Conceptualizing cognitive aging as a step-sequential process is useful in identifying particular stages of cognitive function and impairment.
Objective:
We applied latent transition analysis (LTA) to determine 1) whether the underlying structure of cognitive profiles found at every measurement occasion are uniform across three waves of assessment, 2) whether class-instability is predictive of distal outcomes, and 3) whether class-reversions from impaired to non-impaired using latent modelling is lower than when using clinical criteria of mild cognitive impairment (MCI).
Methods:
A mover-stayer LTA model with dementia as a distal outcome was specified to model transitions of ten neuropsychological measures over three annual waves in the Rush Memory and Aging Project (n = 1,661). The predictive validity of the mover-stayer status for incident Alzheimer’s disease (AD) was then assessed.
Results:
We identified a five-class model across the three time-points: Mixed-Domain Impairment, Memory-Specific Impairment, Frontal Impairment, Average, and Superior Cognition. None of the individuals in the Impairment classes reverted to the Average or Superior classes. Conventional MCI classification identified 26.4% and 14.1% at Times 1 and 2 as false-positive cases. “Movers” had 87% increased risk of developing dementia compared to those classified as “Stayers”.
Conclusion:
Our findings support the use of latent variable modelling that incorporates comprehensive neuropsychological assessment to identify and classify cognitive impairment.
DearyI (2000) Looking Down on Human Intelligence: From Psychometrics to the Brain, Oxford University PressUK.
2.
SalthouseTA (1996) The processing-speed theory of adult age differences in cognition. Psychol Rev103, 403–428.
3.
WilsonRS, BeckettLA, BarnesLL, SchneiderJA, BachJ, EvansDA, BennettDA (2002) Individual differences in rates of change in cognitive abilities of older persons. Psychol Aging17, 179–193.
4.
HultschDF, DixonRA, MacDonaldSWS (2002) Variability in reaction time performance of younger and older adults. J Gerontol B Psychol Sci Soc Sci57, P101–P115.
5.
GunstadJ, PaulRH, BrickmanAM, CohenRA, ArnsM, RoeD, LawrenceJJ, GordonE (2006) Patterns of cognitive performance in middle-aged and older adults: A cluster analytic examination. J Geriatr Psychiatry Neurol19, 59–64.
6.
EppigJS, EdmondsEC, CampbellL, Sanderson-CiminoM, Delano-WoodL, BondiMW (2017) Statistically derived subtypes and associations with cerebrospinal fluid and genetic biomarkers in mild cognitive impairment: A latent profile analysis. J Int Neuropsychol Soc23, 564–576.
7.
RitchieK, LeiboviciD, LedesertB, TouchonJ (1996) A typology of sub-clinical senescent cognitive disorder. Br J Psychiatry168, 470–476.
8.
ZammitAR, HallCB, KatzMJ, Muniz-TerreraG, EzzatiA, BennettDA, LiptonRB (2018) Class-specific incidence of all-cause dementia and Alzheimer’s disease: A latent class approach. J Alzheimers Dis66, 347–357.
9.
ZammitAR, Muniz-TerreraG, KatzMJ, HallCB, EzzatiA, BennettDA, LiptonRB (2019) Subtypes based on neuropsychological performance predict incident dementia: Findings from the rush memory and aging project. J Alzheimers Dis67, 125–135.
10.
CollinsLM, LanzaST (2010) Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences, John Wiley & Sons, Hoboken, New Jersey.
11.
HyattSL, CollinsLM (2000) Using latent transition analysis to examine the relationship between perceived parental permissiveness and the onset of substance use. In Multivariate applications in substance use research: New methods for new questions. Lawrence Erlbaum Associates Publishers, Mahwah, NJ, US, pp. 259–288.
12.
BennettDA, SchneiderJA, BuchmanAS, BarnesLL, BoylePA, WilsonRS (2012) Overview and findings from the rush Memory and Aging Project. Curr Alzheimer Res9, 646–663.
13.
BennettDA, BuchmanAS, BoylePA, BarnesLL, WilsonRS, SchneiderJA (2018) Religious Orders Study and Rush Memory and Aging Project. J Alzheimers Dis64, S161–s189.
14.
WechslerD (1987) Wechsler Memory Scale - Revised, The Psychological Corporation, San Antonio.
15.
WelshKA, ButtersN, MohsRC, BeeklyD, EdlandS, FillenbaumG, HeymanA (1994) The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). Part V. A normative study of the neuropsychological battery. Neurology44, 609–614.
16.
MorrisJC, HeymanA, MohsRC, HughesJP, van BelleG, FillenbaumG, MellitsED, ClarkC (1989) The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). Part I. Clinical and neuropsychological assessment of Alzheimer’s disease. Neurology39, 1159–1165.
17.
KaplanEF, GoodglassH, WeintraubS (1983) The Boston Naming Test, Lea & Febiger, Philadelphia.
18.
MonschAU, BondiMW, ButtersN, SalmonDP, KatzmanR, ThalLJ (1992) Comparisons of verbal fluency tasks in the detection of dementia of the Alzheimer type. Arch Neurol49, 1253–1258.
19.
WechslerD (1997) Adult Intelligence Scale-III, Psychological Corporation, San Antonio, TX.
20.
CooperJA, SagarHJ, JordanN, HarveyNS, SullivanEV (1991) Cognitive impairment in early, untreated Parkinson’s disease and its relationship to motor disability. Brain114 (Pt 5), 2095–2122.
ZammitAR, HallCB, BennettDA, EzzatiA, KatzMJ, Muniz-TerreraG, LiptonRB (2019) Neuropsychological latent classes at enrollment and postmortem neuropathology. Alzheimers Dement15, 1195–1207.
27.
LamarM, ZondermanAB, ResnickS (2002) Contribution of specific cognitive processes to executive functioning in an aging population. Neuropsychology16, 156–162.
FusterJ (1997), The Prefrontal Cortex Anatomy, Physiology and Neuropsychology of the Frontal Lobe.
30.
BennettDA, WilsonRS, SchneiderJA, EvansDA, BeckettLA, AggarwalNT, BarnesLL, FoxJH, BachJ (2002) Natural history of mild cognitive impairment in older persons. Neurology59, 198–205.
31.
McKhannGM, KnopmanDS, ChertkowH, HymanBT, JackCRJr, KawasCH, KlunkWE, KoroshetzWJ, ManlyJJ, MayeuxR, MohsRC, MorrisJC, RossorMN, ScheltensP, CarrilloMC, ThiesB, WeintraubS, PhelpsCH (2011) 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 Dement7, 263–269.
32.
NylundKL (2007) Latent transition analysis: Modeling extensions and an application to peer victimization (Doctoral dissertation). University of California Los Angeles.
33.
MuthénLK,
MuthénBO (1998–2016) MPlus User’s Guide. Muthén & Muthén, Los Angeles, CA.
34.
SPSS Inc. (Released 2016) IBM SPSS Statistics for Windows. IBM Corp., Armonk, NY.
35.
WilsonRS, BeckettLA, BennettDA, AlbertMS, EvansDA (1999) Change in cognitive function in older persons from a community population: Relation to age and Alzheimer disease. Arch Neuro56, 1274–1279.
36.
WeintraubS, WicklundAH, SalmonDP (2012) The neuropsychological profile of Alzheimer disease. Cold Spring Harb Perspect Med2, a006171.
37.
MeehlPE (1954) Clinical versus statistical prediction: A theoretical analysis and a review of the evidence,
University of Minnesota Press, Minneapolis, MN.
38.
GroveWM, MeehlPE (1996) Comparative efficiency of informal (subjective, impressionistic) and formal (mechanical, algorithmic) prediction procedures: The clinical–statistical controversy. Psychol Public Policy Law2, 293–323.
39.
WestenD, WeinbergerJ (2004) When clinical description becomes statistical prediction. Am Psychol59, 595–613.
40.
GroveWM, ZaldDH, LebowBS, SnitzBE, NelsonC (2000) Clinical versus mechanical prediction: A meta-analysis. Psychol Assess12, 19–30.
41.
ÆgisdóttirS, WhiteMJ, SpenglerPM, MaughermanAS, AndersonLA, CookRS, NicholsCN, LampropoulosGK, WalkerBS, CohenG, RushJD (2006) The meta-analysis of clinical judgment project: Fifty-six years of accumulated research on clinical versus statistical prediction. Couns Psychol34, 341–382.
42.
EdmondsEC, McDonaldCR, MarshallA, ThomasKR, EppigJ, WeigandAJ, Delano-WoodL, GalaskoDR, SalmonDP, BondiMW (2019) Early versus late MCI: Improved MCI staging using a neuropsychological approach. Alzheimers Dement15, 699–708.
43.
ThomasKR, EppigJS, WeigandAJ, EdmondsEC, WongCG, JakAJ, Delano-WoodL, GalaskoDR, SalmonDP, EdlandSD, BondiMW (2019) Artificially low mild cognitive impairment to normal reversion rate in the Alzheimer’s disease neuroimaging initiative. Alzheimers Dement15, 561–569.
BondiMW, JakAJ, Delano-WoodL, JacobsonMW, DelisDC, SalmonDP (2008) Neuropsychological contributions to the early identification of Alzheimer’s disease. Neuropsychol Rev18, 73–90.
46.
SalthouseTA, SoubeletA (2014) Heterogeneous ability profiles may be a unique indicator of impending cognitive decline. Neuropsychology28, 812–818.
47.
SalmonDP, BondiMW (2009) Neuropsychological assessment of dementia. Annu Rev Psychol60, 257–282.
48.
ChristensenH, MackinnonAJ, KortenAE, JormAF, HendersonAS, JacombP, RodgersB (1999) An analysis of diversity in the cognitive performance of elderly community dwellers: Individual differences in change scores as a function of age. Psychol Aging14, 365–379.
49.
DixonRA, HertzogC, FriesenIC, HultschDF (1993) Assessment of intraindividual change in text recall of elderly adults. In Narrative discourse in neurologically impaired and normal aging adults,
BrownwellHH,
JoanetteY, eds. Singular, San Diego, pp. 77–101.
HoferSM, PiccininAM (2009) Integrative data analysis through coordination of measurement and analysis protocol across independent longitudinal studies. Psychol Methods14, 150–164.
