The potential of precision psychiatry: what is in reach?

Kambeitz-Ilankovic, Lana; Koutsouleris, Nikolaos; Upthegrove, Rachel

doi:10.1192/bjp.2022.23

Cited by 13 publications

(8 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Advocates for precision psychiatry routinely argue that the approach can be applied to common mental disorders including depression, anxiety and stress-related disorders (e.g., Fernandes et al, 2017 ; Williams and Hack, 2021 ). For example, a recent article suggests that “polygenic risk scores may be useful for prediction of vulnerability to depression and resilience under stress” ( Kambeitz-Ilankovic et al, 2022 ).…”

Section: The Ambit Of Precision Psychiatry and The Need For Multiple ...mentioning

confidence: 99%

Restoring the missing person to personalized medicine and precision psychiatry

et al. 2023

View full text Add to dashboard Cite

Precision psychiatry has emerged as part of the shift to personalized medicine and builds on frameworks such as the U.S. National Institute of Mental Health Research Domain Criteria (RDoC), multilevel biological “omics” data and, most recently, computational psychiatry. The shift is prompted by the realization that a one-size-fits all approach is inadequate to guide clinical care because people differ in ways that are not captured by broad diagnostic categories. One of the first steps in developing this personalized approach to treatment was the use of genetic markers to guide pharmacotherapeutics based on predictions of pharmacological response or non-response, and the potential risk of adverse drug reactions. Advances in technology have made a greater degree of specificity or precision potentially more attainable. To date, however, the search for precision has largely focused on biological parameters. Psychiatric disorders involve multi-level dynamics that require measures of phenomenological, psychological, behavioral, social structural, and cultural dimensions. This points to the need to develop more fine-grained analyses of experience, self-construal, illness narratives, interpersonal interactional dynamics, and social contexts and determinants of health. In this paper, we review the limitations of precision psychiatry arguing that it cannot reach its goal if it does not include core elements of the processes that give rise to psychopathological states, which include the agency and experience of the person. Drawing from contemporary systems biology, social epidemiology, developmental psychology, and cognitive science, we propose a cultural-ecosocial approach to integrating precision psychiatry with person-centered care.

show abstract

Section: The Ambit Of Precision Psychiatry and The Need For Multiple ...mentioning

confidence: 99%

Restoring the missing person to personalized medicine and precision psychiatry

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Importantly, persisting brain alterations might be predictive of future illness course and long-term outcome (Vall & Wade, 2015 ). The use of biologically objective measurements, such as brain structure, to stratify individuals might enable novel possibilities for personalized precision medicine including improved individualization of prognosis and treatment (Kambeitz-Ilankovic, Koutsouleris, & Upthegrove, 2022 ). A major limitation of previous sMRI studies in AN is the use of mass univariate analyses, which test each brain region separately for group differences, while psychiatric disorders are theorized to constitute brain network disorders (Fornito, Zalesky, & Breakspear, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Predicting long-term outcome in anorexia nervosa: a machine learning analysis of brain structure at different stages of weight recovery

Arold,

Bernardoni,

Geisler

et al. 2023

Psychol. Med.

View full text Add to dashboard Cite

Background Anorexia nervosa (AN) is characterized by sizable, widespread gray matter (GM) reductions in the acutely underweight state. However, evidence for persistent alterations after weight-restoration has been surprisingly scarce despite high relapse rates, frequent transitions to other psychiatric disorders, and generally unfavorable outcome. While most studies investigated brain regions separately (univariate analysis), psychiatric disorders can be conceptualized as brain network disorders characterized by multivariate alterations with only subtle local effects. We tested for persistent multivariate structural brain alterations in weight-restored individuals with a history of AN, investigated their putative biological substrate and relation with 1-year treatment outcome. Methods We trained machine learning models on regional GM measures to classify healthy controls (HC) (N = 289) from individuals at three stages of AN: underweight patients starting intensive treatment (N = 165, used as baseline), patients after partial weight-restoration (N = 115), and former patients after stable and full weight-restoration (N = 89). Alterations after weight-restoration were related to treatment outcome and characterized both anatomically and functionally. Results Patients could be classified from HC when underweight (ROC-AUC = 0.90) but also after partial weight-restoration (ROC-AUC = 0.64). Alterations after partial weight-restoration were more pronounced in patients with worse outcome and were not detected in long-term weight-recovered individuals, i.e. those with favorable outcome. These alterations were more pronounced in regions with greater functional connectivity, not merely explained by body mass index, and even increases in cortical thickness were observed (insula, lateral orbitofrontal, temporal pole). Conclusions Analyzing persistent multivariate brain structural alterations after weight-restoration might help to develop personalized interventions after discharge from inpatient treatment.

show abstract

“…The prospect of this strategy is that patients instead can be stratified based on objective and replicable psychosocial, biochemical and/ or neurobiological characteristics. Interventions would ideally be tailored to these individual profiles and thereby maximise clinical response [12]. Furthermore, precision medicine enables the optimisation of resource allocation within patient populations, e.g., by efficient allocation of relapse prevention and interventions to high-risk patient groups.…”

Section: Introductionmentioning

confidence: 99%

Deep phenotyping towards precision psychiatry of first-episode depression — the Brain Drugs-Depression cohort

et al. 2023

View full text Add to dashboard Cite

Background Major Depressive Disorder (MDD) is a heterogenous brain disorder, with potentially multiple psychosocial and biological disease mechanisms. This is also a plausible explanation for why patients do not respond equally well to treatment with first- or second-line antidepressants, i.e., one-third to one-half of patients do not remit in response to first- or second-line treatment. To map MDD heterogeneity and markers of treatment response to enable a precision medicine approach, we will acquire several possible predictive markers across several domains, e.g., psychosocial, biochemical, and neuroimaging. Methods All patients are examined before receiving a standardised treatment package for adults aged 18–65 with first-episode depression in six public outpatient clinics in the Capital Region of Denmark. From this population, we will recruit a cohort of 800 patients for whom we will acquire clinical, cognitive, psychometric, and biological data. A subgroup (subcohort I, n = 600) will additionally provide neuroimaging data, i.e., Magnetic Resonance Imaging, and Electroencephalogram, and a subgroup of patients from subcohort I unmedicated at inclusion (subcohort II, n = 60) will also undergo a brain Positron Emission Tomography with the [11C]-UCB-J tracer binding to the presynaptic glycoprotein-SV2A. Subcohort allocation is based on eligibility and willingness to participate. The treatment package typically lasts six months. Depression severity is assessed with the Quick Inventory of Depressive Symptomatology (QIDS) at baseline, and 6, 12 and 18 months after treatment initiation. The primary outcome is remission (QIDS ≤ 5) and clinical improvement (≥ 50% reduction in QIDS) after 6 months. Secondary endpoints include remission at 12 and 18 months and %-change in QIDS, 10-item Symptom Checklist, 5-item WHO Well-Being Index, and modified Disability Scale from baseline through follow-up. We also assess psychotherapy and medication side-effects. We will use machine learning to determine a combination of characteristics that best predict treatment outcomes and statistical models to investigate the association between individual measures and clinical outcomes. We will assess associations between patient characteristics, treatment choices, and clinical outcomes using path analysis, enabling us to estimate the effect of treatment choices and timing on the clinical outcome. Discussion The BrainDrugs-Depression study is a real-world deep-phenotyping clinical cohort study of first-episode MDD patients. Trial Registration Registered at clinicaltrials.gov November 15th, 2022 (NCT05616559).

show abstract

The potential of precision psychiatry: what is in reach?

Cited by 13 publications

References 44 publications

Restoring the missing person to personalized medicine and precision psychiatry

Restoring the missing person to personalized medicine and precision psychiatry

Predicting long-term outcome in anorexia nervosa: a machine learning analysis of brain structure at different stages of weight recovery

Deep phenotyping towards precision psychiatry of first-episode depression — the Brain Drugs-Depression cohort

Contact Info

Product

Resources

About