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Clinical and sociodemographic features of the Texas resilience against depression (T-RAD) study: Findings from the
Madhukar H Trivedi1, Manish K Jha1, Joshua S Elmore1
1Center for Depression Research and Clinical Care, Peter O'Donnell Jr. Brain Institute and Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Objective:
The burden of major depressive disorder is compounded by a limited understanding of its risk factors, the limited efficacy of treatments, and the lack of precision approaches to guide treatment selection. The Texas Resilience Against Depression (T-RAD) study was designed to explore the etiology of depression by collecting comprehensive socio-demographic, clinical, behavioral, neurophysiological/neuroimaging, and biological data from depressed individuals (D2K) and youth at risk for depression (RAD).
Methods:
This report details the baseline sociodemographic, clinical, and functional features from the initial cohort (D2K N = 1040, RAD N = 365).
Results:
Of the total T-RAD sample, n = 1078 (76.73 %) attended ≥2 in-person visits, and n = 845 (60.14 %) attended ≥4 in-person visits. Most D2K (84.82 %) had a primary diagnosis of any depressive disorder, with a bipolar disorder diagnosis being prevalent (13.49 %). RAD participants (75.89 %) did not have a psychiatric diagnosis, but other non-depressive diagnoses were present. D2K participants had 9-item Patient Health Questionnaire scores at or near the moderate range (10.58 ± 6.42 > 24 yrs.; 9.73 ± 6.12 10-24 yrs). RAD participants were in the non-depressed range (2.19 ± 2.65). While the age ranges in D2K and RAD differ, the potential to conduct analyses that compare at-risk and depressed youth is a strength of the study. The opportunity to examine the trajectory of depressive symptoms in the D2K cohort over the lifespan is unique.
Limitations:
As a longitudinal study, missing data were common.
Conclusion:
T-RAD will allow data to be collected from multiple modalities on a clinically well-characterized sample. These data will drive important discoveries on diagnosis, treatment, and prevention of depression.
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