Postdoctoral researcher in the Snyder Lab. My research focuses on the human gut microbiome, and I am involved in multiple multiomic projects investigating how physiological systems through the human body interact across different lifestyles and health states. I perform both wet and dry lab aspects of multiomics analyses, and am involved in two coronavirus research projects including handling of positive SARS-COV-2 samples.
Michael Snyder, Postdoctoral Faculty Sponsor
Real-time alerting system for COVID-19 and other stress events using wearable data.
Early detection of infectious diseases is crucial for reducing transmission and facilitating early intervention. In this study, we built a real-time smartwatch-based alerting system that detects aberrant physiological and activity signals (heart rates and steps) associated with the onset of early infection and implemented this system in a prospective study. In a cohort of 3,318 participants, of whom 84 were infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), this system generated alerts for pre-symptomatic and asymptomatic SARS-CoV-2 infection in 67 (80%) of the infected individuals. Pre-symptomatic signals were observed at a median of 3 days before symptom onset. Examination of detailed survey responses provided by the participants revealed that other respiratory infections as well as events not associated with infection, such as stress, alcohol consumption and travel, could also trigger alerts, albeit at a much lower mean frequency (1.15 alert days per person compared to 3.42 alert days per person for coronavirus disease 2019 cases). Thus, analysis of smartwatch signals by an online detection algorithm provides advance warning of SARS-CoV-2 infection in a high percentage of cases. This study shows that a real-time alerting system can be used for early detection of infection and other stressors and employed on an open-source platform that is scalable to millions of users.
View details for DOI 10.1038/s41591-021-01593-2
View details for PubMedID 34845389
New-onset vegetarian diet shows differences in fatty acid metabolites in European American and African American women.
Nutrition, metabolism, and cardiovascular diseases : NMCD
BACKGROUND AND AIMS: The type of fat consumed in animal-based western diets, typically rich in the saturated fat palmitate, has been implicated in cardiometabolic disease risk. In contrast, the most abundant mono- and polyunsaturated fats, more typical in a vegetarian or plant-based diet, potentiate less deleterious effects. This study determined differences in plasma and urine metabolites when switching from omnivorous to vegetarian diet, including metabolites involved in fatty acid utilization.METHODS AND RESULTS: A prospective cohort of 38 European (EA) and African American (AA) omnivorous females were matched by age (25.7±5.3y) and BMI (22.4±1.9kg/m2). Pre-intervention samples were collected while subjects consumed habitual animal-based diet. Changes in metabolites were assessed by ultra-high-performance liquid chromatography-tandem mass spectroscopy (Metabolon, Inc.) upon completing four days of novel vegetarian diet provided by the Vanderbilt Metabolic Kitchen. Changes in several diet-derived metabolites were observed, including increases in compounds derived from soy food metabolism along with decreases in metabolites of xanthine and histidine. Significant changes occurred in metabolites of saturated, monounsaturated and polyunsaturated fatty acids along with significant differences between EA and AA women in changes in plasma concentrations of acylcarnitines, which reflect the completeness of fatty acid oxidation (versus storage).CONCLUSION: These data suggest improvements in fatty acid metabolism (oxidation vs storage), a key factor in energy homeostasis, may be promoted rapidly by adoption of a vegetarian (plant-based) diet. Mechanistic differences in response to diet interventions must be understood to effectively provide protection against the widespread development of obesity and cardiometabolic disease in population subgroups, such as AA women.
View details for DOI 10.1016/j.numecd.2021.05.013
View details for PubMedID 34176710
Early Detection of SARS-CoV-2 and other Infections in Solid Organ Transplant Recipients and Household Members using Wearable Devices.
Transplant international : official journal of the European Society for Organ Transplantation
The increasing global prevalence of SARS-CoV-2 and the resulting COVID-19 disease pandemic pose significant concerns for clinical management of solid organ transplant recipients (SOTR). Wearable devices that can measure physiologic changes in biometrics including heart rate, heart rate variability, body temperature, respiratory, activity (such as steps taken per day) and sleep patterns and blood oxygen saturation, show utility for the early detection of infection before clinical presentation of symptoms. Recent algorithms developed using preliminary wearable datasets show that SARS-CoV-2 is detectable before clinical symptoms in >80% of adults. Early detection of SARS-CoV-2, influenza, and other pathogens in SOTR, and their household members, could facilitate early interventions such as self-isolation and early clinical management of relevant infection(s). Ongoing studies testing the utility of wearable devices such as smartwatches for early detection of SARS-CoV-2 and other infections in the general population are reviewed here, along with the practical challenges to implementing these processes at scale in pediatric and adult SOTR, and their household members. The resources and logistics, including transplant specific analyses pipelines to account for confounders such as polypharmacy and comorbidities, required in studies of pediatric and adult SOTR for the robust early detection of SARS-CoV-2 and other infections are also reviewed.
View details for DOI 10.1111/tri.13860
View details for PubMedID 33735480
Real-time Alerting System for COVID-19 Using Wearable Data.
medRxiv : the preprint server for health sciences
Early detection of infectious disease is crucial for reducing transmission and facilitating early intervention. We built a real-time smartwatch-based alerting system for the detection of aberrant physiological and activity signals (e.g. resting heart rate, steps) associated with early infection onset at the individual level. Upon applying this system to a cohort of 3,246 participants, we found that alerts were generated for pre-symptomatic and asymptomatic COVID-19 infections in 78% of cases, and pre-symptomatic signals were observed a median of three days prior to symptom onset. Furthermore, by examining over 100,000 survey annotations, we found that other respiratory infections as well as events not associated with COVID-19 (e.g. stress, alcohol consumption, travel) could trigger alerts, albeit at a lower mean period (1.9 days) than those observed in the COVID-19 cases (4.3 days). Thus this system has potential both for advanced warning of COVID-19 as well as a general system for measuring health via detection of physiological shifts from personal baselines. The system is open-source and scalable to millions of users, offering a personal health monitoring system that can operate in real time on a global scale.
View details for DOI 10.1101/2021.06.13.21258795
View details for PubMedID 34189532
View details for PubMedCentralID PMC8240687
Pre-symptomatic detection of COVID-19 from smartwatch data.
Nature biomedical engineering
Consumer wearable devices that continuously measure vital signs have been used to monitor the onset of infectious disease. Here, we show that data from consumer smartwatches can be used for the pre-symptomatic detection of coronavirus disease 2019 (COVID-19). We analysed physiological and activity data from 32 individuals infected with COVID-19, identified from a cohort of nearly 5,300 participants, and found that 26 of them (81%) had alterations in their heart rate, number of daily steps or time asleep. Of the 25 cases of COVID-19 with detected physiological alterations for which we had symptom information, 22 were detected before (or at) symptom onset, with four cases detected at least nine days earlier. Using retrospective smartwatch data, we show that 63% of the COVID-19 cases could have been detected before symptom onset in real time via a two-tiered warning system based on the occurrence of extreme elevations in resting heart rate relative to the individual baseline. Our findings suggest that activity tracking and health monitoring via consumer wearable devices may be used for the large-scale, real-time detection of respiratory infections, often pre-symptomatically.
View details for DOI 10.1038/s41551-020-00640-6
View details for PubMedID 33208926
Gut microbiota diversity across ethnicities in the United States
2018; 16 (12): e2006842
Composed of hundreds of microbial species, the composition of the human gut microbiota can vary with chronic diseases underlying health disparities that disproportionally affect ethnic minorities. However, the influence of ethnicity on the gut microbiota remains largely unexplored and lacks reproducible generalizations across studies. By distilling associations between ethnicity and differences in two US-based 16S gut microbiota data sets including 1,673 individuals, we report 12 microbial genera and families that reproducibly vary by ethnicity. Interestingly, a majority of these microbial taxa, including the most heritable bacterial family, Christensenellaceae, overlap with genetically associated taxa and form co-occurring clusters linked by similar fermentative and methanogenic metabolic processes. These results demonstrate recurrent associations between specific taxa in the gut microbiota and ethnicity, providing hypotheses for examining specific members of the gut microbiota as mediators of health disparities.
View details for DOI 10.1371/journal.pbio.2006842
View details for Web of Science ID 000455108400016
View details for PubMedID 30513082
View details for PubMedCentralID PMC6279019
Phylosymbiosis: Relationships and Functional Effects of Microbial Communities across Host Evolutionary History
2016; 14 (11): e2000225
Phylosymbiosis was recently proposed to describe the eco-evolutionary pattern, whereby the ecological relatedness of host-associated microbial communities parallels the phylogeny of related host species. Here, we test the prevalence of phylosymbiosis and its functional significance under highly controlled conditions by characterizing the microbiota of 24 animal species from four different groups (Peromyscus deer mice, Drosophila flies, mosquitoes, and Nasonia wasps), and we reevaluate the phylosymbiotic relationships of seven species of wild hominids. We demonstrate three key findings. First, intraspecific microbiota variation is consistently less than interspecific microbiota variation, and microbiota-based models predict host species origin with high accuracy across the dataset. Interestingly, the age of host clade divergence positively associates with the degree of microbial community distinguishability between species within the host clades, spanning recent host speciation events (~1 million y ago) to more distantly related host genera (~108 million y ago). Second, topological congruence analyses of each group's complete phylogeny and microbiota dendrogram reveal significant degrees of phylosymbiosis, irrespective of host clade age or taxonomy. Third, consistent with selection on host-microbiota interactions driving phylosymbiosis, there are survival and performance reductions when interspecific microbiota transplants are conducted between closely related and divergent host species pairs. Overall, these findings indicate that the composition and functional effects of an animal's microbial community can be closely allied with host evolution, even across wide-ranging timescales and diverse animal systems reared under controlled conditions.
View details for DOI 10.1371/journal.pbio.2000225
View details for Web of Science ID 000392113500007
View details for PubMedID 27861590
View details for PubMedCentralID PMC5115861