Analysis of lipid profile in lipid storage myopathy

• Intra-muscular triglycerides investigation by NARP-HPLC-APCI-MS.
• Fatty acid composition of lipid storage myopathies.
• 35 FAMEs and 107 TGs were identified.
• The entire dataset was elaborated performing a principal component analysis.

Lipid dysmetabolism disease is a condition in which lipids are stored abnormally in organs and tissues throughout the body, causing muscle weakness (myopathy). Usually, the diagnosis of this disease and its characterization goes through dosage of Acyl CoA in plasma accompanied with evidence of droplets of intra-fibrils lipids in the patient muscle biopsy. However, to understand the pathophysiological mechanisms of lipid storage diseases, it is useful to identify the nature of lipids deposited in muscle fiber. In this work fatty acids and triglycerides profile of lipid accumulated in the muscle of people suffering from myopathies syndromes was characterized. In particular, the analyses were carried out on the muscle biopsy of people afflicted by lipid storage myopathy, such as multiple acyl-coenzyme A dehydrogenase deficiency, and neutral lipid storage disease with myopathy, and by the intramitochondrial lipid storage dysfunctions, such as deficiencies of carnitine palmitoyltransferase II enzyme. A single step extraction and derivatization procedure was applied to analyze fatty acids from muscle tissues by gas chromatography with a flame ionization detector and with an electronic impact mass spectrometer. Triglycerides, extracted by using n-hexane, were analyzed by high performance liquid chromatography coupled to mass spectrometer equipped with an atmospheric pressure chemical ionization interface. The most representative fatty acids in all samples were: C16:0 in the 13–24% range, C18:1n9 in the 20–52% range, and C18:2n6 in the 10–25% range. These fatty acids were part of the most representative triglycerides in all samples. The data obtained was statistically elaborated performing a principal component analysis. A satisfactory discrimination was obtained among the different diseases. Using component 1 vs component 3 a 43.3% of total variance was explained. Such results suggest the important role that lipid profile characterization can have in supporting a correct diagnosis.