Dalarna University's logo and link to the university's website

du.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Plasma proteomic signatures of a direct measure of insulin sensitivity in two population cohorts
Stanford University School of Medicine, Stanford, CA, USA; VA Palo Alto Health Care System, Palo Alto, CA, USA.
VA Palo Alto Health Care System, Palo Alto, CA, USA; Stanford University School of Medicine, Stanford, CA, USA.
Uppsala University, Uppsala.
Stanford University School of Medicine, Stanford, CA, USA.
Show others and affiliations
Number of Authors: 152023 (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-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).

RESULTS: A standard LASSO model incorporating 53 proteins in addition to routinely available clinical variables increased the M value R2 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 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 randomised LASSO and stability selection algorithm selected only two proteins per cohort (three unique proteins), which improved R2 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 R2 with randomised LASSO and stability selection were less marked in cross-cohort analyses (RISC to ULSAM R2 0.444 [0.391, 0.497]; ULSAM to RISC R2 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
2023. Vol. 66, no 9, p. 1643-1654
Keywords [en]
Euglycaemic–hyperinsulinaemic clamp, Insulin resistance, Insulin sensitivity, LASSO, Plasma proteomics, Population study, Stability selection
National Category
Endocrinology and Diabetes
Identifiers
URN: urn:nbn:se:du-46249DOI: 10.1007/s00125-023-05946-zISI: 001012589300002PubMedID: 37329449Scopus ID: 2-s2.0-85161984668OAI: oai:DiVA.org:du-46249DiVA, id: diva2:1770903
Available from: 2023-06-20 Created: 2023-06-20 Last updated: 2023-09-01Bibliographically approved

Open Access in DiVA

fulltext(1220 kB)24 downloads
File information
File name FULLTEXT01.pdfFile size 1220 kBChecksum SHA-512
1566d849fe024b906c6b28b65c1f1623899aa9b1527e3b1857ae839c9cbcdac048cb8955b99b41026bfb8f2df8f177d24378e6ef11e21880b19bc54cd1c8c72f
Type fulltextMimetype application/pdf

Other links

Publisher's full textPubMedScopus

Authority records

Ärnlöv, Johan

Search in DiVA

By author/editor
Ärnlöv, Johan
By organisation
Medical Science
In the same journal
Diabetologia
Endocrinology and Diabetes

Search outside of DiVA

GoogleGoogle Scholar
Total: 24 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 9 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf