The plasma proteome identifies expected and novel proteins correlated with micronutrient status in undernourished Nepalese children

Abstract

Micronutrient deficiencies are common in undernourished societies yet remain inadequately assessed due to the complexity and costs of existing assays. A plasma proteomics-based approach holds promise in quantifying multiple nutrient:protein associations that reflect biological function and nutritional status. To validate this concept, in plasma samples of a cohort of 500 6- to 8-y-old Nepalese children, we estimated cross-sectional correlations between vitamins A (retinol), D (25-hydroxyvitamin D), and E (α-tocopherol), copper, and selenium, measured by conventional assays, and relative abundance of their major plasma-bound proteins, measured by quantitative proteomics using 8-plex iTRAQ mass tags. The prevalence of low-to-deficient status was 8.8% (<0.70 μmol/L) for retinol, 19.2% (<50 nmol/L) for 25-hydroxyvitamin D, 17.6% (<9.3 μmol/L) for α-tocopherol, 0% (<10 μmol/L) for copper, and 13.6% (<0.6 μmol/L) for selenium. We identified 4705 proteins, 982 in >50 children. Employing a linear mixed effects model, we observed the following correlations: retinol:retinol-binding protein 4 (r = 0.88), 25-hydroxyvitamin D:vitamin D-binding protein (r = 0.58), α-tocopherol:apolipoprotein C-III (r = 0.64), copper:ceruloplasmin (r = 0.65), and selenium:selenoprotein P isoform 1 (r = 0.79) (all P < 0.0001), passing a false discovery rate threshold of 1% (based on P value-derived q values). Individual proteins explained 34-77% (R(2)) of variation in their respective nutrient concentration. Adding second proteins to models raised R(2) to 48-79%, demonstrating a potential to explain additional variation in nutrient concentration by this strategy. Plasma proteomics can identify and quantify protein biomarkers of micronutrient status in undernourished children. The maternal micronutrient supplementation trial, from which data were derived as a follow-up activity, was registered at clinicaltrials.gov as NCT00115271.

FIGURE 1

Plasma retinol and RBP4 relative abundance distributions in Nepalese children 6–8 y of age (n = 500). (A) Histogram showing the frequency distribution of retinol concentrations: range = 0.30–2.11 μmol/L, 8.8% (n = 44) deficient (<0.70 μmol/L, dark gray), 45.6% (n = 228), marginal (0.70 to <1.05 μmol/L, medium gray), and 45.6% (n = 228) adequate (≥1.05 μmol/L, light gray) in status. (B) Plasma retinol by relative abundance of RBP4 by a traditional estimation method using a master plasma pool in one randomly assigned iTRAQ channel within each 8-plex experiment to normalize the protein distribution across iTRAQ runs. (C) Plasma retinol by relative abundance of RBP4 by an estimation method that relies on an LME model that combines abundance estimates from all 72 iTRAQ experiments (25). R² values represent the proportion of variance in the nutrient explained by the fitted values of the nutrient-protein regression models. The P value in B is derived from testing the hypothesis of no association between the nutrient and protein abundance, whereas the P value in C is derived from testing the fixed effects slope for the protein abundance in the LME model. Shading of circles in B and C corresponds to bars. Horizontal lines indicate cutoffs for changes in micronutrient status. iTRAQ, isobaric tags for relative and absolute quantification; LME, linear mixed effects (model); RBP4, retinol binding protein isoform 4.

FIGURE 2

Plasma 25-hydroxyvitamin D and VDBP relative abundance distributions in Nepalese children 6–8 y of age (n = 500). (A) Frequency distribution of 25-hydroxyvitamin D concentrations: range, 18.6–173.5 nmol/L, 19.2% (n = 96) deficient (<50 nmol/L, dark gray), and 80.8% (n = 404, medium gray) adequate (≥50 nmol/L) in status. (B,C) Plasma 25-hydroxyvitamin D by relative abundance of VDBP by traditional master plasma pool normalization and LME-adjusted methods, respectively (see Fig. 1 for details). LME, linear mixed effects (model); VDBP, vitamin D binding protein; 25(OH)D, 25-hydroxyvitamin D.

FIGURE 3

Plasma α-tocopherol and Apo C-III relative abundance distributions in Nepalese children 6–8 y of age (n = 500). (A) Frequency distribution of α-tocopherol concentrations: range, 4.1–26.9 μmol/L, 17.6% (n = 88) deficient (<9.3 μmol/L, dark gray), 37.4% (n = 187) marginal (9.3 to <12 μmol/L, medium gray), and 45% (n = 225) adequate (≥12 μmol/L, light gray) in status. (B,C) Plasma α-tocopherol by relative abundance of Apo C-III by traditional master plasma pool normalization and LME-adjusted methods, respectively (see Fig. 1 for details). LME, linear mixed effects (model).

FIGURE 4

Plasma copper and Cp relative abundance distributions in Nepalese children 6–8 y of age (n = 494). (A) Plasma copper concentrations: range, 11.6–35.8 μmol/L, 100% were adequate (>10 μmol/L, gray). Six implausible values (4 <5 μmol/L, and 1 each at 62.3 μmol/L and 100.5 μmol/L) were removed from this analysis. (B,C) Plasma copper by relative abundance of Cp by traditional master plasma pool normalization and LME-adjusted methods, respectively (see Fig. 1 for details). Cp, ceruloplasmin; LME, linear mixed effects (model).

FIGURE 5

Plasma selenium and SEPP1 relative abundance distributions in Nepalese children 6–8 y of age (n = 499). (A) Plasma selenium concentrations: range, 0.4–2.1 μmol/L; 13.6% (n = 68) deficient (<0.6 μmol/L, dark gray) and 86.4% (n = 431) adequate (≥0.6 μmol/L, medium gray) in status. (B,C) Plasma selenium by relative abundance of SEPP1 by traditional master plasma pool normalization and LME-adjusted methods, respectively (see Fig. 1 for details). LME, linear mixed effects (model); SEPP1, selenoprotein P isoform 1.