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Record W4365516047 · doi:10.3389/fnins.2023.1151820

Accelerated hypometabolism with disease progression associated with faster cognitive decline among amyloid positive patients

2023· article· en· W4365516047 on OpenAlexfundno aff
Zhengshi Yang, Jeffrey L. Cummings, Jefferson W. Kinney, Dietmar Cordes

Bibliographic record

VenueFrontiers in Neuroscience · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchNational Institutes of HealthGenentechNational Institute of Neurological Disorders and StrokeIXICONational Institute of General Medical SciencesH. Lundbeck A/SServierEisaiNorthern California Institute for Research and EducationPfizerBiogenBioClinicaUniversity of Southern CaliforniaNovartis Pharmaceuticals CorporationU.S. Department of DefenseEli Lilly and CompanyBristol-Myers SquibbCleveland ClinicF. Hoffmann-La RocheAlzheimer's Drug Discovery FoundationAlzheimer's Disease Neuroimaging InitiativeMeso Scale DiagnosticsNational Institute on AgingAlzheimer's Association
KeywordsDementiaCognitive declineMedicineConfoundingInternal medicineAlzheimer's Disease Neuroimaging InitiativePositron emission tomographyAlzheimer's diseaseNeuroimagingFluorodeoxyglucoseCarbohydrate metabolismNeuropsychologyOncologyCognitionPsychologyDiseasePsychiatryNuclear medicine

Abstract

fetched live from OpenAlex

Objective: To evaluate the progression of brain glucose metabolism among participants with biological signature of Alzheimer's disease (AD) and its relevance to cognitive decline. Method: We studied 602 amyloid positive individuals who underwent 18F-fluorodeoxyglucose PET (FDG-PET) scan, 18F-AV-45 amyloid PET (AV45-PET) scan, structural MRI scan and neuropsychological examination, including 116 cognitively normal (CN) participants, 314 participants diagnosed as mild cognitive impairment (MCI), and 172 participants diagnosed as AD dementia. The first FDG-PET scan satisfying the inclusion criteria was considered as the baseline scan. Cross-sectional analysis were conducted with the baseline FDG-PET data to compare the regional differences between diagnostic groups after adjusting confounding factors. Among these participants, 229 participants (55 CN, 139 MCI, and 35 AD dementia) had two-year follow-up FDG-PET data available. Regional glucose metabolism was computed and the progression rates of regional glucose metabolism were derived from longitudinal FDG-PET scans. Then the group differences of regional progression rates were examined to assess whether glucose metabolism deficit accelerates or becomes stable with disease progression. The association of cognitive decline rate with baseline regional glucose metabolism, and progression rate in longitudinal data, were evaluated. Results: Participants with AD dementia showed substantial glucose metabolism deficit than CN and MCI at left hippocampus, in addition to the traditionally reported frontal and parietal-temporal lobe. More substantial metabolic change was observed with the contrast AD - MCI than the contrast MCI - CN, even after adjusting time duration since cognitive symptom onset. With the longitudinal data, glucose metabolism was observed to decline the most rapidly in the AD dementia group and at a slower rate in MCI. Lower regional glucose metabolism was correlated to faster cognitive decline rate with mild-moderate correlations, and the progression rate was correlated to cognitive decline rate with moderate-large correlations. Discussion and conclusion: Hippocampus was identified to experience hypometabolism in AD pathology. Hypometabolism accelerates with disease progression toward AD dementia. FDG-PET, particularly longitudinal scans, could potentially help predict how fast cognition declines and assess the impact of treatment in interventional trials.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.004
Threshold uncertainty score0.656

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.297
Teacher spread0.280 · 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 teacher head, 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".

Quick stats

Citations13
Published2023
Admission routes1
Has abstractyes

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