Differences in Rate of Gestational Diabetes Between Active Duty and Non-active Duty Beneficiaries at a Military Treatment Facility

2020 ◽  
Author(s):  
Tolulope Odedokun ◽  
Larissa F Weir ◽  
James K Aden
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Medical Center ◽  
Cohort Analysis ◽  
Sample Size Calculation ◽  
The United States ◽  
Active Duty ◽  
Military Population

Abstract Introduction Gestational diabetes mellitus (GDM) affects approximately 1-14% of all pregnancies in the United States and has significant maternal and neonatal consequences. Developing GDM can increase a patient’s risk of developing overt diabetes in the future which may impact a soldier’s readiness. The purpose of this study is to compare the incidence of GDM in active duty females compared with civilian dependents. Materials and Methods This retrospective cohort analysis was performed at a military medical center with IRB approval. Active duty and dependent status women who delivered between June 1, 2014 and April 30, 2015 were identified along with incidence of GDM. Sample size calculation determined a need for 391 women in each group to observe a 5% difference in rate of GDM with a power of 80%. Chi-squared analysis was used to compare rates of GDM. Results Rates of GDM were similar between the two cohorts (active duty = 9.95%, dependent = 9.72%, P = .91). Age, gravidity, and prepregnancy BMI were also similar between groups. The rate of diet-controlled GDM were different between the two cohorts (active duty = 53.8%, dependent = 34.2%, P = .02). Conclusions This study highlights active duty females have similar rates of GDM as dependents. Gestational diabetes mellitus is known to affect short- and long-term maternal and neonatal outcomes and can impact a soldier’s readiness. Further research is required to determine the long-term impact of GDM in active duty females and best practices to decrease rates of GDM in the military population.

Prevalence and Trends in Gestational Diabetes Mellitus among Women in the United States, 2006–2016

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 121-OR ◽  
Author(s):  
TAO ZHOU ◽  
DIANJIANYI SUN ◽  
XIANG LI ◽  
YORIKO HEIANZA ◽  
HOIRUN NISA ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
United States ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
The United States

scholarly journals Breastfeeding Duration and Development of Dysglycemia in Women Who Had Gestational Diabetes Mellitus: Evidence from the GUSTO Cohort Study

Nutrients ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 408
Author(s):  
Sumali S. Hewage ◽  
Xin Yu Hazel Koh ◽  
Shu E. Soh ◽  
Wei Wei Pang ◽  
Doris Fok ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Tolerance Test ◽  
Diabetes Risk ◽  
Breastfeeding Duration ◽  
Oral Glucose ◽  
Inverse Association ◽  
Index Pregnancy

(1) Background: Breastfeeding has been shown to support glucose homeostasis in women after a pregnancy complicated by gestational diabetes mellitus (GDM) and is potentially effective at reducing long-term diabetes risk. (2) Methods: Data from the Growing Up in Singapore Towards healthy Outcomes (GUSTO) study were analyzed to understand the influence of breastfeeding duration on long-term dysglycemia (prediabetes and diabetes) risk in women who had GDM in the index pregnancy. GDM and dysglycemia four to seven years postpartum were determined by the oral glucose tolerance test (OGTT). A Poisson regression model with a robust error variance was used to estimate incidence rate ratios (IRRs) for dysglycemia four to seven years post-delivery according to groupings of the duration of any breastfeeding (<1, ≥1 to <6, and ≥6 months). (3) Results: Women who had GDM during the index pregnancy and complete breastfeeding information and OGTT four to seven years postpartum were included in this study (n = 116). Fifty-one women (44%) had postpartum dysglycemia. Unadjusted IRRs showed an inverse association between dysglycemia risk and ≥1 month to <6 months (IRR 0.91; 95% confidence interval [CI] 0.57, 1.43; p = 0.68) and ≥6 months (IRR 0.50; 95% CI 0.27, 0.91; p = 0.02) breastfeeding compared to <1 month of any breastfeeding. After adjusting for key confounders, the IRR for the ≥6 months group remained significant (IRR 0.42; 95% CI 0.22, 0.80; p = 0.008). (4) Conclusions: Our results suggest that any breastfeeding of six months or longer may reduce long-term dysglycemia risk in women with a history of GDM in an Asian setting. Breastfeeding has benefits for mothers beyond weight loss, particularly for those with GDM.

scholarly journals Lactation Duration and Long-term Risk for Incident Type 2 Diabetes in Women With a History of Gestational Diabetes Mellitus

Diabetes Care ◽  
2020 ◽  
Vol 43 (4) ◽  
pp. 793-798 ◽  
Author(s):  
Sylvia H. Ley ◽  
Jorge E. Chavarro ◽  
Mengying Li ◽  
Wei Bao ◽  
Stefanie N. Hinkle ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Incident Type ◽  
Incident Type 2 Diabetes ◽  
History Of ◽  
Lactation Duration

Long Term Predictors of Post-Partum Glucose Metabolism in Women with Gestational Diabetes Mellitus

2010 ◽  
Vol 118 (08) ◽  
pp. 485-489 ◽  
Author(s):  
G. Seghieri ◽  
F. Tesi ◽  
A. De Bellis ◽  
R. Anichini ◽  
G. Fabbri ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Glucose Metabolism ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Post Partum

scholarly journals Recent Advances in Gestational Diabetes Mellitus

2021 ◽  
Vol 10 (10) ◽  
pp. 2202
Author(s):  
Katrien Benhalima
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Clinical Medicine ◽  
Metabolic Risk ◽  
Special Issue ◽  
Recent Advances ◽  
Adverse Pregnancy ◽  
New Biomarkers

The incidence of gestational diabetes mellitus (GDM) and overt diabetes in pregnancy is rising globally. GDM leads to increased risks for maternal and neonatal adverse pregnancy outcomes. In addition, GDM is also associated with an increased long-term metabolic risk in mothers and offspring [1]. Although much is known about GDM, evidence gaps persist. For instance, more research is needed on how to prevent GDM, on whether screening and treatment of GDM in early pregnancy are beneficial, on non-fasting biomarkers to screen for GDM, on new biomarkers to predict pregnancy complications, and on how to reduce the long-term metabolic risk in mothers and infants after delivery. To address this important health issue, the present Special Issue in the Journal of Clinical Medicine was dedicated to recent advances in the field of GDM. This Special Issue published 16 articles on this topic. [...]

scholarly journals Geospatial Analysis of Birth Records to Target Programming for Mothers With Gestational Diabetes Mellitus in Michigan, 2013

2018 ◽  
Vol 134 (1) ◽  
pp. 27-35 ◽  
Author(s):  
Elizabeth MacQuillan ◽  
Amy Curtis ◽  
Kathleen Baker ◽  
Rajib Paul
Keyword(s):  
Diabetes Mellitus ◽  
Logistic Regression ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Hot Spot ◽  
The United States ◽  
The State ◽  
Maternal Child Health ◽  
Birth Data ◽  
Birth Records

Objectives: The incidence of gestational diabetes mellitus (GDM) in the United States has increased during the past several decades. The objective of this study was to use birth records and a combination of statistical and geographic information system (GIS) analyses to evaluate GDM rates among subgroups of pregnant women in Michigan. Materials and Methods: We obtained data on maternal demographic and health-related characteristics and regions of residence from 2013 Michigan birth records. We geocoded (ie, matched to maternal residence) the birth data, calculated proportions of births to women with GDM, and used logistic regression models to determine predictors of GDM. We calculated odds ratios (ORs) from the exponentiated beta statistic of the logistic regression test. We also used kernel density estimations and local indicators of spatial association (LISA) analyses to determine GDM rates in regions in the state and identify GDM hot spots (ie, areas with a high GDM rate surrounded by areas with a high GDM rate). Results: We successfully geocoded 104 419 of 109 168 (95.6%) births in Michigan in 2013. Of the geocoded births, 5185 (5.0%) were to mothers diagnosed with GDM. LISA maps showed a hot spot of 8 adjacent counties with high GDM rates in southwest Michigan. Of 11 064 births in the Southwest region, 829 (7.5%) were to mothers diagnosed with GDM, the highest rate in the state and a result confirmed by geospatial analyses. Practice Applications: Birth data and GIS analyses may be used to measure statewide pregnancy-associated disease risk and identify populations and geographic regions in need of targeted public health and maternal–child health interventions.

Abstract P051: Lifestyle Change In Mid-life And Long-term Weight Gain In Women With And Without History Of Gestational Diabetes Mellitus

Circulation ◽  
2021 ◽  
Vol 143 (Suppl_1) ◽  
Author(s):  
Jiaxi Yang ◽  
Janet Rich-Edwards ◽  
Molin Wang ◽  
Wafaie W Fawzi ◽  
Cuilin Zhang ◽  
...  
Keyword(s):  
Physical Activity ◽  
Diabetes Mellitus ◽  
Body Weight ◽  
Weight Gain ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Weight Change ◽  
Lifestyle Change ◽  
History Of

Introduction: Identifying strategies to mitigate gradual long-term weight gain is critical for preventing obesity and its related chronic diseases, particularly for persons at high risk, such as women with a history of gestational diabetes mellitus (GDM). We prospectively examined the independent associations between lifestyle changes in mid-life with long-term weight change among women with and without a history of GDM. Hypothesis: We hypothesized that favorable improvements in lifestyle would be associated with less long-term weight gain, particularly among women with a history of GDM. Methods: We used data from the longitudinal Nurses’ Health Study II, with self-reported lifestyle, diet via food frequency questionnaire, and body weight updated every 2-4 years. We analyzed repeated 4-year changes of the following lifestyle factors among parous women after age 40: adherence to a healthy dietary pattern (Alternate Healthy Eating Index score [AHEI]), physical activity (MET-hrs/wk), moderate alcohol intake (servings/d), and non-smoking, in relation to concurrent 4-year change in body weight (lb). We used multivariable generalized estimating equation models to estimate the least-squares mean of 4-year weight change and 95% confidence interval (CI) for each lifestyle change category (e.g., decrease, remain stable, and increase). Results: Our analysis included 61,637 women, of which 3,444 (5.6%) had a history of GDM. Mean of repeated 4-year weight change after age 40 was 3.0 lb (SD=14.3). Improving diet was associated with favorable 4-year weight change, particularly among women with a history of GDM vs. without GDM (AHEI score change from low to high: -6.3 lb [CI: -9.3, -3.4] vs. -2.7 lb [CI: -3.2, -2.2], respectively; p-interaction=0.04). Increasing physical activity was associated with weight maintenance for GDM women only (MET-hrs/wk change from low to high: 0.6 lb [95% CI: -0.6, 1.7] vs. 2.0 lb [95% CI: 1.8, 2.2] for GDM vs. non-GDM, respectively; p-interaction=0.01). Reducing alcohol (decreased servings/d: 1.9 lb [95% CI: 1.2, 2.6] and 2.8 lb [95% CI: 2.6, 2.9] for GDM vs. non-GDM, respectively) and smoking cessation (recent quitter: 9.8 lb [95% CI: 7.1, 12.5] and 8.5 lb [95% CI: 8.0, 9.1] for GDM vs. non-GDM, respectively) were associated with similar patterns in weight change for women with and without prior GDM. Further, the joint association of improving both diet and physical activity from low to high was related to -12.3 lb (95% CI: -19.5, -5.0) and -6.1 lb (95% CI: -8.0, -4.2) of weight loss for GDM vs. non-GDM women, respectively. Conclusions: We observed that attainable improvements in diet quality and physical activity were associated with weight gain prevention. These findings support continued efforts to improve lifestyle as a beneficial strategy to prevent long-term weight gain, particularly among women with a history of GDM.

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