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Figure 1.

						Expression patterns of Alzheimer disease (AD) signature proteins discriminate between plasma samples from patients with AD and controls. We discovered a panel of 18 secreted intercellular signaling proteins in plasma that distinguishes between blinded samples from patients with AD and control subjects with high accuracy. The same proteins also predicted progression to AD in preclinical patients with mild cognitive impairment (MCI) several years before clinical diagnosis for AD was made.The concentrations of these proteins generate a characteristic pattern for each plasma sample. We arranged 222 plasma samples from patients with diagnoses of AD, other dementia (OD) (frontotemporal dementia, Lewy body dementia, vascular dementia, corticobasal degeneration), or MCI and cognitively healthy individuals by pattern similarity with a cluster algorithm. Samples are arranged in columns, proteins in rows. Red shades in the node map indicate increased expression in AD plasma samples as compared with samples from controls without dementia; blue shades in the node map indicate reduced expression. List of signaling proteins from top to bottom including alternative names for chemokines: CCL18/PARC, IL-11, angiopoientin 2 (ANG-2), TRAIL-R4, CXCL8/IL-8, insulinlike growth factor–binding protein 6 (IGFBP-6), intercellular adhesion molecule 1 (ICAM-1), CCL5/RANTES, platelet-derived growth factor BB (PDGF-BB), epidermal growth factor (EGF), IL-3, granulocyte colony-stimulating factor (G-CSF), IL-1α, tumor necrosis factor α (TNF-α), glial cell line–derived neurotrophic factor (GDNF), CCL15/macrophage inflammatory protein 1δ (MIP-1δ), macrophage colony-stimulating factor (M-CSF), and CCL7/macrophage chemoattractant protein 3 (MCP-3).

Expression patterns of Alzheimer disease (AD) signature proteins discriminate between plasma samples from patients with AD and controls. We discovered a panel of 18 secreted intercellular signaling proteins in plasma that distinguishes between blinded samples from patients with AD and control subjects with high accuracy. The same proteins also predicted progression to AD in preclinical patients with mild cognitive impairment (MCI) several years before clinical diagnosis for AD was made.20The concentrations of these proteins generate a characteristic pattern for each plasma sample. We arranged 222 plasma samples from patients with diagnoses of AD, other dementia (OD) (frontotemporal dementia, Lewy body dementia, vascular dementia, corticobasal degeneration), or MCI and cognitively healthy individuals by pattern similarity with a cluster algorithm. Samples are arranged in columns, proteins in rows. Red shades in the node map indicate increased expression in AD plasma samples as compared with samples from controls without dementia; blue shades in the node map indicate reduced expression. List of signaling proteins from top to bottom including alternative names for chemokines: CCL18/PARC, IL-11, angiopoientin 2 (ANG-2), TRAIL-R4, CXCL8/IL-8, insulinlike growth factor–binding protein 6 (IGFBP-6), intercellular adhesion molecule 1 (ICAM-1), CCL5/RANTES, platelet-derived growth factor BB (PDGF-BB), epidermal growth factor (EGF), IL-3, granulocyte colony-stimulating factor (G-CSF), IL-1α, tumor necrosis factor α (TNF-α), glial cell line–derived neurotrophic factor (GDNF), CCL15/macrophage inflammatory protein 1δ (MIP-1δ), macrophage colony-stimulating factor (M-CSF), and CCL7/macrophage chemoattractant protein 3 (MCP-3).

Figure 2.

						Correlation networks of Alzheimer disease (AD) signature proteins in plasma of controls without dementia and patients with AD. Cytokines, chemokines, growth factors, and related secreted signaling proteins are part of a soluble network of proteins in the extracellular space that cells use to communicate with each other. Here, we focus only on the 18 proteins of the AD signature in plasma samples from 79 controls without dementia (A) and 84 patients with AD (B). The relationship between 2 proteins can be expressed as a correlation coefficient calculated from all measurements for these 2 proteins in patients or controls. The correlations between the 18 proteins are visualized as lines with different strokes and colors depending on their strength and type of relationship. Note the considerable differences in these relationships in patients with AD compared with controls without dementia. Red lines indicate Spearman rank correlation coefficients R ≥ 0.4 and green lines, R ≤ −0.4. M-CSF indicates macrophage colony-stimulating factor; PDGF-BB, platelet-derived growth factor BB; ICAM-1, intercellular adhesion molecule 1; GDNF, glial cell line–derived neurotrophic factor; G-CSF, granulocyte colony-stimulating factor; ANG-2, angiopoientin 2; EGF, epidermal growth factor; IGFBP-6, insulinlike growth factor–binding protein 6; tumor necrosis factor α, TNF-α.

Correlation networks of Alzheimer disease (AD) signature proteins in plasma of controls without dementia and patients with AD. Cytokines, chemokines, growth factors, and related secreted signaling proteins are part of a soluble network of proteins in the extracellular space that cells use to communicate with each other. Here, we focus only on the 18 proteins of the AD signature in plasma samples from 79 controls without dementia (A) and 84 patients with AD (B). The relationship between 2 proteins can be expressed as a correlation coefficient calculated from all measurements for these 2 proteins in patients or controls. The correlations between the 18 proteins are visualized as lines with different strokes and colors depending on their strength and type of relationship. Note the considerable differences in these relationships in patients with AD compared with controls without dementia. Red lines indicate Spearman rank correlation coefficients R ≥ 0.4 and green lines, R ≤ −0.4. M-CSF indicates macrophage colony-stimulating factor; PDGF-BB, platelet-derived growth factor BB; ICAM-1, intercellular adhesion molecule 1; GDNF, glial cell line–derived neurotrophic factor; G-CSF, granulocyte colony-stimulating factor; ANG-2, angiopoientin 2; EGF, epidermal growth factor; IGFBP-6, insulinlike growth factor–binding protein 6; tumor necrosis factor α, TNF-α.

1.
Blennow  Kde Leon  MJZetterberg  H Alzheimer's disease. Lancet 2006;368 (9533) 387- 403
PubMedArticle
2.
Alzheimer's Association, Early Onset Dementia: A National Challenge, A Future Crisis.   Chicago, IL Alzheimer's Association2006;
3.
Ferri  CPPrince  MBrayne  C  et al. Alzheimer's Disease International, Global prevalence of dementia: a Delphi consensus study. Lancet 2005;366 (9503) 2112- 2117
PubMedArticle
4.
McKhann  GDrachman  DFolstein  MKatzman  RPrice  DStadlan  EM Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology 1984;34 (7) 939- 944
PubMedArticle
5.
Knopman  DSDeKosky  STCummings  JL  et al.  Practice parameter: diagnosis of dementia (an evidence-based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology 2001;56 (9) 1143- 1153
PubMedArticle
6.
Dubois  BFeldman  HHJacova  C  et al.  Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS-ADRDA criteria. Lancet Neurol 2007;6 (8) 734- 746
PubMedArticle
7.
Storandt  MGrant  EAMiller  JPMorris  JC Rates of progression in mild cognitive impairment and early Alzheimer's disease. Neurology 2002;59 (7) 1034- 1041
PubMedArticle
8.
Sunderland  TLinker  GMirza  N  et al.  Decreased beta-amyloid1-42 and increased tau levels in cerebrospinal fluid of patients with Alzheimer disease. JAMA 2003;289 (16) 2094- 2103
PubMedArticle
9.
Hansson  OZetterberg  HBuchhave  PLondos  EBlennow  KMinthon  L Association between CSF biomarkers and incipient Alzheimer's disease in patients with mild cognitive impairment: a follow-up study. Lancet Neurol 2006;5 (3) 228- 234
PubMedArticle
10.
Price  JCKlunk  WELopresti  BJ  et al.  Kinetic modeling of amyloid binding in humans using PET imaging and Pittsburgh Compound-B. J Cereb Blood Flow Metab 2005;25 (11) 1528- 1547
PubMedArticle
11.
Pike  KESavage  GVillemagne  VL  et al.  Beta-amyloid imaging and memory in non-demented individuals: evidence for preclinical Alzheimer's disease. Brain 2007;130 (pt 11) 2837- 2844
PubMedArticle
12.
Jack  CR  JrLowe  VJSenjem  ML  et al.  11C PiB and structural MRI provide complementary information in imaging of Alzheimer's disease and amnestic mild cognitive impairment. Brain 2008;131 (pt 3) 665- 680
PubMedArticle
13.
Anderson  L Candidate-based proteomics in the search for biomarkers of cardiovascular disease. J Physiol 2005;563 (pt 1) 23- 60
PubMedArticle
14.
Ludwig  JAWeinstein  JN Biomarkers in cancer staging, prognosis and treatment selection. Nat Rev Cancer 2005;5 (11) 845- 856
PubMedArticle
15.
Vasan  RS Biomarkers of cardiovascular disease: molecular basis and practical considerations. Circulation 2006;113 (19) 2335- 2362
PubMedArticle
16.
Steinman  L Elaborate interactions between the immune and nervous systems. Nat Immunol 2004;5 (6) 575- 581
PubMedArticle
17.
Wyss-Coray  T Inflammation in Alzheimer disease: driving force, bystander or beneficial response? Nat Med 2006;12 (9) 1005- 1015
PubMed
18.
Britschgi  MWyss-Coray  T Immune cells may fend off Alzheimer disease. Nat Med 2007;13 (4) 408- 409
PubMedArticle
19.
Britschgi  MWyss-Coray  T Systemic and acquired immune responses in Alzheimer's disease. Int Rev Neurobiol 2007;82205- 233
PubMed
20.
Ray  SBritschgi  MHerbert  C  et al.  Classification and prediction of clinical Alzheimer's diagnosis based on plasma signaling proteins. Nat Med 2007;13 (11) 1359- 1362
PubMedArticle
21.
Assini  ACammarata  SVitali  A  et al.  Plasma levels of amyloid beta-protein 42 are increased in women with mild cognitive impairment. Neurology 2004;63 (5) 828- 831
PubMedArticle
22.
Ideker  TThorsson  VRanish  JA  et al.  Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 2001;292 (5518) 929- 934
PubMedArticle
23.
Blalock  EMGeddes  JWChen  KCPorter  NMMarkesbery  WRLandfield  PW Incipient Alzheimer's disease: microarray correlation analyses reveal major transcriptional and tumor suppressor responses. Proc Natl Acad Sci U S A 2004;101 (7) 2173- 2178
PubMedArticle
24.
Biomarkers Definitions Working Group, Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther 2001;69 (3) 89- 95
PubMedArticle
25.
El Khoury  JToft  MHickman  SE  et al.  Ccr2 deficiency impairs microglial accumulation and accelerates progression of Alzheimer-like disease. Nat Med 2007;13 (4) 432- 438
PubMedArticle
26.
Tsai  KJTsai  YCShen  CK G-CSF rescues the memory impairment of animal models of Alzheimer's disease. J Exp Med 2007;204 (6) 1273- 1280
PubMedArticle
27.
Shyu  WCLin  SZLee  CCLiu  DDLi  H Granulocyte colony-stimulating factor for acute ischemic stroke: a randomized controlled trial. CMAJ 2006;174 (7) 927- 933
PubMedArticle
28.
Schäbitz  WRSchneider  A New targets for established proteins: exploring G-CSF for the treatment of stroke. Trends Pharmacol Sci 2007;28 (4) 157- 161
PubMedArticle
Neurological Review
February 2009

Blood Protein Signature for the Early Diagnosis of Alzheimer Disease

Author Affiliations

Author Affiliations:Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California (Drs Britschgi and Wyss-Coray); and VA Palo Alto Health Care System, Palo Alto, California (Dr Wyss-Coray).

 

DAVID E.PLEASUREMD

Arch Neurol. 2009;66(2):161-165. doi:10.1001/archneurol.2008.530
Abstract

Alzheimer disease (AD) has become one of the main health concerns for the elderly population in the United States. Current treatments target symptoms only, but several advanced clinical trials are testing new drugs that are potentially disease modifying. Because AD is still difficult to diagnose in its earliest stages and the disease process is estimated to start many years before current clinical diagnosis is made, accurate and simple diagnostic tools are urgently needed. We recently described a blood-based panel of secreted signaling proteins that distinguishes between blinded samples from patients with AD and control subjects with high accuracy. The same proteins also predicted progression to AD in preclinical patients with mild cognitive impairment several years before clinical diagnosis for AD was made. Herein, we describe these findings and discuss the potential for a more general application of our proteomic approach in understanding and diagnosing disease.

Published online December 8, 2008 (doi:10.1001/archneurol.2008.530).

Alzheimer disease (AD) is an age-dependent neurodegenerative disorder and the major cause of dementia in the elderly population.1The increase in life expectancy and the lack of effective treatments continue to lead to a rapid increase in patients with AD and threaten to wreak havoc on health care systems in industrialized nations.2It is currently assumed that the disease process leading to AD starts years before the typical patient receives a diagnosis, resulting in up to 15 million patients today with either incipient or clinically manifest AD.3Current diagnosis of AD is largely based on exclusion of other causes for dementia4and in the United States, the diagnostic accuracy reaches 80% sensitivity and 70% specificity.5As of today, there are no simple molecular tests available to aid in the diagnosis of dementias6and an estimated quarter million of patients with AD per year are not diagnosed at all in the United States.2Even more challenging, and restricted to specialized clinics, is the diagnosis of patients with mild cognitive impairment (MCI), a population with greatly increased risk to develop AD.6,7

The best-characterized molecular biomarkers for AD are tau protein and β-amyloid (Aβ) peptide. The ratio of phosphorylated tau and Aβ concentrations in cerebrospinal fluid (CSF) is a highly sensitive and specific indicator for AD and a predictor of progression from MCI to AD.8,9Other advanced possible biomarkers for AD include various imaging modalities, most notably positron emission tomography imaging with Pittsburgh compound B, which appears to specifically label Aβ deposits in the brain.1012However, CSF collection is invasive and most imaging methods are expensive; routine testing in elderly at-risk patients on a larger scale is therefore not practical or realistic. A blood-based or other simple and inexpensive test will be necessary for such purpose.

Simple blood-based molecular biomarkers that detect AD in its earliest stages would be particularly useful to manage the disease as early as possible and to develop new treatments. While there is a long history for using molecular biomarkers in blood as surrogate markers for physiological and disease-related processes in various tissues,1315no such markers are available for neurological disorders to date. This is in part explained by the preconceived notion that the brain is relatively isolated from the blood by the blood-brain barrier. However, over the past decade, it has become clear that the brain maintains intricate relationships with the immune system, for example, and that secreted proteins from the brain can regulate physiological processes throughout the body.16In AD, the characteristic amyloid plaques and tangles in the brain are accompanied by prominent local stimulation of innate immune and inflammatory responses,17and there is increasing evidence from AD mouse models that peripheral immune cells can be recruited from the blood to the brain and modulate the disease.18Other studies have reported differences in the distribution or reactivity of blood cell subsets or abnormal levels of cytokines, chemokines, and growth factors in the brain parenchyma, CSF, or blood of patients with MCI or AD.19

Based on this body of scientific evidence, we formulated the hypothesis that pathological processes associated with AD (or other central nervous system diseases) would produce disease-specific molecular changes in the blood. We focused specifically on secreted signaling proteins, including cytokines, chemokines, and growth factors, as these are the primary means of communication between cells in our body. Imbalances in the network of communication between cells in disease may not only be a diagnostic indicator but could potentially reveal mechanistic insight into pathophysiological processes. Herein, we review our recently published discovery of an AD-specific signature of signaling proteins in blood20and discuss its potential relevance for clinical application and basic research.

PROTEIN SIGNATURE IN BLOOD PREDICTS PROGRESSION TO AD IN PATIENTS WITH MCI YEARS BEFORE DIAGNOSIS OF AD

To find a blood-based signature for AD, we measured with a proteomic multiplex method 120 cytokines, chemokines, growth factors, and related signaling proteins in plasma of roughly 40 patients with mild to moderate AD and 40 age-matched controls without dementia from 7 different AD research centers and clinics in the United States and Europe. Statistical comparison of the measurements led to the discovery of 18 proteins whose concentrations in plasma of patients with AD is characteristically changed (proteins are listed in the Figure 1legend). To confirm that this panel is a potential AD signature, we tested it in a similarly sized, independent, blinded sample set containing plasma from patients with AD, patients with other types of dementia, and controls without dementia. The 18 proteins were able to distinguish between samples from patients with AD and various controls with almost 90% accuracy.20The same proteins also discriminated patients with AD from patients with other neurological disorders or rheumatoid arthritis. Together, this indicated that we discovered a relatively robust AD-specific protein signature in blood.

Such an AD signature would be most helpful if it could identify those patients with MCI whose disease later converted to AD. We therefore analyzed plasma samples from 47 patients with MCI who gave blood at the point of diagnosis of MCI and who were followed up clinically.9,21At follow-up diagnosis 2 to 6 years later, 22 patients' disease had progressed to AD and the prediction by the AD signature was consistent with the clinical diagnosis for 20 of them. Moreover, the same proteins also entirely discriminated AD converters from 8 patients with MCI who developed other types of dementia. Interestingly, 7 of the 17 samples from patients who continued to have MCI were classified as being AD converters and it will be interesting to see if this prediction is correct.20Our data indicate that a highly specific plasma biomarker signature can identify presymptomatic AD in patients with MCI years before a clinical diagnosis is made.

AD SIGNATURE SHOWS SPECIFIC PROTEIN EXPRESSION PATTERNS IN THE BLOOD

To explore and compare expression patterns of the 18 signaling proteins in plasma of patients with AD, other dementia, or MCI and healthy individuals, we used a so-called clustering algorithm that arranges samples in clusters based on similarity of expression patterns of the 18 proteins. Most of the samples from patients with AD and patients with MCI whose disease converted to AD were grouped into 1 main cluster, whereas most other samples were arranged into another cluster (Figure 1), illustrating that the 18 proteins can be used to discriminate patients with AD from patients with other dementias or healthy individuals.

AD SIGNATURE INDICATES IMBALANCE IN THE NETWORK OF SECRETED INTERCELLULAR SIGNALING PROTEINS

Cytokines, chemokines, growth factors, and related secreted signaling proteins are part of a soluble network of proteins in the extracellular space that cells use to communicate with each other. We call the universe of these intercellular communication factors the communicome, in reference to the other systemwide groups or -omesof molecules (eg, transcriptome, proteome). The 120 proteins that we measured are only a subgroup of the communicome and were selected with special emphasis on the fact that immune and inflammatory mechanisms in the brain and the periphery are increasingly implicated in AD.17,19The relationship between factors of the communicome can directly be assessed by correlation networks.22Focusing only on the 18 proteins of the AD signature, it becomes already quite obvious that the relationship between these factors changes considerably in patients with AD compared with controls without dementia (Figure 2). For example, while granulocyte colony-stimulating factor is strongly embedded in a network and correlated with 7 of the other 17 proteins in healthy individuals (Figure 2A), these relationships are all lost in patients with AD; instead, a weak correlation with IL-3 is observed (Figure 2B). In general, there seem to be more positively coregulated factors within these 18 proteins in patients with AD than in controls without dementia. If one considers the correlation network in controls without dementia as the baseline or homeostatic network, then the network for patients with AD could be considered off-baseline or imbalanced. Because of the coregulation of these 18 factors with other factors, this imbalance may “spread” into other parts of the communicome, disrupting existing communication or signaling networks. Computer-based analysis of biological pathways for these 18 factors points to dysregulation of hematopoiesis, inflammation, and apoptosis.20Interestingly, an independent microarray study comparing expression of hippocampal genes between AD and nondiseased control brains found similar pathways to be impaired.23

RELEVANCE FOR CLINICAL APPLICATION AND BASIC RESEARCH

The National Institutes of Health Biomarkers Definitions Working Group standardized the definition of biomarker as

a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.24(p91)

An AD signature in blood such as the one we describe would fall under this definition. Clinically, a molecular blood biomarker for AD might have advantages over more complex and sometimes more invasive tests, such as CSF protein measurements, magnetic resonance, or positron emission tomography imaging, particularly as an early screening tool. Still, these existing tests could be used to follow up on a positive test result with a blood biomarker to establish a final or more differentiated diagnosis, much like prostate-specific antigen measurements can trigger biopsies or other testing for prostate cancer. Before our current blood biomarker can be used in the clinic, however, it needs to be validated in larger independent groups of samples obtained from other clinical centers.

One of the most intriguing scientific questions arising from our work is what might cause the observed changes in the communicome and the apparent disruption of relationships between communication factors in the blood in patients with AD. Understanding these causes may provide novel information about disease mechanisms and help identify new targets for therapeutic intervention. Interestingly, several of the 18 proteins that are part of the AD signature promote production and lineage commitment of myeloid cells toward macrophages, stimulate egression of these cells from the bone marrow, and control recruitment to target tissues. Because these factors were found to be reduced in AD plasma in our study, macrophages and related cells may be impaired in AD and not be recruited as efficiently to sites of injury in the brain. Indeed, studies in mouse models for AD support the concept that peripheral macrophages are recruited to the brain where they appear to limit disease progression.18,25In related studies, treatment of “AD mice” with granulocyte colony-stimulating factor led to increased infiltration of the brain by hematopoietic stem cell–derived cells and improved memory function in comparison with mock-treated mice.26Granulocyte colony-stimulating factor, which we observed to be reduced in AD plasma and “disconnected” in the AD relationship network (Figure 2), is currently undergoing clinical trials for stroke.27,28Although it is still unclear how the studies in mice with AD-like pathological features relate to the human disease, our findings in plasma of patients with AD offer a framework to study the potential role of macrophages and related cell types in AD.

CONCLUSIONS

By focusing on secreted signaling proteins or intercellular communication factors rather than the entire plasma proteome, we were able to identify an AD blood signature that can potentially be used as a biomarker for the simple and early diagnosis of AD.20While it does not predict the risk of a healthy person to develop AD, our current biomarker seems to be an early indicator of the disease process and conversion from MCI to AD. No doubt more work is necessary to validate this novel AD biomarker but our study suggests that changes in the cellular communicome in the blood can in principle be used to characterize a central nervous system disease. It is conceivable that other combinations of secreted signaling proteins can be used as surrogate markers of disease progression or even indicators of disease risk for AD as well as other diseases. Proteins that are part of such signatures may provide mechanistic insight into disease-associated biological pathways and serve as new targets for therapeutic strategies.

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Article Information

Correspondence:Tony Wyss-Coray, PhD, Department of Neurology and Neurological Sciences, Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5235 (twc@stanford.edu).

Accepted for Publication:May 13, 2008.

Published Online:December 8, 2008 (doi:10.1001/archneurol.2008.530).

Author Contributions:Study concept and design: Britschgi and Wyss-Coray. Analysis and interpretation of data: Britschgi and Wyss-Coray. Drafting of the manuscript: Britschgi. Critical revision of the manuscript for important intellectual content: Wyss-Coray. Obtained funding: Wyss-Coray.

Financial Disclosure:Dr Wyss-Coray is a founder and paid consultant of Satoris, Inc, which funded part of the described work and is pursuing the development of blood-based molecular markers for neurological diseases.

Funding/Support:This work was supported by the Veterans Administration, the National Institute on Aging, and the John Douglas French Foundation.

References
1.
Blennow  Kde Leon  MJZetterberg  H Alzheimer's disease. Lancet 2006;368 (9533) 387- 403
PubMedArticle
2.
Alzheimer's Association, Early Onset Dementia: A National Challenge, A Future Crisis.   Chicago, IL Alzheimer's Association2006;
3.
Ferri  CPPrince  MBrayne  C  et al. Alzheimer's Disease International, Global prevalence of dementia: a Delphi consensus study. Lancet 2005;366 (9503) 2112- 2117
PubMedArticle
4.
McKhann  GDrachman  DFolstein  MKatzman  RPrice  DStadlan  EM Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology 1984;34 (7) 939- 944
PubMedArticle
5.
Knopman  DSDeKosky  STCummings  JL  et al.  Practice parameter: diagnosis of dementia (an evidence-based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology 2001;56 (9) 1143- 1153
PubMedArticle
6.
Dubois  BFeldman  HHJacova  C  et al.  Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS-ADRDA criteria. Lancet Neurol 2007;6 (8) 734- 746
PubMedArticle
7.
Storandt  MGrant  EAMiller  JPMorris  JC Rates of progression in mild cognitive impairment and early Alzheimer's disease. Neurology 2002;59 (7) 1034- 1041
PubMedArticle
8.
Sunderland  TLinker  GMirza  N  et al.  Decreased beta-amyloid1-42 and increased tau levels in cerebrospinal fluid of patients with Alzheimer disease. JAMA 2003;289 (16) 2094- 2103
PubMedArticle
9.
Hansson  OZetterberg  HBuchhave  PLondos  EBlennow  KMinthon  L Association between CSF biomarkers and incipient Alzheimer's disease in patients with mild cognitive impairment: a follow-up study. Lancet Neurol 2006;5 (3) 228- 234
PubMedArticle
10.
Price  JCKlunk  WELopresti  BJ  et al.  Kinetic modeling of amyloid binding in humans using PET imaging and Pittsburgh Compound-B. J Cereb Blood Flow Metab 2005;25 (11) 1528- 1547
PubMedArticle
11.
Pike  KESavage  GVillemagne  VL  et al.  Beta-amyloid imaging and memory in non-demented individuals: evidence for preclinical Alzheimer's disease. Brain 2007;130 (pt 11) 2837- 2844
PubMedArticle
12.
Jack  CR  JrLowe  VJSenjem  ML  et al.  11C PiB and structural MRI provide complementary information in imaging of Alzheimer's disease and amnestic mild cognitive impairment. Brain 2008;131 (pt 3) 665- 680
PubMedArticle
13.
Anderson  L Candidate-based proteomics in the search for biomarkers of cardiovascular disease. J Physiol 2005;563 (pt 1) 23- 60
PubMedArticle
14.
Ludwig  JAWeinstein  JN Biomarkers in cancer staging, prognosis and treatment selection. Nat Rev Cancer 2005;5 (11) 845- 856
PubMedArticle
15.
Vasan  RS Biomarkers of cardiovascular disease: molecular basis and practical considerations. Circulation 2006;113 (19) 2335- 2362
PubMedArticle
16.
Steinman  L Elaborate interactions between the immune and nervous systems. Nat Immunol 2004;5 (6) 575- 581
PubMedArticle
17.
Wyss-Coray  T Inflammation in Alzheimer disease: driving force, bystander or beneficial response? Nat Med 2006;12 (9) 1005- 1015
PubMed
18.
Britschgi  MWyss-Coray  T Immune cells may fend off Alzheimer disease. Nat Med 2007;13 (4) 408- 409
PubMedArticle
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Britschgi  MWyss-Coray  T Systemic and acquired immune responses in Alzheimer's disease. Int Rev Neurobiol 2007;82205- 233
PubMed
20.
Ray  SBritschgi  MHerbert  C  et al.  Classification and prediction of clinical Alzheimer's diagnosis based on plasma signaling proteins. Nat Med 2007;13 (11) 1359- 1362
PubMedArticle
21.
Assini  ACammarata  SVitali  A  et al.  Plasma levels of amyloid beta-protein 42 are increased in women with mild cognitive impairment. Neurology 2004;63 (5) 828- 831
PubMedArticle
22.
Ideker  TThorsson  VRanish  JA  et al.  Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 2001;292 (5518) 929- 934
PubMedArticle
23.
Blalock  EMGeddes  JWChen  KCPorter  NMMarkesbery  WRLandfield  PW Incipient Alzheimer's disease: microarray correlation analyses reveal major transcriptional and tumor suppressor responses. Proc Natl Acad Sci U S A 2004;101 (7) 2173- 2178
PubMedArticle
24.
Biomarkers Definitions Working Group, Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther 2001;69 (3) 89- 95
PubMedArticle
25.
El Khoury  JToft  MHickman  SE  et al.  Ccr2 deficiency impairs microglial accumulation and accelerates progression of Alzheimer-like disease. Nat Med 2007;13 (4) 432- 438
PubMedArticle
26.
Tsai  KJTsai  YCShen  CK G-CSF rescues the memory impairment of animal models of Alzheimer's disease. J Exp Med 2007;204 (6) 1273- 1280
PubMedArticle
27.
Shyu  WCLin  SZLee  CCLiu  DDLi  H Granulocyte colony-stimulating factor for acute ischemic stroke: a randomized controlled trial. CMAJ 2006;174 (7) 927- 933
PubMedArticle
28.
Schäbitz  WRSchneider  A New targets for established proteins: exploring G-CSF for the treatment of stroke. Trends Pharmacol Sci 2007;28 (4) 157- 161
PubMedArticle
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