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Record W4386203700 · doi:10.2196/52199

The Influence of Branched-Chain Amino Acid Supplementation on Fatigue and Tryptophan Metabolism After Acute and Chronic Exercise in Older Adults: Protocol for a Pilot Randomized Controlled Trial

2023· article· en· W4386203700 on OpenAlexvenueno aff
Ronna Robbins, Tiffany Cortes, Jason C. O’Connor, Rozmin Jiwani, Monica C. Serra

Bibliographic record

VenueJMIR Research Protocols · 2023
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsnot available
FundersGeriatric Research Education and Clinical CenterUniversity of Texas Health Science Center at San AntonioNational Institute on AgingNIH Clinical CenterNational Institutes of HealthU.S. Department of Veterans Affairs
KeywordsKynurenineKynurenic acidMedicineRandomized controlled trialKynurenine pathwayAmino acidPhysical exerciseMetabolitePhysical therapyTryptophanInternal medicineBiochemistryChemistry

Abstract

fetched live from OpenAlex

BACKGROUND: Fatigue is a strong predictor of negative health outcomes in older adults. Kynurenine, a metabolite of tryptophan, is strongly associated with fatigue. Reductions in fatigue are observed with exercise; however, exercise training does not completely alleviate symptoms. Branched-chain amino acids (BCAAs) have been shown to have advantageous effects on exercise performance and compete with kynurenine for transport into the central nervous system. Thus, the combination of BCAA and exercise may exert synergized effects of mental and physical fatigue. Therefore, we hypothesize that BCAA added to exercise will shift kynurenine metabolism toward enhanced synthesis of kynurenic acid, thereby reducing fatigue. OBJECTIVE: This randomized, double-blind, placebo-controlled trial aims to compare the effects of acute (approximately 45 min) and chronic (8 wk) exercise with and without BCAA supplementation on mental and physical fatigue and assess whether the hypothesized outcomes are modulated by changes in kynurenine metabolism in 30 older adults (n=15, 50% per group). METHODS: Older adults (aged 60-80 y) who do not exercise >2 days per week and self-report fatigue (≥3 on a scale of 1-10) will be recruited. Participants will be randomized to either the exercise+BCAA group or exercise+placebo group. Participants will engage in high-volume, moderate-intensity, whole-body exercise training (aerobic and resistance exercise; either in-person or web-based sessions) 3 times per week for 8 weeks. In addition, participants will consume daily either 100 mg/kg body weight of BCAA (2:1:1 leucine:isoleucine:valine) or placebo (maltodextrin) throughout the 8-week intervention. BCAA and placebo powders will be identical in color and dissolved in 400 mL of water and 2.5 g of a calorie-free water flavor enhancer. Muscle biopsies will be collected before and after the intervention after a 12-hour fast to examine changes in the biomarkers of tryptophan metabolism and inflammation. Our primary outcomes include changes in mental and physical fatigue and metabolism after the 8-week exercise training between the 2 groups. Mental and physical fatigue will be measured before and after the intervention. Mental fatigue will be subjectively assessed through the completion of validated questionnaires. Physical fatigue will be measured by isometric handgrip, 1-repetition maximum, chair rise, 400-meter walk, and cardiopulmonary exercise tests. RESULTS: The study was funded in March 2022, with an anticipated projected data collection period lasting from January 2023 through December 2023. CONCLUSIONS: The discovery that kynurenine concentrations are associated with fatigue and are responsive to BCAA supplementation during exercise training could have important implications for the development of future interventions, both lifestyle and pharmacologic, to treat fatigue in older adults. TRIAL REGISTRATION: ClinicalTrials.gov NCT05484661; https://www.clinicaltrials.gov/study/NCT05484661. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52199.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.202
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
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 head0.084
GPT teacher head0.496
Teacher spread0.412 · 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 designRandomized trial
Domainnot available
GenreProtocol

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

Citations7
Published2023
Admission routes1
Has abstractyes

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