Hostname: page-component-8448b6f56d-c4f8m Total loading time: 0 Render date: 2024-04-19T21:34:34.577Z Has data issue: false hasContentIssue false
  • English
  • Français

Partial Least Squares Analysis of Alzheimer’s Disease Biomarkers, Modifiable Health Variables, and Cognition in Older Adults with Mild Cognitive Impairment

Published online by Cambridge University Press:  19 October 2021

Jessica Stark ,
Daniela J. Palombo ,
Jasmeet P. Hayes Open the ORCID record for Jasmeet P. Hayes [Opens in a new window] ,
Kelly J. Hiersche ,
Alexander N. Hasselbach ,
Scott M. Hayes Open the ORCID record for Scott M. Hayes [Opens in a new window]  and
for the Alzheimer’s Disease Neuroimaging Initiative
Jessica Stark
Affiliation:
Department of Psychology, The Ohio State University, Columbus, OH, USA
Daniela J. Palombo
Affiliation:
Department of Psychology, University of British Columbia, Vancouver, BC, Canada
Jasmeet P. Hayes
Affiliation:
Department of Psychology, The Ohio State University, Columbus, OH, USA Chronic Brain Injury Initiative, The Ohio State University, Columbus, OH, USA
Kelly J. Hiersche
Affiliation:
Department of Psychology, The Ohio State University, Columbus, OH, USA
Alexander N. Hasselbach
Affiliation:
Department of Psychology, The Ohio State University, Columbus, OH, USA
Scott M. Hayes*
Affiliation:
Department of Psychology, The Ohio State University, Columbus, OH, USA Chronic Brain Injury Initiative, The Ohio State University, Columbus, OH, USA
*
Correspondence and reprint requests to: Scott M. Hayes, Ph.D., Department of Psychology, The Ohio State University, Psychology Building, 1835 Neil Ave., Columbus, OH 43210, USA. E-mail: hayes.1074@osu.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives:

To identify novel associations between modifiable physical and health variables, Alzheimer’s disease (AD) biomarkers, and cognitive function in a cohort of older adults with Mild Cognitive Impairment (MCI).

Methods:

Metrics of cardiometabolic risk, stress, inflammation, neurotrophic/growth factors, AD, and cognition were assessed in 154 MCI participants (Mean age = 74.1 years) from the Alzheimer’s Disease Neuroimaging Initiative. Partial Least Squares analysis was employed to examine associations among these physiological variables and cognition.

Results:

Latent variable 1 revealed a unique combination of AD biomarkers, neurotrophic/growth factors, education, and stress that were significantly associated with specific domains of cognitive function, including episodic memory, executive function, processing speed, and language, representing 45.2% of the cross-block covariance in the data. Age, body mass index, and metrics tapping basic attention or premorbid IQ were not significant.

Conclusions:

Our data-driven analysis highlights the significant relationships between metrics associated with AD pathology, neuroprotection, and neuroplasticity, primarily with tasks tapping episodic memory, executive function, processing speed, and verbal fluency rather than more basic tasks that do not require mental manipulation (basic attention and vocabulary). These data also indicate that biological metrics are more strongly associated with episodic memory, executive function, and processing speed than chronological age in older adults with MCI.

Keywords

Healthy AgingNeuropsychologyNeuroprotectionNeuroplasticityMemoryExecutive Function
Type
Research Article
Information
Journal of the International Neuropsychological Society , Volume 28 , Issue 8 , September 2022 , pp. 781 - 789
Copyright
Copyright © INS. Published by Cambridge University Press, 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.)

Footnotes

*

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

References

REFERENCES

Abdi, H. & Williams, L.J. (2013). Partial least squares methods: partial least squares correlation and partial least square regression. In Reisfeld, B. & Mayeno, A. N. (Eds.), Methods in molecular biology (Vol. 930, pp. 549–570). Clifton, NJ: Humana Press. doi: 10.1007/978-1-62703-059-5_21 Google Scholar
Baker, L.D., Frank, L.L., Foster-schubert, K., Pattie, S., Wilkinson, C.W., Mctiernan, A., … Craft, S. (2011). Effects of Aerobic exercise on mild cognitive impairment. 67, 71–79. doi: 10.1001/archneurol.2009.307.Effects CrossRefGoogle Scholar
Buckner, R.L. (2004). Memory and executive function in aging and ad: multiple factors that cause decline and reserve factors that compensate. Neuron, 44, 195–208. doi: 10.1016/j.neuron.2004.09.006 CrossRefGoogle Scholar
Chiu, M.J., Chen, Y.F., Chen, T.F., Yang, S.Y., Yang, F.P.G., Tseng, T.W., … Horng, H.E. (2014). Plasma Tau as a window to the brain — negative associations with brain volume and memory function in mild cognitive impairment and early Alzheimer’s disease. Human Brain Mapping, 35, 3132–3142.CrossRefGoogle Scholar
Diniz, B.S.O., Pinto, J.A., & Forlenza, O.V. (2008). Do CSF total tau, phosphorylated tau, and b-amyloid 42 help to predict progression of mild cognitive impairment to Alzheimer’s disease? A systematic review and meta-analysis of the literature. World Journal of Biological Psychiatry, 9, 172–182. doi: 10.1080/15622970701535502 CrossRefGoogle Scholar
Farooqui, A.A., Farooqui, T., Panza, F., & Frisardi, V. (2012). Metabolic syndrome as a risk factor for neurological disorders. Cellular and Molecular Life Sciences, 69, 741–762. doi: 10.1007/s00018-011-0840-1 CrossRefGoogle ScholarPubMed
Kovacevic, N., Abdi, H., Beaton, D., & McIntosh, A.R. (2013). Revisiting PLS resampling: comparing significance versus reliability across range of simulations. In Abdi, Herve, Chin, W.W., Esposito Vinzi, V., Russolillo, G., & Trinchera, L. (Eds.), New perspectives in partial least squares and related methods (pp. 159–170). New York: Springer New York.CrossRefGoogle Scholar
Krishnan, A., Williams, L.J., McIntosh, A.R., & Abdi, H. (2011). Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review. NeuroImage, 56, 455–475. doi: 10.1016/j.neuroimage.2010.07.034 CrossRefGoogle ScholarPubMed
Lista, I. & Sorrentino, G. (2010). Biological mechanisms of physical activity in preventing cognitive decline. Cellular and Molecular Neurobiology, 30, 493–503. doi: 10.1007/s10571-009-9488-x CrossRefGoogle ScholarPubMed
Matura, S., Köhler, J., Reif, A., Fusser, F., Karakaya, T., Scheibe, M., … Pantel, J. (2019). Intrinsic functional connectivity, CSF biomarker profiles and their relation to cognitive function in mild cognitive impairment. Acta Neuropsychiatrica. doi: 10.1017/neu.2019.49 Google ScholarPubMed
Maurya, S.K., Mishra, J., Abbas, S., & Bandyopadhyay, S. (2016). Cypermethrin stimulates GSK3β-dependent Aβ and p-tau proteins and cognitive loss in young rats: reduced HB-EGF signaling and downstream neuroinflammation as critical regulators. Molecular Neurobiology, 53, 968–982. doi: 10.1007/s12035-014-9061-6 CrossRefGoogle ScholarPubMed
Meyer, P.F., Savard, M., Poirier, J., Labonté, A., Rosa-Neto, P., Weitz, T.M., … Breitner, J. (2018). Bi-directional association of Cerebrospinal Fluid Immune markers with stage of Alzheimer’s disease pathogenesis. Journal of Alzheimer’s Disease, 63, 577–590. doi: 10.3233/JAD-170887 CrossRefGoogle ScholarPubMed
Meyer, P.F., Savard, M., Poirier, J., Morgan, D., & Breitner, J. (2019). Hypothesis: cerebrospinal fluid protein markers suggest a pathway toward symptomatic resilience to AD pathology. Alzheimer’s & Dementia, 15, 1160–1171. doi: 10.1016/j.jalz.2019.05.007 CrossRefGoogle ScholarPubMed
Miranda, M., Morici, J.F., Zanoni, M.B., & Bekinschtein, P. (2019). Brain-derived Neurotrophic factor: a key molecule for memory in the healthy and the pathological brain. Frontiers in Cellular Neuroscience, 13, 1–25. doi: 10.3389/fncel.2019.00363 CrossRefGoogle ScholarPubMed
Nathan, P.J., Lim, Y.Y., Abbott, R., Galluzzi, S., Marizzoni, M., Babiloni, C., … Frisoni, G.B. (2017). Association between CSF biomarkers, hippocampal volume and cognitive function in patients with amnestic mild cognitive impairment (MCI). Neurobiology of Aging, 53, 1–10. doi: 10.1016/j.neurobiolaging.2017.01.013 CrossRefGoogle Scholar
Salthouse, T.A. (2010). Selective review of cognitive aging. Journal of the International Neuropsychological Society, 16, 754–760. doi: 10.1017/S1355617710000706 CrossRefGoogle ScholarPubMed
Shim, J.W. & Madsen, J.R. (2018). VEGF signaling in neurological disorders. International Journal of Molecular Sciences. doi: 10.3390/ijms19010275 CrossRefGoogle ScholarPubMed
Stern, Y. (2013). Cognitive reserve in ageing. Lancet Neurology, 11(11), 1006–1012. doi: 10.1016/S1474-4422(12)70191-6.Cognitive CrossRefGoogle Scholar
Taipa, R., das Neves, S.P., Sousa, A.L., Fernandes, J., Pinto, C., Correia, A.P., … Sousa, N. (2019). Proinflammatory and anti-inflammatory cytokines in the CSF of patients with Alzheimer’s disease and their correlation with cognitive decline. Neurobiology of Aging, 76, 125–132. doi: 10.1016/j.neurobiolaging.2018.12.019 CrossRefGoogle ScholarPubMed
Tromp, D., Dufour, A., Lithfous, S., Pebayle, T., & Després, O. (2015). Episodic memory in normal aging and Alzheimer disease: Insights from imaging and behavioral studies. Ageing Research Reviews, 24, 232–262. doi: 10.1016/j.arr.2015.08.006 CrossRefGoogle ScholarPubMed
Udeh-Momoh, C.T., Su, B., Evans, S., Zheng, B., Sindi, S., Tzoulaki, I., … Middleton, L. (2020). Cortisol, Amyloid-Beta, and reserve predicts Alzheimer’s disease progression for cognitively normal older adults. Journal of Alzheimer’s Disease, 1–10. doi: 10.3233/JAD-181030 Google Scholar
Van Roon, P., Zakizadeh, J., & Chartier, S. (2014). Partial Least Squares tutorial for analyzing neuroimaging data. The Quantitative Methods for Psychology, 10, 200–215. doi: 10.20982/tqmp.10.2.p200 CrossRefGoogle Scholar
Yaffe, K., Kanaya, A., Lindquist, K., Simonsick, E.M., Harris, T., Shorr, R.I., … Newman, A.B. (2004). The metabolic syndrome, inflammation, and risk of cognitive decline. Journal of the American Medical Association, 292, 2237–2242. doi: 10.1001/jama.292.18.2237 CrossRefGoogle ScholarPubMed
Supplementary material: File

Stark et al. supplementary material

Stark et al. supplementary material

Download Stark et al. supplementary material(File)
File 582.8 KB