Evidence map›Paper›PMID 42897521›Full record

ArticleBMC endocrine disorders2026

Current status of anti-obesity medications and performance, an EHR based survey.

Xiaoyang Ruan, Rui Li, Liwei Wang, Shuyu Lu, Andrew Wen, Murali Sameer, Hongfang Liu

Abstract read
In one paragraph

Article in BMC endocrine disorders, 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

7 authors.

Xiaoyang RuanDepartment of Quantitative and Systems Health Sciences, Dell Medical School, University of Texas at Austin, 1501 Red River St, Austin, TX, 78712, USA.
Rui LiDepartment of Health Data Science and AI, McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
Liwei WangDepartment of Quantitative and Systems Health Sciences, Dell Medical School, University of Texas at Austin, 1501 Red River St, Austin, TX, 78712, USA.
Shuyu LuDepartment of Health Data Science and AI, McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
Andrew WenDepartment of Quantitative and Systems Health Sciences, Dell Medical School, University of Texas at Austin, 1501 Red River St, Austin, TX, 78712, USA.
Murali SameerDepartment of Surgery, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
Hongfang LiuDepartment of Quantitative and Systems Health Sciences, Dell Medical School, University of Texas at Austin, 1501 Red River St, Austin, TX, 78712, USA. Hongfang.Liu@austin.utexas.edu.

Funding

Semi-structured Information Retrieval in Clinical Text for Cohort IdentificationR01LM011934 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI HERSH, WILLIAM R, LIU, HONGFANG · 2014 to 2025
$5.1M
EMED: An Ethical Mixture-of-Experts Digital Twin Framework for Medical Device SurveillanceOT2OD038051 · OD · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Hanjie Chen, HONGFANG LIU · 2025 to 2026
$4.0M
Predictive modeling of Alzheimer's Disease Related Dementias (ADRD) in the elderly population empowered by knowledge-driven data miningR01HG012748 · NHGRI · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI HONGFANG LIU · 2023 to 2026
$3.1M
Facilitate Observational Studies of Alzheimer's Disease and Alzheimer's Disease-Related Dementias Using Ontology and Natural Language ProcessingR01AG072799 · NIA · YALE UNIVERSITY · PI HONGFANG LIU, Cui Tao · 2025 to 2026
$1.7M
POI-KB: Design and Development of a Knowledgebase for Accelerating Perioperative Organ Injury Research and TranslationR24HL180372 · NHLBI · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Holger K. Eltzschig, HONGFANG LIU · 2025 to 2026
$1.1M
Cancer Prevention and Research Institute of Texas RR230020FDA HHS U01 FD008720NHGRI NIH HHS R01 HG012748NHGRI NIH HHS R01HG012748NHLBI NIH HHS R24 HL180372NHLBI NIH HHS R24HL180372NIA NIH HHS R01 AG072799NIA NIH HHS R01AG072799NIBIB NIH HHS OT2OD038051NIH HHS OT2 OD038051NIH HHS R01LM011934NLM NIH HHS R01 LM011934U.S. Food and Drug Administration U01FD008720
6 · The paper itself

Abstract

backgroundWhile randomized clinical trials (RCTs) establish the efficacy of anti-obesity medications (AOMs), their real-world effectiveness, inter-individual variability, and weight regain post discontinuation remain poorly characterized across diverse, large scale populations.

methodsElectronic health records with pharmacy dispensation of obesity or overweight individuals were retrieved from local UT-Physician and nationwide EPIC COSMOS databases. AOMs were analyzed for short to long-term effectiveness on obesity relevant metrics.

resultsFrom the two cohorts, 71,318 (11·7%) and 1 M (27·9%) individuals documented AOM exposures, respectively, with about 70% individuals experiencing more than one treatment session. Tirzepatide and Semaglutide outperformed other AOMs and were associated with up to 9·6[IQR:11]% and 6·2[IQR:11]% weight loss, respectively, and both improved blood pressure, HbA1c, and lipid profile. The median exposure durations were approximately 4·6 months for the two GLP-1/GIP AOMs, far short of the 72-week duration typically required for peak therapeutic benefit in RCTs. All AOMs showed substantial inter-individual variability in responses, regardless of exposure length. Substantial differences were found between RCT and real-world performance. For example, while Tirzepatide led both cohorts, nearly 50% and 25% of individuals didn't achieve the 10% and 5% weight loss target respectively, after a minimum 72 weeks exposure, highlighting a notable effectiveness gap. Critically, we identified that Metformin co-therapy acted as a modifier of the 'rebound effect,' significantly attenuating (median 1·9 - 2·6%, p < 0.0001) weight regain compared to semaglutide or tirzepatide monotherapy.

conclusionOur findings confirm real-world AOM effectiveness and reveal challenges of the 'one-size-fits-all' approach of current pharmacotherapy. The marked inter-individual variability suggests that stratified, personalized pharmacotherapy is essential to transform transient weight loss into sustainable metabolic health. CLINICAL TRIAL: Not applicable.

Indexed as

Anti-Obesity AgentsElectronic Health RecordsObesityFemaleGlucagon-Like PeptidesHumansMaleSemaglutideSurveys and QuestionnairesTirzepatideWeight LossAnti-Obesity AgentsGlucagon-Like PeptidesSemaglutideTirzepatideAnti-obesity medicationBMIBody weightEHR

Identifiers

PMID42897521
PMCPMC13681875

What OpenQuestion 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 OpenQuestion graph.