Evidence map›Paper›PMID 36381471›Full record

ReviewClinical nutrition research2022

Obesity, Diabetes Mellitus, and Metabolic Syndrome: Review in the Era of COVID-19.

Behnaz Abiri, Amirhossein Ramezani Ahmadi, Mahdi Hejazi, Shirin Amini

Open access · diamondAbstract readReview
In one paragraph

Review in Clinical nutrition research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 35% 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

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
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

4 authors at 4 institutions in 1 country.

Behnaz AbiriObesity Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran 19839-63113, Iran.ORCID https://orcid.org/0000-0002-9921-6260
Amirhossein Ramezani AhmadiIsfahan Endocrine and Metabolism Research Center, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran.ORCID https://orcid.org/0000-0003-2581-963X
Mahdi HejaziDepartment of Nutrition, School of Public Health, Iran University of Medical Sciences, Tehran 14166-34793, Iran.ORCID https://orcid.org/0000-0002-4382-9623
Shirin AminiDepartment of Nutrition, Shoushtar Faculty of Medical Sciences, Shoushtar 64517-73865, Iran.ORCID https://orcid.org/0000-0003-0339-8029
Iran University of Medical Sciences · IRIsfahan University of Medical Sciences · IRIslamic Azad University, Shoushtar Branch · IRResearch Institute for Endocrine Sciences · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronavirus disease 2019 (COVID-19), a novel coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is now at pandemic levels leading to considerable morbidity and mortality throughout the globe. Patients with obesity, diabetes, and metabolic syndrome (MetS) are mainly susceptible and more probably to get severe side effects when affected by this virus. The pathophysiologic mechanisms for these notions have not been completely known. The pro-inflammatory milieu observed in patients with metabolic disruption could lead to COVID-19-mediated host immune dysregulation, such as immune dysfunction, severe inflammation, microvascular dysfunction, and thrombosis. The present review expresses the current knowledge regarding the influence of obesity, diabetes mellitus, and MetS on COVID-19 infection and severity, and their pathophysiological mechanisms.

Indexed as

COVID-19Diabetes mellitusMetabolic syndromeObesitySARS-CoV-2

Identifiers

PMID36381471
PMCPMC9633974
OpenAlexW4309245131

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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 OpenQuestion graph.