Oriol Grau-Rivera1,2,3,4, Irene Navalpotro-Gomez5,6,7, Gonzalo Sánchez-Benavides5,7,8, Marc Suárez-Calvet5,6,7,8, Marta Milà-Alomà5,7,8,9, Eider M Arenaza-Urquijo5,7,8, Gemma Salvadó5,7,8, Aleix Sala-Vila5,7, Mahnaz Shekari5,7,9, José Maria González-de-Echávarri5,7, Carolina Minguillón5,7,8, Aida Niñerola-Baizán10,11, Andrés Perissinotti10,11, Maryline Simon12, Gwendlyn Kollmorgen13, Henrik Zetterberg14,15,16,17, Kaj Blennow14,16, Juan Domingo Gispert5,7,11, José Luis Molinuevo18,19. 1. Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain. ograu@barcelonabeta.org. 2. Servei de Neurologia, Hospital del Mar, Barcelona, Spain. ograu@barcelonabeta.org. 3. IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain. ograu@barcelonabeta.org. 4. Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain. ograu@barcelonabeta.org. 5. Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain. 6. Servei de Neurologia, Hospital del Mar, Barcelona, Spain. 7. IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain. 8. Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain. 9. Universitat Pompeu Fabra, Barcelona, Spain. 10. Servei de Medicina Nuclear, Hospital Clínic, Barcelona, Spain. 11. Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain. 12. Roche Diagnostics International Ltd, Rotkreuz, Switzerland. 13. Roche Diagnostics GmbH, Penzberg, Germany. 14. Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden. 15. UK Dementia Research Institute at UCL, London, UK. 16. Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden. 17. Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK. 18. Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain. jlmolinuevo@fpmaragall.org. 19. Current affiliation: H. Lundbeck A/S, Copenhagen, Denmark. jlmolinuevo@fpmaragall.org.
Abstract
BACKGROUND: Recognizing clinical manifestations heralding the development of Alzheimer's disease (AD)-related cognitive impairment could improve the identification of individuals at higher risk of AD who may benefit from potential prevention strategies targeting preclinical population. We aim to characterize the association of body weight change with cognitive changes and AD biomarkers in cognitively unimpaired middle-aged adults. METHODS: This prospective cohort study included data from cognitively unimpaired adults from the ALFA study (n = 2743), a research platform focused on preclinical AD. Cognitive and anthropometric data were collected at baseline between April 2013 and November 2014. Between October 2016 and February 2020, 450 participants were visited in the context of the nested ALFA+ study and underwent cerebrospinal fluid (CSF) extraction and acquisition of positron emission tomography images with [18F]flutemetamol (FTM-PET). From these, 408 (90.1%) were included in the present study. We used data from two visits (average interval 4.1 years) to compute rates of change in weight and cognitive performance. We tested associations between these variables and between weight change and categorical and continuous measures of CSF and neuroimaging AD biomarkers obtained at follow-up. We classified participants with CSF data according to the AT (amyloid, tau) system and assessed between-group differences in weight change. RESULTS: Weight loss predicted a higher likelihood of positive FTM-PET visual read (OR 1.27, 95% CI 1.00-1.61, p = 0.049), abnormal CSF p-tau levels (OR 1.50, 95% CI 1.19-1.89, p = 0.001), and an A+T+ profile (OR 1.64, 95% CI 1.25-2.20, p = 0.001) and was greater among participants with an A+T+ profile (p < 0.01) at follow-up. Weight change was positively associated with CSF Aβ42/40 ratio (β = 0.099, p = 0.032) and negatively associated with CSF p-tau (β = - 0.141, p = 0.005), t-tau (β = - 0.147 p = 0.004) and neurogranin levels (β = - 0.158, p = 0.002). In stratified analyses, weight loss was significantly associated with higher t-tau, p-tau, neurofilament light, and neurogranin, as well as faster cognitive decline in A+ participants only. CONCLUSIONS: Weight loss predicts AD CSF and PET biomarker results and may occur downstream to amyloid-β accumulation in preclinical AD, paralleling cognitive decline. Accordingly, it should be considered as an indicator of increased risk of AD-related cognitive impairment. TRIAL REGISTRATION: NCT01835717 , NCT02485730 , NCT02685969 .
BACKGROUND: Recognizing clinical manifestations heralding the development of Alzheimer's disease (AD)-related cognitive impairment could improve the identification of individuals at higher risk of AD who may benefit from potential prevention strategies targeting preclinical population. We aim to characterize the association of body weight change with cognitive changes and AD biomarkers in cognitively unimpaired middle-aged adults. METHODS: This prospective cohort study included data from cognitively unimpaired adults from the ALFA study (n = 2743), a research platform focused on preclinical AD. Cognitive and anthropometric data were collected at baseline between April 2013 and November 2014. Between October 2016 and February 2020, 450 participants were visited in the context of the nested ALFA+ study and underwent cerebrospinal fluid (CSF) extraction and acquisition of positron emission tomography images with [18F]flutemetamol (FTM-PET). From these, 408 (90.1%) were included in the present study. We used data from two visits (average interval 4.1 years) to compute rates of change in weight and cognitive performance. We tested associations between these variables and between weight change and categorical and continuous measures of CSF and neuroimaging AD biomarkers obtained at follow-up. We classified participants with CSF data according to the AT (amyloid, tau) system and assessed between-group differences in weight change. RESULTS:Weight loss predicted a higher likelihood of positive FTM-PET visual read (OR 1.27, 95% CI 1.00-1.61, p = 0.049), abnormal CSF p-tau levels (OR 1.50, 95% CI 1.19-1.89, p = 0.001), and an A+T+ profile (OR 1.64, 95% CI 1.25-2.20, p = 0.001) and was greater among participants with an A+T+ profile (p < 0.01) at follow-up. Weight change was positively associated with CSF Aβ42/40 ratio (β = 0.099, p = 0.032) and negatively associated with CSF p-tau (β = - 0.141, p = 0.005), t-tau (β = - 0.147 p = 0.004) and neurogranin levels (β = - 0.158, p = 0.002). In stratified analyses, weight loss was significantly associated with higher t-tau, p-tau, neurofilament light, and neurogranin, as well as faster cognitive decline in A+ participants only. CONCLUSIONS:Weight loss predicts AD CSF and PET biomarker results and may occur downstream to amyloid-β accumulation in preclinical AD, paralleling cognitive decline. Accordingly, it should be considered as an indicator of increased risk of AD-related cognitive impairment. TRIAL REGISTRATION: NCT01835717 , NCT02485730 , NCT02685969 .
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