Evidence map›Paper›PMID 32982969›Full record

ReviewFrontiers in endocrinology2020

Diabetes and Sarcopenic Obesity: Pathogenesis, Diagnosis, and Treatments.

Mina Wang, Yan Tan, Yifan Shi, Xu Wang, Zehuan Liao, Peng Wei

Abstract readReview
In one paragraph

Review in Frontiers in endocrinology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 133 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
133citing papers in PubMed, 6 pooled it
–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

133 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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73 more citing papers are in PubMed but not listed here.

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

6 authors.

Mina WangSchool of Traditional Chinese Medicine, School of Life Sciences, Beijing University of Chinese Medicine, Beijing, China.
Yan TanSchool of Traditional Chinese Medicine, School of Life Sciences, Beijing University of Chinese Medicine, Beijing, China.
Yifan ShiSchool of Traditional Chinese Medicine, School of Life Sciences, Beijing University of Chinese Medicine, Beijing, China.
Xu WangSchool of Traditional Chinese Medicine, School of Life Sciences, Beijing University of Chinese Medicine, Beijing, China.
Zehuan LiaoSchool of Biological Sciences, Nanyang Technological University, Singapore, Singapore.
Peng WeiSchool of Traditional Chinese Medicine, School of Life Sciences, Beijing University of Chinese Medicine, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sarcopenic obesity and diabetes are two increasing health problems worldwide, which both share many common risk factors, such as aging, and general obesity. The pathogenesis of sarcopenic obesity includes aging, physical inactivity, malnutrition, low-grade inflammation, insulin resistance, and hormonal changes. Nevertheless, there are two major reasons to cause diabetes: impaired insulin secretion and impaired insulin action. Furthermore, the individual diagnosis of obesity and sarcopenia should be combined to adequately define sarcopenic obesity. Also, the diagnosis of diabetes includes fasting plasma glucose test (FPG), 2-h oral glucose tolerance test (OGTT), glycated hemoglobin (A1C), and random plasma glucose coupled with symptoms. Healthy diet and physical activity are beneficial to both sarcopenic obesity and diabetes, but there are only recommended drugs for diabetes. This review consolidates and discusses the latest research in pathogenesis, diagnosis, and treatments of diabetes and sarcopenic obesity.

Indexed as

AgingDiabetes Mellitus, Type 2HumansInsulin ResistanceInsulin SecretionObesitySarcopeniaagingdiabetesinflammationinsulin resistancesarcopenic obesity

Identifiers

PMID32982969
PMCPMC7477770

What OpenQuestion holds

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LicenceCC BY
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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.