Relationships between menopausal and mood symptoms and EEG sleep measures in a multi-ethnic sample of middle-aged women: the SWAN sleep study

Abstract

Study objectives: Examine associations of vasomotor and mood symptoms with visually scored and computer-generated measures of EEG sleep.

Design: Cross-sectional analysis.

Setting: Community-based in-home polysomnography (PSG).

Participants: 343 African American, Caucasian, and Chinese women; ages 48-58 years; pre-, peri- or post-menopausal; participating in the Study of Women's Health Across the Nation Sleep Study (SWAN Sleep Study).

Interventions: None.

Measurements and results: Measures included PSG-assessed sleep duration, continuity, and architecture, delta sleep ratio (DSR) computed from automated counts of delta wave activity, daily diary-assessed vasomotor symptoms (VMS), questionnaires to collect mood (depression, anxiety) symptoms, medication, and lifestyle information, and menopausal status using bleeding criteria. Sleep outcomes were modeled using linear regression. Nocturnal VMS were associated with longer sleep time. Higher anxiety symptom scores were associated with longer sleep latency and lower sleep efficiency, but only in women reporting nocturnal VMS. Contrary to expectations, VMS and mood symptoms were unrelated to either DSR or REM latency.

Conclusions: Vasomotor symptoms moderated associations of anxiety with EEG sleep measures of sleep latency and sleep efficiency and was associated with longer sleep duration in this multi-ethnic sample of midlife women.

Keywords: Anxiety; REM latency; REM sleep; delta (slow wave) sleep; delta sleep ratio; depressive symptoms; menopausal status; race/ethnicity; sleep continuity; vasomotor symptoms.

Figure 1

Vasomotor symptoms (VMS)-by-State Trait Anxiety Inventory–State scale (STAI) interactions for sleep latency (A) and sleep efficiency (B), adjusted for age, race/ethnicity, and menopausal status. Estimated mean sleep latency (in min) and sleep efficiency (percent) are plotted on the Y-axes and STAI score on both X-axes. After covariate adjustment, the β coefficients remained significant for the VMS-by-STAI interaction terms (β = 0.04, P = 0.04 for the natural log transformed sleep latency and β = −0.38, P < 0.05 for sleep efficiency) and for the VMS main effects (β = −0.72, P = 0.02 for sleep latency and β = 7.02, P = 0.02 for sleep efficiency). See text for details.