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1. Disrupting Electronic Health Records Systems: The Next Generation

Author

Celi, Leo Anthony, Marshall, Jeffrey David, Lai, Yuan, and Stone, David J

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

The health care system suffers from both inefficient and ineffective use of data. Data are suboptimally displayed to users, undernetworked, underutilized, and wasted. Errors, inefficiencies, and increased costs occur on the basis of unavailable data in a system that does not coordinate the exchange of information, or adequately support its use. Clinicians’ schedules are stretched to the limit and yet the system in which they work exerts little effort to streamline and support carefully engineered care processes. Information for decision-making is difficult to access in the context of hurried real-time workflows. This paper explores and addresses these issues to formulate an improved design for clinical workflow, information exchange, and decision making based on the use of electronic health records.

Published
2015
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2. Metadata Correction: Making Big Data Useful for Health Care: A Summary of the Inaugural MIT Critical Data Conference

Author

Badawi, Omar, Brennan, Thomas, Celi, Leo Anthony, Feng, Mengling, Ghassemi, Marzyeh, Ippolito, Andrea, Johnson, Alistair, Mark, Roger G, Mayaud, Louis, Moody, George, Moses, Christopher, Naumann, Tristan, Nikore, Vipan, Pimentel, Marco, Pollard, Tom J, Santos, Mauro, Stone, David J, and Zimolzak, Andrew

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Published
2015
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3. Beyond Open Big Data: Addressing Unreliable Research

Author

Moseley, Edward T, Hsu, Douglas J, Stone, David J, and Celi, Leo Anthony

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

The National Institute of Health invests US $30.9 billion annually in medical research. However, the subsequent impact of this research output on society and the economy is amplified dramatically as a result of the actual medical treatments, biomedical innovations, and various commercial enterprises that emanate from and depend on these findings. It is therefore a great concern to discover that much of published research is unreliable. We propose extending the open data concept to the culture of the scientific research community. By dialing down unproductive features of secrecy and competition, while ramping up cooperation and transparency, we make a case that what is published would then be less susceptible to the sometimes corrupting and confounding pressures to be first or journalistically attractive, which can compromise the more fundamental need to be robustly correct.

Published
2014
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4. Crowdsourcing Knowledge Discovery and Innovations in Medicine

Author

Celi, Leo Anthony, Ippolito, Andrea, Montgomery, Robert A, Moses, Christopher, and Stone, David J

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

Clinicians face difficult treatment decisions in contexts that are not well addressed by available evidence as formulated based on research. The digitization of medicine provides an opportunity for clinicians to collaborate with researchers and data scientists on solutions to previously ambiguous and seemingly insolvable questions. But these groups tend to work in isolated environments, and do not communicate or interact effectively. Clinicians are typically buried in the weeds and exigencies of daily practice such that they do not recognize or act on ways to improve knowledge discovery. Researchers may not be able to identify the gaps in clinical knowledge. For data scientists, the main challenge is discerning what is relevant in a domain that is both unfamiliar and complex. Each type of domain expert can contribute skills unavailable to the other groups. “Health hackathons” and “data marathons”, in which diverse participants work together, can leverage the current ready availability of digital data to discover new knowledge. Utilizing the complementary skills and expertise of these talented, but functionally divided groups, innovations are formulated at the systems level. As a result, the knowledge discovery process is simultaneously democratized and improved, real problems are solved, cross-disciplinary collaboration is supported, and innovations are enabled.

Published
2014
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5. Making Big Data Useful for Health Care: A Summary of the Inaugural MIT Critical Data Conference

Author

Badawi, Omar, Brennan, Thomas, Celi, Leo Anthony, Feng, Mengling, Ghassemi, Marzyeh, Ippolito, Andrea, Johnson, Alistair, Mark, Roger G, Mayaud, Louis, Moody, George, Moses, Christopher, Naumann, Tristan, Nikore, Vipan, Pimentel, Marco, Pollard, Tom J, Santos, Mauro, Stone, David J, and Zimolzak, Andrew

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

With growing concerns that big data will only augment the problem of unreliable research, the Laboratory of Computational Physiology at the Massachusetts Institute of Technology organized the Critical Data Conference in January 2014. Thought leaders from academia, government, and industry across disciplines—including clinical medicine, computer science, public health, informatics, biomedical research, health technology, statistics, and epidemiology—gathered and discussed the pitfalls and challenges of big data in health care. The key message from the conference is that the value of large amounts of data hinges on the ability of researchers to share data, methodologies, and findings in an open setting. If empirical value is to be from the analysis of retrospective data, groups must continuously work together on similar problems to create more effective peer review. This will lead to improvement in methodology and quality, with each iteration of analysis resulting in more reliability.

Published
2014
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6. Dynamic Clinical Data Mining: Search Engine-Based Decision Support

Author

Celi, Leo Anthony, Zimolzak, Andrew J, and Stone, David J

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

The research world is undergoing a transformation into one in which data, on massive levels, is freely shared. In the clinical world, the capture of data on a consistent basis has only recently begun. We propose an operational vision for a digitally based care system that incorporates data-based clinical decision making. The system would aggregate individual patient electronic medical data in the course of care; query a universal, de-identified clinical database using modified search engine technology in real time; identify prior cases of sufficient similarity as to be instructive to the case at hand; and populate the individual patient's electronic medical record with pertinent decision support material such as suggested interventions and prognosis, based on prior outcomes. Every individual's course, including subsequent outcomes, would then further populate the population database to create a feedback loop to benefit the care of future patients.

Published
2014
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8. Prediction of Blood Lactate Values in Critically Ill Patients: A Retrospective Multi-center Cohort Study

Author

Mamandipoor, Behrooz, Yeung, Wesley, Agha-Mir-Salim, Louis, Stone, David J., Osmani, Venet, and Celi, Leo Anthony

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Computers and Society, Computer Science - Machine Learning, Statistics - Applications
Abstract

Purpose. Elevations in initially obtained serum lactate levels are strong predictors of mortality in critically ill patients. Identifying patients whose serum lactate levels are more likely to increase can alert physicians to intensify care and guide them in the frequency of tending the blood test. We investigate whether machine learning models can predict subsequent serum lactate changes. Methods. We investigated serum lactate change prediction using the MIMIC-III and eICU-CRD datasets in internal as well as external validation of the eICU cohort on the MIMIC-III cohort. Three subgroups were defined based on the initial lactate levels: i) normal group (<2 mmol/L), ii) mild group (2-4 mmol/L), and iii) severe group (>4 mmol/L). Outcomes were defined based on increase or decrease of serum lactate levels between the groups. We also performed sensitivity analysis by defining the outcome as lactate change of >10% and furthermore investigated the influence of the time interval between subsequent lactate measurements on predictive performance. Results. The LSTM models were able to predict deterioration of serum lactate values of MIMIC-III patients with an AUC of 0.77 (95% CI 0.762-0.771) for the normal group, 0.77 (95% CI 0.768-0.772) for the mild group, and 0.85 (95% CI 0.840-0.851) for the severe group, with a slightly lower performance in the external validation. Conclusion. The LSTM demonstrated good discrimination of patients who had deterioration in serum lactate levels. Clinical studies are needed to evaluate whether utilization of a clinical decision support tool based on these results could positively impact decision-making and patient outcomes., Comment: 15 pages, 6 Appendices

Published
2021
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9. Genome sequencing analysis identifies new loci associated with Lewy body dementia and provides insights into its genetic architecture

Author

Chia, Ruth, Sabir, Marya S, Bandres-Ciga, Sara, Saez-Atienzar, Sara, Reynolds, Regina H, Gustavsson, Emil, Walton, Ronald L, Ahmed, Sarah, Viollet, Coralie, Ding, Jinhui, Makarious, Mary B, Diez-Fairen, Monica, Portley, Makayla K, Shah, Zalak, Abramzon, Yevgeniya, Hernandez, Dena G, Blauwendraat, Cornelis, Stone, David J, Eicher, John, Parkkinen, Laura, Ansorge, Olaf, Clark, Lorraine, Honig, Lawrence S, Marder, Karen, Lemstra, Afina, St George-Hyslop, Peter, Londos, Elisabet, Morgan, Kevin, Lashley, Tammaryn, Warner, Thomas T, Jaunmuktane, Zane, Galasko, Douglas, Santana, Isabel, Tienari, Pentti J, Myllykangas, Liisa, Oinas, Minna, Cairns, Nigel J, Morris, John C, Halliday, Glenda M, Van Deerlin, Vivianna M, Trojanowski, John Q, Grassano, Maurizio, Calvo, Andrea, Mora, Gabriele, Canosa, Antonio, Floris, Gianluca, Bohannan, Ryan C, Brett, Francesca, Gan-Or, Ziv, Geiger, Joshua T, Moore, Anni, May, Patrick, Krüger, Rejko, Goldstein, David S, Lopez, Grisel, Tayebi, Nahid, Sidransky, Ellen, Norcliffe-Kaufmann, Lucy, Palma, Jose-Alberto, Kaufmann, Horacio, Shakkottai, Vikram G, Perkins, Matthew, Newell, Kathy L, Gasser, Thomas, Schulte, Claudia, Landi, Francesco, Salvi, Erika, Cusi, Daniele, Masliah, Eliezer, Kim, Ronald C, Caraway, Chad A, Monuki, Edwin S, Brunetti, Maura, Dawson, Ted M, Rosenthal, Liana S, Albert, Marilyn S, Pletnikova, Olga, Troncoso, Juan C, Flanagan, Margaret E, Mao, Qinwen, Bigio, Eileen H, Rodríguez-Rodríguez, Eloy, Infante, Jon, Lage, Carmen, González-Aramburu, Isabel, Sanchez-Juan, Pascual, Ghetti, Bernardino, Keith, Julia, Black, Sandra E, Masellis, Mario, Rogaeva, Ekaterina, Duyckaerts, Charles, Brice, Alexis, Lesage, Suzanne, Xiromerisiou, Georgia, Barrett, Matthew J, Tilley, Bension S, Gentleman, Steve, Logroscino, Giancarlo, and Serrano, Geidy E

Subjects
Biological Sciences, Genetics, Human Genome, Alzheimer's Disease Related Dementias (ADRD), Prevention, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Alzheimer's Disease, Aging, Dementia, Brain Disorders, Acquired Cognitive Impairment, Lewy Body Dementia, Biotechnology, Parkinson's Disease, Neurosciences, Neurodegenerative, 2.1 Biological and endogenous factors, Aetiology, Neurological, Adaptor Proteins, Signal Transducing, Alzheimer Disease, Case-Control Studies, Gene Expression Profiling, Genetic Predisposition to Disease, Genome, Human, Genome-Wide Association Study, Glucosylceramidase, Humans, Lewy Body Disease, Nuclear Proteins, Parkinson Disease, Polymorphism, Single Nucleotide, Tumor Suppressor Proteins, alpha-Synuclein, American Genome Center, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

The genetic basis of Lewy body dementia (LBD) is not well understood. Here, we performed whole-genome sequencing in large cohorts of LBD cases and neurologically healthy controls to study the genetic architecture of this understudied form of dementia, and to generate a resource for the scientific community. Genome-wide association analysis identified five independent risk loci, whereas genome-wide gene-aggregation tests implicated mutations in the gene GBA. Genetic risk scores demonstrate that LBD shares risk profiles and pathways with Alzheimer's disease and Parkinson's disease, providing a deeper molecular understanding of the complex genetic architecture of this age-related neurodegenerative condition.

Published
2021

10. Genome-wide structural variant analysis identifies risk loci for non-Alzheimer’s dementias

Author

Soltis, Anthony R., Viollet, Coralie, Sukumar, Gauthaman, Alba, Camille, Lott, Nathaniel, McGrath Martinez, Elisa, Tuck, Meila, Singh, Jatinder, Bacikova, Dagmar, Zhang, Xijun, Hupalo, Daniel N., Adeleye, Adelani, Wilkerson, Matthew D., Pollard, Harvey B., Dalgard, Clifton L., Black, Sandra E., Gan-Or, Ziv, Keith, Julia, Masellis, Mario, Rogaeva, Ekaterina, Brice, Alexis, Lesage, Suzanne, Xiromerisiou, Georgia, Calvo, Andrea, Canosa, Antonio, Chio, Adriano, Logroscino, Giancarlo, Mora, Gabriele, Krüger, Reijko, May, Patrick, Alcolea, Daniel, Clarimon, Jordi, Fortea, Juan, Gonzalez-Aramburu, Isabel, Infante, Jon, Lage, Carmen, Lleó, Alberto, Pastor, Pau, Sanchez-Juan, Pascual, Brett, Francesca, Aarsland, Dag, Al-Sarraj, Safa, Attems, Johannes, Gentleman, Steve, Hardy, John A., Hodges, Angela K., Love, Seth, McKeith, Ian G., Morris, Christopher M., Morris, Huw R., Palmer, Laura, Pickering-Brown, Stuart, Ryten, Mina, Thomas, Alan J., Troakes, Claire, Albert, Marilyn S., Barrett, Matthew J., Beach, Thomas G., Bekris, Lynn M., Bennett, David A., Boeve, Bradley F., Dawson, Ted M., Dickson, Dennis W., Faber, Kelley, Ferman, Tanis, Ferrucci, Luigi, Flanagan, Margaret E., Foroud, Tatiana M., Ghetti, Bernardino, Gibbs, J. Raphael, Goate, Alison, Goldstein, David S., Graff-Radford, Neill R., Kaufmann, Horacio, Kukull, Walter A., Leverenz, James B., Lopez, Grisel, Mao, Qinwen, Masliah, Eliezer, Monuki, Edwin, Newell, Kathy L., Palma, Jose-Alberto, Perkins, Matthew, Pletnikova, Olga, Renton, Alan E., Resnick, Susan M., Rosenthal, Liana S., Ross, Owen A., Scherzer, Clemens R., Serrano, Geidy E., Shakkottai, Vikram G., Sidransky, Ellen, Tanaka, Toshiko, Tayebi, Nahid, Topol, Eric, Torkamani, Ali, Troncoso, Juan C., Woltjer, Randy, Wszolek, Zbigniew K., Scholz, Sonja W., Baloh, Robert H., Bowser, Robert, Broach, James, Camu, William, Chiò, Adriano, Cooper-Knock, John, Drepper, Carsten, Drory, Vivian E., Dunckley, Travis L., Feldman, Eva, Fratta, Pietro, Gerhard, Glenn, Gibson, Summer B., Glass, Jonathan D., Harms, Matthew B., Heiman-Patterson, Terry D., Jansson, Lilja, Kirby, Janine, Kwan, Justin, Laaksovirta, Hannu, Landers, John E., Landi, Francesco, Le Ber, Isabelle, Lumbroso, Serge, MacGowan, Daniel J.L., Maragakis, Nicholas J., Mouzat, Kevin, Myllykangas, Liisa, Orrell, Richard W., Ostrow, Lyle W., Pamphlett, Roger, Pioro, Erik, Pulst, Stefan M., Ravits, John M., Robberecht, Wim, Rothstein, Jeffrey D., Sendtner, Michael, Shaw, Pamela J., Sidle, Katie C., Simmons, Zachary, Stein, Thor, Stone, David J., Tienari, Pentti J., Traynor, Bryan J., Valori, Miko, Van Damme, Philip, Van Deerlin, Vivianna M., Van Den Bosch, Ludo, Zinman, Lorne, Kaivola, Karri, Chia, Ruth, Ding, Jinhui, Rasheed, Memoona, Fujita, Masashi, Menon, Vilas, Walton, Ronald L., Collins, Ryan L., Billingsley, Kimberley, Brand, Harrison, Talkowski, Michael, Zhao, Xuefang, Dewan, Ramita, Stark, Ali, Ray, Anindita, Solaiman, Sultana, Alvarez Jerez, Pilar, Malik, Laksh, Tienari, Pentti, Mazzini, Letizia, D'Alfonso, Sandra, Moglia, Cristina, and De Jager, Philip L.

Published
2023
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12. Withholding or withdrawing invasive interventions may not accelerate time to death among dying ICU patients

Author

Ramazzotti, Daniele, Clardy, Peter, Celi, Leo Anthony, Stone, David J., and Rudin, Robert S.

Subjects
Computer Science - Computers and Society, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

We considered observational data available from the MIMIC-III open-access ICU database and collected within a study period between year 2002 up to 2011. If a patient had multiple admissions to the ICU during the 30 days before death, only the first stay was analyzed, leading to a final set of 6,436 unique ICU admissions during the study period. We tested two hypotheses: (i) administration of invasive intervention during the ICU stay immediately preceding end-of-life would decrease over the study time period and (ii) time-to-death from ICU admission would also decrease, due to the decrease in invasive intervention administration. To investigate the latter hypothesis, we performed a subgroups analysis by considering patients with lowest and highest severity. To do so, we stratified the patients based on their SAPS I scores, and we considered patients within the first and the third tertiles of the score. We then assessed differences in trends within these groups between years 2002-05 vs. 2008-11. Comparing the period 2002-2005 vs. 2008-2011, we found a reduction in endotracheal ventilation among patients who died within 30 days of ICU admission (120.8 vs. 68.5 hours for the lowest severity patients, p<0.001; 47.7 vs. 46.0 hours for the highest severity patients, p=0.004). This is explained in part by an increase in the use of non-invasive ventilation. Comparing the period 2002-2005 vs. 2008-2011, we found a reduction in the use of vasopressors and inotropes among patients with the lowest severity who died within 30 days of ICU admission (41.8 vs. 36.2 hours, p<0.001) but not among those with the highest severity. Despite a reduction in the use of invasive interventions, we did not find a reduction in the time to death between 2002-2005 vs. 2008-2011 (7.8 days vs. 8.2 days for the lowest severity patients, p=0.32; 2.1 days vs. 2.0 days for the highest severity patients, p=0.74).

Published
2018
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13. Comparative transcriptomics of choroid plexus in Alzheimer’s disease, frontotemporal dementia and Huntington’s disease: implications for CSF homeostasis

Author

Stopa, Edward G, Tanis, Keith Q, Miller, Miles C, Nikonova, Elena V, Podtelezhnikov, Alexei A, Finney, Eva M, Stone, David J, Camargo, Luiz M, Parker, Lisan, Verma, Ajay, Baird, Andrew, Donahue, John E, Torabi, Tara, Eliceiri, Brian P, Silverberg, Gerald D, and Johanson, Conrad E

Subjects
Acquired Cognitive Impairment, Rare Diseases, Aging, Brain Disorders, Dementia, Neurosciences, Alzheimer's Disease, Genetics, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Neurodegenerative, Neurological, Adult, Aged, Aged, 80 and over, Alzheimer Disease, Choroid Plexus, Female, Frontotemporal Dementia, Gene Expression, Homeostasis, Humans, Huntington Disease, Male, Microarray Analysis, Middle Aged, Transcriptome, Choroid plexus transcriptome, Neuroimmune CSF regulation, Blood-CSF barrier inflammatome, Janus kinase/signal transducers and activators of transcription, Peroxisome-proliferator-activated receptor, Cadherin-mediated adhesion, Vascular endothelial growth factor, LRP-1, Choroid plexus methionine, CSF homocysteine, Mechanistic target of rapamycin, Blood–CSF barrier inflammatome, Biological Sciences, Medical and Health Sciences
Abstract

BackgroundIn Alzheimer's disease, there are striking changes in CSF composition that relate to altered choroid plexus (CP) function. Studying CP tissue gene expression at the blood-cerebrospinal fluid barrier could provide further insight into the epithelial and stromal responses to neurodegenerative disease states.MethodsTranscriptome-wide Affymetrix microarrays were used to determine disease-related changes in gene expression in human CP. RNA from post-mortem samples of the entire lateral ventricular choroid plexus was extracted from 6 healthy controls (Ctrl), 7 patients with advanced (Braak and Braak stage III-VI) Alzheimer's disease (AD), 4 with frontotemporal dementia (FTD) and 3 with Huntington's disease (HuD). Statistics and agglomerative clustering were accomplished with MathWorks, MatLab; and gene set annotations by comparing input sets to GeneGo ( http://www.genego.com ) and Ingenuity ( http://www.ingenuity.com ) pathway sets. Bonferroni-corrected hypergeometric p-values of

Published
2018

14. Data Integration for Urban Health

Author

Lai, Yuan, Stone, David J., Celi, Leo Anthony, editor, Majumder, Maimuna S., editor, Ordóñez, Patricia, editor, Osorio, Juan Sebastian, editor, Paik, Kenneth E., editor, and Somai, Melek, editor

Published
2020
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17. Diagnosis of Parkinson's disease on the basis of clinical and genetic classification: a population-based modelling study

Author

Nalls, Mike A, McLean, Cory Y, Rick, Jacqueline, Eberly, Shirley, Hutten, Samantha J, Gwinn, Katrina, Sutherland, Margaret, Martinez, Maria, Heutink, Peter, Williams, Nigel M, Hardy, John, Gasser, Thomas, Brice, Alexis, Price, T Ryan, Nicolas, Aude, Keller, Margaux F, Molony, Cliona, Gibbs, J Raphael, Chen-Plotkin, Alice, Suh, Eunran, Letson, Christopher, Fiandaca, Massimo S, Mapstone, Mark, Federoff, Howard J, Noyce, Alastair J, Morris, Huw, Van Deerlin, Vivianna M, Weintraub, Daniel, Zabetian, Cyrus, Hernandez, Dena G, Lesage, Suzanne, Mullins, Meghan, Conley, Emily Drabant, Northover, Carrie AM, Frasier, Mark, Marek, Ken, Day-Williams, Aaron G, Stone, David J, Ioannidis, John PA, Singleton, Andrew B, and investigators*, Parkinson's Disease Biomarkers Program and Parkinson's Progression Marker Initiative

Subjects
Aging, Prevention, Neurodegenerative, Brain Disorders, Neurosciences, Parkinson's Disease, Clinical Research, 2.1 Biological and endogenous factors, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Aetiology, 4.1 Discovery and preclinical testing of markers and technologies, Neurological, Good Health and Well Being, Aged, Cohort Studies, Disease Progression, Female, Humans, Male, Middle Aged, Models, Statistical, Parkinson Disease, Prodromal Symptoms, Parkinson's Disease Biomarkers Program and Parkinson's Progression Marker Initiative investigators, Clinical Sciences, Neurology & Neurosurgery
Abstract

BackgroundAccurate diagnosis and early detection of complex diseases, such as Parkinson's disease, has the potential to be of great benefit for researchers and clinical practice. We aimed to create a non-invasive, accurate classification model for the diagnosis of Parkinson's disease, which could serve as a basis for future disease prediction studies in longitudinal cohorts.MethodsWe developed a model for disease classification using data from the Parkinson's Progression Marker Initiative (PPMI) study for 367 patients with Parkinson's disease and phenotypically typical imaging data and 165 controls without neurological disease. Olfactory function, genetic risk, family history of Parkinson's disease, age, and gender were algorithmically selected by stepwise logistic regression as significant contributors to our classifying model. We then tested the model with data from 825 patients with Parkinson's disease and 261 controls from five independent cohorts with varying recruitment strategies and designs: the Parkinson's Disease Biomarkers Program (PDBP), the Parkinson's Associated Risk Study (PARS), 23andMe, the Longitudinal and Biomarker Study in PD (LABS-PD), and the Morris K Udall Parkinson's Disease Research Center of Excellence cohort (Penn-Udall). Additionally, we used our model to investigate patients who had imaging scans without evidence of dopaminergic deficit (SWEDD).FindingsIn the population from PPMI, our initial model correctly distinguished patients with Parkinson's disease from controls at an area under the curve (AUC) of 0·923 (95% CI 0·900-0·946) with high sensitivity (0·834, 95% CI 0·711-0·883) and specificity (0·903, 95% CI 0·824-0·946) at its optimum AUC threshold (0·655). All Hosmer-Lemeshow simulations suggested that when parsed into random subgroups, the subgroup data matched that of the overall cohort. External validation showed good classification of Parkinson's disease, with AUCs of 0·894 (95% CI 0·867-0·921) in the PDBP cohort, 0·998 (0·992-1·000) in PARS, 0·955 (no 95% CI available) in 23andMe, 0·929 (0·896-0·962) in LABS-PD, and 0·939 (0·891-0·986) in the Penn-Udall cohort. Four of 17 SWEDD participants who our model classified as having Parkinson's disease converted to Parkinson's disease within 1 year, whereas only one of 38 SWEDD participants who were not classified as having Parkinson's disease underwent conversion (test of proportions, p=0·003).InterpretationOur model provides a potential new approach to distinguish participants with Parkinson's disease from controls. If the model can also identify individuals with prodromal or preclinical Parkinson's disease in prospective cohorts, it could facilitate identification of biomarkers and interventions.FundingNational Institute on Aging, National Institute of Neurological Disorders and Stroke, and the Michael J Fox Foundation.

Published
2015

19. Common dysregulation network in the human prefrontal cortex underlies two neurodegenerative diseases.

Author

Narayanan, Manikandan, Huynh, Jimmy L, Wang, Kai, Yang, Xia, Yoo, Seungyeul, McElwee, Joshua, Zhang, Bin, Zhang, Chunsheng, Lamb, John R, Xie, Tao, Suver, Christine, Molony, Cliona, Melquist, Stacey, Johnson, Andrew D, Fan, Guoping, Stone, David J, Schadt, Eric E, Casaccia, Patrizia, Emilsson, Valur, and Zhu, Jun

Subjects
Prefrontal Cortex, Chromatin, Animals, Mice, Knockout, Humans, Mice, Huntington Disease, Dementia, Alzheimer Disease, Autopsy, Case-Control Studies, Reproducibility of Results, Gene Expression Profiling, Gene Expression Regulation, Gene Regulatory Networks, DNA (Cytosine-5-)-Methyltransferase 1, DNA (Cytosine-5-)-Methyltransferases, differential co‐expression, dysregulatory gene networks, epigenetic regulation of neural differentiation, network alignment, neurodegenerative diseases, differential co-expression, Knockout, DNA (Cytosine-5-)-Methyltransferase, Bioinformatics, Biochemistry and Cell Biology, Other Biological Sciences
Abstract

Using expression profiles from postmortem prefrontal cortex samples of 624 dementia patients and non-demented controls, we investigated global disruptions in the co-regulation of genes in two neurodegenerative diseases, late-onset Alzheimer's disease (AD) and Huntington's disease (HD). We identified networks of differentially co-expressed (DC) gene pairs that either gained or lost correlation in disease cases relative to the control group, with the former dominant for both AD and HD and both patterns replicating in independent human cohorts of AD and aging. When aligning networks of DC patterns and physical interactions, we identified a 242-gene subnetwork enriched for independent AD/HD signatures. This subnetwork revealed a surprising dichotomy of gained/lost correlations among two inter-connected processes, chromatin organization and neural differentiation, and included DNA methyltransferases, DNMT1 and DNMT3A, of which we predicted the former but not latter as a key regulator. To validate the inter-connection of these two processes and our key regulator prediction, we generated two brain-specific knockout (KO) mice and show that Dnmt1 KO signature significantly overlaps with the subnetwork (P = 3.1 × 10(-12)), while Dnmt3a KO signature does not (P = 0.017).

Published
2014

20. Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers.

Author

Shen, Li, Thompson, Paul M, Potkin, Steven G, Bertram, Lars, Farrer, Lindsay A, Foroud, Tatiana M, Green, Robert C, Hu, Xiaolan, Huentelman, Matthew J, Kim, Sungeun, Kauwe, John SK, Li, Qingqin, Liu, Enchi, Macciardi, Fabio, Moore, Jason H, Munsie, Leanne, Nho, Kwangsik, Ramanan, Vijay K, Risacher, Shannon L, Stone, David J, Swaminathan, Shanker, Toga, Arthur W, Weiner, Michael W, Saykin, Andrew J, and Alzheimer’s Disease Neuroimaging Initiative

Subjects
Alzheimer’s Disease Neuroimaging Initiative, Humans, Alzheimer Disease, Cognition, Phenotype, Neuroimaging, Biomarkers, Cognitive Dysfunction, Alzheimer's disease, Genetic association study, Quantitative traits, Biomarker, Experimental Psychology, Medical and Health Sciences, Psychology and Cognitive Sciences
Abstract

The Genetics Core of the Alzheimer's Disease Neuroimaging Initiative (ADNI), formally established in 2009, aims to provide resources and facilitate research related to genetic predictors of multidimensional Alzheimer's disease (AD)-related phenotypes. Here, we provide a systematic review of genetic studies published between 2009 and 2012 where either ADNI APOE genotype or genome-wide association study (GWAS) data were used. We review and synthesize ADNI genetic associations with disease status or quantitative disease endophenotypes including structural and functional neuroimaging, fluid biomarker assays, and cognitive performance. We also discuss the diverse analytical strategies used in these studies, including univariate and multivariate analysis, meta-analysis, pathway analysis, and interaction and network analysis. Finally, we perform pathway and network enrichment analyses of these ADNI genetic associations to highlight key mechanisms that may drive disease onset and trajectory. Major ADNI findings included all the top 10 AD genes and several of these (e.g., APOE, BIN1, CLU, CR1, and PICALM) were corroborated by ADNI imaging, fluid and cognitive phenotypes. ADNI imaging genetics studies discovered novel findings (e.g., FRMD6) that were later replicated on different data sets. Several other genes (e.g., APOC1, FTO, GRIN2B, MAGI2, and TOMM40) were associated with multiple ADNI phenotypes, warranting further investigation on other data sets. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future studies employing next-generation sequencing and convergent multi-omics approaches, and for clinical drug and biomarker development.

Published
2014

21. A Comparative Study on the Lysosomal Cation Channel TMEM175 Using Automated Whole-Cell Patch-Clamp, Lysosomal Patch-Clamp, and Solid Supported Membrane-Based Electrophysiology: Functional Characterization and High-Throughput Screening Assay Development

Author

Bazzone, Andre, primary, Barthmes, Maria, additional, George, Cecilia, additional, Brinkwirth, Nina, additional, Zerlotti, Rocco, additional, Prinz, Valentin, additional, Cole, Kim, additional, Friis, Søren, additional, Dickson, Alexander, additional, Rice, Simon, additional, Lim, Jongwon, additional, Fern Toh, May, additional, Mohammadi, Milad, additional, Pau, Davide, additional, Stone, David J., additional, Renger, John J., additional, and Fertig, Niels, additional

Published
2023
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22. Genome-wide Analyses Identify KIF5A as a Novel ALS Gene

Author

Logullo, Francesco O., Simone, Isabella, Logroscino, Giancarlo, Salvi, Fabrizio, Bartolomei, Ilaria, Borghero, Giuseppe, Murru, Maria Rita, Costantino, Emanuela, Pani, Carla, Puddu, Roberta, Caredda, Carla, Piras, Valeria, Tranquilli, Stefania, Cuccu, Stefania, Corongiu, Daniela, Melis, Maurizio, Milia, Antonio, Marrosu, Francesco, Marrosu, Maria Giovanna, Floris, Gianluca, Cannas, Antonino, Capasso, Margherita, Caponnetto, Claudia, Mancardi, Gianluigi, Origone, Paola, Mandich, Paola, Conforti, Francesca L., Cavallaro, Sebastiano, Mora, Gabriele, Marinou, Kalliopi, Sideri, Riccardo, Penco, Silvana, Mosca, Lorena, Lunetta, Christian, Pinter, Giuseppe Lauria, Corbo, Massimo, Riva, Nilo, Carrera, Paola, Volanti, Paolo, Mandrioli, Jessica, Fini, Nicola, Fasano, Antonio, Tremolizzo, Lucio, Arosio, Alessandro, Ferrarese, Carlo, Trojsi, Francesca, Tedeschi, Gioacchino, Monsurrò, Maria Rosaria, Piccirillo, Giovanni, Femiano, Cinzia, Ticca, Anna, Ortu, Enzo, La Bella, Vincenzo, Spataro, Rossella, Colletti, Tiziana, Sabatelli, Mario, Zollino, Marcella, Conte, Amelia, Luigetti, Marco, Lattante, Serena, Marangi, Giuseppe, Santarelli, Marialuisa, Petrucci, Antonio, Pugliatti, Maura, Pirisi, Angelo, Parish, Leslie D., Occhineri, Patrizia, Giannini, Fabio, Battistini, Stefania, Ricci, Claudia, Benigni, Michele, Cau, Tea B., Loi, Daniela, Calvo, Andrea, Moglia, Cristina, Brunetti, Maura, Barberis, Marco, Restagno, Gabriella, Casale, Federico, Marrali, Giuseppe, Fuda, Giuseppe, Ossola, Irene, Cammarosano, Stefania, Canosa, Antonio, Ilardi, Antonio, Manera, Umberto, Grassano, Maurizio, Tanel, Raffaella, Pisano, Fabrizio, Harms, Matthew B., Goldstein, David B., Shneider, Neil A., Goutman, Stephen, Simmons, Zachary, Miller, Timothy M., Chandran, Siddharthan, Pal, Suvankar, Manousakis, Georgios, Appel, Stanley H., Simpson, Ericka, Wang, Leo, Baloh, Robert H., Gibson, Summer, Bedlack, Richard, Lacomis, David, Sareen, Dhruv, Sherman, Alexander, Bruijn, Lucie, Penny, Michelle, Allen, Andrew S., Appel, Stanley, Bedlack, Richard S., Boone, Braden E., Brown, Robert, Carulli, John P., Chesi, Alessandra, Chung, Wendy K., Cirulli, Elizabeth T., Cooper, Gregory M., Couthouis, Julien, Day-Williams, Aaron G., Dion, Patrick A., Gitler, Aaron D., Glass, Jonathan D., Han, Yujun, Harris, Tim, Hayes, Sebastian D., Jones, Angela L., Keebler, Jonathan, Krueger, Brian J., Lasseigne, Brittany N., Levy, Shawn E., Lu, Yi-Fan, Maniatis, Tom, McKenna-Yasek, Diane, Myers, Richard M., Petrovski, Slavé, Pulst, Stefan M., Raphael, Alya R., Ravits, John M., Ren, Zhong, Rouleau, Guy A., Sapp, Peter C., Sims, Katherine B., Staropoli, John F., Waite, Lindsay L., Wang, Quanli, Wimbish, Jack R., Xin, Winnie W., Phatnani, Hemali, Kwan, Justin, Broach, James R., Arcila-Londono, Ximena, Lee, Edward B., Van Deerlin, Vivianna M., Fraenkel, Ernest, Ostrow, Lyle W., Baas, Frank, Zaitlen, Noah, Berry, James D., Malaspina, Andrea, Fratta, Pietro, Cox, Gregory A., Thompson, Leslie M., Finkbeiner, Steve, Dardiotis, Efthimios, Hornstein, Eran, MacGowan, Daniel J., Heiman-Patterson, Terry, Hammell, Molly G., Patsopoulos, Nikolaos A., Dubnau, Joshua, Nath, Avindra, Kaye, Julia, Finkbeiner, Steven, Wyman, Stacia, LeNail, Alexander, Lima, Leandro, Rothstein, Jeffrey D., Svendsen, Clive N., Van Eyk, Jenny, Maragakis, Nicholas J., Kolb, Stephen J., Cudkowicz, Merit, Baxi, Emily, Benatar, Michael, Taylor, J. Paul, Wu, Gang, Rampersaud, Evadnie, Wuu, Joanne, Rademakers, Rosa, Züchner, Stephan, Schule, Rebecca, McCauley, Jacob, Hussain, Sumaira, Cooley, Anne, Wallace, Marielle, Clayman, Christine, Barohn, Richard, Statland, Jeffrey, Ravits, John, Swenson, Andrea, Jackson, Carlayne, Trivedi, Jaya, Khan, Shaida, Katz, Jonathan, Jenkins, Liberty, Burns, Ted, Gwathmey, Kelly, Caress, James, McMillan, Corey, Elman, Lauren, Pioro, Erik, Heckmann, Jeannine, So, Yuen, Walk, David, Maiser, Samuel, Zhang, Jinghui, Silani, Vincenzo, Ticozzi, Nicola, Gellera, Cinzia, Ratti, Antonia, Taroni, Franco, Lauria, Giuseppe, Verde, Federico, Fogh, Isabella, Tiloca, Cinzia, Comi, Giacomo P., Sorarù, Gianni, Cereda, Cristina, D’Alfonso, Sandra, Corrado, Lucia, De Marchi, Fabiola, Corti, Stefania, Ceroni, Mauro, Mazzini, Letizia, Siciliano, Gabriele, Filosto, Massimiliano, Inghilleri, Maurizio, Peverelli, Silvia, Colombrita, Claudia, Poletti, Barbara, Maderna, Luca, Del Bo, Roberto, Gagliardi, Stella, Querin, Giorgia, Bertolin, Cinzia, Pensato, Viviana, Castellotti, Barbara, Camu, William, Mouzat, Kevin, Lumbroso, Serge, Corcia, Philippe, Meininger, Vincent, Besson, Gérard, Lagrange, Emmeline, Clavelou, Pierre, Guy, Nathalie, Couratier, Philippe, Vourch, Patrick, Danel, Véronique, Bernard, Emilien, Lemasson, Gwendal, Al Kheifat, Ahmad, Al-Chalabi, Ammar, Andersen, Peter, Basak, A. Nazli, Blair, Ian P., Chio, Adriano, Cooper-Knock, Jonathan, de Carvalho, Mamede, Dekker, Annelot, Drory, Vivian, Redondo, Alberto Garcia, Gotkine, Marc, Hardiman, Orla, Hide, Winston, Iacoangeli, Alfredo, Glass, Jonathan, Kenna, Kevin, Kiernan, Matthew, Kooyman, Maarten, Landers, John, McLaughlin, Russell, Middelkoop, Bas, Mill, Jonathan, Neto, Miguel Mitne, Moisse, Mattieu, Pardina, Jesus Mora, Morrison, Karen, Newhouse, Stephen, Pinto, Susana, Pulit, Sara, Robberecht, Wim, Shatunov, Aleksey, Shaw, Pamela, Shaw, Chris, Sproviero, William, Tazelaar, Gijs, van Damme, Philip, van den Berg, Leonard, van der Spek, Rick, van Eijk, Kristel, van Es, Michael, van Rheenen, Wouter, van Vugt, Joke, Veldink, Jan, Weber, Markus, Williams, Kelly L., Zatz, Mayana, Bauer, Denis C., Twine, Natalie A., Nicolas, Aude, Kenna, Kevin P., Renton, Alan E., Faghri, Faraz, Chia, Ruth, Dominov, Janice A., Kenna, Brendan J., Nalls, Mike A., Keagle, Pamela, Rivera, Alberto M., Murphy, Natalie A., van Vugt, Joke J.F.A., Geiger, Joshua T., Van der Spek, Rick A., Pliner, Hannah A., Shankaracharya, Smith, Bradley N., Topp, Simon D., Abramzon, Yevgeniya, Gkazi, Athina Soragia, Eicher, John D., Kenna, Aoife, Messina, Sonia, Simone, Isabella L., Ferrucci, Luigi, Moreno, Cristiane de Araujo Martins, Kamalakaran, Sitharthan, Musunuri, Rajeeva Lochan, Evani, Uday Shankar, Abhyankar, Avinash, Zody, Michael C., Wyman, Stacia K., LeNail, Alex, Van Eyk, Jennifer E., Laaksovirta, Hannu, Myllykangas, Liisa, Jansson, Lilja, Valori, Miko, Ealing, John, Hamdalla, Hisham, Rollinson, Sara, Pickering-Brown, Stuart, Orrell, Richard W., Sidle, Katie C., Hardy, John, Singleton, Andrew B., Johnson, Janel O., Arepalli, Sampath, Polak, Meraida, Asress, Seneshaw, Al-Sarraj, Safa, King, Andrew, Troakes, Claire, Vance, Caroline, de Belleroche, Jacqueline, ten Asbroek, Anneloor L.M.A., Muñoz-Blanco, José Luis, Hernandez, Dena G., Ding, Jinhui, Gibbs, J. Raphael, Scholz, Sonja W., Floeter, Mary Kay, Campbell, Roy H., Landi, Francesco, Bowser, Robert, MacGowan, Daniel J.L., Kirby, Janine, Pioro, Erik P., Pamphlett, Roger, Broach, James, Gerhard, Glenn, Dunckley, Travis L., Brady, Christopher B., Kowall, Neil W., Troncoso, Juan C., Le Ber, Isabelle, Heiman-Patterson, Terry D., Kamel, Freya, Van Den Bosch, Ludo, Strom, Tim M., Meitinger, Thomas, Van Eijk, Kristel R., Moisse, Matthieu, McLaughlin, Russell L., Van Es, Michael A., Boylan, Kevin B., Van Blitterswijk, Marka, Morrison, Karen E., Mora, Jesús S., Drory, Vivian E., Shaw, Pamela J., Turner, Martin R., Talbot, Kevin, Fifita, Jennifer A., Nicholson, Garth A., Esteban-Pérez, Jesús, García-Redondo, Alberto, Rogaeva, Ekaterina, Zinman, Lorne, Cooper-Knock, Johnathan, Brice, Alexis, Goutman, Stephen A., Feldman, Eva L., Gibson, Summer B., Van Damme, Philip, Ludolph, Albert C., Andersen, Peter M., Weishaupt, Jochen H., Trojanowski, John Q., Brown, Robert H., Jr., van den Berg, Leonard H., Veldink, Jan H., Stone, David J., Tienari, Pentti, Chiò, Adriano, Shaw, Christopher E., Traynor, Bryan J., and Landers, John E.

Published
2018
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23. Investigating the genetic architecture of dementia with Lewy bodies: a two-stage genome-wide association study

Author

Guerreiro, Rita, Ross, Owen A, Kun-Rodrigues, Celia, Hernandez, Dena G, Orme, Tatiana, Eicher, John D, Shepherd, Claire E, Parkkinen, Laura, Darwent, Lee, Heckman, Michael G, Scholz, Sonja W, Troncoso, Juan C, Pletnikova, Olga, Ansorge, Olaf, Clarimon, Jordi, Lleo, Alberto, Morenas-Rodriguez, Estrella, Clark, Lorraine, Honig, Lawrence S, Marder, Karen, Lemstra, Afina, Rogaeva, Ekaterina, St George-Hyslop, Peter, Londos, Elisabet, Zetterberg, Henrik, Barber, Imelda, Braae, Anne, Brown, Kristelle, Morgan, Kevin, Troakes, Claire, Al-Sarraj, Safa, Lashley, Tammaryn, Holton, Janice, Compta, Yaroslau, Van Deerlin, Vivianna, Serrano, Geidy E, Beach, Thomas G, Lesage, Suzanne, Galasko, Douglas, Masliah, Eliezer, Santana, Isabel, Pastor, Pau, Diez-Fairen, Monica, Aguilar, Miquel, Tienari, Pentti J, Myllykangas, Liisa, Oinas, Minna, Revesz, Tamas, Lees, Andrew, Boeve, Brad F, Petersen, Ronald C, Ferman, Tanis J, Escott-Price, Valentina, Graff-Radford, Neill, Cairns, Nigel J, Morris, John C, Pickering-Brown, Stuart, Mann, David, Halliday, Glenda M, Hardy, John, Trojanowski, John Q, Dickson, Dennis W, Singleton, Andrew, Stone, David J, and Bras, Jose

Published
2018
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26. Genome-wide structural variant analysis identifies risk loci for non-Alzheimer’s dementias

Author

Kaivola, Karri, primary, Chia, Ruth, additional, Ding, Jinhui, additional, Rasheed, Memoona, additional, Fujita, Masashi, additional, Menon, Vilas, additional, Walton, Ronald L., additional, Collins, Ryan L., additional, Billingsley, Kimberley, additional, Brand, Harrison, additional, Talkowski, Michael, additional, Zhao, Xuefang, additional, Dewan, Ramita, additional, Stark, Ali, additional, Ray, Anindita, additional, Solaiman, Sultana, additional, Alvarez Jerez, Pilar, additional, Malik, Laksh, additional, Dawson, Ted M., additional, Rosenthal, Liana S., additional, Albert, Marilyn S., additional, Pletnikova, Olga, additional, Troncoso, Juan C., additional, Masellis, Mario, additional, Keith, Julia, additional, Black, Sandra E., additional, Ferrucci, Luigi, additional, Resnick, Susan M., additional, Tanaka, Toshiko, additional, Topol, Eric, additional, Torkamani, Ali, additional, Tienari, Pentti, additional, Foroud, Tatiana M., additional, Ghetti, Bernardino, additional, Landers, John E., additional, Ryten, Mina, additional, Morris, Huw R., additional, Hardy, John A., additional, Mazzini, Letizia, additional, D'Alfonso, Sandra, additional, Moglia, Cristina, additional, Calvo, Andrea, additional, Serrano, Geidy E., additional, Beach, Thomas G., additional, Ferman, Tanis, additional, Graff-Radford, Neill R., additional, Boeve, Bradley F., additional, Wszolek, Zbigniew K., additional, Dickson, Dennis W., additional, Chiò, Adriano, additional, Bennett, David A., additional, De Jager, Philip L., additional, Ross, Owen A., additional, Dalgard, Clifton L., additional, Gibbs, J. Raphael, additional, Traynor, Bryan J., additional, Scholz, Sonja W., additional, Soltis, Anthony R., additional, Viollet, Coralie, additional, Sukumar, Gauthaman, additional, Alba, Camille, additional, Lott, Nathaniel, additional, McGrath Martinez, Elisa, additional, Tuck, Meila, additional, Singh, Jatinder, additional, Bacikova, Dagmar, additional, Zhang, Xijun, additional, Hupalo, Daniel N., additional, Adeleye, Adelani, additional, Wilkerson, Matthew D., additional, Pollard, Harvey B., additional, Gan-Or, Ziv, additional, Rogaeva, Ekaterina, additional, Brice, Alexis, additional, Lesage, Suzanne, additional, Xiromerisiou, Georgia, additional, Canosa, Antonio, additional, Chio, Adriano, additional, Logroscino, Giancarlo, additional, Mora, Gabriele, additional, Krüger, Reijko, additional, May, Patrick, additional, Alcolea, Daniel, additional, Clarimon, Jordi, additional, Fortea, Juan, additional, Gonzalez-Aramburu, Isabel, additional, Infante, Jon, additional, Lage, Carmen, additional, Lleó, Alberto, additional, Pastor, Pau, additional, Sanchez-Juan, Pascual, additional, Brett, Francesca, additional, Aarsland, Dag, additional, Al-Sarraj, Safa, additional, Attems, Johannes, additional, Gentleman, Steve, additional, Hodges, Angela K., additional, Love, Seth, additional, McKeith, Ian G., additional, Morris, Christopher M., additional, Palmer, Laura, additional, Pickering-Brown, Stuart, additional, Thomas, Alan J., additional, Troakes, Claire, additional, Barrett, Matthew J., additional, Bekris, Lynn M., additional, Faber, Kelley, additional, Flanagan, Margaret E., additional, Goate, Alison, additional, Goldstein, David S., additional, Kaufmann, Horacio, additional, Kukull, Walter A., additional, Leverenz, James B., additional, Lopez, Grisel, additional, Mao, Qinwen, additional, Masliah, Eliezer, additional, Monuki, Edwin, additional, Newell, Kathy L., additional, Palma, Jose-Alberto, additional, Perkins, Matthew, additional, Renton, Alan E., additional, Scherzer, Clemens R., additional, Shakkottai, Vikram G., additional, Sidransky, Ellen, additional, Tayebi, Nahid, additional, Woltjer, Randy, additional, Baloh, Robert H., additional, Bowser, Robert, additional, Broach, James, additional, Camu, William, additional, Cooper-Knock, John, additional, Drepper, Carsten, additional, Drory, Vivian E., additional, Dunckley, Travis L., additional, Feldman, Eva, additional, Fratta, Pietro, additional, Gerhard, Glenn, additional, Gibson, Summer B., additional, Glass, Jonathan D., additional, Harms, Matthew B., additional, Heiman-Patterson, Terry D., additional, Jansson, Lilja, additional, Kirby, Janine, additional, Kwan, Justin, additional, Laaksovirta, Hannu, additional, Landi, Francesco, additional, Le Ber, Isabelle, additional, Lumbroso, Serge, additional, MacGowan, Daniel J.L., additional, Maragakis, Nicholas J., additional, Mouzat, Kevin, additional, Myllykangas, Liisa, additional, Orrell, Richard W., additional, Ostrow, Lyle W., additional, Pamphlett, Roger, additional, Pioro, Erik, additional, Pulst, Stefan M., additional, Ravits, John M., additional, Robberecht, Wim, additional, Rothstein, Jeffrey D., additional, Sendtner, Michael, additional, Shaw, Pamela J., additional, Sidle, Katie C., additional, Simmons, Zachary, additional, Stein, Thor, additional, Stone, David J., additional, Tienari, Pentti J., additional, Valori, Miko, additional, Van Damme, Philip, additional, Van Deerlin, Vivianna M., additional, Van Den Bosch, Ludo, additional, and Zinman, Lorne, additional

Published
2023
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28. Analysis of neurodegenerative disease-causing genes in dementia with Lewy bodies

Author

Orme, Tatiana, Hernandez, Dena, Ross, Owen A., Kun-Rodrigues, Celia, Darwent, Lee, Shepherd, Claire E., Parkkinen, Laura, Ansorge, Olaf, Clark, Lorraine, Honig, Lawrence S., Marder, Karen, Lemstra, Afina, Rogaeva, Ekaterina, St. George-Hyslop, Peter, Londos, Elisabet, Zetterberg, Henrik, Morgan, Kevin, Troakes, Claire, Al-Sarraj, Safa, Lashley, Tammaryn, Holton, Janice, Compta, Yaroslau, Van Deerlin, Vivianna, Trojanowski, John Q., Serrano, Geidy E., Beach, Thomas G., Lesage, Suzanne, Galasko, Douglas, Masliah, Eliezer, Santana, Isabel, Pastor, Pau, Tienari, Pentti J., Myllykangas, Liisa, Oinas, Minna, Revesz, Tamas, Lees, Andrew, Boeve, Brad F., Petersen, Ronald C., Ferman, Tanis J., Escott-Price, Valentina, Graff-Radford, Neill, Cairns, Nigel J., Morris, John C., Pickering-Brown, Stuart, Mann, David, Halliday, Glenda, Stone, David J., Dickson, Dennis W., Hardy, John, Singleton, Andrew, Guerreiro, Rita, and Bras, Jose

Published
2020
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31. Transthoracic echocardiography and mortality in sepsis: analysis of the MIMIC-III database

Author

Feng, Mengling, McSparron, Jakob I., Kien, Dang Trung, Stone, David J., Roberts, David H., Schwartzstein, Richard M., and Vieillard-Baron, Antoine

Subjects
Mortality -- Analysis -- Usage, Echocardiography -- Usage -- Analysis, Databases -- Usage, Infection -- Patient outcomes, Medical colleges -- Analysis -- Usage, Hospital patients -- Patient outcomes, CD-ROM catalog, Database, CD-ROM database, Health care industry
Abstract

Purpose While the use of transthoracic echocardiography (TTE) in the ICU is rapidly expanding, the contribution of TTE to altering patient outcomes among ICU patients with sepsis has not been examined. This study was designed to examine the association of TTE with 28-day mortality specifically in that population. Methods and results The MIMIC-III database was employed to identify patients with sepsis who had and had not received TTE. The statistical approaches utilized included multivariate regression, propensity score analysis, doubly robust estimation, the gradient boosted model, and an inverse probability-weighting model to ensure the robustness of our findings. Significant benefit in terms of 28-day mortality was observed among the TTE patients compared to the control (no TTE) group (odds ratio = 0.78, 95% CI 0.68-0.90, p < 0.001). The amount of fluid administered (2.5 vs. 2.1 L on day 1, p < 0.001), use of dobutamine (2% vs. 1%, p = 0.007), and the maximum dose of norepinephrine (1.4 vs. 1 mg/min, p = 0.001) were significantly higher for the TTE patients. Importantly, the TTE patients were weaned off vasopressors more quickly than those in the no TTE group (vasopressor-free days on day 28 of 21 vs. 19, p = 0.004). Conclusion In a general population of critically ill patients with sepsis, use of TTE is associated with an improvement in 28-day mortality., Author(s): Mengling Feng [sup.1], Jakob I. McSparron [sup.2], Dang Trung Kien [sup.1], David J. Stone [sup.3], David H. Roberts [sup.4], Richard M. Schwartzstein [sup.4], Antoine Vieillard-Baron [sup.5], Leo Anthony Celi [...]

Published
2018
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32. Parkinson disease among patients treated for benign prostatic hyperplasia with [[alpha].sub.1] adrenergic receptor antagonists

Author

Sasane, Rahul, Bartels, Amy, Field, Michelle, Sierra, Maria I., Duvvuri, Sridhar, Gray, David L., Pin, Sokhom S., Renger, John J., and Stone, David J.

Subjects
Tamsulosin hydrochloride -- Dosage and administration -- Physiological aspects -- Comparative analysis, Prostate -- Hypertrophy, Parkinson's disease -- Risk factors -- Prevention, Terazosin hydrochloride -- Dosage and administration -- Physiological aspects -- Comparative analysis, Health care industry
Abstract

BACKGROUND. Recently the [[alpha].sub.1] adrenergic receptor antagonist terazosin was shown to activate PGK1, a possible target for the mitochondrial deficits in Parkinson disease related to its function as the initial enzyme in ATP synthesis during glycolysis. An epidemiological study of terazosin users showed a lower incidence of Parkinson disease when compared with users of tamsulosin, an [[alpha].sub.1] adrenergic receptor antagonist of a different class that does not activate PGK1. However, prior research on tamsulosin has suggested that it may in fact potentiate neurodegeneration, raising the question of whether it is an appropriate control group. METHODS. To address this question, we undertook an epidemiological study on Parkinson disease occurrence rate in 113,450 individuals from the United States with 5 or more years of follow-up. Patients were classified as tamsulosin users (n = 45,380), terazosin/alfuzosin/doxazosin users (n = 22,690), or controls matched for age, sex, and Charlson comorbidity index score (n = 45,380). RESULTS. Incidence of Parkinson disease in tamsulosin users was 1.53%, which was significantly higher than that in both terazosin/alfuzosin/doxazosin users (1.10%, P < 0.0001) and matched controls (1.01%, P < 0.0001). Terazosin/alfuzosin/ doxazosin users did not differ in Parkinson disease risk from matched controls (P = 0.29). CONCLUSION. These results suggest that zosins may not confer a protective effect against Parkinson disease, but rather that tamsulosin may in some way potentiate Parkinson disease progression. FUNDING. This work was supported by Cerevel Therapeutics., Introduction Parkinson disease (PD) is the second most common neurodegenerative disorder after Alzheimer disease. Due to the aging of the world population, the incidence of PD is increasing, with 6.1 [...]

Published
2021
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33. Genome-wide structural variant analysis identifies risk loci for non-Alzheimer’s dementias

Author

Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) [research center], Luxembourg Centre for Systems Biomedicine (LCSB): Clinical & Experimental Neuroscience (Krüger Group) [research center], Luxembourg Institute of Health - LIH [research center], Fonds National de la Recherche - FnR [sponsor], Kaivola, Karri, Chia, Ruth, Ding, Jinhui, Rasheed, Memoona, Fujita, Masashi, Menon, Vilas, Walton, Ronald L., Collins, Ryan L., Billingsley, Kimberley, Brand, Harrison, Talkowski, Michael, Zhao, Xuefang, Dewan, Ramita, Stark, Ali, Ray, Anindita, Solaiman, Sultana, Alvarez Jerez, Pilar, Malik, Laksh, Dawson, Ted M., Rosenthal, Liana S., Albert, Marilyn S., Pletnikova, Olga, Troncoso, Juan C., Masellis, Mario, Keith, Julia, Black, Sandra E., Ferrucci, Luigi, Resnick, Susan M., Tanaka, Toshiko, Topol, Eric, Torkamani, Ali, Tienari, Pentti, Foroud, Tatiana M., Ghetti, Bernardino, Landers, John E., Ryten, Mina, Morris, Huw R., Hardy, John A., Mazzini, Letizia, D'Alfonso, Sandra, Moglia, Cristina, Calvo, Andrea, Serrano, Geidy E., Beach, Thomas G., Ferman, Tanis, Graff-Radford, Neill R., Boeve, Bradley F., Wszolek, Zbigniew K., Dickson, Dennis W., Chiò, Adriano, Bennett, David A., De Jager, Philip L., Ross, Owen A., Dalgard, Clifton L., Gibbs, J. Raphael, Traynor, Bryan J., Scholz, Sonja W., Soltis, Anthony R., Viollet, Coralie, Sukumar, Gauthaman, Alba, Camille, Lott, Nathaniel, McGrath Martinez, Elisa, Tuck, Meila, Singh, Jatinder, Bacikova, Dagmar, Zhang, Xijun, Hupalo, Daniel N., Adeleye, Adelani, Wilkerson, Matthew D., Pollard, Harvey B., Gan-Or, Ziv, Rogaeva, Ekaterina, Brice, Alexis, Lesage, Suzanne, Xiromerisiou, Georgia, Canosa, Antonio, Chio, Adriano, Logroscino, Giancarlo, Mora, Gabriele, Krüger, Rejko, May, Patrick, Alcolea, Daniel, Clarimon, Jordi, Fortea, Juan, Gonzalez-Aramburu, Isabel, Infante, Jon, Lage, Carmen, Lleó, Alberto, Pastor, Pau, Sanchez-Juan, Pascual, Brett, Francesca, Aarsland, Dag, Al-Sarraj, Safa, Attems, Johannes, Gentleman, Steve, Hodges, Angela K., Love, Seth, McKeith, Ian G., Morris, Christopher M., Palmer, Laura, Pickering-Brown, Stuart, Thomas, Alan J., Troakes, Claire, Barrett, Matthew J., Bekris, Lynn M., Faber, Kelley, Flanagan, Margaret E., Goate, Alison, Goldstein, David S., Kaufmann, Horacio, Kukull, Walter A., Leverenz, James B., Lopez, Grisel, Mao, Qinwen, Masliah, Eliezer, Monuki, Edwin, Newell, Kathy L., Palma, Jose-Alberto, Perkins, Matthew, Renton, Alan E., Scherzer, Clemens R., Shakkottai, Vikram G., Sidransky, Ellen, Tayebi, Nahid, Woltjer, Randy, Baloh, Robert H., Bowser, Robert, Broach, James, Camu, William, Cooper-Knock, John, Drepper, Carsten, Drory, Vivian E., Dunckley, Travis L., Feldman, Eva, Fratta, Pietro, Gerhard, Glenn, Gibson, Summer B., Glass, Jonathan D., Harms, Matthew B., Heiman-Patterson, Terry D., Jansson, Lilja, Kirby, Janine, Kwan, Justin, Laaksovirta, Hannu, Landi, Francesco, Le Ber, Isabelle, Lumbroso, Serge, MacGowan, Daniel J. L., Maragakis, Nicholas J., Mouzat, Kevin, Myllykangas, Liisa, Orrell, Richard W., Ostrow, Lyle W., Pamphlett, Roger, Pioro, Erik, Pulst, Stefan M., Ravits, John M., Robberecht, Wim, Rothstein, Jeffrey D., Sendtner, Michael, Shaw, Pamela J., Sidle, Katie C., Simmons, Zachary, Stein, Thor, Stone, David J., Tienari, Pentti J., Valori, Miko, Van Damme, Philip, Van Deerlin, Vivianna M., Van Den Bosch, Ludo, Zinman, Lorne, Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) [research center], Luxembourg Centre for Systems Biomedicine (LCSB): Clinical & Experimental Neuroscience (Krüger Group) [research center], Luxembourg Institute of Health - LIH [research center], Fonds National de la Recherche - FnR [sponsor], Kaivola, Karri, Chia, Ruth, Ding, Jinhui, Rasheed, Memoona, Fujita, Masashi, Menon, Vilas, Walton, Ronald L., Collins, Ryan L., Billingsley, Kimberley, Brand, Harrison, Talkowski, Michael, Zhao, Xuefang, Dewan, Ramita, Stark, Ali, Ray, Anindita, Solaiman, Sultana, Alvarez Jerez, Pilar, Malik, Laksh, Dawson, Ted M., Rosenthal, Liana S., Albert, Marilyn S., Pletnikova, Olga, Troncoso, Juan C., Masellis, Mario, Keith, Julia, Black, Sandra E., Ferrucci, Luigi, Resnick, Susan M., Tanaka, Toshiko, Topol, Eric, Torkamani, Ali, Tienari, Pentti, Foroud, Tatiana M., Ghetti, Bernardino, Landers, John E., Ryten, Mina, Morris, Huw R., Hardy, John A., Mazzini, Letizia, D'Alfonso, Sandra, Moglia, Cristina, Calvo, Andrea, Serrano, Geidy E., Beach, Thomas G., Ferman, Tanis, Graff-Radford, Neill R., Boeve, Bradley F., Wszolek, Zbigniew K., Dickson, Dennis W., Chiò, Adriano, Bennett, David A., De Jager, Philip L., Ross, Owen A., Dalgard, Clifton L., Gibbs, J. Raphael, Traynor, Bryan J., Scholz, Sonja W., Soltis, Anthony R., Viollet, Coralie, Sukumar, Gauthaman, Alba, Camille, Lott, Nathaniel, McGrath Martinez, Elisa, Tuck, Meila, Singh, Jatinder, Bacikova, Dagmar, Zhang, Xijun, Hupalo, Daniel N., Adeleye, Adelani, Wilkerson, Matthew D., Pollard, Harvey B., Gan-Or, Ziv, Rogaeva, Ekaterina, Brice, Alexis, Lesage, Suzanne, Xiromerisiou, Georgia, Canosa, Antonio, Chio, Adriano, Logroscino, Giancarlo, Mora, Gabriele, Krüger, Rejko, May, Patrick, Alcolea, Daniel, Clarimon, Jordi, Fortea, Juan, Gonzalez-Aramburu, Isabel, Infante, Jon, Lage, Carmen, Lleó, Alberto, Pastor, Pau, Sanchez-Juan, Pascual, Brett, Francesca, Aarsland, Dag, Al-Sarraj, Safa, Attems, Johannes, Gentleman, Steve, Hodges, Angela K., Love, Seth, McKeith, Ian G., Morris, Christopher M., Palmer, Laura, Pickering-Brown, Stuart, Thomas, Alan J., Troakes, Claire, Barrett, Matthew J., Bekris, Lynn M., Faber, Kelley, Flanagan, Margaret E., Goate, Alison, Goldstein, David S., Kaufmann, Horacio, Kukull, Walter A., Leverenz, James B., Lopez, Grisel, Mao, Qinwen, Masliah, Eliezer, Monuki, Edwin, Newell, Kathy L., Palma, Jose-Alberto, Perkins, Matthew, Renton, Alan E., Scherzer, Clemens R., Shakkottai, Vikram G., Sidransky, Ellen, Tayebi, Nahid, Woltjer, Randy, Baloh, Robert H., Bowser, Robert, Broach, James, Camu, William, Cooper-Knock, John, Drepper, Carsten, Drory, Vivian E., Dunckley, Travis L., Feldman, Eva, Fratta, Pietro, Gerhard, Glenn, Gibson, Summer B., Glass, Jonathan D., Harms, Matthew B., Heiman-Patterson, Terry D., Jansson, Lilja, Kirby, Janine, Kwan, Justin, Laaksovirta, Hannu, Landi, Francesco, Le Ber, Isabelle, Lumbroso, Serge, MacGowan, Daniel J. L., Maragakis, Nicholas J., Mouzat, Kevin, Myllykangas, Liisa, Orrell, Richard W., Ostrow, Lyle W., Pamphlett, Roger, Pioro, Erik, Pulst, Stefan M., Ravits, John M., Robberecht, Wim, Rothstein, Jeffrey D., Sendtner, Michael, Shaw, Pamela J., Sidle, Katie C., Simmons, Zachary, Stein, Thor, Stone, David J., Tienari, Pentti J., Valori, Miko, Van Damme, Philip, Van Deerlin, Vivianna M., Van Den Bosch, Ludo, and Zinman, Lorne

Abstract

We characterized the role of structural variants, a largely unexplored type of genetic variation, in two non-Alzheimer’s dementias, namely Lewy body dementia (LBD) and frontotemporal dementia (FTD)/amyotrophic lateral sclerosis (ALS). To do this, we applied an advanced structural variant calling pipeline (GATK-SV) to short-read whole-genome sequence data from 5,213 European-ancestry cases and 4,132 controls. We discovered, replicated, and validated a deletion in TPCN1 as a novel risk locus for LBD and detected the known structural variants at the C9orf72 and MAPT loci as associated with FTD/ALS. We also identified rare pathogenic structural variants in both LBD and FTD/ALS. Finally, we assembled a catalog of structural variants that can be mined for new insights into the pathogenesis of these understudied forms of dementia.

Published
2023

36. The authors reply

Author

Bulgarelli, Lucas, Deliberato, Rodrigo Octavio, Stone, David J., Celi, Leo Anthony, and Johnson, Alistair E. W.

Published
2019
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38. Identification of a sex-specific genetic signature in dementia with Lewy bodies: a meta-analysis of genome-wide association studies

Author

Gibbons, Elizabeth, primary, Rongve, Arvid, additional, de Rojas, Itziar, additional, Shadrin, Alexey, additional, Westra, Kaitlyn, additional, Baumgartner, Allison, additional, Rosendall, Levi, additional, Madaj, Zachary, additional, Hernandez, Dena G., additional, Ross, Owen A., additional, Escott-Price, Valentina, additional, Shepherd, Claire, additional, Parkkinen, Laura, additional, Scholz, Sonja W., additional, Troncoso, Juan C., additional, Pletnikova, Olga, additional, Dawson, Ted, additional, Rosenthal, Liana, additional, Ansorge, Olaf, additional, Clarimon, Jordi, additional, Lleo, Alberto, additional, Morenas-Rodriguez, Estrella, additional, Clark, Lorraine, additional, Honig, Lawrence S, additional, Marder, Karen, additional, Lemstra, Afina, additional, Rogaeva, Ekaterina, additional, George-Hyslop, Peter St., additional, Londos, Elisabet, additional, Zetterberg, Henrik, additional, Morgan, Kevin, additional, Troakes, Claire, additional, Al-Sarraj, Safa, additional, Lashley, Tammaryn, additional, Holton, Janice, additional, Compta, Yaroslau, additional, Van Deerlin, Vivianna, additional, Serrano, Geidy E, additional, Beach, Thomas G, additional, Lesage, Suzanne, additional, Galasko, Douglas, additional, Masliah, Eliezer, additional, Santana, Isabel, additional, Pastor, Pau, additional, Diez-Fairen, Monica, additional, Aguilar, Miquel, additional, Marquie, Marta, additional, Garcia-Gonzalez, Pablo, additional, Olive, Claudia, additional, Puerta, Raquel, additional, Cano, Amanda, additional, Sotolongo-Grau, Oscar, additional, Valero, Sergi, additional, Pytel, Vanesa Veronica, additional, Rosende-Roca, Maitee, additional, Alegret, Montserrat, additional, Tarraga, Lluis, additional, Boada, Merce, additional, Carracedo, Angel, additional, Franco-Macias, Emilio, additional, Perez-Tur, Jordi, additional, Royo, Jose Luis, additional, Garcia-Alberca, Jose Maria, additional, Real, Luis Miguel, additional, Saez, Maria Eugenia, additional, Bullido, Maria Jesus, additional, Calero, Miguel, additional, Medina, Miguel, additional, Mir, Pablo, additional, Sanchez-Juan, Pascual, additional, Alvarez, Victoria, additional, Parveen, Kayenat, additional, Tripathi, Kumar Parijat, additional, Heilmann-Heimbach, Stefanie, additional, Ramirez, Alfredo, additional, Tienari, Pentti J., additional, Bousiges, Olivier, additional, Blanc, Frederic, additional, Fenoglio, Chiara, additional, Padovani, Alessandro, additional, Borroni, Barbara, additional, Pilotto, Andrea, additional, Nobili, Flavio, additional, Saltvedt, Ingvild, additional, Fladby, Tormod, additional, Selbaek, Geir, additional, Bosnes, Ingunn, additional, Brathen, Geir, additional, Hartmann, Annette, additional, Lemstra, Afina W., additional, Rujescu, Dan, additional, Mollenhauer, Brit, additional, Creese, Byron, additional, Chartier-Harlin, Marie-Christine, additional, Athanasiu, Lavinia, additional, Djurovic, Srdjan, additional, Chouliaras, Leonidas, additional, OBrien, John T., additional, Myllykangas, Liisa, additional, Oinas, Minna, additional, Revesz, Tamas, additional, Lees, Andrew, additional, Boeve, Brad F, additional, Petersen, Ronald C., additional, Ferman, Tanis J, additional, Graff-Radford, Neill, additional, Cairns, Nigel J., additional, Morris, John C., additional, Halliday, Glenda M., additional, Hardy, John, additional, Dickson, Dennis W., additional, Singleton, Andrew, additional, Stone, David J., additional, Andreassen, Ole A., additional, Ruiz, Agustin, additional, Aarsland, Dag, additional, Guerreiro, Rita, additional, and Bras, Jose, additional

Published
2022
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41. A comprehensive screening of copy number variability in dementia with Lewy bodies

Author

Kun-Rodrigues, Celia, Orme, Tatiana, Carmona, Susana, Hernandez, Dena G., Ross, Owen A., Eicher, John D., Shepherd, Claire, Parkkinen, Laura, Darwent, Lee, Heckman, Michael G., Scholz, Sonja W., Troncoso, Juan C., Pletnikova, Olga, Dawson, Ted, Rosenthal, Liana, Ansorge, Olaf, Clarimon, Jordi, Lleo, Alberto, Morenas-Rodriguez, Estrella, Clark, Lorraine, Honig, Lawrence S., Marder, Karen, Lemstra, Afina, Rogaeva, Ekaterina, St. George-Hyslop, Peter, Londos, Elisabet, Zetterberg, Henrik, Barber, Imelda, Braae, Anne, Brown, Kristelle, Morgan, Kevin, Troakes, Claire, Al-Sarraj, Safa, Lashley, Tammaryn, Holton, Janice, Compta, Yaroslau, Van Deerlin, Vivianna, Serrano, Geidy E., Beach, Thomas G., Lesage, Suzanne, Galasko, Douglas, Masliah, Eliezer, Santana, Isabel, Pastor, Pau, Diez-Fairen, Monica, Aguilar, Miquel, Tienari, Pentti J., Myllykangas, Liisa, Oinas, Minna, Revesz, Tamas, Lees, Andrew, Boeve, Brad F., Petersen, Ronald C., Ferman, Tanis J., Escott-Price, Valentina, Graff-Radford, Neill, Cairns, Nigel J., Morris, John C., Pickering-Brown, Stuart, Mann, David, Halliday, Glenda M., Hardy, John, Trojanowski, John Q., Dickson, Dennis W., Singleton, Andrew, Stone, David J., Guerreiro, Rita, and Bras, Jose

Published
2019
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47. Prediction of blood lactate values in critically ill patients: a retrospective multi-center cohort study

Author

Harvard University--MIT Division of Health Sciences and Technology, Harvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiology, Mamandipoor, Behrooz, Yeung, Wesley, Agha-Mir-Salim, Louis, Stone, David J., Osmani, Venet, Celi, Leo A., Harvard University--MIT Division of Health Sciences and Technology, Harvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiology, Mamandipoor, Behrooz, Yeung, Wesley, Agha-Mir-Salim, Louis, Stone, David J., Osmani, Venet, and Celi, Leo A.

Abstract

Elevations in initially obtained serum lactate levels are strong predictors of mortality in critically ill patients. Identifying patients whose serum lactate levels are more likely to increase can alert physicians to intensify care and guide them in the frequency of tending the blood test. We investigate whether machine learning models can predict subsequent serum lactate changes. We investigated serum lactate change prediction using the MIMIC-III and eICU-CRD datasets in internal as well as external validation of the eICU cohort on the MIMIC-III cohort. Three subgroups were defined based on the initial lactate levels: (i) normal group (< 2 mmol/L), (ii) mild group (2–4 mmol/L), and (iii) severe group (> 4 mmol/L). Outcomes were defined based on increase or decrease of serum lactate levels between the groups. We also performed sensitivity analysis by defining the outcome as lactate change of > 10% and furthermore investigated the influence of the time interval between subsequent lactate measurements on predictive performance. The LSTM models were able to predict deterioration of serum lactate values of MIMIC-III patients with an AUC of 0.77 (95% CI 0.762–0.771) for the normal group, 0.77 (95% CI 0.768–0.772) for the mild group, and 0.85 (95% CI 0.840–0.851) for the severe group, with only a slightly lower performance in the external validation. The LSTM demonstrated good discrimination of patients who had deterioration in serum lactate levels. Clinical studies are needed to evaluate whether utilization of a clinical decision support tool based on these results could positively impact decision-making and patient outcomes.

Published
2022

49. LRRTM3 Promotes Processing of Amyloid-Precursor Protein by BACE1 and Is a Positional Candidate Gene for Late-Onset Alzheimer's Disease

Author

Majercak, John, Ray, William J., Espeseth, Amy, Simon, Adam, Shi, Xiao-Ping, Wolffe, Carrie, Getty, Krista, Marine, Shane, Stec, Erica, Ferrer, Marc, Strulovici, Berta, Bartz, Steven, Gates, Adam, Xu, Min, Huang, Qian, Ma, Lei, Shughrue, Paul, Burchard, Julja, Colussi, Dennis, Pietrak, Beth, Kahana, Jason, Beher, Dirk, Rosahl, Thomas, Shearman, Mark, Hazuda, Daria, Sachs, Alan B., Koblan, Kenneth S., Seabrook, Guy R., and Stone, David J.

Published
2006
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