Jurnal Reading-glycemic Response

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Glycemic response and healtha systematic review andmeta-analysis: the database, study characteristics, andmacronutrient intakes15

Geoffrey Livesey, Richard Taylor, Toine Hulshof, and John Howlett

ABSTRACT

Background: Reduction of dietary glycemic response has been

proposed as a means of reducing the risk of diabetes and coronary

heart disease. Itsrole in healthmaintenance andmanagement,along-

side unavailable carbohydrate (eg, fiber), is incompletely under-

stood.

Objective: We aimed to assess the evidence relating the glycemic

impact of foods to a role in health maintenance and management of

disease.

Design: We searched the literature for relevant controlled dietary

interventiontrialson glycemic index (GI) accordingto inclusionand

exclusion criteria, extracted the data to a database, and synthesized

the evidence via meta-analyses and meta-regression models.

Results: Among literatureto January 2005, 45 relevant publications

were identified involving972 subjects withgoodhealth or metabolic

disease. With small reductions in GI (10 units), increases in avail-

able carbohydrate, energy, and protein intakes were found in all

studies combined. Falling trends in energy, available carbohydrate,

and protein intakes then occurred with progressive reductions in GI.

Fat intake was essentially unchanged. Unavailable carbohydrate

intake was generally higher for intervention diets but showed notrend with GI (falling or rising). Among studies reporting on GI,

variation in glycemic load was approximately equally explained by

variation in GI and variation in available carbohydrate intake. An

exchange of available and unavailable carbohydrate (1 g/g) was

evident in these studies.

Conclusions: Among GI studies, observed reductions in glycemic

load are most often not solely due to substitution of high for low

glycemic carbohydrate foods. Available carbohydrate intake is a

confounding factor. The role of unavailable carbohydrate remains to

be accounted for. Am J Clin Nutr 2008;87(suppl):223S36S.

KEYWORDS Carbohydrate, glycemic response, glycemic in-

dex, glycemic load, meta-analysis, systematic review

INTRODUCTION

Reduction of the glycemic response to foods, via either re-

duced glycemic index (GI) (1)or reduced glycemic load (GL) (2)

has been proposed as a dietary means to help to combat diabetes

mellitus and possibly coronary heart disease (CHD) (3, 4). Ex-

cessive glycemic response to carbohydrate foods and low un-

available carbohydrate intake are also implicated in stroke (5)

and certain forms of cancer, in particular colorectal cancer, in

some groups (6, 7).

Among early scientific enquiry is that showing that unavail-

able carbohydrate does not adversely elevate blood glucose con-

centrations and so is a useful nutrient source for persons with

diabetes; available carbohydrate was seenas being adverse or not

well tolerated (8). That some available carbohydrate might also

be suitable as an energysource for persons with diabetes became

evident later; this was characterized as low-GI available carbo-hydrate (9). The idea that unavailable carbohydrate might have a

direct or indirect role in glycemic control has renewed interest,

with 8 possible mechanisms proposed (1018). Most recently,

a short narrative review of replacement of ingredient available

carbohydrate with ingredient unavailable carbohydrate sug-

gested reductions in fasting blood glucose or glycated proteins in

persons with diabetes but not in persons in whom fasting blood

glucose was not raised (19). Among all these reports is consid-

eration that for optimal glycemic control, unavailable carbohy-

drate might be used alongside low-GI available carbohydrate to

limit the amount of high-glycemic carbohydrate among food

choices. However, the relative importance of unavailable and

low-glycemic available carbohydrate in health promotion and

management is unknown.

The objectives of the present study were to construct a data-

base of randomized controlled (or similar) intervention studies

that couldbe used to address queries aboutthe possibleroleof GL

and indexes of GL, as modifiable by available and unavailable

carbohydrate, in the management of health and prevention of

disease in respect of common metabolic conditions. In this, the

first of 2 articles in this issue of the Journal, the database is

1 From Independent Nutrition Logic, Wymondham, Norfolk, United

Kingdom (GLand RT); Kellogg Europe, DenBosch, Netherlands (TH); and

Wembley Park, Middlesex, United Kingdom (JH).2 Presented at an ILSI Europe workshop titled Glycemic Response and

Health, held in Nice, France, on 6-8 December 2006.3 Thereview was commissionedby the DietaryCarbohydratesTask Force

of the European Branch of the International Life Sciences Institute (ILSI

Europe) and was funded by industry members Cerestar, Coca-Cola, Danisco,

Groupe Danone, Kellogg, Kraft Foods, National Starch, Nestl, RHM Tech-

nology, Royal Cosun, Sudzucke r, Tate & Lyle Specialty Sweeteners, and

Unilever. The opinions expressed herein are those of the authors and do not

necessarily represent the view of ILSI or ILSI Europe.4 Address reprint requests to ILSI Europe. E-mail: publications@

ilsieurope.be.5 Address correspondence to G Livesey, Independent Nutrition Logic,

Pealerswell House, 21 Bellrope Lane, Wymondham, Norfolk, NR18 0QX

United Kingdom. E-mail: [email protected].

223SAm J Clin Nutr 2008;87(suppl):223S36S. Printed in USA. 2008 American Society for Nutrition

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described together with an assessment of the extent to which the

studiesachievedreductions in GI withoutmodifying the intake of

other macronutrient energy sources. Use of the database to ex-

amine the evidence on the relations between glycemic response

to food and specific aspects of healthis reported separately in the

second article.

MATERIALS AND METHODS

The database was constructed by using 3 processes applied

sequentially: 1) a literature search; 2) examination of titles, ab-

stracts, and full articles; and 3) database construction, data anal-

ysis, and synthesis of evidence. The electronic databases

searched included the Cumulative Index to Nursing & Allied

Health Literature (CINAHL; Internet: www.cinahl.com) from

1982 to January 2005, the Cochrane Central Register of Con-

trolled Trials (CENTRAL; Internet: www.mrw.interscience.

wiley.com/cochrane/cochrane_clcentral_articles_fs.html) to Janu-

ary2005,theElsevierMedicalDatabase(EMBASE;Internet:www.

embase.com) from 1980 to December 2003, the US National Li-

brary of Medicine database (MEDLINE via the PubMed portal;

Internet: www.ncbi.nlm.nih.gov.80/sites/entrez/) from 1950 to Jan-uary 2005; Elseviers Science-Specific Search Engine on the Inter-

net (SCIRUS; Internet: scirus.com) to January 2005, and Black-

wells Nutrition and Food Science database underpinned by the

CommonwealthAgriculturalBureau International (CABI;Internet:

www.nutritionandfoodsciences.org) from 1981 to January 2005.

All field searches for glyc(a)emic index or glyc(a)emic indi-

ces or glyc(a)emic load alone and each together with diabetes

were performed. Previous meta-analyses (4, 2022) and reviews

(23) were also examined to find citations and enabled a relative

assessment of search success. Records were imported into a biblio-

graphic database (Endnote 7, published 2003; ISI Research Soft,

Berkeley, CA) to exclude duplicates automatically, with further

screening manually to exclude remaining duplicates.Potentially relevant studies were identified by screening titles

and abstracts by using the inclusion and exclusion criteria given

in Tables 1 and 2, respectively. Full articles identified as being

potentially relevant were subjected to detailed examination

against these criteria and were included only if the information

could be extracted and the articles were from English language

publications, from a foreign language publication with English

abstract for which additional information could be sought from

the authors, or from unpublished work available by that time.

Study qualitywas assessedon thebasis of criteriain theCochrane

Reviewers Handbook (24). Numeric and alphanumeric (string)

data were double extracted from the source publications to 2

provisional databases by RT and GL independently. Inequalities

in cell contents between the provisional databases were exam-

ined by RT and GL separately and corrections made. Where

inconsistencies remained, agreement was reached by joint con-

sultation of the publication (or report) of the study in question.

The agreed data were imported and stored in a StataCorp (Col-

lege Station, TX) STATA SE 9.0 database file (*.dta) for query

and analysis. Conversion of data reported in common units to SI

units was made by GL and RT independently (Table 3) when

they extracted the data.

Subject groups were classified according to the following

health types: those in good health, those with impaired glucose

tolerance, those with type 1 diabetes mellitus, those with type 2

diabetes mellitus, andthosewith CHD. Subject groups were alsoclassified according to body weight as normal, overweight, or

obese according to body mass index (cutoffs in kg/m2 of 25or

30) or the original author information.

Biochemical riskfactorsextracted were fasting bloodglucose,

fasting insulin, glycated proteins (HbA1c and fructosamine), in-

sulin sensitivity, retrospectively calculated insulin sensitivity by

homeostatic model assessment (HOMA %S), pancreatic B-cell

functioncalculatedretrospectivelyby homeostatic model assess-

ment (HOMA %B), cholesterol (total, LDL, and HDL), and

TABLE 1

Inclusion criteria used when screening titles and abstracts

1) Publications in which glycemic index or load referred to foods or diets

and not to a diabetological parameter

2) Controlled intervention studies

3) Duration of treatment 1 wk

4) Studies in healthy persons, persons with either impaired glucose

tolerance or hyperlipidemia or at risk of coronary artery disease or

having diagnosed type 1 or 2 diabetes

TABLE 2

Exclusion criteria used when screening titles and abstracts

1) Epidemiologic studies

2) Interventions to assess effect on any form of cancer

3) Interventions in animal studies

4) Interventions with a historical design

5) Studies clearly without a control treatment

6) Treatments targeting the use of unavailable carbohydrate, protein, or

fat in place of total or available carbohydrate in the absence of aprimary targeting of a reduction in glycemic index of available

carbohydrate

7) Treatments reducing the glycemic index of the diet by using

inhibitors of carbohydrate digestion

8) Change in glycemic index or load as part of a multiple intervention

not reflected in the control treatment, such as with drugs or lifestyle

factors

9) Interventions specifically to study satiety, which had no relevant

outcomes

10) Publications related to food product characteristics, including

determinations of glycemic index or load

11) Methodologic publications

12) Publications relating to exercise performance

13) Publications on guidelines, reviews, commentaries, and abstracts

14) Study proposals, commentaries, and reviews

TABLE 3

Conversion factors

Variable Conversion factor

Glycemic index % glucose 1.43 % bread

Fasting glucose 1 mg/dL 18.02 mmol/L

Fasting insulin 1 U/mL 0.143 pmol/mL

Serum triglycerides (triacylglycerols) 1 mg/dL 88.5 mmol/L

Fasting capillary blood glucose 0.61 (0.94 fasting venous

glucose)

Metabolizable energy1

Avai lable ca rbohydrate 16.7 kJ/g

Unavailable carbohydrate 8 kJ/g

Protein 16.7 kJ/g

Fat 37.7 kJ/g

1 kcal 4.184 kJ.

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plasma triacylglycerols. Body weight was the only constitutional

risk factor extracted. Dietary risk or nutritional factors extracted

were metabolizable energy, fat, available carbohydrate, unavail-

able carbohydrate, and protein intake, together with the potential

risk factor GI with calculation of GL. Duration of treatment was

extracted either as a continuous variable (weeks) or as a categor-

ical variable or 12 wk. For the glycated proteins, treatment

duration was often low relative to half-life in blood, and so these

were considered with and without adjustment for half-life andduration of treatment.

Random effects meta-analyses and meta-regressions, each

weighted by inverse variance, were undertaken by using meta

and metareg in STATA 9.2 SE (StataCorp) according to Co-

chrane guidelines (25). Meta provides combined means with

95% CIs, the Der Simonian and Laird estimate of between-

studies variance (DSL), Q-test for heterogeneity, and an asymp-

totic z test for the null hypothesis that the true effect is zero.

Metareg was (unless stated otherwise) executed by using re-

stricted maximum likelihood (REML) for the fitting of coeffi-

cients, Knapp & Hartungs coefficient SE for t test of signifi-

cance, likelihood-ratio estimates of between-studies variance

(Tau2

), a likelihood-ratio test for heterogeneity, and an expres-sion of the proportion of total variation due to heterogeneity (I2).

Optionally, with regression models including a constant, the

Monte-Carlo (distribution free) permutations test was used to

assess whether a coefficient differed significantly from zero

(STATA option permut with metareg fitted by the method of

moments). Outliers wereidentified during sensitivity analysisby

means of the statistic 1Bij, ie, the difference between the

coefficient with and without the outlier divided by the SE with-

out. Mean differences between dietary treatments were ex-

pressed either in absolute units when the metric was common to

all studies or as a percentage of the average of the mean dietary

treatments when the metrics differed among studies.

Parallel and crossover studies reporting no crossover effectswere combined. Studies generally reported either treatment dif-

ferences in endmeasurements or differences in changewith time

(change score)for each treatmentor both. When an SE difference

(SED) between treatments in end measures or change in mea-

sures in time was not reported by the original authors, it was

calculated from either CIs or P values, error df, the number of

multiple treatment comparisons reported, and the method for

testing the significance(Students paired ttest, Tukeys test, etc).

Studies often did not report either the dependent(unpaired) or the

independent (paired) SED between treatments. These were

therefore calculated from the dependent SED at the end of the

period of treatment and coefficients of correlation (rp) between

dependent and independent values that were imputed from other

studies, with the use of duration-dependent values of rp when

found appropriate.

Calculation of differences in each study (treatment mean

control mean and independent SED for treatment effects) was

possibleby several waysdepending on theinformationavailable.

The accuracy of a method and the availability of information for

the method dictated preferences. For mean treatment difference

values, the preference was m1m2m3m4. m1 was the re-

ported treatment difference (corrected for starting values). m2

was the treatment difference calculated from reported change

scores. m3 was the same as m2 but first calculating the change

scores from reported start and end values for each treatment. m4

was the difference in reported end values for each treatment in

crossover designs. Similarly, SEDs between treatments took on

methods preferences, SED1SED2SED3. SED1 was the re-

ported value.SED2 wascalculated from changescoreSEs (com-

bined treatment SED across time). SED3 was estimated from

pooled treatment end mean and SD values, the number of par-

ticipants, and rp. Preferences for SDs for start and end mean

values were SD1SD2SD3. SD1 was as originally reported,

whereas SD2 was calculated from dependent SE of means and

the number of observations. SD3 was imputed by using first orsecond order regressions relatingSD1 to the correspondingmean

for those studies imparting this information. The preferences

were elaborated to maximize precision and minimize bias for

combined study treatment effects and was essential to retaining

the maximum number of studies in the analysis. Doing so

avoided bias from dropping studies with incompletely reported

statistics but included bias due to imprecision of SD3, and so

SED3.

Meta-analytic methods make much use of graphic material. In

presenting the results, we give both figures and related data in

tables because this is preferred to ensure provision of both per-

spective and precision for the outcomes (24, 25). Where further

analysis is made to test the significance of a particular perspec-tive, this entailed the provision of further figures and associated

outcomes.

RESULTS

The database and study characteristics

The search for literature to January 2005 yielded 2782 poten-

tially relevant publication records. After the titles and abstracts

were screened and the full articles reviewed, 45 publications

were identified as being relevant and of suitable quality for in-

clusion in the present database. Because some publications re-

ported more than one study, because of repeat observations insome studies (across the duration of treatment), and because

some outcomes were assessed by more than one method, the 45

publicationsyielded 80 conditions contrasting the reportedhigh

versus low GI diets (Table 4). All included studies had control

versus treatment comparisons and designs that were analyzable

as either crossover (20 studies) or parallel (25 studies). Informa-

tion was monitored at different times within each study ( see

Table 4). This was used to help address questions related to the

persistence, gain, or loss of effect with time during treatment.

Unless stated otherwise, independenceof study observations was

preserved by including only the end of treatment results.

The database included 972 participantsper treatment arm with

group mean ages of 10 to 63 y, with both males (n 511) and

females (n 461) represented. Participants were either normal

weight (16 studies), overweight (18 studies), or obese (10 stud-

ies) or were unclassified by weight(1 study). Similar numbers of

participants took part in each treatment arm (770 for the high-GI

treatment and 793 for the low-GI treatment). Study participants

were either healthy (ie, no diagnosis of disease was evident; 13

studies) or hadimpairedglucose tolerance(2 studies, duration of

impairment unknown), had type 1 diabetes (7 studies, mean

duration from diagnosis of 3 to 16 y, 1 unknown duration), had

type 2 diabetes (17 studies, mean duration from diagnosis of0

to 12.5 y, 7 unknown duration), were at risk of primary (4 studies,

duration unknown) or secondary (1 study, duration unknown)

CHD, or had hyperlipidemia (1 study combining Type II a, Type

FOOD, GLYCEMIA, AND HEALTH: DATABASE AND DIETS 225S

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TABLE 4

Study characteristics

Sources (reference)1

Duration

of

diagnosis

Body

weight

band2 Age

Percentage

male

Randomization

reported3Study

design4

No. of

participants

on high GI

No. of

participants

on low GI

Duration

of

treatment

y y % n n w

Normal healthy

Frost et al 1998 (30) (n is 2) n 36 0 r p 6 6 3.0

Frost et al (1998) (30) (n) n 36 0 r p 6 6 3.0

Herrmann et al 2001 (31) (20F) n 27 56 r x 9 9 1.1

Herrmann et al 2001 (31) (30F) n 27 56 r x 9 9 1.1

Jenkins et al 1987 (32) (d1) n 33 100 r x 6 6 1.0

Jenkins et al. 1987 (32) (d2) n 33 100 r x 6 6 2.0

Kiens et al 1996 (26) (d1) n 24 100 r x 7 7 1.0

Kiens et al 1996 (26) (d2) n 24 100 r x 7 7 2.0

Kiens et al 1996 (26) (d3) n 24 100 r x 7 7 3.0

Kiens et al 1996 (26) (d4 is 2) n 24 100 r x 7 7 4.3

Kiens et al 1996 (26) (d4) n 24 100 r x 7 7 4.3

Kurup 1992 (33) (raggi vs rice) n 36 nr p10 35 40 2.1

Kurup 1992 (33) (raggi vs tapioca) n 36 nr p10 35 23 2.1

Bouche et al 2002 (34) ow 46 100 r x 11 11 5.0

Dumesnil 2001 (35) (ph1 vs LGI out) ow 47 100 r x 12 12 0.9

Pereira et al 2004 (36) (out) ow 31 23 r p 17 22 9.6Price (unpublished) ow r p 22 22 12.0

Sloth et al 2004 (37) (d1) ow 30 0 r p 22 23 2.0

Sloth et al 2004 (37) (d2) ow 30 0 r p 22 23 4.0

Sloth et al 2004 (37) (d3) ow 30 0 r p 22 23 6.0

Sloth et al 2004 (37) (d4) ow 30 0 r p 22 23 8.0

Sloth et al 2004 (37) (d5) ow 30 0 r p 22 23 10.0

Agus et al 2000 (38) (out) ob 28 100 r x 10 10 0.9

Carels et al 2005 (39) ob 44 0 r p 23 21 20.0

Ebbeling et al 2003 (40) (d1) ob 16 31 r p 7 7 26.0

Ebbeling et al 2003 (40) (d2) ob 16 31 r p 7 7 52.0

Spieth et al 2000 (41) ob 10 46 nr p 43 64 18.4

Impaired glucose tolerance

Wolever & Mehling 2002 (42) (d1) ow 57 21 r p 11 13 4.0



Wolever & Mehling 2002 (42) (d4 is 2) ow 57 21 r p 11 13 16.0


Slabber et al 1994 (43) (C) ob 35 0 m x 16 16 12.0

Slabber et al 1994 (43) (P) ob 35 0 m p 15 15 12.0

Type 1 diabetes

Calle-Pascual 1988 (44) (T1) n 26 nr p 12 12 4.0

Collier et al 1988 (45) (d1) 3 n 12 86 r x 7 7 3.0

Collier et al 1988 (45) (d2) 3 n 12 86 r x 7 7 6.0

Fontvieille et al 1988 (46) 14.6 n 44 50 r x 8 8 3.0

Fontvieille et al 1992 (47) (mT1) 13.4 n 43 67 r x 12 12 5.0

Giacco et al 2000 (29) 10.3 n 28 37 r p 22 24 24.0

Gilbertson et al 2001 (48) 3.7 n 11 50 r p 49 55 52.0

Lafrance et al 1998 (49) 15.8 n 78 r x 9 9 1.7

Type 2 diabetesJenkins et al 1988 (50) 12.5 n 65 25 r x 8 8 2.0

Brand et al 1991 (1) 5 ow 62 63 r x 16 16 12.0

Calle-Pascual 1988 (44) (T2) ow 59 nr p 12 12 4.0

Fontvieille et al 1992 (47) (mT2) 7.8 ow 56 67 r x 6 6 5.0

Frost et al 1994 (51) 0.5 ow 55 71 r p 26 25 12.0

Jarvi et al 1999 (52) 8 ow 66 75 r x 20 20 3.4

Jimenez-Cruz et al 2003 (53) 8 ow 59 43 r x 14 14 6.0

Kabir et al 2002 (27) ow 59 100 r x 13 13 4.0

Komindr et al 2001 (54) ow 46 0 r x 10 10 4.0

Tsihlias et al 2000 (55) (d1) ow 62 59 r p 29 30 13.0

Tsihlias et al 2000 (55) (d2) ow 62 59 r p 29 30 26.0

Wolever et al 1992 (56) (d1) ow 67 47 r x 15 15 1.0

Wolever et al 1992 (56) (d2) ow 67 47 r x 15 15 2.0

(Continued to the right)

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TABLE 4 (Continued)

No. of meals

with

treatment

Food

intake

control5

Energy

intake

(high GI)6

Available

CHO intake

(high GI)7Fat intake

(high GI)7

Protein

intake

(high GI)7

Unavailable

CHO intake

(high GI)

Change in

unavailable

CHO intake

Glycemic

index

(high GI)8Change

in GI

Glycemic

load8

Change in

glycemic

load

n/d kJ/d %E %E %E g/d g/d % % g eq/d g eq/d

0 lc 8368 48 39 13 23.8 0.0 64 13 153 31

0 lc 8368 48 39 13 23.8 0.0 64 13 153 31

3 lc 7467 65 20 15 669 22 1919 78

3 lc 7333 55 30 15 669 22 1599 64

3 c 11 506 62 20 19 57.8 13.8 74 29 317 134

3 c 11 506 62 20 19 57.8 13.8 74 29 317 134

3 c 10 309 47 41 13 7.3 11.7 64 17 186 51

3 c 10 309 47 41 13 7.3 11.7 64 17 186 51

3 c 10 309 47 41 13 7.3 11.7 64 17 186 51

3 c 10 309 47 41 13 7.3 11.7 64 17 186 51

3 c 10 309 47 41 13 7.3 11.7 64 17 186 51

1 c 8335 59 27 14 17.8 0.9 6811,12 5 20111,12 29

1 c 8335 59 27 14 17.8 3.4 6811,12 5 20111,12 8

0 lc 10 301 42 37 18 19.0 12.0 71 30 184 96

3 a 11 695 55 30 15 26.0 3.0 60 7 231 127

3 c 6276 65 18 17 20.0 12.0 59 23 143 850 lc

0 a 9800 58 21 17 36.0 1.0 6811 13 22911 55

0 a 9800 58 21 17 36.0 1.0 6811 13 22911 55

0 a 9800 58 21 17 36.0 1.0 6811 13 22911 55

0 a 9650 58 22 17 35.0 2.0 6811 13 22411 56

0 a 9650 58 22 17 35.0 2.0 6811 13 22411 56

3 c 6247 67 18 15 25.6 3.4

3 c 6941 53 32 17 57 5 125 8

3 a 6711 55 28 18 9.0 1.0 56 3 124 21

3 a 6021 55 29 18 9.5 0.5 56 3 111 0

3 a 58 28 18

3 a 7170 53 28 17 22.7 13.5 59 5 134 8

3 a 7170 53 28 17 22.7 13.5 59 5 134 8

3 a 7170 53 28 17 22.7 13.5 59 5 134 8

3 a 7170 53 28 17 22.7 13.5 59 5 134 8

3 a 7170 53 28 17 22.7 13.5 59 5 134 8

3 lc 5124 50 30 20

3 lc 5124 50 30 20

1 c 8304 60 20 20 12.9 13.7 6211,12 7 18511,12 21

0 lc 11 452 46 37 17 22.5 16.2 58 9 183 5

0 lc 11 071 47 37 16 23.5 15.5 58 9 180 2

0 lc 8862 45 36 17 33.6 6.1 60 14 144 29

3 c 7477 51 39 21 27.0 0.0 64 26 147 56

2 lc 7653 54 29 17 15.0 24.1 64 14 160 40

0 a 49 34 17 57 1

3 lc 7704 53 29 18 17.1 1.8 66 19 161 36

3 lc 5385 53 26 21 28.0 6.0 65 17 110 21

3 c 6790 46 31 19 26.0 0.0 64 9 120 20

1 c 8531 60 20 20 12.9 13.7 6211,12 7 19011,12 22

3 c 7477 51 39 21 27.0 0.0 64 26 147 56

3 lc 7531 44 32 19.6 9.8 59 4 116 5

3 c 7615 54 29 18 34.0 4.0 59 19 145 40

0 lc 6530 64 20 18 25.0 9.0 56 12 139 45

1 c 61 4

3 lc 6168 58 30 12 6811,12 18 14611,12 39

1 lc 8400 54 29 17 23.1 27.2 62 7 165 39

1 lc 8400 54 29 17 23.1 27.2 62 7 165 39

3 c 6017 59 21 20 33.4 2.4 62 19 131 41

3 c 6017 59 21 20 33.4 2.4 62 19 131 41

(Continued on next page)


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TABLE 4 (Continued)

Sources (reference)1

Duration

of

diagnosis

Body

weight

band2 Age

Percentage

male

Randomization

reported3Study

design4

No. of

participants

on high GI

No. of

participants

on low GI

Duration

of

treatment

y y % n n w

Heilbronn 2002 (57) (d1) 5 ob 57 51 r p 21 24 4.0

Heilbronn 2002 (57) (d2) 5 ob 57 51 r p 21 24 8.0Jimenez-Cruz et al 2004 (58) 7 ob 51 r x 8 8 3.0

Luscombe et al 1999 (59) 6.3 ob 57 67 r x 21 21 4.0

Rizkalla et al 2004 (60) ob 54 100 r x 12 12 4.0

Rizkalla et al 2004 (60) (is 2) ob 54 100 r x 12 12 4.0

Wolever et al 1992 (61) (d1) ob 63 50 r x 6 6 2.0



Zhang et al 2003 (62) 3 51 r p 36 36 21.0

Hyperlipidemia

Jenkins et al 1987 (63) (all) 4.7 n 53 73 nr x13 30 30 4.0

Jenkins et al 1987 (63) (fam) 4.7 n 53 73 nr x13 6 6 4.0

Jenkins et al 1987 (63) (TIIa) 3.8 n 47 33 nr x13 6 6 4.0

Jenkins et al 1987 (63) (TIIb) 6.2 n 51 86 nr x13 7 7 4.0

Jenkins et al 1987 (63) (TIII) 5.3 n 57 100 nr x13 1 1 4.0

Jenkins et al 1987 (63) (TIV) 4.8 n 55 81 nr x13 16 16 4.0

Coronary heart disease risk

Frost et al 1998 (30) (chdr) n 36 0 r p 8 8 3.0

Frost et al 1998 (30) (chdr is 2) n 36 0 r p 8 8 3.0

Frost et al 1996 (64) ow 63 77 r p 15 15 4.0

Frost et al 1996 (64) (is 2) ow 63 77 r p 15 15 4.0

Brynes et al 2003 (28) (st is 2) ow 45 100 r x 17 17 3.4

Brynes et al 2003 (28) (st) ow 45 100 r x 17 17 3.4

Brynes et al 2003 (28) (su is 2) ow 45 100 r x 17 17 3.4

Brynes et al 2003 (28) (su) ow 45 100 r x 17 17 3.4

Frost et al 2004 (65) ow 63 87 r p 29 26 12.0

(Continued to the right)1 Abbreviations in parentheses distinguish data from within the same publication, which might carry more than one study or note an outlying study: out,

study is outlying, ie, the change in glycemic load is not explained by the change in carbohydrate intake or glycemic index. T1, persons with type 1 diabetes;

T2, persons with type 2 diabetes; mT1, mT2, observations presented from a mixture of types of diabetes, results appliedto eachtype with appropriate numbers

of participants; d1, observations are for the first duration of treatment, d2 for the second, and so on; chdr, those participants at coronary heart disease risk; n,

normal participants; 30F, 20F, distinguishes the level of fat intake in two studies in the same publication; fam, refers to a subgroup on which fructosamine

concentrations were measured; all,TIIa,TLIb, TIV, refers to the type of hyperlipidemia; P, C, observations representing thesame subjects initially in a parallel

design and eventually for a subgroup in the cross-over design; ph1, phase 1 diet of the American Heart Foundation; raggi vs rice, study was analyzed in the

publication as 2 sequential studies, here analysis wasmade as a 1 parallel design;raggi vs tapioca, study wasanalyzed in the publicationas 2 sequential studies,

here analysis was made as 1 parallel design; st, starch as the major modification; su, sucrose as the major modification; is 2, insulin sensitivity measured by a

second method on the whole or a subsample of the study populations.2 n, ow, ob, normal, overweight, and obese as classified by original authors or at present by BMI with cutoffs at 25 and 30 (kg/m 2).3 r, randomization reported; nr, randomization not reported.4 x, p, crossover or parallel study design.5 c, lc, a, controlled, limited controlled, and ad libitum food intake controlled.6kJ metabolizable energy (not inclusive of energy from unavailable carbohydrate), kilojoules (1 cal 4.184 kJ), and percentage thereof.7Percentage of metabolizable energy, sum values 99% correspond to alcohol consumption 102% (two cases) unexplained.8

Glycemic index (GI) andload are based on glucose 100g eq/100 g. GI valuesare original author valueswith conversion from bread equivalents whereappropriate, or in the case of relatively simple food exchanges are calculated by using information from relevant food tables and international look-up tables

for GI.9 In the case of Herrmann et al 2001 (31), GI values were given as 65 (high-GI diets) and 45 (low-GI diet) for which values of 66 and 44 were taken

to give a minimum estimate of treatment difference in GI.10 Twostudies by Kurup 1992 (33) presented by authors in one publication as control-treatment sequencedesigns had multiple treatments analyzable and

are included here with parallel design.11 Valuesrequiredassumptionsto be made about theunderlying diet, which mayaffect theaccuracy ofdietary estimates without influence on thetreatment

difference estimates.12 GI values of treatment and control food sources estimated from look-up tables, which may affect the accuracy of both the dietary and the difference

estimates.13 A study by Jenkinset al 1987b (63) waspresented by the authorsas a control-treatment-control designand is presented here by combining controls and

analyzed as a crossover design.

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II b, Type III, mean duration of diagnosis of 4.7 y). The healthtypes were assigned by the authors of the original publications

and reports. It should not be implied from the use of the health

types that particular participants fit a health type uniquely. For

example, all those with diabetes might also be considered at risk

of CHD. Studies includedparticipantstaking medication. Insulin

dosage was reported in all 7 studies of persons with type 1

diabetes, in 2 of 17 studies of persons with type 2 diabetes, and

in 1 of 1 study of participants at risk of secondary CHD. All but

1 study of persons with type 2 diabetes included noninsulin

medication for glycemic control (hypoglycemic agents).

Interventions wereby diet, withintention to exchangethe form

of available carbohydrate (high versus low GI). The extent to

which this was achieved varied between studies. In many cases,

the dietary changes were accompanied by variably higher quan-

tities of unavailable carbohydrate. Thus, the diets are sometimes

qualified collectively as variably lower glycemic and variably

higher unavailable carbohydrate. Three studies were identified

statistically as outliers, ie, belonging to a population of studies

differing from the remainder. These were Agus et al (38),

Dumesnil et al (35), and Pereira et al (36) and were identified

because of the exceptional replacement of available carbohy-

drate with either protein or fat.

Treatmentdurations ranged from 1 to 52 wk. Studies included

interventions with food exchanges at 1 or 2 or 3 (and more) or

probably 3 (and more) meals per day (6, 2, 25, and 12 studies,

respectively). This enabled queries concerning meal numbers

and dose dependency of outcomes in relation to dietary GL orany of its indexes. Diets were intended to meet maintenance and

growth requirements (37 studies) or submaintenance require-

ments (8 studies); no overfeeding studies were encountered.

Control of food intake was categorized as ad libitum (7 studies),

limited controlled food intake (22 studies), and controlled food

intake (16 studies).

All studies werefree-living(no studies of hospitalized patients

or subjects housed in metabolic wards or centers of human nu-

trition). Studies were from all continents: Asia (4 studies), Aus-

tralasia (6 studies), Europe (19 studies), South America (2 stud-

ies), Africa (1 study), and North America (13 studies).

Mortality and morbidity scores were not collected, because

few studies were of sufficient duration to encounter reliable

responses. No data were reported on mortality, cardiovascular

events, or diabetic complications. Changes in the severity of risk

factors were the primary outcomes: fasting glucose, glycated

proteins (HbA1c, fructosamine), fasting insulin, insulin sensitiv-

ity, calculated HOMA %S, calculated HOMA %B, calculated

HOMA %D (the product of HOMA %B HOMA %S), fasting

plasma or serum triacylglycerols, and body weight. Data on

fasting total, HDL, and LDL cholesterol were extracted, ap-

pearedto be confounded, andwerenot investigatedfurtherat this

time. Other outcomes were macronutrient intakes, physical ac-

tivity, and adverse events. Insufficient numbers of studies mon-

itored physical activity to accumulate information in the data-

base;3 that didso provided nodata(2729).The followingintake

TABLE 4 (Continued)

No. of meals

with

treatment

Food

intake

control5

Energy

intake

(high GI)6

Available

CHO intake

(high GI)7Fat intake

(high GI)7

Protein

intake

(high GI)7

Unavailable

CHO intake

(high GI)

Change in

unavailable

CHO intake

Glycemic

index

(high GI)8Change

in GI

Glycemic

load8

Change in

glycemic

load

n/d kJ/d %E %E %E g/d g/d % % g eq/d g eq/d

0 c 6008 61 17 22 29.8 0.5 75 32 164 72

0 c 6008 61 17 22 29.8 0.5 75 32 164 723 lc 8360 54 27 18 30.0 23.0 72 12 194 46

0 lc 7569 53 21 23 30.0 0.0 63 20 151 47

2 lc 9586 38 37 20 21.0 13.0 71 32 155 77

2 lc 9586 38 37 20 21.0 13.0 71 32 155 77

0 c 5807 57 23 19 33.0 1.0 61 20 122 40

0 c 5807 57 23 19 33.0 1.0 61 20 122 40

0 c 5807 57 23 19 33.0 1.0 61 20 122 40

3 lc 59 9

3 lc 7176 49 29 19 21.8 5.9 60 8 125 18

3 lc 7176 49 29 19 21.8 5.9 60 8 125 18

3 lc 7176 49 29 19 21.8 5.9 60 8 125 18

3 lc 7176 49 29 19 21.8 5.9 60 8 125 18

3 lc 7176 49 29 19 21.8 5.9 60 8 125 18

3 lc 7176 49 29 19 21.8 5.9 60 8 125 18

0 lc 8786 51 37 12 29.4 2.8 62 14 166 27

0 lc 8786 51 37 12 29.4 2.8 62 14 166 27

3 lc 8544 44 36 17 18.2 0.7 65 12 145 37

3 lc 8544 44 36 17 18.2 0.7 65 12 145 37

3 a 9020 46 36 18 19.0 12.0 69 20 173 65

3 a 9020 46 36 18 19.0 12.0 69 20 173 65

3 a 9900 51 33 16 19.0 0.0 69 6 209 19

3 a 9900 51 33 16 19.0 0.0 69 6 209 19

3 lc 7360 47 32 18 21.0 6.0 58 7 120 7


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data were noted: 1) metabolizable energy intake (41 studies, 26

had freedom to respond, ie, ad libitum plus limited controlled

food intake; the other 15 had controlled, ie, fixed, food intake),

available carbohydrate intake (41 studies, 26 with freedom to

respond); 2) unavailable carbohydrate intake (36 studies,22 with

freedom to respond); 3) protein intake (39 studies, 24 with free-

dom to respond); 4) fat intake (40 studies, 26 with freedom to

respond); 5) calculated GL (ie, diet GI times available carbohy-

drate intake/100; 38 studies, 24 with freedom to respond); 6)dietary GI (as % glucose) ostensibly of available carbohydrate

(41 studies, 26 with freedom to respond; 6 studies did not fully

report GI values but this was recoverable approximately from

other information available (see footnotes to Table 4); 7) calcu-

lated GI of total carbohydrate (35 studies, 22 with freedom to

respond) were estimated as 100 times the dietary GL divided by

the sum weight of available and unavailable carbohydrate.

Two publications (28, 31) included more than one treatment

(versus control) per study. In one case, dietary fatintake differed

between treatments (30); in the other, the source of carbohydrate

modifying GI differed (sucrose versus starch; 28). This informa-

tion was retained in the database because it was particularly

useful where study results were heterogeneous. One study re-porting an effect on bodyweight (43)informed about the first part

of a crossover study as a parallel design. In this case, analyses

were performed on the crossover design, and the parallel study

was used only for comparison of combined results by study

design.

Common to each of the 45 studies entered into the database

was information for participants that were compliant to diet

(ie, dropouts and noncompliant participants discarded). Twenty-

one studies had reported outcomes on the basis of compliance to

diet analyses, whereas 24 studies reported information that was

both intention-to-treat and compliant-to-diet. Among these,

there were no dropouts in 23 studies, and 1 study (30) with

dropouts presented both intention-to-treat and compliant-to-diet

information. Individual study dropouts ranged from 0% to

61% of study entrants with an unweighted combined study

mean of 8%.

The number of participants per treatment arm per study was

generally small. There were15 studiesfor which thisnumber was

10 units, 15 studies with 10 to 20, 11 studies with 20 to

30, and 4 studies with 30 to 60. Power calculations wereseldom given, and then only for few of the measurements made.

Forty of the 45 studies reported macronutrient intakes and

changes in intake between high to low glycemic carbohydrate

diets. Of these, 38 reported GI values (or information on foods

enabling GI to be estimated). Although many studies reported

designs intended to have similar macronutrient intakes in the

treatment and control arms, the precision with which this was

achieved varied between studies. The possibility that difference

in treatment outcomes covary with unintended differences in

macronutrient intakes was examined, because such differences

between treatments might confound the interpretation of a treat-

ment effect as being due to GI if not accounted for by, for ex-

ample, covariance.To investigate the relation between GI and the energy and

macronutrient intakes from the intervention diets, the studies

were either combined or were divided into 3 categories (Table

5). Thus, foods were eitheravailable ad libitum or were available

in wholly controlled amounts, or were available subject to lim-

ited (or partial) control. In the ad libitum category, food intake

could vary but diet composition was intended to be fixed (food

was provided or largely provided). In the wholly controlled food

intake category, both food intake and composition were fixed

(food was provided or largely provided and may have been ad-

justed in amount if body weight changed). In the intermediate,

TABLE 5

Studies included by food intake control category1

Controlled food intake Limited controlled food intake Ad libitum food intake

Agus et al 2000 (38)2 Bouche et al 2002 (34) Brynes et al 2003 (28) (su)

Brand et al 1991 (1) Collier et al 1988 (45) (d2) Brynes et al 2003 (28) (st)

Calle-Pascual 1988 (44) (exp1_T2) Fontvieille et al 1988 (46) Ebbeling et al 2003 (40) (d2)

Calle-Pascual 1988 (44) (exp2_T2) Frost et al 1994 (52) Sloth et al 2004 (37) (d5)

Carels et al 2005 (39) Frost et al 1996 (65) Wolever & Mehling 2002a (42) (d4)

Fontvieille et al 1992 (47) (exp1) Frost et al 1998 (30) (exp1)

Fontvieille et al 1992 (47) (exp2) Frost et al 1998 (30) (exp2)

Heilbronn 2002 (57) (d2) Frost et al 2004 (64)

Jarvi et al 1999 (52) Giacco et al 2000 (29)

Jenkins et al 1987a (32) (d2) Herrmann et al 2001 (31) (expt2)

Kiens et al 1996 (26) (d4) Jenkins et al 1987b (63) (all)Kurup 1992 (33) (raggi vs rice) Jenkins et al 1988 (50)

Kurup 1992 (33) (expt2 raggi vs tapioca) Jimenez-Cruz et al 2003 (53)

Wolever et al 1992a (61) (d3) Jimenez-Cruz et al 2004 (58)

Wolever et al 1992a (61) (d2) Komindr et al 2001 (54)

Lafrance et al 1998 (49)

Luscombe et al 1999 (59)

Rizkalla et al 2004 (60)

Tsihlias et al 2000 (55) (d2)

1 For abbreviations in parentheses,see Table 4.Controlled food intake,bothfoodintake andcompositionwerefixed(food wasprovided orlargely provided

andmay have been adjusted in amount if body weight changed);limited controlled food intake, scope existedfor both intakesand composition to vary (the diet

wasadvised or largely advised, usuallywith supervision);ad libitum intake, food intake could vary butin these cases diet composition wasintended to be fixed

(food was provided or largely provided).2 Outlier in some analyses.

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partially controlled category, scope existed for both intakes and

composition to vary (the diet was advised or largely advised).

Inequalitiesbetween treatment and control diets could arise in all

3 categories according to the food choices and application of the

dietary prescriptions.

Glycemic index and intake of food components

Univariate analyses using REML random effects meta-

regression (Figure 1, Figure 2, and Figure 3) indicate elevated

intakes of metabolizable energy, protein, and available carbohy-

drate forsmall reductions in GI when comparingtreatmentswith

controls. Thus, regression constants at a hypothetical zerochange in GI are positive in each food intake category for energy

(48 to 1009 kJ/d), protein (1.5 to 15.7g/d), and available

carbohydrate (14.9 to 34.6 g/d). Visual inspection of this

information collected together (Figure 4) suggests a trend

toward greater such effect as control over food intake is pro-

gressively relaxed, from controlled to limited controlled to ad

libitum. Multivariate meta-regressions (legend to Figure 4)

FIGURE 1. Difference in energy intake associated with difference inglycemic index achieved. Observations are grouped by the category of con-trol over food intake (where total is all categories combined). Each study is

represented by a bubble proportional to the inverse (Tau2 SE2). Largebubbles indicate studieswith large weight. AC,available carbohydrate;ME,

metabolizable energy.

FIGURE 2. Difference in protein intake associated with difference in

glycemic index achieved. Observations are grouped by the category of con-trol over food intake (where total is all categories combined). Each study isrepresented by a bubble proportional to the inverse (Tau2 SE2). Large

bubbles indicate studies with small errors. AC, available carbohydrate.

FIGURE 3. Difference in the intake of available carbohydrate associated

with difference in glycemic index achieved. Observations are grouped by thecategory of control over food intake (where total is all categories combined).Each study is represented by a bubble proportional to the inverse (Tau2 SE2).

Large bubbles indicate studies with large weight. AC, available carbohydrate.

FIGURE 4. Confounding incrementsin energy and macronutrient intakes instudies on glycemic index. Top panel: For small reductions in glycemic index

(GI), there is a jump in intake, which is at a theoretical maximum represented by

a regression constant in the univariate model shown. The constant occurs at ahypothetical zero reduction in GI. Results are shown foreach of the 3 macronu-

trient and energy intakes, and these for each of the 3 categories of food intakecontrol, providing results for9 univariate models. The table shows the levels of

statistical significance attained for each of the 9 results from the univariateregressions. Also shown in the table are the levels of significance of effectsoverall*for foodintakecontrolcategoriesand thetrends**acrossthe foodintake

control categories, as obtained by application of themultivariate model. Resultsare converted to units of metabolizable energyfor the purpose of display within

thesameaxis.Almost identicallevels ofstatistical significanceweregivento thetrend by thepermutations test (0.058, 0.34, and0.041in place of thecorrespond-ing values in the figures table). Univariate model: intake constant slope

GI Tau SE. Multivariate model: intake overall constant* trend** (category -1control, 0limited control, 1ad-lib) slope1 GIad-lib slope

2GIlimited control slope3 GI control Tau SE.Contr, controlled;

ltd contr., limited controlled; ad-lib, ad libitum.


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including categorical variables for controlled, limited con-

trolled, and ad libitum food intake categories support that

elevations in intake of metabolizable energy, available car-

bohydrate, and protein are each highly statistically significant

overall, and that each show a trend toward greater elevation as

food intake control is relaxed. Such a trend was significant for

metabolizable energy and protein, although not for available

carbohydrate (Figure 4), likely because of an elevation occur-

ring already in the controlled food intake category. Neverthe-less, the visual inspection and the P value suggest a trend is

plausible for available carbohydrate.

With a progressive reduction in GI, metabolizable energy,

available carbohydrate, and protein intake then each decrease in

the limited control and ad libitum control food intake categories

(Figures 13). Although univariate analysis indicates this to be

significant for protein intake only, more sophisticated multivar-

iate analysis using categorical variables for the level of food

intake control indicated that a fall in metabolizable energy, avail-

able carbohydrate, and protein intake occurs with progressive

reduction in GI, each being very significant (Figure 5). Further-

more, relaxation of control over food intake is associated with a

trend toward greater rates of reduction in energy and proteinintakes, whereas for available carbohydrate such a trend would

appear plausible and more probable than not.

In contrast with metabolizable energy, protein, and available

carbohydrate, the intake of fat appears essentially unchanged by

the interventions, or at best fat intake appears not so sensitive to

change (Figure 6). Nevertheless, statistically significant effects

are not excluded because in the control food intake category a

meta-regression constant equal to 8.3 g fat/d (P kh-t0.004) occurs with a rising slope on the regression line with

progressive reduction in GI (0.36 g/GI%glucose, P kh-t0.013). In thead libitum food intakegroup, however, such a trend

is possiblyreversed, withfat intake becomingreducedalong with

intake of food energy generally, though this remains to be con-

firmed (k 5).

Unavailable carbohydrate intakes after these lower glycemic

interventions are also generally elevated above the intake for

control treatments (Figure 7); the increase achieves statistical

significance only when all food intake control categories arecombined. Thus, the meta-regression constant is 6.9 g/d at the

hypothetical zero difference in GI (P kh-t 0.025). No

FIGURE 5. Confounding falls in energy and macronutrient intakes with

progressive reduction in glycemic index. Top panel: The progressive fall inintakewithreductionin glycemic index(GI)is represented by theslopein the

regression models. Results are shown for each of the 3 macronutrient andenergy intakes, and these for each of the 3 categories of food intake control,

providing results for 9 univariate models. The table shows the levels ofstatistical significance attained for each of the 9 results from the univariateregressions. Also shown in the table are the levels of significance of effects

overall* for food intake control categories and the trends** across the foodintake control categories, as obtained by application of the multivariate

model.Results areconvertedto units ofmetabolizableenergy forthe purposeof display within the same axis. Almost identical levels of significance weregiven tothe trend bythe permutations test (0.003, 0.12, and 0.005 inplace of

corresponding values in the figures table). Univariate model: intakeconstant slope GI Tau SE. Multivariate model: intakeconstant control constantlimited control constant overallslope* GI trend** ( GI category -1control, 0 limited control, 1ad-lib) Tau SE.

FIGURE 6. Difference in fat intake associated with difference in glyce-micindexachieved. Observationsare grouped bythe category ofcontrolover

food intake (where total is all categories combined). Each study is repre-sented by a bubble proportional to the inverse (Tau2 SE2). Large bubbles

indicate studies with large weight. AC, available carbohydrate.

FIGURE 7. Difference in unavailable carbohydrate intake associatedwith differencein glycemic index achieved. Observations aregrouped by the

category of control over food intake (where total is all categories combined).Each study is represented by a bubble proportional to the inverse(Tau2SE2). Large bubbles indicate studieswithlargeweight. AC,available

carbohydrate.

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subsequent fall in intake was evident as the glycemic index got

progressively lower, such as seen for available carbohydrate.

Of possible interest, meta-regression does indicate a falling

trend for available carbohydrate intakes with increases in un-

available carbohydrate intake among these studies (Figure 8),

which would contribute towarda reductionin GL. Therelationis

statistically significant when all studies are combined (regres-

sion slope 1.09, P kh-t 0.019) and of somewhat

consistent sensitivity across categories (1.15 g/g, 1.19 g/g,and 1.37 g/g in the controlled, limited controlled, and ad libi-

tum foods intake groups, respectively), although in no individual

category was the effect statistically significant. These slopes are

consistent with an exchange of1 g available carbohydrate with

1 g unavailable carbohydrate as a result of the lower GI inter-

ventions; this is independent of the absolute decrease in GI.

Heterogeneity (2 - Tau2, the between-studies variance) was

highly significant among the study observations. For metaboliz-

able energy, 2 was 157 reduced to 117 kJ2 after accounting for

the variation in both GI and category of food intake (ie, 25% of

the variance was explained). For available carbohydrate, heter-

ogeneity was reduced 36% from 726 to 462 (g/d)2, whereas for

protein it was reduced 52% from 118 to 56 (g/d)2

. However, ineach case, heterogeneity remained significant (P X 0.001). No reliable (dfnotlow) or significant(P 0.05) patterns

emerged among these data within each health type separately

(healthy, CHD risk, type 1 diabetes, and type 2 diabetes) to

explain these variations. Nor was there evidence of significant

residuals for studies combined by health type separately and

which could have suggested that a health type departs from these

general trends.

Glycemic load

The GL of the intervention diets decreased with progressive

reduction in the GI significantly for all studies combined (P

kh-t 0.001) and for studies combined by food intake cate-gories separately (controlled, P kh-t 0.001; limited con-trolled, P kh-t 0.001; ad libitum, P kh-t 0.05;Figure 9). Each had a corresponding slope on the meta-

regression lines in the range of from 2.9 to 4.5 g glucose equiv-

alent per unit GI (% glucose) (Table 6). The combined average

rate of fall was 3.11 g glucose equivalent perGI % glucose, more

than can be expected from a change in GI alone for carbohydrate

intakes 400 g/d. In view of the overlap of the observations and

meta-regression lines associating GL and GI for the different

food intake control categories (Figure 9) and the limitations of

the study numbers, the statistical significance of the differences

between the slopes of the met-regression lines for the different

food consumption groups has not been assessed.

In all studies combined, treatment differences in available

carbohydrate intake contributed as much to variation in GL as

did GI (Figure 10). Heterogeneity in GL among all studies

combined was 892 g glucose equivalent (2) and was reduced by

54% after accounting for differences in available carbohydrate

intake univariately and by 93% after further accounting for

GI

FIGURE 8. Difference in available carbohydrate intake associated with

unavailable carbohydrate intake found.

FIGURE 9. Difference in glycemic load associated with difference in

glycemic index achieved. Observations are grouped by the category of con-trol over food intake (where total is all categories combined). Each study isrepresented by a bubble proportional to the inverse (Tau2 SE2). Large

bubbles indicate studies with large weight. AC, available carbohydrate.

TABLE 6

Difference in glycemic load (GL) in intervention studies related to

difference in glycemic index (GI) achieved, for each category of food

intake1

All studies

combined

Controlled

intake

Limited

control Ad libitum

k(df) 37 (35) 14 (12) 19 (17) 5 (3)

Slope (g/GI%glu) 3.1 3.2 2.9 4.5

SEE 0.4 0.6 0.50 1.3

P kh-t 0.001 0.001 0.001 0.0463P 0.001 0.001 0.138

Constant (g/d) 10.2 13.2 7 21.9SEE 6.4 12 9.3 16.1

P kh-t 0.122 0.298 0.439 0.2683P 0.894 1.317 0.804

I2 (g2/g2) 0.67 0.24 0.78 0.31

Tau (g/d) 11 10 12 16

P Q 0.001 0.20 0.001 0.31P X 0.001 0.23 0.001 0.26

1 Model: difference in glycemic load slope (SEE) differencein GI

constant (SEE) Tau SE. k, numberof studies; df,degrees of freedom;

P kh-t statistical significance based on knapphartung t; Tau, standarderror among studies; I2, variation among studies (Tau2) as a proportion of

total variation (Tau2 SE2). P Q , probability of significant heterogene-ity; P X, likelihood-ratio test of probability that Tau is 0.


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bivariately. The overall effect on GL was additive, although not

simply additive, because small reductions in GL due to reduced

GI were accompanied (confounded) by the intake of more avail-

able carbohydrate, which would elevate GL (Figure 10). Such

confounding may be either circumstantial or a physiologic re-

sponse to low GI.

Reductions in metabolizable energy and available carbohy-

drate intake associated with the reductions in GL achieved in

these intervention studies as evident in univariate analysis of all

studies combined(Table 7). Thiscomprised elevations of energy

and available carbohydrate intakes (ie, significant positive meta-

regression constants) followed by a slope falling toward lower

GLs. Unavailable carbohydrate intake and fat intake each differ

little or nonsignificantly, because GL reduces with GI univari-

ately, with a marginal exception for fat in the controlled food

intake category.

DISCUSSION

A reduction in GI achieved by dietary intervention is com-

monly accompanied by changes in available carbohydrate in-

take. The resulting change in dietary GL is therefore not solely

theresultof a substitutionof higherGI carbohydrates bylowerGI

carbohydrates. This is reflected in the fact that variance in GL is

explained almost equally by the variance in available carbohy-

drate intake that accompanies variance in GI (Figure 10). Also,

available carbohydrateintakevaries by g/g exchangeof available

and unavailable carbohydrate. In large part, the wider range of

GL observed than expected from the change in GI alone is ex-

plained by elevated intakes of available carbohydrate for lowreductions in GI followed by a trend toward progressive reduc-

tion in the available carbohydrate intake with progressive reduc-

tion in GI. Reductionin theintake of availablecarbohydrate, and

with it metabolizable energy, occurs when GI is reduced by 10

GI units on average or for GL 30 g glucose equivalent (Table 8).

Overall, such reduction in metabolizable energy is contributed to

also by reductions in protein intake.

There is no evidence even when relaxing control over food

intake and composition that aiming for a low-GI diet results in a

rise in energy from fat intake to compensate forthe fall in energy

from available carbohydrate. The generality of an inverse rela-

tion between available carbohydrate and fat intake is therefore

broken in this instance.

TABLE 7

Difference in metabolizable energy, protein, available carbohydrates, fat,

and unavailable carbohydrate intakes in intervention studies related to

difference in glycemic load (GL) achieved, for each category of food

intake1

All studies

combined Controlled

Limited

controlled

Ad

libitum

Metabolizable energy

k 38 14 19 5

Slope (kJ/g eq) 8.01 0.1 15.21 23.42

Constant (kJ/d) 2452 67 4722 420

Protein

k 37 14 18 5

Slope (g/g eq) 0.07 0.04 0.2 0.24

Constant (g/d) 6.21 0.1 141 4.9

Available carbohydrates

k 38 14 19 5Slope (g/g eq) 0.543 0.281 0.803 0.862

Constant (g/d) 213 151 283 222

Fat

k 38 14 19 5

Slope (g/g eq) 0.02 0.092 0.01 0.17

Constant (g/d) 3.3 6.2 2.3 0.8

Unavailable carbohydrates

k 35 13 17 5

Slope (g/g eq) 0.06 0.07 0.07 0.04

Constant (g/d) 4.1 1.3 5.9 3.4

1 P kh-t 0.01.2 P kh-t 0.05.3 P kh-t 0.001.

TABLE 8

Reduction in glycemic index and load required before metabolizable

energy and carbohydrate intake are reduced1

x SE P kh-tUpper

95% CI

Glycemic index (% glucose)


Limited control 10 6 0.140 24

Ad libitum 9 2 0.002 15

Available carbohydrate


Ad libitum 10 4 0.061 22Glycemic load (g glucose equivalents)



Ad libitum 18 6 0.049 36

Available carbohydrate


Ad libitum 26 7 0.023 46

1 For glycemic index, data are intercepts on the xaxis in Figures 1 and

3, and for glycemic load they are the corresponding figures for similar

analyses summarized in Table 7. Data are not shown for protein, which

yielded less consistent information, or for control treatments. Values were

calculated by using nonlinear combination of regression coefficients (ratio:

constant to slope).

FIGURE 10. Contribution of difference in glycemic index (GI; E) anddifference in available carbohydrate (AC; F) intake to the difference inglycemic load (GL) achieved in the intervention studies. Data are (F, slope1 AC) and (E, slope2 GI) from the model GL slope1 AC slope2 GI constant Tau SE. g eq, glucose equivalents.

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The present findings derive from random effects inverse vari-

ance meta-analysis and REML meta-regression across studies.

Publication bias is not excluded among the present studies,

though no analyses were indicative. However, this is strictly

assessable only when fixed-effects analysis is applicable. The

observedeffectsapply to combinedhealth and bodyweight types

(healthy, impaired glucose tolerance, hyperlipidemia, type 1 di-

abetes, type 2 diabetes, primary and secondary CHD risk, and

normal, overweight, and obese weight for height). Generally,there is too little information of a consistent nature or just too

little information to establish the present findings in any one

health or body weight type separately or for interventions of

12 wk duration.

Because a rise in available carbohydrate intake with small

reductions in GI occurred in all 3 food intake control categories

(ad libitum, limited controlled, and controlled), it may prove

difficult to avoid when using GI alone as an intervention tool.

Should this be so, a revised approach to interventions for the

control of the postprandial glycemic response could be needed.

This is particularly so wherever small reductions in GI can be

expected. Likewise, it may be that maximum reduction in GL

through contemporary strategies of GI reduction might not op-timally reach the intended goal. One consideration would be a

moredirect attempt thatreducesthe GL of diets while controlling

fat intake. Before doing so, evidence relating GL to health is

necessary and is addressed in the follow-up paper.

Theauthorsare grateful toG Janecek, University ofEast Anglia,Norwich,

UK, for preliminary discussion of the statistical approach.

The contributions of the authors were as followsGL and RT: data col-

lection; GL:analysis; GLand JH:writing; andTH: comment. GLand RThad

no financial or personal conflicts of interest. JH is currently advising an

industry group comprising food manufacturers and retailers who are prepar-

ing a submission to the authorities in Europe supporting the case for the use

of GI in the labeling of food products. TH works for Kellogg.

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