Using network analysis to illuminate the intergenerational transmission of adversity in the ALSPAC cohort

Objective: The effects of maternal exposure to adverse childhood experiences (ACEs) may be transmitted to subsequent generations through various biopsychosocial mechanisms. However, studies tend to focus on exploring one or two focal pathways with less attention paid to links between different pathways. Using a network approach, this paper explores a range of core prenatal risk factors that may link maternal ACEs to infant preterm birth (PTB) and low birthweight (LBW). Methods: We used data from the Avon Longitudinal Study of Parents and Children (ALSPAC) (n = 8 379) to estimate two mixed graphical network models: Model 1 was constructed using adverse infant outcomes, biopsychosocial and environmental risk factors, forms of ACEs, and sociodemographic factors. In Model 2, ACEs were combined to represent a threshold ACEs score (≥ 4). Network indices were estimated to determine the shortest pathway from ACEs to infant outcomes, and to identify the risk factors that are most vital in bridging these variables. Results: In both models, childhood and prenatal risk factors were highly interrelated. Childhood physical abuse, but not threshold ACEs, was directly linked to LBW. Further, exposure to second-hand smoke, developing gestational hypertension, prenatal smoking, first time pregnancy, not being White, and older age were directly linked to LBW, while developing gestational diabetes, having previous pregnanc(ies), and lower educational attainment were associated with PTB. Only pre-eclampsia was directly linked to both outcomes. Network indices and shortest pathways plots indicate that sexual abuse played a central role in bridging ACEs to other risks and poor infant outcomes. Overall, prenatal smoking was determined as the most influential bridge node. Conclusions: As child physical abuse was directly linked to low birthweight, and child sexual abuse and prenatal smoking were the most influential bridge nodes, they can be considered priority candidate targets for interventions to disrupt intergenerational risk transmission. Further, our study demonstrates the promise of network analysis as an approach for illuminating the intergenerational transmission of adversity in its full complexity.

Neurodevelopmental outcome of very low birth weight and extremely low birth weight newborn at 12 months of corrected age associated with prenatal risk factors

Background: Various neuro-developmental impairment (NDI) among very low birth weight babies (VLBW) and extremely low birth weight (ELBW) babies are common in Indian scenario. This study was designed to assess the impact between prenatal risk factors and neuro-developmental outcomes of premature infants.Methods: This descriptive study was conducted on 143 VLBW and ELBW babies admitted in SNCU of North Bengal Medical College, Darjeeling, West Bengal and discharged babies were followed up.Results: Total 143 neonates were studied among male 82 (57.3%) and female 61 (42.7%) and AGA: SGA ratio was 1.97. Birth weight ranged from 500 to 1500grams with mean was 1199.6±244.14 and the median was 1240 gm. The mean gestational age (Mean± SD) was 29.65±2.032 weeks with range 24-32 weeks and the median was 30 weeks. 28 (19.6%) had PIH, 39 (27.3%) had multiple gestation, 18 (12.6%) had perinatal infection and 25 (17.5%) had birth asphyxia. CRIB II score ranged from 3-18 with mean was 8.021±3.883 and median was 7. 73.4% (105/143) were discharged alive. Significant positive correlations were found among birth weight, gestational age, perinatal infection (p<0.001). Adverse neonatal outcome was associated with CRIB II score ≥10. Total CRIB II score with parameters of NDI like developmental delay, cerebral palsy, visual abnormality, absent ABR showed good correlation (p<0.001). Fisher Exact test revealed significant association between total score and Cerebral palsy (p=0.0005), visual abnormality (p=0.0005), absent ABR (p=0.0002).Conclusions: Perinatal risk factors influence future NDI in very low and extremely low birth weight babies. They should be identified and treated promptly to achieve good outcome.

Prenatal Risk Factors on Children’s Cerebral Palsy Case at RSIA Bunda Jakarta

Introduction: Cerebral Palsy is a disease that is less recognized by the public due to the lack of information related to CP in Indonesia. Aims to analyze prenatal risk factors that influence the evidence of CP RSIA Bunda Jakarta.Methods: This study was conducted in April 2021 at RSIA Bunda Jakarta, an analytical study with a case control design. The sample in this study were 124 respondents from mothers of children who underwent outpatient treatment at the RSIA Bunda Jakartaas cases and controls. each taken by simple random sampling technique. Data were analyzed using the test chi-square to analyze the relationship between variables.Results: The results of statistical analysis showed that the risk factors associated with the incidence of CP were maternal age (p= 0.00; OR= 13.25; 95% CI= 2.93-59.93), preeclampsia (p= 0.00; OR= 2,06; 95% CI= 1,71-2,48), TORCH (p= 0.00; OR= 2.40; 95% CI= 1.92-3.01), and antenatal care (p= 0.00; OR= 41.2; 95% CI= 5.45-317.0). Conclusion: Risk factors affecting the occurrence of CP in children include age <20 or ≥35 years, preeclampsia, TORCH infection and antenatal care visits <4 times. It is necessary to increase intervention programs at various levels of health services to diagnose and prevent the occurrence of cerebral palsy in children so that optimal maternal and child health is achieved. and further research is needed to determine other risk factors that can cause CP in children such as perinatal and postnatal risk factors.

Machine Learning for Predicting the Risk for Childhood Asthma Using Prenatal, Perinatal, Postnatal and Environmental Factors

The prevalence rate for childhood asthma and its associated risk factors vary significantly across countries and regions. In the case of Morocco, the scarcity of available medical data makes scientific research on diseases such as asthma very challenging. In this paper, we build machine learning models to predict the occurrence of childhood asthma using data from a prospective study of 202 children with and without asthma. The association between different factors and asthma diagnosis is first assessed using a Chi-squared test. Then, predictive models such as logistic regression analysis, decision trees, random forest and support vector machine are used to explore the relationship between childhood asthma and the various risk factors. First, data were pre-processed using a Chi-squared feature selection, 19 out of the 36 factors were found to be significantly associated (p-value < 0.05) with childhood asthma; these include: history of atopic diseases in the family, presence of mites, cold air, strong odors and mold in the child’s environment, mode of birth, breastfeeding and early life habits and exposures. For asthma prediction, random forest yielded the best predictive performance (accuracy = 84.9%), followed by logistic regression (accuracy = 82.57%), support vector machine (accuracy = 82.5%) and decision trees (accuracy = 75.19%). The decision tree model has the advantage of being easily interpreted. This study identified important maternal and prenatal risk factors for childhood asthma, the majority of which are avoidable. Appropriate steps are needed to raise awareness about the prenatal risk factors.

Prenatal risk factors and neonatal DNA methylation in very preterm infants

Background Prenatal risk factors are related to poor health and developmental outcomes for infants, potentially via epigenetic mechanisms. We tested associations between person-centered prenatal risk profiles, cumulative prenatal risk models, and epigenome-wide DNA methylation (DNAm) in very preterm neonates. Methods We studied 542 infants from a multi-center study of infants born < 30 weeks postmenstrual age. We assessed 24 prenatal risk factors via maternal report and medical record review. Latent class analysis was used to define prenatal risk profiles. DNAm was quantified from neonatal buccal cells using the Illumina MethylationEPIC Beadarray. Results We identified three latent profiles of women: a group with few risk factors (61%) and groups with elevated physical (26%) and psychological (13%) risk factors. Neonates born to women in higher risk subgroups had differential DNAm at 2 CpG sites. Higher cumulative prenatal risk was associated with methylation at 15 CpG sites, 12 of which were located in genes previously linked to physical and mental health and neurodevelopment. Conclusion We observed associations between prenatal risk factors and DNAm in very preterm infants using both person-centered and cumulative risk approaches. Epigenetics offers a potential biological indicator of prenatal risk exposure.

Prenatal risk factors and neonatal DNA methylation in very preterm infants

Background: Prenatal risk factors are related to poor health and developmental outcomes for infants, potentially via epigenetic mechanisms. We tested associations between person-centered prenatal risk profiles, cumulative prenatal risk models, and epigenome-wide DNA methylation (DNAm) in very preterm neonates.Methods: We studied 542 infants from a multi-center study of infants born &lt;30 weeks postmenstrual age. We assessed 24 prenatal risk factors via maternal report and medical record review. Latent class analysis (LCA) was used to define prenatal risk profiles. DNAm was quantified from neonatal buccal cells using the Illumina MethylationEPIC Beadarray.Results: We identified three latent profiles of women: a group with few risk factors (61%) and groups with elevated physical (26%) and psychological (13%) risk factors. Neonates born to women in higher risk subgroups had differential DNAm at 2 CpG sites. Higher cumulative prenatal risk was associated with methylation at 15 CpG sites, 12 of which were located in genes previously linked to physical and mental health and neurodevelopment.Conclusion: We observed associations between prenatal risk factors and DNAm in very preterm infants using both person-centered and cumulative risk approaches. Epigenetics offers a potential biological indicator of prenatal risk exposure.

Prenatal Sociodemographic Factors Predicting Maltreatment of Children up to 3 Years Old: A Prospective Cohort Study Using Administrative Data in Japan

Identifying risk factors from pregnancy is essential for preventing child maltreatment. However, few studies have explored prenatal risk factors assessed at pregnancy registration. This study aimed to identify prenatal risk factors for child maltreatment during the first three years of life using population-level survey data from pregnancy notification forms. This prospective cohort study targeted all mothers and their infants enrolled for a 3- to 4-month-old health check between October 2013 and February 2014 in five municipalities in Aichi Prefecture, Japan, and followed them until the child turned 3 years old. Administrative records of registration with Regional Councils for Children Requiring Care (RCCRC), which is suggestive of child maltreatment cases, were linked with survey data from pregnancy notification forms registered at municipalities (n = 893). Exact logistic regression was used for analysis. A total of 11 children (1.2%) were registered with RCCRC by 3 years of age. Unmarried marital status, history of artificial abortion, and smoking during pregnancy were significantly associated with child maltreatment. Prenatal risk scores calculated as the sum of these prenatal risk factors, ranging from 0 to 7, showed high predictive power (area under receiver operating characteristic curve 0.805; 95% confidence interval (CI), 0.660–0.950) at a cut-off score of 2 (sensitivity = 72.7%, specificity = 83.2%). These findings suggest that variables from pregnancy notification forms may be predictors of the risk for child maltreatment by the age of three.