Evidence map›Paper›PMID 22916265›Full record

ArticlePloS one2012

Accessing autonomic function can early screen metabolic syndrome.

Kan Sun, Yu Liu, Meng Dai, Mian Li, Zhi Yang, Min Xu, Yu Xu, Jieli Lu, Yuhong Chen, Jianmin Liu and 2 more

Abstract read
In one paragraph

Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

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

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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.

Kan SunKey Laboratory for Endocrine and Metabolic Diseases of Ministry of Health, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, E-Institute of Shanghai Universities, Shanghai, China.
Yu Liu
Meng Dai
Mian Li
Zhi Yang
Min Xu
Yu Xu
Jieli Lu
Yuhong Chen
Jianmin Liu
Guang Ning
Yufang Bi

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundClinical diagnosis of the metabolic syndrome is time-consuming and invasive. Convenient instruments that do not require laboratory or physical investigation would be useful in early screening individuals at high risk of metabolic syndrome. Examination of the autonomic function can be taken as a directly reference and screening indicator for predicting metabolic syndrome. METHODOLOGY AND PRINCIPAL

findingsThe EZSCAN test, as an efficient and noninvasive technology, can access autonomic function through measuring electrochemical skin conductance. In this study, we used EZSCAN value to evaluate autonomic function and to detect metabolic syndrome in 5,887 participants aged 40 years or older. The EZSCAN test diagnostic accuracy was analyzed by receiver operating characteristic curves. Among the 5,815 participants in the final analysis, 2,541 were diagnosed as metabolic syndrome and the overall prevalence was 43.7%. Prevalence of the metabolic syndrome increased with the elevated EZSCAN risk level (p for trend <0.0001). Moreover, EZSCAN value was associated with an increase in the number of metabolic syndrome components (p for trend <0.0001). Compared with the no risk group (EZSCAN value 0-24), participants at the high risk group (EZSCAN value: 50-100) had a 2.35 fold increased risk of prevalent metabolic syndrome after the multiple adjustments. The area under the curve of the EZSCAN test was 0.62 (95% confidence interval [CI], 0.61-0.64) for predicting metabolic syndrome. The optimal operating point for the EZSCAN value to detect a high risk of prevalent metabolic syndrome was 30 in this study, while the sensitivity and specificity were 71.2% and 46.7%, respectively. CONCLUSIONS AND SIGNIFICANCE: In conclusion, although less sensitive and accurate when compared with the clinical definition of metabolic syndrome, we found that the EZSCAN test is a good and simple screening technique for early predicting metabolic syndrome.

Indexed as

AdultBlood PressureBody Mass IndexCholesterol, LDLCross-Sectional StudiesElectrochemistryFemaleHumansMaleMetabolic SyndromeMiddle AgedTriglyceridesWaist CircumferenceCholesterol, LDLTriglycerides

Identifiers

PMID22916265
PMCPMC3423347

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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.