Arbeitsmedizin

Das Kapitel ist in vier Unterkapital gegliedert. "Allgemeines zur Arbeitsmedizin" beinhaltet die Rolle und Ziele der Arbeitsmedizin und die wesentlichen gesetzlichen Grundlagen. "Berufskrankheiten" umfasst die Berufs- und Arbeitsplatzanamnese sowie die wichtigsten Berufskrankheiten und Noxen. Das dritte Unterkapitel behandelt die arbeitsassoziierten Gesundheitsstörungen, die Ergonomie, Arbeitsorganisation und Arbeitslosigkeit. Die Kapitel "Absenzen- und Case Management" sowie "Betriebliche Gesundheitsförderung und Arbeitsgestaltung" zeigen Möglichkeiten auf, wie ein Betrieb die Gesundheit und Arbeitsfähigkeit seiner Mitarbeiter aufrechterhalten und fördern kann. Das Kapitel befähigt die Studierenden, die Interaktion zwischen Arbeit und Gesundheit zu erkennen und adäquate Massnahmen zu ergreifen.[Autoren]]]> Occupational Medicine ; Occupational Diseases oai:serval.unil.ch:BIB_EFD9005BC107 2020-05-30T01:25:30Z phdthesis urnserval https://serval.unil.ch/notice/serval:BIB_EFD9005BC107 Résultats à court terme des arthroplasties totales de hanche non cimentées chez des patients de moins de 60 ans Larequi, Ivan-Philippe Université de Lausanne, Faculté de médecine info:eu-repo/semantics/doctoralThesis phdthesis 1999 fre https://serval.unil.ch/resource/serval:BIB_EFD9005BC107.P001/REF.pdf http://nbn-resolving.org/urn/resolver.pl?urn=urn:nbn:ch:serval-BIB_EFD9005BC1073 info:eu-repo/semantics/altIdentifier/urn/urn:nbn:ch:serval-BIB_EFD9005BC1073 info:eu-repo/semantics/restrictedAccess Restricted: indefinite embargo Copying allowed only for non-profit organizations https://serval.unil.ch/disclaimer application/pdf oai:serval.unil.ch:BIB_EFD90D5BB70B 2020-05-30T01:25:30Z openaire documents urnserval https://serval.unil.ch/notice/serval:BIB_EFD90D5BB70B Association Between Plasma Caffeine and Other Methylxanthines and Metabolic Parameters in a Psychiatric Population Treated With Psychotropic Drugs Inducing Metabolic Disturbances info:doi:10.3389/fpsyt.2018.00573 info:eu-repo/semantics/altIdentifier/doi/10.3389/fpsyt.2018.00573 info:eu-repo/semantics/altIdentifier/pmid/30473668 Delacrétaz, Aurélie Vandenberghe, Frederik Glatard, Anaïs Levier, Axel Dubath, Céline Ansermot, Nicolas Crettol, Séverine Gholam-Rezaee, Mehdi Guessous, Idris Bochud, Murielle von Gunten, Armin Conus, Philippe Eap, Chin B. info:eu-repo/semantics/article article 2018-11-09 Frontiers in Psychiatry, vol. 9 info:eu-repo/semantics/altIdentifier/pissn/1664-0640 <![CDATA[Importance: Multiple studies conducted in the general population identified an association between self-reported coffee consumption and plasma lipid levels. To date, no study assessed whether and which plasma methylxanthines (caffeine and/or its metabolites, i.e., paraxanthine, theophylline, and theobromine) are associated with plasma lipids. In psychiatric patients, an important coffee consumption is often reported and many psychotropic drugs can induce a rapid and substantial increase of plasma lipid levels. Objective: To determine whether plasma methylxanthines are associated with metabolic parameters in psychiatric patients receiving treatments known to induce metabolic disturbances. Design, Setting, and Participants: Data were obtained from a prospective study including 630 patients with metabolic parameters [i.e., body mass index (BMI), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), non-high-density lipoprotein cholesterol (non-HDL-C), and fasting triglycerides (TG)] monitored routinely during psychotropic treatment. Exposures: Plasma methylxanthines levels. Main Outcomes and Measures: Metabolic variables including BMI and plasma lipid levels. Results: Multivariate analyses indicated that BMI, TC, HDL-C, and non-HDL-C increased significantly with increasing total methylxanthines (pcorrected &lt;= 0.05). In addition, compared to patients with plasma caffeine concentration in the lowest quartile, those with caffeine concentration in the highest quartile were twice more prone to suffer from non-HDL hypercholesterolemia (p(corrected) = 0.05), five times more likely to suffer from hypertriglyceridemia (p(corrected) = 0.01) and four times more susceptible to be overweight (p(corrected) = 0.01). Conclusions and Relevance: This study showed that plasma caffeine and other methylxanthines are associated with worsening of metabolic parameters in patients receiving psychotropic treatments known to induce metabolic disturbances. It emphasizes that important caffeine consumption could be considered as an additional environmental risk factor for metabolic worsening in patients receiving such treatments

Association Between Plasma Caffeine and Other Methylxanthines and Metabolic Parameters in a Psychiatric Population Treated With Psychotropic Drugs Inducing Metabolic Disturbances

Importance: Multiple studies conducted in the general population identified an association between self-reported coffee consumption and plasma lipid levels. To date, no study assessed whether and which plasma methylxanthines (caffeine and/or its metabolites, i.e., paraxanthine, theophylline, and theobromine) are associated with plasma lipids. In psychiatric patients, an important coffee consumption is often reported and many psychotropic drugs can induce a rapid and substantial increase of plasma lipid levels.Objective: To determine whether plasma methylxanthines are associated with metabolic parameters in psychiatric patients receiving treatments known to induce metabolic disturbances.Design, Setting, and Participants: Data were obtained from a prospective study including 630 patients with metabolic parameters [i.e., body mass index (BMI), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), non-high-density lipoprotein cholesterol (non-HDL-C), and fasting triglycerides (TG)] monitored routinely during psychotropic treatment.Exposures: Plasma methylxanthines levels.Main Outcomes and Measures: Metabolic variables including BMI and plasma lipid levels.Results: Multivariate analyses indicated that BMI, TC, HDL-C, and non-HDL-C increased significantly with increasing total methylxanthines (pcorrected ≤ 0.05). In addition, compared to patients with plasma caffeine concentration in the lowest quartile, those with caffeine concentration in the highest quartile were twice more prone to suffer from non-HDL hypercholesterolemia (pcorrected = 0.05), five times more likely to suffer from hypertriglyceridemia (pcorrected = 0.01) and four times more susceptible to be overweight (pcorrected = 0.01).Conclusions and Relevance: This study showed that plasma caffeine and other methylxanthines are associated with worsening of metabolic parameters in patients receiving psychotropic treatments known to induce metabolic disturbances. It emphasizes that important caffeine consumption could be considered as an additional environmental risk factor for metabolic worsening in patients receiving such treatments

DNA methylation may partly explain psychotropic drug-induced metabolic side effects: results from a prospective 1-month observational study

Abstract Background Metabolic side effects of psychotropic medications are a major drawback to patients’ successful treatment. Using an epigenome-wide approach, we aimed to investigate DNA methylation changes occurring secondary to psychotropic treatment and evaluate associations between 1-month metabolic changes and both baseline and 1-month changes in DNA methylation levels. Seventy-nine patients starting a weight gain inducing psychotropic treatment were selected from the PsyMetab study cohort. Epigenome-wide DNA methylation was measured at baseline and after 1 month of treatment, using the Illumina Methylation EPIC BeadChip. Results A global methylation increase was noted after the first month of treatment, which was more pronounced (p < 2.2 × 10–16) in patients whose weight remained stable (< 2.5% weight increase). Epigenome-wide significant methylation changes (p < 9 × 10−8) were observed at 52 loci in the whole cohort. When restricting the analysis to patients who underwent important early weight gain (≥ 5% weight increase), one locus (cg12209987) showed a significant increase in methylation levels (p = 3.8 × 10–8), which was also associated with increased weight gain in the whole cohort (p = 0.004). Epigenome-wide association analyses failed to identify a significant link between metabolic changes and methylation data. Nevertheless, among the strongest associations, a potential causal effect of the baseline methylation level of cg11622362 on glycemia was revealed by a two-sample Mendelian randomization analysis (n = 3841 for instrument-exposure association; n = 314,916 for instrument-outcome association). Conclusion These findings provide new insights into the mechanisms of psychotropic drug-induced weight gain, revealing important epigenetic alterations upon treatment, some of which may play a mediatory role

Additional file 1 of DNA methylation may partly explain psychotropic drug-induced metabolic side effects: results from a prospective 1-month observational study

Additional file 1. Appendix: Supplementary Methods and Results. Supplementary figures and tables

Impact of HSD11B1 polymorphisms on BMI and components of the metabolic syndrome in patients receiving psychotropic treatments

Metabolic syndrome (MetS) associated with psychiatric disorders and psychotropic treatments represents a major health issue. 11β-Hydroxysteroid dehydrogenase type 1 (11β-HSD1) is an enzyme that catalyzes tissue regeneration of active cortisol from cortisone. Elevated enzymatic activity of 11β-HSD1 may lead to the development of MetS

The psychosis metabolic risk calculator (PsyMetRiC) for young people with psychosis: International external validation and site-specific recalibration in two independent European samples

Background: Cardiometabolic dysfunction is common in young people with psychosis. Recently, the Psychosis Metabolic Risk Calculator (PsyMetRiC) was developed and externally validated in the UK, predicting up-to six-year risk of metabolic syndrome (MetS) from routinely collected data. The full-model includes age, sex, ethnicity, body-mass index, smoking status, prescription of metabolically-active antipsychotic medication, high-density lipoprotein, and triglyceride concentrations; the partial-model excludes biochemical predictors. Methods: To move toward a future internationally-useful tool, we externally validated PsyMetRiC in two independent European samples. We used data from the PsyMetab (Lausanne, Switzerland) and PAFIP (Cantabria, Spain) cohorts, including participants aged 16-35y without MetS at baseline who had 1-6y follow-up. Predictive performance was assessed primarily via discrimination (C-statistic), calibration (calibration plots), and decision curve analysis. Site-specific recalibration was considered. Findings: We included 1024 participants (PsyMetab n=558, male=62%, outcome prevalence=19%, mean follow-up=2.48y; PAFIP n=466, male=65%, outcome prevalence=14%, mean follow-up=2.59y). Discrimination was better in the full- compared with partial-model (PsyMetab=full-model C=0.73, 95% C.I., 0.68-0.79, partial-model C=0.68, 95% C.I., 0.62-0.74; PAFIP=full-model C=0.72, 95% C.I., 0.66-0.78; partial-model C=0.66, 95% C.I., 0.60-0.71). As expected, calibration plots revealed varying degrees of miscalibration, which recovered following site-specific recalibration. PsyMetRiC showed net benefit in both new cohorts, more so after recalibration. Interpretation: The study provides evidence of PsyMetRiC's generalizability in Western Europe, although further local and international validation studies are required. In future, PsyMetRiC could help clinicians internationally to identify young people with psychosis who are at higher cardiometabolic risk, so interventions can be directed effectively to reduce long-term morbidity and mortality.Funding: NIHR Cambridge Biomedical Research Centre (BRC-1215-20014); The Wellcome Trust (201486/Z/16/Z); Swiss National Research Foundation (320030-120686, 324730- 144064, and 320030-173211); The Carlos III Health Institute (CM20/00015, FIS00/3095, PI020499, PI050427, and PI060507); IDIVAL (INT/A21/10 and INT/A20/04); The Andalusian Regional Government (A1-0055-2020 and A1-0005-2021); SENY Fundacion Research (2005-0308007); Fundacion Marqués de Valdecilla (A/02/07, API07/011); Ministry of Economy and Competitiveness and the European Fund for Regional Development (SAF2016-76046-R and SAF2013-46292-R). Acknowledgements: EFO acknowledges funding support from the Medical Research Council (MRC) and the National Institute for Health Research (NIHR) Imperial Biomedical Research Centre (BRC). This research was supported by the NIHR Cambridge Biomedical Research Centre (BRC1215-20014). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. GMK acknowledges funding support from the Wellcome Trust (Intermediate Clinical Fellowship; grant code: 201486/Z/16/ Z), The MQ: Transforming Mental Health (Data Science Award; grant code: MQDS17/40), the UK Medical Research Council (MICA: Mental Health Data Pathfinder; grant code: MC_PC_17213; and Therapeutic Target Validation in Mental Health; grant code: MR/ S037675/1), and the BMA Foundation (J Moulton grant 2019). PBJ acknowledges funding from the MRC and MQ (as above), programmatic funding from NIHR (RPPG- 0616-20003) and support from the Applied Research Collaboration East of England. RU acknowledges funding support from the NIHR (HTA grant code): 127700 and MRC (Therapeutic Target Validation in Mental Health grant code: MR/S037675/1). This work has been funded in part by the Swiss National Research Foundation (CE and PC: 320030-120686, 324730- 144064, and 320030-173211; CBE, PC and KJP: 320030_200602). NG-T acknowledges funding support from The Carlos III Health Institute (Rio Hortega contract: CM20/00015). JV-B acknowledges funding support from IDIVAL (grant codes: INT/A21/10 and INT/ A20/04). MR-V acknowledges funding support from The Andalusian Regional Government (grant codes: A1- 0055-2020 and A1-0005-2021). BC-F acknowledges the PAFIP researchers who have carried out a great number of outstanding investigations that have notably contributed to improving our knowledge in the field of early psychosis treatment. We would also like to thank the participants and their families for enrolling in these studies. The Santander (Spain) cohort was funded by the following grants to Dr Crespo-Facorro: Instituto de Salud Carlos III (grants FIS00/3095, PI020499, PI050427, and PI060507), Plan Nacional de Drogas Research (grant 2005-Orden sco/3246/2004), SENY Fundacion Research (grant 2005-0308007), Fundacion Marqués de Valdecilla (grant A/02/07, API07/011) and Ministry of Economy and Competitiveness and the European Fund for Regional Development (grants SAF2016-76046-R and SAF2013-46292-R). The funding sources had no role in the writing of the manuscript or in the decision to submit it for publication.PsychosisEarly InterventionRisk Prediction AlgorithmMetabolic SyndromeInternational ValidationPsyMetabPAFI

BMI evolution during psychotropic treatment according to protective or risk <i>MCHR2</i> rs7754794C>T genotype.

<p>Caucasian patients carrying protective (TT) or risk (CC or CT) rs7754794C>T variant. Median, interquartiles and number of observations for each box are indicated.</p