Regular articleMarkers of cholesterol transport are associated with amyloid deposition in the brain
Introduction
Considerable resources and time are being invested in understanding the pathophysiology of the late-onset form of Alzheimer's disease (AD) (Di Paolo and Kim, 2011). Cholesterol levels and factors related to its homeostasis have been implicated in the development of AD in observational and genetic studies; however, the mechanisms underlying the relationship between cholesterol and AD pathology are not well understood (Di Paolo and Kim, 2011). Intervention trials to lower blood low-density lipoprotein cholesterol by statins have shown little effect on treatment and prevention of dementia (McGuinness et al., 2009, McGuinness et al., 2010). This may be because cholesterol homeostasis and metabolism are separated in the periphery and brain because of the blood-brain barrier. Oxysterol metabolites of cholesterol, 24S-hydroxycholesterol (24OHC) and 27-hydroxycholesterol (27OHC), are important exceptions to this rule because they cross the blood-brain barrier directly by diffusion and are involved in regulating cholesterol synthesis via liver X receptors (Bjorkhem, 2006).
Factors related to cholesterol homeostasis have shown promise in genetic and observational studies. The ε4 allele in apolipoprotein E (APOE ε4) remains the strongest risk factor for AD, accounting for 20%–30% of the genetic risk. In addition, recent genomewide association studies have identified several new single nucleotide polymorphisms (SNPs) associated with AD (Harold et al., 2009, Hollingworth et al., 2011, Naj et al., 2011, Seshadri et al., 2010). Of particular interest, were associations between AD and several SNPs within genes related to cholesterol homeostasis, including CLU (clusterin or apolipoprotein J), ABCA1, and ABCA7.
Several blood biomarkers related to cholesterol homeostasis have also been associated with AD (Leoni et al., 2010). Circulating levels of apolipoproteins (ApoE and clusterin) have been associated with Aβ deposition and severity of AD (Kiddle et al., 2012, Schrijvers et al., 2011) as well as progression of AD and mild cognitive impairment (MCI) (Thambisetty et al., 2012, Thambisetty et al., 2010). In addition, oxysterol metabolites of cholesterol, including 24S-hydroxycholesterol (24OHC), which may indicate the presence of neurodegeneration (Leoni, 2009), are associated with AD (Bretillon et al., 2000, Papassotiropoulus et al., 2000), vascular dementia (Lutjohann et al., 2000), and MCI (Lutjohann et al., 2000, Papassotiropoulus et al., 2000), and are correlated with brain volume in cognitively normal adults (Solomon et al., 2009). Recently, our group observed that plasma oxysterol concentrations were higher among cognitively normal individuals who went on to develop cognitive impairment (AD and MCI) during 8 years of follow-up (Hughes et al., 2012).
The use of amyloid-specific radiolabeled ligands (e.g., Pittsburgh compound B-positron emission tomography [PiB-PET]) have opened new avenues of inquiry in both AD and aging through the direct visualization and quantification of fibrillar Aβ plaques in the brain in vivo. This technique has allowed the study of risk factors and determinants of Aβ deposition in the brain of older adults. The aim of this study was to evaluate several indices of cholesterol homeostasis, including genetic and blood biomarkers and their relationship with neuroimaging of Aβ deposition in nondemented elderly adults.
Section snippets
Participants
The Ginkgo Evaluation of Memory (GEM) Study (2000–2008) was a multisite, placebo-controlled, double-blind, randomized clinical trial of daily use of Ginkgo biloba in 3069 community-dwelling participants 72–96 years old (DeKosky et al., 2006). In 2009, approximately 10 ± 3 months after the GEM Study closeout, 194 participants from the Pittsburgh site underwent magnetic resonance imaging and Aβ PET scanning as part of the GEM Neuroimaging Substudy. The design of the Neuroimaging Substudy and
Results
Of the 194 participants in the GEM Neuroimaging Substudy, 178 had plasma samples available at study close. More than half (55%, n = 97) of this cohort of nondemented very elderly adults were Aβ positive for amyloid deposition in the brain. Aβ positivity was associated with high systolic blood pressure and low global cognitive scores (Mini-Mental State Examination score, <27 points), but was not associated with having MCI (Table 1), as presented previously (Mathis et al., 2013). Approximately
Discussion
Aβ deposition is detected frequently among cognitively normal elderly adults. More than 55% of this very elderly sample was Aβ positive. Our findings showed that Aβ status differed little by demographic factors, but was associated significantly with blood pressure, global measures of cognition, and genetic and blood markers of cholesterol transport. The highest levels of ApoE were associated with Aβ status in this cohort. The higher absolute levels of oxysterols and significantly higher ratios
Conclusions
A subset of plasma and genetic factors relating to cholesterol transport were associated with Aβ deposition in the brain of nondemented elderly adults. Concurrent lipid measures and oxysterol cholesterol metabolites do not appear to be related to Aβ deposition in cognitively normal individuals; however, the brain-derived oxysterol 24OHC may be a relevant biomarker for Aβ deposition for older adults with MCI and those taking lipid lowering medications. Although late-life circulating blood
Disclosure statement
T.M. Hughes, L.H. Kuller, R.W. Evans, S. DeKosky, J.D. Williamson, J.C. Price, B. Snitz, A.C. Cohen, and I.M. Kamboh report no disclosures. W.E. Klunk is a coinventor of Pittsburgh compound B (PiB), a technology described in this manuscript. As such, he has a financial interest in the license agreement, which GE Healthcare holds with the University of Pittsburgh. He has served as a consultant to GE Healthcare, Janssen, Pfizer, Lilly, AstraZeneca, Wyeth, Roche, and Elan. C.A. Mathis is a
Acknowledgements
Supported by National Institutes of Health grants P50 AG005133, R37 AG025516, P01 AG025204, AG030653, and AG041718; and a National Institute on Aging T32 postdoctoral training grant (T32 AG000181, recipient: T.M. Hughes). The authors thank B. Hauth, A. Shaw, and R. de la Vega for their expert laboratory analyses.
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