Functional phenotypes in schizophrenia spectrum disorders: defining the constructs and identifying biopsychosocial correlates using data-driven methods.

Sunny X Tang, Katrin H��nsel, Lindsay D Oliver, Erin W Dickie, Colin Hawco, Majnu John, Aristotle Voineskos, James M Gold, Robert W Buchanan, Anil K Malhotra
Author Information
  1. Sunny X Tang: Division of Psychiatry Research, Feinstein Institutes for Medical Research, Northwell Health, New Hyde Park, NY, USA. Stang3@northwell.edu. ORCID
  2. Katrin H��nsel: Division of Psychiatry Research, Feinstein Institutes for Medical Research, Northwell Health, New Hyde Park, NY, USA.
  3. Lindsay D Oliver: Campbell Family Mental Health Research Institute, The Centre for Addiction and Mental Health, Toronto, ON, Canada. ORCID
  4. Erin W Dickie: Campbell Family Mental Health Research Institute, The Centre for Addiction and Mental Health, Toronto, ON, Canada. ORCID
  5. Colin Hawco: Campbell Family Mental Health Research Institute, The Centre for Addiction and Mental Health, Toronto, ON, Canada.
  6. Majnu John: Division of Psychiatry Research, Feinstein Institutes for Medical Research, Northwell Health, New Hyde Park, NY, USA.
  7. Aristotle Voineskos: Campbell Family Mental Health Research Institute, The Centre for Addiction and Mental Health, Toronto, ON, Canada. ORCID
  8. James M Gold: Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA.
  9. Robert W Buchanan: Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA.
  10. Anil K Malhotra: Division of Psychiatry Research, Feinstein Institutes for Medical Research, Northwell Health, New Hyde Park, NY, USA.

Abstract

Functional impairments contribute to poor quality of life in schizophrenia spectrum disorders (SSD). We sought to (Objective I) define the main functional phenotypes in SSD, then (Objective II) identify key biopsychosocial correlates, emphasizing interpretable data-driven methods. Objective I was tested on independent samples: Dataset I (N���=���282) and Dataset II (N���=���317), with SSD participants who underwent assessment of multiple functioning areas. Participants were clustered based on functioning. Objective II was evaluated in Dataset I by identifying key features for classifying functional phenotype clusters from among 65 sociodemographic, psychological, clinical, cognitive, and brain volume measures. Findings were replicated across latent discriminant analyses (LDA) and one-vs.-rest binomial regularized regressions to identify key predictors. We identified three clusters of participants in each dataset, demonstrating replicable functional phenotypes: Cluster 1-poor functioning across domains; Cluster 2-impaired Role Functioning, but partially preserved Independent and Social Functioning; Cluster 3-good functioning across domains. Key correlates were Avolition, anhedonia, left hippocampal volume, and measures of emotional intelligence and subjective social experience. Avolition appeared more closely tied to role functioning, and anhedonia to independent and social functioning. Thus, we found three replicable functional phenotypes with evidence that recovery may not be uniform across domains. Avolition and anhedonia were both critical but played different roles for different functional domains. It may be important to identify critical functional areas for individual patients and target interventions accordingly.

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Grants

  1. R01 MH102313/NIMH NIH HHS
  2. R01 MH102324/NIMH NIH HHS
  3. R01MH102324/U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
  4. R01 MH102318/NIMH NIH HHS
  5. R01MH102318/U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
  6. K23 MH130750-01/U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
  7. K23 MH130750/NIMH NIH HHS

Word Cloud

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