Cargando…

Iron Status of Kenyan Pregnant Women after Adjusting for Inflammation Using BRINDA Regression Analysis and Other Correction Methods

Serum ferritin concentration is the preferred biomarker to assess population iron status in the absence of inflammation. Interpretation of this biomarker is complicated in populations with a high burden of infection, however, because inflammation increases serum ferritin concentration independently...

Descripción completa

Detalles Bibliográficos
Autores principales: Mwangi, Martin N., Echoka, Elizabeth, Knijff, Marthe, Kaduka, Lydia, Werema, Brenda G., Kinya, Frida M., Mutisya, Richard, Muniu, Erastus M., Demir, Ayşe Y., Verhoef, Hans, Bourdet-Sicard, Raphaelle
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6413054/
https://www.ncbi.nlm.nih.gov/pubmed/30781529
http://dx.doi.org/10.3390/nu11020420
_version_ 1783402749564551168
author Mwangi, Martin N.
Echoka, Elizabeth
Knijff, Marthe
Kaduka, Lydia
Werema, Brenda G.
Kinya, Frida M.
Mutisya, Richard
Muniu, Erastus M.
Demir, Ayşe Y.
Verhoef, Hans
Bourdet-Sicard, Raphaelle
author_facet Mwangi, Martin N.
Echoka, Elizabeth
Knijff, Marthe
Kaduka, Lydia
Werema, Brenda G.
Kinya, Frida M.
Mutisya, Richard
Muniu, Erastus M.
Demir, Ayşe Y.
Verhoef, Hans
Bourdet-Sicard, Raphaelle
author_sort Mwangi, Martin N.
collection PubMed
description Serum ferritin concentration is the preferred biomarker to assess population iron status in the absence of inflammation. Interpretation of this biomarker is complicated in populations with a high burden of infection, however, because inflammation increases serum ferritin concentration independently of iron status. We aimed to compare estimates of iron status of Kenyan pregnant women, with circulating ferritin concentrations adjusted for inflammation using newly proposed methods by the BRINDA project, or using previously proposed adjustment methods. We re-analyzed data from pregnant Kenyan women living in a rural area where malaria is highly endemic (n = 470) or in an urban area (n = 402). As proposed by the BRINDA group, we adjusted individual ferritin concentration by internal regression for circulating concentrations of C-reactive protein (CRP) and α(1)-acid glycoprotein (AGP). Other adjustment methods comprised: (a) arithmetic correction factors based on CRP or AGP; (b) exclusion of subjects with inflammation (CRP >5 mg/L or AGP >1 g/L); and (c) higher ferritin cut-off value (<30 μg/L). We additionally adjusted for Plasmodium infection as appropriate. Lastly, we assessed iron status without adjustment for inflammation. All correction methods increased prevalence of iron deficiency compared to the unadjusted estimates. This increase was more pronounced with the internal regression correction method. The iron deficiency prevalence estimate increased from 53% to 87% in rural Kisumu study and from 30% to 41% in the urban Nairobi study after adjusting for inflammation (CRP and AGP) using the BRINDA internal regression method. When we corrected for both inflammation and Plasmodium infection using the regression correction, it resulted in lower prevalence estimates compared to uninfected women. Application of linear regression methods to adjust circulating ferritin concentration for inflammation leads to markedly decreased point estimates for ferritin concentration and increased estimates for the prevalence of iron deficiency in pregnancy.
format Online
Article
Text
id pubmed-6413054
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-64130542019-04-09 Iron Status of Kenyan Pregnant Women after Adjusting for Inflammation Using BRINDA Regression Analysis and Other Correction Methods Mwangi, Martin N. Echoka, Elizabeth Knijff, Marthe Kaduka, Lydia Werema, Brenda G. Kinya, Frida M. Mutisya, Richard Muniu, Erastus M. Demir, Ayşe Y. Verhoef, Hans Bourdet-Sicard, Raphaelle Nutrients Article Serum ferritin concentration is the preferred biomarker to assess population iron status in the absence of inflammation. Interpretation of this biomarker is complicated in populations with a high burden of infection, however, because inflammation increases serum ferritin concentration independently of iron status. We aimed to compare estimates of iron status of Kenyan pregnant women, with circulating ferritin concentrations adjusted for inflammation using newly proposed methods by the BRINDA project, or using previously proposed adjustment methods. We re-analyzed data from pregnant Kenyan women living in a rural area where malaria is highly endemic (n = 470) or in an urban area (n = 402). As proposed by the BRINDA group, we adjusted individual ferritin concentration by internal regression for circulating concentrations of C-reactive protein (CRP) and α(1)-acid glycoprotein (AGP). Other adjustment methods comprised: (a) arithmetic correction factors based on CRP or AGP; (b) exclusion of subjects with inflammation (CRP >5 mg/L or AGP >1 g/L); and (c) higher ferritin cut-off value (<30 μg/L). We additionally adjusted for Plasmodium infection as appropriate. Lastly, we assessed iron status without adjustment for inflammation. All correction methods increased prevalence of iron deficiency compared to the unadjusted estimates. This increase was more pronounced with the internal regression correction method. The iron deficiency prevalence estimate increased from 53% to 87% in rural Kisumu study and from 30% to 41% in the urban Nairobi study after adjusting for inflammation (CRP and AGP) using the BRINDA internal regression method. When we corrected for both inflammation and Plasmodium infection using the regression correction, it resulted in lower prevalence estimates compared to uninfected women. Application of linear regression methods to adjust circulating ferritin concentration for inflammation leads to markedly decreased point estimates for ferritin concentration and increased estimates for the prevalence of iron deficiency in pregnancy. MDPI 2019-02-16 /pmc/articles/PMC6413054/ /pubmed/30781529 http://dx.doi.org/10.3390/nu11020420 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mwangi, Martin N.
Echoka, Elizabeth
Knijff, Marthe
Kaduka, Lydia
Werema, Brenda G.
Kinya, Frida M.
Mutisya, Richard
Muniu, Erastus M.
Demir, Ayşe Y.
Verhoef, Hans
Bourdet-Sicard, Raphaelle
Iron Status of Kenyan Pregnant Women after Adjusting for Inflammation Using BRINDA Regression Analysis and Other Correction Methods
title Iron Status of Kenyan Pregnant Women after Adjusting for Inflammation Using BRINDA Regression Analysis and Other Correction Methods
title_full Iron Status of Kenyan Pregnant Women after Adjusting for Inflammation Using BRINDA Regression Analysis and Other Correction Methods
title_fullStr Iron Status of Kenyan Pregnant Women after Adjusting for Inflammation Using BRINDA Regression Analysis and Other Correction Methods
title_full_unstemmed Iron Status of Kenyan Pregnant Women after Adjusting for Inflammation Using BRINDA Regression Analysis and Other Correction Methods
title_short Iron Status of Kenyan Pregnant Women after Adjusting for Inflammation Using BRINDA Regression Analysis and Other Correction Methods
title_sort iron status of kenyan pregnant women after adjusting for inflammation using brinda regression analysis and other correction methods
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6413054/
https://www.ncbi.nlm.nih.gov/pubmed/30781529
http://dx.doi.org/10.3390/nu11020420
work_keys_str_mv AT mwangimartinn ironstatusofkenyanpregnantwomenafteradjustingforinflammationusingbrindaregressionanalysisandothercorrectionmethods
AT echokaelizabeth ironstatusofkenyanpregnantwomenafteradjustingforinflammationusingbrindaregressionanalysisandothercorrectionmethods
AT knijffmarthe ironstatusofkenyanpregnantwomenafteradjustingforinflammationusingbrindaregressionanalysisandothercorrectionmethods
AT kadukalydia ironstatusofkenyanpregnantwomenafteradjustingforinflammationusingbrindaregressionanalysisandothercorrectionmethods
AT weremabrendag ironstatusofkenyanpregnantwomenafteradjustingforinflammationusingbrindaregressionanalysisandothercorrectionmethods
AT kinyafridam ironstatusofkenyanpregnantwomenafteradjustingforinflammationusingbrindaregressionanalysisandothercorrectionmethods
AT mutisyarichard ironstatusofkenyanpregnantwomenafteradjustingforinflammationusingbrindaregressionanalysisandothercorrectionmethods
AT muniuerastusm ironstatusofkenyanpregnantwomenafteradjustingforinflammationusingbrindaregressionanalysisandothercorrectionmethods
AT demiraysey ironstatusofkenyanpregnantwomenafteradjustingforinflammationusingbrindaregressionanalysisandothercorrectionmethods
AT verhoefhans ironstatusofkenyanpregnantwomenafteradjustingforinflammationusingbrindaregressionanalysisandothercorrectionmethods
AT bourdetsicardraphaelle ironstatusofkenyanpregnantwomenafteradjustingforinflammationusingbrindaregressionanalysisandothercorrectionmethods