BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer's Disease.
ABSTRACT: Alzheimer's disease (AD), the most common form of dementia, shares many aspects of abnormal brain aging. We present a novel magnetic resonance imaging (MRI)-based biomarker that predicts the individual progression of mild cognitive impairment (MCI) to AD on the basis of pathological brain aging patterns. By employing kernel regression methods, the expression of normal brain-aging patterns forms the basis to estimate the brain age of a given new subject. If the estimated age is higher than the chronological age, a positive brain age gap estimation (BrainAGE) score indicates accelerated atrophy and is considered a risk factor for conversion to AD. Here, the BrainAGE framework was applied to predict the individual brain ages of 195 subjects with MCI at baseline, of which a total of 133 developed AD during 36 months of follow-up (corresponding to a pre-test probability of 68%). The ability of the BrainAGE framework to correctly identify MCI-converters was compared with the performance of commonly used cognitive scales, hippocampus volume, and state-of-the-art biomarkers derived from cerebrospinal fluid (CSF). With accuracy rates of up to 81%, BrainAGE outperformed all cognitive scales and CSF biomarkers in predicting conversion of MCI to AD within 3 years of follow-up. Each additional year in the BrainAGE score was associated with a 10% greater risk of developing AD (hazard rate: 1.10 [CI: 1.07-1.13]). Furthermore, the post-test probability was increased to 90% when using baseline BrainAGE scores to predict conversion to AD. The presented framework allows an accurate prediction even with multicenter data. Its fast and fully automated nature facilitates the integration into the clinical workflow. It can be exploited as a tool for screening as well as for monitoring treatment options.
Project description:Aging alters brain structure and function. Personal health markers and modifiable lifestyle factors are related to individual brain aging as well as to the risk of developing Alzheimer's disease (AD). This study used a novel magnetic resonance imaging (MRI)-based biomarker to assess the effects of 17 health markers on individual brain aging in cognitively unimpaired elderly subjects. By employing kernel regression methods, the expression of normal brain-aging patterns forms the basis to estimate the brain age of a given new subject. If the estimated age is higher than the chronological age, a positive brain age gap estimation (BrainAGE) score indicates accelerated atrophy and is considered a risk factor for developing AD. Within this cross-sectional, multi-center study 228 cognitively unimpaired elderly subjects (118 males) completed an MRI at 1.5Tesla, physiological and blood parameter assessments. The multivariate regression model combining all measured parameters was capable of explaining 39% of BrainAGE variance in males (p < 0.001) and 32% in females (p < 0.01). Furthermore, markers of the metabolic syndrome as well as markers of liver and kidney functions were profoundly related to BrainAGE scores in males (p < 0.05). In females, markers of liver and kidney functions as well as supply of vitamin B12 were significantly related to BrainAGE (p < 0.05). In conclusion, in cognitively unimpaired elderly subjects several clinical markers of poor health were associated with subtle structural changes in the brain that reflect accelerated aging, whereas protective effects on brain aging were observed for markers of good health. Additionally, the relations between individual brain aging and miscellaneous health markers show gender-specific patterns. The BrainAGE approach may thus serve as a clinically relevant biomarker for the detection of subtly abnormal patterns of brain aging probably preceding cognitive decline and development of AD.
Project description:INTRODUCTION: Alzheimer's disease (AD) is characterized by the deposition of tau and amyloid in the brain. Although the core cerebrospinal fluid (CSF) AD biomarkers amyloid ? peptide 1-42 (A?1-42), total tau (t-tau) and phosphorylated tau 181 (p-tau181) show good diagnostic sensitivity and specificity, additional biomarkers that can aid in preclinical diagnosis or better track disease progression are needed. Activation of the complement system, a pivotal part of inflammation, occurs at very early stages in the AD brain. Therefore, CSF levels of complement proteins that could be linked to cognitive and structural changes in AD may have diagnostic and prognostic value. METHODS: Using xMAP® technology based assays we measured complement 3 (C3) and factor H (FH) in the CSF of 110 controls (CN), 187 mild cognitive impairment (MCI) and 92 AD subjects of the AD Neuroimaging Initiative (ADNI) at baseline. All ADNI participants underwent clinical follow-up at 12 month intervals and MCI subjects had additional visits at 6 and 18 months. The association between CSF biomarkers and different outcome measures were analyzed using Cox proportional hazard models (conversion from MCI to AD), logistic regression models (classification of clinical groups) and mixed-effects models adjusted for age, gender, education, t-tau/A?1-42 and APOE ?4 presence (baseline and longitudinal association between biomarkers and cognitive scores). RESULTS: Although no association was found between the complement proteins and clinical diagnosis or cognitive measures, lower levels of C3 (??=?-0.12, p?=?0.041) and FH (??=?-0.075, p?=?0.041) were associated with faster cognitive decline in MCI subjects as measured by the AD Assessment Scale-cognitive subscale (ADAS-Cog) test. Furthermore, lower FH levels were associated with larger lateral ventricular volume (p?=?0.024), which is indicative of brain atrophy. CONCLUSIONS: Our study confirms a lack of suitability of CSF C3 and FH as diagnostic biomarkers of AD, but points to their modest potential as prognostic biomarkers and therapeutic targets in cognitively impaired patients.
Project description:Brain iron elevation is implicated in Alzheimer's disease (AD) pathogenesis, but the impact of iron on disease outcomes has not been previously explored in a longitudinal study. Ferritin is the major iron storage protein of the body; by using cerebrospinal fluid (CSF) levels of ferritin as an index, we explored whether brain iron status impacts longitudinal outcomes in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. We show that baseline CSF ferritin levels were negatively associated with cognitive performance over 7 years in 91 cognitively normal, 144 mild cognitive impairment (MCI) and 67 AD subjects, and predicted MCI conversion to AD. Ferritin was strongly associated with CSF apolipoprotein E levels and was elevated by the Alzheimer's risk allele, APOE-?4. These findings reveal that elevated brain iron adversely impacts on AD progression, and introduce brain iron elevation as a possible mechanism for APOE-?4 being the major genetic risk factor for AD.
Project description:To assess the extent to which multiple Alzheimer disease (AD) biomarkers improve the ability to predict future decline in subjects with mild cognitive impairment (MCI) compared with predictions based on clinical parameters alone.All protocols were approved by the institutional review board at each site, and written informed consent was obtained from all subjects. The study was HIPAA compliant. Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline magnetic resonance (MR) imaging and fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET) studies for 97 subjects with MCI were used. MR imaging-derived gray matter probability maps and FDG PET images were analyzed by using independent component analysis, an unbiased data-driven method to extract independent sources of information from whole-brain data. The loading parameters for all MR imaging and FDG components, along with cerebrospinal fluid (CSF) proteins, were entered into logistic regression models (dependent variable: conversion to AD within 4 years). Eight models were considered, including all combinations of MR imaging, PET, and CSF markers with the covariates (age, education, apolipoprotein E genotype, Alzheimer's Disease Assessment Scale-Cognitive subscale score).Combining MR imaging, FDG PET, and CSF data with routine clinical tests significantly increased the accuracy of predicting conversion to AD compared with clinical testing alone. The misclassification rate decreased from 41.3% to 28.4% (P < .00001). FDG PET contributed more information to routine tests (P < .00001) than CSF (P = .32) or MR imaging (P = .08).Imaging and CSF biomarkers can improve prediction of conversion from MCI to AD compared with baseline clinical testing. FDG PET appears to add the greatest prognostic information.
Project description:We investigated why the cerebrospinal fluid (CSF) concentrations of A?42 are lower in mild cognitive impairment (MCI) and Alzheimer's disease (AD) patients. Because A?38/42 and A?40/43 are distinct product/precursor pairs, these four species in the CSF together should faithfully reflect the status of brain ?-secretase activity, and were quantified by specific enzyme-linked immunosorbent assays in the CSF from controls and MCI/AD patients. Decreases in the levels of the precursors, A?42 and 43, in MCI/AD CSF tended to accompany increases in the levels of the products, A?38 and 40, respectively. The ratios A?40/43 versus A?38/42 in CSF (each representing cleavage efficiency of A?43 or A?42) were largely proportional to each other but generally higher in MCI/AD patients compared to control subjects. These data suggest that ?-secretase activity in MCI/AD patients is enhanced at the conversion of A?43 and 42 to A?40 and 38, respectively. Consequently, we measured the in vitro activity of raft-associated ?-secretase isolated from control as well as MCI/AD brains and found the same, significant alterations in the ?-secretase activity in MCI/AD brains.
Project description:Loss of function of TREM2, a key receptor selectively expressed by microglia in the brain, contributes to the development of Alzheimer's disease (AD). We therefore examined whether soluble TREM2 (sTREM2) concentrations in cerebrospinal fluid (CSF) were associated with reduced rates of cognitive decline and clinical progression in subjects with AD or mild cognitive impairment (MCI). We measured sTREM2 in CSF samples from 385 elderly subjects, including cognitively normal controls, individuals with MCI, and subjects with AD dementia (follow-up period: mean, 4 years; range 1.5 to 11.5 years). In subjects with AD defined by evidence of CSF A?1-42 (amyloid ?-peptide 1 to 42; A+) and CSF p-tau181 (tau phosphorylated on amino acid residue 181; T+), higher sTREM2 concentrations in CSF at baseline were associated with attenuated decline in memory and cognition. When analyzed in clinical subgroups, an association between higher CSF sTREM2 concentrations and subsequent reduced memory decline was consistently observed in individuals with MCI or AD dementia, who were positive for CSF A?1-42 and CSF p-tau181 (A+T+). Regarding clinical progression, a higher ratio of CSF sTREM2 to CSF p-tau181 concentrations predicted slower conversion from cognitively normal to symptomatic stages or from MCI to AD dementia in the subjects who were positive for CSF A?1-42 and CSF p-tau181. These results suggest that sTREM2 is associated with attenuated cognitive and clinical decline, a finding with important implications for future clinical trials targeting the innate immune response in AD.
Project description:BACKGROUND: Cerebrospinal fluid (CSF) may be valuable for exploring protein markers for the diagnosis of Alzheimer's disease (AD). The prospect of early detection and treatment, to slow progression, holds hope for aging populations with increased average lifespan. The aim of the present study was to investigate candidate CSF biological markers in patients with mild cognitive impairment (MCI) and AD and compare them with age-matched normal control subjects. METHODS: We applied proteomics approaches to analyze CSF samples derived from 27 patients with AD, 3 subjects with MCI and 30 controls. The AD group was subdivided into three groups by clinical severity according to clinical dementia rating (CDR), a well known clinical scale for dementia. RESULTS: We demonstrated an elevated level of fibrinogen gamma-A chain precursor protein in CSF from patients with mild cognitive impairment and AD compared to the age-matched normal subjects. Moreover, its expression was more prominent in the AD group than in the MCI and correlated with disease severity and progression. In contrast, fibrinogen gamma-A chain precursor protein was detected very low in the age-matched normal group. CONCLUSION: These findings suggest that the CSF level of fibrinogen gamma-A chain precursor may be a candidate biomarker for AD.
Project description:Alzheimer's Disease (AD) and mild cognitive impairment (MCI) are associated with widespread changes in brain structure and function, as indicated by magnetic resonance imaging (MRI) morphometry and 18-fluorodeoxyglucose position emission tomography (FDG PET) metabolism. Nevertheless, the ability to differentiate between AD, MCI and normal aging groups can be difficult. Thus, the goal of this study was to identify the combination of cerebrospinal fluid (CSF) biomarkers, MRI morphometry, FDG PET metabolism and neuropsychological test scores to that best differentiate between a sample of normal aging subjects and those with MCI and AD from the Alzheimer's Disease Neuroimaging Initiative. The secondary goal was to determine the neuroimaging variables from MRI, FDG PET and CSF biomarkers that can predict future cognitive decline within each group. To achieve these aims, a series of multivariate stepwise logistic and linear regression models were generated. Combining all neuroimaging modalities and cognitive test scores significantly improved the index of discrimination, especially at the earliest stages of the disease, whereas MRI gray matter morphometry variables best predicted future cognitive decline compared to other neuroimaging variables. Overall these findings demonstrate that a multimodal approach using MRI morphometry, FDG PET metabolism, neuropsychological test scores and CSF biomarkers may provide significantly better discrimination than any modality alone.
Project description:Mild cognitive impairment (MCI) is a transitional stage between age-related cognitive decline and Alzheimer's disease (AD). For the effective treatment of AD, it would be important to identify MCI patients at high risk for conversion to AD. In this study, we present a novel magnetic resonance imaging (MRI)-based method for predicting the MCI-to-AD conversion from one to three years before the clinical diagnosis. First, we developed a novel MRI biomarker of MCI-to-AD conversion using semi-supervised learning and then integrated it with age and cognitive measures about the subjects using a supervised learning algorithm resulting in what we call the aggregate biomarker. The novel characteristics of the methods for learning the biomarkers are as follows: 1) We used a semi-supervised learning method (low density separation) for the construction of MRI biomarker as opposed to more typical supervised methods; 2) We performed a feature selection on MRI data from AD subjects and normal controls without using data from MCI subjects via regularized logistic regression; 3) We removed the aging effects from the MRI data before the classifier training to prevent possible confounding between AD and age related atrophies; and 4) We constructed the aggregate biomarker by first learning a separate MRI biomarker and then combining it with age and cognitive measures about the MCI subjects at the baseline by applying a random forest classifier. We experimentally demonstrated the added value of these novel characteristics in predicting the MCI-to-AD conversion on data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. With the ADNI data, the MRI biomarker achieved a 10-fold cross-validated area under the receiver operating characteristic curve (AUC) of 0.7661 in discriminating progressive MCI patients (pMCI) from stable MCI patients (sMCI). Our aggregate biomarker based on MRI data together with baseline cognitive measurements and age achieved a 10-fold cross-validated AUC score of 0.9020 in discriminating pMCI from sMCI. The results presented in this study demonstrate the potential of the suggested approach for early AD diagnosis and an important role of MRI in the MCI-to-AD conversion prediction. However, it is evident based on our results that combining MRI data with cognitive test results improved the accuracy of the MCI-to-AD conversion prediction.
Project description: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 ?4 carriers compared to ?3 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.