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

Rahman, Shafiur; Takahashi, Nagahide; Iwabuchi, Toshiki; Nishimura, Tomoko; Harada, Taeko; Okumura, Akemi; Takei, Nori; Nomura, Yoko; Tsuchiya, Kenji J.

doi:10.1186/s12916-021-02093-3

Cited by 13 publications

(10 citation statements)

References 75 publications

(92 reference statements)

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“…Based on an extensive literature review about ADHD, the predictor variables were used in this paper as: child's age [34][35][36][37][38][39][40], sex of the child [34][35][36][37][38][40][41][42], mother's age [35,41,42], allergies, arthritis, asthma, brain injury, headaches, anxiety [34,38,39,43], depression [34,36,38,43], health insurance [34,35], alcohol [36,41], race [28,34,35,37,38,40], family structure [34,35], mother's education [34,35,38,40,42], very low birth weight (LBW) [38], LBW [35,38], premature [35,36,38,...…”

Section: Predictor Variablementioning

confidence: 99%

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

2022

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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.

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Section: Predictor Variablementioning

confidence: 99%

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

2022

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“…By using siblings, it is expected that the indication for the childbearing parent to use ADs is the same and therefore equally distributed among cases and controls. It should be noted that the genetic risk factors for psychiatric disorders in the parent are also controlled for by the sibling comparison design and genetic risk factors of ADHD are considered the main contributors to its etiology ( Thapar et al, 2012 ; Rahman et al, 2021 ; Kraegeloh-Mann, 2022 ). Another limitation of the study is the unequal follow-up time for cases and controls, which may influence the comparison between siblings.…”

Section: Discussionmentioning

confidence: 99%

“…Using a sibling comparison design will match familial factors such as genetics of the childbearing parent ( Frisell et al, 2012 ). Since ADHD is highly heritable ( Coghill and Banaschewski, 2009 ), it is likely that genetic risk factors are the main contributors to the disorder’s etiology, while non-inherited factors play a role as well ( Thapar et al, 2012 ; Rahman et al, 2021 ; Kraegeloh-Mann, 2022 ). This makes a sibling study design suitable for investigating confounding by indication.…”

Section: Introductionmentioning

confidence: 99%

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

Hartwig

Robiyanto²,

Vos³

et al. 2022

Front. Pharmacol.

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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.

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“…Mann-Whitney U test was used to compare birth weights between the SGA and AGA groups, and Spearman's correlation was used to analyze the correlations between birth weight and fatty acids. Birth weight was regressed onto the PUFA metabolites, with gestational age at birth, infant's sex, parity, delivery method (cesarean section), and maternal height as potential confounders according to previous studies (40)(41)(42).…”

Section: Statistical Analysesmentioning

confidence: 99%

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

et al. 2022

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BackgroundLow-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.MethodWe 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.ResultsThe 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.ConclusionsBirth 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.

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

Cited by 13 publications

References 75 publications

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

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

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

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

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