No cross-sectional influence of APOE ɛ4 dose on clinical tests in Alzheimer's disease

Department of Neurology, Georgetown University School of Medicine, Washington, DC, USA.
Neurobiology of aging (Impact Factor: 5.01). 01/2008; 30(8):1327-8. DOI: 10.1016/j.neurobiolaging.2007.11.006
Source: PubMed


This study sought to determine if there are detectible influences on the symptoms of Alzheimer's disease (AD) from the genetic risk factor for AD, the epsilon4 allele of apolipoprotein-E (APOE). Using data from two cohorts of AD patients, a cross-sectional latent variable model of AD was tested with three symptom factors explaining variability in the observed variables after taking a general neurological factor into account. No significant influence of epsilon4 was detected. APOE's effect in AD may occur prior to clinical symptoms, or may simply be more subtle than these instruments can detect.

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Available from: Gregory R. Hancock, Feb 05, 2015
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    ABSTRACT: Alzheimer's disease (AD) is a complex disease process, so finding a single biomarker to track in clinical trials has proven difficult. This paper describes and contrasts statistical methods that might be used with biomarkers in clinical trials for AD, highlighting their differences, limitations and interpretations. The first method is traditional regression, within which one dependent variable, the Best Empirically Supported Indicator (BESI), must be identified. In this approach one biomarker (e.g., the ratio of tau to Abeta42 from CSF) is the indicator for an individual's disease status, and change in that status. The second approach is an exploratory factor analysis (EFA) to consolidate a multitude of candidate dependent variables into a sample-dependent, mathematically-optimized smaller set of 'factors'. The third method is latent variable (LV) modeling of multiple indicators of an entity (e.g., "disease burden"). The LV approach can yield a complex 'dependent variable', the Best Measurement Model Indicator (BMMI). A measurement model represents an entity that several dependent variables reflect or measure, and so can include many 'dependent variables', and estimate their relative contributions to the underlying entity. The selection of a single BESI is an artifact of regression that limits the investigator's ability to utilize all relevant variables representing the entity of interest. EFA results in sample-specific combination of biomarkers that might not generalize to a new sample - and fit of the EFA results cannot be tested. Latent variable methods can be useful to construct powerful, efficient statistical models that optimally combine diverse biomarkers into a single, multidimensional dependent variable that can generalize across samples when they are theory-driven and not sample-dependent. This paper shows that EFA can work to uncover underlying structure, but that it does not always yield solutions that 'fit' the data. It is not recommended as a method to build BMMIs, which will be useful in establishing diagnostic criteria, creating and evaluating benchmarks, and monitoring progression in clinical trials.
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