Important Predictors Of Birthweight Research Articles

Interpretable machine learning to identify important predictors of birth weight: A prospective cohort study.

Predicting birth weight and identifying its risk factors are clinically important. This study aims to use interpretable machine learning to predict birth weight and identity important predictors. This prospective cohort study was conducted in Tongzhou Maternal and Child Health Care Hospital of Beijing, China, recruiting pregnant women between June 2018 and February 2019. We used 24 features to predict infant birth weight, including gestational age, mother's age, parity, history of macrosomia delivery, pre-pregnancy body mass index (BMI), height, father's BMI, lifestyle (diet, physical activity, smoking), and biomarker (fasting glucose and lipids) features. Study outcome was birth weight of infant. We used 8 supervised learning models including 4 individual [linear regression, ridge regression, lasso regression, support vector machines regression (SVR)], and 4 ensemble estimators (random forest, AdaBoost, gradient boosted trees, and voting ensemble for regression) to predict birth weight. Model accuracy was measured by root mean squared error (RMSE) of 10-fold cross validation on the training set and RMSE of prediction on the test set. We used permutation importance algorithm to understand the prediction from the models and what affected them. This study included 4,754 mother-child dyads. RMSEs were lower in voting ensemble for regression, linear regression, and SVR than random forest, AdaBoost, and gradient boosted tree. The 5 most important predictors for infant birth weight were gestational age, fetal sex, preterm birth, mother's height, and pre-pregnancy BMI. After adding ultrasound-measured indicators of fetal growth into predictors, mother's height and pre-pregnancy BMI remained the most important predictors in predicting the outcome. Mother's height and pre-pregnancy BMI were identified as important predictors for infant birth weight. Interpretable machine learning is a promising tool in the prediction of birth weight.

Assessment of individual and mixture effects of element exposure measured in umbilical cord blood on birth weight in Bangladesh

Low birth weight is a leading cause of childhood morbidity and mortality globally, especially in low- and middle-income countries. Individual trace element exposure during pregnancy is associated with birth weight; however, information on potential mixture effects of multiple-elements exposure on birth weight are limited. In this study, we assessed the effects of single element and element mixtures on birth weight among 745 mother-infant pairs from a prospective birth cohort established in Bangladesh from 2008 to 2011. Using inductively coupled plasma mass spectrometry, we measured 56 elements in cord blood collected at delivery. Multivariate linear regression was used to evaluate the individual effect of element exposure on birth weight. Then, we also applied regression model based on categorical variables according the interquartile range (IQR) and restricted cubic spline to explore potential non-linear associations. Bayesian kernel machine regression (BKMR) and weighted quantile sum (WQS) regression were used to assess the mixture effects on birth weight. We found significant negative associations between exposure to lithium (Li), manganese (Mn), cobalt (Co), copper (Cu), yttrium (Y), and erbium (Er) and birth weight, while the concentration of selenium (Se) was positively associated with birth weight. Li was identified as the most important predictor of birth weight by WQS regression and cumulative exposure to element mixtures (Li, Mn, Cu, Co, Y, and Er) was associated with decreased birth weight, birth weight decreasing by 64.73 g with per each unit increment of the element risk score (ERS) based on WQS. Interestingly, the presence of Se during pregnancy may attenuate the risk of low birth weight by its main protective and antagonistic effects against toxic elements.

Associations between Maternal and Fetal Levels of Total Adiponectin, High Molecular Weight Adiponectin, Selected Somatomedins, and Birth Weight of Infants of Smoking and Non-Smoking Mothers.

The aim of the study was to determine the relationships between maternal smoking, total adiponectin, high molecular weight adiponectin (HMW adiponectin), selected somatomedins, and the birth weight of newborns. A total of 78 women with a healthy, singleton pregnancy, 41 active smokers and 37 non-smokers, and their offspring were studied. Total and HMW adiponectin, insulin-like growth factor I (IGF-I), and insulin-like growth factor binding protein-1 (IGFBP-1) and 2 (IGFBP-2) were determined in maternal and cord blood by enzyme-link immunosorbent assay. Serum levels of total and HMW adiponectin were lower in smokers compared to the tobacco abstinent in both the mothers (p = 0.013; p = 0.006) and the infants (p = 0.001; p = 0.047). In smoking women and their children, serum concentrations of IGF-I were significantly lower (p = 0.014; p = 0.042), IGFBP-1 significantly higher (p = 0.009; p = 0.039), and IGFBP-2 did not differ from that observed in the non-smoking group. In multivariate analysis performed on the whole group of mothers, the highest impact of serum cotinine and IGFBP-2 levels were indicated for adiponectin and cotinine and the number of cigarettes/day for HMW adiponectin concentration. In correlation analysis estimated separately for smokers and non-smokers, neonatal birth weight was positively associated with total and HMW adiponectin concentrations in umbilical cord blood. Birth weight was also inversely associated with IGFBP-1 and positively correlated with IGF-I levels in maternal serum as well as in cord blood (r = −0.317, p = 0.005; r = −0.294, p = 0.004; r = 0.245, p = 0.031; r = 0.271, p = 0.009, respectively). The present study showed the levels of total and HMW adiponectin in umbilical cord blood may have a significant effect on fetal development. Both IGF-I and IGFBP-1 concentrations also play an essential role in fetal growth, which is an important predictor of birth weight. Cigarette smoking during pregnancy negatively affected adiponectin and the insulin growth factor profile in the serum of women and the cord blood and may be the reason for the lower birth weight of the smokers newborns compared with the nonsmokers offspring.

Gestational Weight Gain and its Relation with Birth Weight of the Newborn

Gestational weight gain is an important predictor of the health of the newborn. It is affected by body mass index of the women. This study was conducted to find out gestational weight gain according to Institute of Medicine 2009 recommendation and relationship of newborn birth weight to body mass index and gestational weight gain of the women. It was cross sectional, hospital based study. The women, who attended at term pregnancy for delivery and having recorded first trimester body weight, were included in the study. Their body mass index was calculated and they were stratified into 4 groups according to body mass index. The gestational weight gain was calculated by subtracting first trimester body weight from body weight at the time of admission for delivery. All the women were followed till delivery. The newborn birth weight was taken immediately after delivery. A total of 227 women were enrolled in the study. More than half of the women had normal body mass index. There were 84 (37%) overweight and obese women. Mean gestational weight gain was 10.21 kg, and mean weight of the newborn was 3.05 kg. There were equal number of women who had adequate weight gain and less weight gain according to recommendation. Excess weight gain was seen in 34 (15%) women. Women of higher body mass index and women who had gain more weight during pregnancy had larger newborns. Body mass index and gestational weight gain of the women were important predictors of birth weight of the newborn. There is a positive correlation between gestational weight gain of the women and birth weight of the newborn.

Azithromycin-containing intermittent preventive treatment in pregnancy affects gestational weight gain, an important predictor of birthweight in Papua New Guinea - an exploratory analysis.

In Papua New Guinea, intermittent preventive treatment with sulphadoxine-pyrimethamine and azithromycin (SPAZ-IPTp) increased birthweight despite limited impact on malaria and sexually transmitted infections. To explore possible nutrition-related mechanisms, we evaluated associations between gestational weight gain (GWG), enrolment body mass index (BMI) and mid-upper arm circumference (MUAC), and birthweight. We investigated whether the increase in birthweight associated with SPAZ-IPTp may partly be driven by a treatment effect on GWG. The mean GWG rate was 393 g/week (SD 250; n = 948). A 100 g/week increase in GWG was associated with a 14 g (95% CI 2.6, 25.4) increase in birthweight (P = 0.016). Enrolment BMI and MUAC also positively correlated with birthweight. SPAZ-IPTp was associated with increased GWG [58 g/week (26, 900), P < 0.001, n = 948] and with increased birthweight [48 g, 95% CI (8, 880), P = 0.019] when all eligible women were considered (n = 1947). Inclusion of GWG reduced the birthweight coefficient associated with SPAZ-IPTp by 18% from 44 to 36 g (n = 948), although SPAZ-IPTp was not significantly associated with birthweight among women for whom GWG data were available (P = 0.13, n = 948). One month post-partum, fewer women who had received SPAZ-IPTp had a low post-partum BMI (<18.5 kg m(-2) ) [adjusted risk ratio: 0.55 (95% CI 0.36, 0.82), P = 0.004] and their babies had a reduced risk of wasting [risk ratio 0.39 (95% CI 0.21, 0.72), P = 0.003]. SPAZ-IPTp increased GWG, which could explain its impact on birthweight and maternal post-partum BMI. Future trials of SPAZ-IPTp must incorporate detailed anthropometric evaluations to investigate mechanisms of effects on maternal and child health.

Maternal BMI, weight gain during pregnancy, gestational age and Filipino ethnicity are important predictors of birth weight in a multiethnic population in Hawaii

Birth weight remains an important indicator of future health. Mother‐infant pairs (n=334) were studied, using electronic medical record data from Kaiser Permanente Hawaii, to examine determinants of birth weight in an understudied population. Maternal education, maternal age, tobacco use during pregnancy, and gestational diabetes diagnosis did not predict infant birth weight. Maternal BMI and pregnancy weight gain predicted infant birth weight in multiple regression analysis, explaining 5% of the variability in birth weight. Adding gestational age and ethnic group (Native Hawaiian, Asian, Filipino, Pacific Islander, Hawaiian‐Asian, Other Mixed ethnicity, Other ethnicity, with White reference group) to maternal BMI and pregnancy weight gain yielded a model where 48% of the variability in birth weight was explained. Birth weight of Filipinos was 231g lower than Whites, controlling for gestational age, maternal weight gain during pregnancy and pre‐pregnant BMI. These findings confirm findings of earlier studies done in other ethnic populations that show that key predictors of infant birth weight are maternal BMI and weight gain during pregnancy. After controlling for key variables, this study reveals lower birth weight among Filipinos as compared to Whites in Hawaii. USDA AFRI/NIFA Grant #2007–04557

Relation between Maternal Body Composition and Birth Weight

In order to establish the relationship between maternal body composition indicators (fat-free mass, fat mass, total body water) and birth weight, a cross-sectional study was designed, based on 196 pairs of mothers and live singleton newborns with gestational age of 37 weeks or more. Immediately after delivery, the mothers were interviewed to obtain information about different birth weight predictors. An analysis of maternal body composition through bioelectric impedance was held. Multiple linear regression was used to measure the effect of each variable on birth weight. The birth weight mean was 3,251 ± 514 g. Maternal height was 160.44 ± 6.3 cm, total net weight gain was 5.85 ± 5.15 kg, fat mass consisted of 15.84 ± 6.72 kg, and fat-free mass was 50.42 ± 7.65 kg; total body water was 34.82 ± 5.61 liters. The model which included total body water and all predictors found to be associated with birth weight in the bivariate analysis (maternal age, gestational age, gender, placenta weight, and placenta weight squared) was found to be the best in explaining the variability of birth weight (R² = 45.26%). Fat mass was an important predictor only in the subgroup of women within the low tertile of body mass index. In conclusion, fat-free mass and total body water explained a major proportion of the variability of birth weight in comparison with the mother’s weight gain during the pregnancy period, which has already been considered an important predictor of birth weight.

El efecto de variables psicosociales durante el embarazo, en el peso y la edad gestacional del recién nacido

Psychosocial events experienced by pregnant women could have an effect on the newborn. To study the effects of psychosocial events during pregnancy on the birth weight and gestational age of the newborn. One hundred ninety two pregnant women, coming from urban and rural areas, were interviewed at about 35 weeks of gestation. Information from the obstetrical chart was obtained and different inquiries were applied, about personal history, socioeconomic level, emotional state, vital events, family and social support. Fifty six percent of interviewed women did not plan their pregnancy, 32% considered their pregnancy as a negative event in their life, 62% had symptoms of emotional distress and 81% were unsatisfied with the social support that were receiving. When psychosocial factors were included in the multiple regression analyses, their predictive power for birth weight and gestational age improved. Including these variables, the R2 for birth weight was 0.22 and for gestational age, 0.18. Psychosocial factors during pregnancy are important predictors of birth weight and gestational age of the newborn. Measures should be taken to reduce the impact of these factors on the product of pregnancy.

Relation between Birth Weight and Placenta Weight

With the goal to establish a model that relates birth weight to placenta weight, adjusted for the most documented predictors of birth weight, 300 live newborns were studied, all were products of single gestation. Inclusion criteria were newborns with gestational age of 37 weeks or older according to the date of last menstruation, whose mothers did not have diabetes mellitus, high blood pressure, pre-eclampsia, or eclampsia. The weight of the newborn was identified from the anthropometry data collected by previously trained nursing personnel in each of the participating hospitals. Immediately after delivery, the placenta was weighed. Multiple linear regression was used to see the effect of placenta weight and each variable on birth weight. The mean of birth weight was 3,369 g with a standard deviation (SD) of 445 g. Placenta weight had a mean of 537 g (SD: 96 g). The relation between the weight of the placenta and the birth weight was significant, and we found that for each gram increase in placenta weight, birth weight is increased by 1.98 g (SE = 0.25, p < 0.01) and this relation is not linear, since the quadratic term is significant. Placenta weight has a nonlinear relation to the birth weight and is an important predictor of birth weight. Together with the gestational age and the maternal age and size, it explains 32% of the variability of birth weight. Placenta weight can be a ‘sentinel’ indicator of nutritional and/or environmental problems.

Maternal Body Weight And Relation To Birth Weight

In The Stockholm Pregnancy and Weight Development Study the relationships between birth weight and maternal initial body weight as well as weight increase during pregnancy were analysed in 2,295 subjects. As expected, the most important predictor of birth weight was total maternal weight increase, followed by initial maternal body weight. However, in women with an initial BMI value above 24 kg/m2, birth weight did not increase significantly in tact with maternal weight. The frequency of cesarean sections, 12% overall, was not significantly increased with maternal body weight. These data show that birth weight increases with maternal weight only up to the upper normal body weight.

Social determinants of low birth weight in a high-risk population

Over ninety low-weight infants were born per thousand live births in South Carolina, based on 96,000 birth records from 1975 and 1979. Higher incidence of low birth weight for black infants cannot be explained away as a result of black/white differences in age or education of mothers, prenatal care, parity or length of birth intervals. Though all these factors are important predictors of birth weight, an increasing propensity to have low-weight babies persists among black mothers even after all these factors are controlled.

Birth weights among infants born to adolescent and young adult women

Birth weights of 422 infants born to primigravid patients under the age of 16 years were compared with the birth weights of 422 infants born to a racially comparable group of primigravid patients 20 to 24 years old. No significant difference in birth weights was found between the two groups. Although infants of adolescents were born of shorter gestations than infants of older women, overall mean birth weights were similar, since the infants of adolescents were heavier at gestations of less than 37 weeks. Race and sex of the infant were more important predictors of birth weight among the adult group, whereas the maternal gain in weight was a more important factor among the adolescent group. Reduced birth weights reported in previous studies among infants of adolescents may have been due to overrepresentation of risk factors among these mothers rather than to maternal age, per se.