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Greenberg JK, Frumkin M, Xu Z, Zhang J, Javeed S, Zhang JK, Benedict B, Botterbush K, Yakdan S, Molina CA, Pennicooke BH, Hafez D, Ogunlade JI, Pallotta N, Gupta MC, Buchowski JM, Neuman B, Steinmetz M, Ghogawala Z, Kelly MP, Goodin BR, Piccirillo JF, Rodebaugh TL, Lu C, Ray WZ. Preoperative Mobile Health Data Improve Predictions of Recovery From Lumbar Spine Surgery. Neurosurgery 2024; 95:617-626. [PMID: 38551340 DOI: 10.1227/neu.0000000000002911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 01/24/2024] [Indexed: 08/16/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Neurosurgeons and hospitals devote tremendous resources to improving recovery from lumbar spine surgery. Current efforts to predict surgical recovery rely on one-time patient report and health record information. However, longitudinal mobile health (mHealth) assessments integrating symptom dynamics from ecological momentary assessment (EMA) and wearable biometric data may capture important influences on recovery. Our objective was to evaluate whether a preoperative mHealth assessment integrating EMA with Fitbit monitoring improved predictions of spine surgery recovery. METHODS Patients age 21-85 years undergoing lumbar surgery for degenerative disease between 2021 and 2023 were recruited. For up to 3 weeks preoperatively, participants completed EMAs up to 5 times daily asking about momentary pain, disability, depression, and catastrophizing. At the same time, they were passively monitored using Fitbit trackers. Study outcomes were good/excellent recovery on the Quality of Recovery-15 (QOR-15) and a clinically important change in Patient-Reported Outcomes Measurement Information System Pain Interference 1 month postoperatively. After feature engineering, several machine learning prediction models were tested. Prediction performance was measured using the c-statistic. RESULTS A total of 133 participants were included, with a median (IQR) age of 62 (53, 68) years, and 56% were female. The median (IQR) number of preoperative EMAs completed was 78 (61, 95), and the median (IQR) number of days with usable Fitbit data was 17 (12, 21). 63 patients (48%) achieved a clinically meaningful improvement in Patient-Reported Outcomes Measurement Information System pain interference. Compared with traditional evaluations alone, mHealth evaluations led to a 34% improvement in predictions for pain interference (c = 0.82 vs c = 0.61). 49 patients (40%) had a good or excellent recovery based on the QOR-15. Including preoperative mHealth data led to a 30% improvement in predictions of QOR-15 (c = 0.70 vs c = 0.54). CONCLUSION Multimodal mHealth evaluations improve predictions of lumbar surgery outcomes. These methods may be useful for informing patient selection and perioperative recovery strategies.
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Affiliation(s)
- Jacob K Greenberg
- Department of Neurological Surgery, Washington University, St. Louis , Missouri , USA
| | - Madelyn Frumkin
- Department of Psychology and Brain Sciences, Washington University, St. Louis , Missouri , USA
| | - Ziqi Xu
- Department of Computer Science & Engineering, Washington University in St. Louis, St. Louis , Missouri , USA
| | - Jingwen Zhang
- Department of Computer Science & Engineering, Washington University in St. Louis, St. Louis , Missouri , USA
| | - Saad Javeed
- Department of Neurological Surgery, Washington University, St. Louis , Missouri , USA
| | - Justin K Zhang
- Department of Neurological Surgery, Washington University, St. Louis , Missouri , USA
- Department of Neurosurgery, University of Utah, Salt Lake City , Utah , USA
| | - Braeden Benedict
- Department of Neurological Surgery, Washington University, St. Louis , Missouri , USA
| | - Kathleen Botterbush
- Department of Neurological Surgery, Washington University, St. Louis , Missouri , USA
| | - Salim Yakdan
- Department of Neurological Surgery, Washington University, St. Louis , Missouri , USA
| | - Camilo A Molina
- Department of Neurological Surgery, Washington University, St. Louis , Missouri , USA
| | - Brenton H Pennicooke
- Department of Neurological Surgery, Washington University, St. Louis , Missouri , USA
| | - Daniel Hafez
- Department of Neurological Surgery, Washington University, St. Louis , Missouri , USA
| | - John I Ogunlade
- Department of Neurological Surgery, Washington University, St. Louis , Missouri , USA
| | - Nicholas Pallotta
- Department of Orthopedic Surgery, Washington University, St. Louis , Missouri , USA
| | - Munish C Gupta
- Department of Orthopedic Surgery, Washington University, St. Louis , Missouri , USA
| | - Jacob M Buchowski
- Department of Orthopedic Surgery, Washington University, St. Louis , Missouri , USA
| | - Brian Neuman
- Department of Orthopedic Surgery, Washington University, St. Louis , Missouri , USA
| | - Michael Steinmetz
- Department of Neurosurgery, Center for Spine Health, Neurological Institute, Cleveland Clinic Foundation, Cleveland , Ohio , USA
| | - Zoher Ghogawala
- Department of Neurosurgery, Lahey Hospital and Medical Center, Burlington , Massachusetts , USA
| | - Michael P Kelly
- Department of Orthopedic Surgery, Washington University, St. Louis , Missouri , USA
| | - Burel R Goodin
- Department of Anesthesiology, Washington University, St. Louis , Missouri , USA
| | - Jay F Piccirillo
- Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine, St. Louis , Missouri , USA
| | - Thomas L Rodebaugh
- Department of Psychology and Brain Sciences, Washington University, St. Louis , Missouri , USA
| | - Chenyang Lu
- Department of Computer Science & Engineering, Washington University in St. Louis, St. Louis , Missouri , USA
| | - Wilson Z Ray
- Department of Neurological Surgery, Washington University, St. Louis , Missouri , USA
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Kang SJ, Leroux A, Guo W, Dey D, Strippoli MPF, Di J, Vaucher J, Marques-Vidal P, Vollenweider P, Preisig M, Merikangas KR, Zipunnikov V. Integrative Modeling of Accelerometry-Derived Sleep, Physical Activity, and Circadian Rhythm Domains With Current or Remitted Major Depression. JAMA Psychiatry 2024; 81:911-918. [PMID: 38865117 PMCID: PMC11170457 DOI: 10.1001/jamapsychiatry.2024.1321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 03/31/2024] [Indexed: 06/13/2024]
Abstract
Importance Accelerometry has been increasingly used as an objective index of sleep, physical activity, and circadian rhythms in people with mood disorders. However, most prior research has focused on sleep or physical activity alone without consideration of the strong within- and cross-domain intercorrelations; and few studies have distinguished between trait and state profiles of accelerometry domains in major depressive disorder (MDD). Objectives To identify joint and individual components of the domains derived from accelerometry, including sleep, physical activity, and circadian rhythmicity using the Joint and Individual Variation Explained method (JIVE), a novel multimodal integrative dimension-reduction technique; and to examine associations between joint and individual components with current and remitted MDD. Design, Setting, and Participants This cross-sectional study examined data from the second wave of a population cohort study from Lausanne, Switzerland. Participants included 2317 adults (1164 without MDD, 185 with current MDD, and 968 with remitted MDD) with accelerometry for at least 7 days. Statistical analysis was conducted from January 2021 to June 2023. Main Outcomes and Measures Features derived from accelerometry for 14 days; current and remitted MDD. Logistic regression adjusted for age, sex, body mass index, and anxiety and substance use disorders. Results Among 2317 adults included in the study, 1261 (54.42%) were female, and mean (SD) age was 61.79 (9.97) years. JIVE reduced 28 accelerometry features to 3 joint and 6 individual components (1 sleep, 2 physical activity, 3 circadian rhythms). Joint components explained 58.5%, 79.5%, 54.5% of the total variation in sleep, physical activity, and circadian rhythm domains, respectively. Both current and remitted depression were associated with the first 2 joint components that were distinguished by the salience of high-intensity physical activity and amplitude of circadian rhythm and timing of both sleep and physical activity, respectively. MDD had significantly weaker circadian rhythmicity. Conclusions and Relevance Application of a novel multimodal dimension-reduction technique demonstrates the importance of joint influences of physical activity, circadian rhythms, and timing of both sleep and physical activity with MDD; dampened circadian rhythmicity may constitute a trait marker for MDD. This work illustrates the value of accelerometry as a potential biomarker for subtypes of depression and highlights the importance of consideration of the full 24-hour sleep-wake cycle in future studies.
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Affiliation(s)
- Sun Jung Kang
- Genetic Epidemiology Research Branch, Intramural Research Program, National Institute of Mental Health, Bethesda, Maryland
| | - Andrew Leroux
- Department of Biostatistics and Informatics, University of Colorado, Anschutz Medical Campus, Aurora
| | - Wei Guo
- Genetic Epidemiology Research Branch, Intramural Research Program, National Institute of Mental Health, Bethesda, Maryland
| | - Debangan Dey
- Genetic Epidemiology Research Branch, Intramural Research Program, National Institute of Mental Health, Bethesda, Maryland
| | - Marie-Pierre F. Strippoli
- Psychiatric Epidemiology and Psychopathology Research Center, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland
| | - Junrui Di
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Julien Vaucher
- Service of Internal Medicine, Department of Medicine and Specialties, Fribourg Hospital and University of Fribourg, Switzerland
- Service of Internal Medicine, Department of Medicine, Lausanne University Hospital and University of Lausanne, Switzerland
| | - Pedro Marques-Vidal
- Service of Internal Medicine, Department of Medicine, Lausanne University Hospital and University of Lausanne, Switzerland
| | - Peter Vollenweider
- Service of Internal Medicine, Department of Medicine, Lausanne University Hospital and University of Lausanne, Switzerland
| | - Martin Preisig
- Psychiatric Epidemiology and Psychopathology Research Center, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland
| | - Kathleen R. Merikangas
- Genetic Epidemiology Research Branch, Intramural Research Program, National Institute of Mental Health, Bethesda, Maryland
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Vadim Zipunnikov
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
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Druiven SJM, Hovenkamp-Hermelink JHM, Kamphuis J, Haarman BCM, Meesters Y, Riese H, Schoevers RA. Circadian markers as a predictor of response in the treatment of depression-A systematic review. Psychiatry Res 2024; 338:115976. [PMID: 38830322 DOI: 10.1016/j.psychres.2024.115976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 04/29/2024] [Accepted: 05/23/2024] [Indexed: 06/05/2024]
Abstract
Despite many available treatment options for depression, response rates remain suboptimal. To improve outcome, circadian markers may be suitable as markers of treatment response. This systematic review provides an overview of circadian markers that have been studied as predictors of response in treatment of depression. A search was performed (EMBASE, PUBMED, PSYCHINFO) for research studies or articles, randomized controlled trials and case report/series with no time boundaries on March 2, 2024 (PROSPERO: CRD42021252333). Other criteria were; an antidepressant treatment as intervention, treatment response measured by depression symptom severity and/or occurrence of a clinical diagnosis of depression and assessment of a circadian marker at baseline. 44 articles, encompassing 8,772 participants were included in the analysis. Although additional research is needed with less variation in types of markers and treatments to provide definitive recommendations, circadian markers, especially diurnal mood variation and chronotype, show potential to implement as response markers in the clinic.
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Affiliation(s)
- S J M Druiven
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, the Netherlands.
| | - J H M Hovenkamp-Hermelink
- Department of Practice-Oriented Scientific Research (PWO), Alliade Care Group, Heerenveen, the Netherlands
| | - J Kamphuis
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, the Netherlands
| | - B C M Haarman
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, the Netherlands
| | - Y Meesters
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, the Netherlands
| | - H Riese
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, the Netherlands
| | - R A Schoevers
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, the Netherlands
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Zhang Y, Folarin AA, Sun S, Cummins N, Ranjan Y, Rashid Z, Stewart C, Conde P, Sankesara H, Laiou P, Matcham F, White KM, Oetzmann C, Lamers F, Siddi S, Simblett S, Vairavan S, Myin-Germeys I, Mohr DC, Wykes T, Haro JM, Annas P, Penninx BW, Narayan VA, Hotopf M, Dobson RJ. Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing: Retrospective Analysis. J Med Internet Res 2024; 26:e55302. [PMID: 38941600 PMCID: PMC11245656 DOI: 10.2196/55302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/22/2024] [Accepted: 03/29/2024] [Indexed: 06/30/2024] Open
Abstract
BACKGROUND Previous mobile health (mHealth) studies have revealed significant links between depression and circadian rhythm features measured via wearables. However, the comprehensive impact of seasonal variations was not fully considered in these studies, potentially biasing interpretations in real-world settings. OBJECTIVE This study aims to explore the associations between depression severity and wearable-measured circadian rhythms while accounting for seasonal impacts. METHODS Data were sourced from a large longitudinal mHealth study, wherein participants' depression severity was assessed biweekly using the 8-item Patient Health Questionnaire (PHQ-8), and participants' behaviors, including sleep, step count, and heart rate (HR), were tracked via Fitbit devices for up to 2 years. We extracted 12 circadian rhythm features from the 14-day Fitbit data preceding each PHQ-8 assessment, including cosinor variables, such as HR peak timing (HR acrophase), and nonparametric features, such as the onset of the most active continuous 10-hour period (M10 onset). To investigate the association between depression severity and circadian rhythms while also assessing the seasonal impacts, we used three nested linear mixed-effects models for each circadian rhythm feature: (1) incorporating the PHQ-8 score as an independent variable, (2) adding seasonality, and (3) adding an interaction term between season and the PHQ-8 score. RESULTS Analyzing 10,018 PHQ-8 records alongside Fitbit data from 543 participants (n=414, 76.2% female; median age 48, IQR 32-58 years), we found that after adjusting for seasonal effects, higher PHQ-8 scores were associated with reduced daily steps (β=-93.61, P<.001), increased sleep variability (β=0.96, P<.001), and delayed circadian rhythms (ie, sleep onset: β=0.55, P=.001; sleep offset: β=1.12, P<.001; M10 onset: β=0.73, P=.003; HR acrophase: β=0.71, P=.001). Notably, the negative association with daily steps was more pronounced in spring (β of PHQ-8 × spring = -31.51, P=.002) and summer (β of PHQ-8 × summer = -42.61, P<.001) compared with winter. Additionally, the significant correlation with delayed M10 onset was observed solely in summer (β of PHQ-8 × summer = 1.06, P=.008). Moreover, compared with winter, participants experienced a shorter sleep duration by 16.6 minutes, an increase in daily steps by 394.5, a delay in M10 onset by 20.5 minutes, and a delay in HR peak time by 67.9 minutes during summer. CONCLUSIONS Our findings highlight significant seasonal influences on human circadian rhythms and their associations with depression, underscoring the importance of considering seasonal variations in mHealth research for real-world applications. This study also indicates the potential of wearable-measured circadian rhythms as digital biomarkers for depression.
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Affiliation(s)
- Yuezhou Zhang
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Amos A Folarin
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
- NIHR Biomedical Research Centre at University College London Hospitals, NHS Foundation Trust, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
| | - Shaoxiong Sun
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Nicholas Cummins
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Yatharth Ranjan
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Zulqarnain Rashid
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Callum Stewart
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Pauline Conde
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Heet Sankesara
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Petroula Laiou
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Faith Matcham
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- School of Psychology, University of Sussex, Falmer, United Kingdom
| | - Katie M White
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Carolin Oetzmann
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Femke Lamers
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
- Mental Health Program, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | - Sara Siddi
- Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain
- Teaching Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain
- Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain
| | - Sara Simblett
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | | | - Inez Myin-Germeys
- Department of Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium
| | - David C Mohr
- Center for Behavioral Intervention Technologies, Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Til Wykes
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Josep Maria Haro
- Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain
- Teaching Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain
- Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain
| | | | - Brenda Wjh Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
- Mental Health Program, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | - Vaibhav A Narayan
- Janssen Research and Development LLC, Titusville, NJ, United States
- Davos Alzheimer's Collaborative, Geneva, Switzerland
| | - Matthew Hotopf
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Richard Jb Dobson
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
- NIHR Biomedical Research Centre at University College London Hospitals, NHS Foundation Trust, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
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Stolfi F, Abreu H, Sinella R, Nembrini S, Centonze S, Landra V, Brasso C, Cappellano G, Rocca P, Chiocchetti A. Omics approaches open new horizons in major depressive disorder: from biomarkers to precision medicine. Front Psychiatry 2024; 15:1422939. [PMID: 38938457 PMCID: PMC11210496 DOI: 10.3389/fpsyt.2024.1422939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 05/28/2024] [Indexed: 06/29/2024] Open
Abstract
Major depressive disorder (MDD) is a recurrent episodic mood disorder that represents the third leading cause of disability worldwide. In MDD, several factors can simultaneously contribute to its development, which complicates its diagnosis. According to practical guidelines, antidepressants are the first-line treatment for moderate to severe major depressive episodes. Traditional treatment strategies often follow a one-size-fits-all approach, resulting in suboptimal outcomes for many patients who fail to experience a response or recovery and develop the so-called "therapy-resistant depression". The high biological and clinical inter-variability within patients and the lack of robust biomarkers hinder the finding of specific therapeutic targets, contributing to the high treatment failure rates. In this frame, precision medicine, a paradigm that tailors medical interventions to individual characteristics, would help allocate the most adequate and effective treatment for each patient while minimizing its side effects. In particular, multi-omic studies may unveil the intricate interplays between genetic predispositions and exposure to environmental factors through the study of epigenomics, transcriptomics, proteomics, metabolomics, gut microbiomics, and immunomics. The integration of the flow of multi-omic information into molecular pathways may produce better outcomes than the current psychopharmacological approach, which targets singular molecular factors mainly related to the monoamine systems, disregarding the complex network of our organism. The concept of system biomedicine involves the integration and analysis of enormous datasets generated with different technologies, creating a "patient fingerprint", which defines the underlying biological mechanisms of every patient. This review, centered on precision medicine, explores the integration of multi-omic approaches as clinical tools for prediction in MDD at a single-patient level. It investigates how combining the existing technologies used for diagnostic, stratification, prognostic, and treatment-response biomarkers discovery with artificial intelligence can improve the assessment and treatment of MDD.
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Affiliation(s)
- Fabiola Stolfi
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
| | - Hugo Abreu
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
| | - Riccardo Sinella
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
| | - Sara Nembrini
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
| | - Sara Centonze
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
| | - Virginia Landra
- Department of Neuroscience “Rita Levi Montalcini”, University of Turin, Turin, Italy
| | - Claudio Brasso
- Department of Neuroscience “Rita Levi Montalcini”, University of Turin, Turin, Italy
| | - Giuseppe Cappellano
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
| | - Paola Rocca
- Department of Neuroscience “Rita Levi Montalcini”, University of Turin, Turin, Italy
| | - Annalisa Chiocchetti
- Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), Università del Piemonte Orientale, Novara, Italy
- Center for Translational Research on Autoimmune and Allergic Disease (CAAD), Università del Piemonte Orientale, Novara, Italy
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Lin K, Sunko D, Wang J, Yang J, Parsey RV, DeLorenzo C. Investigating the relationship between hippocampus/dentate gyrus volume and hypothalamus metabolism in participants with major depressive disorder. Sci Rep 2024; 14:10622. [PMID: 38724691 PMCID: PMC11082185 DOI: 10.1038/s41598-024-61519-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Accepted: 05/07/2024] [Indexed: 05/12/2024] Open
Abstract
Reduced hippocampal volume occurs in major depressive disorder (MDD), potentially due to elevated glucocorticoids from an overactivated hypothalamus-pituitary-adrenal (HPA) axis. To examine this in humans, hippocampal volume and hypothalamus (HPA axis) metabolism was quantified in participants with MDD before and after antidepressant treatment. 65 participants (n = 24 males, n = 41 females) with MDD were treated in a double-blind, randomized clinical trial of escitalopram. Participants received simultaneous positron emission tomography (PET)/magnetic resonance imaging (MRI) before and after treatment. Linear mixed models examined the relationship between hippocampus/dentate gyrus volume and hypothalamus metabolism. Chi-squared tests and multivariable logistic regression examined the association between hippocampus/dentate gyrus volume change direction and hypothalamus activity change direction with treatment. Multiple linear regression compared these changes between remitter and non-remitter groups. Covariates included age, sex, and treatment type. No significant linear association was found between hippocampus/dentate gyrus volume and hypothalamus metabolism. 62% (38 of 61) of participants experienced a decrease in hypothalamus metabolism, 43% (27 of 63) of participants demonstrated an increase in hippocampus size (51% [32 of 63] for the dentate gyrus) following treatment. No significant association was found between change in hypothalamus activity and change in hippocampus/dentate gyrus volume, and this association did not vary by sex, medication, or remission status. As this multimodal study, in a cohort of participants on standardized treatment, did not find an association between hypothalamus metabolism and hippocampal volume, it supports a more complex pathway between hippocampus neurogenesis and hypothalamus metabolism changes in response to treatment.
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Affiliation(s)
| | | | - Junying Wang
- Department of Applied Mathematics and Statistics, Stony Brook University, New York, NY, USA
| | - Jie Yang
- Department of Family, Population & Preventive Medicine, Stony Brook University, New York, NY, USA
| | - Ramin V Parsey
- Department of Psychiatry and Behavioral Health, Stony Brook University, Stony Brook, NY, USA
| | - Christine DeLorenzo
- Department of Psychiatry and Behavioral Health, Stony Brook University, Stony Brook, NY, USA.
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA.
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7
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Wang J, Wu DD, DeLorenzo C, Yang J. Examining factors related to low performance of predicting remission in participants with major depressive disorder using neuroimaging data and other clinical features. PLoS One 2024; 19:e0299625. [PMID: 38547128 PMCID: PMC10977765 DOI: 10.1371/journal.pone.0299625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 02/13/2024] [Indexed: 04/02/2024] Open
Abstract
Major depressive disorder (MDD), a prevalent mental health issue, affects more than 8% of the US population, and almost 17% in the young group of 18-25 years old. Since Covid-19, its prevalence has become even more significant. However, the remission (being free of depression) rates of first-line antidepressant treatments on MDD are only about 30%. To improve treatment outcomes, researchers have built various predictive models for treatment responses and yet none of them have been adopted in clinical use. One reason is that most predictive models are based on data from subjective questionnaires, which are less reliable. Neuroimaging data are promising objective prognostic factors, but they are expensive to obtain and hence predictive models using neuroimaging data are limited and such studies were usually in small scale (N<100). In this paper, we proposed an advanced machine learning (ML) pipeline for small training dataset with large number of features. We implemented multiple imputation for missing data and repeated K-fold cross validation (CV) to robustly estimate predictive performances. Different feature selection methods and stacking methods using 6 general ML models including random forest, gradient boosting decision tree, XGBoost, penalized logistic regression, support vector machine (SVM), and neural network were examined to evaluate the model performances. All predictive models were compared using model performance metrics such as accuracy, balanced accuracy, area under ROC curve (AUC), sensitivity and specificity. Our proposed ML pipeline was applied to a training dataset and obtained an accuracy and AUC above 0.80. But such high performance failed while applying our ML pipeline using an external validation dataset from the EMBARC study which is a multi-center study. We further examined the possible reasons especially the site heterogeneity issue.
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Affiliation(s)
- Junying Wang
- Department of Applied Mathematics and Statistics, Stony Brook University, New York, New York, United states of America
| | - David D. Wu
- School of Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Christine DeLorenzo
- Department of Psychiatry and Behavioral Health, Stony Brook University, Stony Brook, New York, United States of America
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York, United States of America
| | - Jie Yang
- Department of Family, Population & Preventive Medicine, Stony Brook University, Stony Brook, New York, United States of America
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Donnelly BM, Hsu DT, Gardus J, Wang J, Yang J, Parsey RV, DeLorenzo C. Orbitofrontal and striatal metabolism, volume, thickness and structural connectivity in relation to social anhedonia in depression: A multimodal study. Neuroimage Clin 2023; 41:103553. [PMID: 38134743 PMCID: PMC10777107 DOI: 10.1016/j.nicl.2023.103553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 11/10/2023] [Accepted: 12/12/2023] [Indexed: 12/24/2023]
Abstract
BACKGROUND Social anhedonia is common within major depressive disorder (MDD) and associated with worse treatment outcomes. The orbitofrontal cortex (OFC) is implicated in both reward (medial OFC) and punishment (lateral OFC) in social decision making. Therefore, to understand the biology of social anhedonia in MDD, medial/lateral OFC metabolism, volume, and thickness, as well as structural connectivity to the striatum, amygdala, and ventral tegmental area/nucleus accumbens were examined. A positive relationship between social anhedonia and these neurobiological outcomes in the lateral OFC was hypothesized, whereas an inverse relationship was hypothesized for the medial OFC. The association between treatment-induced changes in OFC neurobiology and depression improvement were also examined. METHODS 85 medication-free participants diagnosed with MDD were assessed with Wisconsin Schizotypy Scales to assess social anhedonia and received pretreatment simultaneous fluorodeoxyglucose positron emission tomography (FDG-PET) and magnetic resonance imaging (MRI), including structural and diffusion. Participants were then treated in an 8-week randomized placebo-controlled double-blind course of escitalopram. PET/MRI were repeated following treatment. Metabolic rate of glucose uptake was quantified from dynamic FDG-PET frames using Patlak graphical analysis. Structure (volume and cortical thickness) was quantified from structural MRI using Freesurfer. To assess structural connectivity, probabilistic tractography was performed on diffusion MRI and average FA was calculated within the derived tracts. Linear mixed models with Bonferroni correction were used to examine the relationships between variables. RESULTS A significantly negative linear relationship between pretreatment social anhedonia score and structural connectivity between the medial OFC and the amygdala (estimated coefficient: -0.006, 95 % CI: -0.0108 - -0.0012, p-value = 0.0154) was observed. However, this finding would not survive multiple comparisons correction. No strong evidence existed to show a significant linear relationship between pretreatment social anhedonia score and metabolism, volume, thickness, or structural connectivity to any of the regions examined. There was also no strong evidence to suggest significant linear relationships between improvement in depression and percent change in these variables. CONCLUSIONS Based on these multimodal findings, the OFC likely does not underlie social anhedonia in isolation and therefore should not be the sole target of treatment for social anhedonia. This is consistent with previous reports that other areas of the brain such as the amygdala and the striatum are highly involved in this behavior. Relatedly, amygdala-medial OFC structural connectivity could be a future target. The results of this study are crucial as, to our knowledge, they are the first to relate structure/function of the OFC with social anhedonia severity in MDD. Future work may need to involve a whole brain approach in order to develop therapeutics for social anhedonia.
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Affiliation(s)
| | - David T Hsu
- Department of Psychiatry and Behavioral Health, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA
| | - John Gardus
- Department of Psychiatry and Behavioral Health, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA
| | - Junying Wang
- Department of Applied Mathematics and Statistics, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA
| | - Jie Yang
- Department of Family, Population & Preventive Medicine, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA
| | - Ramin V Parsey
- Department of Psychiatry and Behavioral Health, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA
| | - Christine DeLorenzo
- Department of Psychiatry and Behavioral Health, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA; Department of Biomedical Engineering, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA.
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