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.     

Association between NME8 Locus Polymorphism and Cognitive Decline,Cerebrospinal Fluid and Neuroimaging Biomarkers in Alzheimer's Disease

Recently, a large meta-analysis of five genome wide association studies (GWAS) identified a novel locus (rs2718058) adjacent to NME8 that played a preventive role in Alzheimer''s disease (AD). However, this link between the single nucleotide polymorphism (SNP) rs2718058 and the pathology of AD have not been mentioned yet. Therefore, this study assessed the strength of association between the NME8 rs2718058 genotypes and AD-related measures including the cerebrospinal fluid (CSF) amyloid beta, tau, P-tau concentrations, neuroimaging biomarkers and cognitive performance, in a large cohort from Alzheimer''s Disease Neuroimaging Initiative (ADNI) database. We used information of a total of 719 individuals, including 211 normal cognition (NC), 346 mild cognitive impairment (MCI) and 162 AD. Although we didn''t observe a positive relationship between rs2718058 and AD, it was significantly associated with several AD related endophenotypes. Among the normal cognitively normal participants, the minor allele G carriers showed significantly associated with higher CDRSB score than A allele carriers (P = 0.021). Occipital gyrus atrophy were significantly associated with NME8 genotype status (P = 0.002), with A allele carriers has more atrophy than the minor allele G carriers in AD patients; lateral ventricle (both right and left) cerebral metabolic rate for glucose (CMRgl) were significantly associated with NME8 genotype (P<0.05), with GA genotype had higher metabolism than GG and AA genotypes in MCI group; the atrophic right hippocampus in 18 months is significantly different between the three group, with GG and AA genotypes had more hippocampus atrophy than GA genotypes in the whole group. Together, our results are consistent with the direction of previous research, suggesting that NME8 rs2718058 appears to play a role in lowering the brain neurodegeneration.     

Rates of Decline in Alzheimer Disease Decrease with Age

Age is the strongest risk factor for sporadic Alzheimer disease (AD), yet the effects of age on rates of clinical decline and brain atrophy in AD have been largely unexplored. Here, we examined longitudinal rates of change as a function of baseline age for measures of clinical decline and structural MRI-based regional brain atrophy, in cohorts of AD, mild cognitive impairment (MCI), and cognitively healthy (HC) individuals aged 65 to 90 years (total n = 723). The effect of age was modeled using mixed effects linear regression. There was pronounced reduction in rates of clinical decline and atrophy with age for AD and MCI individuals, whereas HCs showed increased rates of clinical decline and atrophy with age. This resulted in convergence in rates of change for HCs and patients with advancing age for several measures. Baseline cerebrospinal fluid densities of AD-relevant proteins, Aβ_1–42, tau, and phospho-tau_181p (ptau), showed a similar pattern of convergence with advanced age across cohorts, particularly for ptau. In contrast, baseline clinical measures did not differ by age, indicating uniformity of clinical severity at baseline. These results imply that the phenotypic expression of AD is relatively mild in individuals older than approximately 85 years, and this may affect the ability to distinguish AD from normal aging in the very old. Our findings show that inclusion of older individuals in clinical trials will substantially reduce the power to detect disease-modifying therapeutic effects, leading to dramatic increases in required clinical trial sample sizes with age of study sample.     

Differences in Prefrontal,Limbic, and White Matter Lesion Volumes According to Cognitive Status in Elderly Patients with First-Onset Subsyndromal Depression

The purpose of this preliminary study was to test the hypothesis that subsyndromal depression is associated with the volume of medial prefrontal regional gray matter and that of white matter lesions (WMLs) in the brains of cognitively normal older people. We also explored the relationships between subsyndromal depression and medial prefrontal regional gray matter volume, limbic regional gray matter volume, and lobar WMLs in the brains of patients with mild cognitive impairment (MCI) and Alzheimer''s disease (AD). We performed a cross-sectional study comparing patients with subsyndromal depression and nondepressed controls with normal cognition (n = 59), MCI (n = 27), and AD (n = 27), adjusting for sex, age, years of education, and results of the Mini-Mental State Examination. Frontal WML volume was greater, and right medial orbitofrontal cortical volume was smaller in cognitively normal participants with subsyndromal depression than in those without subsyndromal depression. No volume differences were observed in medial prefrontal, limbic, or WML volumes according to the presence of subsyndromal depression in cognitively impaired patients. The absence of these changes in patients with MCI and AD suggests that brain changes associated with AD pathology may override the changes associated with subsyndromal depression.     

CCL2 is associated with a faster rate of cognitive decline during early stages of Alzheimer's disease

Chemokine (C-C motif) receptor 2 (CCR2)-signaling can mediate accumulation of microglia at sites affected by neuroinflammation. CCR2 and its main ligand CCL2 (MCP-1) might also be involved in the altered metabolism of beta-amyloid (Aβ) underlying Alzheimer''s disease (AD). We therefore measured the levels of CCL2 and three other CCR2 ligands, i.e. CCL11 (eotaxin), CCL13 (MCP-4) and CCL26 (eotaxin-3), in the cerebrospinal fluid (CSF) and plasma of 30 controls and 119 patients with mild cognitive impairment (MCI) at baseline. During clinical follow-up 52 MCI patients were clinically stable for five years, 47 developed AD (i.e. cases with prodromal AD at baseline) and 20 developed other dementias. Only CSF CCL26 was statistically significantly elevated in patients with prodromal AD when compared to controls (p = 0.002). However, in patients with prodromal AD, the CCL2 levels in CSF at baseline correlated with a faster cognitive decline during follow-up (r _s = 0.42, p = 0.004). Furthermore, prodromal AD patients in the highest tertile of CSF CCL2 exhibited a significantly faster cognitive decline (p<0.001) and developed AD dementia within a shorter time period (p<0.003) compared to those in the lowest tertile. Finally, in the entire MCI cohort, CSF CCL2 could be combined with CSF Tau, P-tau and Aβ42 to predict both future conversion to AD and the rate of cognitive decline. If these results are corroborated in future studies, CCL2 in CSF could be a candidate biomarker for prediction of future disease progression rate in prodromal AD. Moreover, CCR2-related signaling pathways might be new therapeutic targets for therapies aiming at slowing down the disease progression rate of AD.     

A blood-based screening tool for Alzheimer's disease that spans serum and plasma: findings from TARC and ADNI

Context

There is no rapid and cost effective tool that can be implemented as a front-line screening tool for Alzheimer''s disease (AD) at the population level.

Objective

To generate and cross-validate a blood-based screener for AD that yields acceptable accuracy across both serum and plasma.

Design, Setting, Participants

Analysis of serum biomarker proteins were conducted on 197 Alzheimer''s disease (AD) participants and 199 control participants from the Texas Alzheimer''s Research Consortium (TARC) with further analysis conducted on plasma proteins from 112 AD and 52 control participants from the Alzheimer''s Disease Neuroimaging Initiative (ADNI). The full algorithm was derived from a biomarker risk score, clinical lab (glucose, triglycerides, total cholesterol, homocysteine), and demographic (age, gender, education, APOE*E4 status) data.

Major Outcome Measures

Alzheimer''s disease.

Results

11 proteins met our criteria and were utilized for the biomarker risk score. The random forest (RF) biomarker risk score from the TARC serum samples (training set) yielded adequate accuracy in the ADNI plasma sample (training set) (AUC = 0.70, sensitivity (SN) = 0.54 and specificity (SP) = 0.78), which was below that obtained from ADNI cerebral spinal fluid (CSF) analyses (t-tau/Aβ ratio AUC = 0.92). However, the full algorithm yielded excellent accuracy (AUC = 0.88, SN = 0.75, and SP = 0.91). The likelihood ratio of having AD based on a positive test finding (LR+) = 7.03 (SE = 1.17; 95% CI = 4.49–14.47), the likelihood ratio of not having AD based on the algorithm (LR−) = 3.55 (SE = 1.15; 2.22–5.71), and the odds ratio of AD were calculated in the ADNI cohort (OR) = 28.70 (1.55; 95% CI = 11.86–69.47).

Conclusions

It is possible to create a blood-based screening algorithm that works across both serum and plasma that provides a comparable screening accuracy to that obtained from CSF analyses.     

An MRI-derived definition of MCI-to-AD conversion for long-term, automatic prognosis of MCI patients

Alzheimer''s disease (AD) and mild cognitive impairment (MCI) are of great current research interest. While there is no consensus on whether MCIs actually “convert” to AD, this concept is widely applied. Thus, the more important question is not whether MCIs convert, but what is the best such definition. We focus on automatic prognostication, nominally using only a baseline brain image, of whether an MCI will convert within a multi-year period following the initial clinical visit. This is not a traditional supervised learning problem since, in ADNI, there are no definitive labeled conversion examples. It is not unsupervised, either, since there are (labeled) ADs and Controls, as well as cognitive scores for MCIs. Prior works have defined MCI subclasses based on whether or not clinical scores significantly change from baseline. There are concerns with these definitions, however, since, e.g., most MCIs (and ADs) do not change from a baseline CDR = 0.5 at any subsequent visit in ADNI, even while physiological changes may be occurring. These works ignore rich phenotypical information in an MCI patient''s brain scan and labeled AD and Control examples, in defining conversion. We propose an innovative definition, wherein an MCI is a converter if any of the patient''s brain scans are classified “AD” by a Control-AD classifier. This definition bootstraps design of a second classifier, specifically trained to predict whether or not MCIs will convert. We thus predict whether an AD-Control classifier will predict that a patient has AD. Our results demonstrate that this definition leads not only to much higher prognostic accuracy than by-CDR conversion, but also to subpopulations more consistent with known AD biomarkers (including CSF markers). We also identify key prognostic brain region biomarkers.     

Selective disruption of the cerebral neocortex in Alzheimer's disease

Background

Alzheimer''s disease (AD) and its transitional state mild cognitive impairment (MCI) are characterized by amyloid plaque and tau neurofibrillary tangle (NFT) deposition within the cerebral neocortex and neuronal loss within the hippocampal formation. However, the precise relationship between pathologic changes in neocortical regions and hippocampal atrophy is largely unknown.

Methodology/Principal Findings

In this study, combining structural MRI scans and automated image analysis tools with reduced cerebrospinal fluid (CSF) Aß levels, a surrogate for intra-cranial amyloid plaques and elevated CSF phosphorylated tau (p-tau) levels, a surrogate for neocortical NFTs, we examined the relationship between the presence of Alzheimer''s pathology, gray matter thickness of select neocortical regions, and hippocampal volume in cognitively normal older participants and individuals with MCI and AD (n = 724). Amongst all 3 groups, only select heteromodal cortical regions significantly correlated with hippocampal volume. Amongst MCI and AD individuals, gray matter thickness of the entorhinal cortex and inferior temporal gyrus significantly predicted longitudinal hippocampal volume loss in both amyloid positive and p-tau positive individuals. Amongst cognitively normal older adults, thinning only within the medial portion of the orbital frontal cortex significantly differentiated amyloid positive from amyloid negative individuals whereas thinning only within the entorhinal cortex significantly discriminated p-tau positive from p-tau negative individuals.

Conclusions/Significance

Cortical Aβ and tau pathology affects gray matter thinning within select neocortical regions and potentially contributes to downstream hippocampal degeneration. Neocortical Alzheimer''s pathology is evident even amongst older asymptomatic individuals suggesting the existence of a preclinical phase of dementia.     

Multimodality imaging of abnormal vascular perfusion and morphology in preclinical 9L gliosarcoma model

Background

This study demonstrates that a dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI) perfusion parameter may indicate vascular abnormality in a brain tumor model and reflects an effect of dexamethasone treatment. In addition, X-ray computed tomography (CT) measurements of vascular tortuosity and tissue markers of vascular morphology were performed to investigate the underpinnings of tumor response to dexamethasone.

Methodology/Principal Findings

One cohort of Fisher 344 rats (N = 13), inoculated intracerebrally with 9L gliosarcoma cells, was treated with dexamethasone (i.p. 3 mg/kg/day) for five consecutive days, and another cohort (N = 11) was treated with equal volume of saline. Longitudinal DSC-MRI studies were performed at the first (baseline), third and fifth day of treatments. Relative cerebral blood volume (rCBV) was significantly reduced on the third day of dexamethasone treatment (0.65±.13) as compared to the fifth day during treatment (1.26±.19, p<0.05). In saline treated rats, relative CBV gradually increased during treatment (0.89±.13, 1.00±.21, 1.13±.23) with no significant difference on the third day of treatment (p>0.05). In separate serial studies, microfocal X-ray CT of ex vivo brain specimens (N = 9) and immunohistochemistry for endothelial cell marker anti-CD31 (N = 8) were performed. Vascular morphology of ex vivo rat brains from micro-CT analysis showed hypervascular characteristics in tumors, and both vessel density (41.32±2.34 branches/mm³, p<0.001) and vessel tortuosity (p<0.05) were significantly reduced in tumors of rats treated with dexamethasone compared to saline (74.29±3.51 branches/mm³). The vascular architecture of rat brain tissue was examined with anti-CD31 antibody, and dexamethasone treated tumor regions showed reduced vessel area (16.45±1.36 µm²) as compared to saline treated tumor regions (30.83±4.31 µm², p<0.001) and non-tumor regions (22.80±1.11 µm², p<0.01).

Conclusions/Significance

Increased vascular density and tortuosity are culprit to abnormal perfusion, which is transiently reduced during dexamethasone treatment.     

A putative Alzheimer's disease risk allele in PCK1 influences brain atrophy in multiple sclerosis

Background

Brain atrophy and cognitive dysfunction are neurodegenerative features of Multiple Sclerosis (MS). We used a candidate gene approach to address whether genetic variants implicated in susceptibility to late onset Alzheimer''s Disease (AD) influence brain volume and cognition in MS patients.

Methods/Principal Findings

MS subjects were genotyped for five single nucleotide polymorphisms (SNPs) associated with susceptibility to AD: PICALM, CR1, CLU, PCK1, and ZNF224. We assessed brain volume using Brain Parenchymal Fraction (BPF) measurements obtained from Magnetic Resonance Imaging (MRI) data and cognitive function using the Symbol Digit Modalities Test (SDMT). Genotypes were correlated with cross-sectional BPF and SDMT scores using linear regression after adjusting for sex, age at symptom onset, and disease duration. 722 MS patients with a mean (±SD) age at enrollment of 41 (±10) years were followed for 44 (±28) months. The AD risk-associated allele of a non-synonymous SNP in the PCK1 locus (rs8192708^G) is associated with a smaller average brain volume (P = 0.0047) at the baseline MRI, but it does not impact our baseline estimate of cognition. PCK1 is additionally associated with higher baseline T2-hyperintense lesion volume (P = 0.0088). Finally, we provide technical validation of our observation in a subset of 641 subjects that have more than one MRI study, demonstrating the same association between PCK1 and smaller average brain volume (P = 0.0089) at the last MRI visit.

Conclusion/Significance

Our study provides suggestive evidence for greater brain atrophy in MS patients bearing the PCK1 allele associated with AD-susceptibility, yielding new insights into potentially shared neurodegenerative process between MS and late onset AD.     

Identification and validation of novel cerebrospinal fluid biomarkers for staging early Alzheimer's disease

Background

Ideally, disease modifying therapies for Alzheimer disease (AD) will be applied during the ‘preclinical’ stage (pathology present with cognition intact) before severe neuronal damage occurs, or upon recognizing very mild cognitive impairment. Developing and judiciously administering such therapies will require biomarker panels to identify early AD pathology, classify disease stage, monitor pathological progression, and predict cognitive decline. To discover such biomarkers, we measured AD-associated changes in the cerebrospinal fluid (CSF) proteome.

Methods and Findings

CSF samples from individuals with mild AD (Clinical Dementia Rating [CDR] 1) (n = 24) and cognitively normal controls (CDR 0) (n = 24) were subjected to two-dimensional difference-in-gel electrophoresis. Within 119 differentially-abundant gel features, mass spectrometry (LC-MS/MS) identified 47 proteins. For validation, eleven proteins were re-evaluated by enzyme-linked immunosorbent assays (ELISA). Six of these assays (NrCAM, YKL-40, chromogranin A, carnosinase I, transthyretin, cystatin C) distinguished CDR 1 and CDR 0 groups and were subsequently applied (with tau, p-tau181 and Aβ42 ELISAs) to a larger independent cohort (n = 292) that included individuals with very mild dementia (CDR 0.5). Receiver-operating characteristic curve analyses using stepwise logistic regression yielded optimal biomarker combinations to distinguish CDR 0 from CDR>0 (tau, YKL-40, NrCAM) and CDR 1 from CDR<1 (tau, chromogranin A, carnosinase I) with areas under the curve of 0.90 (0.85–0.94 95% confidence interval [CI]) and 0.88 (0.81–0.94 CI), respectively.

Conclusions

Four novel CSF biomarkers for AD (NrCAM, YKL-40, chromogranin A, carnosinase I) can improve the diagnostic accuracy of Aβ42 and tau. Together, these six markers describe six clinicopathological stages from cognitive normalcy to mild dementia, including stages defined by increased risk of cognitive decline. Such a panel might improve clinical trial efficiency by guiding subject enrollment and monitoring disease progression. Further studies will be required to validate this panel and evaluate its potential for distinguishing AD from other dementing conditions.     

基于体素的形态测量学在阿尔茨海默病及轻度认知功能障碍研究中的应用

阿尔茨海默病(Alzheimer's disease,AD)是发生于老年和老年前期、以进行性认知功能障碍和行为异常为特征的中枢神经系统退行性疾病,是老年痴呆中最常见类型。轻度认知功能障碍(mild cognitive impairment,MCI)是介于正常衰老和痴呆之间的一种中间状态,指有轻度的记忆或认知损伤,但尚未达到痴呆程度的一种状态,日常生活和社会功能不受影响,其中很大一部分患者最终进展为AD。临床诊断AD患者多已达中晚期,为了能早期诊断AD及预测MCI的转归,有关AD的生物学标注物的研究成为近年来的科研热点。AD患者颅脑的大体病理特征为脑萎缩,其萎缩有别于正常老龄化所致的退行性改变,有其自身特点,这种特定形式的萎缩有可能成为AD早期诊断的生物学标志物。基于体素的形态测量学(voxel-based morphometry,VBM)是一种基于像素水平对脑核磁图像进行自动、全面、客观分析的技术,可以定量分析全脑结构、刻画出局部脑区结构特征,是一种较好的脑形态分析工具,广泛用于阿尔茨海默病及轻度认知功能障碍的研究中,本文综述了近年来其研究进展,期望为临床及科研提供参考。     

Impact of non-HIV and HIV risk factors on survival in HIV-infected patients on HAART: a population-based nationwide cohort study

Background

We determined the impact of three factors on mortality in HIV-infected patients who had been on highly active antiretroviral therapy (HAART) for at least one year: (1) insufficient response to (HAART) and presence of AIDS-defining diseases, (2) comorbidity, and (3) drug and alcohol abuse and compared the mortality to that of the general population.

Methodology/Principal Findings

In a Danish nationwide, population-based cohort study, we used population based registries to identify (1) all Danish HIV-infected patients who started HAART in the period 1 January 1998–1 July 2009, and (2) a comparison cohort of individuals matched on date of birth and gender (N = 2,267 and 9,068, respectively). Study inclusion began 1 year after start of HAART. Patients were categorised hierarchically in four groups according to the three risk factors, which were identified before study inclusion. The main outcome measure was probability of survival from age 25 to 65 years. The probability of survival from age 25 to age 65 was substantially lower in HIV patients [0.48 (95% confidence interval (CI) 0.42–0.55)] compared to the comparison cohort [0.88 (0.86 to 0.90)]. However, in HIV patients with no risk factors (N = 871) the probability of survival was equivalent to that of the general population [0.86 (95% CI 0.77–0.92)]. In contrast, the probability of survival was 0.58 in patients with HIV risk factors (N = 704), 0.30 in patients with comorbidities (N = 479), and 0.03 in patients with drug or alcohol abuse (N = 313).

Conclusions

The increased risk of death in HIV-infected individuals is mainly attributable to risk factors that can be identified prior to or in the initial period of antiretroviral treatment. Mortality in patients without risk factors on a successful HAART is almost identical to that of the non–HIV-infected population.     

Plasma protein profiling of Mild Cognitive Impairment and Alzheimer’s disease using iTRAQ quantitative proteomics

Background

With the promise of disease modifying treatments, there is a need for more specific diagnosis and prognosis of Alzheimer’s disease (AD) and mild cognitive impairment (MCI). Plasma biomarkers are likely to be utilised to increase diagnostic accuracy and specificity of AD and cognitive decline.

Methods

Isobaric tags (iTRAQ) and proteomic methods were used to identify potential plasma biomarkers of MCI and AD. Relative protein expression level changes were quantified in plasma of 411 cognitively normal subjects, 19 AD patients and 261 MCI patients. Plasma was pooled into 4 groups including normal control, AD, amnestic single and multiple domain MCI (aMCI), and nonamnestic single and multiple domain MCI (nMCI). Western-blotting was used to validate iTRAQ data. Integrated function and protein interactions were explored using WEB based bioinformatics tools (DAVID v6.7 and STRING v9.0).

Results

In at least two iTRAQ replicate experiments, 30 proteins were significantly dysregulated in MCI and AD plasma, relative to controls. These proteins included ApoA1, ApoB100, complement C3, C4b-binding protein, afamin, vitamin D-binding protein precursor, isoform 1 of Gelsolin actin regulator, Ig mμ chain C region (IGHM), histidine-rich glycoprotein and fibrinogen β and γ chains. Western-blotting confirmed that afamin was decreased and IGHM was increased in MCI and AD groups. Bioinformatics results indicated that these dysregulated proteins represented a diversity of biological processes, including acute inflammatory response, cholesterol transport and blood coagulation.

Conclusion

These findings demonstrate that expression level changes in multiple proteins are observed in MCI and AD plasma. Some of these, such as afamin and IGHM, may be candidate biomarkers for AD and the predementia condition of MCI.     

Lifespan mental activity predicts diminished rate of hippocampal atrophy

Objective

Epidemiological studies suggest that complex mental activity may reduce the risk for dementia, however an underlying mechanism remains unclear. Our objective was to determine whether individual differences in lifespan complex mental activity are linked to altered rates of hippocampal atrophy independent of global measures of neurodegeneration.

Methods

Thirty seven healthy older individuals had their complex mental activity levels estimated using the Lifetime of Experiences Questionnaire (LEQ) and completed serial MRI investigations at baseline and three years follow-up. Hippocampal volume and semi-automatic quantitation of whole brain volume (WBV) and white matter hyperintensities (WMHs) were compared at both time points.

Results

Higher LEQ scores were correlated with hippocampal volume independent of covariates at the three year follow-up stage (r = 0.43, p = 0.012). Moreover, those with higher LEQ scores experienced less hippocampal atrophy over the follow-up period (r = 0.41, p = 0.02). High LEQ individuals had less than half the hippocampal volume decline of low LEQ individuals in a multivariate analysis (F = 4.47, p = 0.042). No parallel changes were found in measures of WBV and WMHs.

Conclusions

High level of complex mental activity across the lifespan was correlated with a reduced rate of hippocampal atrophy. This finding could not be explained by general differences in intracranial volume, larger hippocampi at baseline, presence of hypertensive disease, gender or low mood. Our results suggest that neuroprotection in medial temporal lobe may be one mechanism underlying the link between mental activity and lower rates of dementia observed in population-based studies. Additional studies are required to further explore this novel finding.     

Peptide fingerprinting of Alzheimer's disease in cerebrospinal fluid: identification and prospective evaluation of new synaptic biomarkers

Background

Today, dementias are diagnosed late in the course of disease. Future treatments have to start earlier in the disease process to avoid disability requiring new diagnostic tools. The objective of this study is to develop a new method for the differential diagnosis and identification of new biomarkers of Alzheimer''s disease (AD) using capillary-electrophoresis coupled to mass-spectrometry (CE-MS) and to assess the potential of early diagnosis of AD.

Methods and Findings

Cerebrospinal fluid (CSF) of 159 out-patients of a memory-clinic at a University Hospital suffering from neurodegenerative disorders and 17 cognitively-healthy controls was used to create differential peptide pattern for dementias and prospective blinded-comparison of sensitivity and specificity for AD diagnosis against the Criterion standard in a naturalistic prospective sample of patients. Sensitivity and specificity of the new method compared to standard diagnostic procedures and identification of new putative biomarkers for AD was the main outcome measure. CE-MS was used to reliably detect 1104 low-molecular-weight peptides in CSF. Training-sets of patients with clinically secured sporadic Alzheimer''s disease, frontotemporal dementia, and cognitively healthy controls allowed establishing discriminative biomarker pattern for diagnosis of AD. This pattern was already detectable in patients with mild cognitive impairment (MCI). The AD-pattern was tested in a prospective sample of patients (n = 100) and AD was diagnosed with a sensitivity of 87% and a specificity of 83%. Using CSF measurements of beta-amyloid1-42, total-tau, and phospho₁₈₁-tau, AD-diagnosis had a sensitivity of 88% and a specificity of 67% in the same sample. Sequence analysis of the discriminating biomarkers identified fragments of synaptic proteins like proSAAS, apolipoprotein J, neurosecretory protein VGF, phospholemman, and chromogranin A.

Conclusions

The method may allow early differential diagnosis of various dementias using specific peptide fingerprints and identification of incipient AD in patients suffering from MCI. Identified biomarkers facilitate face validity for the use in AD diagnosis.     

Inflammatory Proteins in Plasma Are Associated with Severity of Alzheimer’s Disease

Markers of Alzheimer’s disease (AD) are being widely sought with a number of studies suggesting blood measures of inflammatory proteins as putative biomarkers. Here we report findings from a panel of 27 cytokines and related proteins in over 350 subjects with AD, subjects with Mild Cognitive Impairment (MCI) and elderly normal controls where we also have measures of longitudinal change in cognition and baseline neuroimaging measures of atrophy. In this study, we identify five inflammatory proteins associated with evidence of atrophy on MR imaging data particularly in whole brain, ventricular and entorhinal cortex measures. In addition, we observed six analytes that showed significant change (over a period of one year) in people with fast cognitive decline compared to those with intermediate and slow decline. One of these (IL-10) was also associated with brain atrophy in AD. In conclusion, IL-10 was associated with both clinical and imaging evidence of severity of disease and might therefore have potential to act as biomarker of disease progression.     

A Subset of Cerebrospinal Fluid Proteins from a Multi-Analyte Panel Associated with Brain Atrophy,Disease Classification and Prediction in Alzheimer’s Disease

In this exploratory neuroimaging-proteomic study, we aimed to identify CSF proteins associated with AD and test their prognostic ability for disease classification and MCI to AD conversion prediction. Our study sample consisted of 295 subjects with CSF multi-analyte panel data and MRI at baseline downloaded from ADNI. Firstly, we tested the statistical effects of CSF proteins (n = 83) to measures of brain atrophy, CSF biomarkers, ApoE genotype and cognitive decline. We found that several proteins (primarily CgA and FABP) were related to either brain atrophy or CSF biomarkers. In relation to ApoE genotype, a unique biochemical profile characterised by low CSF levels of Apo E was evident in ε4 carriers compared to ε3 carriers. In an exploratory analysis, 3/83 proteins (SGOT, MCP-1, IL6r) were also found to be mildly associated with cognitive decline in MCI subjects over a 4-year period. Future studies are warranted to establish the validity of these proteins as prognostic factors for cognitive decline. For disease classification, a subset of proteins (n = 24) combined with MRI measurements and CSF biomarkers achieved an accuracy of 95.1% (Sensitivity 87.7%; Specificity 94.3%; AUC 0.95) and accurately detected 94.1% of MCI subjects progressing to AD at 12 months. The subset of proteins included FABP, CgA, MMP-2, and PPP as strong predictors in the model. Our findings suggest that the marker of panel of proteins identified here may be important candidates for improving the earlier detection of AD. Further targeted proteomic and longitudinal studies would be required to validate these findings with more generalisability.     

Dietary Factors Impact on the Association between CTSS Variants and Obesity Related Traits

Background/Aims

Cathepsin S, a protein coded by the CTSS gene, is implicated in adipose tissue biology–this protein enhances adipose tissue development. Our hypothesis is that common variants in CTSS play a role in body weight regulation and in the development of obesity and that these effects are influenced by dietary factors–increased by high protein, glycemic index and energy diets.

Methods

Four tag SNPs (rs7511673, rs11576175, rs10888390 and rs1136774) were selected to capture all common variation in the CTSS region. Association between these four SNPs and several adiposity measurements (BMI, waist circumference, waist for given BMI and being a weight gainer–experiencing the greatest degree of unexplained annual weight gain during follow-up or not) given, where applicable, both as baseline values and gain during the study period (6–8 years) were tested in 11,091 European individuals (linear or logistic regression models). We also examined the interaction between the CTSS variants and dietary factors–energy density, protein content (in grams or in % of total energy intake) and glycemic index–on these four adiposity phenotypes.

Results

We found several associations between CTSS polymorphisms and anthropometric traits including baseline BMI (rs11576175 (SNP N°2), p = 0.02, β = −0.2446), and waist change over time (rs7511673 (SNP N°1), p = 0.01, β = −0.0433 and rs10888390 (SNP N°3), p = 0.04, β = −0.0342). In interaction with the percentage of proteins contained in the diet, rs11576175 (SNP N°2) was also associated with the risk of being a weight gainer (p_interaction = 0.01, OR = 1.0526)–the risk of being a weight gainer increased with the percentage of proteins contained in the diet.

Conclusion

CTSS variants seem to be nominally associated to obesity related traits and this association may be modified by dietary protein intake.