Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 370

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Obesity and Outcomes in COVID-19: When an Epidemic and Pandemic Collide

Open access
Sanchis-Gomar, Fabian, Lavie, Carl J., Mehra, Mandeep R., Henry, Brandon Michael, Lippi, Giuseppe·2020·Mayo Clinic Proceedings

Abstract

Obesity has reached epidemic proportions in the United States and in much of the westernized world, contributing to considerable morbidity. Several of these obesity-related morbidities are associated with greater risk for death with coronavirus disease 2019 (COVID-19). Severe acute respiratory syndrome coronavirus 2 penetrates human cells through direct binding with angiotensin-converting enzyme 2 receptors on the cell surface. Angiotensin-converting enzyme 2 expression in adipose tissue is higher than that in lung tissue, which means that adipose tissue may be vulnerable to COVID-19 infection. Obese patients also have worse outcomes with COVID-19 infection, including respiratory failure, need for mechanical ventilation, and higher mortality. Clinicians need to be more aggressive when treating obese, especially severely obese, patients with COVID-19 infection.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.mayocp.2020.05.006

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 8 references

Cited by

See all 370

Metadata

Authors
Sanchis-Gomar, F., Lavie, C. J., Mehra, M. R., Henry, B. M., & Lippi, G.
Venue
Mayo Clinic Proceedings
Publisher
Elsevier BV
Published
2020-07
Volume
95
Issue
7
Pages
1445-1453
ISSN
0025-6196 (ISSN-L)
OpenAlex
W3025048372
Cited by
370
References
8
FWCI
9.33
Type
journal article
Language
en
Open access
bronze

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Obesity and Outcomes in COVID-19: When an Epidemic and Pandemic Collide. https://app.cochat.ai/papers/10.1016/j.mayocp.2020.05.006
The paper
Sanchis-Gomar, F., Lavie, C. J., Mehra, M. R., Henry, B. M., & Lippi, G. (2020). Obesity and Outcomes in COVID-19: When an Epidemic and Pandemic Collide. Mayo Clinic Proceedings. https://doi.org/10.1016/j.mayocp.2020.05.006

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.