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Role of serum biomarkers to optimise a validated clinical risk prediction tool for gestational diabetes
Author(s) -
Abell Sally K.,
Shorakae Soulmaz,
Boyle Jacqueline A.,
De Courten Barbora,
Stepto Nigel K.,
Teede Helena J.,
Harrison Cheryce L.
Publication year - 2019
Publication title -
australian and new zealand journal of obstetrics and gynaecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.734
H-Index - 65
eISSN - 1479-828X
pISSN - 0004-8666
DOI - 10.1111/ajo.12833
Subject(s) - gestational diabetes , adiponectin , medicine , pregnancy , diabetes mellitus , receiver operating characteristic , obstetrics , area under the curve , gestation , endocrinology , insulin resistance , biology , genetics
Background Clinical risk prediction tools for gestational diabetes ( GDM ) may be enhanced by measuring biomarkers in early pregnancy. Aim To evaluate a two‐step GDM risk prediction tool incorporating fasting glucose ( FG ) and serum biomarkers in early pregnancy. Materials and methods High molecular weight ( HMW ) adiponectin, omentin‐1 and interleukin‐6 ( IL ‐6) were measured at 12–15 weeks gestation in women with high risk of GDM from a randomised trial using a clinical risk prediction tool. GDM diagnosis (24–28 weeks) was evaluated using 1998 Australian Diabetes in Pregnancy ( ADIPS ) criteria and newer International Association of the Diabetes and Pregnancy Study Groups ( IADPSG ) criteria. Associations between biomarkers and development of GDM were examined using multivariable regression analysis. Area under the receiver‐operator curve ( AUC ), sensitivity and specificity were calculated to determine classification ability of each model compared to FG and maternal characteristics. Results HMW adiponectin improved prediction of ADIPS GDM ( AUC 0.85, sensitivity 50%, specificity 96.2%, P = 0.04), compared to FG and maternal factors (0.78, 35% and, 98.1%, respectively). HMW adiponectin <1.53 μg/ mL further improved the model ( AUC 0.87, sensitivity 75%, specificity 88.2%, P = 0.01). HMW adiponectin did not improve prediction of IADPSG GDM ( AUC 0.84, sensitivity 64%, specificity 97.9%, P = 0.22) compared to FG and maternal factors (0.79, 56%, 93.8%). Omentin‐1 and IL ‐6 did not significantly improve classification ability for GDM . Conclusions A two‐step approach combining FG and HMW adiponectin to a validated clinical risk prediction tool improved sensitivity and predictive ability for ADIPS GDM . Further research is required to enhance GDM prediction using IADPSG criteria for application in clinical practice.