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1.
Nat Med ; 28(1): 175-184, 2022 01.
Article in English | MEDLINE | ID: covidwho-1541244

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.


Subject(s)
COVID-19/diagnosis , Carrier State/diagnosis , Exercise , Heart Rate/physiology , Wearable Electronic Devices , Accelerometry , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/physiopathology , Carrier State/physiopathology , Early Diagnosis , Female , Fitness Trackers , Humans , Male , Middle Aged , SARS-CoV-2 , Sleep , Young Adult
2.
Diabetes ; 70(12): 2733-2744, 2021 12.
Article in English | MEDLINE | ID: covidwho-1484985

ABSTRACT

The coronavirus disease 2019 (COVID-19) global pandemic continues to spread worldwide with approximately 216 million confirmed cases and 4.49 million deaths to date. Intensive efforts are ongoing to combat this disease by suppressing viral transmission, understanding its pathogenesis, developing vaccination strategies, and identifying effective therapeutic targets. Individuals with preexisting diabetes also show higher incidence of COVID-19 illness and poorer prognosis upon infection. Likewise, an increased frequency of diabetes onset and diabetes complications has been reported in patients following COVID-19 diagnosis. COVID-19 may elevate the risk of hyperglycemia and other complications in patients with and without prior diabetes history. It is unclear whether the virus induces type 1 or type 2 diabetes or instead causes a novel atypical form of diabetes. Moreover, it remains unknown if recovering COVID-19 patients exhibit a higher risk of developing new-onset diabetes or its complications going forward. The aim of this review is to summarize what is currently known about the epidemiology and mechanisms of this bidirectional relationship between COVID-19 and diabetes. We highlight major challenges that hinder the study of COVID-19-induced new-onset of diabetes and propose a potential framework for overcoming these obstacles. We also review state-of-the-art wearables and microsampling technologies that can further study diabetes management and progression in new-onset diabetes cases. We conclude by outlining current research initiatives investigating the bidirectional relationship between COVID-19 and diabetes, some with emphasis on wearable technology.


Subject(s)
COVID-19/complications , Diabetes Mellitus/etiology , SARS-CoV-2 , COVID-19/epidemiology , Diabetes Complications , Diabetes Mellitus/mortality , Humans
3.
Nat Commun ; 12(1): 5757, 2021 10 01.
Article in English | MEDLINE | ID: covidwho-1447304

ABSTRACT

The large amount of biomedical data derived from wearable sensors, electronic health records, and molecular profiling (e.g., genomics data) is rapidly transforming our healthcare systems. The increasing scale and scope of biomedical data not only is generating enormous opportunities for improving health outcomes but also raises new challenges ranging from data acquisition and storage to data analysis and utilization. To meet these challenges, we developed the Personal Health Dashboard (PHD), which utilizes state-of-the-art security and scalability technologies to provide an end-to-end solution for big biomedical data analytics. The PHD platform is an open-source software framework that can be easily configured and deployed to any big data health project to store, organize, and process complex biomedical data sets, support real-time data analysis at both the individual level and the cohort level, and ensure participant privacy at every step. In addition to presenting the system, we illustrate the use of the PHD framework for large-scale applications in emerging multi-omics disease studies, such as collecting and visualization of diverse data types (wearable, clinical, omics) at a personal level, investigation of insulin resistance, and an infrastructure for the detection of presymptomatic COVID-19.


Subject(s)
Data Science/methods , Medical Records Systems, Computerized , Big Data , Computer Security , Data Analysis , Health Information Interoperability , Humans , Information Storage and Retrieval , Software
4.
Am J Clin Nutr ; 114(5): 1655-1665, 2021 11 08.
Article in English | MEDLINE | ID: covidwho-1349771

ABSTRACT

BACKGROUND: Angiotensin-converting enzyme 2 (ACE2) serves protective functions in metabolic, cardiovascular, renal, and pulmonary diseases and is linked to COVID-19 pathology. The correlates of temporal changes in soluble ACE2 (sACE2) remain understudied. OBJECTIVES: We explored the associations of sACE2 with metabolic health and proteome dynamics during a weight loss diet intervention. METHODS: We analyzed 457 healthy individuals (mean ± SD age: 39.8 ± 6.6 y) with BMI 28-40 kg/m2 in the DIETFITS (Diet Intervention Examining the Factors Interacting with Treatment Success) study. Biochemical markers of metabolic health and 236 proteins were measured by Olink CVDII, CVDIII, and Inflammation I arrays at baseline and at 6 mo during the dietary intervention. We determined clinical and routine biochemical correlates of the diet-induced change in sACE2 (ΔsACE2) using stepwise linear regression. We combined feature selection models and multivariable-adjusted linear regression to identify protein dynamics associated with ΔsACE2. RESULTS: sACE2 decreased on average at 6 mo during the diet intervention. Stronger decline in sACE2 during the diet intervention was independently associated with female sex, lower HOMA-IR and LDL cholesterol at baseline, and a stronger decline in HOMA-IR, triglycerides, HDL cholesterol, and fat mass. Participants with decreasing HOMA-IR (OR: 1.97; 95% CI: 1.28, 3.03) and triglycerides (OR: 2.71; 95% CI: 1.72, 4.26) had significantly higher odds for a decrease in sACE2 during the diet intervention than those without (P ≤ 0.0073). Feature selection models linked ΔsACE2 to changes in α-1-microglobulin/bikunin precursor, E-selectin, hydroxyacid oxidase 1, kidney injury molecule 1, tyrosine-protein kinase Mer, placental growth factor, thrombomodulin, and TNF receptor superfamily member 10B. ΔsACE2 remained associated with these protein changes in multivariable-adjusted linear regression. CONCLUSIONS: Decrease in sACE2 during a weight loss diet intervention was associated with improvements in metabolic health, fat mass, and markers of angiotensin peptide metabolism, hepatic and vascular injury, renal function, chronic inflammation, and oxidative stress. Our findings may improve the risk stratification, prevention, and management of cardiometabolic complications.This trial was registered at clinicaltrials.gov as NCT01826591.


Subject(s)
Angiotensin-Converting Enzyme 2/metabolism , Body Composition , COVID-19/metabolism , Diet, Reducing , Obesity/metabolism , Proteome/metabolism , Weight Loss/physiology , Adipose Tissue/metabolism , Adult , Biomarkers/blood , Body Mass Index , Cholesterol, HDL/blood , Cholesterol, LDL/blood , Female , Humans , Inflammation , Insulin Resistance , Male , Middle Aged , Obesity/diet therapy , Oxidative Stress , Pandemics , SARS-CoV-2 , Triglycerides/blood , Weight Reduction Programs
5.
Transpl Int ; 34(6): 1019-1031, 2021 06.
Article in English | MEDLINE | ID: covidwho-1140311

ABSTRACT

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.


Subject(s)
COVID-19 , Organ Transplantation , Wearable Electronic Devices , Adult , Child , Humans , Pandemics , SARS-CoV-2
6.
J Proteome Res ; 19(12): 4735-4746, 2020 12 04.
Article in English | MEDLINE | ID: covidwho-1065786

ABSTRACT

According to the 2020 Metrics of the HUPO Human Proteome Project (HPP), expression has now been detected at the protein level for >90% of the 19 773 predicted proteins coded in the human genome. The HPP annually reports on progress made throughout the world toward credibly identifying and characterizing the complete human protein parts list and promoting proteomics as an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2020-01 classified 17 874 proteins as PE1, having strong protein-level evidence, up 180 from 17 694 one year earlier. These represent 90.4% of the 19 773 predicted coding genes (all PE1,2,3,4 proteins in neXtProt). Conversely, the number of neXtProt PE2,3,4 proteins, termed the "missing proteins" (MPs), was reduced by 230 from 2129 to 1899 since the neXtProt 2019-01 release. PeptideAtlas is the primary source of uniform reanalysis of raw mass spectrometry data for neXtProt, supplemented this year with extensive data from MassIVE. PeptideAtlas 2020-01 added 362 canonical proteins between 2019 and 2020 and MassIVE contributed 84 more, many of which converted PE1 entries based on non-MS evidence to the MS-based subgroup. The 19 Biology and Disease-driven B/D-HPP teams continue to pursue the identification of driver proteins that underlie disease states, the characterization of regulatory mechanisms controlling the functions of these proteins, their proteoforms, and their interactions, and the progression of transitions from correlation to coexpression to causal networks after system perturbations. And the Human Protein Atlas published Blood, Brain, and Metabolic Atlases.


Subject(s)
Proteome , Proteomics , Databases, Protein , Genome, Human , Humans , Mass Spectrometry , Proteome/genetics
7.
Nat Biomed Eng ; 4(12): 1208-1220, 2020 12.
Article in English | MEDLINE | ID: covidwho-933690

ABSTRACT

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.


Subject(s)
COVID-19/diagnosis , COVID-19/prevention & control , Pandemics/prevention & control , Adult , Asymptomatic Diseases , Female , Humans , Male , Monitoring, Physiologic/methods , Retrospective Studies , SARS-CoV-2/pathogenicity , Wearable Electronic Devices
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