Among all individuals in our final analysis sample of 2,405, 1,952 (81.2%) were classified as having no to mild depressive symptoms, 411 (17.1%) were classified as having moderate to moderately severe depressive symptoms, and 42 (1.7%) presented severe depressive symptoms (). The mean age was 69.9 years and comprised roughly equal proportions of men and women. The majority of subjects in the final sample classified themselves as White Non-Hispanic and, with the exception of age and ethnicity, all other covariates were associated with global symptom severity. Subjects with a greater severity of depressive symptoms tended to come from the adverse categories of each covariate (i.e., less educated, greater poverty, not receiving emotional support, history of alcohol consumption, did not pursue leisure time physical activities, and more likely to report taking antidepressants) ().
Frequencies of covariate values across categories of global depressive symptom severity in final sample (N = 2,405)
Factor loadings for each PHQ-9 item are presented in . Factor analysis model fit was verified and acceptable: RMSEA = 0.087. Four items contributed towards affective symptom factor scores, three items contributed towards somatic symptom factor scores and two PHQ-9 items did not contribute towards either factor score. Thus, the possible factor scores for each subject ranged from 0 – 12 and 0 – 9 for affective and somatic factors, respectively.
Factor loadings of individual PHQ-9 items in final sample (N = 2,405)
Physiological dysfunction scores based on clinical cutoffs ranged from 0 to 7, with a median score of 2 (i.e., having two biological or physical measures that exceeded a clinical-relevant value); the percentile-based physiological dysfunction score ranged from 0 to 9, also with a median of 2. These two constructions of physiological dysfunction were highly correlated (Spearman’s ρ = 0.78, p < 0.0001).
shows the results of nested linear regression models using physiological dysfunction scores (based on clinical cutoffs) to predict continuous global depressive symptoms, affective symptom factor scores, and somatic symptom factor scores, sequentially controlling for an additional set of covariates. In the model controlling only for age and sex, greater physiological dysfunction was associated with increasingly severe global depressive symptoms: beta = 0.26 (95% CI = 0.16, 0.37), corresponding to a 0.26-point increase in the overall depressive symptom score for each unit increase in the physiological dysfunction score. Similarly, both affective and somatic depressive symptoms were positively associated with physiological dysfunction: beta = 0.098 (95% CI = 0.06, 0.13); beta = 0.13 (95% CI = 0.06, 0.19), respectively. The component measures driving these three associations were highly similar: body mass index, white blood cell count, glycosylated hemoglobin, resting heart rate, and triglyceride levels. In the fully adjusted models, physiological dysfunction remained positively and significantly associated with global, affective, and somatic depressive symptoms: beta = 0.23 (95% CI = 0.13, 0.32); beta = 0.076 (95% CI = 0.043, 0.11); beta = 0.12 (95% CI = 0.06, 0.18), respectively. Component measure associations from the initial model remained significant in the final models.
Results of multiple regression models predicting global, affective, and somatic depressive symptoms from physiological dysfunction (based on clinical cutoffs), and other covariates (N= 2,405)
Multinomial logistic regression verified the positive association between physiological dysfunction (clinical cutoffs version) and global depressive symptoms. In a fully adjusted model, an increase in the physiological dysfunction score was associated with increased odds of having “moderate to moderately severe depression” (OR = 1.08, 95% CI = 1.001, 1.16) as well as “severe depression” (OR = 1.61, 95% CI = 1.36, 1.91), relative to the “no depression to mild depression” classification.
Linear regression coefficients were smaller in magnitude, yet interpretations were essentially unchanged in all cases when percentile-based physiological dysfunction scores were employed (results not shown); in fully-adjusted models, the associations with global, affective, and somatic depressive symptoms were: beta = 0.17 (95% CI = 0.07, 0.27); beta = 0.05 (95% CI = 0.02, 0.09); beta = 0.10 (95% CI = 0.04, 0.15), respectively.
Multiple imputation analysis produced a fully-adjusted mean coefficient of 0.21 (95% CI = 0.18 – 0.25) for the association between physiological dysfunction (clinical cutoffs version) and global depressive symptoms. This method provided estimates of 0.07 (95% CI = 0.06 – 0.09) and 0.11 (95% CI = 0.09 – 0.13) for affective and somatic symptoms, respectively.