ATN profile classification across two independent prospective cohorts

Débora E Peretti; Federica Ribaldi; Max Scheffler; Linjing Mu; Valerie Treyer; Anton F Gietl; Christoph Hock; Giovanni B Frisoni; Valentina Garibotto

doi:10.3389/fmed.2023.1168470

ATN profile classification across two independent prospective cohorts

Front Med (Lausanne). 2023 Jul 25:10:1168470. doi: 10.3389/fmed.2023.1168470. eCollection 2023.

Authors

Débora E Peretti¹, Federica Ribaldi², Max Scheffler³, Linjing Mu^{4

5}, Valerie Treyer⁴, Anton F Gietl^{4

6}, Christoph Hock^{4

6}, Giovanni B Frisoni^{2

7}, Valentina Garibotto^{1

8

9}

Affiliations

¹ Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Faculty of Medicine, Geneva University Neurocenter, University of Geneva, Geneva, Switzerland.
² Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland.
³ Division of Radiology, Geneva University Hospitals, Geneva, Switzerland.
⁴ Department of Nuclear Medicine, University Hospital of Zurich, University of Zurich, Zurich, Switzerland.
⁵ Institute of Pharmaceutical Sciences, Zurich, Switzerland.
⁶ Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland.
⁷ Memory Clinic, Geneva University Hospitals, Geneva, Switzerland.
⁸ Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland.
⁹ Center for Biomedical Imaging, University of Geneva, Geneva, Switzerland.

Abstract

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.

Keywords: ATN profiles; biomarkers; classification; cognition; positron emission tomography.