Evidence map›Paper›PMID 24587304›Full record

ArticlePloS one2014

Overweight and obesity among low-income Muslim Uyghur women in far western China: correlations of body mass index with blood lipids and implications in preventive public health.

Li Cong, Jin Qiong Zhan, Lan Yang, Wei Zhang, Shu Gang Li, Cheng Chen, Hong Yan Zhang, Zhi Ping Ma, Xiao Ling Hao, Dilixia Simayi and 13 more

Abstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
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

14 citing papers in PubMed.

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  8. Prevalence of Hypertension among Adults in Remote Rural Areas of Xinjiang, China.International journal of environmental research and public health · 2016
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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

23 authors.

Li CongDepartment of Pathology, Shihezi University School of Medicine, Shihezi, Xinjiang, China ; The Key Laboratories for Xinjiang Endemic and Ethnic Diseases, Shihezi University, Shihezi, Xinjiang, China ; Department of Oncology, the First People's Hospital of Suqian, Suqian, Jiangsu, China.
Jin Qiong ZhanDepartment of Laboratory Medicine, JiangXi Mental Health Center, Nanchang, Jiangxi, China.
Lan YangDepartment of Pathology, Shihezi University School of Medicine, Shihezi, Xinjiang, China ; The Key Laboratories for Xinjiang Endemic and Ethnic Diseases, Shihezi University, Shihezi, Xinjiang, China.
Wei ZhangDepartment of Pathology, Shihezi University School of Medicine, Shihezi, Xinjiang, China ; The Key Laboratories for Xinjiang Endemic and Ethnic Diseases, Shihezi University, Shihezi, Xinjiang, China.
Shu Gang LiFaculty of Preventive Medicine, Shihezi University School of Medicine, Shihezi, Xinjiang, China.
Cheng ChenDepartment of Pathology, Shihezi University School of Medicine, Shihezi, Xinjiang, China ; The Key Laboratories for Xinjiang Endemic and Ethnic Diseases, Shihezi University, Shihezi, Xinjiang, China.
Hong Yan ZhangDepartment of Immunization, Xi'an Center for Disease Control and Prevention, Xi'an, Shanxi, China.
Zhi Ping MaDepartment of Pathology, Shihezi University School of Medicine, Shihezi, Xinjiang, China.
Xiao Ling HaoDepartment of Pathology, Shihezi University School of Medicine, Shihezi, Xinjiang, China.
Dilixia SimayiDepartment of Pathology, Shihezi University School of Medicine, Shihezi, Xinjiang, China.
Lin TaoDepartment of Pathology, Shihezi University School of Medicine, Shihezi, Xinjiang, China.
Jin ZhaoDepartment of Pathology, Shihezi University School of Medicine, Shihezi, Xinjiang, China.
A AmanguliDepartment of Obstetrics and Gynecology, the First Affiliated University Hospital, Shihezi University School of Medicine, Shihezi, Xinjiang, China.
Meiliguli MohemaitiDepartment of Obstetrics and Gynecology, the First Affiliated University Hospital, Shihezi University School of Medicine, Shihezi, Xinjiang, China.
Ming Xia JingFaculty of Preventive Medicine, Shihezi University School of Medicine, Shihezi, Xinjiang, China.
Wei WangInstitute of Humanities, Shihezi University School of Medicine, Shihezi, Xinjiang, China.
Abudukeyoumu SaimaitiKashi Prefecture First People's Hospital, Kashi Prefecture Health Bureau, Kashi, Xinjiang, China.
Xiao Guang ZouKashi Prefecture First People's Hospital, Kashi Prefecture Health Bureau, Kashi, Xinjiang, China.
Yan GuDepartment of Obstetrics and Gynecology, Institute of Surgery Research, Daping Hospital, the Third Military Medical University, Chongqing, China.
Li LiDepartment of Obstetrics and Gynecology, Institute of Surgery Research, Daping Hospital, the Third Military Medical University, Chongqing, China.
Ying Hong WangThe Key Laboratories for Xinjiang Endemic and Ethnic Diseases, Shihezi University, Shihezi, Xinjiang, China ; Department of Obstetrics and Gynecology, the First Affiliated University Hospital, Shihezi University School of Medicine, Shihezi, Xinjiang, China.
Feng LiDepartment of Pathology, Shihezi University School of Medicine, Shihezi, Xinjiang, China ; The Key Laboratories for Xinjiang Endemic and Ethnic Diseases, Shihezi University, Shihezi, Xinjiang, China.
Wen Jie ZhangDepartment of Pathology, Shihezi University School of Medicine, Shihezi, Xinjiang, China ; The Key Laboratories for Xinjiang Endemic and Ethnic Diseases, Shihezi University, Shihezi, Xinjiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe pandemic of obesity is a global public health concern. Most studies on obesity are skewed toward high-income and urban settings and few covers low-income populations. This study focused on the prevalence of overweight and obesity and their correlations with blood lipids/metabolites/enzymes (bio-indicators) in a rural community typical of low-income in remote western China.

methodsThis study was performed in a Muslim ethnic Uyghur rural community in Kashi Prefecture of Xinjiang, about 4,407 km (2,739 miles) away from Beijing. Body mass index (BMI) and major blood bio-indicators (25 total items) were measured and demographic information was collected from 1,733 eligible healthy women aged 21 to 71 yrs, of whom 1,452 had complete data for analysis. More than 92% of the women lived on US$1.00/day or less. According to the Chinese criteria, overweight and obesity were defined as BMI at 24 to <28 kg/m(2) and at ≥ 28 kg/m(2), respectively.

resultsThe average BMI among these low-income women was 24.0 ± 4.0 (95% CI, 17.5-33.7) kg/m(2). The prevalence of obesity and overweight was high at 15.1% and 28.9%, respectively. Among 25 bio-indicators, BMI correlated positively with the levels of 11 bio-indicators including triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TCHOL), glucose (GLU), and uric acid (UA); but negatively with the levels of 5 bio-indicators including high-density lipoprotein cholesterol (HDL-C) and apolipoprotein A/B (APO A/B).

conclusionsThis is the first investigation reporting overweight and obesity being common in low-income Muslim Uyghur women, whose BMI correlates with several important blood bio-indicators which are risk factors for diabetes and cardiovascular diseases. These findings may help make preventive public health policies in Uyghur communities. To prevent diabetes and cardiovascular diseases in low-income settings, we therefore propose a cost-effective, two-step strategy first to screen for obesity and then to screen persons with obesity for diabetes and cardiovascular diseases.

Indexed as

Body Mass IndexIslamAdultAgedApolipoproteinsBlood GlucoseBody CompositionCardiovascular DiseasesChinaCholesterol, HDLCholesterol, LDLDiabetes MellitusFemaleHumansMiddle AgedObesityApolipoproteinsBlood GlucoseCholesterol, HDLCholesterol, LDLTriglyceridesUric Acid

Identifiers

PMID24587304
PMCPMC3938656

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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.