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Record W1985270629 · doi:10.3177/jnsv.48.128

Short-Term (-)-Hydroxycitrate Ingestion Increases Fat Oxidation during Exercise in Athletes.

2002· article· en· W1985270629 on OpenAlexaff
Kiwon Lim, Ryu Sungpil, Y. Ohishi, Itaru Watanabe, Hironori Tomi, Heajung Suh, Woong-Ki LEE, Tae-Dong Kwon

Bibliographic record

VenueJournal of Nutritional Science and Vitaminology · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsConestoga Meat Packers (Canada)
FundersKonkuk UniversityDaegu University
KeywordsRespiratory exchange ratioGlycogenCycle ergometerPlaceboAthletesEndurance trainingMedicineTime trialInternal medicineIngestionIncremental exerciseEndocrinologyPhysical therapyHeart rateBlood pressure

Abstract

fetched live from OpenAlex

(-)-Hydroxycitrate (HCA) is known to inhibit increasing malonyl CoA concentration during endurance exercise. Furthermore, a short-term administration of HCA enhances endurance exercise performance in mice. Therefore we investigated the short-term administration of HCA on the exercise performance of athletes. Subjects were administered 250 mg of HCA or placebo as a control (CON) for 5 d, after each time performing cycle ergometer exercise at 60% VO2max for 60 min followed by 80% VO2max until exhaustion. Blood was collected and expired gas samples analyzed at rest and every 15 min. The respiratory exchange ratio was significantly lower in the HCA trial than in the CON trial (p < 0.05). Fat oxidation was significantly increased by short-term administration of HCA, and carbohydrate oxidation was significantly decreased (p < 0.05) during exercise, presumably resulting in increasing the cycle ergometer exercise time to exhaustion after 1 h of 60% VO2max exercise (p < 0.05). These results suggest that a short-term administration of HCA enhances endurance performance with increasing fat oxidation, which spares glycogen utilization during moderate intensity exercise in athletes.

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 score0.812
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.018
GPT teacher head0.248
Teacher spread0.229 · 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

Citations20
Published2002
Admission routes1
Has abstractyes

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