Skeletal muscle mechanisms contributing to improved glycemic control following intense interval exercise and training

How Can Promoting Skeletal Muscle Health and Exercise in Children and Adolescents Prevent Insulin Resistance and Type 2 Diabetes?

Skeletal muscle secretome, through its paracrine and endocrine functions, contributes to the maintenance and regulation of overall physiological health. We conducted a narrative review on the role of skeletal muscle and exercise in maintaining glucose homeostasis, driving insulin resistance (IR), and preventing type 2 diabetes in pediatric populations, especially in the context of overweight and obesity. Myokines such as interleukin (IL)-6, IL-8, and IL-15, as well as irisin, myonectin, and myostatin, appear to play a crucial role in IR. Skeletal muscle can also become a target of obesity-induced and IR-induced inflammation. In the correlation between muscle, IR, and inflammation, the role of infiltration of the immune cells and the microvasculature may also be considered. It remains unclear which exercise approach is the best; however, combining aerobic exercise with resistance training seems to be the most effective strategy for managing IR, with high-intensity activities offering superior metabolic benefits and long-term adherence. Encouraging daily participation in enjoyable and engaging exercise is key for long-term commitment and effective glucose metabolism management. Promoting physical activity in children and adolescents must be a top priority for public health, not only in terms of individual quality of life and well-being but also for community health.

High-Intensity Interval Training Decreases Circulating HMGB1 in Individuals with Insulin Resistance; Plasma Lipidomics Identifies Associated Cardiometabolic Benefits

Background: Exercise is a fundamental primary standard of care for cardiometabolic health. Body Weight (BW) High-Intensity Interval Training (HIIT) is an effective strategy for reducing cardiometabolic markers in individuals with insulin resistance and Type-2 diabetes (T2D). High-mobility group box 1 (HMGB1), a ubiquitous nuclear factor, plays an ample role beyond an alarmin in T2D development and progression. Our group has described this novel role previously, showing the beneficial effect of whole body HMGB1 silencing in decreasing hyperglycemia in diabetic mice. In the present study we tested the hypothesis that BW-HIIT as an effective exercise training modality will decrease cardiometabolic risk with a concomitant decrease in circulating HMGB1 more prominently in insulin resistant individuals compared to non-insulin resistant individuals contrasting to what we can evidence in a preclinical murine model of insulin resistance; Methods: Human and mouse pre- and post-exercise serum/plasma samples were analyzed for Lipidomics as well as Metabolic and Cytokine Multiplex assays. Standard of care, as well as cardiometabolic parameters, was also performed in human subjects; Results: insulin resistant individuals had the most positive effect, primarily with a decrease in the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR). as an index of insulin resistance as well as decreased HMGB1 post-exercise. Lipidomic analysis illustrated the highly beneficial effect of exercise training using a modified HIIT program, showing an enhanced panel of circulating lipids post-exercise exclusively in insulin resistant individuals. Plasma multiplex revealed significant translational heterogeneity in our studies with distinct metabolic hormone responses to exercise conditioning with a decrease in inflammatory markers in insulin resistant individuals; Conclusions: The current study demonstrated that 6-week BW-HIIT training improves cardiometabolic, anti-inflammatory markers, metabolic hormones, and insulin sensitivity in humans, strongly associated with decreased circulating HMGB1. Overall, these experiments reinforce the potential of HMGB1 as a marker of changes in insulin resistance and the positive effect of exercise training on insulin resistance possibly preventing the development of T2D and associated complications.

Effect of eight-week high-intensity interval training versus moderate-intensity continuous training programme on body composition, cardiometabolic risk factors in sedentary adolescents

Objectives

This study aimed to assess and compare the effect of an 8-week high-intensity interval training (HIIT) or moderate-intensity continuous training (MICT) programme on body composition and cardiovascular metabolic outcomes of sedentary adolescents in China.

Methods

Eighteen sedentary normal-weight adolescents (age: 18.5 ± 0.3 years, 11 females) were randomized into three groups. HIIT group protocol consisted of three sessions/week for 8-week of "all out" sprints to reach 85%-95% of HRmax, and MICT group protocol undertook three sessions/week for 8-week of continuous running to reach 65%-75% of HRmax. The control group resumed normal daily activities without any intervention. Blood pressure and body composition were measured, and fasting blood samples were obtained at baseline and 48 h post-trial. Mixed-design ANOVA analysis was employed followed by post hoc t-tests and Bonferroni alpha-correction was used to evaluate interaction, between-group, and within-group differences, respectively.

Results

Results indicated that HIIT and MICT similarly affected body fat mass (p = 0.021, ES = 0.19; p = 0.016, ES = 0.30, respectively), body fat percentage (p = 0.037, ES = 0.17; p = 0.041, ES = 0.28, respectively), visceral fat area (p = 0.001, ES = 0.35; p = 0.003, ES = 0.49, respectively) of body composition. A positive outcome was observed for waist/hip ratio (p = 0.033, ES = 0.43) in HIIT, but not MICT (p = 0.163, ES = 0.33). No significant differences were found between groups for any clinical biomarkers. However, pairwise comparison within the group showed a significant decrease in systolic blood pressure (p = 0.018, ES = 0.84), diastolic blood pressure (p = 0.008, ES = 1.76), and triglyceride (p = 0.004, ES = 1.33) in HIIT, but no significant differences were found in the MICT and Control group.

Conclusion

Both 8-week HIIT and MICT programmes have similar positive effects on reducing body fat mass, fat percentage, and visceral fat area. However, sedentary adolescents may have limited scope to decrease insulin resistance after these 8-week interventions. Notably, the 8-week HIIT intervention was highly effective in increasing cardiometabolic health compared to the MICT. The exercise intensity threshold value and metabolic outcomes of high-intensity interval sprints should be explored further to extend the long-term benefit in this cohort.

Interactive effects of low-volume interval exercise and nutrition on glycemic control

Low-volume interval training has been demonstrated to improve indices of 24 h glycemic control using continuous glucose monitoring in individuals with or at risk for metabolic diseases. Nonetheless, there are inconsistencies in the literature with respect to the effects of interval exercise on 24 h glycemia, which may partly result from different nutritional conditions and/or controls adopted across various studies. This current opinion aims to provide a concise overview of the effects of acute and chronic interval exercise on 24 h glycemic control, while also describing how nutrition can influence and modify these responses. Given the distinct impact of dietary intake on blood glucose regulation, the adoption of diverse dietary control strategies during measurement of 24 h glycemia-spanning from using the participant's habitual diet to providing standardized meals customized to individual energy requirements-may contribute to varying conclusions across studies regarding the influence of interval exercise on 24 h glycemia. In addition, nutritional manipulations surrounding exercise, including whether interval exercise commences in the fasted or fed state, the macronutrient composition of post-exercise meals, and the presence of an energy and/or carbohydrate deficit among participants, offer important context when considering the effects of interval exercise on 24 h glycemia. Additional well-controlled studies are warranted to explore the interactive effects of interval exercise and nutrition on 24 h glycemia. These efforts will assist in refining exercise and nutrition recommendations aimed at improving glycemic control.

Household food insecurity among child and adolescent athletics practitioners: A cross-sectional, descriptive, and exploratory study

OBJECTIVE

The aim of the study was to explore the prevalence of food insecurity among child and adolescent athletics practitioners and to investigate factors associated with exercise performance, dietary habits, body composition, sleep, and socioeconomic status based on food security status.

METHODS

This was a cross-sectional, descriptive, and exploratory study conducted in Campinas, São Paulo, Brazil, between June and July 2023. The convenience sample included children and adolescents (7-17 y old) of both sexes. We evaluated exercise performance, household food insecurity (HFI), dietary-related parameters, and other body composition, lifestyle, and social-related variables. Exercise performance was assessed using the counter movement jump (CMJ), squat jump (SJ), horizontal long jump (HLJ), 50-m sprint test (50-m ST) and throwing strength test (TST). The assessment of HFI was conducted using the food insecurity experience scale (FIES). Comparisons and associations were investigated based on food security status.

RESULTS

The total sample size was comprised of 138 children (n = 42; 30.4%) and adolescents (n = 96; 69.6%). We found an association between food security status and sex (X2(138,1) = 4.42; P = 0.036). SJ was higher in the food security group than in the HFI group (t(117) = 2.112; P = 0.037; ES = 0.39). Sleep- and dietary-related factors did not differ between the groups.

CONCLUSIONS

In summary, the prevalence of HFI among child and adolescent athletics participants was approximately 40%. Regarding exercise performance, SJ was better in the food security group than in the HFI group. Concerning dietary-related data, the HFI group had a lower number of meals per day than the food security group, and other dietary data did not differ between the groups. Body composition and sleep-related parameters were similar between the groups.

Comparison of 10 × 1-minute high-intensity interval training (HIIT) versus 4 × 4-minute HIIT on glucose control and variability in females with type 2 diabetes

Two high-intensity interval training (HIIT) regimens are often used in research and clinical settings. Yet, there has been no direct comparison to determine if one can improve glucose control and variability to a greater extent in individuals living with type 2 diabetes (T2D). Fourteen older females with T2D participated in a semi-randomized control trial where HIIT10 (10 × 1-min intervals at 90% heart rate max; HRmax) and HIIT4 (4 × 4-min intervals at 90% of HRmax) were compared to a control condition (CON; no exercise). Continuous glucose monitoring was used to assess glucose control and variability over 24 h after each condition. Both HIIT10 (-2.1 ± 1.1 mmol/L) and HIIT4 (-2.1 ± 1.3 mmol/L) acutely lowered glucose compared to CON (-0.7 ± 0.8 mmol/L; p = 0.001), with no difference between exercise conditions. This glucose-lowering effect did not persist over the 24-h post-exercise period, as both mean glucose (p = 0.751) and glucose variability (p = 0.168) were not significantly different among conditions. However, exploratory analyses focusing on individuals with less optimal glucose control (above median 24-h mean glucose in the CON condition; n = 7) revealed that 24-h mean glucose (7.4 [7.14-8.92] vs. 8.4 [7.5-9.9] mmol/L; p = 0.048), glucose variability (p = 0.010), and peak glucose (p = 0.048) were lower following HIIT10 compared to CON, while HIIT4 reduced time spent in moderate hyperglycemia compared to CON (p = 0.023). Both HIIT10 and HIIT4 acutely lower glycemia, but the effect does not persist over 24 h. However, in individuals with worse glucose control, HIIT10 may improve mean 24-h glucose and glycemic variability, while HIIT4 may reduce time spent in moderate hyperglycemia.

Exercise and Insulin Resistance Markers in Children and Adolescents With Excess Weight: A Systematic Review and Network Meta-Analysis

Importance

Although benefits have been reported for most exercise modalities, the most effective exercise approaches for reducing insulin resistance in children and adolescents with excess weight and the optimal exercise dose remain unknown.

Objective

To compare exercise training modalities and their association with changes in insulin resistance markers among children and adolescents with excess weight and to establish the optimal exercise dose.

Data Sources

For this systematic review and network meta-analysis, 6 electronic databases (PubMed, EMBASE, Cochrane Central Register of Controlled Trials, Scopus, Web of Science, and CINAHL) were searched for studies from inception to April 1, 2023.

Study Selection

Randomized clinical trials (ie, randomized controlled trials and randomized trials without a control group) were included if they reported outcomes associated with aerobic training, resistance training, high-intensity interval training (HIIT), or a combination of these interventions.

Data Extraction and Synthesis

Data extraction for this systematic review was conducted following a network meta-analysis extension of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guideline. Effect sizes were calculated as the mean difference (MD) with 95% CI using random-effects inverse-variance models with the Hartung-Knapp-Sidik-Jonkman method. The hierarchy of competing interventions was defined using the surface under the cumulative ranking curve. The Cochrane Risk-of-Bias tool, version 2 (RoB2), was used to independently assess the risk of bias of the included studies. The certainty of evidence in consistent networks was assessed using the Grading of Recommendation, Assessment, Development and Evaluation approach. The study protocol was prospectively registered with PROSPERO. Data analyses were conducted between May and June 2023.

Main Outcomes and Measures

The primary outcomes were fasting glucose, insulin, and homeostatic model assessment for insulin resistance (HOMA-IR).

Results

This analysis included 55 studies with a total of 3051 children and adolescents (mean [SD] age, 13.5 [2.3] years; 1537 girls [50.4%] and 1514 boys [49.6%]). Exercise was associated with reductions in fasting insulin (MD, -4.38 μU/mL [95% CI, -5.94 to -2.82 μU/mL]) and HOMA-IR (MD, -0.87 [95% CI, -1.20 to -0.53]). A nonlinear association in both markers was observed, with a required minimal exercise dosage of approximately 900 to 1200 metabolic equivalent of task minutes per week, especially in children and adolescents with insulin resistance at baseline. Combination HIIT and resistance training and concurrent training were the most effective approaches for reducing insulin resistance markers. On average, the certainty of evidence varied from low to moderate.

Conclusions and Relevance

These findings underscore the role of exercise interventions in enhancing insulin resistance markers among children and adolescents with overweight and obesity. It is advisable to include resistance exercises alongside aerobic and HIIT programs for a minimum of two to three 60-minute sessions per week.

Constrained IoT-Based Machine Learning for Accurate Glycemia Forecasting in Type 1 Diabetes Patients

Individuals with diabetes mellitus type 1 (DM1) tend to check their blood sugar levels multiple times daily and utilize this information to predict their future glycemic levels. Based on these predictions, patients decide on the best approach to regulate their glucose levels with considerations such as insulin dosage and other related factors. Nevertheless, modern developments in Internet of Things (IoT) technology and innovative biomedical sensors have enabled the constant gathering of glucose level data using continuous glucose monitoring (CGM) in addition to other biomedical signals. With the use of machine learning (ML) algorithms, glycemic level patterns can be modeled, enabling accurate forecasting of this variable. Constrained devices have limited computational power, making it challenging to run complex machine learning algorithms directly on these devices. However, by leveraging edge computing, using lightweight machine learning algorithms, and performing preprocessing and feature extraction, it is possible to run machine learning algorithms on constrained devices despite these limitations. In this paper we test the burdens of some constrained IoT devices, probing that it is feasible to locally predict glycemia using a smartphone, up to 45 min in advance and with acceptable accuracy using random forest.