Plasma proteomic signatures of a direct measure of insulin sensitivity in two population cohorts.
Daniela ZanettiLaurel StellStefan GustafssonFahim AbbasiPhilip S TsaoJoshua W Knowlesnull nullBjörn ZetheliusJohan ÄrnlövBeverley BalkauMark WalkerLaura C LazzeroniLars LindJohn R PetrieThemistocles L AssimesPublished in: Diabetologia (2023)
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.
Keyphrases
- insulin resistance
- cross sectional
- type diabetes
- public health
- healthcare
- label free
- adipose tissue
- mental health
- machine learning
- metabolic syndrome
- deep learning
- glycemic control
- clinical practice
- health information
- emergency department
- polycystic ovary syndrome
- risk assessment
- neural network
- high fat diet induced
- social media