Adverse Childhood Experiences Predict Common Neurodevelopmental and Behavioral Health Conditions among U.S. Children

Zarei, Kasra; Xu, Guoqing; Zimmerman, Bridget; Giannotti, Michele; Strathearn, Lane

doi:10.3390/children8090761

Cited by 28 publications

(25 citation statements)

References 51 publications

(88 reference statements)

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“…LR is used as supervised learning in the community of ML. In statistics, LR is also used to extract the most informative features [36,38,41,54,55]. The LR-based feature extraction procedure is described as follows:…”

Section: Feature Selection Methodsmentioning

confidence: 99%

“…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%

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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: Feature Selection Methodsmentioning

confidence: 99%

Section: Predictor Variablementioning

confidence: 99%

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

2022

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“…[45] Neural wiring alterations/Cognitive impairment Neural rewiring appears to be associated with a cadre of neurobehavioral alterations. [46,47] Social/Emotional Impairment This can range from mild social interaction issues to trauma-associated body disassociation. [48,49] Elevated risk of suicide This often co-occurs with major depressive disorder.…”

Section: Conditions/attribute Details Referencesmentioning

confidence: 99%

“…Table 4 [37,46,[110][111][112][113][114] illustrates examples of substance abuse connected to ACE. Given the knowledge that this is a significant risk, preventative measures that include the microbiome and protection against dysbiosis not only reduce the risk of substance abuse, but also canaid in protection against impediments to withdrawal.…”

Section: Substance Misuse/abusementioning

confidence: 99%

“…[111] Substance abuse A study using data from the 2016-2019 National Survey of Children's Health; During childhood; Seven ACEs were included (but not child maltreatment). [46] Problematic alcohol and/or tobacco use A longitudinal study of 1179 youths of Lain American origin or descent. The authors concluded that the results suggest a predictive relationship exists for ACEs and specific abused substances.…”

Section: Outcome Details Referencementioning

confidence: 99%

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Using Microbiome-Based Approaches to Deprogram Chronic Disorders and Extend the Healthspan following Adverse Childhood Experiences

Dietert

Dietert²

2022

Microorganisms

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Adverse childhood experiences (ACEs), which can include child trafficking, are known to program children for disrupted biological cycles, premature aging, microbiome dysbiosis, immune-inflammatory misregulation, and chronic disease multimorbidity. To date, the microbiome has not been a major focus of deprogramming efforts despite its emerging role in every aspect of ACE-related dysbiosis and dysfunction. This article examines: (1) the utility of incorporating microorganism-based, anti-aging approaches to combat ACE-programmed chronic diseases (also known as noncommunicable diseases and conditions, NCDs) and (2) microbiome regulation of core systems biology cycles that affect NCD comorbid risk. In this review, microbiota influence over three key cyclic rhythms (circadian cycles, the sleep cycle, and the lifespan/longevity cycle) as well as tissue inflammation and oxidative stress are discussed as an opportunity to deprogram ACE-driven chronic disorders. Microbiota, particularly those in the gut, have been shown to affect host–microbe interactions regulating the circadian clock, sleep quality, as well as immune function/senescence, and regulation of tissue inflammation. The microimmunosome is one of several systems biology targets of gut microbiota regulation. Furthermore, correcting misregulated inflammation and increased oxidative stress is key to protecting telomere length and lifespan/longevity and extending what has become known as the healthspan. This review article concludes that to reverse the tragedy of ACE-programmed NCDs and premature aging, managing the human holobiont microbiome should become a routine part of healthcare and preventative medicine across the life course.

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Examining adverse childhood experiences and attention deficit/hyperactivity disorder: A systematic review

Wojtara,

Syeda,

Singh

et al. 2023

Mental Health Science

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Adverse childhood experiences (ACEs) comprise many dimensions of abuse and neglect in early development. Attention deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder that often begins in childhood. In this review, we investigated the associations between ACEs and ADHD in children. Specifically, the focus is to determine the extent of the relationship between ACE type, cumulative number, and ADHD severity. Furthermore, this study explored all aspects of the bidirectional nature of this relationship including how children with ADHD may experience greater ACEs and the potential contribution of confounding and mediating variables including comorbid conditions and resilience. Selected studies were published between January 2015 and January 2023 on PsychInfo, Google Scholar, PubMed, and Scopus. Selected studies included: (1) The main age group of the study was children; (2)The children had to have been diagnosed with or have parent-reported ADHD; and (3)The research must include ACE. Case studies and those not meeting the inclusion criteria were excluded from this review. Ultimately, 43 studies met the inclusion criteria, were included in this review, and were evaluated using the appropriate risk of bias assessment tools. These studies supported a positive association between ACEs and ADHD including cumulative quantity and select types of ACEs increasing ADHD severity. Previous literature has primarily utilized observational methodologies which prevent researchers from establishing if there are causal associations and if there is a temporal order to ACEs and ADHD development. This review also provides implications for future research.

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Adverse Childhood Experiences Predict Common Neurodevelopmental and Behavioral Health Conditions among U.S. Children

Cited by 28 publications

References 51 publications

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

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

Using Microbiome-Based Approaches to Deprogram Chronic Disorders and Extend the Healthspan following Adverse Childhood Experiences

Examining adverse childhood experiences and attention deficit/hyperactivity disorder: A systematic review

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