Evidence map›Paper›PMID 30572889›Full record

ArticleLipids in health and disease2018

Obesity and lipid-related parameters for predicting metabolic syndrome in Chinese elderly population.

Zhan Gu, Ping Zhu, Qiao Wang, Huayu He, Jingjuan Xu, Li Zhang, Dong Li, Jianying Wang, Xiaojuan Hu, Guang Ji and 2 more

Abstract read
In one paragraph

Article in Lipids in health and disease, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing 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

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

  1. Pooled it
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  20. Metabolic Obesity in People with Normal Body Weight (MONW)-Review of Diagnostic Criteria.International journal of environmental research and public health · 2022
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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.

Zhan GuShanghai Innovation Center of TCM Health Service, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
Ping ZhuSchool of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
Qiao WangShanghai Innovation Center of TCM Health Service, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
Huayu HeShanghai Innovation Center of TCM Health Service, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
Jingjuan XuShanghai Innovation Center of TCM Health Service, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
Li ZhangShanghai Innovation Center of TCM Health Service, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
Dong LiZhangjiang Community Health Service Center of Pudong New District, Shanghai, 201210, China.
Jianying WangShanghai Innovation Center of TCM Health Service, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
Xiaojuan HuShanghai Innovation Center of TCM Health Service, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
Guang JiShanghai Innovation Center of TCM Health Service, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
Lei ZhangShanghai Innovation Center of TCM Health Service, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China. zhanglei37@sina.com.
Baocheng LiuShanghai Innovation Center of TCM Health Service, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China. baochliumed@126.com.

Funding

China Postdoctoral Science Foundation No. 2018M630465National Natural Science Foundation of China No. 81573814National Natural Science Foundation of China No. 81603411National Natural Science Foundation of China No. 816220108030
6 · The paper itself

Abstract

backgroundThe present study evaluated the predictive ability of five known "best" obesity and lipid-related parameters, including body mass index (BMI), waist-to-height ratio (WHtR), triglyceride-to-high-density-lipoprotein-cholesterol (TG/HDL-C), lipid accumulation product (LAP) and visceral adiposity index (VAI), in identifying metabolic syndrome (MetS) in Chinese elderly population.

methodsA total of 6722 elderly Chinese subjects (≥60 years) were recruited into our community-based cross-sectional study from April 2015 to July 2017. The anthropometrics, blood pressure, fasting plasma glucose, blood lipid profiles, family history and health-related behaviours were assessed.

resultsThe prevalence of MetS was 40.4% (32.5% in males and 47.2% in females). With the increase in the number of MetS components (from 0 to 5), all the five parameters showed an increase trend in both genders (all P for trend < 0.001). According to receiver operating characteristic curve (ROC) analyses, all the five parameters performed high predictive value in identifying MetS. The statistical significance of the areas under the curves (AUCs) differences suggested that the AUCs of LAP were the greatest among others in both genders (AUCs were 0.897 in males and 0.875 in females). The optimal cut-off values of LAP were 26.35 in males and 31.04 in females. After adjustment for potentially confounding factors, LAP was strongly associated with the odds of having MetS in both genders, and ORs for MetS increased across quartiles using multivariate logistic regression analysis (P < 0.001).

conclusionLAP appeared to be a superior parameter for predicting MetS in both Chinese elderly males and females, better than VAI, TG/HDL-C, WHtR and BMI.

Indexed as

AgedArea Under CurveBody Mass IndexCross-Sectional StudiesFemaleHumansLipid Accumulation ProductLipoproteins, HDLMaleMetabolic SyndromeMiddle AgedPrognosisROC CurveTriglyceridesHDL-triglycerideLipoproteins, HDLTriglyceridesBody mass indexChinese elderly populationLipid accumulation productMetabolic syndromeObesity and lipid-related parametersTriglyceride-to-high-density-lipoprotein-cholesterolVisceral adiposity indexWaist-to-height ratio

Identifiers

PMID30572889
PMCPMC6302378

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.