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Identification of amnestic mild cognitive impairment among Black and White community-dwelling older adults using NIH Toolbox Cognition tablet battery

Published online by Cambridge University Press:  18 September 2024

Taylor Rigby Open the ORCID record for Taylor Rigby [Opens in a new window] ,
Allyson M. Gregoire ,
Johnathan Reader ,
Yonatan Kahsay ,
Jordan Fisher ,
Anson Kairys Open the ORCID record for Anson Kairys [Opens in a new window] ,
Arijit K. Bhaumik ,
Annalise Rahman-Filipiak ,
Amanda Cook Maher  and
Benjamin M. Hampstead
...Show all authors
Taylor Rigby*
Affiliation:
Michigan Alzheimer’s Disease Research Center, Ann Arbor, MI, USA Department of Veterans Affairs Medical Center, Geriatric Research Education and Clinical Center, Ann Arbor, MI, USA Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
Allyson M. Gregoire
Affiliation:
Michigan Alzheimer’s Disease Research Center, Ann Arbor, MI, USA Department of Neurology, University of Michigan, Ann Arbor, MI, USA
Johnathan Reader
Affiliation:
Michigan Alzheimer’s Disease Research Center, Ann Arbor, MI, USA Department of Neurology, University of Michigan, Ann Arbor, MI, USA
Yonatan Kahsay
Affiliation:
Michigan Alzheimer’s Disease Research Center, Ann Arbor, MI, USA Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
Jordan Fisher
Affiliation:
Michigan Alzheimer’s Disease Research Center, Ann Arbor, MI, USA Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
Anson Kairys
Affiliation:
Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
Arijit K. Bhaumik
Affiliation:
Michigan Alzheimer’s Disease Research Center, Ann Arbor, MI, USA Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA Department of Neurology, University of Michigan, Ann Arbor, MI, USA
Annalise Rahman-Filipiak
Affiliation:
Michigan Alzheimer’s Disease Research Center, Ann Arbor, MI, USA Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
Amanda Cook Maher
Affiliation:
Michigan Alzheimer’s Disease Research Center, Ann Arbor, MI, USA Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
Benjamin M. Hampstead
Affiliation:
Michigan Alzheimer’s Disease Research Center, Ann Arbor, MI, USA Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA Department of Neurology, University of Michigan, Ann Arbor, MI, USA
Judith L. Heidebrink
Affiliation:
Michigan Alzheimer’s Disease Research Center, Ann Arbor, MI, USA Department of Neurology, University of Michigan, Ann Arbor, MI, USA
Voyko Kavcic
Affiliation:
Institute of Gerontology, Wayne State University, Detroit, MI, USA
Bruno Giordani
Affiliation:
Michigan Alzheimer’s Disease Research Center, Ann Arbor, MI, USA Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
*
Corresponding author: T. Rigby; Email: [email protected]
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Abstract

Objectives:

Identify which NIH Toolbox Cognition Battery (NIHTB-CB) subtest(s) best differentiate healthy controls (HC) from those with amnestic mild cognitive impairment (aMCI) and compare the discriminant accuracy between a model using a priori “Norm Adjusted” scores versus “Unadjusted” standard scores with age, sex, race/ethnicity, and education controlled for within the model. Racial differences were also examined.

Methods:

Participants were Black/African American (B/AA) and White consensus-confirmed (HC = 96; aMCI = 62) adults 60–85 years old that completed the NIHTB-CB for tablet. Discriminant function analysis (DFA) was used in the Total Sample and separately for B/AA (n = 80) and White participants (n = 78).

Results:

Picture Sequence Memory (an episodic memory task) was the highest loading coefficient across all DFA models. When stratified by race, differences were noted in the pattern of the highest loading coefficients within the DFAs. However, the overall discriminant accuracy of the DFA models in identifying HCs and those with aMCI did not differ significantly by race (B/AA, White) or model/score type (Norm Adjusted versus Unadjusted).

Conclusions:

Racial differences were noted despite the use of normalized scores or demographic covariates—highlighting the importance of including underrepresented groups in research. While the models were fairly accurate at identifying consensus-confirmed HCs, the models proved less accurate at identifying White participants with an aMCI diagnosis. In clinical settings, further work is needed to optimize computerized batteries and the use of NIHTB-CB norm adjusted scores is recommended. In research settings, demographically corrected scores or within model correction is suggested.

Keywords

Computerized neuropsychological assessmentcomputerized cognitive assessmentcognitionpsychometricsnormscross-culturaldiscriminant function analysis
Type
Research Article
Information
Journal of the International Neuropsychological Society , First View , pp. 1 - 8
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of International Neuropsychological Society

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References

Alzheimer’s Association (2022). More than normal aging: Understanding mild cognitive impairment. Alzheimer’s Disease Facts and Figures. https://www.alz.org/media/Documents/alzheimers-facts-and-figures-special-report-2022.pdf.Google Scholar
Bastin, C., & Salmon, E. (2014). Early neuropsychological detection of Alzheimer’s disease. European Journal of Clinical Nutrition, 68(11), 1192–1199. https://doi.org/10.1038/ejcn.2014.176 CrossRefGoogle ScholarPubMed
Buckley, R. F., Sparks, K. P., Papp, K. V., Dekhtyar, M., Martin, C., Burnham, S., Sperling, R. A., & Rentz, D. M. (2017). Computerized cognitive testing for use in clinical trials: A comparison of the NIH toolbox and cogstate C3 batteries. The Journal of Prevention of Alzheimer’s Disease, 4(1), 3–9. https://doi.org/10.14283/jpad.2017.1 Google ScholarPubMed
Byrd, D. A., & Rivera-Mindt, M. G. (2022). Neuropsychology’s race problem does not begin or end with demographically adjusted norms. Nature Reviews Neurology, 18(3), 125–126. https://doi.org/10.1038/s41582-021-00607-4 CrossRefGoogle ScholarPubMed
Casaletto, K. B., Umlauf, A., Beaumont, J., Gershon, R., Slotkin, J., Akshoomoff, N., & Heaton, R. K. (2015). Demographically corrected normative standards for the English version of the NIH toolbox cognition battery. Journal of the International Neuropsychological Society, 21(5), 378–391. https://doi.org/10.1017/S1355617715000351 CrossRefGoogle ScholarPubMed
Clark, P. C., Kutner, N. G., Goldstein, F. C., Peterson-Hazen, S., Garner, V., Zhang, R., & Bowles, T. (2005). Impediments to timely diagnosis of Alzheimer’s disease in African Americans. Journal of the American Geriatrics Society, 53(11), 2012–2017. https://doi.org/10.1111/j.1532-5415.2005.53569.x CrossRefGoogle ScholarPubMed
Dilworth-Anderson, P., Hendrie, H. C., Manly, J. J., Khachaturian, A. S., & Fazio, S. (2008). Diagnosis and assessment of Alzheimer’s disease in diverse populations. Alzheimer’s & Dementia, 4(4), 305–309. https://doi.org/10.1016/j.jalz.2008.03.001 CrossRefGoogle ScholarPubMed
Fitten, L. J., Ortiz, F., & Pontón, M. (2001). Frequency of Alzheimer’s disease and other dementias in a community outreach sample of hispanics. Journal of the American Geriatrics Society, 49(10), 1301–1308. https://doi.org/10.1046/j.1532-5415.2001.49257.x CrossRefGoogle Scholar
Garcia, S., Askew, R. L., Kavcic, V., Shair, S., Bhaumik, A. K., Rose, E., Campbell, S., May, N., Hampstead, B. M., Dodge, H. H., Heidebrink, J. L., Paulson, H. L., & Giordani, B. (2023). Mild cognitive impairment subtype performance in comparison to healthy older controls on the NIH toolbox and cogstate. Alzheimer Disease & Associated Disorders, 37(4), 328–334. https://doi.org/10.1097/WAD.0000000000000587 CrossRefGoogle ScholarPubMed
Gaugler, J., James, B., Johnson, T., Reimer, J., Solis, M., Weuve, J., & Hohman, T. J. (2022). Alzheimer’s disease facts and figures. Alzheimers & Dementia, 18(4), 700–789. https://doi.org/10.1002/alz.12638 Google Scholar
Gauthier, S., Reisberg, B., Zaudig, M., Petersen, R. C., Ritchie, K., Broich, K., Belleville, S., Brodaty, H., Bennett, D., Chertkow, H., Cummings, J. L., de Leon, M., Feldman, H., Ganguli, M., Hampel, H., Scheltens, P., Tierney, M. C., Whitehouse, P., & Winblad, B. (2006). Mild cognitive impairment. The lancet, 367(9518), 1262–1270. https://doi.org/10.1016/S0140-6736(06)68542-5 CrossRefGoogle ScholarPubMed
Gershon, R. C., Wagster, M. V., Hendrie, H. C., Fox, N. A., Cook, K. F., & Nowinski, C. J. (2013). NIH toolbox for assessment of neurological and behavioral function. Neurology, 80(11_supplement_3), S2–S6. https://doi.org/10.1212/WNL.0b013e3182872e5f CrossRefGoogle ScholarPubMed
Gianattasio, K. Z., Prather, C., Glymour, M. M., Ciarleglio, A., & Power, M. C. (2019). Racial disparities and temporal trends in dementia misdiagnosis risk in the United States. Alzheimer’s & Dementia: Translational Research & Clinical Interventions, 5(1), 891–898. https://doi.org/10.1016/j.trci.2019.11.008 Google ScholarPubMed
Grober, E., Sliwinsk, M., & Korey, S. R. (1991). Development and validation of a model for estimating premorbid verbal intelligence in the elderly. Journal of Clinical and Experimental Neuropsychology, 13(6), 933–949. https://doi.org/10.1080/01688639108405109 CrossRefGoogle Scholar
Grossberg, G. T., Christensen, D. D., Griffith, P. A., Kerwin, D. R., Hunt, G., & Hall, E. J. (2010). The art of sharing the diagnosis and management of Alzheimer’s disease with patients and caregivers: Recommendations of an expert consensus panel. The Primary Care Companion for CNS Disorders, 12(1), 26659. https://doi.org/10.4088/PCC.09cs00833oli Google ScholarPubMed
Hackett, K., Krikorian, R., Giovannetti, T., Melendez‐Cabrero, J., Rahman, A., Caesar, E. E., Chen, J. L., Hristov, H., Seifan, A., Mosconi, L., & Isaacson, R. S. (2018). Utility of the NIH toolbox for assessment of prodromal Alzheimer’s disease and dementia. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 10(1), 764–772. https://doi.org/10.1016/j.dadm.2018.10.002 Google ScholarPubMed
Heaton, R. K. (2004). Revised comprehensive norms for an expanded halstead-reitan battery: Demographically adjusted neuropsychological norms for African American and Caucasian adults, professional manual. Psychological Assessment Resources.Google Scholar
Heaton, R. K., Akshoomoff, N., Tulsky, D., Mungas, D., Weintraub, S., Dikmen, S., Beaumont, J., Casaletto, K. B., Conway, K., Slotkin, J., & Gershon, R. (2014). Reliability and validity of composite scores from the NIH toolbox cognition battery in adults. Journal of the International Neuropsychological Society, 20(6), 588–598. https://doi.org/10.1017/S1355617714000241 CrossRefGoogle ScholarPubMed
Jokel, R., Seixas Lima, B., Fernandez, A., & Murphy, K. J. (2019). Language in amnestic mild cognitive impairment and dementia of Alzheimer’s type: Quantitatively or qualitatively different? Dementia and Geriatric Cognitive Disorders Extra, 9(1), 136–151. https://doi.org/10.1159/000496824 CrossRefGoogle Scholar
Kaduszkiewicz, H., Eisele, M., Wiese, B., Prokein, J., Luppa, M., Luck, T., Jessen, F., Bickel, H., Mosch, E., Pentzek, M., Fuchs, A., Eifflaender-Gorfer, S., Weyerer, S., Konig, H.-H., Brettschneider, C., van den Bussche, H., Maier, W., Scherer, M., Riedel-Heller, S. G., & The Study on Aging, Cognition, and Dementia in Primary Care Patients (AgeCoDe) Study Group (2014). Prognosis of mild cognitive impairment in general practice: Results of the German ageCoDe study. The Annals of Family Medicine, 12(2), 158–165. https://doi.org/10.1370/afm.1596 CrossRefGoogle ScholarPubMed
Kairys, A., Daugherty, A., Kavcic, V., Shair, S., Persad, C., Heidebrink, J., Bhaumik, A., & Giordani, B. (2022). Laptop-administered NIH toolbox and cogstate brief battery in community-dwelling black adults: Unexpected pattern of cognitive performance between MCI and healthy controls. Journal of the International Neuropsychological Society, 28(3), 239–248. https://doi.org/10.1017/S135561772100028X CrossRefGoogle ScholarPubMed
Larner, A. J. (2019). Evaluating cognitive screening instruments with the, likelihood to be diagnosed or misdiagnosed, measure. International Journal of Clinical Practice, 73(2), e13265. https://doi.org/10.1111/ijcp.13265 CrossRefGoogle ScholarPubMed
Lin, P-J., Daly, A. T., Olchanski, N., Cohen, J. T., Neumann, P. J., Faul, J. D., Fillit, H. M., & Freund, K. M. (2021). Dementia diagnosis disparities by race and ethnicity. Medical care, 59(8), 679–686. https://doi.org/10.1097/MLR.0000000000001577 CrossRefGoogle ScholarPubMed
Lippa, R. A., Collaer, M. L., & Peters, M. (2010). Sex differences in mental rotation and line angle judgments are positively associated with gender equality and economic development across 53 nations. Archives of sexual behavior, 39(4), 990–997. https://doi.org/10.1007/s10508-008-9460-8 CrossRefGoogle ScholarPubMed
Manly, J. J. (2005). Advantages and disadvantages of separate norms for African Americans. The Clinical Neuropsychologist, 19(2), 270–275. https://doi.org/10.1080/13854040590945346 CrossRefGoogle ScholarPubMed
Manly, J. J., Miller, S. W., Heaton, R. K., Byrd, D., Reilly, J. U. D. Y., Velasquez, R. J., Saccuzzo, D. P., Grant, I., & HIV Neurobehavioral Research Center (HNRC) Group (1998). The effect of African-American acculturation on neuropsychological test performance in normal and HIV-positive individuals. Journal of the International Neuropsychological Society, 4(3), 291–. https://doi.org/302 CrossRefGoogle ScholarPubMed
Mungas, D., Shaw, C., Hayes-Larson, E., DeCarli, C., Farias, S. T., Olichney, J., & Saucedo, H. H., Gilsanz, P., Glymour, M. M., Whitmer, R. A., & Mayeda, E. R. (2021). Cognitive impairment in racially/ethnically diverse older adults: Accounting for sources of diagnostic bias. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 13(1), e12265. https://doi.org/10.1002/dad2.12265 Google ScholarPubMed
Nasreddine, Z. S., Phillips, N. A., Bédirian, Vérie, Charbonneau, S., Whitehead, V., Collin, I., Cummings, J. L., & Chertkow, H. (2005). The montreal cognitive assessment, MoCA: A brief screening tool for mild cognitive impairment. Journal of the American Geriatrics Society, 53(4), 695–699. https://doi.org/10.1111/j.1532-5415.2005.53221.x CrossRefGoogle Scholar
Petersen, R. C., Lopez, O., Armstrong, M. J., Getchius, T. S. D., Ganguli, M., Gloss, D., Gronseth, G. S., Marson, D., Pringsheim, T., Day, G. S., Sager, M., Stevens, J., & Rae-Grant, A. (2018). Practice guideline update summary: Mild cognitive impairment: Report of the guideline development, dissemination, and implementation subcommittee of the American academy of neurology. Neurology, 90(3), 126–135. https://doi.org/10.1212/WNL.0000000000004826 Google Scholar
Power, M. C., Bennett, E. E., Turner, R. W., Dowling, N. M., Ciarleglio, A., Glymour, M. M., & Gianattasio, K. Z. (2021). Trends in relative incidence and prevalence of dementia across non-hispanic black and white individuals in the United States, 2000-2016. JAMA neurology, 78(3), 275–284. https://doi.org/10.1001/jamaneurol.2020.4471 CrossRefGoogle ScholarPubMed
Rosselli, M., Uribe, I. V., Ahne, E., & Shihadeh, L. (2022). Culture, ethnicity, and level of education in Alzheimer’s disease. Neurotherapeutics, 19(1), 26–54. https://doi.org/10.1007/s13311-022-01193-z CrossRefGoogle ScholarPubMed
Scott, E. P., Sorrell, A., & Benitez, A. (2019). Psychometric properties of the NIH toolbox cognition battery in healthy older adults: Reliability, validity, and agreement with standard neuropsychological tests. Journal of the International Neuropsychological Society, 25(8), 857–867. https://doi.org/10.1017/S1355617719000614 CrossRefGoogle ScholarPubMed
Snitz, B. E., Bieliauskas, L. A., Crossland, A., Basso, M. R., & Roper, B. (2000). PPVT-R as an estimate of premorbid intelligence in older adults. The Clinical Neuropsychologist, 14(2), 181–186.CrossRefGoogle ScholarPubMed
Steenland, K., Goldstein, F. C., Levey, A., & Wharton, W. (2016). A meta-analysis of Alzheimer’s disease incidence and prevalence comparing African-Americans and Caucasians. Journal of Alzheimer’s Disease, 50(1), 71–76. https://doi.org/10.3233/JAD-150778 CrossRefGoogle ScholarPubMed
Vickrey, B. G., Mittman, B. S., Connor, K. I., Pearson, M. L., Della Penna, R. D., Ganiats, T. G., DeMonte, R. W., Chodosh, J., Cui, X., Vassar, S., Duan, N., & Lee, M. (2006). The effect of a disease management intervention on quality and outcomes of dementia care: A randomized, controlled trial. Annals of internal medicine, 145(10), 713–726. https://doi.org/10.7326/0003-4819-145-10-200611210-00004 CrossRefGoogle ScholarPubMed
Voisin, T., & Vellas, B. (2009). Diagnosis and treatment of patients with severe Alzheimer’s disease. Drugs & aging, 26(2), 135–144. https://doi.org/10.2165/0002512-200926020-00005 CrossRefGoogle ScholarPubMed
Ward, A., Tardiff, S., Dye, C., & Arrighi, H. M. (2013). Rate of conversion from prodromal Alzheimer’s disease to Alzheimer’s dementia: A systematic review of the literature. Dementia and Geriatric Cognitive Disorders Extra, 3(1), 320–332. https://doi.org/10.1159/000354370 CrossRefGoogle ScholarPubMed
Weintraub, S., Besser, L., Dodge, H. H., Teylan, M., Ferris, S., Goldstein, F. C., Giordani, B., Kramer, J., Loewenstein, D., Marson, D., Mungas, D., Salmon, D., Welsh-Bohmer, K., Zhou, X.-H., Shirk, S. D., Atri, A., Kukull, W. A., Phelps, C., & Morris, J. C. (2018). Version 3 of the Alzheimer disease centers’ neuropsychological test battery in the uniform data set (UDS). Alzheimer Disease & Associated Disorders, 32(1), 10–17. https://doi.org/10.1097/WAD.0000000000000223 CrossRefGoogle ScholarPubMed
Weintraub, S., Dikmen, S. S., Heaton, R. K., Tulsky, D. S., Zelazo, P. D., Bauer, P. J., Carlozzi, N. E., Slotkin, J., Blitz, D., Wallner-Allen, K., Fox, N. A., Beaumont, J. L., Mungas, D., Nowinski, C. J., Richler, J., Deocampo, J. A., Anderson, J. E., Manly, J. J., Borosh, B., Havlik, R., Conway, K., Edwards, E., Freund, L., King, J. W., Moy, C., Witt, E., & Gershon, R. C. (2013). Cognition assessment using the NIH toolbox. Neurology, 80(11 Supplement 3), S54–S64. https://doi.org/10.1212/WNL.0b013e3182872ded CrossRefGoogle ScholarPubMed
Weintraub, S., Karpouzian‐Rogers, T., Peipert, J. D., Nowinski, C., Slotkin, J., Wortman, K., Ho, E., Rogalski, E., Carlsson, C., Giordani, B., Goldstein, F., Lucas, J., Manly, J. J., Rentz, D., Salmon, D., Snitz, B., Dodge, H. H., Riley, M., Eldes, F., Ustsinovich, V., & Gershon, R. (2022). ARMADA: Assessing reliable measurement in Alzheimer’s disease and cognitive aging project methods. Alzheimer’s & Dementia, 18(8), 1449–1460. https://doi.org/10.1002/alz.12497 CrossRefGoogle ScholarPubMed
Weintraub, S., Salmon, D., Mercaldo, N., Ferris, S., Graff-Radford, N. R., Chui, H., Cummings, J., DeCarli, C., Foster, N. L., Galasko, D., Peskind, E., Dietrich, W., Beekly, D. L., Kukull, W. A., & Morris, J. C. (2009). The Alzheimer’s disease centers’ uniform data set (UDS): The neuropsychological test battery. Alzheimer Disease & Associated Disorders, 23(2), 91–101. https://doi.org/10.1097/WAD.0b013e318191c7dd CrossRefGoogle Scholar
Werry, A. E., Daniel, M., & Bergström, B. (2019). Group differences in normal neuropsychological test performance for older non-hispanic white and black/African American adults. Neuropsychology, 33(8), 1089–1100. https://doi.org/10.1037/neu0000579 CrossRefGoogle ScholarPubMed
Yaffe, K., Falvey, C., Harris, T. B., Newman, A., Satterfield, S., Koster, A., Ayonayon, H., Simonsick, E., & for the Health ABC Study (2013). Effect of socioeconomic disparities on incidence of dementia among biracial older adults: Prospective study. BMJ, 347(dec19 5), f7051. https://doi.org/10.1136/bmj.f7051 CrossRefGoogle ScholarPubMed
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