Establishing a reliable multiple reaction monitoring-based method for the quantification of obesity-associated comorbidities in serum and adipose tissue requires intensive clinical validation.

Multiple reaction monitoring (MRM)-based mass spectrometric quantification of peptides and their corresponding proteins has been successfully applied for biomarker validation in serum. The option of multiplexing offers the chance to analyze various proteins in parallel, which is especially important in obesity research. Here, biomarkers that reflect multiple comorbidities and allow monitoring of therapy outcomes are required. Besides the suitability of established MRM assays for serum protein quantification, it is also feasible for analysis of tissues secreting the markers of interest. Surprisingly, studies comparing MRM data sets with established methods are rare, and therefore the biological and clinical value of most analytes remains questionable. A MRM method using nano-UPLC-MS/MS for the quantification of obesity related surrogate markers for several comorbidities in serum, plasma, visceral and subcutaneous adipose tissue was established. Proteotypic peptides for complement C3, adiponectin, angiotensinogen, and plasma retinol binding protein (RBP4) were quantified using isotopic dilution analysis and compared to the standard ELISA method. MRM method variabilities were mainly below 10%. The comparison with other MS-based approaches showed a good correlation. However, large differences in absolute quantification for complement C3 and adiponectin were obtained compared to ELISA, while less marked differences were observed for angiotensinogen and RBP4. The verification of MRM in obesity was performed to discriminate first lean and obese phenotype and second to monitor excessive weight loss after gastric bypass surgery in a seven-month follow-up. The presented MRM assay was able to discriminate obese phenotype from lean and monitor weight loss related changes of surrogate markers. However, inclusion of additional biomarkers was necessary to interpret the MRM data on obesity phenotype properly. In summary, the development of disease-related MRMs should include a step of matching the MRM data with clinically approved standard methods and defining reference values in well-sized representative age, gender, and disease-matched cohorts.