#AI reads Urine# Urinary Proteomics as a Noninvasive Readout of Obesity-associated Inflammation

This study aimed to detect obesity-related inflammation through a non-invasive method. Morning urine samples were collected from 30 normal-weight individuals (with a mean BMI of 22.8 kg/m²) and 58 obese individuals (with a mean BMI of 32.9 kg/m²). The urinary proteome was analyzed using the Olink Explore 384 Inflammation Panel. Results showed that among the 384 inflammatory proteins, 48 exhibited significant differences between the two groups (P<0.05 after FDR correction). Protein clustering analysis identified 5 protein clusters, some of which were associated with metabolic syndrome risk factors in obese individuals, such as high-density lipoprotein levels, waist circumference, and fat distribution. Pathway analysis revealed a significant enrichment of chemokine-mediated signaling pathways in obese individuals. The study demonstrated that urinary proteomics could serve as a novel non-invasive tool for detecting obesity-associated inflammation, providing potential biomarkers for assessing the risk of obesity-related complications. However, further validation is required.

J Endocr Soc. 2025 Dec 23;10(1):bvaf212. doi: 10.1210/jendso/bvaf212.

Youhe Gao

Statement: During the preparation of this work the author(s) used Deepseek / AI reading for summarizing the content. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

For earlier AI Reads Urine articles：

https://www.keaipublishing.com/en/journals/advances-in-biomarker-sciences-and-technology/ai-reads-urine/