TY  - JOUR
PY  - 2020//
TI  - Identify depressive phenotypes by applying RDOC domains to the PHQ-9
JO  - Psychiatry research
A1  - Gunzler, Douglas
A1  - Sehgal, Ashwini R.
A1  - Kauffman, Kelley
A1  - Davey, Christine Horvat
A1  - Dolata, Jacqueline
A1  - Figueroa, Maria
A1  - Huml, Anne
A1  - Pencak, Julie
A1  - Sajatovic, Martha
SP  - e112872
EP  - e112872
VL  - 286
IS  - 
N2  - Major depression consists of multiple phenotypic traits. Our objective was to characterize depressive phenotypes in the patient health questionnaire (PHQ)-9 using the Research Domain Criteria (RDoC) research framework. Cross-sectional data were examined from the 2013-2014 (N = 5397) and 2015-2016 (N = 5164) National Health and Nutrition Examination Survey, a large, nationally representative U.S. sample. Using both factor analysis and qualitative analysis in mapping scale items along RDoC domains, a four factor model was found to be theoretically appropriate and had an excellent model fit for the PHQ-9. The factor structure consisted of phenotypes describing Negative Valence Systems and Externalizing (anhedonia and depression), Negative Valence Systems and Internalizing (depression, guilt and self-harm), Arousal and Regulatory Systems (sleep, fatigue and appetite) and Cognitive and Sensorimotor Systems (concentration and psychomotor). High correlation between these phenotypes did indicate screening and monitoring for depression study population using a single depression score is likely useful in most circumstances. In multiple indicator multiple cause analysis, differences in the means of the phenotypic traits were found by age, race/ethnicity, sex, and number of comorbidities. Future research should explore whether phenotype expression derived from readily available self-rated depression scales can help to inform more personalized care.<p /> <p>Language: en</p>
LA  - en
SN  - 0165-1781
UR  - http://dx.doi.org/10.1016/j.psychres.2020.112872
ID  - ref1
ER  -