bluuebunny/crossref_metadata_2025_split huggingface dataset:
Associations of a Preconception Biomarker Risk Score with Future Gestational Diabetes
Authors: BADON SYLVIA E. , FERRARA ASSIAMIRA , ZHU YEYI , SRIDHAR SNEHA B. , LEE CATHERINE , EHRLICH SAMANTHA , QUESENBERRY CHARLES , HEDDERSON MONIQUE 
Published: June 2018

Individual preconception biomarkers have been associated with future gestational diabetes mellitus (GDM) risk. Creating a biomarker risk score incorporating several biomarkers may enhance identification of women at high risk for GDM before pregnancy. The objective of this study was to develop a preconception biomarker risk score and assess its association with subsequent GDM. We conducted a nested case-control study within a cohort of 4,098 women with serum and questionnaire data collected as part of a health checkup (1984-1996) and a subsequent pregnancy (1984-2009) in northern California. Two controls were matched to each GDM case (N=256 cases) on year and age at serum collection, age at pregnancy, and number of pregnancies between serum collection and index pregnancy. Biomarkers associated with GDM in univariable models were dichotomized into high/low risk using quartiles. A biomarker risk score was created by summing the number of high risk biomarkers that were significantly associated with GDM in a multivariable model. Receiver-operating-characteristic curve analyses were used to assess the incremental predictive value of the biomarker risk score beyond conventional GDM risk factors obtained from medical records (age, race/ethnicity, BMI, family history, previous GDM). Compared to low risk, high risk levels of total adiponectin (&lt;7.2 μg/mL), SHBG (&lt;44.2 nmol/L), and HOMA-IR (&gt;3.9) were each independently associated with 2.03 (95% CI: 1.37, 3.02), 2.78 (95% CI: 1.87, 4.13), and 2.07 (95% CI: 1.40, 3.06) times the odds of GDM and thus included in the biomarker risk score. For each one unit increase in the biomarker risk score, odds of GDM were 2.04 times greater (95% CI: 1.62, 2.56). The biomarker risk score had relative incremental predictive value beyond conventional GDM risk factors (AUC= 0.73 vs. 0.67, P&lt;0.0001). Our results suggest the biomarker risk score has potential clinical utility in preconception risk assessment for GDM beyond conventional clinical risk factors.

Disclosure
S.E. Badon: None. A. Ferrara: None. Y. Zhu: None. S.B. Sridhar: None. C. Lee: None. S. Ehrlich: None. C. Quesenberry: None. M. Hedderson: None.
Gestational Diabetes Mellitus (GDM) Predicts Future Risk of Serious Liver Disease
Authors: RETNAKARAN RAVI , LUO JIN , SHAH BAIJU R. 
Published: June 2018

Like type 2 diabetes (T2DM), GDM has recently been associated with enhanced hepatic fat deposition. We thus hypothesized that GDM may predict future lifetime risk of serious liver disease, such as cirrhosis, liver failure and transplantation. Using population-based administrative databases, we identified all women in Ontario, Canada, with a live-birth pregnancy between April 1994 and March 2002 (n=698,078). The women were stratified into those who had GDM (n=17,932) and those who did not (n=680,146), and followed for median 17.1 years for the development of serious liver disease (defined by hospitalization for cirrhosis, liver failure or transplantation). Compared to their peers, women with GDM had an elevated risk of this outcome (HR=1.40, 95% CI 1.01-1.94). Since GDM also predicts future risk of T2DM, the women were further stratified based on incident T2DM in the years after delivery (Figure). Women with GDM who developed T2DM (n=8,567) had an elevated risk of serious liver disease (adjusted HR=1.56, 95% CI 1.02-2.39), as did those who did not have GDM but developed T2DM (n=44,148) (adjusted HR=2.48, 95% CI 2.10-2.93). However, there was no increased risk in women with GDM who did not develop T2DM (n=9,365) (adjusted HR=1.15, 95% CI 0.69-1.91). Thus, despite being a diagnosis of young women of childbearing age, GDM predicts future risk of serious liver disease, the development of which may be dependent upon progression to T2DM.

Disclosure
R. Retnakaran: Research Support; Self; Novo Nordisk Inc.. Consultant; Self; Novo Nordisk Inc.. Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc.. Consultant; Self; Sanofi, Eli Lilly and Company. J. Luo: None. B.R. Shah: None.
Implementation of an Electronic Screening Tool to Detect Early GDM Uncovers Deficiencies
Authors: RODEL RACHEL , REEVES SHANE A. , BARBOUR LINDA A. 
Published: June 2018

Intro: Given little consensus on screening/diagnosis for early GDM despite rapidly evolving expert opinion, providers are legitimately confused about who, how and when to screen. In 2017, the ADA modified their risk-based screening guidelines which were adopted by ACOG but implementation of these guidelines is largely unstudied. We compared rates of documented risk assessment before and after ACOG expanded screening criteria to determine rates of early testing and evaluate if an electronic screening form could increase documentation.
Methods: This QI study assessed provider screening practices before and after implementation of a new screening tool. Our academic center includes midwives, residents and OB/MFM faculty. In Oct 2017, in-person education and email surveys informed providers of a new electronic screening tool to promote GDM risk assessment. We reviewed initial visits at &lt;20 weeks gestation. We examined rates of documentation, screening, risk factors and tests ordered.
Results: A random number sequence was used to identify 50 qualifying subjects by coding Nov 2016 and 2017 for a total of 100 patients. Prior to the screening tool 15/50 (30%) had documented risk assessment and 2/3 of these had testing ordered compared to 17/50 (34%) after the screening tool in which 1/2 were tested. A 1 hour, 50g test was ordered in 80% (2016) and in 88% (2017) and an A1c was ordered for the remaining. Of the 100 patients reviewed, 29 would have qualified for early testing based on race, ethnicity and BMI alone, but only 12 were tested.
Discussion: Unexpectedly, we found that early DM/GDM risk assessment is inadequately documented at our academic center and providing an electronic screening tool did little to increase documentation or adherence. Low rates of testing (&lt;50% of high risk patients) are consistent with other data and suggest current strategies may be ineffective. Determining whether this reflects ambiguity over expert consensus or merely provider time constraints warrant active investigation.

Disclosure
R. Rodel: None. S.A. Reeves: None. L.A. Barbour: None.
Breastfeeding at Night Is Rarely Followed by Hypoglycemia in Puerperal Women with Type 1 Diabetes Using Carbohydrate Counting and Modern Insulin Treatment
Authors: RINGHOLM LENE , ROSKJÆR ANN B. , ENGBERG SUSANNE , ANDERSEN HENRIK U. , HOMMEL EVA , SECHER ANNA L. , DAMM PETER , MATHIESEN ELISABETH R. 
Published: June 2018

Aim: Hypoglycemia in association with breastfeeding is a feared condition in women with type 1 diabetes. Thus, routine carbohydrate intake at each night-time breastfeeding is often recommended despite lack of evidence. We evaluated glucose levels during breastfeeding with focus on whether night-time breastfeeding induced hypoglycemia in women with type 1 diabetes.
Methods: Prospective study of 25 consecutive breastfeeding women (mean age 30.8 (SD ±5.9) years, 64% nulliparous, singleton pregnancy) with type 1 diabetes for 18.8 (±10.5) years. All were experienced in carbohydrate counting with 44% on insulin pump and 56% on multiple daily injections. At 33 (±7.2) and 66 (±14.2) days postpartum blinded continuous glucose monitoring (CGM) was applied for six days and the women recorded breastfeedings and carbohydrate intake.
Results: At both CGM periods mean glucose levels were similar at night (11 pm to 7 am) (8.3 (±1.7) and 8.7 (±2.5) mmol/l, p=0.56) and over 24 hours (8.4 (±1.5) and 8.7 (±2.1) mmol/l, p=0.56). The percentage of time &lt;4.0 mmol/l was similar at night (median 5.8% (range 0-20.8) and 3.1% (0-36.1), p=0.24) and over 24 hours (5.0% (0-19.8) and 3.9% (0-22.8), p=0.75). At 33 days postpartum maternal weight was close to the pre-pregnancy weight (79.1 (±15.0) vs. 75.6 (±14.2) kg, p=0.50) while insulin dose was 18% lower than before pregnancy (p=0.04). A total of 340 night-time breastfeedings were recorded with 2 (0-4) breastfeedings per night. Carbohydrate was ingested at 42 (12%) of the breastfeedings. CGM &lt;4.0 mmol/l within three hours after night-time breastfeeding occurred after 5% of breastfeedings.
Conclusion: Despite almost 300 night-time breastfeedings without carbohydrate intake, hypoglycemia within 3 hours after breastfeeding was rare. The recommendation of routine carbohydrate intake at night-time breastfeeding in women with type 1 diabetes on modern insulin treatment who count carbohydrate may be obsolete.

Disclosure
L. Ringholm: None. A.B. Roskjær: None. S. Engberg: None. H.U. Andersen: Advisory Panel; Self; Novo Nordisk A/S. Stock/Shareholder; Self; Novo Nordisk A/S. Advisory Panel; Self; AstraZeneca. E. Hommel: None. A.L. Secher: None. P. Damm: Advisory Panel; Self; Novo Nordisk A/S. Other Relationship; Self; Novo Nordisk A/S. E.R. Mathiesen: Speaker's Bureau; Self; Novo Nordisk A/S. Other Relationship; Self; Novo Nordisk A/S, Novo Nordisk Foundation, Novo Nordisk A/S.
Labor Induction and Cesarean Delivery Risk in Women with Gestational Diabetes Mellitus
Authors: HAMEL MAUREEN S. , KOLE MARTHA B. , ROUSE DWIGHT , WERNER ERIKA 
Published: June 2018

Objective: Previous studies suggest that labor induction is associated with an increased risk of cesarean delivery (CD), but more modern studies question this finding. In particular, it is uncertain how induction affects CD risk among women with gestational diabetes mellitus (GDM), who are often induced. Therefore, we sought to assess the risk of CD among women with GDM who presented for induction compared to those who presented in spontaneous labor.
Methods: Secondary analysis of a prospective cohort study of singleton pregnancies complicated by GDM. Women planning an elective or repeat cesarean were excluded. All women were delivered at a single institution. The primary outcome for this analysis was mode of delivery (CD vs. vaginal delivery). Women who presented for an induction were compared to those who presented in spontaneous labor (including rupture of membranes at 34 weeks or greater). Covariates, including gestational age at delivery were investigated as confounders and adjusted for if the p&lt;0.05. The study had 80% power to detect an 18% decrease in the absolute rate of vaginal deliveries in the induction group compared to the spontaneous labor group with an alpha of 0.05.
Results: Of the 222 women meeting study inclusion criteria, 80 (36%) presented in spontaneous labor and 142 (64%) underwent labor induction. The overall CD rate was 21.6% (n=48). When comparing labor induction to spontaneous labor to, there was no significant difference in the CD rate (21.8% vs. 21.3% p=0.41). Women who were induced compared to those who labored spontaneously differed only with regard to BMI at delivery (p=0.04), and hypertension (p&lt;0.01). Even after adjusting for these covariates, the risk of CD did not differ between groups (aOR 1.01 95% CI 0.51, 2.02). Moreover, the risk remained non-signification even when gestational age was included in the model.
Conclusion: Induction of labor among women with GDM is not associated with an increased risk of CD and should not be avoided in an attempt to minimize CD risk.

Disclosure
M.S. Hamel: None. M.B. Kole: None. D. Rouse: None. E. Werner: None.
Sex Hormone–Binding Globulin, Cardiometabolic Biomarkers, and Gestational Diabetes—A Longitudinal Pregnancy Cohort Study and a Meta-analysis
Authors: LI MENGYING , RAWAL SHRISTI , HINKLE STEFANIE , LIU SIMIN , TEKOLA-AYELE FASIL , ZHANG CUILIN 
Published: June 2018

Background: Sex hormone-binding globulin (SHBG) has been implicated in glucose metabolism and the development of type 2 diabetes. However, its role in glucose metabolism during pregnancy is unclear.
Objectives: This study investigated the prospective associations of SHBG with cardiometabolic biomarkers during pregnancy and gestational diabetes (GDM) risk.
Methods: We conducted a nested case-control study of 107 GDM cases and 214 matched controls without GDM in the NICHD Fetal Growth Studies-Singleton Cohort. GDM was ascertained by medical record review. Blood samples were drawn at 10-14, 15-26, 23-31 and 33-39 gestational weeks (GW). Prospective associations of SHBG with cardiometabolic biomarkers were estimated using Spearman’s correlation coefficient adjusting for potential confounders. Difference in SHBG levels between GDM and non-GDM women was estimated in the present study cohort and a meta-analysis of existing prospective evidence. Odds ratios (OR) for GDM by SHBG quartiles were estimated using conditional logistic regression.
Results: SHBG at GW 10-14 was significantly and inversely related to fasting glucose, fasting insulin, insulin resistance and HbA1c levels, and positively related to high-density lipoprotein and cholesterol levels at GW 15-26. Cases had lower SHBG levels compared to controls at GW 10-14 (mean ± SD: 204.0 ± 97.6 vs. 220.9 ± 102.5 nmol/L) and 15-26 (305.6 ± 124.3 vs. 322.7 ± 105.1 nmol/L), yet the differences were not significant. Meta-analysis of 11 prospective studies, including ours, estimated SHBG levels to be 0.50 SD (95% CI: 0.36-0.65) lower among women with than without GDM.
Conclusions: Higher SHBG levels in early pregnancy was associated with a favorable glucose metabolic profile among pregnant women. Synthesis of prospective studies supports an inverse association between SHBG levels and GDM risk.

Disclosure
M. Li: None. S. Rawal: None. S. Hinkle: None. S. Liu: None. F. Tekola-Ayele: None. C. Zhang: None.
Is Screening for Early Gestational Diabetes Mellitus Associated with a Better Prognosis? An Observational Study Including 8,713 Women
Authors: COSSON EMMANUEL , BANON DORIAN SANDRE , GARY FRANÇOISE , PHARISIEN ISABELLE , PORTAL JEAN-JACQUES , VALENSI PAUL , CARBILLON LIONEL 
Published: June 2018

In addition to screening for gestational diabetes mellitus (GDM) after 24 gestational weeks (GW), the IADPSG proposed to screen in early pregnancy and to refer women with early GDM (eGDM) for immediate care. The usefulness of this strategy is still unknown. We included women with a singleton pregnancy, without personal history of diabetes or bariatric surgery, who delivered in our hospital between 2012 and 2016. We compared the incidence of preeclampsia or large for gestational age infant or shoulder dystocia in women with an early screening before 22 WG -leading to care for eGDM (FPG) 92-125 mg/dL) or diabetes in pregnancy (DIP: FPG≥126 mg/dL) if present- (screened group) or without (unscreened group). Compared with women in the unscreened group (n=4634, 53.3%), those in the screened group (n=4079) had different ethnicities (p&lt;0.0001) and more risk factors for GDM: overweight (48.6 vs. 45.7, p&lt;0.01), family history of diabetes (27.8 vs. 25.0%, p&lt;0.01), personal history of GDM (6.4 vs. 4.3%, p&lt;0.01) and macrosomic infant (3.6 vs. 2.7%, p&lt;0.05). Early screening increased from year 2012 to 2016 (p&lt;0.0001). In the early screened group, the prevalence of GDM and DIP was 22.8% (including 10.3% of eGDM) and 0.9%, respectively. In the unscreened group, the prevalence of GDM was 17.2% and DIP 1.2%. The rate of insulin therapy was 8.6% in the screened group and 5.6% in the unscreened group (p&lt;0.001). The rate of the composite criterion was similar in the screened and unscreened groups (12.5 vs. 11.8%, p=0.3534), also when we only considered women with risk factors for GDM (14.0 vs. 14.9%, p=0.3137). The incidence of other outcomes was similar in both groups. To conclude, a strategy including early FPG measurement during pregnancy may not improve pregnancy prognosis but we still need to adjust the results for confounding factors (ongoing) because the women in the early-screened group had a similar prognosis as those only screened after 22 GW despite a higher risk of complications.

Disclosure
E. Cosson: Consultant; Self; Lilly France. Board Member; Self; Novartis France. Consultant; Self; Roche Diagnostics France. Board Member; Self; Ascensia Diabetes Care. Consultant; Self; Bezins France. Board Member; Self; MSD France. D. Sandre Banon: None. F. Gary: None. I. Pharisien: None. J. Portal: None. P. Valensi: None. L. Carbillon: None.
iPRO—Effects of Increased Dietary Protein with GDMA1
Authors: TROUT KIMBERLY K. 
Published: June 2018

Purpose/Aim: Primary aim of this study was to evaluate outcomes of glucose and insulin responses to meals and snacks using a randomized crossover design of isocaloric diets comparing increased protein (30% protein, 35% carbohydrate, 35% fat) with a decreased protein (15% protein, 50% carbohydrate, 35% fat) diet.
Methods: Women diagnosed with GDMA1 between 28-35 weeks gestation were randomly allocated to receive one of 2 initial dietary treatments for an overnight admission to inpatient research unit where all meals and snacks were provided. After a 3-7 day washout period, participants were readmitted and received the opposite treatment. A continuous glucose monitoring system (CGMS) was inserted on admission and correlated with blood glucose/hormonal data.
Results: Preliminary results (n=6) with mixed model analysis revealed that the p-value for total glucose AUC shows no statistically significant difference between the two treatments (Table 1). However, at 60” after breakfast on day 2, mean BG was 136 mg/dl on lower protein diet (Tx B) and 117 mg/dl on higher protein diet (TxA, p&lt;0.05).
Discussion: These preliminary results suggest that overall carbohydrate restrictions may be relaxed, as total glucose AUC was not significantly different between the 2 treatment groups. However, further analysis may reveal that increased protein and decreased carbohydrate at specific meals (e.g., breakfast) may prove beneficial in meeting blood glucose targets.Table 1TxVariableNMeanStd DevMedianMinMaxATotal Glu AUC63062.98418.623023.812562.043647.79BTotal Gl;u AUC62824.39579.312659.942319.713830.79

Disclosure
K.K. Trout: None.
Impact of Maternal Metabolic Parameters on Birth Weight and Cord Blood Insulin Concentration—Findings from the Born in Guangzhou Study
Authors: WANG JINGYA , KUANG YASHU , PRICE MALCOLM J. , LU JINHUA , HE JIANRONG , XIA HUIMIN , QIU XIU , CHENG KAR KEUNG , NIRANTHARAKUMAR KRISHNARAJAH 
Published: June 2018

Objective: To investigate the relationship between maternal metabolic parameters with birth weight (BW) and cord blood insulin level(CBI).
Research Design and Methods: Participants were mother-baby pairs (n=1,522) from the Born in Guangzhou Cohort Study (delivered during January 2015 to June 2016). Data on maternal pre-pregnancy BMI, Gestational Weight Gain (GWG), mid-pregnancy fasting glucose and lipid levels were collected; and associations with new-born BW and CBI (z-score) were assessed using multivariable linear regression model, controlling for maternal age, ethnic group, parity and early pregnancy cigarettes exposure. The effect estimates (Regression Co-Efficient: RCE) were standardised to compare the strength of associations of the exposures on BW and CBI z-score.
Results: Z-scores for pre-pregnancy BMI (RCE: 0.20[95% CI: 0.15, 0.24]), early GWG (RCE: 0.18[95% CI: 0.13, 0.23]), glucose (RCE: 0.08[95% CI: 0.04, 0.12]) and triglycerides (RCE: 0.12[95% CI: 0.08, 0.16])) were positively associated with BW z-score. Similar associations were observed with CBI z-score (pre-pregnancy BMI (RCE: 0.11[95% CI: 0.06, 0.16]), early GWG (RCE: 0.07[95% CI: 0.00, 0.13]), glucose (RCE: 0.14[95% CI: 0.09, 0.19]) and triglycerides (RCE: 0.07[95% CI: 0.02, 0.12]). Maternal fasting high-density lipoprotein was negatively associated with BW z-score (RCE:-0.04[95% CI:-0.09, -0.00]) and CBI z-score (RCE: -0.04[95% CI:-0.09, 0.01]) but the 95% CI for the latter included the null.
Conclusions: Maternal pre-pregnancy BMI had the strongest association with BW and CBI, emphasising the importance of optimising preconception BMI. Our results also suggest potential under-recognised adverse effects of intrauterine exposure to gestational dyslipidaemia and that this may warrant clinical attention.

Disclosure
J. Wang: None. Y. Kuang: None. M.J. Price: None. J. Lu: None. J. He: None. H. Xia: None. X. Qiu: None. K. Cheng: None. K. Nirantharakumar: None.
Fatty Liver Disease among Adolescent Offspring Born to Women with Pregestational Type 1 Diabetes (The EPICOM Study)
Authors: KNORR SINE , BYTOFT BIRGITTE , LOHSE ZUZANA , CLAUSEN TINE D. , JENSEN RIKKE B. , DAMM PETER , BECK-NIELSEN HENNING , GRAVHOLT CLAUS H. , JENSEN DORTE M. 
Published: June 2018

Intrauterine exposure to maternal type 1 diabetes (T1DM) is associated with a less favourable metabolic profile later in life. Fatty liver disease can be considered as the hepatic manifestation of the cluster of metabolic abnormalities linked to insulin resistance. This study aimed to evaluate the effect of maternal pregestational T1DM on the offspring’s risk of fatty liver disease and any association between maternal glycemic control during pregnancy and later risk of fatty liver disease in the offspring.
The study is a prospective nationwide follow-up study including offspring (n=277) born to mothers with pregestational T1DM between 1993-1999 and matched un-exposed controls (n=303). Age at the time of follow-up was 16.7 years (range 13.0-20.4 years). We used the Fatty Liver Index (FLI) and Waist-to-Height ratio (WHtR) to evaluate risk of fatty liver disease among the offspring. A FLI&gt;60 or a WHtR&gt;0.469 were used as indicators of fatty liver disease. Results are displayed in Table 1. More diabetes exposed offspring had high FLI and WHtR indices compared to un-exposed controls. Adjustment for maternal prepregnancy BMI, gender, age, and pubertal stage did not change overall results. No association between maternal glycemic control during pregnancy and risk of fatty liver disease was found.Table 1: Comparison of indicators of fatty liver disease among offspring born to mothers with T1DM.nExposedControlsp-value(n=277)(n=303)FLI &lt; 30495229 (46)266 (54)FLI&gt;30 &lt; 602518 (72)7 (28)FLI&gt;60149 (64)5 (36)0.02*WHtR &lt;0.469489213 (44)276 (56)WHtR &gt;0.4699164 (70)27 (30)&lt;0.001*Data are displayed as numbers and percent.* Pearsons chi-squared test. FLI&lt;30 rules out and FLI&gt;60 as well as well as a WHtR&gt;0.469 rules in fatty liver disease.
Conclusion: Intrauterine exposure to maternal T1DM may predispose to fatty liver disease.

Disclosure
S. Knorr: None. B. Bytoft: None. Z. Lohse: None. T.D. Clausen: None. R.B. Jensen: None. P. Damm: Advisory Panel; Self; Novo Nordisk A/S. Other Relationship; Self; Novo Nordisk A/S. H. Beck-Nielsen: None. C.H. Gravholt: None. D.M. Jensen: None.