for parameters with a normal distribution across all time point. The Friedman test was employed for parameters with non-normal distributions. A heatmap-based approach was used to visualize correlations between predictors while Cox regression was applied to identify independent predictors of hypertension and metabolic syndrome. The proportional hazards assumption was verified through the visual analysis of Log-Minus-Log (LML) plots. To assess the quality of the models, we constructed ROC curves and calculated the area under the curve (AUC).

Using Cox regression with time-varying covariates allowed us to account for the dynamics of changes in workers’ health. Cox regression analysis revealed that the fasting blood glucose level was the strongest independent predictor of the metabolic syndrome over 5 years of observation. As a predictor of metabolic syndrome, BRI demonstrated the best results across all parameters, including an optimal balance between sensitivity and specificity, as well as overall accuracy. The ROC curve comparison showed that the body roundness index is the most accurate tool for predicting the risk of metabolic syndrome in the occupational group under study. The resulting models can be recommended for establishing risk groups among industrial workers exposed to occupational hazards for further development and implementation of personalized preventive medicine programs aimed at early detection and treatment of cardiometabolic disorders.

Keywords: 
anthropometric indices, predictive model, Cox regression, ROC analysis, cardiometabolic disorders, body roundness index, personalized medicine, risk management
Konstantinova E.D., Gazimova V.G., Ogorodnikova S.Yu., Maslakova T.A., Chaurina D.V., Shastin A.S. Experience in using predictive mathematical models for assessing risks of cardiometabolic disorders in metallurgists. Health Risk Analysis, 2026, no. 1, pp. 95–105. DOI: 10.21668/health.risk/2026.1.09.eng
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Received: 
11.02.2026
Approved: 
11.03.2026
Accepted for publication: 
26.03.2026

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