Growth Mixture Modeling Workgroup

Heterogeneous trajectories of

cognitive decline: Association with neuropathology

Hayden K., Reed B., Chelune G., Manly J., Pietrzak R., Revell A.,

Yang F., Tommet D., Jones R.

March 27, 2009

Hypotheses

1.Cognitive decline is not normal.

This will be revealed by the detection of at least two, and we suspect three population sub-groups with qualitatively distinct trajectories of cognitive decline:

1) little/no change in cognition with aging, and this will be the largest (“normative”) group

2) intermediate change with aging3) rapid decline with aging

Hypotheses

2. Group membership will be differentially associated with AD-type neuropathology

Methods• Participants: Religious Orders Study (N=1,048)

followed annually for up to 15 years with neuropsychological battery; a subset of n=345 had autopsy data.

• Main Outcomes: Global composite of neuropsychological function; among decedents, amyloid and tangle neuropathology (as defined by NIA/Reagan criteria and grouped by tertile).

• Statistical Approach: Random effects growth model with mixture component. Separate multinomial logistic regression of classes on neuropathology.

Growth Mixture Model

a g e5 0 - 6 4

1 2 1 5

y

y

y

1 2 1 5

[1 ] [2 ]

[3 ]

a g e9 0 - 1 0 2

e x c lu d e a g e 7 5 - 7 9 a sre fe re n c e g ro u p

d e n o te s a n in d iv id u a lly - v a ry in g tim e p o in t o f o b se rv a tio n

c

Note: 1 and 2 are baseline and linear slope, respectively. 3 is a retest/learning effect. is regressed on age group dummies used to model non-linearity of trajectories over age using a piecewise approach. c implies latent classes. Individually varying time steps are used to model the regression of y on 2 are modeled as age at observation centered within age group.

Multinomial Regression Models

M o d e l 2 a /b : C la ss o n A m y lo id o r T a n g le s a n dc o v a r ia te s ( A d j u s te d )

cx2

M o d e l 2 : C la ss o n A m y lo id , T a n g le s a n d Z( Fu lly A d j u s te d )

x1

Z

c x1

M o d e l 1 a : C la ss o n A m y lo id( C r u d e )

c x2

M o d e l 1 b : C la ss o n T a n g le s( C r u d e )

c xq

Z

Z = age at baseline, education, gender

Summary of Model Fit (GMM)

Classes Log- Para- Average Class Prob-Extracted Likelihood Scaling* meters P-Value+ aBIC** abilities by Assignment***-------------------------------------------------------------------------------

1 -3653.915 3.878 33 -- 7432 1.00

2 -3210.850 2.634 52 <0.001 6617 0.92 0.94

3 -3132.205 2.314 71 <0.001 6531 0.80 0.93 0.74

4 -3043.62 34.632 90 1.000 6425 0.83 0.92 0.68 0.75 -------------------------------------------------------------------------------Notes: * Scaling Correction Factor for MLR** Sample-Size Adjusted BIC*** Average Latent Class Probabilities for Most Likely Latent Class Membership+ P-value from -2*LL difference test with scaling correction factor, the Satorra-Bentler scaled (mean-adjusted) chi-square (Satorra, 2006).

Table 1. Participant Characteristics at First Visit Mean (SD) or n (%)

Characteristic

Total Cohort

(N=1,066) Class 1 (n=716)

Class 2 (n=233)

Class 3 (n=71)

Age at baseline assessment 76 (7) 75 (7) 78 (7) 79 (8) *** Age group at first observation ***

55-64 27 (3) 22 (3) 3 (1) 2 (3)

65-69 254 (24) 193 (27) 32 (14) 9 (13) 70-74 250 (24) 183 (26) 44 (19) 13 (18)

75-79 214 (20) 140 (20) 53 (23) 12 (17) 80-84 191 (18) 109 (15) 60 (26) 20 (28)

85-89 90 (9) 51 (7) 31 (13) 7 (10)

90+ 35 (3) 17 (2) 10 (4) 8 (11) Male sex 341 (32) 227 (32) 64 (28) 25 (35) Not White, Non-Hispanic 83 (8) 44 (6) 30 (13) 6 (9) ** Years of education **

0-12 29 (3) 12 (2) 12 (5) 4 (6) 12 51 (5) 30 (4) 15 (6) 3 (4) 13-16 244 (23) 158 (22) 59 (25) 23 (32) 17+ 737 (70) 515 (72) 147 (63) 41 (58) MMSE 28 (2) 29 (2) 27 (3) 25 (5) *** SD, standard deviation; MMSE, Mini-Mental State Exam ***, P<0.001; **, P<0.01 on ANOVA or chi-square test of no difference across class

slow moderate fast

Growth Trajectories by Age Group

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ance

60 70 80 90 100 110Age

Note: Model-implied trajectories, net of retest/learning effect, are illustrated.

Years until half-SD decline in cognitive function

Trajectory

Group

Years for

0.5sd

95% Conf. Interval

LL UL

Slow 14.7 11.9 19.1

Moderate 3.0 2.4 3.7

Rapid 0.5 0.5 0.6

Effects shown are unweighted averages over all age groups (Table 1).

Amyloid Burden by Group

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No Decline Mod Decline Fast DeclineClass Membership

Tangle Burden by Group

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No Decline Mod Decline Fast DeclineClass Membership

Odds of Class Membership by level of Neuropathology

Model 1 (Unadjusted)Tertile Contrast Tertile Contrast

2 vs. 1 1.9 * 1.2ns

2 vs. 1 1.4ns

1.2ns

3 vs. 1 2.5 * 3.8 ** 3 vs. 1 5.5 * 13.3 *

Model 2 (Covariate Adjusted)Tertile Contrast Tertile Contrast

2 vs. 1 2.0 * 1.5ns

2 vs. 1 1.5ns

1.5ns

3 vs. 1 2.7 * 4.8 *** 3 vs. 1 5.7 *** 17.8 ***

Model 3 (Fully Adjusted)Tertile Contrast Tertile Contrast

2 vs. 1 1.5ns

0.8ns

2 vs. 1 1.3ns

1.5ns

3 vs. 1 1.5ns

1.5ns

3 vs. 1 4.7 *** 16.5 ***

vs. Slow vs. Slow

Decline Group Decline Group

Amyloid Tangles

Moderate Fast Moderate Fastvs. Slow vs. Slow

Tertiles based on distribution of neuropathology in NIA/Reagan Neuropathologic Criteria for AD "intermediate" and "high" likelihood groups. Cell entries are odds ratios from multinomial logistic regression models. Covariate and fully adjusted models include adjustment for age at baseline assessment, sex, and level of education. Fully adjusted models include covariates and neuropathology. * P<0.05, **P<0.01, *** P<0.001

Summary of Results• The largest proportion of older adults in this

study did not experience significant cognitive decline, and average 15 years until any notable change in cognitive function was detected.

• Pathology does not perfectly account for membership in the 3 groups identifying trajectories of cognitive decline; however presence of tangles at death was strongly associated with the rapid declining group

Things to do for this paper1. See how few repeated observations are

necessary to correctly classify people in trajectory class with reasonable accuracy.

2. Sensitivity analysis on missing data in GLOBCOG (differential missingness might exaggerate decline profiles).

3. Multiple imputation for missing data in multinomial logistic regression models.

4. Write up in 2-2.5k words

Future Studies

1. Alternative class structures (Jones & Tommet)

2. Repeat in sub-domains: memory [episodic, semantic, working], perceptual speed, visuospatial