Evidence map›Paper›PMID 30446432›Full record

Trial reportEBioMedicine2018

Changes in Splicing Machinery Components Influence, Precede, and Early Predict the Development of Type 2 Diabetes: From the CORDIOPREV Study.

Manuel D Gahete, Mercedes Del Rio-Moreno, Antonio Camargo, Juan F Alcala-Diaz, Emilia Alors-Perez, Javier Delgado-Lista, Oscar Reyes, Sebastian Ventura, Pablo Perez-Martínez, Justo P Castaño and 2 more

Abstract readMulticenter StudyRandomized Controlled Trial
In one paragraph

Trial report in EBioMedicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

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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.

Manuel D GaheteMaimonides Institute for Biomedical Research of Cordoba (IMIBIC), Córdoba, Spain.; Universidad de Córdoba, Córdoba, Spain.; Reina Sofia University Hospital, Córdoba, Spain.; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Córdoba, Spain.
Mercedes Del Rio-MorenoMaimonides Institute for Biomedical Research of Cordoba (IMIBIC), Córdoba, Spain.; Universidad de Córdoba, Córdoba, Spain.; Reina Sofia University Hospital, Córdoba, Spain.; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Córdoba, Spain.
Antonio CamargoMaimonides Institute for Biomedical Research of Cordoba (IMIBIC), Córdoba, Spain.; Universidad de Córdoba, Córdoba, Spain.; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Córdoba, Spain.; Lipid and Atherosclerosis Unit, Department of Medicine, Reina Sofia University Hospital, University of Córdoba, Córdoba, Spain.
Juan F Alcala-DiazMaimonides Institute for Biomedical Research of Cordoba (IMIBIC), Córdoba, Spain.; Universidad de Córdoba, Córdoba, Spain.; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Córdoba, Spain.; Lipid and Atherosclerosis Unit, Department of Medicine, Reina Sofia University Hospital, University of Córdoba, Córdoba, Spain.
Emilia Alors-PerezMaimonides Institute for Biomedical Research of Cordoba (IMIBIC), Córdoba, Spain.; Universidad de Córdoba, Córdoba, Spain.; Reina Sofia University Hospital, Córdoba, Spain.; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Córdoba, Spain.
Javier Delgado-ListaMaimonides Institute for Biomedical Research of Cordoba (IMIBIC), Córdoba, Spain.; Universidad de Córdoba, Córdoba, Spain.; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Córdoba, Spain.; Lipid and Atherosclerosis Unit, Department of Medicine, Reina Sofia University Hospital, University of Córdoba, Córdoba, Spain.
Oscar ReyesMaimonides Institute for Biomedical Research of Cordoba (IMIBIC), Córdoba, Spain.; Universidad de Córdoba, Córdoba, Spain.; Reina Sofia University Hospital, Córdoba, Spain.; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Córdoba, Spain.; Department of Computer Sciences, University of Cordoba, Córdoba, Spain.
Sebastian VenturaMaimonides Institute for Biomedical Research of Cordoba (IMIBIC), Córdoba, Spain.; Universidad de Córdoba, Córdoba, Spain.; Reina Sofia University Hospital, Córdoba, Spain.; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Córdoba, Spain.; Department of Computer Sciences, University of Cordoba, Córdoba, Spain.
Pablo Perez-MartínezMaimonides Institute for Biomedical Research of Cordoba (IMIBIC), Córdoba, Spain.; Universidad de Córdoba, Córdoba, Spain.; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Córdoba, Spain.; Lipid and Atherosclerosis Unit, Department of Medicine, Reina Sofia University Hospital, University of Córdoba, Córdoba, Spain.
Justo P CastañoMaimonides Institute for Biomedical Research of Cordoba (IMIBIC), Córdoba, Spain.; Universidad de Córdoba, Córdoba, Spain.; Reina Sofia University Hospital, Córdoba, Spain.; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Córdoba, Spain.
José Lopez-MirandaMaimonides Institute for Biomedical Research of Cordoba (IMIBIC), Córdoba, Spain.; Universidad de Córdoba, Córdoba, Spain.; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Córdoba, Spain.; Lipid and Atherosclerosis Unit, Department of Medicine, Reina Sofia University Hospital, University of Córdoba, Córdoba, Spain.
Raul M LuqueMaimonides Institute for Biomedical Research of Cordoba (IMIBIC), Córdoba, Spain.; Universidad de Córdoba, Córdoba, Spain.; Reina Sofia University Hospital, Córdoba, Spain.; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Córdoba, Spain.. Electronic address: raul.luque@uco.es.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundType-2 diabetes mellitus (T2DM) is a major health problem with increasing incidence, which severely impacts cardiovascular disease. Because T2DM is associated with altered gene expression and aberrant splicing, we hypothesized that dysregulations in splicing machinery could precede, contribute to, and predict T2DM development.

methodsA cohort of patients with cardiovascular disease (CORDIOPREV study) and without T2DM at baseline (at the inclusion of the study) was used (n = 215). We determined the expression of selected splicing machinery components in fasting and 4 h-postprandial peripheral blood mononuclear cells (PBMCs, obtained at baseline) from all the patients who developed T2DM during 5-years of follow-up (n = 107 incident-T2DM cases) and 108 randomly selected non-T2DM patients (controls). Serum from incident-T2DM and control patients was used to analyze in vitro the modulation of splicing machinery expression in control PBMCs from an independent cohort of healthy subjects.

findingsExpression of key splicing machinery components (e.g. RNU2, RNU4 or RNU12) from fasting and 4 h-postprandial PBMCs of incident-T2DM patients was markedly altered compared to non-T2DM controls. Moreover, in vitro treatment of healthy individuals PBMCs with serum from incident-T2DM patients (compared to non-T2DM controls) reduced the expression of splicing machinery elements found down-regulated in incident-T2DM patients PBMCs. Finally, fasting/postprandial levels of several splicing machinery components in the PBMCs of CORDIOPREV patients were associated to higher risk of T2DM (Odds Ratio > 4) and could accurately predict (AUC > 0.85) T2DM development.

interpretationOur results reveal the existence of splicing machinery alterations that precede and predict T2DM development in patients with cardiovascular disease. FUND: ISCIII, MINECO, CIBERObn.

Indexed as

RNA SplicingBiomarkersCardiovascular DiseasesDiabetes Mellitus, Type 2FemaleGlycated HemoglobinHumansLeukocytes, MononuclearMaleMiddle AgedPredictive Value of TestsBiomarkersGlycated Hemoglobinhemoglobin A1c protein, humanOGTTPeripheral blood mononuclear cells (PBMCs)Postprandial stateRNUsSpliceosome

Identifiers

PMID30446432
PMCPMC6286259

What OpenQuestion 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 OpenQuestion graph.