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Assessing phenotypic and polygenic models of ADHD to identify mechanisms of risk for longitudinal trajectories of externalizing behaviors.

ABSTRACT: BACKGROUND:Children with ADHD frequently engage in higher rates of externalizing behaviors in adulthood relative to children without. However, externalizing behaviors vary across development. Little is known about how this risk unfolds across development. Phenotypic and polygenic models of childhood ADHD were used to predict individual differences in adult externalizing trajectories. Supportive parenting, school connectedness, and peer closeness were then examined as causal mechanisms. METHODS:Data were from the National Longitudinal Study of Adolescent to Adult Health (N = 7,674). Externalizing behavior was measured using data from age 18 to 32 and modeled using latent class growth analysis. Child ADHD was measured using retrospective self-report (phenotypic model) and genome-wide polygenic risk scores (polygenic model). Multiple mediation models examined the direct and indirect effects of the phenotypic and polygenic models (separately) on externalizing trajectories through the effects of adolescent supportive parenting, school connectedness, and peer closeness. RESULTS:Phenotypic and polygenic models of ADHD were associated with being in the High Decreasing (3.2% of sample) and Moderate (16.1%) adult externalizing trajectories, but not the severe Low Increasing trajectory (2.6%), relative to the Normal trajectory (78.2%). Associations between both models of ADHD on the High Decreasing and Moderate trajectories were partially mediated through the effects of school connectedness, but not supportive parenting or peer closeness. CONCLUSIONS:Findings shed light on how childhood ADHD affects downstream psychosocial processes that then predict specific externalizing outcomes in adulthood. They also reinforce the importance of fostering a strong school environment for adolescents with (and without) ADHD, as this context plays a critical role in shaping the development of externalizing behaviors in adulthood.

SUBMITTER: Li JJ

PROVIDER: S-EPMC6800752 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Assessing phenotypic and polygenic models of ADHD to identify mechanisms of risk for longitudinal trajectories of externalizing behaviors.

Li James J JJ

Journal of child psychology and psychiatry, and allied disciplines 20190501 11

<h4>Background</h4>Children with ADHD frequently engage in higher rates of externalizing behaviors in adulthood relative to children without. However, externalizing behaviors vary across development. Little is known about how this risk unfolds across development. Phenotypic and polygenic models of childhood ADHD were used to predict individual differences in adult externalizing trajectories. Supportive parenting, school connectedness, and peer closeness were then examined as causal mechanisms.<h ...[more]

PMID: 31044437

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Project description:IntroductionParenthood can be experienced as a pleasant but challenging period for parents, possibly accompanied by parenting stress. Early parenthood in particular is a vulnerable period as many parents experience biological and psychosocial changes related to new parenthood. Previous studies have shown that parenting stress is related to child behavior problems, but few studies have investigated the transactional relations across time between parenting stress and child internalizing and externalizing outcomes separately, examining within-person changes. The first aim of this study was to examine the transactional within-person associations of parenting stress and child internalizing and externalizing behavior problems across childhood from age 9 months to 9 years. As a second aim, we examined parenting as a possible underlying mechanism of the transactional associations by testing whether parental warmth and hostility mediate within-person associations of parenting stress and child behavior across time.MethodData were analyzed from the Growing Up in Ireland longitudinal child cohort study including 7,208 caregiver-child dyads at wave 1 (child's age 9 months), who were followed at child's age three (wave 2), five (wave 3), and 9 years (wave 5). Primary caregiver's and child's age and gender, household income, occupational status, educational status, partner status, and cultural background were covariates assessed at all waves. Data were analyzed using a random intercept cross-lagged panel model (CLPM) in R-lavaan.ResultsBidirectional relations between parenting stress and child behavior were found for both internalizing and externalizing behavior from age 5 to 9, but not for earlier time points.DiscussionOur results did not indicate mediating effects of parental warmth or parental hostility in the associations between parenting stress and child behavior problems. Therefore, we conclude that parenting stress and child internalizing as well as parenting stress and child externalizing behaviors have transactional associations from child's age 5 to 9 years. Future research examining transactional associations of parenting stress and child behaviors should investigate possible other mediations taking a within-person approach by utilizing the RI-CLPM.

Project description:BackgroundInternalizing and externalizing problems account for over 75% of the mental health burden in children and adolescents in the US, with higher burden among minority children. While complex interactions of multilevel factors are associated with these outcomes and may enable early identification of children in higher risk, prior research has been limited by data and application of traditional analysis methods. In this case example focused on Asian American children, we address the gap by applying data-driven statistical and machine learning methods to study clusters of mental health trajectories among children, investigate optimal predictions of children at high-risk cluster, and identify key early predictors.MethodsData from the US Early Childhood Longitudinal Study 2010-2011 were used. Multilevel information provided by children, families, teachers, schools, and care-providers were considered as predictors. Unsupervised machine learning algorithm was applied to identify groups of internalizing and externalizing problems trajectories. For prediction of high-risk group, ensemble algorithm, Superlearner, was implemented by combining several supervised machine learning algorithms. Performance of Superlearner and candidate algorithms, including logistic regression, was assessed using discrimination and calibration metrics via crossvalidation. Variable importance measures along with partial dependence plots were utilized to rank and visualize key predictors.FindingsWe found two clusters suggesting high- and low-risk groups for both externalizing and internalizing problems trajectories. While Superlearner had overall best discrimination performance, logistic regression had comparable performance for externalizing problems but worse for internalizing problems. Predictions from logistic regression were not well calibrated compared to those from Superlearner, however they were still better than few candidate algorithms. Important predictors identified were combination of test scores, child factors, teacher rated scores, and contextual factors, which showed non-linear associations with predicted probabilities.ConclusionsWe demonstrated the application of data-driven analytical approach to predict mental health outcomes among Asian American children. Findings from the cluster analysis can inform critical age for early intervention, while prediction analysis has potential to inform intervention programing prioritization decisions. However, to better understand external validity, replicability, and value of machine learning in broader mental health research, more studies applying similar analytical approach is needed.

Project description:BackgroundTimely recognition of the end of life allows patients to discuss preferences and make advance plans, and clinicians to introduce appropriate care. We examined changes in frailty over 1 year, with the aim of identifying trajectories that could indicate where an individual is at increased risk of all-cause mortality and may require palliative care.MethodsElectronic health records from 13,149 adults (cases) age 75 and over who died during a 1-year period (1 January 2015 to 1 January 2016) were age, sex and general practice matched to 13,149 individuals with no record of death over the same period (controls). Monthly frailty scores were obtained for 1 year prior to death for cases, and from 1 January 2015 to 1 January 2016 for controls using the electronic frailty index (eFI; a cumulative deficit measure of frailty, available in most English primary care electronic health records, and ranging in value from 0 to 1). Latent growth mixture models were used to investigate longitudinal patterns of change and associated impact on mortality. Cases were reweighted to the population level for tests of diagnostic accuracy.ResultsThree distinct frailty trajectories were identified. Rapidly rising frailty (initial increase of 0.022 eFI per month before slowing from a baseline eFI of 0.21) was associated with a 180% increase in mortality (OR 2.84, 95% CI 2.34-3.45) for 2.2% of the sample. Moderately increasing frailty (eFI increase of 0.007 per month, with baseline of 0.26) was associated with a 65% increase in mortality (OR 1.65, 95% CI 1.54-1.76) for 21.2% of the sample. The largest (76.6%) class was stable frailty (eFI increase of 0.001 from a baseline of 0.26). When cases were reweighted to population level, rapidly rising frailty had 99.1% specificity and 3.2% sensitivity (positive predictive value 19.8%, negative predictive value 93.3%) for predicting individual risk of mortality.ConclusionsPeople aged over 75 with frailty who are at highest risk of death have a distinctive frailty trajectory in the last 12 months of life, with a rapid initial rise from a low baseline, followed by a plateau. Routine measurement of frailty can be useful to support clinicians to identify people with frailty who are potential candidates for palliative care, and allow time for intervention.

Dataset Information

Assessing phenotypic and polygenic models of ADHD to identify mechanisms of risk for longitudinal trajectories of externalizing behaviors.

Publications

Assessing phenotypic and polygenic models of ADHD to identify mechanisms of risk for longitudinal trajectories of externalizing behaviors.

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