Five-year biomarker progression variability for Alzheimer's disease dementia prediction: Can a complex instrumental activities of daily living marker fill in the gaps?

Ioannis Tarnanas, Anthoula Tsolaki, Mark Wiederhold, Brenda Wiederhold, Magda Tsolaki, Ioannis Tarnanas, Anthoula Tsolaki, Mark Wiederhold, Brenda Wiederhold, Magda Tsolaki

Abstract

Introduction: Biomarker progressions explain higher variability in cognitive decline than baseline values alone. This study examines progressions of established biomarkers along with a novel marker in a longitudinal cognitive decline.

Methods: A total of 215 subjects were used with a diagnosis of normal, mild cognitive impairment (MCI) or Alzheimer's disease (AD) at baseline. We calculated standardized biomarker progression rates and used them as predictors of outcome within 5 years.

Results: Early cognitive declines were more strongly explained by fluorodeoxyglucose-positron emission tomography, precuneus and medial temporal cortical thickness, and the complex instrumental activities of daily living (iADL) marker progressions. Using Cox proportional hazards model, we found that these progressions were a significant risk factor for conversion from both MCI to AD (adjusted hazard ratio 1.45; 95% confidence interval 1.20-1.93; P = 1.23 × 10(-5)) and cognitively normal to MCI (adjusted hazard ratio 1.76; 95% confidence interval 1.32-2.34; P = 1.55 × 10(-5)).

Discussion: Compared with standard biological biomarkers, complex functional iADL markers could also provide predictive information for cognitive decline during the presymptomatic stage. This has important implications for clinical trials focusing on prevention in asymptomatic individuals.

Keywords: Alzheimer's disease; Biomarker; Biomarker progressions; Cognitive declines; Computerized cognitive assessment; Diagnostics; Early detection; MCI; MRI; PET; Rate of progression.

Figures

Fig. 1
Fig. 1
The graphical representation which illustrates the mean group completion performance profiles of the complex iADL (DOT) from a tablet PC while the users were navigating in 3D space. (A) This is the mean completion time values for the MCI and AD groups while the user was interacting with the different tasks during the complex iADL. (B) This is the mean completion time values for the normal and MCI groups while the user was interacting with the different tasks during the complex iADL, and (C) this is an actual screenshot of DOT, where the user is required to perform fire safety skills and emergency evacuation in the presence of a room fire. A time countdown at the upper left corner is providing gamification and extra pressure for the completion of the task. Abbreviations: iADL, instrumental activities of daily living; DOT, day-out task; 3D, three dimensional; MCI, mild cognitive impairment; AD, Alzheimer's disease.

References

    1. Toyn J. What lessons can be learned from failed Alzheimer's disease trials? Expert Rev Clin Pharmacol. 2015;8:1–3.
    1. Weiner M.W., Veitch D.P., Aisen P.S., Beckett L.A., Cairns N.J., Green R.C. The Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception. Alzheimers Dement. 2012;8:S1–S68.
    1. Insel P.S., Mattsson N., Donohue M.C., Mackin R.S., Aisen P.S., Jack C.R. The transitional association between β-amyloid pathology and regional brain atrophy. Alzheimers Dement. 2015;11:1171–1179.
    1. Sperling R.A., Aisen P.S., Beckett L.A., Bennett D.A., Craft S., Fagan A.M. 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. Alzheimers Dement. 2011;7:280–292.
    1. Sattlecker M., Kiddle S.J., Newhouse S., Proitsi P., Nelson S., Williams S. Alzheimer's disease biomarker discovery using SOMAscan multiplexed protein technology. Alzheimers Dement. 2014;10:724–734.
    1. Prestia A., Caroli A., Wade S.K., van der Flier W.M., Ossenkoppele R., Van Berckel B. Prediction of AD dementia by biomarkers following the NIA-AA and IWG diagnostic criteria in MCI patients from three European memory clinics. Alzheimers Dement. 2015;11:1191–1201.
    1. Selkoe D.J. Resolving controversies on the path to Alzheimer's therapeutics. Nat Med. 2011;17:1060–1065.
    1. Fiandaca M.S., Mapstone M.E., Cheema A.K., Federoff H.J. The critical need for defining preclinical biomarkers in Alzheimer's disease. Alzheimers Dement. 2014;10:S196–S212.
    1. Noel-Storr A.H., Flicker L., Ritchie C.W., Nguyen G.H., Gupta T., Wood P. Systematic review of the body of evidence for the use of biomarkers in the diagnosis of dementia. Alzheimers Dement. 2013;9:e96–e105.
    1. Hill D.L., Schwarz A.J., Isaac M., Pani L., Vamvakas S., Hemmings R. Coalition against major diseases/European Medicines Agency biomarker qualification of hippocampal volume for enrichment of clinical trials in predementia stages of Alzheimer's disease. Alzheimers Dement. 2014;10:421–429.e3.
    1. Brickman A.M., Schupf N., Manly J.J., Stern Y., Luchsinger J.A., Provenzano F.A. APOE ε4 and risk for Alzheimer's disease: Do regionally distributed white matter hyperintensities play a role? Alzheimers Dement. 2014;10:619–629.
    1. Prestia A., Caroli A., Herholz K., Reiman E., Chen K., Jagust W.J. Diagnostic accuracy of markers for prodromal Alzheimer's disease in independent clinical series. Alzheimers Dement. 2013;9:677–686.
    1. Edmonds E.C., Delano-Wood L., Clark L.R., Jak A.J., Nation D.A., McDonald C.R. Susceptibility of the conventional criteria for mild cognitive impairment to false-positive diagnostic errors. Alzheimers Dement. 2015;11:415–424.
    1. Lehmann S., Schraen S., Quadrio I., Paquet C., Bombois S., Delaby C. Impact of harmonization of collection tubes on Alzheimer's disease diagnosis. Alzheimers Dement. 2014;10:S390–S394.e2.
    1. Boccardi M., Bocchetta M., Ganzola R., Robitaille N., Redolfi A., Duchesne S. Operationalizing protocol differences for EADC-ADNI manual hippocampal segmentation. Alzheimers Dement. 2015;11:184–194.
    1. Frisoni G.B., Jack C.R. HarP: the EADC-ADNI harmonized protocol for manual hippocampal segmentation. A standard of reference from a global working group. Alzheimers Dement. 2015;11:107–110.
    1. Young A.L., Oxtoby N.P., Daga P., Cash D.M., Fox N.C., Ourselin S. A data-driven model of biomarker changes in sporadic Alzheimer's disease. Brain. 2014;137:2564–2577.
    1. Rembach A., Faux N.G., Watt A.D., Pertile K.K., Rumble R.L., Trounson B.O. Changes in plasma amyloid beta in a longitudinal study of aging and Alzheimer's disease. Alzheimers Dement. 2014;10:53–61.
    1. Lim Y.Y., Maruff P., Pietrzak R.H., Ellis K.A., Darby D., Ames D. Aβ and cognitive change: Examining the preclinical and prodromal stages of Alzheimer's disease. Alzheimers Dement. 2014;10:743–751.e1.
    1. Tarnanas I., Tsolaki M., Nef T., Muri R., Mosimann U.P. Can a novel computerized cognitive screening test provide additional information for early detection of Alzheimer disease? Alzheimers Dement. 2014;10:790–798.
    1. Dodge H.H., Zhu J., Harvey D., Saito N., Silbert L.C., Kaye J.A. Biomarker progressions explain higher variability in stage-specific cognitive decline than baseline values in Alzheimer disease. Alzheimers Dement. 2014;10:690–703.
    1. Drachman D.A. The amyloid hypothesis, time to move on: Amyloid is the downstream result, not cause, of Alzheimer's disease. Alzheimers Dement. 2014;10:372–380.
    1. Donohue M.C., Jacqmin-Gadda H., Le Goff M., Thomas R.G., Raman R., Gamst A.C. Estimating long-term multivariate progression from short-term data. Alzheimers Dement. 2014;10:S400–S410.
    1. Kodamullil A.T., Younesi E., Naz M., Bagewadi S., Hofmann-apitius M. Computable cause-and-effect models of healthy and Alzheimer's disease states and their mechanistic differential analysis. Alzheimers Dement. 2015;2:1–11.
    1. Geuze E., Vermetten E., Bremner J.D. MR-based in vivo hippocampal volumetrics: 1. Review of methodologies currently employed. Mol Psychiatry. 2005;10:147–159.
    1. Caroli A., Prestia A., Chen K., Ayutyanont N., Landau S.M., Madison C.M. Summary metrics to assess Alzheimer disease-related hypometabolic pattern with 18F-FDG PET: Head-to-head comparison. J Nucl Med. 2012;53:592–600.
    1. McKhann G.M., Knopman D.S., Chertkow H., Hyman B.T., Jack C.R., Kawas C.H. The diagnosis of dementia due to Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011;7:263–269.
    1. McKhann G., Drachman D., Folstein M., Katzman R., Price D., Stadlan E.M. Clinical diagnosis of Alzheimer's disease: Report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology. 1984;34:939–944.
    1. Dickerson B.C., Wolk D.A. Biomarker-based prediction of progression in MCI: Comparison of AD signature and hippocampal volume with spinal fluid amyloid-β and tau. Front Aging Neurosci. 2013;5:55.
    1. Crane P.K., Carle A., Gibbons L.E., Insel P., Mackin R.S., Gross A. Development and assessment of a composite score for memory in the Alzheimer's Disease Neuroimaging Initiative (ADNI) Brain Imaging Behav. 2012;6:502–516.
    1. Crane P.K., Narasimhalu K., Gibbons L.E., Pedraza O., Mehta K.M., Tang Y. Composite scores for executive function items: Demographic heterogeneity and relationships with quantitative magnetic resonance imaging. J Int Neuropsychol Soc. 2008;14:746–759.
    1. Gibbons L.E., Carle A.C., Mackin R.S., Harvey D., Mukherjee S., Insel P. A composite score for executive functioning, validated in Alzheimer's Disease Neuroimaging Initiative (ADNI) participants with baseline mild cognitive impairment. Brain Imaging Behav. 2012;6:517–527.
    1. Tarnanas I., Papagiannopoulos S., Kazis D., Wiederhold M., Widerhold B., Tsolaki M. Reliability of a novel serious game using dual-task gait profiles to early characterize aMCI. Front Aging Neurosci. 2015;7:50.
    1. Tarnanas I., Schlee W., Tsolaki M., Müri R., Mosimann U., Nef T. Ecological validity of virtual reality daily living activities screening for early dementia: Longitudinal study. JMIR Serious Games. 2013;1:e1.
    1. Landau S.M., Harvey D., Madison C.M., Koeppe R.A., Reiman E.M., Foster N.L. Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI. Neurobiol Aging. 2011;32:1207–1218.
    1. Della Rosa P.A., Cerami C., Gallivanone F., Prestia A., Caroli A., Castiglioni I. A standardized [18F]-FDG-PET template for spatial normalization in statistical parametric mapping of dementia. Neuroinformatics. 2014;12:575–593.
    1. Kvartsberg H., Duits F.H., Ingelsson M., Andreasen N., Öhrfelt A., Andersson K. Cerebrospinal fluid levels of the synaptic protein neurogranin correlates with cognitive decline in prodromal Alzheimer's disease. Alzheimers Dement. 2015;11:1180–1190.
    1. Silbert L.C., Dodge H.H., Perkins L.G., Sherbakov L., Lahna D., Erten-Lyons D. Trajectory of white matter hyperintensity burden preceding mild cognitive impairment. Neurology. 2012;79:741–747.
    1. Herrup K., Carrillo M.C., Schenk D., Cacace A., Desanti S., Fremeau R. Beyond amyloid: Getting real about nonamyloid targets in Alzheimer's disease. Alzheimers Dement. 2013;9:452–458.e1.
    1. Malhotra A., Younesi E., Bagewadi S., Hofmann-Apitius M. Linking hypothetical knowledge patterns to disease molecular signatures for biomarker discovery in Alzheimer's disease. Genome Med. 2014;6:97.
    1. Cordell C.B., Borson S., Boustani M., Chodosh J., Reuben D., Verghese J. Alzheimer's Association recommendations for operationalizing the detection of cognitive impairment during the Medicare Annual Wellness Visit in a primary care setting. Alzheimers Dement. 2013;9:141–150.
    1. Vellas B., Carrillo M.C., Sampaio C., Brashear H.R., Siemers E., Hampel H. Designing drug trials for Alzheimer's disease: What we have learned from the release of the phase III antibody trials: A report from the EU/US/CTAD Task Force. Alzheimers Dement. 2013;9:438–444.
    1. Van Harten A.C., Visser P.J., Pijnenburg Y.A.L., Teunissen C.E., Blankenstein M.A., Scheltens P. Cerebrospinal fluid Aβ42 is the best predictor of clinical progression in patients with subjective complaints. Alzheimers Dement. 2013;9:481–487.
    1. Ye B.S., Seo S.W., Yang J.J., Kim H.J., Kim Y.J., Yoon C.W. Comparison of cortical thickness in patients with early-stage versus late-stage amnestic mild cognitive impairment. Eur J Neurol. 2014;21:86–92.
    1. Lista S., Garaci F.G., Ewers M., Teipel S., Zetterberg H., Blennow K. CSF Aβ1-42 combined with neuroimaging biomarkers in the early detection, diagnosis and prediction of Alzheimer's disease. Alzheimers Dement. 2014;10:381–392.
    1. Grill J.D., Di L., Lu P.H., Lee C., Ringman J., Apostolova L.G. Estimating sample sizes for predementia Alzheimer's trials based on the Alzheimer's Disease Neuroimaging Initiative. Neurobiol Aging. 2013;34:62–72.
    1. Leoutsakos J.M., Bartlett A.L., Forrester S.N., Lyketsos C.G. Simulating effects of biomarker enrichment on Alzheimer's disease prevention trials: Conceptual framework and example. Alzheimers Dement. 2014;10:152–161.
    1. Kelley K., Rausch J.R. Sample size planning for longitudinal models: Accuracy in parameter estimation for polynomial change parameters. Psychol Methods. 2011;16:391–405.

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