Explaining the Variability of Alzheimer Disease Fluid Biomarker Concentrations in Memory Clinic Patients Without Dementia
Neurology. 2024-04-23; 102(8):
DOI: 10.1212/wnl.0000000000209219
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1. Neurology. 2024 Apr 23;102(8):e209219. doi: 10.1212/WNL.0000000000209219. Epub
2024 Mar 25.
Explaining the Variability of Alzheimer Disease Fluid Biomarker Concentrations
in Memory Clinic Patients Without Dementia.
Bouteloup V(1), Pellegrin I(1), Dubois B(1), Chene G(1), Planche V(1), Dufouil
C(1); MEMENTO Study Group(1).
Author information:
(1)From the Univ. Bordeaux (V.B., G.C., C.D.), Inserm, Bordeaux Population
Health, UMR1219, Bordeaux; CIC 1401 EC (V.B., G.C., C.D.), Pôle Santé Publique,
CHU de Bordeaux; Laboratory of Immunology and Immunogenetics (I.P.), Resources
Biological Center (CRB), CHU Bordeaux; Univ. Bordeaux (I.P.), CNRS,
ImmunoConcEpT, UMR 5164, Bordeaux; Alzheimer Research Center IM2A (B.D.),
Salpêtrière Hospital, AP-HP, Sorbonne University, Paris; Univ. Bordeaux (V.P.),
CNRS, Institut des Maladies Neuroégénératives, UMR 5293, Bordeaux; Pôle de
Neurosciences Cliniques (V.P.), Centre Mémoire de Ressources et de Recherche,
CHU Bordeaux, France.
BACKGROUND AND OBJECTIVES: Patients’ comorbidities can affect Alzheimer disease
(AD) blood biomarker concentrations. Because a limited number of factors have
been explored to date, our aim was to assess the proportion of the variance in
fluid biomarker levels explained by the clinical features of AD and by a large
number of non-AD-related factors.
METHODS: MEMENTO enrolled 2,323 individuals with cognitive complaints or mild
cognitive impairment in 26 French memory clinics. Baseline evaluation included
clinical and neuropsychological assessments, brain MRI, amyloid-PET, CSF
(optional), and blood sampling. Blood biomarker levels were determined using the
Simoa-HDX analyzer. We performed linear regression analysis of the clinical
features of AD (cognition, AD genetic risk score, and brain atrophy) to model
biomarker concentrations. Next, we added covariates among routine biological
tests, inflammatory markers, demographic and behavioral determinants,
treatments, comorbidities, and preanalytical sample handling in final models
using both stepwise selection processes and least absolute shrinkage and
selection operator (LASSO).
RESULTS: In total, 2,257 participants were included in the analysis (median age
71.7, 61.8% women, 55.2% with high educational levels). For blood biomarkers,
the proportion of variance explained by clinical features of AD was 13.7% for
neurofilaments (NfL), 11.4% for p181-tau, 3.0% for Aβ-42/40, and 1.4% for
total-tau. In final models accounting for non-AD-related factors, the variance
was mainly explained by age, routine biological tests, inflammatory markers, and
preanalytical sample handling. In CSF, the proportion of variance explained by
clinical features of AD was 24.8% for NfL, 22.3% for Aβ-42/40, 19.8% for
total-tau, and 17.2% for p181-tau. In contrast to blood biomarkers, the largest
proportion of variance was explained by cognition after adjustment for
covariates. The covariates that explained the largest proportion of variance
were also the most frequently selected with LASSO. The performance of blood
biomarkers for predicting A+ and T+ status (PET or CSF) remained unchanged after
controlling for drivers of variance.
DISCUSSION: This comprehensive analysis demonstrated that the variance in AD
blood biomarker concentrations was mainly explained by age, with minor
contributions from cognition, brain atrophy, and genetics, conversely to CSF
measures. These results challenge the use of blood biomarkers as isolated
stand-alone biomarkers for AD.
DOI: 10.1212/WNL.0000000000209219
PMID: 38527237 [Indexed for MEDLINE]