Evidence map›Paper›PMID 36568118›Full record

ArticleFrontiers in endocrinology2022

Exploring the common gene signatures and pathogeneses of obesity with Alzheimer's disease

Ting Li, Jingru Qu, Chaofei Xu, Ting Fang, Bei Sun, Liming Chen

Open access · goldAbstract read
In one paragraph

Article in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.9field-weighted citation impact, top 14% 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

6 citing papers in PubMed, 11 citations in OpenAlex.

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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 at 1 institution in 1 country.

Ting LiNHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China.
Jingru QuNHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China.
Chaofei XuNHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China.
Ting FangNHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China.
Bei SunNHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China.
Liming ChenNHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China.
Tianjin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Obesity is a complex condition that influences several organ systems and physiologic systems. Obesity (OB) is closely linked to Alzheimer's disease (AD). However, the interrelationship between them remains unclear. The purpose of this study is to explore the key genes and potential molecular mechanisms in obesity and AD. Methods: The microarray data for OB and AD were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene correlation network analysis (WGCNA) was used to delineate the co-expression modules related to OB and AD. The shared genes existing in obesity and AD were identified through biological process analyses using the DAVID website, which then constructed the Protein-Protein Interaction (PPI) Network and selected the hub genes by Cytoscape. The results were validated in other microarray data by differential gene analysis. Moreover, the hub gene expressions were further determined in mice by qPCR. Results: The WGCNA identifies five modules and four modules as significant modules with OB and AD, respectively. Functional analysis of shared genes emphasized that inflammation response and mitochondrial functionality were common features in the pathophysiology of OB and AD. The results of differential gene analysis in other microarray data were extremely similar to them. Then six important hub genes were selected and identified using cytoHubba, including MMP9, PECAM1, C3AR1, IL1R1, PPARGC1α, and COQ3. Finally, we validated the hub gene expressions Conclusions: Our work revealed the high inflammation/immune response and mitochondrial impairment in OB patients, which might be a crucial susceptibility factor for AD. Meanwhile, we identified novel gene candidates such as MMP9, PECAM1, C3AR1, IL1R1, PPARGC1α, and COQ3 that could be used as biomarkers or potential therapeutic targets for OB with AD.

Indexed as

Alzheimer DiseaseAnimalsInflammationMatrix Metalloproteinase 9MiceObesityTranscriptomeMatrix Metalloproteinase 9Alzheimer’s Diseasedifferential gene analysishub genesobesityweighted gene correlation network analysis (WGCNA)

Identifiers

PMID36568118
PMCPMC9780446
OpenAlexW4311953253

What Socratic holds

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Registered trials

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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 Socratic graph.