Abstract
Abstract Background Hyperglycemia, a condition characterized by a significantly increased glucose level in the blood, can lead to insulin deficiency and pre-diabetes. Lack of insulin production also interferes with lipid metabolism and increases the risk for cardiovascular disease. Thus, diagnosis and treatment of hyperglycemic patients can benefit from advanced proteomics and lipidomics testing approaches. Methods Disease-focused serum samples from 50 specimens were collected and evaluated, including 29 specimens with a glucose level>180 mg/dL and 21 normal lipidemic specimens. The sample proteomics analysis was conducted with a Perfinity IDP workstation (Shimadzu Scientific, Columbia, MD, USA) using on-line protein digestion with an immobilized enzyme reactor (IMER) directly coupled to a HALO-C18 column (Advanced Materials Technology, Wilmington, DE, USA). Lipidomics data were collected with the Lipidyzer platform (AB SCIEX, Framingham, MA, USA). Statistical analysis was conducted using JMP software (SAS Institute, USA) and custom scripts. Results Using targeted lipidomics and proteomics platforms we identified 20 proteins and 575 lipid species across 10 lipid subclasses. We identified specific lipids and lipoproteins dysregulated in hyperglycemic specimens as compared to the normolipidemic samples and linked them to major metabolic pathways. Lipoprotein particles related-proteins apos A1/B/C2/C3/E and lecithin–cholesterol acyltransferase (LCAT) were significantly dysregulated and associated with changes in HDL-mediated lipid transport, very-low-density lipoprotein particle remodeling, acylglycerol homeostasis, and triglyceride homeostasis. Conclusion The conducted integrated analysis of lipid and protein concentrations in hyperglycemia specimens to provide new insights into molecular changes associated with hyperglycemia that may inform new diagnostic and therapeutic strategies. The network analysis can allow improved categorization of patients that can lead to more effective and individualized therapeutic intervention approaches. This study provides new experimental and informatics approaches to better assess underlying metabolic conditions and related diseases, including cardiovascular disease, diabetes, and metabolic syndrome. Disclaimer The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. Use of trade names is for identification only and does not imply endorsement by the CDC.
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