The prevalence of type 2 diabetes is increasing rapidly in the United States, with recent estimates indicating that 79 million Americans currently have prediabetes and as many as one-third of Americans will have type 2 diabetes by the year 2050 (9). Thus, efforts to identify strategies to prevent the development of type 2 diabetes are worthwhile. Elevated postprandial glucose (PPG) typically precedes the development of type 2 diabetes and is an independent risk factor for adverse cardiovascular events, regardless of diabetes status (22,32,34), making PPG a promising target for diabetes prevention. Physical inactivity is gaining acceptance as a key etiological factor in the development and progression of type 2 diabetes. However, despite physical activity guidelines recommending ≥10,000 steps per day (36), the majority of Americans acquire only half of the recommended dose (3).
Epidemiological evidence implicates low levels of physical activity in the development and progression of insulin resistance and type 2 diabetes (18,25). Whereas higher levels of physical activity are associated with reduced risk of developing insulin resistance and cardiovascular diseases (18,25), individuals with type 2 diabetes consistently report lower levels of leisure time physical activity than their healthy counterparts (28) (see Bassuk and Manson  for review). Bed rest, cessation of exercise training, and reduced ambulatory activity lead to declines in insulin sensitivity in healthy individuals (13,17,20,29). Conversely, a single bout of moderate-intensity exercise enhances insulin sensitivity in both healthy and insulin-resistant individuals (17,21).
Although the effect of physical inactivity on insulin sensitivity has been widely studied, classic experimental techniques to assess insulin sensitivity or glucose tolerance, including the hyperinsulinemic–euglycemic clamp, intravenous glucose tolerance testing, and oral glucose tolerance testing (OGTT), do not directly assess glycemic variability in free-living persons consuming mixed meals (6) and, therefore, fail to capture the day-to-day frequency, magnitude, and duration of postprandial hyperglycemia (2,5,8). Thus, the direct effect of changes in physical activity on glycemic control is less clear. This is an important distinction given that PPG is a strong, independent predictor of cardiovascular events and death, regardless of diabetes status (22,32,34).
Direct assessment of PPG is vital to determining the role of daily physical activity in maintaining normal glycemic control. Direct measures of PPG can now be made using continuous glucose monitoring systems (CGMS) capable of recording minute-to-minute measures of blood glucose during multiple days. From these measures, the magnitude and duration of PPG in free-living individuals consuming mixed meals can be determined (16,23).
The specific effects of reducing physical activity on PPG are not well defined. The purpose of this study was to evaluate the effect of reducing daily activity from the current physical activity guidelines (≥10,000 steps per day) to the current norm (∼5000 steps per day) for 3 d on PPG and glycemic variability in healthy habitually active individuals. Given the powerful effect acute changes in physical activity have on experimental measures of insulin sensitivity and glucose disposal, we hypothesized that short-term reductions in physical activity would increase both PPG and glycemic variability in free-living individuals consuming mixed meals.
All protocols were approved by the University of Missouri Health Sciences Institutional Review Board, and written informed consent was obtained from all volunteers. The study’s ClinicalTrials.gov number is NCT00881972.
Young (20–35 yr) generally healthy (determined by detailed medical history questionnaire) recreationally active (routinely acquire ≥10,000 pedometer steps per day) volunteers were recruited for participation. Health status was determined by a detailed medical history questionnaire. Before enrollment in the study, participants were given pedometers and instructed to wear them for 7 d while recording the number of steps taken each day. Volunteers who acquired <10,000 steps per day were excluded from participation. Additional exclusion criteria included having a body mass index ≥30 kg·m−1, smoking, being pregnant, breast-feeding, consuming ≥14 servings of alcohol per week, or being involved in competitive endurance events.
After an overnight (10–12 h) fast, participants were instrumented with CGMS (iPro™ Continuous Glucose Monitor; Medtronic Diabetes, Northridge, CA), pedometers, and Intelligent Device for Energy Expenditure and Physical Activity monitors (IDEEA®; MiniSun, Fresno, CA) and were instructed to maintain habitual physical activity patterns while keeping detailed diet and physical activity records for 3 d (active phase; Fig. 1). Participants were also instructed to allot ≥2 h between meals and snacks to allow for quantification of the 120-min glucose response to meal ingestion (PPG and ΔPPG assessed via CGMS). On the morning of day 4, a 2-h OGTT (75 g of glucose) was performed after an overnight fast (Fig. 1).
After a brief washout period (≥7 d), the protocol was repeated, and participants were instructed to reduce their physical activity to accumulate ≤5000 steps per day for 3 d (inactive phase). Participants self-monitored physical activity during the inactive phase using pedometers. Again, a 2-h OGTT was performed after an overnight fast on the morning of day 4. Four of the 12 participants completed the phases in reverse order (inactive before active) to reduce potential confounding effects of testing sequence.
To ensure that changes in PPG were due to changes in physical activity and not diet, participants were instructed to replicate their dietary patterns precisely (food and beverage intake and timing) across the active and inactive phases. The OGTTs were performed at the same time in the morning across phases to eliminate diurnal influences.
Height, weight, and body composition (dual energy x-ray absorptiometry, Hologic QDR 4500A; Waltham, MA) were measured at baseline. Diet records were analyzed for micro- and macronutrient content using the Food Processor SQL (ESHA Research, Salem, OR). Maximal oxygen consumption (V˙O2max) was determined by indirect calorimetry (TrueOne 2400; ParvoMedics, Salt Lake City, UT) using the standard Bruce protocol (7).
Continuous glucose monitoring
At the onset of each CGMS monitoring period, a glucose sensor was inserted subcutaneously in the periumbilical region and connected to the CGMS for 3 d. Participants recorded precise periods of meal consumption and physical activity during the CGMS monitoring periods. Participants also recorded the timing and results of at least four finger stick glucose readings (ACCU-CHEK Compact Plus; Roche Diagnostics, Indianapolis, IN) each day for calibration against the CGMS recording. On the morning of the OGTTs, record books were collected, the glucose sensor was removed, and data from the CGMS were downloaded and processed using the Solutions Software for CGMS iPro (Medtronic Diabetes). Using the start of meal times documented in the food records, glucose concentrations were extracted from the CGMS recordings at times corresponding to 5 min before meal ingestion and at 30-min intervals up to 120 min after meal ingestion (premeal and 30-, 60-, 90-, and 120-min PPG, respectively). Because glycemic variability seems to be as important as, if not more important than, absolute PPG concentrations, we also calculated ΔPPG, an index of glycemic variability, for the respective time points, where ΔPPG = postmeal − premeal blood glucose concentration (mmol·L−1). Peak ΔPPG was also calculated as ΔPPGpeak = peak postmeal − premeal blood glucose concentration (mmol·L−1), where the peak postmeal glucose value is the highest value recorded within 120 min of meal ingestion. Because we did not see an effect of meal or time on PPG or ΔPPG (PPG and ΔPPG were not different between meals or between days 1, 2, and 3 within each phase), PPG and ΔPPG were pooled across all meals and across all days within each phase for analysis.
On the fourth morning of each phase, OGTTs were performed after an overnight (10–12 h) fast. Blood samples were collected into serum separator or EDTA tubes at 0, 30, 60, and 120 min. Serum samples were allowed to clot for 10 min, and all samples were centrifuged at 2000g for 15 min at 4°C. Serum and plasma were frozen at −80°C for subsequent analysis. Serum glucose was determined using the glucose oxidase method (Sigma, St. Louis, MO), and serum insulin, C-peptide, cortisol, and growth hormone were measured by a chemiluminescent enzyme immunoassay (IMMULITE 1000 Analyzer; Siemens Healthcare Diagnostics, Inc., Deerfield, IL). The areas under the curve for glucose, insulin, C-peptide, cortisol, and growth hormone were calculated using the trapezoidal method.
Surrogate markers of insulin sensitivity and insulin resistance were calculated from the glucose and insulin responses to the OGTT using the Matsuda composite insulin sensitivity index (26), with adjustments to exclude glucose and insulin measures at 90 min, and from fasting glucose and insulin values using the homeostasis model assessment of insulin resistance (27), respectively.
Physical activity was quantified during each phase by pedometer and IDEEA monitors, which can recognize and distinguish the type, onset, duration, and intensity of fundamental movements with 98% accuracy (40). Participants were equipped with pedometers and IDEEA monitors at the time the CGMS monitors were inserted and activated. Similarly, the pedometers and IDEEA monitors were collected at the OGTT. IDEEA data were then downloaded and processed using ActView Software (MiniSun). Pedometer use is well established as a valid index of physical activity levels and correlates strongly with those obtained by accelerometers (35,37). Therefore, pedometers were used in the current study as previously described (20) to allow participants to self-monitor daily activity and to ensure compliance with reduced activity during the inactive phase.
Differences in repeated pre- and postintervention measures were detected using a two-way repeated-measures ANOVA. Where significant main effects were found, Tukey post hoc testing was applied to identify specific between-phase differences. Paired t-tests were performed to detect between-phase differences in paired observations. Statistical significance was set at P < 0.05, and all data are expressed as means ± SE. Statistical analyses were performed using SAS Version 9.1 (SAS Institute, Inc., Cary, NC).
Twelve healthy volunteers (eight men, four women) participated in the study (Table 1).
Participants acquired 12,956 ± 769 pedometer steps per day during the active phase and 4319 ± 256 steps per day during the inactive phase (Fig. 2A), indicating that they were highly compliant with reducing physical activity during the inactive phase. On average, participants self-reported 38 ± 1 min of structured and/or planned physical activity per day during the active phase, with approximately 19 ± 2, 16 ± 4, and 4 ± 2 min of activity being self-reported as low, moderate, and high intensity, respectively, each day. During the inactive phase, participants reported only 3 ± 1 min of low-intensity structured or planned walking per day.
Detailed physical activity data obtained from the IDEEA monitors are displayed in Table 2. Because of technical complications, accelerometer data are unavailable for 5 of the 12 participants. In agreement with the pedometer and self-reported data, the IDEEA analysis revealed significant reductions in the amount of time spent walking, climbing stairs, and running (P < 0.05 vs active phase). Similarly, ambulatory time decreased from 8% ± 1% of each day to 2% ± 1% (P < 0.05). It seems that during the inactive phase, the majority of this time was spent sitting. However, the difference in sitting time between phases did not reach statistical significance (P < 0.07).
Three days of reduced physical activity in the healthy previously active volunteers led to significant increases in PPG at 30 and 60 min and to increases in ΔPPG of 30%–50% at 30, 60, and 90 min after a meal (P < 0.05; Figs. 2B, C). Furthermore, peak PPG increased by 26% (P < 0.05; Fig. 3). However, the reduction in physical activity did not influence premeal blood glucose concentrations measured by the CGMS (5.08 ± 0.09 vs 4.93 ± 0.10 mmol·L−1). The 24-h average glucose concentration did not change (Table 3). However, the minimum and maximum glucose concentrations observed during each phase decreased and increased, respectively (P < 0.05). Similarly, the duration of time spent above target postprandial blood glucose concentrations (>8 mmol·L−1) (19) increased during 3 d of reduced physical activity (P < 0.05).
After 3 d of reduced physical activity (inactive), fasting plasma insulin (23.3 ± 3.2 to 34.2 ± 3.7 pmol·L−1, P < 0.05) and C-peptide (0.43 ± 0.04 to 0.66 ± 0.09 nmol·L−1, P < 0.05) were elevated, whereas fasting glucose (4.64 ± 0.13 to 4.75 ± 0.09) and the glucose responses to the 75-g OGTT (Fig. 3) were not significantly altered. Conversely, both insulin and C-peptide responses to the OGTT were increased significantly after the inactive phase (P < 0.05; Fig. 3), suggesting additional insulin was needed to dispose of the same glucose load. Correspondingly, the Matsuda insulin sensitivity index decreased from 14.26 ± 1.81 to 9.91 ± 0.76 (P < 0.05), and the homeostasis model assessment of insulin resistance increased from 0.83 ± 0.13 to 1.24 ± 0.14 after 3 d of reduced physical activity (P < 0.05).
Responses of the counterregulatory hormones cortisol and growth hormone to the OGTT did not change in response to 3 d of reduced activity (data not shown).
Our data reveal robust changes in PPG and glycemic variability in response to short-term reductions in physical activity, providing new evidence that regular physical activity plays a key role in the day-to-day maintenance of glycemic control. Specifically, we demonstrate that PPG and ΔPPG increase significantly during just 3 d of reduced physical activity (from ∼12,000 to ∼5000 steps per day) in healthy individuals. Notably, these effects were not adequately captured by glucose responses to an OGTT, nor were they reflected by changes in fasting glucose concentrations.
Physical activity is widely recognized as an important component of a healthy lifestyle. Even a single bout of moderate- to vigorous-intensity exercise has been shown to improve insulin sensitivity in healthy individuals as well as those with type 2 diabetes (19,23). More recently, CGMS has been used to demonstrate that a single bout of moderate-intensity exercise significantly reduces the prevalence of hyperglycemia in patients with type 2 diabetes (24,30). Conversely, transient physical inactivity, whether in the form of bed rest, exercise cessation, or reductions in ambulatory activity, can substantially reduce insulin sensitivity (13,17,20,29). However, the effect of short-term reductions in physical activity on glycemic control is less clear. Findings from the present study indicate that a period of reduced physical activity of as short as 3 d leads to increases in ΔPPG by as much as twofold in healthy individuals. Although additional studies are needed to evaluate the effect of chronic physical inactivity on PPG and ΔPPG, these findings are particularly disturbing given the strong association between postprandial hyperglycemia and cardiovascular disease and death and in light of recent evidence that, on average, Americans acquire roughly 5000 steps per day (3).
The health benefits of physical activity are commonly attributed to physiological adaptations to chronic exercise training, including increased cardiorespiratory fitness or lowered adiposity. However, a previous work has established that adiposity and fitness are not significantly altered in response to 10 d of physical inactivity in healthy volunteers (17), suggesting that they were not significantly affected during 3 d of reduced activity in the present study. Although the importance of these factors in predicting health outcomes is widely accepted, the data presented here provide evidence that physical activity directly affects postprandial glycemia, independent of altering fitness or adiposity. Accumulating evidence suggests large swings in blood glucose (glycemic variability) may be as deleterious as or potentially even more damaging than chronic hyperglycemia (10). Large fluctuations in blood glucose initiate the production of reactive oxygen species, increased leukocyte–endothelial interaction, and protein glycosylation (10,38), which may contribute to the progression from insulin resistance to frank type 2 diabetes and may also drive micro- and macrovascular complications in patients with insulin resistance and type 2 diabetes (8,14,34). Thus, maintenance of glycemic control may be one mechanism by which adequate physical activity protects against the development and progression of metabolic and cardiovascular diseases.
Disparities in energy intake and expenditure that result in an energy imbalance may contribute to changes in insulin sensitivity. However, reducing energy intake to maintain energy balance during 1 d of reduced ambulatory activity abrogates only a portion of the effects of reduced physical activity on insulin sensitivity (33), suggesting an imbalance in energy availability does not fully account for changes in insulin sensitivity during short-term changes in physical activity. In addition, although overfeeding has been shown to reduce insulin sensitivity when combined with physical inactivity (12), short-term overfeeding alone does not produce significant changes in insulin sensitivity (1,11). The complex interaction between short-term changes in physical activity and glycemic control is further supported by evidence that a single bout of low- to moderate-intensity exercise has a more robust effect on glycemic control than an isocaloric bout of high-intensity exercise (24). Collectively, these data suggest that the interaction between physical activity levels (and/or intensity) and short-term glycemic control may be more important than that between energy availability and glycemic control. Although confounding effects of an implied positive energy balance resulting from a decline in energy expenditure coupled with no change in energy intake cannot be explicitly ruled out in the present study, it is likely that these conditions more appropriately mimic the physiological state of many individuals who acutely or chronically lower their activity levels but do not modify energy intake (3,39).
We suspect that a reduction in skeletal muscle insulin sensitivity played a role in the increase in PPG and glycemic variability in the present study because many studies have demonstrated the deleterious effects of physical inactivity on skeletal muscle insulin signaling and sensitivity (13,17,20,29). However, little is known about how reductions in physical activity influence the regulation of hepatic glucose production or pancreatic β cell function, factors that may have played a role in the changes in PPG and ΔPPG revealed in this study. Similar to our findings, Olsen et al. (29) recently reported no change in the glucose response to an OGTT coupled with a greater insulin response after 2 wk of reduced ambulatory activity in healthy volunteers. Another study from the same group demonstrated that 2 wk of reduced ambulatory activity altered skeletal muscle insulin signaling and sensitivity but not hepatic glucose production (20). These data support the premise that insulin secretion and/or clearance may adjust to compensate for decreases in skeletal muscle insulin signaling, at least during the early stages of reducing physical activity levels in an attempt to maintain euglycemia.
In the present study, despite evidence of enhanced insulin secretion (higher C-peptide concentrations) during the OGTT, ΔPPG measured by CGMS increased by nearly twofold in healthy individuals in response to reduced physical activity, suggesting the increases in circulating insulin after the OGTT were not replicated in response to mixed meals or were not sufficient to fully compensate for suspected declines in peripheral insulin sensitivity. Furthermore, fasting and premeal glucose concentrations were not elevated during 3 d of reduced activity, suggesting hepatic glucose production was not increased.
In agreement with previous reports, comparisons between PPG measured by CGMS and the laboratory-based OGTT revealed noteworthy differences in this study (15). We speculate that disparities between the two methodologies are likely attributable to differences in the macronutrient content of the 75-g glucose load ingested during the OGTT and the mixed meals consumed during CGMS monitoring. Unlike the OGTT, mixed meals contain fiber, lipids, amino acids, and micronutrients that affect enteric hormones, gastrointestinal motility, and neural impulses thereby influencing rates of absorption, gastric emptying, and insulin secretion. As a result, mixed meals may produce a greater first-phase insulin response and lower plasma glucose concentration relative to the OGTT (31). In addition, OGTTs were performed in the morning after an overnight fast while participants remained supine, whereas CGMS captured PPG responses to meals consumed with and without prior periods of prolonged fasting with no restrictions on posture or movement during the postprandial period. Collectively, these data suggest CGMS may be a more sensitive method for detecting alterations in glycemic control in response to inactivity or other perturbations in free-living populations, including persons without diabetes.
A limitation of this study is the small sample size and homogeneous nature of the research participants. Further study with larger sample sizes is needed to evaluate the effect of transitioning to reduced physical inactivity in more diverse populations to determine whether age, sex, type of daily activity, body composition, glucose tolerance status, ethnicity, and other potential factors affect the degree to which changes in physical activity alter glycemic variability.
In summary, we demonstrate that physical activity plays a fundamental role in the day-to-day maintenance of PPG and glycemic control and that changes in PPG in response to reductions in physical activity levels occur rapidly and likely before changes in adiposity or fitness. These data also emphasize the need to look beyond common clinical measures of glycemic control when assessing the efficacy of interventions designed to improve this outcome. However, above all, our findings support a growing body of evidence implicating low levels of physical activity as an important etiological factor in the development of insulin resistance and reinforce the utility of physical activity in preventing pathologies associated with elevated PPG, including type 2 diabetes and cardiovascular disease.
This project was funded by the University of Missouri Institute of Clinical and Translational Sciences (C.R.M.) and Research Council (J.P.T.) awards and a National Institutes of Health grant T32 AR-048523 (C.R.M.). Medtronic, Inc., supplied CGMS sensors at a discounted rate.
The authors have nothing to disclose.
The results of the present study do not constitute endorsement by the American College of Sports Medicine.
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