Risk Of GDM Research Articles

Study protocol on risk factors for the diagnosis of gestational diabetes mellitus in different trimesters and their relation to maternal and neonatal outcomes (GDM-RIDMAN)

IntroductionGestational diabetes mellitus (GDM) is often associated with adverse pregnancy outcomes. However, the association of risk factors with GDM diagnosis, maternal and neonatal health outcomes is less established when compared...

Fatty liver indices and their association with glucose metabolism in pregnancy – An observational cohort study

AimsNon-invasive hepatic steatosis indices can be used to assess the risk for metabolic (dysfunction) associated fatty liver disease (MAFLD). This may be helpful to detect metabolic disorders in pregnancy, specifically gestational diabetes (GDM). We aimto examine the association of these indices with parameters of glucose metabolism. Methods109 women underwent a metabolic characterization at 16 weeks of gestation andwere classified according to the fatty-liver index (FLI) andhepatic-steatosis index (HSI) into low (G1), intermediate (G2) and high risk (G3). At 26 weeks, participants received an oral glucose tolerance test (OGTT) to assess insulin action, β-cell function and GDM status. ResultsBoth MAFLD indices wereassociated with impaired insulin sensitivityand compensatory increase of insulin release. G3 groups showedimpaired insulin action. The higher circulating insulin concentrations were not able to compensate for insulin resistance in women with higher MAFLD scores, resulting in an increased risk of GDM(OR: 1.05, 95% CI 1.03 to 1.08, p < 0.001 for FLI). MAFLD scores were associated with fetal overgrowth. ConclusionsMaternal MAFLD represents a high-risk obstetric condition. Hepatic steatosis indices are associated with impaired glucose regulation and may provide a useful tool for early risk assessment for impaired glucose metabolism.

A self-efficacy-enhancing physical activity intervention in women with high-risk factors for gestational diabetes mellitus: study protocol for a randomized clinical trial

BackgroundGestational diabetes mellitus (GDM) is one of the most common medical disorders in pregnancy. Evidence has demonstrated that moderate-intensity physical activity may reduce the risk of gestational diabetes. However, women at risk of GDM spend most of their time performing sedentary behaviors. Although researchers identified self-efficacy as a mediator to overcome physical activity barriers, exercise intervention during pregnancy based on self-efficacy theory has not been discussed so far. Furthermore, there is conflicting evidence regarding the effects of a physical exercise intervention on the incidence of GDM and other maternal or neonatal outcomes in women at higher risk for GDM.Methods/designA single-center, parallel, randomized controlled trial will be conducted in a maternal–child health care center. A total of 244 pregnant women at high risk for GDM will be randomized into a study group receiving a self-efficacy-enhancing physical activity intervention or a control group receiving the usual care. The intervention will consist of four group sessions and everyday reminders by WeChat (Tencent, Shenzhen, China). The program will begin at approximately 13–14+6 gestational weeks and end at 36+6 gestational weeks. The primary outcomes will include the incidence of GDM, blood sugar values, and physical activity. The secondary outcomes will include physical activity self-efficacy, gestational weight gain, maternal outcomes, and neonatal outcomes.DiscussionThe findings of this research will contribute toward understanding the effects of a self-efficacy theory-oriented physical activity program on the incidence of GDM, blood sugar values, physical activity level, gestational weight gain, physical activity self-efficacy, maternal outcomes, and neonatal outcomes.Trial registrationChinese Clinical Trial Registry (CHiCTR) ChiCTR2200056355. Registered on February 4, 2022.

Neck circumference as a predictor of gestational diabetes and risk of adverse outcomes in pregnancy of Brazilian woman with overweight and obesity.

To evaluate the association of neck circumference (NC) with gestational diabetes (GDM) and adverse outcomes in women with overweight and obesity. This prospective study included 132 (BMI > 25 kg/m2) pregnant women without and with GDM. Standardized questionnaire and biochemical/physical evaluation were performed during the 1st to 3rd trimester. Fifth-five women were evaluated regarding hypertension in pregnancy, type of delivery and neonatal complications (death, intensive care unit admission and hypoglycemia). Women with (n = 61) and without (n = 71) GDM had similar mean (SD) pre-gestational BMI [30.3 (4.0) vs. 29.4 (3.5) kg/m2, p = 0.16]. Women with GDM were older [32 (6) vs. 28 (6) yrs, p < 0.001] and had greater NC [36.0 (2.7) vs. 34.5 (1.8) cm, p < 0.001]. NC was similar in women with GDM diagnosed in first or third trimester [p = 0.4] and was correlated with FPG [r 0.29, p = 0.01] and systolic [r 0.28, p = 0.001] and diastolic [r 0.25, p = 0.004] blood pressure. NC was associated with GDM [OR 1.25, 95%CI 1.03-1.52] adjusted for age, physical activity, education and familiar history of diabetes. In ROC analysis, the area under the curve was 0.655 and the cut-off value of 34.5 cm had 0.70 of sensitivity and 0.51 of specificity for GDM. Women who had NC ≥ 34.5 vs. < 34.5 cm had higher frequencies of hypertension [32.3 vs. 4.2%, p = 0.01]. In a group of pregnant women with overweight or obesity, NC can be a useful tool for identifying risk of GDM and obstetric adverse outcomes.

1173-P: Metabolomic Signatures of Refined Grains Intake and Risk of Gestational Diabetes

Previous studies linked refined grains intake to risk of gestational diabetes (GDM) ; however, the underlying mechanisms remain understudied. We aimed to identify and validate refined grains-related metabolites as biomarkers for GDM. In a nested case-control study of 91 women with GDM and 180 non-GDM controls (discovery set) in the Pregnancy Environment and Lifestyle Study (PETALS) cohort, we assessed dietary intake of refined grains by a validated food frequency questionnaire and measured fasting serum untargeted metabolomics at gestational weeks (GW) 10-13. We identified metabolites associated with refined grains intake using multivariable linear regression and examined prospective associations of refined grains-related metabolites at GW 10-13 with risk of GDM in later pregnancy using conditional logistic regression. We further examined incremental predictivity of refined grains-related metabolites selected by 10-fold cross-validated LASSO regression beyond conventional risk factors in the discovery set and a random PETALS subsample (38 GDM, 336 non-GDM) as the validation set. Among 821 known metabolites, 42 (mostly lipids/lipid-like molecules and organic oxygen/acids compounds) were associated with refined grains intake in early pregnancy, of which 17 were significantly associated with GDM risk (all FDR &amp;lt; 0.05) . Adding seven out of 17 metabolites selected by LASSO regression to a conventional risk factor-based prediction model increased the C-statistic from 0.71 (95% CI 0.64-0.77) to 0.77 (0.71-0.83) ; P-for-difference &amp;lt; 0.05. Similar improvement was observed in the validation set [0.77 (0.69-0.86) to 0.81 (0.74-0.89) ; P-for-difference &amp;lt; 0.05]. Refined grains intake may be associated with risk of GDM via perturbed metabolic pathways of glycerolipids, glycerophospholipids, and sphingolipids. Refined grains-related metabolites might help improve prediction of GDM risk and inform diet-related preventive strategies. Further validation studies are warranted. Disclosure Y.Zhu: None. S.Zheng: None. A.Ngo: None. R.F.Chehab: None. E.Z.Wang: None. O.Fiehn: None. A.Ferrara: n/a. Funding National Institutes of Health Building Interdisciplinary Research Careers in Women's Health (BIRCWH) Program (K12HD052163) ; National Institute of Diabetes and Digestive and Kidney Diseases (K01DK120807)

105-OR: Microbiome-Derived Metabolite Signatures in Early- to Midpregnancy for Risk of Gestational Diabetes (GDM)

Pre-diagnostic disturbances in gut microbiome and metabolome have been associated with an increased risk of diabetes in non-pregnant populations. However, such prospective studies in pregnant women are scant. We aimed to develop and validate microbiome-derived metabolite predictive markers in early to mid-pregnancy for GDM. We designed a nested case-control study (91 GDM, 180 non-GDM; discovery set) and a random subsample (42 GDM, 372 non-GDM; validation set 1) from the PETALS cohort, and a case-control study from the GLOW trial (35 GDM, 70 non-GDM; validation set 2) . We collected fasting serum untargeted metabolomics data at gestational weeks (GW) 10-13 and 16-by gas chromatography/time-of-flight mass spectrometry (TOF-MS) , liquid chromatography (LC) /quadrupole TOF-MS, and hydrophilic interaction LC/quadrupole TOF-MS. Among 1167 annotated metabolites, 193 microbiome-derived metabolites were identified from the Virtual Metabolic Human Database. Multivariate enrichment analysis examined metabolite-GDM associations. Ten-fold cross-validated LASSO prediction models identified metabolite signatures for GDM. At GW 10-13, the branched-chain amino acids cluster was marginally, positively associated with GDM risk (FDR = 0.073) . At GW 16-19, the ceramides, pyridines, pyrimidine nucleosides, and unsaturated fatty acids clusters were significantly, positively associated with GDM risk (all FDR &amp;lt;0.05) . A 38-metabolite panel at GW 10-13 outperformed the model using conventional risk factors including fasting glucose (discovery AUC: 0.915 vs. 0.743; validation 1: 0.865 vs. 0.731; validation 2: 0.970 vs. 0.742; P &amp;lt;0.01) . Similar results were observed for a 21-metabolite panel at GW 16- (discovery AUC: 0.842 vs. 0.760; validation 1: 0.840 vs. 0.774; P &amp;lt;0.01) . Panels of microbiome-derived metabolites in early to mid-pregnancy can predict GDM risk beyond conventional risk factors, suggesting the role of microbiome-metabolome-host interactions in GDM. Disclosure Y.Zhu: None. S.Susarla: None. D.Barupal: None. A.Ngo: None. R.F.Chehab: None. O.Fiehn: None. A.Ferrara: n/a. Funding National Institutes of Health Building Interdisciplinary Research Careers in Women's Health (BIRCWH) Program (K12HD052163) ; National Institute of Diabetes and Digestive and Kidney Diseases (K01DK120807)

635-P: Maternal Social Support (SS) related to Reproductive Health (RH) and Healthy Lifestyle Behaviors among American Indian and Alaska Native (AIAN) Female Adolescents and Young Adults (AYA) to Minimize Risk for Gestational Diabetes (GDM)

AIAN women have higher risk for GDM. Early preconception counseling (PC) about healthy lifestyle/weight and mothers’ support could reduce these risks. We describe and compare parallel measures of SS with RH/GDM risk: daughters’ perceived SS from mothers, and mothers’ perceived SS for daughters. Methods: Secondary analysis used baseline data from 113 mother-daughter (M-D) dyads from four sites participating in Stopping GDM, an online randomized trial to reduce risks of GDM. Perceived maternal SS was amounts (0=no support, to 5=a lot of support) and types of SS re: RH, healthy lifestyle behaviors, and seeking PC to prevent GDM (instrumental SS, e.g., help get birth control; informational SS, e.g., on pregnancy/GDM) . M-D independently completed online measures of perceived available maternal SS. We performed descriptive, within-dyad comparative/correlational analyses. Results: AIAN AYA (n=113; mean age 17±3yrs; BMI 29±8.3 kg/m2) and their mothers (n=113; age 44.7±9yrs; 45% had &amp;gt; 2-4 college; 44% married) . AYA reported moderately high levels of maternal SS: total (16±5; range:0-20) ; instrumental (12±4; 0-15) ; informational (4±2; 0-5) . Mothers reported high levels of SS to give their daughters: total (20±1; range:15-20) ; instrumental (15±1; 10-15) ; informational (5±0; 0-5) . Mothers reported significantly higher levels of SS to give their daughters compared to their daughters’ perception of available maternal SS (p&amp;lt;.001) . Daughters and mothers’ perceived available maternal SS were positively correlated: total: rS=.218, p=.024; instrumental: rS=.229, p=.016. Conclusion: Maternal SS perceived by AIAN AYA females for RH and GDM prevention was lower than their mothers’ perception of available SS for their daughters. Mothers could potentially provide SS for their daughters. M-D dyadic approach could be beneficial to minimize the risk of GDM. Disclosure D. Charron-Prochownik: None. S.M. Sereika: None. A.F. Fischl: None. N. O'Banion: None. K.L. Gonzales: None. L. Chalmers: None. K.R. Moore: Consultant; Novo Nordisk. Funding RNR014831 grant

156-LB: New Gestational Diabetes Mellitus Risk Algorithm

Background and Objective: GDM is associated with life-long adverse outcomes for mother and baby, and its incidence is increasing. Markers beyond clinical factors are needed to identify women at high risk and catalyze early preventive interventions. Our aim was to develop a risk assessment algorithm that integrates genetic and clinical variables. Methods: We analyzed a retrospective cohort of 711 women from Hospital Clínico San Carlos (HCSC, Madrid, Spain) , with 425 control pregnancies and 286 GDM cases diagnosed per IADPSG criteria. The HCSC cohort was randomly divided into a training/development dataset (70% of cohort) for algorithm development and a test dataset (30% of cohort) for validation. In addition, we tested the model in a cohort of 157 women (89 controls, 68 cases diagnosed per NDDG criteria from Hospital Cruces (Bilbao, Spain) and in a cohort of 416 women (346 controls, 70 cases per IADPSG criteria from HMPMPS, México) . A total of 114 SNPs were selected for this analysis after exhaustive exploration of the databases published to date of SNPs associated with GDM. The SNPs were selected based on their predictive power and population frequency, with the following criteria: OR&amp;gt;1.2, RAF&amp;gt;0.20, p&amp;lt;1×10-5. Discrimination and calibration of risk scores were assessed using the receiver operating characteristic (ROC) curve in the internal and the external validation groups. Results: The algorithm provided a risk score for GDM, integrating 10 SNPs, maternal age, and pregestational BMI. In the training dataset the AUC was 0.74, sensitivity of 77% and specificity of 64%. AUCs in the HCSC, UAEM and Cruces validation sets were 0.71, 0.70 and 0.62 respectively. Conclusions: This new tool for GDM risk assessment suggests that the utilization of genetic markers in combination with clinical characteristics may improve GDM risk evaluation and accelerate adoption of prevention interventions. Our study also highlights the importance of applying consensus criteria for the diagnosis of GDM. Disclosure L. Mendizabal: Employee; Patia Biopharma S. A. L. Castano: None. M. M. Martínez: None. E. Camarillo-romero: None. H. Mendieta zerón: None. J. Garduno-garcia: Speaker's Bureau; Boehringer Ingelheim International GmbH. R. Corcoy: None. L. Simon: Board Member; Nelum Corp., Oncomatryx Biopharma SL, Patia Biopharma S. A., Quimatryx, SunRock Biopharma S. L., Other Relationship; Precsion Diabetes, Inc. M. Zulueta: None. A. L. Calle: None. M. Arregi: Employee; Patia Biopharma S. A. J. Valerio deogracia: None. A. M. Ramos levi: None. A. Barabash: None. N. García de la torre: Other Relationship; Ferrer. E. Arana: None. I. Urrutia: None. S. Gaztambide: None.

1065-P: The Use of OGTT and Phenotypic Characteristics to Identify GDM Subtypes in Mexican Women, an Approach towards Precision Medicine

GDM is a heterogeneous disease. Three subtypes of GDM have been identified, insulin sensitivity defect, insulin secretion defect, and both. Insulin sensitivity defect is associated with higher risk for fetal overgrowth and GDM associated adverse outcomes. Based on the 2hr-OGTT-75g values, the phenotype of GDM-sensitivity is associated with elevated fasting glucose and GDM-secretion by elevated one and/or two hours post-load glucose values. We evaluated the 2hr-OGTT-75g values of 1713 in Mexican pregnant women without previous diagnosis of T2D from the cohort study Cuido mi Embarazo between April 20 and December 2021. We identified 251 cases of GDM (14.6%) . Based on the distribution of 2hr-OGTT-75g values related to known GDM subtypes, 132 (52.6%) had elevated fasting glucose but normal post-load glucose values (GDM-sensitivity) , 90 (35.8%) had elevated post-load glucose but normal fasting glucose values (GDM-secretion) , and 29 (11.6%) patients with elevated fasting and post-load glucose values (GDM-mixed) . Women in the GDM-sensitivity group were younger (27.3 ± 6.5 yr) , nulliparous (56.1%) and more often had a family history of T2D (50.0%) compared to the women in the GDM-secretion and GDM-mixed groups. However, there was no statistical difference between the principal groups regarding pregestational BMI (GDM-sensitivity: 27.7 ± 5.5; GDM-secretion: 27.4 ± 4.4; GDM-mixed: 28.4 ± 6.2 kg/m2, p=0.90) ; although this last group showed the higher prevalence of obesity (29.4% - 23.3% - 38.0% respectively for each group) . To our knowledge, this is the first analysis of GDM subtypes in Mexican pregnant women. The high proportion of GDM diagnoses with normal fasting glucose supports the importance of unavoidably offering a complete OGTT. This strategic approach by classifying GDM subtypes according to OGTT values takes clinical importance in the offering of personalized and timely management care to minimize perinatal and newborn adverse outcomes. Disclosure H.Gallardo-rincón: None. N.Garcia-gonzalez: None. A.H.Perez-hernandez: None. B.Perez-romero: None. C.G.Granillo-ramirez: None. R.Tapia-conyer: None. J.Ortega-montiel: None. L.Martinez-juarez: None. J.Lomelin-gascon: None. A.Montoya: None. E.Reyes-muñoz: None. V.Galicia-hernandez: None. R.Avila-dominguez: None. L.V.Perez-torres: None. Funding Eli Lilly and Company

1039-P: Association of GDM Risk Factors with Glucose at Diagnosis and Treatment in Sweden

Background: The screening strategy in Sweden for GDM is mainly based on random plasma glucose measurements combined with presence of risk factors (previous GDM or LGA (large for gestational age) /macrosomia, obesity, heredity, accelerated growth or polyhydramnios) . The association of risk factors with mean levels of glucose in the diagnostic OGTT in different populations is unclear. Method/Material: Data from Changing Diagnostic Criteria for GDM in Sweden (CDC4G) - a stepped wedge randomised RCT (ISRCTN41918550) where centers during 2018 were randomized to switch to new IADPSG criteria for GDM every month. All pregnant women underwent a venous OGTT during 2018. Mean glucose values by risk factor are presented with 95% CI, significant p value &amp;lt;0. (marked bold in the table) . Results: Of 53 124 pregnancies 69 (13.0%) underwent venous OGTT. Either metformin or Insulin was used in 11% before the diagnostic criteria switch and 18.8% after the switch. Metformin only was used in 9.7% before and in 16.4% after randomization to new criteria. Conclusions: BMI≥35 or Previous LGA/macrosomia were associated with a higher fasting glucose value in OGTT, but previous GDM, accelerated growth and high random blood glucose were statistically significantly associated with higher glucose values in all measurements in the diagnostic OGTT. Women with previous GDM had the highest proportion treated with either insulin and/or metformin Disclosure H.E.Fadl: None. H.Strevens: None. U.Wennerholm: None. M.Persson: None. M.De brun: None. S.Montgomery: None. D.Simmons: Other Relationship; Elsevier, Research Support; Abbott, Hitachi, Ltd., Novo Nordisk, Speaker's Bureau; Sanofi. S.Patil: None. A.Wikström: None. S.P.Jansson: None. V.Sengpiel: None. K.Berntorp: None. L.Ryen: None. F.Ahlsson: None. E.S.Lindholm: None. Funding Swedish Research Council (2018-00470)

Fasting serum fructose is associated with risk of gestational diabetes mellitus

ObjectiveTo investigate the association of fasting serum fructose concentrations and the incidence of GDM.Research design and methodsFive hundred twenty six pregnant women who attended the obstetric clinic of Xinhua Hospital, Chongming Branch were recruited prospectively from September 2019 to November 2020. Fasting serum fructose concentrations were measured by a validated liquid chromatography–tandem mass spectrometry method. GDM was diagnosed according to the criteria of the IADPSG. Independent sample t-test was used to compare the differences between groups. Multiple stepwise regression analysis was used to estimate the associations of serum fructose and other variables. Multivariate logistic regression models were adopted to evaluate the odds ratios (ORs) for GDM.ResultsOf the 526 pregnant women, 110 were diagnosed with GDM. Fasting fructose concentrations were increased significantly in GDM patients compared to those without GDM (1.30 ug/ml vs 1.16 ug/ml, p<0.001). Fasting fructose concentration was independently associated with GDM after adjusting the potential confounders, 1 ug/ml increase in fasting serum fructose level was associated with an 81.1% increased risk of GDM (1.811, [1.155-2.840]). Taking fructose <1.036 ug/ml as the reference, the OR for GDM was significantly higher in fructose ≥1.036 ug/ml group (OR, 1.669; 95% CI, 1.031–2.701) after all the potential confounders were adjusted.ConclusionsIncreased fasting serum fructose levels were independently associated with the incidence of GDM.

Peri-conceptional diet patterns and the risk of Gestational diabetes mellitus in South Indian women.

To identify peri-conceptional diet patterns among women in Bangalore, and examine their associations with risk of gestational diabetes mellitus. BANGLES, started in June 2016, was a prospective observational study, in which women were recruited at 5-16 weeks' gestation. Peri-conceptional diet was recalled at recruitment, using a validated 224-item food frequency questionnaire. GDM was assessed by a 75-gram oral glucose tolerance test at 24-28 weeks' gestation, applying WHO 2013 criteria. Diet patterns were identified using principal component analysis and diet pattern-GDM associations were examined using multivariate logistic regression, adjusting for 'a priori' confounders. Antenatal clinics of two hospitals, Bangalore, South India. 785 pregnant women of varied socio-economic status. GDM prevalence was 22%. Three diet patterns were identified: a) High-diversity, urban (HDU) characterised by diverse, home-cooked and processed foods was associated with older, more affluent, better-educated and urban women; b) Rice-fried snacks-chicken-sweets (RFCS), characterised by low diet-diversity, was associated with younger, less-educated, and lower income, rural and joint families; c) Healthy, traditional vegetarian (HTV), characterised by home-cooked-vegetarian and non-processed foods was associated with less-educated, more affluent, and rural and joint families. The HDU pattern was associated with a lower GDM risk (aOR: 0.80 per SD, 95% CI: 0.64, 0.99, p=0.04) after adjusting for confounders. BMI was strongly related to GDM risk and possibly mediated diet-GDM associations. The findings support global recommendations to encourage women to attain a healthy pre-pregnancy BMI and increase diet-diversity. Both healthy and unhealthy foods in the patterns indicate low-awareness about healthy foods and a need for public-education.

Development and validation of a new gestational diabetes mellitus (gdm) risk algorithm

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

Abstract 180: South Asian Nativity And Cardiometabolic Risk Factors In Pregnant Women In The United States

Introduction: Women of South Asian (SA) descent have high cardiometabolic risk which may impact obstetric and long-term morbidity and mortality. Data regarding contemporary epidemiology of cardiometabolic risk factors in pregnant women are scarce. Therefore, we sought to investigate contemporary epidemiology of cardiometabolic risk factors in pregnant women in the US with a focus on SA-born women in comparison with United States (US)-born and women born in the rest of the world (ROW). Methods: We used the natality database 2016-2020 to investigate the incidence/prevalence of cardiometabolic risk factors (pre-pregnancy diabetes [pre-DM], gestational diabetes [GDM], pre-pregnancy hypertension [pre-HTN], gestational hypertension [GHTN], smoking, and obesity) in the US, by country of nativity. SA women were defined as women born in Bangladesh, India, Nepal, Pakistan, and Sri Lanka. US women were those born in the US (including US Virgin Islands) and ROW refers to women born in other countries. Results: A total of 18,916,536 pregnancies were studied (370,619 SA; 14,745,134 US; and 3,800,783 ROW). SA women had higher risk of pre-DM compared with US and ROW (1.2% vs 0.9% vs 1.0%), GDM (15.7% vs 5.9% vs 8.8%), but lower rates of pre-HTN (0.9% vs 2.3% vs 1.3%), GHTN (4.2% vs 7.8% vs 4.9%), obesity (11.2% vs 29.6% vs 20.2%), and smoking (0.1% vs 8.1% vs 0.6%). Between 2016 and 2020, there was a significant increase in cardiometabolic risk factors in SA women: pre-DM (1.2% to 1.5%), GDM (14% to 17.7%), pre-HTN (0.8% to 1.0%), GHTN (3.7% to 5.0%), obesity (10% to 12.4%). Conclusions: In this contemporary analysis of pregnant women in the US, SA women had higher risk of pre-DM and GDM compared with US and ROW women. Between 2016 and 2020, there was a significant increase in the rates of all cardiometabolic risk factors in SA women. Intensive preventive strategies should be implemented in high-risk SA pregnant women in the US.

Maternal metabolic factors and the association with gestational diabetes: A systematic review and meta-analysis.

Gestational diabetes (GDM) is associated with several adverse outcomes for the mother and child. Higher levels of individual lipids are associated with risk of GDM and metabolic syndrome (MetS), a clustering of risk factors also increases risk for GDM. Metabolic factors can be modified by diet and lifestyle. This review comprehensively evaluates the association between MetS and its components, measured in early pregnancy, and risk for GDM. Databases (Cumulative Index to Nursing and Allied Health Literature, PubMed, Embase, and Cochrane Library) were searched from inception to 5 May 2021. Eligible studies included ≥1 metabolic factor (waist circumference, blood pressure, fasting plasma glucose (FPG), triglycerides, and high‐density lipoprotein cholesterol), measured at <16 weeks' gestation. At least two authors independently screened potentially eligible studies. Heterogeneity was quantified using I 2. Data were pooled by random‐effects models and expressed as odds ratio and 95% confidence intervals (CIs). Of 7213 articles identified, 40 unique articles were included in meta‐analysis. In analyses adjusting for maternal age and body mass index, GDM was increased with increasing FPG (odds ratios [OR] 1.92; 95% CI 1.39–2.64, k = 7 studies) or having MetS (OR 2.52; 1.65, 3.84, k = 3). Women with overweight (OR 2.17; 95% CI 1.89, 2.50, k = 12) or obesity (OR 4.34; 95% CI 2.79–6.74, k = 9) also were at increased risk for GDM. Early pregnancy assessment of glucose or the MetS, offers a potential opportunity to detect and treat individual risk factors as an approach towards GDM prevention; weight loss for pregnant women with overweight or obesity is not recommended.Systematic review registration: PROSPERO CRD42020199225.

Relationship between gestational body mass index change and the risk of gestational diabetes mellitus: a community-based retrospective study of 41,845 pregnant women

BackgroundGestational diabetes mellitus (GDM) is associated with adverse health consequences for women and their offspring. It is associated with maternal body mass index (BMI) and may be associated with gestational weight gain (GWG). But due to the heterogeneity of diagnosis and treatment and the potential effect of GDM treatment on GWG, the association between the two has not been thoroughly clarified. Compared to body weight, BMI has the advantage that it considers height during the whole course of pregnancy. Understanding BMI changes during pregnancy may provide new evidence for the prevention of GDM.MethodsThis study investigated the BMI change of pregnant women based on a retrospective study covering all communities in Tianjin, China. According to the results of GDM screening at 24–28 weeks of gestation, pregnancies were divided into the GDM group and the non-GDM group. We compared gestational BMI change and GWG in the two groups from early pregnancy to GDM screening. GWG was evaluated according to the IOM guidelines. Logistic regression was applied to determine the significance of variables with GDM.ResultsA total of 41,845 pregnant women were included in the final analysis (GDM group, n = 4257 vs. non-GDM group, n = 37,588). BMI gain has no significant differences between the GDM and non-GDM groups at any early pregnancy BMI categories (each of 2 kg/m2), as well as weight gain (P > 0.05). Early pregnancy BMI was a risk factor for GDM (OR 1.131, 95% CI 1.122–1.139). And BMI gain was associated with a decreased risk of GDM in unadjusted univariate analysis (OR 0.895, 95% CI 0.869–0.922). After adjusting on early pregnancy BMI and other confounding factors, the effect of BMI gain was no longer significant (AOR 1.029, 95% CI 0.999–1.061), as well as weight gain (AOR 1.006, 95% CI 0.995–1.018) and GWG categories (insufficient: AOR 1.016, 95% CI 0.911–1.133; excessive: AOR 1.044, 95% CI 0.957–1.138).ConclusionsBMI in early pregnancy was a risk factor for GDM, while BMI gain before GDM screening was not associated with the risk of GDM. Therefore, the optimal BMI in early pregnancy is the key to preventing GDM.

Study on the correlation between homocysteine-related dietary patterns and gestational diabetes mellitus:a reduced-rank regression analysis study

BackgroundThis study aimed to evaluate the association between homocysteine-related dietary patterns and gestational diabetes mellitus.MethodsA total of 488 pregnant women at 24–28 weeks of gestation between January 2019 and December 2020 were included. Demographic characteristics, dietary intake, and multivitamin supplement intake information were collected using a food frequency questionnaire (FFQ); fasting venous blood samples were collected for serum index detection. Serum homocysteine (Hcy), folic acid, and B12 were selected as response variables, and hyperhomocysteinemia (hHcy)-related dietary patterns were extracted using the reduced rank regression.. The relationship between the score of hHcy-related dietary patterns and GDM was analyzed using a multivariate logistic regression model.ResultsThree hHcy-related dietary patterns were extracted. Only mode 2 had a positive and significant relationship with the risk of developing GDM. After adjusting for confounding factors, the risk of GDM was significantly increased in the highest quartile array compared with the lowest quartile of the pattern (OR = 2.96, 95% Confidence Interval: 0.939–9.356, P = 0.004). There was no significant correlation between dietary pattern 1 and GDM risk (P > 0.05).ConclusionsHomocysteine-related dietary patterns were positively associated with gestational diabetes mellitus. Adjusting dietary patterns may contribute to the intervention and prevention of GDM.

Machine Learning Prediction Models for Gestational Diabetes Mellitus: Meta-analysis.

BackgroundGestational diabetes mellitus (GDM) is a common endocrine metabolic disease, involving a carbohydrate intolerance of variable severity during pregnancy. The incidence of GDM-related complications and adverse pregnancy outcomes has declined, in part, due to early screening. Machine learning (ML) models are increasingly used to identify risk factors and enable the early prediction of GDM.ObjectiveThe aim of this study was to perform a meta-analysis and comparison of published prognostic models for predicting the risk of GDM and identify predictors applicable to the models.MethodsFour reliable electronic databases were searched for studies that developed ML prediction models for GDM in the general population instead of among high-risk groups only. The novel Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias of the ML models. The Meta-DiSc software program (version 1.4) was used to perform the meta-analysis and determination of heterogeneity. To limit the influence of heterogeneity, we also performed sensitivity analyses, a meta-regression, and subgroup analysis.ResultsA total of 25 studies that included women older than 18 years without a history of vital disease were analyzed. The pooled area under the receiver operating characteristic curve (AUROC) for ML models predicting GDM was 0.8492; the pooled sensitivity was 0.69 (95% CI 0.68-0.69; P<.001; I2=99.6%) and the pooled specificity was 0.75 (95% CI 0.75-0.75; P<.001; I2=100%). As one of the most commonly employed ML methods, logistic regression achieved an overall pooled AUROC of 0.8151, while non–logistic regression models performed better, with an overall pooled AUROC of 0.8891. Additionally, maternal age, family history of diabetes, BMI, and fasting blood glucose were the four most commonly used features of models established by the various feature selection methods.ConclusionsCompared to current screening strategies, ML methods are attractive for predicting GDM. To expand their use, the importance of quality assessments and unified diagnostic criteria should be further emphasized.

The CDKAL1 rs7747752-Bile Acids Interaction Increased Risk of Gestational Diabetes Mellitus: A Nested Case-Control Study.

AimsThe study aimed to explore additive interactions of CDKAL1 rs7747752 and GUDCA/DCA for GDM risk and whether the interactive effects on the risk of GDM was mediated via increasing lysophosphatidylcholines (LPC) 18:0 and/or saturated fatty acid (SFA) 16:0.MethodsA 1:1 age-matched study nested in a prospective cohort of pregnant women (207 pairs) was organized in Tianjin, China. Additive interactions were used to test interaction effects while mediation analyses and Sobel tests were used to test mediation effects of LPC18:0 and SFA16:0 between copresence of rs7747752 and low GUDCA/DCA, and GDM risk.ResultsThe CDKAL1 rs7747752 was associated with GDM (P<0.05). The rs7747752 C polymorphism markedly enhanced ORs of low GUDCA from 4.04 (0.72-22.8) to 9.02 (1.63-49.7) and low DCA from 1.67 (0.68-4.11) to 4.24 (1.84-9.76), both with significant additive interactions. Further adjustment for LPC18:0 attenuated the interactive effects of rs7747752 and low DCA, with a significant mediation effect (P=0.003). High SFA16:0 did not mediate the interactive effects of rs7747752 and low DCA/GUDCA on GDM risk.ConclusionsThe CDKAL1 rs7747752 C carrier status and low GUDCA/DCA had significant additive interactions on the risk of GDM with the effect from interaction with DCA being partially mediated via increasing LPC18:0.

Increased Central and Peripheral Thyroid Resistance Indices During the First Half of Gestation Were Associated With Lowered Risk of Gestational Diabetes—Analyses Based on Huizhou Birth Cohort in South China

ObjectivesThe study aimed to explore the relationship of thyroid function and resistance indices with subsequent risk of gestational diabetes (GDM).DesignThis was a longitudinal study embedded in the Huizhou Birth Cohort.MethodsA total of 2,927 women of singleton pregnancy were recruited from January to October of 2019. Thyroid central resistance indices were evaluated by Thyroid Feedback Quartile-Based index (TFQI), Thyrotrophy T4 Resistance Index (TT4RI), and TSH Index (TSHI) based on plasma-free thyroxine (FT4) and thyroid-stimulating hormone (TSH) levels during the first half of pregnancy. Thyroid peripheral sensitivity was assessed by free triiodothyronine (FT3) to FT4 ratio (FT3/FT4), a proxy of deiodinase activity. GDM was diagnosed between 24 and 28 weeks of gestation by a standardized 75 g oral glucose tolerance test. Multivariable linear and logistic regression was applied to examine the associations of thyroid markers with GDM risk.ResultsFT3 and FT3/FT4 were positively associated with both fasting and post-load glucose levels, while TSH, TSHI, TT4RI, and TFQI were negatively associated with 1 and 2 h post-load glucose levels. Compared with the lowest quartile, GDM risk in the highest quartile increased by 44% [odds ratio (OR) = 1.44; 95%CI, 1.08–1.92; ptrend = 0.027] for FT3 and 81% (OR = 1.81; 95%CI, 1.33–2.46; ptrend < 0.001) for FT3/FT4, while it lowered by 37% (OR = 0.63; 95%CI, 0.47–0.86; ptrend = 0.002] for TSHI, 28% for TT4RI (OR = 0.72; 95%CI, 0.54–0.97; ptrend = 0.06), and 37% for TFQI (OR = 0.63; 95%CI, 0.46–0.85; ptrend < 0.001).ConclusionsThis longitudinal study indicated that higher FT3 and FT3/FT4 and lower central thyroid resistance indices were associated with increased risk of GDM.