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

Zanetti, D. et al. (2023) Plasma proteomic signatures of a direct measure of insulin sensitivity in two population cohorts. Diabetologia, 66(9), pp. 1643-1654. (doi: 10.1007/s00125-023-05946-z) (PMID:37329449)

Abstract

Aims/hypothesis: The euglycemic hyperinsulinemic clamp (EIC) is a direct measure and the reference-standard in the assessment 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 (R2 82 ). Results: A standard LASSO model incorporating 53 proteins in addition to routinely available clinical variables increased the M-value R2 85 from 0.237 (95% confidence interval: 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 R2 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 R2 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 randomized LASSO and stability selection algorithm selected only two proteins per cohort (three unique proteins) which improved R2 92 but to a lesser degree than standard LASSO models: 0.352 (0.266-0.439) within RISC and 0.495 (0.404-0.585) within ULSAM. Differences in R2 93 explained between randomized and standard LASSO were notably reduced in the cross-cohort analyses despite the much smaller number of proteins selected: RISC to ULSAM range 0.444 (0.391-0.497) ULSAM to RISC range 0.348 (0.300-0.396). Models of proteins alone were as effective as models that included both clinical variables and proteins using either standard or randomized LASSO. The single most consistently selected protein across all analyses and models was IGFBP2. Conclusions/interpretation: A plasma proteomic signature identified through a standard LASSO approach improves the cross-sectional estimation of the M-value over routine clinical variables. However, a small subset of these proteins identified using stability selection algorithm affords much of this improvement especially when considering cross-cohort analyses. Our approach provides opportunities to improve the identification of insulin resistant individuals at risk of IR-related adverse health consequences.

• Item Type: Articles
• Additional Information: This research was supported by a grant from the National Institutes of Health (1R01DK114183). The RISC study was supported by the EU Fifth Framework Programme (EU contract QLG1-CT-2001-01252) with additional funding from Astra Zeneca. The ULSAM study was supported by the Swedish Heart Lung Foundation and the Medical Faculty of Uppsala University, including the Uppsala University Hospital, the Swedish Medical Research Council (MFR5446), the Uppsala Geriatric Fund and the Thuréus Foundation for Geriatric Research. JWK is funded by NIH R01 DK116750, R01 DK120565, R01 DK106236, R01 DK107437, P30DK116074 and ADA 1-19-JDF-10. DZ was supported by the MSCA Postdoctoral Fellowships 2021 (HORIZON-MSCA-2021-PF-01-01).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Petrie, Professor John
• Authors: Zanetti, D., Stell, L., Gustafsson, S., Abbasi, F., Tsao, P. S., Knowles, J. W., RISC Investigators, ., Zethelius, B., Ärnlöv, J., Balkau, B., Walker, M., Lazzeroni, L. C., Lind, L., Petrie, J. R., and Assimes, T. L.
• College/School: College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > Robertson Centre
• Journal Name: Diabetologia
• Publisher: Springer
• ISSN: 0012-186X
• ISSN (Online): 1432-0428
• Published Online: 17 June 2023
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in Diabetologia 66(9):1643-1654
• Publisher Policy: Reproduced under a Creative Commons License