Abstract
Addressing subsyndromal depression in cerebrovascular conditions, diabetes, and obesity reduces morbidity and risk of major depression. However, depression may be masked because self-reported symptoms may not reveal dysphoric (sad) mood. In this study, the first wave (2,812 elders) from the New Haven Epidemiological Study of the Elderly (EPESE) was used. These population-weighted data combined a stratified, systematic, clustered random sample from independent residences and a census of senior housing. Physical conditions included progressive cerebrovascular disease (CVD; hypertension, silent CVD, stroke, and vascular cognitive impairment [VCI]) and co-occurring excess weight and/or diabetes. These conditions and interactions (clusters) simultaneously predicted 20 depression items and a latent trait of depression in participants with subsyndromal (including subthreshold) depression (11≤ Center for Epidemiologic Studies Depression Scale [CES-D] score ≤27). The option for maximum likelihood estimation with standard errors that are robust to non-normality and non-independence in complex random samples (MLR) in Mplus and an innovation created by the author were used for estimating unbiased effects from latent trait models with exhaustive specification. Symptom profiles reveal masked depression in 1) older males, related to the metabolic syndrome (hypertension-overweight-diabetes; silent CVD-overweight; and silent CVD-diabetes) and 2) older females or the full sample, related to several diabetes and/or overweight clusters that involve stroke or VCI. Several other disease clusters are equivocal regarding masked depression; a couple do emphasize dysphoric mood. Replicating findings could identify subgroups for cost-effective screening of subsyndromal depression.