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Record W2748563994 · doi:10.1177/1479164117725478

Metabolic inflexibility in individuals with obesity assessed by near-infrared spectroscopy

2017· article· en· W2748563994 on OpenAlexaff
Rogério N. Soares, Raylene A. Reimer, Patricia K. Doyle–Baker, Juan M. Murias

Bibliographic record

VenueDiabetes and Vascular Disease Research · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineDiabetes mellitusObesityInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

OBJECTIVE: To non-invasively evaluate differences in oxidative metabolism in individuals with obesity compared to normal weight using the near-infrared spectroscopy and vascular occlusion technique during hyperglycaemia. METHODS: ) had five vascular occlusion tests (pre, 30, 60, 90 and 120 min after glucose ingestion). Oxygen utilization was estimated from the area under the curve of the deoxyhemoglobin [HHb] signal during occlusion. Muscle reperfusion was derived from the area above the curve after cuff release. RESULTS: The deoxyhemoglobin area under the curve during occlusion of the normal-weight individuals increased from 15,732 ± 2344 (% . s) at pre to 18,930 ± 3226 (% . s) ( p < 0.05) at 90 min after glucose ingestion. The deoxyhemoglobin area under the curve during occlusion decreased significantly from 14,695 ± 3341 (% . s) at pre to 11,273 ± 1825 (% . s) ( p < 0.05) and 11,360 ± 1750 (% . s) ( p < 0.05) at 30 and 60 min, respectively, after glucose ingestion. The area above the curve of deoxyhemoglobin during reperfusion decreased significantly from 6450 ± 765 (% . s) at pre to 4830 ± 963 (% . s) ( p < 0.05) at 60 min and to 4210 ± 595 (% . s) ( p < 0.01) at 90 min in normal-weight individuals after glucose ingestion, with no changes observed in individuals with obesity. CONCLUSION: This study confirmed in vivo and non-invasively the metabolic inflexibility of skeletal muscle in individuals with obesity during hyperglycaemia.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.027
GPT teacher head0.347
Teacher spread0.320 · 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

Citations12
Published2017
Admission routes1
Has abstractyes

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