Evidence mapPaperPMID 37588983Full record

ArticleFrontiers in endocrinology2023

A study of factors influencing long-term glycemic variability in patients with type 2 diabetes: a structural equation modeling approach.

Yuqin Gan, Mengjie Chen, Laixi Kong, Juan Wu, Ying Pu, Xiaoxia Wang, Jian Zhou, Xinxin Fan, Zhenzhen Xiong, Hong Qi

Open access · goldAbstract read
In one paragraph

Article in Frontiers in endocrinology, 2023. 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
0.6field-weighted citation impact, top 29% 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, 3 citations in OpenAlex.

  1. Article
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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

10 authors at 2 institutions in 1 country.

Yuqin GanSchool of Nursing, Chengdu Medical College, Chengdu, China.
Mengjie ChenSchool of Nursing, Chengdu Medical College, Chengdu, China.
Laixi KongSchool of Nursing, Chengdu Medical College, Chengdu, China.
Juan WuDepartment of Endocrinology and Metabolism, The First Affiliated Hospital of Chengdu Medical College, Chengdu, China.
Ying PuDepartment of Endocrinology and Metabolism, The First Affiliated Hospital of Chengdu Medical College, Chengdu, China.
Xiaoxia WangSchool of Nursing, Chengdu Medical College, Chengdu, China.
Jian ZhouDepartment of Rheumatology and Immunology, The First Affiliated Hospital of Chengdu Medical College, Chengdu, China.
Xinxin FanSchool of Nursing, Chengdu Medical College, Chengdu, China.
Zhenzhen XiongSchool of Nursing, Chengdu Medical College, Chengdu, China.
Hong QiSchool of Nursing, Chengdu Medical College, Chengdu, China.
Chengdu Medical College · CNFirst Affiliated Hospital of Chengdu Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aim: The present study aims to utilize structural equation modeling (SEM) to investigate the factors impacting long-term glycemic variability among patients afflicted with type 2 diabetes. Method: The present investigation is a retrospective cohort study that involved the collection of data on patients with type 2 diabetes mellitus who received care at a hospital located in Chengdu, Sichuan Province, over a period spanning from January 1, 2013, to October 30, 2022. Inclusion criteria required patients to have had at least three laboratory test results available. Pertinent patient-related information encompassing general demographic characteristics and biochemical indicators was gathered. Variability in the dataset was defined by standard deviation (SD) and coefficient of variation (CV), with glycosylated hemoglobin variation also considering variability score (HVS). Linear regression analysis was employed to establish the structural equation models for statistically significant influences on long-term glycemic variability. Structural equation modeling was employed to analyze effects and pathways. Results: Diabetes outpatient special disease management, uric acid variability, mean triglyceride levels, mean total cholesterol levels, total cholesterol variability, LDL variability, baseline glycated hemoglobin, and recent glycated hemoglobin were identified as significant factors influencing long-term glycemic variability. The overall fit of the structural equation model was found to be satisfactory and it was able to capture the relationship between outpatient special disease management, biochemical indicators, and glycated hemoglobin variability. According to the total effect statistics, baseline glycated hemoglobin and total cholesterol levels exhibited the strongest impact on glycated hemoglobin variability. Conclusion: The factors that have a significant impact on the variation of glycosylated hemoglobin include glycosylated hemoglobin itself, lipids, uric acid, and outpatient special disease management for diabetes. The identification and management of these associated factors can potentially mitigate long-term glycemic variability, thereby delaying the onset of complications and enhancing patients' quality of life.

Indexed as

Diabetes Mellitus, Type 2CholesterolGlycated HemoglobinHumansLatent Class AnalysisQuality of LifeRetrospective StudiesUric AcidCholesterolGlycated HemoglobinUric Acidglycated hemoglobinglycemic variabilityinfluential factorsstructural equation modelingtype 2 diabetes mellitus

Identifiers

PMID37588983
PMCPMC10425538
OpenAlexW4385411035

What Socratic holds

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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 Socratic graph.