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American Journal of Clinical Nutrition, Vol. 87, No. 1, 223S-236S, January 2008
© 2008 American Society for Nutrition

Glycemic Response and Health

Glycemic response and health—a systematic review and meta-analysis: the database, study characteristics, and macronutrient intakes1,2,3,4,5

Geoffrey Livesey, Richard Taylor, Toine Hulshof and John Howlett

1 From Independent Nutrition Logic, Wymondham, Norfolk, United Kingdom (GL and RT); Kellogg Europe, Den Bosch, 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 The review was commissioned by the Dietary Carbohydrates Task 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, Nestlé, RHM Technology, Royal Cosun, Südzucker, 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{at}ilsieurope.be.

5 Address correspondence to G Livesey, Independent Nutrition Logic, Pealerswell House, 21 Bellrope Lane, Wymondham, Norfolk, NR18 0QX United Kingdom. E-mail: glivesey{at}inlogic.co.uk.

ABSTRACT

Background: Reduction of dietary glycemic response has been proposed as a means of reducing the risk of diabetes and coronary heart disease. Its role in health maintenance and management, alongside unavailable carbohydrate (eg, fiber), is incompletely understood.

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 intervention trials on glycemic index (GI) according to inclusion and exclusion criteria, extracted the data to a database, and synthesized the evidence via meta-analyses and meta-regression models.

Results: Among literature to January 2005, 45 relevant publications were identified involving 972 subjects with good health or metabolic disease. With small reductions in GI (<10 units), increases in available 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 no trend 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 ({approx}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.

Key Words: Carbohydrate • glycemic response • glycemic index • glycemic load • meta-analysis • systematic review

INTRODUCTION

Reduction of the glycemic response to foods, via either reduced 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). Excessive glycemic response to carbohydrate foods and low unavailable 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 unavailable carbohydrate does not adversely elevate blood glucose concentrations and so is a useful nutrient source for persons with diabetes; available carbohydrate was seen as being adverse or not well tolerated (8). That some available carbohydrate might also be suitable as an energy source for persons with diabetes became evident later; this was characterized as low-GI available carbohydrate (9). The idea that unavailable carbohydrate might have a direct or indirect role in glycemic control has renewed interest, with ≥8 possible mechanisms proposed (10-18). Most recently, a short narrative review of replacement of ingredient available carbohydrate with ingredient unavailable carbohydrate suggested 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 consideration that for optimal glycemic control, unavailable carbohydrate 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 database of randomized controlled (or similar) intervention studies that could be used to address queries about the possible role of 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 described together with an assessment of the extent to which the studies achieved reductions in GI without modifying the intake of other macronutrient energy sources. Use of the database to examine the evidence on the relations between glycemic response to food and specific aspects of health is 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, abstracts, and full articles; and 3) database construction, data analysis, 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 Controlled Trials (CENTRAL; Internet: www.mrw.interscience.wiley.com/cochrane/cochrane_clcentral_articles_fs.html) to January 2005, the Elsevier Medical Database (EMBASE; Internet: www.embase.com) from 1980 to December 2003, the US National Library of Medicine database (MEDLINE via the PubMed portal; Internet: www.ncbi.nlm.nih.gov.80/sites/entrez/) from 1950 to January 2005; Elsevier's Science-Specific Search Engine on the Internet (SCIRUS; Internet: scirus.com) to January 2005, and Blackwell's Nutrition and Food Science database underpinned by the Commonwealth Agricultural Bureau International (CABI; Internet: www.nutritionandfoodsciences.org) from 1981 to January 2005. "All field" searches for "glyc(a)emic index" or "glyc(a)emic indices" or "glyc(a)emic load" alone and each together with "diabetes" were performed. Previous meta-analyses (4, 20-22) and reviews (23) were also examined to find citations and enabled a relative assessment of search success. Records were imported into a bibliographic 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 Go and 2Go, 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 quality was assessed on the basis of criteria in the Cochrane 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 examined by RT and GL separately and corrections made. Where inconsistencies remained, agreement was reached by joint consultation of the publication (or report) of the study in question. The agreed data were imported and stored in a StataCorp (College 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 3Go) when they extracted the data.


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  		TABLE 1. Inclusion criteria used when screening titles and abstracts

 

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  		TABLE 2. Exclusion criteria used when screening titles and abstracts

 

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  		TABLE 3. Conversion factors

 
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, and those with CHD. Subject groups were also classified according to body weight as normal, overweight, or obese according to body mass index (cutoffs in kg/m2 of > 25 or > 30) or the original author information.

Biochemical risk factors extracted were fasting blood glucose, fasting insulin, glycated proteins (HbA1c and fructosamine), insulin sensitivity, retrospectively calculated insulin sensitivity by homeostatic model assessment (HOMA %S), pancreatic B-cell function calculated retrospectively by homeostatic model assessment (HOMA %B), cholesterol (total, LDL, and HDL), and plasma triacylglycerols. Body weight was the only constitutional risk factor extracted. Dietary risk or nutritional factors extracted were metabolizable energy, fat, available carbohydrate, unavailable 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 categorical 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 and duration 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 Cochrane 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 asymptotic z test for the null hypothesis that the true effect is zero. Metareg was (unless stated otherwise) executed by using restricted maximum likelihood (REML) for the fitting of coefficients, Knapp & Hartung's coefficient SE for t test of significance, likelihood-ratio estimates of between-studies variance (Tau2), a likelihood-ratio test for heterogeneity, and an expression 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 were identified during sensitivity analysis by means of the statistic 1<{Delta}Bij, ie, the difference between the coefficient with and without the outlier divided by the SE without. Mean differences between dietary treatments were expressed 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 effects were combined. Studies generally reported either treatment differences in end measurements or differences in change with time (change score) for each treatment or both. When an SE difference (SED) between treatments in end measures or change in measures 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 t test, Tukey's 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 possible by several ways depending on the information available. The accuracy of a method and the availability of information for the method dictated preferences. For mean treatment difference values, the preference was m1>m2>m3>m4. m1 was the reported 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, SED1>SED2>SED3. SED1 was the reported value. SED2 was calculated from change score SEs (combined treatment SED across time). SED3 was estimated from pooled treatment end mean and SD values, the number of participants, and rp. Preferences for SDs for start and end mean values were SD1>SD2>SD3. 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 or second order regressions relating SD1 to the corresponding mean 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 perspective and precision for the outcomes (24, 25). Where further analysis is made to test the significance of a particular perspective, 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 potentially 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 inclusion in the present database. Because some publications reported more than one study, because of repeat observations in some studies (across the duration of treatment), and because some outcomes were assessed by more than one method, the 45 publications yielded 80 conditions contrasting the reported "high versus low GI diets" (Table 4Go). All included studies had control versus treatment comparisons and designs that were analyzable as either crossover (20 studies) or parallel (25 studies). Information was monitored at different times within each study (see Table 4 Go). This was used to help address questions related to the persistence, gain, or loss of effect with time during treatment. Unless stated otherwise, independence of study observations was preserved by including only the end of treatment results.


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  		TABLE 4. Study characteristics

 
The database included 972 participants per 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 studies) 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 had impaired glucose 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 of ~0 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 II b, Type III, mean duration of diagnosis of 4.7 y). The health types 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 included participants taking 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 were by diet, with intention to exchange the 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 quantities 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 carbohydrate with either protein or fat.

Treatment durations 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 or any of its indexes. Diets were intended to meet maintenance and growth requirements (37 studies) or submaintenance requirements (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 were free-living (no studies of hospitalized patients or subjects housed in metabolic wards or centers of human nutrition). Studies were from all continents: Asia (4 studies), Australasia (6 studies), Europe (19 studies), South America (2 studies), 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 sensitivity, calculated HOMA %S, calculated HOMA %B, calculated HOMA %D (the product of HOMA %B x HOMA %S), fasting plasma or serum triacylglycerols, and body weight. Data on fasting total, HDL, and LDL cholesterol were extracted, appeared to be confounded, and were not investigated further at this time. Other outcomes were macronutrient intakes, physical activity, and adverse events. Insufficient numbers of studies monitored physical activity to accumulate information in the database; 3 that did so provided no data (27-29). The following intake 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 freedom to respond); 4) fat intake (40 studies, 26 with freedom to respond); 5) calculated GL (ie, diet GI times available carbohydrate 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 4Go); 7) calculated 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 fat intake differed between treatments (30); in the other, the source of carbohydrate modifying GI differed (sucrose versus starch; 28). This information was retained in the database because it was particularly useful where study results were heterogeneous. One study reporting an effect on body weight (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 were 15 studies for which this number was ≤10 units, 15 studies with >10 to ≤ 20, 11 studies with >20 to ≤30, and 4 studies with >30 to <60. Power calculations were seldom 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 treatment effect as being due to GI if not accounted for by, for example, 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 5Go). Thus, foods were either available ad libitum or were available in wholly controlled amounts, or were available subject to limited (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 adjusted in amount if body weight changed). In the intermediate, partially controlled category, scope existed for both intakes and composition to vary (the diet was advised or largely advised). Inequalities between treatment and control diets could arise in all 3 categories according to the food choices and application of the dietary prescriptions.


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  		TABLE 5. Studies included by food intake control category1

 
Glycemic index and intake of food components
Univariate analyses using REML random effects meta-regression (Figure 1Go, Figure 2Go, and Figure 3Go) indicate elevated intakes of metabolizable energy, protein, and available carbohydrate for small reductions in GI when comparing treatments with controls. Thus, regression constants at a hypothetical "zero" change 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 4Go) suggests a trend toward greater such effect as control over food intake is progressively relaxed, from controlled to limited controlled to ad libitum. Multivariate meta-regressions (legend to Figure 4Go) including categorical variables for controlled, limited controlled, and ad libitum food intake categories support that elevations in intake of metabolizable energy, available carbohydrate, 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 4Go), likely because of an elevation occurring already in the controlled food intake category. Nevertheless, the visual inspection and the P value suggest a trend is plausible for available carbohydrate.


Figure 1
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  		FIGURE 1.. Difference in energy intake associated with difference in glycemic index achieved. Observations are grouped 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 (Tau2 + SE2). Large bubbles indicate studies with large weight. AC, available carbohydrate; ME, metabolizable energy.

 

Figure 2
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  		FIGURE 2.. Difference in protein intake associated with difference in glycemic index achieved. Observations are grouped 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 (Tau2 + SE2). Large bubbles indicate studies with small errors. AC, available carbohydrate.

 

Figure 3
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  		FIGURE 3.. Difference in the intake of available carbohydrate associated with difference in glycemic index achieved. Observations are grouped 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 (Tau2 + SE2). Large bubbles indicate studies with large weight. AC, available carbohydrate.

 

Figure 4
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  		FIGURE 4.. Confounding increments in energy and macronutrient intakes in studies 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 a hypothetical zero reduction in GI. Results are shown for each of the 3 macronutrient and energy intakes, and these for each of the 3 categories of food intake control, providing results for 9 univariate models. The table shows the levels of statistical significance attained for each of the 9 results from the univariate regressions. Also shown in the table are the levels of significance of effects overall* for food intake control categories and the trends** across the food intake control categories, as obtained by application of the multivariate model. Results are converted to units of metabolizable energy for the purpose of display within the same axis. Almost identical levels of statistical significance were given to the trend by the permutations test (0.058, 0.34, and 0.041 in place of the corresponding values in the figure's table). Univariate model: {Delta} intake = constant + slope x {Delta} GI ± Tau ± SE. Multivariate model: {Delta} intake = overall constant* + trend** x (category -1control, 0limited control, +1ad-lib) + slope1 x {Delta} GIad-lib + slope2 x {Delta} GIlimited control + slope3 x {Delta} GI control ± Tau ± SE. Contr, controlled; ltd contr., limited controlled; ad-lib, ad libitum.

 
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 1Go–3Go). Although univariate analysis indicates this to be significant for protein intake only, more sophisticated multivariate analysis using categorical variables for the level of food intake control indicated that a fall in metabolizable energy, available carbohydrate, and protein intake occurs with progressive reduction in GI, each being very significant (Figure 5Go). Furthermore, relaxation of control over food intake is associated with a trend toward greater rates of reduction in energy and protein intakes, whereas for available carbohydrate such a trend would appear plausible and more probable than not.


Figure 5
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  		FIGURE 5.. Confounding falls in energy and macronutrient intakes with progressive reduction in glycemic index. Top panel: The progressive fall in intake with reduction in glycemic index (GI) is represented by the slope in the regression models. Results are shown for each of the 3 macronutrient and energy intakes, and these for each of the 3 categories of food intake control, providing results for 9 univariate models. The table shows the levels of statistical significance attained for each of the 9 results from the univariate regressions. Also shown in the table are the levels of significance of effects overall* for food intake control categories and the trends** across the food intake control categories, as obtained by application of the multivariate model. Results are converted to units of metabolizable energy for the purpose of display within the same axis. Almost identical levels of significance were given to the trend by the permutations test (0.003, 0.12, and 0.005 in place of corresponding values in the figure's table). Univariate model: {Delta} intake = constant + slope x {Delta} GI ± Tau ± SE. Multivariate model: {Delta} intake = constant control + constantlimited control + constant + overall slope* x {Delta} GI + trend** x ({Delta} GI x category -1control, 0limited control, +1ad-lib) ± Tau ± SE.

 
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 6Go). 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-t| = 0.004) occurs with a rising slope on the regression line with progressive reduction in GI (–0.36 g/GI%glucose, P > |kh-t| = 0.013). In the ad libitum food intake group, however, such a trend is possibly reversed, with fat intake becoming reduced along with intake of food energy generally, though this remains to be confirmed (k = 5).


Figure 6
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  		FIGURE 6.. Difference in fat intake associated with difference in glycemic index achieved. Observations are grouped 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 (Tau2 + SE2). Large bubbles indicate studies with large weight. AC, available carbohydrate.

 
Unavailable carbohydrate intakes after these lower glycemic interventions are also generally elevated above the intake for control treatments (Figure 7Go); the increase achieves statistical significance only when all food intake control categories are combined. Thus, the meta-regression constant is 6.9 g/d at the hypothetical zero difference in GI (P > |kh-t| = 0.025). No subsequent fall in intake was evident as the glycemic index got progressively lower, such as seen for available carbohydrate.


Figure 7
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  		FIGURE 7.. Difference in unavailable carbohydrate intake associated with difference in glycemic index achieved. Observations are grouped 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 (Tau2+SE2). Large bubbles indicate studies with large weight. AC, available carbohydrate.

 
Of possible interest, meta-regression does indicate a falling trend for available carbohydrate intakes with increases in unavailable carbohydrate intake among these studies (Figure 8Go), which would contribute toward a reduction in GL. The relation is statistically significant when all studies are combined (regression 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 libitum foods intake groups, respectively), although in no individual category was the effect statistically significant. These slopes are consistent with an exchange of {approx}1 g available carbohydrate with 1 g unavailable carbohydrate as a result of the lower GI interventions; this is independent of the absolute decrease in GI.


Figure 8
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  		FIGURE 8.. Difference in available carbohydrate intake associated with unavailable carbohydrate intake found.

 
Heterogeneity ({tau}2 - Tau2, the between-studies variance) was highly significant among the study observations. For metabolizable energy, {tau}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, heterogeneity 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, in each case, heterogeneity remained significant (P > |X| < 0.001). No reliable (df not low) 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 categories separately (controlled, P > |kh-t| < 0.001; limited controlled, P > |kh-t| < 0.001; ad libitum, P > |kh-t| < 0.05; Figure 9Go). Each had a corresponding slope on the meta-regression lines in the range of from 2.9 to 4.5 g glucose equivalent per unit GI (% glucose) (Table 6Go). The combined average rate of fall was 3.11 g glucose equivalent per GI % 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 9Go) 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.


Figure 9
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  		FIGURE 9.. Difference in glycemic load associated with difference in glycemic index achieved. Observations are grouped 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 (Tau2 + SE2). Large bubbles indicate studies with large weight. AC, available carbohydrate.

 

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  		TABLE 6. Difference in glycemic load (GL) in intervention studies related to difference in glycemic index (GI) achieved, for each category of food intake1

 
In all studies combined, treatment differences in available carbohydrate intake contributed as much to variation in {Delta}GL as did {Delta}GI (Figure 10Go). Heterogeneity in {Delta}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 {Delta}GI 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 available carbohydrate, which would elevate GL (Figure 10Go). Such confounding may be either circumstantial or a physiologic response to low GI.


Figure 10
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  		FIGURE 10.. Contribution of difference in glycemic index (GI; {circ}) and difference in available carbohydrate (AC; •) intake to the difference in glycemic load (GL) achieved in the intervention studies. Data are (•, slope1 x {Delta} AC) and ({circ}, slope2 x {Delta} GI) from the model {Delta}GL = slope1 x {Delta} AC + slope2 x {Delta} GI + constant ± Tau ± SE. g eq, glucose equivalents.

 
Reductions in metabolizable energy and available carbohydrate intake associated with the reductions in GL achieved in these intervention studies as evident in univariate analysis of all studies combined (Table 7Go). This comprised 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 univariately, with a marginal exception for fat in the controlled food intake category.


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  		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

 
DISCUSSION

A reduction in GI achieved by dietary intervention is commonly accompanied by changes in available carbohydrate intake. The resulting change in dietary GL is therefore not solely the result of a substitution of higher GI carbohydrates by lower GI carbohydrates. This is reflected in the fact that variance in GL is explained almost equally by the variance in available carbohydrate intake that accompanies variance in GI (Figure 10Go). Also, available carbohydrate intake varies by g/g exchange of available and unavailable carbohydrate. In large part, the wider range of GL observed than expected from the change in GI alone is explained by elevated intakes of available carbohydrate for low reductions in GI followed by a trend toward progressive reduction in the available carbohydrate intake with progressive reduction in GI. Reduction in the intake of available carbohydrate, and with it metabolizable energy, occurs when GI is reduced by {approx}10 GI units on average or for GL 30 g glucose equivalent (Table 8Go). Overall, such reduction in metabolizable energy is contributed to also by reductions in protein intake.


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  		TABLE 8. Reduction in glycemic index and load required before metabolizable energy and carbohydrate intake are reduced1

 
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 for the fall in energy from available carbohydrate. The generality of an inverse relation between available carbohydrate and fat intake is therefore broken in this instance.

The present findings derive from random effects inverse variance 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 observed effects apply to combined health and body weight types (healthy, impaired glucose tolerance, hyperlipidemia, type 1 diabetes, 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 optimally reach the intended goal. One consideration would be a more direct attempt that reduces the 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.

ACKNOWLEDGMENTS

The authors are grateful to G Janecek, University of East Anglia, Norwich, UK, for preliminary discussion of the statistical approach.

The contributions of the authors were as follows—GL and RT: data collection; GL: analysis; GL and JH: writing; and TH: comment. GL and RT had no financial or personal conflicts of interest. JH is currently advising an industry group comprising food manufacturers and retailers who are preparing 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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