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A Predictive Model of the Progression to Alzheimer's Disease in Patients with Mild Cognitive Impairment Based on the MRI Enlarged Perivascular Spaces.
Chen, Jun; Yang, Jingwen; Shen, Dayong; Wang, Xi; Lin, Zihao; Chen, Hao; Cui, Guiyun; Zhang, Zuohui.
Afiliación
  • Chen J; Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
  • Yang J; Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
  • Shen D; Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
  • Wang X; Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
  • Lin Z; Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
  • Chen H; Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
  • Cui G; Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
  • Zhang Z; Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
J Alzheimers Dis ; 101(1): 159-173, 2024.
Article en En | MEDLINE | ID: mdl-39177602
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Imagen por Resonancia Magnética / Progresión de la Enfermedad / Enfermedad de Alzheimer / Disfunción Cognitiva / Sistema Glinfático Límite: Aged / Aged80 / Female / Humans / Male Idioma: En Revista: J Alzheimers Dis Asunto de la revista: GERIATRIA / NEUROLOGIA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Países Bajos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Imagen por Resonancia Magnética / Progresión de la Enfermedad / Enfermedad de Alzheimer / Disfunción Cognitiva / Sistema Glinfático Límite: Aged / Aged80 / Female / Humans / Male Idioma: En Revista: J Alzheimers Dis Asunto de la revista: GERIATRIA / NEUROLOGIA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Países Bajos