Evidence map›Paper›PMID 27622506›Full record

ArticlePloS one2016

Metabolic Risk Factors of Type 2 Diabetes Mellitus and Correlated Glycemic Control/Complications: A Cross-Sectional Study between Rural and Urban Uygur Residents in Xinjiang Uygur Autonomous Region.

Guo-Li Du, Yin-Xia Su, Hua Yao, Jun Zhu, Qi Ma, Ablikm Tuerdi, Xiao-Dong He, Li Wang, Zhi-Qiang Wang, Shan Xiao and 2 more

Open access · goldAbstract readComparative Study
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
1.5field-weighted citation impact, top 16% 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

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

12 authors at 2 institutions in 1 country.

Guo-Li DuDepartment of Endocrinology, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Yin-Xia SuThe Key Laboratory of Xinjiang Metabolic Disease, Clinical Medical Research Institute, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Hua YaoThe Key Laboratory of Xinjiang Metabolic Disease, Clinical Medical Research Institute, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Jun ZhuDepartment of Endocrinology, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Qi MaThe Key Laboratory of Xinjiang Metabolic Disease, Clinical Medical Research Institute, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Ablikm TuerdiDepartment of Endocrinology, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Xiao-Dong HeDepartment of Endocrinology, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Li WangThe Key Laboratory of Xinjiang Metabolic Disease, Clinical Medical Research Institute, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Zhi-Qiang WangDepartment of Occupational and Environmental Health, Xinjiang Medical University, Urumqi, China.
Shan XiaoDepartment of Endocrinology, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Shu-Xia WangDepartment of Health Check Centre, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Li-Ping SuDepartment of Pathology, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Xinjiang Medical University · CNFirst Affiliated Hospital of Xinjiang Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiabetes is a major global public health problem driven by a high prevalence of metabolic risk factors.

objectiveTo describe the differences of metabolic risk factors of type 2 diabetes, as well as glycemic control and complicated diabetic complications between rural and urban Uygur residents in Xinjiang Uygur Autonomous Region of China.

methodsThis comparative cross-sectional study, conducted among 2879 urban and 918 rural participants in Xinjiang, China, assessed the metabolic risk factors of diabetes and related complications differences between urban and rural settlements.

resultsCompared to rural areas, urban participants had higher education level and more average income, little physical activity, less triglycerides and higher HDL-c (p < 0.05 respectively). Differences in metabolic risk factors by urban/rural residence included overweight or obesity, triglycerides (≥1.71mmol/l), HDL-c (< 1.04 mmol/l), alcohol intake, and physical inactivity (p < 0.01 respectively). There was significant difference regarding the prevalence of HbA1c >8% (48.1% versus 54.5%, p = 0.019) between rural and urban diabetic participants. No significant difference in the prevalence of type 2 diabetic complications between urban and rural participants (74.9% versus 72.2%; p = 0.263) was detected. Compared to rural participants, the most prevalent modifiable risk factors associated with diabetic complications in urban participants were obesity (BMI ≥ 28 Kg/m2), HDL-c (< 1.04 mmol/l), physical inactivity and irregular eating habits (p = 0.035, p = 0.001, p < 0.001, and p = 0.013, respectively).

conclusionsUrban settlers were significantly more likely to have metabolic risk factors highlighting the need for public health efforts to improve health outcomes for these vulnerable populations. Diabetes related complications risk factors were prevalent amongst rural and urban diabetes settlers.

Indexed as

AdultAgedBlood GlucoseBody Mass IndexChinaCross-Sectional StudiesDiabetes Mellitus, Type 2FemaleHumansLipidsMaleMiddle AgedRisk FactorsRural PopulationUrban PopulationYoung AdultBlood GlucoseLipids

Identifiers

PMID27622506
PMCPMC5021360
OpenAlexW2519497864

What OpenQuestion holds

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LicenceCC BY
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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.