Dynamic biomarkers and the pathophysiology of Alzheimer's disease3D PIB and CSF biomarker associations with hippocampal atrophy in ADNI subjects
Introduction
Alzheimer's disease (AD), the most common neurodegenerative disorder, is becoming increasingly prevalent among those 65 years and older. Confronted by the grim outlook of tripled AD prevalence by year 2050 (Hebert et al., 2001), scientists are relentlessly working toward earlier diagnosis and disease-modifying strategies that could one day allow primary or secondary disease prevention.
Early and presymptomatic diagnosis can be established by the use of biomarkers. Biomarkers are complementary to the use of clinical outcomes in clinical trials and could one day be used as surrogate endpoints. Good biomarkers should accurately reflect disease progression, predict clinical measures, and demonstrate change with therapeutic interventions that correlate with clinical improvement (Cummings 2009).
Cerebrospinal fluid (CSF) measures of amyloid beta protein (Aβ) and CSF tau, as well as Pittsburgh Compound B (PIB) imaging and hippocampal atrophy are four established AD biomarkers. They all reflect different aspects of AD pathophysiology. Aβ made of 42 amino acids (Aβ42) has been most clearly associated with AD pathogenesis. Aβ is an unstable peptide that tends to polymerize. In its monomeric form, Aβ is soluble and readily measured in the CSF. In AD, increased production, decreased clearance, or a combination of both cause significant increases in the total amount of Aβ. After polymerization, Aβ is sequestered in the brain tissue in the form of amyloid plaques comprised of insoluble fibrillar amyloid. Presumably because of this sequestration, CSF Aβ42 levels are low in persons with AD (Blennow and Hampel 2003). Low CSF Aβ42 has demonstrated 90%–96% sensitivity and 77%–80% specificity in discriminating AD from cognitively normal elderly (NC) (Andreasen et al., 2001, Galasko et al., 1998, Shaw et al., 2009) and 59%–79% sensitivity and 65%–100% specificity for predicting progression to AD dementia in subjects with mild cognitive impairment (MCI) (Hampel et al., 2004, Mattsson et al., 2009). Also, the Aβ42/Aβ40 ratio was recently reported to have 86% sensitivity and 60% specificity for detecting incipient AD in MCI (Brys et al., 2009). Although low CSF Aβ42 is a good indicator of AD pathology, CSF Aβ42 levels do not correlate well with cognitive measures (Wallin et al., 2006).
Tau is a microtubule-associated stabilizing protein which when hyperphosphorylated detaches from the microtubules and disrupts axonal transport. Similar to Aβ, tau also tends to aggregate in AD and forms intraneuronal neurofibrillary tangle lesions. Presumably as cells die tau is released into the interstitium and is transported to the CSF. Phosphorylated tau (p-tau) is similarly released from tangles and appears in the CSF. CSF total tau (t-tau) and p-tau are significantly elevated in subjects with AD (Andreasen et al., 2001, Blennow et al., 1995, Clark et al., 2003, Galasko et al., 1998). High CSF t-tau shows 70% sensitivity and 92% specificity in differentiating AD from normal controls (NC) and 83%–86% sensitivity and 56%–90% specificity for predicting progression to AD in MCI (Hampel et al., 2004). p-tau shows 68% sensitivity and 73% specificity in differentiating AD from NC and 73%–84% sensitivity and 47%–88% specificity in diagnosing incipient AD in the MCI stages (Brys et al., 2009, Mattsson et al., 2009). Unlike Aβ42, t-tau and p-tau have been associated with cognitive decline (Buerger et al., 2002, Buerger et al., 2005, Riemenschneider et al., 2002, Wallin et al., 2006). Research reports by several groups suggest that a combined biomarker measure based on both Aβ42 and tau may improve diagnostic accuracy (Galasko et al., 1998, Hansson et al., 2006, Mattsson et al., 2009, Shaw et al., 2009, Visser et al., 2009).
Recent advances in molecular imaging have allowed us to visualize amyloid deposition in vivo, in subjects with AD, using positron emission tomography (PET). Of the available amyloid tracers PIB has been widely used as an AD biomarker although other compounds are also being simultaneously developed (Mathis et al., 2007, Small et al., 2006). The scientific evidence for high cortical PIB retention in AD and low retention in the majority of NC subjects is compelling (Mathis et al., 2007, Mintun et al., 2006). PIB retention in amyloid-rich regions has been reported to be high in postmortem specimens (Ikonomovic et al., 2008, Thompson et al., 2009). A bimodal distribution has been described in MCI with some subjects showing AD-like and others NC-like PIB retention patterns (Kemppainen et al., 2007, Pike et al., 2007). Compared with low PIB retention, high retention conveys substantially higher risk for progression from MCI to AD dementia (87% vs. 7%; Okello et al., 2009) and for cognitive decline in NC (hazard ratio = 4.9; Morris et al., 2009). Although PIB binding shows the expected correlation with cognitive function (Jack et al., 2008b, Jack et al., 2009, Mormino et al., 2009, Pike et al., 2007, Tolboom et al., 2009), longitudinal PIB studies surprisingly have suggested that PIB retention levels off in the dementia stages (Engler et al., 2006). Finally PIB PET has demonstrated better performance than conventional fluorodeoxyglucose (FDG) PET imaging with greater effect sizes and improved spatial resolution for differentiating AD from NC subjects (Ziolko et al., 2006).
Hippocampal atrophy is the most established AD structural imaging biomarker. Hippocampal atrophy is seen in normal aging but is greatly accelerated and steadily progressive in AD (Jack et al., 1997, Jack et al., 1998, Jack et al., 2000). Hippocampal atrophy shows a strong correlation with cognitive decline (de Toledo-Morrell et al., 2000, Fleischman et al., 2005, Mortimer et al., 2004) as well as with AD pathologic markers such as neuronal and neurofibrillary tangle counts and Braak and Braak pathological staging (Bobinski et al., 1995, Bobinski et al., 1997, Schonheit et al., 2004, Zarow et al., 2005). Using an advanced 3-dimensional (3D) hippocampal mapping technique our group has demonstrated that hippocampal atrophy can predict which MCI subjects would progress to AD during 3-year follow-up (Apostolova et al., 2006b) and that it can detect atrophic changes in cognitively normal elderly 3 years prior to diagnosis of MCI and 6 years prior to diagnosis of AD dementia (Apostolova et al., 2010).
Further advancing biomarker development several research groups have taken the next step toward surrogacy validation (Cummings, 2009) by investigating whether various biomarkers correlate with each other. Region of interest (ROI) studies have reported that CSF p-tau has a stronger association with baseline hippocampal volume and longitudinal volume change compared with CSF t-tau (Hampel et al., 2005, Henneman et al., 2009), while CSF Aβ42 shows either a weak (Henneman et al., 2009) or lack of an association (Fagan et al., 2009) with hippocampal volumetric measures. Negative associations between global PIB retention and ROI-measured hippocampal volume were independently reported in a large NC sample (Storandt et al., 2009), in a relatively large pooled sample consisting of NC, MCI, and AD subjects (Jack et al., 2008b) and in small samples of MCI and NC (Mormino et al., 2009), no association in AD subjects was also reported (Mormino et al., 2009).
Here we aimed to uncover the 3D hippocampal regional associations between several CSF and 1 amyloid imaging biomarker in a large sample from the Alzheimer's Disease Neuroimaging Initiative (ADNI). As CSF tau is thought to be an indicator of neuronal injury we postulated that CSF tau measures will have stronger associations with hippocampal volume and radial distance than CSF Aβ42. We also hypothesized that the brain parenchymal amyloid measure—PIB PET—will show stronger association with structural hippocampal changes than the CSF amyloid measure—a peripheral measure that while being correlated with amyloid load in the brain shows an imperfect correlation. As previous studies have reported effects of ApoE4 genotype on hippocampal volume (Fleisher et al., 2005, Mueller and Weiner, 2009, Mueller et al., 2008, van de Pol et al., 2007), CSF Abeta and PIB binding (Morris et al., 2010) in addition to examining the association in the pooled sample we also modeled the effects in APoE4 carriers and noncarriers separately.
Section snippets
Subjects
Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI). The ADNI was launched in 2003 by the National Institute on Aging (NIA), the National Institute of Biomedical Imaging and Bioengineering (NIBIB), the Food and Drug Administration (FDA), private pharmaceutical companies and nonprofit organizations, as a $60 million, 5-year public-private partnership. The primary goal of ADNI has been to test
Demographic characteristics
The mean demographic characteristics of the diagnostic groups in the CSF (n = 388), the PIB (n = 98), and combined CSF/PIB (n = 49) samples are shown in Table 1. There were no significant age and education differences among the groups in any of the 3 samples. In the CSF (n = 388) sample, the MCI group had significantly more males (66%; p = 0.023) relative to both the NC (51%) and the AD groups (57%). Sex distribution was not significantly different between the diagnostic groups in the PIB or
Discussion
A recent expert position paper reviewed the current evidence on imaging and biofluid AD biomarkers and proposed a timely revision of the temporal order of biomarker abnormalities in AD (Jack et al., 2010). The authors posited that the first AD-associated abnormalities are CSF Aβ depletion and cortical amyloid deposition. These events, occurring largely during the cognitively normal stage, precede tau-mediated synaptic and neuronal injury, which are the substrate for hippocampal and cortical
Disclosure statement
GE Healthcare holds a license agreement with the University of Pittsburgh based on the PIB technology described in this manuscript. Dr. Mathis is a coinventor of PIB and, as such, has a financial interest in this license agreement. Dr. Petersen is consultant for Elan Pharmaceuticals, serves on the Safety Monitoring Committee for Elan Pharmaceuticals and Wyeth Pharmaceuticals and is a GE Healthcare consultant. The remaining authors have no potential financial or personal conflicts of interest
Acknowledgements
Data used in preparing this article were obtained from the Alzheimer's Disease Neuroimaging Initiative database (www.loni.ucla.edu/ADNI). Many Alzheimer's Disease Neuroimaging initiative (ADNI) investigators have therefore contributed to the design and implementation of ADNI or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators is available at www.loni.ucla.edu/ADNI/Collaboration/ADNI_Citation.shtml.
Data collection and
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