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Plasma biosignature and brain pathology related to persistent cognitive impairment in late-life depression

Molecular Psychiatry volume 20, pages 594–601 (2015)Cite this article

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Abstract

Cognitive impairment is highly prevalent among individuals with late-life depression (LLD) and tends to persist even after successful treatment. The biological mechanisms underlying cognitive impairment in LLD are complex and likely involve abnormalities in multiple pathways, or ‘cascades,’ reflected in specific biomarkers. Our aim was to evaluate peripheral (blood-based) evidence for biological pathways associated with cognitive impairment in older adults with LLD. To this end, we used a data-driven comprehensive proteomic analysis (multiplex immunoassay including 242 proteins), along with measures of structural brain abnormalities (gray matter atrophy and white matter hyperintensity volume via magnetic resonance imaging), and brain amyloid-β (Aβ) deposition (PiB-positron emission tomography). We analyzed data from 80 older adults with remitted major depression (36 with mild cognitive impairment (LLD+MCI) and 44 with normal cognitive (LLD+NC)) function. LLD+MCI was associated with differential expression of 24 proteins (P<0.05 and q-value <0.30) related mainly to the regulation of immune–inflammatory activity, intracellular signaling, cell survival and protein and lipid homeostasis. Individuals with LLD+MCI also showed greater white matter hyperintensity burden compared with LLD+NC (P=0.015). We observed no differences in gray matter volume or brain Aβ deposition between groups. Machine learning analysis showed that a group of three proteins (Apo AI, IL-12 and stem cell factor) yielded accuracy of 81.3%, sensitivity of 75% and specificity of 86.4% in discriminating participants with MCI from those with NC function (with an averaged cross-validation accuracy of 76.3%, sensitivity of 69.4% and specificity of 81.8% with nested cross-validation considering the model selection bias). Cognitive impairment in LLD seems to be related to greater cerebrovascular disease along with abnormalities in immune–inflammatory control, cell survival, intracellular signaling, protein and lipid homeostasis, and clotting processes. These results suggest that individuals with LLD and cognitive impairment may be more vulnerable to accelerated brain aging and shed light on possible mediators of their elevated risk for progression to dementia.

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References

  1. Byers AL, Yaffe K, Covinsky KE, Friedman MB, Bruce ML . High occurrence of mood and anxiety disorders among older adults: The National Comorbidity Survey Replication. Arch Gen Psychiatry 2010; 67: 489–496.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Viana MC, Andrade LH . Lifetime prevalence, age and gender distribution and age-of-onset of psychiatric disorders in the São Paulo Metropolitan Area, Brazil: results from the São Paulo Megacity Mental Health Survey. Rev Bras Psiquiatr. 2012; 34: 249–260.

    Article  PubMed  Google Scholar 

  3. Butters MA, Whyte EM, Nebes RD, Begley AE, Dew MA, Mulsant BH et al. The nature and determinants of neuropsychological functioning in late-life depression. Arch Gen Psychiatry. 2004; 61: 587–595.

    Article  PubMed  Google Scholar 

  4. Bhalla RK, Butters MA, Becker JT, Houck PR, Snitz BE, Lopez OL et al. Patterns of mild cognitive impairment after treatment of depression in the elderly. Am J Geriatr Psychiatry 2009; 17: 308–316.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Reynolds CF 3rd, Butters MA, Lopez O, Pollock BG, Dew MA, Mulsant BH et al. Maintenance treatment of depression in old age: a randomized, double-blind, placebo-controlled evaluation of the efficacy and safety of donepezil combined with antidepressant pharmacotherapy. Arch Gen Psychiatry. 2011; 68: 51–60.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Köhler S, Thomas AJ, Barnett NA, O'Brien JT . The pattern and course of cognitive impairment in late-life depression. Psychol Med 2010; 40: 591–602.

    Article  PubMed  Google Scholar 

  7. Diniz BS, Butters MA, Albert SM, Dew MA, Reynolds CF 3rd . Late-life depression and risk of vascular dementia and Alzheimer's disease: systematic review and meta-analysis of community-based cohort studies. Br J Psychiatry 2013; 202: 329–335.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Singh I, Rose N . Biomarkers in psychiatry. Nature 2009; 460: 202–207.

    Article  CAS  PubMed  Google Scholar 

  9. Alexopoulos GS, Meyers BS, Young RC, Campbell S, Silbersweig D, Charlson M et al. 'Vascular depression' hypothesis. Arch Gen Psychiatry. 1997; 54: 915–922.

    Article  CAS  PubMed  Google Scholar 

  10. Culang-Reinlieb ME, Johnert LC, Brickman AM, Steffens DC, Garcon E, Sneed JR et al. MRI-defined vascular depression: a review of the construct. Int J Geriatr Psychiatry 2011; 26: 1101–1108.

    PubMed  Google Scholar 

  11. Kohler S, Thomas AJ, Lloyd A, Barber R, Almeida OP, O’Brien JT et al. White matter hyperintensities, cortisol levels, brain atrophy and continuing cognitive deficits in late-life depression. BJP 2010; 196: 143–149.

    Article  Google Scholar 

  12. Dotson VM, Davatzikos C, Kraut MA, Resnick SM . Depressive symptoms and brain volumes in older adults: a longitudinal magnetic resonance imaging study. J Psychiatry Neurosci 2009; 34: 367–375.

    PubMed  PubMed Central  Google Scholar 

  13. Egger K, Schocke M, Weiss E, Auffinger S, Esterhammer R, Goebel G et al. Pattern of brain atrophy in elderly patients with depression revealed by voxel-based morphometry. Psychiatry Res 2008; 164: 237–244.

    Article  PubMed  Google Scholar 

  14. Butters MA, Klunk WE, Mathis CA, Price JC, Ziolko SK, Hoge JA et al. Imaging Alzheimer’s pathology in late-life depression with PET and Pittsburgh Compound-B. Alzheimer Dis Assoc Disord 2008; 22: 261–268.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Kumar A, Kepe V, Barrio JR, Siddarth P, Manoukian V, Elderkin-Thompson V et al. Protein binding in patients with late-life depression. Arch Gen Psychiatry 2011; 68: 1143–1150.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Alexopoulos GS, Morimoto SS . The inflammation hypothesis in geriatric depression. Int J Geriatr Psychiatry 2011; 26: 1109–1118.

    PubMed  PubMed Central  Google Scholar 

  17. Diniz BS, Teixeira AL, Campos AC, Miranda AS, Rocha NP, Talib LL et al. Reduced serum levels of adiponectin in elderly patients with major depression. J Psychiatr Res 2012; 46: 1081–1085.

    Article  PubMed  Google Scholar 

  18. Teixeira AL, Barbosa IG, Diniz BS, Kummer A . Circulating levels of brain-derived neurotrophic factor: correlation with mood, cognition and motor function. Biomark Med 2010; 4: 871–887.

    Article  CAS  PubMed  Google Scholar 

  19. Diniz BS, Reynolds CF 3rd, Begley A, Dew MA, Anderson SJ, Lotrich F et al. Brain-derived neurotrophic factor levels in late-life depression and comorbid mild cognitive impairment: a longitudinal study. J Psychiatr Res 2014; 49: 96–101.

    Article  PubMed  Google Scholar 

  20. Pomara N, Bruno D, Sarreal AS, Hernando RT, Nierenberg J, Petkova E et al. Lower CSF amyloid beta peptides and higher F2-isoprostanes in cognitively intact elderly individuals with major depressive disorder. Am J Psychiatry 2012; 169: 523–530.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Diniz BS, Talib LL, Joaquim HP, de Paula VR, Gattaz WF, Forlenza OV et al. Platelet GSK3B activity in patients with late-life depression: marker of depressive episode severity and cognitive impairment? World J Biol Psychiatry 2011; 12: 216–222.

    Article  PubMed  Google Scholar 

  22. Xiao Z, Prieto D, Conrads TP, Veenstra TD, Issaq HJ . Proteomic patterns: their potential for disease diagnosis. Mol Cell Endocrinol 2005; 230: 95–106.

    Article  CAS  PubMed  Google Scholar 

  23. Templin MF, Stoll D, Bachmann J, Joos TO . Protein microarrays and multiplexed sandwich immunoassays: what beats the beads? Comb Chem High Throughput Screen 2004; 7: 223–229.

    Article  CAS  PubMed  Google Scholar 

  24. Arnold SE, Xie SX, Leung YY, Wang LS, Kling MA, Han X et al. Plasma biomarkers of depressive symptoms in older adults. Transl Psychiatry 2012; 2: e65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Ray S, Britschgi M, Herbert C, Takeda-Uchimura Y, Boxer A, Blennow K et al. Classification and prediction of clinical Alzheimer's diagnosis based on plasma signaling proteins. Nat Med 2007; 13: 1359–1362.

    Article  CAS  PubMed  Google Scholar 

  26. Hu WT, Chen-Plotkin A, Arnold SE, Grossman M, Clark CM, Shaw LM et al. Novel CSF biomarkers for Alzheimer's disease and mild cognitive impairment. Acta Neuropathol 2010 Jun; 119: 669–678.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Domenici E, Willé DR, Tozzi F, Prokopenko I, Miller S, McKeown A et al. Plasma protein biomarkers for depression and schizophrenia by multi analyte profiling of case-control collections. PLoS One 2010; 5: e9166.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Schwarz E, Guest PC, Rahmoune H, Harris LW, Wang L, Leweke FM et al. Identification of a biological signature for schizophrenia in serum. Mol Psychiatry 2012; 17: 494–502.

    Article  CAS  PubMed  Google Scholar 

  29. Klunk WE, Engler H, Nordberg A, Wang Y, Blomqvist G, Holt DP et al. Imaging brain amyloid in Alzheimer's disease with Pittsburgh Compound-B. Ann Neurol 2004; 55: 306–319.

    Article  CAS  PubMed  Google Scholar 

  30. Petersen RC . Mild cognitive impairment as a diagnostic entity. J Int Med 2004; 256: 183–194.

    Article  CAS  Google Scholar 

  31. Wu M, Rosano C, Butters M, Whyte E, Nable M, Crooks R et al. A fully automated method for quantifying and localizing white matter hyperintensities on MR images. Psychiatry Res 2006; 148: 133–142.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Price JC, Klunk WE, Lopresti BJ, Lu X, Hoge JA, Ziolko SK et al. Kinetic modeling of amyloid binding in humans using PET imaging and Pittsburgh Compound-B. J Cereb Blood Flow Metab 2005; 25: 1528–1547.

    Article  CAS  PubMed  Google Scholar 

  33. Cohen AD, Price JC, Weissfeld LA, James J, Rosario BL, Bi W et al. Basal cerebral metabolism may modulate the cognitive effects of Abeta in mild cognitive impairment: an example of brain reserve. J Neurosci 2009; 29: 14770–14778.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Cohen AD, Mowrey W, Weissfeld LA, Aizenstein HJ, McDade E, Mountz JM et al. Classification of amyloid-positivity in controls: comparison of visual read and quantitative approaches. Neuroimage 2013; 71: 207–215.

    Article  PubMed  Google Scholar 

  35. Meltzer CC, Cantwell MN, Greer PJ, Ben-Eliezer D, Smith G, Frank G et al. Does cerebral blood flow decline in healthy aging? A PET study with partial-volume correction. J Nucl Med 2000; 41: 1842–1848.

    CAS  PubMed  Google Scholar 

  36. Wang X, Lin Y, Song C, Sibille E, Tseng GC . Detecting disease-associated genes with confounding variable adjustment and the impact on genomic meta-analysis: with application to major depressive disorder. BMC Bioinformatics 2012; 13: 52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Jia Li, Tseng G . An adaptively weighted statistic for detecting differential gene expression when combining multiple transcriptomic studies. Ann Appl Stat 2011; 5: 994–1019.

    Article  Google Scholar 

  38. Spijker S, Van Zanten JS, De Jong S, Penninx BW, van Dyck R, Zitman FG et al. Stimulated gene expression profiles as a blood marker of major depressive disorder. Biol Psychiatry 2010; 68: 179–186.

    Article  CAS  PubMed  Google Scholar 

  39. Conrad CD . Chronic stress-induced hippocampal vulnerability: the glucocorticoid vulnerability hypothesis. Rev Neurosci 2008; 19: 395–411.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Thomas AJ, Ferrier N, Kalaria RN, Davis S, O'brien JT . Cell adhesion molecule expression in the dorsolateral prefrontal cortex and anterior cingulate cortex in major depression in the elderly. Br J Psychiatry 2002; 181: 129–134.

    Article  PubMed  Google Scholar 

  41. Tibshirani R . A bias correction for the minimum error rate in cross-validation. Ann Appl Stat 2009; 3: 822–829.

    Article  Google Scholar 

  42. Varma S, Simon R . Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics 2006; 7: 91.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Jack CR Jr. . Alzheimer disease: new concepts on its neurobiology and the clinical role imaging will play. Radiology 2012; 263: 344–361.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Hughes TM, Kuller LH, Lopez OL, Becker JT, Evans RW, Sutton-Tyrrell K et al. Markers of cholesterol metabolism in the brain show stronger associations with cerebrovascular disease than Alzheimer's disease. J Alzheimers Dis 2012; 30: 53–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Lotze MT . Interleukin 12: cellular and molecular immunology of an important regulatory cytokine. Introduction. Ann N Y Acad Sci 1996; 795: xiii–x.

    Article  CAS  PubMed  Google Scholar 

  46. Del Vecchio M, Bajetta E, Canova S, Lotze MT, Wesa A, Parmiani G et al. Interleukin-12: biological properties and clinical application. Clin Cancer Res 2007; 13: 4677–4685.

    Article  CAS  PubMed  Google Scholar 

  47. Douillard-Guilloux G, Guilloux JP, Lewis DA, Sibille E . Anticipated brain molecular aging in major depression. Am J Geriatr Psychiatry 2013; 21: 450–460.

    Article  PubMed  Google Scholar 

  48. Kapczinski F, Vieta E, Andreazza AC, Frey BN, Gomes FA, Tramontina J et al. Allostatic load in bipolar disorder: implications for pathophysiology and treatment. Neurosci Biobehav Rev 2008; 32: 675–692.

    Article  PubMed  Google Scholar 

  49. Sibille E . Molecular aging of the brain, neuroplasticity, and vulnerability to depression and other brain-related disorders. Dialogues Clin Neurosci 2013; 15: 53–65.

    PubMed  PubMed Central  Google Scholar 

  50. Diniz BS, Reynolds CF 3rd, Butters MA, Dew MA, Firmo JO, Lima-Costa MF et al. The effect of gender, age, and symptom severity in late-life depression on the risk of all-cause mortality: the Bambuí Cohort Study of Aging. Depress Anxiety advance online publication, 18 December 2013; doi:10.1002/da.22226(e-pub ahead of print).

  51. Alexopoulos GS . The vascular depression hypothesis: 10 years later. Biol Psychiatry. 2006; 60: 1304–1305.

    Article  PubMed  Google Scholar 

  52. Sexton CE, Mackay CE, Ebmeier KP . A systematic review and meta-analysis of magnetic resonance imaging studies in late-life depression. Am J Geriatr Psychiatry 2013; 21: 184–195.

    Article  PubMed  Google Scholar 

  53. Andreescu C, Butters MA, Begley A, Rajji T, Wu M, Meltzer CC et al. Gray matter changes in late life depression—a structural MRI analysis. Neuropsychopharmacology 2008; 33: 2566–2572 (e-pub ahead of print).

    Article  PubMed  Google Scholar 

  54. Tsopelas C, Stewart R, Savva GM, Brayne C, Ince P, Thomas A et al. Neuropathological correlates of late-life depression in older people. Br J Psychiatry. 2011; 198: 109–114.

    Article  PubMed  Google Scholar 

  55. Koivunen J, Scheinin N, Virta JR, Aalto S, Vahlberg T, Någren K et al. Amyloid PET imaging in patients with mild cognitive impairment: a 2-year follow-up study. Neurology 2011; 76: 1085–1090.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

Supported in part by MH080240, MH080240-S1 (MAB), P30 MH90333 (ACISR for Late Life Depression Prevention and Treatment; CFR, MAB, FL), the Alzheimer's Disease Research Center (P50 AG05133; JTB, OLL, WEK, HJA, MAB) and The John A Hartford Foundation Center of Excellence in Geriatric Psychiatry (BSD, CFR); R01 CA181450 (MTL); UFMG Intramural Research Grant 01/2013 (BSD); CNPq grant 472138/2013-8 (BSD).

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Authors and Affiliations

  1. Department of Mental Health, Faculty of Medicine, Federal University of Minas Gerais, Belo Horizonte, Brazil, MG

    B S Diniz

  2. Department of Psychiatry, University of Pittsburgh School of Medicine and Western Psychiatric Institute and Clinic, Pittsburgh, PA, USA,

    E Sibille, H J Aizenstein, F Lotrich, J T Becker, W E Klunk, C F Reynolds & M A Butters

  3. Department of Statistics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA

    Y Ding & G Tseng

  4. Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    O L Lopez

  5. Department of Surgery, University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA

    M T Lotze

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Correspondence to M A Butters.

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Diniz, B., Sibille, E., Ding, Y. et al. Plasma biosignature and brain pathology related to persistent cognitive impairment in late-life depression. Mol Psychiatry 20, 594–601 (2015). https://doi.org/10.1038/mp.2014.76

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