| 1. |
- Azevedo, Flavio, et al.
(author)
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Social and moral psychology of COVID-19 across 69 countries
- 2023
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In: Scientific Data. - : NATURE PORTFOLIO. - 2052-4463. ; 10:1
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Journal article (peer-reviewed)abstract
- The COVID-19 pandemic has affected all domains of human life, including the economic and social fabric of societies. One of the central strategies for managing public health throughout the pandemic has been through persuasive messaging and collective behaviour change. To help scholars better understand the social and moral psychology behind public health behaviour, we present a dataset comprising of 51,404 individuals from 69 countries. This dataset was collected for the International Collaboration on Social & Moral Psychology of COVID-19 project (ICSMP COVID-19). This social science survey invited participants around the world to complete a series of moral and psychological measures and public health attitudes about COVID-19 during an early phase of the COVID-19 pandemic (between April and June 2020). The survey included seven broad categories of questions: COVID-19 beliefs and compliance behaviours; identity and social attitudes; ideology; health and well-being; moral beliefs and motivation; personality traits; and demographic variables. We report both raw and cleaned data, along with all survey materials, data visualisations, and psychometric evaluations of key variables.
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| 2. |
- Kanoni, Stavroula, et al.
(author)
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Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis.
- 2022
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In: Genome biology. - : Springer Science and Business Media LLC. - 1474-760X .- 1465-6906 .- 1474-7596. ; 23:1
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Journal article (peer-reviewed)abstract
- Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery.To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N=1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism.Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.
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| 3. |
- Van Bavel, Jay J., et al.
(author)
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National identity predicts public health support during a global pandemic
- 2022
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In: Nature Communications. - : Nature Portfolio. - 2041-1723. ; 13:1
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Journal article (peer-reviewed)abstract
- Understanding collective behaviour is an important aspect of managing the pandemic response. Here the authors show in a large global study that participants that reported identifying more strongly with their nation reported greater engagement in public health behaviours and support for public health policies in the context of the pandemic. Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national samples. Study 2 (N = 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic (r = -0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics.
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| 4. |
- Kehoe, Laura, et al.
(author)
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Make EU trade with Brazil sustainable
- 2019
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In: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 364:6438, s. 341-
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Journal article (other academic/artistic)
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| 5. |
- Bourached, Anthony, et al.
(author)
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Scaling behaviours of deep learning and linear algorithms for the prediction of stroke severity
- 2023
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In: BRAIN COMMUNICATIONS. - 2632-1297. ; 6:1
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Journal article (peer-reviewed)abstract
- Deep learning has allowed for remarkable progress in many medical scenarios. Deep learning prediction models often require 105-107 examples. It is currently unknown whether deep learning can also enhance predictions of symptoms post-stroke in real-world samples of stroke patients that are often several magnitudes smaller. Such stroke outcome predictions however could be particularly instrumental in guiding acute clinical and rehabilitation care decisions. We here compared the capacities of classically used linear and novel deep learning algorithms in their prediction of stroke severity. Our analyses relied on a total of 1430 patients assembled from the MRI-Genetics Interface Exploration collaboration and a Massachusetts General Hospital-based study. The outcome of interest was National Institutes of Health Stroke Scale-based stroke severity in the acute phase after ischaemic stroke onset, which we predict by means of MRI-derived lesion location. We automatically derived lesion segmentations from diffusion-weighted clinical MRI scans, performed spatial normalization and included a principal component analysis step, retaining 95% of the variance of the original data. We then repeatedly separated a train, validation and test set to investigate the effects of sample size; we subsampled the train set to 100, 300 and 900 and trained the algorithms to predict the stroke severity score for each sample size with regularized linear regression and an eight-layered neural network. We selected hyperparameters on the validation set. We evaluated model performance based on the explained variance (R2) in the test set. While linear regression performed significantly better for a sample size of 100 patients, deep learning started to significantly outperform linear regression when trained on 900 patients. Average prediction performance improved by similar to 20% when increasing the sample size 9x [maximum for 100 patients: 0.279 +/- 0.005 (R2, 95% confidence interval), 900 patients: 0.337 +/- 0.006]. In summary, for sample sizes of 900 patients, deep learning showed a higher prediction performance than typically employed linear methods. These findings suggest the existence of non-linear relationships between lesion location and stroke severity that can be utilized for an improved prediction performance for larger sample sizes. Bourached et al. contrast linear and deep learning-based algorithms in their prediction performances of stroke severity depending on the training set sample sizes. They find that linear regression outperforms deep learning-based algorithms for smaller training samples comprising lesion location information of 100 patients, while deep learning excels in the case of larger samples (N = 900).
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| 6. |
- Frebel, Anna, et al.
(author)
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Discovery of HE 1523-0901, a strongly r-process-enhanced metal-poor star with detected uranium
- 2007
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In: Astrophysical Journal. - 0004-637X .- 1538-4357. ; 660:2, s. L117-L120
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Journal article (peer-reviewed)abstract
- We present age estimates for the newly discovered, very r-process-enhanced metal-poor star HE 1523 - 0901 ([Fe/H] = -2.95) based on the radioactive decay of Th and U. The bright (V = 11.1) giant was found among a sample of bright metal-poor stars selected from the Hamburg/ESO Survey. From an abundance analysis of a high-resolution (R = 75,000) VLT/UVES spectrum, we find HE 1523 - 0901 to be strongly overabundant in r- process elements ([r/Fe] = 1.8). The abundances of heavy neutron-capture elements (Z > 56) measured in HE 1523 - 0901 match the scaled solar r- process pattern extremely well. We detect the strongest optical U line at 3859.57 angstrom. For the first time, we are able to employ several different chronometers, such as the U/Th, U/Ir, Th/ Eu, and Th/Os ratios to measure the age of a star. The weighted average age of HE 1523 - 0901 is 13.2 Gyr. Several sources of uncertainties are assessed in detail.
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| 7. |
- Heaney, Liam G., et al.
(author)
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Eosinophilic and Noneosinophilic Asthma : An Expert Consensus Framework to Characterize Phenotypes in a Global Real-Life Severe Asthma Cohort
- 2021
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In: Chest. - : Elsevier BV. - 0012-3692. ; 160:3, s. 814-830
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Journal article (peer-reviewed)abstract
- Background: Phenotypic characteristics of patients with eosinophilic and noneosinophilic asthma are not well characterized in global, real-life severe asthma cohorts. Research Question: What is the prevalence of eosinophilic and noneosinophilic phenotypes in the population with severe asthma, and can these phenotypes be differentiated by clinical and biomarker variables? Study Design and Methods: This was an historical registry study. Adult patients with severe asthma and available blood eosinophil count (BEC) from 11 countries enrolled in the International Severe Asthma Registry (January 1, 2015-September 30, 2019) were categorized according to likelihood of eosinophilic phenotype using a predefined gradient eosinophilic algorithm based on highest BEC, long-term oral corticosteroid use, elevated fractional exhaled nitric oxide, nasal polyps, and adult-onset asthma. Demographic and clinical characteristics were defined at baseline (ie, 1 year before or closest to date of BEC). Results: One thousand seven hundred sixteen patients with prospective data were included; 83.8% were identified as most likely (grade 3), 8.3% were identified as likely (grade 2), and 6.3% identified as least likely (grade 1) to have an eosinophilic phenotype, and 1.6% of patients showed a noneosinophilic phenotype (grade 0). Eosinophilic phenotype patients (ie, grades 2 or 3) showed later asthma onset (29.1 years vs 6.7 years; P < .001) and worse lung function (postbronchodilator % predicted FEV1, 76.1% vs 89.3%; P = .027) than those with a noneosinophilic phenotype. Patients with noneosinophilic phenotypes were more likely to be women (81.5% vs 62.9%; P = .047), to have eczema (20.8% vs 8.5%; P = .003), and to use anti-IgE (32.1% vs 13.4%; P = .004) and leukotriene receptor antagonists (50.0% vs 28.0%; P = .011) add-on therapy. Interpretation: According to this multicomponent, consensus-driven, and evidence-based eosinophil gradient algorithm (using variables readily accessible in real life), the severe asthma eosinophilic phenotype was more prevalent than previously identified and was phenotypically distinct. This pragmatic gradient algorithm uses variables readily accessible in primary and specialist care, addressing inherent issues of phenotype heterogeneity and phenotype instability. Identification of treatable traits across phenotypes should improve therapeutic precision.
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| 8. |
- Perez-de-Llano, Luis, et al.
(author)
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Impact of pre-biologic impairment on meeting domain-specific biologic responder definitions in patients with severe asthma
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In: Annals of Allergy, Asthma and Immunology. - 1081-1206.
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Journal article (peer-reviewed)abstract
- Background: There is little agreement on clinically useful criteria for identifying real-world responders to biologic treatments for asthma. Objective: To investigate the impact of pre-biologic impairment on meeting domain-specific biologic responder definitions in adults with severe asthma. Methods: This was a longitudinal, cohort study across 22 countries participating in the International Severe Asthma Registry (https://isaregistries.org/) between May 2017 and January 2023. Change in 4 asthma domains (exacerbation rate, asthma control, long-term oral corticosteroid [LTOCS] dose, and lung function) was assessed from biologic initiation to 1 year post-treatment (minimum 24 weeks). Pre- to post-biologic changes for responders and nonresponders were described along a categorical gradient for each domain derived from pre-biologic distributions (exacerbation rate: 0 to 6+/y; asthma control: well controlled to uncontrolled; LTOCS: 0 to >30 mg/d; percent-predicted forced expiratory volume in 1 second [ppFEV1]: <50% to ≥80%). Results: Percentage of biologic responders (ie, those with a category improvement pre- to post-biologic) varied by domain and increased with greater pre-biologic impairment, increasing from 70.2% to 90.0% for exacerbation rate, 46.3% to 52.3% for asthma control, 31.1% to 58.5% for LTOCS daily dose, and 35.8% to 50.6% for ppFEV1. The proportion of patients having improvement post-biologic tended to be greater for anti–IL-5/5R compared with for anti-IgE for exacerbation, asthma control, and ppFEV1 domains, irrespective of pre-biologic impairment. Conclusion: Our results provide realistic outcome-specific post-biologic expectations for both physicians and patients, will be foundational to inform future work on a multidimensional approach to define and assess biologic responders and response, and may enhance appropriate patient selection for biologic therapies. Trial Registration: The ISAR database has ethical approval from the Anonymous Data Ethics Protocols and Transparency (ADEPT) committee (ADEPT0218) and is registered with the European Union Electronic Register of Post-Authorization studies (ENCEPP/DSPP/23720). The study was designed, implemented, and reported in compliance with the European Network Centres for Pharmacoepidemiology and Pharmacovigilance (ENCEPP) Code of Conduct (EUPAS38288) and with all applicable local and international laws and regulation, and registered with ENCEPP (https://www.encepp.eu/encepp/viewResource.htm?id=38289). Governance was provided by ADEPT (registration number: ADEPT1220).
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| 9. |
- Wang, Thomas J., et al.
(author)
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2-Aminoadipic acid is a biomarker for diabetes risk
- 2013
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In: Journal of Clinical Investigation. - 0021-9738. ; 123:10, s. 4309-4317
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Journal article (peer-reviewed)abstract
- Improvements in metabolite-profiling techniques are providing increased breadth of coverage of the human metabolome and may highlight biomarkers and pathways in common diseases such as diabetes. Using a metabolomics platform that analyzes intermediary organic acids, purines, pyrimidines, and other compounds, we performed a nested case-control study of 188 individuals who developed diabetes and 188 propensity-matched controls from 2,422 normoglycemic participants followed for 12 years in the Framingham Heart Study. The metabolite 2-aminoadipic acid (2-AAA) was most strongly associated with the risk of developing diabetes. Individuals with 2-AAA concentrations in the top quartile had greater than a 4-fold risk of developing diabetes. Levels of 2-AAA were not well correlated with other metabolite biomarkers of diabetes, such as branched chain amino acids and aromatic amino acids, suggesting they report on a distinct pathophysiological pathway. In experimental studies, administration of 2-AAA lowered fasting plasma glucose levels in mice fed both standard chow and high-fat diets. Further, 2-AAA treatment enhanced insulin secretion from a pancreatic beta cell line as well as murine and human islets. These data highlight a metabolite not previously associated with diabetes risk that is increased up to 12 years before the onset of overt disease. Our findings suggest that 2-AAA is a marker of diabetes risk and a potential modulator of glucose homeostasis in humans.
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