5th Leonard Berg Symposium
Posted 24 February 2006
October 7-8, 2005, Eric P. Newman Education Center, Washington University School of Medicine, St. Louis, Missouri, USA
Alzheimer's Disease Research Center (ADRC)
Department of Neurology
Washington University School of Medicine
John C. Morris, MD, Principal Investigator & Director
Please direct questions and comments to:
Tom Meuser, Ph.D.
Director of Education
Coordinator, Berg Symposium Series
Washington University ADRC
4488 Forest Park Avenue, Suite 130
St. Louis, MO 63108
3140 286-2882 / Fax: 286-2443
A Summary of the Presented Scientific Content
Report prepared by science writer Patricia G. McCaffrey, Ph.D., under contract to the Washington University ADRC. Members of the Symposium Faculty Panel Planning Committee reviewed and contributed to this report prior to online publication.
The 5th Annual Berg Symposium was convened October 7-8, 2005 at Washington University in St. Louis with the goal of bringing together experts on the biology, epidemiology, and clinical study of Alzheimer disease (AD) to present the state of the art in efforts to identify the earliest signs and predictors of Alzheimer's. The talks represented the full range of activities in the ongoing search for clinically useful antecedent markers of disease.
Laura Almasy, Ph.D., Southwest Foundation for Biomedical Research;
Randy L. Buckner, Ph.D., Washington University, St. Louis, Missouri;
Sherrilynne Fuller, Ph.D., University of Washington, Seattle;
James E. Galvin, M.D., Washington University, St. Louis, Missouri;
Alison M. Goate, Ph.D., Washington University, St. Louis, Missouri;
David M. Holtzman, M.D., Washington University, St. Louis, Missouri;
Claudia H. Kawas, M.D., University of California, Irvine;
Zaven S. Khachaturian, Ph.D., Khachaturian, Radebaugh & Associates, Inc.;
William E. Klunk, M.D., Ph.D., University of Pittsburgh, Pennsylvania;
Eric B. Larson, M.D., M.PH., University of Washington, Seattle;
Jeffrey D. Milbrandt, M.D., Washington University, St. Louis, Missouri;
Mark A. Mintun, M.D., Washington University, St. Louis, Missouri;
John C. Morris, M.D., Washington University, St. Louis, Missouri;
Dennis J. Selkoe, M.D., Harvard University, Boston, Massachusetts;
John Q. Trojanowski, M.D., University of Pennsylvania;
Kristine Yaffe, M.D., University of California, San Francisco.
1. Introduction: Why Antecedent Biomarkers?
In the past few years, AD has been recognized to be a chronic condition whose characteristic dementia is preceded by a long asymptomatic period (preclinical AD), which may last decades. In this period, underlying disease processes result in cerebral lesions, including amyloid plaques and neurofibrillary tangles, which gradually accumulate without the appearance of discernable symptoms (at least as assessed with current instruments). Eventually, the lesions cause sufficient brain damage for cognitive impairment to be expressed.
This preclinical period of AD may represent a "window of opportunity" for interventions aimed at stopping the underlying disease process to preserve mental capacities. By the time the earliest detectable signs of cognitive decline begin, damage already is extensive in vulnerable brain regions (Price and Morris, 1999), and likely irreversible. To treat the preclinical stages of AD, clinicians will need antecedent biomarkers that by definition detect changes that precede the appearance of cognitive symptoms. Such biomarkers will allow preclinical AD to be detected and therapeutic options to be initiated when they are most likely to be effective.
There currently are no treatments that slow or arrest the progression of AD. The last 20 years have seen a sizable investment in AD research, described in a keynote address by Zaven Khachaturian, former director at the Office of Alzheimer's Disease Research at the National Institutes of Health. The payoff has been a considerable increase in our understanding of the genetic and molecular pathways involved in AD. Now, as researchers move to translate understanding into therapeutics, the availability of biomarkers will affect the ease with which new therapies and preventive strategies are developed. Using biomarkers as surrogates to monitor disease progression and the response to experimental therapies has the potential to greatly simplify and shorten clinical trials, as these now rely solely on clinical assessment of dementia severity.
Epidemiological studies have been useful to identify risk factors for AD. In the first session of the symposium, Claudia Kawas (University of California, Irvine) and Kristine Yaffe (University of San Francisco) reviewed the evidence for a number for vitamins, drugs, and lifestyle factors that have been implicated in reducing risk for AD. Ultimately, these studies need to be translated into double-blind (where possible), randomized, placebo-controlled primary prevention trials. Prevention trials, usually long and expensive, might be streamlined and simplified with biomarkers that signal risk and measure progression.
The hypothesis underlying the search for antecedent markers is that the ongoing cerebral changes occurring in people with preclinical AD can be detected by means of objectively measured biomarkers, prior to the onset of dementia. These measures could include levels of gene expression, proteins, metabolites, or other biochemical indicators related to disease progression, neuroimaging correlates, or early cognitive or personality changes that precede the development of dementia.
The hope is that, in the future, AD will be managed in the way cardiovascular disease is handled now. In his keynote address, Dennis Selkoe of Harvard Medical School described a scenario 20 years from now, where physicians will use lifestyle factors and diagnostic measures to define risk in their patients, followed by manipulation of diet, lifestyle, and medications to delay or prevent onset of disease. In economic and human terms, achieving even a modest delay in onset of dementia could make a big difference, avoiding suffering for patients, their families, and caregivers and saving billions of dollars (Brookmeyer et al., 1998).
2. Can Cognitive Function Be Used as an Early Biomarker?
The clinical phase of AD begins with the onset of symptoms and the diagnosis of dementia. Impaired memory and executive function result in interference of normal activities. As the disease progresses, there is further deterioration of cognition, behavior, and function. The Clinical Dementia Rating (CDR) stages the disease, with a rating of 0 for normal cognitive function, and 0.5 signaling mild cognitive impairment (MCI) and early-stage AD. A CDR of 1.0 represents mild AD.
In the past, most research has focused on the clinical phase, but increasingly the long preclinical phase is being examined. Up to 15 years before the diagnosis of AD, prospective studies may show group differences in cognitive performance between those persons who do progress to AD and those who do not. For example, poor performance on tests that query memory and executive function is associated with a higher risk of developing AD (Elias et al., 2000; Kawas et al., 2003). However, the significant heterogeneity of cognitive abilities in older adults means that it is hard to draw boundaries between normal aging, MCI, and very mild AD.
One challenge is to define the cognitive changes of normal aging to allow clinicians to establish a threshold for abnormal function and determine predictors of cognitive decline. James Galvin of Washington University described the results of a longitudinal study of 80 nondemented individuals from age 80 through autopsy (Galvin et al., 2005). By following these elderly participants with extensive annual assessments, the researchers hoped to determine the prevalence of dementia, define its pathologic basis, and determine antecedent markers of cognitive decline. The assessments included annual clinical evaluations, psychometric testing, and neuropathological examination in those who came to autopsy. A critical feature of this study was that clinical diagnoses rested on the observations of informants, in addition to the examination of the participant. This informant-based method has been shown by the Washington University group to be more sensitive to early dementia in individual cases than psychometric performance. The participants had a mean age of 80 years at entry when all were CDR = 0 and had minimal depressive features. During follow-up (average of 8 years), 49 percent of the subjects became demented. The vast majority (92 percent) of those who developed dementia had AD. The older adults who remained nondemented demonstrated stable cognitive performance over time. Impairment in non-memory domains, particularly in judgment and problem solving skills, was as likely as memory impairment to be the first sign of cognitive change.
At autopsy, even subjects with only minimal cognitive impairment showed histopathological evidence of AD. In addition, 34 percent of nondemented individuals showed histopathological AD at death. These individuals, who potentially represent a group with preclinical AD, showed an absence of practice effects on certain cognitive tests during life, suggesting that this absence could signal preclinical dementia in some individuals.
The presence of depressive features, as reported by an informant, also predicted dementia in the longitudinal study reported by Dr. Galvin. This finding agrees with work presented by Kristine Yaffe from the University of California at San Francisco that implicates depression as a risk factor for and possible prodrome of AD. A meta-analysis of six prospective studies (Jorm et al., 2001) showed that risk of dementia doubled in older adults with depressive symptoms. The results raised the possibility that depressive symptoms could be a precursor to dementia but a mechanistic link has not been shown. Yaffe also showed that depressive symptoms doubled risk of cognitive decline in older women (Yaffe et al., 1999). New work has established that the association is unlikely to be due to vascular factors (Barnes et al., 2006). While it is unclear whether depression is a risk factor for AD, the results suggest that older adults should be aggressively monitored for depressive symptoms. Future studies should be undertaken to determine whether treatment of depressive symptoms reduces the risk of dementia.
3. Where to Look for Other Biomarkers?
By the time cognitive effects can be measured, substantial damage to synapses and neurons may already be present. Biological changes that precede symptoms may also precede the development of brain damage. In some cases, genetic mutations are clear predictors of subsequent development of AD. Mutations in the genes for the amyloid precursor protein (APP) and in the presenilin proteins (PS1 and PS2) cause autosomal-dominantly inherited familial AD (FAD). These mutations are biomarkers that define populations destined to develop AD. Because of this, asymptomatic gene carriers would be expected to manifest changes in antecedent biomarkers. Therefore, studying these rare individuals should prove extremely valuable in identifying candidate biomarkers for the much more common sporadic, late-onset form of AD.
Alison Goate of Washington University outlined the contribution of genetic studies to antecedent biomarker discovery. One genetic form of AD occurs in people with Down syndrome (DS). Most individuals with DS have three copies of the entire chromosome 21, which includes the APP gene. All of these develop AD neuropathology by the fourth or fifth decade of life. A small percentage of individuals with DS have partial trisomy of chromosome 21, and the individuals who have only two copies of the APP gene do not develop AD neuropathology (Prasher et al., 1998). Like late-onset AD, the age of onset and extent of AD in DS is influenced by ApoE genotype, where the presence of the E4 allele gives a worse prognosis than E3, and E2 is protective. Longitudinal studies in people with DS could lead to the identification of novel biomarkers.
Early-onset FAD resulting from mutations in APP, PS1, or PS2 is transmitted as a dominant trait. Most people (>80 percent) who get FAD have mutations in PS1, which also result in the earliest onset. A recent study has also shown that an additional genetic defect, the duplication of the APP gene, causes FAD (Rovelet-Lecrux et al., 2006). Because mutations are dominant such that essentially all carriers get the disease, the mutations are a highly specific biomarker of risk. The overall sensitivity is low, however, because the mutations occur infrequently in the population. In addition, the exact age of onset is difficult to predict in FAD, with distinct but overlapping ranges for different mutations and added contribution from ApoE alleles.
Like DS cases, individuals with FAD mutations are potentially highly informative for biomarker discovery because they will eventually develop dementia, and the risk of misdiagnosis is low. The relevance of FAD mutations and pathways to late-onset AD remains unknown. The ultimate utility of such approaches hinges on whether common disease mechanisms exist between early-onset FAD caused by the mutations and sporadic, or late-onset AD. For example, the finding that many FAD mutations do not cause a significant increase in levels of total amyloid-β (Aβ) peptides, but instead skew Aβ peptide production in favor of the Aβ42 form, suggested that the ratio of Aβ40 to Aβ42 might serve as a marker for AD. This remains to be evaluated.
The ApoE gene contributes to the genetic risk of developing AD, but ApoE genotype on its own is not a clinically useful biomarker. The ApoE gene occurs as three variants, of which ApoE4 increases the risk for AD, while ApoE2 decreases it. The ApoE protein binds to Aβ in cerebrospinal fluid, and is found in senile plaques in AD brain. ApoE4 shows a dose-dependent effect on the age of onset of AD, and there is evidence that it interacts with environmental risk factors as well—the risk for AD after severe head injury is much greater in people with one or more ApoE4 alleles. But ApoE4 is neither necessary nor sufficient to cause AD, since 50 percent of AD cases do not carry an ApoE4 allele. Some ApoE4 carriers never develop AD, and some carriers will develop other dementias. So, while ApoE4 is a useful biomarker (as is family history) to identify individuals at high or low risk for AD for biomarker studies, ApoE genotyping itself is not recommended for presymptomatic testing or diagnosis.
Currently, 50 percent of AD cases have no known genetic risk factors, and unraveling the complex genetics of late-onset or sporadic disease remains one of the biggest challenges to understanding the pathology of AD and devising new searches for mechanism-based biomarkers. One way of untangling the genetics of a complex disease is by quantitative trait analysis or the use of quantitative phenotypes in genetic analysis. An example of the power of quantitative trait analysis was presented by Laura Almasy from the Southwest Foundation for Biomedical Research in San Antonio, Texas, who presented her work on discovering genes that help determine plasma homocysteine concentration (Souto et al., 2005).
High plasma homocysteine is a risk factor for AD, and also for cardiovascular disease and stroke. Understanding the genes that contribute to homocysteine levels could reveal how this risk factor relates to disease, and also suggest additional biomarkers and appropriate interventions. Studying a large Spanish family with an inherited, idiopathic clotting disorder, Almasy and colleagues measured as many clotting-related proteins as they could. They then correlated each quantitative phenotype to the clotting phenotype, giving evidence for a common gene or set of genes which influence both homocysteine levels and the risk for thrombosis. A genomewide linkage scan led them to narrow in on two genes, the 5,10 methylenetetrahydrofolate reductase (MTHFR) gene, which has a polymorphism that correlates with higher plasma homocysteine levels, and a novel disease-linked gene, nicotinamide N-methyltransferase (NNMT), which they are now sequencing for variants.
Quantitative trait analysis gains power from larger families and extended pedigrees. In a late-onset condition like AD, extended pedigrees are hard to come by, but the message was that within the constraints of the disease, larger families give more power to discover genetic linkages. When one identifies a large family with an extended pedigree, it is important to measure as many things as possible, Almasy said.
The risk of developing AD is the product of genes and the environment, and understanding the lifestyle factors that contribute to disease leads to etiological hypotheses, which could lead to suggestions for biomarkers. Eric Larson of the Group Health Cooperative's Center for Health Studies and the University of Washington School of Medicine in Seattle surveyed the landscape of lifestyle factors that are linked to AD.
Lifestyle factors that affect the risk of AD include early life conditions, education and professional activity, social networks, physical activity, other nonphysical leisure time activity, and possibly, the availability of medical care. Early life factors related to better or worse socioeconomic status can affect later risk of AD (Moceri et al., 2000). These findings are consistent with the brain reserve hypothesis, which states that cognitive capacity is established early, and then declines at variable rates through the lifespan. "Extra" capacity or reserve delays the onset of cognitive decline, as does anything that slows the rate of decline. Protective effects of education, leisure time mental activity, and professional activity suggest also that cognitive vitality may be preserved by education, complexity of occupation, and cognitively stimulating leisure activities.
Studies over the last 5 years have consistently indicated that physical activity and exercise improve cognition in older adults. Larson reported new results on the reduction in risk of dementia and AD with exercise in a study of 1,740 people older than 65 (Larson et al., 2006). After age 80, there was a separation of dementia-free survival curves for people who exercised three or more times per week compared to those who exercised less. The greatest effect of exercise on survival was observed for the groups with the lowest physical fitness scores, suggesting that even modest activity has a significant effect compared to no activity. This study follows on several other observational studies (Laurin et al., 2001; Yaffe et al., 2001; Abbott et al., 2004; Podewils et al., 2005) showing an association of physical activity with delayed onset of dementia.
Overall, recent evidence supports the notion that lower rates of age-related decline in cognition and risk of dementia and AD are associated with rather "simple" changes in lifestyle and environmental effects. According to one study (Manton et al., 2005), a decline in chronic disability prevalence occurred in the period of 1982-1999. National long-term care survey data suggest this might be due to overall decline in rate of severe cognitive impairment (decline in prevalence from 5.7 percent-2.9 percent), mainly due to a decline in mixed dementia. The authors attribute this change to an increased proportion of better-educated persons among the oldest old, decrease in strokes (treatment of hypertension), and the use of medications that might have neuroprotective actions. The magnitude of these effects, including lifestyle effects, may not be large. But, if the results of research on lifestyle effects are valid, and Manton is correct, lifestyle changes could have profound effects on disability and public health in an aging society.
4. Current Work on Biochemical Markers
Jeffrey Millbrandt (Washington University) presented an overview of the state of the art of biomarker discovery in cancer, a field where many pioneering studies have been done. Techniques using expression profiling, proteomics, metabolomics, and lipidomics have been applied to creating tools for population screening, diagnosis, prognosis, selection of therapy, and therapeutic monitoring and detection of recurrences.
Gene expression profiling was the first and most common technique for marker discovery. High-throughput, genomewide microarray analysis can be used to discover genes that can be used as stand-alone screens, for example, the M1C1 marker for colorectal cancer (Welsh et al., 2003). Alternatively, expression data can yield a tumor profile comprising the activity of many genes, and with the use of pattern recognition algorithms and similarity clustering, these fingerprints can be diagnostic. In breast cancer, such a gene signature was described that is diagnostic of outcome by indicating which tumors are likely to metastasize (van de Vijver et al., 2002). In lymphoma, the profiling of many tumors led to clustering of expression patterns based on prognosis (Rosenwald et al., 2002). This approach can also work in samples of multiple tumor types, in the case of discovery of a "metastatic fingerprint" by comparing a wide variety of tumors (Ramaswamy et al., 2003).
Cancer therapy has turned out to be the leading edge in personalized medicine, and currently there are five clinical trials underway that diagnose disease based on molecular abnormalities, then target therapies directed at those abnormalities. In this way, patients receive therapy consistent with their molecular defect and overall genotype.
Newer approaches take the same concept of a fingerprint, but focus on proteins. Proteomics can be used to discover either single protein markers, or patterns of expression. Using 2D gel separation or liquid chromatography in tandem with mass spectrometry in ever more sophisticated configurations yields rapid assays on small-sample volumes that generate hundreds of thousands of data points per sample. These techniques have wide potential utility for early diagnosis, early warning of disease recurrence, or drug toxicity. Initial results using proteomics for early diagnosis of ovarian cancer were greeted with enthusiasm (Petricoin et al., 2002), but the results remain to be replicated.
Other "omics" technologies, including lipidomics and metobolomics, have the potential to monitor cell processes by measuring the composition in tissue, serum, urine or cerebral spinal fluid (CSF). Lipid profiles can be used to monitor energy homeostasis, cell signaling, proliferation, or apoptosis, processes important in many diseases including AD. Metabolomics, or global measurements of low-molecular-weight molecules in biological systems, theoretically allows the readout of pathway activity, and the detection of changes due to altered gene expression, nutrition, or pathology. Both techniques present separation and analytical challenges, but applications include toxicology, and surrogate markers for disease (for example, see Brindle et al., 2002).
A caveat of all these techniques and a major hurdle to moving into the clinic are that it can be hard to standardize complex processes of sample handling and purification. Analysis platforms vary, as in the use of different gene chips, for example. Population studies may introduce biological variability. Finally, it can be hard to reproduce studies because technology is evolving so rapidly (the moving target problem).
In the case of AD, the search for biochemical changes preceding dementia encompasses the same techniques of genomics, proteomics, lipidomics, metabolomics, or the analysis of almost any parameter of cell function. Searches can involve cells or bodily fluids such as blood, urine, and CSF. The methods used comprise two general approaches: biased and unbiased. Biased approaches start with biomarkers that have been identified for clinically detectable AD and ask whether these markers can also pick up incipient AD in either retrospective or prospective, longitudinal studies. In this approach, the goal is to determine if abnormalities linked with AD pathology (e.g., amyloid-β metabolism or changes in tau protein phosphorylation) that are present in clinical AD are also detected in antecedent AD. These approaches entail population-based studies or studies in specific research populations. Unbiased approaches use large-scale, hypothesis-free methods (e.g., proteomics) to look for differences in either AD versus control samples or in subjects more or less likely to have antecedent AD.
David Holtzman (Washington University) described work using a biased approach based on the known pathogenesis and pathology of preclinical AD. Pathological hallmarks of AD include the aggregation and deposition of amyloid-β (Aβ) peptide in plaques in the brain (extracellular Aβ) and aggregation and buildup of hyperphosphorylated tau in neurofibrillary tangles and neuropil threads in paired helical filaments (intracellular). Levels of Aβ peptide are influenced by many genes and other processes that affect APP transcription, translation, and processing, as well as Aβ secretion, degradation, and clearance, so measuring this protein should give a readout of the net of several events. In addition, the metabolism of Aβ and tau are ongoing processes in the chronic phase of preclinical AD, so changes in their metabolism may be present before symptoms appear.
Drawing an analogy to cardiovascular disease, where imaging (angiogram, CT scan) and fluid biomarkers (LDL, HDL) have revolutionized diagnosis and management, Holtzman is investigating amyloid imaging and the amount of Aβ (1-42) and tau proteins in cerebral spinal fluid as potential antecedent biomarkers for AD. These proteins are on average altered in AD versus age-matched controls, where CSF measures of Aβ decrease in AD, while tau goes up (Sunderland et al., 2003). The sensitivity of decreases in Aβ42 in relation to the clinical diagnosis of AD is between 70-100 percent, while specificity is 40-90 percent. For increases in tau, sensitivity is 40-85 percent, while specificity is 65-85 percent. Extending this work, Holtzman and colleagues showed that in patients with CDR 0, 0.5 and 1.0, there is no change in CSF Aβ40, while Aβ42 is already down in people with CDR 0.5, and tau and phospho-tau are increased.
To try to correlate CSF Aβ peptide measures with actual levels of brain amyloid, Holtzman turned to the ongoing work of Mark Mintun (Washington University) and colleagues who have been conducting in vivo imaging using PET scans and 11C-Pittsburgh compound B (PIB) tracer. In a group of 24 participants, 48-81 years old with various levels of cognitive function/impairment, they measured Aβ40, Aβ42, tau, and phosphorylated tau in CSF, and Aβ40 and Aβ42 in plasma. Their study found an inverse relationship between in vivo brain amyloid load as measured by PIB binding and CSF Aβ42 levels. Importantly, there was no overlap between groups: all individuals with positive PIB binding had lower CSF Aβ42 than those who were negative for PIB binding. There was no such relationship between the PIB binding and CSF Aβ40. There was no significant correlation between brain amyloid load and plasma Aβ, CSF tau, or p-tau. Their results suggest that CSF Aβ42 alone could be an indicator of the amyloid status in the brain.
Interestingly, PIB binding and CSF Aβ42 levels did not consistently correspond with clinical diagnosis. The researchers found three instances of elevated PIB and depressed CSF Aβ42 in cognitively normal individuals. The implication is that these are three cases of preclinical AD. These findings suggest that in vivo amyloid imaging and decreased CSF Aβ42 may be antecedent biomarkers, but further studies with larger numbers of individuals and clinical follow-up will be needed to determine if this is the case.
Other studies support the idea that decreased CSF Aβ42 may be an antecedent AD biomarker. Decreased CSF Aβ42 is found in presymptomatic carriers with familial AD mutations (Moonis et al., 2005). A group of cognitively normal ApoE4+ older individuals (mean age in the late fifties) showed a mean decrease in CSF Aβ42 but no change in tau compared to an age-matched group of ApoE4- individuals (Sunderland et al., 2004), and similar results were obtained by Holtzman's group.
To confirm and to extend these findings require longitudinal studies focusing on individuals in the age range when preclinical AD is present. The plan is to continue comparing imaging techniques with CSF Aβ42 but also assess other promising CSF and plasma antecedent markers, including isoprostanes, homocysteine, and new markers. Starting with larger groups enriched for preclinical AD, the intention is to follow subjects longitudinally. Later, population-based studies can be used to determine how candidate biomarkers predict outcome.
Holtzman also covered his work on an unbiased approach using two-dimensional electrophoresis of CSF to discover biomarkers for preclinical AD. Using a novel method of two-dimensional differential gel electrophoresis (2D DIGE; Hu et al., 2005) followed by mass spectrometry identification of proteins, Holtzman is undertaking the comparative proteomics of human cerebral spinal fluid. Up to 2,100 spots could be assessed on single gels, most in the 10-50 kDa range, and less than 5 percent of the proteins varied significantly between two tests on the same person. Variation in protein levels overall between individuals is much greater than variation between subsequent samples of the same individual. In an experiment where replicate CSF samples were taken from six people 2 weeks apart, the proteomic profile of each person was reproducible enough to be able to match each repeat sample to the correct person by standard analysis techniques.
Current goals are to identify proteins or peptides in human CSF which differ between individuals with very mild and mild AD and controls. To do this, the researchers are studying 12 subjects, half of whom have a CDR of 0 and half with a CDR of 1, who displayed a high (CDR = 0) or low (CDR = 1) CSF Aβ42 in the preliminary proteomic study. Comparison between the two groups with 2D-DIGE/mass spec resulted in matching ~500 spots across six gels comparing six controls, six AD, and pooled samples. Of the 500, 18 spots were identified with differences of p <0.05 between AD and control samples. The next step is to validate three promising candidates in the original CSF samples by ELISA. Following this, the markers can be assessed in larger sample sets, along with other potential markers.
Howard Schulman from Biomarker Discovery Sciences at PPD in Menlo Park, California, described his company's LC-MS- based high-throughput proteomics platform that is being applied to generate differential protein expression profiles of biofluids, with the goal of identifying new biomarkers for a number of diseases. Their technology can profile >600 proteins identified from 1 ml of CSF. Using a 2D analysis with 3 mls CSF, they can routinely profile thousands of proteins, including more than 2,000 proteins that have been identified. The project has identified several differentially expressed proteins in two small independent 1D pilot studies on AD, working with academic collaborators. Differentially expressed proteins are implicated in a variety of pathways and include APP, the α-2-HS-glycoprotein precursor, chromogranin A precursor, MAC25 (insulin-like growth factor binding protein 7), SPARC-like protein 1 precursor, complement C3, and apolipoprotein A-1. Among these, chromogranin A is found in amyloid-β plaques. Interestingly, chromogranin A accumulates in Lewy bodies in brain cortical regions, along with APP and synaptophysin, potentially because of reduced axonal transport. Previously identified as a putative AD biomarker, its abundance in CSF decreased in AD compared to controls.
John Trojanowski (University of Pennsylvania) updated the audience on the biomarkers portion of the NIA Alzheimer's Neuroimaging Initiative (ADNI), a longitudinal multisite observational program to collect imaging and biomarker data that will lead to improved methods to facilitate treatment trials for therapies for AD. Funded jointly by the NIA and industry partners, with additional support from the Alzheimer's Association, the Institute for the Study of Aging and the NIH Foundation, the study will involve 800 subjects ages 55-90, with MCI (N = 400), AD (N = 200), or no cognitive impairments (N = 200). All subjects will be followed for 3 years and evaluated at defined time intervals by clinical examination as well as by neuroimaging (MRI, FDG-PET). Blood and urine samples will be collected at the same time intervals from all subjects, while CSF will be obtained in 20-50 percent of participants. Enrollment began in August of 2005, and the study will conclude in 2010.
The ADNI biomarker core was established >1 year ago at the University of Pennsylvania, and its mission is to bank all biological fluids from ADNI subjects, determine their ApoE genotype, and measure homocysteine and isoprostane levels in blood, urine, and CSF. Previous studies have shown increased isoprostane 8,12-iso-iPF2a-VI in urine of people with MCI (Pratico et al., 2002), and the biomarker core researchers hope to correlate urine and CSF levels of this and other species of isoprostanes with stages in the onset and progression of AD. Since quantitative assays of CSF tau and Aβ can help distinguish patients with AD from those with other neurodegenerative disorders, as well as from cognitively normal subjects (Grossman et al., 2005), the ADNI biomarker core will measure normal and altered forms of CSF tau and Aβ in addition to CSF sulfatides. All assays for the analytes studied in the biomarker core will be optimized and standardized prior to their use with ADNI samples. Other funding will be sought for additional analyses through peer-reviewed ADNI "add-on studies," such as the analysis of 12(S)HETE and 15(S)HETE, markers of lipid peroxidation that have been shown to be elevated in AD and MCI (Yao et al., 2005).
In order to help researchers access the ever-growing scientific literature on AD biomarkers, Sherrilyn Fuller of the University of Washington in Seattle has developed the Biomarkers in Alzheimer's Disease Knowledgebase. Fuller demonstrated the use of this relational database, which is accessible online (www.telemakus.net). By allowing researchers to rapidly retrieve and link results from many studies, the database may help identify new targets for biomarker research.
5. Imaging as a Source of Antecedent Information
The advent in the past 2 years of in vivo imaging of amyloid using the PET tracer Pittsburgh compound B (PIB) offers the opportunity to look directly at amyloid deposition as a preclinical marker of Alzheimer disease. In his talk, William Klunk from Pittsburgh described the development of the PET-PIB imaging method, which uses a radiolabeled thioflavin T derivative to detect amyloid deposits (Mathis et al., 2003; Bacskai et al., 2003). PIB binding to brain homogenates correlates with the amount of Aβ determined by ELISA (Klunk et al., 2003), and PIB is specific for amyloid, and does not bind tangles or α-synuclein. PET-PIB was used in humans to obtain the first images of amyloid in the living brain (Klunk et al., 2004).
Further imaging studies on subjects classified as cognitively normal, MCI, or AD revealed that many MCI patients had amyloid levels equal to those of the AD patients. This supports the theory that MCI is not a precursor to AD, but it is AD. More precisely, the results suggest that most MCI patients destined for AD already have a nearly full load of AD amyloid. In this group, some asymptomatic people also showed PIB retention. Both of these findings reinforce the idea that the optimal time to treat may be prior even to a diagnosis of MCI, at some asymptomatic stage with evidence of earlier pathology indicated by amyloid imaging or other biomarker.
A comparison of PET-PIB and FDG imaging in 30 subjects with AD, MCI, or normal controls showed that PIB retention was superior in classifying subjects with AD versus control. PIB analysis achieves 100 percent specificity and sensitivity for AD, while FDG analysis achieves 83.3 percent specificity and 87.5 percent sensitivity for AD. Using PIB, the MCI subjects could be classified as AD-like (high PIB), control-like (no PIB), or transitional (intermediate PIB). More follow-up will be needed to determine the fate of the different MCI groups identified this way.
The possibility of using amyloid imaging to detect in vivo decreases in amyloid load was investigated by analyzing PIB binding to the postmortem brains of two individuals who had received the AN1792 amyloid vaccine (Ferrer et al., 2004; Masliah et al., 2005). PIB staining was less than expected for AD brain frontal cortex, and the researchers saw neuritic elements, "shadow plaques," and tangles where plaques would normally have been detected. These studies suggest that PIB binding is sensitive to decreased amyloid load in AN1792-treated cases, and that it may be possible to detect in vivo decreases in amyloid load caused by anti-amyloid therapies. The focal nature of amyloid clearance means the entire brain will need to be monitored.
Klunk described a long list of ongoing and future studies, aimed at refining data analysis, expanding the population studied to include both cross-sectional and longitudinal designs, MCI and normal aging, and imaging in presymptomatic, autosomal-dominant familial AD as well as other populations of interest.
Propagation of imaging technology requires the initiation of multicenter PET-PIB studies, and the topic of Mark Mintun's talk was the state of PIB imaging at Washington University. Mintun's group has replicated and extended Klunk and colleagues' findings of a highly significant increase in PIB binding in AD versus old or young controls. They have also studied a larger group of 41 nondemented controls and found several subjects over the age of 60 demonstrated update of PIB that is indistinguishable to that see in AD subjects, suggesting the presence of preclinical pathology. Interestingly, the elevation in asymptomatic people is often in the precuneous region, suggesting that accumulation of amyloid in selected brain regions may account for early pathology. Mintun has also developed methods to analyze PIB binding quantitatively, and worked with Holtzman and Fagan to show that PIB binding correlates with low CSF Aβ42 content.
Randy Buckner (Washington University, St. Louis) and colleagues compared five modes of brain imaging (PIB-PET, MRI, FDG-PET, fMRI, and H20-PET) from a variety of investigators and institutions to try to link together biochemical, functional, and anatomical changes taking place in AD. The different techniques converged to suggest a default activity network in the brain as an early and important locus of amyloid deposition and pathological changes in AD (Buckner et al., 2005). Default activity is defined as what the brain does when it is not engaged in a specific task. Sometimes likened to daydreaming, or a way of internal monitoring of brain activity, the default pathway seems to be important in memory, and the authors show by fMRI that parts of this network do, in fact, take part in successful memory retrieval in young subjects. While Buckner's earlier work (Lustig et al., 2003) and that of Greicius and colleagues (2004) have shown that default network activity is disrupted in AD, the new studies go further to show that the anatomical regions involved in default activity also accumulate amyloid and eventually display metabolic abnormalities and structural changes. Putting this all together, the authors speculate that there may be a relationship between levels of brain or metabolic activity over many years and subsequent amyloid deposition in the posterior cortical areas. The damage done early in this region could be part of the reason AD strikes preferentially at memory functions, and understanding these early events could lead to new imaging-based antecedent markers.
6. Looking Forward
The prospects for identifying and validating antecedent biomarkers for AD are improving with the increasing number of techniques available to study the early stages of disease. The need for longitudinal studies of emerging biomarkers is clear, and will be addressed in part by the ADNI study. John Morris and colleagues at Washington University are also launching a new longitudinal assessment of middle-age to elderly cognitively normal individuals. This "Adult Children Study" will evaluate potential indicators of incipient disease through analysis of cognition, personality, genetics, biomarkers, and neuroimaging in a group of 240 normal healthy people between the ages of 45 and 74. The subjects will be divided between those with a parent with AD, and those for whom neither parent has AD. All subjects will undergo MRI, FDG-PET, PIB-PET amyloid imaging, and psychometric testing with a follow-up every 3 years, and biomarker analysis will be performed. Integrated studies like this and others, which build on present knowledge with well-designed clinical investigations, offer hope of reaching the goal of identifying antecedent markers and opening the way to effective treatments for AD.
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