Altered metabolism of mothers of young children with Autism Spectrum Disorder: a case control study.

BACKGROUND: Previous research studies have demonstrated abnormalities in the metabolism of mothers of young children with autism. METHODS: Metabolic analysis was performed on blood samples from 30 mothers of young children with Autism Spectrum Disorder (ASD-M) and from 29 mothers of young typically-developing children (TD-M). Targeted metabolic analysis focusing on the folate one-carbon metabolism (FOCM) and the transsulfuration pathway (TS) as well as broad metabolic analysis were performed. Statistical analysis of the data involved both univariate and multivariate statistical methods. RESULTS: Univariate analysis revealed significant differences in 5 metabolites from the folate one-carbon metabolism and the transsulfuration pathway and differences in an additional 48 metabolites identified by broad metabolic analysis, including lower levels of many carnitine-conjugated molecules. Multivariate analysis with leave-one-out cross-validation allowed classification of samples as belonging to one of the two groups of mothers with 93% sensitivity and 97% specificity with five metabolites. Furthermore, each of these five metabolites correlated with 8-15 other metabolites indicating that there are five clusters of correlated metabolites. In fact, all but 5 of the 50 metabolites with the highest area under the receiver operating characteristic curve were associated with the five identified groups. Many of the abnormalities appear linked to low levels of folate, vitamin B12, and carnitine-conjugated molecules. CONCLUSIONS: Mothers of children with ASD have many significantly different metabolite levels compared to mothers of typically developing children at 2-5 years after birth.

Quantitative Assessment of Balance for Accurate Prediction of Return to Sport From Sport-Related Concussion.

BACKGROUND: Determining when athletes are able to return to sport after sports-related concussion (SRC) can be difficult. HYPOTHESIS: A multimodal algorithm using cognitive testing, postural stability, and clinical assessment can predict return to sports after SRC. STUDY DESIGN: Prospective cohort. LEVEL OF EVIDENCE: Level 2b. METHODS: Athletes were evaluated within 2 to 3 weeks of SRC. Clinical assessment, Immediate Post Concussion and Cognitive Testing (ImPACT), and postural stability (Equilibrate) were conducted. Resulting data and machine learning techniques were used to optimize an algorithm discriminating between patients ready to return to sports versus those who are not yet recovered. A Fisher discriminant analysis with leave-one-out cross-validation assessed every combination of 2 to 5 factors to optimize the algorithm with lowest combination of type I and type II errors. RESULTS: A total of 193 athletes returned to contact sports after SRC at a mean 84.6 days (±88.8). Twelve subjects (6.2%) sustained repeat SRC within 12 months after return to sport. The combination of (1) days since injury, (2) total symptom score, and (3) nondominant foot tandem eyes closed postural stability score created the best algorithm for discriminating those ready to return to sports after SRC with lowest type I error (13.85%) and type II error (11.25%). The model was able to discriminate between patients who were ready to successfully return to sports versus those who were not with area under the receiver operating characteristic (ROC) curve of 0.82. CONCLUSION: The algorithm predicts successful return to sports with an acceptable sensitivity and specificity. Tandem balance with eyes closed measured with a video-force plate discriminated athletes ready to return to sports from SRC when combined in multivariate analysis with symptom score and time since injury. The combination of these factors may pose advantages over computerized neuropsychological testing when evaluating young athletes with SRC for return to contact sports. CLINICAL RELEVANCE: When assessing young athletes sustaining an SRC in a concussion clinic, measuring postural stability in tandem stance with eyes closed combined with clinical assessment and cognitive recovery is effective to determine who is ready to successfully return to sports.

Towards a Multivariate Biomarker-Based Diagnosis of Autism Spectrum Disorder: Review and Discussion of Recent Advancements.

An ever-evolving understanding of autism spectrum disorder (ASD) pathophysiology necessitates that diagnostic standards also evolve from being observation-based to include quantifiable clinical measurements. The multisystem nature of ASD motivates the use of multivariate methods of statistical analysis over common univariate approaches for discovering clinical biomarkers relevant to this goal. In addition to characterization of important behavioral patterns for improving current diagnostic instruments, multivariate analyses to date have allowed for thorough investigation of neuroimaging-based, genetic, and metabolic abnormalities in individuals with ASD. This review highlights current research using multivariate statistical analyses to quantify the value of these behavioral and physiological markers for ASD diagnosis. A detailed discussion of a blood-based diagnostic test for ASD using specific metabolite concentrations is also provided. The advancement of ASD biomarker research promises to provide earlier and more accurate diagnoses of the disorder.