The journal of prevention of Alzheimer's disease · 2016
Longitudinal Trajectories of Clinical Decline in Amyloid Positive and Negative Populations.
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Trzepacz P T, Hochstetler H, Wang S, Yu P, Case M, Henley D B, Degenhardt E, Leoutsakos J-M, Lyketsos C G
Аннотация
BACKGROUND: Brain beta-amyloid status portends different trajectories of clinical decline.
OBJECTIVE: Determine trajectories and predictive baseline variable(s).
DESIGN: Longitudinal, up to 24 months.
SETTING: ADNI sites.
PARTICIPANTS: Healthy control (n=325), early and late mild cognitive impairment (n=279; n=372), and Alzheimer's dementia (n=216) subjects from ADNI-1/GO/2.
MEASUREMENTS: Baseline amyloid status was based on first available CSF Aβ1-42 or, [11C]PiB or [18F]florbetapir (FBP) PET. Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog13) and Functional Activities Questionnaire (FAQ) were co-analyzed using Growth Mixture Modeling (GMM) to define latent class trajectories for each amyloid group. Classification and Regression Tree (CART) analysis determined which variables best predicted trajectory class membership using a number of variables available to clinicians.
RESULTS: GMMs found two trajectory classes (C1, C2) each for amyloid-positive (P; n=722) and negative (N; n=470) groups. Most (90%) in the negative group were C2N with mildly impaired baseline ADAS-Cog13, normal FAQ and nonprogression; 10% were C1N with moderately impaired baseline FAQ and ADAS-Cog13 and trajectory of moderately worsening scores on the FAQ. C1P (26%) had more impaired baseline FAQ and ADAS-Cog13 than C2P (74%) and a steeper declining trajectory. CART yielded 4 decision nodes (FAQ <10.5, FAQ <6.5, MMSE ≥26.5, age <75.5) in positive and 1 node (FAQ <6.5) in negative groups, with 91.4% and 92.8% accuracy for class assignments, respectively.
CONCLUSIONS: The trajectory pattern of greater decline in amyloid positive subjects was predicted by greater baseline impairment of cognition and function. While most amyloid-negative subjects had nonprogression irrespective of their diagnosis, a subgroup declined similarly to the gradually declining amyloid-positive group. CART predicted likely trajectory class, with known amyloid status, using variables accessible in a clinical setting, but needs replication.
OBJECTIVE: Determine trajectories and predictive baseline variable(s).
DESIGN: Longitudinal, up to 24 months.
SETTING: ADNI sites.
PARTICIPANTS: Healthy control (n=325), early and late mild cognitive impairment (n=279; n=372), and Alzheimer's dementia (n=216) subjects from ADNI-1/GO/2.
MEASUREMENTS: Baseline amyloid status was based on first available CSF Aβ1-42 or, [11C]PiB or [18F]florbetapir (FBP) PET. Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog13) and Functional Activities Questionnaire (FAQ) were co-analyzed using Growth Mixture Modeling (GMM) to define latent class trajectories for each amyloid group. Classification and Regression Tree (CART) analysis determined which variables best predicted trajectory class membership using a number of variables available to clinicians.
RESULTS: GMMs found two trajectory classes (C1, C2) each for amyloid-positive (P; n=722) and negative (N; n=470) groups. Most (90%) in the negative group were C2N with mildly impaired baseline ADAS-Cog13, normal FAQ and nonprogression; 10% were C1N with moderately impaired baseline FAQ and ADAS-Cog13 and trajectory of moderately worsening scores on the FAQ. C1P (26%) had more impaired baseline FAQ and ADAS-Cog13 than C2P (74%) and a steeper declining trajectory. CART yielded 4 decision nodes (FAQ <10.5, FAQ <6.5, MMSE ≥26.5, age <75.5) in positive and 1 node (FAQ <6.5) in negative groups, with 91.4% and 92.8% accuracy for class assignments, respectively.
CONCLUSIONS: The trajectory pattern of greater decline in amyloid positive subjects was predicted by greater baseline impairment of cognition and function. While most amyloid-negative subjects had nonprogression irrespective of their diagnosis, a subgroup declined similarly to the gradually declining amyloid-positive group. CART predicted likely trajectory class, with known amyloid status, using variables accessible in a clinical setting, but needs replication.