Disease progression model in subjects with mild cognitive impairment from the Alzheimer's disease neuroimaging initiative: CSF biomarkers predict population subtypes

Abstract

Aim: The objective is to develop a semi-mechanistic disease progression model for mild cognitive impairment (MCI) subjects. The model aims to describe the longitudinal progression of ADAS-cog scores from the Alzheimer's disease neuroimaging initiative trial that had data from 198 MCI subjects with cerebrospinal fluid (CSF) information who were followed for 3 years.

Method: Various covariates were tested on disease progression parameters and these variables fell into six categories: imaging volumetrics, biochemical, genetic, demographic, cognitive tests and CSF biomarkers.

Results: CSF biomarkers were associated with both baseline disease score and disease progression rate in subjects with MCI. Baseline disease score was also correlated with atrophy measured using hippocampal volume. Progression rate was also predicted by executive functioning as measured by the Trail B-test.

Conclusion: CSF biomarkers have the ability to discriminate MCI subjects into sub-populations that exhibit markedly different rates of disease progression on the ADAS-cog scale. These biomarkers can therefore be utilized for designing clinical trials enriched with subjects that carry the underlying disease pathology.

Figure 1

Distribution of CSF biomarkers in MCI subjects. The solid and dashed lines (1A, 1B and 1D) represent the density of the sub-populations based on the parameters of the CSF mixture models while the dotted vertical lines are the cut-off thresholds separating the two sub-populations. (A, B, D) , With disease pathology; , Without disease pathology

Figure 2

Results of the stratified visual predictive check; x-axis are jittered for clarity. Open symbols are observed data while lines and shaded areas represent the median and 90% prediction intervals. (A) Non-progressers without pathologic CSF [log CSF p-tau_181P : Aβ1–42 ratio ≤−1.86] and (B) progressers with pathologic CSF [log CSF p-tau_181P : A β1–42 ratio > −1.86]

Figure 3

Influence of APOE may no longer be apparent once the data are dichotomized by CSF status. (A) entire MCI population, (B) progressers with pathologic CSF [log CSF p-tau_181P : Aβ1–42 ratio > −1.86] and (C) non-progressers without pathologic CSF [log CSF p-tau_181P : Aβ1–42 ratio ≤−1.86]. APOE ε4 was dichotomized into carrier (one or two alleles) and non-carrier status. Error bars represent standard error (SE) and lines are simple linear regression through the data to allow visualization of trends. ○ APOE4 non-carrier; • APOE4 carrier

Figure 4

Influence of cholesterol is no longer apparent once the data are dichotomized by CSF status. (A) entire MCI population, (B) Progressers with pathologic CSF [log CSF p-tau_181P : Aβ1–42 ratio > −1.86] and (C) non-progressers without pathologic CSF [log CSF p-tau_181P : A⁻¹β1–42 ratio ≤−1.86]. Total serum cholesterol was dichotomized into high cholesterol (≥200 mg dl⁻¹) and normal cholesterol (<200 mg dl⁻¹). Error bars represent standard error (SE) and lines are simple linear regression through the data to allow visualization of trends. (A, B, C) ○ normal cholesterol; • high cholesterol

Figure 5

(A) Influence of hippocampal volume on baseline disease score for non-progressers, (B) impact of hippocampal volume on baseline score for progressers and (C) disease progression rate affected by Trail B test in progressers. Covariates were dichotomized to create roughly equal groups (> Median and ≤ Median) in each panel. Median hippocampal volume in progressers and non-progressers were 3045 and 3334 mm³ respectively. Median Trail B test in progressers was 109 s. Error bars represent standard error (SE) and lines are simple linear regression through the data to allow visualization of trends. (A, B) ○ low hippocampal volume; • high hippocampal volume. (C) ○ lower Trail B test time; • higher Trail B test time

Figure 6

Differential effect of age on (A) AD subjects vs. (B) MCI progressers. For dichotomization the median age for AD subjects and MCI progressers were 76 and 74 years respectively. Error bars represent standard error (SE) and lines are simple linear regression through the data to allow visualization of trends. (A, B) ○ lower age group; • higher age group

Figure 7

Relationship between CSF p-tau_181P and Aβ1–42 for MCI subjects that have (A) not converted to AD and (B) converted to AD. In both panels triangles represent subjects with log CSF p-tau_181P : Aβ1–42 ratio > −1.86, while squares represent subjects with log CSF p-tau_181P : Aβ1–42 ratio ≤−1.86. In both panels open symbols refer to correct assignment i.e. low ratio subjects who do not convert and high ratio subjects who convert to AD. In contrast, filled symbols represent incorrect assignment i.e. high ratio subjects who have not converted and low ratio subjects who have converted to AD. (A) ▴ log CSF p-tau_181p : Aβ1-42 ratio > −1.86; □ log CSF p-tau_181p : Aβ1-42 ratio ≤−1.86. (B) ▵ log CSF p-tau_181p : Aβ1-42 ratio > −1.86; log CSF p-tau_181p : Aβ1-42 ratio ≤−1.86