Specific triacylglycerol, diacylglycerol, and lyso-phosphatidylcholine species for the prediction of type 2 diabetes: a ~ 16-year prospective study in Chinese

Pharmacokinetics of soy-derived lysophosphatidylcholine compared with that of glycerophosphocholine: a randomized controlled trial

Lysophosphatidylcholine (LPC) is present in various foods and contains a choline moiety such as in glycerophosphocholine (GPC). However, the potential of LPC as a choline source remains unclear. This study investigated the single-dose pharmacokinetics of 480 mg soy-derived LPC in 12 healthy men compared with that of either soy oil with the same lipid amount (placebo) or GPC with the same choline amount. Both LPC and GPC supplementation increased plasma choline, serum phospholipid, and serum triglyceride concentrations, but neither of them significantly elevated plasma trimethylamine N-oxide concentration. In addition, although the intake of LPC slightly increased plasma LPC16:0, LPC18:2, and total LPC concentrations, their concentrations remained within physiological ranges. No adverse events were attributed to the LPC supplementation. To the best of our knowledge, this study is the first to compare LPC and GPC pharmacokinetics in humans and shows that LPC can be a source of choline.

Lipidomic studies revealing serological markers associated with the occurrence of retinopathy in type 2 diabetes

PURPOSE

The duration of type 2 diabetes mellitus (T2DM) and blood glucose levels have a significant impact on the development of T2DM complications. However, currently known risk factors are not good predictors of the onset or progression of diabetic retinopathy (DR). Therefore, we aimed to investigate the differences in the serum lipid composition in patients with T2DM, without and with DR, and search for potential serological indicators associated with the development of DR.

METHODS

A total of 622 patients with T2DM hospitalized in the Department of Endocrinology of the First Affiliated Hospital of Xi'an JiaoTong University were selected as the discovery set. One-to-one case-control matching was performed according to the traditional risk factors for DR (i.e., age, duration of diabetes, HbA1c level, and hypertension). All cases with comorbid chronic kidney disease were excluded to eliminate confounding factors. A total of 42 pairs were successfully matched. T2DM patients with DR (DR group) were the case group, and T2DM patients without DR (NDR group) served as control subjects. Ultra-performance liquid chromatography-mass spectrometry (LC-MS/MS) was used for untargeted lipidomics analysis on serum, and a partial least squares discriminant analysis (PLS-DA) model was established to screen differential lipid molecules based on variable importance in the projection (VIP) > 1. An additional 531 T2DM patients were selected as the validation set. Next, 1:1 propensity score matching (PSM) was performed for the traditional risk factors for DR, and a combined 95 pairings in the NDR and DR groups were successfully matched. The screened differential lipid molecules were validated by multiple reaction monitoring (MRM) quantification based on mass spectrometry.

RESULTS

The discovery set showed no differences in traditional risk factors associated with the development of DR (i.e., age, disease duration, HbA1c, blood pressure, and glomerular filtration rate). In the DR group compared with the NDR group, the levels of three ceramides (Cer) and seven sphingomyelins (SM) were significantly lower, and one phosphatidylcholine (PC), two lysophosphatidylcholines (LPC), and two SMs were significantly higher. Furthermore, evaluation of these 15 differential lipid molecules in the validation sample set showed that three Cer and SM(d18:1/24:1) molecules were substantially lower in the DR group. After excluding other confounding factors (e.g., sex, BMI, lipid-lowering drug therapy, and lipid levels), multifactorial logistic regression analysis revealed that a lower abundance of two ceramides, i.e., Cer(d18:0/22:0) and Cer(d18:0/24:0), was an independent risk factor for the occurrence of DR in T2DM patients.

CONCLUSION

Disturbances in lipid metabolism are closely associated with the occurrence of DR in patients with T2DM, especially in ceramides. Our study revealed for the first time that Cer(d18:0/22:0) and Cer(d18:0/24:0) might be potential serological markers for the diagnosis of DR occurrence in T2DM patients, providing new ideas for the early diagnosis of DR.

Decreased in n-3 DHA enriched triacylglycerol in small extracellular vesicles of diabetic patients with cardiac dysfunction

PURPOSE

Diabetic cardiomyopathy is the leading cause of death in diabetic patients, and the mechanism by which factors other than hyperglycemia contribute to the development of diabetic cardiomyopathy is unknown. Serum small extracellular vesicles (sEVs) carry bioactive proteins or nuclei, which enter into remote tissues and modulate cell functions. However, in diabetic conditions, the changes of lipids carried by sEVs has not been identified. Our study aims to explore the changes of lipids in sEVs in diabetic patients with cardiovascular disease, we hope to provide new ideas for understanding the role of lipid metabolism in the pathogenesis of diabetic cardiomyopathy.

METHODS

SEVs samples derived from serum of health controls (Ctrl), diabetic patients without cardiovascular diseases (DM), and diabetic patients with cardiovascular diseases (DM-CAD) were used for lipidomics analysis. Because AC16 cells are also treated with those sEVs to confirm the entrance of cells and effects on insulin sensitivity, a lipidomics analysis on cells was also performed.

RESULTS AND CONCLUSIONS

In this study, we found that docosahexaenoic acid (DHA)-triacylglycerides of sEVs from serums of DM-CAD patients decreased significantly, and those sEVs could enter into AC16 cells and diminish insulin sensitivity. In addition, DHA-triacylglycerides were also decreased in cells treated with sEVs from DM-CAD. Therefore, DHA-triacylglycerides carried by sEVs may mediate intercellular signaling and be associated with the incidence of diabetic cardiovascular complications.

Ten metabolites-based algorithm predicts the future development of type 2 diabetes in Chinese

INTRODUCTION

Type 2 diabetes (T2D) is a heterogeneous metabolic disease with large variations in the relative contributions of insulin resistance and β-cell dysfunction across different glucose tolerance subgroups and ethnicities. A more precise yet feasible approach to categorize risk preceding T2D onset is urgently needed. This study aimed to identify potential metabolic biomarkers that could contribute to the development of T2D and investigate whether their impact on T2D is mediated through insulin resistance and β-cell dysfunction.

METHODS

A non-targeted metabolomic analysis was performed in plasma samples of 196 incident T2D cases and 196 age- and sex-matched non-T2D controls recruited from a long-term prospective Chinese community-based cohort with a follow-up period of ∼ 16 years.

RESULTS

Metabolic profiles revealed profound perturbation of metabolomes before T2D onset. Overall metabolic shifts were strongly associated with insulin resistance rather than β-cell dysfunction. In addition, 188 out of the 578 annotated metabolites were associated with insulin resistance. Bi-directional mediation analysis revealed putative causal relationships among the metabolites, insulin resistance and T2D risk. We built a machine-learning based prediction model, integrating the conventional clinical risk factors (age, BMI, TyG index and 2hG) and 10 metabolites (acetyl-tryptophan, kynurenine, γ-glutamyl-phenylalanine, DG(18:2/22:6), DG(38:7), LPI(18:2), LPC(P-16:0), LPC(P-18:1), LPC(P-20:0) and LPE(P-20:0)) (AUROC = 0.894, 5.6% improvement comparing to the conventional clinical risk model), that successfully predicts the development of T2D.

CONCLUSIONS

Our findings support the notion that the metabolic changes resulting from insulin resistance, rather than β-cell dysfunction, are the primary drivers of T2D in Chinese adults. Metabolomes as a valuable phenotype hold potential clinical utility in the prediction of T2D.