(PO18-09-22) Constructing a Plasma Nutriproteome for Population Assessment: Analytical Considerations

The ePoster content is available to NUTRITION 2022 LIVE ONLINE attendees who are logged in.<br><br>If you are registered, log in and then come back here to use this button.

Presenting Author(s)

Hyunju Kim, PhD MPH
– Assistant Scientist, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States

Co-Author(s)

Kerry J. Schulze, PhD
– Associate Scientist, Center for Human Nutrition, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States
Casey M. Rebholz, PhD, MS, MNSP, MPH
– Associate Professor, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States
Gwen Sincerbeaux, MS
– Associate, Johns Hopkins University, Spirit Lake, Idaho, United States
LW

Lee S. Wu, MS
– Center for Human Nutrition, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States
SB

Sarah Baker, MSPH
– Research Specialist, Center for Human Nutrition, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States
JY

James D. Yager
– Johns Hopkins Bloomberg School of Public Health
PC

Parul Christian, DrPH
– Professor, Center for Human Nutrition, JiVitA Project, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States
LD

Luigi M. De Luca
– Johns Hopkins Bloomberg School of Public Health
JG

John D. Groopman, PhD
– Johns Hopkins University
RC

Robert N. Cole, PhD
– Johns Hopkins University
Keith P. West, Jr., DrPH, MPH, RD
– Professor, Center for Human Nutrition, Dept of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States

Disclosure(s):

Hyunju Kim, PhD MPH: No relevant financial relationship(s) with ineligible companies to disclose.

Gwen Sincerbeaux, MS: No relevant financial relationship(s) with ineligible companies to disclose.

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 (r_p) 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 r_p vs Spearman rank (r_s) 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 r_por r_s) 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 r_p (0.92 vs. 0.87) and r_s (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 r_p and 87 (29 - 639) by r_s. For >50% of proteins in 13 nutriproteomes, r_pwas stronger than r_s (in either +/- direction), favoring use of r_p. Log₂-transforming folate (B₉), Zn & cholesterol increased proteome size by 39, 223 & 875 proteins and strengthened r_p 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