Daniel S McConnell1, Sybil L Crawford2, Nancy A Gee3, Joyce T Bromberger4, Rasa Kazlauskaite5, Nancy E Avis6, Carolyn J Crandall7, Hadine Joffe8, Howard M Kravitz9, Carol A Derby10, Ellen B Gold11, Samar R El Khoudary12, Sioban Harlow13, Gail A Greendale14, Bill L Lasley15. 1. Department of Epidemiology, School of Public Health, The University of Michigan, Ann Arbor, MI, United States. Electronic address: danmcc@umich.edu. 2. Graduate School of Nursing, University of Massachusetts Medical School, Worcester, MA, United States. 3. Center for Health and the Environment, John Muir Institute of the Environment, University of California, Davis, CA, United States. 4. Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh PA, United States. 5. Rush University Medical Center, Chicago, IL, United States. 6. Department of Social Sciences and Health Policy, Wake Forest University School of Medicine, Winston-Salem, NC, United States. 7. David Geffen School of Medicine at University of California, Department of Medicine, Los Angeles, CA, United States. 8. Mary Horrigan Connors Center for Women's Health and Gender Biology, Paula A. Johnson Associate Professor of Psychiatry in the Field of Women's Health, Harvard Medical School, Vice Chair for Research, Department of Psychiatry, Brigham and Women's Hospital, Psychosocial Oncology and Palliative Care, Dana Farber Cancer Institute, Dana Farber/Harvard Cancer Center Breast Cancer Program, Harvard Medical School, Boston, MA, United States. 9. Department of Psychiatry, Rush University Medical Center, Department of Preventive Medicine, Rush University Medical Center, Chicago, IL, United States. 10. Albert Einstein College of Medicine, Saul R. Korey Department of Neurology, Department of Epidemiology and Population Health, Bronx, NY, United States. 11. Department of Public Health Sciences, University of California Davis School of Medicine, Davis, CA, United States. 12. Epidemiology Data Center, University of Pittsburgh, Pittsburgh, PA, United States. 13. Department of Epidemiology, School of Public Health, The University of Michigan, Ann Arbor, MI, United States. 14. Division of Geriatrics, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States. 15. Center for Health and the Environment, University of California Davis, Davis, CA, United States.
Abstract
OBJECTIVE: The menopausal transition is characterized by progressive changes in ovarian function and increasing circulating levels of gonadotropins, with some women having irregular menstrual cycles well before their final menstrual period. These observations indicate a progressive breakdown of the hypothalamic-pituitary-ovarian axis often associated with an increase in menopausal symptoms. Relationships between vasomotor symptoms (VMS) and depressed mood and sleep as well as a bidirectional association between VMS and depressed mood in mid-life women have been reported, but the endocrine foundations and hormone profiles associated with these symptoms have not been well described. Our objective was to determine the relationship between daily urinary hormone profiles and daily logs of affect and VMS during the early perimenopausal transition. STUDY DESIGN: SWAN, the Study of Women's Health Across the Nation, is a large, mutli-ethnic, multisite cohort study of 3302 women aged 42-52 at baseline, designed to examine predictors of health and disease in women as they traversed the menopause. Inclusion criteria were: an intact uterus and at least one ovary present, at least one menstrual period in the previous three months, no use of sex steroid hormones in the previous three months, and not pregnant or lactating. A subset (n = 849) of women aged 43-53 years from all study sites in the first Daily Hormone Study collection were evaluated for this substudy. OUTCOME MEASURES: We measured daily VMS, and urinary hormones: follicle stimulating hormone (FSH), luteinizing hormone (LH), pregnanediol glucuronide (PdG) and estradiol (estrone conjugate, E1C). RESULTS: A variable pattern of LH and negative LH feedback were the hormone patterns most strongly associated with increased VMS. In contrast, no hormone pattern was significantly related to negative mood. CONCLUSION: Fluctuations of LH associated with low progesterone production were associated with VMS but not negative mood, suggesting different endocrine patterns may be related to increased negative mood than to the occurrence of VMS.
OBJECTIVE: The menopausal transition is characterized by progressive changes in ovarian function and increasing circulating levels of gonadotropins, with some women having irregular menstrual cycles well before their final menstrual period. These observations indicate a progressive breakdown of the hypothalamic-pituitary-ovarian axis often associated with an increase in menopausal symptoms. Relationships between vasomotor symptoms (VMS) and depressed mood and sleep as well as a bidirectional association between VMS and depressed mood in mid-life women have been reported, but the endocrine foundations and hormone profiles associated with these symptoms have not been well described. Our objective was to determine the relationship between daily urinary hormone profiles and daily logs of affect and VMS during the early perimenopausal transition. STUDY DESIGN: SWAN, the Study of Women's Health Across the Nation, is a large, mutli-ethnic, multisite cohort study of 3302 women aged 42-52 at baseline, designed to examine predictors of health and disease in women as they traversed the menopause. Inclusion criteria were: an intact uterus and at least one ovary present, at least one menstrual period in the previous three months, no use of sex steroid hormones in the previous three months, and not pregnant or lactating. A subset (n = 849) of women aged 43-53 years from all study sites in the first Daily Hormone Study collection were evaluated for this substudy. OUTCOME MEASURES: We measured daily VMS, and urinary hormones: follicle stimulating hormone (FSH), luteinizing hormone (LH), pregnanediol glucuronide (PdG) and estradiol (estrone conjugate, E1C). RESULTS: A variable pattern of LH and negative LH feedback were the hormone patterns most strongly associated with increased VMS. In contrast, no hormone pattern was significantly related to negative mood. CONCLUSION: Fluctuations of LH associated with low progesterone production were associated with VMS but not negative mood, suggesting different endocrine patterns may be related to increased negative mood than to the occurrence of VMS.
Authors: Hsin-Fang Chung; Nirmala Pandeya; Annette J Dobson; Diana Kuh; Eric J Brunner; Sybil L Crawford; Nancy E Avis; Ellen B Gold; Ellen S Mitchell; Nancy F Woods; Joyce T Bromberger; Rebecca C Thurston; Hadine Joffe; Toyoko Yoshizawa; Debra Anderson; Gita D Mishra Journal: Psychol Med Date: 2018-02-12 Impact factor: 7.723