The DOI matches its Crossref record; 161 of the 190 references it cites were found in a registry or an index, and 2 are not checked yet.
Diabetes, obesity, metabolism, and SARS-CoV-2 infection: the end of the beginning
Open accessAbstract
The increased prevalence of obesity, diabetes, and cardiovascular risk factors in people hospitalized with severe COVID-19 illness has engendered considerable interest in the metabolic aspects of SARS-CoV-2-induced pathophysiology. Here, I update concepts informing how metabolic disorders and their co-morbidities modify the susceptibility to, natural history, and potential treatment of SARS-CoV-2 infection, with a focus on human biology. New data informing genetic predisposition, epidemiology, immune responses, disease severity, and therapy of COVID-19 in people with obesity and diabetes are highlighted. The emerging relationships of metabolic disorders to viral-induced immune responses and viral persistence, and the putative importance of adipose and islet ACE2 expression, glycemic control, cholesterol metabolism, and glucose- and lipid-lowering drugs is reviewed, with attention to controversies and unresolved questions. Rapid progress in these areas informs our growing understanding of SARS-CoV-2 infection in people with diabetes and obesity, while refining the therapeutic strategies and research priorities in this vulnerable population.
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“Increased inflammation, defective adaptative immune responses, endothelial dysfunction and coagulation-related disorders are all phenomena that have been well described in obesity and might represent a plausible explanation to the link we observed between high BMI and long COVID [33].”
From Female gender is associated with long COVID syndrome: a prospective cohort study (2022) · cites this paper as [33]
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Metadata
- Authors
- Drucker, D. J.
- Affiliations
- University of Toronto (CA)
- Corresponding author
- Drucker, Daniel J.
- ORCID
- Drucker, Daniel J.
- Venue
- Cell Metabolism
- Publisher
- Elsevier BV
- Published
- 2021-03
- Volume
- 33
- Issue
- 3
- Pages
- 479-498
- ISSN
- 1550-4131 (ISSN-L)
- PMC
- PMC7825982
- OpenAlex
- W3124063857
- Cited by
- 272
- References
- 190
- Citations by year
- 2026: 16 · 2025: 25 · 2024: 38 · 2023: 49 · 2022: 86 · 2021: 58
- FWCI
- 6.76
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 366 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Cell Metabolism (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Canadian Institutes of Health Research (154321)
- University of Toronto
- Novo Nordisk
- Novo Nordisk Fonden
- Pfizer (GLP-2)
- Topics
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Keywords
- SARS-CoV-2, COVID-19 pandemic, obesity, diabetes mellitus, metabolic disorders, ACE2 expression, adipose tissue, pancreatic islets, glycemic control, cholesterol metabolism, antidiabetic drugs, lipid-lowering drugs, immune response, viral persistence, disease severity, genetic susceptibility, comorbidities
- MeSH terms
- Heart Disease Risk Factors, Angiotensin-Converting Enzyme 2, COVID-19, SARS-CoV-2, COVID-19 Drug Treatment, Blood Glucose, Cholesterol, Diabetes Mellitus, Glucose, Humans, Hyperglycemia, Metabolic Diseases, Obesity, Comorbidity, Genetic Predisposition to Disease
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 ›
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