There is a growing body of evidence that a high dietary intake of fructose is associated with the development of metabolic risk factors including insulin resistance (5), fatty liver (24), hyperlipidemia (2,22,23), hypertension (6), and reduced lipid oxidation (1). Specifically, a study by Stanhope et al. (32) demonstrated that an ad libitum diet consisting of 25% of calories from fructose induced hepatic insulin resistance, increased de novo lipogenesis (DNL), and increased visceral adiposity in obese adults within only 10 wk.
The proposed metabolic dysregulation associated with high fructose consumption relates to the unique metabolism of fructose. Fructose is almost exclusively phosphorylated to fructose-1-phosphate in an adenosine triphosphate-dependent process catalyzed by fructokinase in hepatic cells. This results in a rapid and relatively unregulated production of triose phosphates, which may subsequently be converted to pyruvate for conversion to lactate or acetyl coenzyme A (33). Large concentrations of acetyl coenzyme A can stimulate hepatic DNL and simultaneously inhibit hepatic lipid oxidation, resulting in fatty acid reesterification and VLDL triglyceride (TG) synthesis (30). The consequence of the elevation in VLDL TG synthesis is an increased supply of TG to the adipose tissue and ectopic tissues such as muscles and the liver, increasing the size of TG depots. This leads to impaired insulin signaling, dyslipidemia, and low-grade inflammation (11).
Despite the mounting evidence demonstrating adverse adaptations to a high-fructose diet, these studies often use unrealistically high levels of fructose, some in excess of 25% of total energy intake (1,32), which may not be clinically relevant. Specifically, Stanhope et al. (32) used an additional 25% of a fructose drink to an ad libitum diet. Moreover, research from Abdel-Sayed et al. (1) used an additional 234 g fructose·d−1 for 7 d on healthy young males. Both of these aforementioned studies are well above the average intake of approximately 70 g of added sugar in the form of fructose. Moreover, there are no studies to date researching the metabolic effects of increased physical activity (PA) during a high-fructose diet. PA is a lifestyle modification often implemented to decrease risk factors associated with metabolic syndrome and may be a clinically relevant tool to use to combat the deleterious effects of a high-fructose diet. A previous review by Bassuk and Manson (4) determined through various epidemiological studies that physically active individuals are at 30%–50% lower risk of developing type 2 diabetes and heart disease because of a possible reduction in body weight, insulin resistance, hypertension (25), athrogenic dyslipidemia, and inflammation, all previously mentioned risk factors of a high-fructose diet. Hence, public health initiatives should focus on increased PA in conjunction with a diet low in fructose to attenuate metabolic syndrome risk factors.
Moreover, physical inactivity is associated with low-grade inflammation, as evidenced by increased concentrations of proinflammatory markers such as monocyte chemoattractant protein 1 and interleukin 6 (IL-6) (14). Because fructose consumption is independently associated with increased low-grade inflammatory markers (9) and increased rates of DNL (1,6,23,24,32), we expect previously reported deleterious health outcomes from chronic fructose consumption to be confounded by physical inactivity.
Our aim was to test the hypothesis that a diet rich in fructose would increase postprandial lipidemia to a greater extent when coupled with physical inactivity, independent of energy intake. Secondly, our aim was to test the hypothesis that after ingesting a high-fructose diet for 2 wk, proinflammatory markers associated with elevated lipid production would be elevated and these effects would be ameliorated with high PA.
SUBJECTS AND METHODS
Twenty-two healthy male (n = 11) and female subjects (n = 11) between the ages of 18 and 25 yr were recruited from the Syracuse University community. Subjects had to be recreationally active, as determined by a PA questionnaire (3–4 d·wk−1 of moderate-to-vigorous activity for at least 20–60 min·d−1), with a body mass index (BMI) <27 kg·m−2 (mean, 22.5 ± 1.6 kg·m−2). All subjects completed an informed consent form approved by the Syracuse University institutional review board before participating in this study. Exclusion criteria included the use of lipid and/or glucose-lowering medications or other medications that may affect glucose and lipid metabolism (e.g., antidepressants, oral contraceptives, etc.), chronic nonsteroidal anti-inflammatory drug use (more than two times a week), daily antioxidant supplementation, orthopedic limitations to walking, type 2 diabetes or glucose intolerance, overt cardiovascular disease, hypertension, and/or an abnormal lipid profile. Subjects were excluded if they were currently ingesting more than one high-fructose drink per day (>20 g). Women started the intervention period within the first 7 d of their menstrual cycle to minimize the potential effects of estrogen on glucose/insulin concentration (12).
Initially, subjects participated in a 1-wk control period to determine their normal PA (Table 1) and dietary habits. The two interventions were separated by a 2-wk washout period, and participants were randomly allocated to each intervention using a counterbalanced crossover experimental design. During each intervention period, the subjects’ usual ad libitum diet was supplemented with an additional 75 g of fructose per day. The first intervention involved 2 wk of high PA levels (>12,500 steps) (FR+active). The second intervention involved low PA (<4500 steps) (FR+inactive). A study day with a fructose-rich meal was given at the beginning and at the end of each 14-d intervention, as described in the following section.
All subjects completed a medical history, PA, and sedentary behavior questionnaire before the start of the intervention. After a review of the subject’s activity level with the use of a validated questionnaire monitoring PA at work, during leisure time, and during sport (10), the potential subjects were excluded from participation if their activity level included regular structured exercise more than five times per week or less than two times per week. Height and weight were measured, and body composition was assessed by air displacement plethysmography (BOD POD system; Life Measurement, Inc., Concorde, CA) (29). After anthropometric measurements were completed, subjects performed a graded exercise test on the treadmill using a protocol that has been previously published (13) to determine peak oxygen consumption (V˙O2peak).
After the initial visit, the subjects underwent a nutritional consultation with a registered dietitian to ensure compliance with the dietary intervention and to estimate normal fructose intake. Subjects were instructed to refrain from ingesting any added sugar such as sweetened beverages, fruit juices, pastries, and cookies aside from the study drinks during the intervention period. Furthermore, estimates of food intake during the control and intervention periods were collected by random 24-h recall (via telephone) two times per week using the US Department of Agriculture five-step multiple-pass method (8). The same registered dietitian administered the recall to all subjects. Recalls were then analyzed using Diet Analysis Plus (version 7; Thomson Wadsworth, Thomson Corp., Independence, KY). After the initial visit, the subjects began a 1-wk control period, at which time, they were instructed to maintain normal activities of daily living and dietary habits. During this period, PA (steps) was monitored with the use of an accelerometer (ActiGraph® GT3X activity monitor; ActiGraph Corp., Pensacola, FL), which was later uploaded to the computer for further analysis. Subjects also were given pedometers (Accusplit®, Livermore, CA) to provide visual feedback regarding daily step count. Subjects were asked to refrain from any additional exercise throughout the study duration.
At the end of the 7-d control period, the subjects arrived at the Human Performance Laboratory at Syracuse University at 0700 h after a 12-h fast and no exercise 24 h before testing. Subjects had a catheter inserted into the antecubital vein by a registered nurse. Subjects then rested in a supine position for 30 min before obtaining two baseline blood samples (10 mL each). After baseline blood samples were obtained, a test meal was prepared for subjects. The test meal included the following: 139.5 g of Wegmans® large eggs, 65.55 g of Thomas Better Start Light® Multi-Grain English Muffins, 22.54 g of I Can’t Believe Its Not Butter® Mediterranean Blend butter, and a high-fructose corn syrup drink consisting of 20.6 g of Swanson® fructose, 16.9 g of NOW® sports glucose, 1.1 g of Great Value® artificial sweetener, and 236.5 g of Vintage® sodium-free carbonated water (600 kcal, 45% CHO (25% fructose and 20% complex), 40% fat, 15% protein, and 5 g of fiber). After the test meal, blood samples were obtained at the following time points: 5, 10, 15, 20, 30, 40, 50, 60, 75, 90, 120, 150, 180, 210, 240, and 360 min. For the duration of the study day, subjects were instructed to sit on a reclining chair and abstain from any strenuous activity.
The subjects consumed a fructose-rich diet containing 74.9 g·d−1 of fructose (two 20-oz Lemon Lime WPOP® drinks; Rochester, NY) along with their ad libitum diet during both conditions. The ad libitum diet was chosen on the basis that sugar-sweetened beverages are usually consumed in conjunction with an ad libitum diet (32). Previous research has indicated that within 7 d, metabolic abnormalities can occur with a high-fructose diet (1); therefore, a 2-wk intervention was chosen to ensure that changes will occur. The subjects collected their beverages twice weekly from the Human Performance Laboratory. They were required to return their empty drink bottles to the laboratory once per week to assess drink compliance and to record step counts. Each subject met with a registered dietitian who assisted in maintaining eating habits and recording of weight during these weekly visits to the laboratory.
On the day after the 14-d intervention period, a postintervention test meal was provided using the same procedures as those in visit 1. After visit 2, subjects were instructed to maintain their normal activities of daily living and dietary habits for 2 wk. Previous research has indicated that a 2-wk washout period is adequate to normalize metabolic markers associated with hyperlipidemia (20). Visits 3 and 4 were then completed before and a day after the alternate 2-wk intervention period, respectively, using the same procedures as those outlined for visits 1 and 2.
A lipid profile (Cholestech LDX; Biosite International, San Diego, CA) was performed on the samples taken at −5, 0, 60, 120, 180, 240, and 360 min and measured TG, VLDL, total cholesterol, and glucose concentrations. The use of this equipment has previously been validated (26). In addition, blood samples obtained at −5, 0, 5, 10, 15, 20, 30, 40, 50, 60, 75, 90, 120, 150, 180, 210, 240, and 360 min were transferred to BD Vacutainer® Plus Plastic EDTA tubes (Franklin Lakes, NJ), separated by centrifugation, divided into two sets of polypropylene tubes, and stored at −80°C for subsequent analysis. Insulin, TNF-α, and IL-6 were analyzed using Luminex xMap Technology (Linco Research, St.Charles, MO) on a Luminex 100/200 platform (Luminex Corp., Austin, TX). All procedures followed the manufacturer’s instructions (Millipore, Billerica, MA), with quality controls within expected ranges for each assay (insulin: interassay coefficient of variation (CV) = 5.0%, intraassay CV = 4.0%; TNF-α: interassay CV = 9.9%, intraassay CV = 10.6%; IL-6: interassay CV = 10.3%, intraassay CV = 11.9%). C-reactive protein (CRP) assays were performed using the Quantikine assay kit (R&D Systems, Inc., Minneapolis, MN) (interassay CV = 6.5%, intraassay CV = 4.2%). Insulin sensitivity was calculated by the homeostatic model assessment method as previously described by Levy et al. (19) and the quantitative insulin sensitivity check index (7).
All results were reported as mean ± SEM using SPSS 19.0 (Chicago, IL). Descriptive variables (n = 22) and dietary analysis were analyzed using a two-way repeated-measures ANOVA to depict differences in pre- and postintervention weight, BMI, percent body fat, and macronutrient consumption. Postprandial responses for all blood variables were determined by calculating the total area under the curve (tAUC) or incremental AUC (iAUC) (Excel; Microsoft Corp., Redmond, WA) and absolute change from peak to baseline concentrations (Δpeak) for all variables. A between-subject analysis was performed on all variables to depict differences in genders. A log transformation (log10) was used for data that were not normally distributed on the basis of visual appearance of skewed data. Transformations were applied so that the data more closely met the assumptions of normality on the basis of parametric analysis statistical procedures (18). Lipid and inflammatory variables were analyzed using a three-way ANOVA with repeated measures to assess the changes in lipid measures and inflammatory markers over the 6-h test day: 2 (high vs low PA) × 2 (pre- vs postintervention) × 18 (time points). If a significant interaction was found, differences between time points were analyzed using a paired t-test with Bonferroni correction factor. Statistical significance for AUC and Δpeak concentrations was computed using a two-way repeated-measures ANOVA (intervention × pre–post). Pearson correlation coefficient was computed to determine any correlations between fasting TG and inflammatory variables. A priori significance was set at P < 0.05. Sample size was determined by a previous research with similar methodology (3).
Table 1 presents the subject characteristics of the study participants. Males were significantly heavier and taller and had a lower body fat percentage than the females (P < 0.05). There were no significant differences in weight (preintervention, 67.4 ± 9.1 kg; postintervention, 10.2 kg), BMI, or percent body fat after either intervention for both males and females (P < 0.05) (data not shown). There were no gender differences in any of the lipid or inflammatory markers; therefore, all subjects were combined for further analysis. No significant differences in fasting metabolic, inflammatory, and glucose markers for both pre- and post-FR+active and -FR+ inactive interventions were observed (data not shown). Moreover, there were no significant correlations between lipid and inflammatory markers (P > 0.05).
The baseline energy intake for all subjects was 2701 kcal (Table 1). Energy intake was not significantly different between baseline and either intervention, and we observed no change in subjects’ body weight (P > 0.05). Likewise, there was no difference in the macronutrient composition between the interventions (P > 0.05) (Table 2). The subjects consumed an additional 75 g of fructose per day from the drink provided (500 kcal, 0 g of fat, 135 g of CHO, and 74.9 g of fructose).
Glucose and insulin
The test meal induced a significant postprandial response in both glucose and insulin concentrations (P < 0.05) (Fig. 1A–B). Glucose tAUC and postprandial Δpeak concentrations were not different after either intervention, but a significant intervention–time interaction occurred in insulin concentrations (Fig. 1B). Specifically, insulin tAUC for FR+active intervention decreased from pre- to postintervention, whereas there was no change in insulin tAUC after the inactive intervention (P = 0.04) (Fig. 1B). These differences in AUC can be accounted for by a 19% lower Δpeak insulin response after the FR+active intervention, whereas the Δpeak insulin in the FR+inactive condition was 21% higher postintervention (P < 0.01) (Fig. 1C).
TG concentrations significantly increased in response to the test meal under both interventions (P < 0.01) (Fig. 2A). After log transformation, TG tAUC concentrations significantly increased from pre- to post-FR+inactive intervention (P = 0.04), whereas there was no change from pre- to post-FR+active intervention (Fig. 2B). Similarly, Δpeak TG increased by 88% as a result of the FR+inactive intervention but decreased by 5% (P < 0.01) with the FR+active intervention (Fig. 2C).
Figure 3A represents the 6-h postprandial response of VLDL concentrations after a test meal. Both pre- and post-FR+active and -FR+inactive interventions demonstrated a significant main effect across time for VLDL concentrations, at which point, concentrations peaked at 3 h (P = 0.03). The Δpeak VLDL induced a significant intervention–time interaction, such that the difference from pre- to post-FR+inactive intervention was significantly larger than the difference from pre- to post-FR+active intervention. The inactivity induced an 84% increase in Δpeak VLDL, whereas only a 33% increase after the FR+active intervention was observed (P = 0.009) (Fig. 3B).
There was a significant main effect across time after the meal for total cholesterol (P = 0.03), but tAUC and Δpeak cholesterol were not significantly different between interventions (data not shown).
In response to the fructose test meal, a significant meal effect was observed for TNF-α concentrations, such that the test meal resulted in an increase in TNF-α concentrations over the course of 6 h (P = 0.02); however, there were no changes in CRP concentrations over time (P > 0.05) (data not shown). The FR+active and FR+inactive interventions did not result in any significant differences in TNF-α or CRP tAUC, iAUC, or Δpeak concentrations (P > 0.05).
In response to the test meal, IL-6 concentration showed a significant main effect of time (P = 0.02) (Fig. 4A). Furthermore, Δpeak IL-6 demonstrated a significant intervention–time interaction, such that Δpeak IL-6 concentrations decreased by 30% after the FR+active intervention, whereas they increased by 116% after the FR+inactive intervention from pre- to postintervention (P = 0.048) (Fig. 4B).
The purpose of the current study was to determine whether manipulation of PA levels would alter individual susceptibility for metabolic risk factors associated with consumption of a moderate dose of excess fructose (approximately 75 g·d−1) over a 2-wk period, independent of energy intake. This study demonstrated in young healthy individuals that consumption of an additional 75 g of fructose per day in conjunction with physical inactivity (approximately 4200 steps per day) resulted in increased postprandial lipidemia and is a precursor to low-grade inflammation. These results were not observed when fructose was consumed during the 2-wk high PA intervention (approximately 13,000 steps per day), suggesting that the higher level of PA conferred protection against the metabolic stress of the additional fructose.
Previous research has identified that a diet high in fructose induces hepatic DNL, causing an increase in plasma TG and VLDL concentrations (1,28,32). In addition, although not studied in this particular study, physical inactivity may reduce LPL activity, resulting in reduced rates of fatty acid use and oxidation in peripheral tissues (34). Therefore, we speculated that the increased rates of DNL associated with high fructose consumption seen previously (32), coupled with the reduction in LPL activity with sedentary behavior (34) and, thus, reduced clearance rates of blood lipids, likely explain the increases observed in TG and VLDL concentrations. In agreement with previous research (15,34), postprandial TG and VLDL concentrations in this study were elevated by 88% and 84% from baseline, respectively, in the FR+inactive intervention; however, no changes were observed in the active intervention.
Although glucose concentration was unaffected by the additional fructose intake during either 2-wk intervention, insulin concentration was 19% lower during the FR+active intervention when compared with that during the FR+inactive intervention. This is consistent with previous research showing decreased plasma insulin concentrations with PA (21). Regular PA has beneficial effects on insulin sensitivity by enhancing insulin signaling, glucose transport, and substrate metabolism in muscles (21). Therefore, although a high-fructose diet increases postprandial plasma lipids, which may alter the insulin-signaling cascade, increased PA seems to offset these deleterious consequences, most likely by altering intracellular substrate use.
Our data show a 116% increase in postprandial-induced peak IL-6 concentration in response to the FR+inactive intervention (Fig. 4), which was not observed in response to the FR+active intervention. Although the current study did not find changes in CRP and TNF-α (Table 2), the changes in IL-6 are in line with those in the study of Højbjerre et al. (16) who demonstrated an association between physical inactivity (without dietary modification) and elevated IL-6 concentrations. In the present study, the low PA (approximately 50% reduction in steps per day) during 2 wk seemed sufficient to increase postprandial IL-6 systemic concentration. In contrast, we found a 30% decrease in IL-6 levels after PA (increasing steps per day by approximately 50%). PA is a known protector against increases in inflammatory markers (17). The acute increases in IL-6 levels that occur after exercise stimulate the release of many anti-inflammatory cytokines, particularly from the skeletal muscle, which may cause a long-term effect of attenuating low-grade inflammation and chronic IL-6 release (17,27). As stated in previous research, physical inactivity increases IL-6 concentrations, which is known to reduce the expression of insulin substrate receptor 1 and glucose transporter type 4 receptors in adipocytes and decrease insulin-stimulated glucose transport, resulting in insulin resistance and glucose intolerance (17). On the basis of previous studies and results from the current study, it is possible to speculate that a longer duration of physical inactivity, in conjunction with a diet high in fructose, could lead to deleterious effects of insulin signaling as a consequence of chronically elevated IL-6 concentrations (3).
Although proinflammatory markers are often up-regulated with physical inactivity (31), the addition of PA did not cause any change in TNF-α concentrations. These results were not unexpected because research has suggested that TNF-α concentrations are not affected by exercise (16). Moreover, 2 wk of a moderately high-fructose diet may not be long enough to induce up-regulation of TNF-α.
In conclusion, in a population of young, healthy individuals, being physically inactive (approximately 4200 steps per day) while consuming an additional 75 g of fructose resulted in increased postprandial lipidemia and signs of potential low-grade inflammation, independent of energy intake, in as few as 2 wk. However, increased PA levels (approximately 13,000 steps per day) seem to protect against these adverse changes. Thus, low PA may increase susceptibility to both metabolic and cardiovascular risk factors within just 2 wk in a nonclinical population. While future research in clinical populations and additional dietary modifications should be explored in combination with other lifestyle factors, it becomes evident that basic advice concerning increased exercise needs to continue in the clinical setting.
This study was supported by a National Institutes of Health grant (R21DK084467-01) and the Syracuse University School of Education Research and Creative Grant.
The authors’ responsibilities were as follows: A. J. B. conducted the main part of the intervention, interpreted the data, and wrote the manuscript; J. R. was the registered dietician on the study and conducted all dietary assessments and analysis; L. W. assisted with data interpretation and writing of the manuscript; T. J. F. assisted with data interpretation and writing of manuscript; and J. A. K. assisted with data analysis and interpretation and writing of manuscript. All authors read and approved the final manuscript.
None of the authors declared a conflict of interest.
The results of the present study do not constitute endorsement by the American College of Sports Medicine.
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