Evidence map›Paper›PMID 32276657›Full record

ArticleBMC research notes2020

Green coffee extract modifies body weight, serum lipids and TNF-α in high-fat diet-induced obese rats.

Cimi Ilmiawati, Fajar Fitri, Zelly Dia Rofinda, Mohamad Reza

Abstract read
In one paragraph

Article in BMC research notes, 2020. 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.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Anti-Obesity Effects of a Mixture ofMolecules (Basel, Switzerland) · 2022
    Article
  9. Article
  10. 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

4 authors.

Cimi IlmiawatiDepartment of Pharmacology, Faculty of Medicine, Andalas University, Main Campus at Limau Manis, Gedung A Lantai 1, Pauh, Padang, 25166, West Sumatra, Indonesia. ilmiawati@med.unand.ac.id.
Fajar FitriPostgraduate Student, Graduate Program of Biomedical Science, Faculty of Medicine, Andalas University, Padang, West Sumatra, Indonesia.
Zelly Dia RofindaDepartment of Clinical Pathology, Faculty of Medicine, Andalas University, Padang, West Sumatra, Indonesia.
Mohamad RezaDepartment of Biology, Faculty of Medicine, Andalas University, Padang, West Sumatra, Indonesia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveCurrently, there are many efforts to find functional nutrients for obesity management, and the green coffee extract is a potential candidate. This study aimed to examine the effect of low dose administration of  green coffee extract on body weight, serum lipids, and TNF-α level in high-fat diet-induced obese rats.

resultsAdministration of green coffee extract to high-fat diet-induced obese male Wistar rats (Rattus norvegicus) reduced body weight, total serum cholesterol, and triglyceride at the dose of 10, 20, and 40 mg/kg BW/day; lowered serum LDL-cholesterol at the treatment dose of 20 mg/kg BW/day (p < 0.05). The effective dose to decrease serum TNF-α level was 40 mg/kg BW/day, while the effective dose to improve the lipid profile was 10 mg/kg BW/day. These results support the potential use of green coffee extract as a functional nutrient in the management of obesity.

Indexed as

CoffeaAnimalsBody WeightCholesterol, LDLDiet, High-FatDisease Models, AnimalMaleObesityPlant ExtractsRatsRats, WistarTriglyceridesTumor Necrosis Factor-alphaCholesterol, LDLPlant ExtractsTriglyceridesTumor Necrosis Factor-alphaBody weightCholesterolGreen coffee extractLipid profileObesityTNF-α

Identifiers

PMID32276657
PMCPMC7149906

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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.