Evidence map›Paper›PMID 42465874›Full record

ArticlemedRxiv : the preprint server for health sciences2026

GLP Medications and Severe Post-COVID-19 Outcomes Among Individuals with Type 2 Diabetes Mellitus.

Zachary Butzin-Dozier, Lin-Chiun Wang, Yunwen Ji, Manav Kumar, A Jerrod Anzalone, Eric Hurwitz, Rena C Patel, Ariana Budhihartanto, John B Buse, Steven Johnson and 2 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. 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

12 authors.

Zachary Butzin-DozierStanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0001-6419-0008
Lin-Chiun WangSchool of Public Health, University of California, Berkeley, Berkeley, CA, USA.
Yunwen JiSchool of Public Health, University of California, Berkeley, Berkeley, CA, USA.
Manav KumarSchool of Public Health, University of California, Berkeley, Berkeley, CA, USA.
A Jerrod AnzaloneUniversity of Nebraska Medical Center, Omaha, NE, USA.
Eric HurwitzUniversity of North Carolina at Chapel Hill, Chapel Hill, NC, USA.ORCID 0000-0002-6581-7754
Rena C PatelUniversity of Alabama at Birmingham, Birmingham, AL, USA.
Ariana BudhihartantoSchool of Public Health, University of California, Berkeley, Berkeley, CA, USA.
John B BuseUniversity of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Steven JohnsonUniversity of Minnesota, Minneapolis, MN, USA.
Carolyn BramanteUniversity of Minnesota, Minneapolis, MN, USA.ORCID 0000-0001-5858-2080
Rachel WongRenaissance School of Medicine, Stony Brook University, New York, NY, USA.

Funding

Applying a Targeted Machine Learning and Causal Inference Approach to Analyzing Long-Term Sequelae of COVID-19 Infection Through the National COVID Cohort Collaborative.K01AI182501 · UNIVERSITY OF CALIFORNIA BERKELEY · 2025 to 2025
$56k
NIAID NIH HHS K01 AI182501
6 · The paper itself

Abstract

Background: Glucagon-like peptide-1 receptor agonist-based therapies (GLP) have recently emerged as promising treatments across a wide range of health conditions. These medications may have protective effects against severe long-term consequences of COVID-19 by promoting weight loss, exerting antihyperglycemic and anti-inflammatory effects, and providing cardiovascular and endothelial protection. Methods: We evaluated electronic health record data from a retrospective cohort of individuals in the National Clinical Cohort Collaborative. We included individuals with type 2 diabetes mellitus and comorbid COVID-19 who were prescribed either GLP (treatment) or a sodium-glucose co-transporter 2 inhibitor (SGLT2i) and subsequently developed acute COVID-19 between October 1, 2021, and April 1, 2023. We compared the 12-month cumulative incidence of mortality and Long COVID (Long COVID diagnosis and probable Long COVID via computational phenotype) between groups. We applied targeted maximum likelihood estimation to compare outcome risks by exposure status, controlling for covariates of interest. Results: We analyzed data from 14,215 individuals with COVID-19 and comorbid type 2 diabetes (mean age, 60 years; mean BMI, 37). Compared to SGLT2i, a prescription for GLP medication was associated with a lower risk of mortality (adjusted risk ratio [aRR] 0.71; 95% CI 0.53, 0.95), but not Long COVID diagnosis (aRR 1.01; 95% CI 0.80, 1.27) or probable Long COVID (aRR 0.94; 95% CI 0.88, 1.01). Conclusions: We found that among individuals with type 2 diabetes and comorbid COVID-19, a prescription for GLP vs. SGLT2i medications was associated with a lower risk of mortality, but not Long COVID.

Identifiers

PMID42465874
PMCPMC13370558

What Socratic holds

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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 Socratic graph.