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Record W3029393633 · doi:10.1093/sleep/zsaa056.412

0415 Metabolic Dysfunction and Sleep Disruption in Models of Alzheimer’s Disease

2020· article· en· W3029393633 on OpenAlexaff
Caitlin M. Carroll, Molly Stanley, Shannon L. Macauley

Bibliographic record

VenueSLEEP · 2020
Typearticle
Languageen
FieldMedicine
TopicBiochemical effects in animals
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNeurodegenerationAlzheimer's diseaseNeuroscienceMedicineInternal medicineWakefulnessEndocrinologyDementiaCarbohydrate metabolismPsychologyDiseaseElectroencephalography

Abstract

fetched live from OpenAlex

Abstract Introduction Metabolic perturbations and sleep disruptions are a cause and consequence of Alzheimer’s disease pathophysiology. A bidirectional relationship exists where impairments in sleep and metabolism contribute to the development of Alzheimer’s disease while the presence of Alzheimer’s disease pathology leads to decreased cerebral metabolism, peripheral glucose intolerance, and disrupted sleep. While the effects of type 2 diabetes (T2D) and Alzheimer’s disease on sleep have been explored separately, no previous studies have examined the effect of acute glycemic variability, a defining feature of T2D, on sleep in the context of Alzheimer’s disease. The goal of this study is to determine how glycemic variability drives sleep disruptions by modifying the relationship between cerebral glucose metabolism and neuronal activity. Methods Biosensors are implanted bilaterally into the hippocampus of APP/PS1, a model of amyloid-beta (Aβ) overexpression, and P301S, a model of tau deposition and neurodegeneration, mice to measure ISF glucose and lactate, markers of cerebral metabolism and neuronal activity, respectively. Simultaneous cortical EEG/EMG recordings are used for sleep/wake scoring and analysis. Results Glycemic fluctuations cause a decoupling of the typical relationships between cerebral metabolism and neuronal activity, while also increasing arousal in 3-month-old, wildtype mice. The presence of AD-like pathology results in a similar, albeit muted cerebral metabolic response to peripheral glycemic variability, but a diminished effect on wakefulness, likely due to age- and pathology-dependent increases in overall time spent awake. Conversely, in aged, P301S mice, cerebral metabolic responsiveness is lost and a ceiling effect on wakefulness emerges, suggesting differential effects on sleep with tau accumulation. Moreover, aged mice show progressive disruptions to overall sleep quality and quantity, highlighting the synergism existing between AD-like pathology and glucose intolerance in sleep dysfunction. Lactate seems to be a common driver of disruption in this synergistic cycle. Conclusion This study represents a novel approach to defining the dynamic interplay between risk factors for AD and T2D and suggests a feedforward loop of disease progression where disrupted sleep can alter the relationship between neuronal activity, metabolism, and pathology. Support NIH/NIA 1K01AG050719. Harold and Mary Eagle Fund for Alzheimer’s Research New Vision Award through Donors Cure Foundation

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.034
GPT teacher head0.280
Teacher spread0.246 · 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 designBench or experimental
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

Citations0
Published2020
Admission routes1
Has abstractyes

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