Development of a Biomarker Panel to Distinguish Risk of Progressive Chronic Kidney Disease
Chronic kidney disease (CKD) patients typically progress to kidney failure, but the rate of progression differs per patient or may not occur at all. Current CKD screening methods are sub-optimal at predicting progressive kidney function decline. This investigation develops a model for predicting pro...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Biomedicines |
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| Online Access: | https://www.mdpi.com/2227-9059/8/12/606 |
| _version_ | 1797544813715783680 |
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| author | Evan Owens Ken-Soon Tan Robert Ellis Sharon Del Vecchio Tyrone Humphries Erica Lennan David Vesey Helen Healy Wendy Hoy Glenda Gobe |
| author_facet | Evan Owens Ken-Soon Tan Robert Ellis Sharon Del Vecchio Tyrone Humphries Erica Lennan David Vesey Helen Healy Wendy Hoy Glenda Gobe |
| author_sort | Evan Owens |
| collection | DOAJ |
| description | Chronic kidney disease (CKD) patients typically progress to kidney failure, but the rate of progression differs per patient or may not occur at all. Current CKD screening methods are sub-optimal at predicting progressive kidney function decline. This investigation develops a model for predicting progressive CKD based on a panel of biomarkers representing the pathophysiological processes of CKD, kidney function, and common CKD comorbidities. Two patient cohorts are utilised: The CKD Queensland Registry (n = 418), termed the Biomarker Discovery cohort; and the CKD Biobank (n = 62), termed the Predictive Model cohort. Progression status is assigned with a composite outcome of a ≥30% decline in eGFR from baseline, initiation of dialysis, or kidney transplantation. Baseline biomarker measurements are compared between progressive and non-progressive patients via logistic regression. In the Biomarker Discovery cohort, 13 biomarkers differed significantly between progressive and non-progressive patients, while 10 differed in the Predictive Model cohort. From this, a predictive model, based on a biomarker panel of serum creatinine, osteopontin, tryptase, urea, and eGFR, was calculated via linear discriminant analysis. This model has an accuracy of 84.3% when predicting future progressive CKD at baseline, greater than eGFR (66.1%), sCr (67.7%), albuminuria (53.2%), or albumin-creatinine ratio (53.2%). |
| first_indexed | 2024-03-10T14:05:51Z |
| format | Article |
| id | doaj.art-6226d37f1a9049828007b2414ee456c7 |
| institution | Directory Open Access Journal |
| issn | 2227-9059 |
| language | English |
| last_indexed | 2024-03-10T14:05:51Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj.art-6226d37f1a9049828007b2414ee456c72023-11-21T00:39:27ZengMDPI AGBiomedicines2227-90592020-12-0181260610.3390/biomedicines8120606Development of a Biomarker Panel to Distinguish Risk of Progressive Chronic Kidney DiseaseEvan Owens0Ken-Soon Tan1Robert Ellis2Sharon Del Vecchio3Tyrone Humphries4Erica Lennan5David Vesey6Helen Healy7Wendy Hoy8Glenda Gobe9NHMRC CKD CRE (CKD.QLD), The University of Queensland, Brisbane 4067, AustraliaNHMRC CKD CRE (CKD.QLD), The University of Queensland, Brisbane 4067, AustraliaFaculty of Medicine, The University of Queensland, Brisbane 4067, AustraliaFaculty of Medicine, The University of Queensland, Brisbane 4067, AustraliaFaculty of Medicine, The University of Queensland, Brisbane 4067, AustraliaRenal Medicine, Metro South Hospital and Health Service, Logan Hospital, Meadowbrook 4131, AustraliaFaculty of Medicine, The University of Queensland, Brisbane 4067, AustraliaNHMRC CKD CRE (CKD.QLD), The University of Queensland, Brisbane 4067, AustraliaNHMRC CKD CRE (CKD.QLD), The University of Queensland, Brisbane 4067, AustraliaNHMRC CKD CRE (CKD.QLD), The University of Queensland, Brisbane 4067, AustraliaChronic kidney disease (CKD) patients typically progress to kidney failure, but the rate of progression differs per patient or may not occur at all. Current CKD screening methods are sub-optimal at predicting progressive kidney function decline. This investigation develops a model for predicting progressive CKD based on a panel of biomarkers representing the pathophysiological processes of CKD, kidney function, and common CKD comorbidities. Two patient cohorts are utilised: The CKD Queensland Registry (n = 418), termed the Biomarker Discovery cohort; and the CKD Biobank (n = 62), termed the Predictive Model cohort. Progression status is assigned with a composite outcome of a ≥30% decline in eGFR from baseline, initiation of dialysis, or kidney transplantation. Baseline biomarker measurements are compared between progressive and non-progressive patients via logistic regression. In the Biomarker Discovery cohort, 13 biomarkers differed significantly between progressive and non-progressive patients, while 10 differed in the Predictive Model cohort. From this, a predictive model, based on a biomarker panel of serum creatinine, osteopontin, tryptase, urea, and eGFR, was calculated via linear discriminant analysis. This model has an accuracy of 84.3% when predicting future progressive CKD at baseline, greater than eGFR (66.1%), sCr (67.7%), albuminuria (53.2%), or albumin-creatinine ratio (53.2%).https://www.mdpi.com/2227-9059/8/12/606chronic kidney diseaseprogressionbiomarkersprogressive |
| spellingShingle | Evan Owens Ken-Soon Tan Robert Ellis Sharon Del Vecchio Tyrone Humphries Erica Lennan David Vesey Helen Healy Wendy Hoy Glenda Gobe Development of a Biomarker Panel to Distinguish Risk of Progressive Chronic Kidney Disease Biomedicines chronic kidney disease progression biomarkers progressive |
| title | Development of a Biomarker Panel to Distinguish Risk of Progressive Chronic Kidney Disease |
| title_full | Development of a Biomarker Panel to Distinguish Risk of Progressive Chronic Kidney Disease |
| title_fullStr | Development of a Biomarker Panel to Distinguish Risk of Progressive Chronic Kidney Disease |
| title_full_unstemmed | Development of a Biomarker Panel to Distinguish Risk of Progressive Chronic Kidney Disease |
| title_short | Development of a Biomarker Panel to Distinguish Risk of Progressive Chronic Kidney Disease |
| title_sort | development of a biomarker panel to distinguish risk of progressive chronic kidney disease |
| topic | chronic kidney disease progression biomarkers progressive |
| url | https://www.mdpi.com/2227-9059/8/12/606 |
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