Elevated risk of attention deficit hyperactivity disorder (ADHD) in Japanese children with higher genetic susceptibility to ADHD with a birth weight under 2000 g

Development and validation of a nomogram prediction model for ADHD in children based on individual, family, and social factors

OBJECTIVES

A reliable, user-friendly, and multidimensional prediction tool can help to identify children at high risk for ADHD and facilitate early recognition and family management of ADHD. We aimed to develop and validate a risk nomogram for ADHD in children aged 3-17 years in the United States based on clinical manifestations and complex environments.

METHODS

A total of 141,356 cases were collected for the prediction model. Another 54,444 cases from a new data set were utilized for performing independent external validation. The LASSO regression was used to control possible variables. A final risk nomogram for ADHD was established based on logistic regression, and the discrimination and calibration of the established nomogram were evaluated by bootstrapping with 1000 resamples.

RESULTS

A final risk nomogram for ADHD was established based on 13 independent predictors, including behavioral problems, learning disabilities, age, intellectual disabilities, anxiety symptoms, gender, premature birth, maternal age at childbirth, parent-child interaction patterns, etc. The C-index of this model was 0.887 in the training set, and 0.862 in the validation set. Internal and external validation proved that the model was reliable.

CONCLUSIONS

A nomogram, a statistical prediction tool that assesses individualized ADHD risk for children is helpful for the early identification of children at high risk for ADHD and the construction of a conceptual model of society-family-school collaborative diagnosis, treatment, and management of ADHD.

The relationship between screen time and attention deficit/hyperactivity disorder in Chinese preschool children under the multichild policy: a cross-sectional survey

BACKGROUND

Long screen time has become a public health problem that cannot be ignored. The association between screen time and attention-deficit/hyperactivity disorder (ADHD) in preschool children has received widespread attention.

METHODS

A questionnaire was used to survey 2452 people. ADHD symptoms were assessed by the Conners Child Behavior Scale. Considering that the ADHD symptoms of boys and girls might be different, we stratified the data by gender. Logistic regression model was used for regression analysis. To exclude the influence of multichild family and obesity level, we also conducted a sensitivity analysis. P values were two-tailed with a significance level at 0.05.

RESULTS

The results showed that the association between screen time and ADHD symptoms in preschool children was significant (OR = 1.826, 95%CI: 1.032, 3.232). After grouping the genders, the correlation was not significant. There was an association between screen time and ADHD symptoms in children from families with multiple children. However, after excluding overweight and obese children from the overall population, the association between screen time and ADHD symptoms did not have statistical significance.

CONCLUSIONS

The issue of screen time for preschoolers needs to be taken seriously. Although the results indicate a significant correlation between screen time and ADHD symptoms, clearer evidence is needed to provide recommendations to policy makers.

Examining the relationship between birth weight and attention-deficit hyperactivity disorder diagnosis

Background

Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental condition that is prevalent in children worldwide. We evaluated the potential relationship between birth weight and ADHD using newly released data from the National Survey of Children's Health 2019-2020.

Methods

This population-based survey study used parent recollection data that were collected and submitted by 50 states and the District of Columbia to the National Survey of Children's Health database from the National Survey of Children's Health database. Those aged < 3 years and without birth weight or ADHD records were excluded. Children were stratified according to ADHD diagnosis and birth weight: very low birth weight (VLBW, < 1,500 g), low birth weight (LBW, 1,500-2,500 g), and normal birth weight (NBW, ≥ 2,500 g). Multivariable logistic regression was applied to examine the causal association between birth weight and ADHD while controlling for child and household characteristics.

Results

The final sample consisted of 60,358 children, of whom 6,314 (9.0%) were reported to have an ADHD diagnosis. The prevalence of ADHD was 8.7% in NBW children, 11.5% in LBW, and 14.4% in VLBW. Compared with NBW children, LBW children [adjusted odds ratio (aOR), 1.32 (95% CI, 1.03-1.68)], and VLBW children [aOR, 1.51 (95% CI, 1.06-2.15)] had a significantly higher risk of ADHD after adjusting all variables. These associations persisted in the male subgroups.

Conclusion and relevance

This study found that LBW and VLBW children were at a higher risk of ADHD.

Annual Research Review: Perspectives on progress in ADHD science - from characterization to cause

The science of attention-deficit/hyperactivity disorder (ADHD) is motivated by a translational goal - the discovery and exploitation of knowledge about the nature of ADHD to the benefit of those individuals whose lives it affects. Over the past fifty years, scientific research has made enormous strides in characterizing the ADHD condition and in understanding its correlates and causes. However, the translation of these scientific insights into clinical benefits has been limited. In this review, we provide a selective and focused survey of the scientific field of ADHD, providing our personal perspectives on what constitutes the scientific consensus, important new leads to be highlighted, and the key outstanding questions to be addressed going forward. We cover two broad domains - clinical characterization and, risk factors, causal processes and neuro-biological pathways. Part one focuses on the developmental course of ADHD, co-occurring characteristics and conditions, and the functional impact of living with ADHD - including impairment, quality of life, and stigma. In part two, we explore genetic and environmental influences and putative mediating brain processes. In the final section, we reflect on the future of the ADHD construct in the light of cross-cutting scientific themes and recent conceptual reformulations that cast ADHD traits as part of a broader spectrum of neurodivergence.

In utero antidepressant exposure not associated with ADHD in the offspring: A case control sibling design

Recent studies have reported an association between antidepressant (AD) use during pregnancy and the risk to develop attention-deficit/hyperactivity disorder (ADHD) in the offspring. However, the association might be confounded by risk factors in the pregnant parent. To control for unmeasured factors between pregnancies carried by the same parent, we set up a case-control sibling study using the University of Groningen prescription database IADB.nl. Children receiving medication for ADHD (cases) before the age of 16 years were matched to siblings not receiving such medication (controls). Exposure was defined as at least two prescriptions for any AD during pregnancy, i.e., the period of 39 weeks before the birth date of the offspring. Secondary analyses were performed to assess the effects of the degree of exposure (the amount of Defined Daily Doses) and the type of AD exposed to. Univariate and multivariate logistic regression was used to estimate odds ratios (ORs) with corresponding 95% confidence intervals (CI). In total, 2,833 children (1,304 cases and 1,529 controls) were included in the analysis. Exposure rate to ADs among cases and controls was 2.2% and 2.4%, respectively. After adjusting for the birth date of the child (as a proxy for the date of pregnancy), age of the pregnant parent at birth, use of psychostimulants, opioids, and antiepileptic drugs by the pregnant parent in the 15 months before birth of the child, an adjusted OR of 1.11 (95% CI 0.67–1.83) was found for the risk of ADHD in the offspring when exposed in utero to ADs. This indicates no increased risk of ADHD in offspring following in utero exposure to ADs. The secondary analyses revealed no statistically significant associations either. The present study provides further evidence that an association between in utero AD exposure and ADHD in offspring might not exist. This perceived association may be caused (at least partially) by confounding by indication. The extent to which depression in the pregnant parent could cause mental disorders such as ADHD in offspring, and the mechanisms involved, should be investigated in further studies.

Predicting Children with ADHD Using Behavioral Activity: A Machine Learning Analysis

Attention deficit hyperactivity disorder (ADHD) is one of childhood’s most frequent neurobehavioral disorders. The purpose of this study is to: (i) extract the most prominent risk factors for children with ADHD; and (ii) propose a machine learning (ML)-based approach to classify children as either having ADHD or healthy. We extracted the data of 45,779 children aged 3–17 years from the 2018–2019 National Survey of Children’s Health (NSCH, 2018–2019). About 5218 (11.4%) of children were ADHD, and the rest of the children were healthy. Since the class label is highly imbalanced, we adopted a combination of oversampling and undersampling approaches to make a balanced class label. We adopted logistic regression (LR) to extract the significant factors for children with ADHD based on p-values (<0.05). Eight ML-based classifiers such as random forest (RF), Naïve Bayes (NB), decision tree (DT), XGBoost, k-nearest neighborhood (KNN), multilayer perceptron (MLP), support vector machine (SVM), and 1-dimensional convolution neural network (1D CNN) were adopted for the prediction of children with ADHD. The average age of the children with ADHD was 12.4 ± 3.4 years. Our findings showed that RF-based classifier provided the highest classification accuracy of 85.5%, sensitivity of 84.4%, specificity of 86.4%, and an AUC of 0.94. This study illustrated that LR with RF-based system could provide excellent accuracy for classifying and predicting children with ADHD. This system will be helpful for early detection and diagnosis of ADHD.

Neighborhood environmental exposures and incidence of attention deficit/hyperactivity disorder: A population-based cohort study

BACKGROUND

Emerging studies have associated low greenspace and high air pollution exposure with risk of child attention deficit/hyperactivity disorder (ADHD). Population-based studies are limited, however, and joint effects are rarely evaluated. We investigated associations of ADHD incidence with greenspace, air pollution, and noise in a population-based birth cohort.

METHODS

We assembled a cohort from administrative data of births from 2000 to 2001 (N ∼ 37,000) in Metro Vancouver, Canada. ADHD was identified by hospital records, physician visits, and prescriptions. Cox proportional hazards models were applied to assess associations between environmental exposures and ADHD incidence adjusting for available covariates. Greenspace was estimated using vegetation percentage derived from linear spectral unmixing of Landsat imagery. Fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) were estimated using land use regression models; noise was estimated using a deterministic model. Exposure period was from birth until the age of three. Joint effects of greenspace and PM_2.5 were analysed in two-exposure models and by categorizing values into quintiles.

RESULTS

During seven-year follow-up, 1217 ADHD cases were diagnosed. Greenspace was associated with lower incidence of ADHD (hazard ratio, HR: 0.90 [0.81-0.99] per interquartile range increment), while PM_2.5 was associated with increased incidence (HR: 1.11 [1.06-1.17] per interquartile range increment). NO₂ (HR: 1.01 [0.96, 1.07]) and noise (HR: 1.00 [0.95, 1.05]) were not associated with ADHD. There was a 50% decrease in the HR for ADHD in locations with the lowest PM_2.5 and highest greenspace exposure, compared to a 62% increase in HR in locations with the highest PM_2.5 and lowest greenspace exposure. Effects of PM_2.5 were attenuated by greenspace in two-exposure models.

CONCLUSIONS

We found evidence suggesting environmental inequalities where children living in greener neighborhoods with low air pollution had substantially lower risk of ADHD compared to those with higher air pollution and lower greenspace exposure.

Linoleic acid and linoleate diols in neonatal cord blood influence birth weight

BACKGROUND

Low-birth-weight infants exhibit a high risk for postnatal morbidity. Cytochrome P450 (CYP) and epoxide hydrolase (EH) are involved in the metabolism of factors responsible for low-birth-weight in infants. Both CYPs and EHs have high substrate specificity and are involved in polyunsaturated fatty acid (PUFA) metabolism. The CYP pathway produces epoxy fatty acids (EpFAs), which are further degraded by soluble EH (sEH). Additionally, sEH inhibition enhances the action of EpFAs and suppresses inflammatory responses. During pregnancy, excessive activation of maternal inflammatory response is a significant factor associated with low-birth-weight. However, the association of EpFAs, which have potential anti-inflammatory properties, with the low-birth-weight of infants remains uninvestigated. This study aimed to clarify the association between the umbilical cord serum EpFA and low-birth-weight using data obtained from the Hamamatsu Birth Cohort for Mothers and Children (HBC Study) by analyzing the umbilical cord blood samples.

METHOD

We selected a subgroup of 200 infants (106 boys and 94 girls), quantified EpFA concentration in their cord blood samples collected at birth, and examined its correlation with birth weight.

RESULTS

The comparison between the low-birth-weight and normal-birth-weight groups revealed no significant correlation between PUFA and EpFA concentrations, but a significant correlation was observed in the linoleate diol concentrations of the two groups. Furthermore, birth weight did not significantly correlate with PUFA, EpFA, and diol concentrations in cord blood; however, multiple regression analysis showed a significant negative correlation of birth weight with the concentration of linoleic acid (LA) (r = -0.101, p = 0.016) as well as LA-derived dihydroxyoctadecenoic acid (diHOME) (r = -0.126, p = 0.007), 9,10-diHOME (r = -0.115, p = 0.014), and 12,13-diHOME (r = -0.126, p = 0.007) after adjusting for obstetric factors, including gestational age, infant's sex, childbirth history, delivery method, and maternal height.

CONCLUSIONS

Birth weight was significantly correlated with the concentration of LA and linoleate diol diHOME after adjusting for obstetric confounders. Our results show that CYP and sEH involved in PUFA metabolism may influence the birth weight of infants. Further validation is needed to provide insights regarding maternal intervention strategies required to avoid low-birth-weight in infants in the future.