A Practical Guide to Adjust Micronutrient Biomarkers for Inflammation Using the BRINDA Method

Abstract

The Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) research group was formed over a decade ago to improve the interpretation of micronutrient biomarkers in settings with inflammation. The BRINDA inflammation adjustment method uses regression correction to adjust for the confounding effects of inflammation on select micronutrient biomarkers and has provided important insights to micronutrient research, policy, and programming. However, users may face challenges when applying the BRINDA inflammation adjustment methods to their own data due to varying guidance on the adjustment approach for different biomarkers and the need to develop statistical programming to conduct these analyses. This may result in lost opportunities to have results of micronutrient data readily available during critical decision-making periods. Our research objectives are to 1) provide an all-in-one summary of the BRINDA method in adjusting multiple micronutrient biomarkers for inflammation, 2) evaluate whether malaria as a binary variable should be included in the BRINDA inflammation adjustment method, and 3) present standardized and user-friendly BRINDA adjustment R package and SAS macro. This paper serves as a practical guidebook for the BRINDA inflammation adjustment approach and aids users to use the BRINDA R package and SAS to streamline their analyses.

Keywords: BRINDA; inflammation adjustment; micronutrients; software.

FIGURE 1.

Inflammation markers used to adjust micronutrient biomarkers among preschool-age children and women of reproductive age based on the latest publications. If only one inflammation biomarker is available, for micronutrient biomarkers that need to be adjusted by both AGP and CRP, it is still recommended to use that inflammation marker to adjust for inflammation. AGP, α-1-acid glycoprotein. *Serum zinc needs to be adjusted by both AGP and CRP only if there is a negative (r < −0.1) and marginally significant (P < 0.1) Spearman correlation between serum zinc and either AGP or CRP in preschool-age children.

FIGURE 2.

A decision-making tree on whether serum zinc should be adjusted for inflammation in preschool-age children. We recommend checking the decile plots of serum zinc and AGP/CRP as the first step before using this figure to determine whether serum zinc should be adjusted. AGP, α-1-acid glycoprotein.

FIGURE 3.

Procedures of checking data and applying the BRINDA adjustment method for micronutrient biomarkers that require adjustment for inflammation. The relation between iron and inflammation markers should be positive, whereas the relation between micronutrient biomarkers such as vitamin A or zinc and inflammation markers should be negative. For soluble transferrin receptor, only AGP should be included in the BRINDA adjustment; for zinc, adjustment using both AGP and CRP is needed only if there is a negative (r < −0.1) and marginally significant (P < 0.1) Spearman correlation between serum zinc and either AGP or CRP in PSC. AGP, α-1-acid glycoprotein; MB, micro-nutrient biomarkers; PSC, preschool-age children; WRA, women of reproductive age.

FIGURE 4.

Comparison of the prevalence of micronutrient deficiencies using the BRINDA method with and without malaria in (A) WRA and (B) PSC. Five datasets in WRA (n = 4768) and 8 datasets in PSC (n = 6662) for ferritin, 5 datasets in WRA (n = 4768) and 7 datasets in PSC (n = 6251) for sTfR, 7 datasets in PSC (n = 6251) for RBP, 3 datasets in PSC (n = 1023) for serum retinol, and 2 datasets in PSC (n = 1841) for zinc were used in the analysis. Error bars show prevalence ± standard error. RBP, retinol binding protein; sTfR, soluble transferrin receptor.