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. Author manuscript; available in PMC: 2013 Mar 12.
Published in final edited form as: Ann Nutr Metab. 2012;61(2):135–141. doi: 10.1159/000342084

Exercise training with weight loss and either a high or low glycemic diet reduces metabolic syndrome severity in older adults

Steven K Malin 1, Nicole Niemi 1, Thomas PJ Solomon 1, Jacob M Haus 1, Karen R Kelly 1, Julianne Filion 1,5, Michael Rocco 2, Sangeeta R Kashyap 3,5, Hope Barkoukis 4, John P Kirwan 1,4,5
PMCID: PMC3586384  NIHMSID: NIHMS417647  PMID: 23036993

Abstract

Background

The efficacy of combining carbohydrate quality with exercise on metabolic syndrome risk is unclear. Thus, we determined the effects of exercise training with a low or high glycemic diet on metabolic syndrome severity (Z-score).

Methods

Twenty-one adults (66.2 ± 1.1 yr; BMI = 35.3 ± 0.9 kg/m2) with metabolic syndrome were randomized to 12 weeks of exercise (60 minutes/d for 5 d/week at ~85% HRmax) and provided a low-glycemic (n=11; LoGIx) or high glycemic (n=10; HiGIx) diet. Z-scores were determined from: blood pressure, triglycerides (TG), high-density lipoproteins (HDL), fasting plasma glucose (FPG), and waist circumference (WC) before and after the intervention. Body composition, aerobic fitness, insulin resistance, and non-esterfied fatty acid (NEFA) suppression were also assessed.

Results

LoGIx and HiGIx decreased body mass and insulin resistance and increased aerobic fitness comparably (p < 0.05). LoGIx and HiGIx decreased the Z-score similarly, as each intervention decreased blood pressure, TG, FPG, and WC (p < 0.05). HiGIx tended to suppress NEFA during insulin stimulation compared to LoGIx (p = 0.06).

Conclusions

Our findings highlight that exercise with weight loss reduces metabolic syndrome severity whether individuals were randomized to a high or low glycemic index diet.

Keywords: aging, obesity, lifestyle modification, diabetes, impaired glucose tolerance

Introduction

Approximately 35 million adults in the U.S. over the age of 60 years have metabolic syndrome [1]. The components of metabolic syndrome (i.e. central obesity, hypertension, dyslipidemia and hyperglycemia) increase risk for type 2 diabetes and cardiovascular disease. Given that nearly 20% of the U.S. population is expected to be over the age of 65 years by 2030, the number of individuals with metabolic syndrome will rise. Thus, there is a need to identify optimal treatments to prevent metabolic syndrome, and reduce the associated health and economic burden.

The U.S. Diabetes Prevention Program demonstrated that lifestyle modification, consisting of increased physical activity and low-fat diet, reduced metabolic syndrome prevalence compared to metformin treatment or placebo [2]. Exercise training reduces cardiometabolic risk factors, including blood pressure, lipids and insulin resistance, in older adults with metabolic syndrome [3,4]. Despite reports that greater weight loss reverses the constellation of metabolic syndrome risk factors [5], exercise training with hypocaloric diet has not translated into greater reductions in cardiometabolic risk factors compared to exercise training alone [6,7]. This suggests that exercise may be sufficient to correct many of the underlying metabolic syndrome risk factors. However, it may be possible to enhance the effect of exercise on these risk factors by adjusting the glycemic content of the diet.

Although lifestyle interventions often recommend carbohydrate-based diets, few studies have determined the role of carbohydrate type in combination with exercise on metabolic syndrome severity. High glycemic index carbohydrates increase plasma glucose concentrations, blood lipids, ectopic lipid storage, and the demand for insulin [8], whereas low-glycemic index diets improve glycemic control and lower triglycerides, blood pressure, and hyperinsulinemia [9,10]. We have shown that a low-glycemic index diet combined with exercise training raised insulin sensitivity [11] and lowered glucose concentrations, inflammation, and systolic blood pressure to a greater extent than a high glycemic index diet in older adults [12,13]. To date, however, no study has determined the efficacy of a low glycemic index diet combined with exercise on metabolic syndrome severity (i.e. Z-score), as opposed to metabolic syndrome being present or absent. Considering metabolic syndrome in this way has clinical relevance as it allows a change in cardiometabolic health to be measured across a continuum. Therefore, the purpose of this study was to determine the effect of exercise training with a low glycemic diet, compared to a high glycemic diet, on metabolic syndrome severity and insulin resistance in older adults. We hypothesized that a low glycemic diet with exercise training would reduce metabolic syndrome severity and insulin resistance more than a high glycemic diet.

Methods

Subjects

Twenty-one older (66.2 ± 1.1 yr) obese (BMI = 35.5 ± 0.9 kg/m2) adults (see Table 2) who met the National Cholesterol Education Program Adult Treatment Panel (ATP) III criteria for metabolic syndrome were used in this analysis from our past work [13,14,15]. Subjects were non-smoking, weight stable (<2 kg in previous 6 months), sedentary (less than <30 min/d <3 d/week), and free of chronic disease (i.e. hematological, renal, hepatic, cardiovascular) or medications known to affect the primary outcomes. Participants were randomly assigned to exercise training with a low-glycemic index diet (LoGIx; n=11) or a high glycemic index diet (HiGIx; n=10). Post-menopausal women were not on hormone replacement therapy, and all subjects provided signed informed consent approved by our Institutional Review Board.

Table 2.

Effects of HiGIx and LoGIx on body composition, blood pressure, lipids, insulin resistance, and aerobic fitness.

Metabolic Syndrome - HiGIx Metabolic Syndrome - LoGIx ANOVA (p-value)
Pre Post Pre Post Test Group x Test
n (M,F) 10 (3F, 7M) - 11 (7F, 4M) - - -
Age (years) 65.6 ± 1.3 - 67.2 ± 1.6 - - -
Weight (kg) 107.9 ± 4.3 96.9 ± 3.8 93.6 ± 4.0 86.8 ± 3.5 ≤ 0.001 ≤ 0.09
Body Mass Index (kg/m2) 36.7 ± 0.9 33.1 ± 1.2 33.8 ± 1.3 31.4 ± 1.3 ≤ 0.001 ≤ 0.10
Fat Mass (kg) 45.1 ± 2.1 36.2 ± 3.2 41.6 ± 2.1 35.9 ± 2.2 ≤ 0.001 ≤ 0.07
Fat Free Mass (kg) 62.4 ± 4.0 60.7 ± 3.7 52.1 ± 3.3 50.9 ± 2.8 ≤ 0.01 ≤ 0.67
Hemoglobin A1c (%) 5.6 ± 0.2 5.4 ± 0.1 5.8 ± 0.2 5.7 ± 0.2 ≤ 0.02 ≤ 0.97
2-hour Glucose (mg/dl) 137.4 ± 11.6 131.5 ± 16.8 152.5 ± 8.7 142.9 ± 9.8 ≤ 0.18 ≤ 0.39
Fasting Plasma Insulin (μU/ml) 21.9 ± 5.8 11.9 ± 1.4 31.2 ± 8.5 15.2 ± 2.0 ≤ 0.01 ≤ 0.53
2-hour Plasma Insulin (μU/ml) 111.6 ± 27.7 88.1 ± 24.2 164.4 ± 34.8 94.0 ± 31.9 ≤ 0.02 ≤ 0.22
HOMA-IR 4.3 ± 0.6 2.7 ± 0.3 5.2 ± 0.7 3.2 ± 0.5 ≤ 0.003 ≤ 0.80
TC (mg/dL) 205.6 ± 9.5 183.5 ± 9.7 200.8 ± 12.0 180.6 ± 10.3 ≤ 0.002 ≤ 0.87
LDL (mg/dL) 136.2 ± 6.8 123.0 ± 7.5 120.3 ± 9.3 112.1 ± 9.9 ≤ 0.05 ≤ 0.63
VO2max (ml/kg/min) 22.2 ± 1.5 28.3 ± 2.7 19.6 ± 0.9 25.2 ± 1.9 ≤ 0.0001 ≤ 0.87
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Data are mean ± standard error of mean. There were no statistical differences at baseline between groups for any outcome. HiGIx = high glycemic index; LoGIx = low-glycemic index.

Aerobic Fitness

Maximum oxygen consumption (VO2max) was determined using a continuous incremental treadmill exercise test (Jaeger Oxygcon Pro; Viasys, Yorba Linda, CA). VO2max was determined as previously described (12). Maximal heart rate (HRmax) obtained during this test was used during exercise training.

Body Composition

Height was measured without shoes using a wall-mounted stadiometer and weight was recorded on a digital scale in a hospital gown. Dual-x-ray absorptiometry (DEXA; Lunar Prodigy, Madison, WI) was used to quantify total fat mass and fat free-mass.

Inpatient Control Period

Pre and post treatment assessments of cardiometabolic risk factors and insulin resistance were conducted during a 3-day inpatient stay in the Clinical Research Unit. Subjects were provided weight-maintenance meals (resting metabolic rate x 1.2 activity factor; 55% CHO, 30% fat, 15% protein). Following the intervention, cardiometabolic risk factor measurements and insulin resistance were made approximately 16–18 hours after the last exercise bout. Subjects maintained their respective dietary intervention at the time of post-testing.

Exercise Training and Nutritional Intervention

All participants underwent a 12-week supervised exercise training program, consisting of treadmill-walking and cycle ergometer exercise at approximately 85% of HRmax. All meals and fluids were provided to the participants throughout the intervention. A registered dietitian (H.B.) created the diets to be isocaloric to the individual requirements at baseline (i.e. resting metabolic rate measurement x 1.2 activity factor). As a result, weight loss during the interventions was primarily due to exercise energy expenditure, not caloric restriction. The macronutrient composition, including fiber, was matched between groups. However, the LoGIx subjects received a diet corresponding to a GI of 40 arbitrary units (au), while the HiGIx subjects were provided an 80 au GI diet (see Table 1). Recipes were identical between diets, with only substitutions made between the types of carbohydrate. Dietary compliance was determined by daily food container weigh backs, and diet analysis was performed with the Nutritionist Pro software (Axxya systems, Stafford, TX).

Table 1.

Low and high glycemic index diets while exercise training.

Metabolic Syndrome- HiGIx Metabolic Syndrome - LoGIx
GI (au) 80.3 ± 0.6* 40.3 ± 0.2
GL (au) 241.7 ± 16.9* 105.0 ± 4.8
Energy Intake (kcal/d) 2082.0 ± 131.7 1827.7 ± 81.7
Carbohydrate (gram/d) 301.0 ± 20.2 256.7 ± 11.8
Fiber (gram/d) 30.5 ± 2.1 29.5 ± 1.3
Fat (gram/d) 88.6 ± 5.6 78.9 ± 3.5
Protein (gram/d) 66.3 ± 3.9 58.2 ± 2.3
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Data are mean ± standard error of mean. GI = glycemic index. GL = glycemic load. Significant compared to LoGIx,

*

p < 0.05.

Cardiometabolic Risk Factors

After an overnight fast, blood pressure was measured in the seated position after 10 minutes of rest and was based on the average of 3 measurements. Mean arterial pressure (MAP) was calculated as: MAP = 2/3(DBP) + (1/3SBP). Waist circumference (WC) was measured up to 3 times using a plastic tape measure approximately 2 cm above the umbilicus. Measurements within 0.5 cm were used for analysis. It is possible that we overestimated our waist circumference results by measuring at the umbilicus compared to the NCEP boney landmark recommendation [15]. However, we believe our waist circumference data are valid since they parallel the change in visceral adiposity as measured by CT scans in our previous work [13,14,15]. Fasting plasma glucose (FPG), triglyceride (TG), and high density lipoprotein (HDL) measurements were obtained from blood sampling. Sex-specific Z-scores were calculated to indicate changes in metabolic syndrome severity before and after the intervention [16]. The equations used were: Z-scoremen = [(40-HDL)/10.3] + [(TG-150/66.5)] + [(FPG-100)/13.4] + [(WC-102)/8.5] + [(MAP-100)/10.0], and Z-scorewomen = [(50-HDL)/12.4] + [(TAG-150/66.5)] + [(FPG-100)/13.4] + [(WC-88)/11.7] + [(MAP-100)/10.03]. ATP III scores were also calculated for each subject based on the sum of risk factors meeting metabolic syndrome criteria.

Insulin Resistance

After an overnight fast, a 2-hour euglycemic-hyperinsulinemic clamp was performed as previously described [14]. In summary, a constant infusion (40 mU/m2·min−1) of insulin was administered via an indwelling catheter placed in an antecubital vein. Glucose (20%) was infused at a variable rate to maintain plasma glucose at 90 mg/dl. Arterialized plasma samples were collected from a retrograde hand vein warmed to 60°C. Blood samples were collected every 5 minutes for the analysis of glucose and every 15 minutes for analysis of insulin. Non-esterfied fatty acid (NEFA) plasma samples were collected at baseline and 120 minutes. Insulin stimulated NEFA suppression was calculated as: [1− (NEFAclamp/NEFAbase) * 100]. Respiratory gases (VO2 and VCO2) were analyzed via indirect calorimetry for 20 minutes in the fasted and insulin stimulated state (last 20 minutes of clamp) while the subject rested in the supine position. The last 10-minutes were averaged for determination of substrate oxidation [17,18]. Non-oxidative glucose disposal was calculated as: glucose infusion rate – total carbohydrate oxidation rate. Homeostatic model assessment (HOMA-IR), a surrogate for hepatic insulin resistance, was calculated as fasting glucose (mg/dl) x fasting insulin (uU/ml)/ 405.

Glucose Tolerance

After an overnight fast, blood samples were collected from an antecubital vein and a 75 gram glucose load was administered orally. Blood samples were then obtained at 120 minutes.

Biochemical Analysis

Plasma glucose was determined using a glucose oxidase assay (YSI 2300 STAT Plus, Yellow Springs, OH). Plasma insulin was measured by radioimmunoassay (Millipore, Billerica, MA). Plasma triglycerides and cholesterol were analyzed using enzymatic methods with an automated platform (Roche Modular Diagnositcs, Indianapolois, IN). Plasma NEFA was measured by enzymatic colorimetry (Wako Chemicals, Richmond, VA).

Statistical Analysis

Data are expressed as mean ± standard error of the mean (SEM). Group means were compared using the R statistical software package (Version 2.4.0, The R Foundation, Vienna, Austria, 2006). Diet and baseline variables were assessed by unpaired t-tests. Baseline variables were not different between groups. Outcomes were assessed using a two-way (group x test) repeated measures ANOVA. Unpaired t-tests were used to determine statistical differences between group mean differences (i.e. change from pre to post) when there was a significant group x test interaction. Pearson’s correlation was used to examine associations between outcomes. Significance was accepted as α ≤ 0.05.

Results

Exercise and Diet Compliance

Exercise adherence and dietary data were previously reported [13,14]. However, dietary intake data are reported here for clarity (i.e. ~55% carbohydrate, 28% fat and 16% protein; Table 1). By design, glycemic index and load were statistically different between groups (p < 0.05; Table 1).

Metabolic Syndrome Severity

There was no statistical differences at baseline between HiGIx and LoGIx for systolic blood pressure (135.2 ± 6.0 vs. 132.9 ± 3.6 mmHg; p = 0.75), diastolic blood pressure (80.6 ± 3.4 vs. 80.5 ± 3.2 mmHg; p = 0.99), triglycerides (139.1 ± 21.1 vs. 180.9 ± 23.6 mg/dl; p = 0.20), HDL (42.4 ± 3.7 vs. 46.8 ± 3.9 mg/dl; p = 0.42) and waist circumference (121.9 ± 2.3 vs. 115.0 ± 3.9 cm; p = 0.16). HiGIx and LoGIx reduced body mass, waist circumference, and fat mass by approximately 8% each (p < 0.05; Figure 1a and 1b, Table 2). Both groups lowered the metabolic syndrome Z-score (p = 0.001; Figure 1a) and ATP III score (HiGIx = pre: 3.6 ± 0.2 vs. post: 2.4 ± 0.5; LoGIx = pre: 3.6 ± 0.2 vs. post: 2.2 ± 0.3; p = 0.001). There were no group differences between the reduction in Z-score (p ≤ 0.85), ATP III score (p ≤ 0.85) or metabolic syndrome presence (HiGIx = 5 out of 10 vs. LoGIx = 6 out of 11). Consistent with these data, systolic and diastolic blood pressure and triglycerides were lowered by HiGIx and LoGIx (Figure 1a and 1b; p < 0.05). Neither GI group had effects on HDL.

Figure 1.

Figure 1

Figure 1

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Figure 1a. Effects of HiGIx and LoGIx on metabolic syndrome Z-score outcomes. Data are mean ± standard error of mean. ^LoGIx compared to HiGIx (group x test interaction; p <0.05). *Test effect (p < 0.05).

Figure 1b. Effects of HiGIx and LoGIx on metabolic syndrome criteria. Data are mean ± standard error of mean. ^LoGIx compared to HiGIx (group x test interaction; p <0.05).*Test effect (p < 0.05).

Glycemic Control

Baseline fasting glucose concentrations were not different between HiGIx and LoGIx (103.8 ± 3.5 vs. 112.3 ± 5.2 mg/dl; p = 0.19). Fasting glucose concentrations were reduced comparably between HiGIx and LoGIx (Figure 1a and 1b; p <0.05), and both groups lowered HbA1c (p < 0.05; Table 2).

Insulin Resistance and NEFA suppression

HiGIx and LoGIx reduced insulin resistance measured by clamp and HOMA-IR to a similar extent (p < 0.05; Table 2 & 3). Decreased insulin resistance was a result of both increased non-oxidative glucose disposal (p < 0.05) and carbohydrate oxidation (p < 0.03; Table 3). HiGIx showed a strong trend to increase insulin stimulated NEFA suppression, compared to LoGIx (p = 0.06) and both groups decreased lipid oxidation during insulin stimulation (p < 0.03; Table 3).

Table 3.

Effects of HiGIx and LoGIx on insulin sensitivity, carbohydrate and fat metabolism.

Metabolic Syndrome - HiGIx Metabolic Syndrome - LoGIx ANOVA (p-value)
Pre Post Pre Post Test Group x Test
Fasted
NEFA (mM) 0.56 ± 0.04 0.57 ± 0.03 0.60 ± 0.04 0.55 ± 0.04 ≤ 0.19 ≤ 0.21
CHO utilization (%) 35.7 ± 2.7 27.9 ± 2.9 26.3 ± 4.5 31.1 ± 4.8 ≤ 0.68 ≤ 0.10
Lipid utilization (%) 65.3 ± 2.8 72.1 ± 2.9 73.7 ± 4.5 68.9 ± 4.8 ≤ 0.68 ≤ 0.10
Clamp
GDRI (mg/kg-FFM/min/μU/ml) 0.04 ± 0.01 0.08 ± 0.01 0.04 ± 0.01 0.06 ± 0.01 ≤ 0.001 ≤ 0.95
Insulin (uU/ml) 96.6 ± 5.2 92.7 ± 5.2 101.1 ± 8.1 101.6 ± 7.2 ≤ 0.66 ≤ 0.58
NOGD (mg/kg-FFM/min) 1.4 ± 0.6 3.7 ± 0.7 1.0 ± 0.5 2.1 ± 0.4 ≤ 0.003 ≤ 0.13
CHO utilization (%) 55.8 ± 2.1 63.0 ± 4.0 52.9 ± 3.4 56.3 ± 2.3 ≤ 0.03 ≤ 0.98
Lipid utilization (%) 44.2 ± 6.8 36.9 ± 4.0 47.0 ± 3.4 43.7 ± 2.3 ≤ 0.03 ≤ 0.98
NEFA (mM) 0.15 ± 0.3 0.11 ± 0.3 0.13 ± 0.3 0.15 ± 0.3 ≤ 0.73 ≤ 0.12
NEFA suppression (%) 74.1 ± 4.8 80.7 ± 4.9 76.8 ± 6.4 71.9 ± 5.9 ≤ 0.85 ≤ 0.06
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Data are mean ± standard error of mean. There were no statistical differences at baseline between groups for any outcome. GDRI = glucose disposal rate divided by insulin. NOGD = non-oxidative glucose disposal. CHO = carbohydrate. NEFA = non-esterfied fatty acid.

Correlations

Reductions in fasting insulin concentrations (r = −0.38; p = 0.09), total cholesterol (r = 0.49; p < 0.05), and total fat mass (r = 0.46; p < 0.04) were correlated with lower metabolic syndrome severity (i.e. Z-score). Insulin resistance calculated by HOMA-IR (r = 0.48; p < 0.05), but not the clamp (r = −0.29; p = 0.20), was correlated with lower metabolic syndrome severity. Finally, the change in NEFAsuppression correlated with the change in fat mass (r = −0.45; p < 0.05).

Discussion

The novel finding from this study is that 12-weeks of exercise can successfully reduce metabolic syndrome severity, and that a low-glycemic index diet has little added benefit on metabolic syndrome severity when compared to a high glycemic index diet matched on fiber content in older men and women. Lifestyle modification, consisting of a low-fat diet and increased physical activity, reduces metabolic syndrome prevalence by approximately 45% in glucose intolerant adults and post-menopausal women [2,6]. This is consistent with previous work from our lab demonstrating significant improvements in aerobic fitness and reductions in cardiometabolic risk factors after high intensity exercise and diet induced weight loss in older men and women with metabolic syndrome [7]. Aerobic fitness is related to reductions in many of the underlying metabolic syndrome risk factors (e.g. insulin resistance, inflammation, and blood pressure) [19,20]. In the current study, both exercise groups increased VO2max by approximately 27%, however, only reductions in body fat correlated with decreased metabolic syndrome severity. Decreased body fat was correlated with increased insulin stimulated NEFA suppression. Reduced availability of NEFA may have contributed to less inflammation and improved cardiometabolic health [21,22]. Thus, these observations suggest that increasing physical activity leads to fat loss, which decreases cardiometabolic risk in adults with metabolic syndrome.

Aerobic exercise, and to some extent resistance exercise, decreases triglyceride and increases HDL concentrations [23], reduces waist circumference [24,25], and lowers blood pressure [26]. Although exercise may not improve all ATP III criteria outcomes, the collective change in these outcomes (i.e. Z-score) has clinical utility. We found that exercise training with high or low glycemic index diet, decreased metabolic syndrome severity. Moreover, 5 out of 10 in the HiGIx group and 6 out of 10 in the LoGIx group no longer had the metabolic syndrome after the intervention. These findings are not entirely surprising given that HiGIx and LoGIx improved insulin sensitivity, aerobic fitness, and adiposity comparably [27]. Regardless, our data are consistent with previous work showing that aerobic exercise training, with or without resistance exercise, reduces metabolic syndrome severity in middle-aged men and women regardless of exercise intensity, duration, or caloric deficit [16, 28, 29]. We acknowledge the small sample size may have limited our ability to detect statistical differences between metabolic syndrome severity. Thus, future studies with larger sample sizes are warranted to examine whether exercise training without weight loss, or at lower exercise intensities, while consuming either a low or high glycemic diet yields similar results on metabolic syndrome severity [30,31].

Glucose abnormalities are found in approximately 40% of adults with metabolic syndrome [32]. Although exercise decreases/maintains glucose concentrations [7,29,33], Cheong et al. reported that a lifestyle intervention consisting of increased steps and consumption of low-glycemic foods, compared to walking only, had similar reductions in HbA1c in adults with type 2 diabetes [34]. Similarly, we show that 12 weeks of supervised exercise training, independent of strict dietary control, was effective at lowering HbA1c levels in adults with metabolic syndrome. LoGIx and HiGIx likely improved glycemic control because of reduced insulin resistance [14]. Interestingly, similar reductions in HbA1c occurred despite increased NEFA suppression in 80% of the individuals after HiGIx compared to only 20% in the LoGIx intervention. Although kinetic tracer studies would have more accurately assessed lipid flux in this study, we previously demonstrated that exercise training with approximately 8% weight loss reduced NEFA concentrations by decreasing palmitate turnover rates and increasing lipid utilization in obese older adults [35]. In the current study, individuals lost approximately 8% body weight, and we found a significant correlation between fat mass loss and NEFA suppression. Together, these observations suggest that fat mass loss, not increased fat utilization, leads to decreased NEFA mobilization [21,22]. However, we cannot rule out the possibility that exercise training while consuming a high glycemic index carbohydrate diet increased extramyocellular lipid storage [36]. Regardless of the exact mechanism, changes in NEFA concentrations after the HiGIx intervention did not translate into enhanced glycemic control. Thus, the clinical relevance of altered lipid availability after the HiGIx intervention remains unclear. Longer duration clinical trials comparing HiGIx and LoGIx are needed to confirm our observation, as differences in glycemic control may become evident after 12 months [10].

In conclusion, these findings indicate that exercise training with approximately 8% weight loss reduces metabolic syndrome severity whether individuals followed a low or high GI diet. However, similar fiber intakes between diets may reduce the “real-world” relevance of these findings because fiber intake was approximately 15 g/d more than what the typical American is currently consuming. Thus, it remains possible that a low glycemic index and high fiber diet combined with exercise may have greater efficacy in lowering metabolic severity compared to a high glycemic index and low fiber diet. More studies are needed to fully understand the utility of exercise training with different types of carbohydrates on reducing cardiovascular and type 2 diabetes risk in adults with metabolic syndrome.

Acknowledgments

S.K.M, H.B., and J.P.K developed the study hypothesis. H.B. designed the diet interventions. S.K.M was primarily responsible for data analysis and statistical integrity. S.K.M, N.F., T.P.J.S, J.M.H., K.R.K., J.F, M.R., H.B., and J.P.K assisted with data collection, analysis, editing, and study organization. S.K.M and J.P.K. wrote the manuscript. We thank the nursing staff for technical assistance and the dedicated participants for their effort. This research was supported by National Institutes of Health Grants RO1 AG-12834 (J.P.K.), and National Institutes of Health National Center for Research Resources, 1UL1RR024989, Cleveland, OH. J.M.H and K.R.K were supported by T32 HL-007887 and T32 DK007319, respectively.

Footnotes

The authors report no conflict of interest.

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