Evidence map›Paper›PMID 33161898›Full record

ArticleBMC medicine2020

Contribution of cystatin C- and creatinine-based definitions of chronic kidney disease to cardiovascular risk assessment in 20 population-based and 3 disease cohorts: the BiomarCaRE project.

Dietrich Rothenbacher, Martin Rehm, Licia Iacoviello, Simona Costanzo, Hugh Tunstall-Pedoe, Jill J F Belch, Stefan Söderberg, Johan Hultdin, Veikko Salomaa, Pekka Jousilahti and 14 more

Open access · goldAbstract read
In one paragraph

Article in BMC medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed
6.3field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

46 citing papers in PubMed, 82 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

24 authors at 13 institutions in 7 countries.

Dietrich RothenbacherInstitute of Epidemiology and Medical Biometry, Ulm University, Helmholtzstr. 22, 89081, Ulm, Germany. dietrich.rothenbacher@uni-ulm.de.
Martin RehmInstitute of Epidemiology and Medical Biometry, Ulm University, Helmholtzstr. 22, 89081, Ulm, Germany.
Licia IacovielloDepartment of Epidemiology and Prevention, IRCCS Neuromed, Pozzilli, Italy.
Simona CostanzoDepartment of Epidemiology and Prevention, IRCCS Neuromed, Pozzilli, Italy.
Hugh Tunstall-PedoeCardiovascular Epidemiology Unit, Institute of Cardiovascular Research, University of Dundee, Dundee, UK.
Jill J F BelchVascular Medicine Unit, Institute of Cardiovascular Disease, University of Dundee, Dundee, UK.
Stefan SöderbergDepartment of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden.
Johan HultdinDepartment of Medical Biosciences, Clinical Chemistry, Umeå University, Umeå, Sweden.
Veikko SalomaaFinnish Institute for Health and Welfare, Helsinki, Finland.
Pekka JousilahtiFinnish Institute for Health and Welfare, Helsinki, Finland.
Allan LinnebergCenter for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, The Capital Region, Copenhagen, Denmark.
Susana SansCatalan Department of Health, 08005, Barcelona, Spain.
Teresa PadróCardiovascular ICCC-Program, Research Institute Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau, Barcelona, Spain.
Barbara ThorandInstitute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Christa MeisingerIndependent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Frank KeeQueen's University of Belfast, UK Clinical Research Collaboration Centre of Excellence for Public Health, Belfast, UK.
Amy Jayne McKnightCentre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen's University of Belfast, Belfast, UK.
Tarja PalosaariFinnish Institute for Health and Welfare, Helsinki, Finland.
Kari KuulasmaaFinnish Institute for Health and Welfare, Helsinki, Finland.
Christoph WaldeyerClinic for General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany.
Tanja ZellerClinic for General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany.
Stefan BlankenbergClinic for General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany.
Wolfgang KoenigInstitute of Epidemiology and Medical Biometry, Ulm University, Helmholtzstr. 22, 89081, Ulm, Germany.
BiomarCaRE consortium
Finnish Institute for Health and Welfare · FIHelmholtz-Institute Ulm · DEIstituto Neurologico Mediterraneo · ITUmeå University · SEUniversität Hamburg · DEUniversity of Dundee · GBCenter for Clinical Research and Prevention · DKGerman Centre for Cardiovascular Research · DEHelmholtz Zentrum München · DEHospital de Sant Pau · ESLudwig-Maximilians-Universität München · DEQueen's University Belfast · GBUnited Kingdom Clinical Research Collaboration · GB

Funding

Medical Research Council G0601463Seventh Framework Programme HEALTH-F2-2011-278913
6 · The paper itself

Abstract

backgroundChronic kidney disease has emerged as a strong cardiovascular risk factor, and in many current guidelines, it is already considered as a coronary heart disease (CHD) equivalent. Routinely, creatinine has been used as the main marker of renal function, but recently, cystatin C emerged as a more promising marker. The aim of this study was to assess the comparative cardiovascular and mortality risk of chronic kidney disease (CKD) using cystatin C-based and creatinine-based equations of the estimated glomerular filtration rate (eGFR) in participants of population-based and disease cohorts.

methodsThe present study has been conducted within the BiomarCaRE project, with harmonized data from 20 population-based cohorts (n = 76,954) from 6 European countries and 3 cardiovascular disease (CVD) cohorts (n = 4982) from Germany. Cox proportional hazards models were used to assess hazard ratios (HRs) for the various CKD definitions with adverse outcomes and mortality after adjustment for the Systematic COronary Risk Evaluation (SCORE) variables and study center. Main outcome measures were cardiovascular diseases, cardiovascular death, and all-cause mortality.

resultsThe overall prevalence of CKD stage 3-5 by creatinine- and cystatin C-based eGFR, respectively, was 3.3% and 7.4% in the population-based cohorts and 13.9% and 14.4% in the disease cohorts. CKD was an important independent risk factor for subsequent CVD events and mortality. For example, in the population-based cohorts, the HR for CVD mortality was 1.72 (95% CI 1.53 to 1.92) with creatinine-based CKD and it was 2.14 (95% CI 1.90 to 2.40) based on cystatin-based CKD compared to participants without CKD. In general, the HRs were higher for cystatin C-based CKD compared to creatinine-based CKD, for all three outcomes and risk increased clearly below the conventional threshold for CKD, also in older adults. Net reclassification indices were larger for a cystatin-C based CKD definition. Differences in HRs (between the two CKD measures) in the disease cohorts were less pronounced than in the population-based cohorts.

conclusionCKD is an important risk factor for subsequent CVD events and total mortality. However, point estimates of creatinine- and cystatin C-based CKD differed considerably between low- and high-risk populations. Especially in low-risk settings, the use of cystatin C-based CKD may result in more accurate risk estimates and have better prognostic value.

Indexed as

Heart Disease Risk FactorsCardiovascular DiseasesCoronary DiseaseCreatinineCystatin CFemaleHumansMaleMiddle AgedPrognosisRisk AssessmentCreatinineCystatin CAdverse outcomeChronic kidney diseaseCohort studyCreatinineCystatin CEstimated glomerular filtration rate

Identifiers

PMID33161898
PMCPMC7650190
OpenAlexW3103702296

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.