Identify depressive phenotypes by applying RDOC domains to the PHQ-9

Douglas Gunzler; Ashwini R Sehgal; Kelley Kauffman; Christine Horvat Davey; Jacqueline Dolata; Maria Figueroa; Anne Huml; Julie Pencak; Martha Sajatovic

doi:10.1016/j.psychres.2020.112872

Identify depressive phenotypes by applying RDOC domains to the PHQ-9

Psychiatry Res. 2020 Feb 19:286:112872. doi: 10.1016/j.psychres.2020.112872. Online ahead of print.

Authors

Douglas Gunzler¹, Ashwini R Sehgal², Kelley Kauffman², Christine Horvat Davey³, Jacqueline Dolata², Maria Figueroa², Anne Huml², Julie Pencak², Martha Sajatovic⁴

Affiliations

¹ Center for Health Care Research & Policy, The MetroHealth System, Case Western Reserve University, 2500 MetroHealth Drive, Cleveland, OH 44109, United States. Electronic address: dgunzler@metrohealth.org.
² Center for Reducing Health Disparities, The MetroHealth System, Case Western Reserve University, Cleveland, OH, United States.
³ Case Western Reserve University, Cleveland, OH, United States.
⁴ Department of Psychiatry and of Neurology, Case Western Reserve University School of Medicine, Neurological and Behavioral Outcomes Center, University Hospitals Cleveland Medical Center, Cleveland, Ohio, United States.

Abstract

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.

Keywords: Factor analysis; Major depression; Multiple indicator multiple cause modeling; National health and nutrition examination survey; Patient health questionnaire-9; Research domain criteria.

Abstract

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