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Large-scale lipidomics identifies associations between plasma sphingolipids and T2DM incidence
Wee Siong Chew, Federico Torta, Shanshan Ji, Hyungwon Choi, Husna Begum, Xueling Sim, Chin Meng Khoo, Eric Yin Hao Khoo, Wei-Yi Ong, Rob M. Van Dam, Markus R. Wenk, E. Shyong Tai, Deron R. Herr
Wee Siong Chew, Federico Torta, Shanshan Ji, Hyungwon Choi, Husna Begum, Xueling Sim, Chin Meng Khoo, Eric Yin Hao Khoo, Wei-Yi Ong, Rob M. Van Dam, Markus R. Wenk, E. Shyong Tai, Deron R. Herr
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Clinical Research and Public Health Endocrinology

Large-scale lipidomics identifies associations between plasma sphingolipids and T2DM incidence

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Abstract

BACKGROUND Sphingolipids (SPs) are ubiquitous, structurally diverse molecules that include ceramides, sphingomyelins (SMs), and sphingosines. They are involved in various pathologies, including obesity and type 2 diabetes mellitus (T2DM). Therefore, it is likely that perturbations in plasma concentrations of SPs are associated with disease. Identifying these associations may reveal useful biomarkers or provide insight into disease processes.METHODS We performed a lipidomics evaluation of molecularly distinct SPs in the plasma of 2302 ethnically Chinese Singaporeans using electrospray ionization mass spectrometry coupled with liquid chromatography. SP profiles were compared to clinical and biochemical characteristics, and subjects were evaluated with follow-up visits for 11 years.RESULTS We found that ceramides correlated positively but hexosylceramides correlated negatively with BMI and homeostatic model assessment of insulin resistance (HOMA-IR). Furthermore, SPs with a d16:1 sphingoid backbone correlated more positively with BMI and HOMA-IR, while d18:2 SPs correlated less positively, relative to canonical d18:1 SPs. We also found that higher concentrations of 2 distinct SMs were associated with a higher risk of T2DM (HR 1.45 with 95% CI 1.18–1.78 for SM d16:1/18:0 and HR 1.40 with 95% CI 1.17–1.68 for SM d18:1/18:0).CONCLUSIONS We identified significant associations between SPs and obesity/T2DM characteristics, specifically, those of hexosylceramides, d16:1 SPs, and d18:2 SPs. This suggests that the balance of SP metabolism, rather than ceramide accumulation, is associated with the pathology of obesity. We further identified 2 specific SPs that may represent prognostic biomarkers for T2DM.FUNDING National University Health System (NUHSRO/2014/085/AF-Partner/01) and the National Research Foundation Investigatorship grant (NRF-NRFI2015-05).

Authors

Wee Siong Chew, Federico Torta, Shanshan Ji, Hyungwon Choi, Husna Begum, Xueling Sim, Chin Meng Khoo, Eric Yin Hao Khoo, Wei-Yi Ong, Rob M. Van Dam, Markus R. Wenk, E. Shyong Tai, Deron R. Herr

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Figure 2

Correlations between SPs and key clinical characteristics.

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Correlations between SPs and key clinical characteristics.
SP/clinical c...
SP/clinical characteristics network showing plasma SP correlations with 7 key clinical characteristics. Pearson’s correlation coefficients based on univariate logistic regression are represented as connecting lines (edges) between the clinical characteristics’ nodes and SPs’ nodes. Only correlations with an absolute value of 0.2 and above were shown. Edge width is proportional to correlation strength from minimum 0.2 to maximum 0.58. Size of circles (nodes) is proportional to degree (total number of connections with other nodes). Red edge color indicates positive correlation while blue edge indicates negative correlation. Node color was grouped according to SP type and clinical characteristic: clinical characteristic nodes are gray, Cer nodes are red, HexCer nodes are blue, Hex2Cer nodes are green, and nodes for the other SPs are yellow. The graph was generated using the Force Atlas layout in Gephi 0.9.2. S1P, sphingosine-1-phosphate; SM, sphingomyelin; SPH, sphingosine.

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