Skip to main content Accessibility help

Biomarker-based subtyping of depression and anxiety disorders using Latent Class Analysis. A NESDA study

  • Lian Beijers (a1), Klaas J. Wardenaar (a1), Fokko J. Bosker (a2), Femke Lamers (a3), Gerard van Grootheest (a3), Marrit K. de Boer (a2), Brenda W.J.H. Penninx (a3) and Robert A. Schoevers (a2)...



Etiological research of depression and anxiety disorders has been hampered by diagnostic heterogeneity. In order to address this, researchers have tried to identify more homogeneous patient subgroups. This work has predominantly focused on explaining interpersonal heterogeneity based on clinical features (i.e. symptom profiles). However, to explain interpersonal variations in underlying pathophysiological mechanisms, it might be more effective to take biological heterogeneity as the point of departure when trying to identify subgroups. Therefore, this study aimed to identify data-driven subgroups of patients based on biomarker profiles.


Data of patients with a current depressive and/or anxiety disorder came from the Netherlands Study of Depression and Anxiety, a large, multi-site naturalistic cohort study (n = 1460). Thirty-six biomarkers (e.g. leptin, brain-derived neurotrophic factor, tryptophan) were measured, as well as sociodemographic and clinical characteristics. Latent class analysis of the discretized (lower 10%, middle, upper 10%) biomarkers were used to identify different patient clusters.


The analyses resulted in three classes, which were primarily characterized by different levels of metabolic health: ‘lean’ (21.6%), ‘average’ (62.2%) and ‘overweight’ (16.2%). Inspection of the classes’ clinical features showed the highest levels of psychopathology, severity and medication use in the overweight class.


The identified classes were strongly tied to general (metabolic) health, and did not reflect any natural cutoffs along the lines of the traditional diagnostic classifications. Our analyses suggested that especially poor metabolic health could be seen as a distal marker for depression and anxiety, suggesting a relationship between the ‘overweight’ subtype and internalizing psychopathology.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the or variations. ‘’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Biomarker-based subtyping of depression and anxiety disorders using Latent Class Analysis. A NESDA study
      Available formats

      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      Biomarker-based subtyping of depression and anxiety disorders using Latent Class Analysis. A NESDA study
      Available formats

      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      Biomarker-based subtyping of depression and anxiety disorders using Latent Class Analysis. A NESDA study
      Available formats


This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

Corresponding author

Author for correspondence: Lian Beijers, E-mail:


Hide All
Abosi, O, Lopes, S, Schmitz, S and Fiedorowicz, JG (2018) Cardiometabolic effects of psychotropic medications. Hormone Molecular Biology and Clinical Investigation [Epub ahead of print].
Bartova, L, Berger, A and Pezawas, L (2010) Is there a personalized medicine for mood disorders? European Archives of Psychiatry and Clinical Neuroscience 260, 121126.
Baumeister, H and Parker, G (2012) Meta-review of depressive subtyping models. Journal of Affective Disorders 139, 126140.
Beck, AT, Epstein, N, Brown, G and Steer, RA (1988) An inventory for measuring clinical anxiety: psychometric properties. Journal of Consulting and Clinical Psychology 56, 893.
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 57, 289300.
Blaine, B (2008) Does depression cause obesity? Journal of Health Psychology 13, 11901197.
Bornstein, SR, Schuppenies, A, Wong, ML and Licinio, J (2006) Approaching the shared biology of obesity and depression: the stress axis as the locus of gene-environment interactions. Molecular Psychiatry 11, 892902.
Carroll, BJ (1982) Use of the dexamethasone suppression test in depression. Journal of Clinical Psychiatry 43, 4450.
Chaves, PHM, Xue, QL, Guralnik, JM, Ferrucci, L, Volpato, S and Fried, LP (2004) What constitutes normal hemoglobin concentration in community-dwelling disabled older women? Journal of the American Geriatrics Society 52, 18111816.
Cizza, G, Ronsaville, DS, Kleitz, H, Eskandari, F, Mistry, S, Torvik, S, Sonbolian, N, Reynolds, JC, Blackman, MR, Gold, PW, Martinez, PE and P.O.W.E.R. (Premenopausal Women, Alendronate, Depression) Study Group O (2012) Clinical subtypes of depression are associated with specific metabolic parameters and circadian endocrine profiles in women: the power study. PloS ONE 7, e28912.
Cooney, G (2013) Exercise for depression. Journal of Evidence-Based Medicine 6, 307308.
Cramér, H (1946) The two-dimensional case. In Mathematical Methods of Statistics. Princeton: Princeton University Press, p. 282.
Cuijpers, P, van Straten, A, Bohlmeijer, E, Hollon, SD and Andersson, G (2010) The effects of psychotherapy for adult depression are overestimated: a meta-analysis of study quality and effect size. Psychological Medicine 40, 211223.
Cuijpers, P, Berking, M, Andersson, G, Quigley, L, Kleiboer, A and Dobson, KS (2013) A meta-analysis of cognitive-behavioural therapy for adult depression, alone and in comparison with other treatments. Canadian Journal of Psychiatry 58, 376385.
Cuijpers, P, Ebert, DD, Acarturk, C, Andersson, G and Cristea, IA (2016) Personalized psychotherapy for adult depression: a meta-analytic review. Behavior Therapy 47, 966980.
de Vos, S, Wardenaar, KJ, Bos, EH, Wit, EC and de Jonge, P (2015) Decomposing the heterogeneity of depression at the person-, symptom-, and time-level: latent variable models versus multimode principal component analysis. BMC Medical Research Methodology 15, 88.
de Wit, LM, van Straten, A, Lamers, F, Cuijpers, P and Penninx, BWJH (2015) Depressive and anxiety disorders: associated with losing or gaining weight over 2 years? Psychiatry Research 227, 230237.
e Silva, JAC (2013) Personalized medicine in psychiatry: new technologies and approaches. Metabolism 62, S40S44.
Ghanei Gheshlagh, R, Parizad, N, Sayehmiri, K, Zamanian-Azodi, M and Rashidy-pour, A (2015) Is there a relationship between metabolic syndrome and depression? A systematic review and meta-analysis. Koomesh 16, 488494.
Gibson-Smith, D, Bot, M, Milaneschi, Y, Twisk, JW, Visser, M, Brouwer, IA and Penninx, BWJH (2016) Major depressive disorder, antidepressant use, and subsequent 2-year weight change patterns in the Netherlands study of depression and anxiety. Journal of Clinical Psychiatry 77, e144e151.
Giltay, EJ, Enter, D, Zitman, FG, Penninx, BWJH, van Pelt, J, Spinhoven, P and Roelofs, K (2012) Salivary testosterone: associations with depression, anxiety disorders, and antidepressant use in a large cohort study. Journal of Psychosomatic Research 72, 205213.
Goldberg, D (2011) The heterogeneity of ‘major depression’. World Psychiatry 10, 226228.
Hasler, G (2010) Pathophysiology of depression: do we have any solid evidence of interest to clinicians? World Psychiatry 9, 155161.
Hasler, G, Drevets, WC, Manji, HK and Charney, DS (2004) Discovering endophenotypes for major depression. Neuropsychopharmacology 29, 17651781.
Hastie, T, Tibshirani, R and Friedman, J (2011) The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edn. New York: Springer Series in Statistics, pp. 501520.
Hettema, JM (2008) The nosologic relationship between generalized anxiety disorder and major depression. Depression and Anxiety 25, 300316.
Hiles, SA, Révész, D, Lamers, F, Giltay, E and Penninx, BWJH (2016) Bidirectional prospective associations of metabolic syndrome components with depression, anxiety, and antidepressant use. Depression and Anxiety 33, 754764.
Insel, T, Cuthbert, B, Garvey, M, Heinssen, R, Pine, D, 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, 748751.
Kapur, S, Phillips, a G and Insel, TR (2012) Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it? Molecular Psychiatry 17, 11741179.
Kendell, R and Jablensky, A (2003) Distinguishing between the validity and utility of psychiatric diagnoses. American Journal of Psychiatry 160, 412.
Kendler, KS (2014) The structure of psychiatric science. American Journal of Psychiatry 171, 931938.
Kessler, RC (2012) The costs of depression. Psychiatric Clinics of North America 35, 114.
Korte, SM, Prins, J, Krajnc, AM, Hendriksen, H, Oosting, RS, Westphal, KG, Korte-Bouws, GAH and Olivier, B (2015) The many different faces of major depression: it is time for personalized medicine. Elsevier European Journal of Pharmacology 753, 88104.
Lamers, F, Vogelzangs, N, Merikangas, K, De Jonge, P, Beekman, A and Penninx, B (2013) Evidence for a differential role of HPA-axis function, inflammation and metabolic syndrome in melancholic versus atypical depression. Molecular Psychiatry 18, 692699.
Leuchter, AF, Cook, IA, Hamilton, SP, Narr, KL, Toga, A, Hunter, AM, Faull, K, Whitelegge, J, Andrews, AM, Loo, J, Way, B, Nelson, SF, Horvath, S and Lebowitz, BD (2010) Biomarkers to predict antidepressant response. Current Psychiatry Reports 12, 553562.
Licht, CMM, de Geus, EJC, Zitman, FG, Hoogendijk, WJG, van Dyck, R and Penninx, BWJH (2008) Association between major depressive disorder and heart rate variability in the Netherlands Study of Depression and Anxiety (NESDA). Archives of General Psychiatry 65, 13581367.
Łojko, D, Buzuk, G, Owecki, M, Ruchała, M and Rybakowski, JK (2015) Atypical features in depression: association with obesity and bipolar disorder. Journal of Affective Disorders 185, 7680.
Lubke, GH and Muthén, B (2005) Investigating population heterogeneity with factor mixture models. Psychological Methods 10, 2139.
Luppino, FS, de Wit, LM, Bouvy, PF, Stijnen, T, Cuijpers, P, Penninx, BWJH and Zitman, FG (2010) Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies. Archives of General Psychiatry 67, 220229.
Luppino, FS, Bouvy, PF, Giltay, EJ, Penninx, BWJH and Zitman, FG (2014) The metabolic syndrome and related characteristics in major depression: inpatients and outpatients compared metabolic differences across treatment settings. General Hospital Psychiatry 36, 509515.
Mannan, M, Mamun, A, Doi, S and Clavarino, A (2016) Is there a bi-directional relationship between depression and obesity among adult men and women? Systematic review and bias-adjusted meta analysis. Asian Journal of Psychiatry 21, 5166.
Marazziti, D, Rutigliano, G, Baroni, S, Landi, P and Dell'Osso, L (2014) Metabolic syndrome and major depression. CNS Spectrums 19, 293304.
Marquand, AF, Wolfers, T, Mennes, M, Buitelaar, J and Beckmann, CF (2016) Beyond lumping and splitting: a review of computational approaches for stratifying psychiatric disorders. Cognitive Neuroscience and Neuroimaging 1, 433447.
Meyer, JM and Ginsburg, GS (2002) The path to personalized medicine. Current Opinion in Chemical Biology 6, 434438.
Milaneschi, Y, Hoogendijk, W, Lips, P, Heijboer, AC, Schoevers, R, van Hemert, AM, Beekman, ATF, Smit, JH and Penninx, B (2014) The association between low vitamin D and depressive disorders. Molecular Psychiatry 19, 444451.
Milaneschi, Y, Lamers, F, Bot, M, Drent, ML and Penninx, BWJH (2017) Leptin dysregulation is specifically associated with major depression with atypical features: evidence for a mechanism connecting obesity and depression. Biological Psychiatry 81, 807814.
Miller, DB and O'Callaghan, JP (2013) Personalized medicine in major depressive disorder—opportunities and pitfalls. Metabolism 62, S34S39.
Monroe, SM and Anderson, SF (2015) Depression: the shroud of heterogeneity. Current Directions in Psychological Science 24, 227231.
Morgan, GB, Hodge, KJ and Baggett, AR (2016) Latent profile analysis with nonnormal mixtures: a Monte Carlo examination of model selection using fit indices. Computational Statistics and Data Analysis 93, 146161.
Morita, T, Senzaki, K, Ishihara, R, Umeda, K, Iwata, N, Nagai, T, Hida, H, Nabeshima, T, Yukawa, K, Ozaki, N and Noda, Y (2014) Plasma dehydroepiandrosterone sulfate levels in patients with major depressive disorder correlate with remission during treatment with antidepressants. Human Psychopharmacology 29, 280286.
Muthen, LK and Muthen, BO (2007) Mplus User's Guide. 5th Edn. Los Angeles, CA: Muthén & Muthén, pp. 197200.
Nutt, DJ, Ballenger, JC, Sheehan, D and Wittchen, HU (2002) Generalized anxiety disorder: comorbidity, comparative biology and treatment. International Journal of Neuropsychopharmacology 5, 315325.
Nylund, KL, Asparouhov, T and Muthén, BO (2007) Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Structural Equation Modeling 14, 535569.
Ozomaro, U, Wahlestedt, C and Nemeroff, CB (2013) Personalized medicine in psychiatry: problems and promises. BMC Medicine 11, 132.
Patas, K, Penninx, BWJH, Bus, BAA, Vogelzangs, N, Molendijk, ML, Elzinga, BM, Bosker, FJ and Voshaar, RCO (2014) Association between serum brain-derived neurotrophic factor and plasma interleukin-6 in major depressive disorder with melancholic features. Brain, Behavior, and Immunity 36, 7179.
Penninx, BWJH, Beekman, ATF, Smit, JH, Zitman, FG, Nolen, WA, Spinhoven, P, Cuijpers, P, De Jong, PJ, Van Marwijk, HWJ and Assendelft, WJJ (2008) The Netherlands Study of Depression and Anxiety (NESDA): rationale, objectives and methods. International Journal of Methods in Psychiatric Research 17, 121140.
Quak, J, Doornbos, B, Roest, AM, Duivis, HE, Vogelzangs, N, Nolen, WA, Penninx, BWJH, Kema, IP and de Jonge, P (2014) Does tryptophan degradation along the kynurenine pathway mediate the association between pro-inflammatory immune activity and depressive symptoms? Elsevier Psychoneuroendocrinology 45, 202210.
Rhebergen, D, van der Steenstraten, IM, Sunderland, M, de Graaf, R, Ten Have, M, Lamers, F, Penninx, BWJH and Andrews, G (2013) An examination of generalized anxiety disorder and dysthymic disorder by latent class analysis. Psychological Medicine 44, 17011712.
Roest, AM, de Jonge, P, Williams, CD, de Vries, YA, Schoevers, RA and Turner, EH (2015) Reporting bias in clinical trials investigating the efficacy of second-generation antidepressants in the treatment of anxiety disorders: a report of 2 meta-analyses. JAMA Psychiatry 72, 500510.
Rush, AJ, Gullion, CM, Basco, MR, Jarrett, RB and Trivedi, MH (1996) The inventory of depressive symptomatology (IDS): psychometric properties. Psychological Medicine 26, 477486.
Sardinha, A and Nardi, AE (2014) The role of anxiety in metabolic syndrome. Expert Review of Endocrinology & Metabolism 7, 6371.
Simon, GE (2011) What little we know about tailoring depression treatment for individual patients. Depression and Anxiety 28, 435438.
Simon, GE and Perlis, RH (2010) Personalized medicine for depression: can we match patients with treatments? American Journal of Psychiatry 167, 14451455.
Tang, SW, Helmeste, DM and Leonard, BE (2010) Antidepressant compounds: a critical review. Depression: From Psychopathology to Pharmacotherapy 27, 119.
Turner, EH and Rosenthal, R (2008) Efficacy of antidepressants. Biomedical Journal 336, 516517.
Van Loo, HM, De Jonge, P, Romeijn, J-W, Kessler, RC and Schoevers, RA (2012) Data-driven subtypes of major depressive disorder: a systematic review. BMC Medicine 10, 156.
van Reedt Dortland, AKB, Giltay, EJ, van Veen, T, van Pelt, J, Zitman, FG and Penninx, BWJH (2009) Associations between serum lipids and major depressive disorder: results from the Netherlands Study of Depression and Anxiety (NESDA). Physicians Postgraduate Press, Inc. The Journal of Clinical Psychiatry 71, 1478736.
Van Reedt Dortland, AKB, Giltay, EJ, Van Veen, T, Zitman, FG and Penninx, BWJH (2010) Metabolic syndrome abnormalities are associated with severity of anxiety and depression and with tricyclic antidepressant use. Acta Psychiatrica Scandinavica 122, 3039.
Vancampfort, D, Correll, CU, Wampers, M, Sienaert, P, Mitchell, AJ, De Herdt, A, Probst, M, Scheewe, TW and De Hert, M (2014) Metabolic syndrome and metabolic abnormalities in patients with major depressive disorder: a meta-analysis of prevalences and moderating variables. Psychological Medicine 44, 20172028.
van Santen, A, Vreeburg, SA, Van der Does, AJW, Spinhoven, P, Zitman, FG and Penninx, BWJH (2011) Psychological traits and the cortisol awakening response: results from the Netherlands Study of Depression and Anxiety. Elsevier Psychoneuroendocrinology 36, 240248.
Veen, G, Giltay, EJ, Licht, CMM, Vreeburg, SA, Cobbaert, CM, Penninx, BWJH and Zitman, FG (2013) Evening salivary alpha-amylase, major depressive disorder, and antidepressant use in the Netherlands Study of Depression and Anxiety (NESDA). Elsevier Psychiatry Research 208, 4146.
Vogelzangs, N, Beekman, AT, van Reedt Dortland, AK, Schoevers, RA, Giltay, EJ, de Jonge, P and Penninx, BW (2014) Inflammatory and metabolic dysregulation and the 2-year course of depressive disorders in antidepressant users. Neuropsychopharmacology 39, 16241634.
Wardenaar, KJ, Wanders, RBK, ten Have, M, de Graaf, R and de Jonge, P (2017) Using a hybrid subtyping model to capture patterns and dimensionality of depressive and anxiety symptomatology in the general population. Journal of Affective Disorders 215, 125134.
Whiteford, H, Ferrari, A and Degenhardt, L (2016) Global burden Of disease studies: implications For mental And substance Use disorders. Health affairs (Project Hope) 35, 11141120.
World Health Organization (WHO) (2016) Investing in treatment for depression and anxiety leads to fourfold return. (


Related content

Powered by UNSILO
Type Description Title
Supplementary materials

Beijers et al. supplementary material
Figure S1 and Tables S1-S4

 Word (389 KB)
389 KB

Biomarker-based subtyping of depression and anxiety disorders using Latent Class Analysis. A NESDA study

  • Lian Beijers (a1), Klaas J. Wardenaar (a1), Fokko J. Bosker (a2), Femke Lamers (a3), Gerard van Grootheest (a3), Marrit K. de Boer (a2), Brenda W.J.H. Penninx (a3) and Robert A. Schoevers (a2)...


Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed.