Evidence map›Paper›PMID 35066742›Full record

ArticleEndocrine2022

Gradient variability coefficient: a novel method for assessing glycemic variability and risk of hypoglycemia.

Jingzhen Li, Jingyi Lu, Igbe Tobore, Yuhang Liu, Abhishek Kandwal, Lei Wang, Xiaojing Ma, Wei Lu, Yuqian Bao, Jian Zhou and 1 more

Abstract read
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In one paragraph

Article in Endocrine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 30% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. 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

11 authors at 2 institutions in 1 country.

Jingzhen Li *Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China.
Jingyi Lu *Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 200233, Shanghai, China.
Igbe ToboreShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China.
Yuhang LiuShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China.
Abhishek KandwalShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China.
Lei WangShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China.
Xiaojing MaDepartment of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 200233, Shanghai, China.
Wei LuDepartment of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 200233, Shanghai, China.
Yuqian BaoDepartment of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 200233, Shanghai, China.
Jian ZhouDepartment of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 200233, Shanghai, China. zhoujian@sjtu.edu.cn.ORCID http://orcid.org/0000-0002-1534-2279
Zedong NieShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China. zd.nie@siat.ac.cn.ORCID http://orcid.org/0000-0002-6079-9964
Chinese Academy of Sciences · CNShanghai Jiao Tong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveDespite the clinical importance of glycemic variability and hypoglycemia, thus far, there is no consensus on the optimum method for assessing glycemic variability and risk of hypoglycemia simultaneously. RESEARCH DESIGN AND

methodsA novel metric, the gradient variability coefficient (GVC), was proposed for characterizing glycemic variability and risk of hypoglycemia. A total of 208 daily records of CGM encompassing 104 patients with T1DM and 2380 daily records from 1190 patients with T2DM were obtained in our study. Simulated CGM waveforms were used to assess the ability of GVC and other metrics to capture the amplitude and frequency of glucose fluctuations. In addition, the association between GVC and the risk of hypoglycemia was evaluated by receiver operating characteristic (ROC) curve.

resultsThe results of simulated CGM waveforms indicated that, compared with the widely used metrics of glycemic variability including standard deviation of sensor glucose (SD), coefficient of variation (CV), and mean amplitude of glycemic excursion (MAGE), GVC could reflect both the amplitude and frequency of glucose oscillations. In addition, the area under the curve (AUC) of ROC was 0.827 in T1DM and 0.873 in T2DM, indicating good performance in predicting hypoglycemia.

conclusionsThe proposed GVC might be a clinically useful tool in characterizing glycemic variability and the assessment of hypoglycemia risk in patients with diabetes.

Indexed as

Diabetes Mellitus, Type 1Diabetes Mellitus, Type 2HypoglycemiaBlood GlucoseBlood Glucose Self-MonitoringGlycated HemoglobinHumansBlood GlucoseGlycated HemoglobinContinuous glucose monitoringGlycemic variabilityGradient variability coefficientHypoglycemia

Identifiers

PMID35066742
OpenAlexW4207044397

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Registered trials

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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.