Evidence map›Paper›PMID 28474384›Full record

ArticleObesity (Silver Spring, Md.)2017

Exploring genetic susceptibility to obesity through genome functional pathway analysis.

Alexander P Gabrielli, Ann M Manzardo, Merlin G Butler

Open access · bronzeAbstract read
In one paragraph

Article in Obesity (Silver Spring, Md.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.1field-weighted citation impact, top 19% 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

7 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Autonomic nervous system dysfunction in Prader-Willi syndrome.Clinical autonomic research : official journal of the Clinical Autonomic Research Society · 2023
    Review
  3. A Call to Action-The Need to Address Obesity in the Black Community.Journal of the National Medical Association · 2020
    Article
  4. Article
  5. GeneAnalytics Pathways and Profiling of Shared Autism and Cancer Genes.International journal of molecular sciences · 2019
    Article
  6. Racial Disparities in Obesity Treatment.Current obesity reports · 2018
    Review
  7. 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

3 authors at 1 institution in 1 country.

Alexander P GabrielliDepartments of Psychiatry and Behavioral Sciences and Pediatrics, University of Kansas Medical Center, Kansas City, Kansas, USA.
Ann M ManzardoDepartments of Psychiatry and Behavioral Sciences and Pediatrics, University of Kansas Medical Center, Kansas City, Kansas, USA.
Merlin G ButlerDepartments of Psychiatry and Behavioral Sciences and Pediatrics, University of Kansas Medical Center, Kansas City, Kansas, USA.
University of Kansas Medical Center · US

Funding

Research Design and Analysis Core (RDAC)P30HD002528 · NICHD · UNIVERSITY OF KANSAS LAWRENCE · PI DURHAM, DIANNE NONE · 1985 to 2015
$22.1M
NICHD NIH HHS P30 HD002528
6 · The paper itself

Abstract

objectiveObesity has been reaching epidemic levels in recent decades, with a growing body of research identifying predisposing genetic components. To explore the relationship of genetic factors contributing to obesity, an analytical computer-based gene-profiling approach utilizing an updated list of clinically relevant and known obesity-related genes was undertaken.

methodsAn updated list of 494 genes reportedly associated with obesity was compiled, and the GeneAnalytics profiling software was utilized to interrogate genomic databases from GeneCards® to cross-reference obesity gene sets against tissues and cells, diseases, genetic pathways, gene ontology (GO)-biological processes and GO-molecular functions, phenotypes, and compounds.

resultsObesity-related fields identified by GeneAnalytics algorithms included 8 diseases, 46 pathways, 62 biological processes, 22 molecular functions, 148 phenotypes, and 286 compounds impacting adipogenesis, signal transduction by G-protein coupled receptors, and lipid metabolism involving insulin-related genes (IGF1, INS, IRS1). GO-biological processes identified feeding behavior, cholesterol metabolic process, and glucose and cholesterol homeostasis pathways, while GO-molecular processes pertained to receptor binding, affecting glucose homeostasis, body weight, and circulating insulin and triglyceride levels.

conclusionsThe gene-profiling model suggests that pathogenesis of obesity relates to the coordination of biological responses to glucose and intracellular lipids possibly through a disruption of biochemical cascades and cellular signaling arising from affected receptors.

Indexed as

Genetic Predisposition to DiseaseAdultFemaleGenomicsHumansMaleObesityYoung Adult

Identifiers

PMID28474384
PMCPMC5444946
OpenAlexW2611402552

What Socratic holds

Textmetadata
LicenceTDM
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 Socratic graph.