Brett A Clementz1, Rebekah L Trotti1, Godfrey D Pearlson2, Matcheri S Keshavan3, Elliot S Gershon4, Sarah K Keedy4, Elena I Ivleva5, Jennifer E McDowell1, Carol A Tamminga6. 1. Departments of Psychology and Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens, Georgia. 2. Olin Neuropsychiatry Research Center, Institute of Living at Hartford Hospital, Hartford, Connecticut; Department of Psychiatry, Yale University, New Haven, Connecticut. 3. Department of Psychiatry, Beth Israel Deaconess, Harvard Medical School, Boston, Massachusetts. 4. Department of Psychiatry, University of Chicago, Chicago, Illinois. 5. Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas. 6. Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas. Electronic address: Carol.tamminga@utsouthwestern.edu.
Abstract
BACKGROUND: Psychiatry aspires to the molecular understanding of its disorders and, with that knowledge, to precision medicine. Research supporting such goals in the dimension of psychosis has been compromised, in part, by using phenomenology alone to estimate disease entities. To this end, we are proponents of a deep phenotyping approach in psychosis, using computational strategies to discover the most informative phenotypic fingerprint as a promising strategy to uncover mechanisms in psychosis. METHODS: Doing this, the Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) has used biomarkers to identify distinct subtypes of psychosis with replicable biomarker characteristics. While we have presented these entities as relevant, their potential utility in clinical practice has not yet been demonstrated. RESULTS: Here we carried out an analysis of clinical features that characterize biotypes. We found that biotypes have unique and defining clinical characteristics that could be used as initial screens in the clinical and research settings. Differences in these clinical features appear to be consistent with biotype biomarker profiles, indicating a link between biological features and clinical presentation. Clinical features associated with biotypes differ from those associated with DSM diagnoses, indicating that biotypes and DSM syndromes are not redundant and are likely to yield different treatment predictions. We highlight 3 predictions based on biotype that are derived from individual biomarker features and cannot be obtained from DSM psychosis syndromes. CONCLUSIONS: In the future, biotypes may prove to be useful for targeting distinct molecular, circuit, cognitive, and psychosocial therapies for improved functional outcomes.
BACKGROUND: Psychiatry aspires to the molecular understanding of its disorders and, with that knowledge, to precision medicine. Research supporting such goals in the dimension of psychosis has been compromised, in part, by using phenomenology alone to estimate disease entities. To this end, we are proponents of a deep phenotyping approach in psychosis, using computational strategies to discover the most informative phenotypic fingerprint as a promising strategy to uncover mechanisms in psychosis. METHODS: Doing this, the Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) has used biomarkers to identify distinct subtypes of psychosis with replicable biomarker characteristics. While we have presented these entities as relevant, their potential utility in clinical practice has not yet been demonstrated. RESULTS: Here we carried out an analysis of clinical features that characterize biotypes. We found that biotypes have unique and defining clinical characteristics that could be used as initial screens in the clinical and research settings. Differences in these clinical features appear to be consistent with biotype biomarker profiles, indicating a link between biological features and clinical presentation. Clinical features associated with biotypes differ from those associated with DSM diagnoses, indicating that biotypes and DSM syndromes are not redundant and are likely to yield different treatment predictions. We highlight 3 predictions based on biotype that are derived from individual biomarker features and cannot be obtained from DSM psychosis syndromes. CONCLUSIONS: In the future, biotypes may prove to be useful for targeting distinct molecular, circuit, cognitive, and psychosocial therapies for improved functional outcomes.
Authors: Frederike Stein; Tina Meller; Katharina Brosch; Simon Schmitt; Kai Ringwald; Julia Katharina Pfarr; Susanne Meinert; Katharina Thiel; Hannah Lemke; Lena Waltemate; Dominik Grotegerd; Nils Opel; Andreas Jansen; Igor Nenadić; Udo Dannlowski; Axel Krug; Tilo Kircher Journal: Schizophr Bull Date: 2021-10-21 Impact factor: 9.306
Authors: Carol A Tamminga; Brett A Clementz; Godfrey Pearlson; Macheri Keshavan; Elliot S Gershon; Elena I Ivleva; Jennifer McDowell; Shashwath A Meda; Sarah Keedy; Vince D Calhoun; Paulo Lizano; Jeffrey R Bishop; Matthew Hudgens-Haney; Ney Alliey-Rodriguez; Huma Asif; Robert Gibbons Journal: Neuropsychopharmacology Date: 2020-09-26 Impact factor: 8.294
Authors: Brett A Clementz; David A Parker; Rebekah L Trotti; Jennifer E McDowell; Sarah K Keedy; Matcheri S Keshavan; Godfrey D Pearlson; Elliot S Gershon; Elena I Ivleva; Ling-Yu Huang; S Kristian Hill; John A Sweeney; Olivia Thomas; Matthew Hudgens-Haney; Robert D Gibbons; Carol A Tamminga Journal: Schizophr Bull Date: 2022-01-21 Impact factor: 9.306
Authors: Synthia Guimond; Feng Gu; Holly Shannon; Sinead Kelly; Luke Mike; Gabriel A Devenyi; M Mallar Chakravarty; John A Sweeney; Godfrey Pearlson; Brett A Clementz; Carol Tamminga; Matcheri Keshavan Journal: Schizophr Bull Date: 2021-10-21 Impact factor: 7.348