Evidence map›Paper›PMID 41662250›Full record

ArticleEpigenetics2026

Early characterization of pregnancy glycemic traits in gestational diabetes mellitus by plasma cell-free mRNA and non-coding RNA.

Songchang Chen, Yuwei Liu, Tingyu Yang, Zunmin Wan, Jinghua Sun, Xuanyou Zhou, Jiayi Li, Jiayi Huang, Lanlan Zhang, Sijia Guo and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Epigenetics, 2026. 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
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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

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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

15 authors.

Songchang ChenInstitute of Reproduction and Development, Shanghai Key Laboratory of Reproduction and Development, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China.
Yuwei LiuState Key Laboratory of Genome and Multi-Omics Technologies, BGI Research, Shenzhen, China.
Tingyu YangState Key Laboratory of Genome and Multi-Omics Technologies, BGI Research, Shenzhen, China.
Zunmin WanState Key Laboratory of Genome and Multi-Omics Technologies, BGI Research, Shenzhen, China.
Jinghua SunState Key Laboratory of Genome and Multi-Omics Technologies, BGI Research, Shenzhen, China.
Xuanyou ZhouInstitute of Reproduction and Development, Shanghai Key Laboratory of Reproduction and Development, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China.
Jiayi LiState Key Laboratory of Genome and Multi-Omics Technologies, BGI Research, Shenzhen, China.
Jiayi HuangState Key Laboratory of Genome and Multi-Omics Technologies, BGI Research, Shenzhen, China.
Lanlan ZhangInternational Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Sijia GuoState Key Laboratory of Genome and Multi-Omics Technologies, BGI Research, Shenzhen, China.
Yuxuan KangState Key Laboratory of Genome and Multi-Omics Technologies, BGI Research, Shenzhen, China.
Fang ChenState Key Laboratory of Genome and Multi-Omics Technologies, BGI Research, Shenzhen, China.
Hefeng HuangInstitute of Reproduction and Development, Shanghai Key Laboratory of Reproduction and Development, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China.
Chenming XuInstitute of Reproduction and Development, Shanghai Key Laboratory of Reproduction and Development, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China.
Wen-Jing WangState Key Laboratory of Genome and Multi-Omics Technologies, BGI Research, Shenzhen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, the incidence of gestational diabetes mellitus (GDM) has been steadily increasing, posing risks to the long-term health of both mother and child. We aim to characterize blood glucose levels of pregnant women and predict the risk of GDM during early pregnancy through plasma cell-free mRNA and non-coding RNA (cfRNA). Here, we collected plasma samples from 108 pregnant women (54 with GDM and 54 controls) at around 16 weeks of gestation. Following high-throughput sequencing, we performed differentially abundant genes analysis and evaluated correlations between cfRNA profiles and blood glucose levels. Based on these findings, we developed a predictive model utilizing cf-mRNA and cf-lncRNA signatures. We found that ribosomal genes (

Indexed as

Blood GlucoseCell-Free Nucleic AcidsDiabetes, GestationalRNA, Long NoncodingRNA, MessengerRNA, UntranslatedAdultFemaleHumansInsulin-Like Growth Factor IIPregnancyBlood GlucoseCell-Free Nucleic AcidsInsulin-Like Growth Factor IIRNA, Long NoncodingRNA, MessengerRNA, UntranslatedCell-free RNAcharacterizationgestational diabetes mellitusprediction

Identifiers

PMID41662250
PMCPMC12893693

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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.