Research Conducted by the Clinical & Translational Neuroscience Section
The Clinical & Translational Neuroscience Section (CTNS), within the Laboratory of Behavioral Neuroscience at NIA, researches the biologic basis for how Alzheimer’s disease develops in humans. The findings from some of their research studies can be found by selecting the tabs below.
While we previously reported on the association between plasma concentration of clusterin and measures of AD pathology; we recently examined the relationship between the AD risk variant clusterin gene (CLU) and longitudinal changes in brain function during aging. This analysis was performed in the neuroimaging substudy of the BLSA (BLSA-NI) and used resting state cerebral blood flow (rCBF) to measure longitudinal changes in brain function. We showed that even non-demented older individuals who carry one or more of the AD-related risk alleles of CLU show significantly greater increments in rCBF within memory circuits of the brain in comparison to risk allele non-carriers.
Fig-2. Associations between longitudinal changes in resting regional cerebral blood flow and the Alzheimer’s disease risk variant rs11136000 single nucleotide polymorphism in the clusterin gene in cognitively normal older individuals. Highlighted regions show significantly greater longitudinal increases in regional cerebral blood flow in carriers of the risk variant C-allele within the right anterior cingulate cortex (left panel) and bilateral hippocampi (right panel) (R and L, right and left cerebral hemispheres, respectively).
Similarly, while we have previously reported on the association between plasma concentrations of several complement-related proteins and AD pathology, we recently studied the effects of the AD risk variant, Complement Receptor-1 (CR1) gene, on brain amyloid deposition in non-demented older individuals within the BLSA-NI. We showed, somewhat unexpectedly that risk allele carriers of CR1 show lower brain amyloid burden relative to non-carriers. Equally importantly, we reported that an interaction between the CR1 and Apolipoprotein E (APOE) genes modulates brain amyloid burden. These findings are important in drawing attention to alternate, non-amyloid pathways underlying risk for AD, such as neuroinflammation as well as the need to study gene x gene interactions to gain a better understanding of such mechanisms. Using data collected by the National Alzheimer’s Disease Coordinating Center and available through NIA Genetics of Alzheimer’s Disease Data Storage Site ( NIAGADS), we examined whether any of the novel AD risk genes influence the AAO of AD. This phenotype is believed to have a heritability that is distinct from disease risk. It is also important to study in the context of strategies to delay the onset of AD. We showed that the novel AD risk variant Phosphatidylinositol binding clathrin assembly protein (PICALM) exerts a small effect on the AAO, with risk allele carriers showing a lower age at onset of AD.
Although systemic inflammation is known to be closely related to the pathogenesis of AD, the precise molecular mechanisms underlying this association are poorly understood. A detailed characterization of the biological pathways implicated in inflammation and their relationship to the onset of AD symptoms is critical to develop disease-modifying treatments targeting inflammation in AD. We have recently applied an integrated, systems-biology approach to study the role of the acute phase protein, A2M in AD pathogenesis. Combining population-level epidemiological analyses with multi-tissue gene expression and brain proteomics data, we show that higher A2M concentrations in serum are associated with a nearly three-fold greater risk of AD in males and with Cerebrospinal fluid (CSF) concentrations of the neuronal injury markers, t-tau and p-tau. We also describe an A2M-gene network that includes Regulator of Calcineurin-1 (RCAN1), an inhibitor of the well characterized tau phosphatase, calcinerin. We find that brain A2M protein levels are negatively correlated with protein levels of calcineurin. We believe that these novel findings have several translational implications including explaining sex-specific differences in AD risk and in the rational design of clinical trials targeting inflammation in AD.
Fig. 7 - Schematic representation of the study design. (A) Using the Biomarkers for Older Controls at Risk for Dementia (BIOCARD) and Alzheimer’s Disease Neuroimaging Initiative (ADNI) study, we explored associations between A2M and CSF measures of preclinical AD pathophysiology and risk of MCI/ AD. (B) Using publicly available gene expression data acquired from the Genetype-Tissue Expression (GTEx) project, we explored the association between blood and brain A2M gene expression. (C) Using publicly available gene expression data acquired from Gene Expression Omnibus (GEO), we explored global gene networks driving A2M gene expression (C.I.) and brain specific gene expression correlations (C.II.). Protein expression data from autopsy samples in the BLSA was used to validate gene expression findings (C.III.).
We showed that insulin resistance (IR) is not related to the extent of AD pathology in non-demented individuals and that midlife IR is associated with longitudinal changes in brain function during aging.
Fig-3. Differences in longitudinal change in regional cerebral blood flow between impaired glucose tolerance (IGT) and normal glucose tolerance (NGT) groups. Blue areas indicate significantly greater longitudinal decreases in regional cerebral blood flow in the IGT group; yellow areas, greater longitudinal increases in regional cerebral blood flow in the IGT group. Abbreviations: IGT, impaired glucose tolerance; L, left; NGT, normal glucose tolerance; R, right.
Despite a large body of evidence showing that obesity is associated with several adverse health outcomes, the underlying mechanisms governing obesity-related behaviors are poorly understood. Unless we understand what drives our inability to resist the temptation of calorie-dense foods, we will not be able to devise strategies to combat the global obesity epidemic. In a contemporary context, we are familiar with the stereotype of obese individuals as “headless, hungry and unhealthy” (Rebecca Puhl and colleagues, Yale University) that portray them as weak-willed, susceptible to over-eating and therefore vulnerable to poor health. In a recent study, we show that the relationships between obesity and obesity-related behaviors may be much more complex. We studied the commonly carried risk variant of the obesity-linked gene, fat mass and obesity-associated (FTO), and showed that in addition to increasing body mass index (BMI) when individuals age, it also reduces brain function within regions intrinsic to impulse control and taste-responsiveness. We suggest that these reductions in brain function in the ventro-medial prefrontal cortex (vmPFC) may be responsible for increasing impulsivity and a preference for calorie-dense foods during aging in individuals who carry the obesity-risk allele of FTO.
In participants within the Baltimore Longitudinal Study of Aging (BLSA), risk allele carriers of the FTO single nucleotide polymorphisms (SNP) rs1410285 show greater increases in BMI over time (panel-A) and a reduction in brain function within the medial prefrontal cortex (mPFC), measured by 15O-water positron emission tomography (PET) imaging (panel-B). Longitudinal decreases in mPFC function within regions important for impulse control and taste responsiveness may in turn, mediate observed increases in impulsivity and a greater intake of dietary fat (panel-C) in obesity-risk allele carriers of FTO.
NEO Personality Inventory-revised (NEO-PI-R). Figure modified with permission from Yi-Fang Chuang et al; FTO genotype and aging: pleiotropic longitudinal effects on adiposity, brain function, implusivity and diet, Molecular Psychiatry, 2014, [Epub ahead of print]
Fig-4.; The fat mass and obesity-related gene exerts pleiotropic longitudinal actions on BMI as well as brain function, impulsivity, and macronutrient intake, suggesting shared biological mechanisms underlying a predisposition to obesity as well as obesity-related behaviors.
While midlife obesity and overweight are linked to greater risk of AD, we do not know whether these risk factors accelerate the onset of AD symptoms or whether they are related to the severity of pathology in the brain. Analyzing data from the BLSA, we recently showed that higher adiposity at midlife (50 years of age) accelerates the age-at-onset of AD and is also associated with greater neurofibrillary pathology in the brain. These findings are of considerable public health importance as they suggest that maintaining a healthy body mass index at midlife may have long-lasting protective effects against the onset of AD symptoms decades later.
Figure 5 - Mid-Life BMI
Relationship between 10th percentile survival time for Alzheimer’s disease (AD) and midlife BMI with 90% confidence intervals from the accelerated failure time model using the log normal distribution. The 10th percentile survival time for AD is defined as the age when 10% of the subjects in the sample will develop AD. It shows that a unit increase in midlife BMI is associated with an earlier age-of-onset of AD by an average of 6.7 months.
Plasma clusterin concentration is related to severity, pathology, and progression in Alzheimer’s disease (AD). In this study, we combined proteomic analysis of plasma with distinct endophenotypes of AD such as hippocampal volume derived by magnetic resonance imaging (MRI), rate of clinical progression and brain amyloid deposition to identify clusterin (also known as apolipoprotein-J/apoJ) as a biologically relevant blood biomarker of AD.
Fig 1. Schematic study design combining multi-modal neuroimaging with mass spectrometry-based proteomic analysis of plasma for discovery of biologically relevant biomarkers of AD
In this study, we combined proteomic analysis of plasma with 11C-PiB PET imaging to identify plasma proteins predictive of brain amyloid deposition in non-demented older individuals. It was striking that in this unbiased study (i.e. without any a priori assumptions about the identity or nature of candidate biomarkers), the strongest signal associated with brain amyloid burden was from apolipoprotein-E (apoE).