Fisher discriminant analysis for classification of autism spectrum disorders based on folate-related metabolism markers

Folic acid and autism: updated evidences

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that impairs communication, socialization, and behavior. The association of ASD with folic acid has been investigated due to the importance of this vitamin for neurological health. This study is an update of the publication 'Folic acid and autism: What do we know?' and aims to systematically review studies examining the relationship between folic acid and ASD. The search resulted in 2,389 studies on folic acid and ASD, which were selected by two reviewers based on their titles and abstracts. Studies meeting the inclusion criteria were fully read. The 52 included studies involved 10,429 individuals diagnosed with ASD and assessed the intake of vitamin B6, folic acid, and vitamin B12; serum levels of these vitamins, homocysteine, and methionine; therapeutic interventions using folic acid; and the association between maternal exposure to this vitamin and the risk of ASD. The evidence of insufficient folic acid intake in most individuals with ASD remains consistent in this update. No association was found between maternal exposure to folic acid and the risk of ASD in their children. Despite observed improvements in communication, socialization, and behavior in individuals with ASD following folic acid interventions, it is crucial to consider the individuality and complexity of ASD. Given the relevance of the topic, there remains a need for more high-quality research and clinical trials characterized by rigorous methodological designs.

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.

Water-soluble vitamin insufficiency, deficiency and supplementation in children and adolescents with a psychiatric disorder: a systematic review and meta-analysis

Nutrition is fundamental for brain development, but relatively little is known about water-soluble vitamin (WSV) levels and the effect of supplementation on psychiatry symptoms in children and adolescents (CAD) with psychiatric disorders. Our team systematically reviewed all studies concerning WSV abnormalities or supplementation in CAD with any psychiatric disorder. We searched for original studies published between 1990 and 15/05/2020 which were not based on retrospective chart review and which included WSV blood level measurements or investigated the effect of WSV supplementation on psychiatric symptoms in psychiatric patients aged 18 or under. Forty-two articles were included, 69% of which (N = 29) examined Autism Spectrum Disorders (ASD), with most of these assessing folate or vitamin B₁₂ supplementation (N = 22, 75.9% of ASD studies). Meta-analyses showed significantly lower vitamin B₁₂ levels in ASD and ADHD patients vs. healthy controls (HC), while folate levels were higher in ADHD patients vs. HC. Most of the studies (9/10, 90%) showed a decrease in symptoms as measured by clinical scales after supplementation. There was significant heterogeneity between the studies, however many found different types of vitamin abnormalities in CAD with psychiatric disorders.

Towards the Development of a Diagnostic Test for Autism Spectrum Disorder: Big Data Meets Metabolomics

Autism spectrum disorder (ASD) is defined as a neurodevelopmental disorder which results in impairments in social communications and interactions as well as repetitive behaviors. Despite current estimates showing that approximately 2.2% of children are affected in the United States, relatively little about ASD pathophysiology is known in part due to the highly heterogenous presentation of the disorder. Given the limited knowledge into the biological mechanisms governing its etiology, the diagnosis of ASD is performed exclusively based on an individual's behavior assessed by a clinician through psychometric tools. Although there is no readily available biochemical test for ASD diagnosis, multivariate statistical methods show considerable potential for effectively leveraging multiple biochemical measurements for classification and characterization purposes. In this work, markers associated with the folate dependent one-carbon metabolism and transulfuration (FOCM/TS) pathways analyzed via both Fisher Discriminant Analysis and Support Vector Machine showed strong capability to distinguish between ASD and TD cohorts. Furthermore, using Kernel Partial Least Squares regression it was possible to assess some degree of behavioral severity from metabolomic data. While the results presented need to be replicated in independent future studies, they represent a promising avenue for uncovering clinically relevant ASD biomarkers.

Fisher discriminant model based on LASSO logistic regression for computed tomography imaging diagnosis of pelvic rhabdomyosarcoma in children

Computed tomography (CT) has been widely used for the diagnosis of pelvic rhabdomyosarcoma (RMS) in children. However, it is difficult to differentiate pelvic RMS from other pelvic malignancies. This study aimed to analyze and select CT features by using least absolute shrinkage and selection operator (LASSO) logistic regression and established a Fisher discriminant analysis (FDA) model for the quantitative diagnosis of pediatric pelvic RMS. A total of 121 pediatric patients who were diagnosed with pelvic neoplasms were included in this study. The patients were assigned to an RMS group (n = 36) and a non-RMS group (n = 85) according to the pathological results. LASSO logistic regression was used to select characteristic features, and an FDA model was constructed for quantitative diagnosis. Leave-one-out cross-validation and receiver operating characteristic (ROC) curve analysis were used to evaluate the diagnostic ability of the FDA model. Six characteristic variables were selected by LASSO logistic regression, all of which were CT morphological features. Using these CT features, the following diagnostic models were established: (RMS group)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{1}=-14.283+6.613{x}_{1}+5.333{x}_{2}+5.753{x}_{3}+12.361{x}_{4}+8.095{x}_{5}-0.715{x}_{6}$$\end{document}G1=-14.283+6.613x1+5.333x2+5.753x3+12.361x4+8.095x5-0.715x6; (Non-RMS group)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{2}=-2.008+3.539{x}_{1}+1.080{x}_{2}+1.154{x}_{3}+2.307{x}_{4}+1.656{x}_{5}+1.380{x}_{6}$$\end{document}G2=-2.008+3.539x1+1.080x2+1.154x3+2.307x4+1.656x5+1.380x6, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{1}$$\end{document}x1, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{2}$$\end{document}x2, … and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{6}$$\end{document}x6 are lower than normal muscle density (1 = yes; 0 = no), multinodular fusion (1 = yes; 0 = no), enhancement at surrounding blood vessels (1 = yes; 0 = no), heterogeneous progressive centripetal enhancement (1 = yes; 0 = no), ring enhancement (1 = yes; 0 = no), and hemorrhage (1 = yes; 0 = no), respectively. The calculated area under the ROC curve (AUC) of the model was 0.992 (0.982–1.000), with a sensitivity of 94.4%, a specificity of 96.5%, and an accuracy of 95.9%. The calculated sensitivity, specificity and accuracy values were consistent with those from cross-validation. An FDA model based on the CT morphological features of pelvic RMS was established and could provide an easy and efficient method for the diagnosis and differential diagnosis of pelvic RMS in children.

Predicting the Occurrence of Advanced Schistosomiasis Based on FISHER Discriminant Analysis of Hematological Biomarkers

We established a model that predicts the possibility of chronic schistosomiasis (CS) patients developing into advanced schistosomiasis (AS) patients using special biomarkers that were detected in human peripheral blood. Blood biomarkers from two cohorts (132 CS cases and 139 AS cases) were examined and data were collected and analyzed by univariate and multivariate logistic regression analysis. Fisher discriminant analysis (FDA) for advanced schistosomiasis was established based on specific predictive diagnostic indicators and its accuracy was assessed using data of 109 CS. The results showed that seven indicators including HGB, MON, GLB, GGT, APTT, VIII, and Fbg match the model. The accuracy of the FDA was assessed by cross-validation, and 86.7% of the participants were correctly classified into AS and CS groups. Blood biomarker data from 109 CS patients were converted into the discriminant function to determine the possibility of occurrence of AS. The results demonstrated that the possibility of occurrence of AS and CS was 62.1% and 89.0%, respectively, and the accuracy of the established model was 81.4%. Evidence displayed that Fisher discriminant analysis is a reliable predictive model in the clinical field. It’s an important guide to effectively control the occurrence of AS and lay a solid foundation for achieving the goal of schistosomiasis elimination.

Advanced machine learning algorithms for flood susceptibility modeling - performance comparison: Red Sea, Egypt

Floods are among the most devastating environmental hazards that directly and indirectly affect people's lives and activities. In many countries, sustainable environmental management requires the assessment of floods and the likely flood-prone areas to avoid potential hazards. In this study, the performance and capabilities of seven machine learning algorithms (MLAs) for flood susceptibility mapping were tested, evaluated, and compared. These MLAs, including support vector machine (SVM), random forest (RF), multivariate adaptive regression spline (MARS), boosted regression tree (BRT), functional data analysis (FDA), general linear model (GLM), and multivariate discriminant analysis (MDA), were tested for the area between Safaga and Ras Gharib cities, Red Sea, Egypt. A geospatial database was developed with eleven flood-related factors, namely altitude, slope aspect, lithology, land use/land cover (LULC), slope length (LS), topographic wetness index (TWI), slope angle, profile curvature, plan curvature, stream power index (SPI), and hydrolithology units. In addition, 420 actual flooded areas were recorded from the study area to create a flood inventory map. The inventory data were randomly divided into training group with 70% and validation group with 30%. The flood-related factors were tested with a multicollinearity test, the variance inflation factor (VIF) was less than 2.135, the tolerance (TOL) was more than 0.468, and their importance was evaluated with a partial least squares (PLS) method. The results show that RF performed the best with the highest AUC (area under curve) value of 0.813, followed by GLM with 0.802, MARS with 0.801, BRT with 0.777, MDA with 0.768%, FDA with 0.763, and SVM with 0.733. The results of this study and the flood susceptibility maps could be useful for environmental mitigation, future development activities in the area, and flood control areas.

SYNGAP1 and Methylenetetrahydrofolate in Cerebrospinal Fluid: Cognitive Development after Oral Folate (5-Methyltetrahydrofolate) Supplementation in a 5-Year-Old Girl

Synaptic Ras GTPase-activating protein 1 (SYNGAP1), also called Ras-GAP 1 or RASA5, is a cerebral protein with a role in brain synaptic function. Its expression affects the development, structure, function, and plasticity of neurons. Mutations in the gene cause a neurodevelopment disorder termed mental retardation-type 5, also called SYNGAP1 syndrome. This syndrome can cause many neurological symptoms including pharmaco-resistant epilepsy, intellectual disability, language delay, and autism spectrum disorder. The syndrome naturally evolves as epileptic encephalopathy with handicap and low intellectual level. A treatment to control epilepsy, limit any decrease in social capacities, and improve intellectual development is really a challenging goal for these patients. The etiologic investigation performed in a 5-year-old girl with early epileptic absence seizures (onset at 6 months) and psychomotor delay (language) revealed a low methylenetetrahydrofolate level in cerebrospinal fluid in a lumbar puncture, confirmed by a second one (35 nmol/L and 50 nmol/L vs. 60–100 nmol/L normal), associated with normal blood and erythrocyte folate levels. Hyperhomocysteinemia, de vivo disease, and other metabolic syndromes were excluded by metabolic analysis. No genetic disorders (like methylenetetrahydrofolate reductase and methenyltetrahydrofolate synthetase) with folate metabolism were found. The physical examination showed only a minor kinetic ataxia. An oral folate (5-methyltetrahydrofolate) supplementation was started with oral vitamin therapy. The child showed good progress in language with this new treatment; epilepsy was well balanced with only one antiepileptic drug. The SYNGAP1 mutation was identified in this patient's genetic analysis. Since the start of folate supplementation/vitamin therapy, the patient's neurologic development has improved. To our knowledge, no association between these two pathologies has been linked and no patient with this SYNGAP1 mutation has ever showed much intellectual progress. Low cerebral methylenetetrahydrofolate levels could be associated with SYNGAP1 mutations. One of the hypotheses is the link of folate metabolism with epigenetic changes including methylation process. One inborn metabolic activity in folate metabolism may be associated with SYNGAP1 disease with epigenetic repercussions. Further studies should assess the link of SYNGAP1 and methyltetrahydrofolate and the evolution of SYNGAP1 patients with oral folate supplementation or vitamin therapy.

Potential Novel Therapies for Neurodevelopmental Diseases Targeting Oxidative Stress

Neurodevelopmental disorders are a category of diseases that is not yet fully understood. Due to their common traits and pathways, often it is difficult to differentiate between them based on their symptoms only. A series of hypotheses are trying to define their etiology, such as neuroinflammation, neurodegeneration, and immunology, but none have managed to explain their multifactorial manifestation. One feature that may link all theories is that of oxidative stress, with a redox imbalance as well as several other markers of oxidative damage (on lipids, proteins, and nucleic acids) being observed in both postmortem samples of the brain of patients with schizophrenia and autism spectrum disorders. However, the implication of oxidative stress in pathology is still distrustfully looked upon. For this purpose, in the current paper, we were interested in reviewing the implications of oxidative stress in these disorders as well as the impact of N-acetylcysteine on the oxidative status with a focus on the glutathione level and N-methyl-D-aspartate receptor. We were also interested in finding papers targeting the use of antioxidant properties of different plant extracts.

Alterations in Gut Vitamin and Amino Acid Metabolism are Associated with Symptoms and Neurodevelopment in Children with Autism Spectrum Disorder

Metabolic disturbance may be implicated in the pathogenesis of autism. This study aimed to investigate the gut metabolomic profiles of autistic children and to analyze potential interaction between gut metabolites with autistic symptoms and neurodevelopment levels. We involved 120 autistic and 60 neurotypical children. Autistic symptoms and neurodevelopment levels were assessed. Fecal samples were analyzed using untargeted liquid chromatography-tandem mass spectrometry methods. Our results showed the metabolic disturbances of autistic children involved in multiple vitamin and amino acid metabolism pathways, with the strongest enrichment identified for tryptophan metabolism, retinol metabolism, cysteine-methionine metabolism, and vitamin digestion and absorption. Differential gut metabolites were correlated to autistic symptoms and neurodevelopment levels. Our findings improved the understanding of the perturbations of metabolome networks in autism.

Sulfur amino acid metabolism and related metabotypes of autism spectrum disorder: A review of biochemical evidence for a hypothesis

There are multiple lines of evidence for an impaired sulfur amino acid (SAA) metabolism in autism spectrum disorder (ASD). For instance, the concentrations of methionine, cysteine and S-adenosylmethionine (SAM) in body fluids of individuals with ASD is significantly lower while the concentration of S-adenosylhomocysteine (SAH) is significantly higher as compared to healthy individuals. Reduced methionine and SAM may reflect impaired remethylation pathway whereas increased SAH may reflect reduced S-adenosylhomocysteine hydrolase activity in the catabolic direction. Reduced SAM/SAH ratio reflects an impaired methylation capacity. We hypothesize multiple mechanisms to explain how the interplay of oxidative stress, neuroinflammation, mercury exposure, maternal use of valproate, altered gut microbiome and certain genetic variants may lead to these SAA metabotypes. Furthermore, we also propose a number of mechanisms to explain the metabolic consequences of abnormal SAA metabotypes. For instance in the brain, reduced SAM/SAH ratio will result in melatonin deficiency and hypomethylation of a number of biomolecules such as DNA, RNA and histones. In addition to previously proposed mechanisms, we propose that impaired activity of "radical SAM" enzymes will result in reduced endogenous lipoic acid synthesis, reduced molybdenum cofactor synthesis and impaired porphyrin metabolism leading to mitochondrial dysfunction, porphyrinuria and impaired sulfation capacity. Furthermore depletion of SAM may also lead to the disturbed mTOR signaling pathway in a subgroup of ASD. The proposed "SAM-depletion hypothesis" is an inclusive model to explain the relationship between heterogeneous risk factors and metabotypes observed in a subset of children with ASD.

The Role of Lipidomics in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental syndrome commonly diagnosed in early childhood; it is usually characterized by impairment in reciprocal communication and speech, repetitive behaviors, and social withdrawal with loss in communication skills. Its development may be affected by a variety of environmental and genetic factors. Trained physicians diagnose and evaluate the severity of ASD based on clinical evaluations of observed behaviors. As such, this approach is inevitably dependent on the expertise and subjective assessment of those administering the clinical evaluations. There is a need to identify objective biological markers associated with diagnosis or clinical severity of the disorder. Several important issues and concerns exist regarding the diagnostic competence of the many abnormal plasma metabolites produced in the different biochemical pathways evaluated in individuals with ASD. The search for high-performing bio-analytes to diagnose and follow-up ASD development is still a major target in medicine. Dysregulation in the oxidative stress response and proinflammatory processes are major etiological causes of ASD pathogenesis. Furthermore, dicarboxylic acid metabolites, cholesterol-related metabolites, phospholipid-related metabolites, and lipid transporters and mediators are impaired in different pathological conditions that have a role in the ASD etiology. A mechanism may exist by which pro-oxidant environmental stressors and abnormal metabolites regulate clinical manifestations and development of ASD.

Blood homocysteine levels in children with autism spectrum disorder: An updated systematic review and meta-analysis

Results of studies on peripheral blood levels of homocysteine (Hcy) in children with autism spectrum disorder (ASD) are inconsistent, and conclusions from two previous meta-analyses on this subject published in 2012 are already outdated. Therefore, we conducted an updated systematic review and meta-analysis to quantitatively summarize the peripheral blood Hcy data in children with ASD compared with healthy controls (HC). We searched PubMed, EMBASE, PsycINFO, PsycARTICLES, Web of Science, and Cochrane Library databases from inception to September 2019 for eligible studies, with no language restriction. Using random-effects model, we computed summary statistics. Thirty-one studies (3304 participants including 1641 cases) were included. The pooled results showed that the peripheral blood Hcy levels were significantly elevated in children with ASD when compared to HC (Hedges's g = 0.56, 95% CI = 0.36 to 0.76, P < 0.001). By sensitivity analyses, we confirmed that our results were quite robust. Additionally, no publication bias was observed in this meta-analysis. In conclusion, our study support the association of increased circulating Hcy levels with ASD in children, and the involvement of Hcy in the occurrence of ASD. However, in view of the significant between-study heterogeneity, the conclusions should be interpreted cautiously and more investigation is required.

A machine learning framework for multi-hazards modeling and mapping in a mountainous area

This study sought to produce an accurate multi-hazard risk map for a mountainous region of Iran. The study area is in southwestern Iran. The region has experienced numerous extreme natural events in recent decades. This study models the probabilities of snow avalanches, landslides, wildfires, land subsidence, and floods using machine learning models that include support vector machine (SVM), boosted regression tree (BRT), and generalized linear model (GLM). Climatic, topographic, geological, social, and morphological factors were the main input variables used. The data were obtained from several sources. The accuracies of GLM, SVM, and functional discriminant analysis (FDA) models indicate that SVM is the most accurate for predicting landslides, land subsidence, and flood hazards in the study area. GLM is the best algorithm for wildfire mapping, and FDA is the most accurate model for predicting snow avalanche risk. The values of AUC (area under curve) for all five hazards using the best models are greater than 0.8, demonstrating that the model’s predictive abilities are acceptable. A machine learning approach can prove to be very useful tool for hazard management and disaster mitigation, particularly for multi-hazard modeling. The predictive maps produce valuable baselines for risk management in the study area, providing evidence to manage future human interaction with hazards.

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.

Blood biomarker levels of methylation capacity in autism spectrum disorder: a systematic review and meta-analysis

OBJECTIVE

To compare the peripheral blood levels of methionine (Met), S-adenosylmethionine (SAM), S-adenosylhomocysteine (SAH), and the SAM/SAH ratio (the most core and predictive indices of cellular methylation ability) between patients with autism spectrum disorder (ASD) and control subjects.

METHODS

PubMed, Embase, PsycINFO, Web of Science, and Cochrane Library were searched from inception to August 2, 2019, without language restriction. The random-effects model was used to summarize effect sizes.

RESULTS

We retrieved 1,493 records, of which 22 studies met inclusion criteria. Our overall analyses revealed that individuals with ASD had significantly decreased levels of Met (22 studies; Hedges' g = -0.62; 95% confidence interval [CI]: -0.89, -0.35), SAM (8 studies; Hedges' g = -0.60; 95% CI: -0.86, -0.34), and the SAM/SAH ratio (8 studies; Hedges' g = -0.98; 95% CI: -1.30, -0.66) and significantly increased levels of SAH (8 studies; Hedges' g = 0.69; 95% CI: 0.43, 0.94). The findings of the overall analyses were quite stable after being verified by sensitivity analyses and in agreement with the corresponding outcomes of subgroup analyses. Additionally, our results from meta-analytic techniques confirmed that the effect estimates of this meta-analysis did not originate from publication bias.

CONCLUSION

Individuals with ASD have substantially aberrant peripheral blood levels of Met, SAM, SAH, and the SAM/SAH ratio, which supports the association between impaired methylation capacity and ASD. Therefore, further investigations into these indices as potential biomarkers for diagnosis and therapeutic targets of ASD are warranted.

Multivariate Analysis of Plasma Metabolites in Children with Autism Spectrum Disorder and Gastrointestinal Symptoms Before and After Microbiota Transfer Therapy

Current diagnosis of autism spectrum disorder (ASD) is based on assessment of behavioral symptoms, although there is strong evidence that ASD affects multiple organ systems including the gastrointestinal (GI) tract. This study used Fisher discriminant analysis (FDA) to evaluate plasma metabolites from 18 children with ASD and chronic GI problems (ASD + GI cohort) and 20 typically developing (TD) children without GI problems (TD − GI cohort). Using three plasma metabolites that may represent three general groups of metabolic abnormalities, it was possible to distinguish the ASD + GI cohort from the TD − GI cohort with 94% sensitivity and 100% specificity after leave-one-out cross-validation. After the ASD + GI participants underwent Microbiota Transfer Therapy with significant improvement in GI and ASD-related symptoms, their metabolic profiles shifted significantly to become more similar to the TD − GI group, indicating potential utility of this combination of plasma metabolites as a biomarker for treatment efficacy. Two of the metabolites, sarcosine and inosine 5′-monophosphate, improved greatly after treatment. The third metabolite, tyramine O-sulfate, showed no change in median value, suggesting it and correlated metabolites to be a possible target for future therapies. Since it is unclear whether the observed differences are due to metabolic abnormalities associated with ASD or with GI symptoms (or contributions from both), future studies aiming to classify ASD should feature TD participants with GI symptoms and have larger sample sizes to improve confidence in the results.

Establishment and validation of an immunodiagnostic model for prediction of breast cancer

Serum autoantibodies that react with tumor-associated antigens (TAAs) can be used as potential biomarkers for diagnosis of cancer. This study aims to evaluate the immunodiagnostic value of 11 anti-TAAs autoantibodies for detection of breast cancer (BC) and establish a diagnostic model for distinguishing BC from normal human controls (NHC) and benign breast diseases (BBD). Sera from 10 BC patients and 10 NHC were used to detect 11 anti-TAAs autoantibodies by western blotting. The 11 anti-TAAs autoantibodies were further assessed in 983 sera by relative quantitative enzyme-linked immunosorbent assay (ELISA). Binary logistic regression and Fisher linear discriminant analysis were conducted to establish a prediction model by using 184 BC and 184 NHC (training cohort, n = 568) and validated by leave-one-out cross-validation. Logistic regression model was selected to establish the prediction model. Results were validated using an independent validation cohort (n = 415). The five anti-TAAs (p53, cyclinB1, p16, p62, 14-3-3ξ) autoantibodies were selected to construct the model with the area under the curve (AUC) of 0.943 (95% CI, 0.919–0.967) in training cohort and 0.916 (95% CI, 0.886–0.947) in the validation cohort. In the identification of BC and BBD, AUCs were 0.881 (95% CI, 0.848–0.914) and 0.849 (95% CI, 0.803–0.894) in training and validation cohort, respectively. In summary, our study indicates that the immunodiagnostic model can distinguish BC from NHC and BC from BBD and this model may have a potential application in immunodiagnosis of breast cancer.

Fisher linear discriminant analysis for classification and prediction of genomic susceptibility to stomach and colorectal cancers based on six STR loci in a northern Chinese Han population

Objective

Gastrointestinal cancer is the leading cause of cancer-related death worldwide. The aim of this study was to verify whether the genotype of six short tandem repeat (STR) loci including AR, Bat-25, D5S346, ER1, ER2, and FGA is associated with the risk of gastric cancer (GC) and colorectal cancer (CRC) and to develop a model that allows early diagnosis and prediction of inherited genomic susceptibility to GC and CRC.

Methods

Alleles of six STR loci were determined using the peripheral blood of six colon cancer patients, five rectal cancer patients, eight GC patients, and 30 healthy controls. Fisher linear discriminant analysis (FDA) was used to establish the discriminant formula to distinguish GC and CRC patients from healthy controls. Leave-one-out cross validation and receiver operating characteristic (ROC) curves were used to validate the accuracy of the formula. The relationship between the STR status and immunohistochemical (IHC) and tumor markers was analyzed using multiple correspondence analysis.

Results

D5S346 was confirmed as a GC- and CRC-related STR locus. For the first time, we established a discriminant formula on the basis of the six STR loci, which was used to estimate the risk coefficient of suffering from GC and CRC. The model was statistically significant (Wilks’ lambda = 0.471, χ2 = 30.488, df = 13, and p = 0.004). The results of leave-one-out cross validation showed that the sensitivity of the formula was 73.7% and the specificity was 76.7%. The area under the ROC curve (AUC) was 0.926, with a sensitivity of 73.7% and a specificity of 93.3%. The STR status was shown to have a certain relationship with the expression of some IHC markers and the level of some tumor markers.

Conclusions

The results of this study complement clinical diagnostic criteria and present markers for early prediction of GC and CRC. This approach will aid in improving risk awareness of susceptible individuals and contribute to reducing the incidence of GC and CRC by prevention and early detection.