| 1. |
- Heinzel, Andreas, et al.
(författare)
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Validation of Plasma Biomarker Candidates for the Prediction of eGFR Decline in Patients With Type 2 Diabetes
- 2018
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Ingår i: Diabetes Care. - : American Diabetes Association. - 1935-5548 .- 0149-5992. ; 41:9, s. 1947-1954
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Tidskriftsartikel (refereegranskat)abstract
- RESEARCH DESIGN AND METHODS: We studied participants in PROVALID (PROspective cohort study in patients with type 2 diabetes mellitus for VALIDation of biomarkers), a prospective multinational cohort study of patients with type 2 diabetes and a follow-up of more than 24 months (n = 2,560; baseline median eGFR, 84 mL/min/1.73 m2; urine albumin-to-creatinine ratio, 8.1 mg/g). The 17 biomarkers were measured at baseline in 481 samples using Luminex and ELISA. The prediction of eGFR decline was evaluated by linear mixed modeling.RESULTS: In univariable analyses, 9 of the 17 markers showed significant differences in median concentration between stable and fast-progressing patients. A linear mixed model for eGFR obtained by variable selection exhibited an adjusted R2 of 62%. A panel of 12 biomarkers was selected by the procedure and accounted for 34% of the total explained variability, of which 32% was due to 5 markers. The individual contribution of each biomarker to the prediction of eGFR decline on top of clinical predictors was generally low. When included into the model, baseline eGFR exhibited the largest explained variability of eGFR decline (R2 of 79%), and the contribution of each biomarker dropped below 1%.CONCLUSIONS: In this longitudinal study of patients with type 2 diabetes and maintained eGFR at baseline, 12 of the 17 candidate biomarkers were associated with eGFR decline, but their predictive power was low.OBJECTIVE: The decline of estimated glomerular filtration rate (eGFR) in patients with type 2 diabetes is variable, and early interventions would likely be cost-effective. We elucidated the contribution of 17 plasma biomarkers to the prediction of eGFR loss on top of clinical risk factors.
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| 2. |
- Kammer, Michael, et al.
(författare)
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Integrative analysis of prognostic biomarkers derived from multiomics panels helps discrimination of chronic kidney disease trajectories in people with type 2 diabetes
- 2019
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Ingår i: Kidney International. - : Elsevier BV. - 0085-2538. ; 96:6, s. 1381-1388
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Tidskriftsartikel (refereegranskat)abstract
- Clinical risk factors explain only a fraction of the variability of estimated glomerular filtration rate (eGFR) decline in people with type 2 diabetes. Cross-omics technologies by virtue of a wide spectrum screening of plasma samples have the potential to identify biomarkers for the refinement of prognosis in addition to clinical variables. Here we utilized proteomics, metabolomics and lipidomics panel assay measurements in baseline plasma samples from the multinational PROVALID study (PROspective cohort study in patients with type 2 diabetes mellitus for VALIDation of biomarkers) of patients with incident or early chronic kidney disease (median follow-up 35 months, median baseline eGFR 84 mL/min/1.73 m2, urine albumin-to-creatinine ratio 8.1 mg/g). In an accelerated case-control study, 258 individuals with a stable eGFR course (median eGFR change 0.1 mL/min/year) were compared to 223 individuals with a rapid eGFR decline (median eGFR decline -6.75 mL/min/year) using Bayesian multivariable logistic regression models to assess the discrimination of eGFR trajectories. The analysis included 402 candidate predictors and showed two protein markers (KIM-1, NTproBNP) to be relevant predictors of the eGFR trajectory with baseline eGFR being an important clinical covariate. The inclusion of metabolomic and lipidomic platforms did not improve discrimination substantially. Predictions using all available variables were statistically indistinguishable from predictions using only KIM-1 and baseline eGFR (area under the receiver operating characteristic curve 0.63). Thus, the discrimination of eGFR trajectories in patients with incident or early diabetic kidney disease and maintained baseline eGFR was modest and the protein marker KIM-1 was the most important predictor.
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| 3. |
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| 4. |
- Culina, Antica, et al.
(författare)
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Connecting the data landscape of long-term ecological studies : The SPI-Birds data hub
- 2021
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Ingår i: Journal of Animal Ecology. - : John Wiley & Sons. - 0021-8790 .- 1365-2656. ; 90:9, s. 2147-2160
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Tidskriftsartikel (refereegranskat)abstract
- The integration and synthesis of the data in different areas of science is drastically slowed and hindered by a lack of standards and networking programmes. Long-term studies of individually marked animals are not an exception. These studies are especially important as instrumental for understanding evolutionary and ecological processes in the wild. Furthermore, their number and global distribution provides a unique opportunity to assess the generality of patterns and to address broad-scale global issues (e.g. climate change). To solve data integration issues and enable a new scale of ecological and evolutionary research based on long-term studies of birds, we have created the SPI-Birds Network and Database ()-a large-scale initiative that connects data from, and researchers working on, studies of wild populations of individually recognizable (usually ringed) birds. Within year and a half since the establishment, SPI-Birds has recruited over 120 members, and currently hosts data on almost 1.5 million individual birds collected in 80 populations over 2,000 cumulative years, and counting. SPI-Birds acts as a data hub and a catalogue of studied populations. It prevents data loss, secures easy data finding, use and integration and thus facilitates collaboration and synthesis. We provide community-derived data and meta-data standards and improve data integrity guided by the principles of Findable, Accessible, Interoperable and Reusable (FAIR), and aligned with the existing metadata languages (e.g. ecological meta-data language). The encouraging community involvement stems from SPI-Bird's decentralized approach: research groups retain full control over data use and their way of data management, while SPI-Birds creates tailored pipelines to convert each unique data format into a standard format. We outline the lessons learned, so that other communities (e.g. those working on other taxa) can adapt our successful model. Creating community-specific hubs (such as ours, COMADRE for animal demography, etc.) will aid much-needed large-scale ecological data integration.
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| 5. |
- Szöllösi, E, et al.
(författare)
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Determinants of distribution and prevalence of avian malaria in blue tit populations across Europe: separating host and parasite effects.
- 2011
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Ingår i: Journal of evolutionary biology. - : Wiley. - 1420-9101 .- 1010-061X. ; 24, s. 2014-2024
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Tidskriftsartikel (refereegranskat)abstract
- Although avian malarial parasites are globally distributed, the factors that affect the geographical distribution and local prevalence of different parasite lineages across host populations or species are still poorly understood. Based on the intense screening of avian malarial parasites in nine European blue tit populations, we studied whether distribution ranges as well as local adaptation, host specialization and phylogenetic relationships can determine the observed prevalences within populations. We found that prevalence differed consistently between parasite lineages and host populations, indicating that the transmission success of parasites is lineage specific but is partly shaped by locality-specific effects. We also found that the lineage-specific estimate of prevalence was related to the distribution range of parasites: lineages found in more host populations were generally more prevalent within these populations. Additionally, parasites with high prevalence that were also widely distributed among blue tit populations were also found to infect more host species. These findings suggest that parasites reaching high local prevalence can also realize wide distribution at a global scale that can have further consequences for host specialization. Although phylogenetic relationships among parasites did not predict prevalence, we detected a close match between a tree based on the geographic distance of the host populations and the parasite phylogenetic tree, implying that neighbouring host populations shared a related parasite fauna.
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