Evidence map›Paper›PMID 34706716›Full record

ArticleLipids in health and disease2021

Waist-to-height ratio, an optimal anthropometric indicator for metabolic dysfunction associated fatty liver disease in the Western Chinese male population.

Jinwei Cai, Cuiting Lin, Shuiqing Lai, Yingshan Liu, Min Liang, Yingfen Qin, Xinghuan Liang, Aihua Tan, Yong Gao, Zheng Lu and 6 more

Open access · goldAbstract read
In one paragraph

Article in Lipids in health and disease, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 1 pooled it
3.8field-weighted citation impact, top 6% 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

30 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 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

16 authors at 2 institutions in 1 country.

Jinwei Cai *The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China.
Cuiting Lin *School of Pharmaceutical Sciences, Southern Medical University, Guangzhou, Guangdong, China.
Shuiqing LaiDepartment of Endocrinology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China.
Yingshan LiuThe Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China.
Min LiangDepartment of Endocrinology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
Yingfen QinDepartment of Endocrinology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
Xinghuan LiangDepartment of Endocrinology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
Aihua TanCenter for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
Yong GaoCenter for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
Zheng LuCenter for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
Chunlei WuCenter for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
Shengzhu HuangThe Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China.
Xiaobo YangCenter for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
Haiying ZhangCenter for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
Jian KuangThe Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China. kuangjian@gdph.org.cn.ORCID http://orcid.org/0000-0001-5166-477X
Zengnan MoCenter for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
Guangxi Medical University · CNGuangdong Academy of Medical Sciences · CN

Funding

Guangxi key Laboratory for Genomic and Personalized Medicine 16-380-54, 17-259-45, 19-050-22, 19-185-33, 20-065-33Key-Area Research and Development Program of Guangdong Province 2019B020230001Major Project of Guangxi Innovation Driven AA18118016National Key Research and Development Program of China 2017YFC0908000National Key Research & Development Projects 2018YFC1314104Natural Science Foundation of Guangxi Province 2017GXNSFAA198097
6 · The paper itself

Abstract

backgroundNon-alcoholic fatty liver disease (NAFLD) has been entitled as metabolic-dysfunction associated fatty liver disease (MAFLD). Therefore anthropometric indicators of adiposity may provide a non-invasive predictive and diagnostic tool for this disease. This study intended to validate and compare the MAFLD predictive and diagnostic capability of eight anthropometric indicators.

methodsThe study involved a population-based retrospective cross-sectional design. The Fangchenggang area male health and examination survey (FAMHES) was used to collect data of eight anthropometric indicators, involving body mass index (BMI), waist-to-height ratio (WHtR), waist-hip ratio (WHR), body adiposity index (BAI), cardiometabolic index (CMI), lipid accumulation product (LAP), visceral adiposity index (VAI), and abdominal volume index (AVI). Receiver operating characteristics (ROC) curves and the respective areas under the curves (AUCs) were utilized to compare the diagnostic capacity of each indicator for MAFLD and to determine the optimal cutoff points. Binary logistic regression analysis was applied to identify the odds ratios (OR) with 95% confidence intervals (95% CI) for all anthropometric indicators and MAFLD. The Spearman rank correlation coefficients of anthropometric indicators, sex hormones, and MAFLD were also calculated.

resultsAll selected anthropometric indicators were significantly associated with MAFLD (P < 0.001), with an AUC above 0.79. LAP had the highest AUC [0.868 (95% CI, 0.853-0.883)], followed by WHtR [0.863 (95% CI, 0.848-0.879)] and AVI [0.859 (95% CI, 0.843-0.874)]. The cutoff values for WHtR, LAP and AVI were 0.49, 24.29, and 13.61, respectively. WHtR [OR 22.181 (95% CI, 16.216-30.340)] had the strongest association with MAFLD, regardless of potential confounders. Among all the anthropometric indicators, the strongest association was seen between LAP and sex hormones.

conclusionAll anthropometric indicators were associated with MAFLD. WHtR was identified as the strongest predictor of MAFLD in young Chinese males, followed by LAP and AVI. The strongest association was found between LAP and sex hormones.

Indexed as

Waist-Height RatioAdiposityAdultArea Under CurveBody Mass IndexChinaCross-Sectional StudiesFatty LiverHealth SurveysHumansLogistic ModelsMaleMetabolic DiseasesRetrospective StudiesROC CurveWaist-Hip RatioAbdominal volume indexAnthropometric indicatorLipid accumulation productMetabolic-dysfunction associated fatty liver diseaseNon-alcoholic fatty liver diseaseVisceral fatWaist-to-height ratio

Identifiers

PMID34706716
PMCPMC8549212
OpenAlexW3208120311

What OpenQuestion holds

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LicenceCC BY
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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.