Evidence map›Paper›PMID 42539052›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Early identification of suboptimal responders to metformin in type 2 diabetes using long-term real-world HbA1c trajectories.

Eunsol Yang, Andrew Riselli, Fei Xu, Sneha B Sridhar, Mark Kvale, Kathleen M Giacomini, Monique M Hedderson, Sook Wah Yee, Rada M Savic

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

9 authors.

Eunsol YangDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco, 1700 Fourth Street, San Francisco, CA 94158, United States of America.ORCID 0000-0003-2581-349X
Andrew RiselliDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco, 1700 Fourth Street, San Francisco, CA 94158, United States of America.
Fei XuDivision of Research, Kaiser Permanente Northern California, 4480 Hacienda Drive, Pleasanton, CA 94588, USA.
Sneha B SridharDivision of Research, Kaiser Permanente Northern California, 4480 Hacienda Drive, Pleasanton, CA 94588, USA.
Mark KvaleInstitute for Human Genetics, University of California, San Francisco, 513 Parnassus Avenue, San Francisco, CA 94143, United States of America.
Kathleen M GiacominiDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco, 1700 Fourth Street, San Francisco, CA 94158, United States of America.
Monique M HeddersonDivision of Research, Kaiser Permanente Northern California, 4480 Hacienda Drive, Pleasanton, CA 94588, USA.
Sook Wah YeeDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco, 1700 Fourth Street, San Francisco, CA 94158, United States of America.
Rada M SavicDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco, 1700 Fourth Street, San Francisco, CA 94158, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: Metformin remains the primary treatment for type 2 diabetes, yet over 40% of patients fail to maintain glycaemic control. We aimed to identify patients unlikely to respond to metformin prior to treatment initiation and to evaluate whether on-treatment management can improve glycaemic outcomes in suboptimal responders, informing early treatment decisions. Materials and Methods: We analyzed 59,881 longitudinal HbA1c measurements from 7,105 patients with type 2 diabetes receiving metformin monotherapy using real-world electronic health records from Kaiser Permanente Northern California with up to six years of follow-up. We integrated demographic, clinical, genetic, and pharmacological factors to characterize metformin responder phenotypes and quantify the impact of adherence and weight control on time to glycaemic failure. Results: Three distinct trajectory-based phenotypes were identified: good (63.6%), poor (8.9%), and non-responders (27.5%). Poor responders initially achieved glycaemic targets but lost control within 2.5 years, while non-responders showed minimal HbA1c reduction and failed within 1 year. Five baseline factors-HbA1c, age at diagnosis, body mass index, sex, and estimated glomerular filtration rate-classified phenotypes with good discrimination (area under the receiver operating characteristic curve = 0.84). Incorporating on-treatment HbA1c further enhanced identification of non-responders. Among suboptimal responders, weight control and improved adherence delayed glycaemic failure by approximately 7 months; however, eventual glycaemic failure remained likely. Conclusions: We characterized three clinically relevant metformin responder phenotypes and showed that suboptimal responders can be identified early using baseline features. Poor and non-responders are unlikely to achieve durable glycaemic control with metformin alone and may require alternative treatment strategies.

Indexed as

HbA1c trajectoriesMetforminPhenotype stratificationReal-world dataTreatment responseType 2 diabetes

Identifiers

PMID42539052
PMCPMC13419552

What Socratic 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 Socratic graph.