(Climacteric[TA])
2,577 results
  • Perimenopause and metabolic vulnerability: hormones, body composition and lifestyle changes. [Review]
    Climacteric. 2026 Aug 24; :1-16. [Online ahead of print]Lobato VA, Minari TP, Pisani LPC
  • Perimenopause is a transitional phase characterized by substantial hormonal fluctuations, particularly declining estradiol levels, accompanied by age-related metabolic changes. Increasing evidence suggests that this period constitutes a critical window of metabolic vulnerability, marked by adverse alterations in body composition, energy metabolism and cardiometabolic health. Notably, these change…
  • Literature review of estradiol/dydrogesterone in perimenopausal and postmenopausal women. [Review]
    Climacteric. 2026 Aug 18; :1-11. [Online ahead of print]Nappi RE, Binkowska M, … Stevenson JCC
  • Menopausal hormone therapy (MHT) with 17β-estradiol/dydrogesterone (E/D) or Femoston® at a range of doses has been shown to provide effective relief from climacteric symptoms and prevention of osteoporosis in postmenopausal women. However, less is known about personalizing MHT for women who require lower doses, or those with obesity or overweight. A literature search was conducted using PubMed to…
  • Identification and importance analysis of osteoporosis risk factors in perimenopausal women based on the Transformer-LSTM hybrid model and SMOTE. [Journal Article]
    Climacteric. 2026 Jul 29; :1-20. [Online ahead of print]Hu HX, Wang W, Liang HMC
  • CONCLUSIONS: This study provides a scientific basis and quantitative reference for the early identification, prevention, clinical assessment and health management of osteoporosis. The significant performance improvements of the proposed model (3.76-19.0% across key metrics) validate its superiority in capturing complex feature correlations and identifying minority class samples, addressing the core challenges of imbalanced clinical data and limited feature mining capability of traditional models. The identification of core risk factors and their quantitative importance not only enhances the interpretability of machine learning-based osteoporosis risk assessment but also enables targeted screening and personalized intervention for perimenopausal women, which is of great significance for improving the early diagnosis rate of osteoporosis and reducing the social and economic burden of related fractures.