@article{878ca1fc167e425eb2b17d0793c8a7ec,
title = "Statistically Driven Metabolite and Lipid Profiling of Patients from the Undiagnosed Diseases Network",
abstract = "Advancements in molecular separations coupled with mass spectrometry have enabled metabolome analyses for clinical cohorts. A population of interest for metabolome profiling is patients with rare disease for which abnormal metabolic signatures may yield clues into the genetic basis, as well as mechanistic drivers of the disease and possible treatment options. We undertook the metabolome profiling of a large cohort of patients with mysterious conditions characterized through the Undiagnosed Diseases Network (UDN). Due to the size and enrollment procedures, collection of the metabolomes for UDN patients took place over 2 years. We describe the study designed to adjust for measurements collected over a long time scale and how this enabled statistical analyses to summarize the metabolome of individual patients. We demonstrate the removal of time-based batch effects, overall statistical characteristics of the UDN population, and two case studies of interest that demonstrate the utility of metabolome profiling for rare diseases.",
author = "Webb-Robertson, {Bobbie Jo M.} and Stratton, {Kelly G.} and Kyle, {Jennifer E.} and Kim, {Young Mo} and Bramer, {Lisa M.} and Waters, {Katrina M.} and Koeller, {David M.} and Metz, {Thomas O.}",
note = "Funding Information: We thank the Undiagnosed Diseases Network investigators and are grateful for the participation of the patients and family members and their referring clinicians. This work was funded by the Undiagnosed Diseases Network (1U01TR001395) supported by the National Institutes of Health (NIH) Common Fund. The authors thank the following individuals, institutions, and funding sources for providing or making available the reference population samples: Dr. Mary Samuels, at Oregon Health Science University (via the Oregon Clinical and Translational Research Institute, supported by the National Center for Advancing Translational Sciences of the NIH under award number UL1TR0000128); Dr. Joseph Quinn, at Oregon Health Science University (via the Oregon Alzheimer{\textquoteright}s Disease Center Biorepository, supported by grant number NIA-AG008017 from the NIH National Institute on Aging); samples were courtesy of Dr. Rizwan Hamid, at the Vanderbilt University Medical Center, and Dr. Devin Oglesbee, at the Mayo Clinic (with support by Mayo Clinic{\textquoteright}s Department of Laboratory Medicine and Pathology). Mass spectrometry analyses were performed in the Environmental Molecular Sciences Laboratory, a national scientific user facility sponsored by the Department of Energy (DOE) Office of Biological and Environmental Research and located at Pacific Northwest National Laboratory (PNNL). We also thank Mr. Michael Perkins at PNNL for graphic design. PNNL is operated by Battelle Memorial Institute for the DOE under contract DEAC05-76RLO1830. Publisher Copyright: {\textcopyright} 2019 American Chemical Society.",
year = "2020",
month = jan,
day = "21",
doi = "10.1021/acs.analchem.9b03522",
language = "English (US)",
volume = "92",
pages = "1796--1803",
journal = "Analytical Chemistry",
issn = "0003-2700",
number = "2",
}