Evidence map›Paper›PMID 41202182›Full record

SynthesisJAMA2025

Discordance in Creatinine- and Cystatin C-Based eGFR and Clinical Outcomes: A Meta-Analysis.

Michelle M Estrella, Shoshana H Ballew, Yingying Sang, Morgan E Grams, Josef Coresh, Aditya Surapaneni, Natalia Alencar de Pinho, Johan Ärnlöv, Hermann Brenner, Juan-Jesus Carrero and 18 more

Abstract readComparative StudyMeta-Analysis
In one paragraph

Synthesis in JAMA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–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.

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

10 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Observational
  8. Article
  9. Review
  10. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

28 authors.

Michelle M EstrellaKidney Health Research Collaborative, University of California San Francisco and San Francisco Veterans Affairs Health Care System, San Francisco.
Shoshana H BallewOptimal Aging Institute, New York University Grossman School of Medicine, New York.
Yingying SangOptimal Aging Institute, New York University Grossman School of Medicine, New York.
Morgan E GramsDivision of Precision Medicine, Department of Medicine, New York University Grossman School of Medicine, New York.
Josef CoreshOptimal Aging Institute, New York University Grossman School of Medicine, New York.
Aditya SurapaneniDivision of Precision Medicine, Department of Medicine, New York University Grossman School of Medicine, New York.
Natalia Alencar de PinhoCentre for Research in Epidemiology and Population Health (CESP), Paris-Saclay University, Inserm U1018, Versailles Saint-Quentin University, Clinical Epidemiology Team, Villejuif, France.
Johan ÄrnlövSchool of Health and Social Studies, Dalarna University, Falun, Sweden.
Hermann BrennerDivision of Clinical Epidemiology and Aging Research, German Cancer Research Center and Network Aging Research, Heidelberg University, Heidelberg, Germany.
Juan-Jesus CarreroDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Huddinge, Sweden.
Teresa K ChenKidney Health Research Collaborative, University of California San Francisco and San Francisco Veterans Affairs Health Care System, San Francisco.
Debbie L CohenDivision of Renal Electrolyte and Hypertension, Perelman School of Medicine, University of Pennsylvania, Philadelphia.
Mary CushmanDepartments of Medicine and Pathology and Laboratory Medicine, University of Vermont Larner College of Medicine, Burlington.
Ron T GansevoortDepartment of Nephrology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
Shih-Jen HwangFramingham Heart Study, Framingham, Massachusetts.
Lesley A InkerDivision of Nephrology, Department of Medicine, Tufts Medical Center, Boston, Massachusetts.
Joachim H IxNephrology Section, Veterans Affairs San Diego Health Care System, San Diego, California.
Keiko KabasawaDepartment of Health Promotion Medicine, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan.
Tsuneo KontaDepartment of Public Health and Hygiene, Yamagata University, Yamagata, Japan.
Jennifer S LeesSchool of Cardiovascular & Metabolic Health, University of Glasgow, Glasgow, United Kingdom.
Kevan R PolkinghorneSchool of Public Health and Preventive Medicine and Department of Medicine, Monash University, Clayton, Victoria, Australia.
Michael G ShlipakKidney Health Research Collaborative, University of California San Francisco and San Francisco Veterans Affairs Health Care System, San Francisco.
Robin W M VernooijDepartment of Nephrology and Hypertension, University Medical Center Utrecht, Utrecht, the Netherlands.
David C WheelerCentre for Kidney and Bladder Health, University College London, London, United Kingdom.
Ashok Kumar YadavDepartment of Experimental Medicine and Biotechnology, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
Andrew S LeveyDivision of Nephrology, Department of Medicine, Tufts Medical Center, Boston, Massachusetts.
Kai-Uwe EckardtDepartment of Nephrology and Hypertension, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Chronic Kidney Disease Prognosis Consortium Investigators and Collaborators

Funding

Prospective Renal Insufficiency Cohort Evaluation:PRICEU01DK060984 · NIDDK · UNIVERSITY OF PENNSYLVANIA · PI COHEN, DEBBIE L · 2001 to 2025
$17.0M
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
Infrastructure for mentored access to CHS data and specimensR01HL172803 · NHLBI · UNIVERSITY OF WASHINGTON · PI James S Floyd, Michelle Christina Odden · 2025 to 2026
$2.7M
MDRD LONG TERM FOLLOW UP STUDYU01DK035073 · NIDDK · CLEVELAND CLINIC FOUNDATION · PI BECK, GERALD J · 1986 to 2004
$650k
NHLBI NIH HHS R01 HL172803NIDDK NIH HHS R01 DK100446NIDDK NIH HHS U01 DK035073NIDDK NIH HHS U01 DK060984Wellcome Trust
6 · The paper itself

Abstract

Importance: Estimated glomerular filtration rates (eGFRs) can differ according to whether creatinine or cystatin C is used for the eGFR calculation, but the prevalence and importance of these differences remain unclear. Objectives: To evaluate the prevalence of a discordance between cystatin C-based eGFR (eGFRcys) and creatinine-based eGFR (eGFRcr), identify characteristics associated with greater discordance, and evaluate associations of discordance with adverse outcomes. Data Sources: Participants in the Chronic Kidney Disease Prognosis Consortium (CKD-PC). Study Selection: Participants with concurrent cystatin C and creatinine measurements and clinical outcome measurement. Data Extraction and Synthesis: Between April 2024 and August 2025, data were synthesized using individual-level meta-analysis. Main Outcomes and Measures: The primary independent measurement was a large negative eGFR difference (eGFRdiff), defined as an eGFRcys that was at least 30% lower than eGFRcr. Secondary (dependent) outcomes included all-cause and cardiovascular mortality, atherosclerotic cardiovascular disease, heart failure, and kidney failure with replacement therapy. Results: A total of 821 327 individuals from 23 outpatient cohorts (mean [SD] age, 59 [12] years; 48% female; 13.5% with diabetes; 40% with hypertension) and 39 639 individuals from 2 inpatient cohorts (mean [SD] age, 67 [16] years; 31% female; 30% with diabetes; 72% with hypertension) were included. Among outpatient participants, 11% had a large negative eGFRdiff (range, 3%-50%). Among inpatients, 35% had a large negative eGFRdiff. Among outpatient participants, at a mean (SD) follow-up of 11 (4) years, a large negative eGFRdiff, compared with an eGFRdiff between -30% and 30%, was associated with higher rates of all-cause mortality (28.4 vs 16.8 per 1000 person-years [PY]; hazard ratio [HR], 1.69 [95% CI, 1.57-1.82]), cardiovascular mortality (6.1 vs 3.8 per 1000 PY; HR, 1.61 [95% CI, 1.48-1.76]), atherosclerotic cardiovascular disease (13.3 vs 9.8 per 1000 PY; HR, 1.35 [95% CI, 1.27-1.44]), heart failure (13.2 vs 8.6 per 1000 PY; HR, 1.54 [95% CI, 1.40-1.68]), and kidney failure with replacement therapy (2.7 vs 2.1 per 1000 PY; HR, 1.29 [95% CI, 1.13-1.47]). Conclusions and Relevance: In the CKD-PC, 11% of outpatient participants and 35% of hospitalized patients had an eGFRcys that was at least 30% lower than their eGFRcr. In the outpatient setting, presence of eGFRcys at least 30% lower than eGFRcr was associated with significantly higher rates of all-cause mortality, cardiovascular events, and kidney failure.

Indexed as

CreatinineCystatin CGlomerular Filtration RateKidney Failure, ChronicRenal Insufficiency, ChronicAgedAtherosclerosisCardiovascular DiseasesFemaleHeart FailureHumansMaleMiddle AgedPrognosisRenal Replacement TherapyCreatinineCystatin C

Identifiers

PMID41202182
PMCPMC12595547

What OpenQuestion holds

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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 OpenQuestion graph.