Real World Interstitial Glucose Profiles of a Large Cohort of Physically Active Men and Women

The use of continuous glucose monitors (CGMs) in individuals living without diabetes is increasing. The purpose of this study was to profile various CGM metrics around nutritional intake, sleep and exercise in a large cohort of physically active men and women living without any known metabolic disease diagnosis to better understand the normative glycemic response to these common stimuli. A total of 12,504 physically active adults (age 40 ± 11 years, BMI 23.8 ± 3.6 kg/m2; 23% self-identified as women) wore a real-time CGM (Abbott Libre Sense Sport Glucose Biosensor, Abbott, USA) and used a smartphone application (Supersapiens Inc., Atlanta, GA, USA) to log meals, sleep and exercise activities. A total of >1 M exercise events and 274,344 meal events were analyzed. A majority of participants (85%) presented an overall (24 h) average glucose profile between 90 and 110 mg/dL, with the highest glucose levels associated with meals and exercise and the lowest glucose levels associated with sleep. Men had higher mean 24 h glucose levels than women (24 h-men: 100 ± 11 mg/dL, women: 96 ± 10 mg/dL). During exercise, the % time above >140 mg/dL was 10.3 ± 16.7%, while the % time <70 mg/dL was 11.9 ± 11.6%, with the remaining % within the so-called glycemic tight target range (70-140 mg/dL). Average glycemia was also lower for females during exercise and sleep events (p < 0.001). Overall, we see small differences in glucose trends during activity and sleep in females as compared to males and higher levels of both TAR and TBR when these active individuals are undertaking or competing in endurance exercise training and/or competitive events.

Association between pre-exercise food ingestion timing and reactive hypoglycemia: Insights from a large database of continuous glucose monitoring data

Using a large database of continuous glucose monitoring (CGM) data, this study aimed to gain insights into the association between pre-exercise food ingestion timing and reactive hypoglycemia. A group of 6,761 users self-reported 48,799 pre-exercise food ingestion events and logged minute-by-minute CGM, which was used to detect reactive hypoglycemia (<70 mg/dL) in the first 30 min of exercise. A linear and a non-linear binomial logistic regression model was used to investigate the association between food ingestion timing and the probability of experiencing reactive hypoglycemia. An analysis of variance was conducted to compare the predictive ability of the models. On average, reactive hypoglycemia was detected in 8.34 ± 3.04% of the total events, with <15% of individuals experiencing hypoglycemia in >20% of their events. The majority of the reactive hypoglycemia events were found with pre-exercise food timing between ∼30 and ∼90 min, with a peak at ∼60 min. The superior accuracy (62.05 vs 45.1%) and F-score (0.75 vs 0.59) of the non-linear vs the linear model were statistically superior (P < 0.0001). These results support the notion of an unfavourable 30-to-90 min pre-exercise food ingestion time window which can significantly impact the likelihood of reactive hypoglycemia in some individuals.

Glycaemic patterns of male professional athletes with type 1 diabetes during exercise, recovery and sleep: Retrospective, observational study over an entire competitive season

AIMS

To analyse glycaemic patterns of professional athletes with type 1 diabetes during a competitive season.

MATERIALS AND METHODS

We analysed continuous glucose monitoring data of 12 professional male cyclists with type 1 diabetes during exercise, recovery and sleep on days with competitive exercise (CE) and non-competitive exercise (NCE). We assessed whether differences exist between CE and NCE days and analysed associations between exercise and dysglycaemia.

RESULTS

The mean glycated haemoglobin was 50 ± 5 mmol/mol (6.7 ± 0.5%). The athletes cycled on 280.8 ± 28.1 days (entire season 332.6 ± 18.8 days). Overall, time in range (3.9-10 mmol/L) was 70.0 ± 13.7%, time in hypoglycaemia (<3.9 mmol/L) was 6.4 ± 4.7% and time in hyperglycaemia (>10 mmol/L) was 23.6 ± 12.5%. During the nights of NCE days, athletes spent 10.1 ± 7.4% of time in hypoglycaemia, particularly after exercise in the endurance zones. The CE days were characterized by a higher time in hyperglycaemia compared with NCE days (25.2 ± 12.5% vs. 22.2 ± 12.1%, p = .012). This was driven by the CE phase, where time in range dropped to 60.4 ± 13.0% and time in hyperglycaemia was elevated (38.5 ± 12.9%). Mean glucose was higher during CE compared with NCE sessions (9.6 ± 0.9 mmol/L vs. 7.8 ± 1.1 mmol/L, p < .001). The probability of hyperglycaemia during exercise was particularly increased with longer duration, higher intensity and higher variability of exercise.

CONCLUSIONS

The analysis of glycaemic patterns of professional endurance athletes revealed that overall glycaemia was generally within targets. For further improvement, athletes, team staff and caregivers may focus on hyperglycaemia during competitions and nocturnal hypoglycaemia after NCE.

An evaluation of the role of the exercise training dose for changes in exercise capacity following a standard cardiac rehabilitation program

BACKGROUND

To retrospectively characterize and compare the dose of exercise training (ET) within a large cohort of patients demonstrating different levels of improvement in exercise capacity following a cardiac rehabilitation (CR) program.

METHODS

A total of 2310 patients who completed a 12-week, center-based, guidelines-informed CR program between January 2018 and December 2019 were included in the analysis. Peak metabolic equivalents (MET_peak) were determined pre- and post-CR during which total duration (ET time) and intensity [percent of heart rate peak (%HR_peak)] of supervised ET were also obtained. Training responsiveness was quantified on the basis of changes in MET_peak from pre- to post-CR. A cluster analysis was performed to identity clusters demonstrating discrete levels of responsiveness (i.e., negative, low, moderate, high, and very-high). These were compared for several baseline and ET-derived variables which were also included in a multivariable linear regression model.

RESULTS

At pre-CR, baseline MET_peak was progressively lower with greater training responsiveness (F_(4,2305) = 44.2, P < 0.01, η²_p = 0.71). Likewise, average training duration (F_(4,2305) = 10.7 P < 0.01, η²_p = 0.02) and %HR_peak (F_(4,2305) = 25.1 P < 0.01, η²_p = 0.042) quantified during onsite ET sessions were progressively greater with greater training responsiveness. The multivariable linear regression model confirmed that baseline MET_peak, training duration and intensity during ET, BMI, and age (P < 0.001) were significant predictors of MET_peak post-CR.

CONCLUSIONS

Along with baseline MET_peak, delta BMI, and age, the dose of ET (i.e., training duration and intensity) predicts MET_peak at the conclusion of CR. A re-evaluation of current approaches for exercise intensity prescription is recommended to extend the benefits of completing CR to all patients.

Nutritional intake when cycling under racing and training conditions in professional male cyclists with type 1 diabetes

This study sought to detail and compare the in-ride nutritional practices of a group of professional cyclists with type 1 diabetes (T1D) under training and racing conditions. We observed seven male professional road cyclists with T1D (Age: 28 ± 4 years, HbA_1c: 6.4 ± 0.4% [46 ± 4 mmol.mol^-1], VO_2max: 73.9 ± 4.3 ml.kg ^-1.min^-1) during pre-season training and during a Union Cycliste Internationale multi-stage road cycling race (Tour of Slovenia). In-ride nutritional, interstitial glucose, and performance variables were quantified and compared between the two events.    The in-ride energy intake was similar between training and racing conditions     (p = 0.909), with carbohydrates being the major source of fuel in both events during exercise at a rate of 41.9 ± 6.8 g.h^-1 and 45.4 ± 15.5 g.h^-1 (p = 0.548), respectively. Protein consumption was higher during training (2.6 ± 0.6 g.h^-1) than race rides (1.9 ± 0.9 g.h^-1; p = 0.051).   A similar amount of time was spent within the euglycaemic range (≥70-≤180 mg.dL^-1): training 77.1 ± 32.8% vs racing 73.4 ± 3.9%; p = 0.818. These data provide new information on the in-ride nutritional intake in professional cyclists with T1D during different stages of the competitive season.

Use and Perception of Telemedicine in People with Type 1 Diabetes During the COVID-19 Pandemic: A 1-Year Follow-Up

Background: In the spring of 2020, our research group circulated a worldwide survey with the aim of gathering information on the use and perception of telemedicine in people living with type 1 diabetes at the start of the COVID-19 pandemic. The data suggested that a large number of respondents had rapidly adopted to telemedicine, as in-person visits were not possible, and that this was perceived positively by many. In this study, we conducted a 1-year follow-up to investigate changes in opinions and experiences to telemedicine over the past year of the pandemic. Methods: An anonymous questionnaire was distributed through social media (Twitter, Facebook, and Instagram) between May 9 and May 15, 2021, using an open-access web-based platform (SurveyMonkey.com). The survey was identical to that used in the original study, covering questions relating to the use and perception of telemedicine, diabetes treatment and control, and medical supplies during the COVID-19 pandemic. The questionnaire was available in English, Spanish, German, French, and Italian. We compared the results from the two surveys descriptively and statistically, results were stratified according to age, gender, and HbA_1c. Results: There were 531 survey responses from 40 countries (Europe 54%, North America 36%, South America 2%, and Africa and Asia 2%). A large percentage of respondents (67%) reported meeting with their health care provider remotely since the beginning of the pandemic, a significant increase compared with the 28% in the 2020 survey (P < 0.001). Eighty-three percent of respondents found remote appointments to be somewhat-to-extremely useful, similar to the 86% satisfaction rate in the previous survey (P = 0.061). Remote appointments were most frequently undertaken through telephone (50%) and video call (45%), which are significant changes compared with those in 2020 (72% and 28%, respectively, P < 0.001). Forty-five percent of respondents in 2021 were likely to consider remote appointments instead of in-person appointments in the future-being significantly lower than the 75% in the initial survey (P < 0.001)-whereas 37% indicated they would not. The majority of respondents (84%) reported no issues in their access to diabetes supplies and medication over the past year. Conclusions: This study showed that the use of telemedicine in the form of remote appointments increased during the COVID-19 pandemic in people living with type 1 diabetes, with high levels of satisfaction. However, a remarkable decline took place in the past year in the proportion of patients stating a willingness to continue with remote appointments beyond the pandemic. It seems that a personalized approach is needed since a substantial proportion of respondents in this follow-up still indicated a preference for in-person diabetes care, hence the use of telemedicine should be considered on an individual basis.

Assessment of bike handling during cycling individual time trials with a novel analytical technique adapted from motorcycle racing

A methodology to study bike handling of cyclists during individual time trials (ITT) is presented. Lateral and longitudinal accelerations were estimated from GPS data of professional cyclists (n=53) racing in two ITT of different length and technical content. Acceleration points were plotted on a plot (g-g diagram) and they were enclosed in an ellipse. A correlation analysis was conducted between the area of the ellipse and the final ITT ranking. It was hypothesized that a larger area was associated to a better performance. An analytical model for the bike-cyclist system dynamics was used to conduct a parametric analysis on the influence of riding position on the shape of the g-g diagram. A moderate (n=27, r=-0.40, p=0.038) and a very large (n=26, r=-0.83, p<0.0001) association were found between the area of the enclosing ellipse and the final ranking in the two ITT. Interestingly, this association was larger in the shorter race with higher technical content. The analytical model suggested that maximal decelerations are highly influenced by the cycling position, road slope and speed. This investigation, for the first time, explores a novel methodology that can provide insights into bike handling, a large unexplored area of cycling performance.

Post-exercise recovery for the endurance athlete with type 1 diabetes: a consensus statement

There has been substantial progress in the knowledge of exercise and type 1 diabetes, with the development of guidelines for optimal glucose management. In addition, an increasing number of people living with type 1 diabetes are pushing their physical limits to compete at the highest level of sport. However, the post-exercise recovery routine, particularly with a focus on sporting performance, has received little attention within the scientific literature, with most of the focus being placed on insulin or nutritional adaptations to manage glycaemia before and during the exercise bout. The post-exercise recovery period presents an opportunity for maximising training adaption and recovery, and the clinical management of glycaemia through the rest of the day and overnight. The absence of clear guidance for the post-exercise period means that people with type 1 diabetes should either develop their own recovery strategies on the basis of individual trial and error, or adhere to guidelines that have been developed for people without diabetes. This Review provides an up-to-date consensus on post-exercise recovery and glucose management for individuals living with type 1 diabetes. We aim to: (1) outline the principles and time course of post-exercise recovery, highlighting the implications and challenges for endurance athletes living with type 1 diabetes; (2) provide an overview of potential strategies for post-exercise recovery that could be used by athletes with type 1 diabetes to optimise recovery and adaptation, alongside improved glycaemic monitoring and management; and (3) highlight the potential for technology to ease the burden of managing glycaemia in the post-exercise recovery period.

Oxynet: A collective intelligence that detects ventilatory thresholds in cardiopulmonary exercise tests

The problem of the automatic determination of the first and second ventilatory thresholds (VT1 and VT2) from cardiopulmonary exercise test (CPET) still leads to controversy. The reliability of the gold standard methodology (i.e. expert visual inspection) feeds into the debate and several authors call for more objective automatic methods to be used in the clinical practice. In this study, we present a framework based on a collaborative approach, where a web-application was used to crowd-source a large number (1245) of CPET data of individuals with different aerobic fitness. The resulting database was used to train and test an artificial intelligence (i.e. a convolutional neural network) algorithm. This automatic classifier is currently implemented in another web-application and was used to detect the ventilatory thresholds in the available CPET. A total of 206 CPET were used to evaluate the accuracy of the estimations against the expert opinions. The neural network was able to detect the ventilatory thresholds with an average mean absolute error of 178 (198) mlO₂/min (11.1%, r = 0.97) and 144 (149) mlO₂/min (6.1%, r = 0.99), for VT1 and VT2 respectively. The performance of the neural network in detecting VT1 deteriorated in case of individuals with poor aerobic fitness. Our results suggest the potential for a collective intelligence system to outperform isolated experts in ventilatory thresholds detection. However, the inclusion of a larger number of VT1 examples certified by a community of experts will be likely needed before the abilities of this collective intelligence can be translated into the clinical use of CPET.

Use and perception of telemedicine in people with type 1 diabetes during the COVID-19 pandemic-Results of a global survey

Introduction

The COVID-19 pandemic has forced rapid reconsideration as to the way in which health care is delivered. One potential means to provide care while avoiding unnecessary person-to-person contact is to offer remote services (telemedicine). This study aimed to (1) gather real-time information on the use and perception of telemedicine in people living with type 1 diabetes and (2) assess the challenges, such as restricted access to health care and/or medical supplies.

Methods

An anonymous questionnaire was widely distributed between 24 March and 5 May 2020 using an open-access web-based platform. Data were analysed descriptively, and results were stratified according to age, sex and HbA1c.

Results

There were 7477 survey responses from individuals in 89 countries. Globally, 30% reported that the pandemic had affected their healthcare access due to cancelled physical appointments with their healthcare providers. Thirty-two per cent reported no fundamental change in their medical follow-up during this period, with 9% stating that no personal contact was established with their doctors over the duration of the study. Twenty-eight per cent received remote care through telephone (72%) or video-calls (28%). Of these, 86% found remote appointments useful and 75% plan to have remote appointments in the future. Glucose control, indicated by HbA1c, was positively associated with positive perception of telemedicine. In males, 45% of respondents with an HbA1c > 9% rated telemedicine not useful compared to those with lower HbA1c, while 20% of females with an HbA1c > 9% rated it not useful (χ2 = 14.2, P = .0016).

Conclusion

Remote appointments have largely been perceived as positive in people with type 1 diabetes with the majority (75%) stating that they would consider remote appointments beyond the pandemic. Age and level of education do not appear to influence perception of telemedicine, whereas poor glucose control, particularly in males, seems to negatively affect perception.

Evaluation of Factors Related to Glycemic Management in Professional Cyclists With Type 1 Diabetes Over a 7-Day Stage Race

OBJECTIVE

To investigate factors related to glycemic management among members of a professional cycling team with type 1 diabetes over a 7-day Union Cycliste Internationale World Tour stage race.

RESEARCH DESIGN AND METHODS

An observational evaluation of possible factors related to glycemic management and performance in six male professional cyclists with type 1 diabetes (HbA_1c 6.4 ± 0.6%) during the 2019 Tour of California.

RESULTS

In-ride time spent in euglycemia (3.9-10.0 mmol/L glucose) was 63 ± 11%, with a low percentage of time spent in level 1 (3.0-3.9 mmol/L; 0 ± 1% of time) and level 2 (<3.0 mmol/L; 0 ± 0% of time) hypoglycemia over the 7-day race. Riders spent 25 ± 9% of time in level 1 (10.1-13.9 mmol/L) and 11 ± 9% in level 2 (>13.9 mmol/L) hyperglycemia during races. Bolus insulin use was uncommon during races, despite high carbohydrate intake (76 ± 23 g ⋅ h^-1). Overnight, the riders spent progressively more time in hypoglycemia from day 1 (6 ± 12% in level 1 and 0 ± 0% in level 2) to day 7 (12 ± 12% in level 1 and 2 ± 4% in level 2) (χ²[1] > 4.78, P < 0.05).

CONCLUSIONS

Professional cyclists with type 1 diabetes have excellent in-race glycemia, but significant hypoglycemia during recovery overnight, throughout a 7-day stage race.

Glycemic responses to strenuous training in male professional cyclists with type 1 diabetes: a prospective observational study

INTRODUCTION

This prospective observational study sought to establish the glycemic, physiological and dietary demands of strenuous exercise training as part of a 9-day performance camp in a professional cycling team with type 1 diabetes (T1D).

RESEARCH DESIGN AND METHODS

Sixteen male professional cyclists with T1D on multiple daily injections (age: 27±4 years; duration of T1D: 11±5 years; body mass index: 22±2 kg/m²; glycated hemoglobin: 7%±1% (50±6 mmol/mol); maximum rate of oxygen consumption: 73±4 mL/kg/min) performed road cycle sessions (50%-90% of the anaerobic threshold, duration 1-6 hours) over 9 consecutive days. Glycemic (Dexcom G6), nutrition and physiological data were collected throughout. Glycemic data were stratified into predefined glycemic ranges and mapped alongside exercise physiology and nutritional parameters, as well as split into daytime and night-time phases for comparative analysis. Data were assessed by means of analysis of variance and paired t-tests. A p value of ≤0.05 (two-tailed) was statistically significant.

RESULTS

Higher levels of antecedent hypoglycemia in the nocturnal hours were associated with greater time spent in next-day hypoglycemia overall (p=0.003) and during exercise (p=0.019). Occurrence of nocturnal hypoglycemia was associated with over three times the risk of next-day hypoglycemia (p<0.001) and a twofold risk of low glucose during cycling (p<0.001). Moreover, there was trend for a greater amount of time spent in mild hypoglycemia during the night compared with daytime hours (p=0.080).

CONCLUSION

The higher prevalence of nocturnal hypoglycemia was associated with an increased risk of next-day hypoglycemia, which extended to cycle training sessions. These data highlight the potential need for additional prebed carbohydrates and/or insulin dose reduction strategies around exercise training in professional cyclists with T1D.

TRIAL REGISTRATION NUMBER

DRKS00019923.

Diabetes Technology and Exercise

Advances in technologies such as glucose monitors, exercise wearables, closed-loop systems, and various smartphone applications are helping many people with diabetes to be more physically active. These technologies are designed to overcome the challenges associated with exercise duration, mode, relative intensity, and absolute intensity, all of which affect glucose homeostasis in people living with diabetes. At present, optimal use of these technologies depends largely on motivation, competence, and adherence to daily diabetes care requirements. This article discusses recent technologies designed to help patients with diabetes to be more physically active, while also trying to improve glucose control around exercise.

An equation to predict the maximal lactate steady state from ramp-incremental exercise test data in cycling

OBJECTIVES

The maximal lactate steady state (MLSS) represents the highest exercise intensity at which an elevated blood lactate concentration ([Lac]_b) is stabilized above resting values. MLSS quantifies the boundary between the heavy-to-very-heavy intensity domains but its determination is not widely performed due to the number of trials required.

DESIGN

This study aimed to: (i) develop a mathematical equation capable of predicting MLSS using variables measured during a single ramp-incremental cycling test and (ii) test the accuracy of the optimized mathematical equation.

METHODS

The predictive MLSS equation was determined by stepwise backward regression analysis of twelve independent variables measured in sixty individuals who had previously performed ramp-incremental exercise and in whom MLSS was known (MLSS_obs). Next, twenty-nine different individuals were prospectively recruited to test the accuracy of the equation. These participants performed ramp-incremental exercise to exhaustion and two-to-three 30-min constant-power output cycling bouts with [Lac]_b sampled at regular intervals for determination of MLSS_obs. Predicted MLSS (MLSS_pred) and MLSS_obs in both phases of the study were compared by paired t-test, major-axis regression and Bland-Altman analysis.

RESULTS

The predictor variables of MLSS were: respiratory compensation point (Wkg^-1), peak oxygen uptake (V˙O_2peak) (mlkg^-1min^-1) and body mass (kg). MLSS_pred was highly correlated with MLSS_obs (r=0.93; p<0.01). When this equation was tested on the independent group, MLSS_pred was not different from MLSS_obs (234±43 vs. 234±44W; SEE 4.8W; r=0.99; p<0.01).

CONCLUSIONS

These data support the validity of the predictive MLSS equation. We advocate its use as a time-efficient alternative to traditional MLSS testing in cycling.

Critical power: How different protocols and models affect its determination

In cycling, critical power (CP) and work above CP (W') can be estimated through linear and nonlinear models. Despite the concept of CP representing the upper boundary of sustainable exercise, overestimations may be made as the models possess inherent limitations and the protocol design is not always appropriate.

OBJECTIVES

To measure and compare CP and W' through the exponential (CP_exp), 3-parameter hyperbolic (CP_3-hyp), 2-parameter hyperbolic (CP_2-hyp), linear (CP_linear), and linear 1/time (CP_1/time) models, using different combinations of TTE trials of different durations (approximately 1-20min).

DESIGN

Repeated measures.

METHODS

Thirteen healthy young cyclists (26±3years; 69.0±9.2kg; 174±10cm; 60.4±5.9mLkg^-1min^-1) performed five TTE trials on separate days. CP and W' were modeled using two, three, four, and/or five trials. All models were compared against a criterion method (CP_3-hyp with five trials; confirmed using the leaving-one-out cross-validation analysis) using smallest worthwhile change (SWC) and concordance correlation coefficient (CCC) analyses.

RESULTS

CP was considerably overestimated when only trials lasting less than 10min were included, independent of the mathematical model used. Following CCC analysis, a number of alternative methods were able to predict our criterion method with almost a perfect agreement. However, the application of other common approaches resulted in an overestimation of CP and underestimation of W', typically these methods only included TTE trials lasting less than 12min.

CONCLUSIONS

Estimations from CP_3-hyp were found to be the most accurate, independently of TTE range. Models that include two trials between 12 and 20min provide good agreement with the criterion method (for both CP and W').

Player's success prediction in rugby union: From youth performance to senior level placing

OBJECTIVES

The study questioned if and to what extent specific anthropometric and functional characteristics measured in youth draft camps, can accurately predict subsequent career progression in rugby union.

DESIGN

Original research.

METHODS

Anthropometric and functional characteristics of 531 male players (U16) were retrospectively analysed in relation to senior level team representation at age 21-24. Players were classified as International (Int: National team and international clubs) or National (Nat: 1st, 2nd and other divisions and dropout). Multivariate analysis of variance (one-way MANOVA) tested differences between Int and Nat, along a combination of anthropometric (body mass, height, body fat, fat-free mass) and functional variables (SJ, CMJ, t_15m, t_30m, VO_2max). A discriminant function (DF) was determined to predict group assignment based on the linear combination of variables that best discriminate groups. Correct level assignment was expressed as % hit rate.

RESULTS

A combination of anthropometric and functional characteristics reflects future level assignment (Int vs. Nat). Players' success can be accurately predicted (hit rate=81% and 77% for Int and Nat respectively) by a DF that combines anthropometric and functional variables as measured at ∼15 years of age, percent body fat and speed being the most influential predictors of group stratification.

CONCLUSIONS

Within a group of 15 year-olds with exceptional physical characteristics, future players' success can be predicted using a linear combination of anthropometric and functional variables, among which a lower percent body fat and higher speed over a 15m sprint provide the most important predictors of the highest career success.

Vascular responsiveness determined by near-infrared spectroscopy measures of oxygen saturation

Vascular impairments at the macro- and microcirculatory levels are associated with increased risk for cardiovascular disease. Flow-mediated dilation (FMD) is currently the most widely used method for non-invasive assessment of vascular endothelial function. Recently, near-infrared spectroscopy (NIRS)-derived measures of tissue oxygen saturation (StO2) have been used to characterize the dynamic response of local tissue perfusion to a brief period of ischaemia. The purpose of the present study was to establish correlations between the reperfusion rate of StO2 and FMD. Ultrasound-derived FMD was quantified after 5 min of distal cuff occlusion of the popliteal artery in 20 healthy young men (26 ± 3 years old). Triplicate measurements of end-diastolic arterial diameter were made every 15 s after cuff release, and FMD response was calculated as the greatest percentage change in diameter from baseline (%FMD). The StO2 was measured using NIRS throughout the duration of each test. Two consecutive FMD tests were performed, separated by 30 min of rest, and were averaged for %FMD and StO2. The %FMD was significantly correlated with the reperfusion slope of StO2 after cuff release (slope 2 StO2; r = 0.63, P = 0.003). In conclusion, the present study established a correlation between slope 2 StO2 and %FMD in healthy young men. These data suggest that NIRS-derived slope 2 StO2 can be used as a measure of vascular endothelial function.