Customize your JAMA Network experience by selecting one or more topics from the list below.
Pereira MA, Swain J, Goldfine AB, Rifai N, Ludwig DS. Effects of a Low–Glycemic Load Diet on Resting Energy Expenditure and
Heart Disease Risk Factors During Weight Loss. JAMA. 2004;292(20):2482–2490. doi:10.1001/jama.292.20.2482
Context Weight loss elicits physiological adaptations relating to energy intake
and expenditure that antagonize ongoing weight loss.
Objective To test whether dietary composition affects the physiological adaptations
to weight loss, as assessed by resting energy expenditure.
Design, Study, and Participants A randomized parallel-design study of 39 overweight or obese young adults
aged 18 to 40 years who received an energy-restricted diet, either low–glycemic
load or low-fat. Participants were studied in the General Clinical Research
Centers of the Brigham and Women’s Hospital and the Children’s
Hospital, Boston, Mass, before and after 10% weight loss. The study was conducted
from January 4, 2001, to May 6, 2003.
Main Outcome Measures Resting energy expenditure measured in the fasting state by indirect
calorimetry, body composition by dual-energy x-ray absorptiometry, cardiovascular
disease risk factors, and self-reported hunger.
Results Resting energy expenditure decreased less with the low–glycemic
load diet than with the low-fat diet, expressed in absolute terms (mean [SE],
96  vs 176  kcal/d; P = .04) or
as a proportion (5.9% [1.5%] vs 10.6% [1.7%]; P = .05).
Participants receiving the low–glycemic load diet reported less hunger
than those receiving the low-fat diet (P = .04).
Insulin resistance (P = .01), serum triglycerides
(P = .01), C-reactive protein (P = .03), and blood pressure (P = .07
for both systolic and diastolic) improved more with the low–glycemic
load diet. Changes in body composition (fat and lean mass) in both groups
were very similar (P = .85 and P = .45, respectively).
Conclusions Changes in dietary composition within prevailing norms can affect physiological
adaptations that defend body weight. Reduction in glycemic load may aid in
the prevention or treatment of obesity, cardiovascular disease, and diabetes
The poor long-term efficacy of conventional obesity treatment has promoted
the notion of a body weight set point,1,2 more
recently termed settling point. According to this
concept, deviations in body weight from baseline elicit physiological adaptations
that antagonize further weight change. During energy restriction, humans and
experimental animals have increased hunger, decreased thyroid hormone levels,
and down-regulation of reproductive and growth functions,3-5 changes
that increase energy intake and lower energy expenditure. To examine this
phenomenon, Leibel et al6 underfed or overfed
participants who were lean or obese to obtain an approximate 10% decrease
or increase in body weight from baseline. Resting energy expenditure (REE)
and total energy expenditure relative to fat-free body mass declined following
weight reduction, whereas total energy expenditure increased following weight
A decline in REE and associated neuroendocrine changes have been consistently
reported during active weight loss, although controversy exists as to whether
these adaptations are permanent6-8 or
transient.9,10 In any event, defended
body weight level is evidently not determined by endogenous mechanisms exclusively,
as demonstrated by the increasing mean body mass index (BMI, calculated as
weight in kilograms divided by the square of height in meters) among genetically
stable populations observed in recent years.11 Thus,
body weight settling point may best be conceptualized as representing the
integrated influences of numerous genetic, behavioral, and environmental factors.1
Previously, the novel dietary factor glycemic load has been proposed
to play a role in body weight regulation based on experimental and theoretical
grounds.12,13 Glycemic load (glycemic
index × carbohydrate amount) is a validated measure of the increase
of blood glucose following a meal.14 A high–glycemic
load diet appears to elicit hormonal changes that limit availability of metabolic
fuels in the postprandial period, stimulating hunger and voluntary food intake.12,13,15 Several short-term
or small-scale studies have reported increased body weight and/or fat loss
on low–glycemic index/glycemic load diets compared with control diets,
although the clinical relevance of these findings remains the subject of debate.16,17 Recently, we examined the effects
that glycemic load has on REE in 10 overweight young men.18 After
1 week consuming energy-restricted diets providing 50% of predicted total
energy requirements, REE decreased by 10.5% on the high–glycemic load
diet compared with 4.6% on the low–glycemic load diet (P = .04).
The goal of this study was to determine whether dietary composition
can influence the physiological adaptations of a weight-reducing diet, as
assessed by REE. To test this hypothesis, we studied 2 energy-restricted diets
with large differences in glycemic load. Because glycemic load has been linked
to risk for heart disease in epidemiological studies, we also examined several
conventional and novel cardiovascular disease risk factors as secondary end
We randomly assigned 46 participants to low–glycemic load or low-fat
diet groups using a parallel-design study, which was performed at the General
Clinical Research Centers of the Brigham and Women’s Hospital and the
Children’s Hospital in Boston, Mass, and conducted from January 4, 2001,
to May 6, 2003. The participants were given a standard weight–maintaining
diet during a 9-day run-in period and then were admitted to a metabolic unit
for 3 days to obtain baseline measurements. The food preparation, inpatient
stays, and most data collection were performed at the Brigham and Women’s
Hospital, while the body composition analysis and laboratory assay of C-reactive
protein were performed at the Children’s Hospital. At discharge, participants
began the experimental or control diets, providing 60% of predicted energy
requirements. After achieving a 10% reduction in body weight, participants
were readmitted for 5 days to obtain final measurements of study end points
(during active weight loss on the low-glycemic load and low-fat diets). All
foods, for both inpatient and outpatient phases, were prepared in a metabolic
kitchen. Ethics approval for human subjects research was provided by institutional
review boards at both hospitals. All participants gave written informed consent
Participants were recruited primarily through posted fliers and newspaper
advertisements in the Boston metropolitan area. Inclusion criteria were assessed
by telephone, an inperson interview, and a physician-conducted physical examination.
Participants meeting the following criteria were included in the study: aged
between 18 and 40 years; BMI of at least 27 and weight of less than 135 kg
(<300 lb); change in body weight of less than 10% during the past year;
good general health; no medical conditions or medications that might affect
body weight, appetite, or energy expenditure; nonsmoker; not regularly engaged
in heavy/vigorous physical activities; normal laboratory screening test results,
including complete blood cell count, serum electrolytes, thyroid-stimulating
hormone, blood glucose, glycosylated hemoglobin, urinalysis, and liver functions
(alanine aminotransferase up to twice normal limit, suggestive of fatty liver,
acceptable); not currently following a special diet; no history of an eating
disorder; no allergies or aversions to foods on the study menu; not taking
dietary supplements; willing to abstain from alcohol consumption for duration
of study; and able to come to research unit on a daily basis to obtain study
foods. In addition, the following inclusion criteria applied to women: not
pregnant during the last year, no plans to become pregnant in the next year,
not lactating, and not taking birth control pills. To characterize the diversity
of the participant population, race/ethnicity was classified by the participants
according to investigator-defined options. All participants were paid US $1500
on completion of the study.
We assessed 67 individuals for eligibility; 46 were enrolled in the
study and randomly assigned to 1 of 2 diet groups (n = 23 in both
the low–glycemic load and low-fat diet groups) (Figure 1). One participant from the low–glycemic load group
and 6 from the low-fat group did not complete the study, for an overall retention
rate of 85%. Five of these noncompleters (1 in low–glycemic load group
and 4 in low-fat group) did not comply with the intervention as evidenced
by inability to achieve the target weight loss goal of 2.5% weight loss per
month despite intensive behavioral counseling by the dietitians. One participant
(low-fat group) dropped out of the study due to work-related scheduling difficulties.
One additional participant (low-fat group) developed an acute febrile illness
with vomitting during the post-weight loss admission. The characteristics
of the remaining participants are shown in Table
1. The 7 noncompleters did not differ from the completers in age
(mean [SD], 30.5 [5.76] years for completers vs 33.1 [5.90] years for noncompleters; P = .26) or in REE (1509  vs 1567 
kcal/d; P = .58) but the noncompleters
did have higher baseline BMI (33.2 [4.59] vs 42.3 [6.03]; P<.001).
Table 2 lists the macronutrient
and micronutrient composition of the run-in, low–glycemic load, and
low-fat diet groups, and Table 3 lists
a representative day’s menu from the low–glycemic load and low-fat
groups. All participants received the same run-in diet before and during the
first inpatient admission. This diet, resembling a “typical” US
diet,19 was intended to stabilize body weight
and provide the same nutrient profile to participants in both groups before
collection of baseline clinical end points. The low-fat diet was low in fat,
high in carbohydrate and glycemic load, and generally consistent with National
Cholesterol Education Program guidelines for a heart healthy diet.20 This diet satisfied recommendations for servings
of whole grain products, fruits and vegetables, and saturated fat and cholesterol.
The low–glycemic load diet was designed to be as low in glycemic load
as possible, while providing more than ample carbohydrate to prevent ketosis.
Glycemic load was reduced by modifications of both the amount and type of
carbohydrate. Thus, some high–glycemic index carbohydrate in the low-fat
diet (eg, conventional bread, instant oatmeal, corn) was replaced with food
that had other macronutrients (eg, cheese, soy, vegetable oil) or a low glycemic
index (eg, whole kernel bread, steel-cut oats, pasta). Mean daily predicted
glycemic load was calculated as grams of available carbohydrate × glycemic
index (using white bread as 100%) and summed over all foods.
Total energy intake for the low–glycemic load and low-fat groups
was 60% of energy requirements, with a minimum of 1100 kcal/d. We calculated
energy requirement from REE and an activity factor.21 Resting
energy expenditure was determined before beginning the diets using the Harris
Benedict equation21 and during the first inpatient
stay by indirect calorimetry. For most participants, these methods yielded
results within 10% and the value obtained from the Harris Benedict equation
was used throughout the study; for 3 participants, energy requirements were
adjusted within the first week of the weight loss diet according to data obtained
by indirect calorimetry.
For the run-in diet, we created a 3-day menu cycle, and for the low-glycemic
load and low-fat diet groups, a 7-day menu cycle (calculated with Food Processor
version 7.9, ESHA Research, Salem, Ore). Percentage of calories at meals was
distributed as 25% breakfast, 30% lunch, 30% dinner, and 15% snack. The macronutrient
ratio for each meal was similar within each diet. Investigators and staff
participated in extensive quality control and taste testing procedures until
all meals for low–glycemic load and low-fat groups were of acceptable
appeal with respect to appearance and palatability. All recipes were standardized
for production consistency and individual foods were weighed to within 0.5
g. Care was taken in preparing foods to minimize changes that might affect
glycemic index, such as cooking times, reheating, freezing, or overripening.
Participants were required to eat only the food provided and to consume
1 meal (lunch) onsite Monday through Friday. The remainder of the day’s
food was provided as take-home meals. Weekend food was provided in its entirety.
Spatulas to clean out all dishes were provided for participants at home and
at the feeding site. A dietitian or nutrition assistant was present throughout
mealtime to monitor and facilitate adherence. Special attention was given
to participants who had difficulty following the diet. Participants were encouraged
to consume their meals at regularly scheduled times throughout the day and
to not skip mealtimes. Participants were allowed to consume selected noncaloric
beverages ad libitum and caffeinated beverages up to 3 servings per day, but
were not allowed any alcoholic drinks. To minimize the likelihood of micronutrient
deficiencies or marked micronutrient differences between the low–glycemic
load and low-fat diet group, each participant was provided a daily multivitamin.
Emergency meals were provided for use in the event of unexpected situations
(eg, severe weather) that might prevent participants from coming to the on-site
meal. Emergency bars consistent with diet assignment (low–glycemic load
diet: apple cinnamon crunch [Zone Perfect, The ZonePerfect Nutrition Co, Columbus,
Ohio]; low-fat diet: apple cinnamon [PowerBar Performance, PowerBar, Berkeley,
Calif]) were also provided to participants in the event of extreme hunger,
to minimize deviations from dietary prescription.
The low–glycemic load and low-fat diets were designed to produce
10% weight loss during a 6- to 10-week period if no other foods were consumed.
We provided the participants with daily logs for recording instances of nonadherence
to the diet, as well as any adverse effects, hunger levels, or exercise bouts.
This information was used in conjunction with changes in body weight to evaluate
adherence on an ongoing basis throughout the trial. The dietitians provided
behavioral support and encouragement on a daily basis, and special attention
was given to the few participants whose weight loss did not meet expectations.
Before enrollment, height and weight measurements were performed by
a calibrated balance beam scale and physical activity level was assessed using
a modified version of the Seven-Day Physical Activity Questionnaire.22 Body composition (fat and lean mass) was measured
before beginning the weight-reducing diet and again after weight loss by dual-energy
x-ray absorptiometry using Hologic instrumentation (Model QDR 4500, Hologic,
During the outpatient run-in period before the baseline admission, and
during the outpatient weight loss phase between clinic admissions, participants
arrived in clinic at noon for measurement of body weight to complete a daily
questionnaire and eat the lunch meal. Daily body weight was measured before
the lunch meal, with participants wearing light clothing, shoes removed, and
pockets emptied. Daily weight measurements were used to track the progress
of the participants toward their goal of 10% weight loss, and served as the
criterion measure for dietary adherence. In response to the question, “How
hungry have you been over the past 24 hours?” which was asked before
lunch, participants circled a number from 1 to 10, with 1 corresponding to
“not hungry at all” and 10 corresponding to “extremely hungry.”
In response to the question, “How hungry are you right now?” which
was asked before lunch, participants made a single vertical mark on a 10-cm
line anchored to the left by the text “not hungry at all” and
on the right by the text “extremely hungry.” A more in-depth questionnaire
was completed weekly to assess adverse effects and medical problems.
Participants were admitted to the General Clinical Research Center as
inpatients at baseline and after achieving 10% weight loss. When possible,
admissions for women were scheduled during the same menstrual cycle phase
(follicular) to minimize potential confounding of metabolic end points. On
3 mornings of the admission, participants were awakened by a nurse between
6:00 and 6:30 AM. The participants were instructed to empty
their bladder and then were weighed (always with the same scale) wearing a
light hospital gown and no footwear. Subsequently, REE was measured by indirect
calorimetry (Deltatrac I, SensorMedics Corp, Yorba Linda, Calif) with participants
awake and lying quietly in bed. Room temperature was maintained at a constant
level for participant comfort, and lighting and noise were kept to a minimum
during calorimetry. The calorimeter barometer and gas analyzers were calibrated
immediately before each test according to recommendations by the manufacturer.
For each of the 3 days, REE was computed as the mean energy expenditure value
of minutes 11 though 30. The mean of the 3 daily values was then taken as
the best REE estimate for each participant (day-to-day coefficient of variation [CV],
5.6% [84 kcal] at baseline and 4.1% [54 kcal] posttreatment).
Blood pressure (BP) was measured and blood samples were obtained following
calorimetry but before the breakfast meal, with participants in the fasting
state. Blood pressure was obtained in the right arm after participants were
seated quietly for 5 minutes with feet flat on the floor. Three BP readings
were taken by a nurse with an automated unit (Dinamap, Criticon Inc, Tampa,
Fla). The first reading was disregarded and the second 2 readings were averaged.
Blood was drawn from the antecubital vein into plain vacutainers and centrifuged
within 30 minutes, then the serum was stored in cryovials at –80°C
until the assays were performed in batches. Pre–weight loss and post–weight
loss samples for each participant were included in the same assay run to avoid
interassay variability within participants. Blood was drawn in the fasting
state on 3 successive mornings for glucose and insulin and on 2 mornings (first
and third) for lipids and C-reactive protein. Assay results and BP for these
multiple days were averaged within person to minimize intra-individual variability.
To describe the effect of the 2 treatment meals (breakfast) on postprandial
blood glucose and insulin, we sampled blood via an indwelling catheter in
the antecubital vein every 30 minutes for 3 hours following the meal on the
last day of the inpatient stay. The incremental areas under the curve for
glucose and insulin were calculated by the trapezoidal rule.23
All secondary end points were obtained from all completers, with the
exception of serum lipids, which were measured in the first 25 participants.
An ACE hexokinase assay (Alfa Wasserman, West Caldwell, NJ) with a within-assay
CV of less than 1% was used for serum glucose. The Beckman ultrasensitive
immunoassay (Fullerton, Calif) was used for insulin (CV, approximately 2%).
Serum C-reactive protein concentration was measured on a high-sensitivity
immunoturbidimetric assay on a Hitachi 911 analyzer (Roche Diagnostics, Indianapolis,
Ind) with reagents and calibrators from Denka Seiken (Niigata, Japan) (CV,
approximately 2%). An automated Olympus analyzer (Melville, NY) was used to
measure total cholesterol, high-density lipoprotein cholesterol, and triglyceride
levels (CV, approximately 1%). Low-density lipoprotein cholesterol was calculated
by the Friedewald formula.24 The homeostasis
model for insulin resistance was calculated as glucose (mmol/L) × insulin
(μIU/mL) divided by 22.5.25
Before participant enrollment, dietary treatment group (low–glycemic
load vs low-fat) sequence was randomly generated by computer. Randomization
was blocked by sex. Envelopes were prepared separately for male and female
participants, numbered sequentially beginning with 1, enclosed with dietary
group assignment, and sealed until randomization. During the baseline admission
before weight loss, the envelope corresponding to the participant’s
sex and enrollment number was opened by the bionutrition manager of the General
Clinical Research Center. Only the dietary staff was privy to the participant-treatment
assignments. Study personnel who measured the main process measure (body weight)
and the main end point (REE) were masked to the diet group assignment of the
participants, and these data were collected in an objective fashion (eg, digital
reading from an electronic instrument). Other personnel could not be masked
in this fashion because of obvious differences in meal appearance. Participants
also could not be masked, although they were not informed of the study’s
We used general linear models (SAS PROC GLM, SAS Institute Inc, Cary,
NC) to test the effect of dietary treatment (independent variable) on change
in REE and cardiovascular disease risk factors (dependent variables). We adjusted
end points for baseline values using analysis of covariance, as recommended
by Laird,26 to avoid potential bias that might
result if the magnitude of change depended on starting point. Additional analyses
of posttreatment data, rather than change or percent change data, adjusted
for baseline yielded similar results in most cases. Although we also included
sex as a covariate in the models, we were unable to test treatment ×
sex interactions because only 9 men were enrolled in the study. Results are
presented as adjusted means (SE), with statistical significance set at P≤.05.
Because our goal was to compare the effect of these diets on the metabolic
adaptations with 10% weight loss, we did not attempt to assess follow-up outcomes
in the 7 noncompleters. We have no reason to believe that the noncompleters
would differ from the completers in any way that would bias our primary end
point in the hypothesized direction. If any bias did exist, we speculate that
individuals with the greatest decrease in REE would be less likely to complete
the study, an effect that would favor the null hypothesis, as there were more
noncompleters in the low-fat group than in the low–glycemic load group.
However, to address this issue in intention-to-treat models, we used 2 different
strategies to impute REE change scores for the noncompleters: 14% decrease
in REE as reported by Leibel et al6 following
10% weight loss in obese participants, and 8.3% decrease in REE that was equal
to the overall mean response from the 39 completers.
Sample size for the study was based on the mean (SD) difference between
treatments in a previous study (–51  kcal/d for low–glycemic
index and –175  kcal/d for high–glycemic index).18 Forty-six participants (23 per group) were estimated
to provide 80% power to detect a difference between diets in REE of 125 kcal/d
with α = .05.
The 2 weight loss diets differed as intended in their effect on postprandial
glycemia and insulinemia. Incremental area under the curves for glucose (mean
[SE], 2706  vs 1070  mg/dL per minute, P = .003)
and insulin (5581  vs 2044  μIU/mL per minute, P = .003) were more than 2-fold greater for test meals from
the low-fat vs low–glycemic load diet groups, respectively.
Per study design, all participants who completed the protocol lost approximately
10% of their initial body weight (Figure 2).
The mean (SE) time between the baseline and post–weight loss clinic
visits was 69.4 (3.8) days for low-fat and 65.2 (3.3) days for low–glycemic
load groups (P = .41 for treatment effect).
Individual rates of weight loss were nonsignificantly greater in the low–glycemic
load compared with the low-fat groups (1.09 [0.05] vs 0.99 [0.05] kg/wk, P = .19). Changes in body weight and composition
in both groups were very similar (Table 4).
We observed no difference in physical activity, based on daily exercise logs
describing type, frequency, and duration, during the course of the trial between
the 2 groups (101 [9.1] vs 94 [10.4] min/wk for low–glycemic load and
low-fat, respectively, P = .64).
Resting energy expenditure decreased less in the low–glycemic
load group compared with low-fat group following weight loss, whether expressed
in absolute terms (mean [SE], 96  vs 176  kcal/d, P = .04) (Figure 3)
or as a proportion (5.9% [1.5%] vs 10.6% [1.7%], P = .05).
These findings were not materially affected by adjustment for baseline BMI,
body composition, or time to achieve target weight loss. Intention-to-treat
analyses based on imputing values for the 7 noncompleters did not materially
alter these findings (P≤.05 for both models).
Participants in the low–glycemic load group reported less hunger
than those in the low-fat group in response to the question, asked each day
before lunch, “How hungry have you been over the past 24 hours?”
(mean [SD], 3.3 [0.28] vs 4.2 [0.3] units; P = .04).
A nonsignificant difference in the same direction was observed in response
to the question, “How hungry are you right now?” (mean [SD], 3.6
[0.33] vs 4.5 [0.38] units; P = .10).
Effects of dietary treatment on measures of cardiovascular disease risk
with weight loss are shown in Table 5.
Insulin resistance, as assessed by homeostasis model assessment (HOMA) score,
decreased by more than twice as much with weight loss in the low–glycemic
load vs the low-fat group (P = .01). Serum concentrations
of triglyceride levels also decreased more in the low–glycemic load
vs the low-fat group (P = .01). No group differences
were observed for changes in low-density lipoprotein or high-density lipoprotein
cholesterol with weight loss. C-reactive protein declined by nearly 50% with
weight loss in the low–glycemic load group but remained essentially
unchanged in the low-fat group (P = .03
for dietary treatment effect). There was a trend toward larger decrease in
both systolic (P = .07) and diastolic (P = .07) BPs in the low–glycemic load vs
the low-fat group; mean arterial BP declined more in the low–glycemic
load group (P = .04).
The primary finding of our study was that physiological adaptations
that serve to defend baseline body weight can be modified by dietary composition.
The REE declined by 80 kcal/d less and hunger was less on the low–glycemic
load diet vs the low-fat diet during weight loss, similar to results from
a prior short-term study.18 In addition, the
low–glycemic load diet studied here produced favorable changes in insulin
resistance, lipids, chronic inflammation, and BP compared with a conventionally
recommended diet that was lower in saturated fat, cholesterol, and sodium.
We found no evidence to support a previous hypothesis derived from analysis
of nitrogen balance that high–glycemic index, energy-restricted diets
would have adverse effects on body composition,18 although
our study may not be of sufficient length to adequately examine this question.
Two methodological issues warrant discussion. First, to maximize differences
in glycemic load, we did not control for macronutrient composition; therefore,
the question arises as to whether any conventionally recognized effect of
macronutrient composition could explain the study’s primary finding.
The increase in energy expenditure during the postprandial state, termed the thermic effect of food, is greater for protein than for
carbohydrate or fat. However, the REE was measured in our study after a fast
of more than 10 hours, eliminating the possibility of confounding by the thermic
effect of food. Diets with different protein content might also alter REE
through effects on lean body mass. However, changes in body composition as
assessed by dual-energy x-ray absorptiometry scan were not different among
participants who were treated with the low–glycemic load vs the low-fat
diet, and adjustment for change in body composition did not alter the REE
effect. Of particular significance, differences in dietary protein of the
same or greater magnitude as that used in our study did not result in any
differences in REE, either following weight maintenance or weight loss, in
5 previous articles.27-31
A second methodological issue is that measurement of REE was made during
ongoing weight loss. The magnitude of observed effect could change with weight
stabilization and additional research is needed to assess this possibility.
Nevertheless, the physiological adaptations that occur during active weight
loss may be especially relevant to understanding why most obese individuals
become noncompliant with conventional energy-restricted diets long before
a normal body weight has been reached. Indeed, diet-induced differences in
REE were observed after our participants had lost less than half of their
excess adiposity, and after just 1 week of energy restriction in a previous
The difference in REE is too small to account for any significant change
in body composition over the short term. For example, 80 kcal/d over 10 weeks
(5600 kcal) would amount to less than 1 kg of body weight. Thus, our study
does not support claims that popular diets can cause rapid weight loss by
inducing major shifts in energy metabolism. Nevertheless, the REE difference
here could amount to several pounds of weight change per year, given this
effect would persist over the long term. For comparative purposes, an energy
balance of –80 kcal/d could be obtained by walking approximately 1 mile/d
or by decreasing sugar-sweetened soft drink consumption 6 oz/d. Indeed, this
difference (560 kcal/wk) would explain most of the mean difference in rate
of weight loss between groups (0.09 kg/wk × 7500 kcal/kg = 675
A potentially more important question is whether the magnitude of change
in REE during weight loss would predict likelihood of achieving and maintaining
clinically significant weight loss. Some studies32,33 but
not all34 suggest an inverse relationship between
REE and weight gain or regain. An individual experiencing a larger decline
in REE during weight loss may feel more fatigued, cold, and hungry than an
individual experiencing a small decline, and these symptoms may make compliance
with dietary energy restriction increasingly difficult over time.
The physiological mechanisms relating dietary composition to REE during
weight loss remain speculative but may involve altered availability of metabolic
fuels. Blood glucose and free fatty acids are reduced in the postabsorptive
phase following a high– vs low–glycemic index meal, and this reduction
can be sufficient in magnitude to trigger release of stress hormones.12 Low circulating concentrations of metabolic substrate
might directly impair energy metabolism at the cellular level, as occurs with
frank hypoglycemia.35 Alternatively, the decrease
in REE may come from neuroendocrinological adaptations designed to conserve
energy, involving thyroid hormone, growth hormone, sex hormones, or leptin
(an adipocyte-derived factor that acts in the hypothalamus)3,5;
lack of data on these hormones comprises a limitation of our study. Interestingly,
rodents treated with nutrient-controlled high–glycemic index diets compared
with low–glycemic index diets demonstrate an increase in metabolic efficiency
analogous to that observed by our participants taking the low-fat (high–glycemic
Epidemiological analyses have found associations between glycemic load
and high triglycerides, low high-density lipoprotein cholesterol, and elevated
C-reactive protein levels.37 In 1 study,38 individuals in the highest vs lowest quintile of
glycemic load had double the risk of developing heart disease, after controlling
for potentially confounding factors. However, these effects have not previously
been examined in interventional studies. We found that during weight loss,
a diet focused on glycemic load reduction produced greater improvements in
several important cardiovascular disease–related and diabetes mellitus–related
end points than a diet focused on reduction of total and saturated fat in
accordance with conventional practice. We speculate that these improvements
were caused by reduction in insulin concentration; hyperinsulinemia plays
a critical role in development of the insulin resistance syndrome (metabolic
syndrome) consisting of hypertension, dyslipidemia, chronic inflammation,
and other heart disease risk factors in the setting of central obesity.39 Our participants did not have the metabolic syndrome
at baseline (Table 1), sample size was
relatively small, the study was of relatively short duration, and all meals
were prepared in a metabolic kitchen; therefore, the generalizability of these
findings requires further study.
In conclusion, we found that the physiological adaptations to a weight-reducing
diet thought to antagonize ongoing weight loss, involving energy expenditure
and hunger, can be modified by dietary composition. In addition, the low–glycemic
load diet had beneficial effects on several obesity-related risk factors compared
with a low-fat diet that was consistent with current nutritional guidelines.
Incorporation of glycemic load principles into current dietary guidelines
may aid in the treatment of obesity and prevention of cardiovascular disease
and diabetes mellitus, a possibility that warrants evaluation in long-term
randomized controlled trials.
Corresponding Author: David S. Ludwig, MD,
PhD, Department of Medicine, Children’s Hospital, 300 Longwood Ave,
Boston, MA 02115 (email@example.com).
Author Contributions: Dr Ludwig had full access
to all of the data in the study and takes responsibility for the integrity
of the data and the accuracy of the data analysis.
Study concept and design: Pereira, Swain, Ludwig.
Acquisition of data: Pereira, Swain, Goldfine,
Analysis and interpretation of data: Pereira,
Swain, Goldfine, Rifai, Ludwig.
Drafting of the manuscript: Pereira, Swain,
Critical revision of the manuscript for important
intellectual content: Pereira, Swain, Goldfine, Rifai, Ludwig.
Statistical analysis: Pereira.
Obtained funding: Pereira, Ludwig.
Administrative, technical, or material support:
Pereira, Swain, Rifai.
Study supervision: Pereira, Swain, Ludwig.
Funding/Support: This study was supported by
grant R01 DK59240 from the National Institute of Diabetes and Digestive and
Kidney Diseases (Dr Ludwig), grants MO1 RR02635 and M01 RR02172 from the National
Institutes of Health (to the General Clinical Research Center at Brigham and
Women’s Hospital, and Children's Hospital, Boston, Mass, respectively),
and grants from General Mills Corporation (Dr Pereira) and Charles H. Hood
Foundation (Dr Ludwig).
Role of the Sponsors: The funding organizations
did not participate in the design and conduct of the study, in the collection,
analysis, and interpretation of the data, or in the preparation, review, or
approval of the manuscript.
Acknowledgment: We thank Ashley Coyne, MS,
RD, and Eileen Hamilton, DTR, of Brigham and Women’s Hospital, Boston,
Mass, for expert technical assistance.