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Methodology for adding and amending glycaemic index values to a nutrition analysis package

Published online by Cambridge University Press:  09 December 2010

Sharon P. Levis
Affiliation:
Department of Nutrition and Dietetics, Cork University Hospital, Wilton, Cork, Republic of Ireland
Ciara A. McGowan
Affiliation:
UCD Obstetrics and Gynaecology, School of Medicine and Medical Science, University College Dublin, National Maternity Hospital, Dublin 2, Republic of Ireland
Fionnuala M. McAuliffe*
Affiliation:
UCD Obstetrics and Gynaecology, School of Medicine and Medical Science, University College Dublin, National Maternity Hospital, Dublin 2, Republic of Ireland
*
*Corresponding author: Professor F. M. McAuliffe, fax +353 1 6627586, email [email protected]
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Abstract

Since its introduction in 1981, the glycaemic index (GI) has been a useful tool for classifying the glycaemic effects of carbohydrate foods. Consumption of a low-GI diet has been associated with a reduced risk of developing CVD, diabetes mellitus and certain cancers. WISP (Tinuviel Software, Llanfechell, Anglesey, UK) is a nutrition software package used for the analysis of food intake records and 24 h recalls. Within its database, WISP contains the GI values of foods based on the International Tables 2002. The aim of the present study is to describe in detail a methodology for adding and amending GI values to the WISP database in a clinical or research setting, using data from the updated International Tables 2008.

Type
Short Communication
Copyright
Copyright © The Authors 2010

Carbohydrate-rich foods have been classified according to their induced glycaemic response since the 1970s(Reference Crapo, Reaven and Olefsky1Reference Otto and Niklas5). The concept of glycaemic index (GI) was introduced in 1981(Reference Jenkins, Wolever and Taylor6), and GI has since been used as a tool for assessing the glycaemic responses of different carbohydrate foods.

GI is defined as ‘the incremental area under the blood glucose response curve (AUC) of a test food containing 50 g available carbohydrate, expressed as a percentage of the response to the same amount of available carbohydrate from a reference food’(Reference Jenkins, Wolever and Taylor6). The gold standard method for determining the GI value of an individual food is to administer a test food containing 50 g available carbohydrate to at least ten healthy subjects and then to measure the effect on their blood glucose levels over the following 2 h. The area under the 2 h blood glucose response curve (AUC) is then calculated. On a separate day, the same subjects are given a portion of a reference food with a known GI value (i.e. glucose or white bread) containing 50 g available carbohydrate, and the AUC is calculated for this reference food. Finally, the GI of the test food is calculated for each subject by dividing the AUC of the test food by the reference AUC and multiplying by 100. The mean of these values for each of the ten subjects is the final GI value for that particular food(Reference Jenkins, Wolever and Taylor6).

The principle of GI is that foods with a low GI are digested and absorbed more slowly than foods with a high GI and, therefore, help to regulate postprandial blood glucose and insulin levels. In general, many starchy carbohydrates such as refined breads, breakfast cereals and potatoes consumed in Western countries have high GI values. These foods have a high degree of starch gelatinisation and are digested and absorbed rapidly in the body(Reference Brand-Miller and Foster-Powell7). Foods with the lowest GI values include pasta, legumes, most fruits and vegetables, and dairy products(Reference Atkinson, Foster-Powell and Brand-Miller8).

A significant limitation of GI is that it is only a qualitative measure of carbohydrate and does not take into account the effect of carbohydrate portion size on blood glycaemic and insulinaemic responses. The concept of glycaemic load (GL) was introduced in the 1990s by researchers at Harvard University to account for the quantity of carbohydrate consumed and thus described the total glycaemic effect of the diet. The GL, by definition, is the mathematical product of the GI of a food and its carbohydrate content (g) divided by 100 (GL = GI/100 × amount of available carbohydrate)(Reference Salmeron, Manson and Stampfer9).

In recent years, strong evidence has emerged, showing that the consumption of low-GI/GL diets is associated with better glucose control in patients with diabetes mellitus(Reference Thomas and Elliott10), greater fat loss in obese patients(Reference Thomas, Elliott and Baur11), lower cholesterol levels(Reference Thomas, Elliott and Baur11, Reference Opperman, Venter and Oosthuizen12) and, in general, a lower risk of developing diseases including diabetes mellitus, CVD and certain cancers(Reference Thomas, Elliott and Baur11, Reference Barclay, Petocz and McMillan-Price13, Reference Mente, de Koning and Shannon14). However, controversy remains as to whether the GI should be promoted among the general public, and some scientists regard the GI concept as too complex for public understanding(Reference Franz15, Reference Pi-Sunyer16), even though numerous international diabetes organisations fully support the use of the GI. The Australian population has had the greatest success in implementing the GI use particularly with the introduction of the GI Symbol Programme in 2002(Reference Mitchell17). However, from the limited Irish data available, it would appear that the awareness of GI and GL among consumers in Ireland is generally lower; a survey carried out in 2005 found that only 27 % of Irish adults were aware of the GI concept(Reference McGowan and McAuliffe18, 19).

The first ‘International Tables of Glycemic Index Values’ were compiled and published by Dr Brand-Miller and colleagues(Reference Foster-Powell and Brand-Miller20) in Sydney in 1995, and they contain measured GI values of 565 food products. These tables were updated in 2002, containing values for 750 food items(Reference Foster-Powell, Holt and Brand-Miller21). The most recent tables, published in 2008, hold values for 2480 individual food items(Reference Atkinson, Foster-Powell and Brand-Miller8). The tables published in 2008 differentiate GI values derived from studies that used diabetic subjects. Australian researchers also developed cut-off values to define low- and high-GI foods(22).

WISP (Tinuviel Software, Llanfechell, Anglesey, UK) nutrition analysis software is widely used throughout Ireland and the UK both in nutrition research and in dietetic clinical practice for over 20 years. The nutrient database contains over 6000 food items and approximately 125 nutrients (www.tinuvielsoftware.com). The food composition databank is derived from McCance and Widdowson's 6th Edition of The ‘Composition of Foods’ – 2002. In addition, it contains the GI values of foods from the International Tables 2002(Reference Foster-Powell, Holt and Brand-Miller21).

The aim of the present study is to describe in detail a method for adding and amending GI values to the WISP nutrition analysis package. Previous studies have documented comparable methods of estimating GI values for various other nutrition software packages(Reference Flood, Subar and Hull23Reference Schulz, Liese and Mayer-Davis26). However, to our knowledge, a similar method of adding and amending GI values to the WISP database has not been previously described in the literature.

Methods

Each of the food codes in the WISP database was manually checked against the most up-to-date published GI values in the International Tables 2008 for GI and GL values. GI values were assigned to those foods in the database, which did not previously have a GI value, where a relevant value was available. Values already present were changed if more up-to-date or relevant GI values existed in the 2008 data. In situations where values differed from those in the tables published in 2008, they were changed to the more up-to-date value. Certain food codes, which did not have an exact corresponding food in the tables, were given an estimated GI value based on a food or mean of a number of foods, considered to be the closest to the food type/description. Where possible, foods were given GI values that were derived from studies in healthy, non-diabetic subjects. However, if the only relevant value was available from studies in diabetic subjects (table 2), this value was used. In situations where a number of studies in the International Tables measured the GI of one particular food, the mean of the values was calculated and assigned to that food in the WISP database. Where available, mean GI values from studies carried out in the UK were used, as no specific GI testing has been carried out on Irish foods to date. These values were considered to be more representative of foods commonly consumed in Ireland. For each value added or changed in the database, the value considered most relevant was used. For individual foods having multiple food codes in the WISP database, the GI for each of the codes was changed in the database.

Results

Of the 5395 food codes currently in our WISP databank, 664 (12·3 %) had a new GI value assigned or amended according to the International Tables 2008. Altogether, 231 (4·3 %) foods were assigned a new GI value and 433 (8 %) GI values were amended. Table 1 provides a list of the 664 food codes, their original GI value and newly assigned or amended value, along with the rationale for changing the value. The food code refers to the code as assigned to each item in the WISP databank.

Table 1 List of WISP food codes with old and new or revised glycaemic index (GI) values

SMP, skimmed milk powder; UHT, ultra high temperature.

* table 1 refers to the first table of the International GI Tables, 2008(Reference Atkinson, Foster-Powell and Brand-Miller8); table 2 refers to the second table of the International GI Tables, 2008(Reference Atkinson, Foster-Powell and Brand-Miller8).

Discussion

As a result of the present study, there are significantly more foods in the WISP database which have an assigned GI value. We found that 12·3 % of food codes in WISP had either an old GI value or no GI value. It is interesting to note that the food groups where most amendments were made included fruits, fruit drinks, vegetables and soups, pastries and confectioneries. In addition, the GI values already assigned to foods were revised in the light of more recent GI research and changed accordingly. This will enable more accurate WISP analysis involving GI, within ongoing limitations. The changes to the GI values in the database were carried out using the most up-to-date data available. In addition, the values added or amended were made as relevant as possible to the Irish diet. GI values derived from studies involving diabetic subjects were excluded, where alternative data were available.

The methodology in the present study appears to be consistent with the small number of studies that have previously documented methods of estimating GI(Reference Flood, Subar and Hull23Reference Schulz, Liese and Mayer-Davis26), which is important if our methodology is to be implemented by other researchers in the clinical research setting. However, it must be noted that the majority of studies looking at the effects of a low-GI diet have not described their methods of GI estimation in detail.

Certain limitations exist in the current methodology. In general, the area of GI is fraught with difficulty and inaccuracy. Ideally, individual foods need to be tested under laboratory conditions to determine exact GI values, and, in cases where this is not done, there is no established method of calculating or accurately estimating the GI of a given food. Even where the GI has been measured, inter-laboratory variations have been previously documented(Reference Wolever, Vorster and Bjorck27). Differences in foods and food terminology between different countries also contribute to limitations in the use of GI values internationally. It would be of interest to see whether using the most up-to-date GI values results in stronger or weaker correlations with disease markers such as HDL-cholesterol in future research.

Several foods in the WISP database remain without a GI value; hence, any research involving WISP analysis of dietary GI will continue to be limited in its accuracy. However, adding and amending of values carried out in the present study will help to reduce this inaccuracy. Further updates to the GI values in the database are necessary. No GI data exist for foods studied in the Irish population specifically, thus limiting studies on GI carried out in Ireland. However, national dietary surveys carried out in Ireland(Reference Harrington, McGowan and Kiely28Reference O'Brien, Kiely and Harrington30) and the UK(Reference Swan31, 32) have reported similar macro- and micronutrient intakes in both countries, implying that the food choices contributing to these nutrient intakes may be comparable. However, with the limited data available, the present study aimed to assign GI values to foods, as closely as possible to the most recent published GI data.

Conclusion

To our knowledge, this is the first study to describe a method for adding and amending GI values to the nutrition software package WISP. Describing such methods will help to standardise the estimation of GI values and to increase the utility of GI in both clinical and research settings.

Acknowledgements

The present study was funded by the Health Research Board, Ireland. The authors declare no conflicts of interest. S. P. L. devised the methodology and contributed to the writing of the manuscript, C. A. M. contributed to the writing of the manuscript and F. M. M. discussed and revised the manuscript. All authors approved the final version submitted for publication.

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Figure 0

Table 1 List of WISP food codes with old and new or revised glycaemic index (GI) values