The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Painting a new picture of personalised medicine for diabetes
Open accessAbstract
The current focus on delivery of personalised (or precision) medicine reflects the expectation that developments in genomics, imaging and other domains will extend our diagnostic and prognostic capabilities, and enable more effective targeting of current and future preventative and therapeutic options. The clinical benefits of this approach are already being realised in rare diseases and cancer but the impact on management of complex diseases, such as type 2 diabetes, remains limited. This may reflect reliance on inappropriate models of disease architecture, based around rare, high-impact genetic and environmental exposures that are poorly suited to our emerging understanding of type 2 diabetes. This review proposes an alternative 'palette' model, centred on a molecular taxonomy that focuses on positioning an individual with respect to the major pathophysiological processes that contribute to diabetes risk and progression. This model anticipates that many individuals with diabetes will have multiple parallel defects that affect several of these processes. One corollary of this model is that research efforts should, at least initially, be targeted towards identifying and characterising individuals whose adverse metabolic trajectory is dominated by perturbation in a restricted set of processes.
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Cites
The genetic architecture of type 2 diabetes
Fuchsberger, C., Flannick, J., Teslovich, T. M., Mahajan, A., Agarwala, V., Gaulton, K. J., Ma, C., Fontanillas, P., Moutsianas, L., McCarthy, D. J., et al. (2016) · Nature · doi:10.1038/nature18642 · Cited by 1,051
Self-citationExposing the exposures responsible for type 2 diabetes and obesity
Franks, P. W., & McCarthy, M. I. (2016) · Science · doi:10.1126/science.aaf5094 · Cited by 265
Self-citationFlannick, J., Beer, N. L., Bick, A. G., Agarwala, V., Molnes, J., Gupta, N., Burtt, N. P., Florez, J. C., Meigs, J. B., Taylor, H., et al. (2013) · Nature Genetics · doi:10.1038/ng.2794 · Cited by 136
Gale, E. A. M. (2006) · Diabetologia · doi:10.1007/s00125-006-0348-7 · Cited by 71
Cited by
Using machine learning approaches for multi-omics data analysis: A review
Reel, P. S., Reel, S., Pearson, E., Trucco, E., & Jefferson, E. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107739 · Cited by 903
Genetic drivers of heterogeneity in type 2 diabetes pathophysiology
Suzuki, K., Hatzikotoulas, K., Southam, L., Taylor, H. J., Yin, X., Lorenz, K. M., Mandla, R., Huerta-Chagoya, A., Melloni, G. E. M., Kanoni, S., et al. (2024) · Nature · doi:10.1038/s41586-024-07019-6 · Cited by 606
Self-citationBattaglia, M., Ahmed, S., Anderson, M. S., Atkinson, M. A., Becker, D., Bingley, P. J., Bosi, E., Brusko, T. M., DiMeglio, L. A., Evans-Molina, C., et al. (2020) · Diabetes Care · doi:10.2337/dc19-0880 · Cited by 351
Genetic Risk Scores for Diabetes Diagnosis and Precision Medicine
Udler, M. S., McCarthy, M. I., Florez, J. C., & Mahajan, A. (2019) · Endocrine Reviews · doi:10.1210/er.2019-00088 · Cited by 302
Self-citationA novel diabetes typology: towards precision diabetology from pathogenesis to treatment
Herder, C., & Roden, M. (2022) · Diabetologia · doi:10.1007/s00125-021-05625-x · Cited by 140
Metadata
- Authors
- McCarthy, M. I.
- Venue
- Diabetologia
- Published
- 2017-02-07
- Volume
- 60
- Issue
- 5
- Pages
- 793-799
- ISSN
- 0012-186X (ISSN-L)
- 1432-0428
- OpenAlex
- W2586927659
- Cited by
- 255
- References
- 4
- FWCI
- 13.36
- Type
- journal article
- Language
- en
- Open access
- hybrid
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