ArticleNeurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology2024
Prediction model for mild cognitive impairment in patients with type 2 diabetes using the autonomic function test.
Article in Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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2 citing papers in PubMed, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.
- Risk factors for cognitive decline in type 2 diabetes mellitus adults: a systematic review and meta-analysis.Molecular and cellular biochemistry · 2025Pooled it
- Risk Prediction Models for Mild Cognitive Impairment in Patients with Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis.Diabetes, metabolic syndrome and obesity : targets and therapy · 2024Review
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6 authors at 2 institutions in 1 country.
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Abstract
objectiveType 2 diabetes mellitus (T2DM) is a risk factor for cognitive impairment, and reduced heart rate variability (HRV) has been correlated with cognitive impairment in elderly individuals. This study investigated risk factors and validated a predictive model for mild cognitive impairment (MCI) in patients with T2DM using an autonomic function test.
methodsPatients with T2DM, 50-85 years of age, who attended the diabetes clinic at Gyeongsang National University Hospital between March 2018 and December 2019, were included. A total of 201 patients had been screened; we enrolled 124 patients according to the inclusion and exclusion criteria in this study. Cognitive function was assessed using the Montreal Cognitive Assessment-Korean version (MOCA-K); MCI was defined as a total MOCA-K score ≤ 23. Risk factors for MCI in patients with T2DM, including demographic- and diabetes-related factors, and autonomic function test results, were analyzed. Based on multivariate logistic regression, a nomogram was developed as a prediction model for MCI.
resultsThirty-nine of 124 patients were diagnosed with MCI. Age, education, and decreased cardiovagal function were associated with a high risk for MCI, with cardiovagal function exerting the greatest influence. However, diabetes-related factors, such as glycemic control, duration of diabetes, or medications, were not associated with the risk for MCI. The nomogram demonstrated excellent discrimination (area under the curve, 0.832) and was well calibrated.
conclusionApproximately one-third of patients had MCI; as such, carefully evaluating cognitive function in elderly T2DM patients with reduced HRV is important to prevent progression to dementia.
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