Scollr summary
What this paper is about
Among commonly available protein supplements, whey protein at approximately 30–35 g/d was associated with the largest gains in lean body mass when combined with RT, although this dose–response estimate is exploratory.
Full abstract
Read the full abstract
Background Protein supplementation is frequently utilized in conjunction with resistance training for the purpose of enhancing muscle mass and strength. Nevertheless, the efficacy of different protein types, dosing thresholds, and population-specific effects is debatable. Methods A systematic search was performed on PubMed, Web of Science, Scopus, SPORTDiscus, Cochrane CENTRAL, EMBASE databases through February 2026. RCTs comparing protein supplements (whey, casein, soy, pea, rice, milk, or blend) with placebo during supervised RT (≥6 weeks) were eligible. A Bayesian random-effects NMA was conducted using gemtc and JAGS. Dose–response curves were fitted with restricted cubic splines. The protocol was registered on PROSPERO (CRD420261340515). Results Thirty-six RCTs (1,552 participants; 89 trial arms) formed a fully connected network of eight nodes. For LBM, whey protein ranked first (SUCRA = 0.91; MD vs. placebo: +1.24 kg, 95% CrI 0.61–1.87), followed by milk (SUCRA = 0.78; +1.18 kg) and casein (SUCRA = 0.72; +0.92 kg). The dose–response curve for whey indicated a plateau at 30–35 g/d. In older adults (≥65 years), the superiority of whey narrowed (SUCRA = 0.89 vs. 0.92 in younger adults), while casein’s SUCRA increased from 0.68 in younger adults to 0.76 in older adults. However, this subgroup analysis was based on only nine trials, and the observed ranking changes may be unstable. Model convergence was satisfactory ( R ̂ ̂ = 1.01; ESS = 3,420), and node-splitting detected no significant inconsistency (all p > 0.10). Conclusion Among commonly available protein supplements, whey protein at approximately 30–35 g/d was associated with the largest gains in lean body mass when combined with RT, although this dose–response estimate is exploratory. Older adults may benefit from casein- or mixed-protein strategies as the advantage of whey is diminished in this population. Plant-based proteins showed smaller effect estimates than dairy-derived proteins, but the evidence certainty was low to very low and the credible intervals were wide, limiting firm conclusions. Systematic review registration Identifier: CRD420261340515.
Direct answer
What can I do from this paper page?
Use this page to scan "Dose–structure–population interactions of protein supplementation combined with resistance training on body composition: a Bayesian network meta-analysis with dose–response modeling" quickly: start with the summary and abstract, then check the authors, source, topics, and related papers. From here, open Scollr to follow Muscle metabolism and nutrition research, save the paper, or map adjacent work.
Research areas
Follow related topics
Citation
BibTeX
@article{Li2026Dose,
title = {Dose–structure–population interactions of protein supplementation combined with resistance training on body composition: a Bayesian network meta-analysis with dose–response modeling},
author = {Yunxia Li and Gang Qin and Ziyu Wang and Jinghao Zhang},
journal = {Frontiers in Nutrition},
year = {2026},
doi = {10.3389/fnut.2026.1860234},
url = {https://doi.org/10.3389/fnut.2026.1860234}
}
FAQ
Using this paper in a discovery workflow
How do I find related work for this paper?
Use the related papers and topic links on this page as starting points. In Scollr, you can also open the paper and build a literature map around its references, citing papers, and related work.
How can I keep up with new Muscle metabolism and nutrition research papers?
Follow Muscle metabolism and nutrition research in Scollr. New papers from the topic flow into a personalized feed, and you can save useful studies to revisit later.
Can I cite this paper from this page?
This page includes a static BibTeX block for Dose–structure–population interactions of protein supplementation combined with resistance training on body composition: a Bayesian network meta-analysis with dose–response modeling. Always verify the DOI, source, and publication details against the publisher record before submitting a manuscript.
Follow this research in Scollr
Follow the topics and authors behind this paper, save useful studies, and build a literature map when you are ready to go deeper.
Get the app