Evidence mapPaperPMID 41501827Full record

ArticleBMC medicine2026

Urea cycle modulation by combined SGLT2 inhibitors and metformin.

Makoto Harada, Jonathan Adam, Siyu Han, Mengya Shi, Jianhong Ge, Jutta Lintelmann, Alexander Cecil, Sven Zukunft, Cornelia Prehn, Michael Witting and 11 more

Abstract read
In one paragraph

Article in BMC medicine, 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

21 authors.

Makoto HaradaInstitute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Jonathan AdamInstitute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Siyu HanInstitute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Mengya ShiInstitute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Jianhong GeInstitute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Jutta LintelmannMetabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Alexander CecilMetabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Sven ZukunftMetabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Cornelia PrehnMetabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Michael WittingMetabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Markus F ScheererInstitute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Susanne NeschenInstitute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Martin IrmlerInstitute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Johannes BeckersGerman Center for Diabetes Research (DZD), München-Neuherberg, Germany.
Jerzy AdamskiInstitute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Daniel TeupserInstitute of Laboratory Medicine, University Hospital, LMU Munich, Munich, Germany.
Birgit LinkohrInstitute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Christian GiegerInstitute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Martin Hrabě de AngelisGerman Center for Diabetes Research (DZD), München-Neuherberg, Germany.
Annette PetersGerman Center for Diabetes Research (DZD), München-Neuherberg, Germany.
Rui Wang-SattlerInstitute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. rui.wang-sattler@helmholtz-munich.de.

Funding

Bavarian State Ministry of Health, Care and Prevention DigiMed BayernGerman Federal Ministry of Education and Research DZDGerman Federal Ministry of Education and Research (BMBF) KORAInnovative Medicines Initiative 2 No. 821508 (CARDIATEAM)
6 · The paper itself

Abstract

backgroundSodium-glucose co-transporter 2 inhibitors (SGLT2i), when combined with metformin (COMBI), offer multi-organ protective effects in patients with type 2 diabetes (T2D), particularly those at high risk of cardiovascular or renal complications. However, the underlying molecular mechanisms remain poorly understood.

methodsWe profiled 303 targeted serum metabolites in 1494 participants of the KORA study, including T2D patients treated with COMBI therapy, metformin monotherapy, or no glucose-lowering medication. Additionally, metabolomic profiling was quantified on seven tissues (plasma, liver, adrenal glands, adipose tissue, testis, lung, and cerebellum), and related hepatic transcripts were evaluated in 40 mice. Multivariable linear regression analyses, adjusted for age, sex, BMI, lifestyle, glycemic, and cardiovascular risk factors, were applied to human data; tissue-specific regression analyses were conducted for murine samples. Identified metabolites were further investigated using biochemical pathway analyses and literature review.

resultsCOMBI therapy was associated with significant changes in metabolite profiles. In humans, 10 metabolites were significantly altered compared to metformin monotherapy. In mice, 82 altered metabolites were identified in plasma, 52 in liver, 30 in adrenal glands, 12 in adipose tissue, seven in testis, seven in lung, and six in cerebellum. COMBI therapy lowered threonine concentrations in both human serum and murine plasma but raised threonine, glycine, and urea cycle metabolites (citrulline, asymmetric dimethyl arginine (ADMA), and ornithine) in murine liver. This was accompanied by enhanced hepatic expression of Slc38a2, a threonine transporter gene. In humans, urea cycle metabolites correlated strongly with the fibrosis-4 index, a marker of liver fibrosis. Additionally, COMBI therapy elevated ketone body markers, such as hydroxybutyrylcarnitine, across murine liver, plasma, adrenal glands, adipose tissue, and testis.

conclusionsCOMBI therapy modulates amino acid metabolism, the urea cycle, and ketone body production, suggesting potential mechanisms underlying its protective effects against liver fibrosis and male subfertility. These findings provide novel insights into the systemic metabolic actions of COMBI therapy and highlight its translational potential to improve clinical outcomes in T2D patients.

Indexed as

Diabetes Mellitus, Type 2Hypoglycemic AgentsMetforminSodium-Glucose Transporter 2 InhibitorsUreaAgedAnimalsDrug Therapy, CombinationFemaleHumansMaleMiceMiddle AgedHypoglycemic AgentsMetforminSodium-Glucose Transporter 2 InhibitorsUreaFibrosis-4 indexHydroxybutyrylcarnitine (C4-OH)Liver fibrosisMale subfertilityMetabolic dysfunction-associated steatotic liver disease (MASLD)MetforminSodium-glucose co-transporter 2 inhibitors (SGLT2i)ThreonineType 2 diabetesUrea cycle

Identifiers

PMID41501827
PMCPMC12849117

What Socratic holds

Texttitle and abstract
LicenceCC BY
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.