While Study 1 was fully supervised efficacy trial with high training adherence, Study 2 was a free-living effectiveness trial without supervision. Whereas the training adherence in Study 2, in terms of volume, was fairly good compared to other free-living training studies (45, 46), the training adherence, in terms of intensity, was not possible to assess. Since no overall changes in glycemic control and body composition were seen in Study 2, it must be speculated how good the training adherence was. Subjects improved their VO2max, however, indicating that some effect of the training was seen. Still, when compared to our previous study, where 17 weeks of IWT resulted in considerable weight loss and improvements in glycemic control and a much greater increase in VO2max (22), it is clear that subjects included in the current Study 2 had suboptimal training responses. The prescribed volume of IWT (at least three times a week with at least 30 min duration pr. session) in the current Study 2 was approximately 1/3 of the previously prescribed training volume (22), indicating that at least training volume is important for the metabolic improvements seen. As such, and given that higher training volume and intensity (24) and greater improvements in VO2max (9, 15) may be central for increases in RMR, it may be speculated whether a more intense training intervention would have resulted in different RMR results.

Given that the analyses in this paper were secondary to other analyses (27, 28) and that no power calculations were performed for RMR outcomes, it may be speculated whether the negative findings were a result of a lack of power. This is further important since a large heterogeneity was seen for changes in RMR. As such, despite the overall analyses indicated that RMR was unaffected by the training interventions, no final conclusions can be drawn from this paper.

In summary, neither short-term continuous or interval-type training, nor longer term interval training affects RMR in subjects with T2D when no overall changes in body composition are seen. Whereas both FFM and fat mass are important determinants of

REFERENCES

RMR at baseline, only training-induced changes in fat mass and not in FFM seem to be important for training-induced changes in RMR.

# AUTHOR CONTRIBUTIONS

KK designed the studies, analyzed and interpreted the data, and wrote the manuscript. MR-L contributed to the study design. KK, CB, IT, and JN researched the data. All authors reviewed and revised the manuscript, approved the final version, and agreed to be accountable for the content of the work.

# ACKNOWLEDGMENTS

We thank all participants for their participation. Laura Staun Valentiner (CopenRehab, Department of Public Health, Section of Social Medicine, University of Copenhagen) is acknowledged for technical assistance.

# FUNDING

The Centre for Physical Activity Research (CFAS) is supported by a grant from TrygFonden. During the study period, the Centre of Inflammation and Metabolism (CIM) was supported by a grant from the Danish National Research Foundation (DNRF55). The study was further supported by grants from Diabetesforeningen, Augustinusfonden, Fonden til Laegevidenskabens Fremme, and Krista og Viggo Petersens Fond. CIM/CFAS is a member of DD2—the Danish Center for Strategic Research in Type 2 Diabetes (the Danish Council for Strategic Research, grant no. 09-067009 and 09-075724). MR-L was funded by a post doc grant from the Danish Diabetes Academy (through the Novo Nordic Foundation).

women. *J Clin Endocrinol Metab* (1997) 82:3208–12. doi:10.1210/jc.82. 10.3208

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer, OS, and handling editor declared their shared affiliation, and the handling editor states that the process nevertheless met the standards of a fair and objective review.

*Copyright © 2017 Karstoft, Brinkløv, Thorsen, Nielsen and Ried-Larsen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Glycemic and Metabolic Effects of Two Long Bouts of Moderate-Intensity Exercise in Men with Normal Glucose Tolerance or Type 2 Diabetes

*Saeed Reza Eshghi1,2, Kevin Fletcher 1,2, Étienne Myette-Côté1,2, Cody Durrer 3 , Raniah Q. Gabr4 , Jonathan P. Little3 , Peter Senior 2,5, Craig Steinback1,2, Margie H. Davenport1,2, Gordon J. Bell 1,2, Dion R. Brocks 6 and Normand G. Boulé1,2\**

*1 Faculty of Physical Education and Recreation, University of Alberta, Edmonton, AB, Canada, 2Alberta Diabetes Institute, University of Alberta, Edmonton, AB, Canada, 3School of Health and Exercise Sciences, University of British Columbia, Kelowna, BC, Canada, 4National Organization for Drug Control and Research (NODCAR), Giza, Egypt, 5Division of Endocrinology and Metabolism, Department of Medicine, Faculty of Medicine, University of Alberta, Edmonton, AB, Canada, 6 Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada*

*Edited by: Jan Polák, Charles University in Prague, Czechia*

#### *Reviewed by:*

*Andrea Enzo Scaramuzza, Istituti Ospitalieri di Cremona, Italy Niels Jessen, Aarhus University, Denmark*

> *\*Correspondence: Normand G. Boulé<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Diabetes, a section of the journal Frontiers in Endocrinology*

*Received: 24 February 2017 Accepted: 20 June 2017 Published: 11 July 2017*

#### *Citation:*

*Eshghi SR, Fletcher K, Myette-Côté É, Durrer C, Gabr RQ, Little JP, Senior P, Steinback C, Davenport MH, Bell GJ, Brocks DR and Boulé NG (2017) Glycemic and Metabolic Effects of Two Long Bouts of Moderate-Intensity Exercise in Men with Normal Glucose Tolerance or Type 2 Diabetes. Front. Endocrinol. 8:154. doi: 10.3389/fendo.2017.00154*

Background: The glycemic and insulinemic responses following 30–60 min of exercise have been extensively studied, and a dose–response has been proposed between exercise duration, or volume, and improvements in glucose tolerance or insulin sensitivity. However, few studies have examined the effects of longer bouts of exercise in type 2 diabetes (T2D). Longer bouts may have a greater potential to affect glucagon, interleukin-6 (IL-6) and incretin hormones [i.e., glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic peptide (GIP)].

Aim: To examine the effect of two bouts of long-duration, moderate-intensity exercise on incretins, glucagon, and IL-6 responses before and after exercise, as well as in response to an oral glucose tolerance test (OGTT) conducted the following day.

Methods: Twelve men, six with and six without T2D, participated in two separate conditions (i.e., exercise vs. rest) according to a randomized crossover design. On day 1, participants either rested or performed two 90 min bouts of treadmill exercise (separated by 3.5 h) at 80% of their ventilatory threshold. All participants received standardized meals on day 1. On day 2 of each condition, glucose and hormonal responses were measured during a 4-h OGTT.

Results: On day 1, exercise increased IL-6 at the end of the first bout of exercise (exercise by time interaction *p* = 0.03) and GIP overall (main effect of exercise *p* = 0.004). Glucose was reduced to a greater extent in T2D following exercise (exercise by T2D interaction *p* = 0.03). On day 2, GIP and active GLP-1 were increased in the fasting state (*p* = 0.05 and *p* = 0.03, respectively), while plasma insulin and glucagon concentrations were reduced during the OGTT (*p* = 0.01 and *p* = 0.02, respectively) in the exercise compared to the rest condition for both healthy controls and T2D. Postprandial glucose was elevated in T2D compared to healthy control (*p* < 0.05) but was not affected by exercise.

Conclusion: Long-duration, moderate-intensity aerobic exercise can increase IL-6. On the day following exercise, fasting incretins remained increased but postprandial insulin and glucagon were decreased without affecting postprandial glucose. This long duration of exercise may not be appropriate for some people, and further research should investigate why next day glucose tolerance was unchanged.

Keywords: aerobic exercise, glucose tolerance, glucagon, insulin, glucagon-like peptide-1, glucose-dependent insulinotropic peptide

# INTRODUCTION

Exercise recommendations for the prevention and treatment of type 2 diabetes (T2D) emphasize exercise prescriptions designed to target insulin sensitivity or body composition (1, 2). These outcomes have been extensively studied, and it is generally recognized that typical exercise-induced changes in body composition are modest and that changes in insulin sensitivity are short lived (1, 2). Evidence to support, adapt, and fine-tune these recommendations are rapidly accumulating. The most recent (November 2016) position statement of the American Diabetes Association on Physical Activity/Exercise and Diabetes (2) currently recommends:

While exercise interventions based on this paradigm clearly contribute to meaningful reductions in the incidence of diabetes (3, 4) or hyperglycemia (5, 6), an unintended consequence of this success may have been a substantially smaller emphasis on the effects of exercise on other pathophysiologic disturbances present in T2D. For example, Defronzo (7) proposed an "ominous octet" of potential pathophysiologic targets that also includes an increased glucagon secretion and a decreased incretin effect. The effects of exercise on many of these other outcomes are largely unknown in people with T2D.

Insight regarding how exercise could potentially affect glucagon or incretins in T2D may be obtained from studies in other populations. For example, repeated long bouts (e.g., two bouts of 90 min) of moderate-intensity exercise performed on the same day have been shown to lead to reductions in glucagon and other counter-regulatory hormones, as well as reductions in sympathetic nerve activity, which persist until at least the next day in people with type 1 diabetes (T1D) (8) and in healthy participants (9). This has been studied as part of the concept known as hypoglycemia-associated autonomic failure or HAAF (10). Reduced glucagon responses may be problematic in T1D who can experience hypoglycemia in response to exercise or excess insulin and has been studied more extensively. However, T2D and impaired glucose tolerance are characterized by impaired postprandial suppression of glucagon and could potentially benefit from non-pharmacological reductions in glucagon (11–13).

Incretin hormones, such as glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic peptide (GIP), are secreted from the gastrointestinal tract into the portal circulation in response to nutrients. In a nutrient-dependent manner, incretins have been shown to contribute to lowering blood glucose by increasing insulin secretion, decreasing glucagon secretion, and decreasing the rate of gastric emptying [as reviewed by Drucker (14)]. On the other hand, GIP can increase glucagon secretion when glucose is low (15). The GLP-1 receptor has been found in cardiac muscle, smooth muscle of the vasculature, and perhaps skeletal muscle (16, 17). These findings, combined with the established heart rate (HR) increasing effect of GLP-1 (18), suggest that incretins could play a role in cardiometabolic responses to exercise.

Ellingsgaard et al. (19) have shown that increased interleukin-6 (IL-6) during exercise could stimulate secretion of GLP-1 from intestinal L cells and also pancreatic alpha cells. As reviewed by Pedersen (20), it is likely that increased circulating IL-6 during exercise is secreted directly by skeletal muscle and is proportional to the amount of glycogen depletion. *In vivo* human studies in healthy, obese, or T2D have often not observed an effect of exercise on incretin concentrations (21, 22), whereas a study in healthy runners observed increased GLP-1 following a marathon (23). It is unclear if this difference among human studies is due to differences in exercise duration or volume.

The effects of exercise clinical relevant outcomes such as glycated hemoglobin have been extensively studied (6, 24). The objective of this study was to examine the effect of two bouts of long-duration, moderate-intensity exercise on biomarkers, such as plasma glucagon, IL-6, GLP-1, and GIP. It was hypothesized that, compared to rest, two bouts of long-duration (i.e., 90 min) moderate-intensity exercise would increase plasma IL-6 and incretin hormone concentrations in T2D and in healthy participants. On the day after the exercise or rest conditions, participants returned to the laboratory for a 4-h oral glucose tolerance test (OGTT) and it was hypothesized that two bouts of long-duration moderate-intensity exercise would reduce the following day glucagon concentrations immediately before (fasted state) and during the OGTT. These objectives were examined using large amounts of exercise as a proof of concept with the understanding that this large amount of exercise (i.e., 3 h in a single day) is unlikely for most people and unsafe for some.

# MATERIALS AND METHODS

# Research Design

The experimental design involved two conditions that each required visits to the laboratory on two consecutive days (i.e., a total of four visits). On day 1 of each condition, participants were assigned to either exercise or control (i.e., rest) according to a randomized crossover design (**Figure 1**). On day 2 of each condition, participants return to the lab following an overnight fast for a 4-h OGTT. The 2-day exercise and control conditions were separated by at least 2 weeks.

# Participants

Twelve men, six without diabetes and six with physician diagnosed T2D, were recruited for this study. Men were selected since they have higher glucagon concentrations in response to various stimuli (e.g., exercise or hypoglycemia) (25) and previous exercise studies of this nature have shown larger reductions in counter-regulatory responses in men (22). To minimize heterogeneity in T2D and the risk of hypoglycemia with prolonged exercise, participants were required to be treated with lifestyle intervention(s) and metformin only. In order to be eligible, all participants also had to be non-smokers and not taking any beta-blockers. Furthermore, participants were excluded if they had cardiovascular or orthopedic limitations to exercise, or felt they would be unable to walk for 90 min without interruption. Many of the participants with T2D were recruited from our previous exercise studies (26, 27) and were purposefully identified due to their above average level of fitness as potential volunteers due to the long bouts of walking required in the present study. Comparable cohorts of men without diabetes had not previously been studied in our lab, therefore, recruited a convenience sample of healthy counterparts with similar body mass indices.

This study was carried out in accordance with the recommendations of the Tri-Council Policy Statement on "Ethical Conduct for Research Involving Humans" with written informed consent from all subjects. The protocol was approved by the University of Alberta Health Research Ethics Board.

# Baseline Assessment

Participants attended a baseline visit to measure glycated hemoglobin (A1c; DCA Vantage™ A1C Analyzer, Siemens Medical Solutions, Malvern, PA, USA), resting metabolic rate (RMR), and perform a graded submaximal exercise test with indirect calorimetry (TrueMax metabolic measurement system, Parvo Medics, Salt Lake City, UT, USA). HR was measured using a Polar heart rate monitor (Polar Electro, Finland). The submaximal exercise test was performed according to a modified Balke–Ware treadmill protocol where each participant walked at a self-selected speed, determined as comfortable but brisk, while the grade was increased by 1% each minute. The test was ended shortly after participants reached their individual ventilatory threshold (VT) using the V-slope criteria (28) as determined by a trained exercise physiologist.

Once eligibility was confirmed and baseline assessments were performed, a 1-month exercise habituation phase was completed by every participant that included three sessions of exercise per week at 80% of their VT. The duration began with 30 min and gradually progressed until participants could walk for 90 min continuously.

# Experimental Protocol

On day 1 of each condition, participants arrived at lab at 08h00 after a minimum 10-h overnight fast. They were asked to avoid vigorous exercise the day before each testing condition. An intravenous catheter was inserted into an antecubital vein and was kept patent with sterile saline. The exercise condition contained

two 90-min bouts of treadmill exercise at intensity of 80% of the previously determined VT, as described in previous studies (9, 29). The first exercise bout began at 09h00 in the fasted state and the second at 14h00. Blood samples were taken immediately before and after each bout of exercise. Indirect calorimetry and HR measurements were collected during the first and last 10 min of each 90-min bout of exercise. During the non-exercise condition, participants remained sedentary but the above measures (except for HR) were collected at the same times as during the corresponding exercise condition.

The energy intake required to maintain energy balance during non-exercise condition was estimated based on participants' previously measured RMR multiplied by a physical activity level (PAL) of 1.4 (Note: this PAL is typically used to characterize a sedentary lifestyle (30)). As in previous studies of this nature (31, 32), energy intake was kept the same on the exercise and non-exercise conditions. Energy intake was divided in two equal standardized meals (59% carbohydrate, 22% fat, 19% protein) provided 30 min after each exercise bout (see **Figure 1**). As such, the first exercise bout was performed in the fasting state and the second bout started 150 min after the first meal.

On day 2, participants returned to the lab after a minimum 10-h fast. Two fasting blood samples were taken; one 15 min before and the other immediately before the beginning of the OGTT containing 75 g of glucose (Trutol, Thermo Fisher Scientific, Canada). Ten blood samples were collected at specific time points following consumption of the glucose beverage (i.e., 15, 30, 45, 60, 90, 120, 150, 180, 210, and 240 min). Oxygen consumption and carbon dioxide production were collected for 10 min before the OGTT and for the last 10 min of each of the next 4-h periods (see **Figure 1**) using the same metabolic measurement system. Respiratory exchange ratio (RER) was determined as ratio between carbon dioxide production and oxygen consumption, while energy expenditure was calculated assuming non-protein energy equivalents. A metabolic equivalent (MET) was calculated as 1 kcal/kg/h (33). Participants were asked sit continuously throughout the test, with the exception of a bathroom break if required. Appetite rating and HR were collected during the same intervals. Appetite ratings were measured by a 150 mm visual analog scale and included questions on hunger, fullness, prospective food consumption, and desire to eat something sweet, salty, or fatty (34). HR was measured using a standard three-lead ECG over the same intervals as for the indirect calorimetry. Heart rate variability (HRV) is a tool that can be used to investigate the sympathetic and parasympathetic function of the autonomic nervous system. Autonomic nervous system activity can be affected by both hypo- and hyperglycemia. HRV indices included the root mean squared of the successive differences between R–R intervals (rMSSD), the SD of the R–R intervals (SDRR), and the ratio of low frequency spectral power to high frequency spectral power. For both day 1 and day 2, the first and last minutes of the 10-min indirect calorimetry and HR periods were excluded to allow for more stable data.

Participants with T2D refrained from taking their metformin dose on all four testing days. The last metformin dose was consumed more than 12 h prior to first blood sample which was taken on day 1 of each testing condition.

# Blood Samples

Each blood sample was first collected into a 10-mL EDTA vacutainer tube. Subsequently, 2.0 mL was transferred into a tube with 20 µL of a dipeptidyl peptidase (DPP-4) inhibitor (Millipore, MA, USA), 2.0 mL was transferred into a tube with 6.7 µL aprotinin (Millipore, MA, USA), and 0.25 mL whole blood was transferred into 1.0 mL ice-cold 8% perchloric acid. Aprotinin was added to inhibit proteases known to interfere with the determination of glucagon. The DPP-4 inhibitor was added to prevent degradation of active GLP-1. Perchloric acid was added to deproteinize the samples. The EDTA tubes were centrifuged at 1,500 × *g* for 10 min at 4°C. The tubes containing perchloric acid and aprotinin were centrifuged at 2,000 × *g* for 15 min at 4°C. Following centrifugation, the samples were immediately moved to a −80°C freezer until assays were completed.

Non-esterified fatty acids (NEFAs) were analyzed using commercially available kits (Wako Diagnostics, CA, USA), while plasma glucose and lactate were determined enzymatically using spectrophotometric assays. Total GIP, glucagon and insulin were measured using a Multi-Spot® Assay System with a Sector® Imager 2400 (Meso Scale Discovery®, MD, USA). Active GLP-1 was measured separately (Meso Scale Discovery®, MD, USA). Hematocrit was measured only on day 1 for both exercise and non-exercise conditions. Plasma IL-6 was also measured from day 1 plasma samples using a high-sensitivity ELISA (Quantikine HS human IL-6, R&D Systems Ltd., Abingdon, UK). Plasma metformin concentrations were assessed by high performance liquid chromatography in all plasma samples from day 1 as well as fasting samples from day 2. The concentration of phosphate solution used in the mobile phase was 20 mmol/L. The metformin assay was validated to a lower limit of quantitation of 8 ng/mL metformin based on 0.1 mL of human plasma (35). All assays were run in duplicate and the average of the two was reported.

# Statistical Analysis

The primary analyses were conducted using a three-way mixed factorial design ANOVA with *Diabetes* as a between group factor (i.e., T2D vs. healthy control), as well as *Exercise* (i.e., exercise vs. rest conditions) and *Time* (i.e., consecutive blood samples) as repeated measures factors. The number of levels for the time factor differed depending on the time period examined (e.g., day 1 had four consecutive blood samples). For day 2, The *Diabetes* by *Exercise* by *Time* ANOVA showed a significant effect of *Time* for all of the blood sample results (all *p* < 0.01). Therefore, it was deemed more informative to separate the fasting from the post glucose beverage results. For the 10 blood samples taken at different intervals postprandially, we considered both the area under the curve (AUC) and incremental AUC (iAUC). The AUC was calculated by the trapezoid method. The iAUC was calculated by subtracting the average of the two fasting values from the AUC. For the variables that were measured at 1-h intervals postprandially (e.g., calorimetry, HRV, and appetite) the four postprandial values were averaged. Age was a known confounder for HRV and was significantly associated with our HRV outcomes; we, therefore, considered age as a covariate for the statistical analyses on HRV. For each ANOVA, we examined interaction effects and main effects, but did not conduct the many possible *post hoc* comparisons due to lack of statistical power. Sphericity was tested using Mauchly's test of sphericity. In the events where Mauchly's sphericity test was significant the Greenhouse–Geisser correction was used.

Baseline characteristics were compared between groups using independent *t*-tests. Secondary analyses also examined the bivariate correlations among variables (e.g., IL-6 vs. GLP-1). Statistical tests were two-tailed, and *p*-values ≤ 0.05 were considered significant. Statistical analyses were performed with SPSS 21 (SPSS, Inc., Chicago, IL, USA).

# RESULTS

# Participants

All 12 participants (6 T2D and 6 healthy) completed the study. Baseline characteristics are presented in **Table 1**. T2D and healthy participants had an average age of 60.5 ± 8.5 and 42.5 ± 10.5 years (*p* < 0.01) and an average body mass index (BMI) of 24.8 ± 4.3 and 26.7 ± 3.2 kg/m2 (*p* = 0.39), respectively.

*T2D, type 2 diabetes; BMI, body mass index; A1c, glycated hemoglobin; NA, not applicable; NS, not significant; VT, ventilatory threshold; SBP, systolic blood pressure; DBP, diastolic blood pressure; RMR, resting metabolic rate. Data presented as mean* ± *SD.*

All T2D participants had a well-controlled glycemia as suggested by their A1c (6.4 ± 0.3%). They were treated with 500 to 1,500 mg of metformin per day and the average duration of diabetes diagnosis was 3.9 ± 2.3 years.

# Day 1

### Energy Expenditure and HR

All participants completed both 90-min exercise bouts without requiring adjustments to the exercise intensity. Indirect calorimetry and HR results from the exercise bout and corresponding rest conditions are presented in **Table 2**. As expected, energy expenditure and RER were significantly increased with exercise (main effect of *Exercise p* < 0.001). Energy expenditure corresponded to approximately one MET on the rest day and seven METs during exercise with no significant difference between T2D and healthy participants (see **Table 2** for details). In addition to an increased RER with exercise, a significant *Exercise* by *Diabetes* interaction (*p* = 0.036) and an *Exercise* by *Time* interaction (*p* < 0.001) were observed for RER. These interactions were the result of RER being lower on the rest day in the healthy participants and after lunch in the exercise condition but greater after lunch in the rest condition. During the exercise bouts, HR averaged 121 ± 3 beats per minute during the first 10 min of each bout of exercise, or 72 ± 2% of age predicted maximum HR, and drifted upwards throughout exercise (main effect of time *p* = 0.001).

### Plasma Samples

Results of blood sample analyses from day 1 are summarized in **Table 3** and **Figure 2**. There was a significant *Time* by *Diabetes* interaction (*p*= 0.03) suggesting that glucose changed to a greater extent in T2D over time. In addition, there was a significant main effect of *Exercise* leading to lower overall glucose concentrations in the exercise condition (*p* = 0.02).

There was a significant *Exercise* by *Time* interaction for IL-6 (*p* = 0.03). Visual Inspection of the graph in **Figure 2** suggests that exercise increased IL-6 compared to rest when performed in the fasting state but not when performed after lunch when IL-6 was increased overall compared to fasting. The IL-6 responses

*Outcomes were measured during the first 10 min and last 10 min of each bout of exercise (or rest).*

*T2D, type 2 diabetes; Ex, exercise; RER, respiratory exchange ratio; EE, energy expenditure; METs, metabolic equivalent (kcal/kg/h); HR, heart rate; bpm, beats per minute; NA, not available.*

*Data presented as mean* ± *SD. ANOVA examined main effect of exercise, diabetes, time, and their interactions. Only significant p-values are shown.*

Table 3 | Concentrations of energy substrates and hormones before and after two 90-min moderate-intensity exercise bouts or rest on day 1.

*Outcomes were measured immediately before and immediately after of each bout of exercise (or rest). T2D, type 2 diabetes; Ex, exercise; NEFA, non-esterified fatty acids. Results presented as mean* ± *SEM. ANOVA examined main effect of exercise, diabetes, time, and their interactions. Only significant p-values are shown.*

were similar for participants with and without T2D (**Figure 2**). GIP followed a similar pattern during the first exercise bout compared to rest, but increased to a greater extent in healthy participants following lunch, leading to an *Exercise* by *Time* by *Diabetes* interaction (*p* = 0.03). Overall plasma metformin concentrations decreased from 402 ± 120 to 191 ± 52 ng/ml (main effect of *Time p* = 0.03) throughout day 1 and were not affected by exercise.

# Day 2

### Energy Expenditure, HR, and Appetite

There were main effects of *Time* on energy expenditure and RER during the OGTT (both *p* < 0.001). There were no effects of *Exercise* or *T2D* on energy expenditure in the fasting state or postprandially. RER was greater in participants with diabetes but lower after exercise throughout day 2 (main effect of *T2D* and *Exercise,* both *p* ≤ 0.01 see **Table 4**). There was no statistically significant effect of exercise or diabetes and HRV indices during the OGTT. Overall, ratings for prospective food consumption were higher during the OGTT from the exercise condition compared to the rest condition (*p* = 0.03). However, postprandial fullness decreases following exercise in T2D only (Diabetes by Exercise interaction *p* = 0.04 for fasting; *p* = 0.056 for the mean postprandial values). Participants with T2D had a lower desire to eat something sweet in the fasting (*p* = 0.01) and postprandial state (*p*= 0.03), but a Diabetes by Exercise interaction (*p* = 0.045) indicated that exercise tended to increase the desire to eat something sweet in T2D while decreasing this rating in healthy participants during the OGTT.

#### Plasma Samples

There were significant main effects of *Time* on all energy substrates and hormones on day 2. Therefore, analyses were conducted separately for the fasting and postprandial values. There was a main effect of *Diabetes* on fasting, AUC, and iAUC glucose (all *p* < 0.05) but no main effect of *Exercise* on the AUC and iAUC. However, there was a main effect of *Exercise* on fasting glucose (*p* = 0.05), with a 0.5 and 0.1 mmol/L decrease fasting glucose in the morning following exercise in the T2D and healthy control group, respectively (note: the *Diabetes* by *Exercise* interaction was not significant, *p* = 0.35), see **Figure 3**.

Exercise increased fasting glucagon in the healthy control group but not in T2D (*Exercise* by *Diabetes* interaction, *p* = 0.02), whereas the postprandial iAUC for glucagon was reduced by exercise (main effect of E*xercise*, *p* = 0.01). Fasting insulin was not affected by exercise but iAUC and AUC insulin were reduced (main effect of *Exercise*, *p* = 0.08, *p* = 0.01 and *p* = 0.001, respectively). In terms of the insulin:glucagon ratio, both the iAUC and AUC were reduced following exercise (main effect of *Exercise p* = 0.04 and *p* = 0.004, respectively), see **Figure 3**.

Fasting active GLP-1 and GIP concentrations showed a small increase with exercise (main effect of *Exercise*, both *p* < 0.05). Exercise on the previous day did not affect postprandial incretin hormones during the OGTT, see **Figure 3** and **Table 5**.

Upon arrival on day 2, fasting plasma metformin concentrations were very low and similar between exercise and rest (i.e., control) conditions (76 ± 17 to 83 ± 18 ng/ml, respectively, main effect of *Time, p* = 0.03).

### Bivariate Correlations

There was no significant bivariate correlation between changes in IL-6, insulin or glucagon and changes in active GLP-1 or GIP, either when examining the participants with and without T2D together or separately on day 1. On day 2, there was an inverse association (*r* = −0.60, *p* = 0.038) between the exerciseinduced changes in lactate and HRV as assessed by RMSSD. No associations were found between incretins and glucagon or insulin.

Table 4 | Indirect calorimetry and heart rate variability during fasting and following an oral glucose tolerance test on day 2.

*T2D, type 2 diabetes; Ex, exercise; RER, respiratory exchange ratio; EE, energy expenditure; METs, metabolic equivalent or kilocalories divided by kilograms of body mass and hours (kcal/kg/h); HR, heart rate; bpm, beats per minute; SDRR, SD of the R–R intervals; rMSSD, root mean squared of the successive differences between R–R intervals; LF/HF, the ratio of low frequency spectral power to high frequency spectral power; mean postprandial, average from 10 min at the end of each of the four 1-h postprandial periods;* Δ*Postprandial, mean postprandial minus fasting. Data presented as mean* ± *SD. ANOVA examined main effect of exercise, diabetes, time, and their interactions. Only significant p-values are shown.*

Figure 3 | Day 2 fasting plasma concentrations (−15 and 0 min) and responses to an oral glucose tolerance test (area under the curve = AUC; incremental AUC = iAUC) for glucose, glucagon, insulin, and active glucagon-like peptide-1 (GLP-1), the day after two 90-min bouts of exercise (▪) vs. rest (⚬) in healthy participants (left panels) and in type 2 diabetes (T2D) (right panels). Results from 2 × 2 ANOVA showing main effects of exercise vs. rest, diabetes vs. control, and their interaction. Data shown as mean ± SEM.

# DISCUSSION

To our knowledge, no other study in T2D has examined the glycemic, hormonal, and metabolic responses to exercise of such a high volume in a single day (i.e., 3 h walking). Although other studies have suggested a dose–response relationship between exercise duration and improvements in glucose tolerance or insulin sensitivity (36–38), we did not

*T2D, type 2 diabetes; Ex, exercise; AUC, area under the curve; iAUC, incremental area under the curve; T2D, type 2 diabetes; NEFA, non-esterified fatty acids. For all outcomes, there was a sigificant main effect of time. Data presented as mean* ± *SEM. ANOVA examined main effect of exercise, diabetes, and their interactions. Only significant p-values are shown.*

observe any improvements in glucose tolerance following 3 h of exercise.

Unlike other studies in T2D or obesity which utilized shorter bouts of exercise (21, 22, 39), we observed elevated incretin hormones, particularly GIP, immediately after exercise (**Figure 2**). This increase persisted to the following day in the fasted state but not during the OGTT. It is unclear if these increases are practically meaningful as the increases were small in absolute terms and occurred at times when incretins were low. The increase was nonetheless consistent as we were able to detect these differences with a small sample size. In the participants with T2D who had relatively well-controlled glycemia, postprandial plasma incretin concentrations were not lower in T2D compared to healthy controls. While earlier studies suggested lower incretins in T2D, recent meta-analyses suggest that this is not always the case for both GIP (40) and GLP-1 (41). Another possibility to explain the strong incretin response (particularly for GLP-1) during the OGTT in our participants with T2D was that they were prescribed metformin, an oral hypoglycemic medication that has been shown to increase incretins (21). However, the last metformin dose had been consumed at least 36 h before the OGTT and metformin concentrations had been reduced less than 5% of the concentrations we observed in the hours following a morning dose of metformin (21). However, it is not known if the effect of long term metformin treatment could have persisted beyond 36 h.

According to Ellingsgaard et al. (19) an increased GLP-1 following exercise may be due to increased IL-6. Importantly, the increase in plasma IL-6 during exercise can be directly attributed to secretion from skeletal muscle and IL-6 is thought to be secreted in proportion to glycogen depletion [as reviewed by Pedersen (20)]. We observed that plasma IL-6 only increased compared to rest during the first exercise bout, which was performed in the fasting state and not during the second bout performed after lunch. The design of the present study does not allow us to conclude if the absence of an effect of exercise on plasma IL-6 after lunch was due to the meal itself or to a reduced effect when sequential exercise bouts are performed. However, a recent study found that consuming a carbohydrate beverage during 120 min of cycling abolished leg IL-6 release even though muscle glycogen was reduced to a similar extent compared to fasting exercise (42). IL-6 was also increased by lunch itself, which is consistent with previous studies (43, 44). Therefore, it appears that exerciseinduced IL-6 secretion requires, or at least is more pronounced, with fasting exercise protocols.

A notable finding in the present study was that, in accordance with the hypothesis, two long bouts of exercise enhanced the postprandial suppression of glucagon (i.e., reduced iAUC). The postprandial suppression of glucagon is thought to be impaired in people with T2D (7). While both insulin and glucagon were lowered by exercise during the OGTT, insulin was reduced to a greater extent as reflected as a decrease in both the iAUC and AUC for the insulin:glucagon ratio. Insulin acts to suppress glucagon secretion; therefore, the observation of a lower glucagon in the presence of lower insulin is noteworthy since previous studies using hyperinsulinemic clamp protocols reported a reduced glucagon following exercise when insulin was maintained in the exercise and rest conditions (8, 9, 29). The mechanism by which this form of exercise suppresses postprandial glucagon concentration in T2D cannot be elucidated from this study and is indeed a topic of continued interest and debate (7, 45). Despite postprandial glucagon being reduced in our participants with T2D, postprandial hyperglycemia was not improved. While this may be disappointing from a clinical perspective, the similar concentrations of plasma glucose in both conditions may be considered fortuitous to examine changes in glucoregulatory hormones without needing to clamp glucose at a fixed concentration.

From a theoretical perspective, the absence of a glucose lowering effect of exercise during an OGTT performed on the following day in T2D was unexpected. It is generally believed that the glucose lowering effect of exercise is proportional to the duration or volume of exercise (37, 38). Although our study had a small sample size, the absence of the expected glucose lowering effect of exercise is unlikely to be due low statistical power as the post OGTT glucose AUC was slightly higher (1%) in the exercise condition. The reasons for the unchanged glucose were unclear, although postprandial insulin was reduced suggesting improved insulin sensitivity. Other studies have documented an absence of improvement in OGTT following longer bouts of exercise. For example, Tremblay et al. observed an increase in glucose AUC during an OGTT performed 16 h after 90 min of cycling at 67% of VO2max (46). They attributed this increased to an increased adipose tissue lipolysis, increased NEFA, and a decreased glucose oxidation (46). This explanation is consistent with our observation of a decreased RER (i.e., indicating a decreased carbohydrate oxidation) and a tendency for increased NEFA (*p* = 0.09) during the OGTT from the exercise condition.

A primary limitation of the present study was the small sample size. As a result, our study was underpowered to detect potentially meaningful effects of exercise or diabetes. Our randomized crossover design helped to reduce the impact of this limitation on statistical power when comparing the exercise and rest conditions. However, the between participant comparison of T2D (*n* = 6) to healthy control (*n* = 6) was particularly underpowered for some outcomes.

The validity of conclusions regarding comparisons between T2D and healthy controls was further impaired by these small subgroups which were not matched for possible confounders (e.g., age, BMI, and fitness). BMI and exercise-induced energy expenditure ended up being relatively similar between the healthy and T2D participants; however, the healthy control group was younger and likely had different body composition (e.g., more fat free mass). Age was not associated with most outcomes in our study with the notable exception of HRV. HRV was lower in our T2D participants but these differences were no longer significance after adjusting for age. Although not statistically significant, there were trends to suggest an increase in indices of HRV following exercise in T2D but not in healthy participants. In addition to detecting differences in glucose, our study was able to detect other expected differences between healthy participants and T2D [e.g., RER (47) and glucagon (11)]. Nonetheless, the primary contributions to be retained from this article should be in regards to the exercise vs. control comparison.

The participants with T2D that were recruited for our study were likely more fit, more physically active, and leaner than many people with T2D. Such participants were selected to increase the likelihood of completing the exercise protocol and reduce the risk of injury. However, this selection also introduces potential bias. The phenotypic differences in our participants could influence many of the hormonal and metabolic responses to exercise. For example, participants with lower fitness or greater adiposity may have a different inflammatory profile or different inflammatory

# REFERENCES

1. Canadian Diabetes Association Clinical Practice Guidelines Expert Committee, Sigal RJ, Armstrong MJ, Colby P, Kenny GP, Plotnikoff RC, et al. Physical activity and diabetes. *Can J Diabetes* (2013) 37(Suppl 1):S40–4. doi:10.1016/j.jcjd.2013.01.018

response to exercise (48, 49). This could potentially reduce generalizability of our result.

Another limitation of the study was the reliance on plasma concentrations of hormones taken from peripheral blood samples. These concentrations from the systemic circulation often do not reflect the exposure of other organs to these hormones (e.g., pancreas or liver). In addition, our multiplex hormone assay used has limitations in regards to specificity. For example, the glucagon assay has been shown to have some cross-reactivity with glicentin (or to a lesser extent oxyntomodulin) (50).

In conclusion, exercise can affect a variety of pathological features that can contribute to hyperglycemia. Potential benefits include decreasing postprandial hyperglucagonemia and increasing incretin concentrations. The exercise protocol (i.e., two 90 min bouts of exercise) used in this study is likely not feasible for most people. Larger samples sizes and closer matching of participant characteristics would be required to more carefully address differences between participants with normal glucose tolerance and those with T2D. Future studies should seek to better understand if similar results can be obtained with shorter exercise protocols, as well as the persistency of the observed changes.

# ETHICS STATEMENT

This study was carried out in accordance with the recommendations of the Tri-Council Policy Statement on "Ethical Conduct for Research Involving Humans" with written informed consent from all subjects. The protocol was approved by the University of Alberta Health Research Ethics Board.

# AUTHOR CONTRIBUTIONS

NB, SRE, CS, MD, GP, PS, JL, and DB contributed to the conception of the study and obtained funding for this project. SRE, NB, and ÉM-C collected the data. SRE, NB, ÉM-C, KF, RG, and CD analyzed the data. SRE and NB drafted the manuscript, and all authors critically reviewed the manuscript to provide important intellectual content.

# FUNDING

This study was funded by an Alberta Diabetes Institute Pilot Project Award. SRE and ÉM-C were supported by Alberta Diabetes Institute Graduate Scholarship and KF was supported by an Alberta Diabetes Institute Summer Studentship. JL was supported by a CIHR New Investigator Salary Award (MSH-141980).

3. Pan XR, Li GW, Hu YH, Wang JX, Yang WY, An ZX, et al. Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance.

2. Colberg SR, Sigal RJ, Yardley JE, Riddell MC, Dunstan DW, Dempsey PC, et al. Physical activity/exercise and diabetes: a position statement of the American Diabetes Association. *Diabetes Care* (2016) 39(11):2065–79. doi:10.2337/dc16-1728

The Da Qing IGT and Diabetes Study. *Diabetes Care* (1997) 20(4):537–44. doi:10.2337/diacare.20.4.537

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2017 Eshghi, Fletcher, Myette-Côté, Durrer, Gabr, Little, Senior, Steinback, Davenport, Bell, Brocks and Boulé. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Prevalence of Non-responders for Glucose Control Markers after 10 Weeks of High-Intensity Interval Training in Adult Women with Higher and Lower Insulin Resistance

Cristian Álvarez 1, 2, Rodrigo Ramírez-Campillo1, 2, Robinson Ramírez-Vélez 3 and Mikel Izquierdo4 \*

1 Department of Physical Activity Sciences, Universidad de Los Lagos, Osorno, Chile, 2 Research Nucleus in Health, Physical Activity and Sports, Universidad de Los Lagos, Osorno, Chile, 3 Centro de Estudios en Medición de la Actividad Física, Escuela de Medicina y Ciencias de la Salud, Universidad del Rosario, Bogotá, Colombia, 4 Department of Health Sciences, Public University of Navarra and Centro de Investigación Biomédica en Red (CIBER) en Fragilidad y Envejecimiento Saludable del Instituto de Salud Carlos III, Pamplona, Spain

Edited by: Adeel Safdar, Humber College, Canada

# Reviewed by:

Zsolt Radak, Semmelweis University, Hungary Ayesha Saleem, Humber College, Canada Martin Gibala, McMaster University, Canada

> \*Correspondence: Mikel Izquierdo<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Exercise Physiology, a section of the journal Frontiers in Physiology

Received: 23 November 2016 Accepted: 22 June 2017 Published: 06 July 2017

#### Citation:

Álvarez C, Ramírez-Campillo R, Ramírez-Vélez R and Izquierdo M (2017) Prevalence of Non-responders for Glucose Control Markers after 10 Weeks of High-Intensity Interval Training in Adult Women with Higher and Lower Insulin Resistance. Front. Physiol. 8:479. doi: 10.3389/fphys.2017.00479 Background: Exercise training improves performance and biochemical parameters on average, but wide interindividual variability exists, with individuals classified as responders (R) or non-responders (NRs), especially between populations with higher or lower levels of insulin resistance. This study assessed the effects of high-intensity interval training (HIIT) and the prevalence of NRs in adult women with higher and lower levels of insulin resistance.

Methods: Forty adult women were assigned to a HIIT program, and after training were analyzed in two groups; a group with higher insulin resistance (H-IR, 40 ± 6 years; BMI: 29.5 ± 3.7 kg/m2 ; n = 20) and a group with lower insulin resistance (L-IR, 35 ± 9 years; 27.8 ± 2.8 kg/m2 ; n = 20). Anthropometric, cardiovascular, metabolic, and performance variables were measured at baseline and after 10 weeks of training.

Results: There were significant training-induced changes [delta percent (1%)] in fasting glucose, fasting insulin, and homeostasis model assessment of insulin resistance (HOMA-IR) scores in the H-IR group (−8.8, −26.5, −32.1%, p < 0.0001), whereas no significant changes were observed in the L-IR.  Systolic blood pressure decreased significantly only in the L-IR group (−3.2%, p < 0.010). Both groups showed significant increases in 1RMLE (+12.9, p < 0.010, and +14.7%, p = 0.045). There were significant differences in the prevalence of NRs between the H-IR and L-IR groups for fasting glucose (25 vs. 95%, p < 0.0001) and fasting insulin (p = 0.025) but not for HOMA-IR (25 vs. 45%, p = 0.185).

Conclusion: Independent of the "magnitude" of the cardiometabolic disease (i.e., higher vs. lower insulin resistance), no differences were observed in the NRs prevalence with regard to improved HOMA-IR or to anthropometric, cardiovascular, and muscle performance co-variables after 10 weeks of HIIT in sedentary adult women. This research demonstrates the protective effect of HIIT against cardiometabolic disease progression in a sedentary population.

 Similarly, exercise-based interventions, including resistance training (RT), together with pharmacological, and dietary interventions, represent the cornerstones of T2DM management (ADA, 2011). In addition to the beneficial effects on glycemic control (Umpierre et al., 2013) and other risk factors of T2DM (Chudyk and Petrella, 2011; Figueira et al., 2014), physical exercise is effective in improving muscle strength (Dunstan et al., 2002), cardiovascular function (Cano-Montoya et al., 2016), and functional capacity (Cadore and Izquierdo, 2015). In this regard, combining RT and endurance training is an effective intervention to promote overall physical fitness in T2DM patients (Balducci et al., 2012). More recently, high-intensity interval training (HIIT, i.e., repeated short bursts of high intensity activity with rest breaks in between each bout of exercise) is a time-efficient exercise modality that has emerged as an alternative to continuous traditional endurance exercise training to improve cardiometabolic health (Gibala et al., 2012).

However, despite the frequent reports of "average" exerciserelated changes, there is wide interindividual variability in the results of exercise training (Astorino and Schubert, 2014). Under the same stimulus, some subjects, termed responders (R), achieve benefits after training, while others, termed non-responders (NRs), show an unchanged or worsened response (Bouchard et al., 2012; Bonafiglia et al., 2016; Álvarez et al., 2017). In the literature, this phenomenon has been characterized using several terms, such as low/high responders (Davidsen et al., 2011), non-responders/responders (Sisson et al., 2009), or as an adverse response (Bouchard et al., 2012); in these studies, similar but slightly different methodological criteria have been applied for identifying R and NRs. Genetic (Stephens et al., 2015) and environmental factors (Bouchard and Rankinen, 2001) have been suggested to be responsible for this variability, although not all of the potential environmental factors (e.g., different health status, magnitude of disease, or different mode of exercise training) have been explored.

Furthermore, the prevalence of these unchanged or worsened responses, known as NRs prevalence (i.e., percentage of subjects who do not improve/show a worsened response with regard to a variable), has been reported predominantly after endurance training (Sisson et al., 2009; Bouchard et al., 2012) and RT (Moker et al., 2014; Churchward-Venne et al., 2015). There have been no studies reporting the NRs prevalence associated with risk factors for T2DM after HIIT, which has been shown to improve anthropometric, cardiovascular, metabolic, and performance variables in different cohorts (Astorino and Schubert, 2014; Alvarez et al., 2016). For example, in one study of insulin resistance adult women, there were reductions of −12 to −14% in fasting glucose, −27 to −37% in fasting insulin and ∼40% in homeostasis model assessment of insulin resistance (HOMA-IR) scores after 8 weeks of HIIT (Álvarez et al., 2014). In another study of T2DM subjects, there was a decrease of ∼14% in fasting glucose, with additional decreases of ∼4 mmHg in blood pressure, ∼2% in body mass, ∼4% in waist circumference, and ∼19% in subcutaneous fat after 16 weeks of HIIT (Alvarez et al., 2016). Notably, another study showed that only 2 weeks of HIIT decreased the average 24 h fasting glucose level by approximately −13% (Little et al., 2011). Finally, a study of subjects with poor glucose control showed an improvement (−12%) in the area under the curve for the oral glucose tolerance test (OGTT) results and a 4.2 kg decrease in fat mass after 10 weeks of HIIT (Mancilla et al., 2014).

Latin America has experienced an epidemiological transition characterized by an increasing burden of cardiometabolic disease due to physical inactivity and shifts in diet and lifestyle patterns (Rivera et al., 2014). Evidence in Chilean adults suggests similar associations between low physical activity levels and cardiometabolic risk factors and between health status and overweight/obesity (Vio et al., 2008). Thus, the aim of the present study was to assess the effects 10 weeks of HIIT and the NRs prevalence (as indicated by glucose control variables) in groups with higher and lower levels of insulin resistance. A second aim was to assess other anthropometric, cardiovascular, and performance variables. We hypothesized that independent of the magnitude of the metabolic disease [i.e., higher (HOMA-IR > 5.0) or lower (HOMA-IR < 3.0) levels of insulin resistance], there would be no differences in the NRs prevalence for changes in glucose control parameters after HIIT between women with higher

**Abbreviations:** T2DM, type 2 diabetes mellitus; R, responders; NRs, nonresponders: HIIT, high-intensity interval training; H-IR: higher insulin resistance group; L-IR, lower insulin resistance group; BMI, body mass index; HOMA-IR, homeostasis model assessment of insulin resistance; 1RM, one repetition maximum strength test; 1RMLE, one repetition maximum test of leg extension; 1RMUR, one repetition maximum test of upper row; HbA1c, glycated hemoglobin, OGTT, oral glucose tolerance test; VO2peak, maximum peak of oxygen uptake, VO2max, maximum oxygen uptake.

and lower levels of insulin resistance using the HOMA-IR criteria.

# MATERIALS AND METHODS

# Participants

The first stage of the study was to recruit, using a short telephone survey, adult patients who were previously identified at their last clinical exam as at risk for T2DM and who had dropped out from their regular appointments at the healthcare center. In this first stage, 168 sedentary adult women (aged ≥18 years) with no background of regular exercise training volunteered to be screened.

# Screening and Preliminary Testing

First, subjects were screened for insulin resistance based on a HOMA-IR > 2.6 using both fasting glucose and fasting insulin, and after intervention the subjects were separated into two groups and analyzed based on differences in the "magnitude" of insulin resistance (i.e., a group with a higher level of insulin resistance: HOMA-IR > 5.0, and a group with a lower level insulin resistance: HOMA-IR < 3.0). In the first screening before intervention, 65 individuals classified as "higher insulinresistance" subjects (n = 65) were excluded for multiple reasons (16 due to age > 40 years; 2 due to being recently physically active; 5 due to a diagnosis of hypertension; 5 due to a diagnosis of hypothyroidism; 3 due to musculoskeletal injury; 21 due to no history of T2DM; 6 due to stationary asthma/respiratory disease; and 7 due to having a rural address). Similarly, 25 subjects identified as "lower insulin-resistance" subjects (n = 25) were also excluded for similar reasons (6 due to age ≥40 years; 12 due to being physically active; 2 due to a diagnosis of hypothyroidism; and 5 due to having a rural address). Finally, 78 screened subjects with a higher level of insulin resistance (n = 78) were assigned to 10 weeks of a HIIT program and were analyzed after intervention in two different groups: a group with a higher level of insulin resistance (H-IR, n = 20) and a group with a lower level of insulin resistance (L-IR, n = 20). None of the subjects were taking oral hypoglycemic medications to improve metabolic control of glucose because they all had been recently diagnosed with insulin resistance by our research team. Subjects with < 70% attendance at training sessions were excluded from all statistical analyses after the intervention; after excluding those subjects, the characteristics of the analyzed subject groups were as follows: H-IR group, age 40 ± 6 years, n = 20; L-IR group, age 35 ± 9 years, n = 20 (see flow chart in **Figure 1**). The treatment allocation is described in the flow chart in **Figure 1**.

All participants were informed about the experimental procedures and about possible risks and benefits associated with participation in the study. Written informed consent was obtained before any of the assessments were performed. The study was conducted in accordance with the Declaration of Helsinki and was approved by the institutional review board for the use of human subjects of the local Ethics Committee of the University of los Lagos (Comité de Revisión Científica y Ética Institucional del Departamento de Ciencias de la Actividad Física de la Universidad de Los Lagos). Characteristics of the study participants are provided in **Tables 1**, **2**.

Eligibility criteria included the following: (a) diagnosed with insulin resistance based on the HOMA-IR metabolic marker method and using a cut-off point of HOMA-IR ≥2.6 in a Chilean population (Garmendia et al., 2009), (b) physical inactivity (≤150 min/week of low-moderate physical activity or <75 min/week of vigorous physical activity; O'Donovan et al., 2010), (c) no familial (parents/siblings) history of T2DM, (d) living only in urban areas, and (e) with care provided under the public Chilean healthcare system (i.e., not a private healthcare system). Exclusion criteria included participants with the following characteristics: (a) potential medical or musculoskeletal problems, (b) osteoarthritis, (c) history of ischemic disease, (d) arrhythmia, (e) asthma, (f) chronic obstructive pulmonary disease, and (g) utilization of drugs that modulate metabolic or respiratory control.

# Classification of Responder (R) and Non-responders (NRs)

Using previous criteria applied in exercise-based interventions (Bonafiglia et al., 2016), the interindividual variability in the response to exercise training of the subjects was used to categorize them as responders (R) or non-responders (NRs) using the typical error measurement (TE). Thus, the TE was calculated for all independents variables 3 weeks before the pre-test measurements as described previously (Álvarez et al., 2017) using the following equation:

$$\text{TE} = \text{SD}\_{\text{diff}} / \sqrt{2} \tag{1}$$

where SDdiff is the variance (standard deviation) of the difference in scores observed between the two repeats of each test. A nonresponders participant for HOMA-IR assessments, as well as for all other included co-variables, was defined as an individual who failed to demonstrate a decrease or increase (in favor of beneficial changes) that was greater than two times the TE from zero. A change greater than two times the TE means that there is a high probability (i.e., 12 to 1 odds) that this response is a true physiological adaptation beyond what might be expected to result from technical and/or biological variability (Hopkins, 2000).

# Procedures

### Anthropometric and Cardiovascular Assessments

Anthropometric and blood pressure assessments were carried out during the first week of the allocation stage. Body mass was assessed using a digital scale with an accuracy of 0.1 kg (Omron HBF-INTTM, Omron Healthcare Inc., Lake Forest, IL, USA). Height was assessed with a professional stadiometer (Health o MeterTM Professional, Sunbeam Products Inc., Chicago, IL, USA) with an accuracy of 0.1 cm, and body mass index (BMI) was calculated according to the following formula: kg/m2 . Waist circumference was assessed with an inextensible measuring tape with and accuracy of 0.1 cm (HoechstmassTM, Sulzbach, Germany). Three skinfold measurements of subcutaneous adipose tissue (i.e., tricipital,

supra-iliac, and abdominal skinfolds) were assessed using a LangueTM skinfold caliper (Beta Technology Inc., Santa Cruz, California, USA) according to standard protocols (Marfell-Jones et al., 2006).

Systolic and diastolic blood pressure were assessed using an automatic monitor (Omron HEM 7114TM, Omron Healthcare Inc., Lake Forest, IL, USA) in triplicate (2-min interval between measurements) after 15 min of rest and with the subjects in a seated position following standard classification procedures (Chobanian et al., 2003).

### Plasma Metabolic Markers

The metabolic measurements were carried out in the second week. Subjects arrived at the laboratory of the Riñihue clinic between 8 and 10 in the morning after a 10 h overnight fast. Blood samples (approximately 3.5 mL) were collected in tubes with specific anticoagulant gels for fasting glucose and fasting insulin measurements at baseline and at the 10 week follow-up. Samples were placed on ice and centrifuged at 4,000 rpm (1,700 × g) for 5 min at 4◦C. Plasma samples were immediately transferred to pre-chilled microtubes and stored at −20◦C for later analysis.

Plasma glucose was analyzed via enzymatic methods using standard kits (Wiener Lab Inc., Rosario, Argentina) on an automatic analyzer (Metrolab 2300 PlusTM, Metrolab Biomed Inc., Buenos Aires, Argentina). Fasting insulin was measured via RIA (DPC, Los Angeles, CA, USA). The HOMA-IR index was calculated using the Matthews equation (Matthews et al., 1985): HOMA-IR = [Fasting glucose (mg/dL) × Fasting insulin (µU/dL)]/405). The same blood sampling and preparation procedure was performed at the end of the 10 week follow-up 48 h after the last exercise session in the morning to avoid possible acute effects of exercise.

# Familiarization with the Exercise Training Program

In weeks 3 and 4, in three sessions, the subjects in both the H-IR and L-IR groups underwent a familiarization period for the HIIT protocol, as well as for the 1RMLE- and 1RMUR-tests. In the first and second sessions, the subjects were educated about the cycling machines and the free weights, as well as the exercise machine used for the strength test. In the following four sessions, the subjects underwent HIIT.

# One-Repetition Maximum Test

In week 5, after a familiarization process with the test and before the intervention, both groups performed one-repetition maximum strength tests to establish 1RMLE- and 1RMUR-values as previously described (Izquierdo et al., 2004). The 1RMLEtest involved an exercise machine (OXFORDTM, model EE4002, Santiago, Chile), and in the 1RMUR-test, free weights with bars were used. In brief, for the 1RMLE-test, the subjects began by lifting a load of weights on the machine with both legs. For the 1RMUR-test, the subjects adopted a body flexion angle of 90◦ , grabbed a bar with weights and plates, and with both arms extended, lifted the bar to approximately knee height. The highest load from three attempts per exercise was reported.

# Experimental Protocol

The HIIT program was started in the sixth week and was performed three times per week, for a total of 30 sessions, using exercise bikes (OXFORDTM, model BE2601, OXFORD Inc., Santiago, Chile). Each participant performed a range of 8–12 cycling intervals during the intervention period. The time of each cycling work interval was 60 s, with 120 s of passive rest (sitiing on the bicycle without movement) between work intervals. This rest period was progressively decreased (2 min weeks 1–2, 1.45

# Indicates "small" standardized

&Indicates "moderate" standardized

 ES at level p ≤ 0.05.

 ES at level p ≤ 0.05.

 upperrow.

 Bold values denotes significant (p<0.05) differences in all the respectives comparisons.

×

time.

 †AnalyzedbyRepeatedMeasuresgroup

 ‡AnalyzedbyANOVAone-way/orANCOVA.

 §AnalyzedbyBonferronipost-hoc.

 #Indicates

 "small" standardized ES at level p ≤ 0.05. &Indicates0.05.

 "moderate" standardized ES at level p ≤ ¶Indicates"large"standardizedESatlevelp≤0.05.

TABLE 2 |

HOMA-IR

Frontiers in Physiology | www.frontiersin.org

>

5.0 (H-IR), and a group with a lower level insulin resistance:

Cardiovascular,

 metabolic, and performance

 of the subjects before and after 10-weeks of high-intensity

 HOMA-IR

<

3.0 (L-IR).

 interval training in a group of adult women with a higher level of insulin resistance:

characteristics

min weeks 3–5, 1.30 min weeks 6–8, and 1.15 min weeks 9–10), reaching a time of 1.15 min in the tenth week. Cycle revolutions were maintained at a range of 50–70 rpm and a speed between 20 and 40 km/h during each work interval. Subjects were required to cycle at levels between 8 and 10 points on a modified 0–10 Borg scale during the work interval (Ciolac et al., 2015). This subjective intensity corresponds to a range of 70–100% of the maximum heart rate according to the Karvonen formula (Karvonen and Vuorimaa, 1988). All subjects had good exercise tolerance, and none of the participants reported an injury. Exercise compliance was 82.0 ± 3% in the H-IR group and 79.3 ± 4% in the L-IR group.

# Statistical Analysis

Data are presented as the mean ± standard deviation (SD). Normality and homoscedasticity assumptions for all data were assessed using the Shapiro-Wilk-test and Levene's-test, respectively. The Wilcoxon-test was used for non-parametric data. One-way ANOVA was performed to test differences between groups at baseline. An ANCOVA was conducted to analyze variables that were significantly different at baseline. A repeated-measures ANOVA (group × time) was used to determine differences in all dependent variables between preand post-tests using each group × time. A chi-square test (X 2 ) was used to determine differences between categorical variables for R and NRs by group (H-IR × L-IR). After the intervention, the typical error (TE) were calculated for the pre-post changes for each dependent variable. The subjects were categorized as a R or NRs according to the previously described criteria of a change greater than two times the TE (Bonafiglia et al., 2016). The Bonferroni post-hoc test was applied to establish differences among groups. Additionally, Cohen's-test was used to detect effect sizes (ESs), with threshold values at 0.20, 0.60, 1.2, and 2.0 for small, moderate, large, and very large effects, respectively (Hopkins et al., 2009). ES-values are presented as the mean with 95% confidence limits. Odds ratios (ORs) were used to assess differences in dichotomous NRs variables between groups. All statistical analyses were performed with SPSS statistical software version 18 (SPSSTM Inc., Chicago, Illinois, USA). The alpha level was fixed at p ≤ 0.05 for determining statistical significance in all cases.

# RESULTS

# Anthropometric Measurements

At baseline, there were significant (p ≤ 0.05) differences between groups for abdominal skinfold thickness (**Table 1**). There were significant (p ≤ 0.05) pre-post changes [presented as delta percent (1%)] in waist circumference (−5.2, −3.8%) and in tricipital (−13.3, −13.6%), supra-iliac (−19.4, −13.6%), and abdominal skinfold thicknesses (−18.2, −15.6%) in both the H-IR and L-IR groups (**Table 1**).

# Cardiovascular Measurements

At baseline, there were no significant (p > 0.05) differences between the groups for diastolic or systolic blood pressure (**Table 1**). After intervention, the L-IR group showed significant pre-post changes in systolic blood pressure (−2.3%) (**Table 2**), whereas there were no significant changes in diastolic blood pressure in any group (**Table 2**).

# Metabolic Measurements

At baseline, there were significant (p ≤ 0.05) differences between the groups for fasting glucose, fasting insulin, and HOMA-IR scores (**Table 2**). After intervention, there were no pre-post changes in fasting glucose, fasting insulin, or HOMA-IR scores in L-IR group (**Table 2**). There were significant (p ≤ 0.05) pre-post changes [presented as delta percent (∆%)] in fasting glucose (−8.8%), fasting insulin (−26.5%), and HOMA-IR scores (−32.1%) in the H-IR group (**Table 2**). The ES-values were high for fasting glucose (−1.65; 95%CI −2.07, −1.22) and HOMA-IR scores (−1.23; 95%CI −1.60, −0.85) in the H-IR group (**Table 2**).

FIGURE 2 | Differences in the prevalence of non-responders to decrease HOMA-IR after high-intensity interval training in a higher insulin resistance (H-IR, n = 20), and lower insulin resistance group (L-IR, n = 20) of adult women. OR, odds ratios for suffering a non-response.

# Muscle Performance Measurements

At baseline, there were no significant (p ≤ 0.05) differences between groups for 1RMLE and 1RMUR (**Table 2**). After intervention, there were significant (p ≤ 0.05) pre-post changes in 1RMLE in the H-IR (+12.9) and L-IR (+14.7%) groups (**Table 2**), whereas 1RMUR remained unchanged in both groups. The ES-value was high for 1RMLE (1.25; 95%CI 1.04, 1.45) in the L-IR group (**Table 2**).

# Differences in NRs Prevalence between the H-IR vs. L-IR Groups with Respect to Glucose Control Variables

There were significant differences between the H-IR vs. L-IR groups in NRs prevalence with regard to improved fasting glucose (25.0 vs. 95.0%, p < 0.0001) and fasting insulin (25.0 vs. 60.0%, p = 0.025). There were no significant differences between the H-IR vs. L-IR groups in NRs prevalence with regard to a decrease in HOMA-IR scores (25.0 vs. 45.0%, p = 0.185; **Figure 2**).

# Differences in the NRs Prevalence between the H-IR vs. L-IR Groups with Respect to Other Anthropometric, Cardiovascular, and Performance Variables

There were no significant differences between the H-IR vs. L-IR groups in NRs prevalence with regard to improvements in anthropometric parameters (i.e., body mass, BMI, waist circumference, and tricipital, supra-iliac, and abdominal skinfolds), muscle performance (i.e., 1RMLE and 1RMUR), or cardiovascular parameters (i.e., systolic/diastolic blood pressure; **Table 3**).

The OR analysis for NRs prevalence detected a high risk of being a NRs (>2-fold) associated with improved waist circumference (OR: 2.1, 95%CI 0.1, 3.2), diastolic blood pressure (OR: 2.1, 95%CI 1.5, 2.9), fasting glucose (OR: 4.0, 95%CI 2.2, 14.4), and 1RMUR (OR: 2.1, 95%CI 0.5, 9.0; **Table 3**).

# DISCUSSION

The present study was designed to assess the effects 10 weeks of HIIT and NRs prevalence (as indicated by glucose control parameters) in adult women with higher and lower levels of insulin resistance to test if the "magnitude" of a metabolic disease play a role in increasing or decreasing the NRs prevalence. The major findings of this study indicate that (i) HIIT promotes significantly more benefits in training-induced changes in fasting glucose, fasting insulin and HOMA-IR scores in adult women with higher insulin resistance; (ii) the NRs prevalence was significantly different between the H-IR vs. L-IR groups with regard to improve fasting glucose and fasting insulin but not for HOMA-IR scores; and (iii) both the H-IR and L-IR groups experienced similar positive training-induced changes and similar NRs prevalence with regard to anthropometric (body mass, BMI), cardiovascular (systolic/diastolic blood pressure), and muscle strength performance (1RMLE, 1RMUR) measures.

Several environmental factors related to NRs prevalence have been reported after training interventions. For example, a recent report assessed the effects of RT at different frequencies (3 and 2 days/week) to tests the effect of frequency in older NRs subjects for 12 and 24 weeks. Major differences between both training regimens were found for body mass, which decreased by ∼4.5% at 12 weeks and 23% at 24 weeks. Interestingly, other results included increases in type I (+34.5 vs. +29.4%) and type II muscle fibers (+22.7 vs. +21.1%), as well as increasing leg strength in extension exercises (+0.9 vs. +1.17%) at 12 and 24 weeks, with relatively similar results obtained independent of the training frequency. These results indicated that the frequency of training was not necessarily related to the NRs prevalence for some variables (Churchward-Venne et al., 2015).

There is limited evidence about interindividual variability in exercise training with regard to the NRs prevalence in subjects with low glucose control, and there are several methodological differences in studies comparing the NRs prevalences observed in previous studies (Boulé et al., 2005; Gremeaux et al., 2012; Yates et al., 2013; Moker et al., 2014; Winett et al., 2014; Higgins et al., 2015). For example, for glucose control variables, several authors have observed that after 3 months of strength training (2 days/week, 2 strength exercises at maximal effort), the NRs prevalence for improvements in an OGTT in prediabetic patients was 44%. In the present study, we found a NRs prevalence of 15 and 25% in the H-IR and L-IR groups, respectively, for decreased fasting glucose, with no significant difference between the groups (to see **Table 3**; Winett et al., 2014). Regarding HOMA-IR, the HERITAGE study (Boulé et al., 2005) showed that after 20 weeks of endurance training [30–50 min/session, 55–75% maximum oxygen uptake (VO2max) for 20 weeks], 42% of subjects were NRs for a decrease in HOMA-IR scores. We found similar results regarding a decrease in HOMA-IR scores, with a NRs prevalence of 15 and 20% for the H-IR and L-IR groups, respectively (**Figure 2**). Therefore, considering our 10 weeks of HIIT-based exercise vs. the 20 weeks of endurance exercise in the abovementioned study (Boulé et al., 2005), increasing the environmental "volume" factor of exercise may not necessarily be related to a decrease in the NRs prevalence for improved glucose control variables such as HOMA-IR scores. In a different study (Yates et al., 2013), NRs prevalence of 3% was reported for decreased fasting glucose after 12 months of exercise-based intervention. Similarly, when T2DM subjects were tested after 9 months of endurance training, RT or concurrent training in another study (Stephens et al., 2015), 21% of subjects were NRs for decreased glycated hemoglobin, as well as other body composition and protein markers.

Understanding the NRs prevalence after exercise modes such as HIIT and including populations with higher and lower risks of T2DM, such as those with higher and lower levels of insulin resistance, can be useful for designing more efficient exercise interventions: in this case, populations with higher levels of insulin resistance, such as the H-IR group, defined based on fasting glucose and HOMA-IR scores, are less likely to be NRs after 10 weeks of HIIT. This altered baseline, which we termed previously as "higher insulin resistance," may be in some way related to potential factors for predicting responses

TABLE 3 | Prevalence of non-responders (NRs) on anthropometric, cardiovascular, metabolic, and performance parameters after 10-weeks high-intensity interval training in a group of adult women with a higher level of insulin resistance: HOMA-IR > 5.0 (H-IR), and a group with a lower level insulin of resistance: HOMA-IR < 3.0 (L-IR).

Data are percentage, %/n = number of cases. 1RMLE , one-maximum repetition of leg extension; 1RMUR, one-maximum repetition of upper row. Bold values denotes significant (p < 0.05) differences between NRs from H-IR vs. NRs from the L-IR group at level (p < 0.05).

\*Denotes a high risk (>2-fold) for suffering a non-response.

in future long-term studies. Collectively, and in combination with previous reports (Hecksteden et al., 2013b), these findings indicate that the "magnitude" of changes in response to an acute exercise session can be a potential factor for predicting responses to chronic exercise-based interventions. In this study, the magnitude of changes in plasma variables after volitional exercise was very similar to results from another study where subjects showed decreased fasting insulin after chronic exercise training [walking/running at 60% peak oxygen consumption (VO2peak) for 4 weeks].

Another study (Moker et al., 2014) exploring another co-variable, blood pressure, showed that after 5 months of endurance training (65–80% VO2peak, walking/jogging), RT (8– 12 repetitions per set, 8 exercises, 70–85% of their one-maximum repetition value), or concurrent training, approximately ∼60% of subjects were NRs for a decrease in systolic and diastolic blood pressure. In our study, we found a NRs prevalence of 20 and 15% for decreased systolic blood pressure in the H-IR and L-IR groups, respectively, as well as a more pronounced NRs prevalence of 30 and 45% for decreased diastolic blood pressure in the H-IR and L-IR groups, respectively (**Table 3**). Because none of the intervention groups were diagnosed with hypertension, we hypothesized that genetic together with environmental factors, such as time of intervention, mode of training, and non-hypertensive baseline profiles, may be responsible for these results. However, these results were more positive after 10 weeks of HIIT than the 60% NRs prevalence observed in the aforementioned study following 5 months of intervention. Thus, the volume of training does not appear to play a role in NRs prevalence for decreases in systolic or diastolic blood pressure. Evidence has shown the benefit in terms of decreased systolic blood pressure after HIIT interventions (Ciolac, 2012); however, in this non-hypertensive cohort, we did not observe significant training-induced changes in systolic or diastolic blood pressure (**Table 2**). In other studies, there was an ∼60% NRs prevalence for decreased systolic or diastolic blood pressure after 6 weeks (Higgins et al., 2015) or 6 months (Moker et al., 2014) of HIIT. Interestingly, a study that explored the magnitude of the changes in blood pressure after an acute exercise session reported that this response can be used as a predictive factor for decreases in blood pressure after long-term exercise training (Hecksteden et al., 2013a).

In this study, we found significant training-induced changes in 1RMLE-test results in the H-IR (+12.9%) and L-IR (+14.7%) groups (**Table 2**). Similarly, we found a 10% NRs prevalence for an increase in 1RMLE results in the H-IR group and no NRs (0%) in the L-IR group (**Table 3**). However, in previous studies, RT (10–15 repetitions, four sets of leg extension, 60–80% of the one-maximum repetition value) resulted in a minimum NRs prevalence of ∼1% for an increase in 1RMLE after 12 and 24 weeks of RT (Churchward-Venne et al., 2015). Additionally, despite the fact that our HIIT mode of training is very different methodologically than what was reported in previous studies, the HIIT protocol was able to increase the strength of the lower limbs. These findings are consistent with a previous HIIT-based study (90 s, 6 bouts, 6 weeks), in which HIIT improved several parameters related to power cycling in the lower limbs in adult men (Ziemann et al., 2011).

We observed different ranges of NRs prevalence for other anthropometric (5–30%), blood pressure (55–100%), metabolic (25–95%), and performance (0–35%) variables. These results are consistent with literature reports for blood pressure (59–60%) (Moker et al., 2014), metabolic (7–44%) (Boulé et al., 2005; Yates et al., 2013; Winett et al., 2014; Osler et al., 2015; Stephens et al., 2015), and performance (1%) variables (Churchward-Venne et al., 2015). Finally, our study has some important limitations. Our sample size was limited, but it is similar to the sample sizes used in other exercise training studies (∼10– 20 subjects). Additionally, we lacked a true no-exercise control group, and we did not control the physical activity patterns and diet of subjects after training, although subjects were reminded each week to maintain their baseline patterns of activity and food consumption. The strengths of this study were that we included both the effects of HIIT and NRs prevalences for changes in anthropometric, cardiovascular, and metabolic risk factors and in performance variables. We also included a statistical estimate of the ES for each variable studied.

# REFERENCES

# CONCLUSION

In summary, independent of the "magnitude" of the cardiometabolic disease (i.e., higher vs. lower insulin resistance), no differences were observed in the NRs prevalence with regards to improved HOMA-IR scores, other anthropometric, cardiovascular, and muscle performance variables after 10 weeks of HIIT in sedentary adult women. This research demonstrates the protective effect of HIIT against cardiometabolic disease progression in a sedentary population.

# AUTHOR CONTRIBUTIONS

CA conceived and designed the project. CA and RR-C reviewed the literature studies and conducted data extraction. CA conducted data analyses. CA, RR-C, and MI were responsible for data interpretation. CA, RR-C, and MI drafted the manuscript, and RR-V, and MI revised it critically for intellectual contributions. CA and RR-C coordinate the study development. All authors reviewed and edited the manuscript. All authors read and approved the final manuscript.

# FUNDING

This work was supported by the health promotion program of the Public Health Service of Los Ríos Government (SSVV). MI was funded by research grants from the Spanish Net on Aging and frailty (RETICEF) (ISCIII, FEDER) and Centro de Investigación Biomédica en Red (CIBER) en Fragilidad y Envejecimiento Saludable (CIBERFES; CB16/10/00315) del Instituto de Salud Carlos III (FEDER).

# ACKNOWLEDGMENTS

We are thankful with Mr. Farid (physician), Miss Paulina Carrasco (nursing), and Mrs. Elizabeth Mariangel (nursing), as well as to Mrs Sandra Castro, and Marcial Leal (technician). Finally, we are thankful with Miss DorkaNorambuena, director of the Family Healthcare Center Tomás Rojas Vergara for their regular interest to include the exercise training at level of public health.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer AS and handling Editor declared their shared affiliation, and the handling Editor states that the process nevertheless met the standards of a fair and objective review.

Copyright © 2017 Álvarez, Ramírez-Campillo, Ramírez-Vélez and Izquierdo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Key Points from the Updated Guidelines on Exercise and Diabetes

*Sheri R. Colberg\**

*Human Movement Sciences Department, Old Dominion University, Norfolk, VA, USA*

Keywords: physical activity, exercise, diabetes, guidelines, American Diabetes Association

# INTRODUCTION

No doubt remains that the adoption and maintenance of physical activity is important for overall health and blood glucose management in individuals with diabetes and prediabetes. Recently, the American Diabetes Association (ADA) published updated recommendations and precautions about physical activity and exercise in people with type 1 diabetes, type 2 diabetes, and gestational diabetes (1). Given the importance of these topics, it is worth discussing the key changes and updates included in this ADA position statement (PS).

# Pre-Exercise Health Screening and Evaluation

This PS reiterates that "pre-exercise medical clearance is not necessary for asymptomatic, sedentary individuals who wish to begin low- or moderate-intensity physical activity not exceeding the demands of brisk walking or everyday living" (1). This stance directly opposes a recent recommendation from the American College of Sports Medicine (ACSM) (2) that requires anyone with a metabolic disease (in this case, diabetes) who desires to begin exercising at any level—even doing light activities—to obtain medical clearance from a health-care provider first. The authors of the ADA PS did not agree with this restriction and took the same stance as the prior ADA PS on type 2 diabetes and exercise (3), which I believe is a much better recommendation. Making adults obtain any type of medical clearance prior to starting walking, for example, is an unnecessary barrier that will not necessarily make exercising any safer for them. However, ADA agrees with ACSM that adults with diabetes who plan to exercise at higher intensities than currently undertaken or who would be considered at high risk for cardiovascular disease (e.g., have elevated blood cholesterol, smoke, have a strong family history, etc.) or other health complications from doing such activities are recommended to obtain a pre-training examination from a health-care provider who may or may not recommend exercise stress testing (3).

# RECOMMENDED PHYSICAL ACTIVITY/EXERCISE

All physical movement has the potential to improve physical and mental health (4–6). Since blood glucose management varies with a number of factors, it is critical for recommendations to be tailored for activity type and health complications to be effective (3, 7). In the PS, physical activity is defined as any movement that increases energy use, and exercise is a subset of physical activity that is more planned or structured (1), which is an important distinction.

# Aerobic Exercise Training

As previously recommended, most adults with type 1 or type 2 diabetes should undertake at least 150 min or more of moderate- to vigorous-intensity activity weekly; it is also recommended that these activities occur on at least 3 or more days during the week and that individuals should not allow more than 2 days to elapse between activity sessions to maintain higher levels of insulin sensitivity

#### *Edited by:*

*Jonathan Peter Little, University of British Columbia, Canada*

#### *Reviewed by:*

*Normand Boule, University of Alberta, Canada Jamie F. Burr, University of Guelph, Canada*

> *\*Correspondence: Sheri R. Colberg<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Diabetes, a section of the journal Frontiers in Endocrinology*

*Received: 14 January 2017 Accepted: 07 February 2017 Published: 20 February 2017*

#### *Citation:*

*Colberg SR (2017) Key Points from the Updated Guidelines on Exercise and Diabetes. Front. Endocrinol. 8:33. doi: 10.3389/fendo.2017.00033*

(**Table 1**). However, it is now recognized in this PS that younger or more physically fit individuals may receive similar cardiovascular and fitness benefits from undertaking vigorous-intensity or high-intensity interval training (HIIT), assuming it adds up to a minimum of 75 min/week (1, 8, 9).

Also included in the PS this time for the first time is HIIT, which is a type of training that includes short bursts (seconds to minutes) of very intense activity with recovery periods interspersed that may involve a lower intensity activity or rest. Such training has been demonstrated to result in greater insulin sensitivity and better overall blood glucose levels, at least in adults with type 2 diabetes (9, 10). Adults with type 1 diabetes can engage in HIIT and manage blood glucose with appropriate regimen changes (8, 11), which may include more insulin during and following and activity and reduced dosing overnight, along with food intake to prevent overnight hypoglycemia. Since its safety and efficacy remain unclear for some adults (12, 13), individuals who undertake such training should be clinically stable, already exercising regularly in activities that are moderate in intensity or harder, and possibly supervised when HIIT is started (14). This type of training is definitely not right for everyone.

# Resistance Exercise Training

The PS recommends 2–3 sessions/week of resistance exercise on non-consecutive days using a variety of strength training modalities (1), which is also unchanged from prior recommendations and from guidelines for all adults. Although heavier resistance training improves glycemic control and strength more than lighter weights or home-based activities (15), all resistance training has the potential to result in greater strength, which can translate into improved balance and ability to live independently and undertake activities of daily living.

The main PS update is related to discussing the glycemic impact of resistance exercise in adults with type 1 diabetes (1), which remains unclear (8). It may lower the risk of developing exercise-induced hypoglycemia in type 1 diabetes (16). When both aerobic and resistance exercise are undertaken during a solitary activity session, it has been shown that doing the bout of resistance work first may actually help maintain glycemic balance more so than when aerobic exercise occurs before resistance training (17). Varying the order of the activities based on blood glucose levels may minimize the risk of hypoglycemia.

# Flexibility and Balance Exercises

One major change of this PS is a greater focus on the inclusion of flexibility exercise to improve range of motion around joints in individuals of all ages (18) and balance activities to improve gait and prevent falls in older adults (19). Both flexibility exercises and balance training are recommended to be done minimally 2–3 times/week, especially by older adults (1). Including both is vitally important to living well since limited joint mobility is common in older adults and long-standing diabetes due to advanced glycation end products formed by normal aging and hyperglycemia (20). Stretching increases range of motion around joints and flexibility (18), and balance training can reduce falls risk by improving balance and gait (19).

*Copyright 2016*© *American Diabetes Association from Ref. (1). Reprinted with permission from The American Diabetes Association.*

Lower-body and core strengthening exercises may be considered part of balance training. Yoga may promote improvement in glycemic control, lipid levels, and body composition in adults with type 2 diabetes (21). Tai chi training may improve glycemic control and balance neuropathic symptoms and some dimensions of quality of life in adults with diabetes and neuropathy (22).

# Daily Movement

Engaging in more unstructured daily activity, such as errands, household tasks, dog walking, and gardening, increases daily energy expenditure and assists with weight loss and maintenance (23–25). Increasing daily movement appears to acutely lower postprandial hyperglycemia and possibly improve blood glucose management, especially when undertaken after meals (26–34). It is recommended as part of a whole-day approach and as a starting place for anyone who is currently sedentary and either unwilling or unable to start engaging in more structured activities. For many deconditioned and older individuals with diabetes, increasing daily movement may be an appropriate place to start with physical activity rather than with more structured activities.

# Reduced Sedentary Time and Interrupted Sitting

As demonstrated in adults with type 2 diabetes, encouraging them to interrupt prolonged periods of sitting with 15 min of walking after meals (26) and either light walking or simple bodyweight resistance activities undertaken for 3 min after every 30 min of inactivity (29) improves overall glycemic control. The PS recommends that all adults attempt to lower the total amount of time that they spend each day in sedentary activities and break up prolonged bouts of sitting with some type of light activity for a few minutes at least every 30 min to improve their glycemic management; both should be added to daily structured exercise and unstructured movement rather than being a replacement for them. Research in this area, however, is still in its infancy especially in populations with diabetes—and more studies are needed to better define the best types and timing of activity, not only for managing blood glucose levels but also for preventing type 2 diabetes and reversing prediabetes in the first place.

# PHYSICAL ACTIVITY AND TYPE 2 DIABETES

The impact of exercise on insulin action is transient and, accordingly, activities should be undertaken daily or no less frequently than every other day. It is important to continue to recommend that exercise be undertaken regularly since in many cases, acute effects of aerobic exercise may not last even 24 h. At least one study has shown that if the same volume of exercise is done either as 30 min of moderate exercise daily or 1 h at the same intensity every other day—the glycemic effects over the ensuing 48-h period are similar (35). Exercise does not necessarily need to be prolonged to result in enhanced insulin sensitivity, but if shorter in duration, engaging in harder workouts or highintensity intervals will increase its impact (36, 37). However, daily moderate or high-intensity aerobic or resistance exercise is likely optimal (38–40). Aerobic training may improve overall glycemic control more than resistance training, but both reduce cardiovascular risk markers similarly (41), and a single bout of either may have a similar acute effect in any case (42). To achiever better glycemic management, engaging in combined aerobic and resistance training appears to be superior to undertaking either type of training on its own (43, 44). In fact, the PS states, "Adults should ideally perform both aerobic and resistance exercise training for optimal glycemic and health outcomes" (1), which I firmly believe to be an excellent recommendation.

It is also important for type 2 diabetic youth (children and adolescents) to be more physically active. Their goal should be to meet the activity goals recommended for all youth, which consists of 60 min/day or more of moderate- or vigorous-intensity aerobic activity, with vigorous, muscle-strengthening, and bonestrengthening activities at least 3 days/week (1). Few studies have been done to examine the impact of exercise training and interventions in youth with type 2 diabetes, and those are inconclusive, although it can be assumed that the health and glycemic benefits they would gain are similar to those experienced by adults with type 2 diabetes (45).

# PHYSICAL ACTIVITY AND TYPE 1 DIABETES

This PS is the first in many decades to address the complexities of managing blood glucose with exercise in adults and children with type 1 diabetes (46). Both aerobic and resistance training are recommended for these adults (47–49), and youth with type 1 diabetes should follow general recommendations for children and adolescents (47). Blood glucose responses are impacted by the type, timing, intensity, and duration of exercise, as well as by many other factors. Different activities will likely require individualized adjustments to carbohydrate and food intake and insulin dosing during and after exercise.

Aerobic exercise after meals usually decreases blood glucose levels (50), especially during prolonged activity (34, 51, 52). Doing activity during fasting conditions, however, results in more stable glycemia, with less of a decline or even a small increase in overall levels (53). Engaging in very intense activities either maintains or raises blood glucose (16, 54), depending on duration, which is an important point to keep in mind.

Variable glycemic responses to physical activity (46) make uniform recommendations nearly impossible. In general, individuals will need to increase their carbohydrate intake and/ or reduce circulating insulin levels when engaging in longer duration aerobic activities, along with frequently monitoring blood glucose. These additional recommendations are stated in the PS (1) but come from other studies: for low- to moderateintensity aerobic activities lasting 30–60 min during fasting or basal insulin conditions, ~10–15 g of carbohydrate may suffice to prevent hypoglycemia (55). For activities done after bolus insulin, 30–60 g of carbohydrate per hour may be needed (56, 57), or insulin can be reduced 25–75% to reduce or eliminate the need for carbohydrate intake (58). Basal rate reductions for exercise may reduce hypoglycemia (59).

Continuous glucose monitors (CGM) are more widely available nowadays and have increased in accuracy; for many individuals, wearing such a device may decrease the fear of developing exercise-induced hypoglycemia. They are able to provide blood glucose trends, which can potentially assist the user in preventing hypoglycemia or treating it sooner (60–63). Some issues with CGM use during activity remain, however, as stated in the PS (1): inadequate accuracy (64), sensor filament breakage (62, 63), inability to calibrate (61) time lags between the change in blood glucose and its detection by CGM (65), and variations in sensor performance (66–68). CGM devices are currently being paired with insulin pumps into closed-loop systems run by algorithms. These technological issues with CGM use during exercise are continuing to make regular participation in physical activity a huge hurdle to creating an effective system.

# PHYSICAL ACTIVITY AND PREGNANCY WITH DIABETES

The PS recommends, "Females with pre-existing diabetes of any type should be advised to engage in regular physical activity prior to and during pregnancy" (1). It also reiterates prior recommendations from other organizations that state that "pregnant females with or at risk for gestational diabetes should engage in 20–30 min of moderate-intensity exercise on most or all days of the week" (69–71). Undertaking any type of training (aerobic or resistance) has the ability to improve insulin sensitivity and overall blood glucose management (72). Ideally, physical activity should start prior to pregnancy to reduce gestational diabetes risk (73) but can be initiated safely during pregnancy (69). Regular physical activity is important for other positive pregnancy outcomes as well and should be recommended to all females of childbearing age, both prior to and during pregnancy.

# MINIMIZING EXERCISE-RELATED ADVERSE EVENTS

In the PS (1), it is reiterated that, "Exercise-induced hypoglycemia is common in type 1 diabetes, and to a lesser extent, people with type 2 diabetes using insulin or insulin secretagogues." Some medications (other than insulin) may increase exercise risk, and doses may need to be adjusted (74, 75). Given that fear of hypoglycemia related to exercise is a proven barrier to exercise participation (76), any strategies that will assist in minimizing its occurrence have the potential to increase adherence to exercise training. Other acute strategies to prevent hypoglycemia involve including short sprints, performing resistance exercise before aerobic exercise in the same session, and activity timing (77–82), which are primarily based on the ability of a greater release of counterregulatory hormones during intense activities to maintain blood glucose levels more effectively. Exercise-induced nocturnal hypoglycemia is a major concern (83). Hypoglycemic events occur typically within 6–15 h postexercise (84), although risk can extend out to 48 h (85). Risk of nocturnal hypoglycemia following physical activity may be mitigated with lower basal insulin doses overnight, bedtime snacks, and/or use of CGM, and these strategies should be recommended to assist in preventing delayed-onset lows.

Very intense exercise like sprinting (79), brief but intense aerobic exercise (86), and heavy powerlifting (87, 88) may promote hyperglycemia, especially with elevated starting blood glucose levels (86). A number of strategies can mitigate exercise-induced hyperglycemia, though. For example, it may be modulated with insulin administration, interspersing moderate aerobic activity between intense bouts, and a low-intensity cooldown (89, 90). Another stance taken in the PS (1) is "Overconsumption of carbohydrates before or during exercise, along with aggressive insulin reduction, can promote hyperglycemia during any exercise (58). Exercising with hyperglycemia and elevated blood ketones is not recommended."

Aging combined with diabetes may result in worse blood glucose control; moreover, peripheral neuropathy may be present and skin blood flow and sweating impaired (91–93), which increases the risk of heat-related illness. Chronic hyperglycemia also causes dehydration. These are all fairly new findings. For these reasons, the PS (1) recommends, "Older adults with diabetes or anyone with autonomic neuropathy, cardiovascular complications, or pulmonary disease should avoid exercising outdoors on very hot and/or humid days to prevent heat-related illnesses."

In addition, these statements from the PS are aimed at avoidance of other exercise-related adverse responses (1), which is critical for continued participation: "Active individuals with type 1 diabetes are not at increased risk of tendon injury (94), but this may not apply to sedentary or older individuals with diabetes. Diabetes may lead to exercise-related overuse injuries due to changes in joint structures related to glycemic excursions (95), so exercise training should progress appropriately to avoid excessive aggravation to joint surfaces and structures, particularly when taking statin medications for lipid control (96)."

# MANAGING HEALTH COMPLICATIONS

Finally, many individuals with diabetes carry the burden of having associated health concerns, many of which can impact their ability to exercise safely and effectively. None of these are new ideas, but here is a summary of recommended actions as stated in the PS (1): macrovascular and microvascular diabetesrelated complications can develop and worsen with inadequate blood glucose management (97, 98). Physical activity with vascular diseases can be undertaken safely, but with appropriate precautions. Being active with peripheral neuropathy necessitates proper foot care to prevent, detect, and treat problems early to avoid ulceration and amputation. Autonomic neuropathy may complicate being active; certain precautions are warranted to prevent problems during activity, such as avoiding rapid directional changes (if orthostatic hypotension is present) and preventing dehydration and overheating during exercise with adequate fluid intake. Vigorous aerobic or resistance exercise, jumping, jarring, and head-down activities, and breath-holding should be avoided in anyone with severe non-proliferative and unstable proliferative diabetic retinopathy. Exercise with diabetic kidney disease can be undertaken safely, even during dialysis sessions. Regular stretching and appropriate progression of activities should be done to manage joint changes and diabetes-related orthopedic limitations.

# CONCLUSION

This PS really does not contain any controversial recommendations, other than ADA disagreeing (strongly) with the requirement that ACSM put forth that all individuals with a metabolic condition who are currently sedentary must seek medical clearance prior to getting up off the couch. It is good to be reminded as well that although everyone can benefit from being physically active, specific recommendations and precautions will vary by the type of diabetes, age, activity done, and presence of complications, and exercise prescriptions should be tailored to meet the specific needs

# REFERENCES

of each individual. Overall, this ADA PS provides a comprehensive and current guide to assist individuals of all ages with any type of diabetes with engaging in recommended amounts of regular physical activity safely and effectively and is a much-needed publication. For even more specifics about type 1 diabetes and exercise participation, however, readers are referred to a very recent consensus statement (99) sponsored by the Juvenile Diabetes Research Foundation, which is far more comprehensive for this group of exercisers than this ADA PS ever intended to or realistically could be since it covered all types of diabetes, not just type 1.

# AUTHOR CONTRIBUTIONS

SC wrote and edited this opinion piece with no input from any other authors.

interval exercise for patients with cardiovascular and metabolic diseases. *Clin Med Insights Cardiol* (2015) 9:53–63. doi:10.4137/CMC.S26230

**Conflict of Interest Statement:** The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2017 Colberg. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Exercise after You Eat: Hitting the Postprandial Glucose Target

*Melissa L. Erickson1 \*, Nathan T. Jenkins2 and Kevin K. McCully2*

*1Department of Pathobiology, Cleveland Clinic Foundation, Cleveland, OH, United States, 2Department of Kinesiology, University of Georgia, Athens, GA, United States*

We discuss a novel hypothesis: the effect size of postmeal exercise for attenuating postprandial glucose will be a function of the exercise bout vs. the size of the postprandial glucose response, specifically peak and duration of the postprandial glucose excursion.

Keywords: postmeal exercise, postprandial glucose, type 2 diabetes, continuous glucose monitoring, glycemic control

# INTRODUCTION

Hyperglycemia is a hallmark feature of type 2 diabetes. Sustained high glucose concentrations play a central role in the development of diabetes-related complications (1). Importantly, restoration of glycemic control reduces cardiovascular disease (2). Thus, the primary goal of type 2 diabetes treatment is to achieve and maintain glycemic control. While various therapeutic options are available, glycemic control remains challenging. For example, the long-term performance of hypoglycemic agents is unsatisfactory (2–4).

#### *Edited by:*

*Kristian Karstoft, University of Copenhagen, Denmark*

#### *Reviewed by:*

*Barry Braun, Colorado State University, United States Carmine Grieco, Colorado Mesa University, United States*

> *\*Correspondence: Melissa L. Erickson<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Diabetes, a section of the journal Frontiers in Endocrinology*

*Received: 19 June 2017 Accepted: 22 August 2017 Published: 19 September 2017*

#### *Citation:*

*Erickson ML, Jenkins NT and McCully KK (2017) Exercise after You Eat: Hitting the Postprandial Glucose Target. Front. Endocrinol. 8:228. doi: 10.3389/fendo.2017.00228*

Traditional markers for glycemic control include fasting glucose, hemoglobin A1C (HbA1C), and postprandial glucose. The gold standard for assessing glycemic control is HbA1C, which represents a 3-month average of glucose exposure. Postprandial glucose is gaining more recognition as a key glycemic target for therapeutic intervention, as multiple lines of evidence support its use as a clinical marker. Epidemiological studies have shown that postprandial glucose is a better cardiovascular disease predictor than HbA1C (5–7), as well as fasting glucose (8). In addition, interventional studies have shown that reducing postprandial glucose improves glycemic control (9) and leads to reductions in cardiovascular disease risk in people with type 2 diabetes (10).

Due to the growing body of evidence supporting the link between postprandial glucose and cardiovascular disease, the International Diabetes Federation (IDF) published guidelines for postmeal glucose management. Specifically, the target glucose value 1–2 h after meal ingestion is 160 mg/dL (9.0 mmol/L) (11). Recommendations for treatment of postprandial glucose include pharmacologic and non-pharmacological strategies (11). Interestingly, exercise was not described as a treatment option in the IDF recommendation.

Exercise has been shown to be important for both prevention and treatment of type 2 diabetes, and the American Diabetes Association and American College of Sports Medicine have developed exercise guidelines for people with type 2 diabetes (12). However, these guidelines are not specific to postprandial glucose, and they do not mention exercise timing in relation to meal ingestion. The lack of attention to exercise timing in treatment guidelines highlights a need for more research on postmeal exercise and its effects on diabetes-related outcomes.

Postprandial exercise has been shown to be safe (13) and effective in people with type 2 diabetes (**Figure 1**) (14). Exercise acutely increases glucose uptake in skeletal muscle. This occurs through an insulin-independent process (15, 16) and, therefore, is applicable to type 2 diabetes. Muscle contraction serves as a signal for GLUT-4 receptor translocation on the skeletal muscle plasma membrane (17). GLUT-4 receptors are responsible for transporting glucose from systemic circulation and into skeletal muscle. This effect occurs after just a single bout of exercise, meaning

the glucose-lowering effects can be realized immediately (17). Furthermore, the acute nature of this response indicates that long-term training adaptations are not necessary for beneficial effects on blood glucose to occur. The optimal timing for postmeal exercise has been suggested to be 30 min after the start of a meal (18). This is because peak postmeal glucose values typically occur within 90 min (19), and initiating exercise during this time window will blunt peak glucose excursions protecting the endothelial wall from pro-atherogenic glucose concentrations.

# EFFECTIVE EXERCISE CONSIDERS GLUCOSE LEVELS

A key characteristic of type 2 diabetes is an exaggerated glucose response to a meal, and studies using continuous glucose monitoring (CGM) have shown that glucose excursions in people with type 2 diabetes are well above those of non-diabetic controls, even when treated with hypoglycemic agents (21, 22). An optimal postprandial glucose treatment will produce a glucose response that mimics normal glucose tolerance. Measurable parameters of the postprandial glucose response that can be used for interventional guidance include glucose peak and duration of elevation. Those with normal glucose tolerance do not exceed 140 mg/dL and glucose levels return to preprandial levels after 2 h. Given these parameters, postmeal exercise can be strategically applied to lower peak glucose as well as time of elevation, thus resembling normal glycemic control. Effective exercise for type 2 diabetes requires balance, in that, clinically meaningful glucose reductions should be pursued, while minimizing risk for hypoglycemia (**Figure 2**).

We propose that the effectiveness of exercise will be dependent on the postprandial glucose response, including the glucose peak and duration of elevation. Larger amplitude, longer duration glucose excursions will require more intervention than smaller, shorter glucose excursions to produce a normoglycemic pattern. Therefore, the measurable effect size of an exercise bout will be dependent on the glycemic excursion itself, in that, a higher

Figure 2 | Indicates optimal range of postprandial glucose control. The upper glucose bound is set by the International Diabetes Federation Guidelines, while the lower glucose bound is defined by hypoglycemic risk. Continuous glucose monitoring data are representative of a 24-h glucose profile of an individual with type 2 diabetes. Summary data have been published previously (23).

and longer excursion will experience less reduction from the same exercise bout compared to a smaller and shorter excursion (**Figure 3**). This concept is supported by quantitative comparisons of our previous work. In two distinct studies, we used CGM to assess the effects of postmeal exercise on postprandial glucose excursions in those treated with metformin (20), as well as those treated with more advanced T2D requiring metformin plus additional hypoglycemic agents (23).

We observed different effect sizes for postmeal exerciseinduced reductions in 2-h glucose peak, including a large effect size (0.81) in individuals treated with only metformin (20) vs. a moderate effect size (0.56) in those treated with additional hypoglycemic agents (23). We propose that these differential results can be explained by two key differences among studies, including (1) differences in the applied exercise bout, as well as (2) differences in the amplitude of the postprandial glucose peak. The larger effect size was observed in the study that applied the larger exercise stimulus, i.e., exercise that was longer in duration (50 vs. 30 min) and modestly higher in intensity (60% VO2 max vs. 50% VO2 max). In addition, the amplitude of the 2-h glucose peak measured in the control condition was lower in the metformin study participants compared to that of the metformin plus addon hypoglycemic agent study participants (12.0 vs. 14.5 mmol/L, respectively), indicative of an easier glucose "target" for reduction. This comparison suggests that a larger exercise stimulus (longer in duration, higher in intensity) applied to the smaller glucose peak resulted in a more effective strategy for glucose attenuation. One interpretation of this comparison is that the effectiveness of a postmeal exercise bout is a function of the amount of exercise vs. the size of the glucose peak.

To further explore this concept, a second comparison of the two studies was completed. The aim of this analysis was to use CGM data to quantify the amount of time exercising vs. the amount of time spent in postprandial hyperglycemia. This can be thought of as the percentage of time in which the glucose excursion was being intervened upon by exercise. This analysis revealed differences among our two studies. The percentage of time that was "treated" by exercise was 34% in the metformin study (20), vs. only 16% in the add-on therapy (23). These findings are consistent with the measured effect sizes, in that the larger effect size corresponded with the larger percentage of treated time (ES: 0.81; 34% of time was "treated" with exercise) and the smaller effect size corresponded with the small percentage of treated time (ES: 0.56; 16% of time was "treated" with exercise). These findings further support the concept that the effectiveness of exercise will be a function of the exercise bout vs. the size of the postprandial glucose response.

Significant glucose reductions have been reported in people with type 2 diabetes using a large variety of exercise strategies. This includes continuous (24, 25) and interval protocols (20, 23). In addition, various durations (20–60 min) and intensities have been shown to be effective (26, 27). Furthermore, multiple modes of exercise have been used including walking and cycling (24, 25). More recently, high-intensity interval training has been shown to be a promising approach for improving health outcomes in the people with type 2 diabetes (28–30). Taken together, these studies show there are numerous strategies to prescribe exercise. It is currently not clear if one variable is more important than another for postprandial glucose control. However, in the case of postmeal exercise approaches, it seems evident that maximizing the glucose-lowering power of an exercise bout will require taking the size of the glucose excursion itself, into account.

# COMPLEMENTARY EFFECTS OF DRUGS AND EXERCISE

Hypoglycemic agents are a mainstay in type 2 diabetes treatment. Thus, the combined effects of hypoglycemic agents and exercise should remain a high priority for future investigations. Metformin is the first-line therapy (31) for the prevention and treatment of type 2 diabetes. During disease progression, a variety of hypoglycemic agents can be used for glycemic control. Currently, there are nine available FDA-approved classes of oral hypoglycemic agents (32) and several injectable agents. Some drug classes specifically target and reduce postprandial glucose, including α-glucosidase inhibitors, DPP-4 inhibitors, glinides, GLP-1 derivatives, short-acting sulfonylureas, and insulin regimens (31). All of these medications are recommended to be taken along with regular exercise.

The target tissues and mechanisms of action widely vary among drug classes. Subsequently, these drug classes have differing effects on the 24-h glycemic profile. Some drugs effectively lower fasting glucose, while others target postprandial glucose. Postmeal exercise may be an effective complement to these agents. In fact, the combination of postmeal exercise and hypoglycemic agents has been shown to produce further glucose-lowering effects compared to drug treatment alone (20, 23). Additional experimental studies are needed to determine the interactive effects of postmeal exercise among various drug classes. This will involve appropriately timing medication and exercise in order to avoid hypoglycemia.

Insulin and insulin-analog regimens have been specifically designed to reduce postprandial glucose. Incorporation of postmeal exercise alongside insulin therapy may also have beneficial health effects. If postmeal exercise is effective enough, it could potentially lead to a reduction in insulin dose. A study in participants with type 1 diabetes found that prolonged walking (~40–50 km) led to profound reductions in insulin administration (26%) compared to a sedentary day (33). Future studies should investigate the effectiveness of more feasible exercise strategies, including postmeal exercise, as a complementary treatment to insulin.

For safety reasons, optimal diabetes treatments should have a low probability for eliciting hypoglycemia. The counterregulatory response is a natural physiological process that defends against hypoglycemia, and this can occur if glucose falls too low during exercise. The counter-regulatory release of hormones into the circulation, including glucagon, catecholamines (epinephrine and norepinephrine), cortisol, and growth hormone is triggered when glucose drops below 3.8 mmol/L (34). This effect has been demonstrated experimentally in people with type 2 diabetes (25, 35) and should remain a consideration with prescribing exercise alongside hypoglycemic agents.

# GLUCOSE-GUIDED APPROACH

Our current hypothesis that the effectiveness of an exercise bout will be dependent on the size of the glucose excursion itself. Therefore, optimal exercise approaches will require knowledge of glucose values in real time. One commonly used approach is self-monitoring capillary glucose with finger sticks and glucometers. When timed correctly, this method can be used to assess acute fluctuations in glucose after meals. In addition, CGM will likely play an important role. CGM technology uses a small probe within the subcutaneous tissue that samples and measures glucose concentrations in the interstitial fluid. Future studies should determine the most effective and feasible approach for glucose-guided exercise prescriptions, which may involve a hybrid approach of glucometers and CGM.

# CONCLUSION

Improving the treatment of type 2 diabetes is a major health care need. Taming postprandial glucose excursions can be accomplished by exercising after meals. The effectiveness of an exercise bout for lowering glucose will be dependent upon the size (peak and duration) of the postprandial glucose excursion. Larger excursions necessitate more aggressive intervention, while smaller excursions are easier targets for attenuation. Glucose

# REFERENCES

monitoring techniques, such as glucometers and CGM technology, may have an important role in quantifying the effectiveness of exercise bouts.

# AUTHOR CONTRIBUTIONS

ME, NJ, and KM developed ideas. ME drafted manuscript. NJ and KM edited and approved manuscript.

# ACKNOWLEDGMENTS

The dissertation entitled "Effects of postmeal exercise and hypoglycemic agents on postprandial glucose excursions" informed concepts presented here (36).

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2017 Erickson, Jenkins and McCully. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Running from Disease: Molecular Mechanisms Associating Dopamine and Leptin Signaling in the Brain with Physical Inactivity, Obesity, and Type 2 Diabetes

#### *Gregory N. Ruegsegger1 and Frank W. Booth1,2,3,4\**

*1 Department of Biomedical Sciences, University of Missouri, Columbia, MO, United States, 2 Department of Medical Pharmacology and Physiology, University of Missouri, Columbia, MO, United States, 3 Department of Nutrition and Exercise Physiology, University of Missouri, Columbia, MO, United States, 4 Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO, United States*

#### *Edited by:*

*Jonathan Peter Little, University of British Columbia, Canada*

#### *Reviewed by:*

*Catherine M. Kotz, University of Minnesota, United States Lindsay Spielman, University of British Columbia Okanagan, Canada*

> *\*Correspondence: Frank W. Booth<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Diabetes, a section of the journal Frontiers in Endocrinology*

*Received: 15 March 2017 Accepted: 05 May 2017 Published: 23 May 2017*

#### *Citation:*

*Ruegsegger GN and Booth FW (2017) Running from Disease: Molecular Mechanisms Associating Dopamine and Leptin Signaling in the Brain with Physical Inactivity, Obesity, and Type 2 Diabetes. Front. Endocrinol. 8:109. doi: 10.3389/fendo.2017.00109*

Physical inactivity is a primary contributor to diseases such as obesity, cardiovascular disease, and type 2 diabetes. Accelerometry data suggest that a majority of US adults fail to perform substantial levels of physical activity needed to improve health. Thus, understanding the molecular factors that stimulate physical activity, and physical inactivity, is imperative for the development of strategies to reduce sedentary behavior and in turn prevent chronic disease. Despite many of the well-known health benefits of physical activity being described, little is known about genetic and biological factors that may influence this complex behavior. The mesolimbic dopamine system regulates motivating and rewarding behavior as well as motor movement. Here, we present data supporting the hypothesis that obesity may mechanistically lower voluntary physical activity levels *via* dopamine dysregulation. In doing so, we review data that suggest mesolimbic dopamine activity is a strong contributor to voluntary physical activity behavior. We also summarize findings suggesting that obesity leads to central dopaminergic dysfunction, which in turn contributes to reductions in physical activity that often accompany obesity. Additionally, we highlight examples in which central leptin activity influences physical activity levels in a dopamine-dependent manner. Future elucidation of these mechanisms will help support strategies to increase physical activity levels in obese patients and prevent diseases caused by physical inactivity.

Keywords: physical activity, physical inactivity, motivation, dopamine, obesity, leptin

# INTRODUCTION

Physical inactivity presents a major public health problem. Predictions by Lee et al. (1) estimated that physical inactivity accounts for between 6 and 10% of type 2 diabetes (T2D) and coronary heart disease prevalence, with this percentage further elevated for specific diseases (30% for ischemic heart disease) (2). Moreover, the World Health Organization declared physical inactivity as the fourth leading risk factor for death worldwide, responsible for ~6% of the deaths worldwide in 2008 (1, 2). Accelerometry measurements by Troiano et al. (3) reported that less than 5% of adults met the US guidelines for physical activity, while questionnaire data collected globally in 2009 suggested that 31% of the world's population did not attain minimum recommended levels of physical activity (4). Given the deleterious effects of physical inactivity, understanding molecular mechanisms that influence physical activity adherence is needed. Here, we summarize current knowledge suggesting the mesolimbic dopamine system regulates physical activity, obesity-induced impairments in dopamine signaling may cause physical inactivity, and central leptin resistance in obesity and T2D may alter physical activity in a dopamine-dependent manner. Specifically, our discussion focuses on motivated and self-rewarding (i.e., voluntary wheel running), rather than spontaneous (i.e., cage activity, tremors, etc.), forms of physical activity.

# GENETIC CONTROL OF PHYSICAL ACTIVITY