Alzheimer’s Disease Assessment Scale–Cognitive subscale variants in mild cognitive impairment and mild Alzheimer’s disease: change over time and the effect of enrichment strategies

ADAS-Cog has been the standard measure of cognition in AD clinical trials [20], but recently conducted trials with therapies aimed at slowing down disease progression in subjects at early stages of the disease revealed the limited sensitivity of the original ADAS-Cog 11 to detect change. Sensitivity to change may be compromised by the lack of tests to assess cognitive domains known to be impaired early in the disease, such as attention and executive functions [4], which has been addressed by adding tests to the original instrument [8] (e.g., the ADAS-Cog 13). A second approach to improve sensitivity of the instrument for early AD was to remove items prone to ceiling effects (e.g., ADAS-Cog 3). Data from subjects that score at ceiling inflate variance with negligible benefits in measuring change over time or treatment difference, particularly the possibility to detect improvement in cognition. The ADAS-Cog 3 extracted the items focused on memory (Word Recall, Orientation and Word Recognition) that are among the earliest manifestations of AD [4] and detect impairment in subjects with MCI approximately midway between normal healthy and patients with mild AD [6]. However, this approach is focused solely on a memory measure without considering other important areas of cognition affected at early stages of disease and therefore may not be optimal. The ADAS-Cog 5 combines both approaches and could theoretically be more sensitive than other variants to detect a change over time. It should be noted that our goal was not to validate a new instrument.

In the context of this study, we also sought to determine whether enrichment strategies could improve the sensitivity to change over time of these ADAS-Cog variants. Since the introduction of enrichment as part of inclusion criteria for early stages of AD in 2007 [21], enrichment biomarker strategies have been used in clinical trials to include subjects with prodromal AD (also called MCI due to AD). The enrichment strategy is aimed at detecting the presence of amyloidosis in the brain as a hallmark of AD [22, 23], for example by using CSF biomarkers [24] such as low A? and high Tau. As 60 % of subjects with MCI and 40 % of subjects with AD provided CSF, we used the available CSF baseline data to select enriched populations (i.e., subjects most resembling those to be recruited into clinical trials). This gave us the opportunity to also compare the performance of ADAS-Cog variants in enriched vs. non-enriched groups. For our analyses, t-Tau/A? ratio in the CSF was used as the primary enrichment strategy to determine populations with AD pathology (enriched). In addition, we looked at the impact of single-biomarker modalities such as A? alone, the marker of neurodegeneration Tau and the prominent genetic risk factor for sporadic AD, the ApoE4 allele [25]. It should be noted that these are not defined as enrichment biomarkers in the research criteria for prodromal AD [16].

There is an increasing body of evidence showing that AD starts decades before the clinical symptoms become apparent [26]. With new putative therapies aimed at slowing disease progression by early intervention, there is an increasing interest in identifying subjects at early stages of AD [27]. Advances in the biomarker field allow us to identify subjects with AD pathology before the clinical diagnosis is made [28]. It was hypothesised that using enriched populations in clinical trials reduces variability and increases the magnitude of cognitive decline over time, thus reducing the sample size necessary to detect a drug effect in clinical trials [29].

We analysed cognitive decline on the four ADAS-Cog variants (3-, 5-, 11- and 13-item scales) in ADNI subjects with MCI or mild AD who provided a CSF sample at baseline. The results showed that, in the MCI population, there was a minimal decline over 24 months on all ADAS-Cog variants. The impact of enrichment was detectable but subtle. The largest decline was less than 3 points over 24 months on ADAS-Cog 13 in enriched MCI. This provides some support for use of enrichment as a tool to identify subjects who are more likely to demonstrate a cognitive decline, although, at least in the case of the MCI, the impact upon such changes measured by ADAS-Cog variants is minimal and of questionable clinical relevance to be of any practical use for clinical trials with pharmacological intervention aimed at slowing decline of disease progression.

As expected, decline in mild AD was more pronounced than in MCI but still modest. ADAS-Cog 11 scores of subjects with mild AD declined 3.5 points over 12 months in our analysis. This seems to correspond to the rate of 2.4 points over 6 months reported by Doraiswamy et al. [1]. It should be noted that the rate of decline could be influenced by many factors, such as education, background medication and potentially attrition. The fact that ADNI participants were highly educated, with, on average, 15–16 years of education, has been reported previously [12]. In addition, many ADNI participants, particularly those with mild AD, may be receiving symptomatic treatment [30]. Even though these factors would be of interest, attempting evaluation of all these factors runs the risk that the ultimate sample size would be too small to provide any meaningful results. The impact of enrichment could not be evaluated, as over 90 % of subjects with mild AD enrolled in ADNI studies had positive AD pathology at baseline.

These results were also reflected on the MMSE scale: no worsening in the MCI set and modest worsening in the mild AD set over time. Meta-analysis of MMSE change in patients with mild to moderate AD has previously shown an annual rate of decline of 3.3 points [31]. Similar rates have been reported by other groups [32]. The rate of decline seen in our analysis is more modest, probably due to the relatively earlier stage of disease than in previously published studies. It should be noted, however, that the researchers in the above-mentioned studies analysed data of non-treated patients, while many ADNI participants, especially those with mild AD, were on background medication [30].

The SNR reflects the ratio between mean change and variability, thus allowing direct comparison of the sensitivity of the different ADAS-Cog variants. High SNR values reflect increased sensitivity to detect a change over time, and thus the ADAS-Cog variant with the highest SNR value should theoretically lead to an optimal instrument.

In MCI, the SNRs for each ADAS-Cog variant and for each enrichment strategy were relatively similar but low. The numerically largest SNRs were seen for the ADAS-Cog 5 and ApoE4 enrichment. However, as the 95 % confidence intervals around each of the SNRs were broad, the results did not support a meaningful difference among groups.

We confirmed that the ADAS-Cog instrument is not sensitive to detect change over time at pre-dementia stages of the disease such as MCI (as shown by small SNRs), and therefore the impact of enrichment strategies was subtle. The numerically largest SNR was seen for the enriched set according to ApoE4 risk. ApoE genotype is a well-described genetic risk factor for AD associated with early deficits in episodic recall, higher rates of cognitive decline before the diagnosis of MCI or AD, and age-related memory decline earlier in life (for review, see [33]). The presence of an ApoE4 allele doubles the risk of progression from normal cognition to onset of clinical symptoms [34]. In addition, the ApoE4 allele has been associated with a faster cognitive deterioration in several, but not all, studies of patients with AD. Therefore, ApoE4 genotype is often used as a stratification factor in clinical trials testing novel therapies and, interestingly, is being used as an enrichment strategy in the ongoing TOMMORROW trial [35]. However, as the 95 % confidence interval was broad, it can be concluded that the benefit of such enrichment in MCI seems negligible for this particular instrument. We do not believe that enrichment would allow for significant reduction of the required sample size in MCI if subjects were to be tested using ADAS-Cog.

The SNRs were larger in subjects with mild AD than in subjects with MCI. Similarly to MCI, the numerically highest SNRs were generally seen for the ApoE4-enriched set across all variants. Interestingly, the highest SNRs were seen for both the 5-item and 13-item ADAS-Cog variants. This was supported by the p values with the strongest evidence for a difference seen when comparing the 11- vs. 13-item variants and the 3- vs. 5-item variants. One could speculate that the difference between variants is driven more by the two additional items (Delayed Word Recall and Digit Cancellation) than by removing the items at ceiling in early stages of AD, suggesting the increase in magnitude of the change outweighs the increase in variability.

Limitations of this analysis

In addition to the high education level of ADNI participants [12, 36] discussed earlier, it is also acknowledged that the participating sites in the ADNI study are experienced. This is reflected by the observation that there is less variability in the scores and a more reliable diagnosis (90 % subjects with mild AD had positive AD pathology). This is contrary to recent phase III clinical trials with potential anti-amyloid therapies, which have shown that enrolling participants without evidence of amyloid deposition is common [37].

In mild AD, the number of subjects attending follow-up visits beyond the first year dropped significantly and did not provide a sufficient number of subjects for meaningful analyses. Therefore, changes over time beyond 12 months need to be interpreted with caution. Per regulatory requirement, a minimum of 18 months of treatment is required in clinical trials aimed at disease modification.

It was the purpose of this investigation to analyse subjects with follow-up data. The impact of the clinical characteristics of subjects who withdrew prematurely on change over time was not evaluated, as this would have required assumptions regarding the follow-up data. Such assumptions are the subject of an ongoing scientific debate.

We recognize that the value of the ADAS-Cog variants in our dataset is being judged solely on the basis of a longitudinal change over time, regardless of the treatment status of included subjects. Therefore, the outcome may not necessarily directly translate into a variant with the highest sensitivity to detect differences between treatments.

It must also be noted that the ADAS-Cog variants analysed in this study were collected in the context of ADAS-Cog 11, so the items are likely prone to specific order effects. Moreover, the 13- and 5-item variants contained additional two items that were tested outside the original ADAS-Cog 11. The order effect, where exposure to earlier tests influences performance on later tests, is a well-known phenomenon in cognitive testing [38].