Dataset Information

Methods for estimating insulin resistance from untargeted metabolomics data.

ABSTRACT:

Context

Insulin resistance is associated with multiple complex diseases; however, precise measures of insulin resistance are invasive, expensive, and time-consuming.

Objective

Develop estimation models for measures of insulin resistance, including insulin sensitivity index (SI) and homeostatic model assessment of insulin resistance (HOMA-IR) from metabolomics data.

Design

Insulin Resistance Atherosclerosis Family Study (IRASFS).

Setting

Community based.

Participants

Mexican Americans (MA) and African Americans (AA).

Main outcome

Estimation models for measures of insulin resistance, i.e. SI and HOMA-IR.

Results

Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net regression were used to build insulin resistance estimation models from 1274 metabolites combined with clinical data, e.g. age, sex, body mass index (BMI). Metabolite data were transformed using three approaches, i.e. inverse normal transformation, standardization, and Box Cox transformation. The analysis was performed in one MA recruitment site (San Luis Valley, Colorado (SLV); N = 450) and tested in another MA recruitment site (San Antonio, Texas (SA); N = 473). In addition, the two MA recruitment sites were combined and estimation models tested in the AA recruitment sample (Los Angeles, California; N = 495). Estimated and empiric SI were correlated in the SA (r² = 0.77) and AA (r² = 0.74) testing datasets. Further, estimated and empiric SI were consistently associated with BMI, low-density lipoprotein cholesterol (LDL), and triglycerides. We applied similar approaches to estimate HOMA-IR with similar results.

Conclusions

We have developed a method for estimating insulin resistance with metabolomics data that has the potential for application to a wide range of biomedical studies and conditions.

SUBMITTER: Hsu FC

PROVIDER: S-EPMC10412652 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Methods for estimating insulin resistance from untargeted metabolomics data.

Hsu Fang-Chi FC Palmer Nicholette D ND Chen Shyh-Huei SH Ng Maggie C Y MCY Goodarzi Mark O MO Rotter Jerome I JI Wagenknecht Lynne E LE Bancks Michael P MP Bergman Richard N RN Bowden Donald W DW

Metabolomics : Official journal of the Metabolomic Society 20230809 8

<h4>Context</h4>Insulin resistance is associated with multiple complex diseases; however, precise measures of insulin resistance are invasive, expensive, and time-consuming.<h4>Objective</h4>Develop estimation models for measures of insulin resistance, including insulin sensitivity index (SI) and homeostatic model assessment of insulin resistance (HOMA-IR) from metabolomics data.<h4>Design</h4>Insulin Resistance Atherosclerosis Family Study (IRASFS).<h4>Setting</h4>Community based.<h4>Participan ...[more]

PMID: 37558891

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Dataset Information

Methods for estimating insulin resistance from untargeted metabolomics data.

Context

Objective

Design

Setting

Participants

Main outcome

Results

Conclusions

Publications

Methods for estimating insulin resistance from untargeted metabolomics data.

Similar Datasets

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