3.1. Age and sex effects in atrophy rates
The rates of atrophy (Jacobian values) at each location inside the brain were tested for correlations with age and sex in AD, MCI, and CTL groups independently, as well as in the combined group (ALL). The CDF plots () show that age and sex correlate with atrophic rates, especially in the MCI group, and when all subjects were combined. There was no systematic age difference between the 3 diagnostic groups (mean age was 76.5, 76.0, and 77.0 for AD/MCI/CTL), so these effects are driven by differences in age within the diagnostic groups, not between them. Comparing CDF curves of the same color - for the whole brain versus temporal lobes – gives a clear impression of the power gained by restricting analyses to regions that are known to change the most. For example, the black curves show that age and sex effects are detected with greater effect sizes when focusing on the temporal lobes, as the CDF curves have a steeper gradient at the origin. They also cross the reference line y=20x at a higher point, which means that a higher threshold (critical P-value or C.P.) can be applied to the statistical maps while keeping the false discovery rate to 5% of the voxels shown.
Figure 1 Age and sex differences in atrophic rates are shown across the entire brain and also in an analysis restricted to changes within the temporal lobes. CDF plots for the effects on atrophic rates of age (a) and sex (b) show the statistical significance of (more ...)
The sign of the correlations with age—positive inside tissues and negative in the CSF—indicates faster brain degeneration in younger MCI subjects (), about 1% increase in atrophic rates and 2% increase in ventricular expansion rates for every 10-year decrease in age; AD patients showed a similar but lesser age effect. Healthy controls showed a small but significant age effect in the opposite direction: a few voxels in the CSF and at the boundary of GM/CSF showed positive correlations, i.e. younger age is associated with less ventricular expansion. Atrophic rates were faster in women than men by about 1–1.5%/year, signified by positive correlations between the atrophic rates and sex (female sex was coded arbitrarily as 0; male as 1; ). As expected, the regression coefficient maps, using thresholds derived within the temporal lobes or across the entire brain, are generally consistent in their spatial distributions. However, a broader area reaches significance if restricting the search region to the temporal lobes, as the critical P-values are higher within the temporal lobes than those from the whole brain (results not shown).
When we added education and BMI into this regression model, they did not show significant correlations in any group so were not pursued further as confounds. To better illustrate the age and sex differences in atrophic rates, the MCI group was divided into six sub-groups (in age brackets: 60–<70, 70–<80, and 80–<90 years; further split by sex into female and male). shows the age and sex effects in a straightforward fashion, as group average maps. The rest of correlations tested in this paper were all statistically adjusted for these effects of age and sex.
Figure 2 Average maps of atrophic rates in MCI subjects, subdivided by age and sex. Female MCI subjects (top) are divided into three age groups, 60–70 (N=24), 70–80 (N=59), and 80–90 years (N=37). Male MCI subjects (bottom) are divided (more ...)
As a related question, one might also wonder if age and sex differences were present in the baseline MRI measures. In fact, there were significant age and sex differences in baseline temporal lobe atrophy, within each group independently and in the combined group.
3.2. Correlations between atrophic rates and clinical (cognitive/behavioral) measures
Temporal lobe atrophy rates were correlated with baseline clinical measures () and with their rates of decline (). In AD and MCI, atrophic rates were most strongly correlated with the ADAS-cog, LM-im, and AVLT-5 scores at baseline (). Baseline LM-del, AVLT-del, FAQ, and MMSE also showed significant correlations in MCI (). Anatomical changes over time were also highly correlated with ongoing changes in LM-del, ADAS-cog, CDR-SB, in AD, and CDR-SB, FAQ, LM-im, ADAS-cog, LM-del, in MCI (). The rank order - from highest to lowest effect sizes – is shown for these correlations, with baseline ADAS-cog showing the highest correlations with future atrophic rates. The highest curves show the covariates that are most strongly correlated with the measured atrophic rate.
Figure 3 Whole brain and temporal lobe atrophic rates are correlated with baseline clinical measures in AD (a) and MCI (b). Significant correlations are marked with a critical P value greater than 0.01 or 0.0001. Interestingly, the ADAS-Cog, perhaps the most widely (more ...)
Figure 4 Whole brain and temporal lobe atrophic rates correlated with rates of clinical decline, for various different clinical measures, in AD (a) and MCI (b) groups separately. Significant correlations are marked with critical P>0.01 or >0.0001. (more ...)
Similar but weaker effect sizes (lower CDF curves and critical P-values) were obtained when expanding the search region to the entire brain, relative to restricting to the temporal lobes, comparing curves of the same color on each side of the plot (, ). Using the whole brain ROI, atrophic rates were only significantly correlated with the ADAS-cog at baseline in AD, and baseline measures of ADAS-cog, AVLT-5, LM-del, LM-im and MMSE in MCI (). Likewise, with the whole brain ROI, atrophic rates were only linked to LM-del decline over a year in AD, while the effect sizes were substantially reduced in MCI (). These “butterfly plots” show that there is a clear boosting of power for detecting statistical effects on atrophy when focusing on the regions where greatest changes are expected (i.e., the temporal lobes).
3.3. Correlating atrophic rates with CSF biomarkers
Rates of brain atrophy were significantly correlated with CSF biomarker levels—Aβ, tau, p-tau, and tau/Aβ—at baseline in the combined group of all subjects (blue CDF curves in ). These correlations did not reach statistical significance within each diagnostic group independently, except that the level of CSF Aβ showed weak but significant correlations (critical P=0.004 in the temporal lobes and 0.001 in the whole brain) in MCI (cyan CDF curves in ).Also, there were no detectable correlations between rates of tissue atrophy and the rates of change in the CSF biomarkers within the individual groups, with the exception of tau/Aβ in the whole brain in AD (critical P=0.003). The ratio of tau to Aβ also showed some weak correlations with atrophic rates in the combined group (critical P=0.0004 in the temporal lobes and 0.001 in the whole brain). In the common sample, clinical correlations were compared with the results from CSF biomarkers. Baseline ADAS-cog and CDR-SB rates of decline were more strongly correlated with structural brain atrophy, as indicated by higher CDF curves and higher critical P values, with significant correlations also found in the separate diagnostic groups. Again, the effect sizes are substantially boosted by focusing on a temporal lobe region of interest, rather than including all the voxels in the brain; this is clearly evident as the curves on the right of each plot tend to rise more steeply at the original and intersect the FDR reference line (y=20x) at a higher intersection point, whose x-value denotes the highest P-value threshold that can be applied to the statistical maps while preserving the expected false discovery rate at the conventional level of 5%.
Figure 5 Correlations between atrophic rates and CSF biomarker levels (biomarker and clinical labels are with and without borders, respectively). Whole brain and temporal lobe atrophic rates were correlated with biomarker levels in the following rank order, from (more ...)
3.4. Temporal lobe atrophy rates linked to AD risk genes
Carriers of the E4 allele of the ApoE (apolipoprotein E) gene, a commonly carried risk gene for late-onset AD (Saunders, et al., 1993
, Roses and Saunders, 1994
), showed faster atrophic rates in the temporal lobes overall. Associations were weak but significant within each diagnostic group individually only inside the temporal lobes, but strong when all groups were combined (). The newly discovered risk allele (rs-10845840, which codes for GRIN2b, a glutamate receptor subunit; Stein et al., 2010
) was associated with atrophic rates in the combined group, but more weakly than ApoE was (; higher curves denote stronger effects). When ApoE4 was added to the statistical model that estimated the age and sex effects on the rates of atrophy, the sex effect turned out to be stronger (AD: critical P
=0.001; MCI: 0.02; CTL: n.s.; ALL: 0.02) but the age effect was slightly attenuated (AD: n.s.; MCI: critical P
=0.007; CTL: 0.0008; ALL: 0.01) inside the temporal lobes.
Figure 6 Genetic influences on brain atrophy. The presence of the ApoE4 (marked by solid lines) and the GRIN2b risk gene (also known as SNP rs-10845840; dotted lines; Stein et al., 2010) were associated with faster rates of atrophy in the temporal lobes, with (more ...)
When expanding the search region to the whole brain, the presence of the ApoE4 risk allele was no longer associated with higher atrophic rates in individual diagnostic groups, but the effect remained significant in the combined group.
3.5. Faster temporal lobe atrophy in converters to AD within one year
MCI subjects who converted to AD within a year (13% of the total MCI group) showed faster atrophic rates than nonconverters, as seen in the contrast map and the significance map (). Converters, on average, displayed 2–3% faster atrophic rates than non-converters in the temporal lobes. A similar test in the whole brain did not reach statistical significance (critical P=n.s.).
Figure 7 MCI converters showed faster rates of brain atrophy in temporal lobes than MCI non-converters. The mean difference map shows regions where atrophy rates are faster in converters than non-converters (left panel; blue colors: 3% faster). Red colors show (more ...)
3.6. Correlations between atrophic rates and other risk factors
We evaluated correlations between atrophic rates and histories of cardiovascular, endocrine-metabolic, gastrointestinal disorders, alcohol abuse, drug abuse, and smoking. A medical history of drug abuse was weakly associated with a faster rate of tissue atrophy (critical P=0.0001) in the AD group only, while the other factors had no detectable effect.
3.7. Using covariates to boost power in clinical trials
Given the age and sex effects in atrophic rates, we broke down the MCI groups into six age- and sex-divided sub-groups. The n80s (sample size estimates) and 95% confidence intervals are shown in . In this table, lower numbers are considered better as they imply that smaller sample sizes would be required to detect a 25% change in the rate of disease progress, measured by a specific AD biomarker, in response to a potentially disease-modifying drug. Younger men gave smaller n80s than older men, as expected from the age effects in MCI, where younger MCI subjects showed faster atrophy. For the sample size to be smaller, the atrophic rate may be higher and/or its standard deviation smaller. Women aged 60–70 or 70–80 had smaller n80s than men at similar ages. This is also consistent with the earlier finding that women had marginally faster atrophic rates in MCI (by ~0.5–1.5%/year locally). In other words, trials focusing on younger subjects, or with sub-analyses focusing on women versus men, would be better powered with these measures.
Table 1 The sample sizes (n80s) and 95% confidence intervals (in square brackets) for groups of MCI subjects subdivided by age and sex, and in the combined group with all MCI subjects included. Sub-analyses focusing on women or younger subjects led to smaller (more ...)
3.8. n80 for the CSF biomarkers
To compare structural MRI versus CSF biomarkers, we computed the n80s based on 1-year changes in CSF biomarker levels. Given their poorer reproducibility than MRI, the n80s were much larger than those from neuroimaging measures (). Although clearly not their intended use, tens of thousands to millions of subjects would need to be recruited to detect a potential drug effect using CSF biomarkers as surrogate markers measuring the rate of disease progression.
Table 2 The n80s for AD and MCI using CSF biomarkers versus MRI measures of whole-brain gray matter atrophy and temporal lobe atrophy, with a common sample consisting of 50 AD patients and 122 MCI subjects. The numerical summaries of imaging measures were generated (more ...)