The Annals of Applied Statistics

Early diagnosis of neurological disease using peak degeneration ages of multiple biomarkers

Fei Gao, Yuanjia Wang, and Donglin Zeng

Full-text: Access denied (no subscription detected)

We're sorry, but we are unable to provide you with the full text of this article because we are not able to identify you as a subscriber. If you have a personal subscription to this journal, then please login. If you are already logged in, then you may need to update your profile to register your subscription. Read more about accessing full-text

Abstract

Neurological diseases are due to the loss of structure or function of neurons that eventually leads to cognitive deficit, neuropsychiatric symptoms, and impaired activities of daily living. Identifying sensitive and specific biological and clinical markers for early diagnosis allows recruiting patients into a clinical trial to test therapeutic intervention. However, many biomarker studies considered a single biomarker at one time that fails to provide precise prediction for disease age at onset. In this paper, we use longitudinally collected measurements from multiple biomarkers and measurement error-corrected clinical diagnosis ages to identify which biomarkers and what features of biomarker trajectories are useful for early diagnosis. Specifically, we assume that the subject-specific biomarker trajectories depend on unobserved states of underlying latent variables with the conditional mean follows a nonlinear sigmoid shape. We show that peak degeneration age of the biomarker trajectory is useful for early diagnosis. We propose an Expectation-Maximization (EM) algorithm to obtain the maximum likelihood estimates of all parameters and conduct extensive simulation studies to examine the performance of the proposed methods. Finally, we apply our methods to studies of Alzheimer’s disease and Huntington’s disease and identify a few important biomarkers that can be used for early diagnosis.

Article information

Source
Ann. Appl. Stat., Volume 13, Number 2 (2019), 1295-1318.

Dates
Received: May 2018
Revised: December 2018
First available in Project Euclid: 17 June 2019

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1560758447

Digital Object Identifier
doi:10.1214/18-AOAS1236

Mathematical Reviews number (MathSciNet)
MR3963572

Keywords
Alzheimer’s disease Huntington’s disease inflection point measurement error nonlinear mixed effects model sigmoid function

Citation

Gao, Fei; Wang, Yuanjia; Zeng, Donglin. Early diagnosis of neurological disease using peak degeneration ages of multiple biomarkers. Ann. Appl. Stat. 13 (2019), no. 2, 1295--1318. doi:10.1214/18-AOAS1236. https://projecteuclid.org/euclid.aoas/1560758447


Export citation

References

  • Bateman, R. J., Xiong, C., Benzinger, T. L. S., Fagan, A. M., Goate, A., Fox, N. C., Marcus, D. S., Cairns, N. J., Xie, X. et al. (2012). Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N. Engl. J. Med. 367 795–804.
  • Bernardo, J. M. (1979). Expected information as expected utility. Ann. Statist. 7 686–690.
  • Dubois, B., Feldman, H. H., Jacova, C., DeKosky, S. T., Barberger-Gateau, P., Cummings, J., Delacourte, A., Galasko, D., Gauthier, S. et al. (2007). Research criteria for the diagnosis of Alzheimer’s disease: Revising the NINCDS-ADRDA criteria. Lancet Neurol. 6 734–746.
  • Fjell, A. M., Walhovd, K. B., Fennema-Notestine, C., McEvoy, L. K., Hagler, D. J., Holland, D., Brewer, J. B. and Dale, A. M. (2009). One-year brain atrophy evident in healthy aging. J. Neurosci. 29 15223–15231.
  • Gao, F., Wang, Y. and Zeng, D. (2019). Supplement to “Early diagnosis of neurological disease using peak degeneration ages of multiple biomarkers.” DOI:10.1214/18-AOAS1236SUPPA, DOI:10.1214/18-AOAS1236SUPPB.
  • Garcia, T., Marder, K. and Wang, Y. (2017). Statistical modeling of Huntington disease onset. Handb. Clin. Neurol. 144 47–61.
  • Gneiting, T., Balabdaoui, F. and Raftery, A. E. (2007). Probabilistic forecasts, calibration and sharpness. J. R. Stat. Soc. Ser. B. Stat. Methodol. 69 243–268.
  • Good, I. J. (1952). Rational decisions. J. Roy. Statist. Soc. Ser. B 14 107–114.
  • Huntington Study Group (1996). Unified Huntington’s disease rating scale: Reliability and consistency. Mov. Disord. 11 136–142.
  • Hall, C. B., Lipton, R. B., Sliwinski, M. and Stewart, W. F. (2000). A change point model for estimating the onset of cognitive decline in preclinical Alzheimer’s disease. Stat. Med. 19 1555–1566.
  • Hall, C. B., Ying, J., Kuo, L., Sliwinski, M., Buschke, H., Katz, M. and Lipton, R. B. (2001). Estimation of bivariate measurements having different change points, with application to cognitive ageing. Stat. Med. 20 3695–3714.
  • Hall, C. B., Ying, J., Kuo, L. and Lipton, R. B. (2003). Bayesian and profile likelihood change point methods for modeling cognitive function over time. Comput. Statist. Data Anal. 42 91–109.
  • Hampel, H., Bürger, K., Teipel, S. J., Bokde, A. L. W., Zetterberg, H. and Blennow, K. (2008). Core candidate neurochemical and imaging biomarkers of Alzheimer’s disease. Alzheimer’s Dement. 4 38–48.
  • Hogan, J. W. and Laird, N. M. (1997). Mixture models for the joint distribution of repeated measures and event times. Stat. Med. 16 239–257.
  • Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA 79 2554–2558.
  • Jack, C. R., Knopman, D. S., Jagust, W. J., Shaw, L. M., Aisen, P. S., Weiner, M. W., Petersen, R. C. and Trojanowski, J. Q. (2010). Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol. 9 119–128.
  • Jacqmin-Gadda, H., Commenges, D. and Dartigues, J.-F. (2006). Random changepoint model for joint modeling of cognitive decline and dementia. Biometrics 62 254–260.
  • Jedynak, B. M., Lang, A., Liu, B., Katz, E., Zhang, Y., Wyman, B. T., Raunig, D., Jedynak, C. P., Caffo, B. et al. (2012). A computational neurodegenerative disease progression score: Method and results with the Alzheimer’s disease neuroimaging initiative cohort. NeuroImage 63 1478–1486.
  • Kremer, B., Goldberg, P., Andrew, S. E., Theilmann, J., Telenius, H., Zeisler, J., Squitieri, F., Lin, B., Bassett, A. et al. (1994). A worldwide study of the Huntington’s disease mutation: The sensitivity and specificity of measuring CAG repeats. N. Engl. J. Med. 330 1401–1406.
  • Little, R. J. A. (1995). Modeling the drop-out mechanism in repeated-measures studies. J. Amer. Statist. Assoc. 90 1112–1121.
  • Long, J. D., Paulsen, J. S., Marder, K., Zhang, Y., Kim, J.-I. and Mills, J. A. (2014). Tracking motor impairments in the progression of Huntington’s disease. Mov. Disord. 29 311–319.
  • MacDonald, M. E., Ambrose, C. M., Duyao, M. P., Myers, R. H., Lin, C., Srinidhi, L., Barnes, G., Taylor, S. A., James, M. et al. (1993). A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington’s disease chromosomes. Cell 72 971–983.
  • Nelson, P. T., Alafuzoff, I., Bigio, E. H., Bouras, C., Braak, H., Cairns, N. J., Castellani, R. J., Crain, B. J., Davies, P. et al. (2012). Correlation of Alzheimer disease neuropathologic changes with cognitive status: A review of the literature. J. Neuropathol. Exp. Neurol. 71 362–381.
  • Paulsen, J., Long, J., Ross, C., Harrington, D., Erwin, C., Williams, J., Westervelt, J., Johnson, H., Aylward, E. et al. (2014a). Prediction of manifest Huntington’s disease with clinical and imaging measures: A prospective observational study. Lancet Neurol. 13 1193–1201.
  • Paulsen, J. S., Long, J. D., Johnson, H. J., Aylward, E. H., Ross, C. A., Williams, J. K., Nance, M. A., Erwin, C. J., Westervelt, H. J. et al. (2014b). Clinical and biomarker changes in premanifest Huntington disease show trial feasibility: A decade of the PREDICT-HD study. Front. Aging Neurosci. 6 1–11.
  • Rizopoulos, D. (2012). Joint Models for Longitudinal and Time-to-Event Data: With Applications in R. CRC Press/CRC, New York.
  • Rubinsztein, D. C., Leggo, J., Coles, R., Almqvist, E., Biancalana, V., Cassiman, J.-J., Chotai, K., Connarty, M., Craufurd, D. et al. (1996). Phenotypic characterization of individuals with 30–40 CAG repeats in the Huntington disease (HD) gene reveals HD cases with 36 repeats and apparently normal elderly individuals with 36–39 repeats. Am. J. Hum. Genet. 59 16–22.
  • Samtani, M. N., Farnum, M., Lobanov, V., Yang, E., Raghavan, N., DiBernardo, A. and Narayan, V. (2012). An improved model for disease progression in patients from the Alzheimer’s disease neuroimaging initiative. J. Clin. Pharmacol. 52 629–644.
  • Shaw, L. M., Vanderstichele, H., Knapik-Czajka, M., Clark, C. M., Aisen, P. S., Petersen, R. C., Blennow, K., Soares, H., Simon, A. et al. (2009). Cerebrospinal fluid biomarker signature in Alzheimer’s disease neuroimaging initiative subjects. Ann. Neurol. 65 403–413.
  • Sperling, R. A., Aisen, P. S., Beckett, L. A., Bennett, D. A., Craft, S., Fagan, A. M., Iwatsubo, T., Jack, C. R., Kaye, J. 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. Alzheimer’s Dement. 7 280–292.
  • Tsiatis, A. A. and Davidian, M. (2004). Joint modeling of longitudinal and time-to-event data: An overview. Statist. Sinica 14 809–834.

Supplemental materials

  • Supplement A: Supplement to “Early diagnosis of neurological disease using peak degeneration ages of multiple biomarkers”. This supplement provides additional information on the theorem and proof on model identifiability, protocol for simulation studies, details on estimating the magnitude of measurement error, and residual plots of the examples.
  • Supplement B: R codes. Codes for the EM algorithm and simulation illustrating the proposed methods.