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Deo R, Dubin RF, Ren Y, Murthy AC, Wang J, Zheng H, Zheng Z, Feldman H, Shou H, Coresh J, Grams M, Surapaneni AL, Bhat Z, Cohen JB, Rahman M, He J, Saraf SL, Go AS, Kimmel PL, Vasan RS, Segal MR, Li H, Ganz P. Proteomic cardiovascular risk assessment in chronic kidney disease. Eur Heart J 2023; 44:2095-2110. [PMID: 37014015 PMCID: PMC10281556 DOI: 10.1093/eurheartj/ehad115] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 01/21/2023] [Accepted: 02/16/2023] [Indexed: 04/05/2023] Open
Abstract
AIMS Chronic kidney disease (CKD) is widely prevalent and independently increases cardiovascular risk. Cardiovascular risk prediction tools derived in the general population perform poorly in CKD. Through large-scale proteomics discovery, this study aimed to create more accurate cardiovascular risk models. METHODS AND RESULTS Elastic net regression was used to derive a proteomic risk model for incident cardiovascular risk in 2182 participants from the Chronic Renal Insufficiency Cohort. The model was then validated in 485 participants from the Atherosclerosis Risk in Communities cohort. All participants had CKD and no history of cardiovascular disease at study baseline when ∼5000 proteins were measured. The proteomic risk model, which consisted of 32 proteins, was superior to both the 2013 ACC/AHA Pooled Cohort Equation and a modified Pooled Cohort Equation that included estimated glomerular filtrate rate. The Chronic Renal Insufficiency Cohort internal validation set demonstrated annualized receiver operating characteristic area under the curve values from 1 to 10 years ranging between 0.84 and 0.89 for the protein and 0.70 and 0.73 for the clinical models. Similar findings were observed in the Atherosclerosis Risk in Communities validation cohort. For nearly half of the individual proteins independently associated with cardiovascular risk, Mendelian randomization suggested a causal link to cardiovascular events or risk factors. Pathway analyses revealed enrichment of proteins involved in immunologic function, vascular and neuronal development, and hepatic fibrosis. CONCLUSION In two sizeable populations with CKD, a proteomic risk model for incident cardiovascular disease surpassed clinical risk models recommended in clinical practice, even after including estimated glomerular filtration rate. New biological insights may prioritize the development of therapeutic strategies for cardiovascular risk reduction in the CKD population.
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Affiliation(s)
- Rajat Deo
- Division of Cardiovascular Medicine, Electrophysiology Section, Perelman School of Medicine at the University of Pennsylvania, One Convention Avenue, Level 2 / City Side, Philadelphia, PA 19104, USA
| | - Ruth F Dubin
- Division of Nephrology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USA
| | - Yue Ren
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 215 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Ashwin C Murthy
- Division of Cardiovascular Medicine, Electrophysiology Section, Perelman School of Medicine at the University of Pennsylvania, One Convention Avenue, Level 2 / City Side, Philadelphia, PA 19104, USA
| | - Jianqiao Wang
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 215 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Haotian Zheng
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 215 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Zihe Zheng
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 215 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Harold Feldman
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 215 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 215 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Josef Coresh
- Department of Epidemiology; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
- Department of Medicine, Johns Hopkins University, 2024 E. Monument Street, Room 2-635, Suite 2-600, Baltimore, MD 21287, USA
| | - Morgan Grams
- Department of Epidemiology; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
- Department of Medicine, Johns Hopkins University, 2024 E. Monument Street, Room 2-635, Suite 2-600, Baltimore, MD 21287, USA
| | - Aditya L Surapaneni
- Department of Epidemiology; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
| | - Zeenat Bhat
- Division of Nephrology, University of Michigan, 5100 Brehm Tower, 1000 Wall Street, Ann Arbor, MI 48105, USA
| | - Jordana B Cohen
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 215 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
- Renal, Electrolyte and Hypertension Division, Perelman School of Medicine, University of Pennsylvania, 831 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Mahboob Rahman
- Department of Medicine, Case Western Reserve University School of Medicine, 11100 Euclid Avenue, Wearn Bldg. 3 Floor. Rm 352, Cleveland, OH 44106, USA
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street, SL 18, New Orleans, LA 70112, USA
| | - Santosh L Saraf
- Division of Hematology and Oncology, University of Illinois at Chicago, 1740 West Taylor Street, Chicago, IL 60612, USA
| | - Alan S Go
- Division of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA 94612, USA
- Departments of Epidemiology, Biostatistics and Medicine, University of California at San Francisco, San Francisco, CA, USA
| | - Paul L Kimmel
- Division of Kidney, Urologic, and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, USA
| | - Ramachandran S Vasan
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Section of Cardiology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Mark R Segal
- Department of Epidemiology and Biostatistics, University of California, 550 16th Street, 2nd Floor, Box #0560, San Francisco, CA 94143, USA
| | - Hongzhe Li
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 215 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Peter Ganz
- Division of Cardiology, Zuckerberg San Francisco General Hospital and Department of Medicine, University of California, San Francisco, 1001 Potrero Avenue, 5G1, San Francisco, CA 94110, USA
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Chuki E, Graf A, Ninan A, Tora R, Abijo T, Bliss L, Nilubol N, Weinstein LS, Agarwal SK, Simonds WF, Jha S. Long-Term Outcomes of Parathyroid Autografts in Primary Hyperparathyroidism. J Endocr Soc 2023; 7:bvad055. [PMID: 37284613 PMCID: PMC10154906 DOI: 10.1210/jendso/bvad055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Indexed: 06/08/2023] Open
Abstract
Context Autologous implantation of parathyroid tissue is frequently utilized after parathyroidectomy in patients with heritable forms of primary hyperparathyroidism (PHPT). Data on long-term functional outcome of these grafts is sparse. Objective To investigate long-term outcomes of parathyroid autografts. Methods Retrospective study of patients with PHPT who underwent parathyroid autografts from 1991 to 2020. Results We identified 115 patients with PHPT who underwent 135 parathyroid autografts. Median follow-up duration since graft was 10 (4-20) years. Of the 111 grafts with known functional outcome, 54 (49%) were fully functional, 13 (12%) partially functional, and 44 (40%) nonfunctional at last follow-up. Age at time of graft, thymectomy prior to autograft, graft type (delayed vs immediate), or duration of cryopreservation did not predict functional outcome. There were 45 (83%) post-graft PHPT recurrences among 54 fully functional grafts at a median duration of 8 (4-15) years after grafting. Surgery was performed in 42/45 recurrences, but cure was attained in 18/42 (43%) only. Twelve of 18 (67%) recurrences were graft-related while remaining 6 (33%) had a neck or mediastinal source. Median time to recurrence was 16 (11-25) years in neck or mediastinal source vs 7 (2-13) years in graft-related recurrences. Median parathyroid hormone (PTH) gradient was significantly higher at 23 (20-27) in graft-related recurrence vs 1.3 (1.2-2.5) in neck or mediastinal source (P = .03). Conclusions Post-graft recurrence of PHPT occurs frequently within the first decade after graft and is challenging to localize. Time to recurrence after graft is significantly shorter and PTH gradient higher for graft-related recurrence. Clinical Trial Number: NCT04969926.
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Affiliation(s)
- Elias Chuki
- Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Akua Graf
- Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Anisha Ninan
- Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Rana Tora
- Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Tomilowo Abijo
- National Institute of Diabetes and Digestive and Kidney Diseases,Bethesda, MD 20892, USA
| | - Lynn Bliss
- Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Naris Nilubol
- National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Lee S Weinstein
- Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Sunita K Agarwal
- Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - William F Simonds
- Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Smita Jha
- Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
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