Evidence map›Paper›PMID 34408200›Full record

ArticleScientific reports2021

Longitudinal analysis of antibody decay in convalescent COVID-19 patients.

Weiming Xia, Mingfei Li, Ying Wang, Lewis E Kazis, Kim Berlo, Noureddine Melikechi, Gregory R Chiklis

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
1.9field-weighted citation impact, top 12% of its field
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

19 citing papers in PubMed, 31 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 5 institutions in 2 countries.

Weiming XiaGeriatric Research Education Clinical Center, Bedford VA Healthcare System, Bedford, MA, 01730, USA. weiming.xia@va.gov.
Mingfei LiCenter for Healthcare Organization and Implementation Research, Bedford VA Healthcare System, Bedford, MA, USA.
Ying WangGeriatric Research Education Clinical Center, Bedford VA Healthcare System, Bedford, MA, 01730, USA.
Lewis E KazisCenter for Healthcare Organization and Implementation Research, Bedford VA Healthcare System, Bedford, MA, USA.
Kim BerloGeotop and the Department of Earth and Planetary Sciences, McGill University, Montreal, Canada.
Noureddine MelikechiDepartment of Physics and Applied Physics, Kennedy College of Sciences, University of Massachusetts Lowell, Lowell, MA, USA.
Gregory R ChiklisMRN Diagnostics, Franklin, MA, USA.
Geriatric Research Education and Clinical Center · USBoston University · USMcGill University · CAUniversity of Massachusetts Lowell · USVA New England Healthcare System · US

Funding

Big data and small molecules for Alzheimer's diseaseR01AG063913 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI Weiming Xia · 2024 to 2026
$4.1M
Big data and small molecules for Alzheimer's diseaseRF1AG063913 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI GOLDSTEIN, LEE E., XIA, WEIMING · 2019 to 2021
$2.9M
Precision Brain Health Monitoring for Alzheimer's Disease Risk Detection in the Framingham StudyR01AG072654 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI Rhoda Au, LEE E. GOLDSTEIN · 2025 to 2026
$1.1M
NIA NIH HHS R01 AG063913NIA NIH HHS R01 AG072654NIA NIH HHS RF1 AG063913NIH HHS RF1AG063913U.S. Department of Veterans Affairs COVID19-8900-06
6 · The paper itself

Abstract

Determining the sustainability of antibodies targeting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential for predicting immune response against the Coronavirus disease 2019 (COVID-19). To quantify the antibody decay rates among the varying levels of anti-nucleocapsid (anti-N) Immunoglobulin G (IgG) in convalescent COVID-19 patients and estimate the length of time they maintained SARS-CoV-2 specific antibodies, we have collected longitudinal blood samples from 943 patients over the course of seven months after their initial detection of SARS-CoV-2 virus by RT-PCR. Anti-N IgG levels were then quantified in these blood samples. The primary study outcome was the comparison of antibody decay rates from convalescent patients with high or low initial levels of antibodies using a mixed linear model. Additional measures include the length of time that patients maintain sustainable levels of anti-N IgG. Antibody quantification of blood samples donated by the same subject multiple times shows a gradual decrease of IgG levels to the cutoff index level of 1.4 signal/cut-off (S/C) on the Abbott Architect SARS-CoV-2 IgG test. In addition, this study shows that antibody reduction rate is dependent on initial IgG levels, and patients with initial IgG levels above 3 S/C show a significant 1.68-fold faster reduction rate compared to those with initial IgG levels below 3 S/C. For a majority of the donors naturally occurring anti-N antibodies were detected above the threshold for only four months after infection with SARS-CoV-2. This study is clinically important for the prediction of immune response capacity in COVID-19 patients.

Indexed as

Antibodies, ViralCOVID-19FemaleHumansImmunoglobulin GLinear ModelsLongitudinal StudiesMaleAntibodies, ViralImmunoglobulin G

Identifiers

PMID34408200
PMCPMC8373894
OpenAlexW3194124256

What Socratic holds

Textmetadata
LicenceCC BY
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.