Relationship between thyroid-stimulating hormone, BDNF levels, and hippocampal volume in antipsychotic-naïve first-episode psychosis patients

IntroductionThyroid hormones play an essential role in hippocampal development, a key structure in psychosis. However, the role of these hormones in first-episode psychosis (FEP) has received limited attention. It has been hypothesized that thyroid hormones could cause morphological modifications in the hippocampal structure through the upregulation of brain-derived neurotrophic factor (BDNF). In this study, we primarily aimed to determine the relationship between thyroid-stimulating hormone (TSH) levels, peripheral BDNF levels, and hippocampal volume in antipsychotic-naïve FEP patients. We also aimed to determine whether TSH levels were associated with clinical symptomatology.Materials and methodsA total of 50 antipsychotic-naïve FEP patients were included in the study. At baseline, we collected fasting blood samples and registered sociodemographic and clinical variables (substance use, DUP, PANSS, GAF, and CDSS). Structural T1 MRI was performed at baseline to quantify brain volumes. No control group was used for this study.ResultsOf the 50 patients, more than one-third (36%) presented alterations in TSH levels, mainly elevated levels (32% of patients). The TSH levels were inversely correlated with both peripheral BDNF and hippocampal volume. On the multivariate analysis, the model that best predicted the relative hippocampal volume was a single variable model (TSH levels). No significant association was observed between TSH levels and clinical symptomatology.DiscussionThese results suggest that thyroid hormones could have a neuroprotective effect on the hippocampus in FEP patients, possibly through their effect by increasing BDNF concentrations, which could attenuate brain injury and neuroinflammation. Nevertheless, thyroid hormones could also affect hippocampal volume through other pathways

Determinants of mechanical restraint in an acute psychiatric care unit

Background: Despite numerous attempts to reduce the use of mechanical restraint (MR), this technique continues to be widely applied in many acute psychiatric care settings. In order to reduce MR, a better understanding of the variables associated with its use and duration in different clinical environments is essential. Aim: To determine the proportion of patients subjected to MR and the duration thereof in two acute care psychiatric units; and to identify the variables associated with the use and duration of MR. Methods: Descriptive study of all patients admitted to the acute psychiatric units at the Parc de Salut Mar (Barcelona, Spain) in the year 2018. The number and percentage of patients subjected to MR, as well as the duration of each episode were assessed. The following data were also registered: sociodemographic characteristics, psychiatric diagnosis, and presence of cultural and/or language barriers. Multivariate analyses were performed to assess determinants of MR and its duration. Results: Of the 464 patients, 119 (25.6%) required MR, with a median of 16.4 h per MR. Two factors - a diagnosis of psychotic disorder [Odds ratios (OR) = 0.22; 95%CI: 0.06-0.62; P = 0.005] and the presence of a language barrier (OR = 2.13; 95%CI: 1.2-3.7; P = 0.007) - were associated with a significantly higher risk of MR. Male sex was associated with a longer duration of MR (B = -19.03; 95%CI: -38.06-0.008; P = 0.05). Conclusion: The presence of a language barrier and a psychotic disorder diagnosis are associated with a significantly higher risk of MR. Furthermore, male sex is associated with a longer duration of MR. Individualized restraint protocols that include the required tools are necessary to ultimately limit the use of mechanical restraint

Multidimensional predictors of negative symptoms in antipsychotic-naive first-episode psychosis.

BACKGROUND: Despite a large body of schizophrenia research, we still have no reliable predictors to guide treatment from illness onset. The present study aimed to identify baseline clinical or neurobiological factors - including peripheral brain-derived neurotrophic factor (BDNF) levels and amygdala or hippocampal relative volumes - that could predict negative symptomatology and persistent negative symptoms in first-episode psychosis after 1 year of follow-up. METHODS: We recruited 50 drug-naive patients with first-episode psychosis and 50 age- and sex-matched healthy controls to study brain volumes. We performed univariate and multiple and logistic regression analyses to determine the association between baseline clinical and neurobiological variables, score on the PANSS negative subscale and persistent negative symptoms after 1 year of follow-up. RESULTS: Low baseline serum BDNF levels (p = 0.011), decreased left amygdala relative volume (p = 0.001) and more severe negative symptomatology (p = 0.021) predicted the severity of negative symptoms at 1 year, as measured by the PANSS negative subscale. Low baseline serum BDNF levels (p = 0.012) and decreased left amygdala relative volume (p = 0.010) predicted persistent negative symptoms at 1 year. LIMITATIONS: We were unable to assess negative symptoms and their dimensions with next-generation scales, which were not available when the study was initiated. CONCLUSION: This study shows that a set of variables at baseline, including low BDNF levels, smaller left amygdala relative volume and score on the PANSS negative subscale are significant predictors of outcomes in first-episode psychosis. These findings might offer an initial step for tailoring treatments in first-episode psychosis

Inverse association between negative symptoms and body mass index in chronic schizophrenia

BACKGROUND: We investigated whether negative symptoms, such as poor motivation or anhedonia, were associated with higher body mass index (BMI) in stable patients with schizophrenia chronically treated with antipsychotic medication. METHODS: 62 olanzapine- or clozapine-treated patients with illness duration of at least four years were selected from an international multicenter study on the characterization of negative symptoms. All participants completed the Brief Negative Symptom Scale (BNSS) and the Positive and Negative Syndrome Scale (PANSS). Bivariate correlations between BMI and negative symptoms (BNSS) were explored, as well as multiple regression analyses. We further explored the association of two principal component factors of the BNSS and BMI. Subsidiary analyses re-modeled the above using the negative symptoms subscale of the PANSS and the EMSLEY factor for negative symptoms for convergent validity. RESULTS: Lower negative symptoms (BNSS score) were associated with higher BMI (r=-0.31; p=0.015). A multiple regression analysis showed that negative symptoms (BNSS score) and age were significant predictors of BMI (p=0.037). This was mostly driven by the motivation/pleasure factor of the BNSS. Within this second factor, BMI was negatively associated with anhedonia (r=-0.254; p=0.046) and asociality (r=-0.253; p=0.048), but not avolition (r=-0.169; p=0.188). EMSLEY score was positively associated with BNSS (r=0.873, p<0.001), but negatively associated with BMI (r=-0.308; p=0.015). The association between PANSS and BMI did not reach significance (r=-224, p=0.080). CONCLUSIONS: We conclude that lower negative symptoms were associated with higher BMI (assessed using both the BNSS and EMSLEY) in chronic stable schizophrenia patients, mostly due to lower anhedonia and asociality levels

Relaxed Purifying Selection and Possibly High Rate of Adaptation in Primate Lineage-Specific Genes

Genes in the same organism vary in the time since their evolutionary origin. Without horizontal gene transfer, young genes are necessarily restricted to a few closely related species, whereas old genes can be broadly distributed across the phylogeny. It has been shown that young genes evolve faster than old genes; however, the evolutionary forces responsible for this pattern remain obscure. Here, we classify human–chimp protein-coding genes into different age classes, according to the breath of their phylogenetic distribution. We estimate the strength of purifying selection and the rate of adaptive selection for genes in different age classes. We find that older genes carry fewer and less frequent nonsynonymous single-nucleotide polymorphisms than younger genes suggesting that older genes experience a stronger purifying selection at the protein-coding level. We infer the distribution of fitness effects of new deleterious mutations and find that older genes have proportionally more slightly deleterious mutations and fewer nearly neutral mutations than younger genes. To investigate the role of adaptive selection of genes in different age classes, we determine the selection coefficient (γ = 2Nes) of genes using the MKPRF approach and estimate the ratio of the rate of adaptive nonsynonymous substitution to synonymous substitution (ωA) using the DoFE method. Although the proportion of positively selected genes (γ > 0) is significantly higher in younger genes, we find no correlation between ωA and gene age. Collectively, these results provide strong evidence that younger genes are subject to weaker purifying selection and more tenuous evidence that they also undergo adaptive evolution more frequently

Link between cognitive polygenic risk scores and clinical progression after a first-psychotic episode

Background Clinical intervention in early stages of psychotic disorders is crucial for the prevention of severe symptomatology trajectories and poor outcomes. Genetic variability is studied as a promising modulator of prognosis, thus novel approaches considering the polygenic nature of these complex phenotypes are required to unravel the mechanisms underlying the early progression of the disorder. Methods The sample comprised of 233 first-episode psychosis (FEP) subjects with clinical and cognitive data assessed periodically for a 2-year period and 150 matched controls. Polygenic risk scores (PRSs) for schizophrenia, bipolar disorder, depression, education attainment and cognitive performance were used to assess the genetic risk of FEP and to characterize their association with premorbid, baseline and progression of clinical and cognitive status. Results Schizophrenia, bipolar disorder and cognitive performance PRSs were associated with an increased risk of FEP [false discovery rate (FDR) ⩽ 0.027]. In FEP patients, increased cognitive PRSs were found for FEP patients with more cognitive reserve (FDR ⩽ 0.037). PRSs reflecting a genetic liability for improved cognition were associated with a better course of symptoms, functionality and working memory (FDR ⩽ 0.039). Moreover, the PRS of depression was associated with a worse trajectory of the executive function and the general cognitive status (FDR ⩽ 0.001). Conclusions Our study provides novel evidence of the polygenic bases of psychosis and its clinical manifestation in its first stage. The consistent effect of cognitive PRSs on the early clinical progression suggests that the mechanisms underlying the psychotic episode and its severity could be partially independent

Importance of immunometabolic markers for the classification of patients with major depressive disorder using machine learning

Background: Although there is scientific evidence of the presence of immunometabolic alterations in major depression, not all patients present them. Recent studies point to the association between an inflammatory phenotype and certain clinical symptoms in patients with depression. The objective of our study was to classify major depression disorder patients using supervised learning algo-rithms or machine learning, based on immunometabolic and oxidative stress biomarkers and lifestyle habits.Methods: Taking into account a series of inflammatory and oxidative stress biomarkers (C-reactive protein (CRP), tumor necrosis factor (TNF), 4-hydroxynonenal (HNE) and glutathione), metabolic risk markers (blood pressure, waist circumference and glucose, triglyceride and cholesterol levels) and lifestyle habits of the participants (physical activity, smoking and alcohol consumption), a study was carried out using machine learning in a sample of 171 participants, 91 patients with depression (71.42% women, mean age = 50.64) and 80 healthy subjects (67.50% women, mean age = 49.12).The algorithm used was the support vector machine, performing cross validation, by which the subdivision of the sample in training (70%) and test (30%) was carried out in order to estimate the precision of the model. The prediction of belonging to the patient group (MDD patients versus control subjects), melancholic type (melancholic versus non-melancholic patients) or resistant depression group (treatment-resistant versus non -treatment-resistant) was based on the importance of each of the immunometabolic and lifestyle variables.Results: With the application of the algorithm, controls versus patients, such as patients with melancholic symptoms versus non-melancholic symptoms, and resistant versus non-resistant symptoms in the test phase were optimally classified.The variables that showed greater importance, according to the results of the area under the ROC curve, for the discrimination between healthy subjects and patients with depression were current alcohol consumption (AUC = 0.62), TNF-alpha levels (AUC = 0.61), glutathione redox status (AUC = 0.60) and the performance of both moderate (AUC = 0.59) and vigorous physical exercise (AUC = 0.58). On the other hand, the most important variables for classifying melancholic patients in relation to lifestyle habits were past (AUC = 0.65) and current (AUC = 0.60) tobacco habit, as well as walking routinely (AUC = 0.59) and in relation to immunometabolic markers were the levels of CRP (AUC = 0.62) and glucose (AUC = 0.58).In the analysis of the importance of the variables for the classification of treatment-resistant patients versus non-resistant patients, the systolic blood pressure (SBP) variable was shown to be the most relevant (AUC = 0.67). Other immunometabolic variables were also among the most important such as TNF-alpha (AUC = 0.65) and waist circumference (AUC = 0.64). In this case, sex (AUC = 0.59) was also relevant along with alcohol (AUC = 0.58) and tobacco (AUC = 0.56) consumption.Conclusions: The results obtained in our study show that it is possible to predict the diagnosis of depression and its clinical typology from immunometabolic markers and lifestyle habits, using machine learning techniques. The use of this type of methodology could facilitate the identification of patients at risk of presenting depression and could be very useful for managing clinical heterogeneity

Relationship between immunometabolic status and cognitive performance among major depression disorder patients

Background: Alterations in cognitive performance have been described in patients with major depressive disorder (MDD). However, the specific risk factors of these changes are not yet known. This study aimed to explore whether inmunometabolic parameters are related to cognitive performance in MDD in comparison to healthy controls (HC) METHODS: Sample consisted of 84 MDD patients and 78 HC. Both groups were compared on the results of cognitive performance measured with the Cambridge Neuropsychological Test Automated Battery (CANTAB), the presence of metabolic syndrome (MetS) and an inflammatory/oxidative index calculated by a principal component analysis of peripheral biomarkers (tumor necrosis factor, C-reactive protein and 4-hydroxynonenal). A multiple linear regression was carried out, to study the relationship between inmunometabolic variables and the global cognitive performance, being the latter the dependent variable. Results: Significant differences were obtained in the inflammatory/oxidative index between both groups (F(1157)= 12.93; p < .001), also in cognitive performance (F(1157)= 56.75; p < .001). The inmunometabolic covariate regression model (i.e., condition (HC/MDD), sex, age and medication loading, MetS, inflammatory/oxidative index and the interaction between MetS and inflammatory/oxidative index) was statistically significant (F(7157)= 11.24; p < .01) and explained 31% of variance. The condition, being either MDD or HD, (B=-0.97; p < .001), age (B=-0.28; p < .001) and the interaction between inflammatory/oxidative index and MetS (B=-0.38; p = .02) were factors associated to cognitive performance. Limitations: Sample size was relatively small. The cross-sectional design of the study limits the possibilities of analysis. Conclusions: Our results provide evidence on the conjoint influence of metabolic and inflammatory dysregulation on cognitive dysfunction in MDD patients. In this way, our study opens a line of research in immunometabolic agents to deal with cognitive decline associated with MDD

Corrigendum : Influence of clinical and neurocognitive factors in psychosocial functioning after a first episode non-affective psychosis: differences between males and females

Deficits in psychosocial functioning are present in the early stages of psychosis. Several factors, such as premorbid adjustment, neurocognitive performance, and cognitive reserve (CR), potentially influence functionality. Sex differences are observed in individuals with psychosis in multiple domains. Nonetheless, few studies have explored the predictive factors of poor functioning according to sex in first-episode psychosis (FEP). This study aimed to explore sex differences, examine changes, and identify predictors of functioning according to sex after onset. The initial sample comprised 588 individuals. However, only adults with non-affective FEP (n = 247, 161 males and 86 females) and healthy controls (n = 224, 142 males and 82 females) were included. A comprehensive assessment including functional, neuropsychological, and clinical scales was performed at baseline and at 2-year follow-up. A linear regression model was used to determine the predictors of functioning at 2-year follow-up. FEP improved their functionality at follow-up (67.4% of both males and females). In males, longer duration of untreated psychosis (β = 0.328, p = 0.003) and worse premorbid adjustment (β = 0.256, p = 0.023) were associated with impaired functioning at 2-year follow-up, while in females processing speed (β = 0.403, p = 0.003), executive function (β = 0.299, p = 0.020) and CR (β = −0.307, p = 0.012) were significantly associated with functioning. Our data indicate that predictors of functioning at 2-year follow-up in the FEP group differ according to sex. Therefore, treatment and preventative efforts may be adjusted taking sex into account. Males may benefit from functional remediation at early stages. Conversely, in females, early interventions centered on CR enhancement and cognitive rehabilitation may be recommended