Risk factors for recurrent macrosomia and child outcomes

Family cash transfers in childhood and birthing persons and birth outcomes later in life

Much literature in the US documents an intergenerational transmission of birthing person and perinatal morbidity in socioeconomically disadvantaged groups. A separate line of work indicates that family cash transfers may improve life chances of low-income families well into adulthood. By exploiting a quasi-random natural experiment of a large family cash transfer among a southeastern American Indian (AI) tribe in rural North Carolina, we examine whether a "perturbation" in socioeconomic status during childhood improves birthing person/perinatal outcomes when they become parents themselves. We acquired birth records on 6805 AI and non-AI infants born from 1995 to 2018. Regression methods to examine effect modification tested whether the birthing person's American Indian (AI) status and exposure to the family cash transfer during their childhood years corresponds with improvements in birthing person and perinatal outcomes. Findings show an increase in age at childbearing (coef: 0.15 years, 95% confidence interval [CI]: 0.05, 0.25) and a decrease in pre-pregnancy body mass index (BMI; coef: -0.42, 95% CI: -0.76, -0.09) with increased duration of cash transfer exposure during childhood. The odds of large-for-gestational age at delivery, as well as mean infant birthweight, is also reduced among AI births whose birthing person had relatively longer duration of exposure to the cash transfer. We, however, observe no relation with other birthing person/perinatal outcomes (e.g., tobacco use during pregnancy, preterm birth). In this rural AI population, cash transfers in one generation correspond with improved birthing person and infant health in the next generation.

Analysis of risk factors and construction of a predictive model for macrosomia in deliveries with gestational diabetes

BACKGROUND

Gestational diabetes, a frequent pregnancy complication marked by elevated maternal blood glucose, can cause serious adverse effects for both mother and fetus, including increased amniotic fluid and risks of fetal asphyxia, hypoxia, and premature birth.

OBJECTIVE

To construct a predictive model to analyze the risk factors for macrosomia in deliveries with gestational diabetes.

METHODS

From January 2021 to February 2023, 362 pregnant women with gestational diabetes were selected for the study. They were followed up until delivery. Based on newborn birth weight, the participants were divided into the macrosomia group (birth weight ⩾ 4000 g) and the non-macrosomia group (birth weight < 4000 g). The data of the two groups of pregnant women were compared. ROC curves were plotted to analyze the predictive value of multiple factors for the delivery of macrosomic infants among pregnant women with gestational diabetes. A logistic regression model was constructed to identify the risk factors for delivering macrosomic infants and the model was tested.

RESULTS

A total of 362 pregnant women with gestational diabetes were included, of which 58 (16.02%) had babies with macrosomia. The macrosomia group exhibited higher metrics in several areas compared to those without: pre-pregnancy BMI, fasting glucose, 1 h and 2 h OGTT sugar levels, weight gain during pregnancy, and levels of triglycerides, LDL-C, and HDL-C, all with significant differences (P< 0.05). ROC analysis revealed predictive value for macrosomia with AUCs of 0.761 (pre-pregnancy BMI), 0.710 (fasting glucose), 0.671 (1 h OGTT), 0.634 (2 h OGTT), 0.850 (weight gain), 0.837 (triglycerides), 0.742 (LDL-C), and 0.776 (HDL-C), indicating statistical significance (P< 0.05). Logistic regression identified high pre-pregnancy BMI, fasting glucose, weight gain, triglycerides, and LDL-C levels as independent risk factors for macrosomia, with odds ratios of 2.448, 2.730, 1.884, 16.919, and 5.667, respectively, and all were statistically significant (P< 0.05). The model's AUC of 0.980 (P< 0.05) attests to its reliability and stability.

CONCLUSION

The delivery of macrosomic infants in gestational diabetes may be related to factors such as body mass index before pregnancy, blood-glucose levels, gain weight during pregnancy, and lipid levels. Clinical interventions targeting these factors should be implemented to reduce the incidence of macrosomia.

The impact of macrosomia on cardiometabolic health in preteens: findings from the ROLO longitudinal birth cohort study

BACKGROUND

Macrosomia (birthweight ≥ 4 kg or ≥ 4.5 kg) is strongly associated with a predisposition to childhood obesity, which in turn is linked with adverse cardiometabolic health. Despite this, there is a lack of longitudinal investigation on the impact of high birthweight on cardiometabolic outcomes in youth. The preteen period represents an important window of opportunity to further explore this link, to potentially prevent cardiometabolic profiles worsening during puberty.

METHODS

This is a secondary analysis of 9-11-year-olds (n = 405) born to mothers in the ROLO longitudinal birth cohort study, who previously delivered an infant with macrosomia. Preteens were dichotomised into those born with and without macrosomia, using two common cut-off criteria (birthweight ≥ 4 kg (n = 208) and < 4 kg; ≥ 4.5 kg (n = 65) and < 4.5 kg). Cardiometabolic health was assessed using anthropometry, dual-energy x-ray absorptiometry, blood pressure, heart rate, cardiorespiratory endurance (20-m shuttle run test), and non-fasting serum biomarkers for a subgroup (n = 213). Statistical comparisons between the two groups were explored using independent t-tests, Mann-Whitney U tests, and Chi-square tests. Crude and adjusted linear regression models investigated associations between macrosomia and preteen cardiometabolic outcomes.

RESULTS

In total, 29.3% (n = 119) of preteens had overweight/obesity based on their BMI z-score. Preteens born ≥ 4 kg had lower median (IQR) C3 concentrations (1.38 (1.22, 1.52) g/L vs. 1.4 (1.26, 1.6) g/L, p = 0.043) and lower median (IQR) ICAM-1 concentrations (345.39 (290.34, 394.91) ng/mL vs. 387.44 (312.91, 441.83) ng/mL, p = 0.040), than those born < 4 kg. Those born ≥ 4.5 kg had higher mean (SD) BMI z-scores (0.71 (0.99) vs. 0.36 (1.09), p = 0.016), and higher median (IQR) lean mass (24.76 (23.28, 28.51) kg vs. 23.87 (21.9, 26.79) kg, p = 0.021), than those born < 4.5 kg. Adjusted linear regression analyses revealed birthweight ≥ 4 kg was negatively associated with C3 concentration (g/L) (B = - 0.095, 95% CI = - 0.162, - 0.029, p = 0.005) and birthweight ≥ 4.5 kg was positively associated with weight z-score (B = 0.325, 95% CI = 0.018, 0.633, p = 0.038), height z-score (B = 0.391, 95% CI = 0.079, 0.703, p = 0.014), lean mass (kg) (B = 1.353, 95% CI = 0.264, 2.442, p = 0.015) and cardiorespiratory endurance (B = 0.407, 95% CI = 0.006, 0.808, p = 0.047).

CONCLUSION

This study found no strong evidence to suggest that macrosomia is associated with adverse preteen cardiometabolic health. Macrosomia alone may not be a long-term cardiometabolic risk factor. Trial registration ISRCTN54392969 registered at  www.isrctn.com .

Fitness of INTERGROWTH-21st birth weight standards for Chinese-ethnicity babies

OBJECTIVE

To determine the fitness of the INTERGROWTH-21st birth weight standards (INTERGROWTH21) for ethnic Chinese babies compared with a local reference (FOK2003).

DESIGN

Population-based analysis of territory-wide birth data.

SETTING

All public hospitals in Hong Kong.

PARTICIPANTS

Live births between 24 and 42 complete weeks' gestation during 2006-2017.

MAIN OUTCOME MEASURES

Babies' birth weight Z-scores were calculated using published methods. The two references were compared in three aspects: (1) the proportions of large-for-gestational-age (LGA) or small-for-gestational-age (SGA) infants, (2) the gestation-specific and sex-specific mean birth weight Z-scores and (3) the predictive power for SGA-related complications.

RESULTS

488 896 infants were included. Using INTERGROWTH21, among neonates born <33 weeks' gestation, the mean birth weight Z-scores per week were closer to zero (-0.2 to 0.05), while most of them were further from zero (0.06 to 0.34) after excluding infants with a high risk of abnormal intrauterine growth. Compared with FOK2003, INTERGROWTH21 classified smaller proportions of infants as SGA (8.3% vs 9.6%) and LGA (6.6% vs 7.9%), especially SGA among preterm infants (13.1% vs 17.0%). The area under the receiver operating characteristic curve for predicting SGA-related complications was greater with FOK2003 (0.674, 95% CI 0.670 to 0.677) than INTERGROWTH21 (0.658, 95% CI 0.655 to 0.661) (p<0.001).

CONCLUSIONS

INTERGROWTH21 performed less well than FOK2003, a local reference for ethnic Chinese babies, especially in infants born <33 weeks' gestation. Although the differences are clinically small, both these references performed poorly for extremely preterm infants, and thus a more robust chart based on a larger sample of appropriately selected infants is needed.

Fetal weight estimation based on deep neural network: a retrospective observational study

BACKGROUND

Improving the accuracy of estimated fetal weight (EFW) calculation can contribute to decision-making for obstetricians and decrease perinatal complications. This study aimed to develop a deep neural network (DNN) model for EFW based on obstetric electronic health records.

METHODS

This study retrospectively analyzed the electronic health records of pregnant women with live births delivery at the obstetrics department of International Peace Maternity & Child Health Hospital between January 2016 and December 2018. The DNN model was evaluated using Hadlock's formula and multiple linear regression.

RESULTS

A total of 34824 live births (23922 primiparas) from 49896 pregnant women were analyzed. The root-mean-square error of DNN model was 189.64 g (95% CI 187.95 g-191.16 g), and the mean absolute percentage error was 5.79% (95%CI: 5.70%-5.81%), significantly lower compared to Hadlock's formula (240.36 g and 6.46%, respectively). By combining with previously unreported factors, such as birth weight of prior pregnancies, a concise and effective DNN model was built based on only 10 parameters. Accuracy rate of a new model increased from 76.08% to 83.87%, with root-mean-square error of only 243.80 g.

CONCLUSIONS

Proposed DNN model for EFW calculation is more accurate than previous approaches in this area and be adopted for better decision making related to fetal monitoring.

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 OGTT_0h; Group A2: abnormal OGTT_{1 h}; Group A3: abnormal OGTT_{2 h}; Group B1: abnormal OGTT_{0h+1 h}; Group B2: abnormal OGTT_{0h+2 h}; Group B3: abnormal OGTT_{1 h+2 h}; Group C: abnormal OGTT_{0h+1 h+2 h}; RESULTS: The incidence of macrosomia in group C (21.92%) was higher than other groups. The OR of OGTT_{0h+1 h+2 h} was significant (OGTT_{1 h}: OR=1.577, 95% CI: 0.791, 3.145; OGTT_{2 h}: OR=1.151, 95% CI: 0.572, 2.313; OGTT_{0h+1 h}: OR=1.346, 95% CI: 0.584, 3.101; OGTT_{0h+2 h}: OR=1.327, 95% CI: 0.517, 3.409; OGTT_{1 h+2 h}: OR=0.771, 95% CI: 0.256, 2.322; OGTT_{0h+1 h+2 h}: OR=4.164, 95% CI: 2.095, 8.278) when comparing with OGTT_0h. Subgroup analysis showed abnormal OGTT_{0h+1 h+2 h} might contribute more to macrosomia in pre-pregnancy BMI ≥ 24 kg/m² than those with BMI < 24 kg/m².

CONCLUSION

The effect of abnormal OGTT_{0h+1 h+2 h} 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.

Incidence of macrosomia in Mexico: National and subnational estimations

Fetal macrosomia (FM) is a condition with adverse consequences for both mother and offspring. The occurrence of this condition has increased worldwide. The objectives of this study were: (1) to estimate the incidence of FM at the national and state levels in Mexico in 2020; (2) to estimate the incidence of FM stratified by maternal and newborn characteristics; (3) to identify the states with the highest risk of FM; (4) to georeference the incidence of FM. Open data from the Birth Information Subsystem were used. Relative risks were estimated by adjusted Poisson regression models. The national incidence of FM was 2.75%. The entity with the lowest incidence was Mexico City (1.28%) and the most affected states were Sonora (6.20%), Baja California Sur (5.44%), and Sinaloa (5.36%), located in the north of the country. The incidence of FM at the national level is below that reported in the international literature. The results of this study can be used for the design and implementation of programs, public policies, and interventions.

Association between prenatal exposure to ambient ozone, birth weight, and macrosomia in healthy women

Studies have shown that prenatal ozone exposure is associated with an increased risk of adverse pregnancy outcomes, among which abnormal birth weight is a detrimental factor for diseases in adulthood, but the association between birth weight and ozone is inconclusive. Herein, we conducted this study by enrolling 407 couples of pregnant women and collected their demographical materials, their exposure to ambient ozone was assessed according to the place of their residence. The hourly monitored ozone was first averaged to the daily level, then monthly and whole-gestationally levels. After adjusting confounders, we processed a multivariate generalized addictive analysis to predict the association between prenatal ozone exposure and birth weight. We also divided the cohort into two categories according to whether the infant met the standard of macrosomia, and the occurrence of macrosomia was studied via univariate and multivariate logistic regression analyses as extreme conditions of the effects of ozone exposure on birth weight. We found that the ground-level ozone in Jinan changed with temperature periodically, higher in summer and lower in winter. Over the past 8 years from 2014, the ambient ozone increased by 1.74 μg/m³ per year. Of the 407 singleton-pregnant women, 21 infants were diagnosed with macrosomia. After adjusting confounders, we found that each unit increase in prenatal ozone exposure caused 8.80% [OR_ozone90%CI: 0.912 (0.850, 0.978)] decreased risk of macrosomia, but the splined ambient ozone exposure data was not statistically associated with birth weight, which is probably due to the limited sample size. In conclusion, prenatal ozone exposure is associated with decreased risk of macrosomia but is weakly linked to birth weight.

Risk Factors for Macrosomia in Multipara: A Multi-Center Retrospective Study

The increased incidence of macrosomia has caused an enormous burden after the transition from the almost 40-year one-child policy to the universal two-child policy in 2015 and further to the three-child policy in 2021 in China. However, studies on risk factors of macrosomia in multipara under the new fertility policy in China are limited. We aim to explore the incidence and risk factors for macrosomia in multipara to provide the scientific basis for preventing macrosomia in multipara. A multi-center retrospective study was conducted among 6200 women who had two consecutive deliveries in the same hospital and their second newborn was delivered from January to October 2018 at one of 18 hospitals in 12 provinces in China. Macrosomia was defined as birth weight ≥ 4000 g. Logistic regression models were performed to analyze risk factors for macrosomia in multipara. The incidence of macrosomia in multipara was 7.6% (470/6200) and the recurrence rate of macrosomia in multipara was 27.2% (121/445). After adjusting for potential confounders, a higher prepregnancy BMI, higher gestational weight gain, history of macrosomia, a longer gestation in the subsequent pregnancy were independent risk factors of macrosomia in multipara (p < 0.05). Healthcare education and preconception consultation should be conducted for multipara patients with a history of macrosomia to promote maintaining optimal prepregnancy BMI and avoid excessive gestational weight gain to prevent macrosomia.

Nomogram-based risk prediction of macrosomia: a case-control study

Background

Macrosomia is closely associated with poor maternal and fetal outcome. But there is short of studies on the risk of macrosomia in early pregnancy. The purpose of this study is to establish a nomogram for predicting macrosomia in the first trimester.

Methods

A case-control study involving 1549 pregnant women was performed. According to the birth weight of newborn, the subjects were divided into macrosomia group and non-macrosomia group. The risk factors for macrosomia in early pregnancy were analyzed by multivariate logistic regression. A nomogram was used to predict the risk of macrosomia.

Results

The prevalence of macrosomia was 6.13% (95/1549) in our hospital. Multivariate logistic regression analysis showed that prepregnancy overweight (OR: 2.13 95% CI: 1.18–3.83)/obesity (OR: 3.54, 95% CI: 1.56–8.04), multiparity (OR:1.88, 95% CI: 1.16–3.04), the history of macrosomia (OR: 36.97, 95% CI: 19.90–68.67), the history of GDM/DM (OR: 2.29, 95% CI: 1.31–3.98), the high levels of HbA1c (OR: 1.76, 95% CI: 1.00–3.10) and TC (OR: 1.36, 95% CI: 1.00–1.84) in the first trimester were the risk factors of macrosomia. The area under ROC (the receiver operating characteristic) curve of the nomogram model was 0.807 (95% CI: 0.755–0.859). The sensitivity and specificity of the model were 0.716 and 0.777, respectively.

Conclusion

The nomogram model provides an effective mothed for clinicians to predict macrosomia in the first trimester.

Establishment of a nomogram model to predict macrosomia in pregnant women with gestational diabetes mellitus

AIM

To establish a nomogram model to predict the risk of macrosomia in pregnant women with gestational diabetes mellitus in China.

METHODS

We retrospectively collected the medical records of 783 pregnant women with gestational diabetes who underwent prenatal examinations and delivered at the Affiliated Hospital of Qingdao University from October 2019 to October 2020. The pregnant women were randomly divided into two groups in a 4:1 ratio to generate and validate the model. The independent risk factors for macrosomia in pregnant women with gestational diabetes mellitus were analyzed by multivariate logistic regression, and the nomogram model to predict the risk of macrosomia in pregnant women with gestational diabetes mellitus was established and verified by R software.

RESULTS

Logistic regression analysis showed that prepregnancy body mass index, weight gain during pregnancy, fasting plasma glucose, triglycerides, biparietal diameter and amniotic fluid index were independent risk factors for macrosomia (P < 0.05). The areas under the ROC curve for internal and external validation of the model were 0.813 (95 % confidence interval 0.754-0.862) and 0.903 (95 % confidence interval 0.588-0.967), respectively. The calibration curve was a straight line with a slope close to 1.

CONCLUSIONS

In this study, we constructed a nomogram model to predict the risk of macrosomia in pregnant women with gestational diabetes mellitus. The model has good discrimination and calibration abilities, which can help clinical healthcare staff accurately predict macrosomia in pregnant women with gestational diabetes mellitus.

Predicting the risk of fetal macrosomia at pregnancy in Shandong province: a case-control study

BACKGROUND AND AIM

Macrosomia is used to describe an infant born with excessively high weight, and it brings lots of unexpected risks in clinical work. Macrosomia causes considerable challenges for both physicians and pregnant women. Our objectives were to identify factors in gravida to be associated with the risk of macrosomia, to guide clinical prevention and treatment.

METHODS

The study assessed risk factors of macrosomia by comparison with normal birth weight neonates, and a case-control study was conducted at Shandong Provincial Maternity and Child Healthcare Hospital. We followed and selected the relevant indicators of gravida who gave birth to macrosomia or normal infants, and applied statistical analysis to identify clinical indicators related to macrosomia.

RESULTS

Maternal blood glucose (OR 3.88 (1.07, 14.15)), history of abnormal conception (OR 18.44 (1.05, 322.89)), situation of menarche (OR 13.53 (1.28, 142.66)), and menstrual cycle of gravida (OR 13.24 (1.17, 150.24)) were significant influencing factors of macrosomia, but did not appear in the univariate analysis. Adding gestational age at delivery (OR 4.00 (1.45, 11.09)), triglyceride (OR 0.01 (<0.01, 0.40)), and MBI (OR 46.35 (2.08, >99.99)) of pregnant women, the area under the curve (AUC) curve was drawn for forecasting the risk of macrosomia, and the value of AUC was 0.9174. The triglyceride blood index of pregnant women was the only one that was inversely proportional to the probability of giving birth to macrosomic infants. The low-density lipoprotein (LDL) (OR 0.29 (0.12, 0.72)) and total cholesterol (OR 0.39 (0.20, 0.75)) were important factors in univariate analysis, and both of them were negative correlation factors of macrosomia. All influencing factors in multivariate analysis were selected for drawing a receiver operating characteristic (ROC) curve, and the value of the AUC was 0.9174.

CONCLUSIONS

This analysis could therefore accurately predict the risk of pregnant women who would deliver macrosomic infants.

Contribution of gestational diabetes mellitus heterogeneity and prepregnancy body mass index to large-for-gestational-age infants-A retrospective case-control study

OBJECTIVE

To study the associations between heterogeneity of gestational diabetes mellitus (GDM) subtype/prepregnancy body mass index (pre-BMI) and large-for-gestational-age (LGA) infants of Chinese women.

METHODS

We performed a retrospective case-control study of 299 women with GDM and 204 women with normal glucose tolerance (NGT), using oral glucose tolerance test-based indices performed at 24-25 weeks of gestation. Women with GDM were classified into the following three physiologic subtypes: GDM with a predominant insulin-secretion defect (GDM-dysfunction), GDM with a predominant insulin-sensitivity defect (GDM-resistance), or GDM with both defects (GDM-mixed). We then used a binary logistic regression model to evaluate the potential associations of GDM subtypes and pre-BMI with newborn macrosomia or LGA.

RESULTS

Women with GDM-resistance had a higher pre-BMI (P < 0.001), whereas women in the GDM-dysfunction and GDM-mixed groups had pre-BMIs comparable to the NGT group. In the logistic regression model, women in the GDM-mixed group exhibited an increased risk of bearing newborns with macrosomia and LGA, and women in the GDM-dysfunction group tended to have newborns with LGA after adjusting for pre-BMI and other potential confounders. Women who were overweight or obese prepregnancy manifested an increased risk of having newborns with macrosomia and LGA relative to normal-weight women, regardless of whether values were unadjusted or adjusted for all potential confounders. There was no significant interaction between GDM subtype and pre-BMI for any of the studied outcomes.

CONCLUSIONS

Heterogeneity of GDM (GDM-dysfunction and GDM-mixed) and prepregnancy overweight/obesity were independently associated with LGA in Chinese women. There was no significant interaction between GDM subtypes and pre-BMI for LGA.

The Role of Maternal Weight in the Hierarchy of Macrosomia Predictors; Overall Effect of Analysis of Three Prediction Indicators

So far it has not been established which maternal features play the most important role in newborn macrosomia. The aim of this study is to provide assessment of a hierarchy of twenty six (26) maternal characteristics in macrosomia prediction. A Polish prospective cohort of women with singleton pregnancy (N = 912) which was recruited in the years 2015–2016 has been studied. Two analyses were performed: for probability of macrosomia > 4000 g (n = 97) (vs. 755 newborns 2500–4000 g); and for birthweight > 90th percentile (n = 99) (vs. 741 newborns 10–90th percentile). A multiple logistic regression was used (with 95% confidence intervals (CI)). A hierarchy of significance of potential predictors was established after summing up of three prediction indicators (NRI, IDI and AUC) calculated for the basic prediction model (maternal age + parity) extended with one (test) predictor. ‘Net reclassification improvement’ (NRI) focuses on the reclassification table describing the number of women in whom an upward or downward shift in the disease probability value occurred after a new factor had been added, including the results for healthy and ill women. ‘Integrated discrimination improvement’ (IDI) shows the difference between the value of mean change in predicted probability between the group of ill and healthy women when a new factor is added to the model. The area under curve (AUC) is a commonly used indicator. Results. The macrosomia risk was the highest for prior macrosomia (AOR = 7.53, 95%CI: 3.15–18.00, p < 0.001). A few maternal characteristics were associated with more than three times higher macrosomia odds ratios, e.g., maternal obesity and gestational age ≥ 38 weeks. A different hierarchy was shown by the prediction study. Compared to the basic prediction model (AUC = 0.564 (0.501–0.627), p = 0.04), AUC increased most when pre-pregnancy weight (kg) was added to the base model (AUC = 0.706 (0.649–0.764), p < 0.001). The values of IDI and NRI were also the highest for the model with maternal weight (IDI = 0.061 (0.039–0.083), p < 0.001), and (NRI = 0.538 (0.33–0.746), p < 0.001). Adding another factor to the base model was connected with significantly weaker prediction, e.g., for gestational age ≥ 38 weeks (AUC = 0.602 (0.543–0.662), p = 0.001), (IDI = 0.009 (0.004; 0.013), p < 0.001), and (NRI = 0.155 (0.073; 0.237), p < 0.001). After summing up the effects of NRI, IDI and AUC, the probability of macrosomia was most strongly improved (in order) by: pre-pregnancy weight, body mass index (BMI), excessive gestational weight gain (GWG) and BMI ≥ 25 kg/m². Maternal height, prior macrosomia, fetal sex-son, and gestational diabetes mellitus (GDM) occupied an intermediate place in the hierarchy. The main conclusions: newer prediction indicators showed that (among 26 features) excessive pre-pregnancy weight/BMI and excessive GWG played a much more important role in macrosomia prediction than other maternal characteristics. These indicators more strongly highlighted the differences between predictors than the results of commonly used odds ratios.

Neonatal Macrosomia is an Interfering Factor for Analytes on the Colorado State Newborn Screen

PURPOSE

Neonatal macrosomia is a known complication of maternal obesity and gestational diabetes, and it is a risk factor for obesity and diabetes in offspring. Amino acids and acylcarnitines are biomarkers for obesity in children and adults. These analytes, which are also routinely obtained on the newborn screen, have not been well-characterized in macrosomic newborns. The impact of macrosomia on rates of false-positive results in the newborn screen has also not been well-studied. We test the hypothesis that macrosomia is an interfering factor for amino acids and/or acylcarnitines on the newborn screen.

METHODS

Newborn screening analytes determined by tandem mass spectroscopy were obtained from the Colorado Department of Public Health and Environment archives (2016-2018). This included metabolite concentrations obtained at 24-72 hours of life from newborns with birth weight 2500 to 3999 g (nonmacrosomic, n = 131 896) versus 4000 to 8000 g (macrosomic, n = 7806). Mother/infant phenotypic data were limited to information provided on the newborn screening dried blood spot card. Data were analyzed using Student t-test and chi-squared analysis.

RESULTS

Macrosomic newborns had elevations in C2, C3, dicarboxylic, and long-chain acylcarnitines (specifically C16 and C18 species). C3 and C18:1 were 2 to 3 times more likely to be above predetermined state cutoffs in macrosomic versus nonmacrosomic newborns (both male and female).

MAIN CONCLUSIONS

Macrosomia is an interfering factor for the analytes C3 and C18:1, leading to higher risk of false-positive results for methylmalonic/propionic acidemia and carnitine palmitoyl transferase type 2 deficiency, respectively. Analyte patterns found in macrosomic neonates correspond with similar analyte patterns in obese children and adults.