Glycemic index and glycemic load: Measurement issues and their effect on diet-disease relationships

Abstract

Glycemic index (GI) describes the blood glucose response after consumption of a carbohydrate containing test food relative to a carbohydrate containing reference food, typically glucose or white bread. GI was originally designed for people with diabetes as a guide to food selection, advice being given to select foods with a low GI. The amount of food consumed is a major determinant of postprandial hyperglycemia, and the concept of glycemic load (GL) takes account of the GI of a food and the amount eaten. More recent recommendations regarding the potential of low GI and GL diets to reduce the risk of chronic diseases and to treat conditions other than diabetes, should be interpreted in the light of the individual variation in blood glucose levels and other methodological issues relating to measurement of GI and GL. Several factors explain the large inter- and intra-individual variation in glycemic response to foods. More reliable measurements of GI and GL of individual foods than are currently available can be obtained by studying, under standard conditions, a larger number of subjects than has typically been the case in the past. Meta-analyses suggest that foods with a low GI or GL may confer benefit in terms of glycemic control in diabetes and lipid management. However, low GI and GL foods can be energy dense and contain substantial amounts of sugars or undesirable fats that contribute to a diminished glycemic response. Therefore, functionality in terms of a low glycemic response alone does not necessarily justify a health claim. Most studies, which have demonstrated health benefits of low GI or GL involved naturally occurring and minimally processed carbohydrate containing cereals, vegetables and fruit. These foods have qualities other than their immediate impact on postprandial glycemia as a basis to recommend their consumption. When the GI or GL concepts are used to guide food choice, this should be done in the context of other nutritional indicators and when values have been reliably measured in a large group of individuals.

Full text

REVIEW
Glycemic index and glycemic load: measurement
issues and their effect on diet–disease relationships
BJ Venn and TJ Green
Department of Human Nutrition, University of Otago, Dunedin, New Zealand
Glycemic index (GI) describes the blood glucose response after consumption of a carbohydrate containing test food relative to a
carbohydrate containing reference food, typically glucose or white bread. GI was originally designed for people with diabetes as
a guide to food selection, advice being given to select foods with a low GI. The amount of food consumed is a major
determinant of postprandial hyperglycemia, and the concept of glycemic load (GL) takes account of the GI of a food and the
amount eaten. More recent recommendations regarding the potential of low GI and GL diets to reduce the risk of chronic
diseases and to treat conditions other than diabetes, should be interpreted in the light of the individual variation in blood
glucose levels and other methodological issues relating to measurement of GI and GL. Several factors explain the large inter- and
intra-individual variation in glycemic response to foods. More reliable measurements of GI and GL of individual foods than are
currently available can be obtained by studying, under standard conditions, a larger number of subjects than has typically been
the case in the past. Meta-analyses suggest that foods with a low GI or GL may confer benefit in terms of glycemic control in
diabetes and lipid management. However, low GI and GL foods can be energy dense and contain substantial amounts of sugars
or undesirable fats that contribute to a diminished glycemic response. Therefore, functionality in terms of a low glycemic
response alone does not necessarily justify a health claim. Most studies, which have demonstrated health benefits of low GI or GL
involved naturally occurring and minimally processed carbohydrate containing cereals, vegetables and fruit. These foods have
qualities other than their immediate impact on postprandial glycemia as a basis to recommend their consumption. When the GI
or GL concepts are used to guide food choice, this should be done in the context of other nutritional indicators and when values
have been reliably measured in a large group of individuals.
European Journal of Clinical Nutrition (2007) 61 (Suppl 1), S122–S131; doi:10.1038/sj.ejcn.1602942
Keywords: glycemic index; glycemic load; methodology; diet–disease relationships
Introduction
The glycemic index (GI) concept was introduced by Jenkins
et al. (1981) in the early 1980s as a ranking system for
carbohydrates based on their immediate impact on blood
glucose levels. GI was originally designed for people with
diabetes as a guide to food selection, advice being given to
select foods with a low GI (Jenkins et al., 1983). Lower GI
foods were considered to confer benefit as a result of the
relatively low glycemic response following ingestion com-
pared with high GI foods. The GI concept has been extended
to also take into account the effect of the total amount of
carbohydrate consumed. Thus glycemic load (GL), a product
of GI and quantity of carbohydrate eaten provides an
indication of glucose available for energy or storage follow-
ing a carbohydrate containing meal. Although GI is usually
tested on individual foods, there are methods described
whereby the GI and GL of meals and habitual diets can be
estimated (Wolever and Jenkins, 1986; Salmeron et al.,
1997a). In addition to a role in the treatment of diabetes,
low GI and GL diets have more recently been widely
recommended for the prevention of chronic diseases includ-
ing diabetes, obesity, cancer and heart disease and in the
treatment of cardiovascular risk factors, especially dyslipi-
daemia (Jenkins et al., 2002).
The usefulness of GI and GL has been questioned on
several counts: failure to consider the insulin response
(Coulston et al., 1984), the high intra- and inter-subject
variation in glucose response to a food (Pi-Sunyer, 2002), and
a loss of discriminating power when foods are combined in a
mixed meal (Flint et al., 2004). Furthermore, foods with a
high sugar (sucrose) content and those containing both
Correspondence: Dr B Venn, PO Box 56, Department of Human Nutrition,
University of Otago, Dunedin, New Zealand.
E-mail: bernard.venn@stonebow.otago.ac.nz
European Journal of Clinical Nutrition (2007) 61 (Suppl 1), S122–S131
&
2007 Nature Publishing Group All rights reserved 0954-3007/07 $
30.00
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carbohydrate and fat may have a low GI, but may not be
regarded as particularly appropriate choices because of their
energy density and nature of dietary fat (Freeman, 2005).
This review considers the reliability of the measurement and
the practical application of GI. Its value in relating dietary
attributes to chronic diseases is considered in other papers in
this series.
Definition and measurement
GI is defined as the blood glucose response measured as area
under the curve (AUC) in response to a test food consumed
by an individual under standard conditions expressed as a
percentage of the AUC following consumption of a reference
food consumed by the same person on a different day (FAO/
WHO, 1998). The test food and reference food (usually 50 g
glucose) must contain the same amount of available
carbohydrate (Figure 1). It is important to standardize GI
testing conditions, and the procedure for the measurement
of GI is described in detail in the 1998 FAO/WHO report on
carbohydrates in human nutrition (FAO, 1998). Hundreds of
foods have been tested for GI with the aim of ranking foods
within and between food categories. A GI classification
system is in common use in which foods are categorized as
having low (o55), medium (55–69) or high GI (470) (Brand-
Miller et al., 2003a).
Glucose, a monosaccharide, induces a large glycemic
response and is often used as the reference food and assigned
a GI of 100. Some polysaccharides, such as those present in
instant potato for example, may also result in large glycemic
responses when consumed in an amount containing 50 g
available carbohydrate because of rapid and near complete
digestion and absorption in the small intestine. From the
International Tables, the GI for instant potato, determined as
the mean of six studies, was 85 (Foster-Powell et al., 2002).
Sucrose, a disaccharide of glucose and fructose, has a
somewhat lower GI of 68, resulting from the fructose
component which has an exceptionally low GI of 19. Adding
protein or fat to a carbohydrate containing food can also
lower overall GI (Miller et al., 2006). Resistant starch and
dietary fibre are largely undigested and not absorbed in the
small intestine and therefore contribute little to postprandial
glycemia. However, a lowering of glycemic response has
been found when purified extracts of fibre, particularly of
the type that forms a viscous gel in water such as guar gum,
are added to a test food in sufficient quantity (Jenkins et al.,
1976; Doi et al., 1979; Wolever et al., 1991; Tappy et al.,
1996). GI cannot be predicted from the fibre content of a
carbohydrate containing food or from the terms wholemeal
and wholegrain for which there are no universally accepted
definitions. For example, from the International Tables, the
mean GI of wholemeal bread from 13 studies is 71, while
that of white wheat bread (mean of six studies) is 70 (Foster-
Powell et al., 2002). Whole grains, when largely intact, have
been found to lower GI (Jenkins et al., 1986; 1988; Liljeberg
et al., 1992; Granfeldt et al., 1994; 1995), but wholegrain
products contain a variable proportion of intact grains.
GI does not take into account the amount of carbohydrate
consumed, an important determinant of glycemic response.
For example, watermelon has a high GI (Foster-Powell et al.,
2002) and may not be considered a good food selection as
0
1
2
3
4
5
6
7
8
0 306090120
Time (min)
Blood glucose (mmol/L)
0
2
4
6
8
10
0 30 60 90 120
Time (min)
Blood glucose (mmol/L)
0
2
4
6
8
10
0 30609012
0
Time (min)
Blood glucose (mmol/L)
Figure 1 Example of an individual’s data used to estimate glycemic index (GI). Area under the curve (AUC) refers to the area included between
the baseline and incremental blood glucose points when connected by straight lines. The area under each incremental glucose curve is
calculated using the trapezoid rule (note: only areas above the baseline are used). GI ¼ AUC
Food
/mean (AUC
Reference
)  100.
Glycemic index and glycemic load
BJ Venn and TJ Green
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European Journal of Clinical Nutrition

part of a low GI diet. However, watermelon only contains 5 g
of carbohydrate per 100 g, thus it would have a minimal
glycemic effect. GL takes into account how much carbohy-
drate a serving of a food contains and may be determined by
indirect and direct methods.
The indirect method involves multiplying the GI of a food
by the amount of available carbohydrate in the portion of
food consumed. This method implies that GL is directly
proportional to the amount of the particular food eaten. This
is perhaps counterintuitive, because the blood glucose AUC
does not increase in direct proportion to the amount
consumed. For example, eating six times the amount of
bread results in an approximately threefold increase in AUC
(Brand-Miller et al., 2003c). In other words, as the amount of
food increased, the rate of increase in AUC declines, an effect
shown in Figure 2 (Venn et al., 2006). Therefore, it is implicit
in the calculation of GL that the AUC for both the test and
the reference foods are attenuated to the same degree with
increasing amounts consumed.
Glycemic equivalence is a method of directly determining
GL. For each subject an AUC for glucose is calculated for a
range of doses of the reference food measured on different
days. A standard curve is constructed for each subject with
increasing amounts of the reference on the x axis with its
corresponding AUC for blood glucose on the y axis (Venn
et al., 2006). The AUC in response to a food consumed at any
portion size, typically a usual serving, is compared to that
individual’s glucose standard curve as depicted in Figure 3
(Venn et al., 2006). Using this technique, glycemic equiva-
lence is the amount of glucose that would theoretically
produce the same blood glucose AUC as that particular
portion size of food consumed. Major drawbacks of the direct
method are increased time and cost required to determine
the GL of a food. The reference must be tested at several
doses in each subject and the GL of a food cannot be
estimated from currently available GI values. Data from our
laboratory support the premise that GL is linearly related to
the amount of food consumed that is, GL calculated using
GI  available carbohydrate agrees well with GL measured
directly, at least when food is consumed over a range of usual
intakes (Venn et al., 2006). A GL classification system is used
in which foods are categorized as having low (p10), medium
(410–o20) or high GL (X20).
The relationship between GI and GL is not straightfor-
ward; for example, a high GI food can have a low GL if eaten
in small quantities. Conversely, a low GI food can have a
high GL dependent upon the portion size eaten. This effect is
demonstrated in Table 1, in which various foods from the
International Tables have been selected (Foster-Powell et al.,
2002). A ‘serving size’ of watermelon, a high GI food, has the
same GL as a serving size of high fat ice cream, a low GI food.
Mashed potato and macaroni may be contrasted with the
lower GI food (macaroni) having a higher GL per serving.
0
50
100
150
200
250
300
350
0 12.5 25 37.5 50 62.5 75
Available carbohydrate (g)
Blood glucose iAUC (mmol/L·min)
Figure 2 Blood glucose area under the curve (AUC) responses to
increasing amounts of glucose and granola bar tested in 20 people.
0
25
50
75
100
125
150
175
0 12.5 25 37.5 50 62.5 75
Glycaemic load (g)
Blood glucose iAUC (mmol/L·min)
GL direct measure
iAUC food
Figure 3 Example of an individual’s standard glucose curve
generated using glucose doses of 12.5, 25, 50 and 75 g. A test food
is consumed and the resulting area under the curve (AUC) used to
impute the glycemic load. AUC refers to the area included between
the baseline and incremental blood glucose points when connected
by straight lines. The area under each incremental glucose curve is
calculated using the trapezoid rule (Note: only areas above the
baseline are used.)
Table 1 Examples of GL arranged by classification taken from the
international tables (Foster-Powell et al., 2002)
Food GI Serving
size (g)
Available
carbohydrate (g)
GL
Watermelon 72 120 6 4
Ice cream (high fat) 37 50 9 4
Mashed potato 74 150 20 15
Macaroni 47 180 48 23
Parboiled rice 64 150 36 23
Chocolate bar 65 60 40 26
Porridge 58 250 22 13
Corn flakes 81 30 26 21
Abbreviations: GI, glycaemic index; GL, glycaemic load.
Glycemic index and glycemic load
BJ Venn and TJ Green
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European Journal of Clinical Nutrition

Foods having very different nutrient profiles can have
similar GIs and GLs per serving, such as parboiled rice and
a chocolate bar. GI and GL can also be positively related to
each other, for example comparing porridge and corn flakes,
in which the higher GI food (corn flakes) predicts a higher
GL per serving. Although a food is assigned a fixed GI value,
any food could have a low, medium or high GL because GL is
dependent upon the amount eaten.
The glycemic load of a diet can be calculated by summing
the glycemic loads for all foods consumed in the diet. A low
GL diet could be achieved by choosing small servings of
foods relatively high in carbohydrate having a low GI.
Alternatively, a low GL diet could comprise foods having a
high fat, high protein, low carbohydrate content. The
heterogeneity of foods that could be used to construct a
low GL diet indicates that food selection should not be made
on GL alone. Knowledge of other qualities of the food, for
example fat content, type of fat, energy density, fibre
content and appropriate serving size should be taken into
consideration.
GI and GL labelling
Voluntary GI labelling of foods by food manufacturers occurs
in several countries. Products may need to meet nutritional
compositional requirements to be eligible for GI testing and
labelling, such as a limit on the type or amount of fat
contained in the food. However, compositional require-
ments are not standardized either within a country, where
more than one laboratory may provide a GI-testing service,
or among countries around the world. Standardized elig-
ibility criteria would give consumers, health professionals
and regulators more confidence in the suitability of a food to
display its GI. It could be argued that GL should be labeled
because GL more closely reflects the glycaemic impact
associated with consuming an amount of the food.
Factors affecting the measurement
Postprandial glucose concentrations are dependent upon
several factors. In people with impaired glucose tolerance
and diabetes the glycemic response measured as blood
glucose AUC is increased compared with healthy individuals.
However, GI is the AUC in response to a test food relative to
that of a reference food and given that each person acts as
his/her own control the GI of a food should not differ in
those with and without abnormalities of glucose metabo-
lism. GI testing has been carried out, and values published in
international tables, using normoglycaemic individuals as
well as those with impaired glucose tolerance (Foster-Powell
et al., 2002). Despite broadly comparable results Brouns et al.
(2005) have recommended using people with normal glucose
tolerance for the determination of GI because variability in
glycemic response is greater in people with impaired glucose
tolerance or diabetes.
The use of a test food referenced to a standard could be
used as an argument that GI is a property of food, rather than
a characteristic of the individual consuming the food.
However, postprandial glycemia may be influenced by the
extent to which individuals chew food prior to swallowing
(Read et al., 1986; Suzuki et al., 2005) as well as the expected
biological variation in rates and extent of digestion and
absorption. These variables may not apply equally to test and
reference foods; a reference food commonly used is a glucose
beverage. The observed intra- and inter-individual differ-
ences in GI and GL which are apparent even when measured
under standardized conditions may be further exaggerated
by differing physical and chemical nature of apparently
similar food products (Wolever, 1990). For example, gelati-
nization, the process of rendering starches water soluble;
retrogradation, a realignment of starch molecules during
cooling and storage; starch type; and dietary fibre, are all
factors with potential glycemic-modifying effects. Some of
these factors are affected by cooking times and methods, and
the temperature of the food consumed, potentially provid-
ing a source of variability both in GI measurement and in
day-to-day variability of glycemic responses to the same
food.
Reliability of GI values for individual foods
The 1998 Joint FAO/WHO Expert Consultation on carbo-
hydrates suggested that for the determination of the GI of a
food, six subjects would be required; although the basis for
this number was not given (FAO, 1998). More recently, it has
been recommended that a sample of 10 should be used, on
the grounds that it allows for a ‘reasonable degree of power
and precision for most purposes’, although it was acknowl-
edged that more people would be necessary if greater
precision was required (Brouns et al., 2005). However, there
are strong indications that using 10 people is insufficient to
obtain reliable estimates of GI, particularly if GI levels are
high, because variance increases with the mean. Large
variation in glycemic response between- and within-people
makes it difficult to show differences among foods. For
example, Henry et al. (2005) tested eight varieties of potato
in groups of 10 people and reported mean7s.e.m. GIs
ranging from 5673to94716. Despite a wide range of GIs, it
was not possible to demonstrate statistically significant
differences among the potato varieties. Because of the large
variation there is the potential to miss-classify foods into
categories of low, medium, or high GI.
In an inter-laboratory study, seven laboratories tested the
GIs of centrally provided foods, each using 8–12 participants
(Wolever et al., 2003). A range in mean GI values among
laboratories was obtained for each of the test foods; potato
65.2744.6–98.5720.6; bread (locally sourced) 64.2715.4–
78.9726.1; rice 54.8724.1–85.0728.6; spaghetti 36.4735.8
–69.9718.8; and barley 23.2724.6–47.1749.7. Rice would
have been classified as low GI (54.8724.1) by one laboratory,
Glycemic index and glycemic load
BJ Venn and TJ Green
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European Journal of Clinical Nutrition

medium GI (62.6725.0, 63.378.1, 68.4748.0) by three
laboratories, and high GI (76.9712.9, 85.0728.6,
87.0775.9) by the other three laboratories. It appeared that
results were more consistent when GI was calculated using
capillary blood obtained by finger prick rather than venous
blood. A much better estimate was obtained when data from
the laboratories using capillary blood were combined. Using
a pooled sample of 47 participants, mean GI for rice was 69
with narrower confidence intervals (95% CI: 63, 76). Thus, in
addition to indicating the most appropriate method for
blood sampling, the data demonstrate the enhanced relia-
bility of a measurement when studying large numbers of
individuals. Most of the variation in GI differences between
laboratories was attributed to random within-person varia-
bility (Wolever et al., 2003). There is no ready explanation for
this random day-to-day variability in glycemic response that
occurs even to repeat challenges of the same food under
standardized conditions.
Within-person variability can be reduced to some extent
by increasing the number of replicates for each subject. The
current recommendation is that the reference food should be
tested two or three times in each subject (Brouns et al., 2005).
However, within-person variability is also present for the test
food. Using data obtained from our laboratory in which a
test food and a reference food were tested three times and
four times, respectively, in 20 people, we have calculated
sample sizes necessary to be confident of a difference of 10
GI units between foods. The sample size is dependent on the
level of GI. For a difference of 10 units, between 30 and 40
for instance, it was estimated that 25 people would be
required if the food was tested once and the reference food
three times, or 19 people if the test and reference foods were
both tested twice. These estimates are comparable with the
sample size estimates shown by Brouns et al. (2005). The
same likelihood of detecting differences of 10 GI units
between foods having GIs toward the upper end of the scale
(70 and 80), would require a sample size of 114 people
testing the food once and the reference food three times, or
86 people if the test and reference foods are both tested
twice. Increasing the sample and/or repeating the test food
would appreciably increase the cost of testing, perhaps a
necessary expense, if more precision in GI measurement is to
be achieved.
Mixed meals
The ranking of meals by GI has been found to reflect the
ranking of the major carbohydrate component in the meal.
For example, baked potato was found to have a higher GI
than rice (Jenkins et al., 1984). When these foods were
incorporated into meals, there was a tendency for the
postprandial glycemic ranking of the meals to be maintained
according to the GI ranking of the food that provided the
major carbohydrate source (Wolever and Jenkins, 1986).
However, there is debate as to whether summing the
individual GIs of foods in a meal can be used to reliably
calculate the GI of the meal. Flint et al. (2004) used GI values
taken from the International Table of GI (Foster-Powell et al.,
2002) to predict the GI of 13 simple breakfast meals, each
providing 50 g available carbohydrate. There was no associa-
tion between the GI calculated from the Tables and the
measured GI. It was pointed out that the energy content of
the meals had not been standardized (Brand-Miller and
Wolever, 2005); however, it might be argued that GI testing
is standardized to an available amount of carbohydrate, not
to energy content. In another study, a closer relationship was
reported between calculated GI and glycemic responses to
various breakfast meals, but the agreement was not entirely
consistent (Wolever et al., 2006). Two of the meals, a bagel
with cream cheese and orange juice; and a meal of rye bread,
margarine, cereal, milk, sugar and orange juice each
contained approximately 69 g available carbohydrate. The
mean7s.e. glycemic responses to the meals, measured as
AUC, were similar (148714 and 143713 mmol/l min, re-
spectively). However, using published values the calculated
GIs of the meals were predicted to be 67 and 51, respectively.
Sugiyama et al. (2003) found that the ingestion of milk with
rice resulted in a significantly lower GI than when rice was
eaten alone. When cheese was added to potato, a dramatic
lowering of the potato’s mean7s.e.m. GI from 9378to
3975 was found (Henry et al., 2006). Thus while it is clear
that combining foods does influence GI and that the
addition of protein and fat to a carbohydrate containing
meal can appreciably reduce the glycemic response (Collier
and O’Dea, 1983; Nuttall et al., 1984) there is insufficient
information to accurately predict the effect of different
combinations of foods. Aggregating the GIs of individual
components of a meal does not reliably predict the observed
GI of the meal as a whole.
Published glycemic index and glycemic load values
Care must be taken when using published GI and GL values.
Variability of GI and GL among apparently similar foods has
led to recommendations that some foods, for example rice,
should be tested in the geographical region in which they are
consumed. This may be necessary to account for differences
in variety and cooking conditions (Foster-Powell et al., 2002).
Testing in specific populations may also be important if GI is
not solely a property of food. Mettler et al. (2007) found that
the training state of athletes affected GI of the same food. It
was suggested that Flint et al. (2004) who reported that
combining the GI of single foods did not predict the GI of
mixed meals, chose incorrect GI values from published tables
(Wolever et al., 2006). This problem is likely to occur when
multiple entries for the same food are presented. For
example, in the International Tables baked Russet Burbank
potatoes eaten without added fat (that is butter) are listed as
having GI values of 56, 78, 94 and 111 (Foster-Powell et al.,
2002). Multiple entries for the same foods also complicate
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BJ Venn and TJ Green
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European Journal of Clinical Nutrition

research activities and food selection for individuals trying to
follow a low GI diet. For example, boiled carrots have mean
GI values listed in the International Tables of 92, 49 and 32
(Foster-Powell et al., 2002). If GI were a major criterion in
food selection, a value of 92 might have discouraged people
from eating boiled carrots. On the other hand, low GI values
of 32 and 49 would have suggested boiled carrots as a highly
suitable choice. It has been suggested that reliability of the
estimates might have contributed to the differences in GI
(Foster-Powell et al., 2002), an argument in favour of
studying large numbers of individuals under the standar-
dized conditions described above.
Dietary instruments
Questions have been raised regarding the appropriateness of
the dietary instruments used when examining the relation-
ship between GI and GL and various diseases in observa-
tional studies (Pi-Sunyer, 2002). Food frequency
questionnaires used in several studies were not designed
specifically to obtain information on GI and GL. Individual
foods were not assigned GI or GL values, rather they were
collapsed into categories. Most of the published studies have
not described how foods were grouped, but an Australian
study gave some insight into the process (Hodge et al., 2004).
The food frequency questionnaire used in the Australian
study had a category of ‘cereal foods, cakes and biscuits’.
Within that category were 17 items, two of which were
‘muesli’ and ‘other breakfast cereals’. A GI value of 46% was
assigned to muesli and 62% to ‘other breakfast cereals.’ The
use of a single value to describe the GI of breakfast cereals
seemed inappropriate given that the GIs of Australian cereals
range from 30 (All-Bran) to 85 (Rice Bubbles) (Foster-Powell
et al., 2002). Pi-Sunyer has drawn attention to the even
broader groupings that were used in the Health Profes-
sionals’ Follow-up Study and the Nurses’ Health Study.
Categories used were—all whole grains; all refined grains;
all cold breakfast cereals; all fruit; and all fruit juices (Pi-
Sunyer, 2002). Such observations do raise some concerns
about the degree of confidence that can be placed on the
findings of these cohort studies with respect to dietary GI or
GL and disease outcome. On the other hand one might argue
that misclassification leads to an underestimate of the true
association between GI and GL and disease (see paper by
Mann in this series). The use of dietary questionnaires
specifically designed to gather information on GI and GL,
such as those currently being developed, may be expected to
generate data which can be interpreted with greater
confidence (Flood et al., 2006; Neuhouser et al., 2006).
Overall comment on reliability
Biological variation, differing chemical and physical struc-
ture of apparently similar foods and method of food
preparation and consumption may all contribute to the
marked inter- and intra-individual variation observation in
the glycemic response to foods. GI and GL testing on larger
numbers of individuals than previously undertaken or
increasing the number of replicates carried out on an
individual will improve the reliability and precision of GI
estimates. Specifying origin and other details of product (for
example, variety of fruit or vegetable) will further enhance
confidence in the measurement. However, the usefulness of
the index will always be limited to some extent by the
variation between and within individuals. It is common
practice to place foods into broad categories of GI. Classifica-
tion works well when there is a large separation in GI values
between foods, but there are still uncertainties into which
category of GI many foods belong because of the variability
around group mean GI values. More certainty in the relative
ranking of foods by GI would be attained if larger group sizes
were used to estimate GI. The degree to which limitations of
currently available data influence present use of the concepts
of GI and GL in understanding cause or influencing
management of disease is considered further in the section
on recommendations.
Glycemic index and glycemic load and human
health
The GI/GL concept has been widely advocated as a means of
identifying foods that might protect against chronic diseases
or be useful in disease management. Potential protection
against diabetes and cardiovascular disease is considered in
the paper by Mann (2007). Several other health issues are
discussed here.
Long-term glycemic control in diabetes
The GI concept was used in the management of diabetes
before being used in other clinical situations. Glycated
haemoglobin (HbA
1C
) is measured in people with diabetes
to assess overall glycemic control over a period of approxi-
mately 2–3 months prior to the measurement being made
(Goldstein et al., 2004). Fructosamine, another glycated
protein, is also occasionally used as a measure of glucose
control over the preceding 2–3 weeks. Several studies have
been carried out in people with diabetes to examine the
effect on HbA
1C
or fructosamine of diets differing principally
with respect to GI. Data from these studies form the basis of
two meta-analyses. There was a modest reduction in HbA
1C
in people consuming low GI diets, estimated to be 0.33%
units (95% CI: 0.07, 0.59) in one meta-analysis (Brand-Miller
et al., 2003b), and 0.27% units (95% CI: 0.03, 0.5) in the
other (Opperman et al., 2004). Fructosamine concentrations
were also lower in favour of low GI dietary interventions. In
one meta-analysis, the estimated difference between low and
high GI dietary periods was 0.19 mmol/l (95% CI: 0.06, 0.32)
Glycemic index and glycemic load
BJ Venn and TJ Green
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European Journal of Clinical Nutrition

(Brand-Miller et al., 2003b), and in the other 0.1 mmol/l
(95% CI: 0.00, 0.20) (Opperman et al., 2004). Although these
reductions in HbA
1C
or fructosamine are small it is important
to note that these effects are in addition to other dietary
changes or pharmacological treatments used in diabetes
management. Whether changes in glycated proteins of this
magnitude affect long-term health outcomes is untested.
Trials using drugs such as acarbose, which lower postprandial
hyperglycaemia, suggest that acarbose may be effective in
reducing cardiovascular complications in people with type 2
diabetes mellitus (Hanefeld et al., 2004). However, reductions
in HbA
1C
of 0.6–0.8% were achieved in these trials (Hanefeld
et al., 2004; van de Laar et al., 2005). Nevertheless, any
dietary strategy that resulted in improved glycaemic control
would be welcome and given the difference in the acute
effect that low and high GI foods have on postprandial
hyperglycaemia, the proposition that changing foods in the
diet from high to low GI might improve markers of glycemic
control is entirely plausible. However, some caveats may be
appropriate. In many of the studies included in the meta-
analyses described above, the low GI foods tended to have
low energy density and a high fibre content, such as whole
fruit, oats, whole grain, pulses and pasta (Frost et al., 1998;
Heilbronn et al., 2002). Thus, modest changes in glycaemic
control were achieved under study conditions that required
people to be compliant with relatively major changes in
dietary habits. The findings may not necessarily apply to the
many low GI functional and convenience foods currently
available, which may be relatively high in sugars and energy
dense.
Glycemic index and glycemic load and blood lipids
Relationships between dietary GI and blood lipid fractions
have been assessed in several prospective observational
studies. A reasonably consistent finding has been an inverse
association between fasting HDL cholesterol concentrations
and dietary GI (Liu et al., 2001; Amano et al., 2004; Slyper
et al., 2005), although one study found no association
(Murakami et al., 2006). Ma et al. (2006) found inverse
associations between dietary GI and GL in a cross-sectional
analysis, but the associations were lost during follow-up. An
inverse association between GI and HDL-cholesterol con-
centration has also been found in a nationally representative
sample of US adults (Ford and Liu, 2001).
Findings from intervention trials differed from those of
observational studies. Kelly et al. (2004) conducted a meta-
analysis of intervention trials that had examined the effect of
low GI diets on coronary heart disease risk factors. Results
from that analysis showed limited and weak evidence of an
inverse relationship between GI and total cholesterol, with
no effect of dietary GI on LDL and HDL cholesterol,
triglycerides, fasting glucose and fasting insulin. Opperman
et al. (2004) conducted a meta-analysis of 14 randomized
controlled trials relating to the effects on blood lipids of
altering the GI of test diets. There was a difference in total
and LDL-cholesterol concentrations of 0.33 (95% CI: 0.18,
0.47) mmol/l and 0.15 (95% CI: 0.00, 0.31) mmol/l, favoring
the low GI diets, but no difference in HDL cholesterol
concentrations between people consuming low and high GI
diets.
Thus, the reasonably consistent finding in observational
studies of an inverse association between dietary GI and HDL
cholesterol concentrations is not confirmed by meta-ana-
lyses of randomized controlled trials in which people
consumed diets that had been designed specifically to
achieve differences in GI (Kelly et al., 2004; Opperman
et al., 2004). On the other hand, the meta-analyses show
differences in total and LDL cholesterol not found in the
observational data. There is no obvious explanation for this
inconsistency.
Glycemic index and glycemic load and insulin
response
The GI of a food is affected not only by the rate of absorption
of carbohydrate, but also by the rate of glucose removal from
the plasma. When comparing two breakfast cereals with
different GI values (131733 and 54.577.2), the rate of
glucose removal was a major determinant of postprandial
hyperglycaemia (Schenk et al., 2003). It was found that the
lower GI breakfast cereal had induced hyperinsulinaemia
earlier than the higher GI cereal, resulting in an earlier
increase in more rapid removal of glucose from circulation. It
has been known for some time that insulin response cannot
be predicted based solely on the glycemic response to a food.
Collier and O’Dea (1983) found marked differences in the
glycemic response to potato with or without added butter,
but a very similar insulin response. The effect of GI on
insulin response may also depend upon insulin sensitivity.
Dietary GI has not been shown to have a marked effect on
insulin sensitivity whereas dietary fibre has (McAuley and
Mann, 2006).
Glycemic index and satiety
An important justification for the claim of an overall health
benefit of low GI foods is that low GI foods may aid weight
control because they promote satiety (Brand-Miller et al.,
2002). Ideally, weight loss studies comparing low and high
GI diets would need to assess differences between diets based
on ad libitum intake to show that the apparently greater
satiating effect of low GI foods led to a reduced energy
intake. Holt and colleagues have carried out the most
comprehensive study investigating the relationship between
GI and satiety, reporting the same work in several articles
(Holt et al., 1995; Holt et al., 1996; Holt et al., 1997). Iso-
energetic (1000 kJ) servings of 38 foods were tested for satiety
rating and glucose and insulin response. The food with the
Glycemic index and glycemic load
BJ Venn and TJ Green
S128
European Journal of Clinical Nutrition

highest satiety score was boiled potato. When comparing a
high GI food (potato) with a low GI food (white pasta) on an
iso-energetic, equi-carbohydrate (49 g) basis, the high GI
food had the highest satiety rating. The opposite was true
when comparing oranges (lower GI) and white bread (higher
GI), where the lower GI food had the higher satiety rating.
Porridge and natural muesli had similar glycemic and
insulinemic scores, but porridge had a greater satiety index
than muesli (Po0.001). These results suggest that there is
little or no relationship between GI and satiety, at least when
comparing food portions of equal energy content. Rather,
energy density appeared to be inversely related to satiety,
presumably because of the high bulk required to obtain a
serving containing 1000 kJ when low energy-dense foods
were tested. When iso-energetic, iso-volumetric carbohy-
drate-containing beverages were tested, high GI beverages
resulted in lower energy intakes during a subsequent meal,
while low GI beverages were found not to suppress appetite
and food intake in the short-term (Anderson et al., 2002). A
review of the effect of glycemic carbohydrates on short-term
satiety has been published (Anderson and Woodend, 2003).
One conclusion was that high GI carbohydrates suppress
short-term (1 h) food intake more effectively than low GI
carbohydrates, whereas low GI carbohydrates appeared to be
more effective over longer periods (6 h).
How dietary GI and GL affects satiety and food intake over
a number of years is not entirely clear. The effectiveness of
dietary GI and GL on weight loss or maintenance is covered
by van Damm in this series. The results of several observa-
tional studies have shown little difference in body mass
index (BMI) across categories of GI and GL (Salmeron et al.,
1997a, b; Hodge et al., 2004; Schulze et al., 2004). Murakami
et al. (2006) found a positive association between GI and
body mass index in Japanese female farmers, but no
association between GL and body mass index. On the other
hand, Ford and Liu reported inverse associations between GI
and GL and body mass index in a nationally representative
sample of US adults (Ford and Liu, 2001). These contra-
dictory findings might suggest that dietary GI and GL is not
a major determinant of dietary energy intake over the long-
term. A plausible reason is that GI appears not to be related
to energy density. Potatoes and lentils for example represent
foods with widely differing GIs but comparable energy
densities of around 3–4 kJ/g. On the other hand, cakes,
cookies and fresh oranges have similar GIs in the low to
medium range, but energy densities some 10-fold different
(Holt et al., 1996).
Recommendations
The FAO/WHO Report on Carbohydrates in Human Nutri-
tion suggests that the concept of GI provides a useful means
of selecting the most appropriate carbohydrate containing
foods for the maintenance of health and the treatment of
several disease states (FAO, 1998). Since the publication of
that report some of the limitations of the GI and GL concepts
have become increasingly apparent. With regard to measure-
ment there is clearly a need to study a larger number of
subjects under standard conditions to obtain more precise
estimates of the GI and GL of individual foods. The
introduction of instruments for assessing dietary intake in
epidemiological studies that have been designed to include
more direct measures of GI and GL will enhance the
confidence in findings from such studies. Despite these
reservations it does appear that distinguishing between foods
with appreciable differences in the indices may produce
some benefit in terms of glycemic control in diabetes and
lipid management. However, caution should be exercised in
food choice based solely on GI or GL because low GI and GL
foods may be energy dense and contain substantial amounts
of sugars or undesirable fatty acids that contribute to the
diminished glycemic response but not necessarily to good
health outcomes. This may apply especially to some of the
manufactured products that have been GI and GL tested and
are available in many countries. Given that most of the
studies which have demonstrated a health benefit of low GI
and GL involved the use of naturally occurring and
minimally processed foods it would seem to be appropriate
for such products to be further tested for their health benefits
directly, rather than on the basis of their functionality (that
is, a low glycemic response). Although some data suggest
that the low GI effect is not explained by the dietary fibre
content of the foods it remains conceivable that food
structure or composition explain some of the health
benefits. GI may be a useful indicator to guide food choice
if for example bread with a high GI is replaced on a slice-for-
slice basis with a lower GI bread, thereby achieving a lower
GL. However, the complexity of the relationship between GI
and GL is probably not well understood whereby GI and the
amount of a food eaten are both important determinants of
the postprandial glycemic response. For the present it would
seem appropriate that when GI or GL are used to guide food
choice, it should only be done in the context of other
nutritional indicators and when values have been measured
in a large group of individuals.
Acknowledgements
We wish to thank Dr Jennie Brand-Miller, Professor Gary
Frost, Professor Philip James, Professor Simin Liu, Professor
Jim Mann, Dr Gabriele Riccardi, Dr M Robertson and
Professor HH Vorster for their valuable comments.
Conflict of interest
During the preparation and peer-review of this paper in
2006, the authors and peer-reviewers declared the following
interests.
Authors
Dr Bernard J Venn: None declared.
Glycemic index and glycemic load
BJ Venn and TJ Green
S129
European Journal of Clinical Nutrition

Dr Tim Green: Affiliated with GI Otago, a commercial
glycemic index testing service.
Peer-reviewers
Dr Jennie Brand-Miller: Publishing books in the popular
press: ‘The New Glucose Revolution Series’; Director of a
University-based service for GI testing; Director of a not-for-
profit food-labelling programme based on the GI.
Professor Gary Frost: None declared.
Professor Philip James: None declared.
Professor Simin Liu: None declared.
Professor Jim Mann: None declared.
Dr Gabriele Riccardi: None declared.
Dr M Robertson: Research Grant from National Chemical
and Starch.
Professor HH Vorster: Member and Director of the Africa
Unit for Transdisciplinary health Research (AUTHeR), Re-
search grant from the South African Sugar Association.
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