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Sökning: WFRF:(Gregorich Mariella) > (2023) > Different roles of ...

  • Kammer, MichaelMedical University of Vienna (författare)

Different roles of protein biomarkers predicting eGFR trajectories in people with chronic kidney disease and diabetes mellitus : a nationwide retrospective cohort study

  • Artikel/kapitelEngelska2023

Förlag, utgivningsår, omfång ...

  • 2023-03-29
  • Springer Science and Business Media LLC,2023

Nummerbeteckningar

  • LIBRIS-ID:oai:lup.lub.lu.se:f6877e14-d5e2-4c7c-bfda-fad547ae64ab
  • https://lup.lub.lu.se/record/f6877e14-d5e2-4c7c-bfda-fad547ae64abURI
  • https://doi.org/10.1186/s12933-023-01808-5DOI

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

Ingår i deldatabas

Klassifikation

  • Ämneskategori:art swepub-publicationtype
  • Ämneskategori:ref swepub-contenttype

Anmärkningar

  • 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.

Ämnesord och genrebeteckningar

Biuppslag (personer, institutioner, konferenser, titlar ...)

  • Heinzel, AndreasMedical University of Vienna (författare)
  • Hu, KarinMedical University of Vienna (författare)
  • Meiselbach, HeikeFriedrich-Alexander University Erlangen-Nürnberg (författare)
  • Gregorich, MariellaMedical University of Vienna (författare)
  • Busch, MartinUniversitätsklinikum Jena (författare)
  • Duffin, Kevin LEli Lilly and Company (författare)
  • Gomez, Maria FLund University,Lunds universitet,Diabetiska komplikationer,Forskargrupper vid Lunds universitet,Diabetic Complications,Lund University Research Groups(Swepub:lu)mphy-mgo (författare)
  • Eckardt, Kai-UweFriedrich-Alexander University Erlangen-Nürnberg (författare)
  • Oberbauer, RainerMedical University of Vienna (författare)
  • Medical University of ViennaFriedrich-Alexander University Erlangen-Nürnberg (creator_code:org_t)
  • BEAt-DKD Consortium

Sammanhörande titlar

  • Ingår i:Cardiovascular Diabetology: Springer Science and Business Media LLC22, s. 1-101475-2840

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