Hippocampal atrophy and white matter lesions characteristics can predict evolution to dementia in patients with vascular mild cognitive impairment.

Cerebral small vessel disease Dementia Neuroimaging Vascular cognitive impairment hippocampus

Journal

Journal of the neurological sciences
ISSN: 1878-5883
Titre abrégé: J Neurol Sci
Pays: Netherlands
ID NLM: 0375403

Informations de publication

Date de publication:
06 Aug 2024
Historique:
received: 13 06 2024
revised: 01 08 2024
accepted: 04 08 2024
medline: 11 8 2024
pubmed: 11 8 2024
entrez: 11 8 2024
Statut: aheadofprint

Résumé

Vascular mild cognitive impairment (VMCI) is a transitional condition that may evolve into Vascular Dementia(VaD). Hippocampal volume (HV) is suggested as an early marker for VaD, the role of white matter lesions (WMLs) in neurodegeneration remains debated. Evaluate HV and WMLs as predictive markers of VaD in VMCI patients by assessing: (i)baseline differences in HV and WMLs between converters to VaD and non-converters, (ii) predictive power of HV and WMLs for VaD, (iii) associations between HV, WMLs, and cognitive decline, (iv)the role of WMLs on HV. This longitudinal multicenter study included 110 VMCI subjects (mean age:74.33 ± 6.63 years, 60males/50females) from the VMCI-Tuscany Study database. Subjects underwent brain MRI and cognitive testing, with 2-year follow-up data on VaD progression. HV and WMLs were semi-automatically segmented and measured. ANCOVA assessed group differences, while linear and logistic regression models evaluated predictive power. After 2 years, 32/110 VMCI patients progressed to VaD. Converting patients had lower HV(p = 0.015) and higher lesion volumes in the posterior thalamic radiation (p = 0.046), splenium of the corpus callosum (p = 0.016), cingulate gyrus (p = 0.041), and cingulum hippocampus(p = 0.038). HV alone did not fully explain progression (p = 0.059), but combined with WMLs volume, the model was significant (p = 0.035). The best prediction model (p = 0.001) included total HV (p = 0.004) and total WMLs volume of the posterior thalamic radiation (p = 0.005) and cingulate gyrus (p = 0.005), achieving 80% precision, 81% specificity, and 74% sensitivity. Lower HV were linked to poorer performance on the Rey Auditory-Verbal Learning Test delayed recall (RAVLT) and Mini Mental State Examination (MMSE). HV and WMLs are significant predictors of progression from VMCI to VaD. Lower HV correlate with worse cognitive performance on RAVLT and MMSE tests.

Sections du résumé

BACKGROUND BACKGROUND
Vascular mild cognitive impairment (VMCI) is a transitional condition that may evolve into Vascular Dementia(VaD). Hippocampal volume (HV) is suggested as an early marker for VaD, the role of white matter lesions (WMLs) in neurodegeneration remains debated.
OBJECTIVES OBJECTIVE
Evaluate HV and WMLs as predictive markers of VaD in VMCI patients by assessing: (i)baseline differences in HV and WMLs between converters to VaD and non-converters, (ii) predictive power of HV and WMLs for VaD, (iii) associations between HV, WMLs, and cognitive decline, (iv)the role of WMLs on HV.
METHODS METHODS
This longitudinal multicenter study included 110 VMCI subjects (mean age:74.33 ± 6.63 years, 60males/50females) from the VMCI-Tuscany Study database. Subjects underwent brain MRI and cognitive testing, with 2-year follow-up data on VaD progression. HV and WMLs were semi-automatically segmented and measured. ANCOVA assessed group differences, while linear and logistic regression models evaluated predictive power.
RESULTS RESULTS
After 2 years, 32/110 VMCI patients progressed to VaD. Converting patients had lower HV(p = 0.015) and higher lesion volumes in the posterior thalamic radiation (p = 0.046), splenium of the corpus callosum (p = 0.016), cingulate gyrus (p = 0.041), and cingulum hippocampus(p = 0.038). HV alone did not fully explain progression (p = 0.059), but combined with WMLs volume, the model was significant (p = 0.035). The best prediction model (p = 0.001) included total HV (p = 0.004) and total WMLs volume of the posterior thalamic radiation (p = 0.005) and cingulate gyrus (p = 0.005), achieving 80% precision, 81% specificity, and 74% sensitivity. Lower HV were linked to poorer performance on the Rey Auditory-Verbal Learning Test delayed recall (RAVLT) and Mini Mental State Examination (MMSE).
CONCLUSIONS CONCLUSIONS
HV and WMLs are significant predictors of progression from VMCI to VaD. Lower HV correlate with worse cognitive performance on RAVLT and MMSE tests.

Identifiants

pubmed: 39128160
pii: S0022-510X(24)00298-3
doi: 10.1016/j.jns.2024.123163
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

123163

Informations de copyright

Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.

Auteurs

Carlo Manco (C)

Department of Medicine, Surgery and Neuroscience, University of Siena, 53100 Siena, Italy.

Rosa Cortese (R)

Department of Medicine, Surgery and Neuroscience, University of Siena, 53100 Siena, Italy. Electronic address: rosa.cortese@unisi.it.

Matteo Leoncini (M)

Siena Imaging SRL, 53100 Siena, Italy.

Domenico Plantone (D)

Department of Medicine, Surgery and Neuroscience, University of Siena, 53100 Siena, Italy.

Giordano Gentile (G)

Department of Medicine, Surgery and Neuroscience, University of Siena, 53100 Siena, Italy; Siena Imaging SRL, 53100 Siena, Italy.

Ludovico Luchetti (L)

Department of Medicine, Surgery and Neuroscience, University of Siena, 53100 Siena, Italy; Siena Imaging SRL, 53100 Siena, Italy.

Jian Zhang (J)

Siena Imaging SRL, 53100 Siena, Italy.

Ilaria Di Donato (I)

Neurology, San Giuseppe Hospital, Empoli, Italy.

Emilia Salvadori (E)

Department of Biomedical and Clinical Sciences, University of Milano, Italy.

Anna Poggesi (A)

NEUROFARBA Department, Neuroscience Section, University of Florence, Florence, Italy.

Mirco Cosottini (M)

Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy.

Mario Mascalchi (M)

Department of Clinical and Experimental Biomedical Sciences -"Mario Serio", University of Florence, Florence, Italy.

Antonio Federico (A)

Department of Medicine, Surgery and Neuroscience, University of Siena, 53100 Siena, Italy.

Maria Teresa Dotti (MT)

Department of Medicine, Surgery and Neuroscience, University of Siena, 53100 Siena, Italy.

Marco Battaglini (M)

Department of Medicine, Surgery and Neuroscience, University of Siena, 53100 Siena, Italy; Siena Imaging SRL, 53100 Siena, Italy.

Domenico Inzitari (D)

NEUROFARBA Department, Neuroscience Section, University of Florence, Florence, Italy.

Leonardo Pantoni (L)

Department of Biomedical and Clinical Sciences, University of Milano, Italy.

Nicola De Stefano (N)

Department of Medicine, Surgery and Neuroscience, University of Siena, 53100 Siena, Italy.

Classifications MeSH