Evidence map›Paper›PMID 35313587›Full record

ArticlemedRxiv : the preprint server for health sciences2022

Biomarkers Selection for Population Normalization in SARS-CoV-2 Wastewater-based Epidemiology.

Shu-Yu Hsu, Mohamed B Bayati, Chenhui Li, Hsin-Yeh Hsieh, Anthony Belenchia, Jessica Klutts, Sally A Zemmer, Melissa Reynolds, Elizabeth Semkiw, Hwei-Yiing Johnson and 5 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2022. 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

15 authors.

Shu-Yu HsuSchool of Natural Resources, University of Missouri, Columbia, MO 65201, USA.
Mohamed B BayatiSchool of Natural Resources, University of Missouri, Columbia, MO 65201, USA.
Chenhui LiSchool of Natural Resources, University of Missouri, Columbia, MO 65201, USA.
Hsin-Yeh HsiehSchool of Natural Resources, University of Missouri, Columbia, MO 65201, USA.
Anthony BelenchiaBureau of Environmental Epidemiology, Division of Community and Public Health, Missouri Department of Health and Senior Services, Jefferson City, MO, USA.
Jessica KluttsWater Protection Program, Missouri Department of Natural Resources, Jefferson City, MO, USA.
Sally A ZemmerWater Protection Program, Missouri Department of Natural Resources, Jefferson City, MO, USA.
Melissa ReynoldsBureau of Environmental Epidemiology, Division of Community and Public Health, Missouri Department of Health and Senior Services, Jefferson City, MO, USA.
Elizabeth SemkiwBureau of Environmental Epidemiology, Division of Community and Public Health, Missouri Department of Health and Senior Services, Jefferson City, MO, USA.
Hwei-Yiing JohnsonBureau of Environmental Epidemiology, Division of Community and Public Health, Missouri Department of Health and Senior Services, Jefferson City, MO, USA.
Trevor FoleyMissouri Department of Corrections, Jefferson City, MO, USA.
Chris G WiebergWater Protection Program, Missouri Department of Natural Resources, Jefferson City, MO, USA.
Jeff WenzelBureau of Environmental Epidemiology, Division of Community and Public Health, Missouri Department of Health and Senior Services, Jefferson City, MO, USA.
Marc C JohnsonDepartment of Molecular Microbiology and Immunology, University of Missouri, School of Medicine and the Christopher S. Bond Life Sciences Center, Columbia, MO 65201, USA.
Chung-Ho LinSchool of Natural Resources, University of Missouri, Columbia, MO 65201, USA.

Funding

Wastewater Detection of COVID-19U01DA053893 · NIDA · MISSOURI STATE DEPT/ HEALTH & SENIOR SRV · PI WENZEL, JEFF · 2021 to 2022
$4.0M
NIDA NIH HHS U01 DA053893
6 · The paper itself

Abstract

Wastewater-based epidemiology (WBE) has been one of the most cost-effective approaches to track the SARS-CoV-2 levels in the communities since the COVID-19 outbreak in 2020. Normalizing SARS-CoV-2 concentrations by the population biomarkers in wastewater can be critical for interpreting the viral loads, comparing the epidemiological trends among the sewersheds, and identifying the vulnerable communities. In this study, five population biomarkers, pepper mild mottle virus (pMMoV), creatinine (CRE), 5-hydroxyindoleacetic acid (5-HIAA), caffeine (CAF) and its metabolite paraxanthine (PARA) were investigated for their utility in normalizing the SARS-CoV-2 loads through developed direct and indirect approaches. Their utility in assessing the real-time population contributing to the wastewater was also evaluated. The best performed candidate was further tested for its capacity for improving correlation between normalized SARS-CoV-2 loads and the clinical cases reported in the City of Columbia, Missouri, a university town with a constantly fluctuated population. Our results showed that, except CRE, the direct and indirect normalization approaches using biomarkers allow accounting for the changes in wastewater dilution and differences in relative human waste input over time regardless flow volume and population at any given WWTP. Among selected biomarkers, PARA is the most reliable population biomarker in determining the SARS-CoV-2 load per capita due to its high accuracy, low variability, and high temporal consistency to reflect the change in population dynamics and dilution in wastewater. It also demonstrated its excellent utility for real-time assessment of the population contributing to the wastewater. In addition, the viral loads normalized by the PARA-estimated population significantly improved the correlation ( HIGHLIGHT bullet points: The paraxanthine (PARA), the metabolite of the caffeine, is a more reliable population biomarker in SARS-CoV-2 wastewater-based epidemiology studies than the currently recommended pMMoV genetic marker.SARS-CoV-2 load per capita could be directly normalized using the regression functions derived from correlation between paraxanthine and population without flowrate and population data.Normalizing SARS-CoV-2 levels with the chemical marker PARA significantly improved the correlation between viral loads per capita and case numbers per capita.The chemical marker PARA demonstrated its excellent utility for real-time assessment of the population contributing to the wastewater.

Identifiers

PMID35313587
PMCPMC8936110

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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 OpenQuestion graph.