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Mendes AJ, Ribaldi F, Lathuiliere A, Ashton NJ, Zetterberg H, Abramowicz M, Scheffler M, Assal F, Garibotto V, Blennow K, Frisoni GB. Comparison of plasma and neuroimaging biomarkers to predict cognitive decline in non-demented memory clinic patients. Alzheimers Res Ther 2024; 16:110. [PMID: 38755703 PMCID: PMC11097559 DOI: 10.1186/s13195-024-01478-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 05/07/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND Plasma biomarkers of Alzheimer's disease (AD) pathology, neurodegeneration, and neuroinflammation are ideally suited for secondary prevention programs in self-sufficient persons at-risk of dementia. Plasma biomarkers have been shown to be highly correlated with traditional imaging biomarkers. However, their comparative predictive value versus traditional AD biomarkers is still unclear in cognitively unimpaired (CU) subjects and with mild cognitive impairment (MCI). METHODS Plasma (Aβ42/40, p-tau181, p-tau231, NfL, and GFAP) and neuroimaging (hippocampal volume, centiloid of amyloid-PET, and tau-SUVR of tau-PET) biomarkers were assessed at baseline in 218 non-demented subjects (CU = 140; MCI = 78) from the Geneva Memory Center. Global cognition (MMSE) was evaluated at baseline and at follow-ups up to 5.7 years. We used linear mixed-effects models and Cox proportional-hazards regression to assess the association between biomarkers and cognitive decline. Lastly, sample size calculations using the linear mixed-effects models were performed on subjects positive for amyloid-PET combined with tau-PET and plasma biomarker positivity. RESULTS Cognitive decline was significantly predicted in MCI by baseline plasma NfL (β=-0.55), GFAP (β=-0.36), hippocampal volume (β = 0.44), centiloid (β=-0.38), and tau-SUVR (β=-0.66) (all p < 0.05). Subgroup analysis with amyloid-positive MCI participants also showed that only NfL and GFAP were the only significant predictors of cognitive decline among plasma biomarkers. Overall, NfL and tau-SUVR showed the highest prognostic values (hazard ratios of 7.3 and 5.9). Lastly, we demonstrated that adding NfL to the inclusion criteria could reduce the sample sizes of future AD clinical trials by up to one-fourth in subjects with amyloid-PET positivity or by half in subjects with amyloid-PET and tau-PET positivity. CONCLUSIONS Plasma NfL and GFAP predict cognitive decline in a similar manner to traditional imaging techniques in amyloid-positive MCI patients. Hence, even though they are non-specific biomarkers of AD, both can be implemented in memory clinic workups as important prognostic biomarkers. Likewise, future clinical trials might employ plasma biomarkers as additional inclusion criteria to stratify patients at higher risk of cognitive decline to reduce sample sizes and enhance effectiveness.
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Affiliation(s)
- Augusto J Mendes
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland.
- Geneva Memory Center, Department of Rehabilitation and Geriatrics,, Geneva University Hospitals, Geneva, Switzerland.
| | - Federica Ribaldi
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics,, Geneva University Hospitals, Geneva, Switzerland
| | - Aurelien Lathuiliere
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics,, Geneva University Hospitals, Geneva, Switzerland
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK
- NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London, UK
- Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer?s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Marc Abramowicz
- Genetic Medicine, Diagnostics Dept, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Max Scheffler
- Division of Radiology, Geneva University Hospitals, Geneva, Switzerland
| | - Frédéric Assal
- Division of Neurology, Department of Clinical Neurosciences, Faculty of Medicine, Geneva University Hospitals, University of Geneva, Geneva, Switzerland
| | - Valentina Garibotto
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Faculty of Medicine, Geneva University Neurocenter, University of Geneva, Geneva, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Geneva, Switzerland
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
| | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics,, Geneva University Hospitals, Geneva, Switzerland
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Mathoux G, Boccalini C, Peretti DE, Arnone A, Ribaldi F, Scheffler M, Frisoni GB, Garibotto V. A comparison of visual assessment and semi-quantification for the diagnostic and prognostic use of [ 18F]flortaucipir PET in a memory clinic cohort. Eur J Nucl Med Mol Imaging 2024; 51:1639-1650. [PMID: 38182839 DOI: 10.1007/s00259-023-06583-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 12/22/2023] [Indexed: 01/07/2024]
Abstract
PURPOSE [18F]Flortaucipir PET is a powerful diagnostic and prognostic tool for Alzheimer's disease (AD). Tau status definition is mainly based in the literature on semi-quantitative measures while in clinical settings visual assessment is usually preferred. We compared visual assessment with established semi-quantitative measures to classify subjects and predict the risk of cognitive decline in a memory clinic population. METHODS We included 245 individuals from the Geneva Memory Clinic who underwent [18F]flortaucipir PET. Amyloid status was available for 207 individuals and clinical follow-up for 135. All scans were blindly evaluated by three independent raters who visually classified the scans according to Braak stages. Standardized uptake value ratio (SUVR) values were obtained from a global meta-ROI to define tau positivity, and the Simplified Temporo-Occipital Classification (STOC) was applied to obtain semi-quantitatively tau stages. The agreement between measures was tested using Cohen's kappa (k). ROC analysis and linear mixed-effects models were applied to test the diagnostic and prognostic values of tau status and stages obtained with the visual and semi-quantitative approaches. RESULTS We found good inter-rater reliability in the visual interpretation of tau Braak stages, independently from the rater's expertise (k>0.68, p<0.01). A good agreement was equally found between visual and SUVR-based classifications for tau status (k=0.67, p<0.01). All tau-assessment modalities significantly discriminated amyloid-positive MCI and demented subjects from others (AUC>0.80) and amyloid-positive from negative subjects (AUC>0.85). Linear mixed-effect models showed that tau-positive individuals presented a significantly faster cognitive decline than the tau-negative group (p<0.01), independently from the classification method. CONCLUSION Our results show that visual assessment is reliable for defining tau status and stages in a memory clinic population. The high inter-rater reliability, the substantial agreement, and the similar diagnostic and prognostic performance of visual rating and semi-quantitative methods demonstrate that [18F]flortaucipir PET can be robustly assessed visually in clinical practice.
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Affiliation(s)
- Gregory Mathoux
- Diagnostic Department, Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, University of Geneva, Geneva, Switzerland
- Università degli Studi Milano-Bicocca, Milano, Italy
| | - Cecilia Boccalini
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Faculty of Medicine, Geneva University Neurocenter, University of Geneva , Geneva, Switzerland
- Università Vita e Salute San Raffaele, Milano, Italy
| | - Debora E Peretti
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Faculty of Medicine, Geneva University Neurocenter, University of Geneva , Geneva, Switzerland
| | - Annachiara Arnone
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Faculty of Medicine, Geneva University Neurocenter, University of Geneva , Geneva, Switzerland
| | - Federica Ribaldi
- Department of Rehabilitation and Geriatrics, Memory Clinic, Geneva University and University Hospitals, Geneva, Switzerland
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
| | - Max Scheffler
- Division of Radiology, Geneva University Hospitals, Geneva, Switzerland
| | - Giovanni B Frisoni
- Department of Rehabilitation and Geriatrics, Memory Clinic, Geneva University and University Hospitals, Geneva, Switzerland
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
| | - Valentina Garibotto
- Diagnostic Department, Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, University of Geneva, Geneva, Switzerland.
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Faculty of Medicine, Geneva University Neurocenter, University of Geneva , Geneva, Switzerland.
- CIBM Center for Biomedical Imaging, Geneva, Switzerland.
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Mendes AJ, Ribaldi F, Lathuiliere A, Ashton NJ, Janelidze S, Zetterberg H, Scheffler M, Assal F, Garibotto V, Blennow K, Hansson O, Frisoni GB. Head-to-head study of diagnostic accuracy of plasma and cerebrospinal fluid p-tau217 versus p-tau181 and p-tau231 in a memory clinic cohort. J Neurol 2024; 271:2053-2066. [PMID: 38195896 PMCID: PMC10972950 DOI: 10.1007/s00415-023-12148-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 12/01/2023] [Accepted: 12/03/2023] [Indexed: 01/11/2024]
Abstract
BACKGROUND AND OBJECTIVE Phosphorylated tau (p-tau) 217 has recently received attention because it seems more reliable than other p-tau variants for identifying Alzheimer's disease (AD) pathology. Thus, we aimed to compare the diagnostic accuracy of plasma and CSF p-tau217 with p-tau181 and p-tau231 in a memory clinic cohort. METHODS The study included 114 participants (CU = 33; MCI = 67; Dementia = 14). The p-tau variants were correlated versus continuous measures of amyloid (A) and tau (T)-PET. The p-tau phospho-epitopes were assessed through: (i) effect sizes (δ) between diagnostic and A ± and T ± groups; (ii) receiver operating characteristic (ROC) analyses in A-PET and T-PET. RESULTS The correlations between both plasma and CSF p-tau217 with A-PET and T-PET (r range 0.64-0.83) were stronger than those of p-tau181 (r range 0.44-0.79) and p-tau231 (r range 0.46-0.76). Plasma p-tau217 showed significantly higher diagnostic accuracy than p-tau181 and p-tau231 in (i) differences between diagnostic and biomarker groups (δrange: p-tau217 = 0.55-0.96; p-tau181 = 0.51-0.67; p-tau231 = 0.53-0.71); (ii) ROC curves to identify A-PET and T-PET positivity (AUCaverage: p-tau217 = 0.96; p-tau181 = 0.76; p-tau231 = 0.79). On the other hand, CSF p-tau217 (AUCaverage = 0.95) did not reveal significant differences in A-PET and T-PET AUC than p-tau181 (AUCaverage = 0.88) and p-tau231 (AUCaverage = 0.89). DISCUSSION Plasma p-tau217 demonstrated better performance in the identification of AD pathology and clinical phenotypes in comparison with other variants of p-tau in a memory clinic cohort. Furthermore, p-tau217 had comparable performance in plasma and CSF. Our findings suggest the potential of plasma p-tau217 in the diagnosis and screening for AD, which could allow for a decreased use of invasive biomarkers in the future.
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Affiliation(s)
- Augusto J Mendes
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland.
- Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland.
| | - Federica Ribaldi
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland
| | - Aurelien Lathuiliere
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- King's College London, Institute of Psychiatry, Psychology and Neuroscience Maurice Wohl Institute Clinical Neuroscience Institute, London, UK
- NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London, UK
- Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
| | | | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Max Scheffler
- Division of Radiology, Geneva University Hospitals, Geneva, Switzerland
| | - Frédéric Assal
- Division of Neurology, Department of Clinical Neurosciences, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Valentina Garibotto
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Geneva, Switzerland
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Paris Brain Institute, ICM, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Neurodegenerative Disorder Research Center, Division of Life Sciences and Medicine, and Department of Neurology, Institute on Aging and Brain Disorders, University of Science and Technology of China and First Affiliated Hospital of USTC, Hefei, People's Republic of China
| | - Oskar Hansson
- Clinical Memory Research Unit, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland
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Pizzini FB, Boscolo Galazzo I, Natale V, Ribaldi F, Scheffler M, Caranci F, Lovblad KO, Menegaz G, Frisoni GB, Gunther M. Insights into single-timepoint ASL hemodynamics: what visual assessment and spatial coefficient of variation can tell. LA RADIOLOGIA MEDICA 2024; 129:467-477. [PMID: 38329703 PMCID: PMC10943156 DOI: 10.1007/s11547-024-01777-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 01/03/2024] [Indexed: 02/09/2024]
Abstract
PURPOSE Arterial spin labeling (ASL) represents a noninvasive perfusion biomarker, and, in the study of nonvascular disease, the use of the single-timepoint ASL technique is recommended. However, the obtained cerebral blood flow (CBF) maps may be highly influenced by delayed arterial transit time (ATT). Our aim was to assess the complexity of hemodynamic information of single-timepoint CBF maps using a new visual scale and comparing it with an ATT proxy, the "coefficient of spatial variation" (sCoV). MATERIAL AND METHODS Individual CBF maps were estimated in a memory clinic population (mild cognitive impairment, dementia and cognitively unimpaired controls) and classified into four levels of delayed perfusion based on a visual rating scale. Calculated measures included global/regional sCoVs and common CBF statistics, as mean, median and standard deviation. One-way ANOVA was performed to compare these measures across the four groups of delayed perfusion. Spearman correlation was used to study the association of global sCoV with clinical data and CBF statistics. RESULTS One hundred and forty-four participants (72 ± 7 years, 53% women) were included in the study. The proportion of maps with none, mild, moderate, and severe delayed perfusion was 15, 20, 37, and 28%, respectively. SCoV demonstrated a significant increase (p < 0.05) across the four groups, except when comparing none vs mild delayed perfusion groups (pBonf > 0.05). Global sCoV values, as an ATT proxy, ranged from 67 ± 4% (none) to 121 ± 24% (severe delayed) and were significantly associated with age and CBF statistics (p < 0.05). CONCLUSION The impact of ATT delay in single-time CBF maps requires the use of a visual scale or sCoV in clinical or research settings.
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Affiliation(s)
| | | | - Valerio Natale
- Dept. of Diagnostic and Public Health, Rivoli Hospital, Rivoli, Turin, Italy
| | - Federica Ribaldi
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
| | - Max Scheffler
- Division of Radiology, Geneva University Hospitals, Geneva, Switzerland
| | - Ferdinando Caranci
- Department of Medicine of Precision, School of Medicine, "Luigi Vanvitelli" University of Campania, Naples, Italy
| | - Karl-Olof Lovblad
- Division of Radiology, Geneva University Hospitals, Geneva, Switzerland
| | - Gloria Menegaz
- Department of Engineering for Innovation Medicine, University of Verona, Verona, Italy
| | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Memory Clinic, Geneva University Hospitals, Geneva, Switzerland
| | - Matthias Gunther
- Imaging Physics, Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
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Kurnaz S, Durmaz O. The relationship between metacognitive processes and cognitive performances in older adults with no significant impairment: a cross-sectional study. Psychogeriatrics 2024; 24:322-328. [PMID: 38247025 DOI: 10.1111/psyg.13077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 11/30/2023] [Accepted: 01/06/2024] [Indexed: 01/23/2024]
Abstract
BACKGROUND Metacognitive dysfunctions have been implicated in several neuropsychiatric conditions, while cognitive performances have been evaluated by measuring cognitive domains in older adults. This study investigated a relationship between metacognitive processes and cognitive performances in older adults. METHODS A sociodemographic form, the Standardised Mini-Mental State Examination (SMMSE) and the Metacognitions Questionnaire-30 (MCQ-30) were applied to participants aged >65 years who had no significant cognitive decline defined as normal or with mild cognitive impairment. RESULTS 'Negative beliefs about worry' and 'need to control thoughts' domains of MCQ-30 were related to cognitive performance measured with SMMSE. Increased negative beliefs about worry were a predicting factor for total cognitive performance as a means of contributing to cognitive impairment, whereas an increased need to control thoughts was related to having a less likely cognitive impairment. CONCLUSIONS Metacognitive dysfunctional processes, in particular about worry, might contribute to determining more decent outcomes for cognitive conditions in older adults with no significant cognitive dysfunction.
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Affiliation(s)
- Samet Kurnaz
- Department of Psychiatry, Şehit Prof. Dr. İlhan Varank Sancaktepe Training & Research Hospital, Istanbul, Turkey
| | - Onur Durmaz
- Department of Psychiatry, Erenköy Mental Health and Neurology Training & Research Hospital, Istanbul, Turkey
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Peretti DE, Ribaldi F, Scheffler M, Chicherio C, Frisoni GB, Garibotto V. Prognostic value of imaging-based ATN profiles in a memory clinic cohort. Eur J Nucl Med Mol Imaging 2023; 50:3313-3323. [PMID: 37358619 PMCID: PMC10542279 DOI: 10.1007/s00259-023-06311-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 06/17/2023] [Indexed: 06/27/2023]
Abstract
PURPOSE The ATN model represents a research framework used to classify subjects based on the presence or absence of Alzheimer's disease (AD) pathology through biomarkers for amyloid (A), tau (T), and neurodegeneration (N). The aim of this study was to assess the relationship between ATN profiles defined through imaging and cognitive decline in a memory clinic cohort. METHODS One hundred-eight patients from the memory clinic of Geneva University Hospitals underwent complete clinical and neuropsychological evaluation at baseline and 23 ± 5 months after inclusion, magnetic resonance imaging, amyloid and tau PET scans. ATN profiles were divided into four groups: normal, AD pathological change (AD-PC: A + T-N-, A + T-N +), AD pathology (AD-P: A + T + N-, A + T + N +), and suspected non-AD pathology (SNAP: A-T + N-, A-T-N + , A-T + N +). RESULTS Mini-Mental State Examination (MMSE) scores were significantly different among groups, both at baseline and follow-up, with the normal group having higher average MMSE scores than the other groups. MMSE scores changed significantly after 2 years only in AD-PC and AD-P groups. AD-P profile classification also had the largest number of decliners at follow-up (55%) and the steepest global cognitive decline compared to the normal group. Cox regression showed that participants within the AD-P group had a higher risk of cognitive decline (HR = 6.15, CI = 2.59-14.59), followed by AD-PC (HR = 3.16, CI = 1.17-8.52). CONCLUSION Of the different group classifications, AD-P was found to have the most significant effect on cognitive decline over a period of 2 years, highlighting the value of both amyloid and tau PET molecular imaging as prognostic imaging biomarkers in clinical practice.
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Affiliation(s)
- Débora E Peretti
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocentre and Faculty of Medicine, University of Geneva, Geneva, Switzerland.
| | - Federica Ribaldi
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Centre, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland
| | - Max Scheffler
- Division of Radiology, Geneva University Hospitals, Geneva, Switzerland
| | - Christian Chicherio
- Geneva Memory Centre, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland
- Centre for Interdisciplinary Study of Gerontology and Vulnerability (CIGEV), University of Geneva, Geneva, Switzerland
| | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Centre, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland
| | - Valentina Garibotto
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocentre and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
- Centre for Biomedical Imaging, University of Geneva, Geneva, Switzerland
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Peretti DE, Ribaldi F, Scheffler M, Mu L, Treyer V, Gietl AF, Hock C, Frisoni GB, Garibotto V. ATN profile classification across two independent prospective cohorts. Front Med (Lausanne) 2023; 10:1168470. [PMID: 37559930 PMCID: PMC10407659 DOI: 10.3389/fmed.2023.1168470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 07/10/2023] [Indexed: 08/11/2023] Open
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.
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Affiliation(s)
- Débora E. Peretti
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Faculty of Medicine, Geneva University Neurocenter, University of Geneva, Geneva, Switzerland
| | - Federica Ribaldi
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
| | - Max Scheffler
- Division of Radiology, Geneva University Hospitals, Geneva, Switzerland
| | - Linjing Mu
- Department of Nuclear Medicine, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
- Institute of Pharmaceutical Sciences, Zurich, Switzerland
| | - Valerie Treyer
- Department of Nuclear Medicine, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Anton F. Gietl
- Department of Nuclear Medicine, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland
| | - Christoph Hock
- Department of Nuclear Medicine, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland
| | - Giovanni B. Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Memory Clinic, Geneva University Hospitals, Geneva, Switzerland
| | - Valentina Garibotto
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Faculty of Medicine, Geneva University Neurocenter, University of Geneva, Geneva, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
- Center for Biomedical Imaging, University of Geneva, Geneva, Switzerland
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Chen Q, Chen F, Long C, Zhu Y, Jiang Y, Zhu Z, Lu J, Zhang X, Nedelska Z, Hort J, Zhang B. Spatial navigation is associated with subcortical alterations and progression risk in subjective cognitive decline. Alzheimers Res Ther 2023; 15:86. [PMID: 37098612 PMCID: PMC10127414 DOI: 10.1186/s13195-023-01233-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 04/18/2023] [Indexed: 04/27/2023]
Abstract
BACKGROUND Subjective cognitive decline (SCD) may serve as a symptomatic indicator for preclinical Alzheimer's disease; however, SCD is a heterogeneous entity regarding clinical progression. We aimed to investigate whether spatial navigation could reveal subcortical structural alterations and the risk of progression to objective cognitive impairment in SCD individuals. METHODS One hundred and eighty participants were enrolled: those with SCD (n = 80), normal controls (NCs, n = 77), and mild cognitive impairment (MCI, n = 23). SCD participants were further divided into the SCD-good (G-SCD, n = 40) group and the SCD-bad (B-SCD, n = 40) group according to their spatial navigation performance. Volumes of subcortical structures were calculated and compared among the four groups, including basal forebrain, thalamus, caudate, putamen, pallidum, hippocampus, amygdala, and accumbens. Topological properties of the subcortical structural covariance network were also calculated. With an interval of 1.5 years ± 12 months of follow-up, the progression rate to MCI was compared between the G-SCD and B-SCD groups. RESULTS Volumes of the basal forebrain, the right hippocampus, and their respective subfields differed significantly among the four groups (p < 0.05, false discovery rate corrected). The B-SCD group showed lower volumes in the basal forebrain than the G-SCD group, especially in the Ch4p and Ch4a-i subfields. Furthermore, the structural covariance network of the basal forebrain and right hippocampal subfields showed that the B-SCD group had a larger Lambda than the G-SCD group, which suggested weakened network integration in the B-SCD group. At follow-up, the B-SCD group had a significantly higher conversion rate to MCI than the G-SCD group. CONCLUSION Compared to SCD participants with good spatial navigation performance, SCD participants with bad performance showed lower volumes in the basal forebrain, a reorganized structural covariance network of subcortical nuclei, and an increased risk of progression to MCI. Our findings indicated that spatial navigation may have great potential to identify SCD subjects at higher risk of clinical progression, which may contribute to making more precise clinical decisions for SCD individuals who seek medical help.
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Affiliation(s)
- Qian Chen
- Department of Radiology, Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Futao Chen
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Cong Long
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Yajing Zhu
- Department of Radiology, Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Yaoxian Jiang
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Zhengyang Zhu
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Jiaming Lu
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Xin Zhang
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Zuzana Nedelska
- Memory Clinic, Department of Neurology, 2nd Faculty of Medicine, Charles University, University Hospital Motol, Prague, Czechia
| | - Jakub Hort
- Memory Clinic, Department of Neurology, 2nd Faculty of Medicine, Charles University, University Hospital Motol, Prague, Czechia
| | - Bing Zhang
- Department of Radiology, Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, 210008, China.
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China.
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China.
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China.
- Jiangsu Key Laboratory of Molecular Medicine, Nanjing, China.
- Institute of Brain Science, Nanjing University, Nanjing, China.
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Altomare D, Stampacchia S, Ribaldi F, Tomczyk S, Chevalier C, Poulain G, Asadi S, Bancila B, Marizzoni M, Martins M, Lathuiliere A, Scheffler M, Ashton NJ, Zetterberg H, Blennow K, Kern I, Frias M, Garibotto V, Frisoni GB. Plasma biomarkers for Alzheimer's disease: a field-test in a memory clinic. J Neurol Neurosurg Psychiatry 2023; 94:420-427. [PMID: 37012066 DOI: 10.1136/jnnp-2022-330619] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 01/28/2023] [Indexed: 04/05/2023]
Abstract
BACKGROUND The key Alzheimer's disease (AD) biomarkers are traditionally measured with techniques/exams that are either expensive (amyloid-positron emission tomography (PET) and tau-PET), invasive (cerebrospinal fluid Aβ42 and p-tau181), or poorly specific (atrophy on MRI and hypometabolism on fluorodeoxyglucose-PET). Recently developed plasma biomarkers could significantly enhance the efficiency of the diagnostic pathway in memory clinics and improve patient care. This study aimed to: (1) confirm the correlations between plasma and traditional AD biomarkers, (2) assess the diagnostic accuracy of plasma biomarkers as compared with traditional biomarkers, and (3) estimate the proportion of traditional exams potentially saved thanks to the use of plasma biomarkers. METHODS Participants were 200 patients with plasma biomarkers and at least one traditional biomarker collected within 12 months. RESULTS Overall, plasma biomarkers significantly correlated with biomarkers assessed through traditional techniques: up to r=0.50 (p<0.001) among amyloid, r=0.43 (p=0.002) among tau, and r=-0.23 (p=0.001) among neurodegeneration biomarkers. Moreover, plasma biomarkers showed high accuracy in discriminating the biomarker status (normal or abnormal) determined by using traditional biomarkers: up to area under the curve (AUC)=0.87 for amyloid, AUC=0.82 for tau, and AUC=0.63 for neurodegeneration status. The use of plasma as a gateway to traditional biomarkers using cohort-specific thresholds (with 95% sensitivity and 95% specificity) could save up to 49% of amyloid, 38% of tau, and 16% of neurodegeneration biomarkers. CONCLUSION The implementation of plasma biomarkers could save a remarkable proportion of more expensive traditional exams, making the diagnostic workup more cost-effective and improving patient care.
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Affiliation(s)
- Daniele Altomare
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Sara Stampacchia
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
- Laboratory of Cognitive Neuroscience (LNCO), Center of Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Federica Ribaldi
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Szymon Tomczyk
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Claire Chevalier
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Géraldine Poulain
- Sérotheque Centrale / Biotheque SML, Diagnostic Department, Geneva University Hospitals, Geneva, Switzerland
| | - Saina Asadi
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
| | - Bianca Bancila
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
| | - Moira Marizzoni
- Laboratory of Biological Psychiatry, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Laboratory of Neuroimaging and Alzheimer's Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Marta Martins
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Aurelien Lathuiliere
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Max Scheffler
- Division of Radiology, Geneva University Hospitals, Geneva, Switzerland
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Institute Clinical Neuroscience Institute, King's College London, London, UK
- NIHR Biomedical Research Centre for Mental Health and Biomedical Research, Unit for Dementia, South London and Maudsley, NHS Foundation, London, UK
- Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute, UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, People's Republic of China
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
| | - Ilse Kern
- Division of Laboratory Medicine, Diagnostic Department, Geneva University Hospitals, Geneva, Switzerland
| | - Miguel Frias
- Division of Laboratory Medicine, Diagnostic Department, Geneva University Hospitals, Geneva, Switzerland
| | - Valentina Garibotto
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
| | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
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10
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Ribaldi F, Palomo R, Altomare D, Scheffler M, Assal F, Ashton NJ, Zetterberg H, Blennow K, Abramowicz M, Garibotto V, Chicherio C, Frisoni GB. The taxonomy of subjective cognitive decline: proposal and first clinical evidence from the Geneva memory clinic cohort. RESEARCH SQUARE 2023:rs.3.rs-2570068. [PMID: 36824709 PMCID: PMC9949231 DOI: 10.21203/rs.3.rs-2570068/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
Background Subjective Cognitive Decline (SCD) is characterized by subjective cognitive complaints without objective cognitive impairment and is considered a risk factor for cognitive decline and dementia. However, most SCD patients will not develop neurodegenerative disorders, yet they may suffer from minor psychiatric, neurological, or somatic comorbidities. The aim of the present study is to provide a taxonomy of the heterogeneous SCD entity by isolating homogenous SCD subgroups with specific clinical features and cognitive trajectories. Methods Participants were fifty-five SCD individuals consecutively recruited at the Geneva Memory Center. Based on clinical reports, they were classified into three clinically pre-defined subgroups: (i) those with psychological or psychiatric comorbidities (Psy), (ii) those with somatic comorbidities (SomCom), (iii) and those with no apparent cause (NAC). Baseline demographics, clinical, cognitive, and biomarker differences among the SCD subgroups were assessed. Longitudinal cognitive changes (average 3 years follow-up) were modeled using a linear mixed model. Results Out of the 55 SCD cases, 16 were SomCom, 18 Psy, and 21 NAC. 47% were female, mean age was 71 years. We observed higher frequency of APOE ε4 carriers in NAC (53%) compared to SomCom (14%) and Psy (0%, P=0.023) and lower level of plasma Aβ42 in NAC (6.8±1.0) compared to SomCom (8.4±1.1; P=0.031). SomCom subjects were older (74 years) than Psy (67 years, P=0.011), and had greater medial temporal lobe atrophy(1.0±1.0) than Psy (0.2±0.6) and NAC (0.4±0.5, P=0.005). SomCom have worse episodic memory performances(14.5±3.5) than Psy (15.8±0.4) and SomCom (15.1±0.7, P=0.032). We observed a slightly steeper, yet not statistically significant, cognitive decline in NAC (β=-0.48) compared to Psy (β=-0.28) and SomCom (β=-0.24). Conclusions NAC feature higher proportion of APOE ε4 carriers, lower plasma Aβ42, worse memory performance, and a trend towards steeper cognitive decline than SomCom and Psy. Taken together, these findings suggest that NAC are at higher risk of cognitive decline due to AD. The proposed clinical taxonomy might be implemented in clinical practice to identify SCD at higher risk. However, such taxonomy should be tested on an independent cohort with larger sample size.
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11
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Caprioglio C, Ribaldi F, Visser LNC, Minguillon C, Collij LE, Grau-Rivera O, Zeyen P, Molinuevo JL, Gispert JD, Garibotto V, Moro C, Walker Z, Edison P, Demonet JF, Barkhof F, Scheltens P, Alves IL, Gismondi R, Farrar G, Stephens AW, Jessen F, Frisoni GB, Altomare D. Analysis of Psychological Symptoms Following Disclosure of Amyloid-Positron Emission Tomography Imaging Results to Adults With Subjective Cognitive Decline. JAMA Netw Open 2023; 6:e2250921. [PMID: 36637820 PMCID: PMC9857261 DOI: 10.1001/jamanetworkopen.2022.50921] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
IMPORTANCE Individuals who are amyloid-positive with subjective cognitive decline and clinical features increasing the likelihood of preclinical Alzheimer disease (SCD+) are at higher risk of developing dementia. Some individuals with SCD+ undergo amyloid-positron emission tomography (PET) as part of research studies and frequently wish to know their amyloid status; however, the disclosure of a positive amyloid-PET result might have psychological risks. OBJECTIVE To assess the psychological outcomes of the amyloid-PET result disclosure in individuals with SCD+ and explore which variables are associated with a safer disclosure in individuals who are amyloid positive. DESIGN, SETTING, AND PARTICIPANTS This prospective, multicenter study was conducted as part of The Amyloid Imaging to Prevent Alzheimer Disease Diagnostic and Patient Management Study (AMYPAD-DPMS) (recruitment period: from April 2018 to October 2020). The setting was 5 European memory clinics, and participants included patients with SCD+ who underwent amyloid-PET. Statistical analysis was performed from July to October 2022. EXPOSURES Disclosure of amyloid-PET result. MAIN OUTCOMES AND MEASURES Psychological outcomes were defined as (1) disclosure related distress, assessed using the Impact of Event Scale-Revised (IES-R; scores of at least 33 indicate probable presence of posttraumatic stress disorder [PTSD]); and (2) anxiety and depression, assessed using the Hospital Anxiety and Depression scale (HADS; scores of at least 15 indicate probable presence of severe mood disorder symptoms). RESULTS After disclosure, 27 patients with amyloid-positive SCD+ (median [IQR] age, 70 [66-74] years; gender: 14 men [52%]; median [IQR] education: 15 [13 to 17] years, median [IQR] Mini-Mental State Examination [MMSE] score, 29 [28 to 30]) had higher median (IQR) IES-R total score (10 [2 to 14] vs 0 [0 to 2]; P < .001), IES-R avoidance (0.00 [0.00 to 0.69] vs 0.00 [0.00 to 0.00]; P < .001), IES-R intrusions (0.50 [0.13 to 0.75] vs 0.00 [0.00 to 0.25]; P < .001), and IES-R hyperarousal (0.33 [0.00 to 0.67] vs 0.00 [0.00 to 0.00]; P < .001) scores than the 78 patients who were amyloid-negative (median [IQR], age, 67 [64 to 74] years, 45 men [58%], median [IQR] education: 15 [12 to 17] years, median [IQR] MMSE score: 29 [28 to 30]). There were no observed differences between amyloid-positive and amyloid-negative patients in the median (IQR) HADS Anxiety (-1.0 [-3.0 to 1.8] vs -2.0 [-4.8 to 1.0]; P = .06) and Depression (-1.0 [-2.0 to 0.0] vs -1.0 [-3.0 to 0.0]; P = .46) deltas (score after disclosure - scores at baseline). In patients with amyloid-positive SCD+, despite the small sample size, higher education was associated with lower disclosure-related distress (ρ = -0.43; P = .02) whereas the presence of study partner was associated with higher disclosure-related distress (W = 7.5; P = .03). No participants with amyloid-positive SCD+ showed probable presence of PTSD or severe anxiety or depression symptoms at follow-up. CONCLUSIONS AND RELEVANCE The disclosure of a positive amyloid-PET result to patients with SCD+ was associated with a bigger psychological change, yet such change did not reach the threshold for clinical concern.
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Affiliation(s)
- Camilla Caprioglio
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Federica Ribaldi
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Leonie N. C. Visser
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Karolinska Institutet, Stockholm/Solna, Sweden
- Department of Medical Psychology, Amsterdam Public Health Research Institute, Amsterdam University Medical Centers (UMC)–Location VUmc, Amsterdam, the Netherlands
| | - Carolina Minguillon
- Barcelonaßeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Lyduine E. Collij
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers (UMC)–Location VUmc, Amsterdam, the Netherlands
| | - Oriol Grau-Rivera
- Barcelonaßeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Philip Zeyen
- Department of Psychiatry, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - José Luis Molinuevo
- Barcelonaßeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- H. Lundbeck A/S, Denmark
| | - Juan Domingo Gispert
- Barcelonaßeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Barcelona, Spain
| | - Valentina Garibotto
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
- Center for Biomedical Imaging (CIBM), Geneva, Switzerland
| | - Christian Moro
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Zuzana Walker
- Division of Psychiatry, University College London, London, United Kingdom
- Margaret’s Hospital, Essex Partnership University NHS Foundation Trust, Essex, United Kingdom
| | - Paul Edison
- Division of Neurology, Department of Brain Sciences, Imperial College London, United Kingdom
| | | | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers (UMC)–Location VUmc, Amsterdam, the Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Philip Scheltens
- Alzheimer Center, Department of Neurology, Amsterdam University Medical Centers (UMC)–Location VUmc, Amsterdam, the Netherlands
| | - Isadora Lopes Alves
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers (UMC)–Location VUmc, Amsterdam, the Netherlands
| | | | | | | | - Frank Jessen
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- German Center for Neurodegenerative Diseases (DZNE), Bonn-Cologne, Germany
- Excellence Cluster Cellular Stress Responses in Aging-Related Diseases (CECAD), Medical Faculty, University of Cologne, Germany
| | - Giovanni B. Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Daniele Altomare
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
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12
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Ribaldi F, Rolandi E, Vaccaro R, Colombo M, Battista Frisoni G, Guaita A. The clinical heterogeneity of subjective cognitive decline: a data-driven approach on a population-based sample. Age Ageing 2022; 51:6770075. [PMID: 36273347 DOI: 10.1093/ageing/afac209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND subjective cognitive decline (SCD) refers to the subjective experience of cognitive decline in the absence of detectable cognitive impairment. SCD has been largely studied as a risk condition for cognitive decline. Empirical observations suggest that persons with SCD are heterogeneous, including individuals with early Alzheimer's disease and others with psychological vulnerabilities and/or physical comorbidity. The semiology of SCD is still in its infancy, and the features predicting cognitive decline are poorly defined. The present study aims to identify subgroups of SCD using a data-driven approach and study their clinical evolution across 8 years. METHODS the study population is the InveCe.Ab population-based cohort, including cognitively unimpaired people aged 70-74 years and followed for 8 years. Hierarchical cluster analysis (HCA) was carried out to identify distinct SCD subgroups based on nine clinical and cognitive features. Longitudinal changes by baseline SCD status were estimated using linear mixed models for cognitive decline and Cox proportional-hazard model for all-cause dementia risk. RESULTS out of 956 individuals, 513 were female (54%); and the mean age was 72.1 (SD = 1.3), education was 7.2 (3.3), and 370 (39%) reported cognitive complaints (SCD). The HCA resulted in two clusters (SCD1 and SCD2). SCD2 were less educated and had more comorbidities, cardiovascular risk and depressive symptoms than SCD1 and controls. SCD2 presented steeper cognitive decline (Mini-Mental State Examination; β = -0.31) and increased all-cause dementia risk (hazard-ratio = 3.4). CONCLUSIONS at the population level, basic clinical information can differentiate individuals with SCD at higher risk of developing dementia, underlining the heterogeneous nature of this population even in a sample selected for a narrow age range, in a specific geographic area.
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Affiliation(s)
- Federica Ribaldi
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland.,Department of Rehabilitation and Geriatrics, Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Elena Rolandi
- "Golgi Cenci" Foundation, Corso San Martino 10, Abbiategrasso 20081, Italy.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia 27100, Italy
| | - Roberta Vaccaro
- "Golgi Cenci" Foundation, Corso San Martino 10, Abbiategrasso 20081, Italy
| | - Mauro Colombo
- "Golgi Cenci" Foundation, Corso San Martino 10, Abbiategrasso 20081, Italy
| | - Giovanni Battista Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland.,Department of Rehabilitation and Geriatrics, Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Antonio Guaita
- "Golgi Cenci" Foundation, Corso San Martino 10, Abbiategrasso 20081, Italy
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13
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Boccalini C, Peretti DE, Ribaldi F, Scheffler M, Stampacchia S, Tomczyk S, Rodriguez C, Montandon ML, Haller S, Giannakopoulos P, Frisoni GB, Perani D, Garibotto V. Early-phase 18F-Florbetapir and 18F-Flutemetamol images as proxies of brain metabolism in a memory clinic setting. J Nucl Med 2022; 64:jnumed.122.264256. [PMID: 35863896 PMCID: PMC9902851 DOI: 10.2967/jnumed.122.264256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 07/15/2022] [Accepted: 07/15/2022] [Indexed: 11/16/2022] Open
Abstract
Background: Alzheimer's disease (AD) neuropathologic changes are β-amyloid (Aβ) deposition, pathologic tau, and neurodegeneration. Dual-phase amyloid-PET might be able to evaluate Aβ deposition and neurodegeneration with a single tracer injection. Early-phase amyloid-PET scans provide a proxy for cerebral perfusion, which has shown good correlations with neural dysfunction measured through metabolic consumption, while the late frames depict amyloid distribution. Our study aims to assess the comparability between early-phase amyloid-PET scans and 18F-fluorodeoxyglucose (18F-FDG)-PET brain topography at the individual level, and their ability to discriminate patients. Methods: 166 subjects evaluated at the Geneva Memory Center, ranging from cognitively unimpaired to Mild Cognitive Impairment (MCI) and dementia, underwent early-phase amyloid-PET - using either 18F-florbetapir (eFBP) (n = 94) or 18F-flutemetamol (eFMM) (n = 72) - and 18F-FDG-PET. Aβ status was assessed. Standardized uptake value ratios (SUVR) were extracted to evaluate the correlation of eFBP/eFMM and their respective 18F-FDG-PET scans. The single-subject procedure was applied to investigate hypometabolism and hypoperfusion maps and their spatial overlap by Dice coefficient. Receiver operating characteristic analyses were performed to compare the discriminative power of eFBP/eFMM, and 18F-FDG-PET SUVR in AD-related metaROI between Aβ-negative healthy controls and cases in the AD continuum. Results: Positive correlations were found between eFBP/eFMM and 18F-FDG-PET SUVR independently of Aβ status and Aβ radiotracer (R>0.72, p<0.001). eFBP/eFMM single-subject analysis revealed clusters of significant hypoperfusion with good correspondence to hypometabolism topographies, independently of the underlying neurodegenerative patterns. Both eFBP/eFMM and 18F-FDG-PET SUVR significantly discriminated AD patients from controls in the AD-related metaROIs (AUCFBP = 0.888; AUCFMM=0.801), with 18F-FDG-PET performing slightly better, however not significantly (all p-value higher than 0.05), than others (AUCFDG=0.915 and 0.832 for subjects evaluated with 18F-FBP and 18F-FMM, respectively). Conclusion: The distribution of perfusion was comparable to that of metabolism at the single-subject level by parametric analysis, particularly in the presence of a high neurodegeneration burden. Our findings indicate that eFBP/eFMM imaging can replace 18F-FDG-PET imaging, as they reveal typical neurodegenerative patterns, or allow to exclude the presence of neurodegeneration. The finding shows cost-saving capacities of amyloid-PET and supports the routine use of the modality for individual classification in clinical practice.
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Affiliation(s)
- Cecilia Boccalini
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Vita-Salute San Raffaele University, Milan, Italy
- In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Débora Elisa Peretti
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Federica Ribaldi
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Memory Clinic, Geneva University Hospitals, Geneva, Switzerland
| | - Max Scheffler
- Division of Radiology, Diagnostic Department, Geneva University Hospitals, Geneva, Switzerland
| | - Sara Stampacchia
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Szymon Tomczyk
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
| | - Cristelle Rodriguez
- Division of Institutional Measures, Medical Direction, University Hospitals of Geneva, Geneva, Switzerland
- Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Marie-Louise Montandon
- Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Department of Rehabilitation and Geriatrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Sven Haller
- CIMC–Centre d’Imagerie Médicale de Cornavin, Geneva, Switzerland
- Faculty of Medicine of University of Geneva, Geneva, Switzerland
- Division of Radiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Panteleimon Giannakopoulos
- Division of Institutional Measures, Medical Direction, University Hospitals of Geneva, Geneva, Switzerland
- Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Giovanni B. Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Memory Clinic, Geneva University Hospitals, Geneva, Switzerland
| | - Daniela Perani
- Vita-Salute San Raffaele University, Milan, Italy
- In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Nuclear Medicine Unit, San Raffaele Hospital, Milan, Italy
| | - Valentina Garibotto
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland; and
- CIBM Center for Biomedical Imaging, Geneva, Switzerland
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