Affiliations: Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
Correspondence:
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Correspondence to: Zuohui Zhang, Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, China. E-mail: zuohuizhang@xzhmu.edu.cn.
Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
Abstract: Background:Mild cognitive impairment (MCI) is a heterogeneous condition that can precede various forms of dementia, including Alzheimer’s disease (AD). Identifying MCI subjects who are at high risk of progressing to AD is of major clinical relevance. Enlarged perivascular spaces (EPVS) on MRI are linked to cognitive decline, but their predictive value for MCI to AD progression is unclear. Objective:This study aims to assess the predictive value of EPVS for MCI to AD progression and develop a predictive model combining EPVS grading with clinical and laboratory data to estimate conversion risk. Methods:We analyzed 358 patients with MCI from the ADNI database, consisting of 177 MCI-AD converters and 181 non-converters. The data collected included demographic information, imaging data (including perivascular spaces grade), clinical assessments, and laboratory test results. Variable selection was conducted using the Least Absolute Shrinkage and Selection Operator (LASSO) method, followed by logistic regression to develop predictive model. Results:In the univariate logistic regression analysis, both moderate (OR = 5.54, 95% CI [3.04–10.18]) and severe (OR = 25.04, 95% CI [10.07–62.23]) enlargements of the centrum semiovale perivascular space (CSO-PVS) were found to be strong predictors of disease progression. LASSO analyses yielded 12 variables, refined to six in the final model: APOE4 genotype, ADAS11 score, CSO-PVS grade, and volumes of entorhinal, fusiform, and midtemporal regions, with an AUC of 0.956 in the training and 0.912 in the validation cohort. Conclusions:Our predictive model, emphasizing EPVS assessment, provides clinicians with a practical tool for early detection and management of AD risk in MCI patients.
Keywords: Alzheimer’s disease, mild cognitive impairment, nomogram, perivascular space
