Evidence map›Paper›PMID 41048014›Full record

ArticleAnnals of medicine2025

Nuclear magnetic resonance combined with genetic algorithm with linear discriminant analysis (GA-LDA) is a suitable model for discriminating urinary metabolomic profiles of individuals with glycemic disorders.

Ângela Waleska Freire de Sousa Papa, Anne Natália Almeida de Oliveira, Rannapaula Lawrynhuk Urbano Ferreira, Geovanna Beatriz Oliveira Rosendo, Karine Cavalcanti Maurício Sena-Evangelista, Josivan Gomes Lima, Fernando Barbosa, Camilo de Lelis Medeiros de Morais, Kássio Michell Gomes de Lima, Renata Mendonça Araújo and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of medicine, 2025. 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

11 authors.

Ângela Waleska Freire de Sousa PapaGraduate Program in Nutrition, Federal University of Rio Grande do Norte, Natal, RN, Brazil.ORCID 0000-0003-2089-928X
Anne Natália Almeida de OliveiraNuclear Magnetic Resonance Laboratory, Institute of Chemistry, Federal University of Rio Grande do Norte, Natal, RN, Brazil.ORCID 0000-0002-1672-8683
Rannapaula Lawrynhuk Urbano FerreiraPostgraduate Program in Health Sciences, Federal University of Rio Grande do Norte, Natal, RN, Brazil.ORCID 0000-0002-5176-1972
Geovanna Beatriz Oliveira RosendoPostgraduate Program in Health Sciences, Federal University of Rio Grande do Norte, Natal, RN, Brazil.ORCID 0000-0002-2466-5336
Karine Cavalcanti Maurício Sena-EvangelistaGraduate Program in Nutrition, Federal University of Rio Grande do Norte, Natal, RN, Brazil.ORCID 0000-0001-9729-1243
Josivan Gomes LimaDepartment of Clinical Medicine, Endocrine Unit, Federal University of Rio Grande do Norte, Natal, RN, Brazil.ORCID 0000-0002-1627-7018
Fernando BarbosaASTox Laboratory, Department of Clinical Analyses, Toxicology and Food Science, University of São Paulo - Ribeirão Preto, Ribeirão Preto, SP, Brazil.ORCID 0000-0002-2498-0619
Camilo de Lelis Medeiros de MoraisBiological Chemistry and Chemometrics, Institute of Chemistry, Federal University of Rio Grande do Norte, Natal, RN, Brazil.ORCID 0000-0003-2573-787X
Kássio Michell Gomes de LimaBiological Chemistry and Chemometrics, Institute of Chemistry, Federal University of Rio Grande do Norte, Natal, RN, Brazil.ORCID 0000-0002-3827-3800
Renata Mendonça AraújoNuclear Magnetic Resonance Laboratory, Institute of Chemistry, Federal University of Rio Grande do Norte, Natal, RN, Brazil.ORCID 0000-0001-5514-5192
Lucia Fatima Campos PedrosaGraduate Program in Nutrition, Federal University of Rio Grande do Norte, Natal, RN, Brazil.ORCID 0000-0002-5436-5115

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionMetabolomics is essential in identifying biomarkers involved in the development and progression of diabetes. The study aimed to verify the discrimination among metabolites in the urine of individuals with glycemic alterations using Nuclear Magnetic Resonance (

methodsThe preliminary case-control study was performed with three groups: patients with type 2 diabetes (T2D,

resultsWe identified characteristic signals of various metabolites by comparing the chemical shift data with the literature. Our analysis revealed twenty-one distinct metabolic regions, emphasizing citrate, creatinine, glucose, urea, acetate, and glycine. The most pronounced differences were observed in individuals with T2D compared to groups C and PD. We evaluated a range of algorithms to determine the optimal model. The Genetic Algorithm with Linear Discriminant Analysis (GA-LDA) model exhibited remarkable accuracy, sensitivity, and specificity rates of 100% in discriminating between the studied groups.

conclusionThe

Indexed as

Diabetes Mellitus, Type 2MetabolomicsPrediabetic StateAdultAgedAlgorithmsBiomarkersCase-Control StudiesDimensionality ReductionDiscriminant AnalysisFemaleGenetic AlgorithmsHumansMagnetic Resonance SpectroscopyMaleMiddle AgedBiomarkersmetabolomicsnuclear magnetic resonanceType 2 diabetesurine

Identifiers

PMID41048014
PMCPMC12502107

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.