Re: Kind of carbs matters...

[Guest guest]

>>they found was the kind of carbohydrate what minded, not GL or amount.

Amen. However, quantity can also matter, its just much harder to overeat on the

unrefined, unprocessed carbohydrates when consumed in their natural state.

Regard

jeff

[Guest guest]

Hi Willie:

Thanks for that link. Might what he really found be that eating

foods (which include 'refined carbs') which have high caloric value

in small volume is what is REALLY correlated with BMI?

The very obvious reason being that after eating such foods people

need to eat more food volume before achieving satiation than if they

instead had eaten bulkier foods?

If so then what is important for BMI is eating foods with low caloric

content per unit volume, rather than low GI as he suggests.

Please take this as a question rather than as an assertion. But you

can see which way I am leaning on the subject.

Rodney.

>

>

> See http://aje.oupjournals.org/cgi/content/abstract/161/4/359 where

> they found was the kind of carbohydrate what minded, not GL or

amount.

>

> ************************************************

> Carbohydrate Type, Not Amount, Linked to Obesity

> ************************************************

>

> Wed Feb 16, 2:46 PM ET Health - Reuters

> By Alison McCook

>

> NEW YORK (Reuters Health) - When it comes to carbohydrates, it's

not

> how much you eat, but which kind, that makes a difference to your

> bathroom scale, new research shows.

>

> People who are overweight do not appear to eat more carbohydrates

> overall than people who weigh less, the researchers report in the

> American Journal of Epidemiology. However, they found that

overweight

> people tend to eat more refined carbohydrates, such as white bread

> and pasta, which cause a rapid spike in blood sugar.

>

> " Total amount of carbohydrate is not related to body weight, " Dr.

> Yunsheng Ma of the University of Massachusetts Medical School in

> Worcester told Reuters Health. " It's the type of carbohydrate

that's

> important. "

>

> These findings suggest that low-carbohydrate diets, which recommend

> people cut back on all carbohydrates, are missing the mark, Ma

added.

>

> " Carbohydrates are not the enemy, " he said in an interview. " But

you

> have to watch the kind of enemy. "

>

> Ma explained that refined carbohydrates are often found in

processed

> foods that contain a lot of sugar. This type of carbohydrate has

> what's called a high glycemic index, meaning it causes a rapid

> increase in blood sugar. The body stores that sugar in muscle, but

if

> it is not used, it becomes fat, he noted.

>

> In contrast, whole grains, fruits and vegetables have carbohydrates

> that don't have such high glycemic index, Ma said.

>

> In the report, Ma and his colleagues note that in the last 20

years,

> the rate of obesity has increased, despite the fact that people are

> eating less fat. To help investigate the role carbohydrates play in

> obesity, the researchers measured the height and weight of 572

> healthy people, and asked them to regularly report what

carbohydrates

> they ate. Ma's team followed study participants for one year.

>

> They found that people with a higher body mass index -- a measure

of

> weight that factors in height -- tended to eat carbohydrates with a

> higher glycemic index. The amount of carbohydrates people ate had

no

> influence on body mass index.

>

> " Refined carbohydrates are no good, but the total amount of

> carbohydrates is okay, " Ma noted.

>

> He added that some countries now include a food's glycemic index on

> the labeling, which can be helpful for people trying to lose weight

> or deal with diabetes.

>

> SOURCE: American Journal of Epidemiology, February 15; 2005

> ************************************************************

>

> Regards

> Willie

[Guest guest]

>

>

> See http://aje.oupjournals.org/cgi/content/abstract/161/4/359 where

> they found was the kind of carbohydrate what minded, not GL or

amount.

>

> ************************************************

> Carbohydrate Type, Not Amount, Linked to Obesity

> ************************************************

>

> Wed Feb 16, 2:46 PM ET Health - Reuters

> By Alison McCook

>

> NEW YORK (Reuters Health) - When it comes to carbohydrates, it's

not

> how much you eat, but which kind, that makes a difference to your

> bathroom scale, new research shows.

>

> People who are overweight do not appear to eat more carbohydrates

> overall than people who weigh less, the researchers report in the

> American Journal of Epidemiology. However, they found that

overweight

> people tend to eat more refined carbohydrates, such as white bread

> and pasta, which cause a rapid spike in blood sugar.

>

> " Total amount of carbohydrate is not related to body weight, " Dr.

> Yunsheng Ma of the University of Massachusetts Medical School in

> Worcester told Reuters Health. " It's the type of carbohydrate

that's

> important. "

>

> These findings suggest that low-carbohydrate diets, which recommend

> people cut back on all carbohydrates, are missing the mark, Ma

added.

>

> " Carbohydrates are not the enemy, " he said in an interview. " But

you

> have to watch the kind of enemy. "

>

> Ma explained that refined carbohydrates are often found in

processed

> foods that contain a lot of sugar. This type of carbohydrate has

> what's called a high glycemic index, meaning it causes a rapid

> increase in blood sugar. The body stores that sugar in muscle, but

if

> it is not used, it becomes fat, he noted.

>

> In contrast, whole grains, fruits and vegetables have carbohydrates

> that don't have such high glycemic index, Ma said.

>

> In the report, Ma and his colleagues note that in the last 20

years,

> the rate of obesity has increased, despite the fact that people are

> eating less fat. To help investigate the role carbohydrates play in

> obesity, the researchers measured the height and weight of 572

> healthy people, and asked them to regularly report what

carbohydrates

> they ate. Ma's team followed study participants for one year.

>

> They found that people with a higher body mass index -- a measure

of

> weight that factors in height -- tended to eat carbohydrates with a

> higher glycemic index. The amount of carbohydrates people ate had

no

> influence on body mass index.

>

> " Refined carbohydrates are no good, but the total amount of

> carbohydrates is okay, " Ma noted.

>

> He added that some countries now include a food's glycemic index on

> the labeling, which can be helpful for people trying to lose weight

> or deal with diabetes.

>

> SOURCE: American Journal of Epidemiology, February 15; 2005

> ************************************************************

Hi All,

Carbohydrates and body mass index (BMI) have been suggested

to be correlated and the carbohydrates may cause increased BMI.

The below pdf-available paper suggests that it may be a much

more complicated situation.

From Table 3, it seems that the BMI is determined by high glycemic

index of foods, higher % calories from fat, greater energy intake,

less

exercise, being female, being older, having less education and it

being

any season other than Fall.

Ma Y, Olendzki B, Chiriboga D, Hebert JR, Li Y, Li W, M,

Gendreau

K, Ockene IS.

Association between Dietary Carbohydrates and Body Weight.

Am J Epidemiol. 2005 Feb 15;161(4):359-67.

PMID: 15692080 [PubMed - in process]

The role of dietary carbohydrates in weight loss has received

considerable attention in light of the current obesity epidemic. The

authors investigated the association of body mass index (weight

(kg)/height (m)2) with dietary intake of carbohydrates and with

measures of the induced glycemic response, using data from an

observational study of 572 healthy adults in central Massachusetts.

Anthropometric measurements, 7-day dietary recalls, and physical

activity recalls were collected quarterly from each subject

throughout a 1-year study period. Data were collected between 1994

and 1998. Longitudinal analyses were conducted, and results were

adjusted for other factors related to body habitus. Average body mass

index was 27.4 kg/m2 (standard deviation, 5.5), while the average

percentage of calories from carbohydrates was 44.9 (standard

deviation, 9.6). Mean daily dietary glycemic index was 81.7 (standard

deviation, 5.5), and glycemic load was 197.8 (standard deviation,

105.2). Body mass index was found to be positively associated with

glycemic index, a measure of the glycemic response associated with

ingesting different types of carbohydrates, but not with daily

carbohydrate intake, percentage of calories from carbohydrates, or

glycemic load. Results suggest that the type of carbohydrate may be

related to body weight. However, further research is required to

elucidate this association and its implications for weight

management.

Abbreviations: BMI, body mass index; FFQ, food frequency

questionnaire; SD, standard deviation; 7DDR, 7-day dietary recall.

INTRODUCTION

Over the last 20 years, the prevalence of obesity has increased

despite a decrease in the reported percentage of energy intake

derived from daily fat intake (1). Issues regarding the accuracy of

dietary self-report notwithstanding, this observation suggests that

factors other than dietary fat intake may be responsible for this

observed increase in obesity. The role of dietary carbohydrates in

weight gain has become an important question in the public

consciousness (2–5). Carbohydrates have been traditionally classified

as simple (monomeric and dimeric) or complex (polymeric) on the basis

of their chemical structure. A critical defect of this classification

is its inability to predict the plasma glucose and insulin responses

associated with different types of carbohydrates (6). The glycemic

index, developed two decades ago by et al. (7), allows

comparison of different foods based on their physiologic effects

rather than on their chemical composition. A positive association

between glycemic index and body weight has been shown in several

short-term experimental studies and limited observational studies (8–

15). The possible biologic mechanisms of glycemic index on body

weight are thought to be related to insulin levels, hunger and

satiation, and basic metabolic processes (16–18). However, the long-

term effect of glycemic index and total carbohydrates on body weight

is currently unknown.

Because controlled clinical trials are expensive and difficult to

conduct, it is of value to obtain further insight into the impact of

glycemic index and total carbohydrate intake on body mass index (BMI)

from existing data sets derived from carefully conducted

observational studies. The Seasonal Variation of Blood Cholesterol

Study (SEASONS), a large prospective study, was designed to quantify

the magnitude and timing of seasonal changes in blood lipids and to

identify the major factors contributing to this variation, including

diet and physical activity (19, 20). We used longitudinal data from

this study to evaluate the relation between BMI and carbohydrate-

related dietary factors (glycemic index, glycemic load, daily

carbohydrate intake, and percentage of calories from carbohydrates)

while controlling for the effects of physical activity and energy

intake.

...

We distinguished cross-sectional (between-subject)

effects from longitudinal (temporal change within subject)

effects of predictors (33, 34).

...

RESULTS

Of the 641 subjects entering the Seasonal Variation of Blood

Cholesterol Study, 572 (89.2 percent) participated in at least two

clinic visits yielding both BMI and glycemic index values and were

included in the analyses. Tables 1 and 2 present the characteristics

of the study sample. About half (51.9 percent) of the subjects were

men. They were middle-aged, primarily White, well educated, employed,

non-smokers, and generally overweight. The distribution of glycemic

index (mean = 81.7, standard deviation (SD), 5.5; white bread = 100)

is illustrated in figure 1. Differences in educational levels, BMI,

total caloric intake, percentage of calories from fat, glycemic

index, and physical activity levels were found between men and women.

Between included and excluded participants, only age (47.8 years (SD,

12.3) for those included and 42.0 years (SD, 11.3) for those

excluded) and race/ethnicity (86.7 percent White for those included

vs. 76.6 percent for those excluded) were significantly different (p

< 0.01 and p = 0.01, respectively). Gender, BMI, and smoking status

were comparable.

TABLE 1. Characteristics of study participants at baseline,

Seasonal Variation of Cholesterol Study, Worcester, Massachusetts,

1994–1998

----------------------------------------------------------------------

Variable Total (n = 572)-----Total-----Men (n = 297)----Women (n =

275)

----------------------------------------------------------------------

----------

----------------------------------------No. %------- No. %----------

--No. %

----------------------------------------------------------------------

----------

Age in years (mean (SD)) 47.8 (12.3) 48.3 (12.4) 47.2 (12.0)

Race/ethnicity

White 484 86.7 250 88.7 225 84.6

Hispanic 44 7.9 18 6.16 26 9.8

Other 30 5.4 15 5.14 15 5.6

Educational level*

Less than high school 32 5.6 13 4.4 19 7.0

High school or vocational degree 137 24.1 58 19.6 79 28.9

Some college or associates degree 179 31.5 99 33.4 80 29.3

College/graduate 221 38.8 126 42.6 95 34.8

Employment*

Full-time 384 67.4 224 75.4 160 58.6

Part-time 87 15.3 31 10.4 56 20.5

Unemployed/retired 99 17.4 42 14.1 57 20.9

Current smoking#

Yes 96 17.1 53 18.4 43 15.8

No** 464 82.9 235 81.6 229 84.2

Body mass index*** classification*

Normal (18.5–24.9) 205 37.5 81 28.6 124 47.0

Overweight (25–29.9) 210 38.4 127 44.9 83 31.4

Obese (30) 132 24.1 75 26.5 57 21.6

Mean (SD) 27.38 (5.5) 27.9 (4.5) 26.8 (6.3)

----------------------------------------------------------------------

----------

* p < 0.05 in chi-square tests comparing men and women.

SD, standard deviation.

# Because of missing values, the total number of subjects differs.

** Defined as never having smoked or having quit smoking at least 1

year prior to enrollment.

*** Weight (km)/height (m)2).

--------------------------------------------------------------------

TABLE 2. Relevant dietary factors and physical activity (mean

(standard deviation)) of study participants at baseline, Seasonal

Variation of Cholesterol Study, Worcester, Massachusetts, 1994–1998

----------------------------------------------------------------------

----------

Variable----Total (n = 572)----Men (n = 297)----Women (n = 275)

----------------------------------------------------------------------

----------

Dietary factor

Total energy (kcal/day)* 2,055 (9.55) 2,227 (895) 1,882 (983)

% of energy from carbohydrate* 44.9 (9.6) 43.4 (9.3) 46.4 (9.7)

% of energy from fat 36.7 (9.0) 37.2 (8.9) 36.1 (9.1)

Carbohydrate (g/day) 231.2 (125.1) 241.1 (106.5) 221.2 (141.1)

Glycemic index* 81.7 (5.5) 82.3 (5.2) 81.1 (5.8)

Glycemic load 197.8 (105.2) 202.7 (91.1) 192.8 (118.0)

Physical activity (MET-hours/day)

Total* 30.6 (6.2) 32.2 (7.6) 29.0 (4.0)

Household 4.7 (5.0) 4.4 (5.6) 5.0 (4.3)

Occupational* 4.9 (7.1) 6.5 (8.7) 3.3 (4.1)

Leisure-time/sports* 2.0 (3.1) 2.2 (3.5) 1.8 (2.7)

----------------------------------------------------------------------

----------

* p < 0.05 in a t test comparing men and women.

MET, metabolic equivalent task.

Table 3 presents determinants of BMI. Daily dietary glycemic

index, percentage of calories from fat, and caloric intake were

associated with higher BMI; female gender, increased physical

activity, and higher educational levels were associated with lower

BMI. The BMI of study participants was lower in the fall compared

with the winter. As a focus of this paper, we report associations of

BMI with glycemic index, glycemic load, and carbohydrates, adjusted

for daily caloric intake, percentage of calories from fat, physical

activity, age, gender, educational level, and season of the year.

Daily dietary glycemic index was positively associated with BMI, with

a five-unit increase in glycemic index being significantly associated

with an increase of 0.75 units in BMI (p = 0.01) from cross-sectional

(between-subject) effects and an increase of 0.04 unit in BMI (p =

0.02) from longitudinal effects (temporal change within subject). We

conducted similar analyses with separate models to evaluate

percentage of calories from carbohydrates, total carbohydrate intake,

and glycemic load, and the results showed no significantly

statistical associations with BMI regarding both cross-sectional and

longitudinal effects (data not shown). However, we noted that the

cross-sectional effects of percentage of calories from carbohydrates

and total carbohydrates on BMI had small negative regression

coefficients: –0.08 and –0.002, respectively.

TABLE 3. Predictors of body mass index,* Seasonal Variation of

Cholesterol Study, Worcester, Massachusetts, 1994–1998

----------------------------------------------------------------------

----------

-----Coefficient 95% CI p value

----------------------------------------------------------------------

----------

Daily dietary glycemic index##

Cross-sectional effect 0.75 0.21, 1.30 0.007

Longitudinal effect 0.04 0.01, 0.07 0.02

% of calories from fat

Cross-sectional effect 0.09 0.03, 0.16 0.005

Longitudinal effect 0.005 –0.0004, 0.01 0.07

Energy intake (kcal/day)

Cross-sectional effect –0.01 –0.08, 0.05 0.68

Longitudinal effect 0.005 0.001, 0.009 0.02

Total physical activity (MET#-hours/day)***

Cross-sectional effect –0.61 –1.14, –0.08 0.02

Longitudinal effect –0.03 –0.06, –0.0001 0.049

Gender (male as referent)

Female –1.65 –2.62, –0.68 0.001

Age (years)**** 0.03 –0.34, 0.41 0.86

Educational level (<high school as referent)

High school or vocational degree –1.67 –3.69, 0.36 0.11

Some college or associates degree –2.41 –4.44, –0.38 0.02

College/graduate –4.01 –6.07, –1.96 <0.001

Season of the year (winter as referent)

Spring 0.02 –0.04, 0.08 0.44

Summer –0.05 0.12, 0.02 0.14

Fall –0.11 –0.17, –0.05 0.001

----------------------------------------------------------------------

----------

* Weight (kg)/height (m)2.

# CI, confidence interval; MET, metabolic equivalent task.

## A five-unit change in glycemic index.

** A 100-kcal change in energy intake.

*** A five-unit change in total physical activity.

**** A 10-year change in age.

-----------------------------------------------------

Similarly, we examined the association between daily dietary

glycemic index and caloric intake adjusted for physical activity,

age, gender, educational level, and season of the year. Glycemic

index was not found to be a significant predictor of energy intake

(data not shown).

DISCUSSION

Results from our study support the hypothesis that type of

carbohydrate may be related to body weight. However, percentage of

calories from carbohydrates, daily total carbohydrate intake, and

glycemic load were not found to be related to BMI, and glycemic index

was also found not to be related to daily caloric intake.

Validity and reliability of glycemic index and glycemic load are

of vital importance to our investigation and to some extent depend on

the characteristics of the dietary assessment method. The 7DDR has

been shown to be extremely competent in terms of both cross-sectional

and longitudinal assessment of diet, especially macronutrients (21).

To minimize the influence of missing values, we identified and

assigned substitute values of glycemic index for the foods with a

higher carbohydrate content, for which glycemic index values were not

available. The glycemic index, glycemic load, and total carbohydrate

values in our study are also comparable to those reported in several

studies that used an FFQ as a dietary assessment tool (35–39). For

example, for the 131-item FFQ used in the Women's Health Study (40),

the reported overall dietary glycemic index was 75 (SD, 5.0),

glycemic load was 166 (SD, 32), and total carbohydrate intake was 221

g/day (SD, 36) (40).

The hypothetical association between glycemic index and body

weight is supported by evidence from short-term animal studies as

well as from epidemiologic studies (8–15). Short-term studies in

humans and animals have provided evidence that a high-glycemic-index

diet affects hunger and nutrient partitioning in a way that promotes

body fat storage. Compared with rats fed amylose (a low-glycemic-

index starch), those fed amylopectin (a high-glycemic-index starch),

under nutrient- and energy-controlled conditions for 3–5 weeks,

exhibited physiologic changes that favored fat deposition, including

larger adipocyte diameter, increased glucose incorporation into

lipids, greater fatty acid synthase, and Glut 4 gene _expression in

fat tissue (41, 42). By 7 weeks, animals fed a high-glycemic-index

diet developed increased epididymal fat mass (9); by 32 weeks, they

developed marked obesity compared with animals fed a low-glycemic-

index diet (8).

Furthermore, several short-term experimental studies in humans

suggest that body weight is positively associated with dietary

glycemic index (13). Obese, hyperinsulinemic women appear to lose

more weight after 12 weeks of consuming an energy-restricted, low-

glycemic-index diet compared with women who consume an energy-

restricted, high-glycemic-index diet (11). Similarly, it has been

observed that obese men have reduced adiposity, as assessed by dual-

energy x-ray absorptiometry, after 5 weeks on an energy- and nutrient-

controlled, low-glycemic-index diet compared with a high-glycemic-

index diet (13). BMI decreased significantly during an average of 4

months in obese children prescribed an ad libitum low-glycemic-index

diet compared with those prescribed an energy-restricted, low-fat

diet (12).

Some epidemiologic studies provide further evidence that the

glycemic effect of the diet might influence weight control. For

example, a lower-glycemic-index diet has been found to predict lower

waist-to-hip ratio and waist circumference independent of

carbohydrate, fat, and fiber intakes (14), and, in a study of young

adults, low fiber consumption (glycemic index was not assessed)

predicted higher 10-year weight gain, higher waist-to-hip ratio, and

higher 2-hour postprandial glucose insulin concentrations (a measure

of insulin resistance) to a greater extent than did total or

saturated fat consumption (15). Although fiber and glycemic index are

not equivalent, they tend to be related because viscous dietary

fibers and foods in which the natural cell wall architecture remains

intact (e.g., legumes) generally have lower glycemic indexes.

Results from analysis of both cross-sectional and longitudinal

data in our study support findings from a retrospective cohort study

of 107 children attending an outpatient obesity program, in which a

low-glycemic-index diet was associated with greater weight loss

within 4 months of follow-up (12). The average height of subjects in

our study was 5 feet 7 inches (170 cm). Given this height, the cross-

sectional effect of glycemic index on BMI from our study is

calculated to be the equivalent of a difference of 4.8 pounds of

weight (2.2 kg of mass) for a five-unit change in glycemic index

across subjects, or 9.5 pounds (4.3 kg) for a 10-unit change. This

effect is clinically relevant, since it is comparable to that

achieved in clinical trials involving changes in dietary intake of

fat and saturated fat (43, 44). With knowledge of how foods

containing carbohydrate may be classified (e.g., the glycemic indexes

for Kellogg's Corn Flakes (Kellogg's, Battle Creek, Michigan) and

Cheerios (General Mills, Golden Valley, Minnesota) are 92 and 74,

respectively), persons are able to make appropriate substitutions

acceptable to their palate and lifestyle. Therefore, a change of 5–10

units in glycemic index is possible and of practical significance in

terms of their BMI (45).

Because there were only small changes in glycemic index and BMI

longitudinally over the 1-year observational period of this study,

the association between glycemic index and BMI that was found comes

from the cross-sectional analysis in which we observed a larger

association between glycemic index and BMI than in the longitudinal

analysis. This observation is essentially the converse of what is

seen in large-scale intervention trials, where large, relatively

predictable changes in serum cholesterol values occur with large

changes in dietary fatty acid intake, but there is no observable

difference cross-sectionally (46). Here, the spontaneous change in

glycemic index or BMI was small, so the variance became very large in

relation to the change score because of the simple fact that the

variance of a difference is the sum of the variance of the two

measures from which the difference is computed (e.g., here, mean

weight change = –0.01 pounds (variance, 83.36) and mean change in

glycemic index = –0.14 (variance, 38.81)). Therefore, we were unable

to detect a stronger association in the longitudinal analyses from

this observational study.

Diets low in carbohydrates have been purported to enhance weight

loss (2, 3, 47). Studies have reported greater weight loss in those

who consumed a low-carbohydrate diet compared with a diet higher in

carbohydrate at 6 months (48–50), but weight loss was not sustained

at 12 months (48, 51). Therefore, the long-term effect of a low-

carbohydrate diet on weight loss remains controversial (52). Our

data, derived from a free-living population, did not support the

hypothesis that low total carbohydrate intake is related to lower BMI

and are consistent with 1-year follow-up data from clinical trials

(48, 51).

The glycemic load is the glycemic index of a food multiplied by

its carbohydrate content in grams. It has been suggested that

glycemic load better reflects a food's physiologic effect than either

the amount of carbohydrate or the glycemic index alone (5). Ebbeling

et al. (53) tested a reduced-glycemic-load diet for treating obesity

in 14 pediatric subjects aged 13–21 years. Results showed that a

decrease of 17 units in glycemic load led to a 1.3-unit decrease in

BMI, which was significant in comparison with the control group that

consumed a conventional low-fat, low-calorie diet. However, our data

did not support this hypothesis. The possible explanation is that

daily total carbohydrate intake appeared to be negatively associated

with BMI, and glycemic index was positively associated with BMI.

Therefore, the combination of glycemic index and carbohydrates

diminished the association between glycemic load and BMI. Further

investigation is warranted.

It has also been suggested that glycemic index influences hunger

(16, 17) and therefore may be related to overeating (18). Ludwig (54)

summarized 16 studies that examined the effects of glycemic index on

hunger, and all but one demonstrated that low-glycemic-index foods

increased satiety, delayed return of hunger, and decreased ad libitum

food intake compared with high-glycemic-index foods in humans. For

example, a study by Leathwood and Pollet (10) reported lower blood

glucose levels and slower return of hunger after meals containing

bean puree (a low-glycemic-index starch) compared with meals

containing potato (a high-glycemic-index starch). Holt et al. (55)

showed that glycemic and insulinemic responses to various breakfast

cereals are inversely related to satiety score. Ludwig et al. (56)

found that high-glycemic-index meals induce a sequence of hormonal

and metabolic changes that promote excessive food intake in obese

subjects. However, our data indicated that high glycemic index is

unrelated to total caloric intake. Again, further research is

warranted.

Strengths of this study include the following: 1) Longitudinal

analysis was conducted, and two to five measures for dietary factors

and BMI from each subject provided more reliable values for the

analyses and therefore more stable results; 2) recall bias was less

likely in our study because the 7DDR is a food assessment tool

designed to recall food intake during 7 days, in contrast to an FFQ

requiring estimation of average exposure over a much longer period,

typically 3 months to 1 year; 3) information on physical activity

also was collected and controlled for in the analyses; and 4) the

data analysis method used in this study enabled us to examine the

presence of cross-sectional and longitudinal effects in the same

model. This method has been widely used and accepted in the

statistical field (33, 34, 57), although it has not been widely used

in most epidemiologic analyses of longitudinal data.

There are some potential limitations to this study. Unlike in an

intervention study, we were not able to create large contrasts over

time, thus obscuring a possible causal relation between high-glycemic-

index foods and weight. Published glycemic index values are also

subject to error. The average coefficient of variation for glycemic

responses from 11 normal subjects who consumed 50 g of glucose an

average of eight times each was 25 percent. The mean coefficient of

variation for the glycemic index for 22 normal subjects who consumed

50 g of carbohydrate from white bread was 22 percent (58). The

coefficients of variation were 13 percent for the glycemic index

value of Kellogg's Corn Flakes as tested in four laboratories and 26

percent for potato as tested in 10 laboratories (28), although, if a

standardized method is used, the results for glycemic index agree

about as well as data on carbohydrate, protein, fat, and fiber from

24-hour dietary recalls (i.e., with a coefficient of variation of ±5

percent) (59–61). Although the 7DDR may have minimized recall bias

compared with the FFQ, it was designed to focus on dietary fat. As

with most FFQs, it underestimates total carbohydrate (by an average

of 33 g; SD, 68) compared with multiple 24-hour recalls (21). Still,

to estimate effect, the absolute amount of bias is relatively

unimportant if the degree of linear agreement is good.

Dietary fiber, particularly soluble fiber, is a very important

factor affecting food digestion and absorption rate, which in turn

affects the glycemic index value (62, 63). We attempted to break food

items down into individual components to calculate glycemic index and

glycemic load, but the process is not as precise as it is with the

detail available from 24-hour dietary recall data. However, to our

knowledge, there are no published glycemic index data based on 24-

hour recalls.

Other potential limitations to our study include the fact that our

study population consisted largely of White, middle-class subjects

who were members of a health maintenance organization. In addition,

because the study protocol involved a lengthy series of clinic visits

and diet assessments, participants who stayed in the study were

highly motivated. Selection factors relating to the participants'

interest in their own health and time availability for participation

may have created a fairly homogeneous and health-conscious study

group. For these reasons, our findings might not be generalizable to

other socioeconomic strata or to other cultural and ethnic groups.

In conclusion, results from our study suggest that daily

dietary glycemic index is independently and positively associated

with BMI. This finding is consistent with the hypothesis that with

increased glycemic index, more insulin is produced and more fat is

stored, suggesting that type of carbohydrate may be related to body

weight. Our data did not support the current public trend of lowering

total carbohydrate intake for weight loss or of lowering glycemic

load for weight loss, as suggested by other researchers (5, 53).

These results add valuable information about the role of glycemic

index, glycemic load, carbohydrates, and management of body weight

and about the potential prevention of obesity. However, further

research is needed to deepen understanding of the relation between

body weight and dietary carbohydrates.

Cheers, Al Pater.