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Weight loss predicts Alzheimer’s disease biomarker positivity in cognitively unimpaired middle‐aged adults
Author(s) -
GrauRivera Oriol,
Navalpotro Irene,
SánchezBenavides Gonzalo,
SuárezCalvet Marc,
MilàAlomà Marta,
ArenazaUrquijo Eider M,
Salvadó Gemma,
SalaVila Aleix,
CrousBou Marta,
GonzálezdeEchávarri José Maria,
Minguillón Carolina,
Farrar Gill,
Buckley Chris J,
NiñerolaBaizán Aida,
Perissinotti Andrés,
Simon Maryline,
Kollmorgen Gwendlyn,
Eichenlaub Udo,
Zetterberg Henrik,
Blennow Kaj,
Gispert Juan Domingo,
Molinuevo Jose Luis
Publication year - 2020
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.045137
Subject(s) - medicine , biomarker , neuropsychology , apolipoprotein e , weight loss , cognitive decline , oncology , cognition , psychology , gastroenterology , disease , dementia , psychiatry , biochemistry , chemistry , obesity
Background Weight loss is common in Alzheimer’s disease (AD) and may start before cognitive impairment, but little is known about the relationship between weight change and AD biomarkers in the preclinical Alzheimer’s continuum . We aimed to assess the association between weight change and AD biomarkers and cognitive performance in cognitively unimpaired (CU) adults from the ALFA+ study. Method We analyzed data from 295 CU middle‐aged adults with two consecutive (mean interval 3.7 years) weight measures and neuropsychological assessments (n=267). Preclinical Alzheimer's Cognitive Composite (PACC) was computed as an average of z‐scores of episodic memory, semantic fluency and processing speed measures. We measured cerebrospinal fluid (CSF) levels of Aβ42, Aβ40, p‐tau, t‐tau, neurofilament light (NfL) and neurogranin using the Roche NeuroToolKit and Elecsys® immunoassays (n=280), and performed a [ 18 F]flutemetamol PET scan in the follow‐up visit (n=252). PET results were dichotomized (Aβ positive or negative) based on visual read. Using established CSF cutoffs, we grouped participants in three biomarker categories: A‐T‐ (both Aβ and tau negative), A+T‐ (Aβ positive only) and A+T+ (both Aβ and tau positive). Linear and logistic regression and Kernel‐weighted local polynomial smoothing were used for analyses, which were adjusted by age, sex, body mass index, APOE genotype and years of education, as appropriate. Result Weight loss predicted amyloid PET positivity (odds ratio 1.1, p=0.028) and was associated with significantly higher CSF levels of p‐tau (p=0.005), t‐tau (p=0.005) and neurogranin (p=0.002) (Table 1). Weight loss ≥5% (a standard cutoff for clinically relevant weight change) yielded 87% specificity and 30% sensitivity for amyloid PET positivity. Participants from the A+T+ group experienced significantly higher weight loss compared to A+T‐ (p=0.0029) and A‐T‐ (p=0.0005) groups (Figure 1). Weight loss was not associated with longitudinal changes in cognitive performance and preceded cognitive decline when using Aβ42/40 as a proxy of AD change progression (Table 1, Figure 2). Conclusion Weight loss predicts amyloid PET positivity in CU middle‐aged adults. Our results suggest that weight loss is mainly related with changes in tau‐related and synaptic dysfunction biomarkers and may precede cognitive decline in the preclinical stage of the Alzheimer’s continuum.