Skip to content
DraconiticResearch
Search Sources API
203,234 studies31 sourcesUpdated 56 min ago
Peer-reviewedCC BY-NC-NDBMC pregnancy and childbirth

A risk assessment model for gestational diabetes mellitus integrating maternal demographics and hematological indices in Thai women.

  • Thaveesak Sai-Ong1,2
  • Donrawee Waeyeng3
  • Tanaporn Khamphaya4,5
  • Yanisa Rattanapan6,7
  • Chutima Jansakun6
  • Atsuko Ikeda8
  • Supabhorn Yimthiang9,10
10 affiliations
  1. 1Environmental, Safety Technology and Health Program, School of Public Health, Walailak University, Nakhon Si Thammarat, Thailand.
  2. 2Office of Disease Prevention and Control Region 11, Nakhon Si Thammarat, Thailand.
  3. 3Department of Environmental Health, School of Public Health, Walailak University, Nakhon Si Thammarat, Thailand.
  4. 4Department of Occupational Health and Safety, School of Public Health, Walailak University, Nakhon Si Thammarat, Thailand.
  5. 5Excellence Center for Public Health Research, Walailak University, Nakhon Si Thammarat, Thailand.
  6. 6School of Allied Health Sciences, Walailak University, Nakhon Si Thammarat, Thailand.
  7. 7Hematology and Transfusion Science Research Center, Walailak University, Nakhon Si Thammarat, Thailand.
  8. 8Center for Environmental and Health Sciences, Hokkaido University, Sapporo, Japan.
  9. 9Environmental, Safety Technology and Health Program, School of Public Health, Walailak University, Nakhon Si Thammarat, Thailand. [email protected].
  10. 10Excellence Center for Public Health Research, Walailak University, Nakhon Si Thammarat, Thailand. [email protected].
Read full textPDFDOICite

Abstract

Background

Universal screening for Gestational Diabetes Mellitus (GDM) is the gold standard, but it can be difficult and expensive in resource-limited settings. Many current prediction models rely on specialized tests that are not available in primary care. This study aimed to create a cost-effective risk assessment model using common maternal demographic and blood test data to help identify GDM risk in Thai pregnant women.Methods

We conducted a cross-sectional study with 200 pregnant women (24-28 weeks of gestation) at a secondary care hospital in Southern Thailand. We analyzed routine antenatal data, including complete blood count and other lab results. A risk-scoring system was built using multivariate logistic regression. We then tested the model's performance using Receiver Operating Characteristic (ROC) curve analysis to determine the optimal cut-off point.Results

Six independent predictors were found maternal age over 35 years, systolic blood pressure above 130 mmHg, glycosuria, hemoglobin at least 11 g/dL, platelets at least 260.50 × 10³/µL, and neutrophil-to-lymphocyte ratio (NLR) of 2.89 or higher. The combined risk model performed better (AUC = 0.804; 95% CI: 0.74-0.87) than single-marker models. With a risk score cut-off of 2.5 (indicating three or more risk factors), the model had a sensitivity of 71.4% and a specificity of 78.1%.Conclusion

The new risk assessment model, which uses basic demographic and routine blood test data, offers a reliable and affordable way to screen for GDM. This method enables early risk identification and helps healthcare providers use resources more efficiently, potentially reducing unnecessary tests in resource-limited settings.

Related

  1. Integrating probiotics into a one-day diabetes clinic: outcomes in gestational diabetes.
    Wei Wang, Nana Cai, Min Ma +5
    BMC pregnancy and childbirthPeer-reviewedCC BY-NC-NDFull textObstetrics, gynaecology & paediatricsEurope PMC
  2. The CKM–GDM multi-organ precision model: reframing gestational diabetes as an early cardiovascular–kidney–metabolic event in Southeast Asia
    Ashutosh Mishra
    Frontiers in endocrinologyPeer-reviewedCC BYFull textEndocrinology & metabolismEurope PMC
  3. A machine learning-based predictive model for gestational diabetes mellitus based on first trimester clinical indicators
    Weina Xu, Xin Jiang, Huaqing Huang +1
    Scientific ReportsPeer-reviewedCC BY-NC-ND 4.0Obstetrics, gynaecology & paediatricsCrossref
  4. Maternity versus non‐maternity healthcare settings for the diagnosis of gestational diabetes or diabetes in pregnancy
    Nora Cryns, Julia Schroer, Anna H Noel-Storr +10
    The Cochrane Database of Systematic ReviewsPeer-reviewedPublisher termsFull textObstetrics, gynaecology & paediatricsPMC
  5. Pregnancy Complicated by Citrin Deficiency and Gestational Diabetes Mellitus: Perioperative Metabolic Management and Postoperative Analgesic Challenges Following Caesarean Delivery
    Ei Ei Phyo Naing, Ei Mon Mon Kyaw
    CureusPeer-reviewedCC BYFull textGeneral medicineEurope PMC

Cite

BibTeXRIS
BibTeX
@article{saiong2026risk,
  title = {{A risk assessment model for gestational diabetes mellitus integrating maternal demographics and hematological indices in Thai women.}},
  author = {Thaveesak Sai-Ong and Donrawee Waeyeng and Tanaporn Khamphaya and Yanisa Rattanapan and Chutima Jansakun and Atsuko Ikeda and Supabhorn Yimthiang},
  journal = {BMC pregnancy and childbirth},
  year = {2026},
  month = jul,
  doi = {10.1186/s12884-026-09608-x},
  url = {https://europepmc.org/article/MED/42897525},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - A risk assessment model for gestational diabetes mellitus integrating maternal demographics and hematological indices in Thai women.
AU  - Thaveesak Sai-Ong
AU  - Donrawee Waeyeng
AU  - Tanaporn Khamphaya
AU  - Yanisa Rattanapan
AU  - Chutima Jansakun
AU  - Atsuko Ikeda
AU  - Supabhorn Yimthiang
PY  - 2026
DA  - 2026/07/01
JO  - BMC pregnancy and childbirth
DO  - 10.1186/s12884-026-09608-x
UR  - https://europepmc.org/article/MED/42897525
AB  - Background  Universal screening for Gestational Diabetes Mellitus (GDM) is the gold standard, but it can be difficult and expensive in resource-limited settings. Many current prediction models rely on specialized tests that are not available in primary care. This study aimed to create a cost-effective risk assessment model using common maternal demographic and blood test data to help identify GDM risk in Thai pregnant women.Methods  We conducted a cross-sectional study with 200 pregnant women (24-28 weeks of gestation) at a secondary care hospital in Southern Thailand. We analyzed routine antenatal data, including complete blood count and other lab results. A risk-scoring system was built using multivariate logistic regression. We then tested the model's performance using Receiver Operating Characteristic (ROC) curve analysis to determine the optimal cut-off point.Results  Six independent predictors were found maternal age over 35 years, systolic blood pressure above 130 mmHg, glycosuria, hemoglobin at least 11 g/dL, platelets at least 260.50 × 10³/µL, and neutrophil-to-lymphocyte ratio (NLR) of 2.89 or higher. The combined risk model performed better (AUC = 0.804; 95% CI: 0.74-0.87) than single-marker models. With a risk score cut-off of 2.5 (indicating three or more risk factors), the model had a sensitivity of 71.4% and a specificity of 78.1%.Conclusion  The new risk assessment model, which uses basic demographic and routine blood test data, offers a reliable and affordable way to screen for GDM. This method enables early risk identification and helps healthcare providers use resources more efficiently, potentially reducing unnecessary tests in resource-limited settings.
SN  - 1471-2393
ER  -