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Depression is more than the sum score of its parts: individual DSM symptoms have different risk factors

Published online by Cambridge University Press:  02 December 2013

E. I. Fried ,
R. M. Nesse ,
K. Zivin ,
C. Guille  and
S. Sen
E. I. Fried
Affiliation:
Cluster of Excellence ‘Languages of Emotion’, Freie Universität Berlin, Germany Department of Education and Psychology, Freie Universität Berlin, Germany
R. M. Nesse
Affiliation:
Department of Psychology, University of Michigan, Ann Arbor, MI, USA Department of Psychiatry, University of Michigan Medical School, Ann Arbor, MI, USA
K. Zivin
Affiliation:
Department of Psychiatry, University of Michigan Medical School, Ann Arbor, MI, USA Department of Veterans Affairs, National Serious Mental Illness Treatment Resource and Evaluation Center, Ann Arbor, MI, USA Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
C. Guille
Affiliation:
Medical University of South Carolina, Charleston, SC, USA
S. Sen*
Affiliation:
Department of Psychiatry, University of Michigan Medical School, Ann Arbor, MI, USA
*
*Address for correspondence: Dr S. Sen, Rachel Upjohn Building, 4250 Plymouth Rd, Ann Arbor, MI 48109-5734, USA. (Email: srijan@umich.edu)
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Abstract

Background

For diagnostic purposes, the nine symptoms that compose the DSM-5 criteria for major depressive disorder (MDD) are assumed to be interchangeable indicators of one underlying disorder, implying that they should all have similar risk factors. The present study investigates this hypothesis, using a population cohort that shifts from low to elevated depression levels.

Method

We assessed the nine DSM-5 MDD criterion symptoms (using the Patient Health Questionnaire; PHQ-9) and seven depression risk factors (personal and family MDD history, sex, childhood stress, neuroticism, work hours, and stressful life events) in a longitudinal study of medical interns prior to and throughout internship (n = 1289). We tested whether risk factors varied across symptoms, and whether a latent disease model could account for heterogeneity between symptoms.

Results

All MDD symptoms increased significantly during residency training. Four risk factors predicted increases in unique subsets of PHQ-9 symptoms over time (depression history, childhood stress, sex, and stressful life events), whereas neuroticism and work hours predicted increases in all symptoms, albeit to varying magnitudes. MDD family history did not predict increases in any symptom. The strong heterogeneity of associations persisted after controlling for a latent depression factor.

Conclusions

The influence of risk factors varies substantially across DSM depression criterion symptoms. As symptoms are etiologically heterogeneous, considering individual symptoms in addition to depression diagnosis might offer important insights obfuscated by symptom sum scores.

Keywords

Depressiondepressive symptomsDSMheterogeneitypsychiatric diagnosisresidency
Type
Original Articles
Information
Psychological Medicine , Volume 44 , Issue 10 , July 2014 , pp. 2067 - 2076
Copyright
Copyright © Cambridge University Press 2013 

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References

Angst, J, Clayton, P (1986). Premorbid personality of depressive, bipolar, and schizophrenic patients with special reference to suicidal issues. Comprehensive Psychiatry 27, 511–532.Google Scholar
APA (2000). Diagnostic and Statistical Manual of Mental Disorders, 4th edn. American Psychiatric Association: Washington, DC.Google Scholar
APA (2013). Diagnostic and Statistical Manual of Mental Disorders, 5th edn. American Psychiatric Association: Washington, DC.Google Scholar
Baas, KD, Cramer, AOJ, Koeter, MWJ, van de Lisdonk, EH, van Weert, HC, Schene, AH (2011). Measurement invariance with respect to ethnicity of the Patient Health Questionnaire-9 (PHQ-9). Journal of Affective Disorders 129, 229–235.Google Scholar
Baumeister, H, Parker, JD (2012). Meta-review of depressive subtyping models. Journal of Affective Disorders 139, 126–140.Google Scholar
Beck, AT, Steer, RA, Garbin, MG (1988). Psychometric properties of the Beck Depression Inventory: 25 years of evaluation. Clinical Psychology Review 8, 77–100.Google Scholar
Beekman, ATF, Deeg, DJH, van Tilburg, T, Smit, JH, Hooijer, C, van Tilburg, W (1995). Major and minor depression in later life: a study of prevalence and risk factors. Journal of Affective Disorders 36, 65–75.CrossRefGoogle ScholarPubMed
Bollen, KA (1989). Structural Equations with Latent Variables. Wiley: New York.CrossRefGoogle Scholar
Borsboom, D, Cramer, AOJ, Schmittmann, VD, Epskamp, S, Waldorp, LJ (2011). The small world of psychopathology. PloS One 6, e27407.CrossRefGoogle ScholarPubMed
Butterfield, PS (1988). The stress of residency: a review of the literature. Archives of Internal Medicine 148, 1428–1435.CrossRefGoogle ScholarPubMed
Colman, I, Naicker, K, Zeng, Y, Ataullahjan, A, Senthilselvan, A, Patten, SB (2011). Predictors of long-term prognosis of depression. Canadian Medical Association Journal 183, 1969–1976.Google Scholar
Coryell, W, Winokur, G, Shea, T, Maser, JD, Endicott, J, Akiskal, HS (1994). The long-term stability of depressive subtypes. American Journal of Psychiatry 151, 199–204.Google Scholar
Costa, PT Jr., McCrae, RR (1997). Stability and change in personality assessment: the revised NEO Personality Inventory in the year 2000. Journal of Personality Assessment 68, 86–94.Google Scholar
Cramer, AOJ, Borsboom, D, Aggen, SH, Kendler, KS (2011). The pathoplasticity of dysphoric episodes: differential impact of stressful life events on the pattern of depressive symptom inter-correlations. Psychological Medicine 42, 957–965.Google Scholar
Cramer, AOJ, Waldorp, LJ, van der Maas, HLJ, Borsboom, D (2010). Comorbidity: a network perspective. Behavioral and Brain Sciences 33, 137–150.Google Scholar
Crockett, LJ, Randall, BA, Shen, Y-L, Russell, ST, Driscoll, AK (2005). Measurement equivalence of the center for epidemiological studies depression scale for Latino and Anglo adolescents: a national study. Journal of Consulting and Clinical Psychology 73, 47–58.Google Scholar
Duffy, TP (2005). Glory days: what price glory? The Pharos of Alpha Omega Alpha-Honor Medical Society 68, 22–30.Google Scholar
Epskamp, S, Cramer, AOJ, Waldorp, LJ, Schmittmann, VD, Borsboom, D (2012). qgraph: network visualizations of relationships in psychometric data. Journal of Statistical Software 48, 1–18.Google Scholar
Furukawa, TA, Streiner, DL, Azuma, H, Higuchi, T, Kamijima, K, Kanba, S, Ozaki, N, Aoba, A, Murasaki, M, Miura, S (2005). Cross-cultural equivalence in depression assessment: Japan-Europe-North American study. Acta Psychiatrica Scandinavica 112, 279–285.CrossRefGoogle ScholarPubMed
Gilmer, WS, McKinney, WT (2003). Early experience and depressive disorders: human and non-human primate studies. Journal of Affective Disorders 75, 97–113.Google Scholar
Gutman, DA, Nemeroff, CB (2003). Persistent central nervous system effects of an adverse early environment: clinical and preclinical studies. Physiology and Behavior 79, 471–478.CrossRefGoogle ScholarPubMed
Hamilton, M (1960). A rating scale for depression. Journal of Neurology, Neurosurgery and Psychiatry 23, 56–62.CrossRefGoogle ScholarPubMed
Hasler, G, Drevets, WC, Manji, HK, Charney, DS (2004). Discovering endophenotypes for major depression. Neuropsychopharmacology 29, 1765–1781.CrossRefGoogle ScholarPubMed
Hasler, G, Northoff, G (2011). Discovering imaging endophenotypes for major depression. Molecular Psychiatry 16, 604–619.CrossRefGoogle ScholarPubMed
Hek, K, Demirkan, A, Lahti, J, Terracciano, A, Teumer, A, Cornelis, MC, Amin, N, Bakshis, E, Baumert, J, Ding, J, Liu, Y, Marciante, K, Meirelles, O, Nalls, MA, Sun, YV, Vogelzangs, N, Yu, L, Bandinelli, S, Benjamin, EJ, Bennett, DA, Boomsma, D, Cannas, A, Coker, LH, de Geus, E, De Jager, PL, Diez-Roux, AV, Purcell, S, Hu, FB, Rimm, EB, Hunter, DJ, Jensen, MK, Curhan, G, Rice, K, Penman, AD, Rotter, JI, Sotoodehnia, N, Emeny, R, Eriksson, JG, Evans, DA, Ferrucci, L, Fornage, M, Gudnason, V, Hofman, A, Illig, T, Kardia, S, Kelly-Hayes, M, Koenen, K, Kraft, P, Kuningas, M, Massaro, JM, Melzer, D, Mulas, A, Mulder, CL, Murray, A, Oostra, BA, Palotie, A, Penninx, B, Petersmann, A, Pilling, LC, Psaty, B, Rawal, R, Reiman, EM, Schulz, A, Shulman, JM, Singleton, AB, Smith, AV, Sutin, AR, Uitterlinden, AG, Völzke, H, Widen, E, Yaffe, K, Zonderman, AB, Cucca, F, Harris, T, Ladwig, KH, Llewellyn, DJ, Räikkönen, K, Tanaka, T, van Duijn, CM, Grabe, HJ, Launer, LJ, Lunetta, KL, Mosley, TH Jr., Newman, AB, Tiemeier, H, Murabito, J (2013). A genome-wide association study of depressive symptoms. Biological Psychiatry 73, 667–678.Google Scholar
Hu, L, Bentler, PM (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling 6, 1–55.Google Scholar
Insel, T (2013). Director's blog: transforming diagnosis. National Institute of Mental Health (www.nimh.nih.gov/about/director/2013/transforming-diagnosis.shtml). Accessed 6 June 2013.Google Scholar
Jang, KL, Livesley, WJ, Taylor, S, Stein, MB, Moon, EC (2004). Heritability of individual depressive symptoms. Journal of Affective Disorders 80, 125–133.Google Scholar
Jones, RN (2006). Identification of measurement differences between English and Spanish language versions of the Mini-Mental State Examination. Detecting differential item functioning using MIMIC modeling. Medical Care 44, 124–133.CrossRefGoogle ScholarPubMed
Jöreskog, K, Goldberger, A (1975). Estimation of a model of multiple indicators and multiple causes of a single latent variable. Journal of the American Statistical Association 10, 631–639.Google Scholar
Katschnig, H, Pakesch, G, Egger-Zeidner, E (1986). Life stress and depressive subtypes: a review of present diagnostic criteria and recent research results. In Life Events and Psychiatric Disorders: Controversial Issues (ed. Katschnig, H.), pp. 201–245. Cambridge University Press: Cambridge.Google Scholar
Keller, MC, Neale, MC, Kendler, KS (2007). Association of different adverse life events with distinct patterns of depressive symptoms. American Journal of Psychiatry 164, 1521–1529.Google Scholar
Keller, MC, Nesse, RM (2005). Is low mood an adaptation? Evidence for subtypes with symptoms that match precipitants. Journal of Affective Disorders 86, 27–35.Google Scholar
Keller, MC, Nesse, RM (2006). The evolutionary significance of depressive symptoms: different adverse situations lead to different depressive symptom patterns. Journal of Personality and Social Psychology 91, 316–330.Google Scholar
Kendler, KS, Aggen, SH, Neale, MC (2013). Evidence for multiple genetic factors underlying DSM-IV criteria for major depression. Journal of the American Medical Association Psychiatry 70, 599–607.Google Scholar
Kendler, KS, Kuhn, J, Prescott, CA (2004). The interrelationship of neuroticism, sex, and stressful life events in the prediction of episodes of major depression. American Journal of Psychiatry 161, 631–636.Google Scholar
Kendler, KS, Myers, J, Prescott, CA (2005). Sex differences in the relationship between social support and risk for major depression: a longitudinal study of opposite-sex twin pairs. American Journal of Psychiatry 162, 250–256.Google Scholar
Kendler, KS, Zachar, P (2008). The incredible insecurity of psychiatric nosology. In Philosophical Issues in Psychiatry (ed. Kendler, K. S. and Parnas, J.), pp. 368–383. The Johns Hopkins University Press: Baltimore, MD.Google Scholar
Kessler, RC, Berglund, P, Demler, O, Jin, R, Koretz, D, Merikangas, KR, Rush, AJ, Walters, EE, Wang, PS (2003). The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication. Journal of the American Medical Association 289, 3095–3105.CrossRefGoogle ScholarPubMed
Kessler, RC, Chiu, WT, Demler, O, Walters, EE (2005). Prevalence, severity, and comorbidity of twelve-month DSM-IV disorders in the National Comorbidity Survey Replication (NCS-R). Archives of General Psychiatry 62, 617–627.CrossRefGoogle Scholar
Lichtenberg, P, Belmaker, RH (2010). Subtyping major depressive disorder. Psychotherapy and Psychosomatics 79, 131–135.Google Scholar
Lopez, AD, Mathers, CD, Ezzati, M, Jamiso, DT, Murray, CJL (2006). Global Burden of Disease and Risk Factors. Oxford University Press and The World Bank: New York.CrossRefGoogle ScholarPubMed
Lux, V, Kendler, KS (2010). Deconstructing major depression: a validation study of the DSM-IV symptomatic criteria. Psychological Medicine 40, 1679–1690.Google Scholar
Mazure, CM (1998). Life stressors as risk factors in depression. Clinical Psychology: Science and Practice 5, 291–313.Google Scholar
McGlinchey, JB, Zimmerman, M, Young, D, Chelminski, I (2006). Diagnosing major depressive disorder VIII: are some symptoms better than others? Journal of Nervous and Mental Disease 194, 785–790.Google Scholar
Muthén, LK, Muthén, BO (1998–2012). Mplus User's Guide. Seventh Edition. Muthén & Muthén: Los Angeles, CA.Google Scholar
Myung, W, Song, J, Lim, S-W, Won, H-H, Kim, S, Lee, Y, Kang, HS, Lee, H, Kim, J-W, Carroll, BJ, Kim, DK (2012). Genetic association study of individual symptoms in depression. Psychiatry Research 198, 400–406.Google Scholar
Nierenberg, AA, Trivedi, MH, Fava, M, Biggs, MM, Shores-Wilson, K, Wisniewski, SR, Balasubramani, GK, Rush, AJ (2007). Family history of mood disorder and characteristics of major depressive disorder: a STAR*D (sequenced treatment alternatives to relieve depression) study. Journal of Psychiatric Research 41, 214–221.Google Scholar
Oquendo, MA, Barrera, A, Ellis, SP, Li, S, Burke, AK, Grunebaum, M, Endicott, J, Mann, JJ (2004). Instability of symptoms in recurrent major depression: a prospective study. American Journal of Psychiatry 161, 255–261.Google Scholar
Ostergaard, SD, Jensen, SOW, Bech, P (2011). The heterogeneity of the depressive syndrome: when numbers get serious. Acta Psychiatrica Scandinavica 124, 495–496.Google Scholar
Paykel, ES (2003). Life events and affective disorders. Acta Psychiatrica Scandinavica 108, 61–66.CrossRefGoogle Scholar
Piccinelli, M, Wilkinson, G (2000) Gender differences in depression. Critical review. British Journal of Psychiatry 177, 486–492.CrossRefGoogle ScholarPubMed
Power, RA, Keers, R, Ng, MY, Butler, AW, Uher, R, Cohen-Woods, S, Ising, M, Craddock, N, Owen, MJ, Korszun, A, Jones, L, Jones, I, Gill, M, Rice, JP, Hauser, J, Henigsberg, N, Maier, W, Zobel, A, Mors, O, Placentino, AS, Rietschel, M, Souery, D, Kozel, D, Preisig, M, Lucae, S, Binder, EB, Aitchison, KJ, Tozzi, F, Muglia, P, Breen, G, Craig, IW, Farmer, AE, Müller-Myhsok, B, McGuffin, P, Lewis, CM (2012). Dissecting the genetic heterogeneity of depression through age at onset. American Journal of Medical Genetics. Part B, Neuropsychiatric Genetics 159B, 859–868.CrossRefGoogle ScholarPubMed
R Development Core Team (2008). R: a language and environment for statistical computing. R Foundation for Statistical Computing: Vienna.Google Scholar
Regier, DA, Narrow, WE, Clarke, DE, Kraemer, HC, Kuramoto, SJ, Kuhl, EA, Kupfer, DJ (2012). DSM-5 field trials in the United States and Canada, Part II: test-retest reliability of selected categorical diagnoses. American Journal of Psychiatry 170, 59–70.Google Scholar
Schmittmann, VD, Cramer, AOJ, Waldorp, LJ, Epskamp, S, Kievit, RA, Borsboom, D (2013). Deconstructing the construct: a network perspective on psychological phenomena. New Ideas in Psychology 31, 43–53.Google Scholar
Sen, S, Kranzler, HR, Didwania, AK, Schwartz, AC, Amarnath, S, Kolars, JC, Dalack, GW, Nichols, B, Guille, C (2013). Effects of the 2011 duty hour reforms on interns and their patients: a prospective longitudinal cohort study. Journal of the American Medical Association Internal Medicine 173, 657–662.Google ScholarPubMed
Sen, S, Kranzler, HR, Krystal, JH, Speller, H, Chan, G, Gelernter, J, Guille, C (2010). A prospective cohort study investigating factors associated with depression during medical internship. Archives of General Psychiatry 67, 557–565.Google Scholar
Shafer, AB (2006). Meta-analysis of the factor structures of four depression questionnaires: Beck, CES-D, Hamilton, and Zung. Journal of Clinical Psychology 62, 123–146.Google Scholar
Shanafelt, T, Habermann, T (2002). Medical residents' emotional well-being. Journal of the American Medical Association 288, 1846–1847.Google Scholar
Solomon, DA, Keller, MB, Leon, AC, Mueller, TI, Lavori, PW, Shea, MT, Coryell, W, Warshaw, M, Turvey, C, Maser, JD, Endicott, J (2000). Multiple recurrences of major depressive disorder. American Journal of Psychiatry 157, 229–233.Google Scholar
Spitzer, RL, Kroenke, K, Williams, JB (1999). Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. Primary Care Evaluation of Mental Disorders. Patient Health Questionnaire. Journal of the American Medical Association 282, 1737–1744.Google Scholar
Staner, L (2010). Comorbidity of insomnia and depression. Sleep Medicine Reviews 14, 35–46.Google Scholar
Tansey, KE, Guipponi, M, Perroud, N, Bondolfi, G, Domenici, E, Evans, D, Hall, SK, Hauser, J, Henigsberg, N, Hu, X, Jerman, B, Maier, W, Mors, O, O'Donovan, M, Peters, TJ, Placentino, A, Rietschel, M, Souery, D, Aitchison, KJ, Craig, I, Farmer, A, Wendland, JR, Malafosse, A, Holmans, P, Lewis, G, Lewis, CM, Stensbøl, TB, Kapur, S, McGuffin, P, Uher, R (2012). Genetic predictors of response to serotonergic and noradrenergic antidepressants in major depressive disorder: a genome-wide analysis of individual-level data and a meta-analysis. PLOS Medicine 9, e1001326.Google Scholar
Taylor, SE, Way, BM, Welch, WT, Hilmert, CJ, Lehman, BJ, Eisenberger, NI (2006). Early family environment, current adversity, the serotonin transporter promoter polymorphism, and depressive symptomatology. Biological Psychiatry 60, 671–676.Google Scholar
Widaman, K (1993). Common factor analysis versus principal component analysis: differential bias in representing model parameters? Multivariate Behavioral Research 28, 263–311.Google Scholar
Williams, CD, Taylor, TR, Makambi, K, Harrell, J, Palmer, JR, Rosenberg, L, Adams-Campbell, LL (2007). CES-D four-factor structure is confirmed, but not invariant, in a large cohort of African American women. Psychiatry Research 150, 173–180.Google Scholar
Wood, AM, Taylor, PJ, Joseph, S (2010). Does the CES-D measure a continuum from depression to happiness? Comparing substantive and artifactual models. Psychiatry Research 177, 120–123.Google Scholar
Zimmerman, M, Chelminski, I, McGlinchey, JB, Young, D (2006 a). Diagnosing major depressive disorder X: can the utility of the DSM-IV symptom criteria be improved? Journal of Nervous and Mental Disease 194, 893–897.Google Scholar
Zimmerman, M, McGlinchey, JB, Young, D, Chelminski, I (2006 b). Diagnosing major depressive disorder I: A psychometric evaluation of the DSM-IV symptom criteria. Journal of Nervous and Mental Disease 194, 158–163.Google Scholar
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