Objective: The purpose of this study was to investigate associations between profiles of vasomotor menopausal symptoms (VMS) during the menopausal transition with the prevalence of diabetes.
Methods: VMS and diabetes were measured at baseline and 3-year intervals for 15 years in 4,895 women in the Australian Longitudinal Study on Women’s Health who were aged 45 to 50 years at baseline in 1996. Latent class analysis and generalized estimating equation models for binary repeated measures were performed. The VMS profiles were labeled as mild, moderate, early severe, and late severe.
Results: The prevalence of diabetes in the total group was 9.0%. Compared with mild VMS, the odds of diabetes were higher in those with a late severe profile (though not statistically significant; adjusted odds ratio, 1.28; 95% CI, 0.97-1.68) and in those with an early severe profile (adjusted odds ratio, 1.67; 95% CI, 1.20-2.32). Adjustment for body mass index attenuated this association, but the odds of diabetes were still significantly higher in women with an early severe profile than in women with mild VMS (odds ratio, 1.55; 95% CI, 1.11-2.17). The moderate profile was not associated with diabetes.
Conclusions: Women with an early severe VMS profile are more likely to have diabetes across a period of 15 years. This association is not explained by body mass index or other potential confounders. Our findings imply that the predictive value of VMS for diabetes may vary with the timing of VMS relative to menopause.
From the School of Population Health, University of Queensland, Brisbane, Australia.
Received August 21, 2013; revised and accepted October 24, 2013.
Funding/support: The Australian Longitudinal Study on Women’s Health was funded by the Australian Commonwealth Department of Health and Aging. G.-C.M.H.-G. and G.D.M. were supported by the Australian National Health and Medical Research Council (grant APP1000986).
Financial disclosure/conflicts of interest: None reported.
Address correspondence to: Gerrie-Cor M. Herber-Gast, PhD, School of Population Health, University of Queensland, Herston, QLD 4006, Australia. E-mail: email@example.com