Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
Diabetologia
Published in: Diabetologia 5/2016

01-05-2016 | Article

First-trimester multimarker prediction of gestational diabetes mellitus using targeted mass spectrometry

Authors: Tina Ravnsborg, Lise Lotte T. Andersen, Natacha D. Trabjerg, Lars M. Rasmussen, Dorte M. Jensen, Martin Overgaard

Published in: Diabetologia | Issue 5/2016

Login to get access

Abstract

Aims/hypothesis

Gestational diabetes mellitus (GDM) is associated with an increased risk of pre-eclampsia, macrosomia and the future development of type 2 diabetes mellitus in both mother and child. Although an early and accurate prediction of GDM is needed to allow intervention and improve perinatal outcome, no single protein biomarker has yet proven useful for this purpose. In the present study, we hypothesised that multimarker panels of serum proteins can improve first-trimester prediction of GDM among obese and non-obese women compared with single markers.

Methods

A nested case–control study was performed on first-trimester serum samples from 199 GDM cases and 208 controls, each divided into an obese group (BMI ≥27 kg/m2) and a non-obese group (BMI <27 kg/m2). Based on their biological relevance to GDM or type 2 diabetes mellitus or on their previously reported potential as biomarkers for these diseases, a number of proteins were selected for targeted nano-flow liquid chromatography (LC) MS analysis. This resulted in the development and validation of a 25-plex multiple reaction monitoring (MRM) MS assay.

Results

After false discovery rate correction, six proteins remained significantly different (p<0.05) between obese GDM patients (n=135) and BMI-matched controls (n=139). These included adiponectin, apolipoprotein M and apolipoprotein D. Multimarker models combining protein levels and clinical data were then constructed and evaluated by receiver operating characteristic (ROC) analysis. For the obese, non-obese and all GDM groups, these models achieved marginally higher AUCs compared with adiponectin alone.

Conclusions/interpretation

Multimarker models combining protein markers and clinical data have the potential to predict women at a high risk of developing GDM.
Appendix
Available only for authorised users
Literature
1.
Group HSCR, Metzger BE, Lowe LP et al (2008) Hyperglycemia and adverse pregnancy outcomes. N Engl J Med 358:1991–2002CrossRef
2.
Kim C, Newton KM, Knopp RH (2002) Gestational diabetes and the incidence of type 2 diabetes: a systematic review. Diabetes Care 25:1862–1868CrossRefPubMed
3.
Ovesen PG, Jensen DM, Damm P, Rasmussen S, Kesmodel US (2015) Maternal and neonatal outcomes in pregnancies complicated by gestational diabetes. A nation-wide study. J Matern Fetal Neonatal Med: 1–5
4.
5.
Chu SY, Callaghan WM, Kim SY et al (2007) Maternal obesity and risk of gestational diabetes mellitus. Diabetes Care 30:2070–2076CrossRefPubMed
6.
Jensen DM, Damm P, Sorensen B et al (2003) Proposed diagnostic thresholds for gestational diabetes mellitus according to a 75-g oral glucose tolerance test. Maternal and perinatal outcomes in 3260 Danish women. Diabet Med 20:51–57CrossRefPubMed
7.
Lind T, Phillips PR (1991) Influence of pregnancy on the 75-g OGTT. A prospective multicenter study. The Diabetic Pregnancy Study Group of the European Association for the Study of Diabetes. Diabetes 40(Suppl 2):8–13CrossRefPubMed
8.
International Association of Diabetes and Pregnancy Study Groups Consensus Panel, Metzger BE, Gabbe SG et al (2010) International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care 33:676–682CrossRefPubMedCentral
9.
Mills JL, Jovanovic L, Knopp R et al (1998) Physiological reduction in fasting plasma glucose concentration in the first trimester of normal pregnancy: the diabetes in early pregnancy study. Metabolism 47:1140–1144CrossRefPubMed
10.
Corrado F, D’Anna R, Cannata ML, Interdonato ML, Pintaudi B, Di Benedetto A (2012) Correspondence between first-trimester fasting glycaemia, and oral glucose tolerance test in gestational diabetes diagnosis. Diabetes Metab 38:458–461CrossRefPubMed
11.
Ferreira AF, Rezende JC, Vaikousi E, Akolekar R, Nicolaides KH (2011) Maternal serum visfatin at 11–13 weeks of gestation in gestational diabetes mellitus. Clin Chem 57:609–613CrossRefPubMed
12.
Georgiou HM, Lappas M, Georgiou GM et al (2008) Screening for biomarkers predictive of gestational diabetes mellitus. Acta Diabetol 45:157–165CrossRefPubMed
13.
Lacroix M, Battista MC, Doyon M et al (2013) Lower adiponectin levels at first trimester of pregnancy are associated with increased insulin resistance and higher risk of developing gestational diabetes mellitus. Diabetes Care 36:1577–1583CrossRefPubMedPubMedCentral
14.
Nanda S, Savvidou M, Syngelaki A, Akolekar R, Nicolaides KH (2011) Prediction of gestational diabetes mellitus by maternal factors and biomarkers at 11 to 13 weeks. Prenat Diagn 31:135–141CrossRefPubMed
15.
Ahnstrom J, Lindqvist PG, Walle U, Dahlback B (2010) Plasma levels of apolipoprotein M in normal and complicated pregnancy. Acta Obstet Gynecol Scand 89:1214–1217CrossRefPubMed
16.
Worda C, Leipold H, Gruber C, Kautzky-Willer A, Knofler M, Bancher-Todesca D (2004) Decreased plasma adiponectin concentrations in women with gestational diabetes mellitus. Am J Obstet Gynecol 191:2120–2124CrossRefPubMed
17.
Maged AM, Moety GA, Mostafa WA, Hamed DA (2014) Comparative study between different biomarkers for early prediction of gestational diabetes mellitus. J Matern Fetal Neonatal Med 27:1108–1112CrossRefPubMed
18.
Smirnakis KV, Plati A, Wolf M, Thadhani R, Ecker JL (2007) Predicting gestational diabetes: choosing the optimal early serum marker. Am J Obstet Gynecol 196:410, e1–6; discussion 410 e6–7
19.
Caglar GS, Ozdemir ED, Cengiz SD, Demirtas S (2012) Sex-hormone-binding globulin early in pregnancy for the prediction of severe gestational diabetes mellitus and related complications. J Obstet Gynaecol Res 38:1286–1293CrossRefPubMed
20.
Su YX, Hong J, Yan Q et al (2010) Increased serum retinol-binding protein-4 levels in pregnant women with and without gestational diabetes mellitus. Diabetes Metab 36:470–475CrossRefPubMed
21.
Tepper BJ, Kim YK, Shete V, Shabrova E, Quadro L (2010) Serum retinol-binding protein 4 (RBP4) and retinol in a cohort of borderline obese women with and without gestational diabetes. Clin Biochem 43:320–323CrossRefPubMedPubMedCentral
22.
Lewandowski KC, Stojanovic N, Bienkiewicz M et al (2008) Elevated concentrations of retinol-binding protein-4 (RBP-4) in gestational diabetes mellitus: negative correlation with soluble vascular cell adhesion molecule-1 (sVCAM-1). Gynecol Endocrinol 24:300–305CrossRefPubMed
23.
Qiu C, Sorensen TK, Luthy DA, Williams MA (2004) A prospective study of maternal serum C-reactive protein (CRP) concentrations and risk of gestational diabetes mellitus. Paediatr Perinat Epidemiol 18:377–384CrossRefPubMed
24.
Ekelund CK, Jorgensen FS, Petersen OB, Sundberg K, Tabor A, Danish Fetal Medicine Research Group (2008) Impact of a new national screening policy for Down’s syndrome in Denmark: population based cohort study. BMJ 337:a2547CrossRefPubMedPubMedCentral
25.
Jensen DM, Molsted-Pedersen L, Beck-Nielsen H, Westergaard JG, Ovesen P, Damm P (2003) Screening for gestational diabetes mellitus by a model based on risk indicators: a prospective study. Am J Obstet Gynecol 189:1383–1388CrossRefPubMed
26.
Overgaard M, Cangemi C, Jensen ML, Argraves WS, Rasmussen LM (2015) Total and isoform-specific quantitative assessment of circulating fibulin-1 using selected reaction monitoring MS and time-resolved immunofluorometry. Proteomics Clin Appl 9:767–775CrossRefPubMed
27.
Lou Y, Wu C, Wu M, Xie C, Ren L (2014) The changes of neutrophil gelatinase-associated lipocalin in plasma and its expression in adipose tissue in pregnant women with gestational diabetes. Diabetes Res Clin Pract 104:136–142CrossRefPubMed
28.
Chen D, Fang Q, Chai Y, Wang H, Huang H, Dong M (2007) Serum resistin in gestational diabetes mellitus and early postpartum. Clin Endocrinol 67:208–211CrossRef
29.
Kuzmicki M, Telejko B, Szamatowicz J et al (2009) High resistin and interleukin-6 levels are associated with gestational diabetes mellitus. Gynecol Endocrinol 25:258–263CrossRefPubMed
30.
Noureldeen AF, Qusti SY, Al-Seeni MN, Bagais MH (2014) Maternal leptin, adiponectin, resistin, visfatin and tumor necrosis factor-alpha in normal and gestational diabetes. Indian J Clin Biochem 29:462–470CrossRefPubMedPubMedCentral
31.
Vitoratos N, Deliveliotou A, Dimitrakaki A et al (2011) Maternal serum resistin concentrations in gestational diabetes mellitus and normal pregnancies. J Obstet Gynaecol Res 37:112–118CrossRefPubMed
32.
D’Anna R, Baviera G, Corrado F, Giordano D, Recupero S, Di Benedetto A (2009) First trimester serum neutrophil gelatinase-associated lipocalin in gestational diabetes. Diabet Med 26:1293–1295CrossRefPubMed
33.
Yassine H, Borges CR, Schaab MR et al (2013) Mass spectrometric immunoassay and MRM as targeted MS-based quantitative approaches in biomarker development: potential applications to cardiovascular disease and diabetes. Proteomics Clin Appl 7:528–540CrossRefPubMedPubMedCentral
34.
Heegaard NH, Ostergaard O, Bahl JM et al (2015) Important options available–from start to finish–for translating proteomics results to clinical chemistry. Proteomics Clin Appl 9:235–252CrossRefPubMed
35.
Agger SA, Marney LC, Hoofnagle AN (2010) Simultaneous quantification of apolipoprotein A-I and apolipoprotein B by liquid-chromatography-multiple-reaction-monitoring mass spectrometry. Clin Chem 56:1804–1813CrossRefPubMedPubMedCentral
36.
Kuzyk MA, Smith D, Yang J et al (2009) Multiple reaction monitoring-based, multiplexed, absolute quantitation of 45 proteins in human plasma. Mol Cell Proteomics 8:1860–1877CrossRefPubMedPubMedCentral
37.
Kaur P, Rizk NM, Ibrahim S et al (2012) iTRAQ-based quantitative protein expression profiling and MRM verification of markers in type 2 diabetes. J Proteome Res 11:5527–5539CrossRefPubMed
38.
39.
Kim SM, Park JS, Norwitz ER et al (2012) Identification of proteomic biomarkers in maternal plasma in the early second trimester that predict the subsequent development of gestational diabetes. Reprod Sci 19:202–209CrossRefPubMed
40.
41.
42.
Biesheuvel CJ, Vergouwe Y, Oudega R, Hoes AW, Grobbee DE, Moons KG (2008) Advantages of the nested case–control design in diagnostic research. BMC Med Res Methodol 8:48CrossRefPubMedPubMedCentral
43.
Nielsen LR, Ekbom P, Damm P et al (2004) HbA1c levels are significantly lower in early and late pregnancy. Diabetes Care 27:1200–1201CrossRefPubMed
Metadata
Title
First-trimester multimarker prediction of gestational diabetes mellitus using targeted mass spectrometry
Authors
Tina Ravnsborg
Lise Lotte T. Andersen
Natacha D. Trabjerg
Lars M. Rasmussen
Dorte M. Jensen
Martin Overgaard
Publication date
01-05-2016
Publisher
Springer Berlin Heidelberg
Published in
Diabetologia / Issue 5/2016
Print ISSN: 0012-186X
Electronic ISSN: 1432-0428
DOI
https://doi.org/10.1007/s00125-016-3869-8

Other articles of this Issue 5/2016

Diabetologia 5/2016 Go to the issue

Article

Tobacco smoking is associated with DNA methylation of diabetes susceptibility genes

Article

Gastric bypass surgery vs intensive lifestyle and medical intervention for type 2 diabetes: the CROSSROADS randomised controlled trial

Article

Incidence of register-based diabetes 10 years after a stepwise diabetes screening programme: the ADDITION-Denmark study

Review

Insights into metabolic disease from studying genetics in isolated populations: stories from Greece to Greenland

MINI-Review

The role of leptin in diabetes: metabolic effects

Article

Exosome-like vesicles released from lipid-induced insulin-resistant muscles modulate gene expression and proliferation of beta recipient cells in mice
Live Webinar | 27-06-2024 | 18:00 (CEST)

Keynote webinar | Spotlight on medication adherence

Reserve your place

Live: Thursday 27th June 2024, 18:00-19:30 (CEST)

WHO estimates that half of all patients worldwide are non-adherent to their prescribed medication. The consequences of poor adherence can be catastrophic, on both the individual and population level.

Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.

Prof. Kevin Dolgin
Prof. Florian Limbourg
Prof. Anoop Chauhan
Developed by: Springer Medicine
Obesity Clinical Trial Summary

At a glance: The STEP trials

Read more

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.

Developed by: Springer Medicine
Image Credits