Evidence map›Paper›PMID 27007212›Full record

ArticleJournal of magnetic resonance imaging : JMRI2016

Quantification of renal steatosis in type II diabetes mellitus using dixon-based MRI.

Takeshi Yokoo, Haley R Clark, Ivan Pedrosa, Qing Yuan, Ivan Dimitrov, Yue Zhang, Ildiko Lingvay, Muhammad S Beg, I Alexandru Bobulescu

Open access · bronzeAbstract read
In one paragraph

Article in Journal of magnetic resonance imaging : JMRI, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
2.6field-weighted citation impact, top 12% 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

28 citing papers in PubMed, 45 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

9 authors at 3 institutions in 1 country.

Takeshi YokooDepartment of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA. Takeshi.Yokoo@UTSouthwestern.edu.
Haley R ClarkDepartment of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Ivan PedrosaDepartment of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Qing YuanDepartment of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Ivan DimitrovAdvanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Yue ZhangDepartment of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Ildiko LingvayDepartment of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Muhammad S BegDepartment of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
I Alexandru BobulescuDepartment of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
The University of Texas Southwestern Medical Center · USSouthwestern Medical Center · USPhilips (United States) · US

Funding

UT Southwestern Center for Translational Medicine (UL1/KL2/TL1)UL1TR001105 · NCATS · UT SOUTHWESTERN MEDICAL CENTER · PI TOTO, ROBERT DANIEL · 2013 to 2017
$24.5M
NON-INVASIVE PHYSIOLOGIC PREDICTORS OF AGGRESSIVENESS IN RENAL CELL CARCINOMAR01CA154475 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI PEDROSA, IVAN · 2011 to 2024
$4.3M
Protein Phosphatase Regulation of the Principal Renal Sodium Transporter NHE3K01DK090282 · NIDDK · UT SOUTHWESTERN MEDICAL CENTER · PI BOBULESCU, ION ALEXANDRU · 2011 to 2015
$434k
NCATS NIH HHS UL1 TR001105NCI NIH HHS R01 CA154475NIDDK NIH HHS K01 DK090282
6 · The paper itself

Abstract

purposeTo evaluate renal lipid content in subjects with and without type II diabetes mellitus (DM2) using Dixon-based magnetic resonance imaging (MRI). MATERIALS AND

methodsThis retrospective study was approved by the Institutional Review Board and compliant with the Health Insurance Portability and Accountability Act. Sixty-nine adults with or without DM2 (n = 29, n = 40) underwent 3T MRI of the abdomen using 3D multiecho Dixon gradient-echo acquisition and proton-density fat fraction (FF) reconstruction. FF values were recorded within segmented regions of interest in the kidneys and liver. The FF measurement error was estimated from the within-subject difference between the right and left kidneys using Bland-Altman analysis. Correlation between renal FF, hepatic FF, and body mass index (BMI) was evaluated. The association between renal FF and DM2 was evaluated by Wilcoxon rank sum test as well as by multivariate regression to correct for potential confounding effects of age, sex, BMI, creatinine, and hepatic FF. P < 0.05 was considered statistically significant.

resultsPer-subject 95% limits of agreement of the renal FF measurement were [-3.26%, +3.22%]. BMI was significantly correlated with renal FF (r = 0.266, P = 0.027) and with liver FF (r = 0.344, P = 0.006). Correlation between renal and hepatic FF did not reach statistical significance (r = 0.215, P = 0.090). Median renal FF (±interquartile range) was 2.18% (±2.52%) in the DM2 cohort, significantly higher than 0.80% (±2.63%) in the non-DM2 cohort (P < 0.001). After correcting for potential confounders, the relationship between DM2 and renal FF remained statistically significant (P = 0.005).

conclusionRenal lipid content can be measured noninvasively using Dixon-based MRI and may be increased in subjects with DM2 compared to those without DM2. J. Magn. Reson. Imaging 2016;44:1312-1319.

Indexed as

Lipid MetabolismDiabetes Mellitus, Type 2Diabetic NephropathiesFemaleHumansImage EnhancementImage Interpretation, Computer-AssistedMagnetic Resonance ImagingMaleMiddle AgedMolecular ImagingReproducibility of ResultsSensitivity and Specificitychronic kidney diseasediabetesDixon techniqueobesityproton-density fat fractionrenal steatosis

Identifiers

PMID27007212
PMCPMC5035175
OpenAlexW2304226423

What OpenQuestion 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 OpenQuestion graph.