Evidence mapPaperPMID 38523249Full record

ArticleDiabetes & metabolism journal2024

A New Tool to Identify Pediatric Patients with Atypical Diabetes Associated with Gene Polymorphisms.

Sophie Welsch, Antoine Harvengt, Paola Gallo, Manon Martin, Dominique Beckers, Thierry Mouraux, Nicole Seret, Marie-Christine Lebrethon, Raphaël Helaers, Pascal Brouillard and 2 more

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In one paragraph

Article in Diabetes & metabolism journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 30% of its field
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

2 citing papers in PubMed, 1 citations in OpenAlex.

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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 at 6 institutions in 1 country.

Sophie WelschPediatrics Unit, Institute for Experimental and Clinical Research, UCLouvain, Brussels, Belgium.
Antoine HarvengtPediatrics Unit, Institute for Experimental and Clinical Research, UCLouvain, Brussels, Belgium.
Paola GalloPediatric Endocrinology Unit, Saint-Luc University Clinics, Brussels, Belgium.
Manon MartinLouvain Institute of Biomolecular Science and Technology (IBST) Unit, UCLouvain, Brussels, Belgium.
Dominique BeckersPediatric Endocrinology and Diabetology Unit, CHU-UCL Namur sites Saint-Elisabeth and Mont-Godinne, Namur, Belgium.
Thierry MourauxPediatric Endocrinology and Diabetology Unit, CHU-UCL Namur sites Saint-Elisabeth and Mont-Godinne, Namur, Belgium.
Nicole SeretPediatric Endocrinology and Diabetology Unit, Clinique CHC MontLégia (CHC MontLégia Clinic), Liège, Belgium.
Marie-Christine LebrethonPediatric Endocrinology Unit, CHU of Liège site ND-des Bruyères, Liège, Belgium.
Raphaël HelaersHuman Molecular Genetics, de Duve Institute, UCLouvain, Brussels, Belgium.
Pascal BrouillardHuman Molecular Genetics, de Duve Institute, UCLouvain, Brussels, Belgium.
Miikka VikkulaHuman Molecular Genetics, de Duve Institute, UCLouvain, Brussels, Belgium.
Philippe A LysyPediatrics Unit, Institute for Experimental and Clinical Research, UCLouvain, Brussels, Belgium.
de Duve Institute · BECHU Dinant Godinne UCL Namur · BECliniques Universitaires Saint-Luc · BECentre Hospitalier Chrétien · BECentre Hospitalier Universitaire de Liège · BEUCLouvain · BE

Funding

Action de Recherche ConcertéeCliniques Universitaires Saint-LucFondation Saint-LucFonds De La Recherche Scientifique - FNRSInnovirisUCLouvain
6 · The paper itself

Abstract

backgruoundRecent diabetes subclassifications have improved the differentiation between patients with type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus despite several overlapping features, yet without considering genetic forms of diabetes. We sought to facilitate the identification of monogenic diabetes by creating a new tool that we validated in a pediatric maturity-onset diabetes of the young (MODY) cohort.

methodsWe first created the DIAgnose MOnogenic DIAbetes (DIAMODIA) criteria based on the pre-existing, but incomplete, MODY calculator. This new score is composed of four strong and five weak criteria, with patients having to display at least one weak and one strong criterion.

resultsThe effectiveness of the DIAMODIA criteria was evaluated in two patient cohorts, the first consisting of patients with confirmed MODY diabetes (n=34) and the second of patients with T1DM (n=390). These DIAMODIA criteria successfully detected 100% of MODY patients. Multiple correspondence analysis performed on the MODY and T1DM cohorts enabled us to differentiate MODY patients from T1DM. The three most relevant variables to distinguish a MODY from T1DM profile were: lower insulin-dose adjusted A1c score ≤9, glycemic target-adjusted A1c score ≤4.5, and absence of three anti-islet cell autoantibodies.

conclusionWe validated the DIAMODIA criteria, as it effectively identified all monogenic diabetes patients (MODY cohort) and succeeded to differentiate T1DM from MODY patients. The creation of this new and effective tool is likely to facilitate the characterization and therapeutic management of patients with atypical diabetes, and promptly referring them for genetic testing which would markedly improve clinical care and counseling, as well.

Indexed as

Diabetes Mellitus, Type 1Diabetes Mellitus, Type 2AdolescentChildChild, PreschoolCohort StudiesDiagnosis, DifferentialFemaleGlycated HemoglobinHumansInsulinMalePolymorphism, GeneticGlycated HemoglobinInsulinDiabetes mellitusDiabetes mellitus, type 1Diabetes mellitus, type 2Genetic testingPediatrics

Identifiers

PMID38523249
PMCPMC11449816
OpenAlexW4393156346

What Socratic holds

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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 Socratic graph.