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Cross-Sectional Association of Blood Selenium with Glycemic Biomarkers among U.S. Adults with Normoglycemia in the National Health and Nutrition Examination Survey 2013–2016
Selenium (Se) remains to have an inconsistent relationship with glycemic biomarkers and the risk of developing type 2 diabetes (T2D). Few studies have investigated the relationship between blood Se and glycemic biomarkers among people with normoglycemia. We conducted a cross-sectional analysis using...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9570941/ https://www.ncbi.nlm.nih.gov/pubmed/36235626 http://dx.doi.org/10.3390/nu14193972 |
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author | Yang, Jingli Chen, En Choi, Cheukling Chan, Kayue Yang, Qinghua Rana, Juwel Yang, Bo Huang, Chuiguo Yang, Aimin Lo, Kenneth |
author_facet | Yang, Jingli Chen, En Choi, Cheukling Chan, Kayue Yang, Qinghua Rana, Juwel Yang, Bo Huang, Chuiguo Yang, Aimin Lo, Kenneth |
author_sort | Yang, Jingli |
collection | PubMed |
description | Selenium (Se) remains to have an inconsistent relationship with glycemic biomarkers and the risk of developing type 2 diabetes (T2D). Few studies have investigated the relationship between blood Se and glycemic biomarkers among people with normoglycemia. We conducted a cross-sectional analysis using the U.S. National Health and Nutrition Examination Survey 2013–2016. Multivariable linear regression models were developed to examine the associations of blood Se with glycemic biomarkers, namely, fasting plasma glucose (FPG), hemoglobin A1c (HbA1c), insulin, and the oral glucose tolerance test (OGTT). Blood Se was treated as continuous (per log-10 increment) and categorical exposure (in quartiles) in separate regression models. We assessed the dose–response relationships by restricted cubic spline analysis. After excluding the participants with T2D or incomplete data, 2706 participants were analyzed. The highest quartile of blood Se was associated with increased FPG [adjusted β = 0.12, 95% Confidence Intervals (CI) = 0.04, 0.20], OGTT (adjusted β = 0.29, 95% CI = 0.02, 0.56), HbA1c (adjusted β = 0.04, 95% CI = 0.00, 0.07), and insulin (adjusted β = 2.50, 95% CI = 1.05, 3.95) compared with the lowest quartile. Positive associations were also observed between every log-10 increment of blood Se level and glycemic biomarkers, except for OGTT. A positive linear dose–response relationship existed between blood Se and FPG (P(overall) = 0.003, P(nonlinear) = 0.073) and insulin (P(overall) = 0.004, P(nonlinear) =0.060). BMI, age, and smoking status modified the associations of the highest quartile of Se (compared with the lowest quartile) with glycemic biomarkers. Overall, positive associations of blood Se with glycemic biomarkers were observed among U.S. adults with normoglycemia. These findings implied that people with normoglycemia need to be aware of the level of Se and other mineral intakes from diet and supplements. Further research is required to identify the mechanisms of excess Se in the progression of diabetes. |
format | Online Article Text |
id | pubmed-9570941 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95709412022-10-17 Cross-Sectional Association of Blood Selenium with Glycemic Biomarkers among U.S. Adults with Normoglycemia in the National Health and Nutrition Examination Survey 2013–2016 Yang, Jingli Chen, En Choi, Cheukling Chan, Kayue Yang, Qinghua Rana, Juwel Yang, Bo Huang, Chuiguo Yang, Aimin Lo, Kenneth Nutrients Article Selenium (Se) remains to have an inconsistent relationship with glycemic biomarkers and the risk of developing type 2 diabetes (T2D). Few studies have investigated the relationship between blood Se and glycemic biomarkers among people with normoglycemia. We conducted a cross-sectional analysis using the U.S. National Health and Nutrition Examination Survey 2013–2016. Multivariable linear regression models were developed to examine the associations of blood Se with glycemic biomarkers, namely, fasting plasma glucose (FPG), hemoglobin A1c (HbA1c), insulin, and the oral glucose tolerance test (OGTT). Blood Se was treated as continuous (per log-10 increment) and categorical exposure (in quartiles) in separate regression models. We assessed the dose–response relationships by restricted cubic spline analysis. After excluding the participants with T2D or incomplete data, 2706 participants were analyzed. The highest quartile of blood Se was associated with increased FPG [adjusted β = 0.12, 95% Confidence Intervals (CI) = 0.04, 0.20], OGTT (adjusted β = 0.29, 95% CI = 0.02, 0.56), HbA1c (adjusted β = 0.04, 95% CI = 0.00, 0.07), and insulin (adjusted β = 2.50, 95% CI = 1.05, 3.95) compared with the lowest quartile. Positive associations were also observed between every log-10 increment of blood Se level and glycemic biomarkers, except for OGTT. A positive linear dose–response relationship existed between blood Se and FPG (P(overall) = 0.003, P(nonlinear) = 0.073) and insulin (P(overall) = 0.004, P(nonlinear) =0.060). BMI, age, and smoking status modified the associations of the highest quartile of Se (compared with the lowest quartile) with glycemic biomarkers. Overall, positive associations of blood Se with glycemic biomarkers were observed among U.S. adults with normoglycemia. These findings implied that people with normoglycemia need to be aware of the level of Se and other mineral intakes from diet and supplements. Further research is required to identify the mechanisms of excess Se in the progression of diabetes. MDPI 2022-09-24 /pmc/articles/PMC9570941/ /pubmed/36235626 http://dx.doi.org/10.3390/nu14193972 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yang, Jingli Chen, En Choi, Cheukling Chan, Kayue Yang, Qinghua Rana, Juwel Yang, Bo Huang, Chuiguo Yang, Aimin Lo, Kenneth Cross-Sectional Association of Blood Selenium with Glycemic Biomarkers among U.S. Adults with Normoglycemia in the National Health and Nutrition Examination Survey 2013–2016 |
title | Cross-Sectional Association of Blood Selenium with Glycemic Biomarkers among U.S. Adults with Normoglycemia in the National Health and Nutrition Examination Survey 2013–2016 |
title_full | Cross-Sectional Association of Blood Selenium with Glycemic Biomarkers among U.S. Adults with Normoglycemia in the National Health and Nutrition Examination Survey 2013–2016 |
title_fullStr | Cross-Sectional Association of Blood Selenium with Glycemic Biomarkers among U.S. Adults with Normoglycemia in the National Health and Nutrition Examination Survey 2013–2016 |
title_full_unstemmed | Cross-Sectional Association of Blood Selenium with Glycemic Biomarkers among U.S. Adults with Normoglycemia in the National Health and Nutrition Examination Survey 2013–2016 |
title_short | Cross-Sectional Association of Blood Selenium with Glycemic Biomarkers among U.S. Adults with Normoglycemia in the National Health and Nutrition Examination Survey 2013–2016 |
title_sort | cross-sectional association of blood selenium with glycemic biomarkers among u.s. adults with normoglycemia in the national health and nutrition examination survey 2013–2016 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9570941/ https://www.ncbi.nlm.nih.gov/pubmed/36235626 http://dx.doi.org/10.3390/nu14193972 |
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