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Challenges and Strategies for Current Classifications of Depressive Disorders: Proposal for Future Diagnostic Standards

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Major Depressive Disorder

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1305))

Abstract

The Diagnostic and Statistical Manual of Mental Disorder, Fourth Edition (DSM-IV) was revised based on a combination of a categorical and a dimensional approach such that in the DSM, Fifth Edition (DSM-5), depressive disorders have been separated as a distinctive disease entity from bipolar disorders, consistent with the deconstruction of Kraepelinian dualism. Additionally, the diagnostic thresholds of depressive disorders may be reduced due to the addition of “hopelessness” to the subjective descriptors of depressed mood and the removal of the “bereavement exclusion.” Manic/hypomanic, psychotic, and anxious symptoms in major depressive disorder (MDD) and other depressive disorders are described using the transdiagnostic specifiers of “with mixed features,” “with psychotic features,” and “with anxious distress,” respectively. Additionally, due to the polythetic and operational characteristics of the DSM-5 diagnostic criteria, the heterogeneity of MDD is inevitable. Thus, 227 different symptom combinations fulfill the DSM-5 diagnostic criteria for MDD. This heterogeneity of MDD is criticized in view of the Wittgensteinian analogy of language game. Depression subtypes determined by disturbances in monoamine levels and the severity of the disease have been identified in the literature. According to a review of the Gottesman and Gould criteria, neuroticism, morning cortisol, cortisol awakening response, asymmetry in frontal cortical activity on electroencephalography (EEG), and probabilistic reward learning, among other variables, are evidenced as endophenotypes for depressive disorders. Network analysis has been proposed as a potential method to compliment the limitations of current diagnostic criteria and to explore the pathways between depressive symptoms, as well as to identify novel and interesting relationships between depressive symptoms. Based on the literature on network analysis in this field, no differences in the centrality index of the DSM and non-DSM symptoms were repeatedly present among patients with MDD. Furthermore, MDD and other depressive syndromes include two of the Research Domain Criteria (RDoC), including the Loss construct within the Negative Valence Systems domains and various Reward constructs within the Positive Valence Systems domain.

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References

  1. Insel TR (2012) Next-generation treatments for mental disorders. Sci Transl Med 4:155psc19

    Article  CAS  Google Scholar 

  2. Insel TR, Cuthbert BN (2015) Brain disorder? Precisely. Science 348:499–500

    Article  CAS  PubMed  Google Scholar 

  3. Beyond MG (2010) DSM: seeking a brain-based classification of mental illness. Science 327:1437

    Article  Google Scholar 

  4. Whooley O, Horowitz AV (2013) The paradox of professional success: grand ambition, furious resistances, and the derailment of the DSM-5 revision process. Springer, New York, pp 75–92

    Google Scholar 

  5. Park S-C, Kim Y-K (2019) Contemporary issues in depressive disorder. Psychiatry Investig 16:633–635

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Park S-C, Kim Y-K (2019) Diagnostic issues of depressive disorders from Kraepelinian dualism to the diagnostic and statistical manual of mental disorders, fifth edition. Psychiatry Investig 16:636–644

    Article  PubMed  PubMed Central  Google Scholar 

  7. Kim Y-K, Park S-C (2019) Classification of psychiatric disorders. Adv Exp Med Biol 1192:17–25

    Article  CAS  PubMed  Google Scholar 

  8. Kupfer DJ, Regier DA (2011) Neuroscience, clinical evidence, and the future of psychiatric classification in DSM-5. Am J Psychiatry 168(7):672–674

    Article  PubMed  Google Scholar 

  9. Uher R, Payne JL, Pavlova B, Perlis RH (2014) Major depressive disorder in DSM-5: implications for clinical practice and research of changes from DSM-IV. Depress Anxiety 31:459–471

    Article  PubMed  Google Scholar 

  10. Park S-C, Kim Y-K (2018) Depression in DSM-5: changes, controversies and future direction. In: Kim Y-K (ed) Understanding depression: Volume 2. Clinical manifestations, diagnosis and treatment. Springer, New York, pp 3–14

    Chapter  Google Scholar 

  11. Park S-C, Choi J (2017) Issues in the classification of psychotic depression. In: Kim Y-K (ed) Major depressive disorder: risk factors, characteristics and treatment options. NOVA Science Publishers, Inc., New York, pp 49–67

    Google Scholar 

  12. Block TS, Kushner H, Kalin N, Nelson C, Belanoff J, Schatzberg A (2018) Combined analysis of mifepristone for psychotic depression: plasma levels associated with clinical response. Biol Psychiatry 84:46–54

    Article  CAS  PubMed  Google Scholar 

  13. Østergaard SD, Bille J, Søltoft-Jensen H, Lauge N, Bech P (2012) The validity of severity-psychosis hypothesis in depression. J Affect Disord 140:48–56

    Article  PubMed  Google Scholar 

  14. Maj M, Pirozzi R, Magliano L, Fiorillo A, Bartoli L (2007) Phenomenology and prognostic significance of delusions in major depressive disorder: a 10-year prospective follow-up study. J Clin Psychiatry 68:1411–1417

    Article  PubMed  Google Scholar 

  15. Goldberg D, Kendler KS, Sirovata PJ, Regier DA (eds) (2010) Diagnostic issues in depression and generalized anxiety disorder: refining the research agenda for DSM-V. American Psychiatric Association, Washington, DC

    Google Scholar 

  16. Starcevic V, Portman ME (2013) The status quo as a good outcome: how the DSM-5 diagnostic criteria for generalized anxiety disorder remained unchanged from the DSM-IV criteria. Aust N Z J Psychiatry 47:995–997

    Article  PubMed  Google Scholar 

  17. Zimmerman M, Kerr S, Kiefer R, Balling C, Dalrymple K (2019) What is anxious depression? Overlap and agreement between different definitions. J Psychiatr Res 109:133–138

    Article  PubMed  Google Scholar 

  18. World Health Organization (1992) The ICD-10 classification of mental and behavioral disorders, clinical descriptions and diagnostic guidelines. World Health Organization, Geneva

    Google Scholar 

  19. Zisook S, Corruble E, Duan N, Iglewicz A, Karam EG, Lanouette N, Lebowitz B, Pies R, Reynolds C, Seay K, Katherine SM, Simon N, Young IT (2012) The bereavement exclusion and DSM-5. Depress Anxiety 29:425–443

    Article  PubMed  Google Scholar 

  20. Wakefield JC, Schmitz MF (2013) When does depression become a disorder? Using recurrence rates to evaluate the validity of proposed changes in major depression diagnostic thresholds. World Psychiatry 12:44–52

    Article  PubMed  PubMed Central  Google Scholar 

  21. Bandini J (2015) The medicalization of bereavement: (Ab)normal grief in the DSM-5. Death Stud 39:347–352

    Article  PubMed  Google Scholar 

  22. Allsopp K, Read J, Corcoran R, Kinderman P (2019) Heterogeneity in psychiatric diagnostic classification. Psychiatry Res 279:15–22

    Article  PubMed  Google Scholar 

  23. Hill MO (1973) Diversity and evenness: a unifying notation and its consequences. Ecology 54:427–432

    Article  Google Scholar 

  24. Hannah L, Kay JA (1977) Concentration in modern industry: theory, measurement, and the U.K. experience. The MacMillan Press, London

    Book  Google Scholar 

  25. Nunes A, Trappenberg T, Alda M (2020) We need an operational framework for heterogeneity in psychiatry research. J Psychiatry Neurosci 45:3–6

    Article  PubMed  PubMed Central  Google Scholar 

  26. Zimmerman M, Chelminski I, McGlinchey JB, Young D (2006) Diagnosing major depressive disorder X: can the utility of the DSM-IV symptom criteria be improved? J Nerv Ment Dis 194:893–897

    Article  PubMed  Google Scholar 

  27. Zimmerman M, Emmert-Aronson BO, Brown TA (2011) Concordance between a simpler definition of major depressive disorder and diagnostic and statistical manual of mental disorder, fourth edition: an independent replication in an outpatient sample. Compr Psychiatry 52:261–264

    Article  PubMed  Google Scholar 

  28. Østegaard SD, Jensen SOW, Pech P (2011) The heterogeneity of the depressive syndrome: when number get serious. Acta Psychiatr Scand 124:495–496

    Article  Google Scholar 

  29. Zimmerman M, Ellison W, Young D, Chelminski I, Dalrymple K (2015) How many different ways do patients meet the diagnostic criteria for major depressive disorder? Compr Psychiatry 56:29–34

    Article  PubMed  Google Scholar 

  30. Freedman R, Lewis DA, Michels DA, Pine DS, Schultz SK, Tamminga CA, Gabbard GO, Gau SSF, Javitt DC, Oquendo MA, Shrout PE, Vieta E, Yager J (2013) The initial field trials of DSM-5: new Blooms and old thorns. Am J Psychiatry 170:1–3

    Article  PubMed  Google Scholar 

  31. Park S-C, Kim J-M, Jun T-Y, Lee M-S, Kim J-B, Yim H-Y, Park YC (2017) How many different symptom combinations fulfill the diagnostic criteria for major depressive disorder? Results from the CRESCEND study. Nordic J Psychiatry 71:217–222

    Article  Google Scholar 

  32. Simmons WK, Burrows K, Avery JA, Kerr KL, Bodurka J, Savage CR, Drevets WC (2016) Depression-related increases and decreases in appetite: dissociable patterns of aberrant activity in reward and interoceptive neurocircuitry. Am J Psychiatry 173:418–428

    Article  PubMed  PubMed Central  Google Scholar 

  33. Rosenman S, Nasti J (2012) Psychiatric diagnoses are not mental processes: Wittgenstein on conceptual confusion. Aust N Z J Psychiatry 46:1046–1052

    Article  PubMed  Google Scholar 

  34. Wittgenstein L (2001) Philosophical Investigations. (Germantext, with a revised English translation, trans GEM Anscombe), 3rd edn. Blackwell, Oxford

    Google Scholar 

  35. Beijers L, Wardenaar KJ, van Loo HM, Schoevers RA (2019) Data-driven biological subtypes of depression: systemic review of biological approaches to depression subtyping. Mol Psychiatry 24:888–900

    Article  PubMed  Google Scholar 

  36. Lamers F, de Jonge P, Nolen WA, Smit JH, Zitman FG, Beekman AT, Penninx BW (2010) Identifying depressive subtypes in a large cohort study: result from the netherlands study of depression and anxiety (NESDA). J Clin Psychiatry 71:1582–1589

    Article  PubMed  Google Scholar 

  37. Lamers F, Rhebergen D, Merikangas KR, de Jonge P, Beekman ATF, Pennix BWJH (2012) Stability and transitions of depressive subtypes over a 2-year follow-up. Psychol Med 42:2083–2093

    Article  CAS  PubMed  Google Scholar 

  38. Zimmerman M, Martinez JH, Young D, Chelminski I, Dalrymple K (2013) Severity classification on the Hamilton depression rating scale. J Affect Disord 150:384–388

    Article  PubMed  Google Scholar 

  39. Zimmerman M, Balling C, Chelminski I, Dalrymple K (2018) Understanding the severity of depression: which symptoms of depression are the best indicators of depression severity? Compr Psychiatry 87:84–88

    Article  PubMed  Google Scholar 

  40. Zimmerman M, Balling C, Chelminski I, Dalrymple K (2019) Symptom presence versus symptom intensity in understanding the severity of depression: implications for documentation in electronic medical records. J Affect Disord 26:344–347

    Article  Google Scholar 

  41. Zimmerman M, Balling C, Chelminski I, Dalrymple K (2019) Understanding the severity of depression: do nondepressive symptoms influence global ratings of depression severity? CNS Spectr 12:1–4. https://doi.org/10.1017/S1092852919001548

    Article  Google Scholar 

  42. Cannon TD, Keller MC (2006) Endophenotypes in the genetic analyses of mental disorders. Annu Rev Clin Psychol 2:267–290

    Article  PubMed  Google Scholar 

  43. Gottesman I, Gould TD (2003) The phenotype concept in psychiatry: etymology and strategic intentions. Am J Psychiatry 160:636–645

    Article  PubMed  Google Scholar 

  44. Miller GA, Rochstroh B (2013) Endophenotypes in psychopathology research: where do we stand? Annu Rev Clin Psychol 9:177–213

    Article  PubMed  Google Scholar 

  45. Goldstetin BL, Klein D (2014) A review of selected candidate endophenotypes for depression. Annu Rev Clin Psychol 34:417–427

    Article  Google Scholar 

  46. Chan RCK, Gottesman II (2008) Neurological soft signs as candidate endophenotypes for schizophrenia: a shooting star or a Northern star? Neurosci Biobehav Rev 32:957–971

    Article  PubMed  Google Scholar 

  47. Gould TD, Gottesman II (2006) Psychiatric endophenotypes and the development of valid animal models. Genes Brain Behav 5:113–119

    Article  CAS  PubMed  Google Scholar 

  48. Saxe GN (2017) Network psychiatry: computational methods to understand the complexity of psychiatric disorders. J Am Acad Child Adolesc Psychiatry 56:639–641

    Article  PubMed  Google Scholar 

  49. Barabasi AL (2017) Network medicine form obesity to the “diseasome”. N Engl J Med 357:404–407

    Article  Google Scholar 

  50. Russell JD, Neill EL, Carrion VG, Weems CF (2017) The network structure of posttraumatic stress symptoms in children and adolescents exposed to disaster. J Am Acad Child Adolesc Psychiatry 56:669–677

    Article  PubMed  Google Scholar 

  51. Opsahl T, Agneessens F, Skvoretz J (2010) 2010. Node centrality in weighted networks: Generalizing degrees and shortest paths. Soc Netw 32:245–251

    Article  Google Scholar 

  52. McNally RJ, Robinaugh DJ, Wu GW, Wang L, Deserno MK, Borsboom D (2015) Mental disorders as causal systems a network approach to posttraumatic stress disorder. Clin Psychol Sci 3:836–849

    Article  Google Scholar 

  53. Young G (2015) Causality in psychiatry: a hybrid symptom network construct model. Front Psychiatry 6:164

    Article  PubMed  PubMed Central  Google Scholar 

  54. Borsboom D, Cramer AOJ (2013) Network analysis: an integrative approach to the structure of psychopathology. Annu Rev Clin Psychol 9:91–121

    Article  PubMed  Google Scholar 

  55. Fried EI, Epskamp S, Nesse RM, Tuerlinckx F, Borsboom D (2016) What are ‘good’ depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis. J Affect Disord 189:314–329

    Article  PubMed  Google Scholar 

  56. Armour C, Fried EI, Deserno MK, Tsai J, Pietrzak RH (2017) A network analysis of DSM-5 posttraumatic stress disorder symptoms and correlates in U.S. military veterans. J Affect Disord 45:49–59

    Google Scholar 

  57. van Borkulo C, Boschloo L, Borsboom D, Penninx BWJH, Waldorp LJ, Schoevers RA (2015) Association of symptom network structure with the course of depression. JAMA Psychiatry 72:1219–1226

    Article  PubMed  Google Scholar 

  58. Kendler KS, Aggen SH, Flint J, Borsboom D, Fried EI (2018) The centrality of DSM and non-DSM depressive symptoms in Han Chinese women with major depression. J Affect Disord 227:739–744

    Article  PubMed  Google Scholar 

  59. Pe ML, Kircanski K, Thompson RJ, Bringmann LF, Tuerlinckx F, Mestdagh M, Mata J, Jaeggi SM, Buschkuehl M, Joindes J, Kuppens P, Gotilib IH (2015) Emotion-network density in major depressive disorder. Clin Psychol Sci 3:292–300

    Article  Google Scholar 

  60. Santos H Jr, Fried EI, Asafu-Adjei J, Ruiz RJ (2017) Network structure of perinatal depressive symptoms in Latinas: relationship to stress and reproductive biomarkers. Res Nurs Health 40:218–228

    Article  PubMed  PubMed Central  Google Scholar 

  61. Wood ML, Gibb BE (2015) Integrating NIMH Research Domain Criteria (RDoC) into depression research. Curr Opin Psychol 4:6–12

    Article  Google Scholar 

  62. National Institute of Mental Health. The National Institute of Mental Health strategic plan (NIH Publication No. 08-6368). 2008. Retrieved from: http://www.nimh.nih.gov/about/strategic-planning-reports/index.shtml

  63. Cuthbert BN, Insel TR (2013) Toward the future of psychiatric diagnosis: the seven pillars of RDoC. BMC Med 11:126

    Article  PubMed  PubMed Central  Google Scholar 

  64. Dillon DG, Rosso IM, Pechtel P, Killgore WDS, Rauch SL, Pizzagalli DA (2014) Peril and pleasure: an RDoC-inspired examination of threat responses and reward processing in anxiety and depression. Depress Anxiety 31:233–249

    Article  PubMed  Google Scholar 

  65. Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium: a mega-analysis of genome-wide association studies for major depressive disorder. Mol Psychiatry 2013; 18: 497–511.

    Google Scholar 

  66. Disner SG, Beevers CG, Haigh EAP, Beck AT (2011) Neural mechanisms of the cognitive model of depression. Nat Rev Neurosci 12:467–477

    Article  CAS  PubMed  Google Scholar 

  67. Gibb BE (2014) Depression in children. In: Gotlib IH, Hammen CL (eds) Handbook of depression, 3rd edn. Guilford, New York, pp 374–390

    Google Scholar 

  68. Hamilton JP, Etkin A, Furman DJ, Lemus MG, Johnson RF, Gotlib IH (2012) Functional neuroimaging of major depressive disorder: a meta-analysis and new integration of baseline activation and neural response data. Am J Psychiatry 169:693–703

    Article  PubMed  Google Scholar 

  69. Olino TM (2016) Future research directions in the positive valence systems: measurement, development, and implications for youth unipolar depression. J Clin Child Adolesc Psychol 45:681–705

    Article  PubMed  PubMed Central  Google Scholar 

  70. Heim C, Binder EB (2012) Current research trends in early life stress and depression: review of human studies on sensitive periods, gene-environment interactions, and epigenetics. Exp Neurol 233:12–111

    Article  Google Scholar 

  71. Karg K, Burmeister M, Shedden K, Sen S (2011) The serotonin transporter variant (5-HTTLPR), stress, and depression meta-analysis revisited: evidence of genetic moderation. Arch Gen Psychiatry 68:444–454

    Article  PubMed  PubMed Central  Google Scholar 

  72. Risch N, Herrell R, Lehner T, Liang K-Y, Eaves L, Hoh J, Greim A, Kovacs M, Ott J, Merikangas KR (2009) Interaction between the serotonin transporter gene (5-HTTLPR), stressful life events, and risk of depression: a meta-analysis. J Am Med Assoc 301:2462–2471

    Article  CAS  Google Scholar 

  73. Gee DG, Humphreys KL, Flannery J, Goff B, Telzer EH, Shapiro M, Hare TA, Bookheimer SY, Tottenham N (2013) A developmental shift from positive to negative connectivity in human amygdala-prefrontal circuitry. J Neurosci 33:4584–4593

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Hankin BL, Badanes LS, Abela JRZ, Watamura SE (2010) Hypothalamic–pituitary–adrenal axis dysregulation in dysphoric children and adolescents: cortisol reactivity to psychosocial stress from preschool through middle adolescence. Biol Psychiatry 68:484–490

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Harrison AJ, Gibb BE (2015) Attentional biases in currently depressed children: an eye-tracking study of biases in sustained attention to emotional stimuli. J Clin Child Adolesc Psychol 44:1008–1014

    Article  PubMed  Google Scholar 

  76. Kellough JL, Beevers CG, Ellis AJ, Wells TT (2008) Time course of selective attention in clinically depressed young adults: an eye tracking study. Behav Res Ther 46:1238–1243

    Article  PubMed  PubMed Central  Google Scholar 

  77. Silk JS, Siegle GJ, Whalen DJ, Ostapenko LJ, Ladouceur CD, Dahl RE (2009) Pubertal changes in emotional information processing: pupillary, behavioral, and subjective evidence during emotional word identification. Dev Psychopathol 21:7–26

    Article  PubMed  PubMed Central  Google Scholar 

  78. Lenroot RK, Schmitt JE, ORdaz SJ, Wallace GL, Neale MC, Lerch JP, Kendler KS, Evans AC, Giedd JN (2009) Differences in genetic and environmental influences on the human cerebral cortex associated with development during childhood and adolescence. Hum Brain Mapp 30:163–174

    Article  PubMed  Google Scholar 

  79. Schmitt JE, Neale MC, Fassassi B, Perez J, Lenroot RK, Wells EM, Giedd JN (2014) The dynamic role of genetics on cortical patterning during childhood and adolescence. Proc Natl Acad Sci 111:6774–6779

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2019R1A2C1090146).

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Correspondence to Yong-Ku Kim .

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Park, SC., Kim, YK. (2021). Challenges and Strategies for Current Classifications of Depressive Disorders: Proposal for Future Diagnostic Standards. In: Kim, YK. (eds) Major Depressive Disorder. Advances in Experimental Medicine and Biology, vol 1305. Springer, Singapore. https://doi.org/10.1007/978-981-33-6044-0_7

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