Evidence map›Paper›PMID 36631877›Full record

ReviewDiabetology & metabolic syndrome2023

Biochemical and molecular biomarkers: unraveling their role in gestational diabetes mellitus.

Masoumeh Karami, Seyyed Hossein Mousavi, Mohammad Rafiee, Reza Heidari, Seyedeh Zahra Shahrokhi

Abstract readReview
In one paragraph

Review in Diabetology & metabolic syndrome, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

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  18. Genetic and Epigenetic Factors in Gestational Diabetes Mellitus Pathology.International journal of molecular sciences · 2023
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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

5 authors.

Masoumeh KaramiDepartment of Biochemistry, School of Medicine, AJA University of Medical Sciences, Tehran, Iran.
Seyyed Hossein MousaviDepartment of Cardiology, School of Medicine, AJA University of Medical Sciences, Tehran, Iran.
Mohammad RafieeDepartment of Medical Laboratory Sciences, School of Paramedical Sciences, Zanjan University of Medical Sciences, Zanjan, Iran.
Reza HeidariMedical Biotechnology Research Center, AJA University of Medical Sciences, Tehran, Iran.
Seyedeh Zahra ShahrokhiDepartment of Biochemistry, School of Medicine, AJA University of Medical Sciences, Tehran, Iran. zahra.shahrokhi@ajaums.ac.ir.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gestational diabetes mellitus (GDM) is the most prevalent metabolic disorder during pregnancy, causing short- and long-term complications for both mother and baby. GDM is a multifactorial disease, and it may be affected by interactions between genetic, epigenetic, and environmental factors. However, the exact etiology is poorly understood. Despite the high prevalence of GDM, there is still debate regarding the optimal time for screening, the diagnostic threshold to apply, and the best strategies for treatment. Identifying effective strategies for therapeutic purposes as well as accurate biomarkers for prognostic and diagnostic purposes will reduce the GDM incidence and improve its management. In recent years, new biochemical and molecular biomarkers such as microRNAs, single-nucleotide polymorphisms, and DNA methylation have received great interest in the diagnosis of GDM. In this review, we discuss current and future diagnostic approaches for the detection of GDM and evaluate lifestyle and pharmacological strategies for GDM prevention.

Indexed as

BiomarkerGestational diabetes mellitusInsulin resistanceMicroRNAβ-cell dysfunction

Identifiers

PMID36631877
PMCPMC9832639

What Socratic 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 Socratic graph.