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Record W2604580271 · doi:10.1097/mco.0000000000000380

AMP-activated protein kinase, fatty acid metabolism, and insulin sensitivity

2017· review· en· W2604580271 on OpenAlexaff
Brennan K. Smith, Gregory R. Steinberg

Bibliographic record

VenueCurrent Opinion in Clinical Nutrition & Metabolic Care · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAMPKLipogenesisInsulin resistanceAMP-activated protein kinaseProtein kinase AFatty acid metabolismBeta oxidationAdipose tissueFatty acid synthesisInsulinType 2 diabetesFatty acidChemistryEndocrinologyKinaseMedicineBiochemistryDiabetes mellitus

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Insulin resistance is an important risk factor for metabolic diseases such as type 2 diabetes, cardiovascular disease and certain cancers. A common characteristic of strategies that improve insulin sensitivity involves the activation of the energy sensing enzyme of the cell, AMP-activated protein kinase (AMPK). The purpose of this review is to explore the mechanisms associated with AMPK activation to improve insulin sensitivity with a focus on fatty acid metabolism. We will also discuss the literature surrounding direct AMPK activators to improve insulin resistance and important considerations for the design of direct AMPK activators. RECENT FINDINGS: AMPK activation can decrease de novo lipogenesis, increase fatty acid oxidation and promote mitochondrial integrity to improve insulin sensitivity. Drugs targeted to directly activate AMPK show therapeutic promise, yet in vivo data is lacking. SUMMARY: Designing a drug to directly activate AMPK may improve insulin resistance by reducing liver de novo lipogenesis and increasing brown and white adipose tissue mitochondrial function. However, in vivo experimental procedures to support this notion are not extensive and more research is required.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.004

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 head0.174
GPT teacher head0.456
Teacher spread0.282 · 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 designNot applicable
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

Citations54
Published2017
Admission routes1
Has abstractyes

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