به سوی یک مدل چند عاملی از بیماری آلزایمر
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|30790||2012||10 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Neurobiology of Aging, Volume 33, Issue 10, October 2012, Pages 2262–2271
Relations among antecedent biomarkers of Alzheimer disease (AD) were evaluated using causal modeling; although correlation cannot be equated to causation, causation does require correlation. Individuals aged 43 to 89 years (N = 220) enrolled as cognitively normal controls in longitudinal studies had clinical and psychometric assessment, structural magnetic resonance imaging (MRI), cerebrospinal fluid (CSF) biomarkers, and brain amyloid imaging via positron emission tomography with Pittsburgh Compound B (PIB) obtained within 1 year. CSF levels of Aβ42 and tau were minimally correlated, indicating they represent independent processes. Aβ42, tau, and their interaction explained 60% of the variance in PIB. Effects of APOE genotype and age on PIB were indirect, operating through CSF markers. Only spurious relations via their common relation with age were found between the biomarkers and regional brain volumes or cognition. Hence, at least 2 independent hypothesized processes, one reflected by CSF Aβ42 and one by CSF tau, contribute to the development of fibrillar amyloid plaques preclinically. The lack of correlation between these 2 processes and brain volume in the regions most often affected in AD suggests the operation of a third process related to brain atrophy.
It is increasingly accepted that the pathologic changes that lead to the eventual diagnosis of symptomatic Alzheimer disease (AD) begin long before there is sufficient cognitive impairment to warrant a clinical diagnosis of the disease (Jack et al., 2009 and Price et al., 2009). Recent advances (Klunk et al., 2004) make it possible to image fibrillar amyloid plaques, a pathologic hallmark of AD, providing one avenue to detection of pathology prior to clinical diagnosis. There is a strong inverse relation between fibrillar amyloid plaque burden as assessed by positron emission tomography (PET) imaging using the amyloid tracer, Pittsburgh Compound-B (PIB), with levels of cerebrospinal fluid (CSF) Aβ42 in cognitively healthy individuals (Fagan et al., 2006, Fagan et al., 2009a and Tolboom et al., 2009). This has been interpreted as suggesting that an early step in the process leading to AD is sequestering of Aβ42 in plaques (Hong et al., 2011), thereby reducing the level in the CSF. The amount of plaque burden also is associated with increased levels of CSF total tau and phospho-tau181 (ptau; Fagan et al., 2009b). This relation has often been interpreted in terms of the amyloid cascade hypothesis (Selkoe, 1991). In its simplest form the hypothesis states that Aβ42 peptides aggregate to form amyloid plaques which, in turn, lead to synaptic loss and cell death, reflected in elevated CSF tau, thereby causing dementia. Recent reviews, however, suggest that the process may not be that simple (Holtzman et al., 2011, Hyman, 2011, Pimplikar, 2009 and Small and Duff, 2008). Other variables associated with one or more of the CSF biomarkers and PIB include age and apolipoprotein (APOE) genotype, the major genetic susceptibility factor associated with late-onset AD ( Morris et al., 2010, Rowe et al., 2010, Sunderland et al., 2004 and Vemuri et al., 2010). Mixed results have been reported for forebrain structure ( Apostolova et al., 2010 and Becker et al., 2011; Chételat et al., 2010, Fagan et al., 2009a, Mormino et al., 2009, Oh et al., 2011 and Tosun et al., 2010). Concurrent measures of cognition, however, are uncorrelated with the CSF measures ( Fagan et al., 2009) or PIB ( Mormino et al., 2009, Oh et al., 2011 and Storandt et al., 2009) in cognitively normal individuals. We examined all of these variables in cognitively normal individuals using causal modeling in an effort to explore theoretical models of their interrelations. To the best of our knowledge there has been no prior attempt to do so. Causal modeling is a statistical procedure using regression analysis that is designed to determine if empirical data are consistent with a theoretical model. It requires that 3 conditions exist if X is a potential cause of Y (Cohen et al., 2003). One, there must be a correlation between X and Y; that is, although correlation cannot be equated to causation, causation does require correlation. Two, X must precede Y in time. Three, the relation between X and Y must not be spurious; a spurious relation is one in which X and Y are related because both are influenced by a third variable, Z. For example, wrinkled skin and slowed reaction times are correlated because both are associated with age, not because either causes the other. Of course, although correlation cannot be equated to causation, causation does require correlation. Longitudinal study ultimately is required to verify causality, but those results for preclinical AD may not be available for many years. Similarly, longitudinal study is necessary to determine the temporal order of appearance of the various processes, even if they are independent. In the meantime, models built on cross-sectional data can provide useful suggestions about avenues of investigation of various underlying pathophysiolocal processes.