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1. The Space Omics and Medical Atlas (SOMA) and international astronaut biobank

Author

Overbey, Eliah G., Kim, JangKeun, Tierney, Braden T., Park, Jiwoon, Houerbi, Nadia, Lucaci, Alexander G., Garcia Medina, Sebastian, Damle, Namita, Najjar, Deena, Grigorev, Kirill, Afshin, Evan E., Ryon, Krista A., Sienkiewicz, Karolina, Patras, Laura, Klotz, Remi, Ortiz, Veronica, MacKay, Matthew, Schweickart, Annalise, Chin, Christopher R., Sierra, Maria A., Valenzuela, Matias F., Dantas, Ezequiel, Nelson, Theodore M., Cekanaviciute, Egle, Deards, Gabriel, Foox, Jonathan, Narayanan, S. Anand, Schmidt, Caleb M., Schmidt, Michael A., Schmidt, Julian C., Mullane, Sean, Tigchelaar, Seth Stravers, Levitte, Steven, Westover, Craig, Bhattacharya, Chandrima, Lucotti, Serena, Wain Hirschberg, Jeremy, Proszynski, Jacqueline, Burke, Marissa, Kleinman, Ashley S., Butler, Daniel J., Loy, Conor, Mzava, Omary, Lenz, Joan, Paul, Doru, Mozsary, Christopher, Sanders, Lauren M., Taylor, Lynn E., Patel, Chintan O., Khan, Sharib A., Suhail Mohamad, Mir, Byhaqui, Syed Gufran Ahmad, Aslam, Burhan, Gajadhar, Aaron S., Williamson, Lucy, Tandel, Purvi, Yang, Qiu, Chu, Jessica, Benz, Ryan W., Siddiqui, Asim, Hornburg, Daniel, Blease, Kelly, Moreno, Juan, Boddicker, Andrew, Zhao, Junhua, Lajoie, Bryan, Scott, Ryan T., Gilbert, Rachel R., Lai Polo, San-huei, Altomare, Andrew, Kruglyak, Semyon, Levy, Shawn, Ariyapala, Ishara, Beer, Joanne, Zhang, Bingqing, Hudson, Briana M., Rininger, Aric, Church, Sarah E., Beheshti, Afshin, Church, George M., Smith, Scott M., Crucian, Brian E., Zwart, Sara R., Matei, Irina, Lyden, David C., Garrett-Bakelman, Francine, Krumsiek, Jan, Chen, Qiuying, Miller, Dawson, Shuga, Joe, Williams, Stephen, Nemec, Corey, Trudel, Guy, Pelchat, Martin, Laneuville, Odette, De Vlaminck, Iwijn, Gross, Steven, Bolton, Kelly L., Bailey, Susan M., Granstein, Richard, Furman, David, Melnick, Ari M., Costes, Sylvain V., Shirah, Bader, Yu, Min, Menon, Anil S., Mateus, Jaime, Meydan, Cem, and Mason, Christopher E.

Published
2024
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2. A second space age spanning omics, platforms and medicine across orbits

Author

Mason, Christopher E., Green, James, Adamopoulos, Konstantinos I., Afshin, Evan E., Baechle, Jordan J., Basner, Mathias, Bailey, Susan M., Bielski, Luca, Borg, Josef, Borg, Joseph, Broddrick, Jared T., Burke, Marissa, Caicedo, Andrés, Castañeda, Verónica, Chatterjee, Subhamoy, Chin, Christopher R., Church, George, Costes, Sylvain V., De Vlaminck, Iwijn, Desai, Rajeev I., Dhir, Raja, Diaz, Juan Esteban, Etlin, Sofia M., Feinstein, Zachary, Furman, David, Garcia-Medina, J. Sebastian, Garrett-Bakelman, Francine, Giacomello, Stefania, Gupta, Anjali, Hassanin, Amira, Houerbi, Nadia, Irby, Iris, Javorsky, Emilia, Jirak, Peter, Jones, Christopher W., Kamal, Khaled Y., Kangas, Brian D., Karouia, Fathi, Kim, JangKeun, Kim, Joo Hyun, Kleinman, Ashley S., Lam, Try, Lawler, John M., Lee, Jessica A., Limoli, Charles L., Lucaci, Alexander, MacKay, Matthew, McDonald, J. Tyson, Melnick, Ari M., Meydan, Cem, Mieczkowski, Jakub, Muratani, Masafumi, Najjar, Deena, Othman, Mariam A., Overbey, Eliah G., Paar, Vera, Park, Jiwoon, Paul, Amber M., Perdyan, Adrian, Proszynski, Jacqueline, Reynolds, Robert J., Ronca, April E., Rubins, Kate, Ryon, Krista A., Sanders, Lauren M., Glowe, Patricia Savi, Shevde, Yash, Schmidt, Michael A., Scott, Ryan T., Shirah, Bader, Sienkiewicz, Karolina, Sierra, Maria A., Siew, Keith, Theriot, Corey A., Tierney, Braden T., Venkateswaran, Kasthuri, Hirschberg, Jeremy Wain, Walsh, Stephen B., Walter, Claire, Winer, Daniel A., Yu, Min, Zea, Luis, Mateus, Jaime, and Beheshti, Afshin

Published
2024
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3. Using Guided Transfer Learning to Predispose AI Agent to Learn Efficiently from Small RNA-sequencing Datasets

Author

Li, Kevin, Nikolić, Danko, Nikolić, Vjekoslav, Andrić, Davor, Sanders, Lauren M., and Costes, Sylvain V.

Subjects
Quantitative Biology - Genomics
Abstract

Given the increasing availability of RNA-seq data and its complex and heterogeneous nature, there has been growing interest in applying AI/machine learning methodologies to work with such data modalities. However, because omics data is characterized by high dimensionality and low sample size (HDLSS), current attempts at integrating AI in this domain require significant human guidance and expertise to mitigate overfitting. In this work we look at how transfer learning can be improved to learn from small RNA-seq sample sizes without significant human interference. The strategy is to gain general prior knowledge about a particular domain of data (e.g. RNA-seq data) by pre-training on a general task with a large aggregate of data, then fine-tuning to various specific, downstream target tasks in the same domain. Because previous attempts have shown traditional transfer learning failing on HLDSS, we propose to improve performance by using Guided Transfer Learning (GTL). Collaborating with Robots Go Mental, the AI we deploy here not only learns good initial parameters during pre-training, but also learns inductive biases that affect how the AI learns downstream tasks. In this approach, we first pre-trained on recount3 data, a collection of over 400,000 mouse RNA-seq samples sourced from thousands of individual studies. With such a large collection, patterns of expression between the ~30,000 genes in mammalian systems were pre-determined. Such patterns were sufficient for the pre-trained AI agent to efficiently learn new downstream tasks involving RNA-seq datasets with very low sample sizes and performed notably better on few-shot learning tasks compared to the same model without pre-training.

Published
2023

4. Explainable machine learning identifies multi-omics signatures of muscle response to spaceflight in mice

Author

Li, Kevin, Desai, Riya, Scott, Ryan T., Steele, Joel Ricky, Machado, Meera, Demharter, Samuel, Hoarfrost, Adrienne, Braun, Jessica L., Fajardo, Val A., Sanders, Lauren M., and Costes, Sylvain V.

Subjects
Quantitative Biology - Quantitative Methods
Abstract

The adverse effects of microgravity exposure on mammalian physiology during spaceflight necessitate a deep understanding of the underlying mechanisms to develop effective countermeasures. One such concern is muscle atrophy, which is partly attributed to the dysregulation of calcium levels due to abnormalities in SERCA pump functioning. To identify potential biomarkers for this condition, multi-omics data and physiological data available on the NASA Open Science Data Repository (osdr.nasa.gov) were used, and machine learning methods were employed. Specifically, we used multi-omics (transcriptomic, proteomic, and DNA methylation) data and calcium reuptake data collected from C57BL/6J mouse soleus and tibialis anterior tissues during several 30+ day-long missions on the international space station. The QLattice symbolic regression algorithm was introduced to generate highly explainable models that predict either experimental conditions or calcium reuptake levels based on multi-omics features. The list of candidate models established by QLattice was used to identify key features contributing to the predictive capability of these models, with Acyp1 and Rps7 proteins found to be the most predictive biomarkers related to the resilience of the tibialis anterior muscle in space. These findings could serve as targets for future interventions aiming to reduce the extent of muscle atrophy during space travel.

Published
2023

5. Author Correction: To boldly go where no microRNAs have gone before: spaceflight impact on risk for small-for-gestational-age infants

Author

Corti, Giada, Kim, JangKeun, Enguita, Francisco J., Guarnieri, Joseph W., Grossman, Lawrence I., Costes, Sylvain V., Fuentealba, Matias, Scott, Ryan T., Magrini, Andrea, Sanders, Lauren M., Singh, Kanhaiya, Sen, Chandan K., Juran, Cassandra M., Paul, Amber M., Furman, David, Calleja-Agius, Jean, Mason, Christopher E., Galeano, Diego, Bottini, Massimo, and Beheshti, Afshin

Published
2024
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6. To boldly go where no microRNAs have gone before: spaceflight impact on risk for small-for-gestational-age infants

Author

Corti, Giada, Kim, JangKeun, Enguita, Francisco J., Guarnieri, Joseph W., Grossman, Lawrence I., Costes, Sylvain V., Fuentealba, Matias, Scott, Ryan T., Magrini, Andrea, Sanders, Lauren M., Singh, Kanhaiya, Sen, Chandan K., Juran, Cassandra M., Paul, Amber M., Furman, David, Calleja-Agius, Jean, Mason, Christopher E., Galeano, Diego, Bottini, Massimo, and Beheshti, Afshin

Published
2024
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7. Cosmic kidney disease: an integrated pan-omic, physiological and morphological study into spaceflight-induced renal dysfunction

Author

Siew, Keith, Nestler, Kevin A., Nelson, Charlotte, D’Ambrosio, Viola, Zhong, Chutong, Li, Zhongwang, Grillo, Alessandra, Wan, Elizabeth R., Patel, Vaksha, Overbey, Eliah, Kim, JangKeun, Yun, Sanghee, Vaughan, Michael B., Cheshire, Chris, Cubitt, Laura, Broni-Tabi, Jessica, Al-Jaber, Maneera Yousef, Boyko, Valery, Meydan, Cem, Barker, Peter, Arif, Shehbeel, Afsari, Fatemeh, Allen, Noah, Al-Maadheed, Mohammed, Altinok, Selin, Bah, Nourdine, Border, Samuel, Brown, Amanda L., Burling, Keith, Cheng-Campbell, Margareth, Colón, Lorianna M., Degoricija, Lovorka, Figg, Nichola, Finch, Rebecca, Foox, Jonathan, Faridi, Pouya, French, Alison, Gebre, Samrawit, Gordon, Peter, Houerbi, Nadia, Valipour Kahrood, Hossein, Kiffer, Frederico C., Klosinska, Aleksandra S., Kubik, Angela, Lee, Han-Chung, Li, Yinghui, Lucarelli, Nicholas, Marullo, Anthony L., Matei, Irina, McCann, Colleen M., Mimar, Sayat, Naglah, Ahmed, Nicod, Jérôme, O’Shaughnessy, Kevin M., Oliveira, Lorraine Christine De, Oswalt, Leah, Patras, Laura Ioana, Lai Polo, San-huei, Rodríguez-Lopez, María, Roufosse, Candice, Sadeghi-Alavijeh, Omid, Sanchez-Hodge, Rebekah, Paul, Anindya S., Schittenhelm, Ralf Bernd, Schweickart, Annalise, Scott, Ryan T., Choy Lim Kam Sian, Terry Chin, da Silveira, Willian A., Slawinski, Hubert, Snell, Daniel, Sosa, Julio, Saravia-Butler, Amanda M., Tabetah, Marshall, Tanuwidjaya, Erwin, Walker-Samuel, Simon, Yang, Xiaoping, Yasmin, Zhang, Haijian, Godovac-Zimmermann, Jasminka, Sarder, Pinaki, Sanders, Lauren M., Costes, Sylvain V., Campbell, Robert A. A., Karouia, Fathi, Mohamed-Alis, Vidya, Rodriques, Samuel, Lynham, Steven, Steele, Joel Ricky, Baranzini, Sergio, Fazelinia, Hossein, Dai, Zhongquan, Uruno, Akira, Shiba, Dai, Yamamoto, Masayuki, A.C.Almeida, Eduardo, Blaber, Elizabeth, Schisler, Jonathan C., Eisch, Amelia J., Muratani, Masafumi, Zwart, Sara R., Smith, Scott M., Galazka, Jonathan M., Mason, Christopher E., Beheshti, Afshin, and Walsh, Stephen B.

Published
2024
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8. Understanding how space travel affects the female reproductive system to the Moon and beyond

Author

Mathyk, Begum, Imudia, Anthony N., Quaas, Alexander M., Halicigil, Cihan, Karouia, Fathi, Avci, Pinar, Nelson, Nicolas G., Guzeloglu-Kayisli, Ozlem, Denbo, Miriah, Sanders, Lauren M., Scott, Ryan T., Basar, Murat, Guevara-Cerdán, Ana Paula, Strug, Michael, Monseur, Brent, Kayisli, Umit A., Szewczyk, Nathaniel, Mason, Christopher E., Young, Steven L., Tasoglu, Savas, Costes, Sylvain V., and Beheshti, Afshin

Published
2024
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11. Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs

Author

Sanders, Lauren M., Yang, Jason H., Scott, Ryan T., Qutub, Amina Ann, Martin, Hector Garcia, Berrios, Daniel C., Hastings, Jaden J. A., Rask, Jon, Mackintosh, Graham, Hoarfrost, Adrienne L., Chalk, Stuart, Kalantari, John, Khezeli, Kia, Antonsen, Erik L., Babdor, Joel, Barker, Richard, Baranzini, Sergio E., Beheshti, Afshin, Delgado-Aparicio, Guillermo M., Glicksberg, Benjamin S., Greene, Casey S., Haendel, Melissa, Hamid, Arif A., Heller, Philip, Jamieson, Daniel, Jarvis, Katelyn J., Komarova, Svetlana V., Komorowski, Matthieu, Kothiyal, Prachi, Mahabal, Ashish, Manor, Uri, Mason, Christopher E., Matar, Mona, Mias, George I., Miller, Jack, Myers Jr., Jerry G., Nelson, Charlotte, Oribello, Jonathan, Park, Seung-min, Parsons-Wingerter, Patricia, Prabhu, R. K., Reynolds, Robert J., Saravia-Butler, Amanda, Saria, Suchi, Sawyer, Aenor, Singh, Nitin Kumar, Soboczenski, Frank, Snyder, Michael, Soman, Karthik, Theriot, Corey A., Van Valen, David, Venkateswaran, Kasthuri, Warren, Liz, Worthey, Liz, Zitnik, Marinka, and Costes, Sylvain V.

Subjects
Quantitative Biology - Other Quantitative Biology, Computer Science - Machine Learning
Abstract

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space., Comment: 28 pages, 4 figures

Published
2021

12. Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health

Author

Scott, Ryan T., Antonsen, Erik L., Sanders, Lauren M., Hastings, Jaden J. A., Park, Seung-min, Mackintosh, Graham, Reynolds, Robert J., Hoarfrost, Adrienne L., Sawyer, Aenor, Greene, Casey S., Glicksberg, Benjamin S., Theriot, Corey A., Berrios, Daniel C., Miller, Jack, Babdor, Joel, Barker, Richard, Baranzini, Sergio E., Beheshti, Afshin, Chalk, Stuart, Delgado-Aparicio, Guillermo M., Haendel, Melissa, Hamid, Arif A., Heller, Philip, Jamieson, Daniel, Jarvis, Katelyn J., Kalantari, John, Khezeli, Kia, Komarova, Svetlana V., Komorowski, Matthieu, Kothiyal, Prachi, Mahabal, Ashish, Manor, Uri, Martin, Hector Garcia, Mason, Christopher E., Matar, Mona, Mias, George I., Myers, Jr., Jerry G., Nelson, Charlotte, Oribello, Jonathan, Parsons-Wingerter, Patricia, Prabhu, R. K., Qutub, Amina Ann, Rask, Jon, Saravia-Butler, Amanda, Saria, Suchi, Singh, Nitin Kumar, Soboczenski, Frank, Snyder, Michael, Soman, Karthik, Van Valen, David, Venkateswaran, Kasthuri, Warren, Liz, Worthey, Liz, Yang, Jason H., Zitnik, Marinka, and Costes, Sylvain V.

Subjects
Quantitative Biology - Other Quantitative Biology, Computer Science - Machine Learning
Abstract

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are necessary to enable Earth-independence, rather than Earth-reliance. Promising developments in the fields of artificial intelligence and machine learning for biology and health can address these needs. We propose an appropriately autonomous and intelligent Precision Space Health system that will monitor, aggregate, and assess biomedical statuses; analyze and predict personalized adverse health outcomes; adapt and respond to newly accumulated data; and provide preventive, actionable, and timely insights to individual deep space crew members and iterative decision support to their crew medical officer. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration, on future applications of artificial intelligence in space biology and health. In the next decade, biomonitoring technology, biomarker science, spacecraft hardware, intelligent software, and streamlined data management must mature and be woven together into a Precision Space Health system to enable humanity to thrive in deep space., Comment: 31 pages, 4 figures

Published
2021

13. Tranexamic acid versus placebo in individuals with intracerebral haemorrhage treated within 2 h of symptom onset (STOP-MSU): an international, double-blind, randomised, phase 2 trial

Author

Yassi, Nawaf, Zhao, Henry, Churilov, Leonid, Wu, Teddy Y, Ma, Henry, Nguyen, Huy-Thang, Cheung, Andrew, Meretoja, Atte, Mai, Duy Ton, Kleinig, Timothy, Jeng, Jiann-Shing, Choi, Philip M C, Duc, Phuc Dang, Brown, Helen, Ranta, Annemarei, Spratt, Neil, Cloud, Geoffrey C, Wang, Hao-Kuang, Grimley, Rohan, Mahawish, Karim, Cho, Der-Yang, Shah, Darshan, Nguyen, Thai My Phuong, Sharma, Gagan, Yogendrakumar, Vignan, Yan, Bernard, Harrison, Emma L, Devlin, Michael, Cordato, Dennis, Martinez-Majander, Nicolas, Strbian, Daniel, Thijs, Vincent, Sanders, Lauren M, Anderson, David, Parsons, Mark W, Campbell, Bruce C V, Donnan, Geoffrey A, and Davis, Stephen M

Published
2024
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14. Machine learning multi-omics analysis reveals cancer driver dysregulation in pan-cancer cell lines compared to primary tumors

Author

Sanders, Lauren M, Chandra, Rahul, Zebarjadi, Navid, Beale, Holly C, Lyle, A Geoffrey, Rodriguez, Analiz, Kephart, Ellen Towle, Pfeil, Jacob, Cheney, Allison, Learned, Katrina, Currie, Rob, Gitlin, Leonid, Vengerov, David, Haussler, David, Salama, Sofie R, and Vaske, Olena M

Subjects
Cancer, Digestive Diseases, Rare Diseases, Biotechnology, Human Genome, Genetics, 2.1 Biological and endogenous factors, Aetiology, Good Health and Well Being, Humans, Proteomics, Multiomics, Neoplasms, Machine Learning, Cell Line
Abstract

Cancer cell lines have been widely used for decades to study biological processes driving cancer development, and to identify biomarkers of response to therapeutic agents. Advances in genomic sequencing have made possible large-scale genomic characterizations of collections of cancer cell lines and primary tumors, such as the Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA). These studies allow for the first time a comprehensive evaluation of the comparability of cancer cell lines and primary tumors on the genomic and proteomic level. Here we employ bulk mRNA and micro-RNA sequencing data from thousands of samples in CCLE and TCGA, and proteomic data from partner studies in the MD Anderson Cell Line Project (MCLP) and The Cancer Proteome Atlas (TCPA), to characterize the extent to which cancer cell lines recapitulate tumors. We identify dysregulation of a long non-coding RNA and microRNA regulatory network in cancer cell lines, associated with differential expression between cell lines and primary tumors in four key cancer driver pathways: KRAS signaling, NFKB signaling, IL2/STAT5 signaling and TP53 signaling. Our results emphasize the necessity for careful interpretation of cancer cell line experiments, particularly with respect to therapeutic treatments targeting these important cancer pathways.

Published
2022

15. Biomonitoring and precision health in deep space supported by artificial intelligence

Author

Scott, Ryan T., Sanders, Lauren M., Antonsen, Erik L., Hastings, Jaden J. A., Park, Seung-min, Mackintosh, Graham, Reynolds, Robert J., Hoarfrost, Adrienne L., Sawyer, Aenor, Greene, Casey S., Glicksberg, Benjamin S., Theriot, Corey A., Berrios, Daniel C., Miller, Jack, Babdor, Joel, Barker, Richard, Baranzini, Sergio E., Beheshti, Afshin, Chalk, Stuart, Delgado-Aparicio, Guillermo M., Haendel, Melissa, Hamid, Arif A., Heller, Philip, Jamieson, Daniel, Jarvis, Katelyn J., Kalantari, John, Khezeli, Kia, Komarova, Svetlana V., Komorowski, Matthieu, Kothiyal, Prachi, Mahabal, Ashish, Manor, Uri, Garcia Martin, Hector, Mason, Christopher E., Matar, Mona, Mias, George I., Myers, Jr, Jerry G., Nelson, Charlotte, Oribello, Jonathan, Parsons-Wingerter, Patricia, Prabhu, R. K., Qutub, Amina Ann, Rask, Jon, Saravia-Butler, Amanda, Saria, Suchi, Singh, Nitin Kumar, Snyder, Michael, Soboczenski, Frank, Soman, Karthik, Van Valen, David, Venkateswaran, Kasthuri, Warren, Liz, Worthey, Liz, Yang, Jason H., Zitnik, Marinka, and Costes, Sylvain V.

Published
2023
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16. Biological research and self-driving labs in deep space supported by artificial intelligence

Author

Sanders, Lauren M., Scott, Ryan T., Yang, Jason H., Qutub, Amina Ann, Garcia Martin, Hector, Berrios, Daniel C., Hastings, Jaden J. A., Rask, Jon, Mackintosh, Graham, Hoarfrost, Adrienne L., Chalk, Stuart, Kalantari, John, Khezeli, Kia, Antonsen, Erik L., Babdor, Joel, Barker, Richard, Baranzini, Sergio E., Beheshti, Afshin, Delgado-Aparicio, Guillermo M., Glicksberg, Benjamin S., Greene, Casey S., Haendel, Melissa, Hamid, Arif A., Heller, Philip, Jamieson, Daniel, Jarvis, Katelyn J., Komarova, Svetlana V., Komorowski, Matthieu, Kothiyal, Prachi, Mahabal, Ashish, Manor, Uri, Mason, Christopher E., Matar, Mona, Mias, George I., Miller, Jack, Myers, Jr., Jerry G., Nelson, Charlotte, Oribello, Jonathan, Park, Seung-min, Parsons-Wingerter, Patricia, Prabhu, R. K., Reynolds, Robert J., Saravia-Butler, Amanda, Saria, Suchi, Sawyer, Aenor, Singh, Nitin Kumar, Snyder, Michael, Soboczenski, Frank, Soman, Karthik, Theriot, Corey A., Van Valen, David, Venkateswaran, Kasthuri, Warren, Liz, Worthey, Liz, Zitnik, Marinka, and Costes, Sylvain V.

Published
2023
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17. Identification of a differentiation stall in epithelial mesenchymal transition in histone H3–mutant diffuse midline glioma

Author

Sanders, Lauren M, Cheney, Allison, Seninge, Lucas, van den Bout, Anouk, Chen, Marissa, Beale, Holly C, Kephart, Ellen Towle, Pfeil, Jacob, Learned, Katrina, Lyle, A Geoffrey, Bjork, Isabel, Haussler, David, Salama, Sofie R, and Vaske, Olena M

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Biomedical and Clinical Sciences, Genetics, Human Genome, Rare Diseases, Neurosciences, Pediatric Cancer, Cancer, Brain Cancer, Pediatric, Cancer Genomics, Biotechnology, Brain Disorders, Generic health relevance, Cell Differentiation, Child, Child, Preschool, Epithelial-Mesenchymal Transition, Glioma, Histones, Humans, Mutation, glioma, H3K27M mutation, epithelial mesenchymal transition
Abstract

BackgroundDiffuse midline gliomas with histone H3 K27M (H3K27M) mutations occur in early childhood and are marked by an invasive phenotype and global decrease in H3K27me3, an epigenetic mark that regulates differentiation and development. H3K27M mutation timing and effect on early embryonic brain development are not fully characterized.ResultsWe analyzed multiple publicly available RNA sequencing datasets to identify differentially expressed genes between H3K27M and non-K27M pediatric gliomas. We found that genes involved in the epithelial-mesenchymal transition (EMT) were significantly overrepresented among differentially expressed genes. Overall, the expression of pre-EMT genes was increased in the H3K27M tumors as compared to non-K27M tumors, while the expression of post-EMT genes was decreased. We hypothesized that H3K27M may contribute to gliomagenesis by stalling an EMT required for early brain development, and evaluated this hypothesis by using another publicly available dataset of single-cell and bulk RNA sequencing data from developing cerebral organoids. This analysis revealed similarities between H3K27M tumors and pre-EMT normal brain cells. Finally, a previously published single-cell RNA sequencing dataset of H3K27M and non-K27M gliomas revealed subgroups of cells at different stages of EMT. In particular, H3.1K27M tumors resemble a later EMT stage compared to H3.3K27M tumors.ConclusionsOur data analyses indicate that this mutation may be associated with a differentiation stall evident from the failure to proceed through the EMT-like developmental processes, and that H3K27M cells preferentially exist in a pre-EMT cell phenotype. This study demonstrates how novel biological insights could be derived from combined analysis of several previously published datasets, highlighting the importance of making genomic data available to the community in a timely manner.

Published
2020

18. Hydra: A mixture modeling framework for subtyping pediatric cancer cohorts using multimodal gene expression signatures.

Author

Pfeil, Jacob, Sanders, Lauren M, Anastopoulos, Ioannis, Lyle, A Geoffrey, Weinstein, Alana S, Xue, Yuanqing, Blair, Andrew, Beale, Holly C, Lee, Alex, Leung, Stanley G, Dinh, Phuong T, Shah, Avanthi Tayi, Breese, Marcus R, Devine, W Patrick, Bjork, Isabel, Salama, Sofie R, Sweet-Cordero, E Alejandro, Haussler, David, and Vaske, Olena Morozova

Subjects
Humans, Neoplasms, Neuroblastoma, Cluster Analysis, Models, Statistical, Gene Expression Profiling, Computational Biology, Gene Expression Regulation, Neoplastic, Child, Tumor Microenvironment, Transcriptome, Biomarkers, Tumor, Precision Medicine, Models, Statistical, Gene Expression Regulation, Neoplastic, Biomarkers, Tumor, Bioinformatics, Mathematical Sciences, Biological Sciences, Information and Computing Sciences
Abstract

Precision oncology has primarily relied on coding mutations as biomarkers of response to therapies. While transcriptome analysis can provide valuable information, incorporation into workflows has been difficult. For example, the relative rather than absolute gene expression level needs to be considered, requiring differential expression analysis across samples. However, expression programs related to the cell-of-origin and tumor microenvironment effects confound the search for cancer-specific expression changes. To address these challenges, we developed an unsupervised clustering approach for discovering differential pathway expression within cancer cohorts using gene expression measurements. The hydra approach uses a Dirichlet process mixture model to automatically detect multimodally distributed genes and expression signatures without the need for matched normal tissue. We demonstrate that the hydra approach is more sensitive than widely-used gene set enrichment approaches for detecting multimodal expression signatures. Application of the hydra analysis framework to small blue round cell tumors (including rhabdomyosarcoma, synovial sarcoma, neuroblastoma, Ewing sarcoma, and osteosarcoma) identified expression signatures associated with changes in the tumor microenvironment. The hydra approach also identified an association between ATRX deletions and elevated immune marker expression in high-risk neuroblastoma. Notably, hydra analysis of all small blue round cell tumors revealed similar subtypes, characterized by changes to infiltrating immune and stromal expression signatures.

Published
2020

19. Comparative RNA-seq analysis aids in diagnosis of a rare pediatric tumor.

Author

Sanders, Lauren M, Rangaswami, Arun, Bjork, Isabel, Lam, Du Linh, Beale, Holly C, Kephart, Ellen Towle, Durbin, Ann, Learned, Katrina, Currie, Rob, Lyle, A Geoffrey, Pfeil, Jacob, Shah, Avanthi Tayi, Lee, Alex G, Leung, Stanley G, Behroozfard, Inge H, Breese, Marcus R, Peralez, Jennifer, Hazard, Florette K, Lacayo, Norman, Spunt, Sheri L, Haussler, David, Salama, Sofie R, Sweet-Cordero, E Alejandro, and Vaske, Olena M

Subjects
Humans, Glioma, Teratoma, Peritoneal Neoplasms, Ovarian Neoplasms, Rare Diseases, Prognosis, Sequence Analysis, RNA, Base Sequence, Child, Female, Whole Exome Sequencing, RNA-Seq, neoplasm of the nervous system, Sequence Analysis, RNA
Abstract

Gliomatosis peritonei is a rare pathologic finding that is associated with ovarian teratomas and malignant mixed germ cell tumors. The occurrence of gliomatosis as a mature glial implant can impart an improved prognosis to patients with immature ovarian teratoma, making prompt and accurate diagnosis important. We describe a case of recurrent immature teratoma in a 10-yr-old female patient, in which comparative analysis of the RNA sequencing gene expression data from the patient's tumor was used effectively to aid in the diagnosis of gliomatosis peritonei.

Published
2019

20. Barriers to accessing public cancer genomic data.

Author

Learned, Katrina, Durbin, Ann, Currie, Robert, Kephart, Ellen Towle, Beale, Holly C, Sanders, Lauren M, Pfeil, Jacob, Goldstein, Theodore C, Salama, Sofie R, Haussler, David, Vaske, Olena Morozova, and Bjork, Isabel M

Subjects
Humans, Neoplasms, Information Dissemination, Genomics, Datasets as Topic
Published
2019

21. Loss of MAT2A compromises methionine metabolism and represents a vulnerability in H3K27M mutant glioma by modulating the epigenome

Author

Golbourn, Brian J., Halbert, Matthew E., Halligan, Katharine, Varadharajan, Srinidhi, Krug, Brian, Mbah, Nneka E., Kabir, Nisha, Stanton, Ann-Catherine J., Locke, Abigail L., Casillo, Stephanie M., Zhao, Yanhua, Sanders, Lauren M., Cheney, Allison, Mullett, Steven J., Chen, Apeng, Wassell, Michelle, Andren, Anthony, Perez, Jennifer, Jane, Esther P., Premkumar, Daniel R. David, Koncar, Robert F., Mirhadi, Shideh, McCarl, Lauren H., Chang, Yue-Fang, Wu, Yijen L., Gatesman, Taylor A., Cruz, Andrea F., Zapotocky, Michal, Hu, Baoli, Kohanbash, Gary, Wang, Xiuxing, Vartanian, Alenoush, Moran, Michael F., Lieberman, Frank, Amankulor, Nduka M., Wendell, Stacy G., Vaske, Olena M., Panigrahy, Ashok, Felker, James, Bertrand, Kelsey C., Kleinman, Claudia L., Rich, Jeremy N., Friedlander, Robert M., Broniscer, Alberto, Lyssiotis, Costas, Jabado, Nada, Pollack, Ian F., Mack, Stephen C., and Agnihotri, Sameer

Published
2022
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22. Management of people after stroke in 383 Victorian general practices, 2014–2018: analysis of linked stroke registry and general practice data.

Author

Olaiya, Muideen T, Kim, Joosup, Pearce, Christopher, Bam, Kiran, Cadilhac, Dominique A, Andrew, Nadine E, Sanders, Lauren M, Thrift, Amanda G, Nelson, Mark R, Gall, Seana, and Kilkenny, Monique F

Abstract

Objective: To evaluate the management in Victorian general practice of people who have been hospitalised with stroke or transient ischaemic attacks (TIA). Study design: Retrospective observational study; analysis of linked Australian Stroke Clinical Registry (AuSCR) and general practice data. Setting: 383 general practices in the Eastern Melbourne, South Eastern Melbourne, and Gippsland primary health networks (Victoria), 1 January 2014 – 31 December 2018. Participants: Adults who had been hospitalised with acute stroke or TIA and had at least two encounters with the same general practice during the observation period (7–18 months after the acute event). Main outcome measures: Assessment of cardiometabolic risk factors (blood pressure, serum lipids, blood glucose, urinary protein); prescribing of guideline‐recommended prevention medications (blood pressure‐, lipid‐, or glucose‐lowering, antithrombotic agents); attainment of guideline targets for cardiometabolic risk factors at final assessment during observation period. Results: During 2014–2018, 3376 eligible AuSCR registrants (1465 women, 43.4%) had at least two encounters with one of the 383 general practices during the observation period; median age at stroke onset was 73.9 (interquartile range, 64.4–81.9) years, 737 events were TIAs (21.8%). Blood pressure was assessed in 2718 patients (80.5%), serum lipids in 1830 (54.2%), blood glucose in 1708 (50.6%). Prevention medications were prescribed for 2949 patients (87.4%), including lipid‐lowering (2427, 71.9%) and blood pressure‐lowering agents (2363, 70.0%). Blood glucose targets had been achieved by 1346 of 1708 patients assessed for this risk factor (78.8%), blood pressure targets by 1935 of 2717 (71.2%), and serum lipid targets by 765 of 1830 (41.8%). The incidence of having risk factors assessed was lower among patients aged 60 years or younger (incidence rate ratio [IRR], 0.97; 95% confidence interval [CI], 0.92–1.03) and those over 80 years of age (IRR, 0.92; 95% CI 0.88–0.97) than for those aged 61–80 years, and for women (IRR, 0.91; 95% CI, 0.87–0.95) and people with dementia (IRR, 0.89; 95% CI, 0.81–0.98). The likelihood of having classes of prevention medication prescribed was lower for patients aged 60 years or younger (IRR, 0.92; 95% CI, 0.88–0.97) and those over 80 years of age (IRR, 0.96; 95% CI, 0.92–0.997) than for patients aged 61–80 years, and for women (IRR, 0.95; 95% CI, 0.91–0.98) and people with dementia (IRR, 0.88; 95% CI, 0.78–0.98). Conclusions: The general practice management of people who have been hospitalised with stroke or TIA could be improved. Effective monitoring of cardiometabolic risk factors will enable general practitioners to optimise care for people who need careful attention to prevent adverse secondary events. [ABSTRACT FROM AUTHOR]

Published
2024
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26. Author Correction: Loss of MAT2A compromises methionine metabolism and represents a vulnerability in H3K27M mutant glioma by modulating the epigenome

Author

Golbourn, Brian J., Halbert, Matthew E., Halligan, Katharine, Varadharajan, Srinidhi, Krug, Brian, Mbah, Nneka E., Kabir, Nisha, Stanton, Ann-Catherine J., Locke, Abigail L., Casillo, Stephanie M., Zhao, Yanhua, Sanders, Lauren M., Cheney, Allison, Mullett, Steven J., Chen, Apeng, Wassell, Michelle, Andren, Anthony, Perez, Jennifer, Jane, Esther P., Premkumar, Daniel R. David, Koncar, Robert F., Mirhadi, Shideh, McCarl, Lauren H., Chang, Yue-Fang, Wu, Yijen L., Gatesman, Taylor A., Cruz, Andrea F., Zapotocky, Michal, Hu, Baoli, Kohanbash, Gary, Wang, Xiuxing, Vartanian, Alenoush, Moran, Michael F., Lieberman, Frank, Amankulor, Nduka M., Wendell, Stacy G., Vaske, Olena M., Panigrahy, Ashok, Felker, James, Bertrand, Kelsey C., Kleinman, Claudia L., Rich, Jeremy N., Friedlander, Robert M., Broniscer, Alberto, Lyssiotis, Costas, Jabado, Nada, Pollack, Ian F., Mack, Stephen C., and Agnihotri, Sameer

Published
2022
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27. Multi-omic analyses and organoid models for identification of therapeutic vulnerabilities and developmental origins in childhood cancer

Author

Sanders, Lauren M

Subjects
Bioinformatics
Abstract

Pediatric cancers are different from adult cancers in that they often have few targetable DNA mutations, and in most cases are thought to be developmental in origin rather than environmental. While overall survival rates for pediatric cancer have increased in the past few decades, there remain several difficult-to-treat pediatric cancer types, including deadly pediatric diffuse midline and brainstem gliomas. However, advances in genomics data generation and analysis methods have made it possible to start identifying the dominant signaling pathways, developmental origins, and ultimately therapeutic vulnerabilities of these tumors.Here I present my work in the UCSC Treehouse Childhood Cancer Initiative using comparative gene expression analysis methods to identify a rare cancer subtype with treatment and outcome implications (Chapter 2). This work demonstrates the utility of gene expression data for molecular subtyping in the clinic, especially in rare or difficult-to-diagnose pediatric cancers. I also present my work identifying a developmental window of opportunity for the histone H3 K27M mutation event, which characterizes a majority of brainstem gliomas and is associated with especially poor overall survival (Chapter 3). The cell-of-origin and developmental timing for gliomagenesis in this tumor type has been a subject of much research in the past decade, and my work helps to address this clinically relevant issue. Finally, in Chapter 4 I present an analysis of multiple types of genomic data across thousands of primary tumors and diverse cancer laboratory models (cell lines, organoids, and patient-derived mouse xenografts). I report that cancer organoids have the advantage of being relatively accurate cellular representations of primary tumors, and time- and cost-effective. Therefore, in Chapter 5 I present the development of a novel human embryonic stem cell based cerebral organoid model of the histone H3 K27M mutation in early human embryonic brain development. This model can be used to answer outstanding questions in the field to better help treat these devastating tumors.

Published
2020

28. Melbourne Mobile Stroke Unit and Reperfusion Therapy: Greater Clinical Impact of Thrombectomy Than Thrombolysis

Author

Zhao, Henry, Coote, Skye, Easton, Damien, Langenberg, Francesca, Stephenson, Michael, Smith, Karen, Bernard, Stephen, Cadilhac, Dominique A., Kim, Joosup, Bladin, Christopher F., Churilov, Leonid, Crompton, Douglas E., Dewey, Helen M., Sanders, Lauren M., Wijeratne, Tissa, Cloud, Geoffrey, Brooks, Duncan M., Asadi, Hamed, Thijs, Vincent, Chandra, Ronil V., Ma, Henry, Desmond, Patricia M., Dowling, Richard J., Mitchell, Peter J., Yassi, Nawaf, Yan, Bernard, Campbell, Bruce C.V., Parsons, Mark W., Donnan, Geoffrey A., and Davis, Stephen M.

Published
2020
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30. sj-docx-1-him-10.1177_18333583231184004 – Supplemental material for Stroke clinical coding education program in Australia and New Zealand

Author

Kilkenny, Monique F, Sanders, Ailie, Burns, Catherine, Sanders, Lauren M, Ryan, Olivia, Read, Carla, Lum On, Miriam, Ranta, Anna, Purvis, Tara, Inman, Carys, Cadilhac, Dominique A, Carter, Helen, Rowlands, Stella, Nedkoff, Lee, and Olaiya, Muideen T

Subjects
Public Health and Health Services not elsewhere classified, Health and Community Services
Abstract

Supplemental material, sj-docx-1-him-10.1177_18333583231184004 for Stroke clinical coding education program in Australia and New Zealand by Monique F Kilkenny, Ailie Sanders, Catherine Burns, Lauren M Sanders, Olivia Ryan, Carla Read, Miriam Lum On, Anna Ranta, Tara Purvis, Carys Inman, Dominique A Cadilhac, Helen Carter, Stella Rowlands, Lee Nedkoff and Muideen T Olaiya in Health Information Management Journal

Published
2023
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34. Cancer microenvironment and genomics: evolution in process

Author

Leong, Stanley P., primary, Witz, Isaac P., additional, Sagi-Assif, Orit, additional, Izraely, Sivan, additional, Sleeman, Jonathan, additional, Piening, Brian, additional, Fox, Bernard A., additional, Bifulco, Carlo B., additional, Martini, Rachel, additional, Newman, Lisa, additional, Davis, Melissa, additional, Sanders, Lauren M., additional, Haussler, David, additional, Vaske, Olena M., additional, and Witte, Marlys, additional

Published
2021
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39. Abstract 6154: H3K27M gliomas are characterized by a stall in the epithelial-mesenchymal transition

Author

CHENEY, ALLISON R., primary, Sanders, Lauren M., additional, Seninge, Lucas, additional, Beale, Holly C., additional, Kephart, Ellen Towle, additional, Pfeil, Jacob, additional, Learned, Katrina, additional, Lyle, A. Geoffrey, additional, Bjork, Isabel, additional, Haussler, David, additional, Salama, Sofie R., additional, and Vaske, Olena M., additional

Published
2020
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40. Abstract A44: Comparative gene expression analysis for identification and prioritization of therapeutic targets in a cohort of childhood cancers

Author

Sanders, Lauren M., primary, Lyle, A. Geoffrey, additional, Beale, Holly C., additional, Kephart, Ellen Towle, additional, Learned, Katrina, additional, Peralez, Jennifer, additional, Lacayo, Norman, additional, Rangaswami, Arun, additional, Spunt, Sheri L., additional, Bjork, Isabel, additional, Haussler, David, additional, Salama, Sofie R., additional, and Vaske, Olena M., additional

Published
2020
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41. Abstract B06: Candidate differentiation stall in epithelial mesenchymal transition in H3K27M diffuse midline glioma

Author

Cheney, Allison R., primary, Sanders, Lauren M., additional, Seninge, Lucas, additional, Beale, Holly C., additional, Kephart, Ellen Towle, additional, Pfeil, Jacob, additional, Learned, Katrina, additional, Lyle, A. Geoffrey, additional, Bjork, Isabel, additional, Haussler, David, additional, Salama, Sofie R., additional, and Vaske, Olena M., additional

Published
2020
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44. Comparative RNA-seq analysis aids in diagnosis of a rare pediatric tumor

Author

Sanders, Lauren M., primary, Rangaswami, Arun, additional, Bjork, Isabel, additional, Lam, Du Linh, additional, Beale, Holly C., additional, Kephart, Ellen Towle, additional, Durbin, Ann, additional, Learned, Katrina, additional, Currie, Rob, additional, Lyle, A. Geoffrey, additional, Pfeil, Jacob, additional, Shah, Avanthi Tayi, additional, Lee, Alex G., additional, Leung, Stanley G., additional, Behroozfard, Inge H., additional, Breese, Marcus R., additional, Peralez, Jennifer, additional, Hazard, Florette K., additional, Lacayo, Norman, additional, Spunt, Sheri L., additional, Haussler, David, additional, Salama, Sofie R., additional, Sweet-Cordero, E. Alejandro, additional, and Vaske, Olena M., additional

Published
2019
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47. Stroke clinical coding education program in Australia and New Zealand

Author

Kilkenny, Monique F, Sanders, Ailie, Burns, Catherine, Sanders, Lauren M, Ryan, Olivia, Read, Carla, Lum On, Miriam, Ranta, Anna, Purvis, Tara, Inman, Carys, Cadilhac, Dominique A, Carter, Helen, Rowlands, Stella, Nedkoff, Lee, and Olaiya, Muideen T

Abstract

Background: Accurate coded diagnostic data are important for epidemiological research of stroke.Objective: To develop, implement and evaluate an online education program for improving clinical coding of stroke.Method: The Australia and New Zealand Stroke Coding Working Group co-developed an education program comprising eight modules: rationale for coding of stroke; understanding stroke; management of stroke; national coding standards; coding trees; good clinical documentation; coding practices; and scenarios. Clinical coders and health information managers participated in the 90-minute education program. Pre- and post-education surveys were administered to assess knowledge of stroke and coding, and to obtain feedback. Descriptive analyses were used for quantitative data, inductive thematic analysis for open-text responses, with all results triangulated.Results: Of 615 participants, 404 (66%) completed both pre- and post-education assessments. Respondents had improved knowledge for 9/12 questions (p< 0.05), including knowledge of applicable coding standards, coding of intracerebral haemorrhage and the actions to take when coding stroke (all p< 0.001). Majority of respondents agreed that information was pitched at an appropriate level; education materials were well organised; presenters had adequate knowledge; and that they would recommend the session to colleagues. In qualitative evaluations, the education program was beneficial for newly trained clinical coders, or as a knowledge refresher, and respondents valued clinical information from a stroke neurologist.Conclusion: Our education program was associated with increased knowledge for clinical coding of stroke. To continue to address the quality of coded stroke data through improved stroke documentation, the next stage will be to adapt the educational program for clinicians.

Published
2024
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49. Cognitive function modifies the effect of physiological function on the risk of multiple falls-a population-based study

Author

Martin, Kara L, Blizzard, Leigh, Srikanth, Velandai K, Wood, Amanda G, Thomson, Russell, Sanders, Lauren M, Callisaya, Michele L, Martin, Kara L, Blizzard, Leigh, Srikanth, Velandai K, Wood, Amanda G, Thomson, Russell, Sanders, Lauren M, and Callisaya, Michele L

Abstract

Background. There is a poor understanding of the interplay between cognitive and physiological functions in leading to falls. We hypothesized that poorer physiological function would modify the effect of poorer cognitive function on increased risk of falling in older people.Methods. A range of cognitive (executive function/attention, memory, processing speed, and visuospatial ability) and physiological functions (vision, proprioception, sway, leg strength, reaction time) were measured using standardized tests in 386 randomly selected adults aged 60-86. Incident falls were recorded over 12 months. Log-multinomial regression was used to model the relationships and test for interactions between cognition and physiological function in explaining the risk of single or multiple falls.Results. Overall, 94 people (24.4%) had a single fall, and 78 (20.2%) had multiple falls. No significant associations were observed between cognitive function and the risk of single falls. The risk of multiple falls was increased with poorer function in Stroop dot time (RR = 1.03, 95% CI [1.01, 1.05], p =. 002) and Stroop word time (RR = 1.02 [1.01, 1.03], p =. 001) and reduced with better function in Category Fluency (RR = 0.94 [0.91, 0.98], p =. 001) and visuospatial function (RR = 0.95 [0.92, 0.98], p <. 001). These associations were amplified by the presence of greater body sway, less ambulatory physical activity, slower reaction time and gait speed, weaker muscle strength, and poorer visual contrast (p for interactions <.05).Conclusions. Preventing falls due to physiological impairments in community-dwelling older people may need to be tailored based on cognitive impairment, a key factor in their inability to compensate for physical decline.

Published
2013

50. Cognitive function, gait, and gait variability in older people: a population-based study

Author

Martin, Kara L, Blizzard, Leigh, Wood, Amanda G, Srikanth, Velandai, Thomson, Russell, Sanders, Lauren M, Callisaya, Michele L, Martin, Kara L, Blizzard, Leigh, Wood, Amanda G, Srikanth, Velandai, Thomson, Russell, Sanders, Lauren M, and Callisaya, Michele L

Abstract

Background.Gait impairments are associated with falls and loss of independence. The study of factors associated with poorer gait may assist in developing methods to preserve mobility in older people. The aim of this study was to examine the associations between a range of cognitive functions and gait and gait variability in a population-based sample of older people.Methods.Gait and intra-individual gait variability measures were obtained using the GAITRite walkway in a sample of older people, aged 60-85 years (N = 422), randomly selected from the Tasmanian electoral roll. Raw scores from a cognitive battery were subjected to principal component analyses deriving four summary domains: executive function/attention, processing speed, memory, and visuospatial ability. Multivariable linear regression was used to examine associations between cognitive domains and gait measures adjusting for age, sex, ambulatory activity, medication use, and mood.Results.The mean age of the sample was 72.0 years (SD = 7.0), with 238 men (56%). Poorer executive function was independently associated with poorer performance in most absolute gait measures and with greater variability in double support phase and step time. Processing speed was associated with absolute gait measures and double support phase variability. Visuospatial ability was only associated with greater double support phase variability, independently of executive function and processing speed. Memory was not independently associated with any gait measure.Conclusions.In community-dwelling older people, executive function/attention and processing speed were associated with many aspects of gait, whereas visuospatial ability may only play a role in double support phase variability.

Published
2013

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