Body mass index and blood volume influence plasma biomarkers and positron emission tomography classification in preclinical Alzheimer's disease

INTRODUCTION Blood-based biomarkers (BBMs) are promising tools for Alzheimer's disease (AD) diagnosis, but their accuracy may be affected by body mass index (BMI) and blood volume (BV) through dilution. We investigated how BMI and BV influence BBM concentrations and PET prediction. METHODS Data...

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Detalles Bibliográficos
Autores: Jacobs, T, Brien, CO, Figueredo, L, Gogola, A, Gaggi, NL, Hurwitz, B, Pirraglia, E, Herzog, S, Ramos-Cejudo, J, Shepherd, TM, Palta, P, Fortea, J, Wisniewski, TM, Betensky, RA, Lopresti, B, Mielke, MM, Convit, A, Osorio, RS
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Institut d’Investigació Biomèdica Sant Pau (IIB Sant Pau)
Repositorio:r-IIB SANT PAU. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica Sant Pau
OAI Identifier:oai:iibsantpau.fundanetsuite.com:p20325
Acceso en línea:https://iibsantpau.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=20325
Access Level:acceso abierto
Palabra clave:Alzheimer's disease
amyloid
blood volume
blood-based biomarkers
body mass index
dilution
glial fibrillary acidic protein
neurodegeneration
neurofilament light
obesity
p-Tau181
p-Tau217
PET
plasma
tau
Descripción
Sumario:INTRODUCTION Blood-based biomarkers (BBMs) are promising tools for Alzheimer's disease (AD) diagnosis, but their accuracy may be affected by body mass index (BMI) and blood volume (BV) through dilution. We investigated how BMI and BV influence BBM concentrations and PET prediction. METHODS Data from 241 cognitively unimpaired participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) were examined to evaluate the influence of BMI/BV on BBMs (A beta 42/40, p-Tau181, p-Tau217, glial fibrillary acidic protein [GFAP], neurofilament light chain [NfL]) and BBM-based PET predictions. RESULTS Elevated BMI/BV associated with lower BBM concentrations, especially for p-Tau217 and NfL, independent of brain amyloid burden. BMI-stratified thresholds improved amyloid PET prediction, with higher BBM thresholds and area under the curve (AUC) values seen in normal weight compared to overweight or obese participants. Drastic BMI/BV declines due to weight loss increased BBM variability and systematic PET misclassification. DISCUSSION Adjusting for BMI/BV in BBM-based diagnostics appears to improve accuracy and reliable detection of AD pathology, especially in preclinical stages.

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