Who are the awesome authors, what is the title of the study, and what year was it published?
Authors: Afaf El-Ansary, Wail M. Hassan, Maha Daghestani, Laila Al-Ayadhi, Abir Ben Bacha
Title: Preliminary evaluation of a novel nine biomarker profile for the prediction of autism spectrum disorder
What is this study about?
The study aimed to investigate the role of mitochondrial dysfunction-related variables in discriminating between individuals with Autism Spectrum Disorder (ASD) and matched control participants. The authors used nine biomarkers, five biomarker ratios, and a combination profile to compare ASD patients and control participants. The participants were recruited from the ART Center clinic, consisting of children diagnosed with ASD, and a pediatric clinic at King Saud Medical City.
What previous research on this topic supports the need for this study?
The authors of a recent study built on previous research that found select biomarkers related to impaired lipid metabolism and neuroinflammation were effective in accurately predicting autism spectrum disorder (ASD) severity and differentiating between individuals with ASD and healthy controls, including those with varying degrees of sensory profile impairments [studies in 2009 & 2016].
Metabolism-related biomarkers have been shown to be more directly linked to an individual with ASD’s unique metabolic signature than other biomarkers, such as genomic, gut microbiome-related, and environmental biomarkers like neurotoxins and diet [another 2016 study & two studies in 2019]. Studies have also reported ASD-specific reductions in multiple metabolites and concurrent declines in intelligence quotient in various brain regions [one 2019 study].
Other research has found elevated levels of lactate, pyruvate, and alanine, and an increase in mitochondrial complex in almost half of ASD participants [a 2010 study], and a significant reduction in the activities of mitochondrial electron respiratory chain complexes in different brain regions of children with ASD [a 2011 study].
In a 2017 study, abnormal levels of mitochondrial plasma markers, including pyruvate, lactate dehydrogenase, creatine kinase, glutathione-S-transferase, caspase 7, and respiratory complex I, were also recorded in children with ASD compared to age- and gender-matched control subjects, with caspase 7 being the most discriminating biomarker between ASD patients and controls. A 2018 study showed that dopaminergic neurons derived from children with ASD displayed decreased neurite development and reduced mitochondrial membrane potential, intracellular calcium levels, ATP production, and total neurite mitochondria numbers.
The current study sought to investigate the role of mitochondrial dysfunction in ASD and its potential connection to other patho-etiological causes, such as glutamate excitotoxicity, oxidative stress, apoptosis, and impaired gut microbiota, based on repeated observations in previous studies [a 3rd study in 2019, another study in 2016, and another study in 2018].
Overall, these findings suggest that mitochondrial dysfunction may be an important mechanism underlying ASD and that identifying biomarkers related to this dysfunction may aid in the diagnosis and treatment of the disorder. The current study was conducted to understand how these issues with energy production in the cells might be related to other causes of autism, such as problems with brain cells, stress, and diet, which have multiple potential potential causes, including problems with the gut bacteria and problems with the way that cells communicate with one another in the brain.
What methods were used?
Autistic children and age-matched healthy controls participated in a study conducted by the Autism Research & Treatment Center (ART Center) clinic. The study involved 37 participants in total, with 13 of them being autistic children and the remaining 24 being healthy controls. Autistic children aged 2-12 years old were included in the study, while the control group was recruited from a pediatric clinic at King Saud medical city with a mean age range of 2-14 years.
The study used a tool called the Childhood Autism Rating Scale (CARS) to measure the severity of the children’s autism. The CARS score is determined by rating the child’s behavior in relation to 15 different criteria, such as how they interact with others, how they communicate verbally and non-verbally, and how they respond to sensory stimuli like taste and touch.
Blood Samples & Other Dependent Variables
Blood samples were collected from the participants after an overnight fast by a qualified lab technician into 3-ml blood collection tubes containing EDTA. Immediately after collection, the blood samples were centrifuged at 4°C at 3,000 g for 20 minutes. The plasma was decanted, dispensed into four 0.75 ml aliquots (to avoid multiple freeze-thaws cycles), and stored at −80°C until analysis.
The levels of Sodium (Na+), Potassium (K+), glutathione (GSH), glutathione-s-transferase (GST), Creatine kinase (CK), lactate dehydrogenase (LDH), Coenzyme Q10, and melatonin (MLTN) were evaluated.
Five ratios, which include Na+/K+, GSH:GST, CK:Cas7, CoQ10: Cas 7, and Cas7: MLTN, were also used to determine predictive differences between the autism and control group.
Area Under the Curve (AUC)
AUC stands for Area Under the Curve and is a measure of the ability of a biomarker or combination of biomarkers to correctly distinguish between two groups. It is used in receiver operating characteristic (ROC) analysis, which is a graphical representation of the relationship between true positive rate and false positive rate at various thresholds. The AUC ranges from 0.5 to 1, where 0.5 indicates random chance, and 1.0 indicates a perfect predictor. An AUC of 0.7 or higher indicates a good predictive performance of the biomarker or combination of biomarkers, while an AUC of 0.5 to 0.7 indicates that the biomarker has no discriminatory power. In this study, the investigators use AUCs to evaluate the predictive power of each individual biomarker, ratio of biomarkers, as well as the combination of different biomarkers to distinguish between autistic and control individuals.
What were the findings?
The study found that there was a complete separation of autistic and control participants using the nine biomarkers alone, the five ratios alone, or all biomarkers and ratios combined, through the use of principal component analysis (PCA) and multidimensional scaling (MDS). Hierarchical clustering further confirmed the separation between the two groups.
Predictors of Autism
Caspase 7 (Cas7) was a very strong predictor of ASD, with a 100% specificity and sensitivity. Glutathione S-transferase (GST) and potassium (K) were also strong predictors of ASD. Combining biomarkers into profiles using PCA appeared to boost AUC values, resulting in an AUC of 1.00 when using all nine biomarkers, five ratios, or the biomarkers and ratios combined. The seven-biomarker profile lacking both Cas7 and GST also had a high AUC.
However, using ratios did not seem beneficial since it either lowered or did not affect AUC values. Finally, using biomarker profiles increased the rate of correct assignment (RCA) to 100% independently of whether the profiles contained nine biomarkers, five ratios, or both.
What are the implications of these findings?
This study has important implications for the diagnosis and early intervention of Autism Spectrum Disorder (ASD). The combination of nine biomarkers, five ratios, or a combination profile can accurately differentiate between autistic patients and control participants. Accurately identifying ASD is often difficult, particularly in young children, as diagnosis is based on behavioral observation and developmental assessments. The non-invasive method of identifying biomarkers used in this study is a significant advantage over other diagnostic methods, as it may decrease anxiety and increase participation in research.
Moreover, identifying the biomarkers related to ASD provides insight into the possible pathophysiology of the disorder, particularly in relation to oxidative stress, energy metabolism, mitochondrial dysfunction, and apoptosis. This information can guide researchers in developing new treatments and interventions for ASD based on targeting these pathological mechanisms. Therefore, this study provides valuable insights into the potential of combining biomarkers to identify and predict the severity of ASD and highlights the importance of considering mitochondrial dysfunction-related variables as etiological mechanisms of ASD to enable the development of novel therapeutic interventions.
What other studies within the library is this one related to?
The study by Al-Yafee et al. (2011) found oxidative stress as a factor associated with autism; oxidative stress is also linked to mitochondrial dysfunction, which is a finding in this study.
The study by Zhang et al. (2020) identified several significantly abnormal metabolites in individuals with ASD, including aconitic acid, suberic acid, 2-hydroxyhippuric acid, and fumaric acid. These metabolites are involved in the Krebs cycle and are released into circulation and urine when mitochondria are damaged, making them potential biomarkers for mitochondrial damage or dysfunction. The levels of these biomarkers were negatively correlated with the abundance of most of the detoxification enzymes deficient in ASD individuals, suggesting a relationship between mitochondrial dysfunction and impaired detoxification mechanisms in individuals with ASD.
Can I read the study somewhere?