Cardiometabolic Biomarkers and Prediction of Kidney Disease Progression: The eGFR Cohort Study

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dc.contributor.authorBarr, E.L.M.
dc.contributor.authorBarzi, F.
dc.contributor.authorMills Kulkalgal, P.
dc.contributor.authorNickels, M.
dc.contributor.authorGraham, S.
dc.contributor.authorPearson, O.
dc.contributor.authorObeyesekere, V.
dc.contributor.authorHoy, W.E.
dc.contributor.authorJones, G.R.D.
dc.contributor.authorLawton, P.D.
dc.contributor.authorBrown, A.D.H.
dc.contributor.authorThomas, M.
dc.contributor.authorSinha, A.
dc.contributor.authorCass, A.
dc.contributor.authorMacIsaac, R.J.
dc.contributor.authorMaple-Brown, L.J.
dc.contributor.authorHughes Wagadagam, J.T.
dc.date.issued2025
dc.descriptionPublished online 17 August 2025
dc.description.abstractBackground: Traditional markers modestly predict chronic kidney disease progression in Aboriginal and Torres Strait Islander people. Therefore, we assessed associations of cardiometabolic and inflammatory clinical biomarkers with kidney disease progression among Aboriginal and Torres Strait Islander people with and without diabetes. Objectives: To identify cardiometabolic and inflammatory clinical biomarkers that predict kidney disease progression in Aboriginal and Torres Strait Islander people. Design: Prospective observational cohort study Setting: Northern Territory, Australia Participants: Aboriginal and Torres Strait Islander participants of the estimated glomerular filtration rate (eGFR) study with (n = 218) and without diabetes (n = 278) Measurements: Baseline biomarkers (expressed as 1 standard deviation increase in logarithmic scale), plasma kidney injury molecule-1 (pKIM-1) (pg/ml), high-sensitivity troponin-T (hs-TnT) (ng/L), troponin-I (hs-TnI) (ng/L), and soluble tumor necrosis factor receptor-1 (sTNFR-1) (pg/ml) were assessed in 496 adults. Annual change in eGFR (ml/min/1.73 m²) and a composite kidney outcome (first of ≥30% eGFR decline with follow-up eGFR <60 ml/min/1.73 m², initiation of kidney replacement therapy or kidney disease-related death) over a median of 3 years. Methods: Linear regression estimated annual change in eGFR (ml/min/1.73 m²). Cox proportional hazards regression estimated hazard ratio (HR) and 95% CI for developing a combined kidney health outcome. Results: In individuals with diabetes, but not those without diabetes, higher baseline hs-TnT (−2.1 [−4.1 to −0.2], P = .033) and sTNFR-1 (−1.8 [−3.5 to −0.1], P = .039) predicted mean (95% CI) eGFR change, after adjusting for age, gender, baseline eGFR, and urinary albumin-to-creatinine ratio. Baseline variables explained 11% of eGFR decline variance; increasing to 27% (P < .001) with biomarkers. In diabetes, hs-TnT and hs-TnI were significantly associated with increased risk of kidney health outcomes. Limitations: Limitations included potential chronic kidney disease misclassification from single creatinine and albumin measurements, limited adjustment for covariates due to a small sample size, and short follow-up restricting long-term outcome assessment. Conclusions: Cardiovascular, kidney, and inflammatory biomarkers are likely associated with kidney function loss in diabetes, with particularly prominent associations for cardiac injury markers.
dc.description.statementofresponsibilityElizabeth L. M. Barr, Federica Barzi, Phillip Mills (Kulkalgal), Maria Nickels, Sian Graham, Odette Pearson, Varuni Obeyesekere, Wendy E. Hoy, Graham R. D. Jones, Paul D. Lawton, Alex D. H. Brown, Mark Thomas, Ashim Sinha, Alan Cass, Richard J. MacIsaac, Louise J. Maple-Brown, and Jaquelyne T. Hughes (Wagadagam)
dc.identifier.citationCanadian Journal of Kidney Health and Disease, 2025; 12:1-11
dc.identifier.doi10.1177/20543581251363126
dc.identifier.issn2054-3581
dc.identifier.issn2054-3581
dc.identifier.orcidBrown, A.D.H. [0000-0003-2112-3918]
dc.identifier.urihttps://hdl.handle.net/2440/147970
dc.language.isoen
dc.publisherSAGE Publishing
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/545202
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1021460
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/GNT1184083
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/631947
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/605837
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1078477
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1194698
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/GNT2026852
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/631947
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1092576
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1174758
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1038721
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1120640
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1079502
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/44126324
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1194677
dc.rights© The Author(s) 2025. Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution- NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
dc.source.urihttps://doi.org/10.1177/20543581251363126
dc.subjectinflammatory markers; novel biomarkers; kidney disease progression; First Nations; epidemiology
dc.titleCardiometabolic Biomarkers and Prediction of Kidney Disease Progression: The eGFR Cohort Study
dc.typeJournal article
pubs.publication-statusPublished

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