Abstract: Objective To investigate the independent influencing factors of endothelial dysfunction in patients with essential hypertension（EH）, and to construct a personalized nomogram model for predicting the occurrence of endothelial dysfunction in EH. Methods The clinical data of 1 752 EH patients admitted to the department of geriatrics and the department of general medicine of the First Affiliated Hospital of Fujian Medical University from August 2000 to May 2016 were retrospectively collected. Endothelial function was evaluated by flow-mediated dilation（FMD）. EH patients were divided into training group（n=1 204） and validation group（n=548） according to the time sequence of enrollment. Then, the patients were segmented into endothelial dysfunction group（FMD≤7.1%） or normal endothelial function group（FMD＞7.1%）. Univariate analysis and multivariate logistic regression analysis were used to analyze the independent influencing factors of endothelial dysfunction, and nomogram model was constructed accordingly. The discrimination, calibration, and clinical applicability of the model were evaluated by area under the receiver operating characteristic curve（AUC）,calibration curve and Hosmer-Lemeshow test and decision curve, respectively. Results There were 441 cases of EH with endothelial dysfunction in the training group, and 103 cases in the validation group. Multivariate logistic analysis showed that a total of 6 independent risk factors were included in the training group, including age（OR=1.02, 95%CI 1.01-1.03）, smoking（OR=3.51, 95%CI 2.40-5.14）, high level of white blood cell（OR=2.14, 95%CI 1.60-2.86）, high level of systolic blood pressure（OR=1.59, 95%CI 1.23-2.05）, high level of fasting blood glucose（OR=1.44, 95%CI 1.12-1.86） and abdominal obesity（OR=1.34, 95%CI 1.04-1.73）（all P＜0.05）. The nomogram of prediction model subsequently was established. After internal validation, the AUC values of the training group and the validation group were 0.70（95%CI 0.67-0.73） and 0.74（95%CI 0.68-0.80）, respectively, indicating good differentiation. Calibration curve and Hosmer-Lemeshow test showed that the predicted results of the model were in good agreement with the actual results（the training group: χ~2=2.05, P=0.36; the validation group: χ~2=0.72, P=0.70）. The decision curve also verified the clinical applicability of nomogram. Conclusion The personalized nomogram constructed in this study has the good prediction ability, calibration and clinical applicability, which can easily and intuitively identify the high-risk groups of EH patients with endothelial dysfunction, and provide clinical basis for early prevention and treatment.