Optimizing lipid control in Taiwanese diabetic patients: A collaborative consensus by the Diabetes Association of the Republic of China (Taiwan) and the Taiwanese Association of Diabetes Educators.

We present an in-depth analysis of dyslipidemia management strategies for patients with diabetes mellitus in Taiwan. It critically examines the disparity between established guideline recommendations and actual clinical practices, particularly in the context of evolving policies affecting statin prescriptions. The focus is on synthesizing the most recent findings concerning lipid management in patients with diabetes mellitus, with a special emphasis on establishing consensus regarding low-density lipoprotein cholesterol treatment targets. The article culminates in providing comprehensive, evidence-based recommendations tailored to the unique needs of those living with diabetes mellitus in Taiwan. It underscores the criticality of personalized care approaches, which incorporate multifaceted factors, and the integration of novel therapeutic options to enhance cardiovascular health outcomes.

Determinants of early chronic kidney disease in patients with recently diagnosed type 2 diabetes mellitus: a retrospective study from the Taiwan Diabetes Registry.

BACKGROUND: We tried to identify the risk factor associate with early chronic kidney disease (CKD) in recently diagnosed type 2 diabetes mellitus patients by utilizing real-world data from Taiwan Diabetes Registry. MATERIALS AND METHODS: Patients with type 2 diabetes mellitus recently diagnosed within 1 year. We divided the study participants into control group and early CKD group. Early CKD was defined as either CKD stage G1 with albuminuria, CKD stage G2 with albuminuria, or CKD stage G3a regardless of albuminuria (Urine-albumin to creatinine ratio (UACR) ≥ 3 mg/mmol). Control group was defined as CKD G1 or CKD G2 without albuminuria. Logistic regression analyses were used to compare differences in clinical characteristics between the subgroups. Linear regression models were employed to examine the factors predicting estimated glomerular filtration rate (eGFR) and UACR. RESULTS: Total 2217 patients with recently diagnosed type 2 diabetes mellitus were included. 1545 patients were assigned to control group and 618 patients were assigned to the early CKD group. Age (odds ratio (OR) 1.215, 95% confidence interval [CI] 1.122-1.316), systolic blood pressure (OR 1.203, 95% CI 1.117-1.296), glycated hemoglobin (OR 1.074, 95% CI 1.023-1.129) and triglyceride (OR 2.18, 95% CI 1.485-3.199) were found to be significant risk factors. Further, presence of bidirectional association between UACR and eGFR was found. CONCLUSIONS: We reported factors associated with early CKD in recently diagnosed type 2 diabetes mellitus patients. Variables that associated with eGFR and UACR were identified respectively, included a mutual influence between UACR and eGFR.

Multi-Ancestry Polygenic Risk Score for Coronary Heart Disease Based on an Ancestrally Diverse Genome-Wide Association Study and Population-Specific Optimization.

BACKGROUND: Predictive performance of polygenic risk scores (PRS) varies across populations. To facilitate equitable clinical use, we developed PRS for coronary heart disease (CHD; PRSCHD) for 5 genetic ancestry groups. METHODS: We derived ancestry-specific and multi-ancestry PRSCHD based on pruning and thresholding and continuous shrinkage priors (polygenic risk score for CHD developed using ancestry-based continuous shrinkage methods) applied to summary statistics from the largest multi-ancestry genome-wide association study meta-analysis for CHD to date, including 1.1 million participants from 5 major genetic ancestry groups. Following training and optimization in the Million Veteran Program, we evaluated the best-performing PRSCHD in 176 988 individuals across 9 diverse cohorts. RESULTS: Multi-ancestry polygenic risk score for CHD developed using pruning and thresholding methods and polygenic risk score for CHD developed using ancestry-based continuous shrinkage methods outperformed ancestry-specific Polygenic risk score for CHD developed using pruning and thresholding methods and polygenic risk score for CHD developed using ancestry-based continuous shrinkage methods across a range of tuning values. Two best-performing multi-ancestry PRSCHD (ie, polygenic risk score for CHD developed using pruning and thresholding methods optimized using a multi-ancestry population and polygenic risk score for CHD developed using ancestry-based continuous shrinkage methods optimized using a multi-ancestry population) and 1 ancestry-specific (PRSCSxEUR) were taken forward for validation. Polygenic risk score for CHD developed using pruning and thresholding methods (PT) optimized using a multi-ancestry population demonstrated the strongest association with CHD in individuals of South Asian genetic ancestry and European genetic ancestry (odds ratio per 1 SD [95% CI, 2.75 [2.41-3.14], 1.65 [1.59-1.72]), followed by East Asian genetic ancestry (1.56 [1.50-1.61]), Hispanic/Latino genetic ancestry (1.38 [1.24-1.54]), and African genetic ancestry (1.16 [1.11-1.21]). Polygenic risk score for CHD developed using ancestry-based continuous shrinkage methods optimized using a multi-ancestry population showed the strongest associations in South Asian genetic ancestry (2.67 [2.38-3.00]) and European genetic ancestry (1.65 [1.59-1.71]), lower in East Asian genetic ancestry (1.59 [1.54-1.64]), Hispanic/Latino genetic ancestry (1.51 [1.35-1.69]), and the lowest in African genetic ancestry (1.20 [1.15-1.26]). CONCLUSIONS: The use of summary statistics from a large multi-ancestry genome-wide meta-analysis improved the performance of PRSCHD in most ancestry groups compared with single-ancestry methods. Despite the use of one of the largest and most diverse sets of training and validation cohorts to date, improvement of predictive performance was limited in African genetic ancestry. This highlights the need for larger Genome-wide association study datasets of underrepresented populations to enhance the performance of PRSCHD.

Data from network meta-analyses can inform clinical practice guidelines and decision-making in diabetes management: perspectives of the taskforce of the guideline workshop.

In recent years, several novel agents have become available to treat individuals with type 2 diabetes (T2D), such as sodium-glucose cotransporter-2 inhibitors (SGLT-2i), tirzepatide, which is a dual glucose-dependent insulinotropic polypeptide receptor agonist (GIP RA)/glucagon-like peptide-1 receptor agonist (GLP-1 RA), and finerenone, a non-steroidal mineralocorticoid receptor antagonist (MRA) that confers significant renal and cardiovascular benefits in individuals with (CKD). New medications have the potential to improve the lives of individuals with diabetes. However, clinicians are challenged to understand the benefits and potential risks associated with these new and emerging treatment options. In this article, we discuss how use of network meta-analyses (NMA) can fill this need.

Precision subclassification of type 2 diabetes: a systematic review.

BACKGROUND: Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients. METHODS: We searched PubMed and Embase for publications that used 'simple subclassification' approaches using simple categorisation of clinical characteristics, or 'complex subclassification' approaches which used machine learning or 'omics approaches in people with established type 2 diabetes. We excluded other diabetes subtypes and those predicting incident type 2 diabetes. We assessed quality, reproducibility and clinical relevance of extracted full-text articles and qualitatively synthesised a summary of subclassification approaches. RESULTS: Here we show data from 51 studies that demonstrate many simple stratification approaches, but none have been replicated and many are not associated with meaningful clinical outcomes. Complex stratification was reviewed in 62 studies and produced reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes. CONCLUSION: Critical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes.

In people with type 2 diabetes there may be differences in the way people present, including for example, their symptoms, body weight or how much insulin they make. We looked at recent publications describing research in this area to see whether it is possible to separate people with type 2 diabetes into different subgroups and, if so, whether these groupings were useful for patients. We found that it is possible to group people with type 2 diabetes into different subgroups and being in one subgroup can be more strongly linked to the likelihood of developing complications over others. This might mean that in the future we can treat people in different subgroups differently in ways that improves their treatment and their health but it requires further study.

Plasma aryl hydrocarbon receptor associated with epicardial adipose tissue in men: a cross-sectional study.

BACKGROUND: Epicardial adipose tissue (EAT) is a type of ectopic fat with endocrine and paracrine functions. Aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor that responds to environmental stimuli. AhR expression is associated with obesity. In this cross-sectional study, we aimed to determine the relationship between circulating AhR concentrations and EAT. METHODS: A total of 30 men with obesity and 23 age-matched men as healthy controls were enrolled. Plasma AhR concentrations were determined at fasting. The EAT thickness was measured on the free wall of the right ventricle from the basal short-axis plane by magnetic resonance imaging. RESULTS: The participants with obesity had a higher plasma AhR level than the controls (81.0 ± 24.5 vs. 65.1 ± 16.4 pg/mL, P = 0.010). The plasma AhR level was positively correlated with EAT thickness (correlation coefficient = 0.380, P = 0.005). After adjusting for fasting glucose levels, plasma AhR levels were still significantly associated with EAT thickness (95% CI 0.458‒5.357, P = 0.021) but not with body mass index (P = 0.168). CONCLUSION: Plasma AhR concentrations were positively correlated with EAT thickness on the free wall of the right ventricle in men. Further investigations are needed to evaluate the causal effects and underlying mechanisms between AhR and EAT.

A Multi-Ancestry Polygenic Risk Score for Coronary Heart Disease Based on an Ancestrally Diverse Genome-Wide Association Study and Population-Specific Optimization.

Background: Predictive performance of polygenic risk scores (PRS) varies across populations. To facilitate equitable clinical use, we developed PRS for coronary heart disease (PRSCHD) for 5 genetic ancestry groups. Methods: We derived ancestry-specific and multi-ancestry PRSCHD based on pruning and thresholding (PRSP+T) and continuous shrinkage priors (PRSCSx) applied on summary statistics from the largest multi-ancestry genome-wide meta-analysis for CHD to date, including 1.1 million participants from 5 continental populations. Following training and optimization of PRSCHD in the Million Veteran Program, we evaluated predictive performance of the best performing PRSCHD in 176,988 individuals across 9 cohorts of diverse genetic ancestry. Results: Multi-ancestry PRSP+T outperformed ancestry specific PRSP+T across a range of tuning values. In training stage, for all ancestry groups, PRSCSx performed better than PRSP+T and multi-ancestry PRS outperformed ancestry-specific PRS. In independent validation cohorts, the selected multi-ancestry PRSP+T demonstrated the strongest association with CHD in individuals of South Asian (SAS) and European (EUR) ancestry (OR per 1SD[95% CI]; 2.75[2.41-3.14], 1.65[1.59-1.72]), followed by East Asian (EAS) (1.56[1.50-1.61]), Hispanic/Latino (HIS) (1.38[1.24-1.54]), and weakest in African (AFR) ancestry (1.16[1.11-1.21]). The selected multi-ancestry PRSCSx showed stronger associacion with CHD in comparison within each ancestry group where the association was strongest in SAS (2.67[2.38-3.00]) and EUR (1.65[1.59-1.71]), progressively decreasing in EAS (1.59[1.54-1.64]), HIS (1.51[1.35-1.69]), and lowest in AFR (1.20[1.15-1.26]). Conclusions: Utilizing diverse summary statistics from a large multi-ancestry genome-wide meta-analysis led to improved performance of PRSCHD in most ancestry groups compared to single-ancestry methods. Improvement of predictive performance was limited, specifically in AFR and HIS, despite use of one of the largest and most diverse set of training and validation cohorts to date. This highlights the need for larger GWAS datasets of AFR and HIS individuals to enhance performance of PRSCHD.

Systematic review of precision subclassification of type 2 diabetes.

Heterogeneity in type 2 diabetes presentation, progression and treatment has the potential for precision medicine interventions that can enhance care and outcomes for affected individuals. We undertook a systematic review to ascertain whether strategies to subclassify type 2 diabetes are associated with improved clinical outcomes, show reproducibility and have high quality evidence. We reviewed publications that deployed 'simple subclassification' using clinical features, biomarkers, imaging or other routinely available parameters or 'complex subclassification' approaches that used machine learning and/or genomic data. We found that simple stratification approaches, for example, stratification based on age, body mass index or lipid profiles, had been widely used, but no strategy had been replicated and many lacked association with meaningful outcomes. Complex stratification using clustering of simple clinical data with and without genetic data did show reproducible subtypes of diabetes that had been associated with outcomes such as cardiovascular disease and/or mortality. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into meaningful groups. More studies are needed to test these subclassifications in more diverse ancestries and prove that they are amenable to interventions.

Unhealthy lifestyle associated with increased risk of macro- and micro-vascular comorbidities in patients with long-duration type 2 diabetes: results from the Taiwan Diabetes Registry.

BACKGROUND: Unhealthy lifestyle has been associated with obesity and type 2 diabetes. Whereas its association with vascular complications in patients with long-duration of type 2 diabetes is still uncertain. METHODS: A total of 1188 patients with long-duration of type 2 diabetes from the Taiwan Diabetes Registry (TDR) data were analyzed. We stratified the severity of unhealthy lifestyle via scoring three factors (sleep duration <7 or >9 h, sit duration ≥ 8h, and meal numbers ≥ with night snack) and analyzed their associations with the development of vascular complications using logistic regression analysis. Besides, we also included 3285 patients with newly diagnosed type 2 diabetes as the comparison. RESULTS: Increased numbers of factors that stand for unhealthy lifestyle were significantly associated with the development of cardiovascular disease, peripheral arterial occlusion disease (PAOD) and nephropathy in patients with long-duration of type 2 diabetes. After adjusting multiple covariables, having ≥ 2 factors of unhealthy lifestyle remained significant associations with cardiovascular disease and PAOD, with an odds ratio (OR) of 2.09 (95% confidence interval [CI] 1.18-3.69) and 2.68 (95% CI 1.21-5.90), respectively. Among individual factor for unhealthy lifestyle behaviors, we revealed that eating ≥ 4 meals per day with night snack increased the risk of cardiovascular disease and nephropathy after multivariable adjustment (OR of 2.60, 95% CI 1.28-5.30; OR of 2.54, 95% CI 1.52-4.26, respectively). Whereas sit duration for ≥ 8 h per day increased the risk of PAOD (OR of 4.32, 95% CI 2.38-7.84). CONCLUSION: Unhealthy lifestyle is associated with increased prevalence of macro- and micro-vascular comorbidities in Taiwanese patients with long-duration type 2 diabetes.

Association of Major Adverse Cardiac Events and Beta-Blockers in Patients with and without Atherosclerotic Cardiovascular Disease: Long-Term Follow-Up Results of the T-SPARCLE and T-PPARCLE Registry in Taiwan.

Beta-blockers are widely used, but the benefit is now challenged in patients at risk of atherosclerotic cardiovascular disease (ASCVD) in the present coronary reperfusion era. We aimed to identify the risk factors of a major adverse cardiac event (MACE) and the long-term effect of beta-blockers in two large cohorts in Taiwan. Two prospective observational cohorts, including patients with known atherosclerosis cardiovascular disease (T-SPARCLE) and patients with at least one risk factor of ASCVD but without clinically evident ASCVD (T-PPARCLE), were conducted in Taiwan. The primary endpoint is the time of first occurrence of a MACE (cardiovascular death, nonfatal stroke, nonfatal myocardial infarction, and cardiac arrest with resuscitation). Between December 2009 and November 2014, with a median 2.4 years follow-up, 11,747 eligible patients (6921 and 4826 in T-SPARCLE and T-PPARCLE, respectively) were enrolled. Among them, 273 patients (2.3%) met the primary endpoint. With multivariate Cox PH model analysis, usage of beta-blocker was lower in patients with MACE (42.9% vs. 52.4%, p < 0.01). In patients with ASCVD, beta-blocker usage was associated with lower MACEs (hazard ratio 0.72; p < 0.001), but not in patients without ASCVD. The event-free survival of beta-blocker users remained higher during the follow-up period (p < 0.005) of ASCVD patients. In conclusion, in ASCVD patients, reduced MACE was associated with beta-blocker usage, and the effect was maintained during a six-year follow-up. Prescribing beta-blockers as secondary prevention is reasonable in the Taiwanese population.

Prediction of the risk of developing end-stage renal diseases in newly diagnosed type 2 diabetes mellitus using artificial intelligence algorithms.

OBJECTIVES: Type 2 diabetes mellitus (T2DM) imposes a great burden on healthcare systems, and these patients experience higher long-term risks for developing end-stage renal disease (ESRD). Managing diabetic nephropathy becomes more challenging when kidney function starts declining. Therefore, developing predictive models for the risk of developing ESRD in newly diagnosed T2DM patients may be helpful in clinical settings. METHODS: We established machine learning models constructed from a subset of clinical features collected from 53,477 newly diagnosed T2DM patients from January 2008 to December 2018 and then selected the best model. The cohort was divided, with 70% and 30% of patients randomly assigned to the training and testing sets, respectively. RESULTS: The discriminative ability of our machine learning models, including logistic regression, extra tree classifier, random forest, gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine were evaluated across the cohort. XGBoost yielded the highest area under the receiver operating characteristic curve (AUC) of 0.953, followed by extra tree and GBDT, with AUC values of 0.952 and 0.938 on the testing dataset. The SHapley Additive explanation summary plot in the XGBoost model illustrated that the top five important features included baseline serum creatinine, mean serum creatine within 1 year before the diagnosis of T2DM, high-sensitivity C-reactive protein, spot urine protein-to-creatinine ratio and female gender. CONCLUSIONS: Because our machine learning prediction models were based on routinely collected clinical features, they can be used as risk assessment tools for developing ESRD. By identifying high-risk patients, intervention strategies may be provided at an early stage.

Toward better diabetes care: Exploration and implementation.

Approximately 40 years ago, I was fortunate enough to step into the field of diabetes. When I had my fellowship training in the USA, I learned how to ask a good scientific question and conduct clinical research. With collaboration with my mentors, Prof. Gerald R Reaven and Prof. Ida Chen, we participated in many clinical trials. We established the Taiwan Diabetes Registry (TDR) to track long-term changes in diabetes profiles. The ultimate purpose of medical research is to provide benefits to patients. Using electronic medical records and point-of-care glucometers, we reduced inpatients' hyperglycemia and hypoglycemia greatly, which was also reflected by the reduction in hospital stays and readmission rates. With the advent of new technology and medications, we have to ponder where we are on the journey toward better diabetes care. We rigorously advocate diabetes care, hold many symposia and publish updated guidelines. We successfully hosted the congress of the 11th International Diabetes Federation Western Pacific Region & 8th Asian Association for the Study of Diabetes Scientific Meeting 2016 at Taipei, Taiwan. As the era of precision medicine is coming, Taiwan could be considered as one of the best places to run precision medicine. The Taiwan Precision Medicine Initiative has enrolled more than half a million residents, and is currently conducting genotyping and data analysis. In conclusion. I witnessed the early days of simple and few choices for diabetes management to the current various modalities in diabetes care. As these new technologies have become available, patients will always remain at the center of the care model with warmth and humility.

Brain-derived neurotrophic factor associated with kidney function.

BACKGROUND: We examined the relationship between brain-derived neurotrophic factor (BDNF) and chronic kidney disease (CKD). METHODS: First, a cross-sectional study was conducted in 480 participants without known diabetes. An oral glucose tolerance test (OGTT) was administered after overnight fasting, and blood samples were collected at 0, 30, and 120 min. Second, a total of 3003 participants were enrolled for the case-control genetic analysis. After assigning them to a case or a control group based on age and CKD status, we investigated the association between BDNF gene variants and susceptibility to CKD. RESULTS: A higher fasting serum BDNF quartile was significantly associated with a lower prevalence of CKD (P value for trend < 0.001). Based on the receiver operating characteristic analysis, the fasting BDNF level had a larger area under the curve for differentiating CKD (0.645, 95% CI 0.583‒0.707) than the BDNF levels at both 30 min (0.547, 95% CI 0.481‒0.612) and 120 min (0.598, 95% CI 0.536‒0.661). A significantly lower CKD prevalence (odds ratio = 0.30, 95% CI 0.12‒0.71) was observed in the highest quartile of fasting BDNF level than that in the lowest quartile, whereas no interquartile differences were observed for BDNF levels determined at 30 or 120 min during the OGTT. Furthermore, BDNF-associated variants, including rs12098908, rs12577517, and rs72891405, were significantly associated with CKD. CONCLUSIONS: The BDNF level at fasting, but not at 30 and 120 min after glucose intake, was an independent indicator of CKD. In addition, significant associations were observed between three BDNF gene variants and CKD.

Guideline Development for Medical Device Technology: Issues for Consideration.

Advances in the development of innovative medical devices and telehealth technologies create the potential to improve the quality and efficiency of diabetes care through collecting, aggregating, and interpreting relevant health data in ways that facilitate more informed decisions among all stakeholder groups. Although many medical societies publish guidelines for utilizing these technologies in clinical practice, we believe that the methodologies used for the selection and grading of the evidence should be revised. In this article, we discuss the strengths and limitations of the various types of research commonly used for evidence selection and grading and present recommendations for modifying the process to more effectively address the rapid pace of device and technology innovation and new product development.

Aryl hydrocarbon receptor deficiency augments dysregulated microangiogenesis and diabetic retinopathy.

Diabetic retinopathy (DR) is a pathophysiologic vasculopathic process with obscure mechanisms and limited effective therapeutic strategies. Aryl hydrocarbon receptor (AhR) is an important regulator of xenobiotic metabolism and an environmental sensor. The aim of the present study was to investigate the role of AhR in the development of DR and elucidate the molecular mechanism of its downregulation. DR was evaluated in diabetes-induced retinal injury in wild type and AhR knockout (AhR-/-) mice. Retinal expression of AhR was determined in human donor and mice eyes by immunofluorescence since AhR activity was examined in diabetes. AhR knockout (AhRKO) mice were used to induce diabetes with streptozotocin, high-fat diet, or genetic double knockout with diabetes spontaneous mutation (Leprdb) (DKO; AhR-/-×Leprdb/db) for investigating structural, functional, and metabolic abnormalities in vascular and epithelial retina. Structural molecular docking simulation was used to survey the pharmacologic AhR agonists targeting phosphorylated AhR (Tyr245). Compared to diabetic control mice, diabetic AhRKO mice had aggravated alterations in retinal vasculature that amplified hallmark features of DR like vasopermeability, vascular leakage, inflammation, blood-retinal barrier breakdown, capillary degeneration, and neovascularization. AhR agonists effectively inhibited inflammasome formation and promoted AhR activity in human retinal microvascular endothelial cells and pigment epithelial cells. AhR activity and protein expression was downregulated, resulting in a decrease in DNA promoter binding site of pigment epithelium-derived factor (PEDF) by gene regulation in transcriptional cascade. This was reversed by AhR agonists. Our study identified a novel of DR model that target the protective AhR/PEDF axis can potentially maintain retinal vascular homeostasis, providing opportunities to delay the development of DR.

Effect of 1PC111, a Fixed-dose Combination of Pitavastatin and Ezetimibe, Versus Pitavastatin or Ezetimibe Monotherapy on Lipid Profiles in Patients With Hypercholesterolemia or Mixed Dyslipidemia: A Randomized, Double-blind, Multicenter, Phase III Study.

PURPOSE: This study aimed to show that the efficacy of 1PC111 is superior to that of either ezetimibe or pitavastatin alone (monotherapy) for the treatment of hypercholesterolemia. METHODS: This was a multicenter, randomized, double-blind, Phase III study. Patients with hypercholesterolemia or mixed dyslipidemia were randomized to receive 1PC111 (which was a fixed-dose combination of pitavastatin 2 mg and ezetimibe 10 mg), pitavastatin 2 mg, or ezetimibe 10 mg daily for 12 weeks. The primary end point was the difference in the percent change in LDL-C from baseline to week 12 between the 1PC111 and each monotherapy group. The secondary end points were the percent change in other lipid profiles from baseline to each visit. All patients were assessed for adverse events until end of study. FINDINGS: A total of 388 patients were randomly assigned to the 1PC111 (n = 128), pitavastatin (n = 132), or ezetimibe (n = 128) group. Generally, baseline characteristics were similar among the 3 groups. A statistically significant decrease in the LDL-C level at week 12 was observed in the 1PC111 group (-50.50% [14.9%]) compared with either the pitavastatin (-36.11% [11.4%]; P < 0.001) or ezetimibe (-19.85% [12.4%]; P < 0.001) group. Also, there was a statistically significant difference between 1PC111 and each monotherapy group in the reduction in total cholesterol, non-HDL-C, and apolipoprotein B levels. Moreover, there was a trend toward more efficient lowering of LDL-C levels in elderly patients (age ≥65 years) than in younger patients (age <65 years) by 1PC111 treatment. In patients given a class I recommendation for atherosclerotic cardiovascular disease prevention, the percentage of patients achieving the LDL-C target of <100 mg/dL at week 12 was significantly higher in the 1PC111 group than in both monotherapy groups (P < 0.001). Overall, the incidence of adverse events was similar among 3 groups. IMPLICATIONS: 1PC111 was more effective in improving lipid profiles and achieving the LDL-C goal than pitavastatin or ezetimibe alone for hypercholesterolemia treatment. Furthermore, 1PC111 may provide more benefit in treating elderly patients. CLINICALTRIALS: gov identifier: NCT04643093.

A Randomized Controlled Trial of R-Form Verapamil Added to Ongoing Metformin Therapy in Patients with Type 2 Diabetes.

CONTEXT: There is a medical need for effective insulin-independent antidiabetic drugs that can promote pancreatic ß-cell function and have a low risk of hypoglycemia in type 2 diabetes mellitus (T2DM) patients. R-form verapamil (R-Vera), which is able to enhance the survival of ß-cells and has higher cardiovascular safety margin compared with racemic verapamil, was developed as a novel approach for T2DM treatment. OBJECTIVE: This randomized, double-blind, placebo-controlled clinical trial was designed to evaluate the efficacy and safety of 3 dosages of R-Vera added to ongoing metformin therapy in T2DM patients who had inadequate glycemic control on metformin alone. METHODS: Participants were randomly assigned in an equal ratio to receive R-Vera 450, 300, or 150 mg per day, or matching placebo, in combination with metformin. The primary endpoint was change in hemoglobin A1c (HbA1c) after 12 weeks of treatment. RESULTS: A total of 184 eligible participants were randomized to receive either R-Vera or placebo plus metformin. At week 12, significant reductions in HbA1c were observed for R-Vera 300 mg/day (-0.36, P = 0.0373) and 450 mg/day (-0.45, P = 0.0098) compared with placebo. The reduction in HbA1c correlated with decreasing fasting plasma glucose levels and improved HOMA2-ß score. Treatment with R-Vera was well tolerated with no hypoglycemic episodes occurring during the trial. CONCLUSION: Addition of R-Vera twice daily to ongoing metformin therapy significantly improved glycemic control in T2DM patients. The favorable efficacy and safety profile of R-Vera 300 mg/day can be considered as the appropriate dose for clinical practice.

Brain-Derived Neurotrophic Factor Reduces Long-Term Mortality in Patients With Coronary Artery Disease and Chronic Kidney Disease.

Objectives: Chronic kidney disease (CKD) is a risk factor for coronary artery disease (CAD). We examined the effects of circulating brain-derived neurotrophic factor (BDNF) on long-term mortality in patients with CAD and CKD. Materials and Methods: We enrolled patients with established CAD in the present study. Serum BDNF and estimated glomerular filtration rate (eGFR) were assessed after overnight fasting. All-cause mortality served as the primary endpoint. Results: All 348 enrolled patients were divided into four groups according to their median BDNF level and CKD status, defined according to eGFR <60 mL/min/1.73 m2. Forty-five patients reached the primary endpoint during the median follow-up time of 6.0 years. Kaplan-Meier survival analysis indicated that the group with low BDNF and CKD had a significantly higher mortality rate than the other three groups (log-rank test p < 0.001). Compared to the high BDNF without CKD group, the low BDNF with CKD group had a hazard ratio (HR) of 3.186 [95% confidence interval (CI): 1.482-6.846] for all-cause mortality according to the multivariable Cox proportional hazard regression analysis after adjusting for age and urine albumin-creatinine ratio (p = 0.003). Furthermore, there was a significantly interactive effect between BDNF and CKD status on the risk of the primary endpoint (odds ratio = 6.413, 95% CI: 1.497-27.47 in the multivariable logistic regression model and HR = 3.640, 95% CI: 1.006-13.173 in the Cox regression model). Conclusion: We observed a synergistic effect between low serum BDNF levels and CKD on the prediction of all-cause mortality in patients with CAD.