Advancing Early Prediction of Gestational Diabetes Mellitus with Circular RNA Biomarkers

Joon Ho Moon; Sung Hee Choi

doi:10.4093/dmj.2025.0277

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Editorial Advancing Early Prediction of Gestational Diabetes Mellitus with Circular RNA Biomarkers: Joon Ho Moon^1,2, Sung Hee Choi^1,2; Diabetes & Metabolism Journal 2025;49(3):403-404.
DOI: https://doi.org/10.4093/dmj.2025.0277
Published online: May 1, 2025

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¹Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea

²Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea

Corresponding author: Sung Hee Choi

Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 82 Gumi-ro 173beon-gil, Bundang-gu, Seongnam 13620, Korea E-mail: shchoimd@gmail.com

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Gestational diabetes mellitus (GDM) is a significant global challenge due to its rising prevalence in pregnant women, the mean age of whom is increasing, and its association with adverse maternal and neonatal outcomes [1,2]. Although the oral glucose tolerance test remains the standard diagnostic method, its administration during the second trimester limits the opportunity for earlier interventions. Identifying women at risk for GDM is clinically important because early diagnosis and treatment significantly reduce adverse neonatal outcomes [3]. Prediction of GDM is based on genetics and consideration of metabolic phenotypes which incorporates conventional diagnostic modeling as well as machine-learning models [4-6]. The recent study by Ma et al. [7] published in Diabetes & Metabolism Journal represents an interesting approach to the field, proposing a novel biomarker-based strategy for early prediction of GDM using circulating circular RNAs (circRNAs).

CircRNAs are a unique class of endogenous non-coding RNAs characterized by their covalently closed loop structures, which confer exceptional stability against exonuclease degradation [8,9]. Unlike linear RNAs, circRNAs lack 5´ caps and 3´ tails, allowing them to persist in various biological fluids and making them attractive candidates as disease biomarkers. They are generated through a process called back-splicing and are increasingly recognized for their roles in gene regulation, acting as microRNA sponges, transcriptional regulators, or protein-binding molecules.

In recent years, circRNAs have emerged as a focus of interest in metabolic disease research, including obesity, type 2 diabetes mellitus, and GDM. Their tissue-specific and metabolic phenotype-dependent expression patterns suggest their involvement in glucose and lipid metabolism, insulin signaling, and inflammatory pathways, which are key in metabolic disorders. Several studies have identified differentially expressed circRNAs in serum or placental tissue of GDM women, indicating their potential as early, non-invasive biomarkers [10-12].

In this multicenter case-control study, the authors harnessed high-throughput sequencing and logistic regression modeling to identify and validate two specific circRNAs—hsa_circ_0031560 and hsa_circ_0000793—as predictive biomarkers for GDM. Notably, the expression of these circRNAs was significantly elevated in both early and mid-gestational serum samples of women who later developed GDM. The authors developed an early GDM prediction model (E-GDMM) that achieved robust diagnostic performance. The model showed area under the curve values exceeding 0.90 in the derivation cohort and maintained high sensitivity and specificity in validation cohorts.

The scientific value of this study lies in several domains. First, it reaffirms the emerging utility of circRNAs as stable, non-coding RNA biomarkers due to their resistance to RNase degradation and consistent expression profiles. Second, by integrating circRNA biomarkers into a clinically applicable logistic regression model, this research bridges molecular diagnostics with pragmatic obstetric screening. The findings also suggest that these circRNAs may originate from placental tissue and reflect underlying pathophysiological mechanisms involved in GDM development, such as insulin resistance and inflammatory signaling, offering avenues for mechanistic exploration.

However, translating circRNA research into clinical application faces several challenges. First, there is limited understanding of their precise biological functions and regulatory networks, especially in the context of complex diseases like GDM. Second, circRNA detection requires technically demanding assays (e.g., RNase R treatment, quantitative real-time polymerase chain reaction with divergent primers) that are not yet standardized for routine clinical diagnostics. Additionally, variability in expression across populations, sample types, and analytical platforms can lead to inconsistencies in reproducibility and limit biomarker validation. Last, many current studies, including promising ones, are based on relatively small or region-specific cohorts, necessitating broader validation before clinical application.

This study does have limitations. The multicenter design improves generalizability within a specific population, but broader external validation in diverse ethnic and geographic cohorts is needed before routine clinical implementation. Additionally, while the E-GDMM shows promise for early screening, its integration with existing clinical workflows, cost-effectiveness, and performance relative to other predictive markers including glycemic indices and body mass index warrant further study. Moreover, the mechanistic roles of hsa_circ_0031560 and hsa_circ_0000793 in GDM pathophysiology remain largely inferential and require direct experimental validation.

Future study directions should include longitudinal measurements assessing the dynamic expression changes of circRNAs across pregnancy, mechanistic investigations using placental and trophoblast models, and clinical trials evaluating whether early interventions based on E-GDMM predictions can improve pregnancy outcomes. Additionally, multi-omic approaches integrating circRNAs with proteomics, metabolomics, and clinical data may refine prediction models and unveil novel therapeutic targets.

In conclusion, Ma et al. [7] suggested an early diagnostic tool for GDM grounded in molecular innovation. However, further research is essential to overcome technical hurdles, ensure reproducibility, and validate clinical utility across diverse populations.

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CONFLICTS OF INTEREST

Sung Hee Choi has been an associate editor of the Diabetes & Metabolism Journal since 2022. She was not involved in the review process of this article. Otherwise, there was no conflict of interest.

REFERENCES

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Advancing Early Prediction of Gestational Diabetes Mellitus with Circular RNA Biomarkers

Diabetes Metab J. 2025;49(3):403-404. Published online May 1, 2025

DOI: https://doi.org/10.4093/dmj.2025.0277

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Advancing Early Prediction of Gestational Diabetes Mellitus with Circular RNA Biomarkers

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Moon JH, Choi SH. Advancing Early Prediction of Gestational Diabetes Mellitus with Circular RNA Biomarkers. Diabetes Metab J. 2025;49(3):403-404.

DOI: https://doi.org/10.4093/dmj.2025.0277.

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