The identification of individuals at high risk of developing hypertension can be of great value to improve the efficiency of primary prevention strategies for hypertension. The objective of this study was to develop a risk prediction model for incident hypertension based on prospective longitudinal data from a general Japanese population. A total of 982 subjects aged 40–59 years without hypertension at baseline were followed up for 10 years (2002–12) for the incidence of hypertension. Hypertension was defined as systolic blood pressure (SBP) ≥ 140 mmHg, diastolic blood pressure (DBP) ≥ 90 mmHg, or the use of antihypertensive agents. The risk prediction model was developed using a Cox proportional hazards model. A simple risk scoring system was also established based on the developed model. During the follow-up period (median 10 years, interquartile range 5–10 years), 302 subjects (120 men and 182 women) developed new-onset hypertension. The risk prediction model for hypertension consisted of age, sex, SBP, DBP, use of glucose-lowering agents, body mass index (BMI), parental history of hypertension, moderate-to-high alcohol intake, and the interaction between age and BMI. The developed model demonstrated good discrimination (Harrell’s C statistic=0.812 [95% confidence interval, 0.791–0.834]; optimism-corrected C statistic based on 200 bootstrap samples=0.804) and calibration (Greenwood-Nam-D’Agostino χ² statistic=12.2). This risk prediction model is a useful guide for estimating an individual’s absolute risk for hypertension and could facilitate the management of Japanese individuals at high risk of developing hypertension in the future.

识别高血压高危人群对于提高高血压一级预防策略的效率具有重要价值。本研究的目的是根据日本一般人群的前瞻性纵向数据，开发一个高血压事件的风险预测模型。共有 982 名年龄在 40-59 岁的受试者在基线时没有高血压，随访了 10 年（2002-12 年）的高血压发病率。高血压定义为收缩压 (SBP) ≥ 140 mmHg、舒张压 (DBP) ≥ 90 mmHg 或使用抗高血压药物。风险预测模型是使用 Cox 比例风险模型开发的。基于所开发的模型，还建立了一个简单的风险评分系统。随访期间（中位 10 年，四分位距 5-10 年），302 名受试者（120 名男性和 182 名女性）出现新发高血压。高血压风险预测模型包括年龄、性别、收缩压、舒张压、降糖药的使用、体重指数（BMI）、父母高血压病史、中度至高酒精摄入量以及年龄与BMI之间的相互作用. 开发的模型表现出良好的辨别力（Harrell 的 C 统计量=0.812 [95% 置信区间，0.791–0.834]；基于 200 个引导样本的乐观校正 C 统计量=0.804）和校准（Greenwood-Nam-D'Agostino χ 中度到高度的酒精摄入量，以及年龄和 BMI 之间的相互作用。开发的模型表现出良好的辨别力（Harrell 的 C 统计量=0.812 [95% 置信区间，0.791–0.834]；基于 200 个引导样本的乐观校正 C 统计量=0.804）和校准（Greenwood-Nam-D'Agostino χ 中度到高度的酒精摄入量，以及年龄和 BMI 之间的相互作用。开发的模型表现出良好的辨别力（Harrell 的 C 统计量=0.812 [95% 置信区间，0.791–0.834]；基于 200 个引导样本的乐观校正 C 统计量=0.804）和校准（Greenwood-Nam-D'Agostino χ²统计量=12.2)。这种风险预测模型是估计个体高血压绝对风险的有用指南，并且可以促进对未来发生高血压的高风险日本个体的管理。