Song Zean, Li Yuanying, Hong Young-Jae, Chiang Chifa, Matsunaga Masaaki, He Yupeng, Ota Atsuhiko, Tamakoshi Koji, Yatsuya Hiroshi
Department of Public Health and Health Systems, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Department of Global and Community Health, Nagoya City University Graduate School of Nursing, Nagoya, Japan.
Nagoya J Med Sci. 2025 May;87(2):220-236. doi: 10.18999/nagjms.87.2.220.
Better identification of individuals at high risk for type 2 diabetes mellitus (T2DM) requires risk-prediction models incorporating novel predictors. Accordingly, this study aimed to evaluate the merits of including long-term systolic blood pressure variability (SBPV) in predicting T2DM incidence in a Japanese cohort of 3017 participants (2446 men, 571 women; age, 36-65 years) in 2007, who were followed up until March 2019. Consecutive SBP values, recorded between 2003 and 2007, were regressed annually for each participant. The slope and root-mean-square error of the regression line were calculated for each individual to represent SBPV. The significance of SBPV was examined by adding it to a multivariate Cox model incorporating age, sex, smoking status, regular exercise, family history of diabetes, body mass index, blood levels of triglycerides, high-density lipoprotein cholesterol, and fasting blood glucose. The c-index, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to compare the performance of the prediction models without (Model 1) and with (Model 2) SBPV. During the 9.8-year follow-up period, 135 participants developed T2DM. Although a statistically significant difference in c-index between Model 1 (0.785) and Model 2 (0.786) was not found, the NRI (8.312% [ < 0.001]) and IDI (0.700% [ = 0.012]) demonstrated that the performance of Model 2 improved compared with Model 1. In conclusion, results suggested that long-term SBPV slightly improved predictive utility for T2DM when added to a conventional prediction model. The study was registered at University Hospital Medical Information Network Clinical Trial registry (UMIN000052544, https://www.umin.ac.jp/).
更好地识别2型糖尿病（T2DM）高危个体需要纳入新预测因素的风险预测模型。因此，本研究旨在评估纳入长期收缩压变异性（SBPV）对预测日本一组3017名参与者（2446名男性，571名女性；年龄36 - 65岁）在2007年T2DM发病率的价值，这些参与者随访至2019年3月。对每位参与者在2003年至2007年期间记录的连续收缩压值进行年度回归分析。计算每条回归线的斜率和均方根误差以代表SBPV。将SBPV添加到包含年龄、性别、吸烟状况、规律运动、糖尿病家族史、体重指数、甘油三酯血水平、高密度脂蛋白胆固醇以及空腹血糖的多变量Cox模型中，检验SBPV的显著性。使用c指数、净重新分类改善（NRI）和综合判别改善（IDI）来比较不含SBPV（模型1）和含SBPV（模型2）的预测模型的性能。在9.8年的随访期内，135名参与者患T2DM。虽然未发现模型1（0.785）和模型2（0.786）的c指数有统计学显著差异，但NRI（8.312%[<0.001]）和IDI（0.700%[=0.012]）表明模型2的性能相较于模型1有所改善。总之，结果表明，长期SBPV添加到传统预测模型中时，对T2DM的预测效用略有提高。该研究已在大学医院医学信息网络临床试验注册中心注册（UMIN编号：000052544，网址：https://www.umin.ac.jp/）。
