Challenges and Opportunities with Causal Discovery Algorithms: Application to Alzheimer’s Pathophysiology

© 2020, The Author(s). Causal Structure Discovery (CSD) is the problem of identifying causal relationships from large quantities of data through computational methods. With the limited ability of traditional association-based computational methods to discover causal relationships, CSD methodologies are gaining popularity. The goal of the study was to systematically examine whether (i) CSD methods can discover the known causal relationships from observational clinical data and (ii) to offer guidance to accurately discover known causal relationships. We used Alzheimer’s disease (AD), a complex progressive disease, as a model because the well-established evidence provides a “gold-standard” causal graph for evaluation. We evaluated two CSD methods, Fast Causal Inference (FCI) and Fast Greedy Equivalence Search (FGES) in their ability to discover this structure from data collected by the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We used structural equation models (which is not designed for CSD) as control. We applied these methods under three scenarios defined by increasing amounts of background knowledge provided to the methods. The methods were evaluated by comparing the resulting causal relationships with the “gold standard” graph that was constructed from literature. Dedicated CSD methods managed to discover graphs that nearly coincided with the gold standard. For best results, CSD algorithms should be used with longitudinal data providing as much prior knowledge as possible

The BIN1 rs744373 SNP is associated with increased tau-PET levels and impaired memory

© 2019, The Author(s). The single nucleotide polymorphism (SNP) rs744373 in the bridging integrator-1 gene (BIN1) is a risk factor for Alzheimer’s disease (AD). In the brain, BIN1 is involved in endocytosis and sustaining cytoskeleton integrity. Post-mortem and in vitro studies suggest that BIN1-associated AD risk is mediated by increased tau pathology but whether rs744373 is associated with increased tau pathology in vivo is unknown. Here we find in 89 older individuals without dementia, that BIN1 rs744373 risk-allele carriers show higher AV1451 tau-PET across brain regions corresponding to Braak stages II–VI. In contrast, the BIN1 rs744373 SNP was not associated with AV45 amyloid-PET uptake. Furthermore, the rs744373 risk-allele was associated with worse memory performance, mediated by increased global tau levels. Together, our findings suggest that the BIN1 rs744373 SNP is associated with increased tau but not beta-amyloid pathology, suggesting that alterations in BIN1 may contribute to memory deficits via increased tau pathology

The Amyloid-beta Pathway in Alzheimer's Disease

Breakthroughs in molecular medicine have positioned the amyloid-β (Aβ) pathway at the center of Alzheimer’s disease (AD) pathophysiology. While the detailed molecular mechanisms of the pathway and the spatial-temporal dynamics leading to synaptic failure, neurodegeneration, and clinical onset are still under intense investigation, the established biochemical alterations of the Aβ cycle remain the core biological hallmark of AD and are promising targets for the development of disease-modifying therapies. Here, we systematically review and update the vast state-of-the-art literature of Aβ science with evidence from basic research studies to human genetic and multi-modal biomarker investigations, which supports a crucial role of Aβ pathway dyshomeostasis in AD pathophysiological dynamics. We discuss the evidence highlighting a differentiated interaction of distinct Aβ species with other AD-related biological mechanisms, such as tau-mediated, neuroimmune and inflammatory changes, as well as a neurochemical imbalance. Through the lens of the latest development of multimodal in vivo biomarkers of AD, this cross-disciplinary review examines the compelling hypothesis- and data-driven rationale for Aβ-targeting therapeutic strategies in development for the early treatment of AD

Detection and sizing of myocardial ischemia and infarction by nuclear magnetic resonance imaging in the canine heart

The usefulness of NMR imaging to size infarcted and hypoperfused, ischemic myocardium was assessed in 16 dogs which underwent coronary artery occlusion and reperfusion. During occlusion, technetium-99 microspheres were injected into the left atrium. Following death, the hearts were excised and underwent NMR imaging with a 0.35 tesla magnet, using multiple spin-echo pulse sequences. The epicardium of the heart was marked to indicate the level of the NMR cross-sectional tomographic image. The heart was subsequently breadloafed into 5 mm sections and the corresponding NMR cross-section was flagged for analysis. Autoradiography was performed to measure the hypoperfused, at-risk zone, and triphenyltetrazolium chloride staining was used to measure infarct size. For the flagged tomographic slice, the size of the NMR abnormality correlated well (r = 0.95), and was comparable to the actual hypoperfused, at-risk zone of the left ventricle. However, NMR estimates of infarct size correlated less well (r = 0.75) with the pathologic measure, and significantly overestimated actual infarct size (p 1 and T2 values were consistently increased (p < 0.0005) in both the hypoperfused and infarct zones, compared to normal myocardium. We conclude that NMR imaging can detect acute myocardial ischemia and infarction, but overestimates infarct size and corresponds better to the area of hypoperfused, ischemic myocardium. In this excised canine heart occlusion-reperfusion model, the NMR abnormality corresponded best to the area including both infarction and the surrounding ischemic region.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/25932/1/0000495.pd

Regional metabolism during coronary occlusion, reperfusion, and reocclusion using phosphorus31 nuclear magnetic resonance spectroscopy in the intact rabbit,

Few studies have examined metabolic consequences of coronary occlusion and reperfusion using phosphorus31 nuclear magnetic resonance (31P-NMR) in an intact animal model. Accordingly, we developed a model to study serial changes in myocardial metabolism in the intact open-chest rabbit. Ten animals underwent 20 +/- 2 minutes of regional coronary occlusion and 60 +/- 10 minutes of reperfusion followed by reocclusion. Cardiac-gated 31P-NMR spectra were obtained with a regional surface coll over the ischemic area during baseline, occlusion, reperfusion, and reocclusion conditions. Phosphocreatine fell with both the initial and second ischemic insults to 65% +/- 5% of baseline for the first occlusion (p p = 0.07), with normal levels reattained in the intervening period of reperfusion (99% +/- 5% of baseline, p = NS). Concordant inverse changes were seen with inorganic phosphates. At occlusion levels of inorganic phosphates were 135% +/- 10% of baseline (p p p p p p = NS). We conclude that reperfusion restores levels of phosphocreatine and adenosine triphosphate while returning levels of inorganic phosphates to baseline. Deleterious changes in high-energy phosphate metabolism are not potentiated by reocclusion in this model. 31P-NMR spectroscopy holds promise as a technique to noninvasively monitor intracellular biochemical processes serially during various interventions in the intact animal model.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/28117/1/0000566.pd

No Effect of One-Year Treatment with Indomethacin on Alzheimer's Disease Progression: A Randomized Controlled Trial

Contains fulltext : 71117.pdf (publisher's version ) (Open Access)BACKGROUND: The objective of this study was to determine whether treatment with the nonselective nonsteroidal anti-inflammatory drug (NSAID) indomethacin slows cognitive decline in patients with Alzheimer's disease (AD). METHODOLOGY/PRINCIPAL FINDINGS: This double-blind, randomized, placebo-controlled trial was conducted between May 2000 and September 2005 in two hospitals in the Netherlands. 51 patients with mild to moderate AD were enrolled into the study. Patients received 100 mg indomethacin or placebo daily for 12 months. Additionally, all patients received omeprazole. The primary outcome measure was the change from baseline after one year of treatment on the cognitive subscale of the AD Assessment Scale (ADAS-cog). Secondary outcome measures included the Mini-Mental State Examination, the Clinician's Interview Based Impression of Change with caregiver input, the noncognitive subscale of the ADAS, the Neuropsychiatric Inventory, and the Interview for Deterioration in Daily life in Dementia. Considerable recruitment problems of participants were encountered, leading to an underpowered study. In the placebo group, 19 out of 25 patients completed the study, and 19 out of 26 patients in the indomethacin group. The deterioration on the ADAS-cog was less in the indomethacin group (7.8+/-7.6), than in the placebo group (9.3+/-10.0). This difference (1.5 points; CI -4.5-7.5) was not statistically significant, and neither were any of the secondary outcome measures. CONCLUSIONS/SIGNIFICANCE: The results of this study are inconclusive with respect to the hypothesis that indomethacin slows the progression of AD

Clinical and biomarker changes in dominantly inherited Alzheimer\u27s disease

BACKGROUND: The order and magnitude of pathologic processes in Alzheimer\u27s disease are not well understood, partly because the disease develops over many years. Autosomal dominant Alzheimer\u27s disease has a predictable age at onset and provides an opportunity to determine the sequence and magnitude of pathologic changes that culminate in symptomatic disease. METHODS: In this prospective, longitudinal study, we analyzed data from 128 participants who underwent baseline clinical and cognitive assessments, brain imaging, and cerebrospinal fluid (CSF) and blood tests. We used the participant\u27s age at baseline assessment and the parent\u27s age at the onset of symptoms of Alzheimer\u27s disease to calculate the estimated years from expected symptom onset (age of the participant minus parent\u27s age at symptom onset). We conducted cross-sectional analyses of baseline data in relation to estimated years from expected symptom onset in order to determine the relative order and magnitude of pathophysiological changes. RESULTS: Concentrations of amyloid-beta (Aβ) 42 in the CSF appeared to decline 25 years before expected symptom onset. Aβ deposition, as measured by positron-emission tomography with the use of Pittsburgh compound B, was detected 15 years before expected symptom onset. Increased concentrations of tau protein in the CSF and an increase in brain atrophy were detected 15 years before expected symptom onset. Cerebral hypometabolism and impaired episodic memory were observed 10 years before expected symptom onset. Global cognitive impairment, as measured by the Mini-Mental State Examination and the Clinical Dementia Rating scale, was detected 5 years before expected symptom onset, and patients met diagnostic criteria for dementia at an average of 3 years after expected symptom onset. CONCLUSIONS: We found that autosomal dominant Alzheimer\u27s disease was associated with a series of pathophysiological changes over decades in CSF biochemical markers of Alzheimer\u27s disease, brain amyloid deposition, and brain metabolism as well as progressive cognitive impairment. Our results require confirmation with the use of longitudinal data and may not apply to patients with sporadic Alzheimer\u27s disease. (Funded by the National Institute on Aging and others; DIAN ClinicalTrials.gov number, NCT00869817.

Effects of traumatic brain injury and posttraumatic stress disorder on development of Alzheimer's disease in Vietnam Veterans using the Alzheimer's Disease Neuroimaging Initiative: Preliminary report

Introduction Traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) have previously been reported to be associated with increased risk of Alzheimer's disease (AD). We are using biomarkers to study Vietnam Veterans with/without mild cognitive impairment with a history of at least one TBI and/or ongoing PTSD to determine whether these contribute to the development of AD. Methods Potential subjects identified by Veterans Administration records underwent an initial telephone screen. Consented subjects underwent clinical evaluation, lumbar puncture, structural magnetic resonance imaging, and amyloid positron emission tomography (PET) scans. Results We observed worse cognitive functioning in PTSD and TBI + PTSD groups, worse global cognitive functioning in the PTSD group, lower superior parietal volume in the TBI + PTSD group, and lower amyloid positivity in the PTSD group, but not the TBI group compared to controls without TBI/PTSD. Medial temporal lobe atrophy was not increased in the PTSD and/or TBI groups. Discussion Preliminary results do not indicate that TBI or PTSD increase the risk for AD measured by amyloid PET. Additional recruitment, longitudinal follow-up, and tau-PET scans will provide more information in the future

Machine learning for comprehensive forecasting of Alzheimer's Disease progression

Most approaches to machine learning from electronic health data can only predict a single endpoint. The ability to simultaneously simulate dozens of patient characteristics is a crucial step towards personalized medicine for Alzheimer’s Disease. Here, we use an unsupervised machine learning model called a Conditional Restricted Boltzmann Machine (CRBM) to simulate detailed patient trajectories. We use data comprising 18-month trajectories of 44 clinical variables from 1909 patients with Mild Cognitive Impairment or Alzheimer’s Disease to train a model for personalized forecasting of disease progression. We simulate synthetic patient data including the evolution of each sub-component of cognitive exams, laboratory tests, and their associations with baseline clinical characteristics. Synthetic patient data generated by the CRBM accurately reflect the means, standard deviations, and correlations of each variable over time to the extent that synthetic data cannot be distinguished from actual data by a logistic regression. Moreover, our unsupervised model predicts changes in total ADAS-Cog scores with the same accuracy as specifically trained supervised models, additionally capturing the correlation structure in the components of ADAS-Cog, and identifies sub-components associated with word recall as predictive of progression