Steven Moore

Ph.D., M.P.H

National Cancer Institute

Senior Investigator

PLCO (Learn more about this study)

PLCO-2001

Dec 1, 2025

Identifying Proteomics Associated with Diet

Nutritional epidemiology studies are limited by recall bias and measurement error from self reported dietary assessment methods (1). Previous work has investigated finding less biased measurements of diet which include using -omics approaches. Metabolomics has been used to identify metabolites that can be used as biomarkers of dietary intake (1). Another methods that could provide similar value is proteomics. Previous studies have used proteomics to identify proteins associated with different dietary patterns (2,3), however to our knowledge no study have evaluated the protein diet associations at the individual food level. Our aim is to study evaluate protein diet associations in the PLCO proteomics data.



1. Guertin KA, Moore SC, Sampson JN, Huang WY, Xiao Q, Stolzenberg-Solomon RZ, Sinha R, Cross AJ. Metabolomics in nutritional epidemiology: identifying metabolites associated with diet and quantifying their potential to uncover diet-disease relations in populations. Am J Clin Nutr. 2014 Jul;100(1):208-17. doi: 10.3945/ajcn.113.078758. Epub 2014 Apr 16. PMID: 24740205; PMCID: PMC4144099.
2. Tong TYN, Smith-Byrne K, Papier K, Atkins JR, Parsaeian M, Key TJ, Travis RC. The plasma proteome of plant-based diets: Analyses of 2920 proteins in 49,615 people. Clin Nutr. 2025 Oct;53:144-154. doi: 10.1016/j.clnu.2025.08.032. Epub 2025 Aug 29. PMID: 40912079.
3. Zhu K, Li R, Yao P, Yu H, Pan A, Manson JE, Rimm EB, Willett WC, Liu G. Proteomic signatures of healthy dietary patterns are associated with lower risks of major chronic diseases and mortality. Nat Food. 2025 Jan;6(1):47-57. doi: 10.1038/s43016-024-01059-x. Epub 2024 Sep 27. PMID: 39333296.

The specific aims for the current project are:

Aim 1: To evaluate protein diet associations to individual foods.
We will use Pearson's correlations and adjusting for age, sex, and smoking status.

Aim 2: To identify protein diet associations to food groups/ categories.
We will use Pearson's correlations and adjusting for age, sex, and smoking status.

Aim 3: Compare protein food associations found in the PLCO dataset with those in the UK biobank.
We will perform the same correlation analysis between proteins and foods that will be completed on the PLCO data on the UK biobank data to strengthen our findings.