ReviewMolecular metabolism2025
Are we giving too much weight to lean mass loss?
Review in Molecular metabolism, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Body Composition and Lifestyle Interventions in Pharmacological Treatment of Acquired Hypothalamic Obesity: A Systematic Review of Randomized Controlled Trials.Obesity (Silver Spring, Md.) · 2026Pooled it
- Effects of Incretin-Based Therapies, Diet and Exercise Interventions, and Bariatric Surgery on Fat-Free Mass in Adults With Overweight or Obesity: A Systematic Review and Meta-Analysis.Diabetes, obesity & metabolism · 2026Pooled it
- The hallmarks of skeletal muscle health.Nature metabolism · 2026Review
- Review
- GLP-1 receptor agonists at immune checkpoint inhibitor initiation with immune-related and supportive-care outcomes in patients with cancer and overweight or obesity without diabetes: a target trial emulation.Cancer immunology, immunotherapy : CII · 2026Article
- Association between creatinine-to-body weight ratio and arterial stiffness: a cross-sectional and longitudinal study in the Chinese population.Frontiers in cardiovascular medicine · 2026Article
- Recent Articles-Skeletal Muscle and Other Topics in Diabetes.Journal of diabetes · 2025Article
- Genetically Predicted Muscle Mass and Function in Relation to Deep Vein Thrombosis: A Two-step Mendelian Randomization Study Highlighting the Mediating Role of BMI.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The global rise in obesity has underscored the critical importance of body composition, particularly the balance between fat mass and lean mass, in determining health outcomes. While excess fat mass is a well-established risk factor for numerous chronic diseases and reduced longevity, lean mass preservation has been widely considered essential for mitigating fall risk and maintaining functional independence. Recent advances in incretin-based weight loss therapies have shown remarkable efficacy in reducing body weight but have raised concerns about the concomitant loss of lean mass. However, emerging evidence suggests that muscle quality - rather than absolute muscle mass - is a more robust predictor of functional capacity and all-cause mortality. Intriguingly, these therapies may enhance muscle quality even while promoting lean mass loss, offering a nuanced perspective on their impact. This review aims to synthesize current evidence on body composition, muscle quality, and weight loss therapies to guide clinicians in tailoring weight loss strategies that optimize both metabolic health and patient outcomes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.