Evidence map›Paper›PMID 39707182›Full record

ArticleMolecular medicine (Cambridge, Mass.)2024

Metabolomic signature of pediatric diabetic ketoacidosis: key metabolites, pathways, and panels linked to clinical variables.

Paolo Spagnolo, David Tweddell, Enis Cela, Mark Daley, Cheril Clarson, C Anthony Rupar, Saverio Stranges, Michael Bravo, Gediminas Cepinskas, Douglas D Fraser

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Article in Molecular medicine (Cambridge, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 2 pooled it
–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

5 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Review
  5. 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

10 authors.

Paolo SpagnoloMedicine, Campus Bio-Medico University of Rome, Via Alvaro del Portillo 21, 00128, Rome, Italy.
David TweddellComputer Science, Western University, London, ON, N6A 3K7, Canada.
Enis CelaPhysiology & Pharmacology, Western University, London, ON, N6A 3K7, Canada.
Mark DaleyComputer Science, Western University, London, ON, N6A 3K7, Canada.
Cheril ClarsonPediatrics, Western University, London, ON, N6A 3K7, Canada.
C Anthony RuparPediatrics, Western University, London, ON, N6A 3K7, Canada.
Saverio StrangesEpidemiology and Biostatistics, Western University, London, ON, N6G 2M1, Canada.
Michael BravoEmergency Department, Hospital for Sick Children, Toronto, ON, M5G 1X8, Canada.
Gediminas CepinskasMedical Biophysics, Western University, London, ON, N6A 3K7, Canada.
Douglas D FraserPhysiology & Pharmacology, Western University, London, ON, N6A 3K7, Canada. douglas.fraser@lhsc.on.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiabetic ketoacidosis (DKA) is a serious complication of type 1 diabetes (T1D), arising from relative insulin deficiency and leading to hyperglycemia, ketonemia, and metabolic acidosis. Early detection and treatment are essential to prevent severe outcomes. This pediatric case-control study utilized plasma metabolomics to explore metabolic alterations associated with DKA and to identify predictive metabolite patterns.

methodsWe examined 34 T1D participants, including 17 patients admitted with severe DKA and 17 age- and sex-matched individuals in insulin-controlled states. A total of 215 plasma metabolites were analyzed using proton nuclear magnetic resonance and direct-injection liquid chromatography/mass spectrometry. Multivariate statistical methods, machine learning techniques, and bioinformatics were employed for data analysis.

resultsAfter adjusting for multiple comparisons, 65 metabolites were found to differ significantly between the groups (28 increased and 37 decreased). Metabolomics profiling demonstrated 100% accuracy in differentiating severe DKA from insulin-controlled states. Random forest analysis indicated that classification accuracy was primarily influenced by changes in ketone bodies, acylcarnitines, and phosphatidylcholines. Additionally, groups of metabolites (ranging in number from 8 to 18) correlated with key clinical and biochemical variables, including pH, bicarbonate, glucose, HbA1c, and Glasgow Coma Scale scores.

conclusionsThese findings underscore significant metabolic disturbances in severe DKA and their associations with critical clinical indicators. Future investigations should explore if metabolic alterations in severe DKA can identify patients at increased risk of complications and/or guide future therapeutic interventions.

Indexed as

Diabetic KetoacidosisMetabolomeMetabolomicsAdolescentBiomarkersCase-Control StudiesChildDiabetes Mellitus, Type 1FemaleHumansMachine LearningMaleMetabolic Networks and PathwaysBiomarkersDiabetic ketoacidosisMetabolitesMetabolomicsPanelsPathwaysPediatric

Identifiers

PMID39707182
PMCPMC11660668

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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.