Children with Autism Spectrum Disorder Exhibit Elevated Physical Activity and Reduced Sedentary Behavior.

According to previous research, individuals with autism spectrum disorder (ASD) have lower levels of physical activity than their typically developed (TD) counterparts. There have been conflicting reports about physical activity (PA) levels in people with ASD. Given the conflicting evidence, further investigation is required. We believe that evaluating PA in individuals with ASD is critical in order to offer PA intervention plans aiming at increasing their health-related physical fitness on a daily, systematic, and individualized basis. In the current study, an ActiGraph monitor (GT3X+) was used to accurately measure PA and sedentary activity in 21 children with autism aged 6.43 ± 2.29 years and 30 TD children aged 7.2 ± 3.14 years. Our data indicated that while the light and moderate activity counts were not significantly different between the two groups, the vigorous activity was significantly higher in ASD compared to TD. This finding was attributed to ASD characteristic stereotypy and self-stimulating behaviors. The significantly higher vigorous PA is discussed in relation to altered neurochemistry, oxidative stress, and neuroinflammation as etiological mechanisms in ASD. This research provides a better understanding of the status of PA participation in individuals with ASD.

The Fidelity of Rheumatoid Arthritis Multivariate Diagnostic Biomarkers Using Discriminant Analysis and Binary Logistic Regression.

Rheumatoid arthritis (RA) is an autoimmune inflammatory disease that causes multi-articular synovitis. The illness is characterized by worsening inflammatory synovitis, which causes joint swelling and pain. Synovitis erodes articular cartilage and marginal bone, resulting in joint deterioration. This bone injury is expected to be permanent. Cytokines play a prominent role in the etiology of RA and could be useful as early diagnostic biomarkers. This research was carried out at Riyadh's King Khalid University Hospital (KKUH). Patients were enrolled from the Rheumatology unit. Seventy-eight RA patients were recruited (67 (85.9%) females and 11 (14.1%) males). Patients were selected for participation by convenience sampling. Demographic data were collected, and disease activity measurements at 28 joints were recorded using the disease activity score (DAS-28). Age- and sex-matched controls from the general population were included in the study. A panel of 27 cytokines, chemokines, and growth factors was determined in patient and control sera. Binary logistic regression (BLR) and discriminant analysis (DA) were used to analyze the data. We show that multiple cytokine biomarker profiles successfully distinguished RA patients from healthy controls. IL-17, IL-4, and RANTES were among the most predictive variables and were the only biomarkers incorporated into both BLR and DA predictive models for pooled participants (men and women). In the women-only models, the significant cytokines incorporated in the model were IL-4, IL-17, MIP-1b, and RANTES for the BLR model and IL-4, IL-1Ra, GM-CSF, IL-17, and eotaxin for the DA model. The BLR and DA men-only models contained one cytokine each, eotaxin for BLR and platelet-derived growth factor-bb (PDGF-BB) for DA. We show that BLR has a higher fidelity in identifying RA patients than DA. We also found that the use of gender-specific models marginally improves detection fidelity, indicating a possible benefit in clinical diagnosis. More research is needed to determine whether this conclusion will hold true in various and larger patient populations.

Multivariate indicators of disease severity in COVID-19.

The novel coronavirus pandemic continues to cause significant morbidity and mortality around the world. Diverse clinical presentations prompted numerous attempts to predict disease severity to improve care and patient outcomes. Equally important is understanding the mechanisms underlying such divergent disease outcomes. Multivariate modeling was used here to define the most distinctive features that separate COVID-19 from healthy controls and severe from moderate disease. Using discriminant analysis and binary logistic regression models we could distinguish between severe disease, moderate disease, and control with rates of correct classifications ranging from 71 to 100%. The distinction of severe and moderate disease was most reliant on the depletion of natural killer cells and activated class-switched memory B cells, increased frequency of neutrophils, and decreased expression of the activation marker HLA-DR on monocytes in patients with severe disease. An increased frequency of activated class-switched memory B cells and activated neutrophils was seen in moderate compared to severe disease and control. Our results suggest that natural killer cells, activated class-switched memory B cells, and activated neutrophils are important for protection against severe disease. We show that binary logistic regression was superior to discriminant analysis by attaining higher rates of correct classification based on immune profiles. We discuss the utility of these multivariate techniques in biomedical sciences, contrast their mathematical basis and limitations, and propose strategies to overcome such limitations.

Prevalence of Syphilis in Pregnant Women Living With Human Immunodeficiency Virus (HIV) From South Africa Using a Molecular-Based Assay.

BACKGROUND: Syphilis is one of the most common sexually transmitted infections (STIs), and it remains a significant public health concern, particularly in low-resource settings including sub-Saharan Africa. There are limited data on the prevalence of syphilis among pregnant women living with HIV in South Africa. This study determined the prevalence of syphilis infection in pregnant women living with HIV by the polymerase chain reaction (PCR). METHODS: This was a cross-sectional study that included 385 pregnant women living with HIV recruited from the antenatal clinic at the King Edward VIII Hospital in Durban, South Africa between October 2020 and April 2021. Treponema pallidum was detected using the Applied BiosystemsTM TaqMan® Assays from stored DNA samples extracted from vaginal swabs. RESULTS: The prevalence of syphilis was 5.2% (20/385). The overall median (Q1-Q3) age of the women was 30.0 years (25.0-36.0). Of the women who tested positive for syphilis, 60.0% had reported STI symptoms (p = 0.030) and of those, 65.0% did not perceive themselves at risk of contracting STIs (p = 0.019). Women who reported having STI symptoms were more likely to test positive for syphilis when compared to women who reported not having any STI symptoms (OR: 2.810; 95% CI 1.119-7.052; p = 0.028). Women who perceived themselves as being at risk of contracting STIs were less likely to test positive for syphilis when compared to women who did not perceive themselves at risk of contracting STIs (OR: 0.328; 95% CI 0.128-0.842; p = 0.020). CONCLUSION: The study has indicated syphilis is prevalent among pregnant women living with HIV in Durban, South Africa however STI risk perception is low. Educational programs on STIs are essential among pregnant women attending antenatal care clinics in Durban.

Bariatric Surgery as Treatment Strategy of Obesity in Saudi People: Effects of Gut Microbiota.

Obesity prevalence is rising globally, as are the number of chronic disorders connected with obesity, such as diabetes, non-alcoholic fatty liver disease, dyslipidemia, and hypertension. Bariatric surgery is also becoming more common, and it remains the most effective and long-term treatment for obesity. This study will assess the influence of Laparoscopic Sleeve Gastrectomy (LSG) on gut microbiota in people with obesity before and after surgery. The findings shed new light on the changes in gut microbiota in Saudi people with obesity following LSG. In conclusion, LSG may improve the metabolic profile, resulting in decreased fat mass and increased lean mass, as well as improving the microbial composition balance in the gastrointestinal tract, but this is still not equivalent to normal weight microbiology. A range of factors, including patient characteristics, geographic dispersion, type of operation, technique, and nutritional and caloric restriction, could explain differences in abundance between studies. This information could point to a novel and, most likely, tailored strategy in obesity therapy, which could eventually be incorporated into health evaluations and monitoring in preventive health care or clinical medicine.

Influenza Type B Complicates a Previously Undiagnosed Case of Pericarditis.

We report the first case of pericarditis exacerbation due to influenza B viral infection while emphasizing the importance of cardiac magnetic resonance (CMR) for the timely diagnosis and ruling out of non-effusive pericarditis in a patient with compatible, unexplained chest pain. The patient presented with left-sided chest pain that was partially relieved by leaning backward and noted persistent fatigue for several days. Pericardial friction rub, electrocardiogram (ECG), and echocardiogram abnormalities were not detected. After discharge on the morning following admission, fatigue and fever several minutes after physical exertion continued. The patient contracted influenza type B, leading to pneumonia and a second hospitalization, during which echocardiography showed moderate pericardial effusion. We conclude that the patient had pericarditis on the first admission because other compatible causes of chest pain were ruled out, symptoms were compatible with non-effusive pericarditis and could not be ruled out since CMR was not done, and the patient tested positive during his second admission for multiple known etiologic agents of pericarditis. We highlight the importance of CMR in screening patients presenting with chest pain of unknown origin to facilitate early detection and intervention.

Synergism between Extracts of Garcinia mangostana Pericarp and Curcuma in Ameliorating Altered Brain Neurotransmitters, Systemic Inflammation, and Leptin Levels in High-Fat Diet-Induced Obesity in Male Wistar Albino Rats.

This study aims to explore the effects of Garcinia mangostana (mangosteen) and Curcuma longa independently and synergistically in modulating induced inflammation and impaired brain neurotransmitters commonly observed in high-fat diet-induced obesity in rodent models. Male albino Wistar rats were divided into four experimental groups. Group I, control, obese, fed on a high-fat diet (HFD), and Group II-IV, fed on HFD then given mangosteen extract (400 mg/kg/day) and/or Curcuma (80 mg/kg/day), or a mixture of both for 6 weeks. Plasma pro-inflammatory cytokines, leptin, and brain serotonin, dopamine, and glutamate were measured in the five studied groups. G. mangostana and Curcuma longa extracts demonstrate antioxidant and DPPH radical scavenging activities. Both induced a significant reduction in the weight gained, concomitant with a non-significant decrease in the BMI (from 0.86 to 0.81 g/cm2). Curcuma either alone or in combination with MPE was more effective. Both extracts demonstrated anti-inflammatory effects and induced a significant reduction in levels of both IL-6 and IL-12. The lowest leptin level was achieved in the synergistically treated group, compared to independent treatments. Brain dopamine was the most affected variable, with significantly lower levels recorded in the Curcuma and synergistically treated groups than in the control group. Glutamate and serotonin levels were not affected significantly. The present study demonstrated that mangosteen pericarp extract (MPE) and Curcuma were independently and in combination effective in treating obesity-induced inflammation and demonstrating neuroprotective properties.

Protective Effects of Bee Pollen on Multiple Propionic Acid-Induced Biochemical Autistic Features in a Rat Model.

Autism spectrum disorders (ASDs) are neurodevelopmental disorders that clinically presented as impaired social interaction, repetitive behaviors, and weakened communication. The use of bee pollen as a supplement rich in amino acids amino acids, vitamins, lipids, and countless bioactive substances may lead to the relief of oxidative stress, neuroinflammation, glutamate excitotoxicity, and impaired neurochemistry as etiological mechanisms autism. Thirty young male Western albino rats were randomly divided as: Group I-control; Group II, in which autism was induced by the oral administration of 250 mg propionic acid/kg body weight/day for three days followed by orally administered saline until the end of experiment and Group III, the bee pollen-treated group, in which the rats were treated with 250 mg/kg body weight of bee pollen for four weeks before autism was induced as described for Group II. Markers related to oxidative stress, apoptosis, inflammation, glutamate excitotoxicity, and neurochemistry were measured in the brain tissue. Our results indicated that while glutathione serotonin, dopamine, gamma-aminobutyric acid (GABA), GABA/Glutamate ratio, and vitamin C were significantly reduced in propionic acid-treated group (p < 0.05), glutamate, IFN-γ, IL-1A, IL-6, caspase-3, and lipid peroxide levels were significantly elevated (p < 0.05). Bee pollen supplementation demonstrates protective potency presented as amelioration of most of the measured variables with significance range between (p < 0.05)−(p < 0.001).

Bee Pollen and Probiotics May Alter Brain Neuropeptide Levels in a Rodent Model of Autism Spectrum Disorders.

Neuropeptides play a major role in maintaining normal brain development in children. Dysfunction of some specific neuropeptides can lead to autism spectrum disorders (ASD) in terms of social interaction and repetitive behavior, but the exact underlying etiological mechanisms are still not clear. In this study, we used an animal model of autism to investigate the role of bee pollen and probiotic in maintaining neuropeptide levels in the brain. We measured the Alpha-melanocyte-stimulating hormone (α-MSH), Beta-endorphin (ß-End), neurotensin (NT), and substance P (SP) in brain homogenates of six studied groups of rats. Group I served as control, given only PBS for 30 days; Group II as an autistic model treated with 250 mg PPA/kg BW/day for 3 days after being given PBS for 27 days. Groups III-VI were denoted as intervention groups. G-III was treated with bee pollen (BP) 250 mg/kg body weight/day; G-IV with Lactobacillus paracaseii (LB) (109 CFU/mL) suspended in PBS; G-V with 0.2 g/kg body weight/day Protexin®, a mixture of probiotics (MPB); and G-VI was transplanted with stool from normal animals (FT) for 27 days prior to the induction of PPA neurotoxicity on the last 3 days of study (days 28-30). The obtained data were analyzed through the use of principal component analysis (PCA), discriminant analysis (DA), hierarchical clustering, and receiver operating characteristic (ROC) curves as excellent statistical tools in the field of biomarkers. The obtained data revealed that brain levels of the four measured neuropeptides were significantly reduced in PPA-treated animals compared to healthy control animals. Moreover, the findings demonstrate the ameliorative effects of bee pollen as a prebiotic and of the pure or mixed probiotics. This study proves the protective effects of pre and probiotics against the neurotoxic effects of PPA presented as impaired levels of α-MSH, ß-End, NT, and SP.

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.

High-fat diet-induced obesity and impairment of brain neurotransmitter pool.

Obesity and the brain are linked since the brain can control the weight of the body through its neurotransmitters. The aim of the present study was to investigate the effect of high-fat diet (HFD)-induced obesity on brain functioning through the measurement of brain glutamate, dopamine, and serotonin metabolic pools. In the present study, two groups of rats served as subjects. Group 1 was fed a normal diet and named as the lean group. Group 2 was fed an HFD for 4 weeks and named as the obese group. Markers of oxidative stress (malondialdehyde, glutathione, glutathione-s-transferase, and vitamin C), inflammatory cytokines (interleukin [IL]-6 and IL-12), and leptin along with a lipid profile (cholesterol, triglycerides, high-density lipoprotein, and low-density lipoprotein levels) were measured in the serum. Neurotransmitters dopamine, serotonin, and glutamate were measured in brain tissue. Fecal samples were collected for observing changes in gut flora. In brain tissue, significantly high levels of dopamine and glutamate as well as significantly low levels of serotonin were found in the obese group compared to those in the lean group (P > 0.001) and were discussed in relation to the biochemical profile in the serum. It was also noted that the HFD affected bacterial gut composition in comparison to the control group with gram-positive cocci dominance in the control group compared to obese. The results of the present study confirm that obesity is linked to inflammation, oxidative stress, dyslipidemic processes, and altered brain neurotransmitter levels that can cause obesity-related neuropsychiatric complications.

Brevibacillus Laterosporus Bacteremia in an Adult.

Brevibacillus laterosporus (B. laterosporus) is an aerobic gram-positive bacillus that is rarely associated with human infection. A review of multiple online databases revealed no other cases of bacteremia in an adult involving this organism. Historically, this "canoe-shaped" microbe has been characterized as a pathogen in invertebrates, and information regarding human infection is scarce. We present a clinical vignette of what we believe to be the first reported case of B. laterosporus bacteremia in an adult human subject.

Host Immune Response to Enterovirus and Parechovirus Systemic Infections in Children.

BACKGROUND: Enterovirus (EV) and parechovirus type A3 (PeV-A3) cause infections ranging from asymptomatic to life-threatening. Host immune responses in children, particularly innate responses to PeV-A3, remain largely unknown. The aim of this study was to determine aspects of the cytokine/chemokine responses to EV and PeV-A3 in cerebrospinal fluid (CSF) and plasma obtained from children with systemic/central nervous system infection. METHODS: A total of 74 salvaged CSF samples (27 with EV, 23 with PeV-A3, and 24 with neither EV nor PeV-A3) and 35 paired blood samples (10 with EV, 14 with PeV-A3, and 11 with neither) were studied. Concentrations of cytokines and chemokines were measured using a customized 21-plex MILLIPLEX MAP Human Cytokine/Chemokine Magnetic Bead Panel. Additionally, clinical characteristics data for all the patients were collected from electronic medical records to evaluate the potential association between the immune response and presentations. RESULTS: We demonstrate that EV and PeV-A3 infections induce different cytokine/chemokine immune responses in children. EV induces more robust responses in CSF with significantly elevated levels of fractalkine, interferon (IFN)-α2, IFN-γ, interleukin (IL)-1Rα, IL-4, IL-8, and tumor necrosis factor α; PeV-A3 induces less robust or absent responses in CSF but robust responses in plasma, with significantly higher concentrations of IFN-α2, IL-15, IL-1Rα, interferon-γ-inducible protein-10, and monocyte chemoattractant protein-1. CONCLUSIONS: High cytokine/chemokine concentrations in the plasma of PeV-A3 patients compared with EV patients could explain higher/more prolonged fever in PeV-A3 patients, whereas relatively low cytokine/chemokine concentrations in PeV-A3 CSF might explain the absence of CSF pleocytosis.

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.

Multivariate profiling of African green monkey and rhesus macaque T lymphocytes.

The complexity of immune responses limits the usefulness of univariate methods in answering complex immunology questions. To demonstrate the utility of a multivariate approach, we employ such approach to compare T cells of African green monkeys (AGMs) and rhesus macaques (RMs). Among the most prominent distinguishing features we found were lower CD3 and higher CD28 surface expression in AGMs compared to RMs. After in vitro stimulation, a larger proportion of AGM T cells secreted cytokines, especially those producing more than one cytokine (i.e. multifunctional cells). To find out whether multifunctional responses associate with protection in other species, we compared T cells of cynomolgus macaques (CMs) infected with wild-type Simian Immunodeficiency Virus (SIV) to those of CMs infected (vaccinated) with a replication-defective virus. Wild-type SIV infection in macaques leads to simian Acquired Immunodeficiency Syndrome (AIDS), which does not happen in animals previously vaccinated with a replication-defective virus. Interestingly, after in vitro stimulation, multifunctional cells were more abundant among T cells of vaccinated CMs. Our results propose T-cell multifunctionality as a potentially useful marker of immunity, although additional verification is needed. Finally, we hope our multivariate model and its associated validation methods will inform future studies in the field of immunology.

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

Effective biomarkers are urgently needed to facilitate early diagnosis of autism spectrum disorder (ASD), permitting early intervention, and consequently improving prognosis. In this study, we evaluate the usefulness of nine biomarkers and their association (combination) in predicting ASD onset and development. Data were analyzed using multiple independent mathematical and statistical approaches to verify the suitability of obtained results as predictive parameters. All biomarkers tested appeared useful in predicting ASD, particularly vitamin E, glutathione-S-transferase, and dopamine. Combining biomarkers into profiles improved the accuracy of ASD prediction but still failed to distinguish between participants with severe versus mild or moderate ASD. Library-based identification was effective in predicting the occurrence of disease. Due to the small sample size and wide participant age variation in this study, we conclude that the use of multi-parametric biomarker profiles directly related to autism phenotype may help predict the disease occurrence more accurately, but studies using larger, more age-homogeneous populations are needed to corroborate our findings.

Identification of Biomarkers of Impaired Sensory Profiles among Autistic Patients.

BACKGROUND: Autism is a neurodevelopmental disorder that displays significant heterogeneity. Comparison of subgroups within autism, and analyses of selected biomarkers as measure of the variation of the severity of autistic features such as cognitive dysfunction, social interaction impairment, and sensory abnormalities might help in understanding the pathophysiology of autism. METHODS AND PARTICIPANTS: In this study, two sets of biomarkers were selected. The first included 7, while the second included 6 biomarkers. For set 1, data were collected from 35 autistic and 38 healthy control participants, while for set 2, data were collected from 29 out of the same 35 autistic and 16 additional healthy subjects. These markers were subjected to a principal components analysis using either covariance or correlation matrices. Moreover, libraries composed of participants categorized into units were constructed. The biomarkers used include, PE (phosphatidyl ethanolamine), PS (phosphatidyl serine), PC (phosphatidyl choline), MAP2K1 (Dual specificity mitogen-activated protein kinase kinase 1), IL-10 (interleukin-10), IL-12, NFκB (nuclear factor-κappa B); PGE2 (prostaglandin E2), PGE2-EP2, mPGES-1 (microsomal prostaglandin synthase E-1), cPLA2 (cytosolic phospholipase A2), 8-isoprostane, and COX-2 (cyclo-oxygenase-2). RESULTS: While none of the studied markers correlated with CARS and SRS as measure of cognitive and social impairments, six markers significantly correlated with sensory profiles of autistic patients. Multiple regression analysis identifies a combination of PGES, mPGES-1, and PE as best predictors of the degree of sensory profile impairment. Library identification resulted in 100% correct assignments of both autistic and control participants based on either set 1 or 2 biomarkers together with a satisfactory rate of assignments in case of sensory profile impairment using different sets of biomarkers. CONCLUSION: The two selected sets of biomarkers were effective to separate autistic from healthy control subjects, demonstarting the possibility to accurately predict the severity of autism using the selected biomarkers. The effectiveness of the identified libraries lied in the fact that they were helpful in correctly assigning the study population as control or autistic patients and in classifying autistic patients with different degree of sensory profile impairment.