δ scores predict mild cognitive impairment and Alzheimer's disease conversions from nondemented states

Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring

Volumn 6, Issue , 2017, Pages 214-221

  Royall, Donald ^a,b,c   Palmer, Raymond ^b  

^a Mays Cancer Center at UT Health San Antonio   (United States)

^b UNIVERSITY OF TEXAS HEALTH SCIENCE CENTER AT SAN ANTONIO   (United States)

^c SOUTH TEXAS VETERANS HEALTH CARE SYSTEM   (United States)

Author keywords

Aging; Cognition; Dementia; Functional status; g; Intelligence; MCI

Indexed keywords

AGED; AGING; ALZHEIMER DISEASE; ARTICLE; CONTROLLED STUDY; DISEASE ASSOCIATION; EDUCATIONAL STATUS; ETHNICITY; FEMALE; HUMAN; INTELLIGENCE; MAJOR CLINICAL STUDY; MALE; MEXICAN AMERICAN; MILD COGNITIVE IMPAIRMENT; PREDICTION; SCORING SYSTEM; SENSITIVITY AND SPECIFICITY; VERY ELDERLY;

EID: 85016164450     PISSN: None     EISSN: 23528729     Source Type: Journal    
DOI: 10.1016/j.dadm.2017.02.002     Document Type: Article

References (44)

1. [1] Royall, D.R., Palmer, R.F., Validation of a latent construct for dementia case-finding in Mexican-Americans. J Alzheimer's Dis 37 (2013), 89–97.
2. [2] Gavett, B.E., Vudy, V., Jeffrey, M., John, S.E., Gurnani, A., Adams, J., The δ latent dementia phenotype in the NACC UDS: cross-validation and extension. Neuropsychology 29 (2015), 344–352.
3. [3] Royall, D.R., Palmer, R.F., Thrombopoietin is associated with δs intercept, and only in Non-Hispanic Whites. Alzheimer's Demen Diagn Assess Dis Monit 3 (2016), 35–42.
4. [4] Royall, D.R., Palmer, R.F., Vidoni, E.D., Honea, R.A., Burns, J.M., The Default Mode Network and related right hemisphere structures may be the key substrates of dementia. J Alzheimer's Dis 32 (2012), 467–478.
5. [5] Gavett, B.E., John, S.E., Gurnani, A.S., Bussell, C.A., Saurman, J.L., The role of Alzheimer's and cerebrovascular pathology in mediating the effects of age, race, and apolipoprotein E genotype on dementia severity in pathologically confirmed Alzheimer's disease. J Alzhiemer's Dis 49 (2016), 531–545.
6. [6] Koppara, A., Wolfsgruber, S., Kleineidam, L., Schmidtke, K., Frölich, L., Kurz, A., et al. The latent dementia phenotype δ is associated with CSF biomarkers of Alzheimer Disease and predicts conversion to AD dementia in subjects with MCI. J Alzheimer's Dis 49 (2016), 547–560.
7. [7] Royall, D.R., Palmer, R.F., Ethnicity moderates dementia's biomarkers. J Alzheimer's Dis 43 (2015), 275–287.
8. [8] Palmer, R.F., Royall, D.R., Future dementia status is almost entirely explained by the latent variable δs intercept and slope. J Alzheimer's Dis 49 (2016), 521–529.
9. [9] McKhann, D., Drockman, D., Folstein, M., Katzman, R., Price, D., Stadlan, E.M., Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group. Neurology 34 (1984), 939–944.
10. [10] Hughes, C.P., Berg, L., Danziger, W.L., Coben, L.A., Martin, R.L., A new clinical scale for the staging of dementia. Br J Psychiatry 140 (1982), 566–572.
11. [11] Yesavage, J.A., Brink, T.L., Rose, T.L., Lum, O., Huang, V., Adey, M., et al. Development and validation of a geriatric depression screening scale: a preliminary report. J Am Geriatr Soc 29 (1982), 164–171.
12. [12] Maixner, S.M., Burke, W.J., Roccaforte, W.H., Wengel, S.P., Potter, J.F., A comparison of two depression scales in a geriatric assessment clinic. Am J Geriatr Psychiatry 3 (1995), 60–67.
13. [13] Mitchell, A.J., Bird, V., Rizzo, M., Meader, N., Which version of the Geriatric Depression Scale is most useful in medical settings and nursing homes? Diagnostic validity meta-analysis. Am J Geriatr Psychiatry 18 (2010), 1066–1077.
14. [14] Folstein, M.F., Folstein, S.E., McHugh, P.R., Mini-mental state: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12 (1975), 189–198.
15. [15] Benton, A., Hamsher, K., Multilingual Aphasia Examination. 1989, AJA Associates, Iowa City, Iowa.
16. [16] Wechsler, D., Wechsler Memory Scale – Third Edition. 1997, The Psychological Corporation, San Antonio, TX.
17. [17] Lawton, M.P., Brody, E.M., Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist 9 (1969), 179–186.
18. [18] Grice, J.W., Computing and evaluation factor scores. Psychol Methods 6 (2001), 430–450.
19. [19] Arbuckle, J.L., Analysis of Moment Structures-AMOS (Version 7.0) [Computer Program]. 2006, SPSS, Chicago.
20. [20] PASW Statistics 18, Release Version 18.0.0, 2009, SPSS, Inc., Chicago, IL.
21. [21] Metz, C.E., Basic principles of ROC analysis. Semin Nucl Med 8 (1978), 283–298 Cognitive Impairment. J Intern Med. 1978;256:240–246.
22. [22] Zweig, M.H., Campbell, G., Receiver-operating characteristic (ROC) plots: A fundamental evaluation tool in clinical medicine. Clin Chem 39 (1993), 561–577.
23. [23] Rosen, W.G., Mohs, R.C., Davis, K.L., A new rating scale for Alzheimer's disease. Am J Psychiatry 141 (1984), 1356–1364.
24. [24] Morris, J.C., Heyman, A., Mohs, R.C., Hughes, J.P., van Belle, G., Fillenbaum, G., et al. The Consortium to Establish a Registry for Alzheimer's Disease (CERAD) Part I. Clinical and neuropsychological assessment of Alzheimer's disease. Neurology 39 (1989), 1159–1165.
25. [25] John, S.E., Gurnani, A.S., Bussell, C., Saurman, J.L., Griffin, J.W., Gavett, B.E., The effectiveness and unique contribution of neuropsychological tests and the δ latent phenotype in the differential diagnosis of dementia in the uniform data set. Neuropsychology 30 (2016), 946–960.
26. [26] White, L.R., Edland, S.D., Hemmy, L.S., Montine, K.S., Zarow, C., Sonnen, J.A., et al. Neuropathologic comorbidity and cognitive impairment in the Nun and Honolulu-Asia Aging Studies. Neurology 86 (2016), 1000–1008.
27. [27] Royall, D.R., Al-Rubaye, S., Bishnoi, R., Palmer, R.F., Serum protein biomarkers of δ fully mediate multiple AD conversion risks and offer targets for intervention. J Prev Alzheimer's Dis, 3, 2016, 283.
28. [28] Degenhardt, E.K., Witte, M.M., Case, M.G., Yu, P., Henley, D.B., Hochstetler, H.M., et al. Florbetapir F18 PET amyloid neuroimaging and characteristics in patients with mild and moderate Alzheimer dementia. Psychosomatics 57 (2016), 208–216.
29. [29] Witte, M.M., Trzepacz, P., Case, M., Yu, P., Hochstetler, H., Quinlivan, M., et al. Association between clinical measures and florbetapir F18 PET neuroimaging, in mild or moderate Alzheimer's disease dementia. J Neuropsychiatry Clin Neurosciences 26 (2014), 214–220.
30. [30] Zhou, J., Greicius, M.D., Gennatas, E.D., Growdon, M.E., Jang, J.Y., Rabinovici, G.D., et al. Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer's disease. Brain 133 (2010), 1352–1367.
31. [31] Dipasquale, O., Cercignani, M., Network functional connectivity and whole-brain functional connectomics to investigate cognitive decline in neurodegenerative conditions. Funct Neurol 31 (2016), 191–203.
32. [32] Wolz, R., Heckemann, R.A., Aljabar, P., Hajnal, J.V., Hammers, A., Lötjönen, J., et al., Alzheimer's Disease Neuroimaging Initiative. Measurement of hippocampal atrophy using 4D graph-cut segmentation: application to ADNI. Neuroimage 52 (2010), 109–118.
33. [33] American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 5th ed., 2013, American Psychiatric Publishing, Arlington, VA.
34. [34] Lindbergh, C.A., Dishman, R.K., Miller, L.S., Functional disability in mild cognitive impairment: a systematic review and meta-analysis. Neuropsychol Rev 26 (2016), 129–159.
35. [35] Johnson, P., Venderwater, L., Wilson, W., Maruff, P., Savage, G., Graham, P., et al. Genetic algorithm with logistic regression for prediction of progression to Alzheimer's disease. Bioinformatics, 15, 2014, S11.
36. [36] Pozueta, A., Rodríguez-Rodríguez, E., Vazquez-Higuera, J.L., Mateo, I., Sánchez-Juan, P., González-Perez, S., et al. Detection of early Alzheimer's disease in MCI patients by combination of MMSE and an episodic memory test. BMC Neurol, 11, 2011, 78.
37. [37] Lange, C., Suppa, P., Frings, L., Brenner, W., Spies, L., Buchert, R., Optimization of statistical single subject analysis of brain FDG PET for the prognosis of Mild Cognitive Impairment-to-Alzheimer's Disease conversion. J Alzheimer's Dis 49 (2015), 945–959.
38. [38] Collij, L.E., Heeman, F., Kuijer, J.P., Ossenkoppele, R., Benedictus, M.R., Möller, C., et al. Application of machine learning to arterial spin labeling in mild cognitive impairment and Alzheimer disease. Radiology 281 (2016), 865–875.
39. [39] Ye, J., Farnum, M., Yang, E., Verbeeck, R., Lobanov, V., Raghavan, N., et al., Alzheimer's Disease Neuroimaging Initiative. Sparse learning and stability selection for predicting MCI to AD conversion using baseline ADNI data. BMC Neurol, 12, 2012, 46.
40. [40] Ewers, M., Brendel, M., RIzk-Jackson, A., Rominger, A., Bartenstein, P., Schuff, N., et al., Alzheimer's Disease Neuroimaging Initiative. Reduced FDG-PET brain metabolism and executive function predict clinical progression in elderly healthy subjects. Neuroimage: Clin 4 (2013), 45–52.
41. [41] Royall, D.R., Palmer, R.F., Aging is a weak but relentless determinant of dementia severity. Oncotarget 7 (2016), 13307–13318.
42. [42] O'Bryant, S.E., Johnson, L., Edwards, M., Soares, H., Devous, M.D., Ross, S., et al., Texas Alzheimer's Research & Care Consortium. The link between C-reactive protein and Alzheimer's disease among Mexican Americans. J Alzheimer's Dis 34 (2013), 701–706.
43. [43] O'Bryant, S.E., Johnson, L., Reisch, J., Edwards, M., Hall, J., Barber, R., et al. Risk factors for mild cognitive impairment among Mexican Americans. Alzheimer's Demen 9 (2013), 622–631.
44. [44] O'Bryant, S.E., Xiao, G., Edwards, M., Devous, M., Barber, R., Simpkins, J., for the Texas Alzheimer's Research and Care Consortium. Biomarkers of Alzheimer's disease among Mexican Americans. J Alzheimer's Dis 34 (2013), 841–849.