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- Edfors, R., et al.
(författare)
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Use of proteomics to identify biomarkers associated with chronic kidney disease and long-term outcomes in patients with myocardial infarction
- 2020
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Ingår i: Journal of Internal Medicine. - : Wiley. - 0954-6820 .- 1365-2796. ; 288:5, s. 581-592
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Tidskriftsartikel (refereegranskat)abstract
- Background Patients with chronic kidney disease (CKD) have poor outcomes following myocardial infarction (MI). We performed an untargeted examination of 175 biomarkers to identify those with the strongest association with CKD and to examine the association of those biomarkers with long-term outcomes. Methods A total of 175 different biomarkers from MI patients enrolled in the Swedish Web-System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies (SWEDEHEART) registry were analysed either by a multiple reaction monitoring mass spectrometry assay or by a multiplex assay (proximity extension assay). Random forests statistical models were used to assess the predictor importance of biomarkers, CKD and outcomes. Results A total of 1098 MI patients with a median estimated glomerular filtration rate of 85 mL min(-1)/1.73 m(2)were followed for a median of 3.2 years. The random forests analyses, without and with adjustment for differences in demography, comorbidities and severity of disease, identified six biomarkers (adrenomedullin, TNF receptor-1, adipocyte fatty acid-binding protein-4, TNF-related apoptosis-inducing ligand receptor 2, growth differentiation factor-15 and TNF receptor-2) to be strongly associated with CKD. All six biomarkers were also amongst the 15 strongest predictors for death, and four of them were amongst the strongest predictors of subsequent MI and heart failure hospitalization. Conclusion In patients with MI, a proteomic approach could identify six biomarkers that best predicted CKD. These biomarkers were also amongst the most important predictors of long-term outcomes. Thus, these biomarkers indicate underlying mechanisms that may contribute to the poor prognosis seen in patients with MI and CKD.
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- Treusch, Sebastian, et al.
(författare)
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Functional links between Aβ toxicity, endocytic trafficking, and Alzheimer's disease risk factors in yeast
- 2011
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Ingår i: Science. - Washington : American association of advancement in science. - 0036-8075 .- 1095-9203. ; 334:6060, s. 1241-1245
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Tidskriftsartikel (refereegranskat)abstract
- Aβ (beta amyloid peptide) is an important contributor to Alzheimer's disease (AD). We modeled Aβ toxicity in yeast by directing the peptide to the secretory pathway. A genome-wide screen for toxicity modifiers identified the yeast homolog of phosphatidylinositol binding clathrin assembly protein (PICALM) and other endocytic factors connected to AD whose relationship to Aβ was previously unknown. The factors identified in yeast modified Aβ toxicity in glutamatergic neurons of Caenorhabditis elegans and in primary rat cortical neurons. In yeast, Aβ impaired the endocytic trafficking of a plasma membrane receptor, which was ameliorated by endocytic pathway factors identified in the yeast screen. Thus, links between Aβ, endocytosis, and human AD risk factors can be ascertained using yeast as a model system.
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- Cars, Thomas, et al.
(författare)
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Effectiveness of Drugs in Routine Care : A Model for Sequential Monitoring of New Medicines Using Dronedarone as Example
- 2018
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Ingår i: Clinical Pharmacology and Therapeutics. - : WILEY. - 0009-9236 .- 1532-6535. ; 103:3, s. 493-501
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Tidskriftsartikel (refereegranskat)abstract
- Although there is no doubt about the scientific value of randomized controlled clinical trials, they are usually conducted in selected populations different fromthose treated in clinical practice. Therefore, it is important to optimize real-time post-marketing evaluation of the effectiveness, safety, and cost of new drugs. Using electronic health records and administrative health databases froma well-defined region with universal access to healthcare, we have built a framework for real-time sequential monitoring of the effectiveness of newly marketed drugs in routine care. We chose the antiarrhythmic agent dronedarone as the study drug and flecainide as the comparator drug for illustration of the model. We demonstrate that this model produces consistent results with increasing precision over time as data accumulates in the clinical systems. We believe that use of this model at the introduction of new drugs can provide complementary evidence, especially in settings of adaptive licensing of new drugs.
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