糖脂代谢病发生非痴呆型血管性认知障碍的风险预测研究

BackgroundWith the aging population in Chinathe incidence of vascular cognitive impairmentVCI will increase year by year. Non-dementia vascular cognitive impairmentVCINDis the most common form of VCI. At present the research shows that glycolipid metabolic diseases will accelerate the process of VCIand the treatment of VCI focuses on controlling risk factorsbut there is a lack of relevant research on VCIND caused by glycolipid metabolic diseases. ObjectiveTo analyze the factors influencing the occurrence of vascular cognitive impairment no dementiaVCINDwith glycolipid metabolic diseaseconstruct a regression modeland conduct risk prediction. MethodsA cross-sectional study was conducted to select410 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. Patients were divided into a cognitive normal group MMSE>26 pointsand a VCIND groupMMSE 26 pointsaccording to the Mini-Mental State Examination Scale MMSE. Multi-factor Logistic regression was used to evaluate the influencing factors of VCIND in middle-aged and elderly patients with glycolipid metabolic diseasesand 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 characteristicROCcurveand the area under the ROC curveAUCwas calculated. ResultsAmong the 410 patientsthere 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 schoolOR=25.98995%CI=5.656-119.33primary schoolOR=6.83995%CI=3.919-11.933 Fazekas gradeOR=1.70095%CI=1.124-2.570were independent risk factors for the occurrence of VCIND in patients with glycolipid metabolismP<0.05. Based on the results of multivariate Logistic regression analysisthe prediction model is logit P=-1.608+ primary school1.923+ below primary school3.285+Fazekas grading0.531. The AUC of this risk prediction regression model is 0.76795%CI=0.721-0.813P<0.001. Hosmer-Lemeshow goodness-of-fit test showed that the model has a good fitting effect2 =13.404P=0.099. ConclusionLow literacy and Fazekas classification are independent risk 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.