Fourier Transform Infrared Spectroscopy Based Complementary Diagnosis Tool for Autism Spectrum Disorder in Children and Adolescents

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that begins early in life and continues lifelong with strong personal and societal implications. It affects about 1%–2% of the children population in the world. The absence of auxiliary methods that can complement the clinical evaluation of ASD increases the probability of false identification of the disorder, especially in the case of very young children. In this study, analytical models for auxiliary diagnosis of ASD in children and adolescents, based on the analysis of patients’ blood serum ATR-FTIR (Attenuated Total Reflectance-Fourier Transform Infrared) spectra, were developed. The models use chemometrics (either Principal Component Analysis (PCA) or Partial Least Squares Discriminant Analysis (PLS-DA)) methods, with the infrared spectra being the X-predictor variables. The two developed models exhibit excellent classification performance for samples of ASD individuals vs. healthy controls. Interestingly, the simplest, unsupervised PCA-based model results to have a global performance identical to the more demanding, supervised (PLS-DA)-based model. The developed PCA-based model thus appears as the more economical alternative one for use in the clinical environment. Hierarchical clustering analysis performed on the full set of samples was also successful in discriminating the two groups.

PLS-DA Model for the Evaluation of Attention Deficit and Hyperactivity Disorder in Children and Adolescents through Blood Serum FTIR Spectra

“…These results shall be considered only as indicative; however, a deficiency in the trace amine phenylethylamine, from which tyrosine is a precursor, was reported for ADHD patients [ 15 , 60 , 61 ]. This result, as well as the increased level of the total protein contents in the blood serum of children with ADHD, is also in consonance with similar observations for children affected by other neurodevelopmental disorders, such as autism [ 27 , 29 , 62 , 63 , 64 ].…”

“…The analysis of body fluids using vibrational spectroscopy (either Raman or infrared) has progressively gained the respect of clinicians as a complementary diagnostic tool, with advantages over other techniques, such as being a sensitive and reliable approach, but also cheap, fast and easily adaptable to the clinical environment [ 27 , 28 ]. When used together with modern chemometric methods, Raman and infrared spectroscopies are powerful analytical instruments, which can efficiently probe the biochemical environment of a given biological sample [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. As they are very sensitive to molecular structure and intermolecular interactions, Raman and infrared spectra provide signatures of a biological sample, such as imprints of its molecular constitution and chemical environment.…”

“…Recently, this approach has been successfully applied to the development of analytical models for evaluation of neurodevelopmental diseases, using principal component analysis (PCA) or partial least-squares discriminant analysis (PLS-DA) applied to Raman infrared spectroscopic data obtained from blood samples [ 27 , 29 , 30 ]. Autism spectrum disorder, bipolar disorder (including identification of its different characteristic phases) and schizophrenia are recent cases of the successful application of this strategy to neurodevelopmental disorders [ 27 , 29 , 30 ].…”

A Review on Integrated ZnO-Based SERS Biosensors and Their Potential in Detecting Biomarkers of Neurodegenerative Diseases

“…On the other hand, functional MR imaging offers valuable information on aspects regarding social skills and independent domestic activities management [55]. It is also worth mentioning an attempt to develop a complementary spectroscopic diagnostic tool based on a chemometric model that relies on infrared (IR) spectroscopic data for ASD detection in children and teenagers 4-17 years old [56]. Principal component analysis (PCA)-and partial least squares discriminant analysis (PLS-DA)-based models analyzing FT-IR spectra recorded on blood serum samples have shown a clear separation of ASD individuals from healthy control samples.…”

Raman Spectra of Blood Serum as Holistic Biomarker for Differential Auxiliary Diagnoses of Attention Deficit and Hyperactivity Disorder (ADHD) in Adults

“…This methodology uses information extracted from the Raman or infrared spectra of blood serum samples of patients, together with multivariate statistical methods, as a spectroscopic biomarker that provides a holistic representation of the biochemical environment of the blood serum. Such an approach was successfully applied to a series of neurobehavioral diseases, including autism spectrum disorder [26,27], schizophrenia, and different phases of bipolar disorder [28], as well as ADHD in children and adolescents [29]. While this holistic approach does not yield information about specific metabolic mechanisms or precise chemical species involved in the disease, it offers a more dependable overall description of the samples and simplifies their distinction from controls, because no information is overlooked, and the entire biochemical environment is subjected to examination.…”