Evidence map›Paper›PMID 36071283›Full record

ReviewNature reviews. Endocrinology2022

Modelling metabolic diseases and drug response using stem cells and organoids.

Wenxiang Hu, Mitchell A Lazar

Open access · bronzeAbstract readReview
In one paragraph

Review in Nature reviews. Endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed
17.0field-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

53 citing papers in PubMed, 85 citations in OpenAlex.

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  17. Gut Microbiota-Derived Metabolites Orchestrate Metabolic Reprogramming in Diabetic Cardiomyopathy: Mechanisms and Therapeutic Frontiers.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2025
    Review
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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

2 authors at 2 institutions in 2 countries.

Wenxiang HuDepartment of Basic Research, Guangzhou Laboratory, Guangdong, China. hu_wenxiang@gzlab.ac.cn.ORCID 0000-0002-6754-5625
Mitchell A LazarInstitute for Diabetes, Obesity, and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA. lazar@pennmedicine.upenn.edu.ORCID 0000-0001-8653-1280
Guangzhou Automobile Group (China) · CNUniversity of Pennsylvania · US

Funding

Nuclear Receptors in Metabolic TissuesR01DK049780 · NIDDK · UNIVERSITY OF PENNSYLVANIA · PI LAZAR, MITCHELL A. · 1995 to 2025
$10.8M
NIDDK NIH HHS R01 DK049780
6 · The paper itself

Abstract

Metabolic diseases, including obesity, diabetes mellitus and cardiovascular disease, are a major threat to health in the modern world, but efforts to understand the underlying mechanisms and develop rational treatments are limited by the lack of appropriate human model systems. Notably, advances in stem cell and organoid technology allow the generation of cellular models that replicate the histological, molecular and physiological properties of human organs. Combined with marked improvements in gene editing tools, human stem cells and organoids provide unprecedented systems for studying mechanisms of metabolic diseases. Here, we review progress made over the past decade in the generation and use of stem cell-derived metabolic cell types and organoids in metabolic disease research, especially obesity and liver diseases. In particular, we discuss the limitations of animal models and the advantages of stem cells and organoids, including their application to metabolic diseases. We also discuss mechanisms of drug action, understanding the efficacy and toxicity of existing therapies, screening for new treatments and pursuing personalized therapies. We highlight the potential of combining stem cell-derived organoids with gene editing and functional genomics to revolutionize the approach to finding treatments for metabolic diseases.

Indexed as

Metabolic DiseasesOrganoidsAnimalsHumansModels, BiologicalObesityStem Cells

Identifiers

PMID36071283
PMCPMC9449917
OpenAlexW4294876596

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.