Evidence map›Paper›PMID 40760251›Full record

ArticleDiabetologia2025

Frequent longitudinal blood microsampling and proteome monitoring identify disease markers and enable timely intervention in a mouse model of type 1 diabetes.

Anirudra Parajuli, Annika Bendes, Fabian Byvald, Virginia M Stone, Emma E Ringqvist, Marta Butrym, Emmanouil Angelis, Sophie Kipper, Stefan Bauer, Niclas Roxhed and 2 more

Abstract read
In one paragraph

Article in Diabetologia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

12 authors.

Anirudra ParajuliDepartment of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden.ORCID http://orcid.org/0000-0001-6886-0566
Annika BendesScience for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Solna, Sweden.ORCID http://orcid.org/0000-0001-9329-2353
Fabian ByvaldDepartment of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden.ORCID http://orcid.org/0000-0003-0516-5448
Virginia M StoneDepartment of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden.ORCID http://orcid.org/0000-0003-3091-2142
Emma E RingqvistDepartment of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden.ORCID http://orcid.org/0000-0001-6303-6076
Marta ButrymDepartment of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden.ORCID http://orcid.org/0000-0002-9285-7276
Emmanouil AngelisTUM School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
Sophie KipperDepartment of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden.ORCID http://orcid.org/0009-0004-8075-1660
Stefan BauerTUM School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.ORCID http://orcid.org/0000-0003-1712-060X
Niclas RoxhedDepartment of Intelligent Systems, KTH Royal Institute of Technology, Stockholm, Sweden.ORCID http://orcid.org/0000-0002-7147-6730
Jochen M SchwenkScience for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Solna, Sweden. jochen.schwenk@scilifelab.se.ORCID http://orcid.org/0000-0001-8141-8449
Malin Flodström-TullbergDepartment of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden. malin.flodstrom-tullberg@ki.se.ORCID http://orcid.org/0000-0003-2685-2052

Funding

Novo Nordisk Fonden NNF18OC0034158Novo Nordisk Fonden NNF24OC0092507Science for Life Laboratory VC-2021-0033Science for Life Laboratory VC-2022-0028Vetenskapsrådet 2020-02969Vetenskapsrådet 2022-01374
6 · The paper itself

Abstract

aims/hypothesisType 1 diabetes manifests after irreversible beta cell damage, highlighting the crucial need for markers of the presymptomatic phase to enable early and effective interventions. Current efforts to identify molecular markers of disease-triggering events lack resolution and convenience. Analysing frequently self-collected dried blood spots (DBS) could enable the detection of early disease-predictive markers and facilitate tailored interventions. Here, we present a novel strategy for monitoring transient molecular changes induced by environmental triggers that enable timely disease interception.

methodsWhole blood (10 μl) was sampled regularly (every 1-5 days) from adult NOD mice infected with Coxsackievirus B3 (CVB3) or treated with vehicle alone. Blood samples (5 μl) were dried on filter discs. DBS samples were analysed by proximity extension assay. Generalised additive models were used to assess linear and non-linear relationships between protein levels and the number of days post infection (p.i.). A multi-layer perceptron (MLP) classifier was developed to predict infection status. CVB3-infected SOCS-1-transgenic (tg) mice were treated with immune- or non-immune sera on days 2 and 3 p.i., followed by monitoring of diabetes development.

resultsFrequent blood sampling and longitudinal measurement of the blood proteome revealed transient molecular changes in virus-infected animals that would have been missed with less frequent sampling. The MLP classifier predicted infection status after day 2 p.i. with over 90% accuracy. Treatment with immune sera on day 2 p.i. prevented diabetes development in all (100%) of CVB3-infected SOCS-1-tg NOD mice while five out of eight (62.5%) of the CVB3-infected controls treated with non-immune sera developed diabetes. CONCLUSIONS/

interpretationOur study demonstrates the utility of frequently collected DBS samples to monitor dynamic proteome changes induced by an environmental trigger during the presymptomatic phase of type 1 diabetes. This approach enables disease interception and can be translated into human initiatives, offering a new method for early detection and intervention in type 1 diabetes. DATA AND CODE AVAILABILITY: Additional data available at https://doi.org/10.17044/scilifelab.27368322 . Additional visualisations are presented in the Shiny app interface https://mouse-dbs-profiling.serve.scilifelab.se/ .

Indexed as

Diabetes Mellitus, Type 1ProteomeAnimalsBiomarkersCoxsackievirus InfectionsDisease Models, AnimalDried Blood Spot TestingFemaleMiceMice, Inbred NODMice, TransgenicBiomarkersProteomeBiomarkersCoxsackievirus BDisease interventionDisease predictionDisease triggerDried blood spotsEnterovirusImmune-mediated diseasesMachine learningMicrosamplingProteomicsProximity extension assayScreeningType 1 diabetes

Identifiers

PMID40760251
PMCPMC12423192

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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.