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- 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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Different roles of protein biomarkers predicting eGFR trajectories in people with chronic kidney disease and diabetes mellitus : a nationwide retrospective cohort study
- 2023
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Ingår i: Cardiovascular Diabetology. - : Springer Science and Business Media LLC. - 1475-2840. ; 22, s. 1-10
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Chronic kidney disease (CKD) is a common comorbidity in people with diabetes mellitus, and a key risk factor for further life-threatening conditions such as cardiovascular disease. The early prediction of progression of CKD therefore is an important clinical goal, but remains difficult due to the multifaceted nature of the condition. We validated a set of established protein biomarkers for the prediction of trajectories of estimated glomerular filtration rate (eGFR) in people with moderately advanced chronic kidney disease and diabetes mellitus. Our aim was to discern which biomarkers associate with baseline eGFR or are important for the prediction of the future eGFR trajectory.METHODS: We used Bayesian linear mixed models with weakly informative and shrinkage priors for clinical predictors (n = 12) and protein biomarkers (n = 19) to model eGFR trajectories in a retrospective cohort study of people with diabetes mellitus (n = 838) from the nationwide German Chronic Kidney Disease study. We used baseline eGFR to update the models' predictions, thereby assessing the importance of the predictors and improving predictive accuracy computed using repeated cross-validation.RESULTS: The model combining clinical and protein predictors had higher predictive performance than a clinical only model, with an [Formula: see text] of 0.44 (95% credible interval 0.37-0.50) before, and 0.59 (95% credible interval 0.51-0.65) after updating by baseline eGFR, respectively. Only few predictors were sufficient to obtain comparable performance to the main model, with markers such as Tumor Necrosis Factor Receptor 1 and Receptor for Advanced Glycation Endproducts being associated with baseline eGFR, while Kidney Injury Molecule 1 and urine albumin-creatinine-ratio were predictive for future eGFR decline.CONCLUSIONS: Protein biomarkers only modestly improve predictive accuracy compared to clinical predictors alone. The different protein markers serve different roles for the prediction of longitudinal eGFR trajectories potentially reflecting their role in the disease pathway.
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| 3. |
- 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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