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Article Dans Une Revue Diabetologia Année : 2023

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

Daniela Zanetti
Laurel Stell
  • Fonction : Auteur
Stefan Gustafsson
  • Fonction : Auteur
Fahim A. Abbasi
  • Fonction : Auteur
Phillip Shih Chung Tsao
  • Fonction : Auteur
Joshua W. Knowles
  • Fonction : Auteur
Ele Ferrannini
  • Fonction : Auteur
Michaela Kozakova
  • Fonction : Auteur
Amalia Gastaldelli
  • Fonction : Auteur
Simon Ward Coppack
  • Fonction : Auteur
Jacqueline M. Dekker
  • Fonction : Auteur
Mark Walker
  • Fonction : Auteur
Andrea Mari
  • Fonction : Auteur
Andrea Tura
  • Fonction : Auteur
Martine Laville
  • Fonction : Auteur
Henning Beck
  • Fonction : Auteur
John J. Nolan
  • Fonction : Auteur
Geremia B. Bolli
  • Fonction : Auteur
Alain Golay
  • Fonction : Auteur
Thomas Konrad
  • Fonction : Auteur
Peter M. Nilsson
Olle Melander
  • Fonction : Auteur
Geltrude Mingrone
Colin G. Perry
  • Fonction : Auteur
John R. Petrie
  • Fonction : Auteur
Michael R. Krebs
  • Fonction : Auteur
Rafael S. Gabriel
  • Fonction : Auteur
Asimina Mitrakou
  • Fonction : Auteur
Piermarco Marco Piatti
  • Fonction : Auteur
Nebojša Malić Lalic
Marku Laakso
  • Fonction : Auteur
Björn Zethelius
  • Fonction : Auteur
Johan Ärnlöv
Mark A. Walker
  • Fonction : Auteur
Laura C. Lazzeroni
Lars L. Lind
  • Fonction : Auteur
John R. Petrie
  • Fonction : Auteur
Themistocles L. Assimes
  • Fonction : Auteur

Résumé

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, 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. Graphical Abstract: [Figure not available: see fulltext.].
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hal-04190902 , version 1 (04-09-2023)

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Daniela Zanetti, Laurel Stell, Stefan Gustafsson, Fahim A. Abbasi, Phillip Shih Chung Tsao, et al.. Plasma proteomic signatures of a direct measure of insulin sensitivity in two population cohorts. Diabetologia, 2023, 66 (9), pp.1643-1654. ⟨10.1007/s00125-023-05946-z⟩. ⟨hal-04190902⟩
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