In one paragraphArticle in Nature communications, 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
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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
10 authors.
Maxwell SalvatoreThe Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA. maxwell.salvatore@pennmedicine.upenn.edu.ORCID http://orcid.org/0000-0002-3659-1514 Bingyu ZhangThe Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA.
Huilin TangThe Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA.
Yiwen LuThe Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, PA, USA.
Anastassia AmaroDivision of Endocrinology, Diabetes and Metabolism, University of Pennsylvania, Philadelphia, PA, USA.
Funding
Technology to Empower Changes in Health (TECH) Network Participant Technologies CenterU24OD023176 · OD · SCRIPPS RESEARCH INSTITUTE, THE · PI TOPOL, ERIC JEFFREY · 2016 to 2022
$204.7MPrecision Medicine Initiative Cohort Program BiobankU24OD023121 · OD · MAYO CLINIC ROCHESTER · PI CEKANOVA, MARIA, CICEK, MINE · 2016 to 2024
$185.5MEnhancing All of Us Data Resources for Nutrition Precision Health: the All of Us Data and Research CenterU2COD023196 · OD · VANDERBILT UNIVERSITY MEDICAL CENTER · PI GLAZER, DAVID, HARRIS, PAUL A. · 2016 to 2022
$143.7MAdaptive Platform for Personalized EngagementU24OD023163 · OD · VIGNET, INC. · PI JAIN, PRADUMAN · 2017 to 2020
$102.6MUniversity of Arizona-Banner Health All of Us Research Program OT2OD026549 · OD · UNIVERSITY OF ARIZONA · PI MORENO, FRANCISCO A, REIMAN, ERIC MICHAEL · 2018 to 2023
$78.9MCalifornia Precision Medicine Research Program ConsortiumOT2OD026552 · OD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ANTON-CULVER, HODA A, OHNO-MACHADO, LUCILA · 2018 to 2023
$73.4MAll of Us PennsylvaniaOT2OD026554 · OD · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI REIS, STEVEN E, VISWESWARAN, SHYAM · 2018 to 2023
$72.1MNew York City Consortium for Precision MedicineOT2OD026556 · OD · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BIER, LOUISE E, GHARAVI, ALI G · 2018 to 2023
$67.3MSouthEast Enrollment Center (SEEC) OT2OD026551 · OD · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI CARRASQUILLO, OLVEEN, COLON, VIVIAN · 2018 to 2023
$62.8MSouthern All of Us NetworkOT2OD026548 · OD · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI FOUAD, MONA N., KORF, BRUCE R · 2018 to 2023
$60.5MIllinois Precision Medicine Consortium OT2OD026557 · OD · NORTHWESTERN UNIVERSITY AT CHICAGO · PI AHSAN, HABIBUL, ARGOS, MARIA · 2018 to 2023
$60.5MThe New England Precision Medicine Consortium of the All of Us Research ProgramOT2OD026553 · OD · MASSACHUSETTS GENERAL HOSPITAL · PI CLARK, CHERYL RENEE, KARLSON, ELIZABETH W · 2018 to 2023
$58.8MDivision of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID) R21AI167418NCATS NIH HHS U01 TR003709NEI NIH HHS R21 EY034179NHLBI NIH HHS R01 HL169458NIAID NIH HHS R21 AI167418NIA NIH HHS R01 AG073435NIA NIH HHS R56 AG074604NIA NIH HHS RF1 AG077820NIDDK NIH HHS R01 DK128237NIH HHS OT2 OD023205NIH HHS OT2 OD023206NIH HHS OT2 OD025276NIH HHS OT2 OD025277NIH HHS OT2 OD025315NIH HHS OT2 OD025337NIH HHS OT2 OD026548NIH HHS OT2 OD026549NIH HHS OT2 OD026550NIH HHS OT2 OD026551NIH HHS OT2 OD026552NIH HHS OT2 OD026553NIH HHS OT2 OD026554NIH HHS OT2 OD026555NIH HHS OT2 OD026556NIH HHS OT2 OD026557NIH HHS U24 OD023121NIH HHS U24 OD023163NIH HHS U24 OD023176NIH HHS U2C OD023196NIMH NIH HHS U24 MH136069NLM NIH HHS R01 LM013519NLM NIH HHS R01 LM014344U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences (NCATS) U01TR003709U.S. Department of Health & Human Services | NIH | National Eye Institute (NEI) R21EY034179U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases) R01DK128237U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) R01AG073435U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) R56AG074604U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) RF1AG077820U.S. Department of Health & Human Services | NIH | U.S. National Library of Medicine (NLM) 1R01LM014344U.S. Department of Health & Human Services | NIH | U.S. National Library of Medicine (NLM) R01LM013519
6 · The paper itselfAbstract
Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are increasingly prescribed for type 2 diabetes (T2D) and weight management, yet their real-world health impacts remain understudied. Using a retrospective cohort design with electronic health record data from 17,267 adults with type 2 diabetes in the All of Us Research Program, we conduct propensity score-matched phenome-wide association studies comparing diagnoses following GLP-1 RA prescription (including semaglutide-specific analyses) to those following sodium-glucose cotransporter-2 inhibitor (SGLT2i) and dipeptidyl peptidase-4 inhibitor (DPP4i) prescriptions between January 2018 and October 2023. We employ both intention-to-treat and per-protocol Cox proportional hazards models alongside restricted mean survival time analyses evaluating up to 974 phenotypes. We identify multiple phenome-wide significant and suggestive associations, including for cardiovascular, genitourinary, dental, and metabolic outcomes. Compared to SGLT2i, semaglutide demonstrates reduced risk for genitourinary infections in women (e.g., candidiasis of vulva and vagina (per-protocol hazard ratio 0.31, 95% confidence interval (0.17-0.55)). Compared to DPP4i, GLP-1 RAs are associated with reduced risk of diseases of hard tissues of teeth (0.45 (0.33-0.61)). Time-to-event analyses reveal modest delays for key diagnoses. These findings underscore differences in downstream diagnostic associations across second-line T2D therapies and highlight semaglutide's distinct profile, with implications for clinical decision-making and personalized prescribing.
Indexed as
Diabetes Mellitus, Type 2Hypoglycemic AgentsPhenomicsAgedDipeptidyl-Peptidase IV InhibitorsElectronic Health RecordsFemaleGlucagon-Like Peptide-1 Receptor AgonistsGlucagon-Like PeptidesHumansMaleMiddle AgedPhenotypeRetrospective StudiesSemaglutideSodium-Glucose Transporter 2 InhibitorsDipeptidyl-Peptidase IV InhibitorsGlucagon-Like Peptide-1 Receptor AgonistsGlucagon-Like PeptidesHypoglycemic AgentsSemaglutideSodium-Glucose Transporter 2 Inhibitors
Identifiers
PMID42185271
PMCPMC13388941
What OpenQuestion holds
Texttitle and abstract
LicenceCC BY-NC-ND
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