Evidence map›Paper›PMID 21253582›Full record

ReviewJournal of biomedicine & biotechnology2011

Rodent models for metabolic syndrome research.

Sunil K Panchal, Lindsay Brown

Open access · hybridAbstract readReview
In one paragraph

Review in Journal of biomedicine & biotechnology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 162 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
162citing papers in PubMed, 1 pooled it
11.7field-weighted citation impact, top 1% 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

162 citing papers in PubMed, 1 synthesis or guideline pooled it, 392 citations in OpenAlex.

  1. Pooled it
  2. Experimental models in diabetes research.Laboratory animal research · 2026
    Review
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  13. Sex-specific characterization of aortic function and inflammation in a new diet-induced mouse model of metabolic syndrome.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2025
    Article
  14. Article
  15. Article
  16. A Review onEndocrine, metabolic & immune disorders drug targets · 2025
    Review
  17. Review
  18. Article
  19. Article
  20. Article

102 more citing papers are in PubMed but not listed here.

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

2 authors at 1 institution in 1 country.

Sunil K PanchalDepartment of Biological and Physical Sciences, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
Lindsay Brown
University of Southern Queensland · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rodents are widely used to mimic human diseases to improve understanding of the causes and progression of disease symptoms and to test potential therapeutic interventions. Chronic diseases such as obesity, diabetes and hypertension, together known as the metabolic syndrome, are causing increasing morbidity and mortality. To control these diseases, research in rodent models that closely mimic the changes in humans is essential. This review will examine the adequacy of the many rodent models of metabolic syndrome to mimic the causes and progression of the disease in humans. The primary criterion will be whether a rodent model initiates all of the signs, especially obesity, diabetes, hypertension and dysfunction of the heart, blood vessels, liver and kidney, primarily by diet since these are the diet-induced signs in humans with metabolic syndrome. We conclude that the model that comes closest to fulfilling this criterion is the high carbohydrate, high fat-fed male rodent.

Indexed as

Biomedical ResearchDisease Models, AnimalRodentiaAnimalsDiabetes Mellitus, Type 2HumansMetabolic SyndromeObesity

Identifiers

PMID21253582
PMCPMC3018657
OpenAlexW2060294481

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.