Evidence map›Paper›PMID 41334441›Full record

ArticleFrontiers in endocrinology2025

Coronary heart disease and type 2 diabetes metabolomic signatures in the Middle East.

Mohamed Elshrif, Keivin Isufaj, Ayman El-Menyar, Ehsan Ullah, Alka Beotra, Mohammed Al-Maadheed, Vidya Mohamed-Ali, Mohamad Saad, Jassim Al Suwaidi

Abstract read
In one paragraph

Article in Frontiers in endocrinology, 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. Review
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

9 authors.

Mohamed ElshrifQatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
Keivin IsufajQatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
Ayman El-MenyarClinical Research, Trauma & Vascular Surgery, Hamad Medical Corporation, Doha, Qatar.
Ehsan UllahQatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
Alka BeotraAnti-doping Lab Qatar, Doha, Qatar.
Mohammed Al-MaadheedAnti-doping Lab Qatar, Doha, Qatar.
Vidya Mohamed-AliAnti-doping Lab Qatar, Doha, Qatar.
Mohamad SaadQatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
Jassim Al SuwaidiDepartment of Cardiology, Heart Hospital, Hamad Medical Corporation, Doha, Qatar.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The growing field of metabolomics has opened new venues for identifying biomarkers of type 2 diabetes (T2D) and predicting its consequences, such as coronary heart disease (CHD). Despite their large size, Middle Eastern populations are underrepresented in omics research. In this study, we aim at investigating metabolomics profiles of T2D stratified by the CHD comorbidity for Middle Eastern population, such as Qatari population. Methods: In this cross-sectional study, we used a total of 641 metabolites from a large cohort of 3,679 Qatari adults from the Qatar BioBank (QBB; 272 T2D and 2,438 non-T2D individuals) and Qatar Cardiovascular Biorepository (QCBio; all CHD patients; 488 T2D and 481 non-T2D individuals). Univariate and pathway enrichment analyses were performed to identify metabolites associated with T2D in the absence or presence of CHD. Machine learning (ML) models, and metabolite risk scores were developed to assess the predictive power of the different combinations of T2D and CHD. Results: Many metabolites were significantly associated with T2D in both the QBB and QCBio cohorts. Among these, we observed 1,5-anhydroglucitol (1,5-AG) (P = 1.33 × 10 Conclusions: Metabolomic profiling has the potential for the early detection of metabolic alterations that precede clinical symptoms of T2D and CHD in the presence of T2D. Risk scores showed great performance in predicting T2D and CHD, but longitudinal data are required to provide evidence for disease risk. Early detection allows timely interventions and improved management strategies for both T2D and CHD patients.

Indexed as

Coronary DiseaseDiabetes Mellitus, Type 2MetabolomeMetabolomicsAdultAgedBiomarkersCohort StudiesCross-Sectional StudiesFemaleHumansMaleMiddle AgedMiddle EastQatarBiomarkerscoronary heart diseasemetabolite risk scoremetabolomicsMiddle Eastern populationspathway enrichment analysispredictive modelingsupervised learningtype 2 diabetes

Identifiers

PMID41334441
PMCPMC12665572

What Socratic holds

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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 Socratic graph.