Effects of Dietary Red Raspberry Consumption on Pre-Diabetes and Type 2 Diabetes Mellitus Parameters.
Stefani A DerrickAleksandra S KristoScott K ReavesAngelos K SikalidisPublished in: International journal of environmental research and public health (2021)
Type 2 diabetes mellitus (T2DM) is a chronic metabolic condition characterized by glucose clearance abnormalities and insufficient insulin response. Left uncontrolled, T2DM can result in serious complications and death. With no cure available currently and the prevalence of major risk factors such as pre-diabetes and the metabolic syndrome continuously increasing, there is an urgent need for effective treatments with limited or no side effects. Red raspberries (RR) contain various phytonutrients with potential for modulating insulin function, glucose, and lipid metabolism. The objective of this literature review was to investigate the potential metabolic benefits of dietary RR in individuals with T2DM and pre-diabetes. A search of major scientific databases was employed to identify peer-reviewed, in vivo, or human studies that utilized whole RR or its functional constituents as treatment. The studies examined provide evidence that RR may offer clinically beneficial effects for the prevention and management of chronic diseases through improvements in glucose handling and insulin sensitivity, adiposity, lipid profiles, ectopic lipid accumulation, inflammation, oxidative stress, and cardiac health. More human trials and in vivo studies are needed to confirm the benefits of dietary RR in T2DM and pre-diabetes and to explore the dose-dependent relationships, optimal duration, and treatment modality.
Keyphrases
- glycemic control
- blood glucose
- type diabetes
- risk factors
- insulin resistance
- oxidative stress
- metabolic syndrome
- endothelial cells
- weight loss
- healthcare
- cardiovascular disease
- public health
- case control
- induced pluripotent stem cells
- mental health
- blood pressure
- adipose tissue
- case report
- left ventricular
- fatty acid
- cardiovascular risk factors
- human health
- social media
- physical activity
- pluripotent stem cells
- health information
- dna damage
- body mass index
- machine learning
- atrial fibrillation
- deep learning