Comparing Variability, Severity, and Persistence of Depressive Symptoms as Predictors of Future Stroke Risk.

Objective: Numerous studies show that depressive symptoms measured at a single assessment predict greater future stroke risk. Longer-term symptom patterns, such as variability across repeated measures or worst symptom level, might better reflect adverse aspects of depression than a single measurement. This prospective study compared five approaches to operationalizing depressive symptoms at annual assessments as predictors of stroke incidence.

Design: Cohort followed for incident stroke over an average of 6.4 years.

Setting: The Adult Changes in Thought cohort follows initially cognitively intact, community- dwelling older adults from a population base defined by membership in Group Health, a Seattle-based nonprofit healthcare organization.

Participants: 3,524 individuals aged 65 years and older.

Measurements: We identified 665 incident strokes using ICD codes. We considered both baseline Center for Epidemiologic Studies-Depression scale (CES-D) score and, using a moving window of three most recent annual CES-D measurements, we compared most recent, maximum, average, and intra-individual variability of CES-D scores as predictors of subsequent stroke using Cox proportional hazards models.

Results: Greater maximum (hazard ratio [HR]: 1.18; 95% CI: 1.07-1.30), average (HR: 1.20; 95% CI: 1.05-1.36) and intra-individual variability (HR: 1.15; 95% CI: 1.06-1.24) in CES-D were each associated with elevated stroke risk, independent of sociodemographics, cardiovascular risks, cognition, and daily functioning. Neither baseline nor most recent CES-D was associated with stroke. In a combined model, intra-individual variability in CES-D predicted stroke, but average CES-D did not.

Conclusions: Capturing the dynamic nature of depression is relevant in assessing stroke risk. Fluctuating depressive symptoms may reflect a prodrome of reduced cerebrovascular integrity.