The effect of maternal and paternal height and weight on antenatal, perinatal and postnatal morphology in sex-stratified analyses

Interpretable deep neural networks for advancing early neonatal birth weight prediction using multimodal maternal factors

BACKGROUND

Neonatal low birth weight (LBW) is a significant predictor of increased morbidity and mortality among newborns. Predominantly, traditional prediction methods depend heavily on ultrasonography, which does not consider risk factors affecting birth weight (BW).

OBJECTIVE

This study introduces a robust deep neural network for a clinical decision-support system designed to early predict neonatal BW, using data available during early pregnancy, with enhanced precision. This innovative system incorporates a comprehensive array of maternal factors, placing particular emphasis on nutritional elements alongside physiological and lifestyle variables.

METHODS

We employed and validated various traditional machine learning models as well as an interpretable deep learning model using the TabNet architecture, noted for its proficient handling of tabular data and high level of interpretability. The efficacy of these models was evaluated against extensive datasets that encompass a broad spectrum of maternal health indicators.

RESULTS

The TabNet model exhibited outstanding predictive capabilities, achieving an accuracy of 96% and an area under the curve (AUC) of 0.96. Significantly, maternal vitamin B12 and folate status emerged as pivotal predictors of BW, emphasizing the crucial role of nutritional factors in influencing neonatal health outcomes.

CONCLUSIONS

Our results demonstrate the substantial benefits of integrating multimodal maternal factors into predictive models for neonatal BW, markedly enhancing the precision over traditional AI methods. The developed decision-support system not only has a possible application in prenatal care but also provides actionable insights that can be leveraged to mitigate the risks associated with LBW, thereby improving clinical decision-making processes and outcomes.

Exploring the Father's Role in Determining Neonatal Birth Weight: A Narrative Review

Birth weight, which exhibits variability across different populations, is influenced by a mix of genetic, environmental, and dietary factors originating from both the mother and father. Maternal characteristics, including age, socioeconomic status, prior pregnancies, weight, height, and weight increase throughout pregnancy, have a substantial influence on fetal growth and the health of the infant. On the other hand, the influence of paternal characteristics on the weight of newborns is still not fully comprehended in a consistent manner. Birth weight is an important factor that can help predict various maternal complications, such as the probability of having a C-section, experiencing postpartum hemorrhage or infections. It can also indicate future health challenges like asthma, cognitive impairment, and chronic diseases such as hypertension and diabetes. Nineteen publications were found through a thorough search of the Medline, PubMed, and Scopus databases, which provide insights into how paternal variables contribute to variations in birth weight. Significantly, the age of the father was found to be associated with higher chances of preterm birth and having a smaller size for gestational age in premature infants, while full-term children were more likely to have a larger size for gestational age. In addition, there is a constant correlation between the height of the father and the birth weight of the child. Taller dads are more likely to have babies with a higher birth weight and a lower likelihood of being small for gestational age (SGA). Although there were some discrepancies in the data about the weight and BMI of fathers, it was found that the height of fathers played a significant role in determining the size of the fetus and the weight of the newborn. While there may be differences in the conducted studies, these findings provide valuable insights into the complex connection between parental characteristics and fetal development. This data can be utilized to enhance clinical treatment strategies and enhance our comprehension of outcomes for neonates. Further homogeneous investigations are required to conclusively validate and build upon these findings.

Twin charts should be the standard reference to assess growth in twin pregnancy

PURPOSE OF REVIEW

Twin pregnancies are characterized by slower growth velocity compared with singletons, especially during the third trimester and, therefore, tend to be smaller than singletons. Growth surveillance is instrumental in the identification of twin pregnancies at risk of adverse outcomes. Whether the phenomenon of slow growth is an adaptive physiological response or represents pathological growth lag, is controversial. In this review, we focus on the evidence related to the types of growth charts employed for twin pregnancies.

RECENT FINDINGS

Consistent evidence has emerged over the past few years from large independent cohorts in different countries suggesting that twin-specific standards perform better in identifying growth-restricted twin pregnancies at risk of adverse perinatal outcomes, without resulting in an increase in interventions or iatrogenic prematurity.

SUMMARY

The current evidence supports the use of twin-specific reference charts. Concerted efforts should be made to derive prospective evidence from large multicentre studies on various aspects of the implementation of twin-specific standards.

Influence of parental anthropometry and gestational weight gain on intrauterine growth and neonatal outcomes: Findings from the MAI cohort study in rural India

Poor foetal growth and subsequent low birth weight are associated with an increased risk for disease later in life. Identifying parental factors that determine foetal growth are important to curbing intergenerational malnutrition, especially among disadvantaged populations in the global south where undernutrition rates are high. The objective of this study was to assess the relationships between parental biometry, intrauterine growth and neonatal outcomes, while factoring in socioeconomic status of historically disadvantaged households in rural India. Using data from the prospective longitudinal cohort, pregnant women from rural Pune, India (n = 134) were assessed between August 2020 and November 2022. Data on socio-demography, ultrasound measurements, parental and foetal anthropometry were collected. Multiple linear regression models were run to predict determinants of foetal intrauterine and neonatal growth (p value<0.05). The dependent variables were ultrasound measurements and neonatal biometry, and independent variables were gestational weight gain, parental and mid-parental height. Mean(±SD) maternal age, maternal height, paternal height and mid-parental height were 22.8±3.7 years, 153.6±5.5cm, 165.9±6.5cm and 159.1±8.7cm, respectively. Pre-pregnancy body mass index and gestational weight gain was 20.5±4.0 kg/m2 and 9.8±3.7kg respectively. Mid-parental height and gestational weight gain were strongly correlated with neonatal growth and foetal intrauterine growth (p<0.05); however, the correlation peaked at 28 weeks of gestation (p<0.05). Gestational weight gain (B = 28.7, p = 0.001) and mid-parental height (B = 14.3, p = 0.001) were identified as strong determinants of foetal-intrauterine growth and neonatal anthropometry at birth. Maternal height was found to influence length of male neonate (B = 0.18, p = 0.001), whereas, paternal height influenced length of the female neonate (B = 0.11, p = 0.01). Parental socio-economic status, biometry and maternal gestational weight gain influence growth of the child starting from the intrauterine period. Our study underlines the need for interventions during pre-pregnancy, as well as during pregnancy, for optimal weight gain and improved foetal and neonatal outcomes.

Does maternal stature modify the association between infants who are small or large for gestational age and adverse perinatal outcomes? A retrospective cohort study

OBJECTIVE

To investigate the effect of maternal stature on adverse birth outcomes and quantify perinatal risks associated with small- and large-for-gestational age infants (SGA and LGA, respectively) born to mothers of short, average, and tall stature.

DESIGN

Retrospective cohort study.

SETTING

USA, 2016-2017.

POPULATION

Women with a singleton live birth (N = 7 325 741).

METHODS

Using data from the National Center for Health Statistics, short and tall stature were defined as <10th and >90th centile of the maternal height distribution. Modified Poisson regression was used to estimate adjusted risk ratios (aRRs) and 95% confidence intervals (95% CIs).

MAIN OUTCOME MEASURES

Preterm birth (<37 weeks of gestation), neonatal intensive care unit (NICU) admission and severe neonatal morbidity/mortality (SNMM).

RESULTS

With increased maternal height, the risk of adverse outcomes increased in SGA infants and decreased in LGA infants compared with infants appropriate-for-gestational age (AGA) (p < 0.001). Infants who were SGA born to women of tall stature had the highest risk of NICU admission (aRR 1.98, 95% CI 1.91-2.05; p < 0.001), whereas LGA infants born to women of tall stature had the lowest risk (aRR 0.85, 95% CI 0.82-0.88; p < 0.001), compared with AGA infants born to women of average stature. LGA infants born to women of short stature had an increased risk of NICU admission and SNMM, compared with AGA infants born to women of average stature (aRR 1.32, 95% CI 1.27-1.38; aRR 1.21, 95% CI 1.13-1.29, respectively).

CONCLUSIONS

Maternal height modifies the association between SGA and LGA status at birth and neonatal outcomes. This quantification of risk can assist healthcare providers in monitoring fetal growth, and optimising neonatal care and follow-up.

Early Mid-pregnancy Blood-Based Proteins as Possible Biomarkers of Increased Infant Birth Size in Sex-Stratified Analyses

The objective of this study was to evaluate the associations of 92 maternal blood-based proteins with increased infant birth size. The study was performed at the Uppsala University Hospital, Sweden, and included 857 mother and child dyads. The mean age of the women was 30.3 years, and 53.2% were nulliparous. Blood samples were collected at mean 18 + 2 weeks' gestation, and the Olink cardiovascular II panel was used to measure 92 proteins, either known to be or suspected to be markers of cardiovascular and inflammatory disease in humans. Multiple linear regression models adjusted for maternal age, parity, pre-conception BMI, height, and smoking were performed to evaluate the association of each individual protein with infant birth size. We also performed sex-stratified analyses. Eight proteins (Matrix metalloproteinase-12 (MMP-12), Prostasin (PRSS8), Adrenomedullin (ADM), Pappalysin-1 (PAPP-A), Angiotensin-converting enzyme 2 (ACE2), Sortilin (SORT1), Lectin-like oxidized LDL receptor 1 (LOX-1), and Thrombomodulin (TM)) were associated with infant birth size after false discovery rate adjustment. In the analyses including only female infants, ten proteins (MMP-12, Growth/differentiation factor 2 (GDF-2), PRSS8, SORT1, ADM, Interleukin-1 receptor antagonist protein (IL-1ra), Leptin (LEP), ACE2, TM, and Tumor necrosis factor receptor superfamily member 11A (TNFRSF11A)) were associated with infant birth size. Two proteins (PAPP-A and PRSS8) were associated with infant birth size among male infants. Our study suggests several proteins as potential biomarkers for increased birth weight, and our findings could act as a base for future research to identify new potential markers that could be added to improve screening for large infants.

A new method for customized fetal growth reference percentiles

BACKGROUND

Customized fetal growth charts assume birthweight at term to be normally distributed across the population with a constant coefficient of variation at earlier gestational ages. Thus, standard deviation used for computing percentiles (e.g., 10th, 90th) is assumed to be proportional to the customized mean, although this assumption has never been formally tested.

METHODS

In a secondary analysis of NICHD Fetal Growth Studies-Singletons (12 U.S. sites, 2009-2013) using longitudinal sonographic biometric data (n = 2288 pregnancies), we investigated the assumptions of normality and constant coefficient of variation by examining behavior of the mean and standard deviation, computed following the Gardosi method. We then created a more flexible model that customizes both mean and standard deviation using heteroscedastic regression and calculated customized percentiles directly using quantile regression, with an application in a separate study of 102, 012 deliveries, 37-41 weeks.

RESULTS

Analysis of term optimal birthweight challenged assumptions of proportionality and that values were normally distributed: at different mean birthweight values, standard deviation did not change linearly with mean birthweight and the percentile computed with the normality assumption deviated from empirical percentiles. Composite neonatal morbidity and mortality rates in relation to birthweight < 10th were higher for heteroscedastic and quantile models (10.3% and 10.0%, respectively) than the Gardosi model (7.2%), although prediction performance was similar among all three (c-statistic 0.52-0.53).

CONCLUSIONS

Our findings question normality and constant coefficient of variation assumptions of the Gardosi customization method. A heteroscedastic model captures unstable variance in customization characteristics which may improve detection of abnormal growth percentiles.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT00912132.

Prevalence and Patterns of Prenatal Alcohol Exposure in Australian Cohort and Cross-Sectional Studies: A Systematic Review of Data Collection Approaches

This study sought to determine data collection approaches in Australian cohort studies and explore the potential impact on reported prenatal alcohol exposure (PAE) prevalence and patterns. Inclusion criteria were that studies related to a general Australian antenatal population where PAE was assessed and reported. Studies were excluded if they were not peer reviewed, examined the prevalence of PAE in pregnancies complicated by alcohol-use disorders, or were published in a language other than English. A systematic search of five electronic databases (PubMed, Embase, CINAHL, Web of Science, and Scopus) was conducted. Risk of bias was assessed using the Effective Public Health Practice Project quality assessment tool. Results were synthesised using MetaXL. Data from 16 separate birth cohorts (n = 78 articles) were included. Included cohorts were either general cohorts that included alcohol as a variable or alcohol-focused cohorts that were designed with a primary focus on PAE. PAE prevalence was estimated as 48% (95% CI: 38 to 57%). When subgroup analysis was performed, estimates of PAE prevalence when self-administered surveys and interviews were used for data collection were 53% (95% CI: 41% to 64%) and 43% (95% CI: 28% to 59%), respectively. Use of trained assessors was an influencing factor of the prevalence estimates when data were collected via interview. Alcohol-focused studies reported higher prevalence of PAE, regardless of method of survey administration. Where interviewer training is not possible, self-administered questionnaires will likely provide the most reliable PAE estimates. No funding sources are relevant to mention. Review was registered with PROSPERO (CRD42020204853).

Is birthweight influenced equally by maternal and paternal anthropometry?

OBJECTIVES

To elucidate the influence of parental biometric factors on fetal birthweight (BW).

STUDY DESIGN

This prospective study was conducted between 2015 and 2017 in Hadassah University Hospital. Inclusion criteria included singletons that were born to healthy mothers at 37-41 weeks' gestation and had no growth abnormality or congenital malformation. Maternal and paternal head circumference, weight, and height were measured. Other data including neonatal head circumference and neonatal birthweight were also collected. Neonatal head circumference and birthweight percentiles were converted to sex-specific ranks according to the neonatal Intergrowth 21 charts (rank = 1 for percentile <3, rank = 2 for percentile 3-10, etc.).

RESULTS

One hundred and ninety-nine trios (mother, father, and neonate) were included in the final analysis. In univariate analysis, maternal head circumference (p = .006), maternal height (p = .001), maternal weight before pregnancy (p < .001), maternal weight at term (p < .001), gestational weight gain (p = .009), paternal height (p = .018), neonatal head circumference (p < .001), and neonatal head circumference percentile rank (p < .001) were significant predictors of neonatal birthweight percentile rank. In multivariate regression, the three factors that were significant independent predictors of neonatal birthweight percentile rank were maternal weight before pregnancy (p = .047), maternal weight at term (p = .01), and neonatal head circumference percentile rank (p < .001). No interaction was found between neonatal sex and any of the tested variables. Neonatal sex-specific multivariate analysis showed that maternal height (p = .013), gestational weight gain (p = .005), and neonatal head circumference percentile rank (p < .001) were predictors of birthweight percentile rank in males. Maternal weight at term (p < .001) and neonatal head circumference percentile rank (p < .001) were predictors of birthweight percentile rank in females.

CONCLUSIONS

Maternal height and weight parameters as well as neonatal head circumference percentile rank were found to be independent predictors of birthweight percentile rank. Paternal parameters did not show any significant association in multivariable analysis. The biological regulation of fetal size is assumed to be the result of strong evolutionary selection. As the fetus must pass through the mother's birth canal, there should be a natural match between maternal and fetal size to ensure the successful birth and survival of mother and offspring.

Maternal Variables as Determinant of Fetal Growth: Study Protocol on Customized Fetal Growth Charts in Malaysia (GROW-My)

Adverse perinatal outcomes such as stillbirth, low birth weight and small for gestational age are still reported to be of high prevalence despite advanced healthcare technology and good quality hospital services in Malaysia. The purpose of this study is to create a model to evaluate individualized birth weight customized for maternal characteristics in a Malaysian population. Three phases are involved in designing the customized fetal growth chart (GROW-My). Baseline data is collected from previous pregnancies in the UKM Medical Centre from year 2010 to 2017. Specific maternal attributes were screened for its completeness and validity, namely maternal height and weight at booking, maternal ethnicity and parity, and the baby's birth weight, for all singleton pregnancies. The design and construction of a Malaysian customized fetal growth chart, Growth Related Optimal Weight (GROW-MY) was based on baseline birth data. The customized chart is used in the implementation phase for testing its feasibility, taking into consideration feedback from caregivers and patients before and after implementation. The current study provides staunch information and data regarding the needs and strategies for using maternal variables for estimating birth weight and the risk of being small for gestational age, in order to facilitate screening and appropriate management. With improved diagnosis of fetal growth restriction, medical care and treatment costs can be reduced.

Novel sex-specific influence of parental factors on small-for-gestational-age newborns

Since fetal programming is sex-specific, there may also be sex-specific in parental influences on newborn birth weight. We aimed to investigate the influence of parental factors on small-for-gestational-age (SGA) infants of different sexes. Based on a pre-pregnancy cohort, multivariate logistic regression was used. 2275 couples were included for analysis. Significant associations were observed among paternal height, pre-pregnancy body mass index (BMI), and SGA in male infants; among maternal height, pre-pregnancy BMI, and SGA in female infants, and among other maternal factors and SGA in both male and female infants. Such sex specificity may be related to genetic, epigenetic, or hormonal influences between parents and infants. In conclusion, there is a sex specificity in the effect of parental height and pre-pregnancy BMI on SGA. The data suggest that future studies on infants should consider the sex-specific differences between the effects of genetic or environmental factors and infants.