Regression calibration utilizing biomarkers developed from high-dimensional metabolites

Yiwen Zhang, Ran Dai, Ying Huang, Ross L. Prentice, Cheng Zheng

Research output: Contribution to journalArticlepeer-review

Abstract

Addressing systematic measurement errors in self-reported data is a critical challenge in association studies of dietary intake and chronic disease risk. The regression calibration method has been utilized for error correction when an objectively measured biomarker is available; however, biomarkers for only a few dietary components have been developed. This paper proposes to use high-dimensional objective measurements to construct biomarkers for many more dietary components and to estimate the diet disease associations. It also discusses the challenges in variance estimation in high-dimensional regression methods and presents a variety of techniques to address this issue, including cross-validation, degrees-of-freedom corrected estimators, and refitted cross-validation (RCV). Extensive simulation is performed to study the finite sample performance of the proposed estimators. The proposed method is applied to the Women's Health Initiative cohort data to examine the associations between the sodium/potassium intake ratio and the total cardiovascular disease.

Original languageEnglish (US)
Article number1215768
JournalFrontiers in Nutrition
Volume10
DOIs
StatePublished - 2023

Keywords

  • biomarker
  • feeding study
  • high-dimensional data
  • measurement error
  • regression calibration

ASJC Scopus subject areas

  • Food Science
  • Endocrinology, Diabetes and Metabolism
  • Nutrition and Dietetics

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