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

BACKGROUND: Bioactive lipids play an important role in insulin secretion and sensitivity, contributing to the pathophysiology of type 2 diabetes (T2D). This study aimed to identify novel lipid species associated with incident T2D in a nested case-control study within a long-term prospective Chinese community-based cohort with a median follow-up of ~ 16 years. METHODS: Plasma samples from 196 incident T2D cases and 196 age- and sex-matched non-T2D controls recruited from the Hong Kong Cardiovascular Risk Factor Prevalence Study (CRISPS) were first analyzed using untargeted lipidomics. Potential predictive lipid species selected by the Boruta analysis were then verified by targeted lipidomics. The associations between these lipid species and incident T2D were assessed. Effects of novel lipid species on insulin secretion in mouse islets were investigated. RESULTS: Boruta analysis identified 16 potential lipid species. After adjustment for body mass index (BMI), triacylglycerol/high-density lipoprotein (TG/HDL) ratio and the presence of prediabetes, triacylglycerol (TG) 12:0_18:2_22:6, TG 16:0_11:1_18:2, TG 49:0, TG 51:1 and diacylglycerol (DG) 18:2_22:6 were independently associated with increased T2D risk, whereas lyso-phosphatidylcholine (LPC) O-16:0, LPC P-16:0, LPC O-18:0 and LPC 18:1 were independently associated with decreased T2D risk. Addition of the identified lipid species to the clinical prediction model, comprised of BMI, TG/HDL ratio and the presence of prediabetes, achieved a 3.8% improvement in the area under the receiver operating characteristics curve (AUROC) (p = 0.0026). Further functional study revealed that, LPC O-16:0 and LPC O-18:0 significantly potentiated glucose induced insulin secretion (GSIS) in a dose-dependent manner, whereas neither DG 18:2_22:6 nor TG 12:0_18:2_22:6 had any effect on GSIS. CONCLUSIONS: Addition of the lipid species substantially improved the prediction of T2D beyond the model based on clinical risk factors. Decreased levels of LPC O-16:0 and LPC O-18:0 may contribute to the development of T2D via reduced insulin secretion.

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 ß-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 ß-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 âˆ¼ 16 years. RESULTS: Metabolic profiles revealed profound perturbation of metabolomes before T2D onset. Overall metabolic shifts were strongly associated with insulin resistance rather than ß-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 ß-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.

[Peripheral blood albumin level in significantly correlated with the severity of atherosclerosis in hypertensive patients].

OBJECTIVE: To investigate the relationship between peripheral blood albumin (Hb) level and the severity of arteriosclerosis in hypertensive patients. METHODS: This retrospective analysis was conducted among 419 randomly selected patients with hypertension. The pulse wave velocity (ba-PWV) of the bilateral limbs was measured using an arteriosclerosis tester. According to the ba-PWV value (the higher value of the two sides), the hypertensive patients were divided into 4 groups, namely normal arterial group [S0 group, ba-PWV < 1400 cm/s; 49 cases (11.7%)], mild arteriosclerosis group [S1 group, ba-PWV of 1400-1800 cm/s; 190 cases (45.3%)], moderate arteriosclerosis group [S2 group, ba-PWV of 1800-2000 cm/s); 69 cases (16.5%)], and severe arteriosclerosis group [S3 group, ba-PWV > 2 000 cm/s; 111 cases (26.5 %)]. The clinical data of the patients were collected and multivariate logistic regression was used to analyze the risk factors of arteriosclerosis. RESULTS: The patients' age, obesity, albumin, alanine aminotransferase (ALT), total bile acid, adenosine deaminase, urea nitrogen, serum creatinine, cystatin C, low-density lipoprotein, red blood cells, hemoglobin (Hb), fibrinogen, and FT3 all differed significantly between S0 group and the 3 arteriosclerosis groups (P < 0.05). Multivariate logistic regression analysis revealed that in hypertensive patients, age was an independent risk factor for severe arteriosclerosis (OR=1.094, 95% CI: 1.052-1.137, P < 0.05) and moderate arteriosclerosis (OR= 1.081, 95% CI: 1.039-1.125, P < 0.05); Hb was an independent risk factor for new-onset severe arteriosclerosis (OR= 1.025, 95% CI: 1.003-1.045, P < 0.05) and moderate arteriosclerosis (OR=1.035, 95% CI: 1.008-1.056, P < 0.05), and an increase of Hb levels by 1 standard deviation was associated with a doubled risk in hypertensive patients. CONCLUSIONS: Peripheral Hb level is significantly correlated with the severity of arteriosclerosis and may serve as a new predictor for arteriosclerosis in hypertensive patients.

A Comparison of Three-Dimensional Speckle Tracking Echocardiography Parameters in Predicting Left Ventricular Remodeling.

Three-dimensional speckle tracking echocardiography (3D STE) is an emerging noninvasive method for predicting left ventricular remodeling (LVR) after acute myocardial infarction (AMI). Previous studies analyzed the predictive value of 3D STE with traditional models. However, no models that contain comprehensive risk factors were assessed, and there are limited data on the comparison of different 3D STE parameters. In this study, we sought to build a machine learning model for predicting LVR in AMI patients after effective percutaneous coronary intervention (PCI) that contains the majority of the clinical risk factors and compare 3D STE parameters values for LVR prediction. We enrolled 135 first-onset AMI patients (120 males, mean age 54 ± 9 years). All patients went through a 3D STE and a traditional transthoracic echocardiography 24 hours after reperfusion. A second echocardiography was repeated at the three-month follow-up to detect LVR (defined as a 20 percent increase in left ventricular end-diastolic volume). Six models were constructed using 15 risk factors. A receiver operator characteristic curve and four performance measurements were used as evaluation methods. Feature importance was used to compare 3D STE parameters. 26 patients (19.3%) had LVR. Our evaluation showed that RF can best predict LVR with the best AUC of 0.96. 3D GLS was the most valuable 3D STE parameters, followed by GCS, global area strain, and global radial strain (feature importance 0.146, 0.089, 0.087, and 0.069, respectively). To sum up, RF models can accurately predict the LVR after AMI, and 3D GLS was the best 3D STE parameters in predicting the LVR.