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Background <p>Diabetes mellitus (DM) is a major modifiable risk factor for dementia, yet cost-effective, scalable methods for early identification of cognitively at-risk individuals remain limited.</p> Objective <p>To evaluate the predictive value of estimated pulse wave velocity (ePWV), alone and in combination with metabolic markers, for incident cognitive impairment in adults with diabetes.</p> Methods <p>We analyzed 893 adults with DM from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2020). ePWV was derived from age and routine blood pressure, and evaluated alone and in interaction with metabolic markers—triglyceride–glucose (TyG) index and HbA1c—for prediction of cognition impairment over nine years. Analyses included Cox regression, K‑means clustering, Kaplan–Meier survival, generalized estimating equations (GEE), ROC curves, and sensitivity tests, including propensity score matching.</p> Results <p>Higher ePWV was linked to cognitive impairment in unadjusted and partially adjusted Cox models (hazard ratio (HR) per standard deviation (SD): 1.32; 95% confidence index (CI) [1.10–1.58]), but the highest quartile lost statistical significance after full biochemical and clinical adjustment. K‑means clustering and unadjusted survival analyses showed significant group differences, which were attenuated after adjustment. GEE analyses confirmed this pattern across repeated measures. Sensitivity analyses indicated robustness to unmeasured confounding. ROC AUCs ranged 0.61–0.69, reflecting limited discrimination.</p> Conclusions <p>ePWV demonstrated modest, adjustment‑sensitive associations with cognitive impairment in DM and limited predictive accuracy. In this context, ePWV is better suited for identifying population‑level vascular risk patterns rather than for precise individual prognostication. Combining ePWV with broader vascular, metabolic, and cognitive markers may improve predictive performance.</p>

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Association of estimated pulse wave velocity and metabolic markers with cognitive impairment risk in diabetes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月30日

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  • Lingjie Kong,
  • Guangning Zhang,
  • Jun Wu,
  • Xizhen Zhou,
  • Linfei Wang,
  • Lingyuan Wu

摘要

Background

Diabetes mellitus (DM) is a major modifiable risk factor for dementia, yet cost-effective, scalable methods for early identification of cognitively at-risk individuals remain limited.

Objective

To evaluate the predictive value of estimated pulse wave velocity (ePWV), alone and in combination with metabolic markers, for incident cognitive impairment in adults with diabetes.

Methods

We analyzed 893 adults with DM from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2020). ePWV was derived from age and routine blood pressure, and evaluated alone and in interaction with metabolic markers—triglyceride–glucose (TyG) index and HbA1c—for prediction of cognition impairment over nine years. Analyses included Cox regression, K‑means clustering, Kaplan–Meier survival, generalized estimating equations (GEE), ROC curves, and sensitivity tests, including propensity score matching.

Results

Higher ePWV was linked to cognitive impairment in unadjusted and partially adjusted Cox models (hazard ratio (HR) per standard deviation (SD): 1.32; 95% confidence index (CI) [1.10–1.58]), but the highest quartile lost statistical significance after full biochemical and clinical adjustment. K‑means clustering and unadjusted survival analyses showed significant group differences, which were attenuated after adjustment. GEE analyses confirmed this pattern across repeated measures. Sensitivity analyses indicated robustness to unmeasured confounding. ROC AUCs ranged 0.61–0.69, reflecting limited discrimination.

Conclusions

ePWV demonstrated modest, adjustment‑sensitive associations with cognitive impairment in DM and limited predictive accuracy. In this context, ePWV is better suited for identifying population‑level vascular risk patterns rather than for precise individual prognostication. Combining ePWV with broader vascular, metabolic, and cognitive markers may improve predictive performance.