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Record W2355524658 · doi:10.1016/s1474-4422(16)30030-8

Cortical atrophy in patients with cerebral amyloid angiopathy: a case-control study

2016· article· en· W2355524658 on OpenAlexfundno aff
Panagiotis Fotiadis, Sanneke van Rooden, Jeroen van der Grond, Aaron P. Schultz, Sergi Martínez‐Ramírez, Eitan Auriel, Yaël D. Reijmer, Anna M. van Opstal, Alison Ayres, Kristin Schwab, Alzheimer’s Disease Neuroimaging Initiative, Trey Hedden, Jonathan Rosand, Anand Viswanathan, Marieke J.H. Wermer, Gisela M. Terwindt, Reisa A. Sperling, Jon̈athan R. Polimeni, Keith A. Johnson, Mark A. van Buchem, Steven M. Greenberg, M. Edip Gurol

Bibliographic record

VenueThe Lancet Neurology · 2016
Typearticle
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsnot available
FundersJanssen Research and DevelopmentNational Institute on AgingNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General HospitalUniversity of California, San DiegoFidelity BiosciencesGenentechNational Institutes of HealthServierCentre d'Imagerie BioMédicaleFujirebio EuropeEisaiHartstichtingAlzheimer's Drug Discovery FoundationZonMwNorthern California Institute for Research and EducationPfizerBiogenBioClinicaNational Center for Research ResourcesF. Hoffmann-La RocheNational Institute of Neurological Disorders and StrokeIXICOTakeda Pharmaceutical CompanyAbbVieNovartis Pharmaceuticals CorporationBristol-Myers Squibb FoundationLundbeckfondenU.S. Department of DefenseEli Lilly and CompanyNederlandse Organisatie voor Wetenschappelijk OnderzoekUniversity of Southern CaliforniaRocheMerckGE HealthcareAlzheimer's Disease Neuroimaging InitiativeJohnson and JohnsonMeso Scale DiagnosticsSanofiHarvard NeuroDiscovery CenterAlzheimer's Association
KeywordsCerebral amyloid angiopathyDementiaMedicineNeuroimagingAlzheimer's diseasePathologyAngiopathyAtrophyDiseaseCognitive declineAmyloid (mycology)Internal medicinePsychiatryEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: Loss of cortical grey matter is a diagnostic marker of many neurodegenerative diseases, and is a key mediator of cognitive impairment. We postulated that cerebral amyloid angiopathy (CAA), characterised by cortical vascular amyloid deposits, is associated with cortical tissue loss independent of parenchymal Alzheimer's disease pathology. We tested this hypothesis in patients with hereditary cerebral haemorrhage with amyloidosis-Dutch type (HCHWA-D), a monogenetic disease with minimal or no concomitant Alzheimer's disease pathology, as well as in patients with sporadic CAA and healthy and Alzheimer's disease controls. METHODS: In this observational case-control study, we included six groups of participants: patients diagnosed with HCHWA-D using genetic testing; healthy controls age-matched to the HCHWA-D group; patients with probable sporadic CAA without dementia; two independent cohorts of healthy controls age-matched to the CAA group; and patients with Alzheimer's disease age-matched to the CAA group. De-identified (but unmasked) demographic, clinical, radiological, and genetic data were collected at Massachusetts General Hospital (Boston, MA, USA), at Leiden University (Leiden, Netherlands), and at sites contributing to Alzheimer's Disease Neuroimaging Initiative (ADNI). The primary outcome measure was cortical thickness. The correlations between cortical thickness and structural lesions, and blood-oxygen-level-dependent time-to-peak (BOLD-TTP; a physiological measure of vascular dysfunction) were analysed to understand the potential mechanistic link between vascular amyloid and cortical thickness. The radiological variables of interest were quantified using previously validated computer-assisted tools, and all results were visually reviewed to ensure their accuracy. RESULTS: Between March 15, 2006, and Dec 1, 2014, we recruited 369 individuals (26 patients with HCHWA-D and 28 age-matched, healthy controls; 63 patients with sporadic CAA without dementia; two healthy control cohorts with 63 and 126 individuals; and 63 patients with Alzheimer's disease). The 26 patients with HCHWA-D had thinner cortices (2·31 mm [SD 0·18]) than the 28 healthy controls (mean difference -0·112 mm, 95% CI -0·190 to -0·034, p=0·006). The 63 patients with sporadic CAA without dementia had thinner cortices (2·17 mm [SD 0·11]) than the two healthy control cohorts (n=63, mean difference -0·14 mm, 95% CI -0·17 to -0·10, p<0·0001; and n=126, -0·10, -0·13 to -0·06, p<0·0001). All differences remained independent in multivariable analyses. The 63 patients with Alzheimer's disease displayed more severe atrophy than the patients with sporadic CAA (2·1 mm [SD 0·14], difference 0·07 mm, 95% CI 0·11 to 0·02, p=0·005). We found strong associations between cortical thickness and vascular dysfunction in the patients with HCHWA-D (ρ=-0·58, p=0·003) or sporadic CAA (r=-0·4, p=0·015), but not in controls. Vascular dysfunction was identified as a mediator of the effect of hereditary CAA on cortical atrophy, accounting for 63% of the total effect. INTERPRETATION: The appearance of cortical thinning in patients with HCHWA-D indicates that vascular amyloid is an independent contributor to cortical atrophy. These results were reproduced in patients with the more common sporadic CAA. Our findings also suggest that CAA-related cortical atrophy is at least partly mediated by vascular dysfunction. Our results also support the view that small vessel diseases such as CAA can cause cortical atrophy even in the absence of Alzheimer's disease, a conclusion that can help radiologists, neurologists, and other clinicians who diagnose these common geriatric conditions. FUNDING: National Institutes of Health.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.259
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations122
Published2016
Admission routes1
Has abstractno

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