About this item:

258 Views | 295 Downloads

Author Notes:

Address correspondence to LML (e-mail: leila.larson@emory.edu).

See publication for full list of author contributions.

We thank the BRINDA statisticians Ravi Varadhan and Janet Peerson for contributing to the statistical approach, as well as Juergen Ehardt, Donnie Whitehead, Kelley Scanlon, Deborah Galuska, and David Thurnham for sharing their inputs.

For their contributions, we thank the BRINDA steering committee [Grant J Aaron, Rafael Flores-Ayala, SMLN, Daniel J Raiten, and PSS (chair)] and BRINDA working group (OYA, Deena Alasfour, Fayrouz A Sakr Ashour, Zulfiqar Bhutta, RE-S, Roland Kupka, LML, Nino Lortkipanidze, Barbara MacDonald, Purnima Menon, Rebecca Merrill, Zuguo Mei, CAN-C, Pura Rayco-Solon, Rahul Rawat, Fabian Rohner, Ofelia P Saniel, Olga L Sarmiento, MS, Saleh Al Shammakhi, VT, Andres B Tschannen, AMW, and JPW).

We also thank Emory University’s Nutrition and Health Sciences Program and the Laney Graduate School for LML’s support in pursuing her PhD.

Report of the collaborative research group called Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA), which was formed in 2012 by the Centers for Disease Control and Prevention (CDC), the Global Alliance for Improved Nutrition (GAIN), and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD).

For additional information on the BRINDA project, please see www.BRINDA-nutrition.org.

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the CDC, the NIH, or the US Agency for International Development.


Research Funding:

Supported by the Bill & Meinda Gates Foundation, CDC, Global Alliance for Improved Nutrition, and Eunice Kennedy Shriver National Institute of Child Health and Human Development.


  • Science & Technology
  • Life Sciences & Biomedicine
  • Nutrition & Dietetics
  • anemia
  • inflammation
  • meta-analysis
  • nutritional assessment
  • retinol-binding protein
  • vitamin A deficiency
  • IRON

Adjusting retinol-binding protein concentrations for inflammation: Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) project

Journal Title:

American Journal of Clinical Nutrition


Volume 106, Number 1


, Pages 390S-401S

Type of Work:

Article | Final Publisher PDF


Background: The accurate estimation of the prevalence of vitamin A deficiency (VAD) is important in planning and implementing interventions. Retinol-binding protein (RBP) is often used in population surveys to measure vitamin A status, but its interpretation is challenging in settings where inflammation is common because RBP concentrations decrease during the acute-phase response.Objectives: We aimed to assess the relation between RBP concentrations and inflammation and malaria in preschool children (PSC) (age range: 6-59 mo) and women of reproductive age (WRA) (age range: 15-49 y) and to investigate adjustment algorithms to account for these effects.Design: Cross-sectional data from 8 surveys for PSC (n = 8803) and 4 surveys for WRA (n = 4191) from the Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) project were analyzed individually and combined with the use of a meta-analysis. Several approaches were explored to adjust RBP concentrations in PSC in inflammation and malaria settings as follows: 1) the exclusion of subjects with C-reactive protein (CRP) concentrations > 5 mg/L or α-1-acid glycoprotein (AGP) concentrations > 1 g/L, 2) the application of arithmetic correction factors, and 3) the use of a regression correction approach. The impact of adjustment on the estimated prevalence of VAD (defined as < 0.7 μmol/L) was examined in PSC.Results: The relation between estimated VAD and CRP and AGP deciles followed a linear pattern in PSC. In women, the correlations between RBP and CRP and AGP were too weak to justify adjustments for inflammation. Depending on the approach used to adjust for inflammation (CRP+AGP), the estimated prevalence of VAD decreased by a median of 11-18 percentage points in PSC compared with unadjusted values. There was no added effect of adjusting for malaria on the estimated VAD after adjusting for CRP and AGP.Conclusions: The use of regression correction (derived from internal data), which accounts for the severity of inflammation, to estimate the prevalence of VAD in PSC in regions with inflammation and malaria is supported by the analysis of the BRINDA data. These findings contribute to the evidence on adjusting for inflammation when estimating VAD with the use of RBP.

Copyright information:

This is an Open Access work distributed under the terms of the Creative Commons Attribution 3.0 Unported License (http://creativecommons.org/licenses/by/3.0/).

Creative Commons License

Export to EndNote