Cognitive Risk Stratification Score in Middle-aged and Older Adults With Type 2 Diabetes: A Cross-Sectional Study.

Zhang, Jinghua; Tam, Wilson Wai San; Lu, Jinhua; Chen, Junjie; Kusuyama, Joji; Dong, Yanhong; Yap, Xin Yi; Zhou, Wentao et al. · J Clin Endocrinol Metab · 2025

cross_sectional · Level IV

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Abstract

Cognitive impairment (CI) affects approximately 45% of middle-aged and older adults with type 2 diabetes mellitus (T2DM) globally. Although formal comprehensive neuropsychological tests are the gold standard for diagnosing CI, they are often time-intensive and may not be feasible in primary care. This study aimed to develop and validate a novel risk stratification score (RSS) to rapidly and comprehensively predict CI risk among middle-aged and older adults with T2DM, offering a streamlined alternative in clinical practice. A cross-sectional study was conducted from July 2023 to February 2024 in a primary care polyclinic in Singapore's western region. Participants aged between 40 and 85 diagnosed with T2DM (n = 150) were included in a convenience sampling. The primary outcome was CI status, which was assessed using formal neuropsychological tests, including the Montreal Cognitive Assessment (MoCA). CI was identified in 49.3% of participants (n = 74). The RSS, incorporating the MoCA, diastolic blood pressure, and Short Physical Performance Battery, demonstrated excellent discrimination, achieving an area under the receiver operating characteristic curve of 0.802 (P < .001). With an optimal cutoff of 0.3, the model showed a sensitivity of 63.5% and specificity of 86.8%, effectively differentiating high- and low-risk CI groups. RSS in clinical practice, exemplified by the Integrated Metabolic Cognitive Risk Stratification Pathway, is a promising tool for rapid CI risk assessment in primary care. Its robust predictive accuracy and ease of use support its application for early intervention in middle-aged and older adults with T2DM. Future studies should validate its use longitudinally and across diverse populations to enhance generalizability.

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