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Record W3176144478 · doi:10.1093/ptj/pzab162

Exercise Training to Decrease Ectopic Intermuscular Adipose Tissue in Individuals With Chronic Diseases: A Systematic Review and Meta-Analysis

2021· review· en· W3176144478 on OpenAlexaff
Mauro Tuñón‐Suárez, Álvaro Reyes, Rodrigo Godoy-Órdenes, Nicolás Quezada, Marcelo Flores‐Opazo

Bibliographic record

VenuePhysical Therapy · 2021
Typereview
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsCanadian Society for Exercise Physiology
Fundersnot available
KeywordsMedicineMeta-analysisSubgroup analysisRandomized controlled trialAerobic exercisePublication biasPopulationPhysical therapyPsychological interventionInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this study was to evaluate the effect of exercise training on ectopic fat within skeletal muscle (intermuscular adipose tissue [IMAT]) in adult populations with chronic diseases. METHODS: A literature search was conducted in relevant databases to identify randomized controlled trials (RCTs) from inception. Selected studies examined the effect of aerobic training (AET), resistance training (RT), or combined training (COM) on IMAT as assessed by noninvasive magnetic resonance imagery or computed tomography. Eligibility was determined using Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Data extraction was performed using the population (P), intervention (I), comparison (C), outcome (O), timing (T), and settings (S) approach. Methodological quality was analyzed by the Cochrane risk of bias assessment. Standardized effect sizes (ES) with 95% CIs were calculated. Heterogeneity among studies was quantified using I2 statistics. Subgroup and meta-regression analyses were included. Risk of publication bias was examined by the Egger regression test. RESULTS: Nineteen RCTs included 962 adults (628 women; age range = 34.8-93.4 years) with different chronic conditions that participated in 10 AET, 12 RT, and 5 COM interventions. The quality of studies was deemed moderate. Overall, the effect of exercise on IMAT was small (ES = 0.24; 95% CI = 0.10 to 0.37; heterogeneity I2 = 0.0%) compared with no exercise or control interventions. Moderate-intensity AET and COM had larger ES compared with RT regardless of intensity. This effect was associated with exercise-induced body weight and fat mass losses. Subgroup analysis revealed larger ES in studies assessing IMAT by magnetic resonance imagery compared with computed tomography in adults and middle-aged individuals compared with older adults and in participants who were HIV+ compared with other diagnoses. CONCLUSION: AET and COM of moderate intensity reduce IMAT in individuals from 18 to 65 years of age who are affected by chronic diseases. This effect is associated with exercise-induced body weight and fat mass losses. In older individuals who are frail and patients at an advanced disease stage, exercise may result in a paradoxical IMAT accumulation. IMPACT: In people affected by chronic conditions, IMAT accumulation induces muscle mass and strength losses, decline in physical performance, inflammation, and metabolic alterations. The present study shows that moderate-intensity AET or COM prevent or reduce IMAT in these conditions. Thus, the deleterious effect of IMAT on skeletal muscle homeostasis may be reverted by a properly prescribed exercise regime. The findings of the present systematic review are critical for physical therapists and health care professionals because they emphasize the therapeutic role of exercise and provide recommendations for exercise prescription that ultimately may have a positive impact on the course of disease, recovery of functionality, and independence. LAY SUMMARY: Aerobic exercise (eg, walking/jogging, cycling) alone or combined with resistance exercise (strength training with free-weights, kettle bells, or gym equipment) is effective in reducing fat streaks that infiltrate muscles and impair muscle function and growth, particularly in adults affected by chronic diseases.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.635
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0100.001
Bibliometrics0.0000.002
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 head0.089
GPT teacher head0.413
Teacher spread0.324 · 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.

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

Citations28
Published2021
Admission routes1
Has abstractyes

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