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MRI and cerebrospinal fluid biomarkers for predicting progression to Alzheimer's disease in patients with mild cognitive impairment: a diagnostic accuracy study.

ABSTRACT: To assess the incremental value of MRI and cerebrospinal fluid (CSF) analysis after a short memory test for predicting progression to Alzheimer's disease from a pragmatic clinical perspective.Diagnostic accuracy study in a multicentre prospective cohort study.Alzheimer Disease Neuroimaging Initiative participants with complete data on neuropsychological assessment, MRI of the brain and CSF analysis.Patients with mild cognitive impairment (MCI; n=181) were included. Mean follow-up was 38.9 months (range 5.5-75.9).Diagnostic accuracy of individual instruments and incremental value of entorhinal cortex volume on MRI and p-?/A? ration in CSF after administration of Rey's Auditory Verbal Learning Memory Test are calculated and expressed as the 'Net Reclassification Improvement' (NRI), which is the change in the percentage of individuals that are correctly diagnosed as Alzheimer or non-Alzheimer case.Tested in isolation, a short memory test, MRI and CSF all substantially contribute to the differentiation of those MCI patients who remain stable during follow-up from those who progress to develop Alzheimer's disease. The memory test, MRI and CSF improved the diagnostic classification by 21% (95% CI 15.1 to 26.9), 22.1% (95% CI 16.1 to 28.1) and 18.8% (95% CI 13.1 to 24.5), respectively. After administration of a short memory test, however, the NRI of MRI is +1.1% (95% CI 0.1 to 3.9) and of CSF is -2.2% (95% CI -5.6 to -0.6).After administration of a brief test of memory, MRI or CSF do not substantially affect diagnostic accuracy for predicting progression to Alzheimer's disease in patients with MCI. The NRI is an intuitive and easy to interpret measure for evaluation of potential added value of new diagnostic instruments in daily clinical practice.

SUBMITTER: Richard E

PROVIDER: S-EPMC3686215 | biostudies-other | 2013 Jun

REPOSITORIES: biostudies-other

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MRI and cerebrospinal fluid biomarkers for predicting progression to Alzheimer's disease in patients with mild cognitive impairment: a diagnostic accuracy study.

Richard Edo E Schmand Ben A BA Eikelenboom Piet P Van Gool Willem A WA

BMJ open 20130620 6

<h4>Objectives</h4>To assess the incremental value of MRI and cerebrospinal fluid (CSF) analysis after a short memory test for predicting progression to Alzheimer's disease from a pragmatic clinical perspective.<h4>Design</h4>Diagnostic accuracy study in a multicentre prospective cohort study.<h4>Setting</h4>Alzheimer Disease Neuroimaging Initiative participants with complete data on neuropsychological assessment, MRI of the brain and CSF analysis.<h4>Participants</h4>Patients with mild cognitiv ...[more]

PMID: 23794572

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Project description:ObjectiveTo compare the effects of a 4-week high-saturated fat/high-glycemic index (HIGH) diet with a low-saturated fat/low-glycemic index (LOW) diet on insulin and lipid metabolism, cerebrospinal fluid (CSF) markers of Alzheimer disease, and cognition for healthy adults and adults with amnestic mild cognitive impairment (aMCI).DesignRandomized controlled trial.SettingVeterans Affairs Medical Center clinical research unit.ParticipantsForty-nine older adults (20 healthy adults with a mean [SD] age of 69.3 [7.4] years and 29 adults with aMCI with a mean [SD] age of 67.6 [6.8] years).InterventionParticipants received the HIGH diet (fat, 45% [saturated fat, > 25%]; carbohydrates, 35%-40% [glycemic index, > 70]; and protein, 15%-20%) or the LOW diet (fat, 25%; [saturated fat, < 7%]; carbohydrates, 55%-60% [glycemic index, < 55]; and protein, 15%-20%) for 4 weeks. Cognitive tests, an oral glucose tolerance test, and lumbar puncture were conducted at baseline and during the fourth week of the diet.Main outcome measuresThe CSF concentrations of β-amyloid (Aβ42 and Aβ40), tau protein, insulin, F2-isoprostanes, and apolipoprotein E, plasma lipids and insulin, and measures of cognition.ResultsFor the aMCI group, the LOW diet increased CSF Aβ42 concentrations, contrary to the pathologic pattern of lowered CSF Aβ42 typically observed in Alzheimer disease. The LOW diet had the opposite effect for healthy adults, ie, decreasing CSF Aβ42, whereas the HIGH diet increased CSF Aβ42. The CSF apolipoprotein E concentration was increased by the LOW diet and decreased by the HIGH diet for both groups. For the aMCI group, the CSF insulin concentration increased with the LOW diet, but the HIGH diet lowered the CSF insulin concentration for healthy adults. The HIGH diet increased and the LOW diet decreased plasma lipids, insulin, and CSF F2-isoprostane concentrations. Delayed visual memory improved for both groups after completion of 4 weeks of the LOW diet.ConclusionOur results suggest that diet may be a powerful environmental factor that modulates Alzheimer disease risk through its effects on central nervous system concentrations of Aβ42, lipoproteins, oxidative stress, and insulin.

Project description:BackgroundAlzheimer's disease (AD) is a widespread neurodegenerative disease that mostly affects the elderly population. Given its prevalence, a precise and efficient stratification system based on AD symptomology that uses functional magnetic resonance imaging (MRI) has great potential in the clinical diagnosis and prognosis estimation of AD patients. It was evident that deep learning methods have performed extremely well in the field of automated stratification of AD based on MRI because of their high predicting accuracy and reliability.MethodsWe proposed a deep convolutional neural network (CNN) and iterated random forest (RF) architecture for MRI image stratification by both anatomical location and image modality using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We employed 3 cross-sectional data sets from the ADNI to conduct our binary-stratification [AD and normal controls (NCs), or AD and mild cognitive impairment (MCI)], and multi-stratification (AD, MCI, and NCs) process using MRI. And the accuracy, recall, specificity, area under the curve of receiver operating characteristic curve (AUC), F1 and Matthew's correlation coefficient (MCC) scores to assess accuracy of auxiliary clinical diagnoses.ResultsCompared to other combinations of algorithms, our model obtained remarkable overall stratification accuracies in all different classification sets. In terms of AD vs. MCI, the mean training AUC of the 3 runs were 85.1% in 95% confidence intervals (CIs). In terms of AD vs. NC, the mean training AUC of the 3 runs was 90.6% in 95% CIs. In terms of the 3 stratifications of AD, MCI, and NC, relative precision, recall, and specificity for each category in the training test (TS) were all near 89%, while the F1 and MCC scores of both sets were 59.9% and 59.5%, respectively.ConclusionsUsing a deep CNN and iterated RF architecture, we showed that brain image stratification is a promising means for evaluating AD, and examining the underlying etiology of the disease, by applying computer and medical images to achieve the early auxiliary diagnosis of AD. However, we still have a long way to go from the discovery of image markers to clinical application.

Project description:BackgroundIndividuals with mild cognitive impairment (MCI) have a substantially increased risk of developing dementia due to Alzheimer's disease (AD). In this study, we developed a multivariate prognostic model for predicting MCI-to-dementia progression at the individual patient level.MethodsUsing baseline data from 259 MCI patients and a probabilistic, kernel-based pattern classification approach, we trained a classifier to distinguish between patients who progressed to AD-type dementia (n = 139) and those who did not (n = 120) during a three-year follow-up period. More than 750 variables across four data sources were considered as potential predictors of progression. These data sources included risk factors, cognitive and functional assessments, structural magnetic resonance imaging (MRI) data, and plasma proteomic data. Predictive utility was assessed using a rigorous cross-validation framework.ResultsCognitive and functional markers were most predictive of progression, while plasma proteomic markers had limited predictive utility. The best performing model incorporated a combination of cognitive/functional markers and morphometric MRI measures and predicted progression with 80% accuracy (83% sensitivity, 76% specificity, AUC = 0.87). Predictors of progression included scores on the Alzheimer's Disease Assessment Scale, Rey Auditory Verbal Learning Test, and Functional Activities Questionnaire, as well as volume/cortical thickness of three brain regions (left hippocampus, middle temporal gyrus, and inferior parietal cortex). Calibration analysis revealed that the model is capable of generating probabilistic predictions that reliably reflect the actual risk of progression. Finally, we found that the predictive accuracy of the model varied with patient demographic, genetic, and clinical characteristics and could be further improved by taking into account the confidence of the predictions.ConclusionsWe developed an accurate prognostic model for predicting MCI-to-dementia progression over a three-year period. The model utilizes widely available, cost-effective, non-invasive markers and can be used to improve patient selection in clinical trials and identify high-risk MCI patients for early treatment.

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MRI and cerebrospinal fluid biomarkers for predicting progression to Alzheimer's disease in patients with mild cognitive impairment: a diagnostic accuracy study.

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MRI and cerebrospinal fluid biomarkers for predicting progression to Alzheimer's disease in patients with mild cognitive impairment: a diagnostic accuracy study.

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