Article ; Online: Development and validation of a prediction model for iron status in a large U.S. cohort of women.
2023 Volume 13, Issue 1, Page(s) 17309
Abstract: Serum iron levels can be important contributors to health outcomes, but it is not often feasible to rely on blood-based measures for a large epidemiologic study. Predictive models that use questionnaire-based factors such as diet, supplement use, recency ...
Abstract | Serum iron levels can be important contributors to health outcomes, but it is not often feasible to rely on blood-based measures for a large epidemiologic study. Predictive models that use questionnaire-based factors such as diet, supplement use, recency of blood donation, and medical conditions could potentially provide a noninvasive alternative for studying health effects associated with iron status. We hypothesized that a model based on questionnaire data could predict blood-based measures of iron status biomarkers. Using iron (mcg/dL), ferritin (mcg/dL), and transferrin saturation (%) based on blood collected at study entry, in a subsample from the U.S.-wide Sister Study (n = 3171), we developed and validated a prediction model for iron with multivariable linear regression models. Model performance based on these cross-sectional data was weak, with R |
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MeSH term(s) | Humans ; Female ; Iron/metabolism ; Cross-Sectional Studies ; Hemoglobins/metabolism ; Ferritins ; Transferrins ; Transferrin |
Chemical Substances | Iron (E1UOL152H7) ; Hemoglobins ; Ferritins (9007-73-2) ; Transferrins ; Transferrin |
Language | English |
Publishing date | 2023-10-12 |
Publishing country | England |
Document type | Journal Article ; Research Support, N.I.H., Intramural ; Research Support, Non-U.S. Gov't |
ZDB-ID | 2615211-3 |
ISSN | 2045-2322 ; 2045-2322 |
ISSN (online) | 2045-2322 |
ISSN | 2045-2322 |
DOI | 10.1038/s41598-023-42993-3 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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