Article in Journal of the American Society of Nephrology : JASN, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
0numbers the graph read from it
0cells of the map it votes in
2citing 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.
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
21 authors.
Adrian M PetzlDivision of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.ORCID 0000-0002-8935-3560
Yue RenDepartment of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.ORCID 0000-0003-1856-8633
Faye L NorbyDivision of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota.ORCID 0000-0002-1975-0405
Jianqiao WangDepartment of Biostatistics, Harvard University, Cambridge, Massachusetts.
Ruth F DubinDepartment of Medicine, University of Texas Southwestern Medical Center, Dallas, Texas.ORCID 0000-0002-0498-1980
Izabele AntanaviciusDivision of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
Mark R SegalDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California.ORCID 0000-0003-4213-3138
Lin Yee ChenCardiovascular Division, Department of Medicine, University of Minnesota, Minneapolis, Minnesota.ORCID 0000-0002-0700-814
Alvaro AlonsoDepartment of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia.ORCID 0000-0002-2225-8323
Eugene P RheeNephrology Division and Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts.ORCID 0000-0002-4797-3200
Nisha BansalDivision of Nephrology, University of Washington Medical Center, Seattle, Washington.ORCID 0000-0002-9583-8628
Paul L KimmelDepartment of Medicine, George Washington University, Washington, DC.
Ramachandran S VasanUniversity of Texas Health Sciences Center, University of Texas School of Public Health San Antonio, San Antonio, Texas.ORCID 0000-0001-7357-5970
Aditya SurapaneniDepartment of Medicine, New York University Grossman School of Medicine, New York, New York.ORCID 0000-0003-4978-5980
Morgan E GramsDepartment of Medicine, New York University Grossman School of Medicine, New York, New York.ORCID 0000-0002-4430-6023
Harold FeldmanPatient-Centered Outcomes Research Institute, Washington, DC.ORCID 0000-0003-4572-2608
Francis E MarchlinskiDivision of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.ORCID 0000-0001-7962-9423
Alan S GoDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California.ORCID 0000-0001-9109-0811
Hongzhe LeeDepartment of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.ORCID 0000-0003-3662-3907
Peter GanzDivision of Cardiology, Department of Medicine, Zuckerberg San Francisco General Hospital, University of California San Francisco, San Francisco, California.ORCID 0000-0002-0437-8882
Rajat DeoDivision of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.ORCID 0000-0002-8250-2607
Funding
UCSF CLINICAL AND TRANSLATIONAL SCIENCE INSTITUTEUL1RR024131 · NCRR · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI JOHNSTON, S. CLAIBORNE · 2006 to 2011
$111.2M
Scientific and Data Coordinating Center(SDCC) for the P*U01DK060990 · NIDDK · UNIVERSITY OF PENNSYLVANIA · PI FELDMAN, HAROLD I · 2001 to 2017
$50.1M
Clinical and Translational Science Collaborative of ClevelandUL1TR000439 · NCATS · CASE WESTERN RESERVE UNIVERSITY · PI KONSTAN, MICHAEL W. · 2012 to 2016
$50.0M
Michigan Institute for Clinical and Health Research (MCHR)UL1TR000433 · NCATS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MASHOUR, GEORGE ALEXANDER · 2012 to 2016
$49.9M
Institutional Clinical and Translational Science AwardUL1TR000003 · NCATS · UNIVERSITY OF PENNSYLVANIA · PI FITZGERALD, GARRET A · 2012 to 2015
$38.9M
Tulane COBRE in Cardiometabolic Diseases Clinical Research CoreP20GM109036 · NIGMS · TULANE UNIVERSITY OF LOUISIANA · PI Katherine Teresa Mills · 2016 to 2026
$25.3M
Continuation of the Coordinating Center for the Chronic Renal Insufficiency Cohort (CRIC) StudyU24DK060990 · NIDDK · UNIVERSITY OF PENNSYLVANIA · PI Amanda Hyre Anderson, Laura M Dember · 2018 to 2026
$21.5M
Zonulin Antibodies in Intestinal Permeabilitiy in IDDMM01RR016500 · NCRR · UNIVERSITY OF MARYLAND BALTIMORE · PI EDELMAN, MARTIN J. · 2002 to 2009
$20.2M
Study of the Care and Outcomes of REnal InsufficiencyU01DK060902 · NIDDK · KAISER FOUNDATION RESEARCH INSTITUTE · PI GO, ALAN S · 2001 to 2025
$19.5M
Limited Competition for the Continuation of the Chronic Renal Insufficiency CohorU01DK061021 · NIDDK · CASE WESTERN RESERVE UNIVERSITY · PI RAHMAN, MAHBOOB · 2001 to 2025
$17.3M
Prospective Renal Insufficiency Cohort Evaluation:PRICEU01DK060984 · NIDDK · UNIVERSITY OF PENNSYLVANIA · PI COHEN, DEBBIE L · 2001 to 2025
$17.0M
Prospective Cohort Study of Chronic Renal InsufficiencyU01DK061028 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI RAO, PANDURANGA S · 2001 to 2025
key pointsUsing proteomics and Mendelian randomization, we identified causal proteins for the development of atrial fibrillation in patients with CKD. Immunologic function, cell growth, and neural development were common biologic pathways underlying atrial fibrillation and left atrial enlargement. Machine learning led to a nine-protein risk model that had a similar predictive performance for incident atrial fibrillation as clinical risk scores.
backgroundCKD is strongly associated with atrial fibrillation. Understanding the biologic pathways for this association and creating predictive models have been challenging. Left atrial enlargement is a substrate for atrial fibrillation, but any overlap between biomarkers of atrial fibrillation and left atrial enlargement in patients with CKD is unknown.
methodsWe evaluated 4590 plasma proteins with SomaScan in two cohorts of adult patients with CKD: the Chronic Renal Insufficiency Cohort (CRIC; n =2654) and the Atherosclerosis Risk in Communities cohort (ARIC; n =1326). Using Mendelian randomization, we identified proteins along the causal pathway to atrial fibrillation. We also identified proteins and corresponding pathways associated with larger echocardiographic left atrial size, a recognized substrate for atrial fibrillation. Finally, we developed and validated a multiprotein risk score for incident atrial fibrillation in the CKD population.
resultsOver 5 years, incident atrial fibrillation occurred among 150 patients in the CRIC and 140 in the ARIC cohort. We identified three proteins causally linked to incident atrial fibrillation: neural EGF like‑like protein 1, cartilage intermediate layer protein 2, and matrix metallopeptidase 12. Pathway analysis revealed an overlap in eight of the top ten canonical pathways for incident atrial fibrillation and left atrial enlargement. A risk model for incident atrial fibrillation composed of proteins had annualized areas under the receiver-operating characteristic curve over 5 years ranging from 0.65 to 0.76, a performance similar to the Cohorts for Heart and Aging Research in Genomic Epidemiology Atrial Fibrillation (CHARGE-AF) clinical risk score.
conclusionsThis study identified causal proteins and biologic mechanisms underlying incident atrial fibrillation in CKD. A proteomic risk score for incident atrial fibrillation in CKD performed similarly to the CHARGE-AF clinical risk score.
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.
Integrating Genomics and Proteomics to Identify Causal Proteins and Biologic Pathways for Incident Atrial Fibrillation in CKD. · full record | Socratic