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Statistically Derived Subtypes and Associations with Cerebrospinal Fluid and Genetic Biomarkers in Mild Cognitive Impairment: A Latent Profile Analysis

  • Joel S. Eppig (a1), Emily C. Edmonds (a2) (a3), Laura Campbell (a3), Mark Sanderson-Cimino (a1), Lisa Delano-Wood (a2) (a3), Mark W. Bondi (a2) (a3) and for the Alzheimer’s Disease Neuroimaging Initiative...

Abstract

Objectives: Research demonstrates heterogeneous neuropsychological profiles among individuals with mild cognitive impairment (MCI). However, few studies have included visuoconstructional ability or used latent mixture modeling to statistically identify MCI subtypes. Therefore, we examined whether unique neuropsychological MCI profiles could be ascertained using latent profile analysis (LPA), and subsequently investigated cerebrospinal fluid (CSF) biomarkers, genotype, and longitudinal clinical outcomes between the empirically derived classes. Methods: A total of 806 participants diagnosed by means of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) MCI criteria received a comprehensive neuropsychological battery assessing visuoconstructional ability, language, attention/executive function, and episodic memory. Test scores were adjusted for demographic characteristics using standardized regression coefficients based on “robust” normal control performance (n=260). Calculated Z-scores were subsequently used in the LPA, and CSF-derived biomarkers, genotype, and longitudinal clinical outcome were evaluated between the LPA-derived MCI classes. Results: Statistical fit indices suggested a 3-class model was the optimal LPA solution. The three-class LPA consisted of a mixed impairment MCI class (n=106), an amnestic MCI class (n=455), and an LPA-derived normal class (n=245). Additionally, the amnestic and mixed classes were more likely to be apolipoprotein e4+ and have worse Alzheimer’s disease CSF biomarkers than LPA-derived normal subjects. Conclusions: Our study supports significant heterogeneity in MCI neuropsychological profiles using LPA and extends prior work (Edmonds et al., 2015) by demonstrating a lower rate of progression in the approximately one-third of ADNI MCI individuals who may represent “false-positive” diagnoses. Our results underscore the importance of using sensitive, actuarial methods for diagnosing MCI, as current diagnostic methods may be over-inclusive. (JINS, 2017, 23, 564–576)

Copyright

COPYRIGHT: © The International Neuropsychological Society 2017 

Corresponding author

Correspondence and reprint requests to: Mark W. Bondi, VA San Diego Healthcare System (116B), 3350 La Jolla Village Drive, San Diego, CA 92161. E-mail: mbondi@ucsd.edu

Footnotes

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Data used in preparation of this article were obtained from the ADNI database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of the ADNI and/or provided data but did not participate in analysis or writing of this article. 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.

Footnotes

References

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Ahmed, S., Brennan, L., Eppig, J., Price, C. C., Lamar, M., Delano-Wood, L., & . . . Jak, A. (2016). Visuoconstructional impairment in subtypes of mild cognitive impairment. Applied Neuropsychology: Adult, 23(1), 43–52. doi: 10.1080/23279095.2014.1003067
Aisen, P. S., Petersen, R. C., Donohue, M. C., Gamst, A., Raman, R., Thomas, R. G., & . . . Jack, C. R. (2010). Clinical core of the Alzheimer’s Disease Neuroimaging Initiative: Progress and plans. Alzheimer’s & Dementia, 6(3), 239–246. doi: 10.1016/j.jalz.2010.03.006
Alzheimer’s Disease Neuroimaging Initiative. (2008). ADNI 2 procedures manual. Retrieved from http://adni.loni.usc.edu/wp-content/uploads/2008/07/adni2-procedures-manual.pdf
Asparouhouv, T., & Muthén, B. (2007). Wald test of mean equality for potential latent class predictors in mixture modeling. Online technical appendix. Los Angeles, CA: Muthén & Muthén. Available for download at https://www.statmodel.com/download/MeanTest1.pdf.
Asparouhov, T., & Muthén, B. (2014). Auxiliary variables in mixture modeling: Three-step approaches using Mplus. Structural Equation Modeling: A Multidisciplinary Journal, 21(3), 329–341. doi: 10.1080/10705511.2014.915181
Asparouhov, T., & Muthén, B. (2015). Auxiliary variables in mixture modeling: Using the BCH method in Mplus to estimate a distal outcome model and an arbitrary secondary model. Mplus Web Notes, 21(2), 1–22. Available for download at https://www.statmodel.com/examples/webnotes/webnote21.pdf
Bakk, Z., & Vermunt, J. K. (2016). Robustness of stepwise latent class modeling with continuous distal outcomes. Structural Equation Modeling: A Multidisciplinary Journal, 23(1), 20–31. doi: 10.1080/10705511.2014.955104
Berlin, K. S., Parra, G. R., & Williams, N. A. (2014). An introduction to latent variable mixture modeling (part 2): Longitudinal latent class growth analysis and growth mixture models. Journal of Pediatric Psychology, 39(2), 188–203. doi: 10.1093/jpepsy/jst085
Berlin, K. S., Williams, N. A., & Parra, G. R. (2014). An introduction to latent variable mixture modeling (part 1): Overview and cross-sectional latent class and latent profile analyses. Journal of Pediatric Psychology, 39(2), 174–187. doi: 10.1093/jpepsy/jst084
Bolck, A., Croon, M., & Hagenaars, J. (2004). Estimating latent structure models with categorical variables: One-step versus three-step estimators. Political Analysis, 12(1), 3–27. doi: 10.1093/pan/mph001
Bondi, M. W., Edmonds, E. C., Jak, A. J., Clark, L. R., Delano-Wood, L., McDonald, C. R., … Salmon, D. P. (2014). Neuropsychological criteria for mild cognitive impairment improves diagnostic precision, biomarker associations, and progression rates. Journal of Alzheimer’s Disease, 42(1), 275–289. doi: 10.3233/JAD-140276
Bray, B. C., Lanza, S. T., & Tan, X. (2015). Eliminating bias in classify-analyze approaches for latent class analysis. Structural Equation Modeling: A Multidisciplinary Journal, 22(1), 1–11. doi: 10.1080/10705511.2014.935265
Clark, L. R., Delano-Wood, L., Libon, D. J., McDonald, C. R., Nation, D. A., Bangen, K. J., . . . Bondi, M. W. (2013). Are empirically-derived subtypes of mild cognitive impairment consistent with conventional subtypes? Journal of the International Neuropsychological Society, 19(06), 635–645. doi: 10.1017/S1355617713000313
Clark, S. L., & Muthén, B. (2009). Relating latent class analysis results to variables not included in the analysis. Submitted for publication. Available for download at https://www.statmodel.com/download/relatinglca.pdf
Collins, L. M., & Lanza, S. T. (2013). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences. Hoboken, NJ: John Wiley & Sons.
Crutch, S. J., Lehmann, M., Schott, J. M., Rabinovici, G. D., Rossor, M. N., & Fox, N. C. (2012). Posterior cortical atrophy. The Lancet Neurology, 11(2), 170–178. doi: 10.1016/S1474-4422(11)70289-7
Crutch, S. J., Schott, J. M., Rabinovici, G. D., Boeve, B. F., Cappa, S. F., Dickerson, B. C., & … Mendez, M. F. (2013). Shining a light on posterior cortical atrophy. Alzheimer’s & Dementia, 9(4), 463–465. doi: 10.1016/j.jalz.2012.11.004
Delano-Wood, L., Bondi, M. W., Sacco, J., Abeles, N., Jak, A. J., Libon, D. J., Bozoki, A. (2009). Heterogeneity in mild cognitive impairment: Differences in neuropsychological profile and associated white matter lesion pathology. Journal of the International Neuropsychological Society, 15(6), 906–914. doi: 10.1017/S1355617709990257
Edmonds, E. C., Delano-Wood, L., Clark, L. R., Jak, A. J., Nation, D. A., & McDonald, C. R., … Alzheimer’s Disease Neuroimaging Initiative. (2015). Susceptibility of the conventional criteria for mild cognitive impairment to false-positive diagnostic errors. Alzheimer’s & Dementia, 11(4), 415–424. doi: 10.1016/j.jalz.2014.03.005
Edmonds, E. C., Eppig, J., Bondi, M. W., Leyden, K. M., Goodwin, B., & Delano-Wood, L., … Alzheimer’s Disease Neuroimaging Initiative. (2016). Heterogeneous cortical atrophy patterns in MCI not captured by conventional diagnostic criteria. Neurology, 87(20), 2108–2116.
Ferman, T. J., Smith, G. E., Boeve, B. F., Graff-Radford, N. R., Lucas, J. A., Knopman, D. S., & . . . Dickson, D. W. (2006). Neuropsychological differentiation of dementia with Lewy bodies from normal aging and Alzheimer’s disease. The Clinical Neuropsychologist, 20(4), 623–636. doi: 10.1080/13854040500376831
Ferman, T. J., Smith, G. E., Kantarci, K., Boeve, B. F., Pankratz, V. S., Dickson, D. W., & . . . Pedraza, O. (2013). Nonamnestic mild cognitive impairment progresses to dementia with Lewy bodies. Neurology, 81(23), 2032–2038. doi: 10.1212/01.wnl.0000436942.55281.47
Freedman, L., & Dexter, L. E. (1991). Visuospatial ability in cortical dementia. Journal of Clinical and Experimental Neuropsychology, 13(5), 677–690. doi:http://dx.doi.org/ 10.1080/01688639108401082
Geldmacher, D. S. (2003). Visuospatial dysfunction in the neurodegenerative diseases. Frontiers in Bioscience: A Journal and Virtual Library, 8, e428–e436. doi: 10.2741/1143
Goodglass, H., & Kaplan, E. (1983). The assessment of aphasia and related disorders. Philadelphia, PA: Lea & Febiger.
Grossi, D., & Trojano, L. (2001). Constructional and visuospatial disorders. In F. Boller & J. Grafman (Eds.), Handbook of neuropsychology (Vol. 4, pp. 99–120). Amsterdam, Netherlands: Elsevier Science, B.V.
Hamilton, J. M., Salmon, D. P., Galasko, D., Raman, R., Emond, J., Hansen, L. A., & . . . Thal, L. J. (2008). Visuospatial deficits predict rate of cognitive decline in autopsy-verified dementia with Lewy bodies. Neuropsychology, 22(6), 729–737. doi: 10.1037/a0012949
Hayden, K. M., Kuchibhatla, M., Romero, H. R., Plassman, B. L., Burke, J. R., Browndyke, J. N., Welsh-Bohmer, K. A. (2014). Pre-clinical cognitive phenotypes for Alzheimer disease: A latent profile approach. The American Journal of Geriatric Psychiatry, 22(11), 1364–1374. doi: 10.1016/j.jagp.2013.07.008
Hipp, J. R., & Bauer, D. J. (2006). Local solutions in the estimation of growth mixture models. Psychological Methods, 11, 36–53. doi: 10.1037/1082-989X.11.1.36
Jefferson, A. L., Cosentino, S. A., Ball, S. K., Bogdanoff, B., Leopold, N., Kaplan, E., Libon, D. J. (2002). Errors produced on the mini-mental state examination and neuropsychological test performance in Alzheimer’s disease, ischemic vascular dementia, and Parkinson’s disease. The Journal of Neuropsychiatry and Clinical Neurosciences, 14(3), 311–320. doi: 10.1176/jnp.14.3.311
Johnson, D. K., Morris, J. C., & Galvin, J. E. (2005). Verbal and visuospatial deficits in dementia with Lewy bodies. Neurology, 65(8), 1232–1238. doi: 10.1212/01.wnl.0000180964.60708.c2
Kao, A. W., Racine, C. A., Quitania, L. C., Kramer, J. H., Christine, C. W., & Miller, B. L. (2009). Cognitive and neuropsychiatric profile of the synucleinopathies: Parkinson’s disease, dementia with Lewy bodies and multiple system atrophy. Alzheimer Disease and Associated Disorders, 23(4), 365–370. doi: 10.1097/WAD.0b013e3181b5065d
Köhler, S., Hamel, R., Sistermans, N., Koene, T., Pijnenburg, Y. A., van der Flier, W. M., & . . . Ramakers, I. (2013). Progression to dementia in memory clinic patients without dementia A latent profile analysis. Neurology, 81(15), 1342–1349. doi: 10.1212/WNL.0b013e3182a82536
Lanza, S. T., Tan, X., & Bray, B. C. (2013). Latent class analysis with distal outcomes: A flexible model-based approach. Structural Equation Modeling: A Multidisciplinary Journal, 20(1), 1–26. doi: 10.1080/10705511.2013.742377
Lezak, M. D. (2004). Neuropsychological assessment. New York, NY: Oxford University Press.
Libon, D. J., Drabick, D. A., Giovannetti, T., Price, C. C., Bondi, M. W., Eppig, J., & . . . Nation, D. A. (2014). Neuropsychological syndromes associated with Alzheimer’s/vascular dementia: A latent class analysis. Journal of Alzheimer’s Disease, 42(3), 999–1014. doi: 10.3233/JAD-132147
Libon, D. J., Xie, S. X., Eppig, J., Wicas, G., Lamar, M., Lippa, C., & . . . Wambach, D. M. (2010). The heterogeneity of mild cognitive impairment: A neuropsychological analysis. Journal of the International Neuropsychological Society, 16(01), 84–93. doi: 10.1017/S1355617709990993
Magidson, J., & Vermunt, J. (2002). Latent class models for clustering: A comparison with K-means. Canadian Journal of Marketing Research, 20(1), 36–43. doi: 10.1.1.128.9157
Mapstone, M., Steffenella, T. M., & Duffy, C. J. (2003). A visuospatial variant of mild cognitive impairment Getting lost between aging and AD. Neurology, 60(5), 802–808. doi: 10.1212/01.WNL.0000049471.76799.DE
McKeith, I. G., Galasko, D., Kosaka, K., Perry, E. K., Dickson, D. W., Hansen, L. A., & . . . Lennox, G. (1996). Consensus guidelines for the clinical and pathologic diagnosis of dementia with Lewy bodies (DLB) Report of the consortium on DLB international workshop. Neurology, 47(5), 1113–1124. doi: 10.1212/WNL.47.5.1113
Molano, J., Boeve, B., Ferman, T., Smith, G., Parisi, J., Dickson, D., & . . . Kantarci, K. (2010). Mild cognitive impairment associated with limbic and neocortical Lewy body disease: A clinicopathological study. Brain, 133(2), 540–556. doi: 10.1093/brain/awp280
Muthén, B. (2004). Latent variable analysis. In D. Kaplan (Eds.), The Sage handbook of quantitative methodology for the social sciences (pp. 345–368). Thousand Oaks, CA: Sage Publications.
Nielson, K. A., Cummings, B. J., & Cotman, C. W. (1996). Constructional apraxia in Alzheimer’s disease correlates with neuritic neuropathology in occipital cortex. Brain Research, 741(1), 284–293. doi: 10.1016/S0006-8993(96)00983-3
Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14(4), 535–569. doi: 10.1080/10705510701575396
Petersen, R. C. (2004). Mild cognitive impairment as a diagnostic entity. Journal of Internal Medicine, 256(3), 183–194. doi: 10.1111/j.1365-2796.2004.01388.x
Petersen, R. C., Aisen, P. S., Beckett, L. A., Donohue, M. C., Gamst, A. C., Harvey, D. J., & . . . Trojanowski, J. Q. (2010). Alzheimer’s Disease Neuroimaging Initiative (ADNI): Clinical characterization. Neurology, 74(3), 201–209. doi: 10.1212/WNL.0b013e3181cb3e25
Petersen, R. C., & Morris, J. C. (2005). Mild cognitive impairment as a clinical entity and treatment target. Archives of Neurology, 62(7), 1160–1163. doi: 10.1001/archneur.62.7.1160
Roesch, S. C., Villodas, M., & Villodas, F. (2010). Latent class/profile analysis in maltreatment research: A commentary on Nooner et al., Pears et al., and looking beyond. Child Abuse & Neglect, 34(3), 155–160. doi: 10.1016/j.chiabu.2010.01.003
Schneider, J. A., Arvanitakis, Z., Leurgans, S. E., & Bennett, D. (2009). The neuropathology of probable Alzheimer disease and mild cognitive impairment. Annals of Neurology, 66(2), 200–208. doi: 10.1002/ana.21706
Shao, A., Liang, L., Yuan, C., & Bian, Y. (2014). A latent class analysis of bullies, victims and aggressive victims in Chinese adolescence: Relations with social and school adjustments. PLoS One, 9(4), e95290. doi: 10.1371/journal.pone.0095290
Shaw, L. M., Vanderstichele, H., Knapik‐Czajka, M., Clark, C. M., Aisen, P. S., Petersen, R. C., Trojanowski, J. Q. (2009). Cerebrospinal fluid biomarker signature in Alzheimer’s disease neuroimaging initiative subjects. Annals of Neurology, 65(4), 403–413. doi: 10.1002/ana.21610
Tein, J. Y., Coxe, S., & Cham, H. (2013). Statistical power to detect the correct number of classes in latent profile analysis. Structural Equation Modeling: A Multidisciplinary Journal, 20(4), 640–657. doi: 10.1080/10705511.2013.824781
Tukey, J. W. (1977). Exploratory data analysis. Reading, MA: Addison-Wesley Publishing Company.
Vermunt, J. K. (2010). Latent class modeling with covariates: Two improved three-step approaches. Political Analysis, 18(4), 450–469. doi: 10.1093/pan/mpq025
Weiner, M. W., Veitch, D. P., Aisen, P. S., Beckett, L. A., Cairns, N. J., Green, R. C., & . . . Morris, J. C. (2013). The Alzheimer’s Disease Neuroimaging Initiative: A review of papers published since its inception. Alzheimer’s & Dementia, 9(5), e111–e194. doi: 10.1016/j.jalz.2013.05.1769
Wilson, R. S., Yu, L., Trojanowski, J. Q., Chen, E. Y., Boyle, P. A., Bennett, D. A., Schneider, J. A. (2013). TDP-43 pathology, cognitive decline, and dementia in old age. JAMA Neurology, 70(11), 1418–1424. doi: 10.1001/jamaneurol.2013.3961
Winblad, B., Palmer, K., Kivipelto, M., Jelic, V., Fratiglioni, L., Wahlund, L. O., & . . . Arai, H. (2004). Mild cognitive impairment–beyond controversies, towards a consensus: Report of the International Working Group on Mild Cognitive Impairment. Journal of Internal Medicine, 256(3), 240–246. doi: 10.1111/j.1365-2796.2004.01380.x
Zlokovic, B. V. (2011). Neurovascular pathways to neurodegeneration in Alzheimer’s disease and other disorders. Nature Reviews Neuroscience, 12(12), 723–738. doi: 10.1038/nrn3114

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Statistically Derived Subtypes and Associations with Cerebrospinal Fluid and Genetic Biomarkers in Mild Cognitive Impairment: A Latent Profile Analysis

  • Joel S. Eppig (a1), Emily C. Edmonds (a2) (a3), Laura Campbell (a3), Mark Sanderson-Cimino (a1), Lisa Delano-Wood (a2) (a3), Mark W. Bondi (a2) (a3) and for the Alzheimer’s Disease Neuroimaging Initiative...

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