Dataset Information

Real-time health monitoring through urine metabolomics.

ABSTRACT: Current healthcare practices are reactive and based on limited physiological information collected months or years apart. By enabling patients and healthy consumers access to continuous measurements of health, wearable devices and digital medicine stand to realize highly personalized and preventative care. However, most current digital technologies provide information on a limited set of physiological traits, such as heart rate and step count, which alone offer little insight into the etiology of most diseases. Here we propose to integrate data from biohealth smartphone applications with continuous metabolic phenotypes derived from urine metabolites. This combination of molecular phenotypes with quantitative measurements of lifestyle reflect the biological consequences of human behavior in real time. We present data from an observational study involving two healthy subjects and discuss the challenges, opportunities, and implications of integrating this new layer of physiological information into digital medicine. Though our dataset is limited to two subjects, our analysis (also available through an interactive web-based visualization tool) provides an initial framework to monitor lifestyle factors, such as nutrition, drug metabolism, exercise, and sleep using urine metabolites.

SUBMITTER: Miller IJ

PROVIDER: S-EPMC6848197 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Real-time health monitoring through urine metabolomics.

Miller Ian J IJ Peters Sean R SR Overmyer Katherine A KA Paulson Brett R BR Westphall Michael S MS Coon Joshua J JJ

NPJ digital medicine 20191111

Current healthcare practices are reactive and based on limited physiological information collected months or years apart. By enabling patients and healthy consumers access to continuous measurements of health, wearable devices and digital medicine stand to realize highly personalized and preventative care. However, most current digital technologies provide information on a limited set of physiological traits, such as heart rate and step count, which alone offer little insight into the etiology o ...[more]

PMID: 31728416

Similar Datasets

Project description:Study description: the study is a clinical trial aimed at assessing the validity of a point-of-care tool developed for colorectal cancer screening using urine metabolites. Objectives: Primary Objective: In the pilot phase, investigators will field test the POC device in Nigeria on 75 patients who are high-risk for CRC and then validate the urine metabolite signature using a large cohort of 645 patients at high risk for CRC and polyps. This will allow us to determine the sensitivity and specificity of this device and these signatures for CRC and polyps. Endpoints: the endpoint will be the calculation of the validity (sensitivity and specificity) of the tool. Positive and negative predictive values will also be calculated. Study population: Patients > 40 years of age with LGI bleeding for more than one-week OR * Patients who are high risk due to a family history of CRC (first-degree relative) * Must be at most 10 years younger than when relative presented with CRC (For example, a patient with a relative diagnosed at age 40 with CRC will be eligible after age 30) * Patients with a diagnosis of stage I-III CRC who have no evidence of disease Both males and females will be involved in the study I. The study will evaluate the validity and acceptability of the POC biosensor device in Nigeria. Description of sites/facilities enrolling participants: the study will be conducted at Obafemi Awolowo University Teaching Hospitals Complex (OAUTHC) which hosts the head office of African Research Group for Oncology (ARGO). The study will also run concurrently at other ARGO collaborating sites. All the ARGO collaborating sites are tertiary health care facilities with experienced personnel to oversee the study. The pilot study will, however, take place in OAUTHC alone. Study duration: The accrual time for the validation study is 2.5 years. Participant Duration: the study will require initial one-time contact. Those that are positive during the screening with the POC biosensor device will be booked for colonoscopy. Hence, for participants with positive results to complete the study may require 2 weeks on average.

Dataset Information

Real-time health monitoring through urine metabolomics.

Publications

Real-time health monitoring through urine metabolomics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets