Evidence map›Paper›PMID 41641313›Full record

ArticleAACE endocrinology and diabetes

Variable Effects of Glucagon Like Peptide-1 Receptor Agonists on Body Composition in Older Women.

Arshad Mohammed, Sneha Mishra

Abstract readCase Reports
In one paragraph

Article in AACE endocrinology and diabetes. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Arshad MohammedMayo Clinic Alix School of Medicine, Mayo Clinic College of Medicine and Science, Phoenix, Arizona.
Sneha MishraDivision of Community Internal Medicine (Mishra), Mayo Clinic, Scottsdale, Arizona.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background/Objective: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) improve glycemic control, promote weight loss, and enhance cardiovascular outcomes. However, their effects on lean mass in older adults are unclear. This case report describes how GLP-1 RAs influenced body composition in 2 older women. Case Report: We measured the body compositions of 2 older women with obesity, who were unable to lose weight through lifestyle changes alone, before and after taking GLP-1 RAs. The first patient (65 years old) received weekly subcutaneous injections of tirzepatide. She had a moderate reduction in body mass index over 4 months, with a modest reduction in fat mass and a substantial reduction in lean mass. The second patient (79 years old) received weekly subcutaneous injections of semaglutide and had a moderate reduction in body mass index over 6 months, with a substantial reduction in fat mass and a modest gain in lean mass. The patient with greater muscle loss was less physically active and tracked food intake less precisely than the patient with less muscle loss. Discussion: Differences in lean mass loss between patients may be influenced by exercise and calorie counting. These findings highlight the importance of lifestyle modifications for preserving lean mass. However, because only 2 patients were studied, the results are entirely anecdotal and therefore cannot be generalized to larger populations. Conclusion: Clinicians should consider recommending individualized exercise programs and dietary strategies for older patients taking GLP-1 RAs. Routine assessment of body composition may help guide interventions and optimize clinical outcomes.

Indexed as

body compositionGLP-1older adults

Identifiers

PMID41641313
PMCPMC12866074

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.