Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-03T12:02:21.883Z Has data issue: false hasContentIssue false
  • English
  • Français

Dimensions of anxiety in Major depressive disorder and their use in predicting antidepressant treatment outcome: an iSPOT-D report

Published online by Cambridge University Press:  26 April 2019

Taylor A. Braund Open the ORCID record for Taylor A. Braund [Opens in a new window] ,
Donna M. Palmer ,
Leanne M. Williams  and
Anthony W. F. Harris
Taylor A. Braund*
Affiliation:
Brain Dynamics Centre, The Westmead Institute for Medical Research, Sydney, NSW, Australia Discipline of Psychiatry, Sydney Medical School, University of Sydney, Sydney, NSW, Australia The Brain Resource Company, Sydney, NSW, Australia
Donna M. Palmer
Affiliation:
Brain Dynamics Centre, The Westmead Institute for Medical Research, Sydney, NSW, Australia The Brain Resource Company, Sydney, NSW, Australia
Leanne M. Williams
Affiliation:
Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC) Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
Anthony W. F. Harris
Affiliation:
Brain Dynamics Centre, The Westmead Institute for Medical Research, Sydney, NSW, Australia Discipline of Psychiatry, Sydney Medical School, University of Sydney, Sydney, NSW, Australia
*
Author for correspondence: Taylor A. Braund, E-mail: tbra.4312@uni.sydney.edu.au
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Major depressive disorder (MDD) commonly co-occurs with clinically significant levels of anxiety. However, anxiety symptoms are varied and have been inconsistently associated with clinical, functional, and antidepressant treatment outcomes. We aimed to identify and characterise dimensions of anxiety in people with MDD and their use in predicting antidepressant treatment outcome.

Method

1008 adults with a current diagnosis of single-episode or recurrent, nonpsychotic, MDD were assessed at baseline on clinical features and cognitive/physiological functioning. Participants were then randomised to one of three commonly prescribed antidepressants and reassessed at 8 weeks regarding symptom change, as well as remission and response, on the 17-item Hamilton Rating Scale Depression (HRSD17) and the 16-item Quick Inventory of Depressive Symptomatology (QIDS-SR16). Exploratory factor analysis was used on items from scales assessing anxiety symptoms, and resulting factors were assessed against clinical features and cognitive/physiological functioning. Factors were also assessed on their ability to predict treatment outcome.

Results

Three factors emerged relating to stress, cognitive anxiety, and somatic anxiety. All factors showed high internal consistency, minimal cross-loadings, and unique clinical and functional profiles. Furthermore, only higher somatic anxiety was associated with poorer QIDS-SR16 remission, even after adjusting for covariates and multiple comparisons.

Conclusions

Anxiety symptoms in people with MDD can be separated onto distinct factors that differentially respond to treatment outcome. Furthermore, these factors do not align with subscales of established measures of anxiety. Future research should consider cognitive and somatic symptoms of anxiety separately when assessing anxiety in MDD and their use in predicting treatment outcome.

Keywords

AnxietyantidepressantDASSexploratory factor analysismajor depressive disorder
Type
Original Articles
Information
Psychological Medicine , Volume 50 , Issue 6 , April 2020 , pp. 1032 - 1042
Copyright
Copyright © Cambridge University Press 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abdi, H (2003) Factor rotations in factor analyses. In Lewis-Beck, M, Bryman, A and Futing, T (ed.), Encyclopedia of Social Sciences Research Methods. Thousand Oaks, CA: Sage. pp. 792–795.Google Scholar
American Psychiatric, A. (1994) Diagnostic and Statistical Manual of Mental Disorders. Washington, DC: American Psychiatric Association.Google Scholar
Azur, MJ, Stuart, EA, Frangakis, C and Leaf, PJ (2011) Multiple imputation by chained equations: what is it and how does it work? International Journal of Methods in Psychiatric Research, 20, 40–49.10.1002/mpr.329CrossRefGoogle ScholarPubMed
Bartlett, AS (1938) Methods of estimating mental factors. Nature 141, 609–610.Google Scholar
Barzi, F and Woodward, M (2004) Imputations of Missing Values in Practice: Results from Imputations of Serum Cholesterol in 28 Cohort Studies. American Journal Of Epidemiology 160, 34–45.CrossRefGoogle ScholarPubMed
Benjamini, Y and Hochberg, Y (1995) Controlling the false discovery rate - a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B-Methodological 57, 289–300.Google Scholar
Bitran, S, Farabaugh, AH, Ameral, VE, Larocca, RA, Clain, AJ, Fava, M and Mischoulon, D (2011) Do early changes in the HAM-D-17 anxiety/somatization factor items affect the treatment outcome among depressed outpatients? Comparison of two controlled trials of St john's wort (Hypericum perforatum) versus a SSRI. International Clinical Psychopharmacology 26, 206–212.CrossRefGoogle ScholarPubMed
Braund, TA, Palmer, DM, Williams, LM and Harris, AW (2019) Characterising anxiety in major depressive disorder and its use in predicting antidepressant treatment outcome: an iSPOT-D report. Australian and New Zealand Journal of Psychiatry e1–e12, doi: 10.1177/0004867419835933.Google ScholarPubMed
Brown, TA, Chorpita, BF, Korotitsch, W and Barlow, DH (1997) Psychometric properties of the depression anxiety stress scales (DASS) in clinical samples. Behaviour Research and Therapy 35, 79–89.CrossRefGoogle Scholar
Castaneda, AE, Suvisaari, J, Marttunen, M, Perala, J, Saarni, SI, Aalto-Setala, T, Lonnqvist, J and Tuulio-Henriksson, A (2011) Cognitive functioning in a population-based sample of young adults with anxiety disorders. European Psychiatry 26, 346–353.CrossRefGoogle Scholar
Cattell, RB (1966) The scree test For The number of factors. Multivariate Behavioral Research 1, 245–276.CrossRefGoogle ScholarPubMed
Chalmers, JA, Quintana, DS, Abbott, MJ and Kemp, AH (2014) Anxiety disorders are associated with reduced heart rate variability: a meta-analysis. Frontiers in Psychiatry 5, 80.CrossRefGoogle ScholarPubMed
Chang, HA, Chang, CC, Tzeng, NS, Kuo, TB, Lu, RB and Huang, SY (2013) Generalized anxiety disorder, comorbid major depression and heart rate variability: a case-control study in Taiwan. Psychiatry Investigation 10, 326–335.10.4306/pi.2013.10.4.326CrossRefGoogle ScholarPubMed
Chen, LF, Chang, CC, Tzeng, NS, Kuo, TJ, Kao, YC, Huang, SY and Chang, HA (2014) Depression, anxiety, and heart rate variability: a case-control study in Taiwan. Journal of Medical Sciences 34, 9–18.Google Scholar
Cleary, P and Guy, W (1977) Factor analysis of the Hamilton depression scale. Drugs Under Experimental and Clinical Research 1, 115–120.Google Scholar
Cohen, H and Benjamin, J (2006) Power spectrum analysis and cardiovascular morbidity in anxiety disorders. Autonomic Neuroscience, 128, 1–8.CrossRefGoogle ScholarPubMed
Costa, PT and McCrae, RR (1989) The NEO-PI/NEO-FFI Manual Supplement. Odessa, FL: Psychological Assessment Resources.Google Scholar
Costello, AB and Osborne, JW (2005) Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. Practical Assessment, Research and Evaluation 10, 1–9.Google Scholar
Derakshan, N and Eysenck, MW (2009) Anxiety, processing efficiency, and cognitive performance New developments from attentional control theory. European Psychologist 14, 168–176.10.1027/1016-9040.14.2.168CrossRefGoogle Scholar
Diener, E, Emmons, RA, Larsen, RJ and Griffin, S (1985) The satisfaction With life scale. Journal of Personality Assessment 49, 71–75.CrossRefGoogle ScholarPubMed
Estabrook, R and Neale, M (2013) A comparison of factor score estimation methods in the presence of missing data: reliability and an application to nicotine dependence. Multivariate Behavioral Research 48, 1–27.CrossRefGoogle Scholar
Eysenck, MW and Derakshan, N (2011) New perspectives in attentional control theory. Personality and Individual Differences 50, 955–960.10.1016/j.paid.2010.08.019CrossRefGoogle Scholar
Eysenck, MW, Derakshan, N, Santos, R and Calvo, MG (2007) Anxiety and cognitive performance: attentional control theory. Emotion 7, 336–353.CrossRefGoogle ScholarPubMed
Farabaugh, A, Mischoulon, D, Fava, M, Wu, SL, Mascarini, A, Tossani, E and Alpert, JE (2005) The relationship between early changes in the HAMD-17 anxiety/somatization factor items and treatment outcome among depressed outpatients. International Clinical Psychopharmacology 20, 87–91.CrossRefGoogle ScholarPubMed
Farabaugh, AH, Bitran, S, Witte, J, Alpert, J, Chuzi, S, Clain, AJ, Baer, L, Fava, M, Mcgrath, PJ, Dording, C, Mischoulon, D and Papakostas, GI (2010) Anxious depression and early changes in the HAMD-17 anxiety-somatization factor items and antidepressant treatment outcome. International Clinical Psychopharmacology 25, 214–217.CrossRefGoogle ScholarPubMed
Ferreri, F, Lapp, LK and Peretti, CS (2011) Current research on cognitive aspects of anxiety disorders. Current Opinion in Psychiatry 24, 49–54.CrossRefGoogle ScholarPubMed
Gaspersz, R, Nawijn, L, Lamers, F and Penninx, B (2018) Patients with anxious depression: overview of prevalence, pathophysiology and impact on course and treatment outcome. Current Opinion in Psychiatry 31, 17–25.10.1097/YCO.0000000000000376CrossRefGoogle ScholarPubMed
Gloster, AT, Rhoades, HM, Novy, D, Klotsche, J, Senior, A, Kunik, M, Wilson, N and Stanley, MA (2008) Psychometric properties of the depression anxiety and stress scale-21 in older primary care patients. Journal of Affective Disorders 110, 248–259.CrossRefGoogle ScholarPubMed
Goldberg, D and Fawcett, J (2012) The importance of anxiety in both major depression and bipolar disorder. Depression and Anxiety 29, 471–478.CrossRefGoogle ScholarPubMed
Goldman, HH, Skodol, AE and Lave, TR (1992) Revising axis V for DSM-IV: a review of measures of social functioning. American Journal of Psychiatry 149, 1148–1156.Google ScholarPubMed
Green, E, Goldstein-Piekarski, AN, Schatzberg, AF, Rush, AJ, Ma, J and Williams, L (2017) Personalizing antidepressant choice by sex, body mass index, and symptom profile: an iSPOT-D report. Personalized Medicine in Psychiatry 1–2, 65–73.CrossRefGoogle Scholar
Gross, JJ and John, OP (2003) Individual differences in two emotion regulation processes: implications for affect, relationships, and well-being. Journal of Personality and Social Psychology 85, 348–362.CrossRefGoogle ScholarPubMed
Hamilton, M (1960) A rating scale for depression. Journal of Neurology, Neurosurgery, and Psychiatry 23, 56–62.CrossRefGoogle ScholarPubMed
Harper, A, Power, M and Grp, W (1998) Development of the World Health Organization WHOQOL-BREF quality of life assessment. Psychological Medicine 28, 551–558.Google Scholar
Horn, JL (1965) A rationale and test for the number of factors in factor-analysis. Psychometrika 30, 179–185.CrossRefGoogle ScholarPubMed
Insel, T, Cuthbert, B, Garvey, M, Heinssen, R, Pine, DS, Quinn, K, Sanislow, C and Wang, P (2010) Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. American Journal of Psychiatry 167, 748–751.CrossRefGoogle Scholar
Ionescu, DF, Niciu, MJ, Henter, ID and Zarate, CA (2013 a) Defining anxious depression: a review of the literature. CNS Spectrums 18, 252–260.10.1017/S1092852913000114CrossRefGoogle ScholarPubMed
Ionescu, DF, Niciu, MJ, Mathews, DC, Richards, EM and Zarate, CA Jr. (2013 b) Neurobiology of anxious depression: a review. Depression and Anxiety, 30, 374–385.10.1002/da.22095CrossRefGoogle ScholarPubMed
Ionescu, DF, Niciu, MJ, Richards, EM and Zarate, CA Jr. (2014) Pharmacologic treatment of dimensional anxious depression: a review. The Primary Care Companion for CNS Disorders 16, e1–e13.Google ScholarPubMed
Josse, J and Husson, F (2013) Handling missing values in exploratory multivariate data analysis methods. Journal de la Société Française de Statistique 153, 79–99.Google Scholar
Josse, J and Husson, F (2016) missMDA: a package for handling missing values in multivariate data analysis. Journal of Statistical Software 70, 1–31.10.18637/jss.v070.i01CrossRefGoogle Scholar
Kaiser, HF (1960) The application of electronic-computers to factor-analysis. Educational and Psychological Measurement 20, 141–151.CrossRefGoogle Scholar
Kemp, AH, Quintana, DS, Felmingham, KL, Matthews, S and Jelinek, HF (2012) Depression, comorbid anxiety disorders, and heart rate variability in physically healthy, unmedicated patients: implications for cardiovascular risk. PloS One 7, e30777.CrossRefGoogle ScholarPubMed
Kessler, RC, Sampson, NA, Berglund, P, Gruber, MJ, Al-Hamzawi, A, Andrade, L, Bunting, B, Demyttenaere, K, Florescu, S, De Girolamo, G, Gureje, O, He, Y, Hu, C, Huang, Y, Karam, E, Kovess-Masfety, V, Lee, S, Levinson, D, Medina Mora, ME, Moskalewicz, J, Nakamura, Y, Navarro-Mateu, F, Browne, MA, Piazza, M, Posada-Villa, J, Slade, T, Ten Have, M, Torres, Y, Vilagut, G, Xavier, M, Zarkov, Z, Shahly, V and Wilcox, MA (2015) Anxious and non-anxious major depressive disorder in the world health organization world mental health surveys. Epidemiology and Psychiatric Sciences 24, 210–226.CrossRefGoogle ScholarPubMed
Licht, CM, De Geus, EJ, Van Dyck, R and Penninx, BW (2009) Association between anxiety disorders and heart rate variability in The Netherlands Study of Depression and Anxiety (NESDA). Psychosomatic Medicine 71, 508–518.CrossRefGoogle Scholar
Little, RJA (1988) Missing-Data adjustments in large surveys. Journal of Business & Economic Statistics 6, 287–296.Google Scholar
Lovibond, PF (1998) Long-term stability of depression, anxiety, and stress syndromes. Journal of Abnormal Psychology 107, 520–526.CrossRefGoogle ScholarPubMed
Lovibond, SH and Lovibond, PF (1995) Manual for the Depression Anxiety Stress Scales. Sydney: Psychology Foundation.Google Scholar
Marshall, A, Altman, DG, Holder, RL and Royston, P (2009) Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines. BMC Medical Research Methodology 9, e1–e8.CrossRefGoogle ScholarPubMed
Mathersul, D, Palmer, DM, Gur, RC, Gur, RE, Cooper, N, Gordon, E and Williams, LM (2009) Explicit identification and implicit recognition of facial emotions: II. Core domains and relationships with general cognition. Journal of Clinical and Experimental Neuropsychology 31, 278–291.CrossRefGoogle ScholarPubMed
Pan, J and Tompkins, WJ (1985) A real-time Qrs detection algorithm. IEEE Transactions on Biomedical Engineering 32, 230–236.CrossRefGoogle ScholarPubMed
Paul, RH, Lawrence, J, Williams, LM, Richard, CC, Cooper, N and Gordon, E (2005) Preliminary validity of “IntegNeuro (TM)”: a new computerized battery of neurocognitive tests. International Journal of Neuroscience, 115, 1549–1567.CrossRefGoogle Scholar
Penttila, J, Helminen, A, Jartti, T, Kuusela, T, Huikuri, HV, Tulppo, MP, Coffeng, R and Scheinin, H (2001) Time domain, geometrical and frequency domain analysis of cardiac vagal outflow: effects of various respiratory patterns. Clinical Physiology 21, 365–376.CrossRefGoogle ScholarPubMed
R Core Team (2019) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
Raiche, G (2010) nFactors: An R Package for Parallel Analysis and non Graphical Solutions to the Cattell Scree Test. R package version 2.3.3.Google Scholar
Raiche, G, Walls, TA, Magis, D, Riopel, M and Blais, JG (2013) Non-Graphical solutions for cattell's scree test. Methodology-European Journal of Research Methods for the Behavioral and Social Sciences 9, 23–29.CrossRefGoogle Scholar
Rush, AJ, Trivedi, MH, Ibrahim, HM, Carmody, TJ, Arnow, B, Klein, DN, Markowitz, JC, Ninan, PT, Kornstein, S, Manber, R, Thase, ME, Kocsis, JH and Keller, MB (2003) The 16-item quick inventory of depressive symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biological Psychiatry 54, 573–583.10.1016/S0006-3223(02)01866-8CrossRefGoogle ScholarPubMed
Saveanu, R, Etkin, A, Duchemin, AM, Goldstein-Piekarski, A, Gyurak, A, Debattista, C, Schatzberg, AF, Sood, S, Day, CV, Palmer, DM, Rekshan, WR, Gordon, E, Rush, AJ and Williams, LM (2015) The international study to predict optimized treatment in depression (iSPOT-D): outcomes from the acute phase of antidepressant treatment. Journal of Psychiatric Research 61, 1–12.CrossRefGoogle Scholar
Severino, GA and Haynes, WDG (2010) Development of an Italian version of the depression anxiety stress scales. Psychology Health & Medicine 15, 607–621.CrossRefGoogle ScholarPubMed
Sheehan, DV, Lecrubier, Y, Sheehan, KH, Amorim, P, Janavs, J, Weiller, E, Hergueta, T, Baker, R and Dunbar, GC (1998) The Mini-international neuropsychiatric interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. Journal of Clinical Psychiatry 59(Suppl 20), 22–33; quiz 34–57.Google ScholarPubMed
Spielberger, CD, Gorsuch, RL, Lushene, R, Vagg, PR and Jacobs, GA (1983) Manual for the State-Trait Anxiety Inventory. Palo Alto, CA: Consulting Psychologists Press.Google Scholar
Tabachnick, BG and Fidell, LS (2007) Using multivariate statistics, 5th Edn. Boston, MA: Allyn & Bacon/Pearson Education.Google Scholar
Thomson, GH (1935) The definition and measurement of “g” (general intelligence). Journal of Educational Psychology 26, 241–262.CrossRefGoogle Scholar
Trivedi, MH, Rush, AJ, Ibrahim, HM, Carmody, TJ, Biggs, MM, Suppes, T, Crismon, ML, Shores-Wilson, K, Toprac, MG, Dennehy, EB, Witte, B and Kashner, TM (2004) The inventory of depressive symptomatology, clinician rating (IDS-C) and self-report (IDS-SR), and the quick inventory of depressive symptomatology, clinician rating (QIDS-C) and self-report (QIDS-SR) in public sector patients with mood disorders: a psychometric evaluation. Psychological Medicine 34, 73–82.CrossRefGoogle ScholarPubMed
Trombello, JM, Pizzagalli, DA, Weissman, MM, Grannemann, BD, Cooper, CM, Greer, TL, Malchow, AL, Jha, MK, Carmody, TJ, Kurian, BT, Webb, CA, Dillon, DG, Mcgrath, PJ, Bruder, G, Fava, M, Parsey, RV, Mcinnis, MG, Adams, P and Trivedi, MH (2018) Characterizing anxiety subtypes and the relationship to behavioral phenotyping in major depression: results from the EMBARC study. Journal of Psychiatric Research 102, 207–215.CrossRefGoogle ScholarPubMed
Van Buuren, S and Groothuis-Oudshoorn, K (2011) Mice: multivariate imputation by chained equations in R. Journal of Statistical Software 45, 1–67.Google Scholar
Williams, LM, Mathersul, D, Palmer, DM, Gur, RC, Gur, RE and Gordon, E (2009) Explicit identification and implicit recognition of facial emotions: I. Age effects in males and females across 10 decades. Journal of Clinical and Experimental Neuropsychology 31, 257–277.CrossRefGoogle ScholarPubMed
Williams, LM, Rush, AJ, Koslow, SH, Wisniewski, SR, Cooper, NJ, Nemeroff, CB, Schatzberg, AF and Gordon, E (2011) International study to predict optimized treatment for depression (iSPOT-D), a randomized clinical trial: rationale and protocol. Trials 12, 4.CrossRefGoogle Scholar
Williams, LM, Cooper, NJ, Wisniewski, SR, Gatt, JM, Koslow, SH, Kulkarni, J, Devarney, S, Gordon, E and John Rush, A (2012) Sensitivity, specificity, and predictive power of the “Brief risk-resilience Index for SCreening,” a brief pan-diagnostic web screen for emotional health. Brain and Behavior 2, 576–589.CrossRefGoogle Scholar
Yeung, AY, Yuliawati, L and Cheung, S (2018) A systematic review and meta-analytic factor analysis of the depression anxiety stress scales: implications for the tripartite model. e1–e62, Available from: psyarxiv.com/bzhgkCrossRefGoogle Scholar
Supplementary material: File

Braund et al. supplementary material

Tables S1-S6 and FIgures S1-S3

Download Braund et al. supplementary material(File)
File 75.6 KB