Study on Risk Prediction of Non-dementia Vascular Cognitive Impairment in Glycolipid Metabolic Diseases

Background With the aging population in China, the incidence of vascular cognitive impairment (VCI) will increase year by year. Non-dementia vascular cognitive impairment (VCIND) is the most common form of VCI. At present, the research shows that glycolipid metabolic diseases will accelerate the process of VCI, and the treatment of VCI focuses on controlling risk factors, but there is a lack of relevant research on VCIND caused by glycolipid metabolic diseases. Objective To analyze the factors influencing the occurrence of VCIND with glycolipid metabolic disease, construct a regression model, and conduct risk prediction. Methods A total of 410 patients with glycolipid metabolic diseases who were hospitalized in the encephalopathy center of Guangdong Provincial Hospital of Traditional Chinese Medicine from March to December 2023 were selected. Patients were divided into a cognitive normal group (MMSE>26 points) and a VCIND group (MMSE≤26 points) according to the Mini-mental State Examination Scale (MMSE). Multivariate Logistic regression was used to evaluate the influencing factors of VCIND in middle-aged and elderly patients with glycolipid metabolic diseases, and the risk prediction model of VCIND in glycolipid metabolic diseases was constructed. The predictive value of the model was evaluated via the receiver's operating characteristic (ROC) curve, and the area under the ROC curve (AUC) was calculated. Results Among the 410 patients, there were 209 cases in the cognitively normal group and 201 cases in VCIND. The results of multivariate Logistic regression analysis showed that low education level [below primary school (OR=25.989, 95%CI=5.656-119.427), primary school (OR=6.839, 95%CI=3.919-11.933) ], Fazekas grade (OR=1.700, 95%CI=1.124-2.570) were independent influencing factors for the occurrence of VCIND in patients with glycolipid metabolism (P<0.05). Based on the results of multivariate Logistic regression analysis, the prediction model was logit (P) =-1.608+ primary school×1.923+ below primary school×3.285+Fazekas grading×0.531. The AUC of this risk prediction regression model was 0.767 (95%CI=0.721-0.813, P<0.001). Hosmer-Lemeshow goodness-of-fit test showed that the model has a good fitting effect (χ2=13.404, P=0.099) . Conclusion Low literacy and Fazekas classification are independent influencing factors for the development of VCIND in a population of patients with glycolipid metabolism. Establishing a risk prediction regression model based on the above risk factors has a good predictive value and helps to identify the high-risk group of developing VCIND in patients with glycolipid metabolism disease at an early stage.