Evidence map›Paper›PMID 38410429›Full record

ArticlemedRxiv : the preprint server for health sciences2024

Effects of Preanalytical Sample Collection and Handling on Comprehensive Metabolite Measurements in Human Urine Biospecimens.

John Braisted, Theresa Henderson, John W Newman, Steven C Moore, Joshua Sampson, Kathleen McClain, Sharon Ross, David J Baer, Ewy A Mathé, Krista A Zanetti

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

John BraistedDivision of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD.ORCID 0000-0001-8093-6463
Theresa HendersonFood Components and Health Laboratory, Agriculture Research Service, United States Department of Agriculture, Beltsville, MD.ORCID 0009-0008-5482-0326
John W NewmanObesity and Metabolism Research, Agriculture Research Service, United States Department of Agriculture, Davis, CA.ORCID 0000-0001-9632-6571
Steven C MooreDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD.ORCID 0000-0002-8169-1661
Joshua SampsonDivision of Cancer Prevention, National Cancer Institute, Rockville, MD.ORCID 0000-0003-2875-3365
Kathleen McClainDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD.ORCID 0000-0001-5520-2097
Sharon RossDivision of Cancer Prevention, National Cancer Institute, Rockville, MD.ORCID 0000-0003-1443-2050
David J BaerFood Components and Health Laboratory, Agriculture Research Service, United States Department of Agriculture, Beltsville, MD.ORCID 0000-0003-3454-2097
Ewy A MathéDivision of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD.ORCID 0000-0003-4491-8107
Krista A ZanettiOffice of Nutrition Research, Division of Program Coordination, Planning, and Strategic Initiatives, Office of the Director, National Institutes of Health, Bethesda, MD.ORCID 0000-0002-4050-8461

Funding

Informatics Research CoreZICTR000410 · NCATS · NATIONAL CENTER FOR ADVANCING TRANSLATIONAL SCIENCES · PI MATHÉ, EWY · 2021 to 2025
$11.7M
Intramural NIH HHS ZIC TR000410
6 · The paper itself

Abstract

Epidemiology studies evaluate associations between the metabolome and disease risk. Urine is a common biospecimen used for such studies due to its wide availability and non-invasive collection. Evaluating the robustness of urinary metabolomic profiles under varying preanalytical conditions is thus of interest. Here we evaluate the impact of sample handling conditions on urine metabolome profiles relative to the gold standard condition (no preservative, no refrigeration storage, single freeze thaw). Conditions tested included the use of borate or chlorhexidine preservatives, various storage and freeze/thaw cycles. We demonstrate that sample handling conditions impact metabolite levels, with borate showing the largest impact with 125 of 1,048 altered metabolites (adjusted P < 0.05). When simulating a case-control study with expected inconsistencies in sample handling, we predicted the occurrence of false positive altered metabolites to be low (< 11). Predicted false positives increased substantially (³63) when cases were simulated to undergo alternate handling. Finally, we demonstrate that sample handling impacts on the urinary metabolome were markedly smaller than those in serum. While changes in urine metabolites incurred by sample handling are generally small, we recommend implementing consistent handling conditions and evaluating robustness of metabolite measurements for those showing significant associations with disease outcomes.

Indexed as

epidemiologyfreeze-thawmetabolomicspreanalytical sample handlingpreservativesstabilityurineurine metabolomics

Identifiers

PMID38410429
PMCPMC10896411

What Socratic holds

Textmetadata
LicenceCC0
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.