Hostname: page-component-745bb68f8f-s22k5 Total loading time: 0 Render date: 2025-01-13T03:47:48.811Z Has data issue: false hasContentIssue false
  • English
  • Français

The intestinal microbiota as a predictor for antidepressant treatment outcome in geriatric depression: a prospective pilot study

Published online by Cambridge University Press:  24 March 2021

S. Melanie Lee ,
Tien S. Dong ,
Beatrix Krause-Sorio Open the ORCID record for Beatrix Krause-Sorio [Opens in a new window] ,
Prabha Siddarth ,
Michaela M. Milillo Open the ORCID record for Michaela M. Milillo [Opens in a new window] ,
Venu Lagishetty ,
Tanya Datta ,
Yesenia Aguilar-Faustino ,
Jonathan P. Jacobs  and
Helen Lavretsky
S. Melanie Lee
Affiliation:
Department of Psychiatry, Semel Institute for Neuroscience, University of California Los Angeles, Los Angeles, CA, USA
Tien S. Dong
Affiliation:
The Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA UCLA Microbiome Center, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
Beatrix Krause-Sorio
Affiliation:
Department of Psychiatry, Semel Institute for Neuroscience, University of California Los Angeles, Los Angeles, CA, USA
Prabha Siddarth
Affiliation:
Department of Psychiatry, Semel Institute for Neuroscience, University of California Los Angeles, Los Angeles, CA, USA
Michaela M. Milillo
Affiliation:
Department of Psychiatry, Semel Institute for Neuroscience, University of California Los Angeles, Los Angeles, CA, USA
Venu Lagishetty
Affiliation:
The Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA UCLA Microbiome Center, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
Tanya Datta
Affiliation:
Department of Psychiatry, Semel Institute for Neuroscience, University of California Los Angeles, Los Angeles, CA, USA
Yesenia Aguilar-Faustino
Affiliation:
Department of Psychiatry, Semel Institute for Neuroscience, University of California Los Angeles, Los Angeles, CA, USA
Jonathan P. Jacobs
Affiliation:
The Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA UCLA Microbiome Center, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA Division of Gastroenterology, Hepatology and Parenteral Nutrition, VA Greater Los Angeles Healthcare System and Department of Medicine and Human Genetics, Los Angeles, CA, USA
Helen Lavretsky*
Affiliation:
Department of Psychiatry, Semel Institute for Neuroscience, University of California Los Angeles, Los Angeles, CA, USA
*
Correspondence should be addressed to: Helen Lavretsky, Professor of Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, 760 Westwood Plaza, 37-456, Los Angeles, CA, 90095, USA. Phone: 310-794-4619. Email: [email protected].
Get access

Abstract

Objectives:

(1) To investigate if gut microbiota can be a predictor of remission in geriatric depression and to identify features of the gut microbiota that is associated with remission. (2) To determine if changes in gut microbiota occur with remission in geriatric depression.

Design:

Secondary analysis of a parent randomized placebo-controlled trial (NCT02466958).

Setting:

Los Angeles, CA, USA (2016-2018)

Participants:

Seventeen subjects with major depressive disorder, over 60 years of age, 41.2% female.

Intervention:

Levomilacipran (LVM) or placebo.

Measurements:

Remission was defined by Hamilton Depression Rating Scale score of 6 or less at 12 weeks. 16S-ribosomal RNA sequencing based fecal microbiota composition and diversity were measured at baseline and 12 weeks. Differences in fecal microbiota were evaluated between remitters and non-remitters as well as between baseline and post-treatment samples. LVM and placebo groups were combined in all the analyses.

Results:

Baseline microbiota showed no community level α-diversity or β-diversity differences between remitters and non-remitters. At the individual taxa level, a random forest classifier created with nine genera from the baseline microbiota was highly accurate in predicting remission (AUC = .857). Of these, baseline enrichment of Faecalibacterium, Agathobacter and Roseburia relative to a reference frame was associated with treatment outcome of remission. Differential abundance analysis revealed significant genus level changes from baseline to post-treatment in remitters, but not in non-remitters.

Conclusions:

This is the first study demonstrating fecal microbiota as a potential predictor of treatment response in geriatric depression. Our findings need to be confirmed in larger prospective studies.

Keywords

antidepressantgut microbiotageriatric depressionMDDpredictor of treatment responseprecision medicinelevomilnacipran
Type
Original Research Article
Information
International Psychogeriatrics , Volume 34 , Special Issue 1: Issue Theme: Varied Angles of Geriatric Depression , January 2022 , pp. 33 - 45
Copyright
© International Psychogeriatric Association 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

(2012). Structure, function and diversity of the healthy human microbiome. Nature, 486, 207–214.CrossRefGoogle Scholar
Auclair, A. L. et al. (2013). Levomilnacipran (F2695), a norepinephrine-preferring SNRI: profile in vitro and in models of depression and anxiety. Neuropharmacology, 70, 338–347.CrossRefGoogle ScholarPubMed
Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.10.1023/A:1010933404324CrossRefGoogle Scholar
Bruno, A., Morabito, P., Spina, E. and Muscatello, M. R. (2016). The Role of Levomilnacipran in the Management of Major Depressive Disorder: A Comprehensive Review. Current Neuropharmacology, 14, 191–199.CrossRefGoogle ScholarPubMed
Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. and Holmes, S. P. (2016). DADA2: high-resolution sample inference from Illumina amplicon data. Nature Methods, 13, 581–583.CrossRefGoogle ScholarPubMed
Cani, P. D. and Knauf, C. (2016). How gut microbes talk to organs: the role of endocrine and nervous routes. Molecular Metabolism, 5, 743–752.CrossRefGoogle ScholarPubMed
Caporaso, J. G. et al. (2010). QIIME allows analysis of high-throughput community sequencing data. Nature Methods, 7, 335–336.CrossRefGoogle ScholarPubMed
Caporaso, J. G. et al. (2012). Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. The ISME Journal, 6, 1621–1624.CrossRefGoogle ScholarPubMed
Cheung, S. G., Goldenthal, A. R., Uhlemann, A. C., Mann, J. J., Miller, J. M. and Sublette, M. E. (2019). Systematic Review of Gut Microbiota and Major Depression. Frontiers in Psychiatry, 10, 34.CrossRefGoogle ScholarPubMed
Dell’Osso, L., Carmassi, C., Mucci, F. and Marazziti, D. (2016). Depression, Serotonin and Tryptophan. Current Pharmaceutical Design, 22, 949–954.CrossRefGoogle ScholarPubMed
Fakhoury, M. (2016). Revisiting the Serotonin Hypothesis: Implications for Major Depressive Disorders. Molecular Neurobiology, 53, 2778–2786.CrossRefGoogle ScholarPubMed
Fedarko, M. W. et al. (2020). Visualizing ‘omic feature rankings and log-ratios using Qurro. NAR Genomics and Bioinformatics, 2, lqaa023.CrossRefGoogle ScholarPubMed
Forlani, C. et al. (2014). Prevalence and gender differences in late-life depression: a population-based study. The American Journal of Geriatric Psychiatry, 22, 370–380.CrossRefGoogle ScholarPubMed
Furet, J. P. et al. (2010). Differential adaptation of human gut microbiota to bariatric surgery-induced weight loss: links with metabolic and low-grade inflammation markers. Diabetes, 59, 3049–3057.CrossRefGoogle ScholarPubMed
Furusawa, Y. et al. (2013). Commensal microbe-derived butyrate induces the differentiation of colonic regulatory T cells. Nature, 504, 446–450.CrossRefGoogle ScholarPubMed
Gadad, B. S. et al. (2018). Peripheral biomarkers of major depression and antidepressant treatment response: current knowledge and future outlooks. Journal of Affective Disorders, 233, 3–14.CrossRefGoogle ScholarPubMed
Galkin, F. et al. (2020). Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning. iScience, 23, 101199.CrossRefGoogle ScholarPubMed
Gao, J. et al. (2018). Impact of the Gut Microbiota on Intestinal Immunity Mediated by Tryptophan Metabolism. Frontiers in Cellular and Infection Microbiology, 8, 13.CrossRefGoogle ScholarPubMed
Gareri, P., De Fazio, P. and De Sarro, G. (2002). Neuropharmacology of depression in aging and age-related diseases. Ageing Research Reviews, 1, 113–134.CrossRefGoogle ScholarPubMed
Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. and Egozcue, J. J. (2017). Microbiome Datasets Are Compositional: And This Is Not Optional. Frontiers in Microbiology, 8, 2224.CrossRefGoogle Scholar
Guilloteau, P., Martin, L., Eeckhaut, V., Ducatelle, R., Zabielski, R. and Van Immerseel, F. (2010). From the gut to the peripheral tissues: the multiple effects of butyrate. Nutrition Research Reviews, 23, 366–384.CrossRefGoogle Scholar
Hao, Z., Wang, W., Guo, R. and Liu, H. (2019). Faecalibacterium prausnitzii (ATCC 27766) has preventive and therapeutic effects on chronic unpredictable mild stress-induced depression-like and anxiety-like behavior in rats. Psychoneuroendocrinology, 104, 132–142.CrossRefGoogle ScholarPubMed
Hopkins, M. J., Sharp, R. and Macfarlane, G. T. (2002). Variation in human intestinal microbiota with age. Digestive and Liver Disease, 34, S12–S18.CrossRefGoogle ScholarPubMed
Huang, R., Wang, K. and Hu, J. (2016). Effect of Probiotics on Depression: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Nutrients, 8.Google ScholarPubMed
Iino, C. et al. (2019). Significant decrease in Faecalibacterium among gut microbiota in nonalcoholic fatty liver disease: a large BMI- and sex-matched population study. Hepatology International, 13, 748–756.CrossRefGoogle ScholarPubMed
Jiang, H. et al. (2015). Altered fecal microbiota composition in patients with major depressive disorder. Brain, Behavior, and Immunity, 48, 186–194.CrossRefGoogle ScholarPubMed
Karlsson, F. H. et al. (2013). Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature, 498, 99–103.CrossRefGoogle ScholarPubMed
Kelly, J. R. et al. (2016). Transferring the blues: Depression-associated gut microbiota induces neurobehavioural changes in the rat. Journal of Psychiatric Research, 82, 109–118.CrossRefGoogle ScholarPubMed
Khan, A., Detke, M., Khan, S. R. and Mallinckrodt, C. (2003). Placebo response and antidepressant clinical trial outcome. The Journal of Nervous and Mental Disease, 191, 211–218.CrossRefGoogle ScholarPubMed
Koeth, R. A. et al. (2013). Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis. Nature Medicine, 19, 576–585.CrossRefGoogle ScholarPubMed
Krause-Sorio, B. et al. (2020). Cortical thickness increases with levomilnacipran treatment in a pilot randomised double-blind placebo-controlled trial in late-life depression. Psychogeriatrics, 20, 140–148.CrossRefGoogle Scholar
Lenze, E. J. et al. (2008). Incomplete response in late-life depression: getting to remission. Dialogues in Clinical Neuroscience, 10, 419–430.Google Scholar
Ley, R. E. (2010). Obesity and the human microbiome. Current Opinion in Gastroenterology, 26, 5–11.CrossRefGoogle ScholarPubMed
Ley, R. E., Turnbaugh, P. J., Klein, S. and Gordon, J. I. (2006). Microbial ecology: human gut microbes associated with obesity. Nature, 444, 1022–1023.CrossRefGoogle ScholarPubMed
Lopez-Siles, M. et al. (2016). Changes in the Abundance of Faecalibacterium prausnitzii Phylogroups I and II in the Intestinal Mucosa of Inflammatory Bowel Disease and Patients with Colorectal Cancer. Inflammatory Bowel Disease, 22, 28–41.CrossRefGoogle ScholarPubMed
Love, M. I., Huber, W. and Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15, 550.CrossRefGoogle ScholarPubMed
Lowry, C. A. et al. (2016). The Microbiota, Immunoregulation, and Mental Health: Implications for Public Health. Current Environmental Health Reports, 3, 270–286.CrossRefGoogle ScholarPubMed
Macedo, D. et al. (2017). Antidepressants, antimicrobials or both? Gut microbiota dysbiosis in depression and possible implications of the antimicrobial effects of antidepressant drugs for antidepressant effectiveness. Journal of Affective Disorders, 208, 22–32.CrossRefGoogle ScholarPubMed
Machiels, K. et al. (2014). A decrease of the butyrate-producing species Roseburia hominis and Faecalibacterium prausnitzii defines dysbiosis in patients with ulcerative colitis. Gut, 63, 1275–1283.CrossRefGoogle ScholarPubMed
Marshe, V. S. et al. (2017). Norepinephrine Transporter Gene Variants and Remission From Depression With Venlafaxine Treatment in Older Adults. The American Journal of Psychiatry, 174, 468–475.CrossRefGoogle ScholarPubMed
Mather, M. and Harley, C. W. (2016). The Locus Coeruleus: Essential for Maintaining Cognitive Function and the Aging Brain. Trends in Cognitive Sciences, 20, 214–226.CrossRefGoogle ScholarPubMed
McArdle, B. H. and Anderson, M. J. (2001). Fitting multivariate models to community data: a comment on distance-based redundancy analysis. Ecology, 82, 290–297.CrossRefGoogle Scholar
McEntee, W. J. and Crook, T. H. (1991). Serotonin, memory, and the aging brain. Psychopharmacology (Berl), 103, 143–149.CrossRefGoogle ScholarPubMed
Meeks, T. W., Vahia, I. V., Lavretsky, H., Kulkarni, G. and Jeste, D. V. (2011). A tune in “a minor” can “b major”: a review of epidemiology, illness course, and public health implications of subthreshold depression in older adults. Journal of Affective Disorders, 129, 126–142.CrossRefGoogle ScholarPubMed
Miquel, S. et al. (2013). Faecalibacterium prausnitzii and human intestinal health. Current Opinion in Microbiology, 16, 255–261.CrossRefGoogle ScholarPubMed
Mitchell, A. J. and Subramaniam, H. (2005). Prognosis of depression in old age compared to middle age: a systematic review of comparative studies. The American Journal of Psychiatry, 162, 1588–1601.CrossRefGoogle ScholarPubMed
Montgomery, S. A., Gommoll, C. P., Chen, C. and Greenberg, W. M. (2015). Efficacy of levomilnacipran extended-release in major depressive disorder: pooled analysis of 5 double-blind, placebo-controlled trials. CNS Spectrums, 20, 148–156.CrossRefGoogle ScholarPubMed
Montgomery, S. A., Mansuy, L., Ruth, A. C., Li, D. and Gommoll, C. (2014). The efficacy of extended-release levomilnacipran in moderate to severe major depressive disorder: secondary and post-hoc analyses from a randomized, double-blind, placebo-controlled study. International Clinical Psychopharmacology, 29, 26–35.CrossRefGoogle ScholarPubMed
Montgomery, S. A., Mansuy, L., Ruth, A., Bose, A., Li, H. and Li, D. (2013). Efficacy and safety of levomilnacipran sustained release in moderate to severe major depressive disorder: a randomized, double-blind, placebo-controlled, proof-of-concept study. The Journal of Clinical Psychiatry, 74, 363–369.CrossRefGoogle ScholarPubMed
Morton, J. T. et al. (2019). Establishing microbial composition measurement standards with reference frames. Nature Communications, 10, 2719.CrossRefGoogle ScholarPubMed
Patel, K., Abdool, P. S., Rajji, T. K. and Mulsant, B. H. (2017). Pharmacotherapy of major depression in late life: what is the role of new agents? Expert Opinion on Pharmacotherapy, 18, 599–609.CrossRefGoogle ScholarPubMed
Petersen, C. and Round, J. L. (2014). Defining dysbiosis and its influence on host immunity and disease. Cellular Microbiology, 16, 1024–1033.CrossRefGoogle ScholarPubMed
Rajilic-Stojanovic, M. et al. (2011). Global and deep molecular analysis of microbiota signatures in fecal samples from patients with irritable bowel syndrome. Gastroenterology, 141, 1792–1801.CrossRefGoogle ScholarPubMed
Rinninella, E. et al. (2019). What is the Healthy Gut Microbiota Composition? A Changing Ecosystem across Age, Environment, Diet, and Diseases. Microorganisms, 7.CrossRefGoogle Scholar
Salanti, G. et al. (2018). Impact of placebo arms on outcomes in antidepressant trials: systematic review and meta-regression analysis. International Journal of Epidemiology, 47, 1454–1464.CrossRefGoogle ScholarPubMed
Sambunaris, A., Bose, A., Gommoll, C. P., Chen, C., Greenberg, W. M. and Sheehan, D. V. (2014a). A Phase III, Double-Blind, Placebo-Controlled, Flexible-Dose Study of Levomilnacipran Extended-Release in Patients With Major Depressive Disorder. Journal of Clinical Psychopharmacology, 34, 47–56.CrossRefGoogle ScholarPubMed
Sambunaris, A., Gommoll, C., Chen, C. and Greenberg, W. M. (2014b). Efficacy of levomilnacipran extended-release in improving functional impairment associated with major depressive disorder: pooled analyses of five double-blind, placebo-controlled trials. International Clinical Psychopharmacology, 29, 197–205.CrossRefGoogle ScholarPubMed
Sarkar, A., Lehto, S. M., Harty, S., Dinan, T. G., Cryan, J. F. and Burnet, P. W. J. (2016). Psychobiotics and the Manipulation of Bacteria-Gut-Brain Signals. Trends in Neurosciences, 39, 763–781.CrossRefGoogle ScholarPubMed
Sender, R., Fuchs, S. and Milo, R. (2016). Are We Really Vastly Outnumbered? Revisiting the Ratio of Bacterial to Host Cells in Humans. Cell, 164, 337–340.CrossRefGoogle ScholarPubMed
Sokol, H. et al. (2008). Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium identified by gut microbiota analysis of Crohn disease patients. Proceedings of the National Academy of Sciences of the United States of America, 105, 16731–16736.CrossRefGoogle ScholarPubMed
Sramek, J. J., Murphy, M. F. and Cutler, N. R. (2016). Sex differences in the psychopharmacological treatment of depression. Dialogues in Clinical Neuroscience, 18, 447–457.Google ScholarPubMed
Storey, J. D. and Tibshirani, R. (2003). Statistical significance for genomewide studies. Proceedings of the National of Academy of Sciences of the United States of America, 100, 9440–9445.Google ScholarPubMed
Thursby, E. and Juge, N. (2017). Introduction to the human gut microbiota. The Biochemical Journal, 474, 1823–1836.CrossRefGoogle Scholar
Tiihonen, K., Ouwehand, A. C. and Rautonen, N. (2010). Human intestinal microbiota and healthy ageing. Ageing Research Reviews, 9, 107–116.CrossRefGoogle ScholarPubMed
Tillisch, K. et al. (2013). Consumption of fermented milk product with probiotic modulates brain activity. Gastroenterology, 144, 1394–1401, 1401 e1391-1394.CrossRefGoogle ScholarPubMed
Tong, M., Jacobs, J. P., McHardy, I. H. and Braun, J. (2014). Sampling of intestinal microbiota and targeted amplification of bacterial 16S rRNA genes for microbial ecologic analysis. Current Protocols in Immunology, 107, 7 41 41-11.CrossRefGoogle ScholarPubMed
Tuohy, K. M., Fava, F. and Viola, R. (2014). ‘The way to a man’s heart is through his gut microbiota’--dietary pro- and prebiotics for the management of cardiovascular risk. The Proceedings of the Nutrition Society, 73, 172–185.CrossRefGoogle ScholarPubMed
Vaiserman, A. M., Koliada, A. K. and Marotta, F. (2017). Gut microbiota: a player in aging and a target for anti-aging intervention. Ageing Research Reviews, 35, 36–45.CrossRefGoogle Scholar
Valles-Colomer, M. et al. (2019). The neuroactive potential of the human gut microbiota in quality of life and depression. Nature Microbiology, 4, 623–632.CrossRefGoogle ScholarPubMed
Vemuri, R. et al. (2018). Gut Microbial Changes, Interactions, and Their Implications on Human Lifecycle: An Ageing Perspective. Biomed Research International, 2018, 4178607.CrossRefGoogle Scholar
Vrieze, A. et al. (2012). Transfer of intestinal microbiota from lean donors increases insulin sensitivity in individuals with metabolic syndrome. Gastroenterology, 143, 913–916 e917.CrossRefGoogle ScholarPubMed
Whiteford, H. A. et al. (2013). Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. Lancet, 382, 1575–1586.CrossRefGoogle ScholarPubMed
Woodmansey, E. J. (2007). Intestinal bacteria and ageing. Journal of Applied Microbiology, 102, 1178–1186.CrossRefGoogle ScholarPubMed
Wu, N. et al. (2013). Dysbiosis signature of fecal microbiota in colorectal cancer patients. Microbial Ecology, 66, 462–470.CrossRefGoogle ScholarPubMed
Yun, H. M., Park, K. R., Kim, E. C., Kim, S. and Hong, J. T. (2015). Serotonin 6 receptor controls Alzheimer’s disease and depression. Oncotarget, 6, 26716–26728.CrossRefGoogle ScholarPubMed
Zheng, P. et al. (2016). Gut microbiome remodeling induces depressive-like behaviors through a pathway mediated by the host’s metabolism. Molecular Psychiatry, 21, 786–796.CrossRefGoogle ScholarPubMed
Supplementary material: File

Lee et al. supplementary material

Figures S1 and S2

Download Lee et al. supplementary material(File)
File 9.1 MB