Evidence map›Paper›PMID 33210835›Full record

ArticleJournal of diabetes investigation2021

Neuroretinal dysfunction revealed by a flicker electroretinogram correlated with peripheral nerve dysfunction and parameters of atherosclerosis in patients with diabetes.

Miyuka Kawai, Tatsuhito Himeno, Yuka Shibata, Nobuhiro Hirai, Yuriko Asada-Yamada, Emi Asano-Hayami, Yohei Ejima, Rina Kasagi, Eriko Nagao, Yukako Sugiura-Roth and 10 more

Open access · goldAbstract readEvaluation Study
In one paragraph

Article in Journal of diabetes investigation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.1field-weighted citation impact, top 50% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Article
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

20 authors at 2 institutions in 1 country.

Miyuka KawaiDivision of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.ORCID https://orcid.org/0000-0002-3008-292X
Tatsuhito HimenoDivision of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.ORCID https://orcid.org/0000-0002-5677-1125
Yuka ShibataDivision of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
Nobuhiro HiraiDivision of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
Yuriko Asada-YamadaDivision of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
Emi Asano-HayamiDivision of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
Yohei EjimaDivision of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
Rina KasagiDivision of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
Eriko NagaoDivision of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
Yukako Sugiura-RothDivision of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
Hiromi Nakai-ShimodaDivision of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
Takayuki NakayamaDepartment of Clinical Laboratory, Aichi Medical University Hospital, Nagakute, Japan.
Yuichiro YamadaDivision of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
Takahiro IshikawaDivision of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
Yoshiaki MorishitaDivision of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
Masaki KondoDivision of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.ORCID https://orcid.org/0000-0003-0558-3404
Shin TsunekawaDivision of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
Yoshiro KatoDivision of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
Jiro NakamuraDivision of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
Hideki KamiyaDivision of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
Aichi Medical University · JPAichi Medical University Hospital · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

AIMS/

introductionDiabetic polyneuropathy (DPN) develops in the early stage of diabetes. However, no common diagnostic protocol has yet been established. Here, to verify that the flicker electroretinogram using a hand-held device can detect the early dysfunction of the peripheral nervous system in patients with diabetes, we investigated the correlation between the progression of DPN and neuroretinal dysfunction. MATERIALS AND

methodsIn total, 184 participants with type 1 or 2 diabetes underwent a flicker electroretinogram (ERG) using a hand-held device RETeval™ and nerve conduction study. Participants were also evaluated for intima-media thickness, ankle-brachial index, toe brachial index and brachial-ankle pulse wave velocity. Parameters of the nerve conduction study were used to diagnose the severity according to Baba's classification. A multiple regression analysis was used to examine the associations of ERG parameters with the severity of DPN categorized by Baba's classification. Diagnostic properties of the device in DPN were evaluated using a receiver operating characteristic curve.

resultsA multiple regression model to predict the severity of DPN was generated using ERG. In the model, moderate-to-severe DPN was effectively diagnosed (area under the receiver operating characteristic curve 0.692, sensitivity 56.5%, specificity 78.3%, positive predictive value 70.6%, negative predictive value 66.1%, positive likelihood ratio 2.60, negative likelihood ratio 0.56). In the patients without diabetic retinopathy, the implicit time and amplitude in ERG significantly correlated with the parameters of the nerve conduction study, brachial-ankle pulse wave velocity and intima-media thickness.

conclusionsElectroretinogram parameters obtained by the hand-held device successfully predict the severity of DPN. The device might be useful to evaluate DPN.

Indexed as

AgedAnkle Brachial IndexAtherosclerosisCarotid Intima-Media ThicknessDiabetes Mellitus, Type 1Diabetes Mellitus, Type 2Diabetic NeuropathiesDiabetic RetinopathyElectroretinographyFemaleHumansMaleMiddle AgedNeural ConductionPeripheral NervesPredictive Value of TestsDiabetic neuropathiesElectroretinographyPoint-of-care testing

Identifiers

PMID33210835
PMCPMC8264400
OpenAlexW3101797290

What OpenQuestion holds

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LicenceCC BY-NC
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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 OpenQuestion graph.