- Clinical Study
- High Serum-Induced AhRL Is Associated with Prevalent Metabolic Syndrome and Future Impairment of Glucose Tolerance in the Elderly
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Youngmi Kim Pak, Hoon Sung Choi, Wook Ha Park, Suyeol Im, P. Monica Lind, Lars Lind, Hong Kyu Lee
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Endocrinol Metab. 2021;36(2):436-446. Published online April 19, 2021
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DOI: https://doi.org/10.3803/EnM.2020.883
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 Abstract
 PDF Supplementary Material PubReader  ePub  Crossref - TDM
- Background
High circulating levels of dioxins and dioxin-like chemicals, acting via the aryl hydrocarbon receptor (AhR), have previously been linked to diabetes. We now investigated whether the serum AhR ligands (AhRL) were higher in subjects with metabolic syndrome (MetS) and in subjects who had developed a worsened glucose tolerance over time.
Methods
Serum AhRL at baseline was measured by a cell-based AhRL activity assay in 70-year-old subjects (n=911) in the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) study. The main outcome measures were prevalent MetS and worsening of glucose tolerance over 5 years of follow-up.
Results
AhRL was significantly elevated in subjects with prevalent MetS as compared to those without MetS, following adjustment for sex, smoking, exercise habits, alcohol intake and educational level (P=0.009). AhRL at baseline was higher in subjects who developed impaired fasting glucose or diabetes at age 75 years than in those who remained normoglycemic (P=0.0081). The odds ratio (OR) of AhRL for worsening glucose tolerance over 5 years was 1.43 (95% confidence interval [CI], 1.13 to 1.81; P=0.003, continuous variables) and 2.81 (95% CI, 1.31 to 6.02; P=0.008, in the highest quartile) adjusted for sex, life style factors, body mass index, and glucose.
Conclusion
These findings support a large body of epidemiologic evidence that exposure to AhR transactivating substances, such as dioxins and dioxin-like chemicals, might be involved in the pathogenesis of MetS and diabetes development. Measurement of serum AhRL in humans can be a useful tool in predicting the onset of metabolic disorders.
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Citations
Citations to this article as recorded by  - An Interactive Online App for Predicting Diabetes via Machine Learning from Environment-Polluting Chemical Exposure Data
Rosy Oh, Hong Kyu Lee, Youngmi Kim Pak, Man-Suk Oh
International Journal of Environmental Research and Public Health.2022; 19(10): 5800. CrossRef - A Novel Aryl Hydrocarbon Receptor Antagonist HBU651 Ameliorates Peripheral and Hypothalamic Inflammation in High-Fat Diet-Induced Obese Mice
Sora Kang, Aden Geonhee Lee, Suyeol Im, Seung Jun Oh, Hye Ji Yoon, Jeong Ho Park, Youngmi Kim Pak
International Journal of Molecular Sciences.2022; 23(23): 14871. CrossRef
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