Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 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.

PubMed Disclaimer

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

Similar articles

See all similar articles

References

    1. GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204–1222. doi: 10.1016/S0140-6736(20)30925-9 - DOI - PMC - PubMed
    1. Bouchi R, Noda M, Hayashino Y, et al. Behavioral change stage might moderate the impact of multifaceted interventions on non-attendance from medical care among patients with type 2 diabetes: The Japan Diabetes Outcome Intervention Trial-2 Large-Scale Trial 007 (J-DOIT2-LT007). J Diabetes Investig. 2023;14(7):907–916. doi: 10.1111/jdi.14012 - DOI - PMC - PubMed
    1. Zou Q, Qu K, Luo Y, Yin D, Ju Y, Tang H. Predicting Diabetes Mellitus With Machine Learning Techniques. Front Genet. 2018;9:515. doi: 10.3389/fgene.2018.00515 - DOI - PMC - PubMed
    1. Li Y, Yang M, Yuan L, Li T, Zhong X, Guo Y. Associations between a polygenic risk score and the risk of gestational diabetes mellitus in a Chinese population: a case-control study. Endocr J. 2023;70(12):1159–1168. doi: 10.1507/endocrj.EJ23-0245 - DOI - PubMed
    1. Miyamori D, Tanaka M, Furuhashi M, et al. Prediction of new onset of diabetes mellitus during a 10-year period by using a combination of levels of alanine aminotransferase and γ-glutamyl transferase. Endocr J. 2021;68(12):1391–1402. doi: 10.1507/endocrj.EJ20-0823 - DOI - PubMed

LinkOut - more resources