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

Abstract

Attention deficit and hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders of childhood. It affects ~10% of the world’s population of children, and about 30–50% of those diagnosed in childhood continue to show ADHD symptoms later, with 2–5% of adults having the condition. Current diagnosis of ADHD is based on the clinical evaluation of the patient, and on interviews performed by clinicians with parents and teachers of the children, which, together with the fact that it shares common symptoms and frequent comorbidities with other neurodevelopmental disorders, makes the accurate and timely diagnosis of the disorder a difficult task. Despite the large effort to identify reliable biomarkers that can be used in a clinical environment to support clinical diagnosis, this goal has never been achieved hitherto. In the present study, infrared spectroscopy was used together with multivariate statistical methods (hierarchical clustering and partial least-squares discriminant analysis) to develop a model based on the spectra of blood serum samples that is able to distinguish ADHD patients from healthy individuals. The developed model used an approach where the whole infrared spectrum (in the 3700–900 cm−1 range) was taken as a holistic imprint of the biochemical blood serum environment (spectroscopic biomarker), overcoming the need for the search of any particular chemical substance associated with the disorder (molecular biomarker). The developed model is based on a sensitive and reliable technique, which is cheap and fast, thus appearing promising to use as a complementary diagnostic tool in the clinical environment.

Rapid and non‐invasive detection of cystic echinococcosis in sheep based on serum fluorescence spectrum combined with machine learning algorithms

“…This functionality allows for the classification of health and disease data [49]. In recent years, SVM, KNN, and PCA-LDA have been widely used in the interpretation of spectral data [25,[50][51][52][53]. These analytical methods can consider the full spectrum rather than a few distinct peaks.…”

Development of a Hybrid Biomimetic Enamel-Biocomposite Interface and a Study of Its Molecular Features Using Synchrotron Submicron ATR-FTIR Microspectroscopy and Multivariate Analysis Techniques

“…Using multivariate statistical methods to analyze an array of spectra of multicomponent systems is useful for identifying information that might have been missed in a simpler analysis. This is especially important for biological samples, which by their nature contain heterogeneous molecular compounds [ 90 ].…”

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.…”

“…This approach offers distinct advantages over other techniques, being a sensitive, reliable, cost-effective, rapid, and easily adaptable methodology within the clinical setting [26,30]. When coupled with contemporary chemometric methods, Raman and infrared spectroscopies have demonstrated their efficacy as powerful analytical instruments for efficient examination of the biochemical environment of a given biological sample [26][27][28][29][30][31][32][33][34][35][36][37][38][39].…”

“…In our prior investigation of ADHD in children and adolescents [29], infrared spectroscopy, coupled with hierarchical clustering (HC) and partial least-squares discriminant analysis (PLS-DA), was employed to construct a model based on the spectra of blood serum samples. This model successfully differentiated ADHD patients from healthy individuals.…”