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Related Experiment Video

Updated: Jul 16, 2026

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS
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Methods for estimating insulin resistance from untargeted metabolomics data.

Fang-Chi Hsu1, Nicholette D Palmer2, Shyh-Huei Chen1

  • 1Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA.

Metabolomics : Official Journal of the Metabolomic Society
|August 9, 2023
PubMed
Summary

Developing accurate insulin resistance (IR) measures is crucial for complex diseases. This study created models using metabolomics data to estimate insulin sensitivity index (SI) and HOMA-IR, offering a more accessible approach.

Keywords:
Elastic netInsulin resistanceLASSOMachine learningMetabolomics

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Area of Science:

  • Metabolomics
  • Biostatistics
  • Human Health

Background:

  • Insulin resistance (IR) is linked to numerous complex diseases.
  • Current methods for measuring IR, such as insulin sensitivity index (SI) and homeostatic model assessment of insulin resistance (HOMA-IR), are invasive, costly, and time-consuming.
  • There is a need for more accessible and efficient methods to assess IR.

Purpose of the Study:

  • To develop and validate estimation models for key measures of insulin resistance (SI and HOMA-IR) using metabolomics data.
  • To assess the feasibility of using metabolomics data combined with clinical factors for IR estimation.
  • To evaluate the performance of these models in diverse populations, including Mexican Americans and African Americans.

Main Methods:

  • Utilized Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net regression.
  • Integrated 1274 metabolites with clinical data (age, sex, BMI) to build estimation models.
  • Employed various data transformation techniques (inverse normal, standardization, Box Cox) for metabolite data.
  • Validated models across different recruitment sites and ethnic groups (Mexican Americans and African Americans).

Main Results:

  • Developed robust estimation models for SI and HOMA-IR using metabolomics and clinical data.
  • Achieved high correlations between estimated and empirical SI in testing datasets (r²=0.77 in Mexican Americans, r²=0.74 in African Americans).
  • Demonstrated consistent associations between estimated SI and key clinical markers like BMI, LDL cholesterol, and triglycerides.

Conclusions:

  • Successfully developed a novel method for estimating insulin resistance using metabolomics data.
  • The developed models show strong predictive power and clinical relevance.
  • This approach has significant potential for broad application in biomedical research and clinical practice for assessing insulin resistance.