Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it?

Molecular Psychiatry volume 17, pages 1174–1179 (2012)Cite this article

Subjects

Abstract

Patients with mental disorders show many biological abnormalities which distinguish them from normal volunteers; however, few of these have led to tests with clinical utility. Several reasons contribute to this delay: lack of a biological ‘gold standard’ definition of psychiatric illnesses; a profusion of statistically significant, but minimally differentiating, biological findings; ‘approximate replications’ of these findings in a way that neither confirms nor refutes them; and a focus on comparing prototypical patients to healthy controls which generates differentiations with limited clinical applicability. Overcoming these hurdles will require a new approach. Rather than seek biomedical tests that can ‘diagnose’ DSM-defined disorders, the field should focus on identifying biologically homogenous subtypes that cut across phenotypic diagnosis—thereby sidestepping the issue of a gold standard. To ensure clinical relevance and applicability, the field needs to focus on clinically meaningful differences between relevant clinical populations, rather than hypothesis-rejection versus normal controls. Validating these new biomarker-defined subtypes will require longitudinal studies with standardized measures which can be shared and compared across studies—thereby overcoming the problem of significance chasing and approximate replications. Such biological tests, and the subtypes they define, will provide a natural basis for a ‘stratified psychiatry’ that will improve clinical outcomes across conventional diagnostic boundaries.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

References

  1. Kupfer D, First M, Regier D (eds). A Research Agenda for DSM-V. American Psychiatric Association: Washington, DC, 2002.

    Google Scholar 

  2. Anderton D . Disease, concepts and classification of. In: Demeny P, McNicoll G (eds). The Encyclopedia of Population, vol. 1 Macmillan Reference: New York, 2003, pp 247–250.

    Google Scholar 

  3. Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig LM et al. Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Standards for Reporting of Diagnostic Accuracy. Clin Chem 2003; 49: 1–6.

    Article  CAS  PubMed  Google Scholar 

  4. Editorial. Lessons of the ‘pink spot’. Br Med J 1967; 1: 382–383.

    Article  Google Scholar 

  5. Carroll BJ, Curtis GC, Mendels J . Neuroendocrine regulation in depression. II. Discrimination of depressed from nondepressed patients. Arch Gen Psychiatry 1976; 33: 1051–1058.

    Article  CAS  PubMed  Google Scholar 

  6. Goldberg IK . Dexamethasone suppression tests in depression and response to treatment. Lancet 1980; 2: 92.

    Article  CAS  PubMed  Google Scholar 

  7. The dexamethasone suppression test: an overview of its current status in psychiatry. The APA Task Force on Laboratory Tests in Psychiatry. Am J Psychiatry 1987); 144: 1253–1262.

  8. Loosen PT, Garbutt JC, Prange AJ . Evaluation of the diagnostic utility of the TRH-induced TSH response in psychiatric disorders. Pharmacopsychiatry 1987; 20: 90–95.

    Article  CAS  PubMed  Google Scholar 

  9. Nuwer MR . On the controversies about clinical use of EEG brain mapping. Brain Topogr 1990; 3: 103–111.

    Article  CAS  PubMed  Google Scholar 

  10. Robins E, Guze SB . Establishment of diagnostic validity in psychiatric illness: its application to schizophrenia. Am J Psychiatry 1970; 126: 983–987.

    Article  CAS  PubMed  Google Scholar 

  11. Andreasen NC . The validation of psychiatric diagnosis: new models and approaches. Am J Psychiatry 1995; 152: 161–162.

    Article  CAS  PubMed  Google Scholar 

  12. Dean K, Stevens H, Mortensen PB, Murray RM, Walsh E, Pedersen CB . Full spectrum of psychiatric outcomes among offspring with parental history of mental disorder. Arch Gen Psychiatry 2010; 67: 822–829.

    Article  PubMed  Google Scholar 

  13. Allardyce J, Suppes T, Van Os J . Dimensions and the psychosis phenotype. Int J Methods Psychiatr Res 2007; 16 (Suppl 1): S34–S40.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Andrews G, Brugha T, Thase ME, Duffy FF, Rucci P, Slade T . Dimensionality and the category of major depressive episode. Int J Methods Psychiatr Res 2007; 16 (Suppl 1): S41–S51.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Kendler KS . Explanatory models for psychiatric illness. Am J Psychiatry 2008; 165: 695–702.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Ioannidis JP . Why most published research findings are false. PLoS Med 2005; 2: e124.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Ioannidis JP . Why most discovered true associations are inflated. Epidemiology 2008; 19: 640–648.

    Article  PubMed  Google Scholar 

  18. Cumming G . Replication and p intervals: p values predict the future only vaguely, but confidence intervals do much better. Perspect Psychol Sci 2008; 3: 286–300.

    Article  PubMed  Google Scholar 

  19. Miller J . What is the probability of replicating a statistically significant effect? Psychon Bull Rev 2009; 16: 617–640.

    Article  PubMed  Google Scholar 

  20. Rothpearl AB, Mohs RC, Davis KL . Statistical power in biological psychiatry. Psychiatry Res 1981; 5: 257–266.

    Article  CAS  PubMed  Google Scholar 

  21. Allen AJ, Griss ME, Folley BS, Hawkins KA, Pearlson GD . Endophenotypes in schizophrenia: a selective review. Schizophr Res 2009; 109: 24–37.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Uher R . Gene-environment interaction: overcoming methodological challenges. Novartis Found Symp 2008; 293: 13–26; discussion 26–30, 68–70.

    Article  CAS  PubMed  Google Scholar 

  23. Collins A, Kim Y, Sklar P, O’Donovan M, Sullivan P . Hypothesis-driven candidate genes for schizophrenia compared to genome-wide association results. Psychol Med 2012; 42: 607–616.

    Article  CAS  PubMed  Google Scholar 

  24. Davidson LL, Heinrichs RW . Quantification of frontal and temporal lobe brain-imaging findings in schizophrenia: a meta-analysis. Psychiatry Res 2003; 122: 69–87.

    Article  PubMed  Google Scholar 

  25. Heinrichs RW . Meta-analysis and the science of schizophrenia: variant evidence or evidence of variants? Neurosci Biobehav Rev 2004; 28: 379–394.

    Article  PubMed  Google Scholar 

  26. Maxwell SE . The persistence of underpowered studies in psychological research: causes, consequences, and remedies. Psychol Methods 2004; 9: 147–163.

    Article  PubMed  Google Scholar 

  27. Van Snellenberg JX, Torres IJ, Thornton AE . Functional neuroimaging of working memory in schizophrenia: task performance as a moderating variable. Neuropsychology 2006; 20: 497–510.

    Article  PubMed  Google Scholar 

  28. Ransohoff DF, Feinstein AR . Problems of spectrum and bias in evaluating the efficacy of diagnostic tests. N Engl J Med 1978; 299: 926–930.

    Article  CAS  PubMed  Google Scholar 

  29. Perlis RH . Translating biomarkers to clinical practice. Mol Psychiatry 2011; 16: 1076–1087.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Grimes DA, Schulz KF . Uses and abuses of screening tests. Lancet 2002; 359: 881–884.

    Article  PubMed  Google Scholar 

  31. Teutsch SM, Bradley LA, Palomaki GE, Haddow JE, Piper M, Calonge N et al. The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) initiative: methods of the EGAPP Working Group. Gen Med 2009; 11: 3–14.

    Article  Google Scholar 

  32. Grosse SD, Rogowski WH, Ross LF, Cornel MC, Dondorp WJ, Khoury MJ . Population screening for genetic disorders in the 21st century: evidence, economics, and ethics. Public Health Genomics 2010; 13: 106–115.

    Article  CAS  PubMed  Google Scholar 

  33. Hoge SK, Appelbaum PS . Ethics and neuropsychiatric genetics: a review of major issues. Int J Neuropsychopharmacol 2012; 1–11; PMID: 22372758.

  34. Gibson PG, McDonald VM, Marks GB . Asthma in older adults. Lancet 2010; 376: 803–813.

    Article  PubMed  Google Scholar 

  35. Scott DL, Wolfe F, Huizinga TWJ . Rheumatoid arthritis. Lancet 2010; 376: 1094–1108.

    Article  PubMed  Google Scholar 

  36. Gurwitz D, Weizman A . Personalized psychiatry: a realistic goal. Pharmacogenomics 2004; 5: 213–217.

    Article  PubMed  Google Scholar 

  37. Brammer M . The role of neuroimaging in diagnosis and personalized medicine—current position and likely future directions. Dialogues Clin Neurosci 2009; 11: 389–396.

    PubMed  PubMed Central  Google Scholar 

  38. de Leon J . The future (or lack of future) of personalized prescription in psychiatry. Pharmacol Res 2009; 59: 81–89.

    Article  PubMed  Google Scholar 

  39. Belli F, Testori A, Rivoltini L, Maio M, Andreola G, Sertoli MR et al. Vaccination of metastatic melanoma patients with autologous tumor-derived heat shock protein gp96-peptide complexes: clinical and immunologic findings. J Clin Oncol 2002; 20: 4169–4180.

    Article  CAS  PubMed  Google Scholar 

  40. Kocher AA, Schuster MD, Szabolcs MJ, Takuma S, Burkhoff D, Wang J et al. Neovascularization of ischemic myocardium by human bone-marrow-derived angioblasts prevents cardiomyocyte apoptosis, reduces remodeling and improves cardiac function. Nat Med 2001; 7: 430–436.

    Article  CAS  PubMed  Google Scholar 

  41. Trusheim MR, Berndt ER, Douglas FL . Stratified medicine: strategic and economic implications of combining drugs and clinical biomarkers. Nat Rev Drug Discov 2007; 6: 287–293.

    Article  CAS  PubMed  Google Scholar 

  42. Slamon DJ, Clark GM, Wong SG, Levin WJ, Ullrich A, McGuire WL . Human-breast cancer—correlation of relapse and survival with amplification of the her-2 neu oncogene. Science 1987; 235: 177–182.

    Article  CAS  PubMed  Google Scholar 

  43. Smith I, Procter M, Gelber RD, Guillaume S, Feyereislova A, Dowsett M et al. 2-year follow-up of trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer: a randomised controlled trial. Lancet 2007; 369: 29–36.

    Article  CAS  PubMed  Google Scholar 

  44. Ferraldeschi R, Newman WG . Pharmacogenetics and pharmacogenomics: a clinical reality. Ann Clin Biochem 2011; 48 (Part 5): 410–417.

    Article  CAS  PubMed  Google Scholar 

  45. Hyman SE . Opinion—can neuroscience be integrated into the DSM-V? Nat Rev Neurosci 2007; 8: 725–U716.

    Article  CAS  PubMed  Google Scholar 

  46. FDA U. Table of Pharmacogenomic Biomarkers in Drug Labels. Available at http://www.fda.gov/drugs/scienceresearch/researchareas/pharmacogenetics/ucm083378.htm (accessed 1 December 2011).

  47. Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry 2010; 167: 748–751.

    Article  PubMed  Google Scholar 

  48. Sullivan PF . The psychiatric GWAS consortium: big science comes to psychiatry. Neuron 2010; 68: 182–186.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Biswal BB, Mennes M, Zuo XN, Gohel S, Kelly C, Smith SM et al. Toward discovery science of human brain function. Proc Natl Acad Sci USA 2010; 107: 4734–4739.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Green MF, Nuechterlein KH, Gold JM, Barch DM, Cohen J, Essock S et al. Approaching a consensus cognitive battery for clinical trials in schizophrenia: the NIMH-MATRICS conference to select cognitive domains and test criteria. Biol Psychiatry 2004; 56: 301–307.

    Article  PubMed  Google Scholar 

  51. Kraemer HC . DSM categories and dimensions in clinical and research contexts. Int J Methods Psychiatr Res 2007; 16: S8–S15.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Nakagawa S, Cuthill IC . Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol Rev 2007; 82: 591–605.

    Article  PubMed  Google Scholar 

  53. Rosenthal R, Rubin DB . A simple, general-purpose display of magnitude of experimental effect. J Educ Psychol 1982; 74: 166–169.

    Article  Google Scholar 

  54. Coe R . It's the effect size, stupid: what effect size is and why it is important. A paper presented at the Annual Conference of the British Educational Research Association 2002.

  55. Committee on a Framework for Developing a New Taxonomy of Disease of the National Academies of Sciences U. (ed) Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease. The National Academies Press: Washington, DC, 2011.

  56. Reis-Filho JS, Pusztai L . Gene expression profiling in breast cancer: classification, prognostication, and prediction. Lancet 2011; 378: 1812–1823.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to thank Dr Bruce Cuthbert for his useful comments on an earlier version of this manuscript. SK's research related to the article is supported by G0701748/1 from the MRC and the Innovative Medicines Initiative (IMI) grant NEWMEDS, under Grant Agreement N8 115008. SK received salary support from the National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London.

Author information

Authors and Affiliations

  1. King's College London, Institute of Psychiatry, London, UK

    S Kapur

  2. Department of Psychiatry and The CIHR Institute of Neurosciences, University of British Columbia, Mental Health and Addiction, Vancouver, BC, Canada

    A G Phillips

  3. National Institute of Mental Health, Bethesda, MD, USA

    T R Insel

Authors
  1. S Kapur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A G Phillips
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. T R Insel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S Kapur.

Ethics declarations

Competing interests

SK has received grant support from GSK and has served as consultant and/or speaker for AstraZeneca, Bioline, BMS-Otsuka, Eli Lilly, Janssen (J&J), Lundbeck, NeuroSearch, Pfizer, Roche, Servier and Solvay Wyeth. AGP serves on the Board of Allon Therapeutics Inc., and holds shares in this corporation. TI has no financial interests to disclose.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kapur, S., Phillips, A. & Insel, T. Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it?. Mol Psychiatry 17, 1174–1179 (2012). https://doi.org/10.1038/mp.2012.105

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/mp.2012.105

Keywords

This article is cited by

Search

Advanced search

Quick links