| 1. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 2. |
|
|
| 3. |
- Mishra, A., et al.
(författare)
-
Stroke genetics informs drug discovery and risk prediction across ancestries
- 2022
-
Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 611
-
Tidskriftsartikel (refereegranskat)abstract
- Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry(1,2). Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis(3), and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach(4), we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry(5). Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.
|
|
| 4. |
|
|
| 5. |
- Bischoff M. M. J., Fang C. M. , De Groot R. A. , Heijnen G. M. M. , Katsnelson M. I. , Kolesnychenko O. Yu. , De Kort R. , Lichtenstein A. I. , Quinn A. J. , de Parma A. L. V. , Yamada T. K. , and Van Kempen H.
(författare)
-
Scanning tunneling spectroscopy study of surface states of 3d metals: Chemical identification, magnetic contrast and orbital Kondo resonance states
- 2003
-
Ingår i: Acta Physica Polonica A. ; , s. 231-
-
Konferensbidrag (refereegranskat)
|
|
| 6. |
|
|
| 7. |
- Busca, A, et al.
(författare)
-
Outcome of COVID-19 in allogeneic stem cell transplant recipients: Results from the EPICOVIDEHA registry
- 2023
-
Ingår i: Frontiers in immunology. - : Frontiers Media SA. - 1664-3224. ; 14, s. 1125030-
-
Tidskriftsartikel (refereegranskat)abstract
- The outcome of COVID-19 in allogeneic hematopoietic stem cell transplantation (HSCT) recipients is almost uniformely considered poor. The aim of present study was to retrospectively analyse the outcome and risk factors for mortality in a large series of patients who developed COVID-19 infection after an allogeneic HSCT.MethodsThis multicenter retrospective study promoted by the European Hematology Association – Infections in Hematology Study Working Group, included 326 adult HSCT patients who had COVID-19 between January 2020 and March 2022.ResultsThe median time from HSCT to the diagnosis of COVID-19 was 268 days (IQR 86-713; range 0-185 days). COVID-19 severity was mild in 21% of the patients, severe in 39% and critical in 16% of the patients. In multivariable analysis factors associated with a higher risk of mortality were, age above 50 years, presence of 3 or more comorbidities, active hematologic disease at time of COVID-19 infection, development of COVID-19 within 12 months of HSCT, and severe/critical infections. Overall mortality rate was 21% (n=68): COVID-19 was the main or secondary cause of death in 16% of the patients (n=53).ConclusionsMortality in HSCT recipients who develop COVID-19 is high and largely dependent on age, comorbidities, active hematologic disease, timing from transplant and severity of the infection.
|
|
| 8. |
- De Kort, A. M., et al.
(författare)
-
Decreased Cerebrospinal Fluid Amyloid beta 38, 40, 42, and 43 Levels in Sporadic and Hereditary Cerebral Amyloid Angiopathy
- 2023
-
Ingår i: Annals of neurology. - : Wiley. - 0364-5134 .- 1531-8249. ; 93:6, s. 1173-86
-
Tidskriftsartikel (refereegranskat)abstract
- Objective: Vascular amyloid beta (A beta) accumulation is the hallmark of cerebral amyloid angiopathy (CAA). The composition of cerebrospinal fluid (CSF) of CAA patients may serve as a diagnostic biomarker of CAA. We studied the diagnostic potential of the peptides A beta 38, A beta 40, A beta 42, and A beta 43 in patients with sporadic CAA (sCAA), hereditary Dutch-type CAA (D-CAA), and Alzheimer disease (AD).Methods: A beta peptides were quantified by immunoassays in a discovery group (26 patients with sCAA and 40 controls), a validation group (40 patients with sCAA, 40 patients with AD, and 37 controls), and a group of 22 patients with D-CAA and 54 controls. To determine the diagnostic accuracy, the area under the curve (AUC) was calculated using a receiver operating characteristic curve with 95% confidence interval (CI).Results: We found decreased levels of all A beta peptides in sCAA patients and D-CAA patients compared to controls. The difference was most prominent for A beta 42 (AUC of sCAA vs controls for discovery: 0.90, 95% CI = 0.82-0.99; for validation: 0.94, 95% CI = 0.89-0.99) and A beta 43 (AUC of sCAA vs controls for discovery: 0.95, 95% CI = 0.88-1.00; for validation: 0.91, 95% CI = 0.83-1.0). All A beta peptides except A beta 43 were also decreased in sCAA compared to AD (CSF A beta 38: AUC = 0.82, 95% CI = 0.71-0.93; CSF A beta 40: AUC = 0.88, 95% CI = 0.80-0.96; CSF A beta 42: AUC = 0.79, 95% CI = 0.66-0.92).Interpretation: A combined biomarker panel of CSF A beta 38, A beta 40, A beta 42, and A beta 43 has potential to differentiate sCAA from AD and controls, and D-CAA from controls. ANN NEUROL 2023
|
|
| 9. |
|
|
| 10. |
|
|
