Evidence map›Paper›PMID 28004765›Full record

ArticleScientific reports2016

Fasting plasma glucose variability and all-cause mortality among type 2 diabetes patients: a dynamic cohort study in Shanghai, China.

Dongli Xu, Hong Fang, Wanghong Xu, Yujie Yan, Yinan Liu, Baodong Yao

Abstract read
In one paragraph

Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
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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

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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

26 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

6 authors.

Dongli XuShanghai Minhang Center for Disease Control and Prevention, Shanghai, China.
Hong FangShanghai Minhang Center for Disease Control and Prevention, Shanghai, China.
Wanghong XuDepartment of Epidemiology, School of Public Health, Fudan University, and The Key Laboratory of Public Health Safety of Ministry of Education (Fudan University), Shanghai, China.
Yujie YanShanghai Minhang Center for Disease Control and Prevention, Shanghai, China.
Yinan LiuShanghai Minhang Center for Disease Control and Prevention, Shanghai, China.
Baodong YaoShanghai Minhang Center for Disease Control and Prevention, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The study aims to examine whether the variation of fasting plasma glucose (FPG), represented by coefficient of variation (CV), independently predicts all-cause mortality among Chinese type 2 diabetes patients. This retrospective cohort study was designed based on a standardized electronic management system of diabetes patients in Shanghai, China. 8871 type 2 diabetes patients were enrolled between 1 January 2007 and 31 December 2007 and were followed-up for all-cause mortality until 31 December 2014. All patients were grouped by the quartiles of CV of FPG. 1136 patients deceased during following-up. After adjusting for other risk factors, CV of FPG was not independently associated with all-cause mortality. Stratified analysis by mean FPG levels (<7 mmol/L and ≥7 mmol/L) observed a significant modifying effect of CV of FPG (P for interact test <0.01). CV of FPG was independently associated with all-cause mortality in patients whose glucose control was poor, with the HRs (95% CI) for the second, third, fourth vs first quartiles of CV of FPG being 1.23(0.94-1.61), 1.23(0.94-1.61), and 1.63(1.25-2.13), respectively. Our results suggest that variability of FPG may be an important predictor of mortality among type 2 diabetes in China, particularly for those with their glycemic status uncontrolled.

Indexed as

MortalityAgedAged, 80 and overBlood GlucoseCardiovascular DiseasesChinaCohort StudiesDiabetes ComplicationsDiabetes Mellitus, Type 2Electronic Health RecordsFemaleFollow-Up StudiesHumansHyperglycemiaInsulinKaplan-Meier EstimateBlood GlucoseInsulin

Identifiers

PMID28004765
PMCPMC5177938

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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.