Glucose Pulse: a simple method to estimate the amount of glucose oxidized during exercise in type 1 diabetic patients

Beyond the Calorie Paradigm: Taking into Account in Practice the Balance of Fat and Carbohydrate Oxidation during Exercise?

Recent literature shows that exercise is not simply a way to generate a calorie deficit as an add-on to restrictive diets but exerts powerful additional biological effects via its impact on mitochondrial function, the release of chemical messengers induced by muscular activity, and its ability to reverse epigenetic alterations. This review aims to summarize the current literature dealing with the hypothesis that some of these effects of exercise unexplained by an energy deficit are related to the balance of substrates used as fuel by the exercising muscle. This balance of substrates can be measured with reliable techniques, which provide information about metabolic disturbances associated with sedentarity and obesity, as well as adaptations of fuel metabolism in trained individuals. The exercise intensity that elicits maximal oxidation of lipids, termed LIPOXmax, FATOXmax, or FATmax, provides a marker of the mitochondrial ability to oxidize fatty acids and predicts how much fat will be oxidized over 45–60 min of low- to moderate-intensity training performed at the corresponding intensity. LIPOXmax is a reproducible parameter that can be modified by many physiological and lifestyle influences (exercise, diet, gender, age, hormones such as catecholamines, and the growth hormone-Insulin-like growth factor I axis). Individuals told to select an exercise intensity to maintain for 45 min or more spontaneously select a level close to this intensity. There is increasing evidence that training targeted at this level is efficient for reducing fat mass, sparing muscle mass, increasing the ability to oxidize lipids during exercise, lowering blood pressure and low-grade inflammation, improving insulin secretion and insulin sensitivity, reducing blood glucose and HbA_1c in type 2 diabetes, and decreasing the circulating cholesterol level. Training protocols based on this concept are easy to implement and accept in very sedentary patients and have shown an unexpected efficacy over the long term. They also represent a useful add-on to bariatric surgery in order to maintain and improve its weight-lowering effect. Additional studies are required to confirm and more precisely analyze the determinants of LIPOXmax and the long-term effects of training at this level on body composition, metabolism, and health.

Personalized Approach for the Management of Exercise-Related Glycemic Imbalances in Type 1 Diabetes: Comparison with Reference Method

BACKGROUND

One of the most frequently adopted strategies to counterbalance the risk of exercise-induced hypoglycemia in patients with type 1 diabetes is carbohydrates supplement. Nevertheless, the estimation of its amount is still challenging. We investigated the efficacy of the personalized Exercise Carbohydrate Requirement Estimation System (ECRES) method compared to a tabular approach to estimate the glucose supplement needed for the prevention of exercise-related glycemic imbalances.

METHOD

Twenty-six patients performed two one-hour constant intensity exercises one week apart; the amount of extra carbohydrates was estimated, in random order, by the personalized ECRES method or through the tabular approach; glycemia was determined every 30 minutes. Continuous glucose monitoring (CGM) metrics were calculated over the 48 hours preceding, and the afternoon and night following the trials.

RESULTS

Applying the personalized ECRES method, a significantly lower amount of carbohydrates was administered to the active patients compared to the tabular approach, median (interquartile range): 9.0 (0.5-21.0) g vs 23.0 (21.0-25.0) g; P < .01; the two methods were similar for the sedentary patients, 18 (13.5-36.0) g vs 23.0 (21.0-27.0) g; P = NS. After overlapping CGM metrics before the exercises, both methods avoided hypoglycemia and resulted in similar glucose levels throughout them. The ECRES method led to CGM metrics within the guidelines for either the afternoon and the night just following the trials, whereas the tabular approach resulted in a significantly greater time below range in the afternoon (11.8% ± 18.2%; P < .05) and time above range during the night (39.3% ± 29.8%; P < .05).

CONCLUSIONS

The results support the validity of the personalized ECRES method: although the estimated amounts of carbohydrates were lower, patients' glycemia was maintained within safe clinical limits.

Carbohydrate Requirement for Exercise in Type 1 Diabetes: Effects of Insulin Concentration

Physical activity is a keystone of a healthy lifestyle as well as of management of patients with type 1 diabetes. The risk of exercise-induced hypoglycemia, however, is a great challenge for these patients. The glycemic response to exercise depends upon several factors concerning the patient him/herself (eg, therapy, glycemic control, training level) and the characteristics of the exercise performed. Only in-depth knowledge of these factors will allow to develop individualized strategies minimizing the risk of hypoglycemia. The main factors affecting the exercise-induced hypoglycemia in patients with T1D have been analyzed, including the effects of insulin concentration. A model is discussed, which has the potential to become the basis for providing patients with individualized suggestions to keep constant glucose levels on each exercise occasion.

Strategies used by Patients with Type 1 Diabetes to Avoid Hypoglycemia in a 24×1-Hour Marathon: Comparison with the Amounts of Carbohydrates Estimated by a Customizable Algorithm

OBJECTIVES

The preferred countermeasure to avoid exercise-related hypoglycemia was investigated in a group of patients with type 1 diabetes participating in a stressful event, a 24×1-hour relay marathon. The carbohydrates actually consumed were compared to those estimated for each patient by applying a customizable algorithm, Exercise Carbohydrates Requirement Estimating Software (ECRES), based on patient's usual therapy and diet and on the exercise characteristics.

METHODS

Glycemia was tested at the start, middle and end of the races. Usual therapies and diets and the adopted countermeasures were recorded in detail.

RESULTS

We studied 19 patients who walked/ran 10.4±2.8 km with a heart rate of 167±11 beats per minute. Of the 19 patients, 7 patients reduced the administered insulin (premeal bolus or basal infusion rate). Glycemia fell by the end of the races (p=0.006; median -1.8 mmol⋅L^-1; interquartile range -0.4 mmol⋅L^-1 to -5.3 mmol⋅L^-1), despite 9 patients being hyperglycemic at the start. Of the patients, 14 concluded the race with glycemia on target, and 4 patients were hyperglycemic. Amounts of carbohydrates actually consumed (median 30 g; interquartile range 0 g to 71 g) were not significantly different from those estimated by ECRES (median 38 g; interquartile range 24 g to 68 g), the 2 quantities being significantly related (R=0.64; p=0.003). ECRES estimated lower carbohydrate levels (-13 g) than the amounts actually consumed by the 4 patients who concluded their exercises with hyperglycemia.

CONCLUSIONS

Patients preferred to consume extra carbohydrates to avoid the possible exercise-induced hypoglycemia. ECRES would provide satisfactory estimates of the carbohydrate requirements, even for a stressful condition, and almost equal to the quantities consumed following medical advice.

Prolonged exercise in type 1 diabetes: performance of a customizable algorithm to estimate the carbohydrate supplements to minimize glycemic imbalances

Physical activity in patients with type 1 diabetes (T1DM) is hindered because of the high risk of glycemic imbalances. A recently proposed algorithm (named Ecres) estimates well enough the supplemental carbohydrates for exercises lasting one hour, but its performance for prolonged exercise requires validation. Nine T1DM patients (5M/4F; 35–65 years; HbA1c 54±13 mmol·mol^-1) performed, under free-life conditions, a 3-h walk at 30% heart rate reserve while insulin concentrations, whole-body carbohydrate oxidation rates (determined by indirect calorimetry) and supplemental carbohydrates (93% sucrose), together with glycemia, were measured every 30 min. Data were subsequently compared with the corresponding values estimated by the algorithm. No significant difference was found between the estimated insulin concentrations and the laboratory-measured values (p = NS). Carbohydrates oxidation rate decreased significantly with time (from 0.84±0.31 to 0.53±0.24 g·min^-1, respectively; p<0.001), being estimated well enough by the algorithm (p = NS). Estimated carbohydrates requirements were practically equal to the corresponding measured values (p = NS), the difference between the two quantities amounting to –1.0±6.1 g, independent of the elapsed exercise time (time effect, p = NS). Results confirm that Ecres provides a satisfactory estimate of the carbohydrates required to avoid glycemic imbalances during moderate intensity aerobic physical activity, opening the prospect of an intriguing method that could liberate patients from the fear of exercise-induced hypoglycemia.

Whole-body glucose oxidation rate during prolonged exercise in type 1 diabetic patients under usual life conditions

OBJECTIVE

Fuel oxidation during exercise was studied in type 1 insulin-dependent (T1DM) patients mainly under quite constant insulin and glycemia; these protocols, however, likely do not reflect patients' usual metabolic conditions. The glucose oxidation rate (GLUox) in T1DM patients under usual life conditions was thus investigated during prolonged exercise (3-h) and its behavior was described mathematically.

MATERIALS/METHODS

Whole-body GLUox was determined in eight T1DM patients (4/8 M; aged 35-59 years) and eight well-matched healthy subjects. Venous blood was drawn prior to and every 30 min until the end of exercise; glycemia, insulin, cortisol, and growth hormone concentrations were determined. Oxygen consumption, carbon dioxide production, and ventilation were measured at rest and thereafter every 30 min of the exercise. To prevent hypoglycemia, patients were given fruit fudge (93% sucrose) prior to / during exercise.

RESULTS

Insulin concentration and glycemia were significantly higher in patients across the entire exercise period (group effect, p<0.001 for both). GLUox decreased significantly with increasing exercise duration (time effect, p<0.001), but no significant difference was detected between the two groups (group effect, p=NS). GLUox, expressed as the percentage of the starting value, was described by an exponential function showing a time constant of 90 min (n=96; mean corrected R(2)=0.666).

CONCLUSIONS

GLUox in T1DM patients was not significantly different from the rate observed in the control subjects. The function describing the time course of GLUox may be useful to correct an estimated GLUox for the duration of exercise and help T1DM patients avoiding exercise-induced glycemic imbalances.

Exercise and glycemic imbalances: a situation-specific estimate of glucose supplement

PURPOSE

The purposes of this study were to describe a newly developed algorithm that estimates the glucose supplement on a patient- and situation-specific basis and to test whether these amounts would be appropriate for maintaining blood glucose levels within the recommended range in exercising type 1 diabetic patients.

METHODS

The algorithm first estimates the overall amount of glucose oxidized during exercise on the basis of the patient's physical fitness, exercise intensity, and duration. The amount of supplemental CHO to be consumed before or during the effort represents a fraction of the burned quantity depending on the patient's usual therapy and insulin sensitivity and on the time of day the exercise is performed. The algorithm was tested in 27 patients by comparing the estimated amounts of supplemental CHO with the actual amounts required to complete 1-h constant-intensity walks. Each patient performed three trials, each of which started at different time intervals after insulin injection (81 walks were performed overall). Glycemia was tested every 15 min.

RESULTS

In 70.4% of the walks, independent of the time of day, the amount of CHO estimated by the algorithm would be adequate to allow the patients to complete the exercise with a glucose level within the selected thresholds (i.e., 3.9-10 mmol·L(-1)).

CONCLUSIONS

The algorithm provided a satisfactory estimate of the CHO needed to complete the exercises. Although the performance of the algorithm still requires testing for different exercise intensities, durations, and modalities, the results indicate its potential usefulness as a tool for preventing immediate exercise-induced glycemic imbalances (i.e., during exercise) in type 1 diabetic patients, in particular for spontaneous physical activities not planned in advance, thus allowing all insulin-dependent patients to safely enjoy the benefits of exercise.