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Glycemic Response and Health |
1 From Independent Nutrition Logic, Wymondham, Norfolk, United Kingdom (GL and RT); Kellogg Europe, Den Bosch, The Netherlands (TH); and Wembley Park, Middlesex, United Kingdom (JH)
2 Presented at an ILSI Europe workshop "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 Speciality Sweeteners, and Unilever. The opinions expressed herein are those of the authors(s) 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. The impact of glycemic response on markers of health remains to be elucidated.
Objective: We assessed the evidence relating the glycemic impact of foods to measures relevant for health maintenance and management of disease.
Design: This was a systematic review and synthesis of interventional evidence from literature reported on glycemic index and markers of health through the use of meta-analyses and meta-regression models.
Results: Data from 45 relevant publications were found to January 2005. Lower glycemic index (GI) diets reduced both fasting blood glucose and glycated proteins independently of variance in available and unavailable carbohydrate intakes. Elevated unavailable carbohydrate added to improvements in both blood glucose and glycated protein control. These effects were greater in persons with poor fasting blood glucose control. No effects were seen on fasting insulin <100 pmol/L; above this, study numbers were few but consistent with prevention of hyperinsulinemia in some but not all overweight persons. Insulin sensitivity according to a variety of measurement methods was improved by lower GI, higher unavailable carbohydrate interventions in persons with type 2 diabetes, in overweight and obese persons, and in all studies combined. Fasting triacylglycerol in addition to body weight reduction related more to glycemic load than to GI. Glycemic load reduction by >17 g glucose equivalents/d was associated with reduced body weight.
Conclusions: Consumption of reduced glycemic response diets are followed by favorable changes in the health markers examined. The case for the use of such diets looks compelling. Unavailable carbohydrate intake is equally important.
Key Words: Carbohydrate • glycemic response • glycemic index • glycemic load • meta-analysis • fasting blood glucose • glycated proteins • fasting insulin • insulin sensitivity • fasting triacylglycerols
INTRODUCTION
Foods vary in their ability to provoke a postprandial glycemic response in humans. This response has been quantified in various ways, including the glycemic index (GI) (1), the relative GI (2), the glycemic load (GL) (3), and others such as the glycemic glucose equivalent (4) or the equivalent GL (5). For the present, information about GI and GL is available for a wide variety of foods (6), and such information has formed the basis of 45 relevant intervention studies in humans to ascertain whether foods with a low impact on blood glucose also have a high impact on disease risk reduction (7). Possibly central to all these approaches is GL, which may be indexed to carbohydrate or to food weight or to serving size for the purpose of describing the food and estimating cumulative intakes (8).
Although information from intervention studies has been reviewed previously (7, 9-12), none of those reviews focused on the contribution of GL versus GI and the role of unavailable carbohydrate in studies on reduced GI, or why GI appears not to show dose-dependent effects on health risk factors, issues that are addressed at present. Such information is critical to an understanding of whether interventions to reduce the glycemic impact of the diet might be useful either as a treatment modality or as a public health measure. It is already clear from epidemiologic studies in the United States and Europe that the intake of both unavailable and available carbohydrate of low GI or low GL each may have a role in prevention of metabolic disease. For example, type 2 diabetes (3, 13, 14), coronary heart disease (2), and health risk markers for both diabetes and heart disease (15, 16) each associate statistically with the glycemic impact of foods (GI or GL). Although epidemiology can provide ideas about possible public health strategy and treatment plans, interventional studies are essential to proving whether such ideas work in practice and to uncovering any unforeseen influences limiting utility of the approach.
A previous article showed that studies that lower GI as a treatment plan may also reduce the intakes of available carbohydrate and metabolizable energy significantly, though without elevating fat intake (7). Low GI diets also may elevate the intake of unavailable carbohydrate and protein to variable extents (7). We therefore implement meta-regression as well as meta-analysis as one of our investigative tools to assess what dietary factors are important in affecting health risk markers.
From a user perspective, meta-analysis and meta-regression call for somewhat different approaches. The latter requires more data, but has the potential to assess the significance of covariates. The former aims for datasets that are homogenous, ie without variation in observations between different studies of the same ‘fixed’ effect (17, 18). However, meta-regression aims for a heterogeneous dataset and attempts to explain the heterogeneity (19, 20), which may otherwise appear to be a ‘random’ effect. Doing so in the present context can provide a broader understanding of the factors influencing health risk. This would seem especially important in nutrition in general where study outcomes can often vary appreciably from one study to another. The heterogeneous data set analyzed at present includes persons who are healthy, glucose intolerant, type 1 or 2 diabetic, at primary (due to family history) or secondary coronary heart disease risk, and hyperlipidemic. Among these subject groups were either normal weight or overweight or obese persons (7). In combining data from such varied health types it was convenient to consider health risk markers to represent a continuum from the healthy state to the diseased state, and this seems to represent the situation for both diabetes and coronary heart disease (21, 22). In addition we considered study outcomes by categories: by health type, by different types of food intake control and by different body weight bands; normal, overweight, and obese (7).
MATERIALS AND METHODS
The construction of a database comprising data extracted from 45 controlled dietary intervention trials on GI reported in the literature to January 2005 has been described previously (7). Information is given there also about the literature search, the inclusion and exclusion criteria, data extraction, and the calculation methods.
The database includes observations from 972 participants per treatment arm of all ages (study group mean ages 10 to 63 y) with both males (511) and females (461) represented. Participants were either normal weight (16 studies), overweight (18 studies) or obese (10 studies) or unclassified by weight (1 study). Similar numbers of participants took part per treatment arm (770 on the high and 793 on 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) or had type 1 diabetes (7 studies, mean duration from diagnosis 3 to 16 y, one unknown duration) or had type 2 diabetes (17 studies, mean duration from diagnosis 
0 to 12.5 y, 7 unknown duration) or were at risk of primary CHD (4 studies, duration unknown) or secondary CHD (1 study, duration unknown) or had hyperlipidemia (1 study combining Type II a, Type II b, and Type III; mean duration from diagnosis 4.7 y). Studies included participants on medication. Insulin dosage was reported in all 7 studies with persons with type 1 diabetes, 2 of 17 studies with persons with type 2 diabetes, and in one of one study with participants at risk of secondary CHD. In all but one study with persons with type 2 diabetes, subjects received non-insulin medication for glycemic control (hypoglycemic agents).
Categorization of studies according health type was based on what authors stated was the study group condition. The same studies were cross categorized also according to body weight as normal, overweight or obese, again according to what authors stated or according to body mass index (bands cut at BMI > 25 or > 30 kg/m2). Interventions were by diet, with intention to exchange the form of available carbohydrate (high versus low GI). All studies were free-living, ie no subjects were hospitalized, housed in metabolic wards or centers of human nutrition.
Biochemical risk factors extracted were fasting blood glucose, fasting insulin, glycated proteins (HbA1c and fructosamine and these combined, glycated albumin and glycated protein), insulin sensitivity, retrospectively calculated insulin sensitivity by homeostatic model assessment (HOMA %S), calculated pancreatic B-cell function (HOMA %B), cholesterol (total, LDL and HDL) and fasting plasma triacylglycerols. Risk factors not reported on here were either a) complex (HOMA model) and so receives only brief comment or b) showed evidence of one or more factors confounding results of simple meta-analyses (total and fractions of cholesterol) or c) were too few to analyze separately (glycated albumin and glycated protein combined). At this time these data simply remain to be more fully assessed.
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 potential risk factors GI and the calculated GL. The last was calculated as GI multiplied by available carbohydrate intake and was expressed as g. glucose equivalents. Duration of treatment was extracted either as a continuous variable (weeks) or as a categorical variable by treatment duration < or 
12 wk.
Data were subjected to random effects meta-analyses and meta-regressions using meta and metareg the latter using restricted maximum likelihood (REML) in Stata 9.2 SE (StataCorp, TX) according to Cochrane guidelines (17). Studies are weighted by inverse variance. Analyses are reported as either random effects or when variation between studies was zero they are reported as fixed effects. Computation of the study effect and dependent SE were as described previously (7) for inputting as the mean effect (Ø) and the SEM effect (seØ) study-by-study (in STATA terminology). For comparison of treatments we use methods difference versus methods average (23). Discussion of this and other approaches can be found elsewhere together with developments in multivariate meta-analyses (20). While the behavior of univariate meta-regression allows valid inferences given valid datasets, the validity and behavior of bivariate meta-regression has only recently been investigated and recommended over multiple uses of univariate analyses (19). Bivariate meta-regression is reported to inflate heterogeneity without systematic bias in the coefficients; this tends to widen the CIs and provide conservative estimates of significant effects. It seems reasonable for the present to make a similar assumption about multivariate meta-regression models in general. We assume ‘measurement error’ is not a cause of bias. This appears reasonable when investigating relations with dietary variables that are highly heterogeneous, as in the present dataset (7). However, failure of this assumption would lead to under strength regression coefficients and conservative estimates of the statistical significance of results. Abbreviations used in the results are provided in the footnotes to Table 1
.
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Fasting blood glucose concentrations
Thirty-six studies reported fasting blood glucose concentrations in either venous plasma or capillary blood and achieved reductions in GI ranging from 4 to 32, with accompanying reductions in GL ranging from 6 to 134 g glucose equivalents. Study durations ranged from 2 to 26 wk.
Subjects were reported as normal healthy (8 studies), glucose intolerant (2 studies), type 1diabetic (4 studies), type 2 diabetic (16 studies), type 1 and type 2 diabetic observations combined (1 study) and at risk of primary (4 studies) or secondary coronary heart disease (1 study). For analysis, fasting venous plasma glucose concentrations were converted to the equivalent capillary blood glucose concentrations (fasting capillary blood glucose = – 0.61 + 0.94 x fasting venous plasma glucose (24)).
The totality of evidence on fasting blood glucose (Figure 1) includes intermediate observations made at timed intervals up to the end of the treatment period in addition to the end of treatment observations. The figure is plotted according to the convention for methods comparison (difference plotted against average). Visual inspection shows no overall treatment difference in fasting blood glucose concentration to be evident when the study population mean is approx. 5 mmol/L. When above 5 mmol/L (22 studies) the lower glycemic treatment outcome departed increasingly from that on the higher glycemic treatment, with the difference being greatest for the highest average of treatment means. After omitting the intermediate observations to obtain independent information, meta-regression revealed a significant relation (slope –0.30 SE 0.10 
mmol/L per mmol/L; P< |kh-t| = 0.010, df 20, Tau 0.6 mmol/L). Further omitting of observations so that all studies had fasting blood glucose > 5 mmol/L and all study participants were fed diets intended at energy maintenance resulted in 18 studies remaining for which meta-regression again indicates a significant relation (Figure 2; slope –0.3 SE 0.12 mmol/L per mmol/L, P < |kh-t| = 0.026, df 16, Tau 0.5 mmol/L).
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), the regression slope remains essentially unaltered at around – 0.3 mmol/L per mmol/L (range – 0.26 to – 0.34). Such similar slopes suggest that none of these dietary factors were sufficiently determinant, either uniquely by themselves or collinearly, to displace fasting blood glucose as an over-riding determinant of the effect size in simple meta-regressions; that is regressions not including interactive terms.
It was found that many interventions that intended to lower the GI of a diet also resulted in increased intakes of unavailable carbohydrates and varied intake of available carbohydrate causing usually a decreased GL. It is important to try to disentangle the effects of each of these. To understand the separate roles of reduced GL of foods and increased unavailable carbohydrates intake on fasting blood glucose, the interaction between these 2 variables and the severity (or impairment) of glycemic control were studied. Severity (S) is quantified here by excessive fasting blood glucose (S = FBG – 5 mmol/L). In this more complex model both interactions were significant (Table 2). Therefore GL clearly has an effect independently of the unavailable carbohydrate content of the diet. Likewise unavailable carbohydrates appear to act independently of GL. Both act to control fasting blood glucose as can be seen in Figure 3 (upper). This helps to explain a reported variability in the effectiveness of the reduced GI diets and illustrates that the GL is as effective as the unavailable carbohydrate in lowering fasting blood glucose. Of the 15 studies in Figure 3 upper, no more than 3 reached 12 wk treatment duration.
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Further, observations (Table 2) indicate that the size of the effects of GL or GI and unavailable carbohydrate is dependent on the persons consuming the diet, that is their level of blood glucose control (S, the severity of impairment); this is all important in enabling any understanding of these studies.
Maximum control of fasting blood glucose was achieved by elevation of unavailable carbohydrate intake together with a lowering of GI. Unavailable carbohydrate had stronger impact than GL on a per g weight basis but remained approximately equally important when taken together with the range of intakes (Figure 4 upper).
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The totality of evidence shows treatment diets to lower the blood glycated protein concentrations (Figure 5). The effect is greater for some persons than others dependent on their level of glycemic control. This figure combines information on fructosamine and glycated hemoglobin, observations from the end of studies, and observations from intermediate time points. Choosing only the independent observations (discarding intermediate time points), statistical significance of this trend toward greater effect in persons with poor blood glucose control is achieved both for the 2 glycated protein types combined and for fructosamine alone; for HbA1c alone, the effect is more probable than not (Table 3). The effect appears greater after adjustment for the half-life of fructosamine (2.5 wk) and HbA1c (4.5 wk). This is because many of the studies are shorter in duration than the 3 half-lives necessary to avoid dilution of effect by the subjects pretreatment diets (ie <12 wk for HbA1c).
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, Figure 4).
Again attempts were made to assess whether after accounting for unavailable carbohydrate intake, the remaining variation attributable to change in GL could be partitioned between GI and available carbohydrate intake. For these studies, the change in GI was in the range from –5 to –31 while the range of change in available carbohydrates intakes (g/d) was from + 43 to –40. Although correlation between the 2 dietary measures was high (r2-adjusted, 0.78) bivariate analysis resolved that any effect of available carbohydrate intake was non-significant (P < |kh-t| = 0.54) while GI has significant effect (P < |kh-t| >0.03), a result consistent with observations on fasting blood glucose. Hence variation in fructosamine concentrations is contributed to in the greater part by variations in unavailable carbohydrate intake and GI (Figure 3 and 4 lower).
Altogether, the observations made imply optimum reduction in fasting blood glucose and fructosamine occur with intakes of unavailable carbohydrate at or above 25g/d (Figure 3 and 4), GL at or below 100g (glucose equivalents) per d (Figure 3) or GI < 45 (Figure 4). Of the 15 studies in Figure 3 and 4 on fasting blood glucose and the 12 studies in Figure 3 and 4 concerning fructosamine, no more than 3 reached 12 wk treatment duration.
Fasting insulin
Eighteen relevant studies reported on fasting insulin concentrations. No treatment effects or meta-regression trends were observed for concentrations < 100 pmol/L in the totality of evidence (Figure 6) or in end of study results whether for all studies combined or by health type or body weight band (Table 4). Fasting insulin concentrations are difficult to interpret without reference to corresponding fasting blood glucose concentrations (25) and so the absence of change in insulin concentration does not necessarily mean an absence of effect on insulin production or effectiveness. Treatment effects on the insulin concentration may depend on hyperinsulinemia > 100pmol/L (Figure 6, Table 4) which developed on the high GI/GL diets in a small number of studies in some overweight or obese persons but not all.
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). For all 18 studies combined, the lower glycemic, variably higher unavailable carbohydrate intervention results in a 20% improvement in insulin sensitivity (P > |z| = 0.004).
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By body weight band, statistically significant increases in insulin sensitivity are apparent for overweight subjects combined (11 studies), obese subjects combined (3 studies) and both these groups combined (14 studies). The mean increase for all normal weight subjects combined (4 studies) suggests a potentially sensitive response but it does not achieve statistical significance.
All methods of assessing insulin sensitivity yield combined means that are positive toward improved sensitivity, and statistically significant in some though not in others (Table 5). Comparison between methods is hampered by the occurrence of heterogeneity and the small number of studies. Most observations were available for HOMA PP (8 studies) the combined mean for which indicates a 30% improvement in insulin sensitivity of glucose disposal in the postprandial state.
For all studies and methods combined (Table 5) the mean improvement in studies of < and 12 wk treatment duration was similar at 20% and 16% respectively for a total of 14 and 4 studies respectively, and with similar 95% CIs (
0 to 35% each). The larger number of studies in the short term indicate a significant effect while over the longer term the effect appears more probable than not.
Fasting plasma triacylglycerols
Thirty-two studies reported on fasting plasma triacylglycerol concentrations. The observations were for healthy subjects (7 studies), persons with type 1 diabetes (4 studies), persons with type 2 diabetes (13 studies), subjects at risk of primary CHD (4 studies), glucose intolerant subjects (1 study), and hyperlipidemic groups (1 study with 4 types). Six of the studies provide repeated observations at various time points. The total evidence included 45 effect estimates (Figure 7). There was no clear evidence for a difference in fasting triacylglycerols following treatment with the lower GI/GL intervention, although triacylglycerols concentrations were reduced among groups with the highest concentrations.
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). Statistically significant effects lowering fasting triacylglycerols were evident for lower GL (dependent on GI) and higher fat intake (independent of GI).
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). Each health type (healthy, type 1 and 2 diabetic and CHD risk) and body weight band (normal, overweight, obese) contributed to varying extends to the statistical significance found for all studies combined, although each category alone was without statistically significant effect. Over all a 10% fall in fasting triacylglycerols requires achievement of a drop in GL by 30 to 100g eq./d for those categories showing large or significant effects (Figure 8).
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Meta-regression indicates body weight to fall with reduction in GL and vice versa (Table 7). The trend was statistically significant for all studies combined and occurred in 2 of the 3 food intake control categories: ad libitum and limited controlled intake but not in the controlled food intake category. Combining the ad libitum and limited controlled food intake categories, reductions in body weight occur when GL is reduced by at least 17 SE 12 g eq./d (intercept on x-axis for y = 0) and most consistently when the reduction is by > 42 g. eq./d (95%CI) (Figure 9).
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).
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Low glycemic response diets are proposed as a means to favorably influence physiologic parameters implicated as markers for conditions including overweight and obesity, diabetes mellitus and risk of coronary heart disease. The present meta-analyses provide evidence that supports the view that intervention to reduce the diets glycemic impact will favorably affect several health risk markers.
For individuals with fasting blood glucose concentrations in excess of 5 mmol/L, there is evidence that fasting blood glucose is reduced by the consumption of lower GI or GL. Higher unavailable carbohydrate has an effect that is additive to that of lower GI (and resulting GL), ie both together have optimum effect. Further, the evidence indicates that the effect of consuming lower GI and higher UC diets is greater in absolute units in persons with poorer control of blood glucose, including persons with both type 1 and type 2 diabetes.
Similarly, for individuals with fasting blood glucose concentrations in excess of 5 mmol/L, there is evidence that both lower GI (and so lower GL) and higher UC in diets reduce the levels of glycated proteins. The evidence is stronger for fructosamine than for glycated hemoglobin. Again, the size of the effect is greater in persons with poorer control over blood glucose.
Although the data suggest that the consumption of lower GI/GL, higher unavailable carbohydrate diets lead to a reduction in fasting insulin concentrations in overweight or obese individuals who had fasting concentrations > 100 pmol/L, the evidence is weak due to few studies. In individuals with fasting insulin levels < 100 pmol/L, meta-analysis provides evidence of no significant effect.
Overall, this review provides evidence that insulin sensitivity is improved by consumption of lower GI/GL, higher unavailable carbohydrate diets. Although the increase is evident in all body weight bands combined, and in nondiabetics, the increase is not shown to be statistically significant in subjects in the normal body weight range.
While a simple meta-analysis of interventions with low GI/GL, variably higher unavailable carbohydrate diets did not provide a combined mean effect on fasting triacylglycerol concentrations, a more sophisticated analysis suggests an apparent absence of effect is due to a confounding factor. After adjustment for fat intake unrelated to GI or load (7), the evidence shows reductions in GL to reduce fasting triacylglycerols.
Over all studies relevant for the purpose taken together, it is evident that intervention with low GI/GL, high unavailable carbohydrate diets do associate significantly with reductions in body weight. Such evidence is consistent with evidence on reductions in food energy intake (7). The present analysis suggests a minimal reduction in GL or index is necessary for this effect to occur (Figure 9 and Table 8 in ref. 7). This could arise either because a threshold needs to be surpassed to achieve an effect or because dietary advice is imprecisely implemented by the researchers, health professionals, and consumers involved.
Although the evidence generally supports the view that intervention with a low GI/GL and higher unavailable carbohydrate diet is associated with favorable changes in a number of health risk markers relevant to persons who are overweight, obese, diabetic or at risk of coronary heart disease, the evidence also indicates for some markers that not all subjects respond equally. For reductions in fasting blood glucose concentrations and glycated proteins, there is evidence that the effects are greatest among those with poorest glycemic control. For reduction in fasting blood glucose, the threshold for this effect is at about 5 mmol/L. For glycated proteins the threshold is at about 3.5 glucose mmol/L. Unpublished meta-analysis of the effect of the low GI sugar fructose indicates a threshold of effect on glycated hemoglobin at about 4% HbA1c (Livesey and Taylor, unpublished observations, 2007). Hence absence of effect in those with good glycemic control is not evident. Also, when fasting glucose is low (<5 mmol/L) and study precision is accounted for, then below median normal fasting blood glucose is elevated to a small but statistically significant extent by such diets (in which case the ‘threshold’ mentioned above is a pivot point). The evidence for a small rise in fasting glucose below 5mmol/L comes with evidence of publication bias consistent with hesitance to report such normalization. While the present data taken together with influence on insulin sensitivity points toward improved control among nondiabetics (in addition to persons with diabetes), a role in disease prevention remains to be established.
The studies reviewed had the intention to treat by reducing the GI of available carbohydrate ingested. Compliant treatments show variable elevations in unavailable carbohydrate and protein intake and reductions in available carbohydrate, metabolizable energy, and GL when food intake was not firmly controlled (7). Even when intake was controlled the reductions in GI were accompanied by significant reductions in available carbohydrate intake and so in GL (7). However, among the health markers examined 3 factors were clearly important, glycaemic index, available carbohydrate intake and unavailable carbohydrate intake. Altogether, improvement in the control of health markers cannot be said to be clearly due to GI alone.
Overall, considering all the markers of health examined here together, it is difficult to establish whether GI is stronger than GL, in part due to their co-linearity (7) and in part because some markers appear affected by available carbohydrate intake while others are not. While there is no affirmative evidence that variation in available carbohydrate intake (± 50g/d) influences fasting blood glucose or glycated proteins in these studies, there is evidence that reductions in available carbohydrate intake do accompany or play a role alongside GI in the beneficial effects of lower GL on fasting triacylglycerols (herein), body weight (herein) and food intake (7). On the other hand, it must also be considered that limiting harm due to glycaemic load by attempts to lower available carbohydrate intake will risk elevating harm from higher total or saturated fat intake. Benefits of reduction in available carbohydrate intake would be expected only in a context of no increase in total or saturated fat intake. It is evident in the studies reviewed here that lower available carbohydrate intake following marked reductions in glycaemic index were not accompanied by elevation in fat intake (7). In this context, the balance favors reduced harm.
The meta-analyses confirm that GI or load has a significantly stronger relation with glycemic control than does available carbohydrate in the present context in which change in fat intake is minimal. Attaining an optimum diet for health therefore requires consideration of glycemic impact of foods eaten in preference to consideration of carbohydrate content alone, each in the context of a balanced diet meeting nutrient requirements. The extent to which reduction in GL using ingredient carbohydrates has similar effects to those in the studies reviewed requires to be more fully evaluated. Fructose appears at least equally effective for HbA1c control (Livesey and Taylor, unpublished observations, 2007) and added unavailable carbohydrate can be effective in glycemic control (8, 26, 27).
The evidence shows also that, independently of increasing unavailable carbohydrate intake, reductions in GI (and so GL) do improve glycemic control. For optimal control over fasting blood glucose concentrations in persons with diabetes, the evidence points toward the need for foods that are of low impact on glycemia (independently of fat intake) and foods that are high in unavailable carbohydrate. Diets that achieve one without the other would be suboptimal for diabetes control.
ACKNOWLEDGMENTS
The authors thank TH for considerable help with structuring a report before a workshop in Nice, which provided comments on the preliminary results, and to G Janecek, University of East Anglia, Norwich, United Kingdom for preliminary discussion of the statistical approach.
The contributions of the authors were as follows—GL and RT: data collection; GL: analysis; JH and GL: 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 glycemic index in the labeling of food products. TH works for Kellogg.
REFERENCES
This article has been cited by other articles:
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  M. N Woods, C. A Wanke, P.-R. Ling, K. M Hendricks, A. M Tang, T. A Knox, C. E Andersson, K. R Dong, S. C Skinner, and B. R Bistrian Effect of a dietary intervention and n-3 fatty acid supplementation on measures of serum lipid and insulin sensitivity in persons with HIV Am. J. Clinical Nutrition, December 1, 2009; 90(6): 1566 - 1578. [Abstract] [Full Text] [PDF]
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n weights for 36 end of study results and 14 intermediate results, totaling 50.
) combinations of GL and UC shown in the figure. All 9 estimates were expressed as a fraction of the central values at 15 g UC/d and 150 g (glucose equivalents)/d GL.
0.02.