Intentos de autolisis en pacientes bipolares I y II

El trastorno bipolar II, que se caracteriza por episodios depresivos graves y períodos de tiipomanía moderados que se suceden de forma espontánea, parece ser algo más que una forma leve de psicosis maníaco-depresiva. Los estudios anteriores indican que es una categoría de diagnóstico válida, diferente del transtorno bipolar I en aspectos genéticos, biológicos, clínicos y farmacológicos (Cassano et al., 1992; Menchón et al., 1993). Por estas razones, se ha distinguido como una entidad separada en el DSM-IV. No obstante, entre los diversos estudios que tratan aspectos importantes del trastorno bipolar II -como la edad de aparición, la frecuencia de los episodios y el comportamiento suicida- aparecen algunas diferencias. Ei riesgo de suicidio inherente a ambos subtipos es un tema especialmente controvertido (Lester, 1993). Algunos estudios atribuyen índices de intentos de suicidio al trastorno bipolar II. (Dunner et al., 1976; Stallone et al., 1980), pero otros no (Cassano et al., 1992; Coryell et al., 1987; Perugi et al., 1988). Además en diversos estudios sobre suicidio consumado se ha observado que los pacientes con trastorno bipolar II están sobrerrepresentados en el grupo de víctimas suicidas consecutivas (Rihmer et al., 1995). El objetivo de este estudio ha sido el de comprobar las características clínicas de los pacientes bipolares en comparación con el tipo bipolar I clásico (especialmente en relación a la conducta suicida)

Estimación de la saturación arterial de oxígeno en función de la altitud

Fundamento y objetivosLa saturación arterial de oxígeno (SAO) es capaz de predecir el desarrollo de mal de altura. Objetivos: estimar los valores de SAO en función de la altitud y, adicionalmente, diseñar un gráfico para usar sobre el terreno que muestre la saturación esperada para cada altitud y sus límites de normalidad.Pacientes y métodoSe registraron valores de SAO a los participantes de 8 actividades de alta montaña en los Alpes, el Himalaya, el Cáucaso y los Andes. Participaron 53 montañeros; 17 de ellos repitieron en más de una actividad. Se registraron 761 mediciones de SAO.ResultadosSe diseñó un modelo de regresión lineal múltiple para estimar los valores de SAO en función de la altitud, ajustados por distintos posibles factores relacionados. Existe una fuerte relación lineal entre altitud y SAO (R2 = 0, 83, p < 0, 001), dando valores 0, 7 puntos mayores en mujeres. La SAO a una determinada altitud no se relaciona con la edad, el peso, la talla, el tabaquismo, la frecuencia cardíaca ni con la experiencia previa en montaña.El cálculo de la estimación de la SAO responde a la siguiente ecuación: SAO = 103, 3 – (altitud × 0, 0047) + (Z), siendo Z = 0, 7 en hombres y 1, 4 en mujeres.Se ha diseñado una gráfica de coordenadas que relaciona la altitud con los valores estimados de SAO con sus límites de normalidad: percentiles 2, 5 y 97, 5.ConclusionesLa sencillez en el cálculo de la SAO estimada para una determinada altitud mediante la gráfica propuesta ayudará en la toma de decisiones precoces sobre el terreno.Background and objectives Arterial Oxygen Saturation (AOS) predicts altitude sickness. Objectives: To estimate the AOS values with relation to altitude. Furthermore, make a graph to use during activity which assesses the AOS for each altitude and the normal range. Patients and method Values of AOS were assessed during eight high mountain activities in the Alps, Himalaya, Caucasus and Andes; 53 mountaineers participated, 17 of them in more than one activity; 761 measurements of AOS were registered. Results A Logistic Regression Model was made to estimate the AOS values dependent on altitude, adjusted to possible related factors. A strong lineal relationship exists between altitude and AOS (R2 = .83, P < .001);.7 points more in women. The AOS in a particular altitude is not related to age, weight, height, smoking, heart rate, or even with previous experiences in mountains. The calculation of the AOS responds to the follow equation: Blood Oxygen Saturation = 103.3 – (altitude ×.0047) + (Z), being Z = .7 in men and 1.4 in women. A scatter plot was made to relate the estimated altitude with the AOS, with their normal limits values: percentiles 2.5 and 97.5. Conclusions The simple calculation of the AOS estimated for a particular altitude with the proposed graphic can help in the early decision-making onsite

Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach

Themed Issue: Precision Medicine and Personalised Healthcare in PsychiatryBACKGROUND: Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment. AIMS: To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder. METHOD: This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi⁺Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework. RESULTS: The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data. CONCLUSIONS: Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.Micah Cearns, Azmeraw T. Amare, Klaus Oliver Schubert, Anbupalam Thalamuthu, Joseph Frank, Fabian Streit, Mazda Adli, Nirmala Akula, Kazufumi Akiyama, Raffaella Ardau, Bárbara Arias, Jean- Michel Aubry, Lena Backlund, Abesh Kumar Bhattacharjee, Frank Bellivier, Antonio Benabarre, Susanne Bengesser, Joanna M. Biernacka, Armin Birner, Clara Brichant-Petitjean, Pablo Cervantes, Hsi- Chung Chen, Caterina Chillotti, Sven Cichon, Cristiana Cruceanu, Piotr M. Czerski, Nina Dalkner, Alexandre Dayer, Franziska Degenhardt, Maria Del Zompo, J. Raymond DePaulo, Bruno Étain, Peter Falkai, Andreas J. Forstner, Louise Frisen, Mark A. Frye, Janice M. Fullerton, Sébastien Gard, Julie S. Garnham, Fernando S. Goes, Maria Grigoroiu-Serbanescu, Paul Grof, Ryota Hashimoto, Joanna Hauser, Urs Heilbronner, Stefan Herms, Per Hoffmann, Andrea Hofmann, Liping Hou, Yi-Hsiang Hsu, Stephane Jamain, Esther Jiménez, Jean-Pierre Kahn, Layla Kassem, Po-Hsiu Kuo, Tadafumi Kato, John Kelsoe, Sarah Kittel-Schneider, Sebastian Kliwicki, Barbara König, Ichiro Kusumi, Gonzalo Laje, Mikael Landén, Catharina Lavebratt, Marion Leboyer, Susan G. Leckband, Mario Maj, the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Mirko Manchia, Lina Martinsson, Michael J. McCarthy, Susan McElroy, Francesc Colom, Marina Mitjans, Francis M. Mondimore, Palmiero Monteleone, Caroline M. Nievergelt, Markus M. Nöthen, Tomas Novák, Claire O, Donovan, Norio Ozaki, Vincent Millischer, Sergi Papiol, Andrea Pfennig, Claudia Pisanu, James B. Potash, Andreas Reif, Eva Reininghaus, Guy A. Rouleau, Janusz K. Rybakowski, Martin Schalling, Peter R. Schofield, Barbara W. Schweizer, Giovanni Severino, Tatyana Shekhtman, Paul D. Shilling, Katzutaka Shimoda, Christian Simhandl, Claire M. Slaney, Alessio Squassina, Thomas Stamm, Pavla Stopkova, Fasil Tekola- Ayele, Alfonso Tortorella, Gustavo Turecki, Julia Veeh, Eduard Vieta, Stephanie H. Witt, Gloria Roberts, Peter P. Zandi, Martin Alda, Michael Bauer, Francis J. McMahon, Philip B. Mitchell, Thomas G. Schulze, Marcella Rietschel, Scott R. Clark and Bernhard T. Baun

Combining schizophrenia and depression polygenic risk scores improves the genetic prediction of lithium response in bipolar disorder patients

Lithium is the gold standard therapy for Bipolar Disorder (BD) but its effectiveness differs widely between individuals. The molecular mechanisms underlying treatment response heterogeneity are not well understood, and personalized treatment in BD remains elusive. Genetic analyses of the lithium treatment response phenotype may generate novel molecular insights into lithium's therapeutic mechanisms and lead to testable hypotheses to improve BD management and outcomes. We used fixed effect meta-analysis techniques to develop meta-analytic polygenic risk scores (MET-PRS) from combinations of highly correlated psychiatric traits, namely schizophrenia (SCZ), major depression (MD) and bipolar disorder (BD). We compared the effects of cross-disorder MET-PRS and single genetic trait PRS on lithium response. For the PRS analyses, we included clinical data on lithium treatment response and genetic information for n = 2283 BD cases from the International Consortium on Lithium Genetics (ConLi+Gen; www.ConLiGen.org). Higher SCZ and MD PRSs were associated with poorer lithium treatment response whereas BD-PRS had no association with treatment outcome. The combined MET2-PRS comprising of SCZ and MD variants (MET2-PRS) and a model using SCZ and MD-PRS sequentially improved response prediction, compared to single-disorder PRS or to a combined score using all three traits (MET3-PRS). Patients in the highest decile for MET2-PRS loading had 2.5 times higher odds of being classified as poor responders than patients with the lowest decile MET2-PRS scores. An exploratory functional pathway analysis of top MET2-PRS variants was conducted. Findings may inform the development of future testing strategies for personalized lithium prescribing in BD

Pattern of neural responses to verbal fluency shows diagnostic specificity for schizophrenia and bipolar disorder

<p>Abstract</p> <p>Background</p> <p>Impairments in executive function and language processing are characteristic of both schizophrenia and bipolar disorder. Their functional neuroanatomy demonstrate features that are shared as well as specific to each disorder. Determining the distinct pattern of neural responses in schizophrenia and bipolar disorder may provide biomarkers for their diagnoses.</p> <p>Methods</p> <p>104 participants underwent functional magnetic resonance imaging (fMRI) scans while performing a phonological verbal fluency task. Subjects were 32 patients with schizophrenia in remission, 32 patients with bipolar disorder in an euthymic state, and 40 healthy volunteers. Neural responses to verbal fluency were examined in each group, and the diagnostic potential of the pattern of the neural responses was assessed with machine learning analysis.</p> <p>Results</p> <p>During the verbal fluency task, both patient groups showed increased activation in the anterior cingulate, left dorsolateral prefrontal cortex and right putamen as compared to healthy controls, as well as reduced deactivation of precuneus and posterior cingulate. The magnitude of activation was greatest in patients with schizophrenia, followed by patients with bipolar disorder and then healthy individuals. Additional recruitment in the right inferior frontal and right dorsolateral prefrontal cortices was observed in schizophrenia relative to both bipolar disorder and healthy subjects. The pattern of neural responses correctly identified individual patients with schizophrenia with an accuracy of 92%, and those with bipolar disorder with an accuracy of 79% in which mis-classification was typically of bipolar subjects as healthy controls.</p> <p>Conclusions</p> <p>In summary, both schizophrenia and bipolar disorder are associated with altered function in prefrontal, striatal and default mode networks, but the magnitude of this dysfunction is particularly marked in schizophrenia. The pattern of response to verbal fluency is highly diagnostic for schizophrenia and distinct from bipolar disorder. Pattern classification of functional MRI measurements of language processing is a potential diagnostic marker of schizophrenia.</p

Evaluation of Commercial Probiotic Products

Although there is a vast number of probiotic products commercially available due to their acceptability and increasing usage, their quality control has continuously been a major concern. This study aimed to assess some commercially available probiotics on the UK market for content in relation to their label claim. Seven products were used for the study. The bacteria content were isolated, identified and enumerated on selective media. The results revealed that all products evaluated contained viable probiotic bacteria but only three out of the seven products (43%) contained the claimed culture concentration or more. None of the multispecies product contained all the labelled probiotic bacteria. Misidentification of some species occurred. The results concurred with previous studies and showed that quality issues with commercial probiotics remain. Since probiotic activity is linked with probiotic concentration and is strain specific, the need exist for a global comprehensive legislation to control the quality of probiotics whose market is gaining huge momentum

Characterisation of age and polarity at onset in bipolar disorder

Background Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools. Aims To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics. Method Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts. Results Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO. Conclusions AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses

Characterisation of age and polarity at onset in bipolar disorder

Background Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools. Aims To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics. Method Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts. Results Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO. Conclusions AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses

Association of polygenic score and the involvement of cholinergic and glutamatergic pathways with lithium treatment response in patients with bipolar disorder

Lithium is regarded as the first-line treatment for bipolar disorder (BD), a severe and disabling mental health&nbsp;disorder that affects about 1% of the population worldwide. Nevertheless, lithium is not consistently effective, with only 30% of patients showing a favorable response to treatment. To provide personalized treatment options for bipolar patients, it is essential to identify prediction biomarkers such as polygenic scores. In this study, we developed a polygenic score for lithium treatment response (Li+PGS) in patients with BD. To gain further insights into lithium’s possible molecular mechanism of action, we performed a genome-wide gene-based analysis. Using polygenic score modeling, via methods incorporating Bayesian regression and continuous shrinkage priors, Li+PGS was developed in the International Consortium of Lithium Genetics cohort (ConLi+Gen: N = 2367) and replicated in the combined PsyCourse (N = 89) and BipoLife (N = 102) studies. The associations of Li+PGS and lithium treatment response — defined in a continuous ALDA scale and a categorical outcome (good response vs. poor response) were tested using regression models, each adjusted for the covariates: age, sex, and the first four genetic principal components. Statistical significance was determined at P &lt; 0.05. Li+PGS was positively associated with lithium treatment response in the ConLi+Gen cohort, in both the categorical (P = 9.8 × 10−12, R2 = 1.9%) and continuous (P = 6.4 × 10−9, R2 = 2.6%) outcomes. Compared to bipolar patients in the 1st decile of the risk distribution, individuals in the 10th decile had 3.47-fold (95%CI: 2.22–5.47) higher odds of responding favorably to lithium. The results were replicated in the independent cohorts for the categorical treatment outcome (P = 3.9 × 10−4, R2 = 0.9%), but not for the continuous outcome (P = 0.13). Gene-based analyses revealed 36 candidate genes that are enriched in biological pathways controlled by glutamate and acetylcholine. Li+PGS may be useful in the development of pharmacogenomic testing strategies by enabling a classification of bipolar patients according to their response to treatment