Evidence map›Paper›PMID 31798675›Full record

ReviewChinese medicine2019

An evaluation of randomized controlled trials on nutraceuticals containing traditional Chinese medicines for diabetes management: a systematic review.

Junnan Shi, Hao Hu, Joanna Harnett, Xiaoting Zheng, Zuanji Liang, Yi-Tao Wang, Carolina Oi Lam Ung

Open access · goldAbstract readReview
In one paragraph

Review in Chinese medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
3.5field-weighted citation impact, top 7% 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

11 citing papers in PubMed, 33 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Pharmacokinetic Herb-Drug Interactions of Glipizide withMolecules (Basel, Switzerland) · 2022
    Article
  6. Article
  7. Article
  8. Review
  9. Article
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  11. Review
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 at 2 institutions in 2 countries.

Junnan Shi1State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macao.
Hao Hu1State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macao.
Joanna Harnett2The University of Sydney School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, New South Wales, Australia.
Xiaoting Zheng1State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macao.
Zuanji Liang1State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macao.
Yi-Tao Wang1State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macao.
Carolina Oi Lam Ung1State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macao.ORCID 0000-0003-1915-5099
University of Macau · MOThe University of Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNutraceuticals containing traditional Chinese medicine (TCM) are promoted for use in the management of diabetes. The evidence to support such use is largely unknown. This study aimed to summarise and evaluate the literature reporting the results of randomized controlled trials (RCTs) investigating the effects of nutraceuticals in people living with diabetes.

methodsLiterature from four electronic databases (PubMed, Scopus, CINAHL and Web of Science) was searched following PRISMA guidelines to yield RCT publications on nutraceutical for diabetes management published since 2009. The quality of reporting was assessed using the CONSORT 2010 checklist statement. Risk-of-bias for each study was assessed using the Cochrane risk of bias tool.

resultsOut of 1978 records identified in the initial search, 24 randomized, double/triple-blinded, controlled trials that investigated the effect of nutraceuticals covering 17 different TCM herbs for diabetes management were selected. Participants included people who were diabetic (n = 16), pre-diabetic (n = 3) or predisposed to diabetes (n = 5). Sample sizes ranged between 23 and 117 for 2 arms, or 99-165 for 3 arms. Comparisons were made against placebo (n = 22), conventional medicine (n = 1), or regular diet (n = 1) for a duration between 4 and 24 weeks. All but one study tested the effect on fasting blood glucose levels (n = 23) or glycated haemoglobin levels (n = 18), and/or postprandial 2-h blood glucose levels (n = 4) as the primary outcomes. Nineteen studies reported some statistically significant reductions in the respective measures while 5 studies showed no effect on primary or secondary outcomes. None of the included studies met all the criteria for the CONSORT guidelines. Incomplete reporting about randomization and blinding, and a lack of ancillary analyses to explore other influential factors and potential harms associated with the use were repeatedly noted. Based on the Cochrane risk-of-bias tool, 19 studies were deemed to have a high risk of bias mainly attributed to sponsor bias.

conclusionsThere is some evidence to suggest positive clinical outcomes in response to the administration of a range of nutraceuticals containing TCM in the management of diabetes. However, these results must be interpreted with caution due to the overall low quality of the trials.

Indexed as

Blood glucoseDiabetesNutraceuticalsRandomized clinical trialRCTSupplementsTCMTraditional Chinese medicine

Identifiers

PMID31798675
PMCPMC6884840
OpenAlexW2989772828

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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.