Differences in abundances of cell-signalling proteins in blood reveal novel biomarkers for early detection of clinical Alzheimer's disease

Insulin-like growth factor binding protein-2 in at-risk adults and autopsy-confirmed Alzheimer brains

Insulin, insulin-like growth factors (IGF) and their receptors are highly expressed in the adult hippocampus. Thus, disturbances in the insulin-IGF signalling pathway may account for the selective vulnerability of the hippocampus to nascent Alzheimer's disease (AD) pathology. In the present study, we examined the predominant IGF-binding protein in the CSF, IGFBP2. CSF was collected from 109 asymptomatic members of the parental history-positive PREVENT-AD cohort. CSF levels of IGFBP2, core AD and synaptic biomarkers were measured using proximity extension assay, ELISA and mass spectrometry. Cortical amyloid-beta (Aβ) and tau deposition were examined using 18F-NAV4694 and flortaucipir. Cognitive assessments were performed during up to 8 years of follow-up, using the Repeatable Battery for the Assessment of Neuropsychological Status. T1-weighted structural MRI scans were acquired, and neuroimaging analyses were performed on pre-specified temporal and parietal brain regions. Next, in an independent cohort, we allocated 241 dementia-free ADNI-1 participants into four stages of AD progression based on the biomarkers CSF Aβ42 and total-tau (t-tau). In this analysis, differences in CSF and plasma IGFBP2 levels were examined across the pathological stages. Finally, IGFBP2 mRNA and protein levels were examined in the frontal cortex of 55 autopsy-confirmed AD and 31 control brains from the Quebec Founder Population (QFP) cohort, a unique population isolated from Eastern Canada. CSF IGFBP2 progressively increased over 5 years in asymptomatic PREVENT-AD participants. Baseline CSF IGFBP2 was positively correlated with CSF AD biomarkers and synaptic biomarkers, and negatively correlated with longitudinal changes in delayed memory (P = 0.024) and visuospatial abilities (P = 0.019). CSF IGFBP2 was negatively correlated at a trend-level with entorhinal cortex volume (P = 0.082) and cortical thickness in the piriform (P = 0.039), inferior temporal (P = 0.008), middle temporal (P = 0.014) and precuneus (P = 0.033) regions. In ADNI-1, CSF (P = 0.009) and plasma (P = 0.001) IGFBP2 were significantly elevated in Stage 2 [CSF Aβ(+)/t-tau(+)]. In survival analyses in ADNI-1, elevated plasma IGFBP2 was associated with a greater rate of AD conversion (hazard ratio = 1.62, P = 0.021). In the QFP cohort, IGFBP2 mRNA was reduced (P = 0.049); however, IGFBP2 protein levels did not differ in the frontal cortex of autopsy-confirmed AD brains (P = 0.462). Nascent AD pathology may induce an upregulation in IGFBP2 in asymptomatic individuals. CSF and plasma IGFBP2 may be valuable markers for identifying CSF Aβ(+)/t-tau(+) individuals and those with a greater risk of AD conversion.

Chemokines in patients with Alzheimer's disease: A meta-analysis

Background

Alzheimer's disease (AD) is the most common neurodegenerative disease in elderly people. Many researches have reported that neuroinflammation is related to AD. Chemokines are a class of small cytokines that play important roles in cell migration and cell communication, which involved in neuroinflammation. Up to now there is no meta-analysis to explore the difference of chemokines between AD patients and healthy elderly individuals.

Method

We searched PubMed, Web of science, Cochrane library, EMBASE and Scopus databases from inception to January 2022. Data were extracted by two independent reviewers, and the Review Manager 5.3 was used for the meta-analysis.

Result

Thirty-two articles were included and analyzed. The total number of participants in the included study was 3,331. We found that the levels of CCL5 (SMD = 2.56, 95% CI: 1.91–3.21), CCL15 (SMD = 3.30, 95% CI: 1.48–5.13) and IP-10 (SMD = 3.88, 95% CI: 1.84–5.91) in the plasma of AD patients were higher than healthy people. MCP-1 protein (SMD = 0.67, 95% CI: 0.29–1.05) in the AD patients' CSF was higher than healthy controls.

Conclusion

These results suggested that chemokines may play an important role in AD. These findings could provide evidences for the diagnosis and treatment of AD.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021278736, identifier: CRD42021278736.

Phosphorylated Tau 181 Serum Levels Predict Alzheimer’s Disease in the Preclinical Stage

Background

There is an urgent need for cost-effective, easy-to-measure biomarkers to identify subjects who will develop Alzheimer’s disease (AD), especially at the pre-symptomatic stage. This stage can be determined in autosomal dominant AD (ADAD) which offers the opportunity to observe the dynamic biomarker changes during the life-course of AD stages. This study aimed to investigate serum biomarkers during different AD stages and potential novel protein biomarkers of presymptomatic AD.

Methods

In the first stage, 32 individuals [20 mutation carriers including 10 with AD, and 10 with mild cognitive impairment (MCI), and 12 healthy controls] from ADAD families were analyzed. All subjects underwent a complete clinical evaluation and a comprehensive neuropsychological battery. Serum samples were collected from all subjects, and antibody arrays were used to analyze 170 proteins in these samples. The most promising biomarkers were identified during this screening and were then measured in serum samples of 12 subjects with pre-MCI and 20 controls.

Results

The serum levels of 13 proteins were significantly different in patients with AD or MCI compared to controls. Of the 13 proteins, cathepsin D, immunoglobulin E, epidermal growth factor receptor (EGFR), matrix metalloproteinase-9 (MMP-9), von Willebrand factor (vWF), haptoglobin, and phosphorylated Tau-181 (p-Tau181) correlated with all cognitive measures (R² = −0.69–0.76). The areas under the receiver operating characteristic curve of these seven proteins were 0.71–0.93 for the classification of AD and 0.57–0.95 for the classification of MCI. Higher levels of p-Tau181 were found in the serum of pre-MCI subjects than in the serum of controls. The p-Tau181 serum level might detect AD before symptoms occur (area under the curve 0.85, sensitivity 75%, specificity 81.67%).

Conclusions

A total of 13 serum proteins showed significant differences between subjects with AD and MCI and healthy controls. The p-Tau181 serum level might be a broadly available and cost-effective biomarker to identify individuals with preclinical AD and assess the severity of AD.

Small molecule modulation of TrkB and TrkC neurotrophin receptors prevents cholinergic neuron atrophy in an Alzheimer's disease mouse model at an advanced pathological stage

Degeneration of basal forebrain cholinergic neurons (BFCNs) in the nucleus basalis of Meynert (NBM) and vertical diagonal band (VDB) along with their connections is a key pathological event leading to memory impairment in Alzheimer's disease (AD). Aberrant neurotrophin signaling via Trks and the p75 neurotrophin receptor (p75^NTR) contributes importantly to BFCN dystrophy. While NGF/TrkA signaling has received the most attention in this regard, TrkB and TrkC signaling also provide trophic support to BFCNs and these receptors may be well located to preserve BFCN connectivity. We previously identified a small molecule TrkB/TrkC ligand, LM22B-10, that promotes cell survival and neurite outgrowth in vitro and activates TrkB/TrkC signaling in the hippocampus of aged mice when given intranasally, but shows poor oral bioavailability. An LM22B-10 derivative, PTX-BD10-2, with improved oral bioavailability has been developed and this study examined its effects on BFCN atrophy in the hAPP^Lond/Swe (APP^L/S) AD mouse model. Oral delivery of PTX-BD10-2 was started after appreciable amyloid and cholinergic pathology was present to parallel the clinical context, as most AD patients start treatment at advanced pathological stages. PTX-BD10-2 restored cholinergic neurite integrity in the NBM and VDB, and reduced NBM neuronal atrophy in symptomatic APP^L/S mice. Dystrophy of cholinergic neurites in BF target regions, including the cortex, hippocampus, and amygdala, was also reduced with treatment. Finally, PTX-BD10-2 reduced NBM tau pathology and improved the survival of cholinergic neurons derived from human induced pluripotent stem cells (iPSCs) after amyloid-β exposure. These data provide evidence that targeting TrkB and TrkC signaling with PTX-BD10-2 may be an effective disease-modifying strategy for combating cholinergic dysfunction in AD. The potential for clinical translation is further supported by the compound's reduction of AD-related degenerative processes that have progressed beyond early stages and its neuroprotective effects in human iPSC-derived cholinergic neurons.

VEGF Signaling in Neurological Disorders

Vascular endothelial growth factor (VEGF) is a potent growth factor playing diverse roles in vasculogenesis and angiogenesis. In the brain, VEGF mediates angiogenesis, neural migration and neuroprotection. As a permeability factor, excessive VEGF disrupts intracellular barriers, increases leakage of the choroid plexus endothelia, evokes edema, and activates the inflammatory pathway. Recently, we discovered that a heparin binding epidermal growth factor like growth factor (HB-EGF)—a class of EGF receptor (EGFR) family ligands—contributes to the development of hydrocephalus with subarachnoid hemorrhage through activation of VEGF signaling. The objective of this review is to entail a recent update on causes of death due to neurological disorders involving cerebrovascular and age-related neurological conditions and to understand the mechanism by which angiogenesis-dependent pathological events can be treated with VEGF antagonisms. The Global Burden of Disease study indicates that cancer and cardiovascular disease including ischemic and hemorrhagic stroke are two leading causes of death worldwide. The literature suggests that VEGF signaling in ischemic brains highlights the importance of concentration, timing, and alternate route of modulating VEGF signaling pathway. Molecular targets distinguishing two distinct pathways of VEGF signaling may provide novel therapies for the treatment of neurological disorders and for maintaining lower mortality due to these conditions.

Phenotype analysis of male transgenic mice overexpressing mutant IGFBP-2 lacking the Cardin-Weintraub sequence motif: Reduced expression of synaptic markers and myelin basic protein in the brain and a lower degree of anxiety-like behaviour

Brain growth and function are regulated by insulin-like growth factors I and II (IGF-I and IGF-II) but also by IGF-binding proteins (IGFBPs), including IGFBP-2. In addition to modulating IGF activities, IGFBP-2 interacts with a number of components of the extracellular matrix and cell membrane via a Cardin-Weintraub sequence or heparin binding domain (HBD1). The nature and the signalling elicited by these interactions are not fully understood. Here, we examined transgenic mice (H1d-hBP2) overexpressing a mutant human IGFBP-2 that lacks a specific heparin binding domain (HBD1) known as the Cardin-Weintraub sequence. H1d-hBP2 transgenic mice have the genetic background of FVB mice and are characterized by severe deficits in brain growth throughout their lifetime (p<0.05). In tissue lysates from brain hemispheres of 12-21day old male mice, protein levels of the GTPase dynamin-I were significantly reduced (p<0.01). Weight reductions were also found in distinct brain regions in two different age groups (12 and 80weeks). In the younger group, impaired weights were observed in the hippocampus (-34%; p<0.001), cerebellum (-25%; p<0.0001), olfactory bulb (-31%; p<0.05) and prefrontal cortex (-29%; p<0.05). At an age of 12weeks expression of myelin basic protein was reduced (p<0.01) in H1d-BP-2 mice in the cerebellum but not in the hippocampus. At 80weeks of age, weight reductions were similarly present in the cerebellum (-28%; p<0.001) and hippocampus (-31; p<0.05). When mice were challenged in the elevated plus maze, aged but not younger H1d-hBP2 mice displayed significantly less anxiety-like behaviour, which was also observed in a second transgenic mouse model overexpressing mouse IGFBP-2 lacking HBD1 (H1d-mBP2). These in vivo studies provide, for the first time, evidence for a specific role of IGFBP-2 in brain functions associated with anxiety and risk behaviour. These activities of IGFBP-2 could be mediated by the Cardin-Weintraub/HBD1 sequence and are altered in mice expressing IGFBP-2 lacking the HBD1.

Computer-Aided Breast Cancer Diagnosis with Optimal Feature Sets: Reduction Rules and Optimization Techniques

This chapter introduces a new method for knowledge extraction from databases for the purpose of finding a discriminative set of features that is also a robust set for within-class classification. Our method is generic and we introduce it here in the field of breast cancer diagnosis from digital mammography data. The mathematical formalism is based on a generalization of the k-Feature Set problem called (α, β)-k-Feature Set problem, introduced by Cotta and Moscato (J Comput Syst Sci 67(4):686-690, 2003). This method proceeds in two steps: first, an optimal (α, β)-k-feature set of minimum cardinality is identified and then, a set of classification rules using these features is obtained. We obtain the (α, β)-k-feature set in two phases; first a series of extremely powerful reduction techniques, which do not lose the optimal solution, are employed; and second, a metaheuristic search to identify the remaining features to be considered or disregarded. Two algorithms were tested with a public domain digital mammography dataset composed of 71 malignant and 75 benign cases. Based on the results provided by the algorithms, we obtain classification rules that employ only a subset of these features.

Identification of Differentially Expressed Genes through Integrated Study of Alzheimer's Disease Affected Brain Regions

Background

Alzheimer’s disease (AD) is the most common form of dementia in older adults that damages the brain and results in impaired memory, thinking and behaviour. The identification of differentially expressed genes and related pathways among affected brain regions can provide more information on the mechanisms of AD. In the past decade, several studies have reported many genes that are associated with AD. This wealth of information has become difficult to follow and interpret as most of the results are conflicting. In that case, it is worth doing an integrated study of multiple datasets that helps to increase the total number of samples and the statistical power in detecting biomarkers. In this study, we present an integrated analysis of five different brain region datasets and introduce new genes that warrant further investigation.

Methods

The aim of our study is to apply a novel combinatorial optimisation based meta-analysis approach to identify differentially expressed genes that are associated to AD across brain regions. In this study, microarray gene expression data from 161 samples (74 non-demented controls, 87 AD) from the Entorhinal Cortex (EC), Hippocampus (HIP), Middle temporal gyrus (MTG), Posterior cingulate cortex (PC), Superior frontal gyrus (SFG) and visual cortex (VCX) brain regions were integrated and analysed using our method. The results are then compared to two popular meta-analysis methods, RankProd and GeneMeta, and to what can be obtained by analysing the individual datasets.

Results

We find genes related with AD that are consistent with existing studies, and new candidate genes not previously related with AD. Our study confirms the up-regualtion of INFAR2 and PTMA along with the down regulation of GPHN, RAB2A, PSMD14 and FGF. Novel genes PSMB2, WNK1, RPL15, SEMA4C, RWDD2A and LARGE are found to be differentially expressed across all brain regions. Further investigation on these genes may provide new insights into the development of AD. In addition, we identified the presence of 23 non-coding features, including four miRNA precursors (miR-7, miR570, miR-1229 and miR-6821), dysregulated across the brain regions. Furthermore, we compared our results with two popular meta-analysis methods RankProd and GeneMeta to validate our findings and performed a sensitivity analysis by removing one dataset at a time to assess the robustness of our results. These new findings may provide new insights into the disease mechanisms and thus make a significant contribution in the near future towards understanding, prevention and cure of AD.

Brain amyloidosis ascertainment from cognitive, imaging, and peripheral blood protein measures

BACKGROUND

The goal of this study was to identify a clinical biomarker signature of brain amyloidosis in the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1) mild cognitive impairment (MCI) cohort.

METHODS

We developed a multimodal biomarker classifier for predicting brain amyloidosis using cognitive, imaging, and peripheral blood protein ADNI1 MCI data. We used CSF β-amyloid 1-42 (Aβ42) ≤ 192 pg/mL as proxy measure for Pittsburgh compound B (PiB)-PET standard uptake value ratio ≥ 1.5. We trained our classifier in the subcohort with CSF Aβ42 but no PiB-PET data and tested its performance in the subcohort with PiB-PET but no CSF Aβ42 data. We also examined the utility of our biomarker signature for predicting disease progression from MCI to Alzheimer dementia.

RESULTS

The CSF training classifier selected Mini-Mental State Examination, Trails B, Auditory Verbal Learning Test delayed recall, education, APOE genotype, interleukin 6 receptor, clusterin, and ApoE protein, and achieved leave-one-out accuracy of 85% (area under the curve [AUC] = 0.8). The PiB testing classifier achieved an AUC of 0.72, and when classifier self-tuning was allowed, AUC = 0.74. The 36-month disease-progression classifier achieved AUC = 0.75 and accuracy = 71%.

CONCLUSIONS

Automated classifiers based on cognitive and peripheral blood protein variables can identify the presence of brain amyloidosis with a modest level of accuracy. Such methods could have implications for clinical trial design and enrollment in the near future.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that a classification algorithm based on cognitive, imaging, and peripheral blood protein measures identifies patients with brain amyloid on PiB-PET with moderate accuracy (sensitivity 68%, specificity 78%).

Plasma proteomics for the identification of Alzheimer disease

Less-invasive biomarkers for early Alzheimer disease (AD) are urgently needed. The present study aimed to establish a panel of plasma proteins that accurately distinguishes early AD from physiological aging and to compare the findings with previous reports. Fifty-eight healthy controls (CON) and 109 patients with AD dementia were randomly split into a training (40%) and a test (60%) sample. Significant proteins to differentiate between the CON and AD dementia groups were identified in a comprehensive panel of 107 plasma analytes in the training sample; the accuracy in differentiating these 2 groups was explored in the test sample. A set of 5 plasma proteins was identified, which differentiated between the CON group and the AD dementia group with a sensitivity of 89.36% and a specificity of 79.17%. A biological pathway analysis showed that 4 of 5 proteins belonged to a common network with amyloid precursor protein and tau. Apolipoprotein E was the only protein that was both significant in the present report and in a previous proteomic study. The study provides a piece of evidence in support of the feasibility of a blood-based biomarker approach in AD diagnostics; however, further research is required because of issues with replicability.

Progress towards a consensus on biomarkers for Alzheimer’s disease: a review of peripheral analytes

Alzheimer’s disease (AD) is the most common cause of dementia in the elderly population and attempts to develop therapies have been unsuccessful because there is no means to target an effective therapeutic window. CNS biomarkers are insightful but impractical for high-throughput population-based screening. Therefore, a peripheral, blood-based biomarker for AD would significantly improve early diagnosis, potentially enable presymptomatic detection and facilitate effective targeting of disease-modifying treatments. The various constituents of blood, including plasma, platelets and cellular fractions, are now being systematically explored as a pool of putative peripheral biomarkers for AD. In this review we cover some less known peripheral biomarkers and highlight the latest developments for their clinical application.

Altered expression of RNA splicing proteins in Alzheimer's disease patients: evidence from two microarray studies

BACKGROUND/AIMS

Dysregulation of pre-mRNA splicing from an altered expression of RNA splice-regulatory proteins may act as the convergence point underlying aberrant gene expression changes in Alzheimer's disease (AD).

METHODS

Two microarray datasets from a control/AD postmortem brain cohort of 31 subjects - 9 controls and 22 AD subjects (National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database) - were used.

RESULTS

Between the two microarray studies, the expression of six splice-regulatory protein genes showed concordant changes in AD. These genes were then correlated with gene expression changes of transcripts reported to be altered in AD. Amyloid beta (A4) precursor protein and tropomyosin receptor kinase B transcripts were found to correlate significantly with the same splice-regulatory proteins in the two studies.

CONCLUSION

This study highlights a susceptibility network that can potentially link a number of susceptibility genes.

Multiplex assessment of a panel of 16 serum molecules for the differential diagnosis of Alzheimer's disease

One of the current challenge in Alzheimer's disease (AD) is the identification of reliable biomarkers that might improve diagnostic accuracy, possibly correlating with the disease progression and patient's response to therapy. As the clinically validated AD biomarkers evaluate cerebrospinal fluid (CSF) parameters, the need for less invasive diagnostic markers is well evident. To this respect, blood circulating cytokines or growth factors have provided some encouraging results, even though no clinically validated to date. In 2007 Ray et al suggested a panel of 18 circulating molecules that might increase AD diagnostic accuracy. In an attempt of replicating their data, we designed a multiplex fluorimetric assay comprising 16 independent analytes and covering 15 out of the 18 described proteins. We collected serum samples from three diagnostic groups: probable AD (n=33), matched healthy controls (CNT, n=23) and non AD demented (NAD, n=14). After correction for age, we found an increased level of EGF-1 in AD in comparison to CNT and NAD, while an increase of TRAIL-R4 was found in NAD. However, evaluation of specificity/sensitivity by ROC curve analysis gave weak evidence of diagnostic accuracy (area under the curve = 0.63 and 0.66 for EGF and TRAIL-R4, respectively). Finally, we tried to find a diagnostic classifier by a multivariate algorithm. We found indication of diagnostic evidence for AD only, while NAD samples did not show a diagnostic pattern.

Plasma biomarkers for Alzheimer's disease: much needed but tough to find

Alzheimer's disease is a complex age-dependent neurodegenerative disease where definitive diagnosis is only possible after autopsy and where there is a long prodromal or preclinical phase. Biomarkers for both early diagnosis and prediction of disease progression are needed and extensive efforts to discover them have been undertaken. In this article, we have attempted to summarize the findings of current studies using proteomics and metabolomics approaches. We are also discussing how the use of emerging technologies and better study designs can support the identification of the much-needed Alzheimer's disease plasma biomarkers.

Unveiling clusters of RNA transcript pairs associated with markers of Alzheimer's disease progression

Background

One primary goal of transcriptomic studies is identifying gene expression patterns correlating with disease progression. This is usually achieved by considering transcripts that independently pass an arbitrary threshold (e.g. p<0.05). In diseases involving severe perturbations of multiple molecular systems, such as Alzheimer’s disease (AD), this univariate approach often results in a large list of seemingly unrelated transcripts. We utilised a powerful multivariate clustering approach to identify clusters of RNA biomarkers strongly associated with markers of AD progression. We discuss the value of considering pairs of transcripts which, in contrast to individual transcripts, helps avoid natural human transcriptome variation that can overshadow disease-related changes.

Methodology/Principal Findings

We re-analysed a dataset of hippocampal transcript levels in nine controls and 22 patients with varying degrees of AD. A large-scale clustering approach determined groups of transcript probe sets that correlate strongly with measures of AD progression, including both clinical and neuropathological measures and quantifiers of the characteristic transcriptome shift from control to severe AD. This enabled identification of restricted groups of highly correlated probe sets from an initial list of 1,372 previously published by our group. We repeated this analysis on an expanded dataset that included all pair-wise combinations of the 1,372 probe sets. As clustering of this massive dataset is unfeasible using standard computational tools, we adapted and re-implemented a clustering algorithm that uses external memory algorithmic approach. This identified various pairs that strongly correlated with markers of AD progression and highlighted important biological pathways potentially involved in AD pathogenesis.

Conclusions/Significance

Our analyses demonstrate that, although there exists a relatively large molecular signature of AD progression, only a small number of transcripts recurrently cluster with different markers of AD progression. Furthermore, considering the relationship between two transcripts can highlight important biological relationships that are missed when considering either transcript in isolation.

GPU-FS-kNN: a software tool for fast and scalable kNN computation using GPUs

Background

The analysis of biological networks has become a major challenge due to the recent development of high-throughput techniques that are rapidly producing very large data sets. The exploding volumes of biological data are craving for extreme computational power and special computing facilities (i.e. super-computers). An inexpensive solution, such as General Purpose computation based on Graphics Processing Units (GPGPU), can be adapted to tackle this challenge, but the limitation of the device internal memory can pose a new problem of scalability. An efficient data and computational parallelism with partitioning is required to provide a fast and scalable solution to this problem.

Results

We propose an efficient parallel formulation of the k-Nearest Neighbour (kNN) search problem, which is a popular method for classifying objects in several fields of research, such as pattern recognition, machine learning and bioinformatics. Being very simple and straightforward, the performance of the kNN search degrades dramatically for large data sets, since the task is computationally intensive. The proposed approach is not only fast but also scalable to large-scale instances. Based on our approach, we implemented a software tool GPU-FS-kNN (GPU-based Fast and Scalable k-Nearest Neighbour) for CUDA enabled GPUs. The basic approach is simple and adaptable to other available GPU architectures. We observed speed-ups of 50–60 times compared with CPU implementation on a well-known breast microarray study and its associated data sets.

Conclusion

Our GPU-based Fast and Scalable k-Nearest Neighbour search technique (GPU-FS-kNN) provides a significant performance improvement for nearest neighbour computation in large-scale networks. Source code and the software tool is available under GNU Public License (GPL) at https://sourceforge.net/p/gpufsknn/.

Multivariate protein signatures of pre-clinical Alzheimer's disease in the Alzheimer's disease neuroimaging initiative (ADNI) plasma proteome dataset

BACKGROUND

Recent Alzheimer's disease (AD) research has focused on finding biomarkers to identify disease at the pre-clinical stage of mild cognitive impairment (MCI), allowing treatment to be initiated before irreversible damage occurs. Many studies have examined brain imaging or cerebrospinal fluid but there is also growing interest in blood biomarkers. The Alzheimer's Disease Neuroimaging Initiative (ADNI) has generated data on 190 plasma analytes in 566 individuals with MCI, AD or normal cognition. We conducted independent analyses of this dataset to identify plasma protein signatures predicting pre-clinical AD.

METHODS AND FINDINGS

We focused on identifying signatures that discriminate cognitively normal controls (n = 54) from individuals with MCI who subsequently progress to AD (n = 163). Based on p value, apolipoprotein E (APOE) showed the strongest difference between these groups (p = 2.3 × 10(-13)). We applied a multivariate approach based on combinatorial optimization ((α,β)-k Feature Set Selection), which retains information about individual participants and maintains the context of interrelationships between different analytes, to identify the optimal set of analytes (signature) to discriminate these two groups. We identified 11-analyte signatures achieving values of sensitivity and specificity between 65% and 86% for both MCI and AD groups, depending on whether APOE was included and other factors. Classification accuracy was improved by considering "meta-features," representing the difference in relative abundance of two analytes, with an 8-meta-feature signature consistently achieving sensitivity and specificity both over 85%. Generating signatures based on longitudinal rather than cross-sectional data further improved classification accuracy, returning sensitivities and specificities of approximately 90%.

CONCLUSIONS

Applying these novel analysis approaches to the powerful and well-characterized ADNI dataset has identified sets of plasma biomarkers for pre-clinical AD. While studies of independent test sets are required to validate the signatures, these analyses provide a starting point for developing a cost-effective and minimally invasive test capable of diagnosing AD in its pre-clinical stages.

Brain transcriptome perturbations in the Hfe(-/-) mouse model of genetic iron loading

Severe disruption of brain iron homeostasis can cause fatal neurodegenerative disease, however debate surrounds the neurologic effects of milder, more common iron loading disorders such as hereditary hemochromatosis, which is usually caused by loss-of-function polymorphisms in the HFE gene. There is evidence from both human and animal studies that HFE gene variants may affect brain function and modify risks of brain disease. To investigate how disruption of HFE influences brain transcript levels, we used microarray and real-time reverse transcription polymerase chain reaction to assess the brain transcriptome in Hfe(-/-) mice relative to wildtype AKR controls (age 10 weeks, n≥4/group). The Hfe(-/-) mouse brain showed numerous significant changes in transcript levels (p<0.05) although few of these related to proteins directly involved in iron homeostasis. There were robust changes of at least 2-fold in levels of transcripts for prominent genes relating to transcriptional regulation (FBJ osteosarcoma oncogene Fos, early growth response genes), neurotransmission (glutamate NMDA receptor Grin1, GABA receptor Gabbr1) and synaptic plasticity and memory (calcium/calmodulin-dependent protein kinase IIα Camk2a). As previously reported for dietary iron-supplemented mice, there were altered levels of transcripts for genes linked to neuronal ceroid lipofuscinosis, a disease characterized by excessive lipofuscin deposition. Labile iron is known to enhance lipofuscin generation which may accelerate brain aging. The findings provide evidence that iron loading disorders can considerably perturb levels of transcripts for genes essential for normal brain function and may help explain some of the neurologic signs and symptoms reported in hemochromatosis patients.

Evaluation of a previously suggested plasma biomarker panel to identify Alzheimer's disease

There is an urgent need for biomarkers in plasma to identify Alzheimer's disease (AD). It has previously been shown that a signature of 18 plasma proteins can identify AD during pre-dementia and dementia stages (Ray et al, Nature Medicine, 2007). We quantified the same 18 proteins in plasma from 174 controls, 142 patients with AD, and 88 patients with other dementias. Only three of these proteins (EGF, PDGF-BB and MIP-1δ) differed significantly in plasma between controls and AD. The 18 proteins could classify patients with AD from controls with low diagnostic precision (area under the ROC curve was 63%). Moreover, they could not distinguish AD from other dementias. In conclusion, independent validation of results is important in explorative biomarker studies.