Evidence map›Paper›PMID 26028594›Full record

SynthesisThe lancet. Diabetes & endocrinology2015

Estimated glomerular filtration rate and albuminuria for prediction of cardiovascular outcomes: a collaborative meta-analysis of individual participant data.

Kunihiro Matsushita, Josef Coresh, Yingying Sang, John Chalmers, Caroline Fox, Eliseo Guallar, Tazeen Jafar, Simerjot K Jassal, Gijs W D Landman, Paul Muntner and 16 more

2 registry-linked trialsAbstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in The lancet. Diabetes & endocrinology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 504 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
504citing papers in PubMed, 9 pooled it
–field-weighted citation impact
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.

NCT03826758 naunknown statusnot on this mapstarted 2021, after this paper: background citation

E-DYNAMIC - Enhanced Dynamic Clinical Decision Support to Identify Patients With Chronic Kidney Disease and Improve Cardiovascular Health

TypeinterventionalSponsorLoyola UniversityRan2021 to 2023Enrolled8,000ConditionsChronic Kidney DiseasesArmsE-DYNAMIC Clinical Decision Support
NCT07171216 narecruitingnot on this mapstarted 2025, after this paper: background citation

The Effect of REgulation of PArathyroId hoRmone in Patients With Chronic Kidney Disease to Investigate the Change in Bone Mineral Density (Biskjoldbruskkirtelhormon og knogletæthed Hos Patienter Med Kronisk Nyresygdom)

TypeinterventionalSponsorHerlev HospitalRan2025 to 2027Enrolled70ConditionsChronic Kidney Disease Mineral and Bone DisorderArmsTreat to target
3 · Its place in the literature

Who cites it

504 citing papers in PubMed, 9 syntheses or guidelines pooled it.

  1. Pooled it
  2. Management of antithrombotic therapies in kidney transplantation candidates and recipients: an ERBP clinical practice document by the DESCaRTES Working Group of ERA and the EKITA Committee of ESOT.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2026
    Guideline
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  10. Trial
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  14. Article
  15. Article
  16. GLP-1 receptor agonists-a potential therapy for chronic kidney disease?Clinical science (London, England : 1979) · 2026
    Review
  17. Article
  18. The PREVENT Risk Equations and Cardiovascular Prevention in East Asia.Journal of atherosclerosis and thrombosis · 2026
    Review
  19. Article
  20. Article

444 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

26 authors.

Kunihiro MatsushitaJohns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Josef CoreshJohns Hopkins Bloomberg School of Public Health, Baltimore, MD. Electronic address: ckdpc@jhmi.edu.
Yingying SangJohns Hopkins Bloomberg School of Public Health, Baltimore, MD.
John ChalmersThe George Institute for Global Health, University of Sydney, Sydney, NSW, Australia.
Caroline FoxNational Heart, Lung, and Blood Institute's Framingham Heart Study and the Center for Population Studies Framingham, MA, USA.
Eliseo GuallarJohns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Tazeen JafarDuke-NUS Graduate Medical School, Singapore.
Simerjot K JassalVA San Diego Healthcare and University of California San Diego, San Diego, CA, USA.
Gijs W D LandmanDiabetes Centre Zwolle, Isala hospital, Zwolle, Netherlands.
Paul MuntnerDepartment of Medicine, University of Alabama at Birmingham, AL, USA.
Paul RoderickPrimary Care and Population Sciences, Faculty of Medicine, University of Southampton, Southampton, UK.
Toshimi SairenchiDepartment of Public Health, Dokkyo Medical University School of Medicine, Tochigi, Japan.
Ben SchöttkerDivision of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Anoop Shankar8816 Manchester Rd, St Louis, MO 63144, USA.
Michael ShlipakDivision of General Internal Medicine, San Francisco Veterans Affairs Medical Center, and Departments of Medicine, Epidemiology, and Biostatistics, University of California San Francisco, San Francisco, CA.
Marcello TonelliDepartment of Medicine, University of Calgary, Calgary, AB, Canada.
Jonathan TownendDepartment of Cardiology, Queen Elizabeth Hospital Birmingham, Birmingham, UK.
Arjan van ZuilenDepartment of Nephrology and Hypertension, University Medical Center Utrecht, Utrecht, Netherlands.
Kazumasa YamagishiDepartment of Public Health Medicine, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.
Kentaro YamashitaDepartment of Cardiology, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Ron GansevoortDepartment of Nephrology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.
Mark SarnakDivision of Nephrology, Tufts Medical Center, Boston, MA, USA.
David G WarnockDepartment of Medicine, University of Alabama at Birmingham, AL, USA.
Mark WoodwardJohns Hopkins Bloomberg School of Public Health, Baltimore, MD; The George Institute for Global Health, University of Sydney, Sydney, NSW, Australia; Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Johan ÄrnlövDepartment of Medical Sciences, Uppsala University, Uppsala, Sweden; School of Health and Social Studies, Dalarna University, Falun, Sweden.
CKD Prognosis Consortium

Funding

VCID and Stroke in a Bi-racial National CohortU01NS041588 · NINDS · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI CUSHMAN, MARY, HOWARD, GEORGE · 2002 to 2022
$96.0M
Institute for Clinical and Translational Research (UL1)UL1RR025005 · NCRR · JOHNS HOPKINS UNIVERSITY · PI FORD, DANIEL ERNEST · 2007 to 2011
$75.8M
Institute for Clinical and Translational ResearchUL1TR001079 · NCATS · JOHNS HOPKINS UNIVERSITY · PI FORD, DANIEL ERNEST · 2013 to 2017
$60.1M
University of Alabama at Birmingham's Diabetes Research CenterP30DK079626 · NIDDK · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI ANANDA BASU · 2013 to 2026
$19.5M
THE EPIDEMIOLOGY OF AGE-RELATED OCULAR DISEASEU10EY006594 · NEI · UNIVERSITY OF WISCONSIN-MADISON · PI KLEIN, RONALD · 1987 to 2018
$17.3M
REGARDS - MI StudyR01HL080477 · NHLBI · WEILL MEDICAL COLL OF CORNELL UNIV · PI LEVITAN, EMILY B, SAFFORD, MONIKA M · 2006 to 2020
$11.6M
Chronic Kidney Disease Prognosis Consortium (CKD-PC)R01DK100446 · NIDDK · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI JOSEF CORESH, Morgan Erika Grams · 2014 to 2026
$10.7M
GENDER DIFFERENCES IN OSTEOPOROSISR01AG007181 · NIA · UNIVERSITY OF CALIFORNIA SAN DIEGO · PI BARRETT-CONNOR, ELIZABETH L · 1999 to 2002
$1.6M
CVD Predictors of Cognitive Function Trajectory over 20 YearsR01AG028507 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BARRETT-CONNOR, ELIZABETH L · 2007 to 2010
$1.2M
Novel Risk Factors for Chronic Kidney DiseaseR01DK073217 · NIDDK · UNIVERSITY OF WISCONSIN-MADISON · PI KLEIN, RONALD · 2006 to 2008
$800k
Novel Cardiac Risk Factors &Endothelial Function in CKDK23DK067303 · NIDDK · TUFTS MEDICAL CENTER · PI MENON, VANDANA · 2004 to 2008
$781k
CARDIOVASCULAR RISK FACTORS--CHRONIC RENAL INSUFFICIENCYK23DK002904 · NIDDK · TUFTS MEDICAL CENTER · PI SARNAK, MARK J · 2000 to 2004
$672k
NCATS NIH HHS UL1 TR001079NCRR NIH HHS UL1 RR025005NEI NIH HHS U10 EY006594NHLBI NIH HHS HHSN268201100005CNHLBI NIH HHS HHSN268201100006CNHLBI NIH HHS HHSN268201100007CNHLBI NIH HHS HHSN268201100008CNHLBI NIH HHS HHSN268201100009CNHLBI NIH HHS HHSN268201100010CNHLBI NIH HHS HHSN268201100011CNHLBI NIH HHS HHSN268201100012CNHLBI NIH HHS N01 HC095159NHLBI NIH HHS N01 HC095169NHLBI NIH HHS R01 HL080477NIA NIH HHS R01 AG007181NIA NIH HHS R01 AG028507NIDDK NIH HHS K23 DK002904NIDDK NIH HHS K23 DK067303NIDDK NIH HHS P30 DK079626NIDDK NIH HHS R01 DK031801NIDDK NIH HHS R01 DK073217NIDDK NIH HHS R01 DK100446NIDDK NIH HHS U01 DK035073NINDS NIH HHS U01 NS041588
6 · The paper itself

Abstract

backgroundThe usefulness of estimated glomerular filtration rate (eGFR) and albuminuria for prediction of cardiovascular outcomes is controversial. We aimed to assess the addition of creatinine-based eGFR and albuminuria to traditional risk factors for prediction of cardiovascular risk with a meta-analytic approach.

methodsWe meta-analysed individual-level data for 637 315 individuals without a history of cardiovascular disease from 24 cohorts (median follow-up 4·2-19·0 years) included in the Chronic Kidney Disease Prognosis Consortium. We assessed C statistic difference and reclassification improvement for cardiovascular mortality and fatal and non-fatal cases of coronary heart disease, stroke, and heart failure in a 5 year timeframe, contrasting prediction models for traditional risk factors with and without creatinine-based eGFR, albuminuria (either albumin-to-creatinine ratio [ACR] or semi-quantitative dipstick proteinuria), or both.

findingsThe addition of eGFR and ACR significantly improved the discrimination of cardiovascular outcomes beyond traditional risk factors in general populations, but the improvement was greater with ACR than with eGFR, and more evident for cardiovascular mortality (C statistic difference 0·0139 [95% CI 0·0105-0·0174] for ACR and 0·0065 [0·0042-0·0088] for eGFR) and heart failure (0·0196 [0·0108-0·0284] and 0·0109 [0·0059-0·0159]) than for coronary disease (0·0048 [0·0029-0·0067] and 0·0036 [0·0019-0·0054]) and stroke (0·0105 [0·0058-0·0151] and 0·0036 [0·0004-0·0069]). Dipstick proteinuria showed smaller improvement than ACR. The discrimination improvement with eGFR or ACR was especially evident in individuals with diabetes or hypertension, but remained significant with ACR for cardiovascular mortality and heart failure in those without either of these disorders. In individuals with chronic kidney disease, the combination of eGFR and ACR for risk discrimination outperformed most single traditional predictors; the C statistic for cardiovascular mortality fell by 0·0227 (0·0158-0·0296) after omission of eGFR and ACR compared with less than 0·007 for any single modifiable traditional predictor.

interpretationCreatinine-based eGFR and albuminuria should be taken into account for cardiovascular prediction, especially when these measures are already assessed for clinical purpose or if cardiovascular mortality and heart failure are outcomes of interest. ACR could have particularly broad implications for cardiovascular prediction. In populations with chronic kidney disease, the simultaneous assessment of eGFR and ACR could facilitate improved classification of cardiovascular risk, supporting current guidelines for chronic kidney disease. Our results lend some support to also incorporating eGFR and ACR into assessments of cardiovascular risk in the general population.

fundingUS National Kidney Foundation, National Institute of Diabetes and Digestive and Kidney Diseases.

Indexed as

Glomerular Filtration RateAdultAlbuminuriaCardiovascular DiseasesCoronary DiseaseCreatinineFemaleFollow-Up StudiesHeart FailureHumansMaleMiddle AgedRisk FactorsStrokeCreatinine

Identifiers

PMID26028594
PMCPMC4594193

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Registered trials

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 OpenQuestion graph.