External validation and extension of a diagnostic model for obstructive coronary artery disease: A cross-sectional predictive evaluation in 4888 patients of the Austrian Coronary Artery disease Risk Determination in Innsbruck by diaGnostic ANgiography (CARDIIGAN) cohort

__Objective__ To externally validate and extend a recently proposed prediction model to diagnose obstructive coronary artery disease (CAD), with the ultimate aim to better select patients for coronary angiography. __Design__ Analysis of individual baseline data of a prospective cardiology cohort. __Setting__ Single-centre secondary and tertiary cardiology clinic. __Participants__ 4888 patients with suspected CAD, without known previous CAD or other heart diseases, who underwent an elective coronary angiography between 2004 and 2008 as part of the prospective Coronary Artery disease Risk Determination In Innsbruck by diaGnostic ANgiography (CARDIIGAN) cohort. Relevant data were recorded as in routine clinical practice. __Main outcome measures__ The probability of obstructive CAD, defined as a stenosis of minimally 50% diameter in at least one of the main coronary arteries, estimated with the predictors age, sex, type of chest pain, diabetes status, hypertension, dyslipidaemia, smoking status and laboratory data. Missing predictor data were multiply imputed. Performance of the suggested models was evaluated according to discrimination (area under the receiver operating characteristic curve, depicted by the c statistic) and calibration. Logistic regression modelling was applied for model updating. __Results__ Among the 4888 participants (38% women and 62% men), 2127 (44%) had an obstructive CAD. The previously proposed model had a c statistic of 0.69 (95% CI 0.67 to 0.70), which was lower than the expected c statistic while correcting for case mix (c=0.80). Regarding calibration, there was overprediction of risk for high-risk patients. All logistic regression coefficients were smaller than expected, especially for the predictor â € chest pain'. Ext

Farmer surveys in Europe suggest that specialized, intensive farms were more likely to perceive negative impacts from COVID-19.

It has been shown that the COVID-19 pandemic affected some agricultural systems more than others, and even within geographic regions, not all farms were affected to the same extent. To build resilience of agricultural systems to future shocks, it is key to understand which farms were affected and why. In this study, we examined farmers' perceived robustness to COVID-19, a key resilience capacity. We conducted standardized farmer interviews (n = 257) in 15 case study areas across Europe, covering a large range of socio-ecological contexts and farm types. Interviews targeted perceived livelihood impacts of the COVID-19 pandemic on productivity, sales, price, labor availability, and supply chains in 2020, as well as farm(er) characteristics and farm management. Our study corroborates earlier evidence that most farms were not or only slightly affected by the first wave(s) of the pandemic in 2020, and that impacts varied widely by study region. However, a significant minority of farmers across Europe reported that the pandemic was "the worst crisis in a lifetime" (3%) or "the worst crisis in a decade" (7%). Statistical analysis showed that more specialized and intensive farms were more likely to have perceived negative impacts. From a societal perspective, this suggests that highly specialized, intensive farms face higher vulnerability to shocks that affect regional to global supply chains. Supporting farmers in the diversification of their production systems while decreasing dependence on service suppliers and supply chain actors may increase their robustness to future disruptions. Supplementary Information The online version contains supplementary material available at 10.1007/s13593-022-00820-5

Relapse according to antipsychotic treatment in schizophrenic patients: a propensity-adjusted analysis

<p>Abstract</p> <p>Objective</p> <p>To compare the rate of relapse as a function of antipsychotic treatment (monotherapy vs. polypharmacy) in schizophrenic patients over a 2-year period.</p> <p>Methods</p> <p>Using data from a multicenter cohort study conducted in France, we performed a propensity-adjusted analysis to examine the association between the rate of relapse over a 2-year period and antipsychotic treatment (monotherapy vs. polypharmacy).</p> <p>Results</p> <p>Our sample consisted in 183 patients; 50 patients (27.3%) had at least one period of relapse and 133 had no relapse (72.7%). Thirty-eight (37.7) percent of the patients received polypharmacy. The most severely ill patients were given polypharmacy: the age at onset of illness was lower in the polypharmacy group (p = 0.03). Patients that received polypharmacy also presented a higher general psychopathology PANSS subscore (p = 0.04) but no statistically significant difference was found in the PANSS total score or the PANSS positive or negative subscales. These patients were more likely to be given prescriptions for sedative drugs (p < 0.01) and antidepressant medications (p = 0.03). Relapse was found in 23.7% of patients given monotherapy and 33.3% given polypharmacy (p = 0.16). After stratification according to quintiles of the propensity score, which eliminated all significant differences for baseline characteristics, antipsychotic polypharmacy was not statistically associated with an increase of relapse: HR = 1.686 (0.812; 2.505).</p> <p>Conclusion</p> <p>After propensity score adjustment, antipsychotic polypharmacy is not statistically associated to an increase of relapse. Future randomised studies are needed to assess the impact of antipsychotic polypharmacy in schizophrenia.</p

Genome-wide association analyses of symptom severity among clozapine-treated patients with schizophrenia spectrum disorders

Clozapine is the most effective antipsychotic for patients with treatment-resistant schizophrenia. However, response is highly variable and possible genetic underpinnings of this variability remain unknown. Here, we performed polygenic risk score (PRS) analyses to estimate the amount of variance in symptom severity among clozapine-treated patients explained by PRSs (R2) and examined the association between symptom severity and genotype-predicted CYP1A2, CYP2D6, and CYP2C19 enzyme activity. Genome-wide association (GWA) analyses were performed to explore loci associated with symptom severity. A multicenter cohort of 804 patients (after quality control N = 684) with schizophrenia spectrum disorder treated with clozapine were cross-sectionally assessed using the Positive and Negative Syndrome Scale and/or the Clinical Global Impression-Severity (CGI-S) scale. GWA and PRS regression analyses were conducted. Genotype-predicted CYP1A2, CYP2D6, and CYP2C19 enzyme activities were calculated. Schizophrenia-PRS was most significantly and positively associated with low symptom severity (p = 1.03 × 10−3; R2 = 1.85). Cross-disorder-PRS was also positively associated with lower CGI-S score (p = 0.01; R2 = 0.81). Compared to the lowest tertile, patients in the highest schizophrenia-PRS tertile had 1.94 times (p = 6.84×10−4) increased probability of low symptom severity. Higher genotype-predicted CYP2C19 enzyme activity was independently associated with lower symptom severity (p = 8.44×10−3). While no locus surpassed the genome-wide significance threshold, rs1923778 within NFIB showed a suggestive association (p = 3.78×10−7) with symptom severity. We show that high schizophrenia-PRS and genotype-predicted CYP2C19 enzyme activity are independently associated with lower symptom severity among individuals treated with clozapine. Our findings open avenues for future pharmacogenomic projects investigating the potential of PRS and genotype-predicted CYP-activity in schizophrenia

Influence of obesity-related risk factors in the aetiology of glioma

BACKGROUND: Obesity and related factors have been implicated as possible aetiological factors for the development of glioma in epidemiological observation studies. We used genetic markers in a Mendelian randomisation framework to examine whether obesity-related traits influence glioma risk. This methodology reduces bias from confounding and is not affected by reverse causation. METHODS: Genetic instruments were identified for 10 key obesity-related risk factors, and their association with glioma risk was evaluated using data from a genome-wide association study of 12,488 glioma patients and 18,169 controls. The estimated odds ratio of glioma associated with each of the genetically defined obesity-related traits was used to infer evidence for a causal relationship. RESULTS: No convincing association with glioma risk was seen for genetic instruments for body mass index, waist-to-hip ratio, lipids, type-2 diabetes, hyperglycaemia or insulin resistance. Similarly, we found no evidence to support a relationship between obesity-related traits with subtypes of glioma-glioblastoma (GBM) or non-GBM tumours. CONCLUSIONS: This study provides no evidence to implicate obesity-related factors as causes of glioma