Identifying clinical clusters with distinct trajectories in first-episode psychosis through an unsupervised machine learning technique

The extreme variability in symptom presentation reveals that individuals diagnosed with a first-episode psychosis (FEP) may encompass different sub-populations with potentially different illness courses and, hence, different treatment needs. Previous studies have shown that sociodemographic and family environment factors are associated with more unfavorable symptom trajectories. The aim of this study was to examine the dimensional structure of symptoms and to identify individuals’ trajectories at early stage of illness and potential risk factors associated with poor outcomes at follow-up in non-affective FEP. One hundred and forty-four non-affective FEP patients were assessed at baseline and at 2-year follow-up. A Principal component analysis has been conducted to identify dimensions, then an unsupervised machine learning technique (fuzzy clustering) was performed to identify clinical subgroups of patients. Six symptom factors were extracted (positive, negative, depressive, anxiety, disorganization and somatic/cognitive). Three distinct clinical clusters were determined at baseline: mild; negative and moderate; and positive and severe symptoms, and five at follow-up: minimal; mild; moderate; negative and depressive; and severe symptoms. Receiving a low-dose antipsychotic, having a more severe depressive symptomatology and a positive family history for psychiatric disorders were risk factors for poor recovery, whilst having a high cognitive reserve and better premorbid adjustment may confer a better prognosis. The current study provided a better understanding of the heterogeneous profile of FEP. Early identification of patients who could likely present poor outcomes may be an initial step for the development of targeted interventions to improve illness trajectories and preserve psychosocial functioning

Cognitive clusters in first-episode psychosis

Impairments in a broad range of cognitive domains have been consistently reported in some individuals with first-episode psychosis (FEP). Cognitive deficits can be observed during the prodromal stage. However, the course of cognitive deficits is still unclear. The aim of this study was to identify cognitive subgroups over time and to compare their sociodemographic, clinical and functional profiles. A total of 114 patients with Schizophrenia Spectrum Disorders were included in the present study. We assessed subjects through psychiatric scales and eight neuropsychological tests at baseline and at two-year follow-up visit. We performed the Partition Around Medoids algorithm with all cognitive variables. Furthermore, we performed a logistic regression to identify the predictors related to the different cognitive clusters at follow-up. Two distinct subgroups were found: the first cluster characterized by cognitive impairment and a second cluster had relatively intact cognition in comparison with norms. Up to 54.7% of patients with cognitive deficits at baseline tended to improve during the first two years of treatment. Patients with intact cognition at follow-up had a higher socioeconomic status, later age of onset, lower negative symptoms and a higher cognitive reserve (CR) at baseline. CR and age of onset were the baseline variables that predicted cognitive impairment. This research allows us to obtain a better understanding of the heterogeneous profile of psychotic disorders. Identifying the characteristics of patients who will present a cognitive impairment could improve early detection and intervention. These results suggest that enhancing CR could contribute to improving the course of the illness. © 2021 Elsevier B.V

Non-motor symptom burden in patients with Parkinson's disease with impulse control disorders and compulsive behaviours : results from the COPPADIS cohort

The study was aimed at analysing the frequency of impulse control disorders (ICDs) and compulsive behaviours (CBs) in patients with Parkinson's disease (PD) and in control subjects (CS) as well as the relationship between ICDs/CBs and motor, nonmotor features and dopaminergic treatment in PD patients. Data came from COPPADIS-2015, an observational, descriptive, nationwide (Spain) study. We used the validated Questionnaire for Impulsive-Compulsive Disorders in Parkinson's Disease-Rating Scale (QUIP-RS) for ICD/CB screening. The association between demographic data and ICDs/CBs was analyzed in both groups. In PD, this relationship was evaluated using clinical features and treatment-related data. As result, 613 PD patients (mean age 62.47 ± 9.09 years, 59.87% men) and 179 CS (mean age 60.84 ± 8.33 years, 47.48% men) were included. ICDs and CBs were more frequent in PD (ICDs 12.7% vs. 1.6%, p < 0.001; CBs 7.18% vs. 1.67%, p = 0.01). PD patients had more frequent previous ICDs history, premorbid impulsive personality and antidepressant treatment (p < 0.05) compared with CS. In PD, patients with ICDs/CBs presented younger age at disease onset, more frequent history of previous ICDs and premorbid personality (p < 0.05), as well as higher comorbidity with nonmotor symptoms, including depression and poor quality of life. Treatment with dopamine agonists increased the risk of ICDs/CBs, being dose dependent (p < 0.05). As conclusions, ICDs and CBs were more frequent in patients with PD than in CS. More nonmotor symptoms were present in patients with PD who had ICDs/CBs compared with those without. Dopamine agonists have a prominent effect on ICDs/CBs, which could be influenced by dose

Phylogenomic analysis of a 55.1 kb 19-gene dataset resolves a monophyletic Fusarium that includes the Fusarium solani Species Complex

Scientific communication is facilitated by a data-driven, scientifically sound taxonomy that considers the end-user¿s needs and established successful practice. In 2013, the Fusarium community voiced near unanimous support for a concept of Fusarium that represented a clade comprising all agriculturally and clinically important Fusarium species, including the F. solani species complex (FSSC). Subsequently, this concept was challenged in 2015 by one research group who proposed dividing the genus Fusarium into seven genera, including the FSSC described as members of the genus Neocosmospora, with subsequent justification in 2018 based on claims that the 2013 concept of Fusarium is polyphyletic. Here, we test this claim and provide a phylogeny based on exonic nucleotide sequences of 19 orthologous protein-coding genes that strongly support the monophyly of Fusarium including the FSSC. We reassert the practical and scientific argument in support of a genus Fusarium that includes the FSSC and several other basal lineages, consistent with the longstanding use of this name among plant pathologists, medical mycologists, quarantine officials, regulatory agencies, students, and researchers with a stake in its taxonomy. In recognition of this monophyly, 40 species described as genus Neocosmospora were recombined in genus Fusarium, and nine others were renamed Fusarium. Here the global Fusarium community voices strong support for the inclusion of the FSSC in Fusarium, as it remains the best scientific, nomenclatural, and practical taxonomic option availabl

CIBERER : Spanish national network for research on rare diseases: A highly productive collaborative initiative

Altres ajuts: Instituto de Salud Carlos III (ISCIII); Ministerio de Ciencia e Innovación.CIBER (Center for Biomedical Network Research; Centro de Investigación Biomédica En Red) is a public national consortium created in 2006 under the umbrella of the Spanish National Institute of Health Carlos III (ISCIII). This innovative research structure comprises 11 different specific areas dedicated to the main public health priorities in the National Health System. CIBERER, the thematic area of CIBER focused on rare diseases (RDs) currently consists of 75 research groups belonging to universities, research centers, and hospitals of the entire country. CIBERER's mission is to be a center prioritizing and favoring collaboration and cooperation between biomedical and clinical research groups, with special emphasis on the aspects of genetic, molecular, biochemical, and cellular research of RDs. This research is the basis for providing new tools for the diagnosis and therapy of low-prevalence diseases, in line with the International Rare Diseases Research Consortium (IRDiRC) objectives, thus favoring translational research between the scientific environment of the laboratory and the clinical setting of health centers. In this article, we intend to review CIBERER's 15-year journey and summarize the main results obtained in terms of internationalization, scientific production, contributions toward the discovery of new therapies and novel genes associated to diseases, cooperation with patients' associations and many other topics related to RD research

Identifying clinical clusters with distinct trajectories in first-episode psychosis through an unsupervised machine learning technique

Altres ajuts: Ministerio de Ciencia, Innovación y Universidades; Fondo Europeo de Desarrollo Regional (FEDER); CIBER of Mental Health (CIBERSAM); CERCA Programme (Generalitat de Catalunya); Ministerio de Economía y Competitividad; Fundació La Caixa (ID 100010434, under the agreement LCF/PR/GN18/50310006).The extreme variability in symptom presentation reveals that individuals diagnosed with a first-episode psychosis (FEP) may encompass different sub-populations with potentially different illness courses and, hence, different treatment needs. Previous studies have shown that sociodemographic and family environment factors are associated with more unfavorable symptom trajectories. The aim of this study was to examine the dimensional structure of symptoms and to identify individuals' trajectories at early stage of illness and potential risk factors associated with poor outcomes at follow-up in non-affective FEP. One hundred and forty-four non-affective FEP patients were assessed at baseline and at 2-year follow-up. A Principal component analysis has been conducted to identify dimensions, then an unsupervised machine learning technique (fuzzy clustering) was performed to identify clinical subgroups of patients. Six symptom factors were extracted (positive, negative, depressive, anxiety, disorganization and somatic/cognitive). Three distinct clinical clusters were determined at baseline: mild; negative and moderate; and positive and severe symptoms, and five at follow-up: minimal; mild; moderate; negative and depressive; and severe symptoms. Receiving a low-dose antipsychotic, having a more severe depressive symptomatology and a positive family history for psychiatric disorders were risk factors for poor recovery, whilst having a high cognitive reserve and better premorbid adjustment may confer a better prognosis. The current study provided a better understanding of the heterogeneous profile of FEP. Early identification of patients who could likely present poor outcomes may be an initial step for the development of targeted interventions to improve illness trajectories and preserve psychosocial functioning

Cognitive clusters in first-episode psychosis

Impairments in a broad range of cognitive domains have been consistently reported in some individuals with first-episode psychosis (FEP). Cognitive deficits can be observed during the prodromal stage. However, the course of cognitive deficits is still unclear. The aim of this study was to identify cognitive subgroups over time and to compare their sociodemographic, clinical and functional profiles. A total of 114 patients with Schizophrenia Spectrum Disorders were included in the present study. We assessed subjects through psychiatric scales and eight neuropsychological tests at baseline and at two-year follow-up visit. We performed the Partition Around Medoids algorithm with all cognitive variables. Furthermore, we performed a logistic regression to identify the predictors related to the different cognitive clusters at follow-up. Two distinct subgroups were found: the first cluster characterized by cognitive impairment and a second cluster had relatively intact cognition in comparison with norms. Up to 54.7% of patients with cognitive deficits at baseline tended to improve during the first two years of treatment. Patients with intact cognition at follow-up had a higher socioeconomic status, later age of onset, lower negative symptoms and a higher cognitive reserve (CR) at baseline. CR and age of onset were the baseline variables that predicted cognitive impairment. This research allows us to obtain a better understanding of the heterogeneous profile of psychotic disorders. Identifying the characteristics of patients who will present a cognitive impairment could improve early detection and intervention. These results suggest that enhancing CR could contribute to improving the course of the illness