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* Center for Pediatric Nutrition Research
Division of Endocrinology
Division of Gastroenterology and Nutrition, Department of Pediatrics, School of Medicine, University of Utah, Salt Lake City, Utah
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONREFERENCES |
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Objective. To investigate whether a low-GI meal replacement (LMR) produced similar metabolic, hormonal, and satiety responses in overweight adolescents as a low-GI whole-food meal (LWM) when compared with a moderately high-GI meal replacement (HMR).
Methods. Randomized, crossover study comparing LMR, HMR, and LWM in 16 (8 male/8 female) adolescents during 3 separate 24-hour admissions. The meal replacements consisted of a shake and a nutrition bar. Identical test meals were provided at breakfast and lunch. Metabolic and hormonal indices were assessed between meals. Measures of participants’ perceived satiety included hunger scales and ad libitum food intake.
Results. The incremental areas under the curve for glucose were 46% and 43% lower after the LMR and LWM, respectively, compared with the HMR. Insulin’s incremental area under the curve was also significantly lower after both low GI test meals (LMR = 36%; LWM = 51%) compared with the HMR. Additional food was requested earlier after the HMR than the LMR (3.1 vs 3.9 hours, respectively), although voluntary energy intake did not differ.
Conclusions. Differences in insulin response between the meal replacements occurred, and prolongation of satiety after the LMR, based on time to request additional food, was observed. We speculate that the prolonged satiety associated with low GI foods may prove an effective method for reducing caloric intake and achieving long-term weight control.
Key Words: glycemic index • glucose • insulin • satiety • obesity • adolescents
Abbreviations: GI, glycemic index • LMR, low-GI meal replacement • LWM, low-GI whole-food meal • HMR, high-GI meal replacement • GCRC, General Clinical Research Center • REE, resting energy expenditure • IGF-1, insulin-like growth factor-1 • IGFBP-3, insulin-like growth factor-binding protein-3 • TG, triacylglycerol • ANOVA, analysis of variance • IAUC, incremental areas under the curve
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONREFERENCES |
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7% of children and adolescents were classified as overweight. By 1999, the number had more than doubled from 12% to 22%.1,2 Obesity among the pediatric population is now recognized as a significant health problem attributable to its myriad medical and psychological consequences, including, but not limited to, dyslipidemia, hyperinsulinemia, hypertension, and diabetes.3 Despite the known negative implications of childhood and adolescent obesity, effective prevention and treatment strategies are lacking. Although the cause of this obesity epidemic has not been well-defined, altered dietary practices and decreased activity levels are believed to be the major environmental contributors. Initially, increased intake of dietary fat was proposed as the source of the problem, but during the past decade the prevalence of obesity has continued to increase despite an overall decrease in fat in the American diet. This decrease in fat intake has been linked to a concomitant increase in carbohydrate consumption,4,5 which has directed attention toward the role of dietary carbohydrates and the glycemic index (GI) of foods on obesity and related disease.
The GI is a measure of a food’s effect on postprandial blood glucose levels compared with the effects of reference standards. It is defined as the area under the glycemic response curve after consumption of 50 g of carbohydrate from a test food divided by the area under the curve after consumption of 50 g of carbohydrate from a reference standard, either white bread or glucose.6,7 Foods that evoke rapid increases in blood glucose levels are classified as high GI and those that elicit minimal glucose fluctuations are labeled low GI. The GI of a food or meal is affected by numerous variables, including physical characteristics and structure of the carbohydrate; processing, cooking, and storage methods; fiber content; coingested foods; and macronutrient composition. Dietary protein, fat, and fiber influence gastric emptying, glucose absorption, and insulin secretion. As these components increase, the GI of the food decreases.8,9
Low-GI diets have been reported to decrease postprandial blood glucose levels and insulin response, improve lipid profiles, increase insulin sensitivity, and reduce lipogenesis when compared with high-GI diets.10–14 The GI of dietary carbohydrates appears to play an important role in metabolic and endocrine responses and, consequently, may significantly affect the risk of cardiovascular disease, type 2 diabetes, and obesity. A low-GI diet could be especially useful in obese children and adolescents, whose insulin secretion rates are generally higher than those of their lean peers.15 Additional research is warranted to expand on the limited number of previously published GI studies in the overweight pediatric population.
To provide a convenient means for individuals to incorporate a low-GI diet into their lifestyle, low-GI meal replacements (LMRs) have been developed. This study investigated whether a LMR produced similar metabolic, hormonal, and satiety responses in overweight adolescents as a low-GI whole-food meal (LWM), as demonstrated by Ludwig et al,13 when compared with a moderately high-GI meal replacement (HMR).
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONREFERENCES |
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95th percentile for age and sex or >30 (kg/m2), and good health status, other than overweight. Adolescents were excluded from study participation if they had a medical history of disease other than overweight or were taking any medication known to affect metabolism. Females were also excluded if they were pregnant or had a history of pregnancy. A total of 16 (8 male/8 female) participants were recruited to participate in this study. Participant characteristic data are presented in Table 1. All 16 participants completed the study protocol.
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Study participants were admitted to the GCRC at 5 PM for each admission. Height in cm, weight in kg, and blood pressure were measured at the start of each visit. A low GI dinner and bedtime snack were provided at 5:30 PM and 9 PM, respectively. All food was consumed. Participants were required to be in bed by 10 PM and were awakened the following morning between 6:30 AM and 7 AM for placement of a peripheral indwelling venous catheter for continuous blood draws. At 7:30 AM, a baseline blood sample was collected, a 10-cm analog hunger scale completed by each of the participants, and a randomly assigned breakfast provided. The participants were asked to consume all of their breakfast within 15 minutes. Additional blood samples were obtained and hunger scale ratings completed at 15-, 30-, 60-, 120-, 180-, and 240-minutes past the baseline blood draw. Four hours after breakfast, the catheter was removed and lunch was provided. Lunch was identical to the randomly assigned meal served at breakfast. After lunch, participants were able to participate in light activities and encouraged to eat until satisfied from ad libitum snack platters that were supplied on request whenever the participants felt "very hungry." Snack platters were left in the participants’ rooms for 10 minutes. Any food taken from the platter but not eaten within 30 minutes was removed from the room. All remaining food was weighed and recorded to determine the amount of food consumed by the participant. Nutrient analyses were performed using Nutritionist IV (Version 4.1; First Data Bank, San Bruno, CA). Participants were discharged 5 hours after lunch and asked to follow their usual diet and activity patterns.
Study Meals
The energy content of all meals and snacks was based on the estimated resting energy expenditure (REE) of each participant using the gender specific Schofield equations based on height and weight. These equations were chosen because they are less invasive than indirect calorimetery and have been validated for use in obese pediatric populations.16
On the evening of each admission, a standard low-GI dinner and bedtime snack were provided to each participant. The dinner consisted of chicken, broccoli, pears, green salad with dressing, and arrowroot biscuits. It provided 30% of the participant’s estimated REE with a macronutrient composition of 30% fat, 25% protein, and 45% carbohydrate. The bedtime snack included ham, cheese, and an apple and provided 10% of the participant’s REE.
One of 3 randomized test meals (LMR, HMR, or LWM) was provided for breakfast and lunch. The LMR included a NutriMeal drink (powdered drink supplement; USANA, Salt Lake City, UT) made with whole milk and served with a NutriBar (chocolate covered nutrition bar; USANA). The HMR contained a Maltodextrin powdered drink supplement (USANA) made with whole milk and 2 drops of lactase (Lactaid Drops; McNeil-PPC, Inc, Fort Washington, PA) per 236 mL of milk and served with an Ensure Bar (chocolate fudge brownie or honey graham; Ross Products Division, Abbott Laboratories, Columbus, OH). Both drinks were identical in appearance and were offered in the same flavors (chocolate, strawberry, and vanilla). The LWM was modeled after the low-GI meal used by Ludwig et al.13 It consisted of scrambled eggs, cheese, ham, apple, and skim milk. The nutrient composition and GI values of the test meals are presented in Table 2. GI values for the meal replacements were determined before the initiation of this study by measuring the blood glucose response of healthy adults after the ingestion of the meal replacements and comparing it to their glucose response after consuming white bread, according to Food and Agriculture Organization/World Health Organization protocol.7 The weighted GI of the whole-food meal was calculated using published GI tables.17
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Blood Analysis
A total of 102 mL of blood was drawn during each admission. Blood samples were analyzed for glucose, insulin, glucagon, insulin-like growth factor-1 (IGF-1), insulin-like growth factor-binding protein-3 (IGFBP-3), and triacylglycerol (TG). Only glucose and insulin were analyzed at 15 minutes. Glucose was measured immediately after the draw on a bedside glucometer (One Touch Sure Step; LifeScan Inc, Milpitas, CA). Accuracy of the glucometer has been evaluated by the manufacturer using least-squares linear regression analysis and found to be 97% "clinically accurate" when compared with reference (YSI 2700) results.18 The remaining blood was sent immediately to a lab (Associated Regional and University Pathologists) for analysis using the following instruments or kits: insulin, Immulite 2000 (DPC, Los Angeles, CA); glucagon, Double Antibody Glucagon (DPC); IGF-1, Nichols Advantage (Nichols Institute Diagnostics, San Juan Capistrano, CA); IGFBP-3, Nichols manual assay (Nichols Institute Diagnostics); and TG, Vitros 950 (Ortho-Clinical Diagnostics, Raritan, NJ).
Statistical Analysis
Descriptive statistics were used to report participant characteristics. Participant responses were evaluated using repeated-measures analysis of variance (ANOVA) with "meal" and "meal and time" as within participant factors, as appropriate. Diet sequence order was included as a between participants categorical factor in the repeated measurements ANOVA models to determine any effect of diet sequence order. One-way repeated measures of ANOVA were used to compare incremental areas under the curve (IAUC) for glucose, insulin, glucagon, and TG for each meal. IAUC was calculated using the trapezoidal rule for values above baseline.7 Pair-wise comparisons between the 3 meals were performed using a paired sample Student t test of the IAUC as a posthoc analysis. To maintain the 
at 0.05, the P values were adjusted using the Tukey-Ciminera-Heyse multiple comparison procedure.19 For variables that were severely skewed, such as energy consumed from the snack tray, the Friedman 2-way ANOVA by ranks was used. The expectation-maximization method was used to impute for 10 missing values. All calculations were performed using SPSS for Windows, version 10.0.7 (SPSS, Inc, Chicago, IL), except for the Tukey-Ciminera-Heyse adjustment to the P values, which was calculated using a simple equation in a Microsoft Excel (Microsoft Corporation, Redmond, WA) spreadsheet.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONREFERENCES |
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.02) and LWM (P .05); however, the IAUC for insulin after the LMR and LWM were about one-third (P .002) and one-half (P .001), respectively, that of the HMR. Glucagon and TG levels increased from baseline after all of the test meals, but no significant differences existed between the test meals for glucagon, TG, IGF-1, and IGFBP-3 (Fig 2).
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.01). However, once a snack platter was requested, no differences were detected between test meals for energy or macronutrients consumed (Table 3). The number of snack platters requested did not vary between test meals.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONREFERENCES |
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Despite the fairly low glycemic value of our HMR, significantly lower glucose and insulin responses were observed in our obese adolescents after both the LMR and LWM compared with the HMR. IAUC for glucose were 46% and 43% lower after the LMR and LWM, respectively, compared with the HMR. However, the IAUC for insulin was much smaller after the LWM than both the HMR (P = .001) and LMR (P = .024), whereas the IAUC for glucose were about equal for the LMR and LWM (P = .957). Insulin’s IAUC after the LWM was 51% lower than that of the HMR, while the area for the LMR was 36% lower. Because glucose levels were below baseline for over half of the morning after the LWM, a lower insulin response was expected compared with the other test meals, but not as large of a variation from the LMR as was observed. We assume that the variations were attributable to the physiologic differences in gastric emptying, digestion, and absorption between a primarily liquid meal replacement and a primarily solid whole food meal. Other researchers have also reported greater IAUC for insulin after ingestion of liquid meals when compared with solid test meals, with no difference in glucose responses.20,21 Despite this insulin difference, Mustad and colleagues22 purport that when energy needs are met, measures of satiety are comparable between liquid and solid diets.
Glucagon, a pancreatic hormone involved in the regulation of glycogen synthesis, is generally released between meals and during fasts and is regulated primarily by blood glucose concentration. When blood glucose levels drop, glucagon is released to stimulate glycogenolysis and lipolysis. In our obese adolescent participants, mean plasma glucagon increased after all 3 test meals. The most dramatic increase was after the LWM, when glucose was below baseline for most of the study period. No significant differences in the IAUC for glucagon were found between the 3 test meals, but the matched dietary protein content of the test meals may have negated any differences attributable to the carbohydrates. Because dietary protein stimulates glucagon secretion as a consequence of amino acid influx, plasma glucagon levels may not vary as much if a meal contains significant amounts of carbohydrate and protein.23
Absorbed fatty acids are reesterified as triglycerides and transported in chylomicra from the intestine via lymphatics to the circulation. Blood TG levels generally peak 3 to 6 hours after fat is consumed.24 Mean TG levels for our participants increased after each of the test meals. All of our test meals provided 30% of the total energy as fat, accounting for the observed increase in TG.
No significant differences were observed between the hunger scale values reported with each test meal; however, there are physiologic, learned, and cognitive components to hunger. Although the 10-cm analog scale has been used in previous studies,13 it is a fairly subjective measurement tool. Because of the close concordance of the response curves for the 3 test meals and the lack of correlation with the metabolic and hormonal data collected over the same time period, we speculate that our participants’ hunger scale responses were based more on external cues, such as time of day, number of hunger scales completed, boredom, etc, than on actual biological hunger. Indeed, previous research has shown that obese individuals respond more to external cues of hunger than nonobese people.25,26
Two possible explanations for the similar ad libitum energy intakes after each of the test meals include: 1) there is no satiety difference between low- and moderately high-GI meals; or 2) intake of food is regulated more by external cues than by actual hunger. Because we did observe a difference in time between lunch and the request for additional food after each test meal, the first of the 2 hypotheses mentioned above may be incorrect. Although the time difference appears small, it is significant; indicating that participants were satisfied for a longer time period after consuming a low-GI meal compared with a moderately high-GI meal.
Two study design issues that should be addressed are the fairly low-GI value of our HMR, which is comparable to that of many low-GI foods used in other studies, and the limited nutrient intake data collected after the test meals. A product with a higher GI would have most likely produced greater differences in glucose, insulin, and satiety responses. However, our objective was to compare meal replacements with identical macronutrient compositions and similar appearance and taste. Based on these criteria, a higher GI product was not available. As for the nutrient intake data, it is complete for the time period that the participants were in the research center. However, the data would have been more complete if the participants had kept food intake records for the remainder of the day. Little is known about the amount and types of foods consumed by the participants once they left the research environment.
Additional studies examining the effects of a long-term low-GI diet are needed. Future studies may also want to address the issue of including female participants resulting from the variation in hormone levels associated with menstrual cycle phase. Although our female participants were scheduled for testing during the first 10 days after the initiation of their menstrual cycle to help control for this variation, actual measurement of sex and puberty hormone levels may help elucidate the effect of hormones on metabolism and appetite during puberty in obese participants.
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CONCLUSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONREFERENCES |
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The LMR was associated with a greater insulin response in terms of IAUC than the LWM, but was otherwise comparable. Although there were no differences in the hunger scale ratings or the amount of additional energy consumed, food was requested later after participants consumed the low-GI test meals, indicating an increase in satiety with the low-GI meal and meal replacement. As the time between meals and snacks increases, a decrease in total energy consumption during a day or a period of a few days may occur. We speculate that the prolonged satiety associated with low-GI foods may prove an effective method for reducing caloric intake and achieving long-term weight control.
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ACKNOWLEDGMENTS |
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We appreciate the study participants and their families for their cooperation and time. We thank Danuta Skorut and Gail Wiebke from the GCRC nutrition staff and the GCRC nursing staff for their assistance with participant care.
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FOOTNOTES |
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Reprint requests to (L.J.M-M.) Center for Pediatric Nutrition Research, Department of Pediatrics, Rm 2A244, School of Medicine, University of Utah, Salt Lake City, UT 84132. Email: laurie.moyer-mileur{at}hsc.utah.edu
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONREFERENCES |
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) from baseline and IAUC for glucose and insulin concentrations 0–240 min after breakfast test meals. Plot symbols: 
, LMR; 
, HMR; 
, LWM.
