Evidence map›Paper›PMID 40114104›Full record

ArticleCardiovascular diabetology2025

Metabolomic profiling reveals early biomarkers of gestational diabetes mellitus and associated hepatic steatosis.

Youngae Jung, Seung Mi Lee, Jinhaeng Lee, Yeonjin Kim, Woojoo Lee, Ja Nam Koo, Ig Hwan Oh, Kue Hyun Kang, Byoung Jae Kim, Sun Min Kim and 14 more

Abstract read
In one paragraph

Article in Cardiovascular diabetology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. 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

24 authors.

Youngae Jung *Integrated Metabolomics Research Group, Metropolitan Seoul Center, Korea Basic Science Institute, University-Industry Cooperate Building, 150 Bugahyeon-ro, Seodaemun-gu, Seoul, 03759, Republic of Korea.
Seung Mi Lee *Department of Obstetrics and Gynecology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
Jinhaeng LeeIntegrated Metabolomics Research Group, Metropolitan Seoul Center, Korea Basic Science Institute, University-Industry Cooperate Building, 150 Bugahyeon-ro, Seodaemun-gu, Seoul, 03759, Republic of Korea.
Yeonjin KimDepartment of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea.
Woojoo LeeDepartment of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea.
Ja Nam KooSeoul Women's Hospital, Incheon, Republic of Korea.
Ig Hwan OhSeoul Women's Hospital, Incheon, Republic of Korea.
Kue Hyun KangSeoul Women's Hospital, Incheon, Republic of Korea.
Byoung Jae KimDepartment of Obstetrics and Gynecology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
Sun Min KimDepartment of Obstetrics and Gynecology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
Jeesun LeeDepartment of Obstetrics and Gynecology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
Ji Hoi KimDepartment of Obstetrics and Gynecology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
Yejin BaeIntegrated Metabolomics Research Group, Metropolitan Seoul Center, Korea Basic Science Institute, University-Industry Cooperate Building, 150 Bugahyeon-ro, Seodaemun-gu, Seoul, 03759, Republic of Korea.
Sang Youn KimDepartment of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
Gyoung Min KimDepartment of Radiology, Yonsei University College of Medicine, Seoul, Republic of Korea.
Sae Kyung JooDepartment of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
Dong Hyeon LeeDepartment of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
Joon Ho MoonDepartment of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
Bo Kyung KooDepartment of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
Sue ShinDepartment of Laboratory Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
Errol R NorwitzDepartment of Obstetrics and Gynecology, Tufts University School of Medicine, Boston, MA, USA.
Geum-Sook HwangIntegrated Metabolomics Research Group, Metropolitan Seoul Center, Korea Basic Science Institute, University-Industry Cooperate Building, 150 Bugahyeon-ro, Seodaemun-gu, Seoul, 03759, Republic of Korea. gshwang@kbsi.re.kr.
Joong Shin ParkDepartment of Obstetrics and Gynecology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. jsparkmd@snu.ac.kr.
Won KimDepartment of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea. drwon1@snu.ac.kr.

Funding

Korea Basic Science Institute A423200Korea National Institute of Health (KNIH) 2022ER090202National Research Foundation of Korea (NRF) 2021R1A2C2005820National Research Foundation of Korea (NRF) 2022M3H9A2096189Patient-Centered Clinical Research Coordinating Center (PACEN) RS-2021-KH120178
6 · The paper itself

Abstract

backgroundThis study aims to identify early metabolomic biomarkers of gestational diabetes mellitus (GDM) and evaluate their association with hepatic steatosis.

methodsWe compared maternal serum metabolomic profiles between women who developed GDM (n = 118) and matched controls (n = 118) during the first (10-14 gestational weeks) and second (24-28 gestational weeks) trimesters using ultra-performance liquid chromatography coupled with mass spectrometry. Mediation analysis was performed to evaluate the mediating role of metabolic dysfunction-associated steatotic liver disease (MASLD) in the relationship between metabolites and subsequent development of GDM. A refined prediction model was developed to predict GDM using established clinical factors and selected metabolites.

resultsSignificant alterations in circulating metabolites, including amino acids, bile acids, and phospholipids, were observed in the GDM group compared to controls during early pregnancy. Mediation analysis revealed that several metabolites, including glycocholic acid (proportion mediated (PM) = 31.9%), butanoyl carnitine (PM = 25.7%), and uric acid (PM = 22.4%), had significant indirect effects on GDM incidence mediated by hepatic steatosis. The refined prediction model composed of clinical factors and selected metabolites in the first trimester demonstrated higher performance in predicting GDM development than the established prediction model composed solely of clinical factors (AUC, 0.85 vs. 0.63, p < 0.001).

conclusionsWomen who developed GDM exhibited altered metabolomic profiles from early pregnancy, which showed a significant correlation with GDM, with MASLD as a mediator. Selected metabolomic biomarkers may serve as predictive markers and potential targets for early risk assessment and intervention in GDM. RESEARCH INSIGHTS: WHAT IS CURRENTLY KNOWN ABOUT THIS TOPIC?: Gestational diabetes mellitus (GDM) is a common pregnancy complication with significant health risks. Early identification of women at high risk for GDM is crucial for timely intervention and improved outcomes. WHAT IS THE KEY RESEARCH QUESTION?: What alterations in circulating metabolites during early pregnancy are associated with subsequent GDM development? Does metabolic dysfunction-associated steatotic liver disease (MASLD) mediate the association between specific metabolites and GDM risk? WHAT IS NEW?: Significant alterations in bile acids, amino acids, phosphatidylethanolamines, and phosphatidylinositols were observed in early pregnancy sera of women who later developed GDM. MASLD significantly mediated the effects of several metabolites on GDM risk, with mediation proportions ranging from 9.7 to 31.9%. A refined prediction model composed of clinical factors and metabolites significantly improved the performance in predicting GDM development. HOW MIGHT THIS STUDY INFLUENCE CLINICAL PRACTICE?: These results provide new insights into early metabolic alterations associated with GDM development and highlight the potential mediating role of MASLD. This comprehensive metabolomic approach may contribute to the development of improved risk prediction models and targeted interventions for GDM prevention.

Indexed as

Diabetes, GestationalFatty LiverMetabolomicsAdultBiomarkersCase-Control StudiesFemaleGestational AgeHumansPredictive Value of TestsPregnancyPregnancy Trimester, FirstRisk AssessmentRisk FactorsBiomarkersEarly biomarkerGestational diabetes mellitusHepatic steatosisMediation analysisMetabolomics

Identifiers

PMID40114104
PMCPMC11927189

What OpenQuestion 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 OpenQuestion graph.