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Personalized Medicine

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Part of the book series: Current Clinical Psychiatry ((CCPSY))

Abstract

Personalized medicine can be defined as the use of specific patient characteristics to guide treatment decisions. This ancient concept, that the cure should fit the patient, has evolved as we learn more about the biological processes underlying psychiatric disease. Our current toolbox for practicing personalized medicine combines understanding the patient’s demographics, history, and environment with the use of modern techniques such as neuroimaging, pharmacogenomics, cellular and molecular phenotyping, and database mining. In this chapter, we will discuss the historical background of personalized medicine, specifically how it relates to psychiatry and to the pharmacological treatment of patients with major depressive disorder (MDD). We will emphasize recent advances in molecular, cellular, and imaging phenotyping in MDD, with a focus on pharmacogenetics and peripheral biomarkers. We will then give some practical recommendations for personalizing treatment of depressed patients and prospects for making individualized treatments more effective.

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References

  1. Abrahams E, Silver M. Integrative neuroscience and personalized medicine. In: Gordon E, Koslow S, editors. Integrative neuroscience and personalized medicine. New York: Oxford University Press; 2010. p. 1–360.

    Google Scholar 

  2. Sur R, Dahm P. History of evidence-based medicine. Indian J Urol. 2011;27:487.

    Article  PubMed Central  PubMed  Google Scholar 

  3. Feinstein AR, Horwitz RI. Problems in the “evidence” of “evidence-based medicine”. Am J Med. 1997;103:529–35.

    Article  CAS  PubMed  Google Scholar 

  4. McGregor AJ. Sex bias in drug research: a call for change. Pharm J. 2016;296. Online. https://www.pharmaceutical-journal.com/opinion/comment/sex-bias-in-drug-research-a-call-for-change/20200727.article.

  5. Holdcroft A. Gender bias in research: how does it affect evidence based medicine? J R Soc Med. 2007;100:2–3.

    Article  PubMed Central  PubMed  Google Scholar 

  6. Klein SL, Schiebinger L, Stefanick ML, Cahill L, Danska J, de Vries GJ, et al. Opinion: sex inclusion in basic research drives discovery. Proc Natl Acad Sci U S A. 2015;112:5257–8.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  7. US General Accounting Office. Drug safety: most drugs withdrawn in recent years had greater health risks for women. Washington, DC: Government Publishing Office; 2001.

    Google Scholar 

  8. US Federal Drug Administration. A drug safety communication: risk of next-morning impairment after use of insomnia drugs. 2013.

    Google Scholar 

  9. Seedat S, Scott KM, Angermeyer MC, Bromet EJ, Ph D, Brugha TS, et al. Cross-national associations between gender and mental disorders in the WHO World Mental Health Surveys. Arch Gen Psychiatry. 2009;66:785–95.

    Article  PubMed Central  PubMed  Google Scholar 

  10. Kornstein SG, Schatzberg AF, Thase ME, Yonkers KA, McCullough JP, Keitner GI, et al. Gender differences in treatment response to sertraline versus imipramine in chronic depression. Am J Psychiatry. 2000;157:1445–52.

    Article  CAS  PubMed  Google Scholar 

  11. Joyce PR, Mulder RT, Luty SE, McKenzie JM, Rae AM. A differential response to nortriptyline and fluoxetine in melancholic depression: the importance of age and gender. Acta Psychiatr Scand. 2003;108:20–3.

    Article  CAS  PubMed  Google Scholar 

  12. Khan A, Brodhead AE, Schwartz KA, Kolts RL, Brown WA. Sex differences in antidepressant response in recent antidepressant clinical trials. J Clin Psychopharmacol. 2005;25:318–24.

    Article  PubMed  Google Scholar 

  13. Young EA, Kornstein SG, Marcus SM, Harvey AT, Warden D, Wisniewski SR, et al. Sex differences in response to citalopram: a STAR∗D report. J Psychiatr Res. 2009;43:503–11.

    Article  PubMed  Google Scholar 

  14. Vermeiden M, van den Broek WW, Mulder PGH, Birkenhäger TK. Influence of gender and menopausal status on antidepressant treatment response in depressed inpatients. J Psychopharmacol. 2010;24:497–502.

    Article  CAS  PubMed  Google Scholar 

  15. Sramek JJ, Murphy MF, Cutler NR. Sex differences in the psychopharmacological treatment of depression. Dialogues Clin Neurosci. 2016;18:447–57.

    PubMed Central  PubMed  Google Scholar 

  16. Bebbington P, Dunn G, Jenkins R, Lewis G, Brugha T, Farrell M, et al. The influence of age and sex on the prevalence of depressive conditions: report from the National Survey of Psychiatric Morbidity. Int Rev Psychiatry. 2003;15:74–83.

    Article  CAS  PubMed  Google Scholar 

  17. Berlanga C, Flores-Ramos M. Different gender response to serotonergic and noradrenergic antidepressants. A comparative study of the efficacy of citalopram and reboxetine. J Affect Disord. 2006;95:119–23.

    Article  CAS  PubMed  Google Scholar 

  18. Gleason CE, Dowling NM, Wharton W, Manson JE, Miller VM, Atwood CS, et al. Effects of hormone therapy on cognition and mood in recently postmenopausal women: findings from the randomized, controlled KEEPS–cognitive and affective study. Brayne C, editor. PLOS Med 2015;12:e1001833.

    Google Scholar 

  19. Schneider LS, Small GW, Hamilton SH, Bystritsky A, Nemeroff CB, Meyers BS. Estrogen replacement and response to fluoxetine in a multicenter geriatric depression trial. Am J Geriatr Psychiatry. 1997;5:97–106.

    Article  CAS  PubMed  Google Scholar 

  20. Gordon JL, Rubinow DR, Eisenlohr-Moul TA, Xia K, Schmidt PJ, Girdler SS. Efficacy of transdermal estradiol and micronized progesterone in the prevention of depressive symptoms in the menopause transition. JAMA Psychiat. 2018;75(2):149–57.

    Article  Google Scholar 

  21. Joffe H, Hickey M. Should hormone therapy be used to prevent depressive symptoms during the menopause transition? JAMA Psychiat. 2018;75(2):125–6.

    Article  Google Scholar 

  22. Kanes S, Colquhoun H, Gunduz-Bruce H, Raines S, Arnold R, Schacterle A, et al. Brexanolone (SAGE-547 injection) in post-partum depression: a randomised controlled trial. Lancet. 2017;390:480–9.

    Article  CAS  PubMed  Google Scholar 

  23. Slowik A, Lammerding L, Hoffmann S, Beyer C. Brain inflammasomes in stroke and depressive disorders: regulation by estrogen. J Neuroendocrinol. 2018;30(2). https://doi.org/10.1111/jne.12482.

    Article  CAS  Google Scholar 

  24. Gilman SE, Cherkerzian S, Buka SL, Hahn J, Hornig M, Goldstein JM. Prenatal immune programming of the sex-dependent risk for major depression. Transl Psychiatry. 2016;6:e822.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  25. Majidi-Zolbanin J, Doosti M-H, Kosari-Nasab M, Salari A-A. Prenatal maternal immune activation increases anxiety- and depressive-like behaviors in offspring with experimental autoimmune encephalomyelitis. Neuroscience. 2015;294:69–81.

    Article  CAS  PubMed  Google Scholar 

  26. Kornstein SG, Sloan DME, Thase ME. Gender-specific differences in depression and treatment response. Psychopharmacol Bull. 2002;36(4):99–112. Available: http://www.ncbi.nlm.nih.gov/pubmed/12858149

    PubMed  Google Scholar 

  27. Givens JL, Houston TK, Van Voorhees BW, Ford DE, Cooper LA. Ethnicity and preferences for depression treatment. Gen Hosp Psychiatry. 2007;29:182–91.

    Article  PubMed  Google Scholar 

  28. Cooper LA, Gonzales JJ, Gallo JJ, Rost KM, Meredith LS, Rubenstein LV, et al. The acceptability of treatment for depression among African-American, Hispanic, and white primary care patients. Med Care. 2003;41:479–89.

    PubMed  Google Scholar 

  29. Alegría M, Chatterji P, Wells K, Cao Z, Chen C, Takeuchi D, et al. Disparity in depression treatment among racial and ethnic minority populations in the United States. Psychiatr Serv. 2008;59:1264–72.

    Article  PubMed Central  PubMed  Google Scholar 

  30. Guarnaccia PJ, Martinez I, Ramirez R, Canino G. Are Ataques de Nervios in Puerto Rican children associated with psychiatric disorder? J Am Acad Child Adolesc Psychiatry. 2005;44:1184–92.

    Article  PubMed  Google Scholar 

  31. Weller SC, Baer RD, Garcia de Alba Garcia J, Salcedo Rocha AL. Susto and nervios: expressions for stress and depression. Cult Med Psychiatry. 2008;32:406–20.

    Article  PubMed  Google Scholar 

  32. Yeung AS, Kam R. Illness beliefs of depressed Chinese American patients in a primary care setting. In: Georgiopoulos AM, Rosenbaum JF, editors. Perspectives in cross-cultural psychiatry. Philadelphia: Lippincott Williams & Wilkins; 2005. p. 21–36.

    Google Scholar 

  33. Georg Hsu LK, Wan YM, Chang H, Summergrad P, Tsang BYP, Chen H. Stigma of depression is more severe in Chinese Americans than Caucasian Americans. Psychiatry Interpersonal Biol Process. 2008;71:210–8.

    Article  CAS  Google Scholar 

  34. Chen JA, Papakostas GI, Youn SJ, Baer L, Clain AJ, Fava M, et al. Association between patient beliefs regarding assigned treatment and clinical response. J Clin Psychiatry. 2011;72:1669–76.

    Article  PubMed  Google Scholar 

  35. Trivedi MH, Rush AJ, Wisniewski SR, Nierenberg AA, Warden D, Ritz L, et al. Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice. Am J Psychiatry. 2006;163:28–40.

    Article  PubMed  Google Scholar 

  36. Murphy E, Hou L, Maher BS, Woldehawariat G, Kassem L, Akula N, et al. Race, genetic ancestry and response to antidepressant treatment for major depression. Neuropsychopharmacology. 2013;38:2598–606.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  37. Pitychoutis PM, Kokras N, Sanoudou D, Dalla C, Papadopoulou-Daifoti Z. Pharmacogenetic considerations for late life depression therapy. Expert Opin Drug Metab Toxicol. 2013;9:989–99.

    Article  CAS  PubMed  Google Scholar 

  38. Davis L, Uezato A, Newell JM, Frazier E. Major depression and comorbid substance use disorders. Curr Opin Psychiatry. 2008;21:14–8.

    Article  PubMed  Google Scholar 

  39. Duntas LH, Maillis A. Hypothyroidism and depression: salient aspects of pathogenesis and management. Minerva Endocrinol. 2013;38:365–77.

    CAS  PubMed  Google Scholar 

  40. Penninx BW, Milaneschi Y, Lamers F, Vogelzangs N. Understanding the somatic consequences of depression: biological mechanisms and the role of depression symptom profile. BMC Med. 2013;11:129.

    Article  PubMed Central  PubMed  Google Scholar 

  41. Whooley MA, Wong JM. Depression and cardiovascular disorders. Annu Rev Clin Psychol. 2013;9:327–54.

    Article  PubMed  Google Scholar 

  42. Sonawalla SB, Papakostas GI, Petersen TJ, Yeung AS, Smith MM, Sickinger AH, et al. Elevated cholesterol levels associated with nonresponse to fluoxetine treatment in major depressive disorder. Psychosomatics. 2002;43:310–6.

    Article  CAS  PubMed  Google Scholar 

  43. Papakostas GI, Petersen T, Iosifescu DV, Burns AM, Nierenberg AA, Alpert JE, et al. Obesity among outpatients with major depressive disorder. Int J Neuropsychopharmacol. 2005;8:59–63.

    Article  PubMed  Google Scholar 

  44. Kloiber S, Ising M, Reppermund S, Horstmann S, Dose T, Majer M, et al. Overweight and obesity affect treatment response in major depression. Biol Psychiatry. 2007;62:321–6.

    Article  PubMed  Google Scholar 

  45. Papakostas GI, Petersen T, Lebowitz BD, Mischoulon D, Ryan JL, Nierenberg AA, et al. The relationship between serum folate, vitamin B12, and homocysteine levels in major depressive disorder and the timing of improvement with fluoxetine. Int J Neuropsychopharmacol. 2005;8:523.

    Article  CAS  PubMed  Google Scholar 

  46. Papakostas GI, Iosifescu DV, Renshaw PF, Lyoo IK, Lee HK, Alpert JE, et al. Brain MRI white matter hyperintensities and one-carbon cycle metabolism in non-geriatric outpatients with major depressive disorder (Part II). Psychiatry Res Neuroimaging. 2005;140:301–7.

    Article  CAS  Google Scholar 

  47. Pan LA, Martin P, Zimmer T, Segreti AM, Kassiff S, McKain BW, et al. Neurometabolic disorders: potentially treatable abnormalities in patients with treatment-refractory depression and suicidal behavior. Am J Psychiatry. 2017;174:42–50.

    Article  PubMed  Google Scholar 

  48. Papakostas GI, Mischoulon D, Shyu I, Alpert JE, Fava M. S-Adenosyl Methionine (SAMe) augmentation of serotonin reuptake inhibitors for antidepressant nonresponders with major depressive disorder: a double-blind, randomized clinical trial. Am J Psychiatry. 2010;167:942–8.

    Article  PubMed  Google Scholar 

  49. Papakostas GI, Cassiello CF, Iovieno N. Folates and S-adenosylmethionine for major depressive disorder. Can J Psychiatr. 2012;57:406–13.

    Article  Google Scholar 

  50. Papakostas GI, Shelton RC, Zajecka JM, Etemad B, Rickels K, Clain A, et al. L-methylfolate as adjunctive therapy for SSRI-resistant major depression: results of two randomized, double-blind, parallel-sequential trials. Am J Psychiatry. 2012;169:1267–74.

    Article  PubMed  Google Scholar 

  51. Pan L, McKain BW, Madan-Khetarpal S, Mcguire M, Diler RS, Perel JM, et al. GTP-cyclohydrolase deficiency responsive to sapropterin and 5-HTP supplementation: relief of treatment-refractory depression and suicidal behaviour. BMJ Case Rep. 2011; 1–3. https://doi.org/10.1136/bcr.03.2011.3927.

    Google Scholar 

  52. Avenevoli S, Swendsen J, He J-P, Burstein M, Merikangas KR. Major depression in the national comorbidity survey–adolescent supplement: prevalence, correlates, and treatment. J Am Acad Child Adolesc Psychiatry. 2015;54:37–44.e2.

    Article  PubMed  Google Scholar 

  53. Ionescu DF, Niciu MJ, Richards EM, Zarate CA. Pharmacologic treatment of dimensional anxious depression. Prim Care Companion CNS Disord. 2014;16(3):PCC.13r01621.

    PubMed Central  PubMed  Google Scholar 

  54. Fava M, Rush AJ, Alpert JE, Balasubramani GK, Wisniewski SR, Carmin CN, et al. Difference in treatment outcome in outpatients with anxious versus nonanxious depression: a STAR*D report. Am J Psychiatry. 2008;165:342–51.

    Article  PubMed  Google Scholar 

  55. Fava M, Uebelacker LA, Alpert JE, Nierenberg AA, Pava JA, Rosenbaum JF. Major depressive subtypes and treatment response. Biol Psychiatry. 1997;42:568–76.

    Article  CAS  PubMed  Google Scholar 

  56. Davidson JRT, Meoni P, Haudiquet V, Cantillon M, Hackett D. Achieving remission with venlafaxine and fluoxetine in major depression: its relationship to anxiety symptoms. Depress Anxiety. 2002;16:4–13.

    Article  CAS  PubMed  Google Scholar 

  57. Papakostas GI, Stahl SM, Krishen A, Seifert CA, Tucker VL, Goodale EP, et al. Bupropion versus SSRIs in anxious depression efficacy of bupropion and the selective serotonin reuptake inhibitors in the treatment of major depressive disorder with high levels of anxiety (anxious depression): a pooled analysis of 10 studies. J Clin Psychiatry. 2008;69869:1287–92.

    Article  Google Scholar 

  58. Ionescu DF, Luckenbaugh DA, Niciu MJ, Richards EM, Slonena EE, Vande Voort JL, et al. Effect of baseline anxious depression on initial and sustained antidepressant response to ketamine. J Clin Psychiatry. 2014;75:e932–8.

    Article  CAS  PubMed  Google Scholar 

  59. Baer L, Trivedi MH, Huz I, Rush AJ, Wisniewski SR, Fava M. Prevalence and impact of obsessive-compulsive symptoms in depression: a STAR∗D report. J Clin Psychiatry. 2015;76:1668–74.

    Article  PubMed  Google Scholar 

  60. Marinova Z, Chuang D-M, Fineberg N. Glutamate-modulating drugs as a potential therapeutic strategy in obsessive-compulsive disorder. Curr Neuropharmacol. 2017;15(7):977–95.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  61. Klengel T, Mehta D, Anacker C, Rex-haffner M, Jens C, Pariante CM, et al. Childhood trauma interactions. Nat Neurosci. 2013;16:33–41.

    Article  CAS  PubMed  Google Scholar 

  62. Nemeroff CB, Heim CM, Thase ME, Klein DN, Rush AJ, Schatzberg AF, et al. Differential responses to psychotherapy versus pharmacotherapy in patients with chronic forms of major depression and childhood trauma. Proc Natl Acad Sci. 2003;100:14293–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Daviss WB. Depressive disorders and ADHD. Moodiness in ADHD. Cham: Springer International Publishing; 2018. p. 91–109.

    Google Scholar 

  64. Daviss WB, Bentivoglio P, Racusin R, Brown KM, Bostic JQ, Wiley L. Bupropion sustained release in adolescents with comorbid attention-deficit/hyperactivity disorder and depression. J Am Acad Child Adolesc Psychiatry. 2001;40:307–14.

    Article  CAS  PubMed  Google Scholar 

  65. Findling RL. Open-label treatment of comorbid depression and attentional disorders with co-administration of serotonin reuptake inhibitors and psychostimulants in children, adolescents, and adults: a case series. J Child Adolesc Psychopharmacol. 1996;6:165–75.

    Article  CAS  PubMed  Google Scholar 

  66. Bair MJ, Robinson RL, Eckert GJ, Stang PE, Croghan TW, Kroenke K. Impact of pain on depression treatment response in primary care. Psychosom Med. 2004;66(1):17–22.

    Article  PubMed  Google Scholar 

  67. Kroenke K, Shen J, Oxman TE, Williams JW, Dietrich AJ. Impact of pain on the outcomes of depression treatment: results from the RESPECT trial. Pain. 2008;134:209–15.

    Article  PubMed  Google Scholar 

  68. Fishbain DA, Cole B, Lewis JE, Gao J. Does pain interfere with antidepressant depression treatment response and remission in patients with depression and pain? An evidence-based structured review. Pain Med. 2014;15:1522–39.

    Article  PubMed  Google Scholar 

  69. Jann MW, Slade JH. Antidepressant agents for the treatment of chronic pain and depression. Pharmacotherapy. 2007;27:1571–87.

    Article  CAS  PubMed  Google Scholar 

  70. Burford NT, Traynor JR, Alt A. Positive allosteric modulators of the μ-opioid receptor: a novel approach for future pain medications. Br J Pharmacol. 2015;172:277–86.

    Article  CAS  PubMed  Google Scholar 

  71. Ehrich E, Turncliff R, Du Y, Leigh-Pemberton R, Fernandez E, Jones R, et al. Evaluation of opioid modulation in major depressive disorder. Neuropsychopharmacology. 2015;40:1448–55.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  72. Fava M, Memisoglu A, Thase ME, Bodkin JA, Trivedi MH, de Somer M, et al. Opioid modulation with buprenorphine/samidorphan as adjunctive treatment for inadequate response to antidepressants: a randomized double-blind placebo-controlled trial. Am J Psychiatry. 2016;173:499–508.

    Article  PubMed  Google Scholar 

  73. Lee RSC, Hermens DF, Porter MA, Redoblado-Hodge MA. A meta-analysis of cognitive deficits in first-episode major depressive disorder. J Affect Disord. 2012;140:113–24.

    Article  PubMed  Google Scholar 

  74. Rock PL, Roiser JP, Riedel WJ, Blackwell AD. Cognitive impairment in depression: a systematic review and meta-analysis. Psychol Med. 2014;44:2029–40.

    Article  CAS  PubMed  Google Scholar 

  75. Baune BT, Fuhr M, Air T, Hering C. Neuropsychological functioning in adolescents and young adults with major depressive disorder – a review. Psychiatry Res. 2014;218:261–71.

    Article  PubMed  Google Scholar 

  76. McDermott LM, Ebmeier KP. A meta-analysis of depression severity and cognitive function. J Affect Disord. 2009;119:1–8.

    Article  PubMed  Google Scholar 

  77. Hinkelmann K, Moritz S, Botzenhardt J, Riedesel K, Wiedemann K, Kellner M, et al. Cognitive impairment in major depression: association with salivary cortisol. Biol Psychiatry. 2009;66:879–85.

    Article  CAS  PubMed  Google Scholar 

  78. Chang HH, Lee IH, Gean PW, Lee SY, Chi MH, Yang YK, et al. Treatment response and cognitive impairment in major depression: association with C-reactive protein. Brain Behav Immun. 2012;26:90–5.

    Article  CAS  PubMed  Google Scholar 

  79. Krogh J, Benros ME, Jørgensen MB, Vesterager L, Elfving B, Nordentoft M. The association between depressive symptoms, cognitive function, and inflammation in major depression. Brain Behav Immun. 2014;35:70–6.

    Article  CAS  PubMed  Google Scholar 

  80. Pimontel MA, Rindskopf D, Rutherford BR, Brown PJ, Roose SP, Sneed JR. A meta-analysis of executive dysfunction and antidepressant treatment response in late-life depression. Am J Geriatr Psychiatry. 2016;24:31–41.

    Article  PubMed  Google Scholar 

  81. Mrazek DA, Hornberger JC, Altar CA, Degtiar I. A review of the clinical, economic, and societal burden of treatment-resistant depression: 1996–2013. Psychiatr Serv. 2014;65:977–87.

    Article  PubMed  Google Scholar 

  82. De Carlo V, Calati R, Serretti A. Socio-demographic and clinical predictors of non-response/non-remission in treatment resistant depressed patients: a systematic review. Psychiatry Res. 2016;240:421–30.

    Article  PubMed  Google Scholar 

  83. Papakostas GI, Petersen T, Homberger CH, Green CH, Smith J, Alpert JE, et al. Hopelessness as a predictor of non-response to fluoxetine in major depressive disorder. Ann Clin Psychiatry. 2007;19:5–8.

    Article  PubMed  Google Scholar 

  84. Coplan JD, Gopinath S, Abdallah CG, Berry BR. A neurobiological hypothesis of treatment-resistant depression †mechanisms for selective serotonin reuptake inhibitor non-efficacy. Front Behav Neurosci. 2014;8:189. eCollection 2014.

    PubMed Central  PubMed  Google Scholar 

  85. The UK ECT Review Group. Efficacy and safety of electroconvulsive therapy in depressive disorders: a systematic review and meta-analysis. Lancet. 2003;361:799–808.

    Article  Google Scholar 

  86. Cusin C, Dougherty DD. Somatic therapies for treatment-resistant depression: ECT, TMS, VNS, DBS. Biol Mood Anxiety Disord. 2012;2:14.

    Article  PubMed Central  PubMed  Google Scholar 

  87. Kedzior KK, Gellersen HM, Brachetti AK, Berlim MT. Deep transcranial magnetic stimulation (DTMS) in the treatment of major depression: an exploratory systematic review and meta-analysis. J Affect Disord. 2015;187:73–83.

    Article  PubMed  Google Scholar 

  88. Lamers F, Vogelzangs N, Merikangas KR, de Jonge P, Beekman ATF, Penninx BWJH. Evidence for a differential role of HPA-axis function, inflammation and metabolic syndrome in melancholic versus atypical depression. Mol Psychiatry. 2013;18:692–9.

    Article  CAS  PubMed  Google Scholar 

  89. Gaspersz R, Nawijn L, Lamers F, Penninx BWJH. Patients with anxious depression. Curr Opin Psychiatry. 2018;31:17–25.

    Article  PubMed  Google Scholar 

  90. Geschwind DH, Flint J. Genetics and genomics of psychiatric disease. Science (80- ). 2015;349:1489–94.

    Article  CAS  Google Scholar 

  91. Cai N, Bigdeli TB, Kretzschmar W, Li Y, Liang J, Song L, et al. Sparse whole-genome sequencing identifies two loci for major depressive disorder. Nature. 2015;523:588–91.

    Article  CAS  Google Scholar 

  92. Hyde CL, Nagle MW, Tian C, Chen X, Paciga SA, Wendland JR, et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat Genet. 2016;48:1031–6.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  93. Hyman S. Mental health: depression needs large human-genetics studies. Nature. 2014;515:189–91.

    Article  CAS  PubMed  Google Scholar 

  94. Porcelli S, Drago A, Fabbri C, Gibiino S, Calati R, Serretti A. Pharmacogenetics of antidepressant response. J Psychiatry Neurosci. 2011;36:87–113.

    Article  PubMed Central  PubMed  Google Scholar 

  95. Fabbri C, Porcelli S, Serretti A. From pharmacogenetics to pharmacogenomics: the way toward the personalization of antidepressant treatment. Can J Psychiatr. 2014;59:62–75.

    Article  Google Scholar 

  96. Laje G, McMahon FJ. Genome-wide association studies of antidepressant outcome: a brief review. Prog Neuro-Psychopharmacol Biol Psychiatry. 2011;35:1553–7.

    Article  CAS  Google Scholar 

  97. Weizman S, Gonda X, Dome P, Faludi G. Pharmacogenetics of antidepressive drugs: a way towards personalized treatment of major depressive disorder. Neuropsychopharmacol Hung. 2012;14:87–101.

    PubMed  Google Scholar 

  98. Altar CA, Carhart JM, Allen JD, Hall-Flavin DK, Dechairo BM, Winner JG. Clinical validity: combinatorial pharmacogenomics predicts antidepressant responses and healthcare utilizations better than single gene phenotypes. Pharmacogenomics J. 2015;15:443–51.

    Article  CAS  PubMed  Google Scholar 

  99. Rosenblat JD, Lee Y, McIntyre RS. Does pharmacogenomic testing improve clinical outcomes for major depressive disorder? J Clin Psychiatry. 2017;78:720–9.

    Article  PubMed  Google Scholar 

  100. Pérez V, Salavert A, Espadaler J, Tuson M, Saiz-Ruiz J, Sáez-Navarro C, et al. Efficacy of prospective pharmacogenetic testing in the treatment of major depressive disorder: results of a randomized, double-blind clinical trial. BMC Psychiatry. 2017;17:250.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  101. Thakur M, Grossman I, McCrory DC, Orlando LA, Steffens DC, Cline KE, et al. Review of evidence for genetic testing for CYP450 polymorphisms in management of patients with nonpsychotic depression with selective serotonin reuptake inhibitors. Genet Med. 2007;9:826–35.

    Article  CAS  PubMed  Google Scholar 

  102. Tsai M-H, Lin K-M, Hsiao M-C, Shen WW, Lu M-L, Tang H-S, et al. Genetic polymorphisms of cytochrome P450 enzymes influence metabolism of the antidepressant escitalopram and treatment response. Pharmacogenomics. 2010;11:537–46.

    Article  CAS  PubMed  Google Scholar 

  103. Kuo HW, Liu SC, Tsou HH, Liu SW, Lin KM, Lu SC, et al. CYP1A2 genetic polymorphisms are associated with early antidepressant escitalopram metabolism and adverse reactions. Pharmacogenomics. 2013;14:1191–201.

    Article  CAS  PubMed  Google Scholar 

  104. Hodgson K, Tansey K, Dernovšek MZ, Hauser J, Henigsberg N, Maier W, et al. Genetic differences in cytochrome P450 enzymes and antidepressant treatment response. J Psychopharmacol. 2014;28:133–41.

    Article  CAS  PubMed  Google Scholar 

  105. Hicks JK, Swen JJ, Thorn CF, Sangkuhl K, Kharasch ED, Ellingrod VL, et al. Clinical pharmacogenetics implementation consortium guideline for CYP2D6 and CYP2C19 genotypes and dosing of tricyclic antidepressants. Clin Pharmacol Ther. 2013;93:402–8.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  106. Dowlati Y, Herrmann N, Swardfager W, Liu H, Sham L, Reim EK, et al. A meta-analysis of cytokines in major depression. Biol Psychiatry. 2010;67:446–57.

    Article  CAS  PubMed  Google Scholar 

  107. Haapakoski R, Mathieu J, Ebmeier KP, Alenius H, Kivimäki M. Cumulative meta-analysis of interleukins 6 and 1β, tumour necrosis factor α and C-reactive protein in patients with major depressive disorder. Brain Behav Immun. 2015;49:206–15.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  108. Miller AH, Raison CL. The role of inflammation in depression: from evolutionary imperative to modern treatment target. Nat Rev Immunol. 2016;16:22–34.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  109. Howren MB, Lamkin DM, Suls J. Associations of depression with C-reactive protein, IL-1, and IL-6: a meta-analysis. Psychosom Med. 2009;71:171–86.

    Article  CAS  PubMed  Google Scholar 

  110. Valkanova V, Ebmeier KP, Allan CL. CRP, IL-6 and depression: a systematic review and meta-analysis of longitudinal studies. J Affect Disord. 2013;150:736–44.

    Article  CAS  PubMed  Google Scholar 

  111. Uher R, Tansey KE, Dew T, Maier W, Mors O, Hauser J, et al. An inflammatory biomarker as a differential predictor of outcome of depression treatment with escitalopram and nortriptyline. Am J Psychiatry. 2014;171:1278–86.

    Article  PubMed  Google Scholar 

  112. Raison CL, Rutherford RE, Woolwine BJ, Shuo C, Schettler P, Drake DF, et al. A randomized controlled trial of the tumor necrosis factor-alpha antagonist infliximab in treatment resistant depression: role of baseline inflammatory biomarkers. JAMA Psychiat. 2013;70:31–41.

    Article  CAS  Google Scholar 

  113. Rapaport MH, Nierenberg AA, Schettler PJ, Kinkead B, Cardoos A, Walker R, et al. Inflammation as a predictive biomarker for response to omega-3 fatty acids in major depressive disorder: a proof-of-concept study. Mol Psychiatry. 2016;21:71–9.

    Article  CAS  PubMed  Google Scholar 

  114. Lanquillon S, Krieg JC, Bening-Abu-Shach U, Vedder H. Cytokine production and treatment response in major depressive disorder. Neuropsychopharmacology. 2000;22:370–9.

    Article  CAS  PubMed  Google Scholar 

  115. O’Brien SM, Scully P, Fitzgerald P, Scott LV, Dinan TG. Plasma cytokine profiles in depressed patients who fail to respond to selective serotonin reuptake inhibitor therapy. J Psychiatr Res. 2007;41:326–31.

    Article  PubMed  Google Scholar 

  116. Yoshimura R, Hori H, Ikenouchi-Sugita A, Umene-Nakano W, Katsuki A, Atake K, et al. Plasma levels of interleukin-6 and selective serotonin reuptake inhibitor response in patients with major depressive disorder. Hum Psychopharmacol. 2013;28:466–70.

    Article  CAS  PubMed  Google Scholar 

  117. Yang JJ, Wang N, Yang C, Shi JY, Yu HY, Hashimoto K. Serum interleukin-6 is a predictive biomarker for ketamine’s antidepressant effect in treatment-resistant patients with major depression. Biol Psychiatry. 2015;77:e19–20.

    Article  CAS  PubMed  Google Scholar 

  118. Eller T, Vasar V, Shlik J, Maron E. Pro-inflammatory cytokines and treatment response to escitalopram in major depressive disorder. Prog Neuro-Psychopharmacol Biol Psychiatry. 2008;32:445–50.

    Article  CAS  Google Scholar 

  119. Rethorst CD, Toups MS, Greer TL, Nakonezny PA, Carmody TJ, Grannemann BD, et al. Pro-inflammatory cytokines as predictors of antidepressant effects of exercise in major depressive disorder. Mol Psychiatry. 2013;18:1119–24.

    Article  CAS  PubMed  Google Scholar 

  120. Noto C, Rizzo LB, Mansur RB, McIntyre RS, Maes M, Brietzke E. Targeting the inflammatory pathway as a therapeutic tool for major depression. Neuroimmunomodulation. 2014;21:131–9.

    Article  CAS  PubMed  Google Scholar 

  121. Grosso G, Pajak A, Marventano S, Castellano S, Galvano F, Bucolo C, et al. Role of omega-3 fatty acids in the treatment of depressive disorders: a comprehensive meta-analysis of randomized clinical trials. PLoS One. 2014;9:e96905.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  122. Fond G, Hamdani N, Kapczinski F, Boukouaci W, Drancourt N, Dargel A, et al. Effectiveness and tolerance of anti-inflammatory drugs’ add-on therapy in major mental disorders: a systematic qualitative review. Acta Psychiatr Scand. 2014;129:163–79.

    Article  CAS  PubMed  Google Scholar 

  123. Lopresti AL, Maker GL, Hood SD, Drummond PD. A review of peripheral biomarkers in major depression: the potential of inflammatory and oxidative stress biomarkers. Prog Neuro-Psychopharmacol Biol Psychiatry. 2014;48:102–11.

    Article  Google Scholar 

  124. Fonseka TM, MacQueen GM, Kennedy SH. Neuroimaging biomarkers as predictors of treatment outcome in major depressive disorder. J Affect Disord. 2018;233:21–35.

    Article  PubMed  Google Scholar 

  125. Korb AS, Hunter AM, Cook IA, Leuchter AF. Rostral anterior cingulate cortex theta current density and response to antidepressants and placebo in major depression. Clin Neurophysiol. 2009;120:1313–9.

    Article  PubMed Central  PubMed  Google Scholar 

  126. Pizzagalli D, Pascual-Marqui RD, Nitschke JB, Oakes TR, Larson CL, Abercrombie HC, et al. Anterior cingulate activity as a predictor of degree of treatment response in major depression: evidence from brain electrical tomography analysis. Am J Psychiatry. 2001;158:405–15.

    Article  CAS  PubMed  Google Scholar 

  127. Mulert C, Juckel G, Brunnmeier M, Karch S, Leicht G, Mergl R, et al. Rostral anterior cingulate cortex activity in the theta band predicts response to antidepressive medication. Clin EEG Neurosci. 2007;38:78–81.

    Article  PubMed  Google Scholar 

  128. Perlis RH. Pharmacogenomic testing and personalized treatment of depression. Clin Chem. 2014;60:53–9.

    Article  CAS  PubMed  Google Scholar 

  129. Chekroud AM, Zotti RJ, Shehzad Z, Gueorguieva R, Johnson MK, Trivedi MH, et al. Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry. 2016;3:243–50.

    Article  PubMed  Google Scholar 

  130. Leuchter AF, Cook IA, Hamilton SP, Narr KL, Toga A, Hunter AM, et al. Biomarkers to predict antidepressant response. Curr Psychiatry Rep. 2010;12:553–62.

    Article  PubMed Central  PubMed  Google Scholar 

  131. Cuijpers P, Ebert DD, Acarturk C, Andersson G, Cristea IA. Personalized psychotherapy for adult depression: a meta-analytic review. Behav Ther. 2016;47:966–80.

    Article  PubMed  Google Scholar 

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Correspondence to Simmie L. Foster .

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FAQs: Common Questions and Answers

FAQs: Common Questions and Answers

  • Q1. When should a provider consider ordering a pharmacogenetic test?

  • A1. The value of ordering genetic tests is still somewhat equivocal, since pharmacogenetic data in MDD are preliminary and must be considered in tandem with other biomarkers and clinical assessment. Still, if the history is suggestive of ultrarapid metabolism (many adequate trials with no response) or poor metabolism (high side effect burden at minimal doses), then it may be useful to employ a lower-cost commercial kit. Available commercial genetic tests for antidepressant response include Avera Health’s GeneFolio, Genomind, GeneSight, and Roche’s AmpliChip test. It is important for a clinician to educate their patients on the utility of these tests and the meaning of the outcomes. Patient expectations also need to be managed carefully, e.g., by reminding the patient that, say, a pharmacogenomic profile is essentially a “probability marker” of success or failure of a certain treatment or treatments, but not a guarantee of either [130].

  • Q2. What is the role of psychotherapy in personalization of therapy for depression?

  • A2. Psychotherapy has been shown in many settings to be as effective as pharmacotherapy for depression and intuitively has great promise for personalization. However, reliable guidelines for personalizing psychotherapy are still lacking. For example, a recent meta-analysis suggests that it would take a substantial amount of time and resources to personalize psychotherapy [131]. However, there are numerous investigations that identify individual moderators of treatment response to therapies such as cognitive-behavioral therapy for depression. A lack of precision in selecting a type of therapy that will work for a specific individual could partly be due to common factors in psychotherapy research. For instance, many active therapies are effective for depression, which could be due to non-specific factors like therapeutic alliance, a supportive environment, and shared goals for treatment. Taken together, similar to the state of the research in pharmacological treatments for depression, personalized psychotherapy approaches are limited, and more research is needed to make firm conclusions or treatment recommendations.

  • Q3. How does patient preference play a role in individualization?

  • A3. Even a treatment predicted by the most sophisticated personalization algorithm won’t work if the patient doesn’t accept it. Therefore any algorithm or treatment plan should include an active discussion of the patient’s expectations and preference.

  • Q4. How can we personalize medicine for patients who don’t respond to any treatment?

  • A4. Even with our current optimized treatments, about 30% of patients with MDD do not respond. The goal of our current method of personalizing medicine is to predict a priori which of our available treatments will be most effective for a patient. Although this method of personalization will likely reduce “non-responders” to some degree (that remains to be determined), it is limited to the treatments and mechanistic targets we currently have available. Then the question becomes—what about the patients who have a distinct mechanism of disease, one that cannot be helped by our current treatments? Here is where the power of personalization to determine mechanisms comes in—with a goal to tailor the treatment to the patient. The physiological biomarkers we identify as being associated with disease in a particular patient can then be tested for their role in contributing to disease. This approach is more similar to the method of personalization in oncology, where the first step is to identify what are the particular mutations carried in a particular patient’s tumor and then (in the case of immunotherapy) develop a patient’s own immune cells to target the tumor. We are still working out ways to do this research in a productive manner in psychiatry. The future holds promise for such an approach.

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Foster, S.L., Petrie, S.R., Mischoulon, D., Fava, M. (2019). Personalized Medicine. In: Shapero, B., Mischoulon, D., Cusin, C. (eds) The Massachusetts General Hospital Guide to Depression. Current Clinical Psychiatry. Humana Press, Cham. https://doi.org/10.1007/978-3-319-97241-1_8

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