A research team led by Alaa Othman1 explored the “lipidome” to identify novel risk factors or biomarkers that predict the incidence of type 2 diabetes. The researchers measured sphingoid base profiles in a prospective cohort of 339 individuals who had already experienced a coronary event. The team determined whether this data could help predict which of these subjects would develop diabetes during the study’s follow-up period. They selected this particular cohort because diabetes and cardiovascular disease are intimately linked – almost half of diabetics die from cardiovascular-related events.

Sphingolipids are a heterogeneous class of lipid molecules defined by a carbon amino-alcohol backbone. The sphingolipid family includes free sphingoid bases, ceramides, sphingomyelins and glycosphingolipids. They exist in cell membranes, are involved in signal transduction and have important regulatory roles in cells.2 Serine palmitoyltransferase is the enzyme at the center of sphingolipid metabolism and is responsible for generating atypical sphingolipids, including 1-deoxysphingolipid.

Selecting a Cohort

The Othman investigators selected 339 individuals from a prospective study of patients who had received an angiography to evaluate coronary artery disease. At the beginning of that study, the research team collected anthropometric data as well as blood samples. They continued to take blood samples at two, four, six and eight years — analyzing them for sphingolipids, fasting glucose, HbA1C, blood lipids and other chemistry of interest.

Part 1: Baseline sphingolipid analysis

Othman et al. devised a cross-sectional study to compare subjects’ sphingoid profiles at the original study’s outset. They also included information about BMI, lipids, blood pressure and drugs. They divided people into those who were diagnosed with metabolic syndrome at the beginning of the study (n=147) and those who were not (n=192), as well as those with normal fasting glucose (n=124), those with impaired fasting glucose (n=107) and those with diabetes (n=108). From this cross-section analysis, the team identified that subjects with metabolic syndrome at baseline had higher levels of deoxysphingolipids, as well as a number of other C20-SA-based sphingolipids.

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Part 2: Prospective-retrospective analysis

The second part of the Othman study looked at 105 subjects with data available from all eight years of the original study. The team compared baseline plasma sphingoid levels in subjects who developed diabetes (n=32) during the study and subjects who did not (n=70). Those who developed type 2 diabetes had significantly higher levels of 1-deoxysphingolipids, HbA1C and triglycerides at baseline compared with those who did not develop diabetes. Statistical analysis revealed that 1-deoxysphingolipids along with HbA1C, triglycerides, hypertension, serum potassium and the presence of metabolic syndrome are significant predictors for type 2 diabetes.

The Ohtmann study confirmed their previous finding, that 1-deoxysphingolipid is elevated in people with impaired fasting glucose, metabolic syndrome and diabetes. Expanding on earlier results, the researchers determined that 1-deoxyshingolipid is predictive for type 2 diabetes, even after adjusting for other risk factors. The study cohort was at a higher risk of developing diabetes than the regular population, so the researchers are now working to determine whether 1-deoxysphingolipid might also predict diabetes in broader, lower risk populations.

Finally, the authors discuss the specific role 1-deoxysphingolipid plays, noting that due to cytotoxicity, it may be causal in diabetes pathogenesis. If that is the case, 1-deoxysphingolipid is not only a predictive biomarker, but also a potential therapeutic target.

  1. Othman A, Saely CH, Muendlein A, et al. Plasma 1-deoxysphingolipids are predictive biomarkers for type 2 diabetes mellitus. BMJ Open Diabetes Research and Care 2015;3:e000073. doi:10.1136/bmjdrc-2014- 000073
  2. Gault CR, Obeid LM, and Hannun YA. An overview of sphingolipid metabolism: from synthesis to breakdown. Adv Exp Med Biol. 2010; 688: 1–23.