David S. Karow

Alzheimer Disease: Quantitative Structural Neuroimaging for Detection and Prediction of Clinical and Structural Changes in Mild Cognitive Impairment

PURPOSE To use structural magnetic resonance (MR) images to identify a pattern of regional atrophy characteristic of mild Alzheimer disease (AD) and to investigate whether presence of this pattern prospectively can aid prediction of 1-year clinical decline and increased structural loss in mild cognitive impairment (MCI). MATERIALS AND METHODS The study was conducted with institutional review board approval and compliance with HIPAA regulations. Written informed consent was obtained from each participant. High-throughput volumetric segmentation and cortical surface reconstruction methods were applied to MR images from 84 subjects with mild AD, 175 with MCI, and 139 healthy control (HC) subjects. Stepwise linear discriminant analysis was used to identify regions that best can aid discrimination of HC subjects from subjects with AD. A classifier trained on data from HC subjects and those with AD was applied to data from subjects with MCI to determine whether presence of phenotypic AD atrophy at baseline was predictive of clinical decline and structural loss. RESULTS Atrophy in mesial and lateral temporal, isthmus cingulate, and orbitofrontal areas aided discrimination of HC subjects from subjects with AD, with fully cross-validated sensitivity of 83% and specificity of 93%. Subjects with MCI who had phenotypic AD atrophy showed significantly greater 1-year clinical decline and structural loss than those who did not and were more likely to have progression to probable AD (annual progression rate of 29% for subjects with MCI who had AD atrophy vs 8% for those who did not). CONCLUSION Semiautomated, individually specific quantitative MR imaging methods can be used to identify a pattern of regional atrophy in MCI that is predictive of clinical decline. Such information may aid in prediction of patient prognosis and increase the efficiency of clinical trials.

Neurons Detect Increases and Decreases in Oxygen Levels Using Distinct Guanylate Cyclases

Homeostatic sensory systems detect small deviations in temperature, water balance, pH, and energy needs to regulate adaptive behavior and physiology. In C. elegans, a homeostatic preference for intermediate oxygen (O2) levels requires cGMP signaling through soluble guanylate cyclases (sGCs), proteins that bind gases through an associated heme group. Here we use behavioral analysis, functional imaging, and genetics to show that reciprocal changes in O2 levels are encoded by sensory neurons that express alternative sets of sGCs. URX sensory neurons are activated by increases in O2 levels, and require the sGCs gcy-35 and gcy-36. BAG sensory neurons are activated by decreases in O2 levels, and require the sGCs gcy-31 and gcy-33. The sGCs are instructive O2 sensors, as forced expression of URX sGC genes causes BAG neurons to detect O2 increases. Both sGC expression and cell-intrinsic dynamics contribute to the differential roles of URX and BAG in O2-dependent behaviors.

Structural MRI biomarkers for preclinical and mild Alzheimer's disease†

Noninvasive MRI biomarkers for Alzheimers disease (AD) may enable earlier clinical diagnosis and the monitoring of therapeutic effectiveness. To assess potential neuroimaging biomarkers, the Alzheimers Disease Neuroimaging Initiative is following normal controls (NC) and individuals with mild cognitive impairment (MCI) or AD. We applied high‐throughput image analyses procedures to these data to demonstrate the feasibility of detecting subtle structural changes in prodromal AD. Raw DICOM scans (139 NC, 175 MCI, and 84 AD) were downloaded for analysis. Volumetric segmentation and cortical surface reconstruction produced continuous cortical surface maps and region‐of‐interest (ROI) measures. The MCI cohort was subdivided into single‐ (SMCI) and multiple‐domain MCI (MMCI) based on neuropsychological performance. Repeated measures analyses of covariance were used to examine group and hemispheric effects while controlling for age, sex, and, for volumetric measures, intracranial vault. ROI analyses showed group differences for ventricular, temporal, posterior and rostral anterior cingulate, posterior parietal, and frontal regions. SMCI and NC differed within temporal, rostral posterior cingulate, inferior parietal, precuneus, and caudal midfrontal regions. With MMCI and AD, greater differences were evident in these regions and additional frontal and retrosplenial cortices; evidence for non‐AD pathology in MMCI also was suggested. Mesial temporal right‐dominant asymmetries were evident and did not interact with diagnosis. Our findings demonstrate that high‐throughput methods provide numerous measures to detect subtle effects of prodromal AD, suggesting early and later stages of the preclinical state in this cross‐sectional sample. These methods will enable a more complete longitudinal characterization and allow us to identify changes that are predictive of conversion to AD. Hum Brain Mapp 2009.

Multi-modal imaging predicts memory performance in normal aging and cognitive decline

This study (n=161) related morphometric MR imaging, FDG-PET and APOE genotype to memory scores in normal controls (NC), mild cognitive impairment (MCI) and Alzheimers disease (AD). Stepwise regression analyses focused on morphometric and metabolic characteristics of the episodic memory network: hippocampus, entorhinal, parahippocampal, retrosplenial, posterior cingulate, precuneus, inferior parietal, and lateral orbitofrontal cortices. In NC, hippocampal metabolism predicted learning; entorhinal metabolism predicted recognition; and hippocampal metabolism predicted recall. In MCI, thickness of the entorhinal and precuneus cortices predicted learning, while parahippocampal metabolism predicted recognition. In AD, posterior cingulate cortical thickness predicted learning, while APOE genotype predicted recognition. In the total sample, hippocampal volume and metabolism, cortical thickness of the precuneus, and inferior parietal metabolism predicted learning; hippocampal volume and metabolism, parahippocampal thickness and APOE genotype predicted recognition. Imaging methods appear complementary and differentially sensitive to memory in health and disease. Medial temporal and parietal metabolism and morphometry best explained memory variance. Medial temporal characteristics were related to learning, recall and recognition, while parietal structures only predicted learning.

Relative Capability of MR Imaging and FDG PET to Depict Changes Associated with Prodromal and Early Alzheimer Disease

PURPOSE To quantify the effect sizes of regional metabolic and morphometric measures in patients with preclinical and mild Alzheimer disease (AD) to aid in the identification of noninvasive biomarkers for the early detection of AD. MATERIALS AND METHODS The study was conducted with institutional review board approval and in compliance with HIPAA regulations. Written informed consent was obtained from each participant or participants legal guardian. Fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET) and magnetic resonance (MR) imaging data were analyzed from 80 healthy control (HC) subjects, 68 individuals with AD, and 156 with amnestic mild cognitive impairment (MCI), 69 of whom had single-domain amnestic MCI. Regions of interest (ROIs) were derived after coregistering FDG PET and MR images by using high-throughput, subject-specific procedures. The Cohen d effect sizes were calculated for 42 predefined ROIs across the brain. Statistical comparison of the largest overall effect sizes for MR imaging and PET was performed. Metabolic effect sizes were determined with and without accounting for regional atrophy. Discriminative accuracy of ROIs showing the largest effect sizes were compared by calculating receiver operating characteristic curves. RESULTS For all disease groups, the hippocampus showed the largest morphometric effect size and the entorhinal cortex showed the largest metabolic effect size. In mild AD, the Cohen d effect size for hippocampal volume (1.92) was significantly larger (P < .05) than that for entorhinal metabolism (1.43). Regression of regional atrophy substantially reduced most metabolic effects. For all group comparisons, the areas under the receiver operating characteristic curves were significantly larger for hippocampal volume than for entorhinal metabolism. CONCLUSION The current results show no evidence that FDG PET is more sensitive than MR imaging to the degeneration occurring in preclinical and mild AD, suggesting that an MR imaging finding may be a more practical clinical biomarker for early detection of AD.

Nitric Oxide Binding to Prokaryotic Homologs of the Soluble Guanylate Cyclase β1 H-NOX Domain

The heme cofactor in soluble guanylate cyclase (sGC) is a selective receptor for NO, an important signaling molecule in eukaryotes. The sGC heme domain has been localized to the N-terminal 194 amino acids of the β1 subunit of sGC and is a member of a family of conserved hemoproteins, called the H-NOX family (Heme-Nitric Oxide and/or OXygen-binding domain). Three new members of this family have now been cloned and characterized, two proteins from Legionella pneumophila (L1 H-NOX and L2 H-NOX) and one from Nostoc punctiforme (Np H-NOX). Like sGC, L1 H-NOX forms a 5-coordinate FeII-NO complex. However, both L2 H-NOX and Np H-NOX form temperature-dependent mixtures of 5- and 6-coordinate FeII-NO complexes; at low temperature, they are primarily 6-coordinate, and at high temperature, the equilibrium is shifted toward a 5-coordinate geometry. This equilibrium is fully reversible with temperature in the absence of free NO. This process is analyzed in terms of a thermally labile proximal FeII-His bond and suggests that in both the 5- and 6-coordinate FeII-NO complexes of L2 H-NOX and Np H-NOX, NO is bound in the distal heme pocket of the H-NOX fold. NO dissociation kinetics for L1 H-NOX and L2 H-NOX have been determined and support a model in which NO dissociates from the distal side of the heme in both 5- and 6-coordinate complexes.

Genetic assessment of age-associated Alzheimer disease risk: Development and validation of a polygenic hazard score

Background Identifying individuals at risk for developing Alzheimer disease (AD) is of utmost importance. Although genetic studies have identified AD-associated SNPs in APOE and other genes, genetic information has not been integrated into an epidemiological framework for risk prediction. Methods and findings Using genotype data from 17,008 AD cases and 37,154 controls from the International Genomics of Alzheimer’s Project (IGAP Stage 1), we identified AD-associated SNPs (at p < 10−5). We then integrated these AD-associated SNPs into a Cox proportional hazard model using genotype data from a subset of 6,409 AD patients and 9,386 older controls from Phase 1 of the Alzheimer’s Disease Genetics Consortium (ADGC), providing a polygenic hazard score (PHS) for each participant. By combining population-based incidence rates and the genotype-derived PHS for each individual, we derived estimates of instantaneous risk for developing AD, based on genotype and age, and tested replication in multiple independent cohorts (ADGC Phase 2, National Institute on Aging Alzheimer’s Disease Center [NIA ADC], and Alzheimer’s Disease Neuroimaging Initiative [ADNI], total n = 20,680). Within the ADGC Phase 1 cohort, individuals in the highest PHS quartile developed AD at a considerably lower age and had the highest yearly AD incidence rate. Among APOE ε3/3 individuals, the PHS modified expected age of AD onset by more than 10 y between the lowest and highest deciles (hazard ratio 3.34, 95% CI 2.62–4.24, p = 1.0 × 10−22). In independent cohorts, the PHS strongly predicted empirical age of AD onset (ADGC Phase 2, r = 0.90, p = 1.1 × 10−26) and longitudinal progression from normal aging to AD (NIA ADC, Cochran–Armitage trend test, p = 1.5 × 10−10), and was associated with neuropathology (NIA ADC, Braak stage of neurofibrillary tangles, p = 3.9 × 10−6, and Consortium to Establish a Registry for Alzheimer’s Disease score for neuritic plaques, p = 6.8 × 10−6) and in vivo markers of AD neurodegeneration (ADNI, volume loss within the entorhinal cortex, p = 6.3 × 10−6, and hippocampus, p = 7.9 × 10−5). Additional prospective validation of these results in non-US, non-white, and prospective community-based cohorts is necessary before clinical use. Conclusions We have developed a PHS for quantifying individual differences in age-specific genetic risk for AD. Within the cohorts studied here, polygenic architecture plays an important role in modifying AD risk beyond APOE. With thorough validation, quantification of inherited genetic variation may prove useful for stratifying AD risk and as an enrichment strategy in therapeutic trials.

Polygenic Overlap Between C-Reactive Protein, Plasma Lipids, and Alzheimer Disease

Background— Epidemiological findings suggest a relationship between Alzheimer disease (AD), inflammation, and dyslipidemia, although the nature of this relationship is not well understood. We investigated whether this phenotypic association arises from a shared genetic basis. Methods and Results— Using summary statistics (P values and odds ratios) from genome-wide association studies of >200 000 individuals, we investigated overlap in single-nucleotide polymorphisms associated with clinically diagnosed AD and C-reactive protein (CRP), triglycerides, and high- and low-density lipoprotein levels. We found up to 50-fold enrichment of AD single-nucleotide polymorphisms for different levels of association with C-reactive protein, low-density lipoprotein, high-density lipoprotein, and triglyceride single-nucleotide polymorphisms using a false discovery rate threshold <0.05. By conditioning on polymorphisms associated with the 4 phenotypes, we identified 55 loci associated with increased AD risk. We then conducted a meta-analysis of these 55 variants across 4 independent AD cohorts (total: n=29 054 AD cases and 114 824 healthy controls) and discovered 2 genome-wide significant variants on chromosome 4 (rs13113697; closest gene, HS3ST1; odds ratio=1.07; 95% confidence interval=1.05–1.11; P=2.86×10−8) and chromosome 10 (rs7920721; closest gene, ECHDC3; odds ratio=1.07; 95% confidence interval=1.04–1.11; P=3.38×10−8). We also found that gene expression of HS3ST1 and ECHDC3 was altered in AD brains compared with control brains. Conclusions— We demonstrate genetic overlap between AD, C-reactive protein, and plasma lipids. By conditioning on the genetic association with the cardiovascular phenotypes, we identify novel AD susceptibility loci, including 2 genome-wide significant variants conferring increased risk for AD.

Novel technique for characterizing prostate cancer utilizing MRI restriction spectrum imaging: proof of principle and initial clinical experience with extraprostatic extension

Background:Standard magnetic resonance imaging (MRI) of the prostate lacks sensitivity in the diagnosis and staging of prostate cancer (PCa). To improve the operating characteristics of prostate MRI in the detection and characterization of PCa, we developed a novel, enhanced MRI diffusion technique using restriction spectrum imaging (RSI-MRI).Methods:We compared the efficacy of our novel RSI-MRI technique with standard MRI for detecting extraprostatic extension (EPE) among 28 PCa patients who underwent MRI and RSI-MRI prior to radical prostatectomy, 10 with histologically proven pT3 disease. RSI cellularity maps isolating the restricted isotropic water fraction were reconstructed based on all b-values and then standardized across the sample with z-score maps. Distortion correction of the RSI maps was performed using the alternating phase-encode technique.Results:27 patients were evaluated, excluding one patient where distortion could not be performed. Preoperative standard MRI correctly identified extraprostatic the extension in two of the nine pT3 (22%) patients, whereas RSI-MRI identified EPE in eight of nine (89%) patients. RSI-MRI correctly identified pT2 disease in the remaining 18 patients.Conclusions:In this proof of principle study, we conclude that our novel RSI-MRI technology is feasible and shows promise for substantially improving PCa imaging. Further translational studies of prostate RSI-MRI in the diagnosis and staging of PCa are indicated.

Prostate diffusion imaging with distortion correction

PURPOSE Diffusion imaging in the prostate is susceptible to distortion from B0 inhomogeneity. Distortion correction in prostate imaging is not routinely performed, resulting in diffusion images without accurate localization of tumors. We performed and evaluated distortion correction for diffusion imaging in the prostate. MATERIALS AND METHODS 28 patients underwent pre-operative MRI (T2, Gadolinium perfusion, diffusion at b=800 s/mm(2)). The restriction spectrum protocol parameters included b-values of 0, 800, 1500, and 4000 s/mm(2) in 30 directions for each nonzero b-value. To correct for distortion, forward and reverse trajectories were collected at b=0 s/mm(2). Distortion maps were generated to reflect the offset of the collected data versus the corrected data. Whole-mount histology was available for correlation. RESULTS Across the 27 patients evaluated (excluding one patient due to data collection error), the average root mean square distortion distance of the prostate was 3.1 mm (standard deviation, 2.2mm; and maximum distortion, 12 mm). CONCLUSION Improved localization of prostate cancer by MRI will allow better surgical planning, targeted biopsies and image-guided treatment therapies. Distortion distances of up to 12 mm due to standard diffusion imaging may grossly misdirect treatment decisions. Distortion correction for diffusion imaging in the prostate improves tumor localization.