Abstract
Big data methodologies, made possible with the increasing generation and availability of digital data and enhanced analytical capabilities, have produced new insights to improve outcomes in many disciplines. Application of big data in the health-care sector is in its early stages, although the potential for leveraging underutilized data to gain a better understanding of disease and improve quality of care is enormous. Owing to the intrinsic characteristics of inflammatory bowel disease (IBD) and the management dilemmas that it imposes, the implementation of big data research strategies not only can complement current research efforts but also could represent the only way to disentangle the complexity of the disease. In this Review, we explore important potential applications of big data in IBD research, including predictive models of disease course and response to therapy, characterization of disease heterogeneity, drug safety and development, precision medicine and cost-effectiveness of care. We also discuss the strengths and limitations of potential data sources that big data analytics could draw from in the field of IBD, including electronic health records, clinical trial data, e-health applications and genomic, transcriptomic, proteomic, metabolomic and microbiomic data.
Key points
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Big data refers to sets of data whose scale and complexity impose the use of dedicated analytical and statistical approaches.
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The distinctive attributes of big data include the four Vs: volume, variety, velocity and veracity.
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Big data approaches have been successfully used in many different areas, including finance and politics, and more recently have been increasingly implemented in health care.
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Big data analytics are innovative approaches to help disentangle the complexity of IBD.
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Potential applications of big data in the field of IBD might include precise phenomapping, the development of predictive models, precision medicine, epidemiological models and drug discovery.
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Researchers will face several potential limitations and challenges when using big data approaches in IBD, including ethical and legal restrictions, heterogeneous data sources, poor quality data and the need for validation.
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References
Ungaro, R., Mehandru, S., Allen, P. B., Peyrin-Biroulet, L. & Colombel, J.-F. Ulcerative colitis. Lancet 389, 1756–1770 (2017).
Torres, J., Mehandru, S., Colombel, J.-F. & Peyrin-Biroulet, L. Crohn’s disease. Lancet 389, 1741–1755 (2016).
de Souza, H. S. P., Fiocchi, C. & Iliopoulos, D. The IBD interactome: an integrated view of aetiology, pathogenesis and therapy. Nat. Rev. Gastroenterol. Hepatol. 14, 739–749 (2017).
Jin, L. et al. Pathway-based analysis tools for complex diseases: a review. Genomics Proteomics Bioinformatics 12, 210–220 (2014).
Actis, G. C. & Rosina, F. Inflammatory bowel disease: an archetype disorder of outer environment sensor systems. World J. Gastrointest. Pharmacol. Ther. 4, 41–46 (2013).
Olivera, P., Danese, S. & Peyrin-Biroulet, L. Next generation of small molecules in inflammatory bowel disease. Gut 66, 199–209 (2017).
Ng, S. C. et al. Environmental risk factors in inflammatory bowel disease: a population-based case-control study in Asia-Pacific. Gut 64, 1063–1071 (2015).
Ng, S. C. et al. Worldwide incidence and prevalence of inflammatory bowel disease in the 21st century: a systematic review of population-based studies. Lancet 390, 2769–2778 (2017).
Bernstein, C. N. Treatment of IBD: where we are and where we are going. Am. J. Gastroenterol. 110, 114–126 (2015).
Actis, G. C., Pellicano, R. & Rosina, F. Inflammatory bowel diseases: current problems and future tasks. World J. Gastrointest. Pharmacol. Ther. 5, 169–174 (2014).
Manyika, J. et al. Big Data: The Next Frontier for Innovation, Competition, and Productivity (McKinsey, 2011).
Philip Chen, C. L. & Zhang, C.-Y. Data-intensive applications, challenges, techniques and technologies: a survey on Big Data. Inf. Sci. 275, 314–347 (2014).
Nickerson, D. W. & Rogers, T. Political campaigns and big data. J. Econ. Perspect. 28, 51–74 (2014).
Kayyali, B., Knott, D. & Van Kuiken, S. The big-data revolution in US health care: accelerating value and innovation. McKinsey https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/the-big-data-revolution-in-us-health-care (2013).
Obermeyer, Z. & Emanuel, E. J. Predicting the future — big data, machine learning, and clinical medicine. N. Engl. J. Med. 375, 1216–1219 (2016).
Wooden, B., Goossens, N., Hoshida, Y. & Friedman, S. L. Using big data to discover diagnostics and therapeutics for gastrointestinal and liver diseases. Gastroenterology 152, 53–67 (2017).
Rumsfeld, J. S., Joynt, K. E. & Maddox, T. M. Big data analytics to improve cardiovascular care: promise and challenges. Nat. Rev. Cardiol. 13, 350–359 (2016).
Raghupathi, W. & Raghupathi, V. Big data analytics in healthcare: promise and potential. Health Inf. Sci. Syst. 2, 3 (2014).
Alonso, S. G., de la Torre Díez, I., Rodrigues, J. J. P. C., Hamrioui, S. & López-Coronado, M. A. systematic review of techniques and sources of big data in the healthcare sector. J. Med. Syst. 41, 183 (2017).
Ginsberg, J. et al. Detecting influenza epidemics using search engine query data. Nature 457, 1012–1014 (2009).
Butler, D. When Google got flu wrong. Nature 494, 155–156 (2013).
Lazer, D., Kennedy, R., King, G. & Vespignani, A. The parable of Google Flu: traps in big data analysis. Science 343, 1203–1205 (2014).
Dinov, I. D. et al. Predictive big data analytics: a study of Parkinson’s disease using large, complex, heterogeneous, incongruent, multi-source and incomplete observations. PLOS ONE 11, 1–28 (2016).
US National Institutes of Health. Big data for knowledge. NIH https://commonfund.nih.gov/bd2k (2018).
Ketchersid, T. Big data in nephrology: friend or foe? Blood Purif. 36, 160–164 (2014).
Auffray, C. et al. Making sense of big data in health research: towards an EU action plan. Genome Med. 8, 71 (2016).
Bellazzi, R. Big data and biomedical informatics: a challenging opportunity. Yearb. Med. Inform. 9, 8–13 (2014).
Corbin, K. How CIOs can prepare for healthcare ‘data tsunami’. CIO https://www.cio.com/article/2860072/healthcare/how-cios-can-prepare-for-healthcare-data-tsunami.html (2014).
Lee, C. H. & Yoon, H.-J. Medical big data: promise and challenges. Kidney Res. Clin. Pract. 36, 3–11 (2017).
Weber, G. M., Mandl, K. D. & Kohane, I. S. Finding the missing link for big biomedical data. JAMA 311, 2479–2480 (2014).
Carroll, S. & Goodstein, D. Defining the scientific method. Nat. Methods 6, 237–237 (2009).
Subramanian, S., Ekbom, A. & Rhodes, J. M. Recent advances in clinical practice: a systematic review of isolated colonic Crohn’s disease: the third IBD? Gut 66, 362–381 (2017).
Ruel, J., Ruane, D., Mehandru, S., Gower-Rousseau, C. & Colombel, J.-F. IBD across the age spectrum: is it the same disease? Nat. Rev. Gastroenterol. Hepatol. 11, 88–98 (2014).
Aloi, M. et al. Phenotype and disease course of early-onset pediatric inflammatory bowel disease. Inflamm. Bowel Dis. 20, 597–605 (2014).
Billiet, T. & Vermeire, S. Differences between adults and children: genetics and beyond. Expert Rev. Gastroenterol. Hepatol. 9, 191–196 (2015).
Peyrin-Biroulet, L. et al. Defining disease severity in inflammatory bowel diseases: current and future directions. Clin. Gastroenterol. Hepatol. 14, 348–354 (2016).
Shivade, C. et al. A review of approaches to identifying patient phenotype cohorts using electronic health records. J. Am. Med. Inform. Assoc. 21, 221–230 (2014).
Altman, R. B. & Ashley, E. A. Using “big data” to dissect clinical heterogeneity. Circulation 131, 232–233 (2015).
D’Haens, G. et al. Early combined immunosuppression or conventional management in patients with newly diagnosed Crohn’s disease: an open randomised trial. Lancet 371, 660–667 (2008).
Peyrin-Biroulet, L. et al. Impact of azathioprine and tumour necrosis factor antagonists on the need for surgery in newly diagnosed Crohn’s disease. Gut 60, 930–936 (2011).
Allen, P. B. et al. Review article: moving towards common therapeutic goals in Crohn’s disease and rheumatoid arthritis. Aliment. Pharmacol. Ther. 45, 1058–1072 (2017).
Gomollón, F. et al. 3rd European evidence-based consensus on the diagnosis and management of Crohn’s disease 2016 —part 1: diagnosis and medical management. J. Crohns Colitis 11, 3–25 (2017).
Cosnes, J. et al. Early administration of azathioprine versus conventional management of Crohn’s Disease: a randomized controlled trial. Gastroenterology 145, 758–765 (2013).
Stallmach, A. et al. Parameters of a severe disease course in ulcerative colitis. World J. Gastroenterol. 20, 12574–12580 (2014).
Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A. & Escobar, G. Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Aff. 33, 1123–1131 (2014).
Olivera, P., Danese, S. & Peyrin-Biroulet, L. JAK inhibition in inflammatory bowel disease. Expert Rev. Clin. Immunol. 13, 693–703 (2017).
Chaudhary, R. & Ghosh, S. Prediction of response to infliximab in Crohn’s disease. Dig. Liver Dis. 37, 559–563 (2005).
Siegel, C. A. & Melmed, G. Y. Predicting response to Anti-TNF Agents for the treatment of crohn’s disease. Therap. Adv. Gastroenterol. 2, 245–251 (2009).
Arijs, I. et al. Mucosal gene signatures to predict response to infliximab in patients with ulcerative colitis. Gut 58, 1612–1619 (2009).
Burke, K. E. et al. Genetic markers predict primary nonresponse and durable response to anti-tumor necrosis factor therapy in ulcerative colitis. Inflamm. Bowel Dis. 24, 1840–1848 (2018).
Boyapati, R. K., Kalla, R., Satsangi, J. & Ho, G. Biomarkers in search of precision medicine in IBD. Am. J. Gastroenterol. 111, 1682–1690 (2016).
Beaugerie, L. et al. Lymphoproliferative disorders in patients receiving thiopurines for inflammatory bowel disease: a prospective observational cohort study. Lancet 374, 1617–1625 (2009).
The I-CARE Study Group. P509 IBD cancer and serious infections in Europe (I-CARE): a European prospective observational study. J. Crohns Colitis 11, S338–S339 (2017).
Harpaz, R. et al. Text mining for adverse drug events: the promise, challenges, and state of the art. Drug Saf. 37, 777–790 (2014).
Arnaud, M. et al. Methods for safety signal detection in healthcare databases: a literature review. Expert Opin. Drug Saf. 16, 721–732 (2017).
Wang, G., Jung, K., Winnenburg, R. & Shah, N. H. A method for systematic discovery of adverse drug events from clinical notes. J. Am. Med. Inform. Assoc. 22, 1196–1204 (2015).
Kaplan, G. G. & Ng, S. C. Understanding and preventing the global increase of inflammatory bowel disease. Gastroenterology 152, 313–321 (2017).
Kaplan, G. G. The global burden of IBD: from 2015 to 2025. Nat. Rev. Gastroenterol. Hepatol. 12, 720–727 (2015).
Sebaa, A., Chikh, F., Nouicer, A. & Tari, A. Medical big data warehouse: architecture and system design, a case study: improving healthcare resources distribution. J. Med. Syst. 42, 59 (2018).
Bram, J. T., Warwick-Clark, B., Obeysekare, E. & Mehta, K. Utilization and monetization of healthcare data in developing countries. Big Data 3, 59–66 (2015).
van der Valk, M. E. et al. Healthcare costs of inflammatory bowel disease have shifted from hospitalisation and surgery towards anti-TNFα therapy: results from the COIN study. Gut 63, 72–79 (2014).
Schuhmacher, A., Gassmann, O. & Hinder, M. Changing R&D models in research-based pharmaceutical companies. J. Transl Med. 14, 105 (2016).
Monteleone, G. et al. Mongersen, an oral SMAD7 antisense oligonucleotide, and Crohn’s disease. N. Engl. J. Med. 372, 1104–1113 (2015).
Celgene. Celgene provides update on GED-0301 (mongersen) inflammatory bowel disease program. Celgene https://ir.celgene.com/press-releases/press-release-details/2017/Celgene-Provides-Update-on-GED-0301-mongersen-Inflammatory-Bowel-Disease-Program/default.aspx (2017).
Hueber, W. et al. Secukinumab, a human anti-IL-17A monoclonal antibody, for moderate to severe Crohn’s disease: unexpected results of a randomised, double-blind placebo-controlled trial. Gut 61, 1693–1700 (2012).
Chen, B. & Butte, A. J. Leveraging big data to transform target selection and drug discovery. Clin. Pharmacol. Ther. 99, 285–297 (2016).
Denny, J. C., Van Driest, S. L., Wei, W. Q. & Roden, D. M. The influence of big (clinical) data and genomics on precision medicine and drug development. Clin. Pharmacol. Ther. 103, 409–418 (2018).
Blackburn, M., Alexander, J., Legan, J. D. & Klabjan, D. Big data and the future of R&D management: the rise of big data and big data analytics will have significant implications for R&D and innovation management in the next decade. Res. Technol. Manag. 60, 43–51 (2017).
Power, A., Berger, A. C. & Ginsburg, G. S. Genomics-enabled drug repositioning and repurposing. JAMA 311, 2063 (2014).
Li, J. et al. A survey of current trends in computational drug repositioning. Brief. Bioinform. 17, 2–12 (2016).
Lamb, J. et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).
Dudley, J. T. et al. Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci. Transl Med. 3, 96ra76 (2011).
Crockett, S. D., Schectman, R., Stürmer, T. & Kappelman, M. D. Topiramate use does not reduce flares of inflammatory bowel disease. Dig. Dis. Sci. 59, 1535–1543 (2014).
Hashimoto, R. E., Brodt, E. D., Skelly, A. C. & Dettori, J. R. Administrative database studies: goldmine or goose chase? Evid. Based Spine Care J. 5, 74–76 (2014).
Bezin, J. et al. The national healthcare system claims databases in France, SNIIRAM and EGB: powerful tools for pharmacoepidemiology. Pharmacoepidemiol. Drug Saf. 26, 954–962 (2017).
Moulis, G. et al. French health insurance databases: what interest for medical research? Rev. Med. Interne 36, 411–417 (2015).
Tuppin, P., de Roquefeuil, L., Weill, A., Ricordeau, P. & Merlière, Y. French national health insurance information system and the permanent beneficiaries sample. Rev. Epidemiol. Sante Publique 58, 286–290 (2010).
Tuppin, P. et al. Value of a national administrative database to guide public decisions: from the système national d’information interrégimes de l’Assurance Maladie (SNIIRAM) to the système national des données de santé (SNDS) in France. Rev. Epidemiol. Sante Publique 65, S149–S167 (2017).
Blotière, P. O. et al. Conditions of prescription of anti-TNF agents in newly treated patients with inflammatory bowel disease in France (2011–2013). Dig. Liver Dis. 48, 620–625 (2016).
Lemaitre, M. et al. Association between use of thiopurines or tumor necrosis factor antagonists alone or in combination and risk of lymphoma in patients with inflammatory bowel disease. JAMA 318, 1679 (2017).
Medicines and Healthcare products Regulatory Agency. The General Practice Research Database (GPRD) — further infromation for patients. NHS Scotland http://www.erskinepractice.scot.nhs.uk/website/S11486/files/GPRD_PatientLeaflet.pdf (2010).
Lewis, J. D., Brensinger, C., Bilker, W. B. & Strom, B. L. Validity and completeness of the General Practice Research Database for studies of inflammatory bowel disease. Pharmacoepidemiol. Drug Saf. 11, 211–218 (2002).
Ludvigsson, J. F. et al. External review and validation of the Swedish national inpatient register. BMC Public Health 11, 450 (2011).
SWIBREG. Swedish Inflammatory Bowel Disease Registry. SWIBREG http://www.swibreg.se/ (2018).
Jakobsson, G. L. et al. Validating inflammatory bowel disease (IBD) in the Swedish National Patient Register and the Swedish Quality Register for IBD (SWIBREG). Scand. J. Gastroenterol. 52, 216–221 (2017).
Schmidt, M. et al. The Danish National patient registry: a review of content, data quality, and research potential. Clin. Epidemiol. 7, 449–490 (2015).
Kreis, K., Neubauer, S., Klora, M., Lange, A. & Zeidler, J. Status and perspectives of claims data analyses in Germany — a systematic review. Health Policy 120, 213–226 (2016).
Cheng, C.-L. et al. Validation of acute myocardial infarction cases in the national health insurance research database in taiwan. J. Epidemiol. 24, 500–507 (2014).
Lichtman, J. H., Leifheit-Limson, E. C. & Goldstein, L. B. Centers for medicare and medicaid services medicare data and stroke research: goldmine or landmine? Stroke 46, 598–604 (2015).
Boyko, E. J., Koepsell, T. D., Gaziano, J. M., Horner, R. D. & Feussner, J. R. US Department of Veterans Affairs medical care system as a resource to epidemiologists. Am. J. Epidemiol. 151, 307–314 (2000).
National Conference of State Legislatures. Collecting health data: all-payers claims databases. NCSL http://www.ncsl.org/research/health/collecting-health-data-all-payer-claims-database.aspx (2018).
All-Payer Claims Database Council. APCD Council. APCD Council https://www.apcdcouncil.org/ (2018).
US Department of Health & Human Services. Health information privacy. HHS https://www.hhs.gov/hipaa/index.html (2018).
Ross, M. K., Wei, W. & Ohno-Machado, L. “Big data” and the electronic health record. Yearb. Med. Inform. 9, 97–104 (2014).
Austin, C. & Kusumoto, F. The application of Big Data in medicine: current implications and future directions. J. Interv. Card. Electrophysiol. 47, 51–59 (2016).
Luo, Y. et al. Natural language processing for EHR-based pharmacovigilance: a structured review. Drug Saf. 40, 1075–1089 (2017).
Zeng, Z., Deng, Y., Li, X., Naumann, T. & Luo, Y. Natural language processing for EHR-based computational phenotyping. IEEE ACM Trans. Comput. Biol. Bioinform. https://doi.org/10.1109/TCBB.2018.2849968 (2018).
Waljee, A. K. et al. Machine learning algorithms for objective remission and clinical outcomes with thiopurines. J. Crohns Colitis 108, 1723–1730 (2017).
Cai, T. et al. The association between arthralgia and vedolizumab using natural language processing. Inflamm. Bowel Dis. 24, 2242–2246 (2018).
Krumholz, H. M. & Peterson, E. D. Open access to clinical trials data. JAMA 312, 1002–1003 (2014).
Taichman, D. B. et al. Sharing clinical trial data — a proposal from the International Committee of Medical Journal Editors. N. Engl. J. Med. 374, 384–386 (2016).
Bertagnolli, M. M. et al. Advantages of a truly open-access data-sharing model. N. Engl. J. Med. 376, 1178–1181 (2017).
Navar, A. M., Pencina, M. J., Rymer, J. A., Louzao, D. M. & Peterson, E. D. Use of open access platforms for clinical trial data. JAMA 315, 1283 (2016).
Clinical Study Data Request. Clinical Study Data Request. CSDR https://clinicalstudydatarequest.com/Default.aspx (2018).
Waljee, A. K. et al. Predicting corticosteroid-free endoscopic remission with vedolizumab in ulcerative colitis. Aliment. Pharmacol. Ther. 47, 763–772 (2018).
Elriz, K. et al. Incidence, presentation, and prognosis of small bowel adenocarcinoma in patients with small bowel Crohn’s disease: a prospective observational study. Inflamm. Bowel Dis. 19, 1823–1826 (2013).
Henriksen, M. et al. Ulcerative colitis and clinical course: results of a 5-year population-based follow-up study (the IBSEN study). Inflamm. Bowel Dis. 12, 543–550 (2006).
Hovde, Ø. et al. Malignancies in patients with inflammatory bowel disease: results from 20 years of follow-up in the IBSEN study. J. Crohns Colitis 11, 571–577 (2016).
US National Library of Medicine. ClinicalTrials.gov https://www.clinicaltrials.gov/ct2/show/NCT02377258 (2016).
US National Library of Medicine. ClinicalTrials.gov https://www.clinicaltrials.gov/ct2/show/NCT03282903 (2018).
Mayo, C. S. et al. Big data in designing clinical trials: opportunities and challenges. Front. Oncol. 7, 187 (2017).
Angus, D. C. Fusing randomized trials with big data: the key to self-learning health care systems? JAMA 314, 767–768 (2015).
Perrin, A. Social media usage: 2005–2015. Pew Research Center http://www.pewinternet.org/2015/10/08/social-networking-usage-2005-2015/ (2015).
Veríssimo, J. M. C. Usage intensity of mobile medical apps: a tale of two methods. J. Bus. Res. 89, 442–447 (2017).
Chou, W. S., Prestin, A., Lyons, C. & Wen, K. Web 2.0 for health promotion: reviewing the current evidence. Am. J. Public Health 103, e9–e18 (2013).
Orozco-Beltran, D., Sánchez-Molla, M., Sanchez, J. J. & Mira, J. J., ValCrònic Research Group. Telemedicine in primary care for patients with chronic conditions: the ValCrònic Quasi-Experimental Study. J. Med. Internet Res. 19, e400 (2017).
Jackson, B. D., Con, D. & De Cruz, P. Design considerations for an eHealth decision support tool in inflammatory bowel disease self-management. Intern. Med. J. 48, 674–681 (2017).
Boelle, P.-Y., Thiébaut, R. & Costagliola, D. Données massives, vous avez dit données massives? Quest. Santé Publique 30, 1–4 (2015).
De Jong, M. et al. Development and feasibility study of a telemedicine tool for all patients with IBD: MyIBDcoach. Inflamm. Bowel Dis. 23, 485–493 (2017).
Jackson, B. D., Gray, K., Knowles, S. R. & De Cruz, P. EHealth technologies in inflammatory bowel disease: a systematic review. J. Crohns Colitis 10, 1103–1121 (2016).
Jaboli, F., Pouillon, L., Bossuyt, P., Danese, S. & Peyrin-Biroulet, L. Telehealth in inflammatory bowel disease: every patient may need a coach! Gastroenterology 154, 1196–1198 (2018).
Cross, R. K., Cheevers, N., Rustgi, A., Langenberg, P. & Finkelstein, J. Randomized, controlled trial of home telemanagement in patients with ulcerative colitis (UC HAT). Inflamm. Bowel Dis. 18, 1018–1025 (2012).
Elkjaer, M. et al. E-Health empowers patients with ulcerative colitis: a randomised controlled trial of the web-guided “Constant-care” approach. Gut 59, 1652–1661 (2010).
Cross, R. K. et al. A randomized controlled trial of telemedicine for patients with inflammatory bowel disease (Tele-IBD) [abstract 903]. Gastroenterology 154, S177 (2018).
Bossuyt, P., Pouillon, L. & Peyrin-Biroulet, L. Primetime for e-health in IBD? Nat. Rev. Gastroenterol. Hepatol. 14, 133–134 (2017).
Bello, C. et al. Usability of a home-based test for the measurement of fecal calprotectin in asymptomatic IBD patients. Dig. Liver Dis. 49, 991–996 (2017).
Heida, A. et al. Agreement between home-based measurement of stool calprotectin and ELISA results for monitoring inflammatory bowel disease activity. Clin. Gastroenterol. Hepatol. 15, 1742–1749 (2017).
Mgudlwa, S. & Iyamu, T. Integration of social media with healthcare big data for improved service delivery. SA J. Inf. Manag. 20, 1–8 (2018).
Martinez, B. et al. Patient understanding of the risks and benefits of biologic therapies in inflammatory bowel disease: insights from a large-scale analysis of social media platforms. Inflamm. Bowel Dis. 23, 1057–1064 (2017).
Panes, J. et al. Imaging techniques for assessment of inflammatory bowel disease: joint ECCO and ESGAR evidence-based consensus guidelines. J. Crohns Colitis 7, 556–585 (2013).
Belle, A. et al. Big data analytics in healthcare. Biomed. Res. Int. 2015, 370194 (2015).
Dilsizian, S. E. & Siegel, E. L. Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Curr. Cardiol. Rep. 16, 441 (2014).
Landewé, R. B. M. & van der Heijde, D. “Big data” in rheumatology: intelligent data modeling improves the quality of imaging data. Rheum. Dis. Clin. North Am. 44, 307–315 (2018).
Liu, J. Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).
Anderson, C. A. et al. Meta-analysis identifies 29 additional ulcerative colitis risk loci, increasing the number of confirmed associations to 47. Nat. Genet. 43, 246–252 (2011).
Franke, A. et al. Genome-wide meta-analysis increases to 71 the number of confirmed Crohn’s disease susceptibility loci. Nat. Genet. 42, 1118–1125 (2010).
Huang, H. et al. Fine-mapping inflammatory bowel disease loci to single-variant resolution. Nature 547, 173–178 (2017).
Abreu, M. T. et al. Mutations in NOD2 are associated with fibrostenosing disease in patients with Crohn’s disease. Gastroenterology 123, 679–688 (2002).
Ellinghaus, D., Bethune, J., Petersen, B. S. & Franke, A. The genetics of Crohn’s disease and ulcerative colitis-status quo and beyond. Scand. J. Gastroenterol. 50, 13–23 (2014).
Mirkov, M. U., Verstockt, B. & Cleynen, I. Genetics of inflammatory bowel disease: beyond NOD2. Lancet Gastroenterol. Hepatol. 2, 224–234 (2017).
Lees, C. W., Barrett, J. C., Parkes, M. & Satsangi, J. New IBD genetics: common pathways with other diseases. Gut 60, 1739–1753 (2011).
Ye, B. D. & McGovern, D. P. B. Genetic variation in IBD: progress, clues to pathogenesis and possible clinical utility. Expert Rev. Clin. Immunol. 12, 1091–1107 (2016).
Ellinghaus, D. et al. Analysis of five chronic inflammatory diseases identifies 27 new associations and highlights disease-specific patterns at shared loci. Nat. Genet. 48, 510–518 (2016).
Zuo, T., Kamm, M. A., Colombel, J. F. & Ng, S. C. Urbanization and the gut microbiota in health and inflammatory bowel disease. Nat. Rev. Gastroenterol. Hepatol. 15, 440–452 (2018).
SYSCID. SYSCID — a systems medicine approach to chronic inflammatory diseases. SYSCID https://syscid.eu (2018).
Schultze, J. L. & Rosenstiel, P. The SYSCID Consortium. Systems medicine in chronic inflammatory diseases. Immunity 48, 608–613 (2018).
Gedela, S. Integration, warehousing, and analysis strategies of Omics data. Methods Mol. Biol. 719, 399–414 (2011).
Berger, B., Peng, J. & Singh, M. Computational solutions for omics data. Nat. Rev. Genet. 14, 333–346 (2013).
Fiocchi, C. Integrating omics: the future of IBD? Dig. Dis. 32, 96–102 (2014).
Chuong, K. H., Mack, D. R., Stintzi, A. & O’Doherty, K. C. Human microbiome and learning healthcare systems: integrating research and precision medicine for inflammatory bowel disease. OMICS 22, 119–126 (2017).
Shah, N. D. et al. Big data and predictive analytics recalibrating expectations. JAMA 320, 27–28 (2018).
Genta, R. M. & Sonnenberg, A. Big data in gastroenterology research. Nat. Rev. Gastroenterol. Hepatol. 11, 386–390 (2014).
Waljee, A. K., Sauder, K., Zhang, Y., Zhu, J. & Higgins, P. D. R. External validation of a thiopurine monitoring algorithm on the SONIC clinical trial dataset. Clin. Gastroenterol. Hepatol. 16, 449–451 (2018).
Waljee, A. K. et al. Predicting hospitalization and outpatient corticosteroid use in inflammatory bowel disease patients using machine learning. Inflamm. Bowel Dis. 24, 45–53 (2018).
Wei, Z. et al. Large sample size, wide variant spectrum, and advanced machine-learning technique boost risk prediction for inflammatory bowel disease. Am. J. Hum. Genet. 92, 1008–1012 (2013).
Menti, E. et al. Bayesian machine learning techniques for revealing complex interactions among genetic and clinical factors in association with extra-intestinal Manifestations in IBD patients. AMIA Annu. Symp. Proc. 2016, 884–893 (2016).
Han, L. et al. A probabilistic pathway score (PROPS) for classification with applications to inflammatory bowel disease. Bioinformatics 34, 985–993 (2018).
Hou, J. K. et al. Automated identification of surveillance colonoscopy in inflammatory bowel disease using natural language processing. Dig. Dis. Sci. 58, 936–941 (2013).
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Nature Reviews Gastroenterology & Hepatology thanks R. Panaccione, X. Roblin and S. Vavricka for their contribution to the peer review of this work.
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L.P.-B., P.O., N.J. and G.N. researched data for the article. L.P.-B., P.O., S.D. and G.N. made substantial contributions to discussion of content for the article. L.P.-B., P.O., N.J. and G.N. wrote the article, and L.P.-B., P.O., S.D. and G.N. reviewed and edited the manuscript before submission.
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P.O. has received financial support for research from Abbvie, Ferring and Takeda and lecture and consulting fees from Abbvie and Takeda. S.D. has received speaking, consultancy or advisory board member fees from Abbvie, Allergan, Biogen, Boehringer-Ingelheim, Celgene, Celltrion, Ferring, Hospira, Johnson and Johnson, Merck, MSD, Mundipharma, Pfizer, Sandoz, Takeda, TiGenix, UCB Pharma and Vifor. L.P.-B. has received consulting fees from Abbvie, Amgen, Biogaran, Boehringer-Ingelheim, Bristol-Myers Squibb, Celltrion, Ferring, Genentech, HAC Pharma, Hospira, Index Pharmaceuticals, Janssen, Lilly, Merck, Mitsubishi, Norgine, Pfizer, Pharmacosmos, Pilege, Sandoz, Takeda, Therakos, Tillotts, UCB Pharma and Vifor and lecture fees from Abbvie, Ferring, HAC Pharma, Janssen, Merck, Mitsubishi, Norgine, Takeda, Therakos, Tillotts and Vifor. N.J. and G.N. declare no competing interests.
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Olivera, P., Danese, S., Jay, N. et al. Big data in IBD: a look into the future. Nat Rev Gastroenterol Hepatol 16, 312–321 (2019). https://doi.org/10.1038/s41575-019-0102-5
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DOI: https://doi.org/10.1038/s41575-019-0102-5
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