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针对炎症调整铁蛋白浓度:反映炎症与贫血营养决定因素的生物标志物(BRINDA)项目

Adjusting ferritin concentrations for inflammation: Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) project.

作者信息

Namaste Sorrel Ml, Rohner Fabian, Huang Jin, Bhushan Nivedita L, Flores-Ayala Rafael, Kupka Roland, Mei Zuguo, Rawat Rahul, Williams Anne M, Raiten Daniel J, Northrop-Clewes Christine A, Suchdev Parminder S

机构信息

Strengthening Partnerships, Results, and Innovations in Nutrition Globally, Arlington, VA;

Helen Keller International, Washington, DC.

出版信息

Am J Clin Nutr. 2017 Jul;106(Suppl 1):359S-371S. doi: 10.3945/ajcn.116.141762. Epub 2017 Jun 14.

DOI:10.3945/ajcn.116.141762
PMID:28615259
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5490647/
Abstract

The accurate estimation of iron deficiency is important in planning and implementing interventions. Ferritin is recommended as the primary measure of iron status, but interpretability is challenging in settings with infection and inflammation. We assessed the relation between ferritin 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 investigated adjustment algorithms to account for these effects. Cross-sectional data from 15 surveys for PSC ( = 27,865) and 8 surveys for WRA (24,844), from the Biomarkers Reflecting the 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 estimate depleted iron stores (ferritin concentration <12 μg/L in PSC and <15 μg/L in WRA) in inflammation and malaria settings as follows: ) increase ferritin-concentration cutoff to <30 μg/L; ) exclude individuals with C-reactive protein (CRP) concentrations >5 mg/L or α-1-acid glycoprotein (AGP) concentrations >1 g/L; ) apply arithmetic correction factors; and ) use a regression correction approach. Depleted iron-store estimates incrementally increased as CRP and AGP deciles decreased (4% compared with 30%, and 6% compared with 29% from highest compared with lowest CRP deciles for pooled PSC and WRA, respectively, with similar results for AGP). Depending on the approach used to adjust for inflammation (CRP plus AGP), the estimated prevalence of depleted iron stores increased by 7-25 and 2-8 absolute median percentage points for PSC and WRA, respectively, compared with unadjusted values. Adjustment for malaria in addition to CRP and AGP did not substantially change the estimated prevalence of depleted iron stores. Our results lend support for the use of internal regression correction to estimate the prevalence of depleted iron stores in regions with inflammation. This approach appears to mathematically reflect the linear relation of ferritin concentrations with acute-phase proteins. More research is warranted to validate the proposed approaches, but this study contributes to the evidence base to guide decisions about how and when to adjust ferritin for inflammation.

摘要

准确估计缺铁情况对于规划和实施干预措施至关重要。铁蛋白被推荐作为铁状态的主要衡量指标,但在存在感染和炎症的情况下,其解读具有挑战性。我们评估了学龄前儿童(PSC)(年龄范围:6 - 59个月)和育龄妇女(WRA)(年龄范围:15 - 49岁)中铁蛋白浓度与炎症及疟疾之间的关系,并研究了用于校正这些影响的算法。对来自反映贫血的炎症和营养决定因素(BRINDA)项目的15项针对PSC(n = 27,865)和8项针对WRA(n = 24,844)的横断面数据进行了单独分析,并通过荟萃分析进行合并。探索了几种方法来估计炎症和疟疾环境中缺铁储备(PSC中铁蛋白浓度<12 μg/L,WRA中铁蛋白浓度<15 μg/L),如下所示:a)将铁蛋白浓度临界值提高到<30 μg/L;b)排除C反应蛋白(CRP)浓度>5 mg/L或α-1酸性糖蛋白(AGP)浓度>1 g/L的个体;c)应用算术校正因子;d)使用回归校正方法。随着CRP和AGP十分位数降低,缺铁储备估计值逐渐增加(合并的PSC和WRA中,与最高CRP十分位数相比最低CRP十分位数分别为4%与30%,AGP的结果相似,为6%与29%)。根据用于校正炎症的方法(CRP加AGP),与未校正值相比,PSC和WRA中铁缺储备估计患病率分别增加了7 - 25和2 - 8个绝对中位数百分点。除了CRP和AGP之外对疟疾进行校正并没有实质性改变缺铁储备估计患病率。我们的结果支持使用内部回归校正来估计炎症地区缺铁储备患病率。这种方法似乎在数学上反映了铁蛋白浓度与急性期蛋白的线性关系。需要更多研究来验证所提出的方法,但本研究为证据库做出了贡献,以指导关于如何以及何时针对炎症校正铁蛋白的决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4dd9/5490647/307a803fc9f6/ajcn141762fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4dd9/5490647/648fff799ffd/ajcn141762fig1.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4dd9/5490647/a31520e32949/ajcn141762fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4dd9/5490647/307a803fc9f6/ajcn141762fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4dd9/5490647/648fff799ffd/ajcn141762fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4dd9/5490647/9bfcd64cd33a/ajcn141762fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4dd9/5490647/8dbf36b63c0a/ajcn141762fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4dd9/5490647/7bb38eab0d65/ajcn141762fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4dd9/5490647/a31520e32949/ajcn141762fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4dd9/5490647/307a803fc9f6/ajcn141762fig6.jpg

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