Diagnostic prediction model development using data from dried blood spot proteomics and a digital mental health assessment to identify major depressive disorder among individuals presenting with low mood

[1]  J. Cooper,et al.  A Combined Digital and Biomarker Diagnostic Aid for Mood Disorders (the Delta Trial): Protocol for an Observational Study , 2020, JMIR research protocols.

[2]  B. Penninx,et al.  Integrating proteomic, sociodemographic and clinical data to predict future depression diagnosis in subthreshold symptomatic individuals , 2019, Translational Psychiatry.

[3]  O. Trifonova,et al.  Evaluation of Dried Blood Spot Sampling for Clinical Metabolomics: Effects of Different Papers and Sample Storage Stability , 2019, Metabolites.

[4]  M. M. Ibrahim,et al.  Low levels of serum complement factor H is associated with increasing progression of bronchiectasis , 2019, Biomedical Research and Therapy.

[5]  Jason D. Cooper,et al.  Multimodel inference for biomarker development: an application to schizophrenia , 2019, Translational Psychiatry.

[6]  S. Deppen,et al.  Developing prediction models for clinical use using logistic regression: an overview. , 2019, Journal of thoracic disease.

[7]  S. Lim,et al.  Association of circulating proinflammatory marker, leucine‐rich‐α2‐glycoprotein (LRG1), following metabolic/bariatric surgery , 2018, Diabetes/metabolism research and reviews.

[8]  D. Steffens,et al.  Increased plasma complement factor H is associated with geriatric depression , 2018, International Psychogeriatrics.

[9]  D. McAlpine,et al.  The Meaning and Predictive Value of Self-rated Mental Health among Persons with a Mental Health Problem , 2018, Journal of health and social behavior.

[10]  L. Aarø,et al.  Measuring mental well-being in Norway: validation of the Warwick-Edinburgh Mental Well-being Scale (WEMWBS) , 2017, BMC Psychiatry.

[11]  A. Young,et al.  Biomarkers for depression: recent insights, current challenges and future prospects , 2017, Neuropsychiatric disease and treatment.

[12]  J. Cooper,et al.  Towards reproducible MRM based biomarker discovery using dried blood spots , 2017, Scientific Reports.

[13]  A. Schene,et al.  Dried Blood Spot sampling in psychiatry: Perspectives for improving therapeutic drug monitoring , 2017, European Neuropsychopharmacology.

[14]  Andrew C. Parnell,et al.  Bayesian methods for proteomic biomarker development , 2015 .

[15]  B. Penninx,et al.  Identification of an Immune-Neuroendocrine Biomarker Panel for Detection of Depression: A Joint Effects Statistical Approach , 2015, Neuroendocrinology.

[16]  Yi Yang,et al.  A fast unified algorithm for solving group-lasso penalize learning problems , 2014, Statistics and Computing.

[17]  Gareth Ambler,et al.  How to develop a more accurate risk prediction model when there are few events , 2015, BMJ : British Medical Journal.

[18]  Y. Nam,et al.  Interactions between the apolipoprotein E ε4 allele status and adverse childhood experiences on depressive symptoms in older adults , 2015, European journal of psychotraumatology.

[19]  María Martín,et al.  UniProt: A hub for protein information , 2015 .

[20]  The Uniprot Consortium,et al.  UniProt: a hub for protein information , 2014, Nucleic Acids Res..

[21]  P. Monagle,et al.  The utility of dried blood spots for proteomic studies: Looking forward to looking back , 2014, Proteomics. Clinical applications.

[22]  P. Guest,et al.  Applications of blood-based protein biomarker strategies in the study of psychiatric disorders , 2014, Progress in Neurobiology.

[23]  J. Cooper,et al.  Proteomic changes in serum of first onset, antidepressant drug-naïve major depression patients. , 2014, The international journal of neuropsychopharmacology.

[24]  E. Swart,et al.  Therapeutic Drug Monitoring by Dried Blood Spot: Progress to Date and Future Directions , 2014, Clinical Pharmacokinetics.

[25]  Seena Fazel,et al.  Risks of all‐cause and suicide mortality in mental disorders: a meta‐review , 2014, World psychiatry : official journal of the World Psychiatric Association.

[26]  Ljubomir J. Buturovic,et al.  Cross-validation pitfalls when selecting and assessing regression and classification models , 2014, Journal of Cheminformatics.

[27]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[28]  S. Fulton,et al.  Metabolic disturbances connecting obesity and depression , 2013, Front. Neurosci..

[29]  K. Moons,et al.  Diagnostic and prognostic prediction models , 2013, Journal of thrombosis and haemostasis : JTH.

[30]  F. J. Langheim,et al.  Psychiatrists’ Use of Electronic Communication and Social Media and a Proposed Framework for Future Guidelines , 2013, Journal of psychiatric practice.

[31]  R. Kessler,et al.  The epidemiology of depression across cultures. , 2013, Annual review of public health.

[32]  J A Bilello,et al.  Assessment of a multi-assay, serum-based biological diagnostic test for major depressive disorder: a Pilot and Replication Study , 2013, Molecular Psychiatry.

[33]  P. Demirev.  Dried blood spots: analysis and applications. , 2013, Analytical chemistry.

[34]  Christiane,et al.  World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. , 2013, JAMA.

[35]  J. Olesen,et al.  The economic cost of brain disorders in Europe , 2012, European journal of neurology.

[36]  K. Utsumi.  [On clinical diagnosis of depression]. , 2012, Seishin shinkeigaku zasshi = Psychiatria et neurologia Japonica.

[37]  Ronald S Duman,et al.  Functional Biomarkers of Depression: Diagnosis, Treatment, and Pathophysiology , 2011, Neuropsychopharmacology.

[38]  E. Walker,et al.  Diagnostic and Statistical Manual of Mental Disorders , 2013 .

[39]  I. Gasquet,et al.  Association between Body Mass Index and depression: the "fat and jolly" hypothesis for adolescents girls , 2011, BMC public health.

[40]  R. Kotov,et al.  Personality and depression: explanatory models and review of the evidence. , 2011, Annual review of clinical psychology.

[41]  Maria Blettner,et al.  Choosing statistical tests: part 12 of a series on evaluation of scientific publications. , 2010, Deutsches Arzteblatt international.

[42]  Brendan MacLean,et al.  Bioinformatics Applications Note Gene Expression Skyline: an Open Source Document Editor for Creating and Analyzing Targeted Proteomics Experiments , 2022 .

[43]  Theo Stijnen,et al.  Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies. , 2010, Archives of general psychiatry.

[44]  Edward T. Bullmore,et al.  Plasma Protein Biomarkers for Depression and Schizophrenia by Multi Analyte Profiling of Case-Control Collections , 2010, PloS one.

[45]  M. Symonds,et al.  A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion , 2010, Behavioral Ecology and Sociobiology.

[46]  Michelle Funk,et al.  Mental health and development: targeting people with mental health conditions as a vulnerable group. , 2010 .

[47]  Andrew H. Miller,et al.  Cytokines and Glucocorticoid Receptor Signaling , 2009, Annals of the New York Academy of Sciences.

[48]  E. Susman,et al.  Depression, cortisol reactivity, and obesity in childhood and adolescence. , 2009, The Journal of adolescent health : official publication of the Society for Adolescent Medicine.

[49]  A. Mitchell,et al.  Clinical diagnosis of depression in primary care: a meta-analysis , 2009, The Lancet.

[50]  David R. Williams,et al.  Cross-national associations between gender and mental disorders in the World Health Organization World Mental Health Surveys. , 2009, Archives of general psychiatry.

[51]  Yvonne Vergouwe,et al.  Prognosis and prognostic research: validating a prognostic model , 2009, BMJ : British Medical Journal.

[52]  P. Filzmoser,et al.  Repeated double cross validation , 2009 .

[53]  T. Dinan.  Inflammatory markers in depression , 2009, Current opinion in psychiatry.

[54]  J. Fleishman,et al.  Self-Rated Mental Health and Racial/Ethnic Disparities in Mental Health Service Use , 2008, Medical care.

[55]  J. Bremner,et al.  The neurobiology of retinoic acid in affective disorders , 2008, Progress in Neuro-Psychopharmacology and Biological Psychiatry.

[56]  L. Hiller,et al.  The Warwick-Edinburgh Mental Well-being Scale (WEMWBS): development and UK validation , 2007, Health and quality of life outcomes.

[57]  J. Fleishman,et al.  Global Self-Rated Mental Health: Associations With Other Mental Health Measures and With Role Functioning , 2007, Medical care.

[58]  R. Spitzer,et al.  The PHQ-9 , 2001, Journal of General Internal Medicine.

[59]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[60]  Fausto Mazzi,et al.  Concordance of the Composite International Diagnostic Interview Version 3.0 (CIDI 3.0) with standardized clinical assessments in the WHO World Mental Health Surveys , 2006, International journal of methods in psychiatric research.

[61]  M. Yuan,et al.  Model selection and estimation in regression with grouped variables , 2006 .

[62]  M. Olfson,et al.  Continuity of antidepressant treatment for adults with depression in the United States. , 2006, The American journal of psychiatry.

[63]  Andrew H. Miller,et al.  Cytokines sing the blues: inflammation and the pathogenesis of depression. , 2006, Trends in immunology.

[64]  D. Posada,et al.  Model selection and model averaging in phylogenetics: advantages of akaike information criterion and bayesian approaches over likelihood ratio tests. , 2004, Systematic biology.

[65]  L. Baxter,et al.  Longitudinal changes in cognition and behavior in asymptomatic carriers of the APOE e4 allele , 2004, Neurology.

[66]  T. B. Üstün,et al.  The World Mental Health (WMH) Survey Initiative version of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI) , 2004, International journal of methods in psychiatric research.

[67]  R. Rosmond.  Obesity and depression: same disease, different names? , 2004, Medical hypotheses.

[68]  Olga V. Demler,et al.  Clinical calibration of DSM‐IV diagnoses in the World Mental Health (WMH) version of the World Health Organization (WHO) Composite International Diagnostic Interview (WMH‐CIDI) , 2004, International journal of methods in psychiatric research.

[69]  S. Jørgensen.  Model Selection and Multimodel Inference: A Practical Information—Theoretic Approach, Second Edition, Kenneth P. Brunham, David R. Anderson, Springer-Verlag, Heidelberg, 2002, 490 pages, hardbound, 31 illustrations , 2004 .

[70]  Christiane,et al.  World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. , 2004, Journal international de bioethique = International journal of bioethics.

[71]  K. Mayer,et al.  Dried blood spots (DBS): a valuable tool for HIV surveillance in developing/tropical countries , 2002, International journal of STD & AIDS.

[72]  Wolzt,et al.  World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. , 2003, The Journal of the American College of Dentists.

[73]  F. Boller,et al.  Association of the Apolipoprotein E ε4 Allele with Late-Onset Depression , 2001, Neuroepidemiology.

[74]  H. Kraemer,et al.  Apolipoprotein E ∈4 Allele Affects the Relationship between Stress and Depression in Caregivers of Patients with Alzheimer's Disease , 2001 .

[75]  Roger W. Johnson,et al.  An Introduction to the Bootstrap , 2001 .

[76]  R. Spitzer,et al.  The PHQ-9: validity of a brief depression severity measure. , 2001, Journal of general internal medicine.

[77]  R. Spitzer,et al.  Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. Primary Care Evaluation of Mental Disorders. Patient Health Questionnaire. , 1999, JAMA.

[78]  M. Maes,et al.  Major depression and activation of the inflammatory response system. , 1999, Advances in experimental medicine and biology.

[79]  S. Kasl,et al.  Subjective ratings of emotional health as a risk factor for major depression in a community sample , 1997, British Journal of Psychiatry.

[80]  K. Krishnan,et al.  Apolipoprotein E-ε4 frequency in geriatric depression , 1996, Biological Psychiatry.

[81]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[82]  B. Leonard,et al.  Changes in immunoglobulin, complement and acute phase protein levels in the depressed patients and normal controls. , 1994, Journal of affective disorders.

[83]  Henry Miller,et al.  The Neurobiology of the , 1993 .

[84]  E. Paykel,et al.  Alpha-1-acid glycoprotein in major depressive and eating disorders. , 1991, Journal of affective disorders.

[85]  K. Krishnan,et al.  Elevated plasma concentrations of alpha 1-acid glycoprotein, a putative endogenous inhibitor of the tritiated imipramine binding site, in depressed patients. , 1990, Archives of general psychiatry.

[86]  Z. Kronfol,et al.  Lymphocyte mitogenesis, immunoglobulin and complement levels in depressed patients and normal controls , 1989, Acta psychiatrica Scandinavica.

[87]  P. McNamara,et al.  Serum protein binding and the role of increased alpha 1-acid glycoprotein in moderately obese male subjects. , 1984, British journal of clinical pharmacology.

[88]  P. McNamara,et al.  Serum alpha 1-acid glycoprotein and the binding of drugs in obesity. , 1983, British journal of clinical pharmacology.

[89]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[90]  R. Guthrie,et al.  A SIMPLE PHENYLALANINE METHOD FOR DETECTING PHENYLKETONURIA IN LARGE POPULATIONS OF NEWBORN INFANTS. , 1963, Pediatrics.