Evidence map›Paper›PMID 39062047›Full record

ReviewBiomedicines2024

Thermogenic Fat as a New Obesity Management Tool: From Pharmaceutical Reagents to Cell Therapies.

Ying Cheng, Shiqing Liang, Shuhan Zhang, Xiaoyan Hui

Abstract readReview
In one paragraph

Review in Biomedicines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Article
  6. Review
  7. 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.

Ying ChengZhongshan Hospital (Xiamen), Fudan University, Xiamen 361015, China.ORCID 0000-0001-5943-8363
Shiqing LiangSchool of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong 999077, China.
Shuhan ZhangSchool of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong 999077, China.
Xiaoyan HuiSchool of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong 999077, China.ORCID 0000-0002-7525-5812

Funding

Lo Kwee-Seong Biomedical Research Fund 7106480; 7106481
6 · The paper itself

Abstract

Obesity is a complex medical condition caused by a positive imbalance between calorie intake and calorie consumption. Brown adipose tissue (BAT), along with the newly discovered "brown-like" adipocytes (called beige cells), functions as a promising therapeutic tool to ameliorate obesity and metabolic disorders by burning out extra nutrients in the form of heat. Many studies in animal models and humans have proved the feasibility of this concept. In this review, we aim to summarize the endeavors over the last decade to achieve a higher number/activity of these heat-generating adipocytes. In particular, pharmacological compounds, especially agonists to the β3 adrenergic receptor (β3-AR), are reviewed in terms of their feasibility and efficacy in elevating BAT function and improving metabolic parameters in human subjects. Alternatively, allograft transplantation of BAT and the transplantation of functional brown or beige adipocytes from mesenchymal stromal cells or human induced pluripotent stem cells (hiPSCs) make it possible to increase the number of these beneficial adipocytes in patients. However, practical and ethical issues still need to be considered before the therapy can eventually be applied in the clinical setting. This review provides insights and guidance on brown- and beige-cell-based strategies for the management of obesity and its associated metabolic comorbidities.

Indexed as

beige adipocytebrown adipose tissuebrown adipose tissue transplantationhuman induced pluripotent stem cellobesityβ3 adrenergic receptor

Identifiers

PMID39062047
PMCPMC11275133

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.