Machine learning techniques in diagnostics and prediction of the clinical features of schizophrenia: a narrative review

BACKGROUND: Schizophrenia is a severe psychiatric disorder associated with a significant negative impact. Early diagnosis and treatment of schizophrenia has a favorable effect on the clinical outcome and patients quality of life. In this context, machine learning techniques open up new opportunities for a more accurate diagnosis and prediction of the clinical features of this illness. AIM: This literature review is aimed to search for information on the use of machine learning techniques in the prediction and diagnosis of schizophrenia and the determination of its clinical features. METHODS: The Google Scholar, PubMed, and eLIBRARY.ru databases were used to search for relevant data. The review included articles that had been published not earlier than January 1, 2010, and not later than March 31, 2023. Combinations of the following keywords were applied for search queries: machine learning, deep learning, schizophrenia, neural network, predictors, artificial intelligence, diagnostics, suicide, depressive, insomnia, and cognitive. Original articles regardless of their design were included in the review. Descriptive analysis was used to summarize the retrieved data. RESULTS: Machine learning techniques are widely used in the functional assessment of patients with schizophrenia. They are used for interpretation of MRI, EEG, and actigraphy findings. Also, models created using machine learning algorithms can analyze speech, behavior, and the creativity of people and these data can be used for the diagnosis of psychiatric disorders. It has been found that different machine learning-based models can help specialists predict and diagnose schizophrenia based on medical history and genetic data, as well as epigenetic information. Machine learning techniques can also be used to build effective models that can help specialists diagnose and predict clinical manifestations and complications of schizophrenia, such as insomnia, depressive symptoms, suicide risk, aggressive behavior, and changes in cognitive functions over time. CONCLUSION: Machine learning techniques play an important role in psychiatry, as they have been used in models that help specialists in the diagnosis of schizophrenia and determination of its clinical features. The use of machine learning algorithms is one of the most promising direction in psychiatry, and it can significantly improve the effectiveness of the diagnosis and treatment of schizophrenia

Genetic Biomarkers of Antipsychotic-Induced Prolongation of the QT Interval in Patients with Schizophrenia.

Antipsychotics (AP) induced prolongation of the QT interval in patients with schizophrenia (Sch) is an actual interdisciplinary problem as it increases the risk of sudden death syndrome. Long QT syndrome (LQTS) as a cardiac adverse drug reaction is a multifactorial symptomatic disorder, the development of which is influenced by modifying factors (APs' dose, duration of APs therapy, APs polytherapy, and monotherapy, etc.) and non-modifying factors (genetic predisposition, gender, age, etc.). The genetic predisposition to AP-induced LQTS may be due to several causes, including causal mutations in the genes responsible for monoheme forms of LQTS, single nucleotide variants (SNVs) of the candidate genes encoding voltage-dependent ion channels expressed both in the brain and in the heart, and SNVs of candidate genes encoding key enzymes of APs metabolism. This narrative review summarizes the results of genetic studies on AP-induced LQTS and proposes a new personalized approach to assessing the risk of its development (low, moderate, high). We recommend implementation in protocols of primary diagnosis of AP-induced LQTS and medication dispensary additional observations of the risk category of patients receiving APs, deoxyribonucleic acid profiling, regular electrocardiogram monitoring, and regular therapeutic drug monitoring of the blood APs levels

The Role of D-Serine and D-Aspartate in the Pathogenesis and Therapy of Treatment-Resistant Schizophrenia

Schizophrenia (Sch) is a severe and widespread mental disorder. Antipsychotics (APs) of the first and new generations as the first-line treatment of Sch are not effective in about a third of cases and are also unable to treat negative symptoms and cognitive deficits of schizophrenics. This explains the search for new therapeutic strategies for a disease-modifying therapy for treatment-resistant Sch (TRS). Biological compounds are of great interest to researchers and clinicians, among which D-Serine (D-Ser) and D-Aspartate (D-Asp) are among the promising ones. The Sch glutamate theory suggests that neurotransmission dysfunction caused by glutamate N-methyl-D-aspartate receptors (NMDARs) may represent a primary deficiency in this mental disorder and play an important role in the development of TRS. D-Ser and D-Asp are direct NMDAR agonists and may be involved in modulating the functional activity of dopaminergic neurons. This narrative review demonstrates both the biological role of D-Ser and D-Asp in the normal functioning of the central nervous system (CNS) and in the pathogenesis of Sch and TRS. Particular attention is paid to D-Ser and D-Asp as promising components of a nutritive disease-modifying therapy for TRS

Genetic Predisposition to Schizophrenia and Depressive Disorder Comorbidity

Background: Patients with schizophrenia have an increased risk of depressive disorders compared to the general population. The comorbidity between schizophrenia and depression suggests a potential coincidence of the pathophysiology and/or genetic predictors of these mental disorders. The aim of this study was to review the potential genetic predictors of schizophrenia and depression comorbidity. Materials and Methods: We carried out research and analysis of publications in the databases PubMed, Springer, Wiley Online Library, Taylor & Francis Online, Science Direct, and eLIBRARY.RU using keywords and their combinations. The search depth was the last 10 years (2010–2020). Full-text original articles, reviews, meta-analyses, and clinical observations were analyzed. A total of 459 articles were found, of which 45 articles corresponding to the purpose of this study were analyzed in this topic review. Results: Overlap in the symptoms and genetic predictors between these disorders suggests that a common etiological mechanism may underlie the presentation of comorbid depression in schizophrenia. The molecular mechanisms linking schizophrenia and depression are polygenic. The most studied candidate genes are GRIN1, GPM6A, SEPTIN4, TPH1, TPH2, CACNA1C, CACNB2, and BCL9. Conclusion: Planning and conducting genome-wide and associative genetic studies of the comorbid conditions under consideration in psychiatry is important for the development of biological and clinical predictors and a personalized therapy strategy for schizophrenia. However, it should be recognized that the problems of predictive and personalized psychiatry in the diagnosis and treatment of schizophrenia and comorbid disorders are far from being resolved

Association of a Single-Nucleotide Variant rs11100494 of the NPY5R Gene with Antipsychotic-Induced Metabolic Disorders

Background: The usage of antipsychotics (APs) is the most robust and scientifically based approach in the treatment of schizophrenia spectrum disorders (SSDs). The efficiency of APs is based on a range of target receptors of the central nervous system (CNS): serotoninergic, dopaminergic, adrenergic, histaminergic and cholinergic. Metabolic disorders are the most severe adverse drug reactions (ADRs) and lead to cardiovascular diseases with a high rate of mortality in patients with SSDs. Neuropeptide Y receptor Y5 (NPY5R) is known in the chain of interaction to target receptors for APs, agouti-related peptide receptors and proopiomelanocortin receptors. We studied the association of the single-nucleotide variants (SNVs) rs11100494 and rs6837793 of the NPY5R gene, and rs16147, rs5573, rs5574 of the NPY gene, with metabolic disorders in Russian patients with SSDs. Methods: We examined 99 patients with SSDs (mean age—24.56 years old). The mean duration of APs monotherapy was 8 weeks. The biochemical blood test included levels of glucose, cholesterol, lipoproteins, alanine aminotransferase (ALT), aspartate aminotransferase (AST), total protein and albumin. Anthropometry included weight, height, waist circumference and hip circumference. We used real-time PCR to study the carriage of major and minor alleles of the SNV rs11100494 (1164C>A) of the NPY5R gene (chromosome localization—4q32.2). Group 1 comprised 25 patients with SSDs taking APs with a change in body weight of more than 6% since the start of APs therapy. Group 2 comprised 74 patients with SSDs taking APs with a change in body weight of less than 6% since the start of APs therapy. Results: We show the significance of genetic risk factors (carriage of major allele C of SNV rs11100494 of the NPY5R gene) for the development of AP-induced weight gain in Russian patients with SSDs. The allele C predisposes to AP-induced weight gain (OR = 33.48 [95% CI: 12.62; 88.82], p-value < 0.001). Additionally, the results of our study demonstrate that first-generation APs (FGAs) are more likely to cause an increase in serum transaminase levels but are less likely to increase body weight. Second-generation APs (SGAs) are more likely to cause weight gain and changes in serum glucose levels. Conclusion: Our study shows the predictive role of the allele C of SNV rs11100494 of the NPY5R gene in the development of AP-induced weight gain. However, we did not find a significant association between biochemical markers and this SNV in Russian patients with SSDs

Genetic Biomarkers of Antipsychotic-Induced Prolongation of the QT Interval in Patients with Schizophrenia

Antipsychotics (AP) induced prolongation of the QT interval in patients with schizophrenia (Sch) is an actual interdisciplinary problem as it increases the risk of sudden death syndrome. Long QT syndrome (LQTS) as a cardiac adverse drug reaction is a multifactorial symptomatic disorder, the development of which is influenced by modifying factors (APs’ dose, duration of APs therapy, APs polytherapy, and monotherapy, etc.) and non-modifying factors (genetic predisposition, gender, age, etc.). The genetic predisposition to AP-induced LQTS may be due to several causes, including causal mutations in the genes responsible for monoheme forms of LQTS, single nucleotide variants (SNVs) of the candidate genes encoding voltage-dependent ion channels expressed both in the brain and in the heart, and SNVs of candidate genes encoding key enzymes of APs metabolism. This narrative review summarizes the results of genetic studies on AP-induced LQTS and proposes a new personalized approach to assessing the risk of its development (low, moderate, high). We recommend implementation in protocols of primary diagnosis of AP-induced LQTS and medication dispensary additional observations of the risk category of patients receiving APs, deoxyribonucleic acid profiling, regular electrocardiogram monitoring, and regular therapeutic drug monitoring of the blood APs levels

Therapeutic and Toxic Effects of Valproic Acid Metabolites

Valproic acid (VPA) and its salts are psychotropic drugs that are widely used in neurological diseases (epilepsy, neuropathic pain, migraine, etc.) and psychiatric disorders (schizophrenia, bipolar affective disorder, addiction diseases, etc.). In addition, the indications for the appointment of valproate have been expanding in recent years in connection with the study of new mechanisms of action of therapeutic and toxic metabolites of VPA in the human body. Thus, VPA is considered a component of disease-modifying therapy for multiple tumors, neurodegenerative diseases (Huntington’s disease, Parkinson’s disease, Duchenne progressive dystrophy, etc.), and human immunodeficiency syndrome. The metabolism of VPA is complex and continues to be studied. Known pathways of VPA metabolism include: β-oxidation in the tricarboxylic acid cycle (acetylation); oxidation with the participation of cytochrome P-450 isoenzymes (P-oxidation); and glucuronidation. The complex metabolism of VPA explains the diversity of its active and inactive metabolites, which have therapeutic, neutral, or toxic effects. It is known that some active metabolites of VPA may have a stronger clinical effect than VPA itself. These reasons explain the relevance of this narrative review, which summarizes the results of studies of blood (serum, plasma) and urinary metabolites of VPA from the standpoint of the pharmacogenomics and pharmacometabolomics. In addition, a new personalized approach to assessing the cumulative risk of developing VPA-induced adverse reactions is presented and ways for their correction are proposed depending on the patient’s pharmacogenetic profile and the level of therapeutic and toxic VPA metabolites in the human body fluids (blood, urine)

Therapeutic and Toxic Effects of Valproic Acid Metabolites

Valproic acid (VPA) and its salts are psychotropic drugs that are widely used in neurological diseases (epilepsy, neuropathic pain, migraine, etc.) and psychiatric disorders (schizophrenia, bipolar affective disorder, addiction diseases, etc.). In addition, the indications for the appointment of valproate have been expanding in recent years in connection with the study of new mechanisms of action of therapeutic and toxic metabolites of VPA in the human body. Thus, VPA is considered a component of disease-modifying therapy for multiple tumors, neurodegenerative diseases (Huntington’s disease, Parkinson’s disease, Duchenne progressive dystrophy, etc.), and human immunodeficiency syndrome. The metabolism of VPA is complex and continues to be studied. Known pathways of VPA metabolism include: β-oxidation in the tricarboxylic acid cycle (acetylation); oxidation with the participation of cytochrome P-450 isoenzymes (P-oxidation); and glucuronidation. The complex metabolism of VPA explains the diversity of its active and inactive metabolites, which have therapeutic, neutral, or toxic effects. It is known that some active metabolites of VPA may have a stronger clinical effect than VPA itself. These reasons explain the relevance of this narrative review, which summarizes the results of studies of blood (serum, plasma) and urinary metabolites of VPA from the standpoint of the pharmacogenomics and pharmacometabolomics. In addition, a new personalized approach to assessing the cumulative risk of developing VPA-induced adverse reactions is presented and ways for their correction are proposed depending on the patient’s pharmacogenetic profile and the level of therapeutic and toxic VPA metabolites in the human body fluids (blood, urine)

Association of the ACTN3 Gene’s Single-Nucleotide Variant Rs1815739 (R577X) with Sports Qualification and Competitive Distance in Caucasian Athletes of the Southern Urals

An elite athlete’s status is associated with a multifactorial phenotype depending on many environmental and genetic factors. Of course, the peculiarities of the structure and function of skeletal muscles are among the most important characteristics in the context of athletic performance. Purpose: To study the associations of SNV rs1815739 (C577T or R577X) allelic variants and genotypes of the ACTN3 gene with qualification and competitive distance in Caucasian athletes of the Southern Urals. Methods: A total of 126 people of European origin who lived in the Southern Urals region took part in this study. The first group included 76 cyclical sports athletes (speed skating, running disciplines in track-and-field): SD (short distances) subgroup—40 sprinters (mean 22.1 ± 2.4 y.o.); LD (long distances) subgroup—36 stayer athletes (mean 22.6 ± 2.7 y.o.). The control group consisted of 50 healthy nonathletes (mean 21.4 ± 2.7 y.o.). We used the Step One Real-Time PCR System (Applied Biosystems, USA) device for real-time polymerase chain reaction. Results: The frequency of the major allele R was significantly higher in the SD subgroup compared to the control subgroup (80% vs. 64%; p-value = 0.04). However, we did not find any significant differences in the frequency of the R allele between the athletes of the SD subgroup and the LD subgroup (80% vs. 59.7%, respectively; p-value > 0.05). The frequency of the X allele was lower in the SD subgroup compared to the LD subgroup (20% vs. 40.3%; p-value = 0.03). The frequency of homozygous genotype RR was higher in the SD subgroup compared to the control group (60.0% vs. 34%; p-value = 0.04). The R allele was associated with competitive distance in the SD group athletes compared to those of the control group (OR = 2.45 (95% CI: 1.02–5.87)). The X allele was associated with competitive distance in the LD subgroup compared to the SD subgroup (OR = 2.7 (95% CI: 1.09–6.68)). Conclusions: Multiplicative and additive inheritance models demonstrated that high athletic performance for sprinters was associated with the homozygous dominant genotype 577RR in cyclical sports athletes of Caucasian origin in the Southern Urals

High-Tech Methods of Cytokine Imbalance Correction in Intervertebral Disc Degeneration

An important mechanism for the development of intervertebral disc degeneration (IDD) is an imbalance between anti-inflammatory and pro-inflammatory cytokines. Therapeutic and non-therapeutic approaches for cytokine imbalance correction in IDD either do not give the expected result, or give a short period of time. This explains the relevance of high-tech medical care, which is part of specialized care and includes the use of new resource-intensive methods of treatment with proven effectiveness. The aim of the review is to update knowledge about new high-tech methods based on cytokine imbalance correction in IDD. It demonstrates promise of new approaches to IDD management in patients resistant to previously used therapies, including: cell therapy (stem cell implantation, implantation of autologous cultured cells, and tissue engineering); genetic technologies (gene modifications, microRNA, and molecular inducers of IDD); technologies for influencing the inflammatory cascade in intervertebral discs mediated by abnormal activation of inflammasomes; senolytics; exosomal therapy; and other factors (hypoxia-induced factors; lysyl oxidase; corticostatin; etc.)