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Institution

University of Gothenburg

EducationGothenburg, Sweden
About: University of Gothenburg is a education organization based out in Gothenburg, Sweden. It is known for research contribution in the topics: Population & Health care. The organization has 23855 authors who have published 65241 publications receiving 2606327 citations. The organization is also known as: Göteborg University & Gothenburg University.


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Journal ArticleDOI
TL;DR: In this paper, the authors argue that part of the reason why anticorruption reforms in countries plagued by widespread corruption fail is that they are based on a theoretical mischaracterization of the problem of systemic corruption.
Abstract: With an increased awareness of the detrimental effects of corruption on development, strategies to fight it are now a top priority in policy circles. Yet, in countries ridden with systemic corruption, few successes have resulted from the investment. On the basis of an interview study conducted in Kenya and Uganda—two arguably typically thoroughly corrupt countries—we argue that part of an explanation to why anticorruption reforms in countries plagued by widespread corruption fail is that they are based on a theoretical mischaracterization of the problem of systemic corruption. More specifically, the analysis reveals that while contemporary anticorruption reforms are based on a conceptualization of corruption as a principal–agent problem, in thoroughly corrupt settings, corruption rather resembles a collective action problem. This, in turn, leads to a breakdown of any anticorruption reform that builds on the principal–agent framework, taking the existence of noncorruptible so-called principals for granted.

567 citations

Journal ArticleDOI
TL;DR: Plasma P-tau18 level increased with progression of Alzheimer’s disease (AD) and differentiated AD dementia from other neurodegenerative diseases, supporting its further development as a blood-based biomarker for AD.
Abstract: Plasma phosphorylated tau181 (P-tau181) might be increased in Alzheimer’s disease (AD), but its usefulness for differential diagnosis and prognosis is unclear. We studied plasma P-tau181 in three cohorts, with a total of 589 individuals, including cognitively unimpaired participants and patients with mild cognitive impairment (MCI), AD dementia and non-AD neurodegenerative diseases. Plasma P-tau181 was increased in preclinical AD and further increased at the MCI and dementia stages. It correlated with CSF P-tau181 and predicted positive Tau positron emission tomography (PET) scans (area under the curve (AUC) = 0.87–0.91 for different brain regions). Plasma P-tau181 differentiated AD dementia from non-AD neurodegenerative diseases with an accuracy similar to that of Tau PET and CSF P-tau181 (AUC = 0.94–0.98), and detected AD neuropathology in an autopsy-confirmed cohort. High plasma P-tau181 was associated with subsequent development of AD dementia in cognitively unimpaired and MCI subjects. In conclusion, plasma P-tau181 is a noninvasive diagnostic and prognostic biomarker of AD, which may be useful in clinical practice and trials. Plasma P-tau18 level increased with progression of Alzheimer’s disease (AD) and differentiated AD dementia from other neurodegenerative diseases, supporting its further development as a blood-based biomarker for AD.

565 citations

Journal ArticleDOI
TL;DR: It is reported that gut microbiota modulate the intestinal eCB system tone, which in turn regulates gut permeability and plasma lipopolysaccharide (LPS) levels, and shows that LPS acts as a master switch to control adipose tissue metabolism both in vivo and ex vivo by blocking cannabinoid‐driven adipogenesis.
Abstract: Obesity is characterised by altered gut microbiota, low-grade inflammation and increased endocannabinoid (eCB) system tone; however, a clear connection between gut microbiota and eCB signalling has yet to be confirmed. Here, we report that gut microbiota modulate the intestinal eCB system tone, which in turn regulates gut permeability and plasma lipopolysaccharide (LPS) levels. The impact of the increased plasma LPS levels and eCB system tone found in obesity on adipose tissue metabolism (e.g. differentiation and lipogenesis) remains unknown. By interfering with the eCB system using CB1 agonist and antagonist in lean and obese mouse models, we found that the eCB system controls gut permeability and adipogenesis. We also show that LPS acts as a master switch to control adipose tissue metabolism both in vivo and ex vivo by blocking cannabinoid-driven adipogenesis. These data indicate that gut microbiota determine adipose tissue physiology through LPS-eCB system regulatory loops and may have critical functions in adipose tissue plasticity during obesity.

565 citations

Journal ArticleDOI
Emanuele Di Angelantonio1, Stephen Kaptoge1, David Wormser1, Peter Willeit1, Adam S. Butterworth1, Narinder Bansal1, Linda M. O’Keeffe1, Pei Gao1, Angela M. Wood1, Stephen Burgess1, Daniel F. Freitag1, Lisa Pennells1, Sanne A.E. Peters2, Carole L. Hart3, Lise Lund Håheim4, Richard F. Gillum5, Børge G. Nordestgaard6, Bruce M. Psaty7, Bu B. Yeap8, Matthew Knuiman8, Paul J. Nietert9, Jussi Kauhanen10, Jukka T. Salonen11, Lewis H. Kuller12, Leon A. Simons13, Yvonne T. van der Schouw2, Elizabeth Barrett-Connor14, Randi Selmer15, Carlos J. Crespo16, Beatriz L. Rodriguez17, W. M. Monique Verschuren, Veikko Salomaa18, Kurt Svärdsudd19, Pim van der Harst20, Cecilia Björkelund21, Lars Wilhelmsen21, Robert B. Wallace22, Hermann Brenner23, Philippe Amouyel24, Elizabeth L M Barr25, Hiroyasu Iso26, Altan Onat27, Maurizio Trevisan28, Ralph B. D'Agostino29, Cyrus Cooper30, Cyrus Cooper31, Maryam Kavousi32, Lennart Welin, Ronan Roussel33, Ronan Roussel34, Frank B. Hu35, Shinichi Sato, Karina W. Davidson36, Barbara V. Howard37, Maarten J.G. Leening32, Annika Rosengren21, Marcus Dörr38, Dorly J. H. Deeg39, Stefan Kiechl, Coen D.A. Stehouwer40, Aulikki Nissinen18, Simona Giampaoli41, Chiara Donfrancesco41, Daan Kromhout42, Jackie F. Price43, Annette Peters, Tom W. Meade44, Edoardo Casiglia45, Debbie A Lawlor46, John Gallacher47, Dorothea Nagel48, Oscar H. Franco32, Gerd Assmann, Gilles R. Dagenais, J. Wouter Jukema49, Johan Sundström19, Mark Woodward50, Eric J. Brunner51, Kay-Tee Khaw1, Nicholas J. Wareham52, Eric A. Whitsel53, Inger Njølstad54, Bo Hedblad55, Sylvia Wassertheil-Smoller56, Gunnar Engström55, Wayne D. Rosamond53, Elizabeth Selvin57, Naveed Sattar3, Simon G. Thompson1, John Danesh1 
University of Cambridge1, Utrecht University2, University of Glasgow3, University of Oslo4, Howard University5, Copenhagen University Hospital6, University of Washington7, University of Western Australia8, Medical University of South Carolina9, University of Eastern Finland10, Analytical Services11, University of Pittsburgh12, University of New South Wales13, University of California, San Diego14, Norwegian Institute of Public Health15, Portland State University16, University of Hawaii17, National Institutes of Health18, Uppsala University19, University Medical Center Groningen20, University of Gothenburg21, University of Iowa22, German Cancer Research Center23, Pasteur Institute24, Baker IDI Heart and Diabetes Institute25, Osaka University26, Istanbul University27, City College of New York28, Boston University29, University of Southampton30, University of Oxford31, Erasmus University Rotterdam32, Paris Diderot University33, French Institute of Health and Medical Research34, Harvard University35, Columbia University Medical Center36, MedStar Health37, Greifswald University Hospital38, VU University Amsterdam39, Maastricht University Medical Centre40, Istituto Superiore di Sanità41, Wageningen University and Research Centre42, University of Edinburgh43, University of London44, University of Padua45, University of Bristol46, Cardiff University47, Ludwig Maximilian University of Munich48, Leiden University Medical Center49, University of Sydney50, University College London51, Medical Research Council52, University of North Carolina at Chapel Hill53, University of Tromsø54, Lund University55, Albert Einstein College of Medicine56, Johns Hopkins University57
07 Jul 2015-JAMA
TL;DR: Because any combination of these conditions was associated with multiplicative mortality risk, life expectancy was substantially lower in people with multimorbidity.
Abstract: IMPORTANCE: The prevalence of cardiometabolic multimorbidity is increasing. OBJECTIVE: To estimate reductions in life expectancy associated with cardiometabolic multimorbidity. DESIGN, SETTING, AND PARTICIPANTS: Age- and sex-adjusted mortality rates and hazard ratios (HRs) were calculated using individual participant data from the Emerging Risk Factors Collaboration (689,300 participants; 91 cohorts; years of baseline surveys: 1960-2007; latest mortality follow-up: April 2013; 128,843 deaths). The HRs from the Emerging Risk Factors Collaboration were compared with those from the UK Biobank (499,808 participants; years of baseline surveys: 2006-2010; latest mortality follow-up: November 2013; 7995 deaths). Cumulative survival was estimated by applying calculated age-specific HRs for mortality to contemporary US age-specific death rates. EXPOSURES: A history of 2 or more of the following: diabetes mellitus, stroke, myocardial infarction (MI). MAIN OUTCOMES AND MEASURES: All-cause mortality and estimated reductions in life expectancy. RESULTS: In participants in the Emerging Risk Factors Collaboration without a history of diabetes, stroke, or MI at baseline (reference group), the all-cause mortality rate adjusted to the age of 60 years was 6.8 per 1000 person-years. Mortality rates per 1000 person-years were 15.6 in participants with a history of diabetes, 16.1 in those with stroke, 16.8 in those with MI, 32.0 in those with both diabetes and MI, 32.5 in those with both diabetes and stroke, 32.8 in those with both stroke and MI, and 59.5 in those with diabetes, stroke, and MI. Compared with the reference group, the HRs for all-cause mortality were 1.9 (95% CI, 1.8-2.0) in participants with a history of diabetes, 2.1 (95% CI, 2.0-2.2) in those with stroke, 2.0 (95% CI, 1.9-2.2) in those with MI, 3.7 (95% CI, 3.3-4.1) in those with both diabetes and MI, 3.8 (95% CI, 3.5-4.2) in those with both diabetes and stroke, 3.5 (95% CI, 3.1-4.0) in those with both stroke and MI, and 6.9 (95% CI, 5.7-8.3) in those with diabetes, stroke, and MI. The HRs from the Emerging Risk Factors Collaboration were similar to those from the more recently recruited UK Biobank. The HRs were little changed after further adjustment for markers of established intermediate pathways (eg, levels of lipids and blood pressure) and lifestyle factors (eg, smoking, diet). At the age of 60 years, a history of any 2 of these conditions was associated with 12 years of reduced life expectancy and a history of all 3 of these conditions was associated with 15 years of reduced life expectancy. CONCLUSIONS AND RELEVANCE: Mortality associated with a history of diabetes, stroke, or MI was similar for each condition. Because any combination of these conditions was associated with multiplicative mortality risk, life expectancy was substantially lower in people with multimorbidity.

564 citations

Journal ArticleDOI
TL;DR: Blood p-tau181 can predict tau and amyloid β pathologies, differentiate Alzheimer's disease from other neurodegenerative disorders, and identify Alzheimer's Disease across the clinical continuum.
Abstract: Summary Background CSF and PET biomarkers of amyloid β and tau accurately detect Alzheimer's disease pathology, but the invasiveness, high cost, and poor availability of these detection methods restrict their widespread use as clinical diagnostic tools. CSF tau phosphorylated at threonine 181 (p-tau181) is a highly specific biomarker for Alzheimer's disease pathology. We aimed to assess whether blood p-tau181 could be used as a biomarker for Alzheimer's disease and for prediction of cognitive decline and hippocampal atrophy. Methods We developed and validated an ultrasensitive blood immunoassay for p-tau181. Assay performance was evaluated in four clinic-based prospective cohorts. The discovery cohort comprised patients with Alzheimer's disease and age-matched controls. Two validation cohorts (TRIAD and BioFINDER-2) included cognitively unimpaired older adults (mean age 63–69 years), participants with mild cognitive impairment (MCI), Alzheimer's disease, and frontotemporal dementia. In addition, TRIAD included healthy young adults (mean age 23 years) and BioFINDER-2 included patients with other neurodegenerative disorders. The primary care cohort, which recruited participants in Montreal, Canada, comprised control participants from the community without a diagnosis of a neurological condition and patients referred from primary care physicians of the Canadian National Health Service for specialist care. Concentrations of plasma p-tau181 were compared with established CSF and PET biomarkers and longitudinal measurements using Spearman correlation, area under the curve (AUC), and linear regression analyses. Findings We studied 37 individuals in the discovery cohort, 226 in the first validation cohort (TRIAD), 763 in the second validation cohort (BioFINDER-2), and 105 in the primary care cohort (n=1131 individuals). In all cohorts, plasma p-tau181 showed gradual increases along the Alzheimer's disease continuum, from the lowest concentrations in amyloid β-negative young adults and cognitively unimpaired older adults, through higher concentrations in the amyloid β-positive cognitively unimpaired older adults and MCI groups, to the highest concentrations in the amyloid β-positive MCI and Alzheimer's disease groups (p Interpretation Blood p-tau181 can predict tau and amyloid β pathologies, differentiate Alzheimer's disease from other neurodegenerative disorders, and identify Alzheimer's disease across the clinical continuum. Blood p-tau181 could be used as a simple, accessible, and scalable test for screening and diagnosis of Alzheimer's disease. Funding Alzheimer Drug Discovery Foundation, European Research Council, Swedish Research Council, Swedish Alzheimer Foundation, Swedish Dementia Foundation, Alzheimer Society Research Program.

563 citations


Authors

Showing all 24120 results

NameH-indexPapersCitations
Peter J. Barnes1941530166618
Luigi Ferrucci1931601181199
Richard H. Friend1691182140032
Napoleone Ferrara167494140647
Timothy A. Springer167669122421
Anders Björklund16576984268
Hua Zhang1631503116769
Kaj Blennow1601845116237
Leif Groop158919136056
Tomas Hökfelt158103395979
Johan G. Eriksson1561257123325
Naveed Sattar1551326116368
Paul Elliott153773103839
Claude Bouchard1531076115307
Hakon Hakonarson152968101604
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023145
2022539
20215,065
20204,657
20194,254
20183,850