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Record W4285076960 · doi:10.1002/jcsm.13030

An umbrella review of systematic reviews of β‐hydroxy‐β‐methyl butyrate supplementation in ageing and clinical practice

2022· review· en· W4285076960 on OpenAlexafffund
Stuart M. Phillips, Kyle J. Lau, Alysha C. D’Souza, Everson Araújo Nunes

Bibliographic record

VenueJournal of Cachexia Sarcopenia and Muscle · 2022
Typereview
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsMedicineClinical PracticeAgeingIntensive care medicineSystematic reviewGerontologyMEDLINEBioinformaticsFamily medicineInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

The compound β-hydroxy-β-methyl butyrate (HMB) is proposed to increase or mitigate the loss of skeletal muscle and improve muscle function. We undertook a review of systematic reviews of HMB supplementation to promote gains or mitigate muscle loss in ageing and clinical populations. Following PRISMA guidelines, we searched for systematic reviews reporting the effect of HMB in our target populations. Dual-energy X-ray absorptiometry (DXA) measured lean soft-tissue mass (LSTM) was accepted as a proxy for muscle. We identified 15 systematic reviews that met our inclusion criteria, which were independently evaluated. The methodological quality of the reviews was assessed using A Measurement Tool to Assess Systematic Reviews (AMSTAR), and standardized effectiveness statements were generated. Five of 15 studies found some evidence that HMB augmented LSTM; the remaining 10 studies reported some evidence favouring no difference (6/10 studies) or insufficient evidence to determine an effect (4/10 studies). Of the 12 studies that evaluated strength, 4/12 found some evidence, 5/12 found some evidence of no effect with one article finding some evidence in favour of patients in peri-hospitalized and no evidence for those that are community-dwelling, 4/12 had insufficient evidence to determine an effect, and 1/12 had insufficient evidence. No]study reported a positive effect of HMB on physical function; however, 2/10 studies found some evidence favouring no effect, and 7/10 studies reported insufficient evidence to determine an effect. The effectiveness of HMB supplementation in augmenting LSTM was heterogeneous, with most reviews finding no effect or inconclusive evidence to determine an effect. Most reviews concluded that HMB supplementation did not affect strength outcome measures or studies were inconclusive. The current evidence is insufficient to assess the impact of HMB supplementation on functional outcome measures. Our analysis shows minor, inconsistent support for HMB as part of an oral nutritional supplement or as a stand-alone supplement (or combined with other amino acids) to increase or promote retention of LSTM, improve strength, and no evidence that it improves physical function in older persons or clinical populations.

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 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)0.049
metaresearch head score (Gemma)0.173
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score0.258

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.173
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0160.013
Bibliometrics0.0300.032
Science and technology studies0.0020.003
Scholarly communication0.0070.010
Open science0.0040.006
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0140.003

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 head0.191
GPT teacher head0.488
Teacher spread0.297 · 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.

Study designMeta-analysis
Domainnot available
GenreReview

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

Citations37
Published2022
Admission routes2
Has abstractyes

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