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[18F]FDDNP PET binding predicts change in executive function in a pilot clinical trial of geriatric depression

Published online by Cambridge University Press:  23 January 2020

Beatrix Krause-Sorio
Affiliation:
Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA 90095, USA
Prabha Siddarth
Affiliation:
Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA 90095, USA
Kelsey T. Laird
Affiliation:
Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA 90095, USA
Linda Ercoli
Affiliation:
Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA 90095, USA
Katherine Narr
Affiliation:
Brain Research Institute, Los Angeles, CA 90095, USA
Jorge R. Barrio
Affiliation:
Department of Molecular and Medical Pharmacology, David Geffen UCLA School of Medicine, Los Angeles, CA 90095, USA
Gary Small
Affiliation:
Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA 90095, USA
Helen Lavretsky*
Affiliation:
Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA 90095, USA
*
Correspondence should be addressed to: Helen Lavretsky, Psychiatry-in-Residence, Jane and Terry Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine at University of California, 760 Westwood Plaza, Los Angeles, CA 90095, USA. Phone: +310 794 4619; Fax: +310 206 4399. Email: [email protected].

Abstract

Objectives:

Geriatric depression often presents with memory and cognitive complaints that are associated with increased risk for Alzheimer’s disease (AD). In a parent clinical trial of escitalopram combined with memantine or placebo for geriatric depression and subjective memory complaints, we found that memantine improved executive function and delayed recall performance at 12 months (NCT01902004). In this report, we used positron emission tomography (PET) to assess the relationship between in-vivo amyloid and tau brain biomarkers and clinical and cognitive treatment response.

Design:

In a randomized double-blind placebo-controlled trial, we measured 2-(1-{6-[(2-[F18]fluoroethyl)(methyl)amino]-2-naphthyl}ethylidene) malononitrile ([18F]FDDNP) binding at baseline and assessed mood and cognitive performance at baseline, posttreatment (6 months), and naturalistic follow-up (12 months).

Participants:

Twenty-two older adults with major depressive disorder and subjective memory complaints completed PET scans and were included in this report.

Results:

Across both treatment groups, higher frontal lobe [18F]FDDNP binding at baseline was associated with improvement in executive function at 6 months (corrected p = .045). This effect was no longer significant at 12 months (corrected p = .12). There was no association of regional [18F]FDDNP binding with change in mood symptoms (corrected p = .2).

Conclusions:

[18F]FDDNP binding may predict cognitive response to antidepressant treatment. Larger trials are required to further test the value of [18F]FDDNP binding as a biomarker for cognitive improvement with antidepressant treatment in geriatric depression.

Type
Original Research Article
Copyright
© International Psychogeriatric Association 2020

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References

American Psychiatric Association (2013). Diagnostic and Statistical Manual of Mental Disorders (5th ed.). Washington, DC: American Psychiatric Association.Google Scholar
Bejanin, A. et al. (2017). Tau pathology and neurodegeneration contribute to cognitive impairment in Alzheimer’s disease. Brain: A Journal of Neurology, 140, 32863300. doi: 10.1093/brain/awx243.CrossRefGoogle ScholarPubMed
Benton, A., Hamsher, K., Varney, R. N. and Spreen, O. (1983). Contribution to Neuropsychological Assessment: A Clinical Manual. New York: Oxford University Press.Google Scholar
Carpenter, C. R. et al. (2014). Predicting geriatric falls following an episode of emergency department care: a systematic review. Academic Emergency Medicine, 21, 10691082. doi: 10.1111/acem.12488.CrossRefGoogle ScholarPubMed
Cho, H. et al. (2016a). In vivo cortical spreading pattern of tau and amyloid in the Alzheimer disease spectrum. Annals of Neurology, 80, 247258. doi: 10.1002/ana.24711.CrossRefGoogle ScholarPubMed
Cho, H. et al. (2016b). Tau PET in Alzheimer disease and mild cognitive impairment. Neurology, 87, 375. doi: 10.1212/WNL.0000000000002892.CrossRefGoogle ScholarPubMed
Cole, G. B. et al. (2018). The value of in vitro binding as predictor of in vivo results: a case for [(18)F]FDDNP PET. Molecular Imaging and Biology, 21, 2534. doi: 10.1007/s11307-018-1210-2.CrossRefGoogle Scholar
Conover, W. J. (2012). The rank transformation—an easy and intuitive way to connect many nonparametric methods to their parametric counterparts. WIREs Computational Statistics, 4, 432438. doi: 10.1002/wics.1216.CrossRefGoogle Scholar
Conover, W. J. and Iman, R. L. (1981). Rank transformations as a bridge between parametric and nonparametric statistics. The American Statistician, 35(3), 24129. doi: 10.2307/2683975.Google Scholar
Delis, D. C. (2000). California Verbal Learning Test: Adult Version Manual. San Antonio, TX: Psychological Corporation.Google Scholar
Diniz, B. S., Butters, M. A., Albert, S. M., Dew, M. A. and Reynolds, C. F., 3rd (2013). Late-life depression and risk of vascular dementia and Alzheimer’s disease: systematic review and meta-analysis of community-based cohort studies. British Journal of Psychiatry, 202, 329–35. doi: 10.1192/bjp.bp.112.118307.CrossRefGoogle ScholarPubMed
Eyre, H. A. et al. (2017). Neural correlates of apathy in late-life depression: a pilot [(18) F]FDDNP positron emission tomography study. Psychogeriatrics: The Official Journal of The Japanese Psychogeriatric Society, 17, 186193. doi: 10.1111/psyg.12213.CrossRefGoogle ScholarPubMed
Folstein, M. F., Folstein, S. E. and Mchugh, P. R. (1975). “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189–98. doi: 10.1016/0022-3956(75)90026-6.CrossRefGoogle ScholarPubMed
Golden, C. J., Marsella, A. J. and Golden, E. E. (1975). Cognitive relationships of resistance to interference. Journal of Consulting and Clinical Psychology, 43, 432432. doi: 10.1037/h0076772.CrossRefGoogle Scholar
Guy, W. (2000). Clinical Global Impressions (CGI) scale, modified. In: Rush, A. J. (Ed.), Task Force for the Handbook of Psychiatric Measures. Handbook of Psychiatric Measures, 1st ed. Washington, DC: American Psychiatric Association.Google Scholar
Hamilton, M. (1967). Development of a rating scale for primary depressive illness. British Journal of Social and Clinical Psychology, 6, 278296. doi: 10.1111/j.2044-8260.1967.tb00530.x.CrossRefGoogle ScholarPubMed
Hostetler, E. D. et al. (2016). Preclinical characterization of 18F-MK-6240, a promising PET tracer for in vivo quantification of human neurofibrillary tangles. Journal of Nuclear Medicine, 57, 15991606. doi: 10.2967/jnumed.115.171678.CrossRefGoogle ScholarPubMed
Hughes, C. P., Berg, L., Danziger, W. L., Coben, L. A. and Martin, R. L. (1982). A new clinical scale for the staging of dementia. British Journal of Psychiatry, 140, 566572. doi: 10.1192/bjp.140.6.566.CrossRefGoogle ScholarPubMed
Ismail, Z., Fischer, C. and Mccall, W. V. (2013). What characterizes late-life depression? Psychiatric Clinics of North America, 36, 483496. doi: 10.1016/j.psc.2013.08.010.CrossRefGoogle ScholarPubMed
Jack, C. R. et al. (2013). Update on hypothetical model of Alzheimer’s disease biomarkers. Lancet Neurology, 12, 207216. doi: 10.1016/S1474-4422(12)70291-0.CrossRefGoogle Scholar
Klunk, W. E. et al. (2004). Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Annals of Neurology, 55, 306319. doi: 10.1002/ana.20009.CrossRefGoogle ScholarPubMed
Kumar, A. et al. (2011). Protein binding in patients with late-life depression. Archives of General Psychiatry, 68, 11431150. doi: 10.1001/archgenpsychiatry.2011.122.CrossRefGoogle ScholarPubMed
Lavretsky, H. et al. (2019). A randomized double-blind placebo-controlled trial of combined escitalopram and memantine for older adults with major depression and subjective memory complaints. American Journal of Geriatric Psychiatry. pii: S1064–7481(19)30474-9. doi: 10.1016/j.jagp.2019.08.011.Google Scholar
Lavretsky, H. et al. (2009). Depression and anxiety symptoms are associated with cerebral FDDNP-PET binding in middle-aged and older nondemented adults. American Journal of Geriatric Psychiatry, 17, 493502. doi: 10.1097/JGP.0b013e3181953b82.CrossRefGoogle ScholarPubMed
Lezac, M., Howieson, D. and Loring, D. (2004). Neuropsychological Assessment. New York: Oxford University Press.Google Scholar
Liu, J. et al. (2007). High-yield, automated radiosynthesis of 2-(1-{6-[(2-[18F]fluoroethyl)(methyl)amino]-2-naphthyl}ethylidene)malononitrile ([18F]FDDNP) ready for animal or human administration. Molecular Imaging and Biology, 9, 616. doi: 10.1007/s11307-006-0061-4.CrossRefGoogle ScholarPubMed
Merrill, D. A. et al. (2012). Self-reported memory impairment and brain PET of amyloid and tau in middle-aged and older adults without dementia. International Psychogeriatrics, 24, 10761084. doi: 10.1017/s1041610212000051.CrossRefGoogle ScholarPubMed
Meyers, J. E. and Meyers, K. R. (1995). Rey Complex Figure Test under four different administration procedures. Clinical Neuropsychologist, 9, 6367. doi: 10.1080/13854049508402059.CrossRefGoogle Scholar
Mitchell, A. J. and Subramaniam, H. (2005). Prognosis of depression in old age compared to middle age: a systematic review of comparative studies. American Journal of Psychiatry, 162, 15881601. doi: 10.1176/appi.ajp.162.9.1588.CrossRefGoogle ScholarPubMed
Murugan, N. A., Nordberg, A. and Ågren, H. (2018). Different positron emission tomography tau tracers bind to multiple binding sites on the tau fibril: insight from computational modeling. ACS Chemical Neuroscience, 9, 17571767. doi: 10.1021/acschemneuro.8b00093.CrossRefGoogle ScholarPubMed
Palmqvist, S. et al. (2017). Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity. Nature Communications, 8, 1214. doi: 10.1038/s41467-017-01150-x.CrossRefGoogle ScholarPubMed
Reitan, R. M. and Wolfson, D. (1988). The Halstead-Reitan Neuropsychological Test Battery and REHABIT: a model for integrating evaluation and remediation of cognitive impairment. Cognitive Rehabilitation, 6, 1017. doi: 10.1007/978-1-4757-9820-3_4.Google Scholar
Rosen, C., Hansson, O., Blennow, K. and Zetterberg, H. (2013). Fluid biomarkers in Alzheimer’s disease—current concepts. Molecular Neurodegener, 8, 20. doi: 10.1186/1750-1326-8-20.CrossRefGoogle ScholarPubMed
Shin, J., Lee, S. Y., Kim, S. H., Kim, Y. B. and Cho, S. J. (2008). Multitracer PET imaging of amyloid plaques and neurofibrillary tangles in Alzheimer’s disease. Neuroimage, 43, 236244. doi: 10.1016/j.neuroimage.2008.07.022.CrossRefGoogle ScholarPubMed
Singh-Manoux, A. et al. (2017). Trajectories of depressive symptoms before diagnosis of dementia: a 28-year follow-up study. JAMA Psychiatry, 74, 712718. doi: 10.1001/jamapsychiatry.2017.0660.CrossRefGoogle ScholarPubMed
Small, G. W. et al. (2012). Prediction of cognitive decline by positron emission tomography of brain amyloid and tau. Archives of Neurology, 69, 215222. doi: 10.1001/archneurol.2011.559.CrossRefGoogle Scholar
Small, G. W. et al. (2006). PET of brain amyloid and tau in mild cognitive impairment. New England Journal of Medicine, 355, 26522663. doi: 10.1056/NEJMoa054625.CrossRefGoogle ScholarPubMed
Sperling, R. A. et al. (2011). Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement, 7, 280292. doi: 10.1016/j.jalz.2011.03.003.CrossRefGoogle ScholarPubMed
Tian, Y. et al. (2016). Venlafaxine treatment reduces the deficit of executive control of attention in patients with major depressive disorder. Scientific Reports, 6, 28028. doi: 10.1038/srep28028.CrossRefGoogle ScholarPubMed
Tunvirachaisakul, C. et al. (2017). Predictors of treatment outcome in depression in later life: a systematic review and meta-analysis. Journal of Affective Disorders, 227, 164182. doi: 10.1016/j.jad.2017.10.008.CrossRefGoogle ScholarPubMed
Vega, J. N. et al. (2016). Altered brain connectivity in early postmenopausal women with subjective cognitive impairment. Frontiers in Neuroscience, 10, 433. doi: 10.3389/fnins.2016.00433.CrossRefGoogle ScholarPubMed
Villemagne, V. L. et al. (2013). Amyloid beta deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: a prospective cohort study. Lancet Neurology, 12, 357367. doi: 10.1016/s1474-4422(13)70044-9.CrossRefGoogle ScholarPubMed
Wagner, S. et al. (2018). Plasma brain-derived neurotrophic factor (pBDNF) and executive dysfunctions in patients with major depressive disorder. World Journal of Biological Psychiatry, 20, 519530. doi: 10.1080/15622975.2018.1425478.CrossRefGoogle ScholarPubMed
Wechsler, D. (1997). The Wechsler Adult Intelligence Scale, III Manual. San Antonio, TX: Psychological Corporation.Google Scholar
Wilkins, C. H., Mathews, J. and Sheline, Y. I. (2009). Late life depression with cognitive impairment: evaluation and treatment. Clinical Interventions in Aging, 4, 5157.Google ScholarPubMed
Winblad, B. et al. (2004). Mild cognitive impairment—beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. Journal of Internal Medicine, 256, 240246. doi: 10.1111/j.1365-2796.2004.01380.x.CrossRefGoogle Scholar
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