Health benefits attributed to 17α-estradiol, a lifespan-extending compound, are mediated through estrogen receptor α
- OUHSC, United States
- The Jackson Laboratory, United States
- Mayo Clinic College of Medicine and Science, United States
- Mayo Clinic, United States
- Albert Einstein College of Medicine, United States
- Oklahoma Medical Research Foundation, United States
Abstract
Metabolic dysfunction underlies several chronic diseases, many of which are exacerbated by obesity. Dietary interventions can reverse metabolic declines and slow aging, although compliance issues remain paramount. 17α-estradiol treatment improves metabolic parameters and slows aging in male mice. The mechanisms by which 17α-estradiol elicits these benefits remain unresolved. Herein, we show that 17α-estradiol elicits similar genomic binding and transcriptional activation through estrogen receptor α (ERα) to that of 17β-estradiol. In addition, we show that the ablation of ERα completely attenuates the beneficial metabolic effects of 17α-E2 in male mice. Our findings suggest that 17α-E2 may act through the liver and hypothalamus to improve metabolic parameters in male mice. Lastly, we also determined that 17α-E2 improves metabolic parameters in male rats, thereby proving that the beneficial effects of 17α-E2 are not limited to mice. Collectively, these studies suggest ERα may be a drug target for mitigating chronic diseases in male mammals.
Data availability
Sequencing data has been deposited in GEO under accession code GSE151039
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Assessment of transcriptional ERa activity following exposure to 17a-E2 and 17b-E2NCBI Gene Expression Omnibus, GSE151039.
Article and author information
Author details
Funding
National Institutes of Health (R00 AG51661,R01 EY030513,T32 AG052363,P30 EY012190,P30 AG038072)
- Shivani N Mann
- Martin-Paul Agbaga
- Derek M Huffman
- Michael B Stout
Harold Hamm Diabetes Center (Pilot Research Funding)
- Shivani N Mann
- Michael B Stout
National Institutes of Health (R01 AG069742)
- Michael B Stout
National Institutes of Health (R01 AG059430)
- Willard M Freeman
Veterans Affairs Oklahoma City (I01BX003906)
- Willard M Freeman
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols (#19-063-SEAHI) of the University of Oklahoma Health Science Center.
Copyright
© 2020, Mann et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Health benefits attributed to 17α-estradiol, a lifespan-extending compound, are mediated through estrogen receptor α
eLife 9:e59616.
https://doi.org/10.7554/eLife.59616
Further reading
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- Medicine
Background:
Identification of individuals with prediabetes who are at high risk of developing diabetes allows for precise interventions. We aimed to determine the role of nuclear magnetic resonance (NMR)-based metabolomic signature in predicting the progression from prediabetes to diabetes.
Methods:
This prospective study included 13,489 participants with prediabetes who had metabolomic data from the UK Biobank. Circulating metabolites were quantified via NMR spectroscopy. Cox proportional hazard (CPH) models were performed to estimate the associations between metabolites and diabetes risk. Supporting vector machine, random forest, and extreme gradient boosting were used to select the optimal metabolite panel for prediction. CPH and random survival forest (RSF) models were utilized to validate the predictive ability of the metabolites.
Results:
During a median follow-up of 13.6 years, 2525 participants developed diabetes. After adjusting for covariates, 94 of 168 metabolites were associated with risk of progression to diabetes. A panel of nine metabolites, selected by all three machine-learning algorithms, was found to significantly improve diabetes risk prediction beyond conventional risk factors in the CPH model (area under the receiver-operating characteristic curve, 1 year: 0.823 for risk factors + metabolites vs 0.759 for risk factors, 5 years: 0.830 vs 0.798, 10 years: 0.801 vs 0.776, all p < 0.05). Similar results were observed from the RSF model. Categorization of participants according to the predicted value thresholds revealed distinct cumulative risk of diabetes.
Conclusions:
Our study lends support for use of the metabolite markers to help determine individuals with prediabetes who are at high risk of progressing to diabetes and inform targeted and efficient interventions.
Funding:
Shanghai Municipal Health Commission (2022XD017). Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZDCX20212501). Shanghai Municipal Human Resources and Social Security Bureau (2020074). Clinical Research Plan of Shanghai Hospital Development Center (SHDC2020CR4006). Science and Technology Commission of Shanghai Municipality (22015810500).
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- Medicine
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