Lifestyle changes, in particular reducing energy intake, are the cornerstone of current approaches to weight loss and prevention of type 2 diabetes. However, there is currently no consensus that one dietary regimen is more effective than another for weight loss (1) or whether particular diets work better for identifiable groups of individuals. There is evidence, however, to suggest that both insulin resistance and insulin secretion play a role in body weight regulation (212). Therefore, dietary factors such as the dietary glycemic load (glycemic load = glycemic index [GI] × available carbohydrate amount) that influence these parameters may theoretically interact with subject-specific characteristics of glucose-insulin dynamics to influence the effect of different hypocaloric diets on weight loss or maintenance (13,14). Weight loss studies using the concept of the dietary glycemic index or glycemic load have shown conflicting results for heterogeneous groups of individuals (1521).

In a 6-month controlled feeding trial in healthy overweight adults with normal glucose tolerance, we tested the hypothesis that individuals with higher insulin secretion lose more weight when randomized to a low–glycemic load diet compared with a high–glycemic load diet.

This study was performed as part of the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE) trial at the Human Nutrition Research Center on Aging at Tufts University with approval from the Tufts-New England Medical Center Human Investigation Review Committee. Written informed consent was obtained from all participants. Healthy women and men aged 24–42 years with a BMI of 25–29.9 kg/m2 and fasting plasma glucose <100 mg/dl were recruited. After a 6-week baseline period when usual energy requirements for weight stability were assessed using the doubly labeled water method (22), participants were randomized for 24 weeks to either a high–glycemic load diet (60% carbohydrate, 20% protein, 20% fat, 15 g fiber/1,000 kcal, mean estimated daily glycemic index of 86, and glycemic load of 116 g/1,000 kcal) or a low–glycemic load diet (40% carbohydrate, 30% protein, 30% fat, 15 g giber/1,000 kcal, mean estimated daily glycemic index of 53, and glycemic load of 45 g/1,000 kcals) at 30% calorie restriction compared with baseline individual energy needs. The glycemic index and glycemic load of the diets were determined using the International Tables of Glycemic Index and Glycemic Load (23) and the Nutrition Data System for Research (version 4.05_33) developed by the Nutrition Coordinating Center, University of Minnesota, Food and Nutrient Database 33, released in 2002 (24). During the 6-month intervention period, all food was provided by the research center, and participants were requested to consume only this food and report additional foods if they were eaten. To maximize adherence to the study diet, regular behavioral group meetings and individual sessions with a dietitian were held. From participants’ reports of leftover food and extra items, actual daily nutrient intake during the intervention period was calculated (24).

Height (±0.1 cm) was measured at baseline, and body weight (±100 g) was measured weekly. Insulin secretion was also estimated at baseline, as was the insulin value at 30 min (INS-30) after a 75-g oral glucose tolerance test (25). Insulin sensitivity in the fasting state was estimated at baseline by the homeostasis model assessment of insulin resistance (HOMA-IR) (26). Glucose was measured by the hexokinase method, and insulin was measured by radioimmunoassay. To examine change in weight at 6 months, we used general linear models adjusting for baseline weight, HOMA-IR and INS-30, and the interaction between diet × HOMA-IR and diet × INS-30 (PROC GLM procedure in SAS software, version 8.2; SAS Institute, Cary, NC).

A total of 32 (25 women and 7 men) of 34 enrolled participants completed the 6-month intervention period and necessary measurements. At baseline, their mean age was 34.6 years, BMI was 27.5 kg/m2, and fasting insulin was 82.5 pmol/l. The mean target energy intake was 1,966 kcal/day, and the mean reported daily energy intake during the intervention did not differ between the two groups (2,017 kcal in the high–glycemic load diet vs. 1,972 kcal in the low–glycemic load diet, P = 0.70).

We examined whether baseline INS-30 predicted change in weight over the 6-month intervention period. We found a diet × INS-30 interaction (P = 0.02) in the multivariate prediction model, and the weight data were stratified into two groups separated by the median INS-30 value (Fig. 1). Participants with high baseline INS-30 lost more weight if randomized to the low–glycemic load diet compared with the high–glycemic load diet (P < 0.05). The reverse was observed in the low–INS-30 group, namely, low–INS-30 participants in the high–glycemic load diet lost more weight than those in the low–glycemic load diet, but the difference was not statistically significant (P = 0.25). We also examined whether baseline HOMA-R predicted weight change, and we found no diet × HOMA-R interaction.

The main finding from this pilot study was that healthy overweight women and men with relatively greater insulin secretion in response to a standard oral glucose tolerance test lost more weight when assigned to a low–glycemic load hypocaloric diet than to a high–glycemic load diet, but there was no differential effect of the two diets on weight loss in individuals who had relatively lower insulin secretion.

Data from human studies on whether insulin secretion predicts future weight gain (4,6,2729) or affects the ability of overweight individuals to lose weight in response to a hypocaloric diet (7,30) are controversial. However, the influence of postchallenge hyperinsulinemia on weight loss may be particularly important for specific dietary compositions, in particular diets that differ in glycemic load or glycemic index, as suggested by animal studies (3133). High–glycemic load diets increase postprandial hyperinsulinemia, which favors fatty acid uptake, inhibition of lipolysis, and energy storage leading to weight gain (34). High–glycemic load diets also lead to other postprandial metabolic changes, including a lower glucose nadir and increase in counterregulatory hormones that may explain increased hunger and increased energy intake in the postabsorptive period, presumably leading to weight gain over time (32). All of these mechanisms may be exacerbated in individuals with high insulin secretory capacity at baseline, which makes them more susceptible to weight gain upon exposure to a high–glycemic load diet (35). These individuals can be hypothesized to do best on low–glycemic load diets, a hypothesis supported by our present findings.

Our results require confirmation in further studies with larger numbers of subjects, but nevertheless offer the first evidence that simple indexes of insulin secretion may help enhance weight loss success in overweight individuals through the use of targeted dietary recommendations specific for insulin secretion status.

Figure 1—

Mean (±SEM) weight change during a 6-month feeding study of a high–(HG) vs. low–(LG) glycemic load diet in overweight adults stratified by baseline insulin secretion based on serum insulin at 30 min after a 75-g oral glucose tolerance test (low INS-30 < 473 pmol/l [66 mU/l] < high INS-30). P values are adjusted for baseline weight. *P < 0.005 for within-group change in weight from baseline.

Figure 1—

Mean (±SEM) weight change during a 6-month feeding study of a high–(HG) vs. low–(LG) glycemic load diet in overweight adults stratified by baseline insulin secretion based on serum insulin at 30 min after a 75-g oral glucose tolerance test (low INS-30 < 473 pmol/l [66 mU/l] < high INS-30). P values are adjusted for baseline weight. *P < 0.005 for within-group change in weight from baseline.

Close modal

This research was supported by National Institutes of Health Grants K23-DK61506 (to A.G.P.), U01-AG20480 (to S.B.R.), and U.S. Department of Agriculture cooperative agreement no. 58-1950-4-401 (to S.B.R. and A.S.G.).

We thank the study participants and staff of the MRU at the USDA Human Nutrition Research Center on Aging.

1.
Dansinger ML, Gleason JA, Griffith JL, Selker HP, Schaefer EJ: Comparison of the Atkins, Ornish, Weight Watchers, and Zone diets for weight loss and heart disease risk reduction: a randomized trial.
JAMA
293
:
43
–53,
2005
2.
Wedick NM, Mayer-Davis EJ, Wingard DL, Addy CL, Barrett-Connor E: Insulin resistance precedes weight loss in adults without diabetes: the Rancho Bernardo Study.
Am J Epidemiol
153
:
1199
–1205,
2001
3.
Odeleye OE, de Courten M, Pettitt DJ, Ravussin E: Fasting hyperinsulinemia is a predictor of increased body weight gain and obesity in Pima Indian children.
Diabetes
46
:
1341
–1345,
1997
4.
Sigal RJ, El-Hashimy M, Martin BC, Soeldner JS, Krolewski AS, Warram JH: Acute postchallenge hyperinsulinemia predicts weight gain: a prospective study.
Diabetes
46
:
1025
–1029,
1997
5.
Swinburn BA, Nyomba BL, Saad MF, Zurlo F, Raz I, Knowler WC, Lillioja S, Bogardus C, Ravussin E: Insulin resistance associated with lower rates of weight gain in Pima Indians.
J Clin Invest
88
:
168
–173,
1991
6.
Schwartz MW, Boyko EJ, Kahn SE, Ravussin E, Bogardus C: Reduced insulin secretion: an independent predictor of body weight gain.
J Clin Endocrinol Metab
80
:
1571
–1576,
1995
7.
McLaughlin T, Abbasi F, Carantoni M, Schaaf P, Reaven G: Differences in insulin resistance do not predict weight loss in response to hypocaloric diets in healthy obese women.
J Clin Endocrinol Metab
84
:
578
–581,
1999
8.
Valdez R, Mitchell BD, Haffner SM, Hazuda HP, Morales PA, Monterrosa A, Stern MP: Predictors of weight change in a bi-ethnic population: the San Antonio Heart Study.
Int J Obes Relat Metab Disord
18
:
85
–91,
1994
9.
Hoag S, Marshall JA, Jones RH, Hamman RF: High fasting insulin levels associated with lower rates of weight gain in persons with normal glucose tolerance: the San Luis Valley Diabetes Study.
Int J Obes Relat Metab Disord
19
:
175
–180,
1995
10.
Baba NH, Sawaya S, Torbay N, Habbal Z, Azar S, Hashim SA: High protein vs. high carbohydrate hypoenergetic diet for the treatment of obese hyperinsulinemic subjects.
Int J Obes Relat Metab Disord
23
:
1202
–1206,
1999
11.
Mosca CL, Marshall JA, Grunwald GK, Cornier MA, Baxter J: Insulin resistance as a modifier of the relationship between dietary fat intake and weight gain.
Int J Obes Relat Metab Disord
28
:
803
–812,
2004
12.
Cornier MA, Donahoo WT, Pereira R, Gurevich I, Westergren R, Enerback S, Eckel PJ, Goalstone ML, Hill JO, Eckel RH, Draznin B: Insulin sensitivity determines the effectiveness of dietary macronutrient composition on weight loss in obese women.
Obes Res
13
:
703
–709,
2005
13.
Jenkins DJ, Wolever TM, Taylor RH, Barker H, Fielden H, Baldwin JM, Bowling AC, Newman HC, Jenkins AL, Goff DV: Glycemic index of foods: a physiological basis for carbohydrate exchange.
Am J Clin Nutr
34
:
362
–366,
1981
14.
Brand-Miller JC, Thomas M, Swan V, Ahmad ZI, Petocz P, Colagiuri S: Physiological validation of the concept of glycemic load in lean young adults.
J Nutr
133
:
2728
–2732,
2003
15.
Wolever TM, Mehling C: High-carbohydrate-low-glycaemic index dietary advice improves glucose disposition index in subjects with impaired glucose tolerance.
Br J Nutr
87
:
477
–487,
2002
16.
Meckling KA, O’Sullivan C, Saari D: Comparison of a low-fat diet to a low-carbohydrate diet on weight loss, body composition, and risk factors for diabetes and cardiovascular disease in free-living, overweight men and women.
J Clin Endocrinol Metab
89
:
2717
–2723,
2004
17.
Ebbeling CB, Leidig MM, Sinclair KB, Seger-Shippee LG, Feldman HA, Ludwig DS: Effects of an ad libitum low-glycemic load diet on cardiovascular disease risk factors in obese young adults.
Am J Clin Nutr
81
:
976
–982,
2005
18.
Brynes AE, Mark Edwards C, Ghatei MA, Dornhorst A, Morgan LM, Bloom SR, Frost GS: A randomised four-intervention crossover study investigating the effect of carbohydrates on daytime profiles of insulin, glucose, non-esterified fatty acids and triacylglycerols in middle-aged men.
Br J Nutr
89
:
207
–218,
2003
19.
Poppitt SD, Keogh GF, Prentice AM, Williams DE, Sonnemans HM, Valk EE, Robinson E, Wareham NJ: Long-term effects of ad libitum low-fat, high-carbohydrate diets on body weight and serum lipids in overweight subjects with metabolic syndrome.
Am J Clin Nutr
75
:
11
–20,
2002
20.
Bouche C, Rizkalla SW, Luo J, Vidal H, Veronese A, Pacher N, Fouquet C, Lang V, Slama G: Five-week, low-glycemic index diet decreases total fat mass and improves plasma lipid profile in moderately overweight nondiabetic men.
Diabetes Care
25
:
822
–828,
2002
21.
Spieth LE, Harnish JD, Lenders CM, Raezer LB, Pereira MA, Hangen SJ, Ludwig DS: A low-glycemic index diet in the treatment of pediatric obesity.
Arch Pediatr Adolesc Med
154
:
947
–951,
2000
22.
Schoeller DA: Recent advances from application of doubly labeled water to measurement of human energy expenditure.
J Nutr
129
:
1765
–1768,
1999
23.
Foster-Powell K, Holt SH, Brand-Miller JC: International table of glycemic index and glycemic load values: 2002.
Am J Clin Nutr
76
:
5
–56,
2002
24.
Schakel SF, Sievert YA, Buzzard IM: Sources of data for developing and maintaining a nutrient database.
J Am Diet Assoc
88
:
1268
–1271,
1988
25.
Hanson RL, Pratley RE, Bogardus C, Narayan KM, Roumain JM, Imperatore G, Fagot-Campagna A, Pettitt DJ, Bennett PH, Knowler WC: Evaluation of simple indices of insulin sensitivity and insulin secretion for use in epidemiologic studies.
Am J Epidemiol
151
:
190
–198,
2000
26.
Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC: Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man.
Diabetologia
28
:
412
–419,
1985
27.
Zavaroni I, Zuccarelli A, Gasparini P, Massironi P, Barilli A, Reaven GM: Can weight gain in healthy, nonobese volunteers be predicted by differences in baseline plasma insulin concentration?
J Clin Endocrinol Metab
83
:
3498
–3500,
1998
28.
Gould AJ, Williams DE, Byrne CD, Hales CN, Wareham NJ: Prospective cohort study of the relationship of markers of insulin resistance and secretion with weight gain and changes in regional adiposity.
Int J Obes Relat Metab Disord
23
:
1256
–1261,
1999
29.
Mayer-Davis EJ, Kirkner GJ, Karter AJ, Zaccaro DJ: Metabolic predictors of 5-year change in weight and waist circumference in a triethnic population: the insulin resistance atherosclerosis study.
Am J Epidemiol
157
:
592
–601,
2003
30.
Alikasifoglu A, Yordam N: The metabolic parameters of obese children and the role of hyperinsulinism on weight loss.
Eur J Pediatr
158
:
269
–270,
1999
31.
Pawlak DB, Bryson JM, Denyer GS, Brand-Miller JC: High glycemic index starch promotes hypersecretion of insulin and higher body fat in rats without affecting insulin sensitivity.
J Nutr
131
:
99
–104,
2001
32.
Ludwig DS, Majzoub JA, Al-Zahrani A, Dallal GE, Blanco I, Roberts SB: High glycemic index foods, overeating, and obesity.
Pediatrics
103
:
E26
,
1999
33.
Pawlak DB, Kushner JA, Ludwig DS: Effects of dietary glycaemic index on adiposity, glucose homoeostasis, and plasma lipids in animals.
Lancet
364
:
778
–785,
2004
34.
Ludwig DS: The glycemic index: physiological mechanisms relating to obesity, diabetes, and cardiovascular disease.
JAMA
287
:
2414
–2423,
2002
35.
Byrnes SE, Miller JC, Denyer GS: Amylopectin starch promotes the development of insulin resistance in rats.
J Nutr
125
:
1430
–1437,
1995

A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances.