Abstract:Abstract: OBJECTIVE Based on the physical examination data of employees in a certain university in Beijing, a hypertension risk prediction model for the campus community was established to facilitate hypertension prevention and control. METHODS The physical examination results of employees in the university in 2022 were statistically analyzed, logistic regression analysis was performed to screen hypertension risk factors, and a Nomogram prediction model was established, and receiver operating characteristic(ROC) curve and calibration curve were used to evaluate the model. RESULTS 2 456 individuals were included, with the prevalence of hypertension was 34.4% . Age ( 41~60 years old OR=2.589, >60 years old OR=13.056 ), higher body mass index (BMI) ( 24.0~27.9 kg/m 2 OR=1.508, ≥28.0 kg/m 2 OR=1.868), abdominal obesity (OR=1.441), high fasting blood glucose (FBG) (≥6.1 mmol/L OR=2.097), and hyperuricemia (>420 μmol/L OR=1.596) were influencing factors for hypertension. The Nomogram model constructed using these risk factors,its area under ROC curve (AUC) was 0.814 (95%CI : 0.795~0.832), and the calibration plot showed that the calibration curve was close to the standard curve, indicating that the model had good predictive ability and stability. CONCLUSION The prevalence of hypertension among employees in the university is high, and age, overweight, obesity, abdominal obesity, high FBG, and hyperuricemia are risk factors for hypertension.