Abstract
AbstractZinc (Zn) is an essential micronutrient, and Zn deficiency remains a major global public health challenge. Recognised biomarkers of population Zn status include blood plasma or serum Zn concentration and proxy data such as dietary Zn intake and prevalence of stunting. Urine Zn concentration is rarely used to assess population Zn status. This study assessed the value of urine Zn concentration as a biomarker of population Zn status using a nationally representative sample of non-pregnant women of reproductive age (WRA) and school-aged children (SAC) in Malawi. Spot (casual) urine samples were collected from 741 WRA and 665 SAC. Urine Zn concentration was measured by inductively coupled plasma mass spectrometry with specific gravity adjustment for hydration status. Data were analysed using a linear mixed model with a spatially correlated random effect for between-cluster variation. The effect of time of sample collection (morning or afternoon), and gender (for SAC), on urine Zn concentration were examined. There was spatial dependence in urine Zn concentration between clusters among SAC but not WRA, which indicates that food system or environmental factors can influence urine Zn concentration. Mapping urine Zn concentration could potentially identify areas where the prevalence of Zn deficiency is greater and thus where further sampling or interventions might be targeted. There was no evidence for differences in urine Zn concentration between gender (P = 0.69) or time of sample collection (P = 0.85) in SAC. Urine Zn concentration was greater in afternoon samples for WRA (P = 0.003). Relationships between urine Zn concentration, serum Zn concentration, dietary Zn intake, and potential food systems covariates warrant further study.
Publisher
Springer Science and Business Media LLC
Subject
Geochemistry and Petrology,General Environmental Science,Water Science and Technology,Environmental Chemistry,General Medicine,Environmental Engineering
Reference43 articles.
1. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov, & F. Csaki (Eds.), Proceedings of the 2nd international symposium on information theory (pp. 267–281). Budapest, Hungary: Akadémiai Kiadó.
2. Baer, M. T., & King, J. C. (1984). Tissue zinc levels and zinc excretion during experimental zinc depletion in young men. American Journal of Clinical Nutrition, 39(4), 556–570.
3. Buckland, S. T., Burnham, K. P., & Augustin, N. H. (1997). Model selection: An integral part of inference. Biometrics, 53(2), 603–618.
4. Canada, H. (2013). Second report on human biomonitoring of environmental chemicals in Canada. Ottawa: Health Canada.
5. Chilimba, A. D. C., Young, S. D., Black, C. R., Rogerson, K. B., Ander, E. L., Watts, M. J., et al. (2011). Maize grain and soil surveys reveal suboptimal dietary selenium intake is widespread in Malawi. Scientific Reports, 1, 72.
Cited by
7 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献