Multivariate Analysis Approach to the Serum Peptide Profile of Morbidly Obese Patients. Issue 4 (21st May 2013)

• Record Type: Journal Article
• Authors: Agostini, M.; Bedin, C.; Enzo, M.V.; Molin, L.; Traldi, P.; D'Angelo, E.; Maschietto, E.; Serraglia, R.; Ragazzi, E.; Prevedello, L.; Foletto, M.; Nitti, D.
• Is Part Of: Disease markers. Volume 34:Issue 4(2013)
• Page Start: 269
• Page End: 278
• Publication Date: 2013-05-21
• Subjects: Obesity -- MALDI-TOF -- serum profile -- principal component analysis -- cluster analysis; Diagnosis -- Periodicals; Biochemical markers -- Periodicals; Pathology -- Periodicals; 616
• DOI: 10.3233/DMA-130971
• Languages: English
• ISSNs: 0278-0240

Abstract

Abstract : Background: Obesity is currently epidemic in many countries worldwide and is strongly related to diabetes and cardiovascular disease. Mass spectrometry, in particular matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) is currently used for detecting different pattern of expressed protein. This study investigated the differences in low molecular weight (LMW) peptide profiles between obese and normal-weight subjects in combination with multivariate statistical analysis. Materials: Serum samples of 60 obese patients and 10 healthy subjects were treated by cut-off membrane (30000 Da) to remove the most abundant proteins. The filtrates containing the LMW protein/peptides were analyzed by MALDI-TOF mass spectrometry. Dataset was elaborated to align and normalize the spectra. We performed cluster analysis and principal component analysis to detect some ionic species that could characterize and classify the subject groups. Results: We observed a down-expression of ionic species at m/z 655.94 and an over-expression of species at m/z 1518.78, 1536.77, 1537.78 and 1537.81 in obese patients. Furthermore we found some ionic species that can distinguish obese patients with diabetes from those with normal glucose level. Conclusion: Serum peptide profile of LMW associate with multivariate statistical approach was revealed as a promising tool to discriminate and characterize obese patients and it was able to stratify them in relation to comorbidity that usually are associated with this disease. Further research involving a larger sample will be required to validate these findings.