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

ABSTRACT: 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 ?-amyloid 1-42 (A?42) ? 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 A?42 but no PiB-PET data and tested its performance in the subcohort with PiB-PET but no CSF A?42 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%).

SUBMITTER: Apostolova LG

PROVIDER: S-EPMC4336101 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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

Apostolova Liana G LG Hwang Kristy S KS Avila David D Elashoff David D Kohannim Omid O Teng Edmond E Sokolow Sophie S Jack Clifford R CR Jagust William J WJ Shaw Leslie L Trojanowski John Q JQ Weiner Michael W MW Thompson Paul M PM

Neurology 20150121 7

<h4>Background</h4>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.<h4>Methods</h4>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 ra ...[more]

PMID: 25609767

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Project description:OBJECTIVE:To identify a neuroimaging signature predictive of brain amyloidosis as a screening tool to identify individuals with mild cognitive impairment (MCI) that are most likely to have high levels of brain amyloidosis or to be amyloid-free. METHODS:The prediction model cohort included 62 MCI subjects screened with structural magnetic resonance imaging (MRI) and (11) C-labeled Pittsburgh compound B positron emission tomography (PET). We identified an anatomical shape variation-based neuroimaging predictor of brain amyloidosis and defined a structural MRI-based brain amyloidosis score (sMRI-BAS). Amyloid beta positivity (A?(+) ) predictive power of sMRI-BAS was validated on an independent cohort of 153 MCI patients with cerebrospinal fluid A?1-42 biomarker data but no amyloid PET scans. We compared the A?(+) predictive power of sMRI-BAS to those of apolipoprotein E (ApoE) genotype and hippocampal volume, the 2 most relevant candidate biomarkers for the prediction of brain amyloidosis. RESULTS:Anatomical shape variations predictive of brain amyloidosis in MCI embraced a characteristic spatial pattern known for high vulnerability to Alzheimer disease pathology, including the medial temporal lobe, temporal-parietal association cortices, posterior cingulate, precuneus, hippocampus, amygdala, caudate, and fornix/stria terminals. A?(+) prediction performance of sMRI-BAS and ApoE genotype jointly was significantly better than the performance of each predictor separately (area under the curve [AUC] = 0.88 vs AUC = 0.70 and AUC = 0.81, respectively) with >90% sensitivity and specificity at 20% false-positive rate and false-negative rate thresholds. Performance of hippocampal volume as an independent predictor of brain amyloidosis in MCI was only marginally better than random chance (AUC = 0.56). INTERPRETATION:As one of the first attempts to use an imaging technique that does not require amyloid-specific radioligands for identification of individuals with brain amyloidosis, our findings could lead to development of multidisciplinary/multimodality brain amyloidosis biomarkers that are reliable, minimally invasive, and widely available.

Project description:BackgroundIdentifying blood-based signatures of brain health and preclinical pathology may offer insights into early disease mechanisms and highlight avenues for intervention. Here, we systematically profiled associations between blood metabolites and whole-brain volume, hippocampal volume, and amyloid-β status among participants of Insight 46-the neuroscience sub-study of the National Survey of Health and Development (NSHD). We additionally explored whether key metabolites were associated with polygenic risk for Alzheimer's disease (AD).MethodsFollowing quality control, levels of 1019 metabolites-detected with liquid chromatography-mass spectrometry-were available for 1740 participants at age 60-64. Metabolite data were subsequently clustered into modules of co-expressed metabolites using weighted coexpression network analysis. Accompanying MRI and amyloid-PET imaging data were present for 437 participants (age 69-71). Regression analyses tested relationships between metabolite measures-modules and hub metabolites-and imaging outcomes. Hub metabolites were defined as metabolites that were highly connected within significant (pFDR < 0.05) modules or were identified as a hub in a previous analysis on cognitive function in the same cohort. Regression models included adjustments for age, sex, APOE genotype, lipid medication use, childhood cognitive ability, and social factors. Finally, associations were tested between AD polygenic risk scores (PRS), including and excluding the APOE region, and metabolites and modules that significantly associated (pFDR < 0.05) with an imaging outcome (N = 1638).ResultsIn the fully adjusted model, three lipid modules were associated with a brain volume measure (pFDR < 0.05): one enriched in sphingolipids (hippocampal volume: ß = 0.14, 95% CI = [0.055,0.23]), one in several fatty acid pathways (whole-brain volume: ß = - 0.072, 95%CI = [- 0.12, - 0.026]), and another in diacylglycerols and phosphatidylethanolamines (whole-brain volume: ß = - 0.066, 95% CI = [- 0.11, - 0.020]). Twenty-two hub metabolites were associated (pFDR < 0.05) with an imaging outcome (whole-brain volume: 22; hippocampal volume: 4). Some nominal associations were reported for amyloid-β, and with an AD PRS in our genetic analysis, but none survived multiple testing correction.ConclusionsOur findings highlight key metabolites, with functions in membrane integrity and cell signalling, that associated with structural brain measures in later life. Future research should focus on replicating this work and interrogating causality.

Project description:To understand how a model of Alzheimer disease pathophysiology based on ?-amyloidosis and neurodegeneration predicts the regional anatomic expansion of hypometabolism and atrophy in persons with mild cognitive impairment (MCI).To define the role of ?-amyloidosis and neurodegeneration in the subsequent progression of topographic cortical structural and metabolic changes in MCI.Longitudinal, observational study with serial brain imaging conducted from March 28, 2006, to January 6, 2015, using a population-based cohort. A total of 96 participants with MCI (all aged >70 years) with serial imaging biomarkers from the Mayo Clinic Study of Aging or Mayo Alzheimer's Disease Research Center were included. Participants were characterized initially as having elevated or not elevated brain ?-amyloidosis (A+ or A-) based on 11C-Pittsburgh compound B positron emission tomography. They were further characterized initially by the presence or absence of neurodegeneration (N+ or N-), where the presence of neurodegeneration was defined by abnormally low hippocampal volume or hypometabolism in an Alzheimer disease-like pattern on 18fluorodeoxyglucose (FDG)-positron emission tomography.Regional FDG standardized uptake value ratio (SUVR) and gray matter volumes in medial temporal, lateral temporal, lateral parietal, and medial parietal regions.In the primary regions of interest (ROI), the A+N+ group (n = 45) had lower FDG SUVR at baseline compared with the A+N- group (n = 17) (all 4 ROIs; P <?.001). The A+N+ group also had lower FDG SUVR at baseline (all 4 ROIs; P <?.01) compared with the A-N- group (n = 12). The A+N+ group had lower medial temporal gray matter volume at baseline (P <?.001) compared with either the A+N- group or A-N- group. The A+N+ group showed large longitudinal declines in FDG SUVR (P <?.05 for medial temporal, lateral temporal, and medial parietal regions) and gray matter volumes (P <?.05 for medial temporal and lateral temporal regions) compared with the A-N+ group (n = 22). The A+N+ group also showed large longitudinal declines compared with the A-N- group on FDG SUVR (P <?.05 for medial temporal and lateral parietal regions) and gray matter volumes (all 4 ROIs; P <?.05) compared with the A+N- group. The A-N+ group did not show declines in FDG SUVR or gray matter volume compared with the A+N- or A-N- groups.Persons with MCI who were A+N+ demonstrated volumetric and metabolic worsening in temporal and parietal association areas, consistent with the expectation that the MCI stage in the Alzheimer pathway heralds incipient isocortical involvement. The A-N+ group, those with suspected non-Alzheimer pathophysiology, lacked a distinctive longitudinal volumetric or metabolic profile.

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Dataset Information

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

Publications

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

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