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4,231 results on '"Tufik, Sergio"'

2. STREAMLINE: An Automated Machine Learning Pipeline for Biomedicine Applied to Examine the Utility of Photography-Based Phenotypes for OSA Prediction Across International Sleep Centers

Author

Urbanowicz, Ryan J., Bandhey, Harsh, Keenan, Brendan T., Maislin, Greg, Hwang, Sy, Mowery, Danielle L., Lynch, Shannon M., Mazzotti, Diego R., Han, Fang, Li, Qing Yun, Penzel, Thomas, Tufik, Sergio, Bittencourt, Lia, Gislason, Thorarinn, de Chazal, Philip, Singh, Bhajan, McArdle, Nigel, Chen, Ning-Hung, Pack, Allan, Schwab, Richard J., Cistulli, Peter A., and Magalang, Ulysses J.

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

While machine learning (ML) includes a valuable array of tools for analyzing biomedical data, significant time and expertise is required to assemble effective, rigorous, and unbiased pipelines. Automated ML (AutoML) tools seek to facilitate ML application by automating a subset of analysis pipeline elements. In this study we develop and validate a Simple, Transparent, End-to-end Automated Machine Learning Pipeline (STREAMLINE) and apply it to investigate the added utility of photography-based phenotypes for predicting obstructive sleep apnea (OSA); a common and underdiagnosed condition associated with a variety of health, economic, and safety consequences. STREAMLINE is designed to tackle biomedical binary classification tasks while adhering to best practices and accommodating complexity, scalability, reproducibility, customization, and model interpretation. Benchmarking analyses validated the efficacy of STREAMLINE across data simulations with increasingly complex patterns of association. Then we applied STREAMLINE to evaluate the utility of demographics (DEM), self-reported comorbidities (DX), symptoms (SYM), and photography-based craniofacial (CF) and intraoral (IO) anatomy measures in predicting any OSA or moderate/severe OSA using 3,111 participants from Sleep Apnea Global Interdisciplinary Consortium (SAGIC). OSA analyses identified a significant increase in ROC-AUC when adding CF to DEM+DX+SYM to predict moderate/severe OSA. A consistent but non-significant increase in PRC-AUC was observed with the addition of each subsequent feature set to predict any OSA, with CF and IO yielding minimal improvements. Application of STREAMLINE to OSA data suggests that CF features provide additional value in predicting moderate/severe OSA, but neither CF nor IO features meaningfully improved the prediction of any OSA beyond established demographics, comorbidity and symptom characteristics., Comment: 23 pages, 7 figures, 1 table, 1 supplemental information document (77 pages), and 7 ancillary files

Published
2023

10. The genetic etiology of periodic limb movement in sleep.

Author

Edelson, Jacob L, Schneider, Logan D, Amar, David, Brink-Kjaer, Andreas, Cederberg, Katie L, Kutalik, Zoltán, Hagen, Erika W, Peppard, Paul E, Tempaku, Priscila Farias, Tufik, Sergio, Evans, Daniel S, Stone, Katie, Tranah, Greg, Cade, Brian, Redline, Susan, Haba-Rubio, Jose, Heinzer, Raphael, Marques-Vidal, Pedro, Vollenweider, Peter, Winkelmann, Juliane, Zou, James, and Mignot, Emmanuel

Subjects
Biological Sciences, Genetics, Minority Health, Sleep Research, Prevention, Human Genome, 2.1 Biological and endogenous factors, Humans, Sleep Initiation and Maintenance Disorders, Cohort Studies, Genome-Wide Association Study, Sleep, Movement, Restless Legs Syndrome, periodic limb movements, genome-wide association study, Mendelian randomization, restless leg syndrome, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biological sciences, Biomedical and clinical sciences, Psychology
Abstract

Study objectivesPeriodic limb movement in sleep is a common sleep phenotype characterized by repetitive leg movements that occur during or before sleep. We conducted a genome-wide association study (GWAS) of periodic limb movements in sleep (PLMS) using a joint analysis (i.e., discovery, replication, and joint meta-analysis) of four cohorts (MrOS, the Wisconsin Sleep Cohort Study, HypnoLaus, and MESA), comprised of 6843 total subjects.MethodsThe MrOS study and Wisconsin Sleep Cohort Study (N = 1745 cases) were used for discovery. Replication in the HypnoLaus and MESA cohorts (1002 cases) preceded joint meta-analysis. We also performed LD score regression, estimated heritability, and computed genetic correlations between potentially associated traits such as restless leg syndrome (RLS) and insomnia. The causality and direction of the relationships between PLMS and RLS was evaluated using Mendelian randomization.ResultsWe found 2 independent loci were significantly associated with PLMS: rs113851554 (p = 3.51 × 10-12, β = 0.486), an SNP located in a putative regulatory element of intron eight of MEIS1 (2p14); and rs9369062 (p = 3.06 × 10-22, β = 0.2093), a SNP located in the intron region of BTBD9 (6p12); both of which were also lead signals in RLS GWAS. PLMS is genetically correlated with insomnia, risk of stroke, and RLS, but not with iron deficiency. Pleiotropy adjusted Mendelian randomization analysis identified a causal effect of RLS on PLMS.ConclusionsBecause PLMS is more common than RLS, PLMS may have multiple causes and additional studies are needed to further validate these findings.

Published
2023

29. Sleep Disturbances and COVID-19 Vaccines

Author

Rosa, Daniela Santoro, Boscardin, Silvia Beatriz, Pires, Gabriel Natan, Andersen, Monica Levy, Tufik, Sergio, BaHammam, Ahmed, editor, Pandi-Perumal, S. R., editor, and Jahrami, Haitham, editor

Published
2023
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44. Sleep Medicine in Brazil

Author

Hachul, Helena, primary, Ninello Polesel, Daniel, additional, Nozoe, Karen Tieme, additional, Poyares, Dalva, additional, Levy Andersen, Monica, additional, and Tufik, Sergio, additional

Published
2023
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