본문 바로가기
KERD
논문KCI 등재

Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables

Giampaolo Perna|Alessandra Alciati|Silvia Daccò|Massimiliano Grassi|Daniela Caldirola
대한신경정신의학회 (Korean Neuropsychiatric Association)|2020.03|PSYCHIATRY INVESTIGATION|제17권 제3호|pp. 193-206|DOI https://doi.org/10.30773/pi.2019.0289

1

조회

0

다운로드

2

인용

0

참고문헌

키워드

DepressionAntidepressantsPersonalized medicineTreatment outcome.Drug targetingClinical markers
주제분류정신과학

초록

Despite several pharmacological options, the clinical outcomes of major depressive disorder (MDD) are often unsatisfactory. Personalized psychiatry attempts to tailor therapeutic interventions according to each patient’s unique profile and characteristics. This approach can be a crucial strategy in improving pharmacological outcomes in MDD and overcoming trial-and-error treatment choices. In this narrative review, we evaluate whether sociodemographic (i.e., gender, age, race/ethnicity, and socioeconomic status) and clinical [i.e., body mass index (BMI), severity of depressive symptoms, and symptom profiles] variables that are easily assessable in clinical practice may help clinicians to optimize the selection of antidepressant treatment for each patient with MDD at the early stages of the disorder. We found that several variables were associated with poorer outcomes for all antidepressants. However, only preliminary associations were found between some clinical variables (i.e., BMI, anhedonia, and MDD with melancholic/atypical features) and possible benefits with some specific antidepressants. Finally, in clinical practice, the assessment of sociodemographic and clinical variables considered in our review can be valuable for early identification of depressed individuals at high risk for poor responses to antidepressants, but there are not enough data on which to ground any reliable selection of specific antidepressant class or compounds. Recent advances in computational resources, such as machine learning techniques, which are able to integrate multiple potential predictors, such as individual/ clinical variables, biomarkers, and genetic factors, may offer future reliable tools to guide personalized antidepressant choice for each patient with MDD.

Abstract

Despite several pharmacological options, the clinical outcomes of major depressive disorder (MDD) are often unsatisfactory. Personalized psychiatry attempts to tailor therapeutic interventions according to each patient’s unique profile and characteristics. This approach can be a crucial strategy in improving pharmacological outcomes in MDD and overcoming trial-and-error treatment choices. In this narrative review, we evaluate whether sociodemographic (i.e., gender, age, race/ethnicity, and socioeconomic status) and clinical [i.e., body mass index (BMI), severity of depressive symptoms, and symptom profiles] variables that are easily assessable in clinical practice may help clinicians to optimize the selection of antidepressant treatment for each patient with MDD at the early stages of the disorder. We found that several variables were associated with poorer outcomes for all antidepressants. However, only preliminary associations were found between some clinical variables (i.e., BMI, anhedonia, and MDD with melancholic/atypical features) and possible benefits with some specific antidepressants. Finally, in clinical practice, the assessment of sociodemographic and clinical variables considered in our review can be valuable for early identification of depressed individuals at high risk for poor responses to antidepressants, but there are not enough data on which to ground any reliable selection of specific antidepressant class or compounds. Recent advances in computational resources, such as machine learning techniques, which are able to integrate multiple potential predictors, such as individual/ clinical variables, biomarkers, and genetic factors, may offer future reliable tools to guide personalized antidepressant choice for each patient with MDD.

참고문헌 (144)

  1. Murray CJ, "The state of US health, 1990-2010 : burden of diseases, injuries, and risk factors", JAMA, 310, pp.591~608 (2013)
  2. World Federation for Mental Health, "Depression: A Global Crisis", World Federation for Mental Health, ~ (2012)
  3. Greenberg PE, "The economic burden of adults with major depressive disorder in the United States(2005 and 2010)", J Clin Psychiatry, 76, pp.155~162 (2015)
  4. Kennedy SH, "Canadian Network for Mood and Anxiety Treatments (CANMAT)2016 Clinical Guidelines for the Management of Adults with Major Depressive Disorder: Section 3. Pharmacological Treatments", Can J Psychiatry, 61, pp.540~560 (2016)
  5. Fornaro M, "The FDA “Black Box” warning on antidepressant suicide risk in young adults: more harm than benefits?", Front Psychiatry, 10, pp.294~ (2019)
  6. Carvalho AF, "Augmentation strategies for treatment-resistant depression : a literature review", J Clin Pharm Ther, 32, pp.415~428 (2007)
  7. Gartlehner G, "Comparative benefits and harms of second-generation antidepressants for treating major depressive disorder : an updated metaanalysis", Ann Intern Med, 155, pp.772~785 (2011)
  8. Trivedi MH, "Exercise as an augmentation strategy for treatment of major depression", J Psychiatr Pract, 12, pp.205~213 (2006)
  9. Lenox-Smith AJ, "Venlafaxine extended release versus citalopram in patients with depression unresponsive to a selective serotonin reuptake inhibitor", Int Clin Psychopharmacol, 23, pp.113~119 (2008)
  10. Rush AJ, "Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps : a STAR*D report", Am J Psychiatry, 163, pp.1905~1917 (2006)
  11. Blier P, "Combination of antidepressant medications from treatment initiation for major depressive disorder : a double-blind randomized study", Am J Psychiatry, 167, pp.281~288 (2010)
  12. Rush AJ, "Combining medications to enhance depression outcomes(CO-MED) : acute and long-term outcomes of a single-blind randomized study", Am J Psychiatry, 168, pp.689~701 (2011)
  13. Fried EI, "Depression is not a consistent syndrome : An investigation of unique symptom patterns in the STAR*D study", J Affect Disord, 172, pp.96~102 (2015)
  14. American Psychiatric Association, "Diagnostic and Statistical Manual of Mental Disorders", American Psychiatric Association, ~ (2013)
  15. Culpepper L, "Major depressive disorder : understanding the significance of residual symptoms and balancing efficacy with tolerability", Am J Med, 128(9 Suppl), pp.S1~S15 (2015)
  16. Akechi T, "Predicting relapse in major depression after successful initial pharmacological treatment", J Affect Disord, 250, pp.108~113 (2019)
  17. Fekadu A, "What happens to patients with treatment-resistant depression? A systematic review of medium to long term outcome studies", J Affect Disord, 116, pp.4~11 (2009)
  18. Hamilton J, "Psychopharmacology and Women. Sex, Gender and Hormones", pp.241~260 (1996), American Psychiatric Association
  19. Kornstein SG, "Gender differences in treatment response to sertraline versus imipramine in chronic depression", Am J Psychiatry, 157, pp.1445~1452 (2000)
  20. Khan A, "Sex differences in antidepressant response in recent antidepressant clinical trials", J Clin Psychopharmacol, 25, pp.318~324 (2005)
  21. Serretti A, "Specificity profile of paroxetine in major depressive disorder : meta-regression of double-blind, randomized clinical trials", J Affect Disord, 132, pp.14~25 (2011)
  22. Gibiino S, "Specificity profile of venlafaxine and sertraline in major depression : metaregression of double-blind, randomized clinical trials", Int J Neuropsychopharmacol, 17, pp.1~8 (2014)
  23. Berlanga C, "Different gender response to serotonergic and noradrenergic antidepressants. A comparative study of the efficacy of citalopram and reboxetine", J Affect Disord, 95, pp.119~123 (2006)
  24. Thase ME, "Relative antidepressant efficacy of venlafaxine and SSRIs: sex-age interactions", J Womens Health(Larchmt), 14, pp.609~616 (2005)
  25. Trivedi MH, "Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D : implications for clinical practice", Am J Psychiatry, 163, pp.28~40 (2006)
  26. Quitkin FM, "Are there differences between women’s and men’s antidepressant responses?", Am J Psychiatry, 159, pp.1848~1854 (2002)
  27. Bagby RM, "Psychosocial and clinical predictors of response to pharmacotherapy for depression", J Psychiatry Neurosci, 27, pp.250~257 (2002)
  28. Lewis-Hall FC, "Fluoxetine vs. tricyclic antidepressants in women with major depressive disorder", J Womens Health, 6, pp.337~343 (1997)
  29. Kornstein SG, "Influence of sex and menopausal status on response, remission, and recurrence in patients with recurrent major depressive disorder treated with venlafaxine extended release or fluoxetine : analysis of data from the PREVENT study", J Clin Psychiatry, 75, pp.62~68 (2014)
  30. Wohlfarth T, "Response to tricyclic antidepressants: independent of gender?", Am J Psychiatry, 161, pp.370~372 (2004)
  31. Cuijpers P, "Gender as predictor and moderator of outcome in cognitive behavior therapy and pharmacotherapy for adult depression: an “individual patient data” meta-analysis", Depress Anxiety, 31, pp.941~951 (2014)
  32. Saveanu R, "The international Study to Predict Optimized Treatment in Depression(iSPOT-D) : outcomes from the acute phase of antidepressant treatment", J Psychiatr Res, 61, pp.1~12 (2015)
  33. Kessler RC, "Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder", Epidemiol Psychiatr Sci, 26, pp.22~36 (2017)
  34. Noma H, "Exploratory analyses of effect modifiers in the antidepressant treatment of major depression : individual-participant data meta-analysis of 2803 participants in seven placebo-controlled randomized trials", J Affect Disord, 250, pp.419~424 (2019)
  35. Webb CA, "Personalized prediction of antidepressant v. placebo response : evidence from the EMBARC study", Psychol Med, 49, pp.1118~1127 (2019)
  36. Marcos LR, "Pharmacotherapy of Hispanic depressed patients : clinical observations", Am J Psychother, 36, pp.505~512 (1982)
  37. Varner RV, "Black and white patients response to antidepressant treatment for major depression", Psychiatr Q, 69, pp.117~125 (1998)
  38. Miranda J, "Effects of primary care depression treatment on minority patients’ clinical status and employment", Arch Gen Psychiatry, 61, pp.827~834 (2004)
  39. Wagner GJ, "Ethnic differences in response to fluoxetine in a controlled trial with depressed HIV-positive patients", Psychiatr Serv, 49, pp.239~240 (1998)
  40. Bailey RK, "Duloxetine in the treatment of major depressive disorder : comparisons of safety and efficacy", J Natl Med Assoc, 98, pp.437~447 (2006)
  41. Roy-Byrne PP, "Paroxetine response and tolerability among ethnic minority patients with mood or anxiety disorders : a pooled analysis", J Clin Psychiatry, 66, pp.1228~1233 (2005)
  42. Lesser IM, "Ethnicity", race and outcome in the treatment of depression : results from STAR*D, 2007, pp.45, 1043~1051
  43. Lesser IM, "Ethnic differences in antidepressant response : a prospective multi-site clinical trial", Depress Anxiety, 27, pp.56~62 (2010)
  44. Lesser IM, "Effects of race and ethnicity on depression treatment outcomes : the CO-MED trial", Psychiatr Serv, 62, pp.1167~1179 (2011)
  45. Murphy E, "Race, genetic ancestry and response to antidepressant treatment for major depression", Neuropsychopharmacology, 38, pp.2598~2606 (2013)
  46. Murphy EJ, "Retention and attrition among African Americans in the STAR*D study : what causes research volunteers to stay or stray", Depress Anxiety, 30, pp.1137~1144 (2013)
  47. Freeman A, "The role of socio-economic status in depression : results from the COURAGE(aging survey in Europe)", BMC Public Health, 16, pp.1098~ (2016)
  48. Lorant V, "Depression and socio-economic risk factors : 7-year longitudinal population study", Br J Psychiatry, 190, pp.293~298 (2007)
  49. Jakubovski E, "Prognostic subgroups for citalopram response in the STAR*D trial", J Clin Psychiatry, 75, pp.738~747 (2014)
  50. Perlman K, "A systematic meta-review of predictors of antidepressant treatment outcome in major depressive disorder", J Affect Disord, 243, pp.503~515 (2019)
  51. Van HL, "Predicting the outcome of antidepressants and psychotherapy for depression : a qualitative, systematic review", Harv Rev Psychiatry, 16, pp.225~234 (2008)
  52. Mandelli L, "High occupational level is associated with poor response to treatment of depression", Eur Neuropsychopharmacol, 26, pp.1320~1326 (2016)
  53. Mandelli L, "Opinion paper : poor response to treatment of depression in people in high occupational levels", Psychol Med, 49, pp.49~54 (2019)
  54. de Wit L, "Depression and obesity : a meta-analysis of community-based studies", Psychiatry Res, 178, pp.230~235 (2010)
  55. Opel N, "Obesity and major depression : Body-mass index(BMI)is associated with a severe course of disease and specific neurostructural alterations", Psychoneuroendocrinology, 51, pp.219~226 (2015)
  56. Oskooilar N, "Body mass index and response to antidepressants in depressed research subjects", J Clin Psychiatry, 70, pp.1609~1610 (2009)
  57. Khan A, "BMI, sex, and antidepressant response", J Affect Disord, 99, pp.101~106 (2007)
  58. Lin CH, "Both body weight and BMI predicts improvement in symptom and functioning for patients with major depressive disorder", J Affect Disord, 161, pp.123~126 (2014)
  59. Papakostas GI, "Obesity among outpatients with major depressive disorder", Int J Neuropsychopharmacol, 8, pp.59~63 (2005)
  60. Uher R, "Body weight as a predictor of antidepressant efficacy in the GENDEP project", J Affect Disord, 118, pp.147~154 (2009)
  61. Jantaratnotai N, "The interface of depression and obesity", Obes Res Clin Pract, 11, pp.1~10 (2017)
  62. Woo YS, "Obesity and its potential effects on antidepressant treatment outcomes in patients with depressive disorders : a literature review", Int J Mol Sci, 17, pp.pii:E80~ (2016)
  63. Green E, "Personalizing antidepressant choice by sex, body mass index, and symptom profile: An iSPOT-D report", Pers Med Psychiatry, 1-2, pp.65~73 (2017)
  64. Jha MK, "Validating pre-treatment body mass index as moderator of antidepressant treatment outcomes : Findings from CO-MED trial", J Affect Disord, 234, pp.34~37 (2018)
  65. Khan A, "Severity of depression and response to antidepressants and placebo : an analysis of the Food and Drug Administration database", J Clin Psychopharmacol, 22, pp.40~45 (2002)
  66. Fournier JC, "Antidepressant drug effects and depression severity : a patient-level meta-analysis", JAMA, 303, pp.47~53 (2010)
  67. Friedman ES, "Baseline depression severity as a predictor of single and combination antidepressant treatment outcome : results from the COMED trial", Eur Neuropsychopharmacol, 22, pp.183~199 (2012)
  68. Bartova L, "Results of the European Group for the Study of Resistant Depression(GSRD)-basis for further research and clinical practice", World J Biol Psychiatry, 20, pp.427~448 (2019)
  69. (2009)[URL]
  70. American Psychiatric Association, "Diagnostic and Statistical Manual of Mental Disorders (4th Ed., Text Revision)", American Psychiatric Pub, ~ (2000)
  71. Pizzagalli DA, "Depression, stress, and anhedonia : toward a synthesis and integrated model", Annu Rev Clin Psychol, 10, pp.393~423 (2014)
  72. Berridge KC, "Parsing reward", Trends Neurosci, 26, pp.507~513 (2003)
  73. Clery-Melin ML, "Reward systems and cognitions in Major Depressive Disorder", CNS Spectr, 24, pp.64~77 (2019)
  74. Lamontagne SJ, "Investigating dopamine and glucocorticoid systems as underlying mechanisms of anhedonia", Psychopharmacology(Berl), 235, pp.3103~3113 (2018)
  75. Treadway MT, "Reconsidering anhedonia in depression : lessons from translational neuroscience", Neurosci Biobehav Rev, 35, pp.537~555 (2011)
  76. Haroon E, "Increased inflammation and brain glutamate define a subtype of depression with decreased regional homogeneity, impaired network integrity, and anhedonia", Transl Psychiatry, 8, pp.189~ (2018)
  77. Davidson KW, "Association of anhedonia with recurrent major adverse cardiac events and mortality 1 year after acute coronary syndrome", Arch Gen Psychiatry, 67, pp.480~488 (2010)
  78. Fawcett J, "Time-related predictors of suicide in major affective disorder", Am J Psychiatry, 147, pp.1189~1194 (1990)
  79. Spijker J, "Determinants of poor 1-year outcome of DSM-III-R major depression in the general population : results of the Netherlands Mental Health Survey and Incidence Study(NEMESIS)", Acta Psychiatr Scand, 103, pp.122~130 (2001)
  80. Vrieze E, "Reduced reward learning predicts outcome in major depressive disorder", Biol Psychiatry, 73, pp.639~645 (2013)
  81. Uher R, "Depression symptom dimensions as predictors of antidepressant treatment outcome : replicable evidence for interest-activity symptoms", Psychol Med, 42, pp.967~980 (2012)
  82. Dunlop BW, "The role of dopamine in the pathophysiology of depression", Arch Gen Psychiatry, 64, pp.327~337 (2007)
  83. Nutt D, "The other face of depression, reduced positive affect : the role of catecholamines in causation and cure", J Psychopharmacol, 21, pp.461~471 (2007)
  84. McCabe C, "Diminished neural processing of aversive and rewarding stimuli during selective serotonin reuptake inhibitor treatment", Biol Psychiatry, 67, pp.439~445 (2010)
  85. Goodwin GM, "Emotional blunting with antidepressant treatments : A survey among depressed patients", J Affect Disord, 221, pp.31~35 (2017)
  86. Blier P, "Serotonin and beyond : therapeutics for major depression", Philos Trans R Soc Lond B Biol Sci, 368, pp.20120536~ (2013)
  87. Blier P, "The noradrenergic symptom cluster : clinical expression and neuropharmacology", Neuropsychiatr Dis Treat, 7(Suppl 1), pp.15~20 (2011)
  88. Cao B, "Pharmacological interventions targeting anhedonia in patients with major depressive disorder : A systematic review", Prog Neuropsychopharmacol Biol Psychiatry, 92, pp.109~117 (2019)
  89. De Berardis D, "The melatonergic system in mood and anxiety disorders and the role of agomelatine : implications for clinical practice", Int J Mol Sci, 14, pp.12458~12483 (2013)
  90. Martinotti G, "Agomelatine increases BDNF serum levels in depressed patients in correlation with the improvement of depressive symptoms", Int J Neuropsychopharmacol, 19, pp.pii:pyw003~ (2016)
  91. Martinotti G, "Agomelatine versus venlafaxine XR in the treatment of anhedonia in major depressive disorder: a pilot study", J Clin Psychopharmacol, 32, pp.487~491 (2012)
  92. Corruble E, "Efficacy of agomelatine and escitalopram on depression, subjective sleep and emotional experiences in patients with major depressive disorder: a 24-wk randomized, controlled, double-blind trial", Int J Neuropsychopharmacol, 16, pp.2219~2234 (2013)
  93. Light SN, "Reduced right ventrolateral prefrontal cortex activity while inhibiting positive affect is associated with improvement in hedonic capacity after 8 weeks of antidepressant treatment in major depressive disorder", Biol Psychiatry, 70, pp.962~968 (2011)
  94. Jouvent R, "Dimensional assessment of onset of action of antidepressants : a comparative study of moclobemide vs. clomipramine in depressed patients with blunted affect and psychomotor retardation", Psychiatry Res, 79, pp.267~275 (1998)
  95. Tomarken AJ, "Assessing the effects of bupropion SR on mood dimensions of depression", J Affect Disord, 78, pp.235~241 (2004)
  96. Lally N, "Neural correlates of change in major depressive disorder anhedonia following open-label ketamine", J Psychopharmacol, 29, pp.596~607 (2015)
  97. McGrath PJ, "Response to a selective serotonin reuptake inhibitor(citalopram)in major depressive disorder with melancholic features : a STAR*D report", J Clin Psychiatry, 69, pp.1847~1855 (2008)
  98. Day CV, "Impairment and distress patterns distinguishing the melancholic depression subtype : an iSPOT-D report", J Affect Disord, 174, pp.493~502 (2015)
  99. Arnow BA, "Depression subtypes in predicting antidepressant response : a report from the iSPOT-D trial", Am J Psychiatry, 172, pp.743~750 (2015)
  100. Bobo WV, "Randomized comparison of selective serotonin reuptake inhibitor(escitalopram)monotherapy and antidepressant combination pharmacotherapy for major depressive disorder with melancholic features : a CO-MED report", J Affect Disord, 133, pp.467~476 (2011)
  101. Gili M, "Clinical patterns and treatment outcome in patients with melancholic, atypical and non-melancholic depressions", PLoS One, 7, pp.e48200~ (2012)
  102. Uher R, "Melancholic, atypical and anxious depression subtypes and outcome of treatment with escitalopram and nortriptyline", J Affect Disord, 132, pp.112~120 (2011)
  103. Yang SJ, "Response to antidepressants in major depressive disorder with melancholic features : the CRESCEND study", J Affect Disord, 144, pp.42~50 (2013)
  104. Valerio MP, "A quantitative review on outcome-to-antidepressants in melancholic unipolar depression", Psychiatry Res, 265, pp.100~110 (2018)
  105. Buhler J, "The predictive power of subgroups : an empirical approach to identify depressive symptom patterns that predict response to treatment", J Affect Disord, 163, pp.81~87 (2014)
  106. Stewart JW, "Do atypical features affect outcome in depressed outpatients treated with citalopram", Int J Neuropsychopharmacol, 13, pp.15~30 (2010)
  107. Stewart JW, "Atypical features and treatment response in the National Institute of Mental Health Treatment of Depression Collaborative Research Program", J Clin Psychopharmacol, 18, pp.429~434 (1998)
  108. Quitkin FM, "Columbia atypical depression. A subgroup of depressives with better response to MAOI than to tricyclic antidepressants or placebo", Br J Psychiatry Suppl, (21), pp.30~34 (1993)
  109. Lonnqvist J, "Moclobemide and fluoxetine in atypical depression : a double-blind trial", J Affect Disord, 32, pp.169~177 (1994)
  110. Sogaard J, "A 12-week study comparing moclobemide and sertraline in the treatment of outpatients with atypical depression", J Psychopharmacol, 13, pp.406~414 (1999)
  111. Henkel V, "Treatment of depression with atypical features : a meta-analytic approach", Psychiatry Res, 141, pp.89~101 (2006)
  112. Bech P, "The predictive validity of atypical neurovegetative depressive symptoms identified by the first principal component in the DUAG trial of moclobemide versus clomipramine", J Affect Disord, 140, pp.253~259 (2012)
  113. Lojko D, "Atypical depression : current perspectives", Neuropsychiatr Dis Treat, 13, pp.2447~2456 (2017)
  114. Lojko D, "Atypical features in depression : Association with obesity and bipolar disorder", J Affect Disord, 185, pp.76~80 (2015)
  115. Milaneschi Y, "Leptin dysregulation is specifically associated with major depression with atypical features : evidence for a mechanism connecting obesity and depression", Biol Psychiatry, 81, pp.807~814 (2017)
  116. Braund TA, "Characterising anxiety in major depressive disorder and its use in predicting antidepressant treatment outcome : an iSPOT-D report", Aust N Z J Psychiatry, 53, pp.782~793 (2019)
  117. Gaspersz R, "Patients with anxious depression : overview of prevalence, pathophysiology and impact on course and treatment outcome", Curr Opin Psychiatry, 31, pp.17~25 (2018)
  118. Gaspersz R, "Longitudinal predictive validity of the DSM-5 anxious distress specifier for clinical outcomes in a large cohort of patients with major depressive disorder", J Clin Psychiatry, 78, pp.207~213 (2017)
  119. Ionescu DF, "Pharmacologic treatment of dimensional anxious depression : a review", Prim Care Companion CNS Disord, 16 (2014)
  120. Fava M, "Difference in treatment outcome in outpatients with anxious versus nonanxious depression : a STAR*D report", Am J Psychiatry, 165, pp.342~351 (2008)
  121. Papakostas GI, "Testing anxious depression as a predictor and moderator of symptom improvement in major depressive disorder during treatment with escitalopram", Eur Arch Psychiatry Clin Neurosci, 261, pp.147~156 (2011)
  122. Braund TA, "Dimensions of anxiety in Major depressive disorder and their use in predicting antidepressant treatment outcome : an iSPOT-D report", Psychol Med, pp.1~11 (2019)
  123. Gaspersz R, "Anxious distress predicts subsequent treatment outcome and side effects in depressed patients starting antidepressant treatment", J Psychiatr Res, 84, pp.41~48 (2017)
  124. Liu RT, "Childhood adversities and depression in adulthood: current findings and future directions", Clin Psychol (New York), 24, pp.140~153 (2017)
  125. Nemeroff CB, "Differential responses to psychotherapy versus pharmacotherapy in patients with chronic forms of major depression and childhood trauma", Proc Natl Acad Sci U S A, 100, pp.14293~14296 (2003)
  126. Williams LM, "Childhood trauma predicts antidepressant response in adults with major depression : data from the randomized international study to predict optimized treatment for depression", Transl Psychiatry, 6, pp.e799~ (2016)
  127. Himmerich H, "Cytokine research in depression : principles, challenges, and open questions", Front Psychiatry, 10, pp.30~ (2019)
  128. Strawbridge R, "Inflammation and clinical response to treatment in depression : A meta-analysis", Eur Neuropsychopharmacol, 25, pp.1532~1543 (2015)
  129. De Berardis D, "Alexithymia and its relationships with acute phase proteins and cytokine release : an updated review", J Biol Regul Homeost Agents, 28, pp.795~799 (2014)
  130. De Berardis D, "Alexithymia and suicide risk in psychiatric disorders : a minireview", Front Psychiatry, 8, pp.148~ (2017)
  131. De Berardis D, "Alexithymia, resilience, somatic sensations and their relationships with suicide ideation in drug naive patients with firstepisode major depression: An exploratory study in the “real world”everyday clinical practice", Early Interv Psychiatry (2019)
  132. Domenico De Berardis, "Alexithymia, Suicide Ideation and Homocysteine Levels in Drug Naïve Patients with Major Depression: A Study in the “Real World” Clinical Practice", Clinical Psychopharmacology and Neuroscience, 17(2), pp.318~322 (2019), 대한정신약물학회[DOI]
  133. Hemming L, "A systematic review and meta-analysis of the association between alexithymia and suicide ideation and behaviour", J Affect Disord, 254, pp.34~48 (2019)
  134. Kaufman J, "Unraveling the genetics of major depression and stressrelated psychiatric disorders: is it time for a paradigm shift?", Biol Psychiatry, 84, pp.82~84 (2018)
  135. Han KM, "Differentiating between bipolar and unipolar depression in functional and structural MRI studies", Prog Neuropsychopharmacol Biol Psychiatry, 91, pp.20~27 (2019)
  136. The National Institute of Mental Health Strategic Plan, "National Institute of Mental Health", National Institute of Mental Health, ~ (2008)
  137. Dean J, "The neurobiology of depression : An integrated view", Asian J Psychiatr, 27, pp.101~111 (2017)
  138. Sanislow CA, "Advancing translational research using NIMH research domain criteria and computational methods", Neuron, 101, pp.779~782 (2019)
  139. Kessler RC, "Machine learning methods for developing precision treatment rules with observational data", Behav Res Ther, 120, pp.103412~ (2019)
  140. Perna G, "The revolution of personalized psychiatry : will technology make it happen sooner", Psychol Med, 48, pp.705~713 (2018)
  141. Lin E, "A deep learning approach for predicting antidepressant response in major depression using clinical and genetic biomarkers", Front Psychiatry, 9, pp.290~ (2018)
  142. Chekroud AM, "Cross-trial prediction of treatment outcome in depression : a machine learning approach", Lancet Psychiatry, 3, pp.243~250 (2016)
  143. Iniesta R, "Combining clinical variables to optimize prediction of antidepressant treatment outcomes", J Psychiatr Res, 78, pp.94~102 (2016)
  144. Iniesta R, "Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables", Sci Rep, 8, pp.5530~ (2018)

관련 논문AI추천

The Present and Future of Precision Medicine in Psychiatry: Focus on Clinical Psychopharmacology of AntidepressantsAlessandro Serretti대한정신약물학회 · Clinical Psychopharmacology and Neuroscience2018 · 제16권 제1호How to Utilize Clinical and Genetic Information for Personalized Treatment of Major Depressive Disorder: Step by Step Strategic ApproachAlessandro Serretti, Chiara Fabbri대한정신약물학회 · Clinical Psychopharmacology and Neuroscience2020 · 제18권 제4호Pharmacogenetics and Depression: A Critical PerspectiveAlessandro Serretti, Chiara Fabbri, Filippo Corponi대한신경정신의학회 · PSYCHIATRY INVESTIGATION2019 · 제16권 제9호The MAKE Biomarker Discovery for Enhancing anTidepressant Treatment Effect and Response (MAKE BETTER) Study: Design and MethodologyHee-Ju Kang, Hee-Young Shin, Jae-Min Kim, Ju-Wan Kim, Myung-Geun Shin, Seon-Young Kim, Sung-Wan Kim대한신경정신의학회 · PSYCHIATRY INVESTIGATION2018 · 제15권 제5호Genetics of Treatment Outcomes in Major Depressive Disorder: Present and FutureAlessandro Serretti, Chiara Fabbri대한정신약물학회 · Clinical Psychopharmacology and Neuroscience2020 · 제18권 제1호A Pharmacogenomic-based Antidepressant Treatment for Patients with Major Depressive Disorder: Results from an 8-week, Randomized, Single-blinded Clinical TrialAlessandro Serretti, Ashwin A Patkar, Changsu Han, Chi-Un Pae, Laura Mandelli, Prakash S Masand, Sheng Min Wang, Soo-Jung Lee, Won-Myong Bahk대한정신약물학회 · Clinical Psychopharmacology and Neuroscience2018 · 제16권 제4호

이 간행물의 다른 논문 (11건)

Major Depressive Disorder: Current Advances and Paradigm ShiftsYong-Ku Kimpp. 179-180Ketamine in Major Depressive Disorder: Mechanisms and Future Perspectives김용구, 신민철pp. 181-192Understanding the Complex of Suicide in Depression: from Research to ClinicsAlessandro Valchera, Maurizio Pompili, Giovanni Martinotti, Silvia Fraticelli, Roberto Latini, Carmine Tomasetti, Raffaella La Rovere, Domenico De Berardis, Gianluca Serafini, Laura Orsolini, Umberto Volpe, Michele Fornaro, Sabatino Trotta, Federica Vellante, Marco Alessandrini, Massimo Di Giannantoniopp. 207-221The Hypothesis on the Prediction of Treatment Response with Buspirone Augmentation along with Serotonergic Antidepressant in Patients with Major Depressive Disorder Using Loudness Dependence of Auditory Evoked Potentials: Two Cases and Review of the Literature for EvidenceYoung-Min Parkpp. 222-224Neural Mechanisms in Eating Behaviors: A Pilot fMRI Study of Emotional ProcessingT. García-Saiz, Jeffrey C.L. Looi, E. Via Virgili, M. Rincón Zamorano, Laura de Anta Tejado, Helena Trebbau López, Jose Luis Carrasco Perera, Marina Díaz-Marsá, Rosa M. Molina-Ruizpp. 225-236
원문 보기