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Record W4220710977 · doi:10.1002/jcsm.12954

Mapping ongoing nutrition intervention trials in muscle, sarcopenia, and cachexia: a scoping review of future research

2022· review· en· W4220710977 on OpenAlexafffund
Camila E. Orsso, Montserrat Montes‐Ibarra, Merran Findlay, Barbara S. van der Meij, M.A.E. de van der Schueren, Francesco Landi, Alessandro Laviano, Carla M. Prado

Bibliographic record

VenueJournal of Cachexia Sarcopenia and Muscle · 2022
Typereview
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsSarcopeniaMedicineClinical trialPsychological interventionCachexiaContext (archaeology)Randomized controlled trialClinical nutritionPhysical therapyIntensive care medicineGerontologyCancerInternal medicineNursingBiology

Abstract

fetched live from OpenAlex

Muscle loss alone, or in the context of sarcopenia or cachexia, is a prevalent condition and a predictor of negative outcomes in aging and disease. As adequate nutrition is essential for muscle maintenance, a growing number of studies has been conducted to explore the role of specific nutrients on muscle mass or function. Nonetheless, more research is needed to guide evidence-based recommendations. This scoping review aimed to compile and document ongoing clinical trials investigating nutrition interventions as a strategy to prevent or treat low muscle mass or function (strength and physical performance), sarcopenia, or cachexia. ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform were searched up to 21 April 2021 for planned and ongoing trials. Randomized controlled trials with ≥20 participants per arm were included based on intent to explore the effects of nutrition interventions on muscle-related outcomes (i.e. muscle mass or strength, physical performance, or muscle synthesis rate) in both clinical and non-clinical conditions (i.e. aging). Two reviewers independently screened records for eligibility, and a descriptive synthesis of trials characteristics was conducted. A total of 113 trials were included in the review. Most trials (69.0%) enroll adults with clinical conditions, such as cancer (19.5%), obesity and metabolic diseases (16.8%), and musculoskeletal diseases (10.7%). The effects of nutrition interventions on age-related muscle loss are explored in 31% of trials. Although nutrition interventions of varied types were identified, food supplements alone (48.7%) or combined with dietary advice (11.5%) are most frequently reported. Protein (17.7%), amino acids (10.6%), and β-hydroxy-β-methylbutyrate (HMB, 6.2%) are the top three food supplements' nutrients under investigation. Primary outcome of most trials (54.9%) consists of measures of muscle mass alone or in combination with muscle strength and/or performance (as either primary or secondary outcomes). Muscle strength and physical performance are primary outcomes of 38% and 31.9% of the trials, respectively. These measurements were obtained using a variety of techniques. Only a few trials evaluate muscle synthesis rate either as a primary or secondary outcome (5.3%). Several nutrition studies focusing on muscle, sarcopenia, and cachexia are underway and can inform future research in this area. Although many trials have similar type of interventions, methodological heterogeneity may challenge study comparisons, and future meta-analyses aiming to provide evidence-based recommendations. Upcoming research in this area may benefit from guidelines for the assessment of therapeutic effects of nutrition interventions.

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)0.127
metaresearch head score (Gemma)0.317
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.873
Threshold uncertainty score0.672

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1270.317
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0200.015
Bibliometrics0.0290.030
Science and technology studies0.0020.003
Scholarly communication0.0120.012
Open science0.0050.006
Research integrity0.0070.004
Insufficient payload (model declined to judge)0.0090.002

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 head0.328
GPT teacher head0.526
Teacher spread0.198 · 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 designSystematic review
DomainMethods
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

Citations66
Published2022
Admission routes2
Has abstractyes

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