| 001 | 165843 | ||
| 005 | 20240229133154.0 | ||
| 024 | 7 | _ | |a 10.1186/s12916-020-01776-7 |2 doi |
| 024 | 7 | _ | |a pmid:33161898 |2 pmid |
| 024 | 7 | _ | |a altmetric:93945391 |2 altmetric |
| 037 | _ | _ | |a DKFZ-2020-02419 |
| 041 | _ | _ | |a eng |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Rothenbacher, Dietrich |0 P:(DE-HGF)0 |b 0 |e First author |
| 245 | _ | _ | |a 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. |
| 260 | _ | _ | |a Heidelberg [u.a.] |c 2020 |b Springer |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1611838570_22973 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 500 | _ | _ | |a #EA:C070# |
| 520 | _ | _ | |a Chronic 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.The 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.The 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.CKD 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. |
| 536 | _ | _ | |a 323 - Metabolic Dysfunction as Risk Factor (POF3-323) |0 G:(DE-HGF)POF3-323 |c POF3-323 |f POF III |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, PubMed, |
| 700 | 1 | _ | |a Rehm, Martin |b 1 |
| 700 | 1 | _ | |a Iacoviello, Licia |b 2 |
| 700 | 1 | _ | |a Costanzo, Simona |b 3 |
| 700 | 1 | _ | |a Tunstall-Pedoe, Hugh |b 4 |
| 700 | 1 | _ | |a Belch, Jill J F |b 5 |
| 700 | 1 | _ | |a Söderberg, Stefan |b 6 |
| 700 | 1 | _ | |a Hultdin, Johan |b 7 |
| 700 | 1 | _ | |a Salomaa, Veikko |b 8 |
| 700 | 1 | _ | |a Jousilahti, Pekka |b 9 |
| 700 | 1 | _ | |a Linneberg, Allan |b 10 |
| 700 | 1 | _ | |a Sans, Susana |b 11 |
| 700 | 1 | _ | |a Padró, Teresa |b 12 |
| 700 | 1 | _ | |a Thorand, Barbara |b 13 |
| 700 | 1 | _ | |a Meisinger, Christa |b 14 |
| 700 | 1 | _ | |a Kee, Frank |b 15 |
| 700 | 1 | _ | |a McKnight, Amy Jayne |b 16 |
| 700 | 1 | _ | |a Palosaari, Tarja |b 17 |
| 700 | 1 | _ | |a Kuulasmaa, Kari |b 18 |
| 700 | 1 | _ | |a Waldeyer, Christoph |b 19 |
| 700 | 1 | _ | |a Zeller, Tanja |b 20 |
| 700 | 1 | _ | |a Blankenberg, Stefan |b 21 |
| 700 | 1 | _ | |a Koenig, Wolfgang |b 22 |
| 700 | 1 | _ | |a consortium, BiomarCaRE |b 23 |e Collaboration Author |
| 773 | _ | _ | |a 10.1186/s12916-020-01776-7 |g Vol. 18, no. 1, p. 300 |0 PERI:(DE-600)2131669-7 |n 1 |p 300 |t BMC medicine |v 18 |y 2020 |x 1741-7015 |
| 909 | C | O | |p VDB |o oai:inrepo02.dkfz.de:165843 |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 0 |6 P:(DE-HGF)0 |
| 913 | 1 | _ | |a DE-HGF |b Gesundheit |l Herz-Kreislauf-Stoffwechselerkrankungen |1 G:(DE-HGF)POF3-320 |0 G:(DE-HGF)POF3-323 |3 G:(DE-HGF)POF3 |2 G:(DE-HGF)POF3-300 |4 G:(DE-HGF)POF |v Metabolic Dysfunction as Risk Factor |x 0 |
| 914 | 1 | _ | |y 2020 |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b BMC MED : 2018 |d 2020-09-05 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2020-09-05 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2020-09-05 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0320 |2 StatID |b PubMed Central |d 2020-09-05 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0501 |2 StatID |b DOAJ Seal |d 2020-09-05 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0500 |2 StatID |b DOAJ |d 2020-09-05 |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b DOAJ : Open peer review |d 2020-09-05 |
| 915 | _ | _ | |a Creative Commons Attribution CC BY (No Version) |0 LIC:(DE-HGF)CCBYNV |2 V:(DE-HGF) |b DOAJ |d 2020-09-05 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |d 2020-09-05 |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |d 2020-09-05 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2020-09-05 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2020-09-05 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1050 |2 StatID |b BIOSIS Previews |d 2020-09-05 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1110 |2 StatID |b Current Contents - Clinical Medicine |d 2020-09-05 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1190 |2 StatID |b Biological Abstracts |d 2020-09-05 |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2020-09-05 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2020-09-05 |
| 915 | _ | _ | |a IF >= 5 |0 StatID:(DE-HGF)9905 |2 StatID |b BMC MED : 2018 |d 2020-09-05 |
| 915 | _ | _ | |a Article Processing Charges |0 StatID:(DE-HGF)0561 |2 StatID |d 2020-09-05 |
| 915 | _ | _ | |a Fees |0 StatID:(DE-HGF)0700 |2 StatID |d 2020-09-05 |
| 920 | 1 | _ | |0 I:(DE-He78)C070-20160331 |k C070 |l C070 Klinische Epidemiologie und Alternf. |x 0 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-He78)C070-20160331 |
| 980 | _ | _ | |a UNRESTRICTED |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|
guest ::