. The preclinical Alzheimer cognitive composite: measuring amyloid-related decline. JAMA Neurol. 2014 Aug 1;71(8):961-70. PubMed.

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  1. Donohue and colleagues have assembled a composite that shows excellent promise as a clinical trial endpoint. The composite is straightforward, easy to interpret, and strengthened by the theoretical and empirical bases underlying its construction. For obvious reasons, it is critical that the composite is optimally sensitive to detect treatment effects in Alzheimer's prevention trials. A treatment that impacts fluid or neuroimaging biomarkers alone would represent a remarkable step forward, but the ultimate Alzheimer's biomarker is cognitive function. Without evidence of cognitive stabilization or amelioration of cognitive deficits, a prevention trial cannot be considered truly successful. Thus, the importance of selecting a cognitive endpoint cannot be overstated.

    It was somewhat surprising to see inclusion of the full 30-point MMSE, which of course has a well-known ceiling effect. In addition, the rationale behind using the total-recall score rather than the much more sensitive free-recall score from the Free and Cued Selective Reminding Test was not provided. In our experience at the Knight Alzheimer's Disease Research Center at Washington University in St. Louis, we have seen considerable ceiling effects on the total recall score from the FCSRT in cognitively normal populations, including those with neuroimaging and CSF evidence of Stages I and II preclinical AD (Sperling et al., 2011). Since two of the measures will likely have pronounced ceiling effects in preclinical populations, it seems that there will be much leverage put on Logical Memory and the Digit-Symbol Substitution test. However, both of these measures are tried and true measures of cognitive functioning and have scores of studies establishing their validity in Alzheimer's disease.

    In the Dominantly Inherited Alzheimer Network Trials Unit (DIAN-TU), we are still in process of determining our cognitive endpoint and have thus far considered several candidates. We have evaluated these candidates in the DIAN observational study cohort, which began data collection in 2009. Many participants from this study will roll over into the DIAN-TU, making the observational cohort an ideal population in which to explore different cognitive endpoints. To date, we have considered endpoints that are similar to ADCS-PACC, using an unweighted z-score composite that includes a word-list recall task, Logical Memory (narrative recall), the five orientation items from the MMSE, and an associative-memory task. We have also focused on theoretically based composites that are specific to domains of episodic memory, executive function, attention, and language. Our biostatistics core has developed a method for optimizing cognitive composites using a stepwise procedure that first selects a single cognitive measure that performs best alone then uses an exhaustive search to find additional measures that add increasing power. This procedure then assigns weights to each measure included in the composite, thus optimizing the weights and increasing statistical power. In the near future, we will nominate our candidate endpoints for consideration. Careful and detailed efforts such as that by Donohue et al. will undoubtedly inform our ultimate decision and we congratulate their team on tackling a complex problem in what is essentially uncharted territory.

    References:

    . Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011 May;7(3):280-92. Epub 2011 Apr 21 PubMed.

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