The study sample included 210 NC, 357 MCI, and 162 AD subjects with baseline and at least one cognitive follow-up assessment. Of these, 83 NC, 175 MCI, and 76 AD had serial PET images including baseline and month 12 (47 NC, 89 MCI and 38 AD in the independent test set) and 96 NC, 155 MCI, and 74 AD subjects had baseline and month 12 CSF measures. Sample sizes for the measures from the serial MRI depended on the different laboratories generating data, so measures were available for 178-200 NC, 297-334 MCI, 132-147 AD (123 NC, 196 MCI, 86AD in the independent test set). 14 NC, 40 MCI, and 11 AD subjects had two PIB scans approximately 12 months apart as part of the PIB add-on study.
3.1 Annualized change in CSF, imaging and cognitive tests
shows the mean and SD of annualized change for key summary measures. The rate of change for measures hypothesized to show early change (CSF, PIB) is greater in NC than AD, with MCI intermediate. For the measures hypothesized to change later in the course of AD development, however, the rate of change is greatest in AD and less in NC than in MCI (FDG PET, MRI, cognitive measures.) These estimates are helpful to us in study design and power calculations for future studies.
Mean (standard deviation) of annualized change for selected ADNI variables, and the NC to AD difference at baseline as a reference for impact of rate of change.
3.2 Longitudinal models predicting trajectories for change in hippocampal volume
In univariate analyses, lower baseline values of CSF Aβ42 and higher values of CSF Tau were associated with more rapid hippocampal atrophy in all three participant groups (). In addition, in the MCI group, presence of an E4 allele, lower years of education, and lower metabolism as measured by the ROI-avg region were also associated with more rapid atrophy. Multivariate analyses, however, suggested that some variables might not predict independently, although it should also be noted that the sample size was reduced considerably, typically by three quarters, when individuals were required to have data on all predictors. For NC, no single variable was a significant predictor of hippocampal decline when all variables were included in the same model, although the coefficients were generally in the expected direction. The typical E4− MCI participant experienced hippocampal atrophy at a rate of 33mm3 per year, on average. MCI who were E4+ had estimated hippocampal atrophy approximately twice as fast as those who were E4-, other variables being equal. An MCI participant with baseline PET ROI-average score one NC-SD better than the average MCI had atrophy about 30% less rapid than an average MCI participant. Among the AD group, the typical E4− participant lost 68mm3 per year in hippocampal volume. Every one NC-SD higher baseline CSF tau level was associated with nearly a 30% faster hippocampal atrophy rate. Taken together, these findings suggest that abnormal values of the CSF biomarkers are indeed associated with more rapid atrophy, in all diagnostic groups. Our findings are limited, however, by the fact that only half of the participants had PET and half had CSF biomarkers, so only 25% had both. Further analysis with the larger samples and longer follow-up of ADNI-2 is needed to determine whether the lack of significance in multivariate analyses reflects mediation or partial mediation through other processes, or is due to the small sample sizes available in ADNI-1 to study all markers simultaneously.
Table 2 Predictors of longitudinal change in hippocampal volume (Freesurfer), based on repeated measures regression models, showing results for coefficient of effect on annual change. Univariate models were not adjusted for other predictors; joint models included (more ...)
3.3 Longitudinal models predicting trajectory of change in ADAS-Cog scores
In , we examined prediction of change in the ADAS-Cog total score. ADAS-Cog increases, representing cognitive impairment, were associated in the NC’s with smaller baseline hippocampal volume, in univariate models, and with presence of ApoE4 in the joint model. In the MCI group, lower baseline CSF Aβ42, higher Tau, lower FDG-PET metabolism, smaller baseline hippocampal volume, and larger ventricles were all associated with more rapid cognitive function worsening, in univariate models. The typical E4− MCI participant with marker levels comparable to an average NC had an estimated increase of half a point per year ADAS-Cog score. In joint models, only the FDG-PET measure remained significant, and each one NC-SD worse metabolism was associated with a 0.40 point faster annualized rate of worsening on the ADAS-Cog. Among AD patients, a typical reference person had an average increase of 2 points per year in ADAS-Cog; higher CSF tau was associated in univariate models with faster ADAS-Cog decline, but not after adjusting for covariates. Lower baseline metabolism, however, remained significantly associated, with each one NC-SD worse metabolism associated with a two-point worse annualized rate of cognitive performance decline. Results for other cognitive outcomes and FDG PET and MRI summaries are in general agreement (not shown).
Table 3 Predictors of longitudinal change in ADAS-Cog Total 11 score, based on repeated measures regression models, showing results for coefficient of effect on annualized change. Univariate models were not adjusted for other predictors; joint models included (more ...)
3.4 Predictors of time to conversion from MCI to AD
A third set of univariate and multivariate analyses examined predictors for conversion from MCI to AD. shows results from survival models for time to conversion; we examined not only fluid and imaging biomarkers, but also baseline cognitive function as a potential predictor, and adjusted for whether participants were already taking cholinesterase inhibitors. Univariate models (not shown) suggested that a number of baseline fluid and imaging biomarkers were associated with shorter time to conversion, including hippocampal and ventricular volume and brain size; complex summaries of FDG PET hypometabolism from the University of Utah; and the P-tau/Aβ42 ratio. In addition, baseline cognition and functional measures were predictive. People who were on Acetylcholinesterase Inhibitors (ACHEI) were also more likely to convert. Multivariate analyses showed that only ACHEI, cognition, and function achieved statistical significance when all variables were included, suggesting that most of the impact of the biomarkers on conversion can be mostly explained via the baseline cognitive and functional scores. When cognitive and functional scores and redundant brain volumetrics were removed, hippocampal volume and FDG PET hypometabolism were significant.
Table 4 Results of survival models for time to conversion from MCI to AD: Table shows predictors that had P values less than 0.10 in model. Ridge regression used to shrink coefficients for smaller values. The rightmost column (P value*) displays P values from (more ...)
3.5 Precision of imaging markers and implications for clinical trials
Finally, we examined the potential of the fluid and imaging biomarkers to improve clinical trials in several different ways. We considered the possibility that they might be used as outcome measures, and calculated the sample size that would be required in a two-arm, one-year clinical trial, with 80% power to detect a 25% improvement in annual rate of decline. shows the results of the comparisons of sample size estimations for a trial in MCI subjects across the most promising MRI and PET biomarkers based on data obtained from 69 MCI subjects. Each different shade in the table identifies a group of measures that were not significantly different from one another. In particular, measures of brain change and hippocampal atrophy required the fewest subjects. The data-driven functional ROI (DD-fROI) required the fewest subjects out of the PET measures and was comparable to many of the top MRI measures.
1.5T MRI vs PET sample size calculations and comparisons: MCI (69 test subjects). Grey-scale bars connect groups of variables for which calculated sample size did not differ significantly under multiple-comparison testing.
An alternative strategy for improving clinical trial design is enrichment of the study population. A trial restricting participation to an enriched MCI population with CSF Aβ to less than 192 pg/mL would only require 225 per group to detect a 25% reduction in rate of change in ADAS-Cog, while an unrestricted study would require 375 people per arm. These sample sizes are based on linear mixed effects models of rate of change over two years of visits every six months, and simulations that replicate ADNI’s missing data.