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Influences of a Cold and Warm Environment on Substrate Metabolism in Women during Running and Cycling

2018· article· en· W3177187290 on OpenAlexaff
Dominique D. Gagnon, Alexus McCue, Sandra C. Dorman, Olivier Serresse

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsLaurentian University
Fundersnot available
KeywordsCardiorespiratory fitnessCyclingVO2 maxAnimal scienceChemistryTreadmillCrossover studyExercise intensityCycle ergometerIncremental exerciseExercise physiologyLipid oxidationTime trialAerobic exerciseInternal medicineMedicineHeart rateBiochemistryBiologyBlood pressure

Abstract

fetched live from OpenAlex

Background Women oxidize more fat than men at rest and during exercise due to differences in hormonal and blood flow regulatory mechanisms, as well as substrate storage and mobilization. Exercise studies examining the effects of varying environments on energy metabolism have consistently demonstrated a greater shift towards lipid utilization in cold compared to warm environments in men. In addition, these studies have been conducted at fixed exercise intensities and workloads only, and have not been validated with different exercise modalities. Thereby, this study aimed to determine whether cold and warm ambient temperatures influence fat and CHO oxidation across a range of exercise intensities during treadmill and cycle ergometer exercise in women. Methods Twelve women (age 22 ± 1.8 yrs, weight 61 ± 6.6 kg, height 1.6 ± 0.09 m, body fat 26 ± 4.7 %, FFM 45 ± 5.8 kg, body surface area 1.6 ± 0.12 m 2 , and V̇O 2 48 ± 5.1 mlO2·kg −1 ·min −1 ) completed four trials, one week apart, during which they performed an incremental peak oxygen consumption (V̇O 2peak ) test on a cycle ergometer or treadmill in a cold (5°C) or warm (35°C) environment. Cardiorespiratory and oxidation variables, including V̇O 2 , maximal fat oxidation rate (MFO), exercise intensity where MFO occurs (Fat max ), and relative contribution of lipids and carbohydrates (CHO) to total energy expenditure crossover point (CC) were assessed via indirect calorimetry. Lactate pre and post tests was also assessed. Analyses of variances and multiple linear regressions were performed. Results MFO was greater in the cold vs. warm for treadmill (0.51 ± 0.19 vs. 0.41 ± 0.14 g•min −1 ; p=0.04) but not during cycling (0.32 ± 0.14 vs. 0.23 ± 0.07 g•min −1 ; p=0.08). MFO was also greater during treadmill vs. cycling exercise, in both temperatures (0.46 ± 0.16 g•min −1 vs. 0.28 ± 0.11 g•min −1 ; p<0.001). Fat max was greater in the cold vs. warm within treadmill (63 ± 27 vs. 49 ± 22 % V̇O 2max ; p=0.02) but not cycling (41 ± 20 vs. 31 ± 12 % V̇O 2max ; p=0.16). CC was greater in the cold vs. warm within treadmill exercise (53 ± 20 vs. 39 ± 17% V̇O 2max ; p=0.04) but not during cycling (36 ± 12 vs. 28 ± 10% V̇O 2max ; p=0.19). Multiple linear regressions further revealed a strong influence of ambient temperature on lipids (p<0.001) and CHO (p<0.001) oxidation rates across exercise intensities. Conclusions Exercising in a cold environment increased MFO, Fat max , and CC during treadmill exercise in women but not during cycling. These results imply that ambient temperature influences energy metabolism over a wide range of exercise intensities during treadmill exercise in women, similarly to what is seen in men. The large variability in fat oxidation in the present results, similar to other studies, will need to be investigated further in both women and men. Support or Funding Information This work was supported by a Laurentian University Research Fund grant. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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 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

Citations1
Published2018
Admission routes1
Has abstractyes

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