Construction of a nomogram model to predict arteriosclerosis in middle-aged and elderly community dwellers: insights from a cohort study

Background The incidence of arteriosclerosis is steadily increasing, and arteriosclerosis is closely associated with cardiovascular diseases. The objective of this research was to create and verify a tool for forecasting arteriosclerosis in middle-aged and elderly individuals within the community. Methods A cohort study was conducted in multiple communities, and 4107 participants over 40 years of age were enrolled. The participants were randomly divided into a derivation cohort ( n = 2875) and a validation cohort ( n = 1232) at a ratio of 7:3. LASSO analysis and multivariate logistic regression were employed to analyze factors influencing arteriosclerosis and to establish a prediction model, which was visualized as a nomogram and then evaluated. The primary outcome is incident arteriosclerosis, defined as baPWV ≥ 1400 cm/s. Results Over an average follow-up period of 3.25 ± 1.14 years, 1688 subjects (41.0%) were diagnosed with arteriosclerosis. Independent risk factors for arteriosclerosis included age, BMI, hypertension, triglyceride levels, glycosylated hemoglobin, sex, and fasting blood glucose. The area under the receiver operating characteristic curve for the derivation and validation cohorts was 0.811 (95% CI: 0.795–0.827) and 0.816 (95% CI: 0.796–0.830), respectively. The Hosmer-Lemeshow test showed good model accuracy ( P = 0.123, P = 0.428). Calibration curves illustrated high consistency between predicted and observed results. Decision curve analysis demonstrated favorable net benefits of the model. Conclusion The predictive model established in this study holds promise for identifying arteriosclerosis in middle-aged and elderly individuals. Understanding the related risk factors and individualized prediction may assist physicians in early detection and intervention, ultimately improving patient prognosis.