Elucidating the biological characteristics of overweight populations based on urine Raman spectroscopy and...

Obesity, a chronic metabolic disease, arises from the interplay of genetic predisposition, endocrine disorders, and environmental factors. It poses a threat to health and imposes significant socioeconomic burdens. Urine Raman spectroscopy provides a non-invasive method for assessing metabolic changes in individuals experiencing weight gain.

Raman spectroscopy was employed to analyze urine samples, developing a simple, non-invasive method for detecting pre-obesity and obesity. The study aims to provide an innovative tool for the early identification of metabolic fluctuations in overweight individuals, addressing key scientific issues in early warning and metabolic mechanism analysis of obesity.

In the study, 31 normal-weight, 27 pre-obese, and 8 obese subjects were enrolled. Clinical data were collected, and urine samples were analyzed using laser Raman spectroscopy coupled with Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA). A classification model was constructed to distinguish pre-obesity, obesity, and control groups, and an in-depth statistical analysis was applied to validate the findings. Gene expression data from the Gene Expression Omnibus (GEO) database were analyzed to identify obesity-associated genes and their key biological pathways.

Raman spectroscopy revealed statistically significant differences in urine peak intensities at 1241 cm^-1, 1397 and 1706 cm^-1, 1596 cm^-1, 1603 cm^-1, 1615 cm^-1, 1620 cm^-1, 1650 cm^-1, and 1654 cm^-1 (P < 0.05). Ten key differentially expressed genes were identified: TNF, IL6, ACTB, IL1B, TP53, MYC, CXCL8, PTGS2, CXCL10, and FN1. A comparative analysis of differential peak positions revealed that alanine, serine, tyrosine, phenylalanine, phospholipids, and creatinine were the major contributors to the diagnostic model for overweight individuals.

Raman spectroscopy, combined with OPLS-DA, enables individuals in the pre-obesity stage to detect subtle metabolic changes early. The results of this study will help in developing personalized health management strategies.