Proteome and genome integration analysis of obesity.
10.1097/CM9.0000000000002644
- Author:
Qigang ZHAO
1
;
Baixue HAN
1
;
Qian XU
1
;
Tao WANG
2
;
Chen FANG
2
;
Rui LI
3
;
Lei ZHANG
4
;
Yufang PEI
1
Author Information
1. Department of Epidemiology and Biostatistics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215123, China.
2. Department of Endocrinology, The Second Affiliated Hospital, Soochow University, Suzhou, Jiangsu 215004, China.
3. Department of Gastroenterology, The First Affiliated Hospital, Soochow University, Suzhou, Jiangsu 215006, China.
4. Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215213, China.
- Publication Type:Journal Article
- MeSH:
Humans;
Proteome/metabolism*;
Proteomics;
Prospective Studies;
Obesity/genetics*;
Genomics;
Genome-Wide Association Study
- From:
Chinese Medical Journal
2023;136(8):910-921
- CountryChina
- Language:English
-
Abstract:
The prevalence of obesity has increased worldwide in recent decades. Genetic factors are now known to play a substantial role in the predisposition to obesity and may contribute up to 70% of the risk for obesity. Technological advancements during the last decades have allowed the identification of many hundreds of genetic markers associated with obesity. However, the transformation of current genetic variant-obesity associations into biological knowledge has been proven challenging. Genomics and proteomics are complementary fields, as proteomics extends functional analyses. Integrating genomic and proteomic data can help to bridge a gap in knowledge regarding genetic variant-obesity associations and to identify new drug targets for the treatment of obesity. We provide an overview of the published papers on the integrated analysis of proteomic and genomic data in obesity and summarize four mainstream strategies: overlap, colocalization, Mendelian randomization, and proteome-wide association studies. The integrated analyses identified many obesity-associated proteins, such as leptin, follistatin, and adenylate cyclase 3. Despite great progress, integrative studies focusing on obesity are still limited. There is an increased demand for large prospective cohort studies to identify and validate findings, and further apply these findings to the prevention, intervention, and treatment of obesity. In addition, we also discuss several other potential integration methods.
