Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-03T23:06:06.771Z Has data issue: false hasContentIssue false
  • English
  • Français

Preliminary Evidence for an Association Between the Composition of the Gut Microbiome and Cognitive Function in Neurologically Healthy Older Adults

Published online by Cambridge University Press:  23 June 2017

Lisa Manderino ,
Ian Carroll ,
M. Andrea Azcarate-Peril ,
Amber Rochette ,
Leslie Heinberg ,
Christine Peat ,
Kristine Steffen ,
James Mitchell  and
John Gunstad
Lisa Manderino
Affiliation:
Department of Psychological Sciences, Kent State University, Kent, Ohio
Ian Carroll
Affiliation:
Department of Cell Biology and Physiology and Microbiome Core Facility, UNC School of Medicine, Chapel Hill, North Carolina
M. Andrea Azcarate-Peril
Affiliation:
Department of Cell Biology and Physiology and Microbiome Core Facility, UNC School of Medicine, Chapel Hill, North Carolina
Amber Rochette
Affiliation:
Department of Psychological Sciences, Kent State University, Kent, Ohio
Leslie Heinberg
Affiliation:
Cleveland Clinic, Lerner College of Medicine of Case Western Reserve University, Bariatric and Metabolic Institute, Cleveland, Ohio
Christine Peat
Affiliation:
Center of Excellence for Eating Disorders, UNC School of Medicine, Chapel Hill, North Carolina
Kristine Steffen
Affiliation:
Department of Pharmaceutical Sciences, College of Pharmacy, Nursing and Allied Sciences, North Dakota State University, Fargo, North Dakota
James Mitchell
Affiliation:
Neuropsychiatric Research Institute; Department of Clinical Neuroscience, School of Medicine and Health Services, University of North Dakota
John Gunstad*
Affiliation:
Department of Psychological Sciences, Kent State University, Kent, Ohio
*
Correspondence and reprint requests to: John Gunstad, 144 Kent Hall, Kent State University, Kent, OH 44242. E-mail: jgunstad@kent.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives: Dysbiosis of the gut microbiome is implicated in numerous human health conditions. Animal studies have linked microbiome disruption to changes in cognitive functioning, although no study has examined this possibility in neurologically healthy older adults. Methods: Participants were 43 community-dwelling older adults (50–85 years) that completed a brief cognitive test battery and provided stool samples for gut microbiome sequencing. Participants performing≥1 SD below normative performance on two or more tests were compared to persons with one or fewer impaired scores. Results: Mann Whitney U tests revealed different distributions of Bacteroidetes (p=.01), Firmicutes (p=.02), Proteobacteria (p=.04), and Verrucomicrobia (p=.003) between Intact and Impaired groups. These phyla were significantly correlated with cognitive test performances, particularly Verrucomicrobia and attention/executive function measures. Conclusions: The current findings suggest that composition of the gut microbiome is associated with cognitive test performance in neurologically healthy older adults. Future studies are needed to confirm these findings and explore possible mechanisms. (JINS, 2017, 23, 700–705)

Keywords

MicrobiotaCognitionAttentionExecutive functionAgingHumans
Type
Brief Communication
Information
Journal of the International Neuropsychological Society , Volume 23 , Issue 8 , September 2017 , pp. 700 - 705
Copyright
Copyright © The International Neuropsychological Society 2017 

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

REFERENCES

Bäckhed, F., Ley, R.E., Sonnenburg, J.L., Peterson, D.A., & Gordon, J.I. (2005). Host-bacterial mutualism in the human intestine. Science, 307(5717), 1915–1920.Google Scholar
Bajaj, J.S., Ahluwalia, V., Steinberg, J.L., Hobgood, S., Boling, P.A., Godschalk, M., & Wade, J.B. (2016). Elderly patients have an altered gut-brain axis regardless of the presence of cirrhosis. Scientific Reports, 6, 38481.Google Scholar
Bajaj, J.S., Ridlon, J.M., Hylemon, P.B., Thacker, L.R., Heuman, D.M., Smith, S., & Gillevet, P.M. (2011). Linkage of gut microbiome with cognition in hepatic encephalopathy. American Journal of Physiology - Gastrointestinal and Liver Physiology, 302(1), G168–G175.Google Scholar
Brandt, J. (1991). The Hopkins Verbal Learning Test: Development of a new memory test with six equivalent forms. Clinical Neuropsychologist, 5, 125–142.Google Scholar
Bruce-Keller, A.J., Salbaum, J.M., Luo, M., Blanchard, E., Taylor, C.M., Welsh, D.A., & Berthoud, H.R. (2015). Obese-type gut microbiota induce neurobehavioral changes in the absence of obesity. Biological Psychiatry, 77(7), 607–615.Google Scholar
Desbonnet, L., Carke, G., Traplin, A., O’Sullivan, O., Crispie, F., Moloney, R.D., & Cryan, J.F. (2015). Microbiota depletion from early adolescence in mice: Implications for brain and behaviour. Brain, Behavior, and Immunity, 48, 165–173.CrossRefGoogle ScholarPubMed
Dikmen, S.S., Heaton, R.K., Grant, I., & Temkin, N.R. (1999). Test-retest reliability and practice effects of expanded Halstead-Reitan Neuropsychological Test Battery. Journal of the International Neuropsychological Society, 5, 346–356.CrossRefGoogle ScholarPubMed
Dubois, B., Slachevsky, A., Litvan, I., & Pillon, B. (2000). The FAB: A frontal assessment battery at bedside. Neurology, 55(11), 1621–1626.Google Scholar
Folstein, M.F., Folstein, S.F., & 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(3), 189–198.CrossRefGoogle Scholar
Golden, J.C., & Freshwater, S.M. (2002). Stroop Color and Word Test: Revised examiner’s manual. Wood Dale, IL: Stoelting Co.Google Scholar
Human Microbiome Project Consortium. (2012). A framework for human microbiome research. Nature, 486(7402), 215–221.Google Scholar
Lezak, M.D., Howieson, D.B., Bigler, E.D., & Tranel, D. (2012). Neuropsychological assessment (5th ed.). New York, NY: Oxford University Press.Google Scholar
Loring, D.W., Martin, R.C., Meador, K.J., & Lee, G.P.. Psychometric construction of the Rey-Osterrieth Complex Figure: Methodological considerations and interrater reliability (1990). Archives of Clinical Neuropsychology, 5(1), 1–14.CrossRefGoogle ScholarPubMed
Magnusson, K.R., Hauck, L., Jeffrey, B.M., Elias, V., Humphrey, A., Nath, R., & Bermudez, L.E. (2015). Relationships between diet-related changes in the gut microbiome and cognitive flexibility. Neuroscience, 300, 128–140.Google Scholar
Mulligan, A.A., Luben, R.N., Bhaniani, A., Parry-Smith, D.J., O’Conner, L., Khawaja, A.P., & Khaw, K. (2014). A new tool for converting food frequency questionnaire data into nutrient and food group values: FETA research methods and availability. BMJ Open, 4(3), e004503.CrossRefGoogle ScholarPubMed
Palmer, B.W., Boone, K.B., Lesser, I.M., & Wohl, M.A. (1998). Base rates of “impaired” neuropsychological test performance among healthy older adults. Archives of Clinical Neuropsychology, 13(6), 503–511.Google Scholar
Sarkar, A., Lehto, S.M., Harty, S., Dinan, T.G., Cryan, J.F., & Burnet, P.W. (2016). Psychobiotics and the manipulation of bacteria–gut–brain signals. Trends in Neurosciences, 39(11), 763–781.CrossRefGoogle ScholarPubMed
Sherwin, E., Sandhu, K.V., Dinan, T.G., & Cryan, J.F. (2016). May the force be with you: The light and dark sides of the microbiota–gut–brain axis in neuropsychiatry. CNS Drugs, 30(11), 1019–1041.Google Scholar
Tilg, H., & Kaser, A. (2011). Gut microbiome, obesity, and metabolic dysfunction. Journal of Clinical Investigation, 121(6), 2126–2132.CrossRefGoogle ScholarPubMed
Topolski, T.D., LoGerfo, J., Patrick, D.L., Williams, B., Walwick, J., & Patrick, M.M.B. (2006). The Rapid Assessment of Physical Activity (RAPA) among older adults. Preventing Chronic Disease, 3(4), A118.Google ScholarPubMed
Trollor, J.N., Smith, E., Agars, E., Kuan, S.A., Baune, B.T., Campbell, L., & Brodaty, H. (2012). The association between systemic inflammation and cognitive performance in the elderly: The Sydney Memory and Ageing Study. Age, 34, 1295–1308.Google Scholar