Below Average Cognitive Ability-An under Researched Risk Factor for Emotional-Behavioural Difficulties in Childhood

Prenatal maternal Inflammation, childhood cognition and adolescent depressive symptoms

BACKGROUND

Accumulating evidence indicates that higher prenatal maternal inflammation is associated with increased depression risk in adolescent and adult-aged offspring. Prenatal maternal inflammation (PNMI) may increase the likelihood for offspring to have lower cognitive performance, which, in turn, may heighten risk for depression onset. Therefore, this study explored the potential mediating role of childhood cognitive performance in the relationship between PNMI and adolescent depressive symptoms in offspring.

METHODS

Participants included 696 mother-offspring dyads from the Child Health and Development Studies (CHDS) cohort. Biomarkers of maternal inflammation [interleukin (IL)-6, IL-8, IL-1 receptor antagonist (IL-1RA) and soluble TNF receptor-II (sTNF-RII)] were assayed from first (T1) and second trimester (T2) sera. Childhood (ages 9-11) cognitive performance was assessed via standardized Peabody Picture Vocabulary Test (PPVT), a measure of receptive vocabulary correlated with general intelligence. Adolescent (ages 15-17) depressive symptoms were assessed via self-report.

RESULTS

There were no significant associations between T1 biomarkers and childhood PPVT or adolescent depressive symptoms. Higher T2 IL1-RA was directly associated with lower childhood PPVT (b = -0.21, SE = 0.08, t = -2.55, p = 0.01), but not with adolescent depressive symptoms. T2 IL-6 was not directly associated with childhood PPVT, but higher T2 IL-6 was directly associated at borderline significance with greater depressive symptoms in adolescence (b = 0.05, SE = 0.03, t = 1.96, p = 0.05). Lower childhood PPVT predicted significantly higher adolescent depressive symptoms (b = -0.07, SE = 0.02, t = -2.99, p < 0.01). There was a significant indirect effect of T2 IL-1RA on adolescent depressive symptoms via childhood PPVT (b = 0.03, 95 % CI = 0.002-0.03) indicating a partially mediated effect. No significant associations were found with T2 sTNF-RII nor IL-8.

CONCLUSIONS

Lower childhood cognitive performance, such as that indicated by a lower PPVT score, represents a potential mechanism through which prenatal maternal inflammation contributes to adolescent depression risk in offspring.

Predicting low cognitive ability at age 5 years using perinatal data and machine learning

BACKGROUND

There are no early, accurate, scalable methods for identifying infants at high risk of poor cognitive outcomes in childhood. We aim to develop an explainable predictive model, using machine learning and population-based cohort data, for this purpose.

METHODS

Data were from 8858 participants in the Growing Up in Ireland cohort, a nationally representative study of infants and their primary caregivers (PCGs). Maternal, infant, and socioeconomic characteristics were collected at 9-months and cognitive ability measured at age 5 years. Data preprocessing, synthetic minority oversampling, and feature selection were performed prior to training a variety of machine learning models using ten-fold cross validated grid search to tune hyperparameters. Final models were tested on an unseen test set.

RESULTS

A random forest (RF) model containing 15 participant-reported features in the first year of infant life, achieved an area under the receiver operating characteristic curve (AUROC) of 0.77 for predicting low cognitive ability at age 5. This model could detect 72% of infants with low cognitive ability, with a specificity of 66%.

CONCLUSIONS

Model performance would need to be improved before consideration as a population-level screening tool. However, this is a first step towards early, individual, risk stratification to allow targeted childhood screening.

IMPACT

This study is among the first to investigate whether machine learning methods can be used at a population-level to predict which infants are at high risk of low cognitive ability in childhood. A random forest model using 15 features which could be easily collected in the perinatal period achieved an AUROC of 0.77 for predicting low cognitive ability. Improved predictive performance would be required to implement this model at a population level but this may be a first step towards early, individual, risk stratification.

Prediction of 2-Year Cognitive Outcomes in Very Preterm Infants Using Machine Learning Methods

Importance

Early intervention can improve cognitive outcomes for very preterm infants but is resource intensive. Identifying those who need early intervention most is important.

Objective

To evaluate a model for use in very preterm infants to predict cognitive delay at 2 years of age using routinely available clinical and sociodemographic data.

Design, Setting, and Participants

This prognostic study was based on the Swedish Neonatal Quality Register. Nationwide coverage of neonatal data was reached in 2011, and registration of follow-up data opened on January 1, 2015, with inclusion ending on September 31, 2022. A variety of machine learning models were trained and tested to predict cognitive delay. Surviving infants from neonatal units in Sweden with a gestational age younger than 32 weeks and complete data for the Bayley Scales of Infant and Toddler Development, Third Edition cognitive index or cognitive scale scores at 2 years of corrected age were assessed. Infants with major congenital anomalies were excluded.

Exposures

A total of 90 variables (containing sociodemographic and clinical information on conditions, investigations, and treatments initiated during pregnancy, delivery, and neonatal unit admission) were examined for predictability.

Main Outcomes and Measures

The main outcome was cognitive function at 2 years, categorized as screening positive for cognitive delay (cognitive index score <90) or exhibiting typical cognitive development (score ≥90).

Results

A total of 1062 children (median [IQR] birth weight, 880 [720-1100] g; 566 [53.3%] male) were included in the modeling process, of whom 231 (21.8%) had cognitive delay. A logistic regression model containing 26 predictive features achieved an area under the receiver operating curve of 0.77 (95% CI, 0.71-0.83). The 5 most important features for cognitive delay were non-Scandinavian family language, prolonged duration of hospitalization, low birth weight, discharge to other destination than home, and the infant not receiving breastmilk on discharge. At discharge from the neonatal unit, the full model could correctly identify 605 of 650 infants who would have cognitive delay at 24 months (sensitivity, 0.93) and 1081 of 2350 who would not (specificity, 0.46).

Conclusions and Relevance

The findings of this study suggest that predictive modeling in neonatal care could enable early and targeted intervention for very preterm infants most at risk for developing cognitive impairment.