Occupational disparities in chronic disease prevalence and metabolic covariation: a cross-sectional study of six occupational groups in Lanzhou

Objectives To investigate the differences in chronic disease prevalence and underlying metabolic profiles among working-age individuals across six typical occupational groups, identify occupation-related associated factors, and provide epidemiological evidence for occupation-specific precise prevention and control of chronic diseases. Methods A cross-sectional study design was adopted. A total of 1,473 on-the-job physical examinees aged 18–64 who received physical examination at the Physical Examination Center of the First Hospital of Lanzhou University from August 2024 to August 2025 were retrospectively included. Structured questionnaires were distributed via targeted online channels to collect sociodemographic characteristics and behavioral and lifestyle data. Physical examinations and laboratory tests were performed to obtain 16 health indicators. Age-standardized prevalence (ASR) of five chronic diseases (hypertension, diabetes mellitus, hyperlipidemia, hyperuricemia, and obesity) was calculated using the direct standardization method, with the age composition of the full study sample as the standard population. Multivariate logistic regression analysis was conducted to assess the independent associations between various factors and chronic diseases, and Spearman rank correlation analysis was used to evaluate the strength of associations between health indicators. Results Occupational disparities in the prevalence of hyperlipidemia and hyperuricemia were the most prominent. After age standardization, the prevalence of hyperlipidemia in police officers (ASR = 0.435, 95% CI 0.368–0.502) and software engineers (ASR = 0.418, 95% CI 0.366–0.470), as well as the prevalence of hyperuricemia in firefighters (ASR = 0.381, 95% CI 0.315–0.447) and police officers (ASR = 0.332, 95% CI 0.267–0.396) were all significantly higher than those in healthcare workers (ASR = 0.218 for hyperlipidemia and 0.143 for hyperuricemia, respectively), with no overlap in 95% CIs across occupational groups. Multivariate logistic regression analysis showed that police officers (OR = 2.499, 95% CI 1.409–4.433) and software engineers (OR = 2.531, 95% CI 1.007–6.361) were independent associated factors for hyperlipidemia; police officers (OR = 3.425, 95% CI 1.821–6.441), firefighters (OR = 4.143, 95% CI 1.916–8.955), and software engineers (OR = 2.957, 95% CI 1.136–7.696) were independent associated factors for hyperuricemia. No statistically significant occupational disparities were observed for hypertension, diabetes mellitus, and obesity. Correlation analysis showed that triglyceride, high-density lipoprotein cholesterol, body mass index, uric acid and alanine aminotransferase formed a moderate correlation cluster (| r | = 0.44–0.54), body mass index was widely correlated with a variety of metabolic indicators ( r = 0.39–0.51), and uric acid was strongly correlated with creatinine ( r = 0.64). Conclusion Police officers, firefighters, and software engineers are occupational groups with elevated prevalence of hyperlipidemia and hyperuricemia. Age standardization is a necessary methodological step for cross-occupational health comparisons. Obesity occupies a core hub position in the metabolic comorbidity network. These findings provide reference evidence for developing occupation-specific, tiered precise prevention and control strategies for chronic diseases.