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Harnessing the power of proteomics in precision diabetes medicine

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Abstract

Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual’s disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.

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Abbreviations

DIA:

Data-independent acquisition

GRS:

Genetic risk scores

GWAS:

Genome-wide association studies

MR:

Mendelian randomisation

PDM:

Precision diabetes medicine

PMDI:

Precision Medicine in Diabetes Initiative

pQTL:

Protein quantitative trait loci

PTM:

Post-translational modification

QTLs:

Quantitative trait loci

RbG:

Recall by genotype

V2F:

Variant-to-function

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    Nigel Kurgan, Jeppe Kjærgaard Larsen & Atul S. Deshmukh

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The authors would like to thank J. Merino and R. A. J. Smit from Novo Nordisk Foundation Center for Basic Metabolic Research for providing critical feedback on the manuscript.

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Work at The Novo Nordisk Foundation Center for Basic Metabolic Research (CBMR) is funded in part by a generous donation from the Novo Nordisk Foundation (grant no. NNF14CC0001). ASD is supported by European Foundation for the Study of Diabetes (EFSD) (grant no. NNF19SA058976). NK acknowledges support by the Natural Sciences and Engineering Research Council of Canada (NSERC) (reference no. PDF-578308-2023).

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Kurgan, N., Kjærgaard Larsen, J. & Deshmukh, A.S. Harnessing the power of proteomics in precision diabetes medicine. Diabetologia 67, 783–797 (2024). https://doi.org/10.1007/s00125-024-06097-5

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