The establishment of the objective diagnostic markers and personalized medical intervention in patients with major depressive disorder: rationale and protocol

Xiaozhen Lv, Tianmei Si, Gang Wang, Huali Wang, Qi Liu, Changqing Hu, Jing Wang, Yunai Su, Yu Huang, Hui Jiang, Xin Yu, Xiaozhen Lv, Tianmei Si, Gang Wang, Huali Wang, Qi Liu, Changqing Hu, Jing Wang, Yunai Su, Yu Huang, Hui Jiang, Xin Yu

Abstract

Background: Major depressive disorders (MDD) is a common mental disorder with high prevalence, frequent relapse and associated with heavy disease burden. Heritability, environment and their interaction play important roles in the development of MDD. MDD patients usually display a wide variation in clinical symptoms and signs, while the diagnosis of MDD is relatively subjective. The treatment response varies substantially between different subtypes of MDD patients and only half respond adequately to the first antidepressant. This study aims to define subtypes of MDD, develop multi-dimension diagnostic test and combined predictors for improving the diagnostic accuracy and promoting personalized intervention in MDD patients.

Methods/design: This is a multi-center, multi-stage and prospective study. The first stage of this study is a case-control study, aims to explore the risk factors for developing MDD and then define the subtypes of MDD using 1200 MDD patients and 1200 healthy controls with a set of questionnaire. The second stage is a diagnostic test, aims to indentify and replicate the potential indicators to assist MDD diagnosis using 600 MDD patients and 300 healthy controls from the first stage with a set of questionnaire, neuropsychological assessment and a series of biomarkers. The third stage is a 96-week longitudinal study, including 8-week acute period treatment and 88-week stable period treatment, aims to identify overall predictors of treatment effectiveness on MDD at week 8 post treatment and to explore the predictors on MDD prognosis in the following 2 years using 600 MDD patients from the first stage with a set of questionnaire, neuropsychological assessment and a series of biomarkers. The primary outcome measure is the change of the total score of 17-Item Hamilton Rating Scale for Depression.

Discussion: This study will provide strong and suitable evidence for enhancing the accuracy of MDD diagnosis and promoting personalized treatment for MDD patients in clinical practice.

Trial registration: ClinicalTrials.gov: NCT02023567 ; registration date: December 2013.

Keywords: Diagnostic; Major depressive disorder; Personalized treatment; Subtype.

Figures

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Research flowchart

References

    1. World Health Organization. Depression. . Accessed on April 22, 2015
    1. Ustun TB, Ayuso-Mateos JL, Chatterji S, Mathers C, Murray CJ. Global burden of depressive disorders in the year 2000. Br J Psychiatry. 2004;184:386–92. doi: 10.1192/bjp.184.5.386.
    1. Ferrari AJ, Charlson FJ, Norman RE, Patten SB, Freedman G, Murray CJ, et al. Burden of depressive disorders by country, sex, age, and year: findings from the global burden of disease study 2010. PLoS Med. 2013;10 doi: 10.1371/journal.pmed.1001547.
    1. Greenberg PE, Kessler RC, Birnbaum HG, Leong SA, Lowe SW, Berglund PA, et al. The economic burden of depression in the United States: how did it change between 1990 and 2000. J Clin Psychiatry. 2003;64:1465–75. doi: 10.4088/JCP.v64n1211.
    1. Phillips MR, Zhang J, Shi Q, Song Z, Ding Z, Pang S, et al. Prevalence, treatment, and associated disability of mental disorders in four provinces in China during 2001–05: an epidemiological survey. Lancet. 2009;373:2041–53. doi: 10.1016/S0140-6736(09)60660-7.
    1. Sullivan PF, Neale MC, Kendler KS. Genetic epidemiology of major depression: review and meta-analysis. Am J Psychiatry. 2000;157:1552–62. doi: 10.1176/appi.ajp.157.10.1552.
    1. Keers R, Uher R. Gene-environment interaction in major depression and antidepressant treatment response. Curr Psychiatry Rep. 2012;14:129–37. doi: 10.1007/s11920-011-0251-x.
    1. Kendler KS, Kessler RC, Walters EE, MacLean C, Neale MC, Heath AC, et al. Stressful life events, genetic liability, and onset of an episode of major depression in women. Am J Psychiatry. 1995;152:833–42. doi: 10.1176/ajp.152.6.833.
    1. Van Loo HM, De Jonge P, Romeijn JW, Kessler RC, Schoevers RA. Data-driven subtypes of major depressive disorder: a systematic review. BMC Med. 2012;10:156. doi: 10.1186/1741-7015-10-156.
    1. Fava M, Kendler KS. Major Depressive Disorder. Neuron. 2000;28:336–41. doi: 10.1016/S0896-6273(00)00112-4.
    1. Lin CH, Huang CJ, Liu SK. Melancholic features in inpatients with major depressive disorder associate with differential clinical characteristics and treatment outcomes. Psychiatry Res. 2016;238:368–73. doi: 10.1016/j.psychres.2015.11.009.
    1. Papakostas GI, Larsen K. Testing anxious depression as a predictor and moderator of symptom improvement in major depressive disorder during treatment with escitalopram. Eur Arch Psychiatry Clin Neurosci. 2011;261:147–56. doi: 10.1007/s00406-010-0149-3.
    1. Iosifescu DV, Clementi-Craven N, Fraguas R, Papakostas GI, Petersen T, Alpert JE, et al. Cardiovascular risk factors may moderate pharmacological treatment effects in major depressive disorder. Psychosom Med. 2005;67:703–6. doi: 10.1097/01.psy.0000170338.75346.d0.
    1. 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 Neuropsychopharmacol Biol Psychiatry. 2014;48:102–11. doi: 10.1016/j.pnpbp.2013.09.017.
    1. Schmidt HD, Shelton RC, Duman RS. Functional biomarkers of depression: diagnosis, treatment, and pathophysiology. Neuropsychopharmacology. 2011;36:2375–94. doi: 10.1038/npp.2011.151.
    1. 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. doi: 10.1016/j.jad.2013.06.004.
    1. Blume J, Douglas SD, Evans DL. Immune suppression and immune activation in depression. Brain Behav Immun. 2011;25:221–9. doi: 10.1016/j.bbi.2010.10.008.
    1. 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. doi: 10.1016/j.biopsych.2009.09.033.
    1. Papakostas GI, Shelton RC, Kinrys G, Henry ME, Bakow BR, Lipkin SH, et al. Assessment of a multi-assay, serum-based biological diagnostic test for major depressive disorder: a pilot and replication study. Mol Psychiatry. 2013;18:332–9. doi: 10.1038/mp.2011.166.
    1. Yamamori H, Ishima T, Yasuda Y, Fujimoto M, Kudo N, Ohi K, et al. Assessment of a multi-assay biological diagnostic test for mood disorders in a Japanese population. Neurosci Lett. 2016;612:167–71. doi: 10.1016/j.neulet.2015.12.019.
    1. 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. doi: 10.1176/appi.ajp.163.1.28.
    1. Hennings JM, Owashi T, Binder EB, Horstmann S, Menke A, Kloiber S, et al. Clinical characteristics and treatment outcome in a representative sample of depressed inpatients - findings from the Munich Antidepressant Response Signature (MARS) project. J Psychiatr Res. 2009;43:215–29. doi: 10.1016/j.jpsychires.2008.05.002.
    1. Perlis RH. A clinical risk stratification tool for predicting treatment resistance in major depressive disorder. Biol Psychiatry. 2013;74:7–14. doi: 10.1016/j.biopsych.2012.12.007.
    1. Rush AJ, Wisniewski SR, Warden D, Luther JF, Davis LL, Fava M, et al. Selecting among second-step antidepressant medication monotherapies: predictive value of clinical, demographic, or first-step treatment features. Arch Gen Psychiatry. 2008;65:870–80. doi: 10.1001/archpsyc.65.8.870.
    1. 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. doi: 10.1016/j.biopsych.2006.10.001.
    1. Papakostas GI, Fava M. Predictors, moderators, and mediators (correlates) of treatment outcome in major depressive disorder. Dialogues Clin Neurosci. 2008;10:439–51.
    1. Licinio J, Dong C, Wong ML. Novel sequence variations in the brain-derived neurotrophic factor gene and association with major depression and antidepressant treatment response. Arch Gen Psychiatry. 2009;66:488–97. doi: 10.1001/archgenpsychiatry.2009.38.
    1. 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. doi: 10.1007/s11920-010-0160-4.
    1. Frieling H, Tadic A. Value of genetic and epigenetic testing as biomarkers of response to antidepressant treatment. Int Rev Psychiatry. 2013;25:572–8. doi: 10.3109/09540261.2013.816657.
    1. Lener MS, Iosifescu DV. In pursuit of neuroimaging biomarkers to guide treatment selection in major depressive disorder: a review of the literature. Ann N Y Acad Sci. 2015;1344:50–65. doi: 10.1111/nyas.12759.
    1. Polyakova M, Stuke K, Schuemberg K, Mueller K, Schoenknecht P, Schroeter ML. BDNF as a biomarker for successful treatment of mood disorders: a systematic & quantitative meta-analysis. J Affect Disord. 2015;174:432–40. doi: 10.1016/j.jad.2014.11.044.
    1. Huang TL, Lin CC. Advances in biomarkers of major depressive disorder. Adv Clin Chem. 2015;68:177–204. doi: 10.1016/bs.acc.2014.11.003.
    1. Thase ME. Using biomarkers to predict treatment response in major depressive disorder: evidence from past and present studies. Dialogues Clin Neurosci. 2014;16:539–44.
    1. Lim SW, Won HH, Kim H, Myung W, Kim S, Kim KK, et al. Genetic prediction of antidepressant drug response and nonresponse in Korean patients. PLoS ONE. 2014;9 doi: 10.1371/journal.pone.0107098.
    1. Lin JY, Jiang MY, Kan ZM, Chu Y. Influence of 5-HTR2A genetic polymorphisms on the efficacy of antidepressants in the treatment of major depressive disorder: a meta-analysis. J Affect Disord. 2014;168:430–8. doi: 10.1016/j.jad.2014.06.012.
    1. Zou YF, Ye DQ, Feng XL, Su H, Pan FM, Liao FF. Meta-analysis of BDNF Val66Met polymorphism association with treatment response in patients with major depressive disorder. Eur Neuropsychopharmacol. 2010;20:535–44. doi: 10.1016/j.euroneuro.2009.12.005.
    1. Niitsu T, Fabbri C, Bentini F, Serretti A. Pharmacogenetics in major depression: a comprehensive meta-analysis. Prog Neuropsychopharmacol Biol Psychiatry. 2013;45:183–94. doi: 10.1016/j.pnpbp.2013.05.011.
    1. Sung SC, Wisniewski SR, Balasubramani GK, Zisook S, Kurian B, Warden D, et al. Does early-onset chronic or recurrent major depression impact outcomes with antidepressant medications? A CO-MED trial report. Psychol Med. 2013;43:945–60. doi: 10.1017/S0033291712001742.
    1. Zhang ZJ. Manual of Behavioral Medicine Scales. Beijing: Chinese Medicine Electronic Audio and Video Press; 2005.
    1. Si TM, Shu L, Dang WM, Su YA, Chen JX, Dong WT, et al. Evaluation of the reliablility and validity of Chinese version of the Mini-International Neuropsychiatric Interview in patients with mental disorders. Chin Ment Health J. 2009;23:493–503.
    1. American Psychiatric Association . Diagnostic and statistical manual of mental disorders. 4. Washington: American Psychiatric Publishing; 2000.
    1. Wang XD, Wang XL, Ma H. Manuals of Mental Health Assessment Scales: the Expanded Edition. Beijing: Chinese Mental Health Journal Publishing; 1999.
    1. Yu X. Handbook of MATRICS Consensus Cognitive Battery Chinese Norm. Beijing: Peking University Medical Press; 2014.
    1. International HapMap Project. . Accessed 11 May 2016
    1. dbSNP. . Accessed 11 May 2016.
    1. Lee PH, Shatkay H. F-SNP: computationally predicted functional SNPs for disease association studies. Nucleic Acids Res. 2008;36:D820–4. doi: 10.1093/nar/gkm904.
    1. Kessler RC, Van Loo HM, Wardenaar KJ, Bossarte RM, Brenner LA, Ebert DD, et al. Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder. Epidemiol Psychiatr Sci. 2016;1–15.
    1. Sikora M, Heffernan J, Avery ET, Mickey BJ, Zubieta JK, Pecina M. Salience Network Functional Connectivity Predicts Placebo Effects in Major Depression. Biol Psychiatry Cogn Neurosci Neuroimaging. 2016;1:68–76. doi: 10.1016/j.bpsc.2015.10.002.
    1. Vasavada MM, Leaver AM, Espinoza RT, Joshi SH, Njau SN, Woods RP, et al. Structural connectivity and response to ketamine therapy in major depression: A preliminary study. J Affect Disord. 2016;190:836–41. doi: 10.1016/j.jad.2015.11.018.

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