Biology subjects

Caskey, C. T.

Publications and source records attributed to Caskey, C. T..

3 recordsLinked to original sources

bioRxiv2018

Unsupervised integration of multimodal dataset identifies novel signatures of health and disease

Modern medicine is rapidly moving towards a data-driven paradigm based on comprehensive multimodal health assessments. We collected 1,385 data features from diverse modalities, including metabolome, microbiome, genetics and advanced imaging, from 1,253 individuals and from a longitudinal validation cohort of 1,083 individuals. We utilized an ensemble of unsupervised machine learning techniques to identify multimodal biomarker signatures of health and disease risk. In particular, our method identified a set of cardiometabolic biomarkers that goes beyond standard clinical biomarkers, which were used to cluster individuals into distinct health profiles. Cluster membership was a better predictor for diabetes than established clinical biomarkers such as glucose, insulin resistance, and BMI. The novel biomarkers in the diabetes signature included 1-stearoyl-2-dihomo-linolenoyl-GPC and 1-(1-enyl-palmitoyl)-2-oleoyl-GPC. Another metabolite, cinnamoylglycine, was identified as a potential biomarker for both gut microbiome health and lean mass percentage. We also identified an early disease signature for hypertension, and individuals at-risk for a poor metabolic health outcome. We found novel associations between an uremic toxin, p-cresol sulfate, and the abundance of the microbiome genera Intestinimonas and an unclassified genus in the Erysipelotrichaceae family. Our methodology and results demonstrate the potential of multimodal data integration, from the identification of novel biomarker signatures to a data-driven stratification of individuals into disease subtypes and stages -- an essential step towards personalized, preventative health risk assessment.

bioinformatics↗

bioRxiv2018

Profound perturbation of the human metabolome by obesity

Obesity is a heterogeneous phenotype that is crudely measured by body mass index (BMI). More precise phenotyping and categorization of risk in large numbers of people with obesity is needed to advance clinical care and drug development. Here, we used non-targeted metabolome analysis and whole genome sequencing to identify metabolic and genetic signatures of obesity. We collected anthropomorphic and metabolic measurements at three timepoints over a median of 13 years in 1,969 adult twins of European ancestry and at a single timepoint in 427 unrelated volunteers. We observe that obesity results in a profound perturbation of the metabolome; nearly a third of the assayed metabolites are associated with changes in BMI. A metabolome signature identifies the healthy obese and also identifies lean individuals with abnormal metabolomes - these groups differ in health outcomes and underlying genetic risk. Because metabolome profiling identifies clinically meaningful heterogeneity in obesity, this approach could help select patients for clinical trials.

genetics↗

bioRxiv2017

Precision Medicine Screening Using Whole Genome Sequencing And Advanced Imaging To Identify Disease Risk In Adults

BACKGROUNDProgress in science and technology have created the capabilities and alternatives to symptom-driven medical care. Reducing premature mortality associated with age-related chronic diseases, such as cancer and cardiovascular disease, is an urgent priority we address using advanced screening detection.\n\nMETHODSWe enrolled active adults for early detection of risk for age-related chronic disease associated with premature mortality. Whole genome sequencing together with: global metabolomics, 3D/4D imaging using non-contrast whole body magnetic resonance imaging and echocardiography, and 2-week cardiac monitoring were employed to detect age-related chronic diseases and risk for diseases.\n\nRESULTSWe detected previously unrecognized age-related chronic diseases requiring prompt (<30 days) medical attention in 17 (8%, 1:12) of 209 study participants, including 4 participants with early stage neoplasms (2%, 1:50). Likely mechanistic genomic findings correlating with clinical data were identified in 52 participants (25%. 1:4). More than three-quarters of participants (n=164, 78%, 3:4) had evidence of age-related chronic diseases or associated risk factors.\n\nCONCLUSIONSPrecision medicine screening using genomics with other advanced clinical data among active adults identified unsuspected disease risks for age-related chronic diseases associated with premature mortality. This technology-driven phenotype screening approach has the potential to extend healthy life among active adults through improved early detection and prevention of age-related chronic diseases. Our success provides a scalable strategy to move medical practice and discovery toward risk detection and disease modification thus achieving healthier extension of life.\n\nSIGNIFICANCE STATEMENTAdvances in science and technology have enabled scientists to analyze the human genome cost-effectively and to combine genome sequencing with noninvasive imaging technologies for alternatives to symptom-driven medical care. Using whole genome sequencing and noninvasive 3D/4D imaging technologies we screened 209 adults to detect age-related chronic diseases, such as cancer and cardiovascular disease. We found unrecognized age-related chronic diseases requiring prompt (<30 days) medical attention in 1:12 study participants, likely genomic findings correlating with clinical data in 1:4 participants, and evidence of age-related chronic diseases or associated risk factors in more than 3 of 4 participants. These results demonstrate that genome sequencing with clinical imaging data can be used for screening and early detection of diseases associated with premature mortality.

genomics↗