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Baglaenko Y, Wagner C, Bhoj VG, Brodin P, Gershwin ME, Graham D, Invernizzi P, Kidd KK, Korsunsky I, Levy M, Mammen AL, Nizet V, Ramirez-Valle F, Stites EC, Williams MS, Wilson M, Rose NR, Ladd V, Sirota M. Making inroads to precision medicine for the treatment of autoimmune diseases: Harnessing genomic studies to better diagnose and treat complex disorders. CAMBRIDGE PRISMS. PRECISION MEDICINE 2023; 1:e25. [PMID: 38550937 PMCID: PMC10953750 DOI: 10.1017/pcm.2023.14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 04/21/2023] [Accepted: 04/25/2023] [Indexed: 06/13/2024]
Abstract
Precision Medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle. Autoimmune diseases are those in which the body's natural defense system loses discriminating power between its own cells and foreign cells, causing the body to mistakenly attack healthy tissues. These conditions are very heterogeneous in their presentation and therefore difficult to diagnose and treat. Achieving precision medicine in autoimmune diseases has been challenging due to the complex etiologies of these conditions, involving an interplay between genetic, epigenetic, and environmental factors. However, recent technological and computational advances in molecular profiling have helped identify patient subtypes and molecular pathways which can be used to improve diagnostics and therapeutics. This review discusses the current understanding of the disease mechanisms, heterogeneity, and pathogenic autoantigens in autoimmune diseases gained from genomic and transcriptomic studies and highlights how these findings can be applied to better understand disease heterogeneity in the context of disease diagnostics and therapeutics.
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Affiliation(s)
| | | | | | | | | | - Daniel Graham
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Pietro Invernizzi
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- European Reference Network on Hepatological Diseases (ERN RARE-LIVER), IRCCS Fondazione San Gerardo dei Tintori, Monza, Italy
| | - Kenneth K. Kidd
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA
| | | | - Michael Levy
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrew L. Mammen
- National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, USA
| | - Victor Nizet
- School of Medicine, University of California San Diego, San Diego, CA, USA
| | | | - Edward C. Stites
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA
| | | | - Michael Wilson
- Weill Institute for Neurosciences, Department of Neurology, UCSF, San Francisco, CA, USA
| | - Noel R. Rose
- Autoimmune Association, Clinton Township, MI, USA
| | | | - Marina Sirota
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA
- Department of Pediatrics, UCSF, San Francisco, CA, USA
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Salis Z, Gallego B, Sainsbury A. Researchers in rheumatology should avoid categorization of continuous predictor variables. BMC Med Res Methodol 2023; 23:104. [PMID: 37101144 PMCID: PMC10134601 DOI: 10.1186/s12874-023-01926-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 04/18/2023] [Indexed: 04/28/2023] Open
Abstract
BACKGROUND Rheumatology researchers often categorize continuous predictor variables. We aimed to show how this practice may alter results from observational studies in rheumatology. METHODS We conducted and compared the results of two analyses of the association between our predictor variable (percentage change in body mass index [BMI] from baseline to four years) and two outcome variable domains of structure and pain in knee and hip osteoarthritis. These two outcome variable domains covered 26 different outcomes for knee and hip combined. In the first analysis (categorical analysis), percentage change in BMI was categorized as ≥ 5% decrease in BMI, < 5% change in BMI, and ≥ 5% increase in BMI, while in the second analysis (continuous analysis), it was left as a continuous variable. In both analyses (categorical and continuous), we used generalized estimating equations with a logistic link function to investigate the association between the percentage change in BMI and the outcomes. RESULTS For eight of the 26 investigated outcomes (31%), the results from the categorical analyses were different from the results from the continuous analyses. These differences were of three types: 1) for six of these eight outcomes, while the continuous analyses revealed associations in both directions (i.e., a decrease in BMI had one effect, while an increase in BMI had the opposite effect), the categorical analyses showed associations only in one direction of BMI change, not both; 2) for another one of these eight outcomes, the categorical analyses suggested an association with change in BMI, while this association was not shown in the continuous analyses (this is potentially a false positive association); 3) for the last of the eight outcomes, the continuous analyses suggested an association of change in BMI, while this association was not shown in the categorical analyses (this is potentially a false negative association). CONCLUSIONS Categorization of continuous predictor variables alters the results of analyses and could lead to different conclusions; therefore, researchers in rheumatology should avoid it.
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Affiliation(s)
- Zubeyir Salis
- The University of New South Wales, Centre for Big Data Research in Health, Kensington, NSW, Australia
| | - Blanca Gallego
- The University of New South Wales, Centre for Big Data Research in Health, Kensington, NSW, Australia
| | - Amanda Sainsbury
- School of Human Sciences, The University of Western Australia, Crawley, Perth, WA, 6009, Australia.
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The promise of precision medicine in rheumatology. Nat Med 2022; 28:1363-1371. [PMID: 35788174 PMCID: PMC9513842 DOI: 10.1038/s41591-022-01880-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 05/23/2022] [Indexed: 01/07/2023]
Abstract
Systemic autoimmune rheumatic diseases (SARDs) exhibit extensive heterogeneity in clinical presentation, disease course, and treatment response. Therefore, precision medicine - whereby treatment is tailored according to the underlying pathogenic mechanisms of an individual patient at a specific time - represents the 'holy grail' in SARD clinical care. Current strategies include treat-to-target therapies and autoantibody testing for patient stratification; however, these are far from optimal. Recent innovations in high-throughput 'omic' technologies are now enabling comprehensive profiling at multiple levels, helping to identify subgroups of patients who may taper off potentially toxic medications or better respond to current molecular targeted therapies. Such advances may help to optimize outcomes and identify new pathways for treatment, but there are many challenges along the path towards clinical translation. In this Review, we discuss recent efforts to dissect cellular and molecular heterogeneity across multiple SARDs and future directions for implementing stratification approaches for SARD treatment in the clinic.
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