Evidence map›Paper›PMID 28425847›Full record

ReviewAdipocyte2017

Form(ul)ation of adipocytes by lipids.

Kfir Lapid, Jonathan M Graff

Open access · goldAbstract readReview
In one paragraph

Review in Adipocyte, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.7field-weighted citation impact, top 17% 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

12 citing papers in PubMed, 24 citations in OpenAlex.

  1. Article
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  5. Review
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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

2 authors at 1 institution in 1 country.

Kfir Lapida Division of Endocrinology, Department of Internal Medicine , UT Southwestern Medical Center , Dallas , TX , USA.ORCID 0000-0002-8898-8695
Jonathan M Graffa Division of Endocrinology, Department of Internal Medicine , UT Southwestern Medical Center , Dallas , TX , USA.
Southwestern Medical Center · US

Funding

Biomarkers, Lineage Analysis--metabolic disease statesR01DK066556 · NIDDK · UT SOUTHWESTERN MEDICAL CENTER · PI GRAFF, JONATHAN M · 2003 to 2017
$5.3M
Invertebrate Models of Fat StorageR01DK064261 · NIDDK · UT SOUTHWESTERN MEDICAL CENTER · PI GRAFF, JONATHAN M · 2003 to 2014
$3.3M
Hormonal Regulation of Fat DepositionR01DK088220 · NIDDK · UT SOUTHWESTERN MEDICAL CENTER · PI GRAFF, JONATHAN M · 2010 to 2014
$2.6M
NIDDK NIH HHS R01 DK064261NIDDK NIH HHS R01 DK066556NIDDK NIH HHS R01 DK088220
6 · The paper itself

Abstract

Lipids have the potential to serve as bio-markers, which allow us to analyze and to identify cells under various experimental settings, and to serve as a clinical diagnostic tool. For example, diagnosis according to specific lipids that are associated with diabetes and obesity. The rapid development of mass-spectrometry techniques enables identification and profiling of multiple types of lipid species. Together, lipid profiling and data interpretation forge the new field of lipidomics. Lipidomics can be used to characterize physiologic and pathophysiological processes in adipocytes, since lipid metabolism is at the core of adipocyte physiology and energy homeostasis. A significant bulk of lipids are stored in adipocytes, which can be released and used to produce energy, used to build membranes, or used as signaling molecules that regulate metabolism. In this review, we discuss how exhaust of lipidomes can be used to study adipocyte differentiation, physiology and pathophysiology.

Indexed as

AdipocytesAdipose TissueCell DifferentiationEnergy MetabolismHumansLipid MetabolismLipidsObesityStem CellsLipidsadipocytesadipose stem cellsadipose tissuebio-markersdifferentiationlipidomicslipids

Identifiers

PMID28425847
PMCPMC5638366
OpenAlexW2595825271

What OpenQuestion holds

Textmetadata
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.