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. 2025 May;87(2):220-236.
doi: 10.18999/nagjms.87.2.220.

Utility of long-term systolic blood pressure variability for predicting the development of type 2 diabetes mellitus

Zean Song  1 , Yuanying Li  1 , Young-Jae Hong  1 , Chifa Chiang  1   2 , Masaaki Matsunaga  3 , Yupeng He  3 , Atsuhiko Ota  3 , Koji Tamakoshi  4 , Hiroshi Yatsuya  1
Affiliations

Utility of long-term systolic blood pressure variability for predicting the development of type 2 diabetes mellitus

Zean Song et al. Nagoya J Med Sci. 2025 May.

Abstract

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% [p < 0.001]) and IDI (0.700% [p = 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/).

Keywords: diabetes; prediction model; systolic blood pressure variability.

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Conflict of interest statement

There are no conflicts of interest to declare.

Figures

Fig. 1
Fig. 1
Calibration plot for type 2 diabetes mellitus cases, showing predicted and observed 10-year risk according to quartiles of Model 2 predicted 10-year risk T2DM: type 2 diabetes mellitus
See this image and copyright information in PMC

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