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Potential Therapeutic Effects of Exogenous Ketone Supplementation for Type 2 Diabetes: A Review

2020· review· en· W3005470332 on OpenAlexafffund
Jeremy J. Walsh, Étienne Myette‐Côté, Helena Neudorf, Jonathan P. Little

Bibliographic record

VenueCurrent Pharmaceutical Design · 2020
Typereview
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsOvernutritionType 2 diabetesMedicineOxidative stressDiabetes mellitusDiseaseInsulin resistancePathophysiologyBioinformaticsInflammationType 2 Diabetes MellitusInternal medicineEndocrinologyObesityBiology

Abstract

fetched live from OpenAlex

Type 2 diabetes (T2D) is among the most prevalent non-communicable lifestyle diseases. We propose that overnutrition and low levels of physical activity can contribute to a vicious cycle of hyperglycemia, inflammation and oxidative stress, insulin resistance, and pancreatic β-cell dysfunction. The pathophysiological manifestations of T2D have a particular impact on the vasculature and individuals with T2D are at high risk of cardiovascular disease. Targeting aspects of the vicious cycle represent therapeutic approaches for improving T2D and protecting against cardiovascular complications. The recent advent of exogenous oral ketone supplements represents a novel, non-pharmacological approach to improving T2D pathophysiology and potentially protecting against cardiovascular disease risk. Herein, we review the emerging literature regarding the effects of exogenous ketone supplementation on metabolic control, inflammation, oxidative stress, and cardiovascular function in humans and highlight the potential application for breaking the vicious cycle of T2D pathophysiology.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.243
GPT teacher head0.483
Teacher spread0.240 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations27
Published2020
Admission routes2
Has abstractyes

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