JOURNAL ARTICLE

Sex steroid hormones and subclinical atherosclerosis progression in postmenopausal women.

  • Published In: European Journal of Endocrinology, 2025, v. 192, n. 3. P. 248 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Chen, Irene J; Stanczyk, Frank Z; Sriprasert, Intira; Karim, Roksana; Shoupe, Donna; Kono, Naoko; Hodis, Howard N; Mack, Wendy J 3 of 3

Abstract

This article focuses on the relationship between serum sex steroid hormones, sex hormone-binding globulin (SHBG), and the progression of subclinical atherosclerosis in postmenopausal women, emphasizing differences based on time since menopause. Using data from the Early versus Late Intervention Trial with Estradiol (ELITE), the study found that higher levels of estradiol, estrone, and SHBG were associated with slower carotid artery intima-media thickness (CIMT) progression in women who initiated hormone therapy (HT) within six years of menopause (early postmenopause), whereas in women ten or more years postmenopause (late postmenopause), these hormone levels correlated with faster CIMT progression. Testosterone levels showed no significant association with CIMT progression. These findings underscore the importance of timing in HT initiation and suggest that the vascular effects of sex hormones and SHBG vary depending on the postmenopausal period, which may inform personalized cardiovascular risk management in postmenopausal women.

Additional Information

  • Source:European Journal of Endocrinology. 2025/03, Vol. 192, Issue 3, p248
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2025
  • ISSN:0804-4643
  • DOI:10.1093/ejendo/lvaf032
  • Accession Number:184039785
  • Copyright Statement:Copyright of European Journal of Endocrinology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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