Evidence map›Paper›PMID 28928179›Full record

ArticleBMJ open2017

Comparison of anthropometric indices for predicting the risk of metabolic syndrome and its components in Chinese adults: a prospective, longitudinal study.

Haoyu Wang, Aihua Liu, Tong Zhao, Xun Gong, Tianxiao Pang, Yingying Zhou, Yue Xiao, Yumeng Yan, Chenling Fan, Weiping Teng and 2 more

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ open, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
84citing papers in PubMed, 1 pooled it
–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

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

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24 more citing papers are in PubMed but not listed here.

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.

Haoyu WangDepartment of Endocrinology and Metabolism, Institute of Endocrinology, Liaoning Provincial Key Laboratory of Endocrine Diseases, First Affiliated Hospital of China Medical University, China Medical University, Shenyang, Liaoning, China.
Aihua LiuDepartment of Endocrinology and Metabolism, Institute of Endocrinology, Liaoning Provincial Key Laboratory of Endocrine Diseases, First Affiliated Hospital of China Medical University, China Medical University, Shenyang, Liaoning, China.
Tong ZhaoDepartment of Endocrinology and Metabolism, Institute of Endocrinology, Liaoning Provincial Key Laboratory of Endocrine Diseases, First Affiliated Hospital of China Medical University, China Medical University, Shenyang, Liaoning, China.
Xun GongDepartment of Endocrinology and Metabolism, Institute of Endocrinology, Liaoning Provincial Key Laboratory of Endocrine Diseases, First Affiliated Hospital of China Medical University, China Medical University, Shenyang, Liaoning, China.
Tianxiao PangDepartment of Endocrinology and Metabolism, Institute of Endocrinology, Liaoning Provincial Key Laboratory of Endocrine Diseases, First Affiliated Hospital of China Medical University, China Medical University, Shenyang, Liaoning, China.
Yingying ZhouDepartment of Endocrinology and Metabolism, Institute of Endocrinology, Liaoning Provincial Key Laboratory of Endocrine Diseases, First Affiliated Hospital of China Medical University, China Medical University, Shenyang, Liaoning, China.
Yue XiaoDepartment of Endocrinology and Metabolism, Institute of Endocrinology, Liaoning Provincial Key Laboratory of Endocrine Diseases, First Affiliated Hospital of China Medical University, China Medical University, Shenyang, Liaoning, China.
Yumeng YanDepartment of Endocrinology and Metabolism, Institute of Endocrinology, Liaoning Provincial Key Laboratory of Endocrine Diseases, First Affiliated Hospital of China Medical University, China Medical University, Shenyang, Liaoning, China.
Chenling FanDepartment of Endocrinology and Metabolism, Institute of Endocrinology, Liaoning Provincial Key Laboratory of Endocrine Diseases, First Affiliated Hospital of China Medical University, China Medical University, Shenyang, Liaoning, China.
Weiping TengDepartment of Endocrinology and Metabolism, Institute of Endocrinology, Liaoning Provincial Key Laboratory of Endocrine Diseases, First Affiliated Hospital of China Medical University, China Medical University, Shenyang, Liaoning, China.
Yaxin LaiDepartment of Endocrinology and Metabolism, Institute of Endocrinology, Liaoning Provincial Key Laboratory of Endocrine Diseases, First Affiliated Hospital of China Medical University, China Medical University, Shenyang, Liaoning, China.
Zhongyan ShanDepartment of Endocrinology and Metabolism, Institute of Endocrinology, Liaoning Provincial Key Laboratory of Endocrine Diseases, First Affiliated Hospital of China Medical University, China Medical University, Shenyang, Liaoning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesOur study aimed to distinguish the ability of anthropometric indices to assess the risk of metabolic syndrome (MetS).

designProspective cohort study.

settingShenyang, China.

participantsA total of 379 residents aged between 40 and 65 were enrolled. 253 of them were free of MetS and had been followed up for 4.5 years.

methodsAt baseline, all the participants underwent a thorough medical examination. A variety of anthropometric parameters were measured and calculated, including waist circumference (WC), body mass index (BMI), a body shape index (ABSI), abdominal volume index (AVI), body adiposity index, body roundness index, conicity index, waist-to-hip ratio and visceral adiposity index (VAI). After 4.5 year follow-up, we re-examined whether participants were suffering from MetS. A receiver operating characteristic (ROC) curve was applied to examine the potential of the above indices to identify the status and risk of MetS. OUTCOMES: Occurrence of MetS.

resultsAt baseline, 33.2% participants suffered from MetS. All of the anthropometric indices showed clinical significance, and VAI was superior to the other indices as it was found to have the largest area under the ROC curve. After a 4.5 year follow-up, 37.8% of men and 23.9% of women developed MetS. ROC curve analysis suggested that baseline BMI was the strongest predictor of MetS for men (0.77 (0.68-0.85)), and AVI was the strongest for women (0.72 (0.64-0.79)). However, no significant difference was observed between WC and both indices. In contrast, the baseline ABSI did not predict MetS in both genders.

conclusionsThe present study indicated that these different indices derived from anthropometric parameters have different discriminatory abilities for MetS. Although WC did not have the largest area under the ROC curve for diagnosing and predicting MetS, it may remain a better index of MetS status and risk because of its simplicity and wide use.

Indexed as

AnthropometryBody ConstitutionAdiposityAdultArea Under CurveBody Mass IndexChinaDiabetes Mellitus, Type 2FemaleHumansHyperglycemiaHypertensionHypertriglyceridemiaIntra-Abdominal FatLipoproteins, HDLLongitudinal StudiesLipoproteins, HDLAbdominal volume indexAnthropometric indexBody mass indexMetabolic syndromeVisceral adiposity indexWaist circumference

Identifiers

PMID28928179
PMCPMC5623484

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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.