Changing diagnostic criteria for gestational diabetes (CDC4G) in Sweden: A stepped wedge cluster randomised trial.

BACKGROUND: The World Health Organisation (WHO) 2013 diagnostic criteria for gestational diabetes mellitus (GDM) has been criticised due to the limited evidence of benefits on pregnancy outcomes in different populations when switching from previously higher glycemic thresholds to the lower WHO-2013 diagnostic criteria. The aim of this study was to determine whether the switch from previous Swedish (SWE-GDM) to the WHO-2013 GDM criteria in Sweden following risk factor-based screening improves pregnancy outcomes. METHODS AND FINDINGS: A stepped wedge cluster randomised trial was performed between January 1 and December 31, 2018 in 11 clusters (17 delivery units) across Sweden, including all pregnancies under care and excluding preexisting diabetes, gastric bypass surgery, or multifetal pregnancies from the analysis. After implementation of uniform clinical and laboratory guidelines, a number of clusters were randomised to intervention (switch to WHO-2013 GDM criteria) each month from February to November 2018. The primary outcome was large for gestational age (LGA, defined as birth weight >90th percentile). Other secondary and prespecified outcomes included maternal and neonatal birth complications. Primary analysis was by modified intention to treat (mITT), excluding 3 clusters that were randomised before study start but were unable to implement the intervention. Prespecified subgroup analysis was undertaken among those discordant for the definition of GDM. Multilevel mixed regression models were used to compare outcome LGA between WHO-2013 and SWE-GDM groups adjusted for clusters, time periods, and potential confounders. Multiple imputation was used for missing potential confounding variables. In the mITT analysis, 47 080 pregnancies were included with 6 882 (14.6%) oral glucose tolerance tests (OGTTs) performed. The GDM prevalence increased from 595/22 797 (2.6%) to 1 591/24 283 (6.6%) after the intervention. In the mITT population, the switch was associated with no change in primary outcome LGA (2 790/24 209 (11.5%) versus 2 584/22 707 (11.4%)) producing an adjusted risk ratio (aRR) of 0.97 (95% confidence interval 0.91 to 1.02, p = 0.26). In the subgroup, the prevalence of LGA was 273/956 (28.8%) before and 278/1 239 (22.5%) after the switch, aRR 0.87 (95% CI 0.75 to 1.01, p = 0.076). No serious events were reported. Potential limitations of this trial are mainly due to the trial design, including failure to adhere to guidelines within and between the clusters and influences of unidentified temporal variations. CONCLUSIONS: In this study, implementing the WHO-2013 criteria in Sweden with risk factor-based screening did not significantly reduce LGA prevalence defined as birth weight >90th percentile, in the total population, or in the subgroup discordant for the definition of GDM. Future studies are needed to evaluate the effects of treating different glucose thresholds during pregnancy in different populations, with different screening strategies and clinical management guidelines, to optimise women's and children's health in the short and long term. TRIAL REGISTRATION: The trial is registered with ISRCTN (41918550).

Ultrasound prediction of fetal macrosomia in pregnancies complicated by diabetes mellitus: a systematic review and meta-analysis.

OBJECTIVES: To report the diagnostic accuracy of ultrasound in identifying fetuses with macrosomia in pregnancies complicated by gestational or pregestational diabetes. METHODS: Medline, Embase and Cochrane databases were searched. Inclusion criteria were singleton pregnancies complicated by diabetes undergoing third-trimester ultrasound evaluation. The index test was represented by ultrasound estimation of fetal macrosomia (estimated fetal weight EFW or abdominal circumference AC >90th or 95th percentile). Subgroup analyses were also performed. Sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio were computed using the hierarchical summary receiver-operating characteristics model. RESULTS: Twenty studies were included in the systematic review including 8,530 pregnancies complicated by diabetes. Ultrasound showed an overall moderate accuracy in identifying fetuses with macrosomia with a sensitivity of 71.2 % (95 % CI 63.1-78.2), a specificity of 88.6 % (95 % CI 83.9-92.0). The interval between ultrasound and birth of two weeks showed the highest sensitivity and specificity (71.6 %, 95 % CI 47.9-87.3 and 91.7, 95 % CI 86.2-95.5). EFW sensitivity and specificity were 76.6 % (95 % CI 70.1-82.3) and 82.9 % (95 % CI 80.9-84.8), while AC 84.8 % (95 % CI 78.2-90.0) and 73.7 % (95 % CI 71.0-76.4). CONCLUSIONS: Ultrasound demonstrates an overall good diagnostic accuracy in detecting fetal macrosomia in pregnancies with diabetes.

Diagnosis and Management of Macrosomia and Shoulder Dystocia: A Comprehensive Review of Major Guidelines.

Importance: Macrosomia represents the most significant risk factor of shoulder dystocia (SD), which is a severe and emergent complication of vaginal delivery. They are both associated with adverse pregnancy outcomes. Objective: The aim of this study was to review and compare the most recently published influential guidelines on the diagnosis and management of fetal macrosomia and SD. Evidence Acquisition: A comparative review of guidelines from the American College of Obstetricians and Gynecologists (ACOG), the Royal College of Obstetricians and Gynaecologists, the National Institute for Health and Care Excellence, the Royal Australian and New Zealand College of Obstetricians and Gynaecologists (RANZCOG), and the Department for Health and Wellbeing of the Government of South Australia on macrosomia and SD was conducted. Results: The ACOG and RANZCOG agree that macrosomia should be defined as birthweight above 4000-4500 g regardless of the gestational age, whereas the National Institute for Health and Care Excellence defines macrosomia as an estimated fetal weight above the 95th percentile. According to ACOG and RANZCOG, ultrasound scans and clinical estimates can be used to rule out fetal macrosomia, although lacking accuracy. Routine induction of labor before 39 weeks of gestation with the sole indication of suspected fetal macrosomia is unanimously not recommended, but an individualized counseling should be provided. Exercise, appropriate diet, and prepregnancy bariatric surgery are mentioned as preventive measures. There is also consensus among the reviewed guidelines regarding the definition and the diagnosis of SD, with the "turtle sign" being the most common sign for its recognition as well as the poor predictability of the reported risk factors. Moreover, there is an overall agreement on the algorithm of SD management with McRoberts technique suggested as first-line maneuver. In addition, appropriate staff training, thorough documentation, and time keeping are crucial aspects of SD management according to all medical societies. Elective delivery for the prevention of SD is discouraged by all the reviewed guidelines. Conclusions: Macrosomia is associated not only with SD but also with maternal and neonatal complications. Similarly, SD can lead to permanent neurologic sequalae, as well as perinatal death if managed in a suboptimal way. Therefore, it is crucial to develop consistent international practice protocols for their prompt diagnosis and effective management in order to safely guide clinical practice and improve pregnancy outcomes.

One-step vs 2-step gestational diabetes mellitus screening and pregnancy outcomes: an updated systematic review and meta-analysis.

OBJECTIVE: This was a systematic review and meta-analysis comparing maternal and neonatal outcomes of patients screened with the 1-step or 2-step screening method for gestational diabetes mellitus. DATA SOURCES: PubMed, Scopus, Cochrane, ClinicalTrials.gov, and LILACS were searched from inception up to September 2022. STUDY ELIGIBILITY CRITERIA: Only randomized controlled trials were included. Studies that had overlapping populations were excluded (International Prospective Register of Systematic Review registration number: CRD42022358903). METHODS: Risk ratios were computed with 95% confidence intervals by 2 authors. Unpublished data were requested. Large for gestational age was the primary outcome. RESULTS: The search yielded 394 citations. Moreover, 7 randomized controlled trials met the inclusion criteria. A total of 54,650 participants were screened for gestational diabetes mellitus by either the 1-step screening method (n=27,163) or the 2-step screening method (n=27,487). For large for gestational age, there was no significant difference found between the groups (risk ratio, 0.99; 95% confidence interval, 0.93-1.05; I2=0%). Newborns of patients who underwent 1-step screening had higher rates of neonatal hypoglycemia (risk ratio, 1.24; 95% confidence interval, 1.14-1.34; I2=0%) and neonatal intensive care unit admissions (risk ratio, 1.13; 95% confidence interval, 1.04-1.21; I2=0%) than newborns of patients who underwent 2-step screening. Patients in the 1-step screening method group were more likely to be diagnosed with gestational diabetes mellitus (risk ratio, 1.73; 95% confidence interval, 1.44-2.09; I2=80%) than patients in the 2-step screening method group. In addition, among trials that tested all patients before randomization and excluded patients with pregestational diabetes mellitus, newborns were more likely to have macrosomia (risk ratio, 1.27; 95% confidence interval, 1.21-1.34; I2=0%). Overall risk of bias assessment was of low concern. CONCLUSION: Large for gestational age did not differ between patients screened using the 1-step screening method and those screened using the 2-step screening method. However, patients randomized to the 1-step screening method had higher rates of neonatal hypoglycemia and neonatal intensive care unit admission and maternal gestational diabetes mellitus diagnosis than the patients randomized to the 2-step screening method.

Comprehensive analysis of macrosomia: exploring the association between first-trimester alanine aminotransferase and uric acid measurements in pregnant women.

OBJECTIVES: Investigating the relationship between liver enzymes, uric acid (UA), and macrosomia will benefit physicians in the early detection of complications that may emerge during/after pregnancy. The study analyzed liver enzyme activity and UA levels in first-trimester pregnant for the risk of macrosomia. METHODS: This retrospective cross-sectional research analyzed the data of pregnant women who gave birth between Jan 2021-2023. All data were extracted from medical records, and UA and AST-ALT were examined in all the participants. RESULTS: Aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels were higher in the macrosomia (p<0.05). Similarly, UA levels were higher in the macrosomia (p<0.001). There was a moderate positive correlation between ALT and birth weight (r=0.168, p<0.01), while we found a strong positive correlation between UA and birth weight (r=0.355, p<0.01). In the ROC (receiver operating characteristic), Area Under the Curve (AUC) for ALT and UA was significant (p<0.0001) but not for AST (p=0.157). UA showed a predictive value for macrosomia with 68.1 % sensitivity and 63.8 % specificity at a 3.15 cut-off (AUC:0.689; p:0.0001; CI:0.644-0.725). CONCLUSIONS: These results indicate that ALT and UA may be potentially important in determining the risk of macrosomia. The UA had a more potent marker for macrosomia than ALT. The occurrence of macrosomia might be more closely related to the mother's metabolic syndrome rather than NAFLD.

Prediction of birthweight and risk of macrosomia in pregnancies complicated by diabetes.

BACKGROUND: Antenatal detection of accelerated fetal growth and macrosomia in pregnancies complicated by diabetes mellitus is important for patient counseling and management. Sonographic fetal weight estimation is the most commonly used tool to predict birthweight and macrosomia. However, the predictive accuracy of sonographic fetal weight estimation for these outcomes is limited. In addition, an up-to-date sonographic fetal weight estimation is often unavailable before birth. This may result in a failure to identify macrosomia, especially in pregnancies complicated by diabetes mellitus where care providers might underestimate fetal growth rate. Therefore, there is a need for better tools to detect and alert care providers to the potential risk of accelerated fetal growth and macrosomia. OBJECTIVE: This study aimed to develop and validate prediction models for birthweight and macrosomia in pregnancies complicated by diabetes mellitus. STUDY DESIGN: This was a completed retrospective cohort study of all patients with a singleton live birth at ≥36 weeks of gestation complicated by preexisting or gestational diabetes mellitus observed at a single tertiary center between January 2011 and May 2022. Candidate predictors included maternal age, parity, type of diabetes mellitus, information from the most recent sonographic fetal weight estimation (including estimated fetal weight, abdominal circumference z score, head circumference-to-abdomen circumference z score ratio, and amniotic fluid), fetal sex, and the interval between ultrasound examination and birth. The study outcomes were macrosomia (defined as birthweights >4000 and >4500 g), large for gestational age (defined as a birthweight >90th percentile for gestational age), and birthweight (in grams). Multivariable logistic regression models were used to estimate the probability of dichotomous outcomes, and multivariable linear regression models were used to estimate birthweight. Model discrimination and predictive accuracy were calculated. Internal validation was performed using the bootstrap resampling technique. RESULTS: A total of 2465 patients met the study criteria. Most patients had gestational diabetes mellitus (90%), 6% of patients had type 2 diabetes mellitus, and 4% of patients had type 1 diabetes mellitus. The overall proportions of infants with birthweights >4000 g, >4500 g, and >90th percentile for gestational age were 8%, 1%, and 12%, respectively. The most contributory predictor variables were estimated fetal weight, abdominal circumference z score, ultrasound examination to birth interval, and type of diabetes mellitus. The models for the 3 dichotomous outcomes had high discriminative accuracy (area under the curve receiver operating characteristic curve, 0.929-0.979), which was higher than that achieved with estimated fetal weight alone (area under the curve receiver operating characteristic curve, 0.880-0.931). The predictive accuracy of the models had high sensitivity (87%-100%), specificity (84%-92%), and negative predictive values (84%-92%). The predictive accuracy of the model for birthweight had low systematic and random errors (0.6% and 7.5%, respectively), which were considerably smaller than the corresponding errors achieved with estimated fetal weight alone (-5.9% and 10.8%, respectively). The proportions of estimates within 5%, 10%, and 15% of the actual birthweight were high (52.3%, 82.9%, and 94.9%, respectively). CONCLUSION: The prediction models developed in the current study were associated with greater predictive accuracy for macrosomia, large for gestational age, and birthweight than the current standard of care that includes estimated fetal weight alone. These models may assist care providers in counseling patients regarding the optimal timing and mode of delivery.

Effects of different dietary patterns during pregnancy on birth outcomes and glucose parameters in women with gestational diabetes mellitus: A systematic review and meta-analysis.

PURPOSE: Dietary interventions are the cornerstone of gestational diabetes mellitus (GDM) treatment. This study aimed to evaluate the effects of dietary patterns during pregnancy on birth outcomes and glucose parameters in women with GDM. METHODS: PubMed, Embase, and The CoChrane Library were searched from the time of database creation to November 30, 2021, along with manual searches. Data analyses were performed using Stata 15.4 software. RESULTS: From 2461 studies, 27 RCTs involving 1923 women were eligible. The pooled results showed that dietary pattern interventions during pregnancy reduced birth weight (WMD: -0.14 kg; 95% CI: -0.24, -0.00), hemoglobin A1 C (HbA1 C) (WMD: -0.19, 95% CI: -0.34, -0.05), and macrosomia incidence (RR 0.65 [95% CI 0.48, 0.88]). Low glycemic index (GI) diet reduced macrosomia incidence (RR 0.31 [95% CI 0.11, 0.93]) and fasting plasma glucose (FPG) levels (WMD: -0.10 mmol/L; 95% CI: -0.14, -0.05); a low carbohydrate (CHO) diet reduced large for gestational age (LGA) incidence (RR 0.33 [95% CI 0.13, 0.82]) and HbA1 C (WMD: -0.32; 95% CI: -0.51, -0.14); dietary approaches to stop hypertension (DASH) diet reduced birth weight (WMD:-0.59 kg; 95% CI: -0.64, -0.55), insulin use (RR 0.31 [95% CI 0.18, 0.56), macrosomia incidence (RR 0.12 [95% CI 0.03, 0.50]), and cesarean sections incidence (RR 0.57 [95% CI 0.40, 0.82]). CONCLUSION: Dietary patterns during pregnancy can improve certain birth outcomes and glycemic parameters. Due to limitations in the quality and number of included studies, the above findings still need to be validated by further randomized controlled trials with high quality and large samples.

Development and validation of a new antenatal warning score system (DRRiP) for neonatal complications of gestational diabetes.

OBJECTIVE: To evaluate the DRRiP (Diabetes Related Risk in Pregnancy) score warning system as a tool for predicting neonatal morbidity in gestational diabetes. METHODS: A retrospective observational cohort study. By applying nine parameters from an antenatal trichotomy of glycemic, ultrasound, and clinical characteristics, DRRiP scores were calculated and assigned to each patient using a checklist tool. Logistic regression models were used to evaluate the association between DRRiP score and adverse fetal outcomes, after adjusting for maternal age and body mass index (calculated as weight in kilograms divided by the square of height in meters). RESULTS: In all, 627 women were studied. DRRiP score was an excellent predictor of macrosomia and shoulder dystocia (both areas under the receiver operating characteristics curves [AUROC] = 0.86), and a modest predictor of preterm delivery, hyperbilirubinemia, neonatal intensive care unit admission and a composite of either of the studied events (AUROC range 0.63-0.69). For the composite outcome, the sensitivity of an amber trigger score of 1 was 68.7% (95% confidence interval [CI] 62.27%-74.63%) and specificity was 48.87% (95% CI 43.85%-53.9%). Specificity at a red trigger score of 3 (89.7%) and a graded increase in post-test probability (90.7% risk at a score of 5) were highly encouraging. CONCLUSION: DRRiP score offers reasonable discriminative performance that could be clinically useful for meaningful risk stratification when making delivery plans.

Evaluating the effect of gestational diabetes mellitus on macrosomia based on the characteristics of oral glucose tolerance test.

BACKGROUND: GDM is always treated as a homogenous disease ignoring the different metabolic characteristics in oral glucose tolerance test (OGTT). We assessed the effect of GDM on macrosomia based on the different characteristics of OGTT. METHODS: We retrospectively divided 998 GDM pregnant women into 7 groups, Group A1: abnormal OGTT0h; Group A2: abnormal OGTT1h; Group A3: abnormal OGTT2h; Group B1: abnormal OGTT0h+1h; Group B2: abnormal OGTT0h+2h; Group B3: abnormal OGTT1h+2h; Group C: abnormal OGTT0h+1h+2h. RESULTS: The incidence of macrosomia in group C (21.92%) was higher than other groups. The OR of OGTT0h+1h+2h was significant (OGTT1h: OR = 1.577, 95% CI: 0.791, 3.145; OGTT2h: OR = 1.151, 95% CI: 0.572, 2.313; OGTT0h+1h: OR = 1.346, 95% CI: 0.584, 3.101; OGTT0h+2h: OR = 1.327, 95% CI: 0.517, 3.409; OGTT1h+2h: OR = 0.771, 95% CI: 0.256, 2.322; OGTT0h+1h+2h: OR = 4.164, 95% CI: 2.095, 8.278) when comparing with OGTT0h. Subgroup analysis showed abnormal OGTT0h+1h+2h might contribute more to macrosomia in pre-pregnancy BMI ≥ 24 kg/m2 than those with BMI < 24 kg/m2. CONCLUSION: The effect of abnormal OGTT0h+1h+2h on macrosomia was significantly greater than other OGTT characteristics, especially for those with pre-pregnancy BMI ≥ 24 kg/m2. Individualized management of GDM based on OGTT characteristics and pre-pregnancy BMI might be needed.

Maternal circulating metabolic biomarkers and their prediction performance for gestational diabetes mellitus related macrosomia.

INTRODUCTION: Gestational diabetes mellitus (GDM), a metabolism-related pregnancy complication, is significantly associated with an increased risk of macrosomia. We hypothesized that maternal circulating metabolic biomarkers differed between women with GDM and macrosomia (GDM-M) and women with GDM and normal neonatal weight (GDM-N), and had good prediction performance for GDM-M. METHODS: Plasma samples from 44 GDM-M and 44 GDM-N were analyzed using Olink Proseek multiplex metabolism assay targeting 92 biomarkers. Combined different clinical characteristics and Olink markers, LASSO regression was used to optimize variable selection, and Logistic regression was applied to build a predictive model. Nomogram was developed based on the selected variables visually. Receiver operating characteristic (ROC) curve, calibration plot, and clinical impact curve were used to validate the model. RESULTS: We found 4 metabolism-related biomarkers differing between groups [CLUL1 (Clusterin-like protein 1), VCAN (Versican core protein), FCRL1 (Fc receptor-like protein 1), RNASE3 (Eosinophil cationic protein), FDR <  0.05]. Based on the different clinical characteristics and Olink markers, a total of nine predictors, namely pre-pregnancy body mass index (BMI), weight gain at 24 gestational weeks (gw), parity, oral glucose tolerance test (OGTT) 2 h glucose at 24 gw, high-density lipoprotein (HDL) and low-density lipoprotein (LDL) at 24 gw, and plasma expression of CLUL1, VCAN and RNASE3 at 24 gw, were identified by LASSO regression. The model constructed using these 9 predictors displayed good prediction performance for GDM-M, with an area under the ROC of 0.970 (sensitivity = 0.955, specificity = 0.886), and was well calibrated (P Hosmer-Lemeshow test = 0.897). CONCLUSION: The Model included pre-pregnancy BMI, weight gain at 24 gw, parity, OGTT 2 h glucose at 24 gw, HDL and LDL at 24 gw, and plasma expression of CLUL1, VCAN and RNASE3 at 24 gw had good prediction performance for predicting macrosomia in women with GDM.

Maternal insulin-like growth factors and insulin-like growth factor-binding proteins for macrosomia prediction in diabetic and nondiabetic pregnancy: A prospective study.

OBJECTIVE: Attributable to the insulin-like growth factor (IGF) axis involvement in fetal growth regulation, possible contribution of the maternal IGF axis to antenatal fetal macrosomia diagnosis is a subject of particular interest in diabetic pregnancy. METHODS: A total of 130 women were prospectively enrolled in a longitudinal single-center cohort study. The four study groups were: type 1 diabetes (n = 40), type 2 diabetes (n = 35), gestational diabetes (n = 40), and control (n = 15). IGF-1 and IGF-2 and insulin-like growth factor-binding protein (IGFBP) 1, 3, 6, and 7 serum levels were analyzed in 11- to 14-week and 30- to 34-week samples with a specific immunoassay. RESULTS: In mothers of large-for-gestational-age neonates (90th percentile), higher (median test) first-trimester IGF-1 (P = 0.007) and lower IGFBP-1 (P = 0.035) were observed. The IGF-1/IGFBP-1 ratio was positively associated with neonatal weight (r = 0.434, P < 0.001). Receiver operating characteristic analysis revealed an association between large for gestational age and the first-trimester IGF-1 (area under the curve [AUC] = 0.747, P < 0.001), IGFBP-1 (AUC = 0.334, P = 0.011), and IGF-1/IGFBP-1 ratio (AUC = 0.750, P < 0.001). IGF-1/IGFBP-1 ratio had better performance for prediction of birth weight over 4000 g (AUC = 0.822, P < 0.001). CONCLUSION: The authors detected different first-trimester IGF-1 and IGF-1/IGFBP-1 thresholds applicable for either supposition or rejection of macrosomia diagnosis. Further investigation is needed to determine how the maternal IGF axis can contribute to fetal macrosomia prediction.

Various screening and diagnosis approaches for gestational diabetes mellitus and adverse pregnancy outcomes: a secondary analysis of a randomized non-inferiority field trial.

INTRODUCTION: We evaluate which screening and diagnostic approach resulted in the greatest reduction in adverse pregnancy outcomes due to increased treatment. RESEARCH DESIGN AND METHODS: This study presents a secondary analysis of a randomized community non-inferiority trial conducted among pregnant women participating in the GULF Study in Iran. A total of 35 430 pregnant women were randomly assigned to one of the five prespecified gestational diabetes mellitus (GDM) screening protocols. The screening methods included fasting plasma glucose (FPG) in the first trimester and either a one-step or a two-step screening method in the second trimester of pregnancy. According to the results, participants were classified into 6 groups (1) First-trimester FPG: 100-126 mg/dL, GDM diagnosed at first trimester; (2) First trimester FPG: 92-99.9 mg/dL, GDM diagnosed at first trimester; (3) First trimester FPG: 92-99.9 mg/dL, GDM diagnosed at second trimester; (4) First trimester FPG: 92-99.9 mg/dL, healthy at second trimester; (5) First trimester FPG<92 mg/dL, GDM diagnosed at second trimester; (6) First trimester FPG<92 mg/dL, healthy at second trimester. For our analysis, we initially used group 6, as the reference and repeated the analysis using group 2, as the reference group. The main outcome of the study was major adverse maternal and neonatal outcomes. RESULTS: Macrosomia and primary caesarean section occurred in 9.8% and 21.0% in group 1, 7.8% and 19.8% in group 2, 5.4% and 18.6% in group 3, 6.6% and 21.5% in group 4, 8.3% and 24.0% in group 5, and 5.4% and 20.0% in group 6, respectively. Compared with group 6 as the reference, there was a significant increase in the adjusted risk of neonatal intensive care unit (NICU) admission in groups 1, 3, and 5 and an increased risk of macrosomia in groups 1, 2, and 5. Compared with group 2 as the reference, there was a significant decrease in the adjusted risk of macrosomia in group 3, a decreased risk of NICU admission in group 6, and an increased risk of hyperglycemia in group 3. CONCLUSIONS: We conclude that screening approaches for GDM reduced the risk of adverse pregnancy outcomes to the same or near the same risk level of healthy pregnant women, except for the risk of NICU admission that increased significantly in groups diagnosed with GDM compared with healthy pregnant women. Individuals with slight increase in FPG (92-100 mg/dL) at first trimester, who were diagnosed as GDM, had an even increased risk of macrosomia in comparison to those group of women with FPG 92-100 mg/dL in the first trimester, who were not diagnosed with GDM, and developed GDM in second trimester TRIAL REGISTRATION: IRCT138707081281N1 (registered: February 15, 2017).

Performance of whole-genome promoter nucleosome profiling of maternal plasma cell-free DNA for prenatal noninvasive prediction of fetal macrosomia: a retrospective nested case-control study in mainland China.

BACKGROUND: Fetal macrosomia is common occurrence in pregnancy, which is associated with several adverse prognosis both of maternal and neonatal. While, the accuracy of prediction of fetal macrosomia is poor. The aim of this study was to develop a reliable noninvasive prediction classifier of fetal macrosomia. METHODS: A total of 3600 samples of routine noninvasive prenatal testing (NIPT) data at 12+ 0-27+ 6 weeks of gestation, which were subjected to low-coverage whole-genome sequencing of maternal plasma cell-free DNA (cfDNA), were collected from three independent hospitals. We identified set of genes with significant differential coverages by comparing the promoter profiling between macrosomia cases and controls. We selected genes to develop classifier for noninvasive predicting, by using support vector machine (SVM) and logistic regression models, respectively. The performance of each classifier was evaluated by area under the curve (AUC) analysis. RESULTS: According to the available follow-up results, 162 fetal macrosomia pregnancies and 648 matched controls were included. A total of 1086 genes with significantly differential promoter profiling were found between pregnancies with macrosomia and controls (p < 0.05). With the AUC as a reference，the classifier based on SVM (CMA-A2) had the best performance, with an AUC of 0.8256 (95% CI: 0.7927-0.8586). CONCLUSIONS: Our study provides that assessing the risk of fetal macrosomia by whole-genome promoter nucleosome profiling of maternal plasma cfDNA based on low-coverage next-generation sequencing is feasible.

Umbilical Cord Biometry and Fetal Abdominal Skinfold Assessment as Potential Biomarkers for Fetal Macrosomia in a Gestational Diabetes Romanian Cohort.

Backgroundand Objectives: Gestational diabetes mellitus (GDM) is a pregnancy-associated pathology commonly resulting in macrosomic fetuses, a known culprit of obstetric complications. We aimed to evaluate the potential of umbilical cord biometry and fetal abdominal skinfold assessment as screening tools for fetal macrosomia in gestational diabetes mellitus pregnant women. Materials and methods: This was a prospective case−control study conducted on pregnant patients presenting at 24−28 weeks of gestation in a tertiary-level maternity hospital in Northern Romania. Fetal biometry, fetal weight estimation, umbilical cord area and circumference, areas of the umbilical vein and arteries, Wharton jelly (WJ) area and abdominal fold thickness measurements were performed. Results: A total of 51 patients were enrolled in the study, 26 patients in the GDM group and 25 patients in the non-GDM group. There was no evidence in favor of umbilical cord area and WJ amount assessments as predictors of fetal macrosomia (p > 0.05). However, there was a statistically significant difference in the abdominal skinfold measurement during the second trimester between macrosomic and normal-weight newborns in the GDM patient group (p = 0.016). The second-trimester abdominal circumference was statistically significantly correlated with fetal macrosomia at term in the GDM patient group with a p value of 0.003, as well as when considering the global prevalence of macrosomia in the studied populations, 0.001, when considering both populations. Conclusions: The measurements of cord and WJ could not be established as predictors of fetal macrosomia in our study populations, nor differentiate between pregnancies with and without GDM. Abdominal skinfold measurement and abdominal circumference measured during the second trimester may be important markers of fetal metabolic status in pregnancies complicated by GDM.

Analytical Comparison of Risk Prediction Models for the Onset of Macrosomia Based on Three Statistical Methods.

Method: A retrospective selection of 93 women who were hospitalized in our hospital from March 2019 to May 2022 with a singleton pregnancy and delivered at term with macrosomia were the study group. And 356 women who delivered a normal size baby during the same period were the control group. The variables that were associated with the onset of macrosomia were screened from maternal medical records. Logistic regression models, random forest, and CART decision tree models were developed using the screened variables as input variables and whether they were macrosomia as outcome variables, respectively. The performance of the three models was evaluated by accuracy, precision, recall, F1 score, and receiver operating characteristic curve (ROC). Result: The risk prediction models for the onset of macrosomia, logistic regression model, random forest model, and decision tree, were successfully developed, with accuracies of 0.904, 1.000, and 0.901 in the training set and 0.926, 0.582, and 0.852 in the validation set, respectively. The AUC in the training set were 0.898, 1.000, and 0.789, and in the validation set were 0.906, 0.913, and 0.731, respectively. In general, the logistic regression model has the highest diagnostic efficiency, followed by the random forest model. Conclusion: Logistic regression models have high application value in the assessment of predicting the risk of macrosomia, and it is suggested that the advantages of logistic regression models and random forest models should be combined in future studies and applications to make them work better in the prediction of the risk of macrosomia.

A predictive model of macrosomic birth based upon real-world clinical data from pregnant women.

BACKGROUND: Fetal macrosomia is associated with an increased risk of several maternal and newborn complications. Antenatal predication of fetal macrosomia remains challenging. We aimed to develop a nomogram model for the prediction of macrosomia using real-world clinical data to improve the sensitivity and specificity of macrosomia prediction. METHODS: In the present study, we performed a retrospective, observational study based on 13,403 medical records of pregnant women who delivered singleton infants at a tertiary hospital in Shanghai from 1 January 2018 through 31 December 2019. We split the original dataset into a training set (n = 9382) and a validation set (n = 4021) at a 7:3 ratio to generate and validate our model. The candidate variables, including maternal characteristics, laboratory tests, and sonographic parameters were compared between the two groups. A univariate and multivariate logistic regression was carried out to explore the independent risk factors for macrosomia in pregnant women. Thus, the regression model was adopted to establish a nomogram to predict the risk of macrosomia. Nomogram performance was determined by discrimination and calibration metrics. All the statistical analysis was analyzed using R software. RESULTS: We compared the differences between the macrosomic and non-macrosomic groups within the training set and found 16 independent risk factors for macrosomia (P < 0.05), including biparietal diameter (BPD), head circumference (HC), femur length (FL), amniotic fluid index (AFI) at the last prenatal examination, pre-pregnancy body mass index (BMI), and triglycerides (TG). Values for the areas under the curve (AUC) for the nomogram model were 0.917 (95% CI, 0.908-0.927) and 0.910 (95% CI, 0.894-0.927) in the training set and validation set, respectively. The internal and external validation of the nomogram demonstrated favorable calibration as well as discriminatory capability of the model. CONCLUSIONS: Our model has precise discrimination and calibration capabilities, which can help clinical healthcare staff accurately predict macrosomia in pregnant women.

Is there a residual risk of large-for-gestational-age infant related to gestational diabetes mellitus when it is treated?

OBJECTIVE: The hyperglycaemia and adverse pregnancy outcomes (HAPO) study, where hyperglycaemia was untreated, showed a continuous association between large-for-gestational-age (LGA) infant and seven increasing categories of fasting plasma glucose (PG), 1-hour and 2-hour PG values after a 75 g oral glucose tolerance test at 24-32 gestational weeks. We evaluated whether the excess risk persisted in the 6th and 7th glucose categories - corresponding to women treated for gestational diabetes mellitus (GDM). PATIENTS AND METHODS: We included 7,190 women meeting the HAPO criteria, of whom 655 (9.2%) were treated for GDM (dietary education in all; insulin therapy in 150 (20.3%)). We evaluated the adjusted odds ratio (aOR) for each glucose category (reference 1st category) for LGA infant. RESULTS: The aOR for LGA linearly increased from the 1st to 5th categories of fasting, 1-hour and 2-hour PG. Specifically, the aORs for the 5th category were 2.20 (95% confidence interval 1.41-3.44), 2.25 (1.11-4.59), and 2.51 (1.63-3.85), respectively. The aORs for the 6th category were globally stable at 2.52 (1.46-4.36), 2.87 (1.48-5.54), and 2.47 (1.46-4.16), respectively. The same was true for the 7th category: 1.41 (0.56-3.55), 2.84 (1.03-7.86), and 3.53 (1.77-7.06), respectively. CONCLUSION: We confirmed the association between increasing PG category and LGA infant in women without GDM. We did not observe a residual risk of LGA infant in women treated for GDM in our hospital, irrespective of elevated fasting, 1-hour, or 2-hour PG diagnosis. The risk of LGA infant was globally similar to that in women with high normal glucose values.