Diabetes Metab J.  2019 Dec;43(6):785-793. 10.4093/dmj.2018.0218.

Early Assessment of the Risk for Gestational Diabetes Mellitus: Can Fasting Parameters of Glucose Metabolism Contribute to Risk Prediction?

Affiliations
  • 1Division of Obstetrics and Feto-Maternal Medicine, Department of Obstetrics and Gynecology, Medical University of Vienna, Vienna, Austria. christian.goebl@meduniwien.ac.at
  • 2Wunschbaby Institut Feichtinger, Vienna, Austria.
  • 3Metabolic Unit, CNR Institute of Neuroscience, Padova, Italy.

Abstract

BACKGROUND
An early identification of the risk groups might be beneficial in reducing morbidities in patients with gestational diabetes mellitus (GDM). Therefore, this study aimed to assess the biochemical predictors of glycemic conditions, in addition to fasting indices of glucose disposal, to predict the development of GDM in later stage and the need of glucose-lowering medication.
METHODS
A total of 574 pregnant females (103 with GDM and 471 with normal glucose tolerance [NGT]) were included. A metabolic characterization was performed before 15+6 weeks of gestation by assessing fasting plasma glucose (FPG), fasting insulin (FI), fasting C-peptide (FCP), and glycosylated hemoglobin (HbA1c). Thereafter, the patients were followed-up until the delivery.
RESULTS
Females with NGT had lower levels of FPG, FI, FCP, or HbA1c at the early stage of pregnancy, and therefore, showed an improved insulin action as compared to that in females who developed GDM. Higher fasting levels of FPG and FCP were associated with a higher risk of developing GDM. Moreover, the predictive accuracy of this metabolic profiling was also good to distinguish the patients who required glucose-lowering medications. Indices of glucose disposal based on C-peptide improved the predictive accuracy compared to that based on insulin. A modified quantitative insulin sensitivity check index (QUICKIc) showed the best differentiation in terms of predicting GDM (area under the receiver operating characteristics curve [ROC-AUC], 72.1%) or need for pharmacotherapy (ROC-AUC, 83.7%).
CONCLUSION
Fasting measurements of glucose and C-peptide as well as the surrogate indices of glycemic condition could be used for stratifying pregnant females with higher risk of GDM at the beginning of pregnancy.

Keyword

Diabetes, gestational; Glucose metabolism disorders; Insulin resistance; Insulin secretion; Metabolic diseases

MeSH Terms

Blood Glucose
C-Peptide
Diabetes, Gestational*
Drug Therapy
Fasting*
Female
Glucose Metabolism Disorders
Glucose*
Hemoglobin A, Glycosylated
Humans
Insulin
Insulin Resistance
Metabolic Diseases
Metabolism*
Pregnancy
ROC Curve
C-Peptide
Glucose
Insulin

Figure

  • Fig. 1 Receiver operating characteristic curves for prediction of gestational diabetes mellitus (GDM) and initiation of pharmacotherapy in GDM (GDM-PT) using (A, D) fasting plasma glucose (FPG), (B, E) fasting C-peptide (FCP), and (C, F) quantitative insulin sensitivity check index from C-peptide (QUICKIc). AUC, area under curve; CI, confidence interval.


Cited by  4 articles

Letter: Early Assessment of the Risk for Gestational Diabetes Mellitus: Can Fasting Parameters of Glucose Metabolism Contribute to Risk Prediction? (Diabetes Metab J 2019;43:785–93)
Ye Seul Yang, Hye Seung Jung
Diabetes Metab J. 2020;44(1):199-200.    doi: 10.4093/dmj.2020.0023.

Response: Early Assessment of the Risk for Gestational Diabetes Mellitus: Can Fasting Parameters of Glucose Metabolism Contribute to Risk Prediction? (Diabetes Metab J 2019;43:785–93)
Christian S. Göbl, Andrea Tura
Diabetes Metab J. 2020;44(1):209-210.    doi: 10.4093/dmj.2020.0029.

Postprandial Free Fatty Acids at Mid-Pregnancy Increase the Risk of Large-for-Gestational-Age Newborns in Women with Gestational Diabetes Mellitus
So-Yeon Kim, Young Shin Song, Soo-Kyung Kim, Yong-Wook Cho, Kyung-Soo Kim
Diabetes Metab J. 2022;46(1):140-148.    doi: 10.4093/dmj.2021.0023.

The Clinical Characteristics of Gestational Diabetes Mellitus in Korea: A National Health Information Database Study
Kyung-Soo Kim, Sangmo Hong, Kyungdo Han, Cheol-Young Park
Endocrinol Metab. 2021;36(3):628-636.    doi: 10.3803/EnM.2020.948.


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