Validation of Plasma Biomarker Candidates for the Prediction of eGFR Decline in Patients With Type 2 Diabetes
(2018) In Diabetes Care 41(9). p.1947-1954- 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... (More)
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.
(Less)
- author
- Heinzel, Andreas
; Kammer, Michael
; Mayer, Gert
; Reindl-Schwaighofer, Roman
; Hu, Karin
; Perco, Paul
; Eder, Susanne
; Rosivall, Laszlo
; Mark, Patrick B.
; Ju, Wenjun
; Kretzler, Matthias
; Gilmour, Peter
; Wilson, Jonathan M.
; Duffin, Kevin L.
; Abdalla, Moustafa
; McCarthy, Mark I.
; Heinze, Georg
; Heerspink, Hiddo L.
; Wiecek, Andrzej
; Gomez, Maria F.
LU
and Oberbauer, Rainer
(Less) - author collaboration
- organization
- publishing date
- 2018
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Diabetes Care
- volume
- 41
- issue
- 9
- pages
- 8 pages
- publisher
- American Diabetes Association
- external identifiers
-
- scopus:85054728054
- pmid:29980527
- ISSN
- 1935-5548
- DOI
- 10.2337/dc18-0532
- language
- English
- LU publication?
- yes
- id
- 01afddd3-1818-4377-8220-cb7f65de9533
- date added to LUP
- 2018-11-05 12:38:21
- date last changed
- 2024-06-10 21:30:38
@article{01afddd3-1818-4377-8220-cb7f65de9533,
abstract = {{<p>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.</p><p>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%.</p><p>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.</p><p>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.</p>}},
author = {{Heinzel, Andreas and Kammer, Michael and Mayer, Gert and Reindl-Schwaighofer, Roman and Hu, Karin and Perco, Paul and Eder, Susanne and Rosivall, Laszlo and Mark, Patrick B. and Ju, Wenjun and Kretzler, Matthias and Gilmour, Peter and Wilson, Jonathan M. and Duffin, Kevin L. and Abdalla, Moustafa and McCarthy, Mark I. and Heinze, Georg and Heerspink, Hiddo L. and Wiecek, Andrzej and Gomez, Maria F. and Oberbauer, Rainer}},
issn = {{1935-5548}},
language = {{eng}},
number = {{9}},
pages = {{1947--1954}},
publisher = {{American Diabetes Association}},
series = {{Diabetes Care}},
title = {{Validation of Plasma Biomarker Candidates for the Prediction of eGFR Decline in Patients With Type 2 Diabetes}},
url = {{http://dx.doi.org/10.2337/dc18-0532}},
doi = {{10.2337/dc18-0532}},
volume = {{41}},
year = {{2018}},
}