Lettie McGuire, EdM

All Publications

• An encyclopedia of the cord blood metabolome reveals maternal-fetal interactions and disease risk. Cell reports. Medicine Lancaster, S., Mataraso, S., Reiss, J. D., Contrepois, K., Trowbridge, C. A., Michael, B., Simms, I., Ellenberger, M., Gibson, M., Clary, M., McGuire, L., Wong, F., Canfield, E., Cotter, D., Wang, T., Yang, Y., Nair, R., Krishnan, P., Rangan, P. V., Bianco, Y. K., Bernstein, J. A., Stevenson, D. K., Cowan, T., Rangan, E., Aghaeepour, N., Kasowski, M., Sylvester, K. G., Snyder, M. P. 2026: 102548

  Abstract

  Metabolites present in the mother traverse the placenta to supply energy, essential nutrients, and communication signals to the fetus. To gain a deeper understanding of fetal metabolism and the impacts of maternal metabolic health and medications on the fetus, we have created CordDB. Using mass spectrometry, we systematically document the metabolites and medications that enter and leave the fetus during birth, as well as the associated health records of the mother and newborn. These data reveal the metabolites consumed by the fetus, microbial metabolites (e.g., 3-indolepropionic acid), metabolites obtained from diet, and medications, as well as create a healthy newborn signature. Our study demonstrates that the mother's microbial interactions and nutrition, premature birth, and the mother's use of drugs such as bupivacaine and betamethasone are linked to variations in the metabolic profiles and health of newborns.

  View details for DOI 10.1016/j.xcrm.2025.102548

  View details for PubMedID 41592564

• Longitudinal wearable sensor data enhance precision of Long COVID detection. PLOS digital health Uwakwe, C. K., Rangan, E. S., Kumar, S., Gutjahr, G., Miao, X., Brooks, A. W., Maguire, P., Mishra, T., McGuire, L., Snyder, M. P. 2025; 4 (11): e0001093

  Abstract

  Despite the millions of individuals struggling with persistent symptoms, Long COVID has remained difficult to diagnose due to limited objective biomarkers, often leading to underdiagnosis or even misdiagnosis. To bridge this gap, we investigated the potential of utilizing wearable sensor data to aid in the diagnosis of Long COVID. We analyzed longitudinal heart rate (HR) data from 126 individuals with acute SARS-CoV-2 infections to develop machine learning models capable of predicting Long COVID status using derived HR features, symptom features, or a combination of both feature sets. The HR features were derived across six analytical categories, including time-domain, Poincaré nonlinear, raw signal, Kullback-Leibler (KL) divergence, variational mode decomposition (VMD), and the Shannon energy envelope (SEE), enabling the capture of heart rate dynamics over various temporal scales and the quantification of day-to-day shifts in HR distributions. The symptom features used in the final models included chest pain, vomiting, excessive sweating, memory loss, brain fog, heart palpitations, and loss of smell. The combined HR- and symptom-feature model demonstrated robust predictive performance, achieving an area under the Receiver Operating Characteristic curve (ROC-AUC) of 95.1% and an area under the Precision-Recall curve (PR-AUC) of 85.9%. These values represent a significant improvement of approximately 5% in both the ROC-AUC and PR-AUC over the symptoms-only model. At the population level, this improvement in discrimination could lead to clinically meaningful reductions in misclassification and improved patient outcomes, achieved through a non-invasive diagnostic tool. These findings suggest that wearable HR data could be used to derive an objective biomarker for Long COVID, thereby enhancing diagnostic precision.

  View details for DOI 10.1371/journal.pdig.0001093

  View details for PubMedID 41264615

• Individual variations in glycemic responses to carbohydrates and underlying metabolic physiology. Nature medicine Wu, Y., Ehlert, B., Metwally, A. A., Perelman, D., Park, H., Brooks, A. W., Abbasi, F., Michael, B., Celli, A., Bejikian, C., Ayhan, E., Lu, Y., Lancaster, S. M., Hornburg, D., Ramirez, L., Bogumil, D., Pollock, S., Wong, F., Bradley, D., Gutjahr, G., Rangan, E. S., Wang, T., McGuire, L., Venkat Rangan, P., Ræder, H., Shipony, Z., Lipson, D., McLaughlin, T., Snyder, M. P. 2025

  Abstract

  Elevated postprandial glycemic responses (PPGRs) are associated with type 2 diabetes and cardiovascular disease. PPGRs to the same foods have been shown to vary between individuals, but systematic characterization of the underlying physiologic and molecular basis is lacking. We measured PPGRs using continuous glucose monitoring in 55 well-phenotyped participants challenged with seven different standard carbohydrate meals administered in replicate. We also examined whether preloading a rice meal with fiber, protein or fat ('mitigators') altered PPGRs. We performed gold-standard metabolic tests and multi-omics profiling to examine the physiologic and molecular basis for interindividual PPGR differences. Overall, rice was the most glucose-elevating carbohydrate meal, but there was considerable interindividual variability. Individuals with the highest PPGR to potatoes (potato-spikers) were more insulin resistant and had lower beta cell function, whereas grape-spikers were more insulin sensitive. Rice-spikers were more likely to be Asian individuals, and bread-spikers had higher blood pressure. Mitigators were less effective in reducing PPGRs in insulin-resistant as compared to insulin-sensitive participants. Multi-omics signatures of PPGR and metabolic phenotypes were discovered, including insulin-resistance-associated triglycerides, hypertension-associated metabolites and PPGR-associated microbiome pathways. These results demonstrate interindividual variability in PPGRs to carbohydrate meals and mitigators and their association with metabolic and molecular profiles.

  View details for DOI 10.1038/s41591-025-03719-2

  View details for PubMedID 40467897

  View details for PubMedCentralID 4266395

• Real-time alerting system for COVID-19 and other stress events using wearable data. Nature medicine Alavi, A., Bogu, G. K., Wang, M., Rangan, E. S., Brooks, A. W., Wang, Q., Higgs, E., Celli, A., Mishra, T., Metwally, A. A., Cha, K., Knowles, P., Alavi, A. A., Bhasin, R., Panchamukhi, S., Celis, D., Aditya, T., Honkala, A., Rolnik, B., Hunting, E., Dagan-Rosenfeld, O., Chauhan, A., Li, J. W., Bejikian, C., Krishnan, V., McGuire, L., Li, X., Bahmani, A., Snyder, M. P. 2021

  Abstract

  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