High concentrations of triglycerides are associated with diabetic kidney disease in new-onset type 2 diabetes in China: Findings from the China Cardiometabolic Disease and Cancer Cohort (4C) Study

Triglyceride-lowering therapy for the prevention of cardiovascular events, stroke, and mortality in patients with diabetes: A meta-analysis of randomized controlled trials

BACKGROUND AND AIMS

Triglyceride (TG)-lowering therapy is efficient for the prevention of cardiovascular disease (CVD) in the general population; however, for diabetic individuals, it is more controversial. The purpose of this study was to pool the results from randomized controlled trials (RCTs) to clarify whether the lowering of TG is beneficial for the prevention of CVD events, stroke, and mortality in subjects with diabetes.

METHODS

MEDLINE, Web of Science, EMBASE, ClinicalTrials.gov, and the Cochrane Central Register for Controlled Trials were searched to identify the relevant literature. We included randomized controlled trials (RCTs) to assess the association of triglyceride-lowering therapy with the prevention of CVD events, stroke, and mortality in diabetic patients.

RESULTS

Overall, 19 studies were included in this meta-analysis. Compared with the control groups, TG lowering was associated with a decreased risk of CVD events (RR = 0.91, 95% CI 0.87-0.95, p = 0.000) and CVD mortality (RR = 0.93, 95% CI 0.86-1.00, p = 0.047). There was no significant correlation between TG-lowering therapy and the incidence of stroke and all-cause mortality (RR = 0.93, 95% CI 0.86-1.02, p = 0.129 and RR = 0.97, 95% CI 0.93-1.01, p = 0.107, respectively). Subgroup analysis showed that the decreased CVD risk resulting from TG-lowering therapy was independent of age, sex, region, duration of follow-up, degree of TG reduction and glycemic control.

CONCLUSIONS

TG-lowering therapy is associated with a reduction in CVD events and cardiovascular-specific mortality, but not in stroke and all-cause mortality. Future large, multicenter RCTs will further confirm these conclusions.

Impact of polygenic risk score for triglyceride trajectory and diabetic complications in subjects with type 2 diabetes based on large electronic medical record data from Taiwan: a case control study

BACKGROUND

The prevalence of diabetic dyslipidemia has gradually increased worldwide and individuals with hypertriglyceridemia often have a high polygenic burden of triglyceride (TG)-increasing variants. However, the contribution of genetic variants to dyslipidemia in patients with type 2 diabetes (T2D) remains limited. Therefore, in this study, we aimed to investigate the genetic characteristics of longitudinal changes in TG levels among patients with T2D and summarize the genetic effects of polygenic risk score (PRS) on TG trajectory and risk of diabetic complications.

METHODS

We conducted a case-control study. A total of 11,312 patients with T2D with longitudinal TG and genetic data were identified from a large hospital database in Taiwan. We then performed a genome-wide association study and calculated the relative PRS.

RESULTS

In total, 21 single-nucleotide polymorphisms (SNPs) related to TG trajectory were identified and yielded an area under the receiver operating characteristic curve (ROC) of 0.712 for high TG trajectory risk among Taiwanese patients with T2D. A cumulative genetic effect was observed for high TG trajectory, even when considering the adherence of a lipid-lowering agent in stratified analysis. An increased PRS increases high TG trajectory risk in a logistic regression model (odds ratio = 1.55; 95% confidence interval [CI] = 1.31-1.83 in the validation cohort). The TG-specific PRS was associated with the risk of diabetic microvascular complications, including diabetic retinopathy and nephropathy (with hazard ratios of 1.11 [95% CI = 1.01-1.21, P = 0.027] and 1.05 [95% CI = 1.01-1.1, P = 0.018], respectively).

CONCLUSIONS

This study may contribute to the identification of patients with T2D who are at risk of abnormal TG levels and diabetic microvascular complications using polygenic information.

Age- and gender-adjusted estimated glomerular filtration rate definition reveals hyperfiltration as a risk factor for renal function deterioration in type 2 diabetes

AIM

To assess the role of hyperfiltration for diabetic kidney disease (DKD) progression.

MATERIALS AND METHODS

A retrospective observational cohort study enrolled type 2 diabetes (T2D) patients with an initial estimated glomerular filtration rate (eGFR) of 60 mL/min/1.73m2 or higher. Patients were categorized into two groups: hyperfiltration (eGFR exceeding the age- and gender-specific 95th percentile values from a prior national cohort study) and normofiltration. Rapid DKD progression was defined as an eGFR decline of more than 5 mL/min/1.73m2/year. We used a linear mixed effect model and Cox regression with time-varying covariate model to compare eGFR changes and identify factors associated with rapid DKD progression.

RESULTS

Of the enrolled 7563 T2D patients, 7.2% had hyperfiltration. The hyperfiltration group exhibited a higher rate of eGFR decline compared with the normofiltration group (-2.0 ± 0.9 vs. -1.1 ± 0.9 mL/min/1.73m2/year; P < .001). During an average follow-up period of 4.65 ± 3.86 years, 24.7% of patients with hyperfiltration experienced rapid DKD progression, compared with 15.7% of patients with normofiltration (P < .001). Cox regression analyses identified that initial hyperfiltration was a significant determinant of rapid DKD progression, with a hazard ratio of 1.66 (95% confidence interval: 1.41-1.95; P < .001). When combined with albuminuria, the risk of progression was further compounded (hazard ratio 1.76-3.11, all P < .001).

CONCLUSIONS

In addition to using the current Kidney Disease: Improving Global Outcomes CGA classification system, considering glomerular hyperfiltration status can improve the accuracy of predicting DKD progression.

Construction of a Nomogram-Based Prediction Model for the Risk of Diabetic Kidney Disease in T2DM

Introduction

To investigate the predictors of diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) patients and establish a nomogram model for predicting the risk of DKD.

Methods

The clinical data of T2DM patients, admitted to the Endocrinology Department of Chengde Central Hospital from October 2019 to September 2020 and divided into a case group or a control group based on whether they had DKD, were collected. The predictive factors of DKD were screened by univariate and multivariate analysis, and a nomogram prediction model was constructed for the risk of DKD in T2DM. Bootstrapping was used for model validation, receiver operating characteristic (ROC) curve and GiViTI calibration curve were used for evaluating the discrimination and calibration of prediction model, and decision analysis curve (DCA) was used for evaluating the practicality of model.

Results

Predictors for DKD are diabetic retinopathy (DR), hypertension, history of gout, smoking history, using insulin, elevation of body mass index (BMI), triglyceride (TG), cystatin C (Cys-C), and reduction of 25 (OH) D. The nomogram prediction model based on the above nine predictors had good representativeness (Bootstrap method: precision: 0.866, Kappa: 0.334), differentiation [the area under curve (AUC) value: 0.868], and accuracy (GiViTI-corrected curved bands, P = 0.836); the DAC curve analysis showed that the prediction model, whose threshold probability was in the range of 0.10 to 0.70, had clinical practical value.

Conclusion

The risk of DKD in T2DM could be predicted accurately by DR, hypertension, history of gout, smoking history, using insulin, elevation of BMI, TG, Cys-C, and reduction of 25 (OH) D.

Interrelation between the lipid accumulation product index and diabetic kidney disease in patients with type 2 diabetes mellitus

Objective

The purpose of this study was to determine the relation between the lipid accumulation product index (LAPI) and diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM).

Methods

Herein, 931 patients were enrolled and their data were collected. Then the interrelation between LAPI and DKD was assessed using multivariate logistic regression analyses (LRAs) and by a restricted cubic spline (RCS).

Results

In total, 931 participants (352 females and 579 males) aged 55 years on average were included in the study. After adjusting for several confounders, the odds ratio for DKD was increased evidently in the third LAPI tertile compared with that in the first LAPI tertile. In addition, the RCS revealed a positive interrelation between LAPI and DKD. In the subgroup analyses, age, sex, hyperlipidemia, hypertension, and HbA1c did not significantly interact with LAPI.

Conclusions

LAPI was higher in the DKD group than in the no-DKD group, and LAPI is positively linked with DKD, which may have potential value to diagnose DKD in clinical practice.

The link between diabetic retinal and renal microvasculopathy is associated with dyslipidemia and upregulated circulating level of cytokines

Purpose

To investigate the mechanisms underlying the correlations between diabetic retinopathy (DR) and diabetic nephropathy (DKD) and examine whether circulating cytokines and dyslipidemia contribute to both DR and DKD in patients with 2 diabetes mellitus (T2DM).

Methods

A total of 122 patients with T2DM were enrolled and categorized into the DM group (without no DR and DKD), DR group [non-proliferative DR (NPDR), and proliferative DR (PDR)] with no DKD), DR complicated with DKD groups (DR+DKD group). The biochemical profile, including fasting blood glucose (FBG), glycated hemoglobin (HbA1c), and lipid profile were estimated, and plasma inflammatory and angiogenic cytokines [monocyte chemoattractant protein-1 (MCP-1), interleukin (IL)-6, IL-8, vascular endothelial growth factor (VEGF)-A, C, D, and placental growth factor (PlGF)] were analyzed by protein microarrays. The atherogenic plasma index (API) was defined as low-density lipoprotein cholesterol (LDL-C)/high-density lipoprotein-cholesterol (HDL-C); atherogenic index (AI) was calculated as [(total cholesterol (TC) -HDL-C)/HDL-C], and atherogenic index of plasma (AIP) was defined as log (TG/HDL-C).

Results

By multivariable disordered regression analysis, after controlling for duration of DM and hypertension, LDL-C (p = 0.019) and VEGF-D (p = 0.029) resulted as independent risk factors for DR. Albumin-to-creatinine ratio (uACR) (p = 0.003) was an independent risk factor for DR with DKD. In DR, NPDR, and PDR groups, grades of A1, A2, and A3 of albuminuria increased with the severity of DR. In A1, A2, and A3 grade groups, the severity of DR (DM, NPDR, and PDR) increased with higher albuminuria grades. Kendall's tau-b correlation coefficient analysis revealed that FBG (p = 0.019), circulating level of PlGF (p = 0.002), and VEGF-D (p = 0.008) were significantly positively correlated with the grades of uACR (p < 0.001), and uACR grades were significantly correlated with DR severity (p < 0.001).

Conclusions

The occurrence and severity of DR are closely correlated with kidney dysfunction. Among the three kidney functional parameters, uACR resulted as the better indicator of DR severity and progression than glomerular filtration (eGFR) and serum creatinine (Scr). Impaired FBG was associated with microalbuminuria, emphasizing that well-controlled FBG is important for both DR and DKD. The link between diabetic retinal and renal microvasculopathy was associated with dyslipidemia and upregulated circulating level of angiogenic cytokines.

Pharmacokinetics of Icosapent Ethyl: An Open-Label, Multiple Oral Dose, Parallel Design Study in Healthy Chinese Subjects

Icosapent ethyl (IPE) is a high-purity prescription form of eicosapentaenoic acid (EPA) ethyl ester that has been approved to lower triglyceride levels in adult patients with severe (≥500 mg/dL) hypertriglyceridemia. Before this study, there were no pharmacokinetics (PK) or safety data in Chinese patients after receiving IPE. The purpose of this study was to evaluate the PK of EPA in plasma and red blood cells and safety after oral administration of IPE capsules for 28 consecutive days in healthy Chinese subjects. It was a randomized, open-label, parallel-designed multiple-dose, phase I study. Twenty-four subjects were enrolled and randomly assigned to 2 groups, including 6 men and 6 women in each group. Group A received IPE 2.0 g/day (1×1 g twice daily), and group B received IPE 4.0 g/day (2×1 g twice daily) with dosing after standard meals for 28 days. During the treatment period, PK samples were collected from all subjects before the morning dose on days 1, 14, 26, and 28. Following completion of the last study drug administration in the morning on day 28, an 18-day posttreatment PK sample collection period was followed. Twenty-four eligible subjects were enrolled in this study, and 1 subject withdrew from the study. The main PK parameters (baseline-corrected maximum observed plasma concentration and area under the plasma concentration-time curve during a dosing interval) of plasma total EPA, RBC EPA, and plasma unesterified EPA increased with dose. Chinese healthy subjects who took IPE capsules orally in the dose range of 2.0 to 4.0 g/day for 28 consecutive days were safe and tolerable.

12(S)-hydroxyeicosatetraenoic acid is significantly increased in diabetic kidney disease and associated with renal function decline

AIMS

12(S)-hydroxyeicosatetraenoic (12(S)-HETE), an alternate arachidonic acid metabolite, has been recently examined in metabolic disease. However, the role of 12(S)-HETE in diabetic kidney disease (DKD) remains unclear. We studied for the first time the relationship of serum 12(S)-HETE and DKD and renal function parameters in a Chinese population.

MATERIALS AND METHODS

We recruited 275 subjects who were diagnosed with type 2 diabetes (T2DM) for more than 10 years, including 149 DKD patients and 126 T2DM patients without DKD. Serum 12(S)-HETE was measured using the enzyme-linked immunosorbent assay.

RESULTS

Serum 12(S)-HETE was significantly higher in DKD patients than controls [384.69 (77.54, 1003.05) pg/ml and 17.77 (8.11, 75.13) pg/ml, respectively, p < 0.0001]. Compared to controls, 12(S)-HETE was significantly increased in both macroalbuminuria and microalbuminuria groups (p < 0.0001). Further, the macroalbuminuria group also had a higher serum 12(S)-HETE level compared to the microalbuminuria group (p = 0.0063). Moreover, serum 12(S)-HETE was positively correlated with the albuminuria level (r = 0.5833, p < 0.0001), serum creatinine (r = 0.2725, p < 0.0001), and was negatively associated with the estimated glomerular filtration rate (r = -0.2085, p = 0.0005). Further, receiver operating characteristic analysis (ROC) revealed that 12(S)-HETE had a good performance of distinguishing DKD from controls (AUC 0.828) with a sensitivity of 0.913 and a specificity of 0.711.

CONCLUSION

Our findings revealed that serum 12(S)-HETE significantly associated with DKD and disease severity, suggesting that serum 12(S)-HETE may be used as a potential biomarker for the early diagnosis of DKD.

Development and Validation of a Model That Predicts the Risk of Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients: A Cross-Sectional Study

Purpose

To develop a nomogram model that predicts the risk of diabetic nephropathy (DN) incidence in type 2 diabetes mellitus (T2DM) patients.

Methods

We collect information from electronic medical record systems. The data were split into a training set (n=521) containing 73.8% of patients and a validation set (n=185) holding the remaining 26.2% of patients based on the date of data collection. Stepwise and multivariable logistic regression analyses were used to screen out DN risk factors. A predictive model including selected risk factors was developed by logistic regression analysis. The results of binary logistic regression are presented through forest plots and nomogram. Lastly, the c-index, calibration plots, and receiver operating characteristic (ROC) curves were used to assess the accuracy of the nomogram in internal and external validation. The clinical benefit of the model was evaluated by decision curve analysis.

Results

Predictors included serum creatinine (Scr), hypertension, glycosylated hemoglobin A1c (HbA1c), blood urea nitrogen (BUN), body mass index (BMI), triglycerides (TG), and Diabetic peripheral neuropathy (DPN). Harrell’s C-indexes were 0.773 (95% CI:0.726–0.821) and 0.758 (95% CI:0.679–0.837) in the training and validation sets, respectively. Decision curve analysis (DCA) demonstrated that the novel nomogram was clinically valuable.

Conclusion

Our simple nomogram with seven factors may help clinicians predict the risk of DN incidence in patients with T2DM.