Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Big Data in Nephrology

Abstract

A huge array of data in nephrology is collected through patient registries, large epidemiological studies, electronic health records, administrative claims, clinical trial repositories, mobile health devices and molecular databases. Application of these big data, particularly using machine-learning algorithms, provides a unique opportunity to obtain novel insights into kidney diseases, facilitate personalized medicine and improve patient care. Efforts to make large volumes of data freely accessible to the scientific community, increased awareness of the importance of data sharing and the availability of advanced computing algorithms will facilitate the use of big data in nephrology. However, challenges exist in accessing, harmonizing and integrating datasets in different formats from disparate sources, improving data quality and ensuring that data are secure and the rights and privacy of patients and research participants are protected. In addition, the optimism for data-driven breakthroughs in medicine is tempered by scepticism about the accuracy of calibration and prediction from in silico techniques. Machine-learning algorithms designed to study kidney health and diseases must be able to handle the nuances of this specialty, must adapt as medical practice continually evolves, and must have global and prospective applicability for external and future datasets.

Key points

  • Big data in nephrology can provide essential information about kidney disease burden, molecular mechanisms, novel risk factors and therapeutic targets.

  • Artificial intelligence and machine-learning approaches that utilize big data could be used for a variety of applications in nephrology, including early diagnosis and prognosis, as well as clinical decision-support systems for personalized selection of therapy.

  • Data curation and standardization enable interoperability, facilitate consolidation and exchange of high-quality data from different sources, create independence from manufacturers and ease competition as comparable products are offered by all market players.

  • Sources of big data in nephrology include patient registries, population surveys, electronic health records, open-access clinical trials, mobile health devices and molecular data repositories.

  • Large-scale acquisition of annotated molecular and clinical data, together with advances in machine learning approaches, open-source computational packages, affordable computation power and cloud storage, will all facilitate more novel data-driven approaches in nephrology.

  • Challenges for the utilization of big data in nephrology include issues relating to data governance and protection, siloed datasets, data heterogeneity, small sample sizes and a lack of consistent research funding.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Characteristics of big data.
Fig. 2: Workflow for use of data for clinical decision support systems in nephrology.
Fig. 3: Timeline of the availability of big data in nephrology.

References

  1. 1.

    Erickson, K. F., Qureshi, S. & Winkelmayer, W. C. The role of big data in the development and evaluation of US dialysis care. Am. J. Kidney Dis. 72, 560–568 (2018).

    PubMed  Article  Google Scholar 

  2. 2.

    Adimadhyam, S. et al. Leveraging the capabilities of the FDA’s sentinel system to improve kidney care. J. Am. Soc. Nephrol. 31, 2506–2516 (2020).

    CAS  PubMed  Article  Google Scholar 

  3. 3.

    Gulshan, V. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402–2410 (2016).

    PubMed  Article  Google Scholar 

  4. 4.

    Escobar, G. J. et al. Automated identification of adults at risk for in-hospital clinical deterioration. N. Engl. J. Med. 383, 1951–1960 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    Hulsen, T. et al. From big data to precision medicine. Front. Med. 6, 34 (2019).

    Article  Google Scholar 

  6. 6.

    Cahan, E. M., Hernandez-Boussard, T., Thadaney-Israni, S. & Rubin, D. L. Putting the data before the algorithm in big data addressing personalized healthcare. NPJ Digit. Med. 2, 78 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  7. 7.

    Liu, F. X., Rutherford, P., Smoyer-Tomic, K., Prichard, S. & Laplante, S. A global overview of renal registries: A systematic review Epidemiology and Health Outcomes. BMC Nephrol. 16, 1–10 (2015).

    Article  Google Scholar 

  8. 8.

    Bikbov, B. et al. Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 395, 709–733 (2020).

    Article  Google Scholar 

  9. 9.

    Friedman, D. J., Parrish, R. G. & Ross, D. A. Electronic health records and US public health: Current realities and future promise. Am. J. Public Health 103, 1560–1567 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Murphy, D. et al. Trends in prevalence of chronic kidney disease in the United States. Ann. Intern. Med. 165, 473–481 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Chan, L. et al. The effect of depression in chronic hemodialysis patients on inpatient hospitalization outcomes. Blood Purif. 43, 226–234 (2017).

    PubMed  Article  Google Scholar 

  12. 12.

    Cheung, A. et al. Impact of atrial fibrillation in patients with chronic kidney disease undergoing transcatheter aortic valve replacement: Insights of the Healthcare Cost and Utilization Project’s National Inpatient Sample. Cardiovasc. Revasc. Med. 19, 21–25 (2018).

    PubMed  Article  Google Scholar 

  13. 13.

    Matsushita, K. et al. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet 375, 2073–2081 (2010).

    PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Zoccali, C., Brancaccio, D. & Nathan, M. J. Causality at the dawn of the ‘omics’ era in medicine and in nephrology. Nephrol. Dial. Transplant. 31, 1381–1385 (2016).

    PubMed  Article  Google Scholar 

  15. 15.

    Weber, G. M., Mandl, K. D. & Kohane, I. S. Finding the missing link for big biomedical data. J. Am. Med. Assoc. 311, 2479–2480 (2014).

    CAS  Google Scholar 

  16. 16.

    Nadkarni, G. N., Coca, S. G. & Wyatt, C. M. Big data in nephrology: promises and pitfalls. Kidney Int. 90, 240–241 (2016).

    PubMed  Article  Google Scholar 

  17. 17.

    Pezoulas, V. C. et al. Medical data quality assessment: on the development of an automated framework for medical data curation. Comput. Biol. Med. 107, 270–283 (2019).

    PubMed  Article  Google Scholar 

  18. 18.

    Danese, M. D., Halperin, M., Duryea, J. & Duryea, R. The generalized data model for clinical research. BMC Med. Inform. Decis. Mak. 19, 1–13 (2019).

    Article  Google Scholar 

  19. 19.

    Fleurence, R. L. et al. Launching PCORnet, a national patient-centered clinical research network. J. Am. Med. Inform. Assoc. 21, 578–582 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  20. 20.

    Murphy, S. N. et al. Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). J. Am. Med. Inform. Assoc. 17, 124–130 (2010).

    PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Klann, J. G., Joss, M. A. H., Embree, K. & Murphy, S. N. Data model harmonization for the all of us research program: transforming i2b2 data into the OMOP common data model. PLoS One 14, 1–13 (2019).

    Article  CAS  Google Scholar 

  22. 22.

    Kush, R. D. et al. FAIR data sharing: the roles of common data elements and harmonization. J. Biomed. Inform. 107, 103421 (2020).

    CAS  PubMed  Article  Google Scholar 

  23. 23.

    Wilkinson, M. D. et al. Comment: the FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 1–9 (2016).

    Article  Google Scholar 

  24. 24.

    Kubben, P., Dumontier, M. & Dekker, A. Fundamentals of clinical data science. (Springer, 2019).

  25. 25.

    Dreyer, N. A. & Garner, S. Registries for robust evidence. JAMA 302, 790–791 (2009).

    CAS  PubMed  Article  Google Scholar 

  26. 26.

    Jager, K. J. & Wanner, C. Fifty years of ERA-EDTA registry — a registry in transition. Kidney Int. Suppl. 5, 12–14 (2015).

    Article  Google Scholar 

  27. 27.

    Choi, N. G., Sullivan, J. E., DiNitto, D. M. & Kunik, M. E. Health care utilization among adults with CKD and psychological distress. Kidney Med. 1, 162–170 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  28. 28.

    Robinson, B. M., Bieber, B., Pisoni, R. L. & Port, F. K. Dialysis outcomes and practice patterns study (DOPPS): Its strengths, limitations, and role in informing practices and policies. Clin. J. Am. Soc. Nephrol. 7, 1897–1905 (2012).

    PubMed  Article  Google Scholar 

  29. 29.

    DOPPS. DPM sampling, study design, and calculation methods. DOPPS https://www.dopps.org/DPM/Data_Sources_Methods.pdf (2020).

  30. 30.

    Dienemann, T. et al. International Network of Chronic Kidney Disease cohort studies (iNET-CKD): a global network of chronic kidney disease cohorts. BMC Nephrol. 17, 1–9 (2016).

    Article  Google Scholar 

  31. 31.

    Saran, R. et al. US Renal Data System 2019 Annual Data Report: epidemiology of kidney disease in the United States. Am. J. Kidney Dis. 75, A6–A7 (2020).

    PubMed  Article  PubMed Central  Google Scholar 

  32. 32.

    Go, A. S., Chertow, G. M., Fan, D., McCulloch, C. E. & Hsu, C. Y. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N. Engl. J. Med. 351, 1296–1305 (2004).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  33. 33.

    Saran, R. et al. US Renal Data System 2014 Annual Data Report: epidemiology of kidney disease in the United States. Am. J. Kidney Dis. 66, A7 (2015).

    Article  Google Scholar 

  34. 34.

    Mendu, M. L. et al. Development of an electronic health record-based chronic kidney disease registry to promote population health management. BMC Nephrol. 20, 1–11 (2019).

    Article  Google Scholar 

  35. 35.

    Norris, K. C. et al. Rationale and design of a multicenter Chronic Kidney Disease (CKD) and at-risk for CKD electronic health records-based registry: CURE-CKD. BMC Nephrol. 20, 1–9 (2019).

    Article  Google Scholar 

  36. 36.

    Navaneethan, S. D. et al. Development and validation of an electronic health record-based chronic kidney disease registry. Clin. J. Am. Soc. Nephrol. 6, 40–49 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Evans, K. et al. UK renal registry 20th annual report: introduction. Nephron 139, 1–11 (2018).

    PubMed  Article  Google Scholar 

  38. 38.

    Pyart, R. et al. The 21st UK renal registry annual report: a summary of analyses of adult data in 2017. Nephron 144, 59–66 (2020).

    PubMed  Article  Google Scholar 

  39. 39.

    Kramer, A. et al. The European Renal Association — European Dialysis and Transplant Association (ERA-EDTA) Registry Annual Report 2016: a summary. Clin. Kidney J. 12, 702–720 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    McDonald, S. P. Australia and New Zealand dialysis and transplant registry. Kidney Int. Suppl. 5, 39–44 (2015).

    Article  Google Scholar 

  41. 41.

    Global Health Data Exchange. http://www.healthdata.org/about/ghdx (2020).

  42. 42.

    Rare Kidney Stone Consortium. http://www.rarekidneystones.org/ (2015).

  43. 43.

    Murdoch, T. B. & Detsky, A. S. The inevitable application of big data to health care. JAMA 309, 1351–1352 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  44. 44.

    Jensen, P. B., Jensen, L. J. & Brunak, S. Mining electronic health records: towards better research applications and clinical care. Nat. Rev. Genet. 13, 395–405 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  45. 45.

    McCartney, P. R. Clinical databases: electronic health records and repositories. MCN Am. J. Matern. Nurs. 38, 186 (2013).

    Article  Google Scholar 

  46. 46.

    Hripcsak, G. et al. Observational health data sciences and informatics (OHDSI): opportunities for observational researchers. Stud. Health Technol. Inform. 216, 574–578 (2015).

    PubMed  PubMed Central  Google Scholar 

  47. 47.

    Johnson, A. E. W. et al. MIMIC-III, a freely accessible critical care database. Sci. Data 3, 1–9 (2016).

    Article  CAS  Google Scholar 

  48. 48.

    Ta, C. N., Dumontier, M., Hripcsak, G., Tatonetti, N. P. & Weng, C. Columbia open health data, clinical concept prevalence and co-occurrence from electronic health records. Sci. Data 5, 1–17 (2018).

    Article  Google Scholar 

  49. 49.

    Centers for Medicare & Medicaid Services. CMS 2008-2010 Data Entrepreneurs’ Synthetic Public Use File (DE-SynPUF) (2019).

  50. 50.

    UK Biobank. Integrating Electronic Health Records into the UK Biobank Resource. http://biobank.ctsu.ox.ac.uk/showcase/showcase/docs/DataLinkageProcess.pdf (2014).

  51. 51.

    Visweswaran, S. et al. Accrual to clinical trials (ACT): a clinical and translational science award consortium network. JAMIA Open 1, 147–152 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    The All of Us Research Program Investigators. The “All of Us” Research Program. N. Engl. J. Med. 381, 668–676 (2019).

    Article  Google Scholar 

  53. 53.

    Cadarette, S. M. & Wong, L. An introduction to health care administrative data. Can. J. Hosp. Pharm. 68, 232–237 (2015).

    PubMed  PubMed Central  Google Scholar 

  54. 54.

    Nadkarni, G. N. et al. Development and validation of an electronic phenotyping algorithm for chronic kidney disease. AMIA Annu. Symp. Proc. 2014, 907–916 (2014).

    PubMed  PubMed Central  Google Scholar 

  55. 55.

    Norton, J. M. et al. Development and validation of a pragmatic electronic phenotype for CKD. Clin. J. Am. Soc. Nephrol. 14, 1306–1314 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  56. 56.

    Wilkerson, M. L., Henricks, W. H., Castellani, W. J., Whitsitt, M. S. & Sinard, J. H. Management of laboratory data and information exchange in the electronic health record. Arch. Pathol. Lab. Med. 139, 319–327 (2015).

    PubMed  Article  PubMed Central  Google Scholar 

  57. 57.

    Mills, S. Electronic health records and use of clinical decision support. Crit. Care Nurs. Clin. North. Am. 31, 125–131 (2019).

    PubMed  Article  PubMed Central  Google Scholar 

  58. 58.

    Abdel-Kader, K. & Jhamb, M. EHR-based clinical trials: the next generation of evidence. Clin. J. Am. Soc. Nephrol. 15, 1050–1052 (2020).

    PubMed  Article  PubMed Central  Google Scholar 

  59. 59.

    Kohane, I. S. Using electronic health records to drive discovery in disease genomics. Nat. Rev. Genet. 12, 417–428 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  60. 60.

    Garcelon, N., Burgun, A., Salomon, R. & Neuraz, A. Electronic health records for the diagnosis of rare diseases. Kidney Int. 97, 676–686 (2020).

    PubMed  Article  Google Scholar 

  61. 61.

    Matsushita, K. et al. Cohort profile: the chronic kidney disease prognosis consortium. Int. J. Epidemiol. 42, 1660–1668 (2013).

    PubMed  Article  Google Scholar 

  62. 62.

    Tomašev, N. et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature 572, 116–119 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  63. 63.

    Makino, M. et al. Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning. Sci. Rep. 9, 11862 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  64. 64.

    Akbilgic, O. et al. Machine learning to identify dialysis patients at high death risk. Kidney Int. Rep. 4, 1219–1229 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  65. 65.

    Ravizza, S. et al. Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data. Nat. Med. 25, 57–59 (2019).

    CAS  PubMed  Article  Google Scholar 

  66. 66.

    Pivovarov, R., Albers, D. J., Sepulveda, J. L. & Elhadad, N. Identifying and mitigating biases in EHR laboratory tests. J. Biomed. Inform. 51, 24–34 (2014).

    PubMed  Article  Google Scholar 

  67. 67.

    Sutton, P. R. & Payne, T. H. Interoperability of electronic health information and care of dialysis patients in the United States. Clin. J. Am. Soc. Nephrol. 14, 1536–1538 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  68. 68.

    Centers for Disease Control and Prevention. Surveillance Strategy Report — How Sharing Data Digitally Benefits Health. https://www.cdc.gov/surveillance/innovation/sharing-data-digitally.html (2018).

  69. 69.

    Krumholz, H. M. & Peterson, E. D. Open access to clinical trials data. JAMA 312, 1002–1003 (2014).

    CAS  PubMed  Article  Google Scholar 

  70. 70.

    Baigent, C. et al. Challenges in conducting clinical trials in nephrology: conclusions from a Kidney Disease — Improving Global Outcomes (KDIGO) Controversies Conference. Kidney Int. 92, 297–305 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  71. 71.

    Kitchlu, A. et al. Representation of patients with chronic kidney disease in trials of cancer therapy. JAMA 319, 2437–2439 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  72. 72.

    Panchapakesan, U. & Pollock, C. Drug repurposing in kidney disease. Kidney Int. 94, 40–48 (2018).

    CAS  PubMed  Article  Google Scholar 

  73. 73.

    Herrington, W. G., Staplin, N. & Haynes, R. Kidney disease trials for the 21st century: innovations in design and conduct. Nat. Rev. Nephrol. 16, 173–185 (2020).

    PubMed  Article  Google Scholar 

  74. 74.

    Sim, I. et al. Time for NIH to lead on data sharing. Science 367, 1308–1309 (2020).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  75. 75.

    Kiley, R., Peatfield, T., Hansen, J. & Reddington, F. Data sharing from clinical trials — a research funder’s perspective. N. Engl. J. Med. 377, 1990–1992 (2017).

    PubMed  Article  PubMed Central  Google Scholar 

  76. 76.

    Mc Cord, K. A. et al. Routinely collected data for randomized trials: promises, barriers, and implications. Trials 19, 29 (2018).

    Article  Google Scholar 

  77. 77.

    Shlipak, M. & Stehman-Breen, C. Observational research databases in renal disease. J Am. Soc. Nephrol. 16, 3477–3484 (2005).

    PubMed  Article  PubMed Central  Google Scholar 

  78. 78.

    Loupy, A. et al. Prediction system for risk of allograft loss in patients receiving kidney transplants: International derivation and validation study. BMJ 366, l4923 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  79. 79.

    Egger, G. F. et al. European Union Clinical Trials Register: on the way to more transparency of clinical trial data. Expert Rev. Clin. Pharmacol. 6, 457–459 (2013).

    CAS  PubMed  Article  Google Scholar 

  80. 80.

    Cochrane Kidney and Transplant. https://kidneyandtransplant.cochrane.org/ (2021).

  81. 81.

    Bierer, B. E., Li, R., Barnes, M. & Sim, I. A global, neutral platform for sharing trial data. N. Engl. J. Med. 374, 2411–2413 (2016).

    PubMed  Article  Google Scholar 

  82. 82.

    Goldacre, B. & Gray, J. Opentrials: towards a collaborative open database of all available information on all clinical trials. Trials 17, 164 (2018).

    Article  Google Scholar 

  83. 83.

    Ross, J. S. et al. Overview and experience of the YODA project with clinical trial data sharing after 5 years. Sci. Data 5, 1–14 (2018).

    Article  CAS  Google Scholar 

  84. 84.

    Pencina, M. J. et al. Supporting open access to clinical trial data for researchers: the Duke Clinical Research Institute-Bristol-Myers Squibb supporting open access to researchers initiative. Am. Heart J. 172, 64–69 (2016).

    PubMed  Article  Google Scholar 

  85. 85.

    Bhattacharya, S. et al. ImmPort, toward repurposing of open access immunological assay data for translational and clinical research. Sci. Data 5, 1–9 (2018).

    CAS  Article  Google Scholar 

  86. 86.

    Chen, J. et al. Assessment of postdonation outcomes in US living kidney donors using publicly available data sets. JAMA Netw. Open 2, e191851 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  87. 87.

    Sim, I. Mobile devices and health. N. Engl. J. Med. 381, 956–968 (2019).

    PubMed  Article  Google Scholar 

  88. 88.

    Sieverdes, J. C. Mobile health considerations for kidney disease and transplantation. mHealth 4, 13–13 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  89. 89.

    Lambert, K., Mullan, J., Mansfield, K. & Owen, P. Should we recommend renal diet–related apps to our patients? An evaluation of the quality and health literacy demand of renal diet–related mobile applications. J. Ren. Nutr. 27, 430–438 (2017).

    PubMed  Article  Google Scholar 

  90. 90.

    Streeper, N. M., Lehman, K. & Conroy, D. E. Acceptability of mobile health technology for promoting fluid consumption in patients with nephrolithiasis. Urology 122, 64–69 (2018).

    PubMed  Article  Google Scholar 

  91. 91.

    Lunde, P., Nilsson, B. B., Bergland, A., Kværner, K. J. & Bye, A. The effectiveness of smartphone apps for lifestyle improvement in noncommunicable diseases: systematic review and meta-analyses. J. Med. Internet Res. 20, 1–12 (2018).

    Article  Google Scholar 

  92. 92.

    Singh, K. et al. Patients’ and nephrologists’ evaluation of patient-facing smartphone apps for CKD. Clin. J. Am. Soc. Nephrol. 14, 523–529 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  93. 93.

    Yang, Y., Chen, H., Qazi, H. & Morita, P. P. Intervention and evaluation of mobile health technologies in management of patients undergoing chronic dialysis: scoping review. JMIR mHealth Uhealth 8, e15549 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  94. 94.

    Pejchinovski, M. & Mischak, H. Clinical proteomics in kidney disease: from discovery to clinical application. Prilozi 38, 39–54 (2018).

    Article  Google Scholar 

  95. 95.

    Bullich, G. et al. A kidney-disease gene panel allows a comprehensive genetic diagnosis of cystic and glomerular inherited kidney diseases. Kidney Int. 94, 363–371 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  96. 96.

    Groopman, E. E., Rasouly, H. M. & Gharavi, A. G. Genomic medicine for kidney disease. Nat. Rev. Nephrol. 14, 83–104 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  97. 97.

    Groopman, E. E. et al. Diagnostic utility of exome sequencing for kidney disease. N. Engl. J. Med. 380, 142–151 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  98. 98.

    Weiss, R. H. Metabolomics and metabolic reprogramming in kidney cancer. Semin. Nephrol. 38, 175–182 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  99. 99.

    Hasin, Y., Seldin, M. & Lusis, A. Multi-omics approaches to disease. Genome Biol. 18, 1–15 (2017).

    Article  CAS  Google Scholar 

  100. 100.

    Papadopoulos, T. et al. Omics databases on kidney disease: where they can be found and how to benefit from them. Clin. Kidney J. 9, 343–352 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  101. 101.

    Hamosh, A., Scott, A. F., Amberger, J. S., Bocchini, C. A. & McKusick, V. A. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 33, 514–517 (2005).

    Article  CAS  Google Scholar 

  102. 102.

    Lenffer, J. OMIA (Online Mendelian Inheritance in Animals): an enhanced platform and integration into the Entrez search interface at NCBI. Nucleic Acids Res. 34, D599–D601 (2006).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  103. 103.

    Parsa, A. et al. Common variants in mendelian kidney disease genes and their association with renal function. J. Am. Soc. Nephrol. 24, 2105–2117 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  104. 104.

    Mallett, A. J. et al. Massively parallel sequencing and targeted exomes in familial kidney disease can diagnose underlying genetic disorders. Kidney Int. 92, 1493–1506 (2017).

    CAS  PubMed  Article  Google Scholar 

  105. 105.

    Tryka, K. A. et al. NCBI’s database of genotypes and phenotypes: DbGaP. Nucleic Acids Res. 42, 975–979 (2014).

    Article  CAS  Google Scholar 

  106. 106.

    Wong, K. M. et al. The dbGaP data browser: a new tool for browsing dbGaP controlled-access genomic data. Nucleic Acids Res. 45, D819–D826 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  107. 107.

    Barrett, T. et al. NCBI GEO: archive for functional genomics data sets — update. Nucleic Acids Res. 41, 991–995 (2013).

    Article  CAS  Google Scholar 

  108. 108.

    Papatheodorou, I. et al. Expression atlas: gene and protein expression across multiple studies and organisms. Nucleic Acids Res. 46, D246–D251 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  109. 109.

    Uhlén, M. et al. Tissue-based map of the human proteome. Science 347, 1260419 (2015).

    Article  CAS  Google Scholar 

  110. 110.

    Thul, P. J. & Lindskog, C. The human protein atlas: a spatial map of the human proteome. Protein Sci. 27, 233–244 (2018).

    CAS  PubMed  Article  Google Scholar 

  111. 111.

    Yamamoto, T., Langham, R. G., Ronco, P., Knepper, M. A. & Thongboonkerd, V. Towards standard protocols and guidelines for urine proteomics: a report on the Human Kidney and Urine Proteome Project (HKUPP) symposium and workshop — 6 October 2007, Seoul, Korea and 1 November 2007, San Francisco, CA, USA. Proteomics 8, 2156–2159 (2008).

    CAS  PubMed  Article  Google Scholar 

  112. 112.

    Shao, C. et al. A tool for biomarker discovery in the urinary proteome: a manually curated human and animal urine protein biomarker database. Mol. Cell. Proteom. 10, 1–8 (2011).

    Article  CAS  Google Scholar 

  113. 113.

    e-LICO An e-Laboratory for Interdisciplinary Collaborative Research in Data Mining and Data-Intensive Science. http://www.e-lico.eu/ (2019).

  114. 114.

    Jupp, S., Klein, J., Schanstra, J. & Stevens, R. Developing a kidney and urinary pathway knowledge base. J. Biomed. Semant. 2, S7 (2011).

    Article  Google Scholar 

  115. 115.

    Helfand, B. T., Mendez, M. G., Pugh, J., Delsert, C. & Goldman, R. D. Maintaining the shape of nerve cells. Mol. Biol. Cell 14, 5069–5081 (2003).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  116. 116.

    Chabardès-Garonne, D. et al. A panoramic view of gene expression in the human kidney. Proc. Natl Acad. Sci. USA 100, 13710–13715 (2003).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  117. 117.

    Willnow, T. E. et al. The European renal genome project. Organogenesis 2, 42–47 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  118. 118.

    Mischak, H. et al. Comprehensive human urine standards for comparability and standardization in clinical proteome analysis. Proteom. Clin. Appl. 4, 464–478 (2010).

    CAS  Article  Google Scholar 

  119. 119.

    Moulos, P. et al. The KUPNetViz: a biological network viewer for multiple -omics datasets in kidney diseases. BMC Bioinformatics 14, 235 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  120. 120.

    Fernandes, M. & Husi, H. Establishment of a integrative multi-omics expression database CKDdb in the context of chronic kidney disease (CKD). Sci. Rep. 7, 1–11 (2017).

    Article  CAS  Google Scholar 

  121. 121.

    Zhao, H. et al. Kidney gene database: a curated and integrated database of genes involved in kidney disease. J. Urol. 172, 2344–2346 (2004).

    CAS  PubMed  Article  Google Scholar 

  122. 122.

    Zhang, Q. et al. Renal Gene Expression Database (RGED): a relational database of gene expression profiles in kidney disease. Database 2014, 1–6 (2014).

    Google Scholar 

  123. 123.

    Gillies, C. E. et al. An eQTL landscape of kidney tissue in human nephrotic syndrome. Am. J. Hum. Genet. 103, 232–244 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  124. 124.

    Qiu, C. et al. Renal compartment–specific genetic variation analyses identify new pathways in chronic kidney disease. Nat. Med. 24, 1721–1731 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  125. 125.

    Ketchersid, T. Big data in nephrology: friend or foe? Blood Purif. 36, 160–164 (2014).

    Article  Google Scholar 

  126. 126.

    Shilo, S., Rossman, H. & Segal, E. Axes of a revolution: challenges and promises of big data in healthcare. Nat. Med. 26, 29–38 (2020).

    CAS  PubMed  Article  Google Scholar 

  127. 127.

    Kaye, J. et al. Including all voices in international datasharing governance. Hum. Genomics 12, 18–23 (2018).

    Article  Google Scholar 

  128. 128.

    Reinholz, D. L. & Andrews, T. C. Breaking down silos working meeting: an approach to fostering cross-disciplinary STEM–DBER collaborations through working meetings. CBE Life Sci. Educ. 18, 1–8 (2019).

    Google Scholar 

  129. 129.

    Davenport, T. & Kalakota, R. The potential for artificial intelligence in healthcare. Futur. Healthc. J. 6, 94–102 (2019).

    Article  Google Scholar 

  130. 130.

    Kruse, C. S., Goswamy, R., Raval, Y. & Marawi, S. Challenges and opportunities of big data in health care: a systematic review. JMIR Med. Inform. 4, e38 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  131. 131.

    Ngiam, K. Y. & Khor, I. W. Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 20, e262–e273 (2019).

    PubMed  Article  Google Scholar 

  132. 132.

    Floege, J., Mak, R. H., Molitoris, B. A., Remuzzi, G. & Ronco, P. Nephrology research — the past, present and future. Nat. Rev. Nephrol. 11, 677–687 (2015).

    PubMed  Article  Google Scholar 

  133. 133.

    Pépin, J. L., Bailly, S. & Tamisier, R. Big data in sleep apnoea: opportunities and challenges. Respirology 25, 486–494 (2019).

    PubMed  Article  Google Scholar 

  134. 134.

    Adibuzzaman, M., DeLaurentis, P., Hill, J. & Benneyworth, B. D. Big data in healthcare — the promises, challenges and opportunities from a research perspective: a case study with a model database. AMIA Annu. Symp. Proc. 2017, 384–392 (2017).

    PubMed  Google Scholar 

  135. 135.

    Price, W. N. & Cohen, I. G. Privacy in the age of medical big data. Nat. Med. 25, 37–43 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  136. 136.

    Jeon, S. et al. Proposal and assessment of a de-identification strategy to enhance anonymity of the observational medical outcomes partnership common data model (OMOP-CDM) in a public cloud-computing environment: anonymization of medical data using privacy models. J. Med. Internet Res. 22, e19597 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  137. 137.

    Meskó, B. & Görög, M. A short guide for medical professionals in the era of artificial intelligence. NPJ Digit. Med. 3, 126 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  138. 138.

    Jha, A. K. et al. How common are electronic health records in the United States? A summary of the evidence. Health Aff. 25, 496–507 (2006).

    Article  Google Scholar 

  139. 139.

    Brennan, S. The biggest computer programme in the world ever! How’s it going? J. Inf. Technol. 22, 202–211 (2007).

    Article  Google Scholar 

  140. 140.

    Lee Ventola, C. Mobile devices and apps for health care professionals: uses and benefits. P T 39, 356–364 (2014).

    PubMed  PubMed Central  Google Scholar 

  141. 141.

    Liu, C., Zhu, Q., Holroyd, K. A. & Seng, E. K. Status and trends of mobile-health applications for iOS devices: a developer’s perspective. J. Syst. Softw. 84, 2022–2033 (2011).

    Article  Google Scholar 

  142. 142.

    Sidey-Gibbons, J. A. M. & Sidey-Gibbons, C. J. Machine learning in medicine: a practical introduction. BMC Med. Res. Methodol. 19, 1–18 (2019).

    Article  Google Scholar 

  143. 143.

    Niel, O. & Bastard, P. Artificial intelligence in nephrology: core concepts, clinical applications, and perspectives. Am. J. Kidney Dis. 74, 803–810 (2019).

    PubMed  Article  Google Scholar 

  144. 144.

    Geddes, C. C., Fox, J. G., Allison, M. E. M., Boulton-Jones, J. M. & Simpson, K. An artificial neural network can select patients at high risk of developing progressive IgA nephropathy more accurately than experienced nephrologists. Nephrol. Dial. Transplant. 13, 67–71 (1998).

    CAS  PubMed  Article  Google Scholar 

  145. 145.

    Lin, K., Hu, Y. & Kong, G. Predicting in-hospital mortality of patients with acute kidney injury in the ICU using random forest model. Int. J. Med. Inform. 125, 55–61 (2019).

    PubMed  Article  Google Scholar 

  146. 146.

    Gabutti, L. et al. Usefulness of artificial neural networks to predict follow-up dietary protein intake in hemodialysis patients. Kidney Int. 66, 399–407 (2004).

    PubMed  Article  Google Scholar 

  147. 147.

    Akl, A. I., Sobh, M. A., Enab, Y. M. & James, T. Artificial intelligence: a new approach for prescription and monitoring of hemodialysis therapy. Am. J. Kidney Dis. 38, 1277–1283 (2001).

    CAS  PubMed  Article  Google Scholar 

  148. 148.

    Barbieri, C. et al. An international observational study suggests that artificial intelligence for clinical decision support optimizes anemia management in hemodialysis patients. Kidney Int. 90, 422–442 (2016).

    PubMed  Article  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge Flavio Vincenti, Sri Lekha Tummalapalli, Vivek Rudrapatna, Douglas Arneson and Zicheng Hu (all University of California, San Francisco) for their valuable suggestions for this manuscript. The authors’ work was supported by the National Institute of Allergy and Infectious Diseases (Bioinformatics Support Contract HHSN316201200036W), the UCSF Bakar Computational Health Sciences Institute and the UCSF Clinical and Translational Sciences Institute, supported in part by the National Center for Advancing Translational Sciences of the National Institutes of Health under award number UL1 TR001872. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information

Affiliations

Authors

Contributions

N.K. researched data for the article and wrote the manuscript, S.B. contributed to data research and edited the manuscript. A.J.B. conceived the manuscript framework and reviewed and edited it before submission.

Corresponding author

Correspondence to Atul J. Butte.

Ethics declarations

Competing interests

A.T.B. is a co-founder and consultant to Personalis and NuMedii; consultant to Samsung, Mango Tree Corporation, and in the recent past, 10x Genomics, Helix, Pathway Genomics, and Verinata (Illumina); has served on paid advisory panels or boards for Geisinger Health, Regenstrief Institute, Gerson Lehman Group, AlphaSights, Covance, Novartis, Genentech, Merck and Roche; is a shareholder in Personalis and NuMedii; is a minor shareholder in Apple, Facebook, Alphabet (Google), Microsoft, Amazon, Snap, 10x Genomics, Illumina, CVS, Nuna Health, Assay Depot, Vet24seven, Regeneron, Sanofi, Royalty Pharma, AstraZeneca, Moderna, Biogen, Paraxel and Sutro, and several other non-health-related companies and mutual funds; and has received honoraria and travel reimbursement for invited talks from Johnson and Johnson, Roche, Genentech, Pfizer, Merck, Lilly, Takeda, Varian, Mars, Siemens, Optum, Abbott, Celgene, AstraZeneca, AbbVie, Westat, and many academic institutions, medical or disease-specific foundations and associations, and health systems. A.T.B. receives royalty payments through Stanford University, for several patents and other disclosures licensed to NuMedii and Personalis. His research has been funded by NIH, Northrup Grumman (as the prime on an NIH contract), Genentech, Johnson and Johnson, FDA, Robert Wood Johnson Foundation, Leon Lowenstein Foundation, Intervalien Foundation, Priscilla Chan and Mark Zuckerberg, the Barbara and Gerson Bakar Foundation, and in the recent past, the March of Dimes, Juvenile Diabetes Research Foundation, California Governor’s Office of Planning and Research, California Institute for Regenerative Medicine, L’Oreal, and Progenity.

Additional information

Peer review information

Nature Reviews Nephrology thanks Luxia Zhang, who co-reviewed with Chao Yang, William Herrington, Min Jun and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Autosomal dominant polycystic kidney disease mutation database: https://pkdb.mayo.edu/

ClinicalStudyDataRequest.com: https://clinicalstudydatarequest.com/Default.aspx

Clinicaltrials.gov: https://clinicaltrials.gov/

Immune Tolerance Network: https://www.immunetolerance.org/

National Kidney Foundation Patient Network: https://www.kidney.org/nkfpatientnetwork

Nephroseq: https://www.nephroseq.org/resource/login.html

PatientsLikeMe: https://www.patientslikeme.com/

Registry Of Kidney Diseases: https://gardn.org.au/registries/registry-of-kidney-diseases/

RenDER data extraction and referencing system: https://render.usrds.org/render/xrender.phtml

Sentinel and Patient-Centered Outcomes Research Network: https://www.sentinelinitiative.org/sentinel/data/distributed-database-common-data-model

The HCUP National Inpatient Sample (NIS): https://www.hcup-us.ahrq.gov/nisoverview.jsp

The NephCure Kidney Network Patient Registry: https://nephcure.org/2015/12/the-nephcure-kidney-network-patient-registry-nkn/

Think Kidneys: https://www.thinkkidneys.nhs.uk/

WHO International Clinical Trials Registry Platform: https://www.who.int/ictrp/en/

Supplementary information

Glossary

Deep learning

A type of machine learning that uses multiple layers to progressively extract higher level features from the input layer of the model. Common deep learning algorithms include convolutional neural networks, recurrent neural networks, general adversarial networks and autoencoders.

k-anonymity

A data anonymization technique that protects the identities of individuals using methods such as suppression and generalization. A dataset is said to have k-anonymity if the information for each individual cannot be distinguished from that of at least k-1 individuals.

l-diversity

A data anonymization approach that relies on introducing further entropy or diversity to the dataset. This model uses generalization and promotes diversity for sensitive values within a group. l-diversity is an extension of the k-anonymity model.

t-closeness

This model is a further refinement of the k-anonymity and l-diversity models. t-closeness is the maximum of the distances between the distribution of values of a sensitive attribute and that of the entire database table. An equivalence class will have t-closeness if the distance between the attribute in the class and whole table is no more than threshold t.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kaur, N., Bhattacharya, S. & Butte, A.J. Big Data in Nephrology. Nat Rev Nephrol (2021). https://doi.org/10.1038/s41581-021-00439-x

Download citation

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing