Evidence map›Paper›PMID 37990710›Full record

ArticleObesity pillars2022

Effect of GLP-1 agonists on weight loss in patients with polycystic ovary syndrome and obesity: A single-center study.

Dushyanth Srinivasan, Holly F Lofton

Open access · diamondAbstract read
In one paragraph

Article in Obesity pillars, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.5field-weighted citation impact, top 19% of its field
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

5 citing papers in PubMed, 8 citations in OpenAlex.

  1. Review
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  4. Observational
  5. Review
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 at 1 institution in 1 country.

Dushyanth SrinivasanDivision of Hospitalist Medicine, Department of Medicine, NYU Grossman School of Medicine, New York, NY, USA.
Holly F LoftonGeneral Internal Medicine, Department of Medicine, NYU Grossman School of Medicine, New York, NY, USA.
New York University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Weight loss of >5% in patients with polycystic ovary syndrome and obesity (PCOS-O) is believed to improve underlying drivers of the syndrome. Weight loss facilitated by GLP-1 agonists in patients with PCOS-O is not well characterized. In this single-center retrospective study, we determined weight loss in patients with PCOS-O with GLP-1 monotherapy versus metformin. Methods: In this brief report, electronic records of 183 adult patients with PCOS-O were reviewed between January 2020 and April 2021. We identified 12 and 19 patients that were treated with metformin and GLP-1 monotherapy respectively. One patient in each cohort had diabetes mellitus. Weights were reviewed at baseline (prior to therapy initiation) and at six-month follow-up. We analyzed change in weight from baseline and proportion with >5% and 10% weight loss using Fisher exact Results: Baseline characteristics were similar between metformin (n = 12) and GLP-1 (n = 19) cohorts with the exception of mean days on medication. Following six months of treatment, mean weight loss was 4.9 kg (4.8%) and 9.1 kg (9.8%) in the metformin and GLP-1 cohorts (p = 0.13) respectively. Similar trends were seen in BMI with reductions of 1.8 kg/m Conclusion: Our study shows improvements in weight with GLP-1 monotherapy versus metformin as demonstrated by overall weight loss and proportion of patients achieving >5% weight loss. Further prospective randomized controlled studies are needed to establish GLP-1 weight loss efficacy in patients with PCOS-O and clinically related outcomes.

Identifiers

PMID37990710
PMCPMC10661916
OpenAlexW4223548520

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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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.