Evidence map›Paper›PMID 41996315›Full record

Observational studyPloS one2026

COVID-19 in hemodialysis patients: New insights into metabolomic profile dynamics from 60 days pre- to 60 days post-diagnosis.

Gabriela F Dias, Chenxi Fan, Maggie Han, Xiaoling Wang, Ohnmar Thwin, Lemuel Fuentes, Xin Wang, Hanjie Zhang, Wensheng Guo, Peter Kotanko and 2 more

Abstract readMulticenter StudyObservational Study
In one paragraph

Observational study in PloS one, 2026. 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

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

12 authors.

Gabriela F DiasRenal Research Institute, New York, New York, United States of America.
Chenxi FanDepartment of Statistics and Applied Probability, University of California, Santa Barbara, California, United States of America.ORCID https://orcid.org/0009-0006-2585-4178
Maggie HanRenal Research Institute, New York, New York, United States of America.
Xiaoling WangRenal Research Institute, New York, New York, United States of America.
Ohnmar ThwinRenal Research Institute, New York, New York, United States of America.
Lemuel FuentesRenal Research Institute, New York, New York, United States of America.
Xin WangRenal Research Institute, New York, New York, United States of America.
Hanjie ZhangRenal Research Institute, New York, New York, United States of America.
Wensheng GuoDepartment of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Peter KotankoRenal Research Institute, New York, New York, United States of America.
Nadja GrobeRenal Research Institute, New York, New York, United States of America.ORCID https://orcid.org/0000-0001-9812-7672
Yuedong WangDepartment of Statistics and Applied Probability, University of California, Santa Barbara, California, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMaintenance hemodialysis patients experience higher morbidity and mortality from COVID-19, partly due to comorbidities like diabetes and cardiovascular disease. However, kidney disease-related metabolic processes may also contribute.

methodsIn this prospective, multi-center, observational study, we analyzed 201 routine serum samples from 30 hemodialysis patients (average age 59.2 ± 13.3 years, 57% male) with confirmed COVID-19, collected from 60 days before and 60 days after diagnosis. Untargeted liquid chromatography/mass spectrometry was used to profile metabolites. Linear and semi-parametric mixed-effects models were applied to assess changes across four phases: baseline (-60 to -15 days), putative incubation period (PIP; -14-0 days), acute (1-14 days), and post-COVID (15-60 days). Because infection and symptoms may vary across individuals, -14-0 days were used as an approximate pre-diagnosis window rather than a precise incubation interval.

resultsAmong 417 metabolomic features, 10 showed significant changes between baseline and PIP. Two metabolites, α-guanidinoglutaric acid and N-acetylneuraminic acid, were identified through library matching, while the remainder were characterized by mass and retention time. Temporal analysis revealed both transient metabolic shifts, which returned to baseline, and persistent changes, which remained altered post-COVID.

conclusionsThese findings suggest that early metabolic changes before COVID-19 diagnosis may be detected in routine serum samples, offering opportunities to develop predictive models for early detection. Identifying these unique metabolomics fingerprints could improve personalized surveillance strategies and enhance understanding of COVID-19's impact on hemodialysis patients.

Indexed as

COVID-19MetabolomeRenal DialysisAgedFemaleHumansMaleMetabolomicsMiddle AgedN-Acetylneuraminic AcidProspective StudiesSARS-CoV-2Time FactorsN-Acetylneuraminic Acid

Identifiers

PMID41996315
PMCPMC13089734

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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.