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Jayaraman P, Crouse A, Nadkarni G, Might M. A Primer in Precision Nephrology: Optimizing Outcomes in Kidney Health and Disease through Data-Driven Medicine. KIDNEY360 2023; 4:e544-e554. [PMID: 36951457 PMCID: PMC10278804 DOI: 10.34067/kid.0000000000000089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 01/04/2023] [Indexed: 03/24/2023]
Abstract
This year marks the 63rd anniversary of the International Society of Nephrology, which signaled nephrology's emergence as a modern medical discipline. In this article, we briefly trace the course of nephrology's history to show a clear arc in its evolution-of increasing resolution in nephrological data-an arc that is converging with computational capabilities to enable precision nephrology. In general, precision medicine refers to tailoring treatment to the individual characteristics of patients. For an operational definition, this tailoring takes the form of an optimization, in which treatments are selected to maximize a patient's expected health with respect to all available data. Because modern health data are large and high resolution, this optimization process requires computational intervention, and it must be tuned to the contours of specific medical disciplines. An advantage of this operational definition for precision medicine is that it allows us to better understand what precision medicine means in the context of a specific medical discipline. The goal of this article was to demonstrate how to instantiate this definition of precision medicine for the field of nephrology. Correspondingly, the goal of precision nephrology was to answer two related questions: ( 1 ) How do we optimize kidney health with respect to all available data? and ( 2 ) How do we optimize general health with respect to kidney data?
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Affiliation(s)
- Pushkala Jayaraman
- The Charles Bronfman Institute for Personalized Medicine Icahn School of Medicine at Mount Sinai, New York, New York
| | - Andrew Crouse
- Hugh Kaul Precision Medicine Institute, University of Alabama at Birmingham, Birmingham, Alabama
| | - Girish Nadkarni
- The Charles Bronfman Institute for Personalized Medicine Icahn School of Medicine at Mount Sinai, New York, New York
- The Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, New York
- Division of Data Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- Barbara T Murphy Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Matthew Might
- Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
- Department of Computer Science, University of Alabama at Birmingham, Birmingham, Alabama
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Paranjpe I, Tsao N, Judy R, Paranjpe M, Chaudhary K, Klarin D, Forrest I, O'Hagan R, Kapoor A, Pfail J, Jaladanki S, Chaudhry F, Vaid A, Duy PQ, He JC, Glicksberg BS, Coca SG, Gupta M, Do R, Damrauer SM, Nadkarni GN. Derivation and validation of genome-wide polygenic score for urinary tract stone diagnosis. Kidney Int 2020; 98:1323-1330. [PMID: 32540406 PMCID: PMC7606592 DOI: 10.1016/j.kint.2020.04.055] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 03/30/2020] [Accepted: 04/16/2020] [Indexed: 11/25/2022]
Abstract
Urinary tract stones have high heritability indicating a strong genetic component. However, genome-wide association studies (GWAS) have uncovered only a few genome wide significant single nucleotide polymorphisms (SNPs). Polygenic risk scores (PRS) sum cumulative effect of many SNPs and shed light on underlying genetic architecture. Using GWAS summary statistics from 361,141 participants in the United Kingdom Biobank, we generated a PRS and determined association with stone diagnosis in 28,877 participants in the Mount Sinai BioMe Biobank. In BioMe (1,071 cases and 27,806 controls), for every standard deviation increase, we observed a significant increment in adjusted odds ratio of a factor of 1.2 (95% confidence interval 1.13-1.26). In comparison, a risk score comprised of GWAS significant SNPs was not significantly associated with diagnosis. After stratifying individuals into low and high-risk categories on clinical risk factors, there was a significant increment in adjusted odds ratio of 1.3 (1.12-1.6) in the low- and 1.2 (1.1-1.2) in the high-risk group for every standard deviation increment in PRS. In a 14,348-participant validation cohort (Penn Medicine Biobank), every standard deviation increment was associated with a significant adjusted odds ratio of 1.1 (1.03 - 1.2). Thus, a genome-wide PRS is associated with urinary tract stones overall and in the absence of known clinical risk factors and illustrates their complex polygenic architecture.
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Affiliation(s)
- Ishan Paranjpe
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Urology, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Noah Tsao
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Renae Judy
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Manish Paranjpe
- Division of Health Sciences and Technology, Harvard Medical School, Boston, Massachusetts, USA
| | - Kumardeep Chaudhary
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Derek Klarin
- Malcolm Randall Veterans Affairs Medical Center, Gainesville, Florida, USA; Division of Vascular Surgery and Endovascular Therapy, University of Florida College of Medicine, Gainesville, Florida, USA; Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Iain Forrest
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ross O'Hagan
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Arjun Kapoor
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - John Pfail
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Suraj Jaladanki
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Fayzan Chaudhry
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Akhil Vaid
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Phan Q Duy
- Medical Scientist Training Program, Yale University School of Medicine, New Haven, Connecticut, USA
| | - John Cijiang He
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Renal Program, James J Peters Veterans Affairs Medical Center at Bronx, New York, New York, USA
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Steven G Coca
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Mantu Gupta
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Scott M Damrauer
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Renal Program, James J Peters Veterans Affairs Medical Center at Bronx, New York, New York, USA.
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Ghimire S, Terhzaz S, Cabrero P, Romero MF, Davies SA, Dow JAT. Targeted renal knockdown of Na +/H + exchanger regulatory factor Sip1 produces uric acid nephrolithiasis in Drosophila. Am J Physiol Renal Physiol 2019; 317:F930-F940. [PMID: 31364377 DOI: 10.1152/ajprenal.00551.2018] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Nephrolithiasis is one of the most common kidney diseases, with poorly understood pathophysiology, but experimental study has been hindered by lack of experimentally tractable models. Drosophila melanogaster is a useful model organism for renal diseases because of genetic and functional similarities of Malpighian (renal) tubules with the human kidney. Here, we demonstrated function of the sex-determining region Y protein-interacting protein-1 (Sip1) gene, an ortholog of human Na+/H+ exchanger regulatory factor (NHERF1), in Drosophila Malpighian tubules and its impact on nephrolithiasis. Abundant birefringent calculi were observed in Sip1 mutant flies, and the phenotype was also observed in renal stellate cell-specific RNA interference Sip1 knockdown in otherwise normal flies, confirming a renal etiology. This phenotype was abolished in rosy mutant flies (which model human xanthinuria) and by the xanthine oxidase inhibitor allopurinol, suggesting that the calculi were of uric acid. This was confirmed by direct biochemical assay for urate. Stones rapidly dissolved when the tubule was bathed in alkaline media, suggesting that Sip1 knockdown was acidifying the tubule. SIP1 was shown to collocate with Na+/H+ exchanger isoform 2 (NHE2) and with moesin in stellate cells. Knockdown of NHE2 specifically to the stellate cells also increased renal uric acid stone formation, and so a model was developed in which SIP1 normally regulates NHE2 activity and luminal pH, ultimately leading to uric acid stone formation. Drosophila renal tubules may thus offer a useful model for urate nephrolithiasis.
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Affiliation(s)
- Saurav Ghimire
- Institute of Molecular, Cell and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Selim Terhzaz
- Institute of Molecular, Cell and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Pablo Cabrero
- Institute of Molecular, Cell and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Michael F Romero
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, Minnesota
| | - Shireen A Davies
- Institute of Molecular, Cell and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Julian A T Dow
- Institute of Molecular, Cell and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
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Vivante A, Hildebrandt F. Exploring the genetic basis of early-onset chronic kidney disease. Nat Rev Nephrol 2016; 12:133-46. [PMID: 26750453 DOI: 10.1038/nrneph.2015.205] [Citation(s) in RCA: 215] [Impact Index Per Article: 26.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
The primary causes of chronic kidney disease (CKD) in children differ from those of CKD in adults. In the USA the most common diagnostic groups of renal disease that manifest before the age of 25 years are congenital anomalies of the kidneys and urinary tract, steroid-resistant nephrotic syndrome, chronic glomerulonephritis and renal cystic ciliopathies, which together encompass >70% of early-onset CKD diagnoses. Findings from the past decade suggest that early-onset CKD is caused by mutations in any one of over 200 different monogenic genes. Developments in high-throughput sequencing in the past few years has rendered identification of causative mutations in this high number of genes feasible. Use of genetic analyses in patients with early onset-CKD will provide patients and their families with a molecular genetic diagnosis, generate new insights into disease mechanisms, facilitate aetiology-based classifications of patient cohorts for clinical studies, and might have consequences for personalized approaches to the prevention and treatment of CKD. In this Review, we discuss the implications of next-generation sequencing in clinical genetic diagnostics and the discovery of novel genes in early-onset CKD. We also delineate the resulting opportunities for deciphering disease mechanisms and the therapeutic implications of these findings.
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Affiliation(s)
- Asaf Vivante
- Department of Medicine, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, Massachusetts 02115, USA.,Talpiot Medical Leadership Program, Sheba Medical Center, Tel-Hashomer 52621, Israel
| | - Friedhelm Hildebrandt
- Department of Medicine, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, Massachusetts 02115, USA
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