Artendiversität bei Mausmakis : die Bedeutung der akustischen Kommunikation

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Do genetic factors protect for early onset lung cancer? A case control study before the age of 50 years

<p>Abstract</p> <p>Background</p> <p>Early onset lung cancer shows some familial aggregation, pointing to a genetic predisposition. This study was set up to investigate the role of candidate genes in the susceptibility to lung cancer patients younger than 51 years at diagnosis.</p> <p>Methods</p> <p>246 patients with a primary, histologically or cytologically confirmed neoplasm, recruited from 2000 to 2003 in major lung clinics across Germany, were matched to 223 unrelated healthy controls. 11 single nucleotide polymorphisms of genes with reported associations to lung cancer have been genotyped.</p> <p>Results</p> <p>Genetic associations or gene-smoking interactions was found for <it>GPX1(Pro200Leu) </it>and <it>EPHX1(His113Tyr)</it>. Carriers of the Leu-allele of <it>GPX1(Pro200Leu) </it>showed a significant risk reduction of OR = 0.6 (95% CI: 0.4–0.8, p = 0.002) in general and of OR = 0.3 (95% CI:0.1–0.8, p = 0.012) within heavy smokers. We could also find a risk decreasing genetic effect for His-carriers of <it>EPHX1(His113Tyr) </it>for moderate smokers (OR = 0.2, 95% CI:0.1–0.7, p = 0.012). Considered both variants together, a monotone decrease of the OR was found for smokers (OR of 0.20; 95% CI: 0.07–0.60) for each protective allele.</p> <p>Conclusion</p> <p>Smoking is the most important risk factor for young lung cancer patients. However, this study provides some support for the T-Allel of <it>GPX1(Pro200Leu) </it>and the C-Allele of <it>EPHX1(His113Tyr) </it>to play a protective role in early onset lung cancer susceptibility.</p

METACOHORTS for the study of vascular disease and its contribution to cognitive decline and neurodegeneration: an initiative of the Joint Programme for Neurodegenerative Disease Research

Dementia is a global problem and major target for health care providers. Although up to 45% of cases are primarily or partly due to cerebrovascular disease, little is known of these mechanisms or treatments because most dementia research still focuses on pure Alzheimer's disease. An improved understanding of the vascular contributions to neurodegeneration and dementia, particularly by small vessel disease, is hampered by imprecise data, including the incidence and prevalence of symptomatic and clinically “silent” cerebrovascular disease, long-term outcomes (cognitive, stroke, or functional), and risk factors. New large collaborative studies with long follow-up are expensive and time consuming, yet substantial data to advance the field are available. In an initiative funded by the Joint Programme for Neurodegenerative Disease Research, 55 international experts surveyed and assessed available data, starting with European cohorts, to promote data sharing to advance understanding of how vascular disease affects brain structure and function, optimize methods for cerebrovascular disease in neurodegeneration research, and focus future research on gaps in knowledge. Here, we summarize the results and recommendations from this initiative. We identified data from over 90 studies, including over 660,000 participants, many being additional to neurodegeneration data initiatives. The enthusiastic response means that cohorts from North America, Australasia, and the Asia Pacific Region are included, creating a truly global, collaborative, data sharing platform, linked to major national dementia initiatives. Furthermore, the revised World Health Organization International Classification of Diseases version 11 should facilitate recognition of vascular-related brain damage by creating one category for all cerebrovascular disease presentations and thus accelerate identification of targets for dementia prevention

Pharmacogenomics Bias - Systematic distortion of study results by genetic heterogeneity

Background: Decision analyses of drug treatments in chronic diseases require modeling the progression of disease and treatment response beyond the time horizon of clinical or epidemiological studies. In many such models, progression and drug effect have been applied uniformly to all patients; heterogeneity in progression, including pharmacogenomic effects, has been ignored. Objective: We sought to systematically evaluate the existence, direction and relative magnitude of a pharmacogenomics bias (PGX-Bias) resulting from failure to adjust for genetic heterogeneity in both treatment response (HT) and heterogeneity in progression of disease (HP) in decision-analytic studies based on clinical study data. Methods: We performed a systematic literature search in electronic databases for studies regarding the effect of genetic heterogeneity on the validity of study results. Included studies have been summarized in evidence tables. In the case of lacking evidence from published studies we sought to perform our own simulation considering both HT and HP. We constructed two simple Markov models with three basic health states (early-stage disease, late-stage disease, dead), one adjusting and the other not adjusting for genetic heterogeneity. Adjustment was done by creating different disease states for presence (G+) and absence (G-) of a dichotomous genetic factor. We compared the life expectancy gains attributable to treatment resulting from both models and defined pharmacogenomics bias as percent deviation of treatment-related life expectancy gains in the unadjusted model from those in the adjusted model. We calculated the bias as a function of underlying model parameters to create generic results. We then applied our model to lipid-lowering therapy with pravastatin in patients with coronary atherosclerosis, incorporating the influence of two TaqIB polymorphism variants (B1 and B2) on progression and drug efficacy as reported in the DNA substudy of the REGRESS trial. Results: We found four studies that systematically evaluated heterogeneity bias. All of them indicated that there is a potential of heterogeneity bias. However, none of these studies explicitly investigated the effect of genetic heterogeneity. Therefore, we performed our own simulation study. Our generic simulation showed that a purely HT-related bias is negative (conservative) and a purely HP-related bias is positive (liberal). For many typical scenarios, the absolute bias is smaller than 10%. In case of joint HP and HT, the overall bias is likely triggered by the HP component and reaches positive values >100% if fractions of „fast progressors" and „strong treatment responders" are low. In the clinical example with pravastatin therapy, the unadjusted model overestimated the true life-years gained (LYG) by 5.5% (1.07 LYG vs. 0.99 LYG for 56-year-old men). Conclusions: We have been able to predict the pharmacogenomics bias jointly caused by heterogeneity in progression of disease and heterogeneity in treatment response as a function of characteristics of patients, chronic disease, and treatment. In the case of joint presence of both types of heterogeneity, models ignoring this heterogeneity may generate results that overestimate the treatment benefit

Peripheral glucose levels and cognitive outcome after ischemic stroke : Results from the Munich Stroke Cohort

Introduction: The relationship between glucose metabolism and stroke outcome is likely to be complex. We examined whether there is a linear or non-linear relationship between glucose measures in the acute phase of stroke and post-stroke cognition, and whether altered glucose metabolism at different time intervals (long- and short-term before stroke, acute phase) is associated with cognitive outcome. Patients and methods: In all, 664 consecutively recruited patients with acute ischemic stroke and without pre-stroke dementia were included in this prospective observational study. Blood samples were taken at admission and fasting on the first morning after stroke. Duration of diabetes was assessed by interview. Cognitive outcome was assessed by the Telephone Interview for Cognitive Status 3 months post-stroke. Dose-response analyses were used to investigate non-linearity. Regression analyses were stratified by diabetes status and adjusted for relevant confounders. Results: Cognitive status was testable in 422 patients (81 with diabetes). There was a non-linear relationship between both admission and fasting glucose levels and cognitive outcome. Lower glucose values were significantly associated with lower Telephone Interview for Cognitive Status scores 3 months post-stroke in patients without diabetes with a similar trend in diabetic patients. There was an inverse association between duration of diabetes and Telephone Interview for Cognitive Status scores (linear regression: −0.10 (95% confidence interval: −0.17 to −0.02) per year increase of diabetes duration), whereas HbA1c was not related to cognitive outcome. Results were supported by sensitivity analyses accounting for attrition. Conclusion: Lower glucose levels in the acute phase of stroke are associated with worse cognitive outcome but the relationship is non-linear. Long-term abnormalities in glucose metabolism are also related to poor outcome but this is not the case for shorter term abnormalities. Altered glucose levels at different stages of stroke may affect stroke outcome through different pathways

Peripheral glucose levels and cognitive outcome after ischemic stroke : Results from the Munich Stroke Cohort

Introduction: The relationship between glucose metabolism and stroke outcome is likely to be complex. We examined whether there is a linear or non-linear relationship between glucose measures in the acute phase of stroke and post-stroke cognition, and whether altered glucose metabolism at different time intervals (long- and short-term before stroke, acute phase) is associated with cognitive outcome. Patients and methods: In all, 664 consecutively recruited patients with acute ischemic stroke and without pre-stroke dementia were included in this prospective observational study. Blood samples were taken at admission and fasting on the first morning after stroke. Duration of diabetes was assessed by interview. Cognitive outcome was assessed by the Telephone Interview for Cognitive Status 3 months post-stroke. Dose-response analyses were used to investigate non-linearity. Regression analyses were stratified by diabetes status and adjusted for relevant confounders. Results: Cognitive status was testable in 422 patients (81 with diabetes). There was a non-linear relationship between both admission and fasting glucose levels and cognitive outcome. Lower glucose values were significantly associated with lower Telephone Interview for Cognitive Status scores 3 months post-stroke in patients without diabetes with a similar trend in diabetic patients. There was an inverse association between duration of diabetes and Telephone Interview for Cognitive Status scores (linear regression: −0.10 (95% confidence interval: −0.17 to −0.02) per year increase of diabetes duration), whereas HbA1c was not related to cognitive outcome. Results were supported by sensitivity analyses accounting for attrition. Conclusion: Lower glucose levels in the acute phase of stroke are associated with worse cognitive outcome but the relationship is non-linear. Long-term abnormalities in glucose metabolism are also related to poor outcome but this is not the case for shorter term abnormalities. Altered glucose levels at different stages of stroke may affect stroke outcome through different pathways

Early MoCA predicts long-term cognitive and functional outcome and mortality after stroke

Objective To examine whether the Montreal Cognitive Assessment (MoCA) administered within 7 days after stroke predicts long-term cognitive impairment, functional impairment, and mortality. Methods MoCA was administered to 274 patients from 2 prospective hospital-based cohort studies in Germany (n = 125) and France (n = 149). Cognitive and functional outcomes were assessed at 6, 12, and 36 months after stroke by comprehensive neuropsychological testing, the Clinical Dementia Rating (CDR) scale, the modified Rankin Scale (mRS), and Instrumental Activities of Daily Living (IADL) and analyzed with generalized estimating equations. All-cause mortality was investigated by Cox proportional hazard models. Analyses were adjusted for demographic variables, education, vascular risk factors, premorbid cognitive status, and NIH Stroke Scale scores. The additive predictive value of MoCA was examined with receiver operating characteristic curves. Results In pooled analyses, a baseline MoCA score = 0.5 (OR 2.53, 95% CI 153-4.18);functional impairment, defined by mRS score >2 (OR 5.03, 95% CI 2.20-11.51) and by LADL score 2, 0.88 vs 0.84, p = 0.047). Conclusion Early cognitive testing by MoCA predicts long-term cognitive outcome, functional outcome, and mortality after stroke. Our results support routine use of the MoCA in stroke patients