Abstract
This population-based study investigated the association of BMI and other predictors with gestational diabetes mellitus (GDM) among Australian Aboriginal and non-Aboriginal mothers. We conducted a state-wide retrospective cohort study that included all singleton births in Western Australia (n = 134,552) between 2012 and 2015 using population health datasets linked by the Western Australian Data Linkage Branch. Associations between GDM and its predictors were estimated as adjusted relative risks (aRRs) from multivariable generalised linear models. Adjusted ratio of relative risks (aRRRs) compared RRs in Aboriginal and non-Aboriginal mothers. Adjusted population attributable fractions estimated the contribution of overweight/obesity to GDM burden, and adjusted predicted probabilities for GDM were plotted against BMI levels. The following predictors had stronger associations with GDM in Aboriginal, compared to non-Aboriginal, mothers: maternal obesity (aRR [95% CI] 3.16 [2.54–3.93]; aRRR 1.57 [1.26–1.94]), previous LGA (aRR 1.70 [1.37–2.12]; aRRR 1.41 [1.13–1.76]) and previous macrosomia (birthweight ≥ 4 kg) (aRR 1.55 [1.24–1.94]; aRRR 1.53 [1.22–1.91]). 46.1% (95% CI: 36.6–54.1) of GDM cases in Aboriginal women (23.3% in non-Aboriginal mothers, 95% CI: 21.6–25.1) were attributed to overweight/obesity. Compared to non-Aboriginal mothers, adjusted GDM probabilities were higher at all BMI levels and showed greater increase with BMI. Overweight/obesity is a key driver of GDM among Aboriginal women. Association between BMI and GDM is stronger in Aboriginal, compared to non-Aboriginal, women especially at higher BMI.
| Original language | English |
|---|---|
| Article number | 102444 |
| Pages (from-to) | 1-8 |
| Number of pages | 8 |
| Journal | Preventive Medicine Reports |
| Volume | 36 |
| DOIs | |
| Publication status | Published - Dec 2023 |
Bibliographical note
Funding Information:M.A.A was supported by a University Postgraduate Award (The University of Western Australia), an Australian Government Research Training Program Scholarship and the Peter and Anne Hector Award. HDB and CCJS were supported by a National Health and Medical Research Council grant (APP 1127265). GP was supported with funding from the National Health and Medical Research Council Project and Investigator Grants #1099655 and #1173991, institutional funding for the WA Health and Artificial Intelligence Consortium, and the Research Council of Norway through its Centres of Excellence funding scheme #262700. The funding bodies had no role in the study design; in the analysis of data and interpretation of results; in the writing of the manuscript; and in our decision to submit this article for publication.
Funding Information:
The authors wish to thank the staff at the Western Australian Data Linkage Branch and the data custodians of the Midwives Notification System, Hospital Morbidity Data Collection, Western Australian Register of Developmental Anomalies and WA Registry of Births, Deaths and Marriages. The authors also gratefully acknowledge the guidance of the Kaadaninny Aboriginal Advisory Committee (Rhonda Marriott, Patricia Elder, Denese Griffin, Aunty Marie Taylor, Corina Martin, Aunty Millie Penny, Lesley Nelson, Wendy Casey, Jade Maddox and Mel Robinson) and Ngangk Yira Council of Elders (Aunty Marie Taylor, Aunty Doreen Nelson, Aunty Doris Getta, Aunty Gladys Yarran, Aunty Millie Penny, Aunty Vivienne Hansen, Uncle Fred Penny and Uncle Mort Hansen).
Publisher Copyright:
© 2023
