Effect of Polymorphism of Uncoupling Protein 3 Gene -55 (C>T) on the Resting Energy Expenditure, Total Body Fat and Regional Body Fat in Chinese.
openalex(2005)
Shanghai Jiaotong University(Shanghai Jiaotong University
Abstract
OBJECTIVE:To investigate the relationship of the C to T variant at the -55 site of the promoter region of uncoupling protein 3 gene (UCP3) with the resting energy expenditure and the parameters of body fat in Chinese population.METHODS:Three hundred Chinese (91 normal weight subjects, 209 overweight/obesity subjects) were genotyped for the UCP3 gene -55(C>T) by using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Resting energy expenditure (REE), fat mass (FM), fat free mass (FFM) and the parameters for regional adipose tissue distribution were measured.RESULTS:Genotype frequencies of UCP3 gene -55(C>T) were not associated with obesity and different types of obesity. The REE level in normal weight subjects with TT homozygotes was higher than that in those with CT heterozygotes and CC homozygotes (P=0.0200). Similar tendency was also observed in overweight/obesity subjects. The FM/FFM exhibited significant difference between the overweight/obesity subjects with a TT genotype and those with a CT or CC genotype (P=0.0096).CONCLUSION:The level of difference in REE caused by the polymorphism of promoter region of UCP3 -55(C>T) may play a role in energy metabolism in Chinese.
MoreTranslated text
Key words
uncoupling protein 3 gene,resting energy expenditure,genetic polymorphism
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined