The Use of Multi-parametric Biomarker Profiles May Increase the Accuracy of ASD Prediction

Combining Anti-Mitochondrial Antibodies, Anti-Histone, and PLA2/COX Biomarkers to Increase Their Diagnostic Accuracy for Autism Spectrum Disorders

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social interaction and restricted and repetitive behaviors. Oxidative stress may be a critical link between mitochondrial dysfunction and ASD as reactive oxygen species (ROS) generated from pro-oxidant environmental toxicants and activated immune cells can result in mitochondrial failure. Recently, mitochondrial dysfunction, autoimmunity, and abnormal lipid mediators have been identified in multiple investigations as an acknowledged etiological mechanism of ASD that can be targeted for therapeutic intervention.

METHODS

The relationship between lipid mediator markers linked to inflammation induction, such as phospholipase A2/cyclooxygenase-2 (PLA2/Cox-2), and the mitochondrial dysfunction marker anti-mitochondrial antibodies (AMA-M2), and anti-histone autoantibodies in the etiology of ASD was investigated in this study using combined receiver operating characteristic (ROC) curve analyses. This study also sought to identify the linear combination for a given set of markers that optimizes the partial area under ROC curves. This study included 40 age- and sex-matched controls and 40 ASD youngsters. The plasma of both groups was tested for PLA2/COX-2, AMA-M2, and anti-histone autoantibodies' levels using ELISA kits. ROC curves and logistic regression models were used in the statistical analysis.

RESULTS

Using the integrated ROC curve analysis, a notable rise in the area under the curve was noticed. Additionally, the combined markers had markedly improved specificity and sensitivity.

CONCLUSIONS

The current study suggested that measuring the predictive value of selected biomarkers related to mitochondrial dysfunction, autoimmunity, and lipid metabolism in children with ASD using a ROC curve analysis could lead to a better understanding of the etiological mechanism of ASD as well as its relationship with metabolism.

Decoding Autism: Uncovering patterns in brain connectivity through sparsity analysis with rs-fMRI data

BACKGROUND

In the realm of neuro-disorders, precise diagnosis and treatment rely heavily on objective imaging-based biomarker identification. This study employs a sparsity approach on resting-state fMRI to discern relevant brain region connectivity for predicting Autism.

NEW METHOD

The proposed methodology involves four key steps: (1) Utilizing three probabilistic brain atlases to extract functionally homogeneous brain regions from fMRI data. (2) Employing a hybrid approach of Graphical Lasso and Akaike Information Criteria to optimize sparse inverse covariance matrices for representing the brain functional connectivity. (3) Employing statistical techniques to scrutinize functional brain structures in Autism and Control subjects. (4) Implementing both autoencoder-based feature extraction and entire feature-based approach coupled with AI-based learning classifiers to predict Autism.

RESULTS

The ensemble classifier with the extracted feature set achieves a classification accuracy of 84.7% ± 0.3% using the MSDL atlas. Meanwhile, the 1D-CNN model, employing all features, exhibits superior classification accuracy of 88.6% ± 1.7% with the Smith 2009 (rsn70) atlas.

COMPARISON WITH EXISTING METHOD (S)

The proposed methodology outperforms the conventional correlation-based functional connectivity approach with a notably high prediction accuracy of more than 88%, whereas considering all direct and noisy indirect region-based functional connectivity, the traditional methods bound the prediction accuracy within 70% to 79%.

CONCLUSIONS

This study underscores the potential of sparsity-based FC analysis using rs-fMRI data as a prognostic biomarker for detecting Autism.

The role and impact of abnormal vitamin levels in autism spectrum disorders

The prevalence of autism spectrum disorder (ASD), a neurodevelopmental disorder with a predominance of social behavioral disorders, has increased dramatically in various countries in recent decades. The interplay between genetic and environmental factors is believed to underlie ASD pathogenesis. Recent analyses have shown that abnormal vitamin levels in early life are associated with an increased risk of autism. As essential substances for growth and development, vitamins have been shown to have significant benefits for the nervous and immune systems. However, it is unknown whether certain vitamin types influence the emergence or manifestation of ASD symptoms. Several studies have focused on vitamin levels in children with autism, and neurotypical children have provided different insights into the types of vitamins and their intake. Here, we review the mechanisms and significance of several vitamins (A, B, C, D, E, and K) that are closely associated with the development of ASD in order to prevent, mitigate, and treat ASD. Efforts have been made to discover and develop new indicators for nutritional assessment of children with ASD to play a greater role in the early detection of ASD and therapeutic remission after diagnosis.

The Rationale for Vitamin, Mineral, and Cofactor Treatment in the Precision Medical Care of Autism Spectrum Disorder

Children with autism spectrum disorder may exhibit nutritional deficiencies due to reduced intake, genetic variants, autoantibodies interfering with vitamin transport, and the accumulation of toxic compounds that consume vitamins. Importantly, vitamins and metal ions are essential for several metabolic pathways and for neurotransmitter functioning. The therapeutic benefits of supplementing vitamins, minerals (Zinc, Magnesium, Molybdenum, and Selenium), and other cofactors (coenzyme Q10, alpha-lipoic acid, and tetrahydrobiopterin) are mediated through their cofactor as well as non-cofactor functions. Interestingly, some vitamins can be safely administered at levels far above the dose typically used to correct the deficiency and exert effects beyond their functional role as enzyme cofactors. Moreover, the interrelationships between these nutrients can be leveraged to obtain synergistic effects using combinations. The present review discusses the current evidence for using vitamins, minerals, and cofactors in autism spectrum disorder, the rationale behind their use, and the prospects for future use.

Clinical efficacy and safety of intravenous ferric carboxymaltose treatment for restless legs symptoms and low serum ferritin in children with autism spectrum disorder

BACKGROUND

Restless legs syndrome (RLS) may be underdiagnosed in children with autism spectrum disorder (ASD) due to difficulty expressing the symptoms in their own words. In addition, administration of oral iron may be particularly difficult in children with ASD.

METHODS

This was a retrospective, open-label case series of children with ASD, restless legs (RL) symptoms, and serum ferritin <30 μg/L, who either had failed or did not tolerate oral iron, and were subsequently treated with intravenous (IV) ferric carboxymaltose (FCM). Patients received a single dose of IV FCM, 15 mg/kg up to a maximum dose of 750 mg. Data collected pre- and eight weeks post-infusion included presenting symptoms, serum ferritin, iron profile, and Clinical Global Impression Scale (CGI-Severity pre- and CGI-Improvement post-infusion). Adverse effects were assessed.

RESULTS

Nineteen children, 4-11 years old (12 male, median age 6, interquartile range (IQR 4-11) were included. A definite RLS diagnosis was identified in 6 verbal children (31.6%). RL symptoms (designated probable RLS) in the 13 other children met all RLS diagnostic criteria except "improvement of symptoms with movement," which was not definitively determined. Baseline median values were: ferritin 10 μg/L (IQR 10-16), iron 66.5 μg/dL (IQR 57-96), TIBC 382 μg/dL (IQR 360-411) and transferrin saturation 19% (IQR 14-28). Median CGI-S was 4 (moderate symptoms) (IQR 3-4). At eight weeks after IV FCM, all measures were improved. Median ferritin was 68 μg/L (IQR 62.5-109, p < 0.00045). Median CGI-I was 1 (very much improved) (IQR 1-2). All children meeting definite RLS criteria improved. Three children in the probable RLS group did not improve. Children meeting the full RLS criteria had lower baseline ferritin levels than those with a probable diagnosis (9 μg/L, IQR 9-10 vs. 13 μg/L, IQR 10-16, Mann-Whitney test p < 0.045). Adverse effects included lightheadedness, gastrointestinal discomfort, fever, and headache among others.

CONCLUSIONS

The majority of children (84.2%) with ASD, restless legs symptoms, and serum ferritin <30 μg/L had clinical improvement and significantly better serum iron parameters after a single IV FCM infusion. Although larger, randomized trials are needed, IV FCM appears to be a promising treatment for this subset of children with ASD.

Discriminant analysis and binary logistic regression enable more accurate prediction of autism spectrum disorder than principal component analysis

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impaired social interaction and restricted, repetitive behavior. Multiple studies have suggested mitochondrial dysfunction, glutamate excitotoxicity, and impaired detoxification mechanism as accepted etiological mechanisms of ASD that can be targeted for therapeutic intervention. In the current study, blood samples were collected from 40 people with autism and 40 control participants after informed consent and full approval from the Institutional Review Board of King Saud University. Sodium (Na⁺), Potassium (K⁺), lactate dehydrogenase (LDH), glutathione-s-transferase (GST), and mitochondrial respiratory chain complex I (MRC1) were measured in plasma of both groups. Predictive models were established to discriminate individuals with ASD from controls. The predictive power of these five variables, individually and in combination, was compared using the area under a ROC curve (AUC). We compared the performance of principal component analysis (PCA), discriminant analysis (DA), and binary logistic regression (BLR) as ways to combine single variables and create the predictive models. K⁺ had the highest AUC (0.801) of any single variable, followed by GST, LDH, Na⁺, and MRC1, respectively. Combining the five variables resulted in higher AUCs than those obtained using single variables across all models. Both DA and BLR were superior to PCA and comparable to each other. In our study, the combination of Na⁺, K⁺, LDH, GST, and MRC1 showed the highest promise in discriminating individuals with autism from controls. These results provide a platform that can potentially be used to verify the efficacy of our models with a larger sample size or evaluate other biomarkers.

Biomarkers of non-communicable chronic disease: an update on contemporary methods

Chronic diseases constitute a major global burden with significant impact on health systems, economies, and quality of life. Chronic diseases include a broad range of diseases that can be communicable or non-communicable. Chronic diseases are often associated with modifications of normal physiological levels of various analytes that are routinely measured in serum and other body fluids, as well as pathological findings, such as chronic inflammation, oxidative stress, and mitochondrial dysfunction. Identification of at-risk populations, early diagnosis, and prediction of prognosis play a major role in preventing or reducing the burden of chronic diseases. Biomarkers are tools that are used by health professionals to aid in the identification and management of chronic diseases. Biomarkers can be diagnostic, predictive, or prognostic. Several individual or grouped biomarkers have been used successfully in the diagnosis and prediction of certain chronic diseases, however, it is generally accepted that a more sophisticated approach to link and interpret various biomarkers involved in chronic disease is necessary to improve our current procedures. In order to ensure a comprehensive and unbiased coverage of the literature, first a primary frame of the manuscript (title, headings and subheadings) was drafted by the authors working on this paper. Second, based on the components drafted in the preliminary skeleton a comprehensive search of the literature was performed using the PubMed and Google Scholar search engines. Multiple keywords related to the topic were used. Out of screened papers, only 190 papers, which are the most relevant, and recent articles were selected to cover the topic in relation to etiological mechanisms of different chronic diseases, the most recently used biomarkers of chronic diseases and finally the advances in the applications of multivariate biomarkers of chronic diseases as statistical and clinically applied tool for the early diagnosis of chronic diseases was discussed. Recently, multivariate biomarkers analysis approach has been employed with promising prospect. A brief discussion of the multivariate approach for the early diagnosis of the most common chronic diseases was highlighted in this review. The use of diagnostic algorithms might show the way for novel criteria and enhanced diagnostic effectiveness inpatients with one or numerous non-communicable chronic diseases. The search for new relevant biomarkers for the better diagnosis of patients with non-communicable chronic diseases according to the risk of progression, sickness, and fatality is ongoing. It is important to determine whether the newly identified biomarkers are purely associations or real biomarkers of underlying pathophysiological processes. Use of multivariate analysis could be of great importance in this regard.

Machine Learning Within Studies of Early-Life Environmental Exposures and Child Health: Review of the Current Literature and Discussion of Next Steps

PURPOSE OF REVIEW

The goal of this article is to review the use of machine learning (ML) within studies of environmental exposures and children's health, identify common themes across studies, and provide recommendations to advance their use in research and practice.

RECENT FINDINGS

We identified 42 articles reporting upon the use of ML within studies of environmental exposures and children's health between 2017 and 2019. The common themes among the articles were analysis of mixture data, exposure prediction, disease prediction and forecasting, analysis of complex data, and causal inference. With the increasing complexity of environmental health data, we anticipate greater use of ML to address the challenges that cannot be handled by traditional analytics. In order for these methods to beneficially impact public health, the ML techniques we use need to be appropriate for our study questions, rigorously evaluated and reported in a way that can be critically assessed by the scientific community.

Paving the Way toward Personalized Medicine: Current Advances and Challenges in Multi-OMICS Approach in Autism Spectrum Disorder for Biomarkers Discovery and Patient Stratification

Autism spectrum disorder (ASD) is a multifactorial neurodevelopmental disorder characterized by impairments in two main areas: social/communication skills and repetitive behavioral patterns. The prevalence of ASD has increased in the past two decades, however, it is not known whether the evident rise in ASD prevalence is due to changes in diagnostic criteria or an actual increase in ASD cases. Due to the complexity and heterogeneity of ASD, symptoms vary in severity and may be accompanied by comorbidities such as epilepsy, attention deficit hyperactivity disorder (ADHD), and gastrointestinal (GI) disorders. Identifying biomarkers of ASD is not only crucial to understanding the biological characteristics of the disorder, but also as a detection tool for its early screening. Hence, this review gives an insight into the main areas of ASD biomarker research that show promising findings. Finally, it covers success stories that highlight the importance of precision medicine and the current challenges in ASD biomarker discovery studies.

Association of Anemia with Neurodevelopmental Disorders in a Nationally Representative Sample of US Children

OBJECTIVE

To examine the associations of anemia with autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), and learning disability in US children.

STUDY DESIGN

We included children and adolescents aged 3-17 years from the National Health Interview Survey (NHIS), 1997-2018. Information about physician-diagnosed history of anemia, ASD, ADHD, and learning disability was reported by a parent or guardian. Multiple logistic regression with sample weights was used to estimate the ORs and 95% CIs of neurodevelopmental disorders according to the presence of anemia.

RESULTS

Of the total population of 213 893 children aged 3-17 years (mean age [SE], 10.01 [0.01] years), 2379 were reported to have a diagnosis of anemia, for a weighted prevalence of 1.06% (95% CI, 1.01-1.12). The prevalence of ASD was 1.94% (95% CI, 1.20-2.68) among children with anemia and 1.07% (95% CI, 1.01-1.14) among those without anemia. The corresponding prevalences were 12.24% (95% CI, 10.47-14.00) and 7.73% (95% CI, 7.58-7.88) for ADHD and 15.03% (95% CI, 13.08-16.99) and 7.75% (95% CI, 7.39-7.70) for learning disability, respectively. Compared with those without anemia, children with anemia were more likely to have neurodevelopmental disorders, with an aOR of 2.07 (95% CI, 1.39-3.08) for ASD, 1.84 (95% CI, 1.55-2.19) for ADHD, and 2.22 (95% CI, 1.90-2.60) for learning disability.

CONCLUSIONS

In a nationally representative sample of US children, we found significant associations between anemia and neurodevelopmental disorders including ASD, ADHD, and learning disability. Further investigation is warranted to assess the causality and elucidate the underlying mechanisms.

Oxidative Stress in Autism Spectrum Disorder

According to the United States Centers for Disease Control and Prevention (CDC), as of July 11, 2016, the reported average incidence of children diagnosed with an autism spectrum disorder (ASD) was 1 in 68 (1.46%) among 8-year-old children born in 2004 and living within the 11 monitoring sites' surveillance areas in the United States of America (USA) in 2012. ASD is a multifaceted neurodevelopmental disorder that is also considered a hidden disability, as, for the most part; there are no apparent morphological differences between children with ASD and typically developing children. ASD is diagnosed based upon a triad of features including impairment in socialization, impairment in language, and repetitive and stereotypic behaviors. The increasing incidence of ASD in the pediatric population and the lack of successful curative therapies make ASD one of the most challenging disorders for medicine. ASD neurobiology is thought to be associated with oxidative stress, as shown by increased levels of reactive oxygen species and increased lipid peroxidation, as well as an increase in other indicators of oxidative stress. Children with ASD diagnosis are considered more vulnerable to oxidative stress because of their imbalance in intracellular and extracellular glutathione levels and decreased glutathione reserve capacity. Several studies have suggested that the redox imbalance and oxidative stress are integral parts of ASD pathophysiology. As such, early assessment and treatment of antioxidant status may result in a better prognosis as it could decrease the oxidative stress in the brain before it can induce more irreversible brain damage. In this review, many aspects of the role of oxidative stress in ASD are discussed, taking into account that the process of oxidative stress may be a target for therapeutic interventions.

Preliminary evaluation of a novel nine-biomarker profile for the prediction of autism spectrum disorder

BACKGROUND

Autism spectrum disorder (ASD) is a complex group of heterogeneous neurodevelopmental disorders the prevalence of which has been in the rise in the past decade. In an attempt to better target the basic causes of ASD for diagnosis and treatment, efforts to identify reliable biomarkers related to the body's metabolism are increasing. Despite an increase in identifying biomarkers in ASD, there are none so far with enough evidence to be used in routine clinical examination, unless medical illness is suspected. Promising biomarkers include those of mitochondrial dysfunction, oxidative stress, energy metabolism, and apoptosis.

METHODS AND PARTICIPANTS

Sodium (Na+), Potassium (K+), glutathione (GSH), glutathione-s-transferase (GST), Creatine kinase (CK), lactate dehydrogenase (LDH), Coenzyme Q10, and melatonin (MLTN) were evaluated in 13 participants with ASD and 24 age-matched healthy controls. Additionally, five ratios, which include Na+/K+, GSH:GST, CK:Cas7, CoQ10: Cas 7, and Cas7:MLTN, were tested to measure their predictive values in discriminating between autistic individuals and controls. These markers, either in absolute values, as five ratios, or combined (9 markers + 5 ratios) were subjected to a principal component analysis and multidimensional scaling (MDS), and hierarchical clustering, which are helpful statistical tools in the field of biomarkers.

RESULTS

Our data demonstrated that both PCA and MDS analysis were effective in separating autistic from control subjects completely. This was also confirmed through the use of hierarchical clustering, which showed complete separation of the autistic and control groups based on nine biomarkers, five biomarker ratios, or a combined profile. Excellent predictive value of the measured profile was obtained using the receiver operating characteristics analysis, which showed an area under the curve of 1.

CONCLUSION

The availability of an improved predictive profile, represented by nine biomarkers plus the five ratios, inter-related different etiological mechanisms in ASD and would be valuable in providing greater recognition of the altered biological pathways in ASD. Our predictive profile could be used for the diagnosis and intervention of ASD.

Generalizability and reproducibility of functional connectivity in autism

Background

Autism is hypothesized to represent a disorder of brain connectivity, yet patterns of atypical functional connectivity show marked heterogeneity across individuals.

Methods

We used a large multi-site dataset comprised of a heterogeneous population of individuals with autism and typically developing individuals to compare a number of resting-state functional connectivity features of autism. These features were also tested in a single site sample that utilized a high-temporal resolution, long-duration resting-state acquisition technique.

Results

No one method of analysis provided reproducible results across research sites, combined samples, and the high-resolution dataset. Distinct categories of functional connectivity features that differed in autism such as homotopic, default network, salience network, long-range connections, and corticostriatal connectivity, did not align with differences in clinical and behavioral traits in individuals with autism. One method, lag-based functional connectivity, was not correlated to other methods in describing patterns of resting-state functional connectivity and their relationship to autism traits.

Conclusion

Overall, functional connectivity features predictive of autism demonstrated limited generalizability across sites, with consistent results only for large samples. Different types of functional connectivity features do not consistently predict different symptoms of autism. Rather, specific features that predict autism symptoms are distributed across feature types.

Electronic supplementary material

The online version of this article (10.1186/s13229-019-0273-5) contains supplementary material, which is available to authorized users.

Reducing toxic stress in the neonatal intensive care unit to improve infant outcomes

In 2011, the American Academy of Pediatrics (AAP) published a technical report on the lifelong effects of early toxic stress on human development, and included a new framework for promoting pediatric health: the Ecobiodevelopmental Framework for Early Childhood Policies and Programs. We believe that hospitalization is a specific form of toxic stress for the neonatal patient, and that toxic stress must be addressed by the nursing profession in order to substantially improve outcomes for the critically ill neonate. Approximately 4% of normal birthweight newborns and 85% of low birthweight newborns are hospitalized each year in the highly technological neonatal intensive care unit (NICU). Neonates are exposed to roughly 70 stressful procedures a day during hospitalization, which can permanently and negatively alter the infant's developing brain. Neurologic deficits can be partly attributed to the frequent, toxic, and cumulative exposure to stressors during NICU hospitalization. However, the AAP report does not provide specific action steps necessary to address toxic stress in the NICU and realize the new vision for pediatric health care outlined therein. Therefore, this paper applies the concepts and vision laid out in the AAP report to the care of the hospitalized neonate and provides action steps for true transformative change in neonatal intensive care. We review how the environment of the NICU is a significant source of toxic stress for hospitalized infants. We provide recommendations for caregiving practices that could significantly buffer the toxic stress experienced by hospitalized infants. We also identify areas of research inquiry that are needed to address gaps in nursing knowledge and to propel nursing science forward. Finally, we advocate for several public policies that are not fully addressed in the AAP technical report, but are vital to the health and development of all newborns.

Iron Deficiency, Cognitive Functions, and Neurobehavioral Disorders in Children

More than 25% of the world's population is affected by anemia, of which more than 50% suffers from iron deficiency anemia (IDA). Children below 7 years of age are the population group that is most vulnerable to iron deficiency. Iron is an essential element in brain metabolism. Iron deficiency can cause changes in neurotransmitter homeostasis, decrease myelin production, impair synaptogenesis, and decline the function of the basal ganglia. Therefore, IDA adversely affects cognitive functions and psychomotor development. Research has shown that iron deficiency is a frequent comorbidity in attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder. Iron deficiency may also induce or exacerbate deficiency of other essential nutrients, which may have a negative impact on the developing brain and other organs in infants. Many nations of the world have programs to control IDA based on the use of iron supplementation, intake of fortified food and drinks, improved food safety, and monitoring of dietary diversity. Based on the current recommendations of the World Health Organization on cost-effectiveness (WHO-CHOICE), iron fortification and iron supplementation programs can be considered cost-effective or even highly cost-effective in most countries of the world to averting cognitive impairment.