Constructing a Plasma Nutriproteome for Population Assessment: Analytical Considerations. (14th June 2022)

• Record Type: Journal Article
• Authors: Kim, Hyunju; Shulze, Kerry; Rebholz, Casey; Sincerbeaux, Gwen; Wu, Lee Shu-Fane; Baker, Sarah; Yager, James; Christian, Parul; De Luca, Luigi; Groopman, John; Cole, Robert; West, Keith
• Is Part Of: Current developments in nutrition. Volume 6(2022)Supplement 1
• Page Start: 770
• Page End: 770
• Publication Date: 2022-06-14
• Subjects: Nutrition -- Periodicals; Nutritional Physiological Phenomena; Nutrition; Periodicals; Periodicals; Fulltext; Internet Resources; Periodicals; 612.3
• DOI: 10.1093/cdn/nzac063.012
• Languages: English
• ISSNs: 2475-2991

Abstract

Abstract: Objectives: Micronutrient status is rarely assessed in low-income settings. Proteomics may offer a proxy by measuring plasma proteins correlated with nutrients on a single platform. However, the proteome is huge, diverse and measured in different ways. We describe an analytic framework and decision process to explore nutrient: protein (N:P) associations for micronutrient status assessment. Methods: In plasma of 435 1 st trimester women in rural Bangladesh, we compared relative protein abundance, revealed by a multiplexed slow off-rate modified aptamer assay (SomaLogic, Inc), to biochemical concentrations of vitamins A, D, E, B9, B12, Zn, Se, Cu, I, & Fe, 5 carotenoids, cholesterol and AGP. After log2 -transforming protein abundance per convention, N:P relationships were summarized by simple linear regression. We assessed reliability by Pearson correlation (rp ) and coefficients of variation (CV) in 20 blind duplicates. To define each plasma nutriproteome [proteins correlating at a false discovery rate < 0.05], in all samples we explored a) normalizing protein abundance to the median of our sample vs not, b) assessing correlations by rp vs Spearman rank (rs ) estimators, and c) log2 -transforming (log2 ) nutrients vs not. We compared differences in the number of proteins and N:P correlative strength (either more negative/positive by rp or rs ) in each proteome when nutrient concentrations were left untransformed vs log2 -transformed. Results: In duplicates, log2 -transformed proteins that were normalized, vs not, to the sample-specific median generated higher median rp (0.92 vs. 0.87) and rs (0.87 vs. 0.85) and lower CV (4.8% vs. 11.7%). The median (IQR) size of the nutriproteomes was 147 (41–340) proteins by rp and 87 (29–639) by rs . For >50% of proteins in 13 nutriproteomes, rp was stronger than rs (in either +/− direction), favoring use of rp . Log2 -transforming folate (B9 ), Zn & cholesterol increased proteome size by 39, 223 & 875 proteins and strengthened rp for >50% of proteins than untransformed nutrients. Other proteomes remained larger when nutrient concentrations remained untransformed. Conclusions: Comparing plasma protein: nutrient associations via methods of normalization, transformation, and correlation offers a framework to guide plasma nutriproteome definition. Funding Sources: Johns Hopkins University.