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

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.

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

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.

3,524 individuals aged 65 years and older.

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.

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.

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