ArticleFrontiers in medicine2026
A clinical prediction nomogram for hemorrhoid risk in health screening populations in Beijing, China: a retrospective cohort study of 35,256 adults.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Hemorrhoids affect approximately 25% of the global population, yet validated risk prediction models for primary prevention are lacking. Routine laboratory biomarkers offer practical advantages for risk prediction in health screening settings. This study aims to identify independent risk factors for hemorrhoid incidence and develop a clinical prediction nomogram incorporating sex, age, and routine laboratory biomarkers in a large health-screening cohort. Methods: This retrospective longitudinal cohort study included 35,256 adults who underwent comprehensive health examinations at Beijing Nuclear Industry Hospital, China, between December 29, 2014, and December 16, 2025. Participants without prior hemorrhoid disease were followed for 60 months through subsequent screening visits. Cox proportional hazards regression identified independent predictors from 16 candidate variables including demographics and laboratory parameters. A nomogram was constructed and validated using discrimination analysis (C-index and time-dependent ROC), calibration curves, and decision curve analysis. Results: Over the 60-month follow-up period, 3,973 of the 35,256 participants (11.27%) developed incident hemorrhoids. Multivariable analysis identified five independent predictors: female sex (HR = 1.41, 95% CI: 1.28-1.55), older age (HR = 1.02, 95% CI: 1.02-1.03 per year), lower hemoglobin (HR = 0.99, 95% CI: 0.99-1.00 per g/L), higher triglycerides (HR = 1.05, 95% CI: 1.02-1.08 per mmol/L), and higher uric acid (HR = 1.001, 95% CI: 1.001-1.002 per μmol/L). The nomogram demonstrated acceptable discrimination (C-index: 0.772, 95% CI: 0.764-0.780), though it systematically underestimated observed risk across the predicted-probability range (∼0%-37%); decision curve analysis showed positive net benefit for threshold probabilities between approximately 5%-40%. Conclusions: This study identified five independent predictors of hemorrhoid incidence-sex, age, hemoglobin, triglycerides, and uric acid-and used them to develop one of the first nomograms integrating routine laboratory biomarkers for hemorrhoid risk prediction in health-screening populations. Using exclusively accessible, low-cost parameters, the model enables individualized risk stratification at routine health screening, facilitating targeted prevention strategies for high-risk individuals.
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