Polypharmacy and specific comorbidities in university primary care settings.

Polypharmacy is associated with adverse events and multimorbidity, but data are limited on its association with specific comorbidities in primary care settings. We measured the prevalence of polypharmacy and inappropriate prescribing, and assessed the association of polypharmacy with specific comorbidities. We did a cross-sectional analysis of 1002 patients aged 50-80years followed in Swiss university primary care settings. We defined polypharmacy as ≥5 long-term prescribed drugs and multimorbidity as ≥2 comorbidities. We used logistic mixed-effects regression to assess the association of polypharmacy with the number of comorbidities, multimorbidity, specific sets of comorbidities, potentially inappropriate prescribing (PIP) and potential prescribing omission (PPO). We used multilevel mixed-effects Poisson regression to assess the association of the number of drugs with the same parameters. Patients (mean age 63.5years, 67.5% ≥2 comorbidities, 37.0% ≥5 drugs) had a mean of 3.9 (range 0-17) drugs. Age, BMI, multimorbidity, hypertension, diabetes mellitus, chronic kidney disease, and cardiovascular diseases were independently associated with polypharmacy. The association was particularly strong for hypertension (OR 8.49, 95%CI 5.25-13.73), multimorbidity (OR 6.14, 95%CI 4.16-9.08), and oldest age (75-80years: OR 4.73, 95%CI 2.46-9.10 vs.50-54years). The prevalence of PPO was 32.2% and PIP was more frequent among participants with polypharmacy (9.3% vs. 3.2%, p<0.006). Polypharmacy is common in university primary care settings, is strongly associated with hypertension, diabetes mellitus, chronic kidney disease and cardiovascular diseases, and increases potentially inappropriate prescribing. Multimorbid patients should be included in further trials for developing adapted guidelines and avoiding inappropriate prescribing

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

Genome-wide association study identifies 30 Loci Associated with Bipolar Disorder

This paper is dedicated to the memory of Psychiatric Genomics Consortium (PGC) founding member and Bipolar disorder working group co-chair Pamela Sklar. We thank the participants who donated their time, experiences and DNA to this research, and to the clinical and scientific teams that worked with them. We are deeply indebted to the investigators who comprise the PGC. The views expressed are those of the authors and not necessarily those of any funding or regulatory body. Analyses were carried out on the NL Genetic Cluster Computer (http://www.geneticcluster.org ) hosted by SURFsara, and the Mount Sinai high performance computing cluster (http://hpc.mssm.edu).Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P<1x10-4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (GWS, p < 5x10-8) in the discovery GWAS were not GWS in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis 30 loci were GWS including 20 novel loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene-sets including regulation of insulin secretion and endocannabinoid signaling. BDI is strongly genetically correlated with schizophrenia, driven by psychosis, whereas BDII is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential new biological mechanisms for BD.This work was funded in part by the Brain and Behavior Research Foundation, Stanley Medical Research Institute, University of Michigan, Pritzker Neuropsychiatric Disorders Research Fund L.L.C., Marriot Foundation and the Mayo Clinic Center for Individualized Medicine, the NIMH Intramural Research Program; Canadian Institutes of Health Research; the UK Maudsley NHS Foundation Trust, NIHR, NRS, MRC, Wellcome Trust; European Research Council; German Ministry for Education and Research, German Research Foundation IZKF of Münster, Deutsche Forschungsgemeinschaft, ImmunoSensation, the Dr. Lisa-Oehler Foundation, University of Bonn; the Swiss National Science Foundation; French Foundation FondaMental and ANR; Spanish Ministerio de Economía, CIBERSAM, Industria y Competitividad, European Regional Development Fund (ERDF), Generalitat de Catalunya, EU Horizon 2020 Research and Innovation Programme; BBMRI-NL; South-East Norway Regional Health Authority and Mrs. Throne-Holst; Swedish Research Council, Stockholm County Council, Söderström Foundation; Lundbeck Foundation, Aarhus University; Australia NHMRC, NSW Ministry of Health, Janette M O'Neil and Betty C Lynch

Improving Genetic Prediction by Leveraging Genetic Correlations Among Human Diseases and Traits

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7 for height to 47 for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait. © 2018 The Author(s)

Geographic and temporal patterns of antimicrobial resistance in pseudomonas aeruginosa over 20 years from the SENTRY Antimicrobial Surveillance Program, 1997-2016

The SENTRY Antimicrobial Surveillance Program was established in 1997 and encompasses over 750 000 bacterial isolates from ≥400 medical centers worldwide. Among the pathogens tested, Pseudomonas aeruginosa remains a common cause of multidrug-resistant (MDR) bloodstream infections and pneumonia in hospitalized patients. In the present study, we reviewed geographic and temporal trends in resistant phenotypes of P. aeruginosa over 20 years of the SENTRY Program. From 1997 to 2016, 52 022 clinically significant consecutive isolates were submitted from ≥200 medical centers representing the Asia-Pacific region, Europe, Latin America, and North America. Only 1 isolate per patient per infection episode was submitted. Isolates were identified by standard algorithms and/or matrix-assisted laser desorption ionization-time of flight mass spectrometry. Susceptibility testing was performed by Clinical and Laboratory Standards Institute (CLSI) methods and interpreted using CLSI and European Committee on Antimicrobial Susceptibility Testing 2018 criteria at JMI Laboratories. The most common infection from which P. aeruginosa was isolated was pneumonia in hospitalized patients (44.6%) followed by bloodstream infection (27.9%), with pneumonia having a slightly higher rate of MDR (27.7%) than bloodstream infections (23.7%). The region with the highest percentage of MDR phenotypes was Latin America (41.1%), followed by Europe (28.4%). The MDR rates were highest in 2005-2008 and have decreased in the most recent period. Colistin was the most active drug tested (99.4% susceptible), followed by amikacin (90.5% susceptible). Over the 20 years of SENTRY Program surveillance, the rate of MDR P. aeruginosa infections has decreased, particularly in Latin America. Whether the trend of decreasing resistance in P. aeruginosa is maintained will be documented in future SENTRY Program and other surveillance reports. © 2019 The Author(s). Published by Oxford University Press on behalf of Infectious Diseases Society of America

Does semen quality of Colossoma macropomum change the productivity of larvae during the reproductive period?

This study aimed to analyze the sperm quality of Colossoma macropomum, during the reproductive period. A total of 23 males of C. macropomum in the breeding season were used. Male gametes were collected after the hormone induction protocol using volumetric syringes for quantitative and qualitative analyses throughout the reproductive season, the fish were captured every 15 days. The following parameters were evaluated: volume, motility rate, motility time, sperm concentration, sperm morphology, fertilization rate, and hatching rate. There was no effect of period within season for semen volume, motility time and sperm concentration. For motility rate a quadratic effect was observed between collection periods. As for sperm morphology, there were differences (p < 0.05) in the probability of occurrences of normal spermatozoa, primary and secondary abnormalities as a function of collection period. For the fertilization rate a quadratic effect was verified and the hatching rate declined linearly throughout the reproductive period. Changes in the qualitative parameters of C. macropomum semen during the reproductive period were observed. In terms of the sperm quality of C. macropomum, the aging process of the spermatozoa is evident and consequently interferes in the fertilization and hatching rates, being more accentuated in the last month of the reproductive season

Inhibition of tumour necrosis factor-alpha by antisense targeting produces immunophenotypical and morphological changes in injury-activated microglia and macrophages

Microglia respond in a stereotypical pattern to a diverse array of pathological states. These changes are coupled to morphological and immunophenotypical alterations and the release of a variety of reactive species, trophic factors and cytokines that modify both microglia and their cellular environment. We examined whether a microglial-produced cytokine, tumour necrosis factor-alpha (TNF-alpha), was involved in the maintenance of microglial activation after spinal cord injury by selective inhibition using TNF-alpha antisense deoxyoligonucleotides (ASOs). Microglia and macrophages harvested from 3 d post-contused rat spinal cord were large and rounded (86.3 +/- 9.6%). They were GSA-IB4-positive (GSA-IB4(+)) (Griffonia simplicifolia lectin, microglia specific; 94.8 +/- 5.1%), strongly OX-42 positive (raised against a type 3 complement/integrin receptor, CD11b; 78.9 +/- 9.1%), ED-1 positive (a lysosomal marker shown to correlate well with immune cell activation; 97.2 +/- 2.6%) and IIA positive (antibody recognizes major histocompatibility complex II; 57.2 +/- 5.6%), indicative of fully activated cells, for up to 48 h after plating. These cells also secreted significant amounts of TNF-alpha (up to 436 pg/microg total protein, 16 h). Fluoroscein isothiocyanate-labelled TNF-alpha ASOs (5, 50 and 200 nm) added to the culture medium were taken up very efficiently into the cells (> 90% cells) and significantly reduced TNF-alpha production by up to 92% (26.5 pg/microg total protein, 16 h, 200 nm TNF-alpha ASOs). Furthermore, few of the treated cells at this time were round (5.4 +/- 2.7%), having become predominantly spindle shaped (74.9 +/- 6.3%) or stellate (21.4 +/- 2.7%); immunophenotypically, although all of them remained GSA-IB4 positive (91.6 +/- 6.2%), many were weakly OX-42 positive and few expressed either ED-1 (12.9 +/- 2.5%) or IIA (19.8 +/- 7.4%). Thus, the secretion of TNF-alpha early in spinal cord injury may be involved in autoactivating microglia/macrophages. However, at the peak of microglial activation after injury, the activation state of microglia/macrophages is not stable and this process may still be reversible by blocking TNF-alpha