Continuous glucose monitoring to measure metabolic impact and recovery in sub-elite endurance athletes

Continuous Glucose Monitoring in Endurance Athletes: Interpretation and Relevance of Measurements for Improving Performance and Health

Blood glucose regulation has been studied for well over a century as it is intimately related to metabolic health. Research in glucose transport and uptake has also been substantial within the field of exercise physiology as glucose delivery to the working muscles affects exercise capacity and athletic achievements. However, although exceptions exist, less focus has been on blood glucose as a parameter to optimize training and competition outcomes in athletes with normal glucose control. During the last years, measuring glucose has gained popularity within the sports community and successful endurance athletes have been seen with skin-mounted sensors for continuous glucose monitoring (CGM). The technique offers real-time recording of glucose concentrations in the interstitium, which is assumed to be equivalent to concentrations in the blood. Although continuous measurements of a parameter that is intimately connected to metabolism and health can seem appealing, there is no current consensus on how to interpret measurements within this context. Well-defined approaches to use glucose monitoring to improve endurance athletes' performance and health are lacking. In several studies, blood glucose regulation in endurance athletes has been shown to differ from that in healthy controls. Furthermore, endurance athletes regularly perform demanding training sessions and can be exposed to high or low energy and/or carbohydrate availability, which can affect blood glucose levels and regulation. In this current opinion, we aim to discuss blood glucose regulation in endurance athletes and highlight the existing research on glucose monitoring for performance and health in this population.

Low carbohydrate high fat ketogenic diets on the exercise crossover point and glucose homeostasis

In exercise science, the crossover effect denotes that fat oxidation is the primary fuel at rest and during low-intensity exercise with a shift towards an increased reliance on carbohydrate oxidation at moderate to high exercise intensities. This model makes four predictions: First, >50% of energy comes from carbohydrate oxidation at ≥60% of maximum oxygen consumption (VO2max), termed the crossover point. Second, each individual has a maximum fat oxidation capacity (FATMAX) at an exercise intensity lower than the crossover point. FATMAX values are typically 0.3–0.6 g/min. Third, fat oxidation is minimized during exercise ≥85%VO2max, making carbohydrates the predominant energetic substrate during high-intensity exercise, especially at >85%VO2max. Fourth, high-carbohydrate low-fat (HCLF) diets will produce superior exercise performances via maximizing pre-exercise storage of this predominant exercise substrate. In a series of recent publications evaluating the metabolic and performance effects of low-carbohydrate high-fat (LCHF/ketogenic) diet adaptations during exercise of different intensities, we provide findings that challenge this model and these four predictions. First, we show that adaptation to the LCHF diet shifts the crossover point to a higher %VO2max (>80%VO2max) than previously reported. Second, substantially higher FATMAX values (>1.5 g/min) can be measured in athletes adapted to the LCHF diet. Third, endurance athletes exercising at >85%VO2max, whilst performing 6 × 800 m running intervals, measured the highest rates of fat oxidation yet reported in humans. Peak fat oxidation rates measured at 86.4 ± 6.2%VO2max were 1.58 ± 0.33 g/min with 30% of subjects achieving >1.85 g/min. These studies challenge the prevailing doctrine that carbohydrates are the predominant oxidized fuel during high-intensity exercise. We recently found that 30% of middle-aged competitive athletes presented with pre-diabetic glycemic values while on an HCLF diet, which was reversed on LCHF. We speculate that these rapid changes between diet, insulin, glucose homeostasis, and fat oxidation might be linked by diet-induced changes in mitochondrial function and insulin action. Together, we demonstrate evidence that challenges the current crossover concept and demonstrate evidence that a LCHF diet may also reverse features of pre-diabetes and future metabolic disease risk, demonstrating the impact of dietary choice has extended beyond physical performance even in athletic populations.

High-Fat Diet Aggravates the Disorder of Glucose Metabolism Caused by Chlorpyrifos Exposure in Experimental Rats

Epidemiological research has demonstrated that the increase in high fat consumption has promoted the morbidity of diabetes. Exposure to organophosphorus pesticides (such as chlorpyrifos) may also increase the risk of diabetes. Although chlorpyrifos is a frequently detected organophosphorus pesticide, the interaction effect between chlorpyrifos exposure and a high-fat diet on glucose metabolism is still unclear. Thus, the effects of chlorpyrifos exposure on glucose metabolism in rats eating a normal-fat diet or a high-fat diet were investigated. The results demonstrated that the glycogen content in the liver decreased and that the glucose content increased in chlorpyrifos-treated groups. Remarkably, the ATP consumption in the chlorpyrifos-treatment group was promoted in the rats eating a high-fat diet. However, chlorpyrifos treatment did not change the serum levels of insulin and glucagon. Notably, the contents of liver ALT and AST changed more significantly in the high-fat chlorpyrifos-exposed group than in the normal-fat chlorpyrifos-exposed group. Chlorpyrifos exposure caused an increase in the liver MDA level and a decrease in the enzyme activities of GSH-Px, CAT, and SOD, and the changes were more significant in the high-fat chlorpyrifos-treatment group. The results indicated that chlorpyrifos exposure led to disordered glucose metabolism in all dietary patterns as a result of antioxidant damage in the liver, in which a high-fat diet may have aggravated its toxicity.

Exercise fatigue diagnosis method based on short-time Fourier transform and convolutional neural network

Reasonable exercise is beneficial to human health. However, it is difficult for ordinary athletes to judge whether they are already in a state of fatigue that is not suitable for exercise. In this case, it is easy to cause physical damage or even life-threatening. Therefore, to health sports, protecting the human body in sports not be injured by unreasonable sports, this study proposes an exercise fatigue diagnosis method based on short-time Fourier transform (STFT) and convolutional neural network (CNN). The method analyzes and diagnoses the real-time electrocardiogram, and obtains whether the current exerciser has exercise fatigue according to the electrocardiogram. The algorithm first performs short-time Fourier transform on the electrocardiogram (ECG) signal to obtain the time spectrum of the signal, which is divided into training set and validation set. The training set is then fed into the convolutional neural network for learning, and the network parameters are adjusted. Finally, the trained convolutional neural network model is applied to the test set, and the recognition result of fatigue level is output. The validity and feasibility of the method are verified by the ECG experiment of exercise fatigue degree. The experimental recognition accuracy rate can reach 97.70%, which proves that the constructed sports fatigue diagnosis model has high diagnostic accuracy and is feasible for practical application.

The Recognition Method of Athlete Exercise Intensity Based on ECG and PCG

Athletes usually arrange their training plans and determine their training intensity according to the coach's experience and simple physical indicators such as heart rate during exercise. However, the accuracy of this method is poor, and the training plan and exercise intensity arranged according to this method can easily cause physical damage, or the training cannot meet the actual needs. Therefore, in order to realize the reasonable arrangement and monitoring of athletes' training, a method of human exercise intensity recognition based on ECG (electrocardiogram) and PCG (Phonocardiogram) is proposed. First, the ECG and PCG signals are fused into a two-dimensional image, and the dataset is marked and divided according to the different motion intensities. Then, the training set is trained with a CNN (convolutional neural network) to obtain the prediction model of the neural network. Finally, the neural network model is used to identify the ECG and PCG signals to judge the exercise intensity of the athlete, so as to adjust the training plan according to the exercise intensity. The recognition accuracy of the model on the dataset can reach 95.68%. Compared with the use of heart rate to detect the physical state during exercise, ECG records the total potential changes in the process of depolarization and repolarization of the heart, and PCG records the waveform of the beating sound of the heart, which contains richer feature information. Combined with the CNN method, the athlete's exercise intensity prediction model constructed by extracting the features of the athlete's ECG and PCG signals realizes the real-time monitoring of the athlete's exercise intensity and has high accuracy and generalization ability.

Continuous Glucose Monitoring in Healthy Adults—Possible Applications in Health Care, Wellness, and Sports

Introduction: Continuous glucose monitoring (CGM) systems were primarily developed for patients with diabetes mellitus. However, these systems are increasingly being used by individuals who do not have diabetes mellitus. This mini review describes possible applications of CGM systems in healthy adults in health care, wellness, and sports. Results: CGM systems can be used for early detection of abnormal glucose regulation. Learning from CGM data how the intake of foods with different glycemic loads and physical activity affect glucose responses can be helpful in improving nutritional and/or physical activity behavior. Furthermore, states of stress that affect glucose dynamics could be made visible. Physical performance and/or regeneration can be improved as CGM systems can provide information on glucose values and dynamics that may help optimize nutritional strategies pre-, during, and post-exercise. Conclusions: CGM has a high potential for health benefits and self-optimization. More scientific studies are needed to improve the interpretation of CGM data. The interaction with other wearables and combined data collection and analysis in one single device would contribute to developing more precise recommendations for users.