Plasma Lipidomic Subclasses and Risk of Hypertension in Middle-Aged and Elderly Chinese

Lipidomic and transcriptomic characteristics of boar seminal plasma extracellular vesicles associated with sperm motility

Seminal plasma extracellular vesicles (SPEVs) play an important role in regulating sperm motility by delivering various cargoes, such as miRNAs, mRNAs, proteins and metabolites. However, information on the lipid compositions of SPEVs and their roles in semen quality is limited. Here, we performed high-throughput transcriptomic and lipidomic analysis on SPEVs isolated from 20 boars with high or low sperm motility. Then, we evaluated the lipid composition and gene expression characteristics of SPEVs and identified the specific lipids and genes related to sperm motility. As a result, a total of 26 lipid classes were identified in SPEVs, and five subclasses, CerG2, CerG3, LPE, LPS and TG, were significantly different in boars with high and low sperm motility. In addition, 195 important lipids and 334 important genes were identified by weighted gene coexpression analysis (WGCNA) and differential expression analysis. We observed that several important genes and lipids in SPEVs potentially influence sperm motility via glycerophospholipid metabolism, glycerolipid metabolism, the sphingolipid signaling pathway and the ferroptosis pathway. Furthermore, we found a significant correlation between the content of 22 lipids and the expression levels of 67 genes (|cor| > 0.8, P < 0.05). Moreover, we observed that three important gene-lipid linkages (CerG1 (d22:0/24:0) - RCAN3, Cer (d18:1/24:0) - SCFD2 and CerG1 (d18:0/24:1) - SCFD2) were strongly correlated with sperm motility. Based on the results, some genes and lipids in SPEVs may play important roles in sperm motility by interacting with sperm through important pathways.

Genetic association analyses highlight apolipoprotein B as a determinant of chronic kidney disease in patients with type 2 diabetes

BACKGROUND

Blood lipid levels were associated with chronic kidney disease (CKD) in patients with type 2 diabetes (T2D), but the genetic basis and causal nature remains unclear.

OBJECTIVE

This study aimed to investigate the relationships of lipids and their fractions with CKD in patients with T2D.

METHODS

Our prospective analysis involved 8,607 White participants with T2D but no CKD at baseline from the UK Biobank. Five common lipid traits were included as exposures. Weighted genetic risk scores (GRSs) for these lipid traits were developed. The causal associations between lipid traits, as well as lipid fractions, and CKD were explored using linear or nonlinear Mendelian randomization (MR). The 10-year predicted probabilities of CKD were evaluated via integrating MR and Cox models.

RESUTLS

Higher GRS of apolipoprotein B (ApoB) was associated with an increased CKD risk (HR[95 % CI]:1.07[1.02,1.13] per SD;P = 0.008) after adjusting for potential confounders. Linear MR indicated a positive association between genetically predicted ApoB levels and CKD (HR[95 % CI]:1.53[1.12,2.09];P = 0.008), but no evidence of associations was found between other lipid traits and CKD in T2D. Regarding 12 ApoB-contained lipid fractions, a significant causal association was found between medium very-low-density lipoprotein particles and CKD (HR[95 % CI]:1.16[1.02,1.32];P = 0.020). Nonlinear MR did not support nonlinearity in these causal associations. The 10-year probability curve showed that ApoB levels was positively associated with the risk of CKD in patients with T2D.

CONCLUSION

Lower ApoB levels were causally associated with a reduced risk of CKD in patients with T2D, positioning ApoB as a potential therapeutic target for CKD prevention in this population.

Plasma lipidomic profiling reveals six candidate biomarkers for the prediction of incident stroke in patients with hypertension

INTRODUCTION

The burden of stroke in patients with hypertension is very high, and its prediction is critical.

OBJECTIVES

We aimed to use plasma lipidomics profiling to identify lipid biomarkers for predicting incident stroke in patients with hypertension.

METHODS

This was a nested case-control study. Baseline plasma samples were collected from 30 hypertensive patients with newly developed stroke, 30 matched patients with hypertension, 30 matched patients at high risk of stroke, and 30 matched healthy controls. Lipidomics analysis was performed by ultrahigh-performance liquid chromatography-tandem mass spectrometry, and differential lipid metabolites were screened using multivariate and univariate statistical methods. Machine learning methods (least absolute shrinkage and selection operator, random forest) were used to identify candidate biomarkers for predicting stroke in patients with hypertension.

RESULTS

Co-expression network analysis revealed that the key molecular alterations of the lipid network in stroke implicate glycerophospholipid metabolism and choline metabolism. Six lipid metabolites were identified as candidate biomarkers by multivariate statistical and machine learning methods, namely phosphatidyl choline(40:3p)(rep), cholesteryl ester(20:5), monoglyceride(29:5), triglyceride(18:0p/18:1/18:1), triglyceride(18:1/18:2/21:0) and coenzyme(q9). The combination of these six lipid biomarkers exhibited good diagnostic and predictive ability, as it could indicate a risk of stroke at an early stage in patients with hypertension (area under the curve = 0.870; 95% confidence interval: 0.783-0.957).

CONCLUSIONS

We determined lipidomic signatures associated with future stroke development and identified new lipid biomarkers for predicting stroke in patients with hypertension. The biomarkers have translational potential and thus may serve as blood-based biomarkers for predicting hypertensive stroke.

Identification of Serum Biomarkers of Ischemic Stroke in a Hypertensive Population Based on Metabolomics and Lipidomics

Hypertensive individuals are at a high risk of stroke, and thus, prevention of stroke in hypertensive patients is essential. Metabolomics and lipidomics can be used to identify diagnostic biomarkers and conduct early assessments of stroke risk in hypertensive populations. In this study, serum samples were collected from 30 hypertensive ischemic stroke (IS), 30 matched hypertensive and 30 matched healthy participants. Metabolomics and lipidomics analyses were conducted via liquid chromatography-tandem mass spectrometry, and the data were analyzed using multivariate and univariate statistical methods. A random forest algorithm and binary logistic regression were used to screen the biomarkers and establish diagnostic model. We detected 21 differential metabolites and 38 differential lipids between the hypertensive IS and healthy group. Moreover, we found 18 differential metabolites and 31 differential lipids between the hypertensive IS and hypertension group. In particular, the following seven metabolites or lipids distinguished the hypertensive IS from the healthy group: 4-hydroxyphenylpyruvic acid, cafestol, phosphatidylethanolamine (PE) (18:0p/18:2), PE (16:0e/20:4), (O-acyI)-1-hydroxy fatty acid (36:3), PE (16:0p/20:3) and PE (18:1p/18:2) (rep). The following seven biomarkers distinguished the hypertensive IS from the hypertension group: diglyceride (DG) (20:1/18:2), PE (18:0p/18:2), PE (16:0e/22:5), phosphatidylcholine (40:7), dimethylphosphatidylethanolamine (50:3), DG (18:1/18:2), and 4-hydroxyphenylpyruvic acid. The aforementioned panels had good diagnostic and predictive ability for hypertensive IS. Our study determines the metabolomic and lipidomic profiles of hypertensive IS patients and thereby identifies potential biomarkers of the presence of IS in hypertensive populations.

Diet-Related Lipidomic Signatures and Changed Type 2 Diabetes Risk in a Randomized Controlled Feeding Study With Mediterranean Diet and Traditional Chinese or Transitional Diets

OBJECTIVE

Few trials studied the links of food components in different diets with their induced lipidomic changes and related metabolic outcomes. Thus, we investigated specific lipidomic signatures with habitual diets and modified diabetes risk by using a trial and a cohort.

RESEARCH DESIGN AND METHODS

We included 231 Chinese with overweight and prediabetes in a randomized feeding trial with Mediterranean, traditional, or transitional diets (control diet) from February to September 2019. Plasma lipidomic profiles were measured at baseline, third month, and sixth month by high-throughput targeted liquid chromatography-mass spectrometry. Associations of the identified lipids with habitual dietary intakes were examined in another lipidomic database of a Chinese cohort (n = 1,117). The relationships between diet-induced changes of lipidomic species and diabetes risk factors were further investigated through both individual lipids and relevant modules in the trial.

RESULTS

Out of 364 lipidomic species, 26 altered across groups, including 12 triglyceride (TAG) fractions, nine plasmalogens, four phosphatidylcholines (PCs), and one phosphatidylethanolamine. TAG fractions and PCs were associated with habitual fish intake while plasmalogens were associated with red meat intake in the cohort. Of the diet-related lipidomic metabolites, 10 TAG fractions and PC(16:0/22:6) were associated with improved Matsuda index (β = 0.12 to 0.42; PFDR < 0.030). Two plasmalogens were associated with deteriorated fasting glucose (β = 0.29 to 0.31; PFDR < 0.014). Similar results were observed for TAG and plasmalogen related modules.

CONCLUSIONS

These fish- and red meat-related lipidomic signatures sensitively reflected different diets and modified type 2 diabetes risk factors, critical for optimizing dietary patterns.