Evidence map›Paper›PMID 35571915›Full record

SynthesisFrontiers in nutrition2022

Lipid-Lowering Efficacy of Kuding Tea in Patients With Metabolic Disorders: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Zhonghui Jiang, Zhuqing Lu, Tianyi Wang, Yilian Wang, Jianfeng Chu, Keji Chen, Zhuye Gao

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in nutrition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

7 authors.

Zhonghui JiangDepartment of Cardiology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Zhuqing LuAcademy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
Tianyi WangAcademy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
Yilian WangAcademy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
Jianfeng ChuAcademy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
Keji ChenDepartment of Cardiology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Zhuye GaoDepartment of Cardiology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Kuding tea (KT), traditional tea material and widely used in China, has been found to have lipid-lowering effect in clinical and experimental studies. However, there has been no systematic review of the evidence on this subject. Methods: Eight electronic databases were searched from database inception until September 2021 for relevant randomized controlled trials (RCTs). We used the Cochrane Reviewer's Handbook to assess the quality of the included studies. Weighted mean difference (WMD) and 95% confidence interval (CI) were used to measure the pooled effect size by random-effects model. Funnel plot, Egger regression test, and the Begg's test was used to assess publication bias. Results: Eight RCTs involving 716 patients were included in our meta-analysis. Comparing with the control group, KT group reduced serum total cholesterol (TC) levels (WMD: -0.56 mmol/L; 95% CI: -0.64, -0.47; Conclusion: KT supplementation can effectively improve lipid profile and KT is a promising approach to reduce blood lipid level in patients with metabolic disorders. Systematic Review Registration: [www.crd.york.ac.uk/prospero], identifier [CRD42020221850].

Indexed as

kuding tea (KT)lipid profilemeta-analysismetabolic disorderssystematic review

Identifiers

PMID35571915
PMCPMC9096904

What OpenQuestion holds

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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.