Association of the brain-derived neurotrophic factor Val66Met polymorphism with negative symptoms severity, but not cognitive function, in first-episode schizophrenia spectrum disorders

2016 ◽  
Vol 38 ◽  
pp. 61-69 ◽  
Author(s):  
G. Mezquida ◽  
R. Penadés ◽  
B. Cabrera ◽  
G. Savulich ◽  
A. Lobo ◽  
...  
Keyword(s):  
Cognitive Function ◽  
Symptom Severity ◽  
Negative Symptoms ◽  
First Episode ◽  
Schizophrenia Spectrum Disorder ◽  
Val66met Polymorphism ◽  
Schizophrenia Spectrum ◽  
Spectrum Disorders ◽  
First Episode Schizophrenia ◽  
The Brain

AbstractObjectiveA functional polymorphism of the brain-derived neurotrophic factor gene (BDNF) Val66Met has been associated with cognitive function and symptom severity in patients with schizophrenia. It has been suggested that the Val66Met polymorphism has a role as a modulator in a range of clinical features of the illness, including symptoms severity, therapeutic responsiveness, age of onset, brain morphology and cognitive function. However, little work has been done in first-episode schizophrenia (FES) spectrum disorders. The objective of this study is to investigate the association of the BDNF Val66Met polymorphism on cognitive function and clinical symptomatology in FES patients.MethodsUsing a cross-sectional design in a cohort of 204 patients with FES or a schizophrenia spectrum disorder and 204 healthy matched controls, we performed BDNF Val66Met genotyping and tested its relationship with cognitive testing (attention, working memory, learning/verbal memory and reasoning/problem-solving) and assessment of clinical symptom severity.ResultsThere was no significant influence of the BDNF allele frequency on cognitive factor scores in either patients or controls. An augmented severity of negative symptoms was found in FES patients that carried the Met allele.ConclusionsThe results of this study suggest that in patients with a first-episode of schizophrenia or a schizophrenia spectrum disorder, the BDNF Val66Met polymorphism does not exert an influence on cognitive functioning, but is associated with negative symptoms severity. BDNF may serve as suitable marker of negative symptomatology severity in FES patients within the schizophrenia spectrum.

scholarly journals Predictors of Remission and Recovery in a First-Episode Schizophrenia Spectrum Disorder Sample: 2-Year Follow-up of the OPUS Trial

2008 ◽  
Vol 53 (10) ◽  
pp. 660-670 ◽  
Author(s):  
Lone Petersen ◽  
Anne Thorup ◽  
Johan Øqhlenschlæger ◽  
Torben Øqstergaard Christensen ◽  
Pia Jeppesen ◽  
...  
Keyword(s):  
Substance Abuse ◽  
Negative Symptoms ◽  
Spectrum Disorder ◽  
First Episode ◽  
Schizophrenia Spectrum Disorder ◽  
Adherence To Medication ◽  
Premorbid Adjustment ◽  
Schizophrenia Spectrum ◽  
First Episode Schizophrenia

Objective: To examine the frequency and predictors of good outcome for patients with first-episode schizophrenia spectrum disorder (SSD). Method: We conducted a 2-year follow-up of a cohort of patients ( n = 547) with first-episode SSD. We evaluated the patients on demographic variables, diagnosis, duration of untreated psychosis (DUP), premorbid functioning, psychotic and negative symptoms, substance abuse, adherence to medication, and service use. ORs were calculated with logistic regression analyses. Results: A total of 369 patients (67%) participated in the follow-up interview. After 2 years, 36% remitted and 17% were considered fully recovered. Full recovery was associated with shorter DUP, better premorbid adjustment, fewer negative symptoms at baseline, no substance abuse at baseline, and adherence to medication and OPUS treatment. Conclusions: Several predictive factors were identified, and focus should be on potentially malleable predictors of outcome, for example, reducing DUP and paying special attention to patients who are unlikely to achieve good outcome, for example, patients with a substance abuse problem and poor premorbid adjustment.

scholarly journals Latent Iron Deficiency as a Marker of Negative Symptoms in Patients with First-Episode Schizophrenia Spectrum Disorder

Nutrients ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1707 ◽  
Author(s):  
Sung-Wan Kim ◽  
Robert Stewart ◽  
Woo-Young Park ◽  
Min Jhon ◽  
Ju-Yeon Lee ◽  
...  
Keyword(s):  
Iron Deficiency ◽  
Negative Symptoms ◽  
Early Stage ◽  
Spectrum Disorder ◽  
First Episode ◽  
Duration Of Treatment ◽  
Schizophrenia Spectrum Disorder ◽  
Dsm 5 ◽  
Schizophrenia Spectrum ◽  
First Episode Schizophrenia

Iron deficiency may alter dopaminergic transmission in the brain. This study investigated whether iron metabolism is associated with negative symptoms in patients with first-episode psychosis. The study enrolled 121 patients with first-episode schizophrenia spectrum disorder, whose duration of treatment was 2 months or less. Negative symptoms were measured using the Positive and Negative Syndrome Scale (PANSS) and Clinician-Rated Dimensions of Psychosis Symptom Severity (Dimensional) scale of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). Prominent negative symptoms were defined as moderate or severe negative symptoms on the Dimensional scale of the DSM-5. Iron deficiency was defined as a serum ferritin ≤ 20 ng/mL. Patients with iron deficiency were significantly more likely to have prominent negative symptoms (45.2 vs. 22.2%; p = 0.014) and a higher PANSS negative symptoms score (p = 0.046) than those with normal ferritin levels. Patients with prominent negative symptoms had significantly lower ferritin levels (p = 0.025). The significance of these results remained after controlling for the duration of illness and other confounding variables. Our finding of an independent association between iron deficiency and negative symptoms in patients at the very early stage of illness implies that iron dysregulation has an effect on negative symptoms in patients with schizophrenia. The possibility of therapeutic intervention with iron should be further investigated.

Ten-year trajectory and outcomes of negative symptoms of patients with first-episode schizophrenia spectrum disorders

2020 ◽  
Vol 220 ◽  
pp. 85-91
Author(s):  
Sherry Kit Wa Chan ◽  
Hei Yan Veronica Chan ◽  
Herbert H. Pang ◽  
Christy Lai Ming Hui ◽  
Yi Nam Suen ◽  
...  
Keyword(s):  
Negative Symptoms ◽  
First Episode ◽  
Schizophrenia Spectrum Disorders ◽  
Schizophrenia Spectrum ◽  
Spectrum Disorders ◽  
First Episode Schizophrenia

scholarly journals Acoustic speech markers for schizophrenia-spectrum disorders: a diagnostic and symptom-recognition tool

2021 ◽  
pp. 1-11
Author(s):  
J. N. de Boer ◽  
A. E. Voppel ◽  
S. G. Brederoo ◽  
H. G. Schnack ◽  
K. P. Truong ◽  
...  
Keyword(s):  
Negative Symptoms ◽  
Structured Interview ◽  
Diagnostic Value ◽  
Spectrum Disorder ◽  
Psychotic Symptoms ◽  
Schizophrenia Spectrum Disorders ◽  
Schizophrenia Spectrum Disorder ◽  
Speech Technology ◽  
Schizophrenia Spectrum ◽  
Spectrum Disorders

Abstract Background Clinicians routinely use impressions of speech as an element of mental status examination. In schizophrenia-spectrum disorders, descriptions of speech are used to assess the severity of psychotic symptoms. In the current study, we assessed the diagnostic value of acoustic speech parameters in schizophrenia-spectrum disorders, as well as its value in recognizing positive and negative symptoms. Methods Speech was obtained from 142 patients with a schizophrenia-spectrum disorder and 142 matched controls during a semi-structured interview on neutral topics. Patients were categorized as having predominantly positive or negative symptoms using the Positive and Negative Syndrome Scale (PANSS). Acoustic parameters were extracted with OpenSMILE, employing the extended Geneva Acoustic Minimalistic Parameter Set, which includes standardized analyses of pitch (F0), speech quality and pauses. Speech parameters were fed into a random forest algorithm with leave-ten-out cross-validation to assess their value for a schizophrenia-spectrum diagnosis, and PANSS subtype recognition. Results The machine-learning speech classifier attained an accuracy of 86.2% in classifying patients with a schizophrenia-spectrum disorder and controls on speech parameters alone. Patients with predominantly positive v. negative symptoms could be classified with an accuracy of 74.2%. Conclusions Our results show that automatically extracted speech parameters can be used to accurately classify patients with a schizophrenia-spectrum disorder and healthy controls, as well as differentiate between patients with predominantly positive v. negatives symptoms. Thus, the field of speech technology has provided a standardized, powerful tool that has high potential for clinical applications in diagnosis and differentiation, given its ease of comparison and replication across samples.

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