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- Glerup, Rie, et al.
(författare)
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Multiplex proteomics as risk predictor of infection in patients treated with hemodialysis-A prospective multicenter study
- 2022
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Ingår i: Hemodialysis International. - : John Wiley & Sons. - 1492-7535 .- 1542-4758. ; 26:2, s. 191-201
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Tidskriftsartikel (refereegranskat)abstract
- Introduction Severe infection is a major problem in hemodialysis patients. Multiplex proteomics might reveal novel insights into disease mechanisms increasing the risk of infection and might also be used as a risk prediction tool. The aims of this study were (1) to evaluate associations between 92 proteins assessed by a proximity extension assay and the development of severe infection in patients on hemodialysis and (2) to develop a risk prediction model for severe infection using prespecified clinical variables and proteomics. Methods Prospective, observational multicenter cohort study with 5-year follow-up. Patients receiving in-center hemodialysis in five facilities in Denmark were included. The primary composite endpoint was death caused by infection, bacteremia, and infections requiring hospitalization of at least 2 days or prolonging a hospital stay. Findings Of 331 patients included 210 patients reached the primary endpoint during follow-up. In adjusted Cox regression analyses, 14 plasma proteins were associated with severe infection. Correcting for multiple testing revealed only cathepsin-L1 and interleukin-6 significantly associated with the primary outcome. Cathepsin-L1-hazard ratio: 1.64 (95% confidence interval [CI] 1.24-2.17) and interleukin-6-hazard ratio: 1.16 (95% CI 1.05-1.29). Apparent C-statistics of the risk prediction model using clinical variables was 0.605, addition of cathepsin-L1 and interleukin-6 to the model improved discrimination slightly: C = 0.625. Discussion Proteomic profiling identified cathepsin-L1 and interleukin-6 as markers for infectious risk in hemodialysis patients. Further studies are needed to replicate the results and to examine possible causality. The developed risk prediction models need considerable improvement before implementation in clinical practice is meaningful.
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| 2. |
- Wu, Ping-Hsun, 1982-, et al.
(författare)
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Novel biomarkers detected by proteomics predict death and cardiovascular events in hemodialysis patients
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Background. End-stage kidney disease increases mortality and the risk of cardiovascular (CV) disease. It is crucial to explore novel biomarkers to predict CV disease in the complex setting of patients receiving hemodialysis (HD). This study investigated the association between 92 targeted proteins with all-cause death, CV death, and composite vascular events (CVEs) in HD patients.Methods. From December 2010 to March 2011, 331 HD patients were included and followed prospectively for 5 years. Serum was analyzed for 92 CV-related proteins using Proseek Multiplex Cardiovascular I panel, a high-sensitivity assay based on proximity extension assay (PEA) technology. The association between biomarkers and all-cause death, CV death, and CVEs was evaluated by Cox-regression analyses.Results. Of the PEA-based proteins, we identified 20 proteins associated with risk of all-cause death, 7 proteins associated with risk of CV death, and 17 proteins associated with risk of CVEs, independent of established risk factors. Interleukin-8 (IL-8), T-cell immunoglobulin and mucin domain 1 (TIM-1), and C-C motif chemokine 20 (CCL20) were associated with increased risk of all-cause death, CV death, and CVE in multivariable-adjusted models. Stem cell factor (SCF) and Galanin peptides (GAL) were associated with both decreased risk of all-cause death and CV death.Conclusions. IL-8, TIM-1, and CCL20 predicted death and CV outcomes in HD patients. Novel findings were that SCF and GAL were associated with a lower risk of all death and CV death. The SCF warrants further study with regards to its possible biological effect in HD patients.
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| 3. |
- Wu, Ping-Hsun, 1982-, et al.
(författare)
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Novel Biomarkers Detected by Proteomics Predict Death and Cardiovascular Events in Hemodialysis Patients
- 2022
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Ingår i: Biomedicines. - : MDPI. - 2227-9059. ; 10:4
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Tidskriftsartikel (refereegranskat)abstract
- End-stage kidney disease increases mortality and the risk of cardiovascular (CV) disease. It is crucial to explore novel biomarkers to predict CV disease in the complex setting of patients receiving hemodialysis (HD). This study investigated the association between 92 targeted proteins with all-cause death, CV death, and composite vascular events (CVEs) in HD patients. From December 2010 to March 2011, 331 HD patients were included and followed prospectively for 5 years. Serum was analyzed for 92 CV-related proteins using Proseek Multiplex Cardiovascular I panel, a high-sensitivity assay based on proximity extension assay (PEA) technology. The association between biomarkers and all-cause death, CV death, and CVEs was evaluated using Cox-regression analyses. Of the PEA-based proteins, we identified 20 proteins associated with risk of all-cause death, 7 proteins associated with risk of CV death, and 17 proteins associated with risk of CVEs, independent of established risk factors. Interleukin-8 (IL-8), T-cell immunoglobulin and mucin domain 1 (TIM-1), and C-C motif chemokine 20 (CCL20) were associated with increased risk of all-cause death, CV death, and CVE in multivariable-adjusted models. Stem cell factor (SCF) and Galanin peptides (GAL) were associated with both decreased risk of all-cause death and CV death. In conclusion, IL-8, TIM-1, and CCL20 predicted death and CV outcomes in HD patients. Novel findings were that SCF and GAL were associated with a lower risk of all-cause death and CV death. The SCF warrants further study with regard to its possible biological effect in HD patients.
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| 4. |
- Wu, Ping-Hsun, 1982-, et al.
(författare)
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Osteoprotegerin predicts cardiovascular events in patients treated with haemodialysis
- 2021
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Ingår i: Nephrology, Dialysis and Transplantation. - : Oxford University Press. - 0931-0509 .- 1460-2385. ; 37:6, s. 1162-1170
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Tidskriftsartikel (refereegranskat)abstract
- Background: Disturbances in bone mineral metabolism are associated with increased mortality and cardiovascular events (CVEs). However, the association between bone-associated protein biomarkers, mortality, and CVEs independent of cytokine activation remains unknown. This study aimed to investigate bone-associated protein biomarkers, and the association with inflammatory cytokines, and cardiovascular outcomes.Methods: This prospective study enrolled hemodialysis (HD) patients in Denmark between December 2010 and March 2011. Using a proximity extension proteomics assay, nine bone-associated proteins were examined: cathepsin D (CTSD), cathepsin L1 (CTSL1), dickkopf-related protein 1 (Dkk-1), fibroblast growth factor 23 (FGF-23), leptin, osteoprotegerin (OPG), receptor activator of nuclear factor kappa-Β ligand (RANKL), TNF-related apoptosis-inducing ligand (TRAIL), and TNF-related apoptosis-inducing ligand receptor 2 (TRAIL-R2). The importance of the bone-associated protein markers was evaluated by a random forest algorithm (RF). The association between bone-associated proteins with all-cause death, cardiovascular death, and CVEs was analyzed in multivariable Cox models adjusted for age, gender, comorbidities, laboratory data, and dialysis duration.Results: We enrolled 331 patients (63.7% men; mean [SD] age, 65 [14.6] years) in a prospective cohort study with five years follow-up. When adjusting for confounders, CTSL1 remained associated with all-cause death, and four biomarkers were associated with CVE. However, the association between bone markers and the outcomes was attenuated after adjusting for inflammatory proteins, and just OPG remained associated with CVE in the adjusted model. Evaluating the importance of bone markers by RF, OPG was the most important marker related to CVEs. OPG also improved the prediction of CVE when added clinical information alone in integrated discrimination improvement and net reclassification improvement analyses.Discussion: OPG, a well-known bone biomarker, was associated with CVEs independent of cytokine activity. In contrast, the association between CVEs and the remaining three bone-associated proteins (TRAIL-R2, CTSD, and CTSL1) was affected by cytokine inflammation activity.
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