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Plasma proteomic signatures of a direct measure of insulin sensitivity in two population cohorts

Title: Plasma proteomic signatures of a direct measure of insulin sensitivity in two population cohorts
Authors: Zanetti D; Stell L; Gustafsson S; Abbasi F; Tsao PS; Knowles JW; Ferrannini E; Kozakova M; Gastaldelli A; Coppack S; Balkau B; Dekker J; Walker M; Mari A; Tura A; Laville M; Beck H; Nolan J; Bolli G; Golay A; Konrad T; Nilsson P; Melander O; Mingrone G; Perry C; Petrie J; Krebs M; Gabriel R; Mitrakou A; Piatti P; Lalic N; Laakso M; Zethelius B; Arnlov J; Lazzeroni LC; Lind L; Petrie JR; Assimes TL
Source: Diabetologia, 2023
Publisher Information: Springer Science and Business Media Deutschland GmbH
Publication Year: 2023
Collection: Newcastle University Library ePrints Service
Description: © 2023, The Author(s).Aims/hypothesis: The euglycaemic–hyperinsulinaemic clamp (EIC) is the reference standard for the measurement of whole-body insulin sensitivity but is laborious and expensive to perform. We aimed to assess the incremental value of high-throughput plasma proteomic profiling in developing signatures correlating with the M value derived from the EIC. Methods: We measured 828 proteins in the fasting plasma of 966 participants from the Relationship between Insulin Sensitivity and Cardiovascular disease (RISC) study and 745 participants from the Uppsala Longitudinal Study of Adult Men (ULSAM) using a high-throughput proximity extension assay. We used the least absolute shrinkage and selection operator (LASSO) approach using clinical variables and protein measures as features. Models were tested within and across cohorts. Our primary model performance metric was the proportion of the M value variance explained (R 2). Results: A standard LASSO model incorporating 53 proteins in addition to routinely available clinical variables increased the M value R 2 from 0.237 (95% CI 0.178, 0.303) to 0.456 (0.372, 0.536) in RISC. A similar pattern was observed in ULSAM, in which the M value R 2 increased from 0.443 (0.360, 0.530) to 0.632 (0.569, 0.698) with the addition of 61 proteins. Models trained in one cohort and tested in the other also demonstrated significant improvements in R 2 despite differences in baseline cohort characteristics and clamp methodology (RISC to ULSAM: 0.491 [0.433, 0.539] for 51 proteins; ULSAM to RISC: 0.369 [0.331, 0.416] for 67 proteins). A randomised LASSO and stability selection algorithm selected only two proteins per cohort (three unique proteins), which improved R 2 but to a lesser degree than in standard LASSO models: 0.352 (0.266, 0.439) in RISC and 0.495 (0.404, 0.585) in ULSAM. Reductions in improvements of R 2 with randomised LASSO and stability selection were less marked in cross-cohort analyses (RISC to ULSAM R 2 0.444 [0.391, 0.497]; ULSAM to RISC R 2 0.348 [0.300, ...
Document Type: article in journal/newspaper
File Description: application/pdf
Language: unknown
Relation: https://eprints.ncl.ac.uk/292061; https://eprints.ncl.ac.uk/fulltext.aspx?url=292061/1E5A9847-571D-4DBA-B75E-9FEF4846A733.pdf&pub_id=292061
Availability: https://eprints.ncl.ac.uk/292061
Rights: https://creativecommons.org/licenses/by/4.0/
Accession Number: edsbas.DF3ED38F
Database: BASE