Effects of anthocyanin supplementation in diet on glycemic and related cardiovascular biomarkers in patients with type 2 diabetes: a systematic review and meta-analysis of randomized controlled trials

The Impact of Cornelian Cherry (Cornus mas L.) on Cardiometabolic Risk Factors: A Meta-Analysis of Randomised Controlled Trials

This meta-analysis aimed to summarise clinical evidence regarding the effect of supplementation with cornelian cherry (Cornus mas L.) on different cardiometabolic outcomes. An extensive literature survey was carried out until 10 April 2024. A total of 415 participants from six eligible studies were included. The overall results from the random-effects model indicated that cornelian cherry supplementation significantly reduced body weight (standardised mean difference [SMD] = -0.27, confidence interval [CI]: -0.52, -0.02, p = 0.03), body mass index (SMD = -0.42, CI: -0.73, -0.12, p = 0.007), fasting blood glucose (SMD = -0.46, CI: -0.74, -0.18, p = 0.001), glycated haemoglobin (SMD = -0.70, CI: -1.19, -0.22, p = 0.005), and HOMA-IR (SMD = -0.89, CI: -1.62, -0.16, p = 0.02), while high-density lipoprotein cholesterol significantly increased (SMD = 0.38, CI: 0.10, 0.65, p = 0.007). A sensitivity analysis showed that cornelian cherry supplementation significantly reduced total plasma triglycerides, total cholesterol, low-density lipoprotein cholesterol, and insulin levels. Cornelian cherry supplementation did not significantly affect waist circumference and liver parameters among the participants. Considering these findings, this meta-analysis indicates that supplementation with cornelian cherry may impact diverse cardiometabolic risk factors among individuals considered to be at a high risk.

Effects of Ellagic Acid on Glucose and Lipid Metabolism: A Systematic Review and Meta-Analysis

Background

Abnormal glucose and lipid metabolism (GALM) serve as both a cause and an inducer for the development of the disease. Improvement and treatment of GALM are an important stage to prevent the occurrence and development of the disease. However, current clinical treatment for GALM is limited. Ellagic acid (EA), a common polyphenol present in foods, has been shown to improve abnormalities in GALM observed in patients suffering from metabolic diseases.

Objective

This study used a meta-analysis method to systematically assess the effects of EA on GALM.

Method

As of November 8, 2023, a comprehensive search was conducted across 5 databases, namely, PubMed, Embase, Web of Science, Cochrane Library, and Google Scholar to identify randomized controlled trials (RCTs) in which EA served as the primary intervention for diseases related to GALM. The risk of bias within the included studies was assessed according to the Cochrane Handbook. All statistical analyzes were performed using RevMan 5.4 software.

Results

In this study, a total of 482 articles were retrieved, resulting in the inclusion of 10 RCTs in the meta-analysis. The results showed that EA could reduce fasting blood glucose (FBG) (p = 0.008), increase insulin secretion (p = 0.01), improve insulin resistance index (HOMA-IR) (p = 0.003), decrease triglyceride (TG) (p = 0.004), and reduce cholesterol (Chol) (p = 0.04) and low-density lipoprotein (LDL-c) (p = 0.0004). EA had no significant effect on waist circumference (WC), body weight (BW), body mass index (BMI), 2 hours after prandial blood glucose (2 h-PG), total cholesterol (TC), and high-density lipoprotein (HDL-c).

Conclusions

The effect of improvement in glucose and lipids of EA was closely related to the dose and the intervention time. EA can improve GALM caused by diseases. To corroborate the findings of this study and improve the reliability of the results, EA is imperative to refine the research methodology and increase the sample size in future investigations.

Comparative Metabolic Profiling of Different Colored Rice Grains Reveals the Distribution of Major Active Compounds and Key Secondary Metabolites in Green Rice

Pigmented rice grains are important resources for health and nutritional perspectives. Thus, a thorough dissection of the variation of nutrients and bioactive metabolites in different colored rice is of global interest. This study applied LC-MS-based widely targeted metabolite profiling and unraveled the variability of metabolites and nutraceuticals in long grain/non-glutinous black (BR), red (RR), green (GR), and white rice (WR) grains. We identified and classified 1292 metabolites, including five flavonoid compounds specific to BR. The metabolite profiles of the four rice grains showed significant variation, with 275-543 differentially accumulated metabolites identified. Flavonoid (flavone, flavonol, and anthocyanin) and cofactor biosynthesis were the most differentially regulated pathways among the four rice types. Most bioactive flavonoids, anthocyanidins (glycosylated cyanidins and peonidins), phenolic acids, and lignans had the highest relative content in BR, followed by RR. Most alkaloids, amino acids and derivatives, lipids, and vitamins (B6, B3, B1, nicotinamide, and isonicotinic acid) had higher relative contents in GR than others. Procyanidins (B1, B2, and B3) had the highest relative content in RR. In addition, we identified 25 potential discriminatory biomarkers, including fagomine, which could be used to authenticate GR. Our results show that BR and RR are important materials for medicinal use, while GR is an excellent source of nutrients (amino acids and vitamins) and bioactive alkaloids. Moreover, they provide data resources for the science-based use of different colored rice varieties in diverse industries.

Anthocyanins and Type 2 Diabetes: An Update of Human Study and Clinical Trial

Anthocyanins are phenolic compounds occurring in fruits and vegetables. Evidence from pre-clinical studies indicates their role in glucose level regulation, gut microbiota improvement, and inflammation reduction under diabetic conditions. Therefore, incorporating these research advancements into clinical practice would significantly improve the prevention and management of type 2 diabetes. This narrative review provides a concise overview of 18 findings from recent clinical research published over the last 5 years that investigate the therapeutic effects of dietary anthocyanins on diabetes. Anthocyanin supplementation has been shown to have a regulatory effect on fasting blood glucose levels, glycated hemoglobin, and other diabetes-related indicators. Furthermore, increased anthocyanin dosages had more favorable implications for diabetes treatment. This review provides evidence that an anthocyanin-rich diet can improve diabetes outcomes, especially in at-risk groups. Future research should focus on optimal intervention duration, consider multiple clinical biomarkers, and analyze anthocyanin effects among well-controlled versus poorly controlled groups of patients with diabetes.

Association of long-term triglyceride-glucose index patterns with the incidence of chronic kidney disease among non-diabetic population: evidence from a functional community cohort

BACKGROUND

The triglyceride-glucose (TyG) index is a reliable surrogate marker of insulin resistance and previous studies have confirmed the association of TyG index with incident chronic kidney disease (CKD). However, the impact of longitudinal patterns of TyG index on CKD risk among non-diabetic population is still unknown. Therefore, this study aimed to investigate the association of longitudinal patterns of TyG index with incident CKD among non-diabetic population.

METHODS

A total of 5484 non-diabetic participants who underwent one health examination per year from 2015 to 2017 were included in this prospective study. TyG index variability and cumulative TyG index were calculated to assess the longitudinal patterns of TyG index. Cox proportional hazard models were performed to estimate the association of TyG index variability or cumulative TyG index with incident CKD.

RESULTS

During a median of 3.82 years follow-up, 879 participants developed CKD. Compared with participants in the lowest quartile, the hazard ratio (HR) and 95% confidence interval (CI) of incident CKD were 1.772 (95% CI: 1.453, 2.162) for the highest TyG index variability quartile and 2.091 (95% CI: 1.646, 2.655) for the highest cumulative TyG index quartile in the fully adjusted models. The best discrimination and reclassification improvement were observed after adding baseline TyG, TyG index variability and cumulative TyG index to the clinical risk model for CKD.

CONCLUSIONS

Both TyG index variability and cumulative TyG index can independently predict incident CKD among non-diabetic population. Monitoring longitudinal patterns of TyG index may assist with prediction and prevention of incident CKD.