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ATN profile classification across two independent prospective cohorts
BACKGROUND: The ATN model represents a research framework used to describe in subjects the presence or absence of Alzheimer’s disease (AD) pathology through biomarkers. The aim of this study was to describe the prevalence of different ATN profiles using quantitative imaging biomarkers in two indepen...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10407659/ https://www.ncbi.nlm.nih.gov/pubmed/37559930 http://dx.doi.org/10.3389/fmed.2023.1168470 |
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author | Peretti, Débora E. Ribaldi, Federica Scheffler, Max Mu, Linjing Treyer, Valerie Gietl, Anton F. Hock, Christoph Frisoni, Giovanni B. Garibotto, Valentina |
author_facet | Peretti, Débora E. Ribaldi, Federica Scheffler, Max Mu, Linjing Treyer, Valerie Gietl, Anton F. Hock, Christoph Frisoni, Giovanni B. Garibotto, Valentina |
author_sort | Peretti, Débora E. |
collection | PubMed |
description | BACKGROUND: The ATN model represents a research framework used to describe in subjects the presence or absence of Alzheimer’s disease (AD) pathology through biomarkers. The aim of this study was to describe the prevalence of different ATN profiles using quantitative imaging biomarkers in two independent cohorts, and to evaluate the pertinence of ATN biomarkers to identify comparable populations across independent cohorts. METHODS: A total of 172 subjects from the Geneva Memory Clinic and 113 volunteers from a study on healthy aging at the University Hospital of Zurich underwent amyloid (A) and tau (T) PET, as well as T1-weigthed MRI scans using site-specific protocols. Subjects were classified by cognition (cognitively unimpaired, CU, or impaired, CI) based on clinical assessment by experts. Amyloid data converted into the standardized centiloid scale, tau PET data normalized to cerebellar uptake, and hippocampal volume expressed as a ratio over total intracranial volume ratio were considered as biomarkers for A, T, and neurodegeneration (N), respectively. Positivity for each biomarker was defined based on previously published thresholds. Subjects were then classified according to the ATN model. Differences among profiles were tested using Kruskal-Wallis ANOVA, and between cohorts using Wilcoxon tests. RESULTS: Twenty-nine percent of subjects from the Geneva cohorts were classified with a normal (A−T−N−) profile, while the Zurich cohort included 64% of subjects in the same category. Meanwhile, 63% of the Geneva and 16% of the Zurich cohort were classified within the AD continuum (being A+ regardless of other biomarkers’ statuses). Within cohorts, ATN profiles were significantly different for age and mini-mental state examination scores, but not for years of education. Age was not significantly different between cohorts. In general, imaging A and T biomarkers were significantly different between cohorts, but they were no longer significantly different when stratifying the cohorts by ATN profile. N was not significantly different between cohorts. CONCLUSION: Stratifying subjects into ATN profiles provides comparable groups of subjects even when individual recruitment followed different criteria. |
format | Online Article Text |
id | pubmed-10407659 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104076592023-08-09 ATN profile classification across two independent prospective cohorts Peretti, Débora E. Ribaldi, Federica Scheffler, Max Mu, Linjing Treyer, Valerie Gietl, Anton F. Hock, Christoph Frisoni, Giovanni B. Garibotto, Valentina Front Med (Lausanne) Medicine BACKGROUND: The ATN model represents a research framework used to describe in subjects the presence or absence of Alzheimer’s disease (AD) pathology through biomarkers. The aim of this study was to describe the prevalence of different ATN profiles using quantitative imaging biomarkers in two independent cohorts, and to evaluate the pertinence of ATN biomarkers to identify comparable populations across independent cohorts. METHODS: A total of 172 subjects from the Geneva Memory Clinic and 113 volunteers from a study on healthy aging at the University Hospital of Zurich underwent amyloid (A) and tau (T) PET, as well as T1-weigthed MRI scans using site-specific protocols. Subjects were classified by cognition (cognitively unimpaired, CU, or impaired, CI) based on clinical assessment by experts. Amyloid data converted into the standardized centiloid scale, tau PET data normalized to cerebellar uptake, and hippocampal volume expressed as a ratio over total intracranial volume ratio were considered as biomarkers for A, T, and neurodegeneration (N), respectively. Positivity for each biomarker was defined based on previously published thresholds. Subjects were then classified according to the ATN model. Differences among profiles were tested using Kruskal-Wallis ANOVA, and between cohorts using Wilcoxon tests. RESULTS: Twenty-nine percent of subjects from the Geneva cohorts were classified with a normal (A−T−N−) profile, while the Zurich cohort included 64% of subjects in the same category. Meanwhile, 63% of the Geneva and 16% of the Zurich cohort were classified within the AD continuum (being A+ regardless of other biomarkers’ statuses). Within cohorts, ATN profiles were significantly different for age and mini-mental state examination scores, but not for years of education. Age was not significantly different between cohorts. In general, imaging A and T biomarkers were significantly different between cohorts, but they were no longer significantly different when stratifying the cohorts by ATN profile. N was not significantly different between cohorts. CONCLUSION: Stratifying subjects into ATN profiles provides comparable groups of subjects even when individual recruitment followed different criteria. Frontiers Media S.A. 2023-07-25 /pmc/articles/PMC10407659/ /pubmed/37559930 http://dx.doi.org/10.3389/fmed.2023.1168470 Text en Copyright © 2023 Peretti, Ribaldi, Scheffler, Mu, Treyer, Gietl, Hock, Frisoni and Garibotto. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Medicine Peretti, Débora E. Ribaldi, Federica Scheffler, Max Mu, Linjing Treyer, Valerie Gietl, Anton F. Hock, Christoph Frisoni, Giovanni B. Garibotto, Valentina ATN profile classification across two independent prospective cohorts |
title | ATN profile classification across two independent prospective cohorts |
title_full | ATN profile classification across two independent prospective cohorts |
title_fullStr | ATN profile classification across two independent prospective cohorts |
title_full_unstemmed | ATN profile classification across two independent prospective cohorts |
title_short | ATN profile classification across two independent prospective cohorts |
title_sort | atn profile classification across two independent prospective cohorts |
topic | Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10407659/ https://www.ncbi.nlm.nih.gov/pubmed/37559930 http://dx.doi.org/10.3389/fmed.2023.1168470 |
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