Evidence map›Paper›PMID 34181137›Full record

ArticleEuropean journal of hybrid imaging2021

Glucose-level dependent brain hypometabolism in type 2 diabetes mellitus and obesity.

Z Képes, Cs Aranyi, A Forgács, F Nagy, K Kukuts, Zs Hascsi, R Esze, S Somodi, M Káplár, J Varga and 2 more

Open access · goldAbstract read
In one paragraph

Article in European journal of hybrid imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
3.2field-weighted citation impact, top 8% 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

13 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.

  1. Pooled it
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  12. Diabetes and dementia: Clinical perspective, innovation, knowledge gaps.Journal of diabetes and its complications · 2022
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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

12 authors at 1 institution in 1 country.

Z KépesFaculty of Medicine, Department of Medical Imaging, Division of Nuclear Medicine and Translational Imaging, University of Debrecen, Nagyerdei krt. 98, Debrecen, Hungary. kepes.zita@med.unideb.hu.
Cs AranyiFaculty of Medicine, Department of Medical Imaging, Division of Nuclear Medicine and Translational Imaging, University of Debrecen, Nagyerdei krt. 98, Debrecen, Hungary.
A ForgácsScanomed Ltd. Nuclear Medicine Centers, Nagyerdei krt. 98, Debrecen, Hungary.
F NagyScanomed Ltd. Nuclear Medicine Centers, Nagyerdei krt. 98, Debrecen, Hungary.
K KukutsScanomed Ltd. Nuclear Medicine Centers, Nagyerdei krt. 98, Debrecen, Hungary.
Zs HascsiScanomed Ltd. Nuclear Medicine Centers, Nagyerdei krt. 98, Debrecen, Hungary.
R EszeFaculty of Medicine, Department of Internal Medicine, University of Debrecen, Nagyerdei krt. 98, Debrecen, Hungary.
S SomodiFaculty of Medicine, Department of Internal Medicine, University of Debrecen, Nagyerdei krt. 98, Debrecen, Hungary.
M KáplárFaculty of Medicine, Department of Internal Medicine, University of Debrecen, Nagyerdei krt. 98, Debrecen, Hungary.
J VargaFaculty of Medicine, Department of Medical Imaging, Division of Nuclear Medicine and Translational Imaging, University of Debrecen, Nagyerdei krt. 98, Debrecen, Hungary.
M EmriFaculty of Medicine, Department of Medical Imaging, Division of Nuclear Medicine and Translational Imaging, University of Debrecen, Nagyerdei krt. 98, Debrecen, Hungary.
I GaraiFaculty of Medicine, Department of Medical Imaging, Division of Nuclear Medicine and Translational Imaging, University of Debrecen, Nagyerdei krt. 98, Debrecen, Hungary.
University of Debrecen · HU

Funding

National Brain Research Program No. 2017-1.2.1-NKP-2017-00002. 2017-1.2.1-NKP-2017-00002National Grant No. GINOP-2.1.1-15-2015-00609 GINOP-2.1.1-15-2015-00609
6 · The paper itself

Abstract

backgroundMetabolic syndrome and its individual components lead to wide-ranging consequences, many of which affect the central nervous system. In this study, we compared the [

methodsIn our prospective study, 51 patients with controlled T2DM (ages 50.6 ± 8.0 years) and 45 non-DM obese participants (ages 52.0 ± 9.6 years) were enrolled. Glucose levels measured before PET/CT examination (pre-PET glucose) as well as laboratory parameters assessing glucose and lipid status were determined. NeuroQ application (NeuroQ

resultsNeuroQ analysis did not reveal significant regional metabolic defects in either group. Voxel-based group comparison revealed significantly (P

conclusionsTo our knowledge, this is the first study to perform pre-PET glucose level corrected comparative analysis of brain metabolism in T2DM and obesity. We also examined the pre-PET glucose level dependency of regional cerebral metabolism in the two groups separately. Large-scale future studies are warranted to perform further correlation analysis with the aim of determining the effects of metabolic disturbances on brain metabolism.

Indexed as

[18F]FDGBrainMetabolismObesityType 2 diabetes mellitus

Identifiers

PMID34181137
PMCPMC8218076
OpenAlexW3111071399

What Socratic holds

Textmetadata
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 Socratic graph.