Open this publication in new window or tab >>Stanford Univ, Dept Med, Div Cardiovasc Med, Sch Med, Stanford, CA 94305 USA.;VA Palo Alto Hlth Care Syst, Palo Alto, CA 94304 USA.;Stanford Univ, Stanford Cardiovasc Inst, Sch Med, Stanford, CA 94305 USA.
Stanford Univ, Dept Med, Div Cardiovasc Med, Sch Med, Stanford, CA 94305 USA.;Stanford Univ, Stanford Diabet Res Ctr, Sch Med, Stanford, CA 94305 USA.;Stanford Univ, Stanford Cardiovasc Inst, Sch Med, Stanford, CA 94305 USA.;Stanford Univ, Stanford Prevent Res Ctr, Sch Med, Stanford, CA USA.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Geriatrics.
Karolinska Inst, Care Sci & Soc, Div Family Med & Primary Care, Dept Neurobiol, Stockholm, Sweden.;Dalarna Univ, Dept Hlth & Social Studies, Falun, Sweden. Ctr Res Epidemiol & Populat Hlth, Clin Epidemiol, Inserm U1018, Villejuif, France.
Newcastle Univ, Translat & Clin Res Inst, Newcastle Upon Tyne, England.
Stanford Univ, Dept Biomed Data Sci, Sch Med, Stanford, CA USA.;Stanford Univ, Dept Psychiat & Behav Sci, Stanford, CA USA.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
Univ Glasgow, Coll Med Vet & Life Sci, Sch Hlth & Wellbeing, Glasgow, Scotland.
Stanford Univ, Dept Med, Div Cardiovasc Med, Sch Med, Stanford, CA 94305 USA.;VA Palo Alto Hlth Care Syst, Palo Alto, CA 94304 USA.;Stanford Univ, Stanford Diabet Res Ctr, Sch Med, Stanford, CA 94305 USA.;Stanford Univ, Stanford Cardiovasc Inst, Sch Med, Stanford, CA 94305 USA.;Stanford Univ, Dept Epidemiol & Populat Hlth, Sch Med, Stanford, CA 94305 USA.
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2023 (English)In: Diabetologia, ISSN 0012-186X, E-ISSN 1432-0428, Vol. 66, no 9, p. 1643-1654Article in journal (Refereed) Published
Abstract [en]
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-through-put 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, 0.396]). Models of proteins alone were as effective as models that included both clinical variables and proteins using either standard or randomised LASSO. The single most consistently selected protein across all analyses and models was IGF-binding protein 2.
Conclusions/interpretation A plasma proteomic signature identified using 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 a 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 insulin resistance-related adverse health consequences.
Place, publisher, year, edition, pages
Springer, 2023
Keywords
Euglycaemic-hyperinsulinaemic clamp, Insulin resistance, Insulin sensitivity, LASSO, Plasma proteomics, Population study, Stability selection
National Category
Endocrinology and Diabetes
Identifiers
urn:nbn:se:uu:diva-512272 (URN)10.1007/s00125-023-05946-z (DOI)001012589300002 ()37329449 (PubMedID)
Funder
Swedish Heart Lung Foundation
2023-09-262023-09-262023-09-26Bibliographically approved