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Record W4318831409 · doi:10.1113/ep090915

Association between haemoglobin A<sub>1c</sub> and whole‐body heat loss during exercise‐heat stress in physically active men with type 2 diabetes

2023· article· en· W4318831409 on OpenAlexafffund
Nathalie V. Kirby, Robert D. Meade, Martin P. Poirier, Ronald J. Sigal, Pierre Boulay, Glen P. Kenny

Bibliographic record

VenueExperimental Physiology · 2023
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsOttawa HospitalUniversité de SherbrookeUniversity of CalgaryCanadian Armed ForcesUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsHeat stressInternal medicineDiabetes mellitusType 2 diabetesEndocrinologyAssociation (psychology)MedicineChemistryBiologyPsychologyAnimal science

Abstract

fetched live from OpenAlex

Abstract Type 2 diabetes is associated with a reduced capacity to dissipate heat. It is unknown whether this impairment is related to glycaemic control (indexed by glycated haemoglobin; haemoglobin A 1c ) is unknown. We evaluated the association between haemoglobin A 1c and whole‐body heat loss (via direct calorimetry), body core temperature, and heart rate in 26 physically active men with type 2 diabetes (43–73 years; HbA 1c 5.1–9.1%) during exercise at increasing rates of metabolic heat production (∼150, 200, 250 W m −2 ) in the heat (40°C, ∼17% relative humidity). Haemoglobin A 1c was not associated with whole‐body heat loss ( P = 0.617), nor the increase in core temperature from pre‐exercise ( P = 0.347). However, absolute core temperature and heart rate were elevated ∼0.2°C ( P = 0.014) and ∼6 beats min −1 ( P = 0.049), respectively, with every percentage point increase in haemoglobin A 1c . Thus, while haemoglobin A 1c does not appear to modify diabetes‐related reductions in capacity for heat dissipation, it may still have important implications for physiological strain during exercise‐heat stress.

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 score0.518
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.012
GPT teacher head0.278
Teacher spread0.266 · 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

Citations10
Published2023
Admission routes2
Has abstractyes

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