Abstract
Background: To aid in the differentiation of individuals with major depressive disorder (MDD) from healthy controls, numerous peripheral biomarkers have been proposed. To date, no comprehensive evaluation of the existence of bias favoring the publication of significant results or inflating effect sizes has been conducted. Methods: Here, we performed a comprehensive review of meta-analyses of peripheral nongenetic biomarkers that could discriminate individuals with MDD from nondepressed controls. PubMed/MEDLINE, EMBASE, and PsycINFO databases were searched through April 10, 2015. Results: From 15 references, we obtained 31 eligible meta-analyses evaluating biomarkers in MDD (21,201 cases and 78,363 controls). Twenty meta-analyses reported statistically significant effect size estimates. Heterogeneity was high (I2 ≥50%) in 29 meta-analyses. We plausibly assumed that the true effect size for a meta-analysis would equal the one of its largest study. A significant summary effect size estimate was observed for 20 biomarkers. We observed an excess of statistically significant studies in 21 meta-analyses. The summary effect size of the meta-analysis was higher than the effect of its largest study in 25 meta-analyses, while 11 meta-analyses had evidence of small-study effects. Conclusions: Our findings suggest that there is an excess of studies with statistically significant results in the literature of peripheral biomarkers for MDD. The selective publication of ‘positive studies' and the selective reporting of outcomes are possible mechanisms. Effect size estimates of meta-analyses may be inflated in this literature.
References
1.
Phillips ML, Chase HW, Sheline YI, Etkin A, Almeida JR, Deckersbach T, Trivedi MH: Identifying predictors, moderators, and mediators of antidepressant response in major depressive disorder: neuroimaging approaches. Am J Psychiatry 2015;172:124-138.
2.
Kato M, Serretti A: Review and meta-analysis of antidepressant pharmacogenetic findings in major depressive disorder. Mol Psychiatry 2010;15:473-500.
3.
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;48:102-111.
4.
Molendijk ML, Spinhoven P, Polak M, Bus BA, Penninx BW, Elzinga BM: Serum BDNF concentrations as peripheral manifestations of depression: evidence from a systematic review and meta-analyses on 179 associations (n = 9,484). Mol Psychiatry 2014;19:791-800.
5.
Dowlati Y, Herrmann N, Swardfager W, Liu H, Sham L, Reim EK, Lanctot KL: A meta-analysis of cytokines in major depression. Biol Psychiatry 2010;67:446-457.
6.
Black CN, Bot M, Scheffer PG, Cuijpers P, Penninx BW: Is depression associated with increased oxidative stress? A systematic review and meta-analysis. Psychoneuroendocrinology 2015;51:164-175.
7.
Lin PY, Tseng PT: Decreased glial cell line-derived neurotrophic factor levels in patients with depression: a meta-analytic study. J Psychiatr Res 2015;63:20-27.
8.
Papakostas GI, Shelton RC, Kinrys G, Henry ME, Bakow BR, Lipkin SH, Pi B, Thurmond L, Bilello JA: Assessment of a multi-assay, serum-based biological diagnostic test for major depressive disorder: a pilot and replication study. Mol Psychiatry 2013;18:332-339.
9.
Insel TR, Landis SC: Twenty-five years of progress: the view from NIMH and NINDS. Neuron 2013;80:561-567.
10.
Ioannidis JP: Why most published research findings are false. PLoS Med 2005;2:e124.
11.
Ioannidis JP: Excess significance bias in the literature on brain volume abnormalities. Arch Gen Psychiatry 2011;68:773-780.
12.
Fusar-Poli P, Radua J, Frascarelli M, Mechelli A, Borgwardt S, Di Fabio F, Biondi M, Ioannidis JP, David SP: Evidence of reporting biases in voxel-based morphometry (VBM) studies of psychiatric and neurological disorders. Hum Brain Mapp 2014;35:3052-3065.
13.
Ioannidis JP, Munafo MR, Fusar-Poli P, Nosek BA, David SP: Publication and other reporting biases in cognitive sciences: detection, prevalence, and prevention. Trends Cogn Sci 2014;18:235-241.
14.
Young SS, Bang H: The file-drawer problem, revisited. Science 2004;306:1133-1134; author reply 1133-1134.
15.
Egger M, Davey Smith G, Schneider M, Minder C: Bias in meta-analysis detected by a simple, graphical test. BMJ 1997;315:629-634.
16.
American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders (DSM-5®). Arlington, American Psychiatric Association Publishing, 2013.
17.
World Health Organization: The ICD-10 Classification of Mental and Behavioural Disorders: Clinical Descriptions and Diagnostic Guidelines. Geneva, World Health Organization, 1992.
18.
Chan MK, Gottschalk MG, Haenisch F, Tomasik J, Ruland T, Rahmoune H, Guest PC, Bahn S: Applications of blood-based protein biomarker strategies in the study of psychiatric disorders. Prog Neurobiol 2014;122:45-72.
19.
Higgins JP, Thompson SG: Quantifying heterogeneity in a meta-analysis. Stat Med 2002;21:1539-1558.
20.
Ioannidis JP, Trikalinos TA: An exploratory test for an excess of significant findings. Clin Trials 2007;4:245-253.
21.
Ioannidis JP: Clarifications on the application and interpretation of the test for excess significance and its extensions. J Math Psychol 2013;57:184-187.
22.
Dupont WD, Plummer WD Jr: Power and sample size calculations. A review and computer program. Control Clin Trials 1990;11:116-128.
23.
Higgins JP, Thompson SG, Deeks JJ, Altman DG: Measuring inconsistency in meta-analyses. BMJ 2003;327:557-560.
24.
Patsopoulos NA, Evangelou E, Ioannidis JP: Heterogeneous views on heterogeneity. Int J Epidemiol 2009;38:1740-1742.
25.
Higgins JP, Thompson SG, Spiegelhalter DJ: A re-evaluation of random-effects meta-analysis. J R Stat Soc Ser A Stat Soc 2009;172:137-159.
26.
Higgins JP: Commentary: heterogeneity in meta-analysis should be expected and appropriately quantified. Int J Epidemiol 2008;37:1158-1160.
27.
Sterne JA, Gavaghan D, Egger M: Publication and related bias in meta-analysis: power of statistical tests and prevalence in the literature. J Clin Epidemiol 2000;53:1119-1129.
28.
Belbasis L, Bellou V, Evangelou E, Ioannidis JP, Tzoulaki I: Environmental risk factors and multiple sclerosis: an umbrella review of systematic reviews and meta-analyses. Lancet Neurol 2015;14:263-273.
29.
Schroeter ML, Steiner J, Mueller K: Glial pathology is modified by age in mood disorders - a systematic meta-analysis of serum S100B in vivo studies. J Affect Disord 2011;134:32-38.
30.
Howren MB, Lamkin DM, Suls J: Associations of depression with C-reactive protein, IL-1, and IL-6: a meta-analysis. Psychosom Med 2009;71:171-186.
31.
Mokhtari M, Arfken C, Boutros N: The DEX/CRH test for major depression: a potentially useful diagnostic test. Psychiatry Res 2013;208:131-139.
32.
Ciufolini S, Dazzan P, Kempton MJ, Pariante C, Mondelli V: HPA axis response to social stress is attenuated in schizophrenia but normal in depression: evidence from a meta-analysis of existing studies. Neurosci Biobehav Rev 2014;47c:359-368.
33.
Belvederi Murri M, Pariante C, Mondelli V, Masotti M, Atti AR, Mellacqua Z, Antonioli M, Ghio L, Menchetti M, Zanetidou S, Innamorati M, Amore M: HPA axis and aging in depression: systematic review and meta-analysis. Psychoneuroendocrinology 2014;41:46-62.
34.
Liu Y, Ho RC-M, Mak A: Interleukin (IL)-6, tumour necrosis factor alpha (TNF-α) and soluble interleukin-2 receptors (sIL-2R) are elevated in patients with major depressive disorder: a meta-analysis and meta-regression. J Affect Disord 2012;139:230-239.
35.
Ogawa S, Fujii T, Koga N, Hori H, Teraishi T, Hattori K, Noda T, Higuchi T, Motohashi N, Kunugi H: Plasma L-tryptophan concentration in major depressive disorder: new data and meta-analysis. J Clin Psychiatry 2014;75:e906-e915.
36.
Lin P-Y, Huang S-Y, Su K-P: A meta-analytic review of polyunsaturated fatty acid compositions in patients with depression. Biol Psychiatry 2010;68:140-147.
37.
Palta P, Samuel LJ, Miller ER, Szanton SL: Depression and oxidative stress: results from a meta-analysis of observational studies. Psychosom Med 2014;76:12-19.
38.
Swardfager W, Herrmann N, Mazereeuw G, Goldberger K, Harimoto T, Lanctôt KL: Zinc in depression: a meta-analysis. Biol Psychiatry 2013;74:872-878.
39.
Carvalho AF, Rocha DQ, McIntyre RS, Mesquita LM, Kohler CA, Hyphantis TN, Sales PM, Machado-Vieira R, Berk M: Adipokines as emerging depression biomarkers: a systematic review and meta-analysis. J Psychiatr Res 2014;59:28-37.
40.
Janes H, Pepe MS: Adjusting for covariates in studies of diagnostic, screening, or prognostic markers: an old concept in a new setting. Am J Epidemiol 2008;168:89-97.
41.
Ioannidis JP, Panagiotou OA: Comparison of effect sizes associated with biomarkers reported in highly cited individual articles and in subsequent meta-analyses. JAMA 2011;305:2200-2210.
42.
Tzoulaki I, Siontis KC, Evangelou E, Ioannidis JP: Bias in associations of emerging biomarkers with cardiovascular disease. JAMA Intern Med 2013;173:664-671.
43.
Janelidze S, Ventorp F, Erhardt S, Hansson O, Minthon L, Flax J, Samuelsson M, Traskman-Bendz L, Brundin L: Altered chemokine levels in the cerebrospinal fluid and plasma of suicide attempters. Psychoneuroendocrinology 2013;38:853-862.
44.
Diniz BS, Sibille E, Ding Y, Tseng G, Aizenstein HJ, Lotrich F, Becker JT, Lopez OL, Lotze MT, Klunk WE, Reynolds CF, Butters MA: Plasma biosignature and brain pathology related to persistent cognitive impairment in late-life depression. Mol Psychiatry 2015;20:594-601.
45.
Ioannidis JP, Tarone R, McLaughlin JK: The false-positive to false-negative ratio in epidemiologic studies. Epidemiology 2011;22:450-456.
46.
Dwan K, Gamble C, Williamson PR, Kirkham JJ: Systematic review of the empirical evidence of study publication bias and outcome reporting bias - an updated review. PLoS One 2013;8:e66844.
47.
Chan AW, Hrobjartsson A, Haahr MT, Gotzsche PC, Altman DG: Empirical evidence for selective reporting of outcomes in randomized trials: comparison of protocols to published articles. JAMA 2004;291:2457-2465.
48.
Turner EH, Matthews AM, Linardatos E, Tell RA, Rosenthal R: Selective publication of antidepressant trials and its influence on apparent efficacy. N Engl J Med 2008;358:252-260.
49.
Roest AM, de Jonge P, Williams CD, de Vries YA, Schoevers RA, Turner EH: 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 2015;72:500-510.
50.
Gold PW: The organization of the stress system and its dysregulation in depressive illness. Mol Psychiatry 2015;20:32-47.
51.
Tomba E, Bech P: Clinimetrics and clinical psychometrics: macro- and micro-analysis. Psychother Psychosom 2012;81:333-343.
52.
Fava GA, Rafanelli C, Tomba E: The clinical process in psychiatry: a clinimetric approach. J Clin Psychiatry 2012;73:177-184.
53.
Fava GA, Guidi J, Grandi S, Hasler G: The missing link between clinical states and biomarkers in mental disorders. Psychother Psychosom 2014;83:136-141.
54.
Lichtenberg P, Belmaker RH: Subtyping major depressive disorder. Psychother Psychosom 2010;79:131-135.
55.
Bech P: Struggle for subtypes in primary and secondary depression and their mode-specific treatment or healing. Psychother Psychosom 2010;79:331-338.
56.
Kunugi H, Hori H, Ogawa S: Biochemical markers subtyping major depressive disorder. Psychiatry Clin Neurosci 2015;69:597-608.
57.
Khan SS, Smith MS, Reda D, Suffredini AF, McCoy JP: Multiplex bead array assays for detection of soluble cytokines: comparisons of sensitivity and quantitative values among kits from multiple manufacturers. Cytometry B Clin Cytom 2004;61:35-39.
58.
Elshal MF, McCoy JP: Multiplex bead array assays: performance evaluation and comparison of sensitivity to ELISA. Methods 2006;38:317-323.
59.
Davis J, Maes M, Andreazza A, McGrath JJ, Tye SJ, Berk M: Towards a classification of biomarkers of neuropsychiatric disease: from encompass to compass. Mol Psychiatry 2015;20:152-153.
60.
Munafo MR, Stothart G, Flint J: Bias in genetic association studies and impact factor. Mol Psychiatry 2009;14:119-120.
61.
Andre F, McShane LM, Michiels S, Ransohoff DF, Altman DG, Reis-Filho JS, Hayes DF, Pusztai L: Biomarker studies: a call for a comprehensive biomarker study registry. Nat Rev Clin Oncol 2011;8:171-176.
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