Evidence map›Paper›PMID 41646761›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Multi-molecular scores map process-specific polygenic diabetes risk to atherosclerosis, cardiometabolic diseases, and vascular complications.

Hui Li, Jakub Morze, Martin Adiels, Joel Kullberg, Jordi Merino, Magdalena Sevilla-Gonzalez, Göran Bergström, Marju Orho-Melander, Stefan Söderberg, Carl Johan Östgren and 5 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

15 authors.

Hui LiDepartment of Life Sciences, SciLifeLab, Chalmers University of Technology, Gothenburg, Sweden.
Jakub MorzeDepartment of Life Sciences, SciLifeLab, Chalmers University of Technology, Gothenburg, Sweden.
Martin AdielsDepartment of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Joel KullbergDepartment of Surgical Sciences, Uppsala University, Uppsala, Sweden.
Jordi MerinoNovo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark.
Magdalena Sevilla-GonzalezDepartment of Medicine, Harvard Medical School, Boston, MA, USA.
Göran BergströmDepartment of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Marju Orho-MelanderDepartment of Clinical Sciences in Malmö, Lund University, Malmö, Sweden.
Stefan SöderbergDepartment of Public Health and Clinical Medicine, and Heart Centre, Umeå University, Umeå, Sweden.
Carl Johan ÖstgrenCentre of Medical Image Science and Visualization, Linköping University, Linköping, Sweden.
Tuuli LappalainenDepartment of Gene Technology, SciLifeLab, KTH Royal Institute of Technology, Stockholm, Sweden.
Tove FallMolecular Epidemiology, Department of Medical Sciences, Uppsala University, Uppsala, Sweden.
Miriam S UdlerDepartment of Medicine, Harvard Medical School, Boston, MA, USA.
Ida HäggströmDepartment of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden.
Clemens WittenbecherDepartment of Life Sciences, SciLifeLab, Chalmers University of Technology, Gothenburg, Sweden.ORCID 0000-0001-7792-877X

Funding

GEneration and assessment of Multi-omic informed Subtypes of Type 2 Diabetes in Diverse Populations (GEMS-T2D)U01DK140757 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI Josep Maria Mercader, KRISTINA Marie UTZSCHNEIDER · 2024 to 2026
$2.3M
Doris Duke Charitable Foundation 2022063NIDDK NIH HHS L30 DK106874NIDDK NIH HHS U01 DK140757
6 · The paper itself

Abstract

Type 2 diabetes (T2D) is etiologically heterogeneous, and process-specific polygenic scores (pPSs) only partly resolve this complexity. Here we integrate fourteen published T2D pPSs with plasma biochemistry, proteomics and NMR metabolomics in 29,425 SCAPIS participants to derive process-specific, polygenic-informed multi-molecular scores (pMMSs) that map genetic risk onto multi-omic molecular signatures. We then test the associations of these scores with subclinical coronary atherosclerosis, incident T2D, macrovascular disease, and microvascular complications in SCAPIS and 458,905 participants from UK Biobank. The novel pMMSs recapitulate the mechanistic interpretability of their underlying pPSs, yet show substantially stronger and more granular associations with molecular traits and disease risk. For example, per standard deviation, the

Identifiers

PMID41646761
PMCPMC12870590

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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.