Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-05T12:32:01.581Z Has data issue: false hasContentIssue false

The potential application of a biomarker approach for the investigation of low-calorie sweetener exposure

Published online by Cambridge University Press:  14 January 2016

C. Logue*
Affiliation:
Northern Ireland Centre for Food and Health, Ulster University, Coleraine BT52 1SA, UK
L. C. Dowey
Affiliation:
Northern Ireland Centre for Food and Health, Ulster University, Coleraine BT52 1SA, UK
J. J. Strain
Affiliation:
Northern Ireland Centre for Food and Health, Ulster University, Coleraine BT52 1SA, UK
H. Verhagen
Affiliation:
Northern Ireland Centre for Food and Health, Ulster University, Coleraine BT52 1SA, UK National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands European Food Safety Authority, Parma, Italy
A. M. Gallagher
Affiliation:
Northern Ireland Centre for Food and Health, Ulster University, Coleraine BT52 1SA, UK
*
*Corresponding author: Caomhán Logue, email [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Low-calorie sweeteners (LCS) are commonly used as sugar substitutes in the diet to provide a desired sweet taste without increased energy intake. The number of LCS available on the market has increased considerably over the years and despite extensive evaluation of their safety prior to approval, debate continues around the effects of consumption on health. In Europe, Member States are obligated to monitor exposure to LCS and methods currently used tend to rely on self-reported dietary intake data alongside LCS concentrations in products. However, the acquisition of accurate data can be costly in terms of resources and time and are inherently imprecise. Although LCS are intensely sweet, they are chemically diverse and a limitation of many studies investigating the health effects of consumption is that they often fail to discern intakes of individual LCS. An approach which objectively assesses intakes of individual LCS would therefore allow robust investigations of their possible effects on health. Biomarker approaches have been utilised for the objective investigation of intakes of a range of dietary components and the feasibility of any such approach depends upon its validity as well as its applicability within the target population. This review aims to provide an overview of current understanding of LCS intake and explore the possibility of implementing a biomarker approach to enhance such understanding. Several commonly used LCS, once absorbed into the body, are excreted via the kidneys; therefore a urinary biomarker approach may be possible for the investigation of short-term exposure to these compounds.

Type
Nutrition Society Irish Section postgraduate meeting
Copyright
Copyright © The Authors 2016 

Since the 1980s, the prevalence of obesity has more than doubled globally( 1 ) with significant implications in terms of the development of chronic conditions such as CVD, type 2 diabetes mellitus and hypertension( Reference Butland, Jebb and Kopelman 2 ). Given that the overall pattern of weight gain has been attributed to a culmination of a host of factors( Reference Butland, Jebb and Kopelman 2 ), it is of no surprise that a multi-faceted approach has been proposed to address the issue. One factor implicated in the development of weight gain, as well as a range of adverse health outcomes, is the over-consumption of non-milk extrinsic sugars from foodstuffs, particularly from sugar-sweetened beverages( Reference Malik, Schulze and Hu 3 Reference Xi, Huang and Reilly 8 ). A logical strategy to address these issues would be to reduce the intakes of these beverages. However in practice, this approach may be difficult given the apparent innate preference for sweet taste among human subjects( Reference Drewnowski, Mennella and Johnson 9 ). To satisfy the desire for sweet-tasting products without exacerbating the problem of overconsumption of non-milk extrinsic sugars, low-calorie sweeteners (LCS) have become more commonly used as substitutes in a wide range of products( Reference Zygler, Wasik and Namiesnik 10 ).

This review will provide an overview of current understanding in relation to intakes of LCS and will consider existing methods for monitoring exposure to these compounds. It will also explore the possibility of implementing a novel biomarker approach for investigating LCS exposure, which could be used to gain a better understanding of LCS consumption and health.

Low-calorie sweeteners

LCS can be broadly divided into two groups; bulk sweeteners (e.g. polyols), which have a similar sweetness to sucrose and are also commonly used for other functional purposes in products( Reference Mortensen 11 , Reference Ghosh and Sudha 12 ), and intense sweeteners, which are many times sweeter than sucrose and are mainly used only for their sweetening properties( Reference Mortensen 11 ). The focus of the remainder of this review will be on intense sweeteners. Intense sweeteners which are currently approved for use in the European Union (EU), together with their general characteristics, are listed in Table 1. The use of LCS has increased considerably over recent years and they can now be found in a wide variety of food and non-food products( Reference Zygler, Wasik and Namiesnik 10 ). The level of sweetness that they impart, relative to sucrose, ranges from thirty times sweeter (cyclamates) to 37 000 times sweeter (advantame) and despite possessing the common characteristic of being intensely sweet, they represent a chemically diverse group of compounds. The overall contribution of LCS to energy intake is negligible and it is upon this basis, along with the fact that they are also non-cariogenic, that they are commonly used. Prior to approval, the safety of LCS consumption in human subjects is established through extensive evaluation of existing safety and toxicological data, usually culminating in the assignment of acceptable daily intake (ADI) and maximum permitted use levels( Reference Renwick 13 Reference Logue, Peters and Gallagher 15 ).

Table 1. Intense sweeteners approved for use in Europe

NHDC, neohesperidine dihydrochalcone; ADI, acceptable daily intake; BW, body weight.

* Relative to sucrose.

Expressed as steviol equivalents.

Acceptable daily intake

An ADI, which is expressed as mg/kg body weight, has been defined as the amount of a chemical that can be consumed daily over the period of a lifetime with no appreciable risk to health( 16 ). To assign an ADI, long-term, multiple-dose animal studies are typically used to initially establish the no observed adverse effect level by identifying the highest level of exposure which causes no adverse effects in the most sensitive species of animals( Reference Renwick 13 Reference Logue, Peters and Gallagher 15 ). To account for variation between species and within human subjects, safety factors are then applied (Fig. 1). In the absence of serious adverse effects (e.g. teratogenicity) in animal studies, an overall safety factor of 100 is usually applied to the no observed adverse effect level( Reference Herrman and Younes 14 ), although higher or lower safety factors can also be applied.

Fig. 1. (Colour online) Safety factors applied to the no observed adverse effect level to establish the acceptable daily intake (Source: Logue et al. ( Reference Logue, Peters and Gallagher 15 )). ADI, acceptable daily intake; NOAEL, no observed adverse effect level.

Health effects of low-calorie sweeteners intake

Despite extensive evaluation of the safety of LCS, the potential health effects of consumption has remained topical within the area of nutrition research( Reference Mortensen 11 , Reference Kroger, Meister and Kava 17 , Reference Marinovich, Galli and Bosetti 18 ). Moreover, the long-term efficacy of using LCS in place of non-milk extrinsic sugars as a weight management tool has yet to be conclusively established( Reference de la Hunty, Gibson and Ashwell 19 Reference Swithers 25 ). A recent meta-analysis of randomised controlled trials and prospective cohort studies into the effects of LCS consumption on weight status, reported that in prospective cohort studies, a small positive association was observed between LCS consumption and increased BMI (0·03 kg/m2) but not body weight or fat mass. However in randomised controlled trials, LCS consumption was associated with modest, albeit significant reductions in body weight (−0·80 kg), BMI (−0·24 kg/m2), fat mass (−1·10 kg) and waist circumference (−0·83 cm)( Reference Miller and Perez 26 ). The potential mechanisms by which LCS consumption might influence appetite and food intake were reviewed by Mattes and Popkin( Reference Mattes and Popkin 27 ) and they concluded that, although the evidence was lacking for many putative mechanisms (such as cephalic phase stimulation, gut peptide response and increased palatability of products), further research in the free living population via long-term  randomised controlled trials was warranted. In addition to body weight status, LCS consumption in relation to a range of adverse health outcomes including cancer( Reference Weihrauch and Diehl 28 , Reference Schernhammer, Bertrand and Birmann 29 ), CVD( Reference Duffey, Steffen and Van Horn 30 , Reference Pereira and Odegaard 31 ), diabetes mellitus( Reference Wiebe, Padwal and Field 32 , Reference Imamura, O'Connor and Ye 33 ) and preterm deliveries( Reference La Vecchia 34 ) has also been investigated. No convincing evidence of a risk in the development of any adverse effects as a result of LCS consumption has been presented to date. The French Agency for Food, Environmental and Occupational Health and Safety recently undertook a review of the evidence with regard to many of these outcomes and concluded that, although the data do not demonstrate a risk, further research is required to establish the long-term beneficial effects of LCS consumption on health( 35 ). Furthermore, the French Agency for Food, Environmental and Occupational Health and Safety also recommended that future cohort studies should aim to distinguish the intakes of individual LCS, so that the effects of single and multiple LCS use can be investigated more effectively( 35 ). The ongoing debate around the safety of LCS consumption has served to fuel a somewhat negative perception within the lay media and the population in general. A recent study by Harricharan et al.( Reference Harricharan, Wills and Metzger 36 ) investigated the attitudes of dietitians from several European countries towards LCS and highlighted a diversity of opinions ranging from negative, ambivalent to positive; they suggested the provision of guidance similar to that which has been undertaken in the USA( 37 ).

Assessment of exposure to low-calorie sweeteners

In accordance with EU Regulation 1333/2008, EU Member States are required to monitor levels of LCS intake within the population to ensure that the ADI is not being exceeded( 38 ). Assessment of exposure to LCS requires the consideration of food intake data along with LCS concentrations within products and can be expressed, according to the International Programme on Chemical Safety( 39 ), as:

$$\displaystyle{{\Sigma ({\rm Food\; LCS\; concentration} \times {\rm Food\; consumption})} \over {{\rm Body\; weight}}}$$

Given that currently there are almost 400 food additives approved for use in the EU, the potential costs associated with collecting accurate intake data at the level of the individual are considerable. However, as the primary aim of monitoring is to ensure that the ADI is not being exceeded( 40 ), a tiered approach is usually adopted, beginning with a conservative screening step and progressing to more refined, and thus costly assessments, if indicated( 39 , 40 ).

Tiered approaches for assessment of low-calorie sweeteners exposure

An example of a tiered approach for assessing exposure to LCS is illustrated in Fig. 2. Tier 1, which is carried out at European wide level, will usually consist of an initial screening method designed to generate a highly conservative estimate so that potentially at risk groups can be easily identified. Examples of such screening methods include the Sweetener Substitution Method( Reference Renwick 41 ) and the Danish Budget Method( Reference Hansen 42 ). In the Danish Budget Method, conservative assumptions are made about the occurrence of LCS in food and beverages and an individual's energy and fluid requirements; this will result in an overestimate of exposure. If, following the initial screening step, the ADI is deemed to be exceeded, further more refined assessments would then be indicated. Tier 2 involves the assessment of actual consumption of foods and beverages known to contain LCS along with the maximum permitted use levels. A Tier 3 assessment would be indicated if the ADI is estimated to be exceeded following the Tier 2 assessment and this will consist of a more refined calculation using actual levels of consumption and actual concentrations in products in order to further elucidate the risk.

Fig. 2. Tiered approach for food additive exposure estimates (adapted from EC( 40 )). MPLs, maximum permitted levels; ADI, acceptable daily intake.

Methods of assessing exposure to low-calorie sweeteners

Dietary intake is traditionally assessed using tools such as food diaries, 24-h recalls and FFQ( Reference Thompson, Subar, Coulston, Boushey and Ferruzzi 43 ). These assessment tools rely on self-reported data and a number of inherent limitations exist which often results in inaccuracies( Reference Livingstone, Prentice and Strain 44 Reference Kipnis, Midthune and Freedman 47 ). The optimal methodology for assessing habitual LCS exposure is a 1–2-week prospective, brand level diary, including information on portion sizes along with brand-specific information on LCS concentrations in products( Reference Renwick 48 ). Given that obtaining such refined individual level data can be time and resource intensive for investigators, as well as labour intensive for participants, a variety of dietary assessment tools have been utilised in the past including 24-h recalls( Reference Devitt, Daveman and Buccino 49 Reference Ha, Ha and Choi 53 ), FFQ covering various durations( Reference Toledo and Ioshi 54 Reference Lino, Costa and Pena 57 ) and food diaries lasting 2 d( Reference van Rooij-van den Bos, Konings and Heida 58 ), 5 d( Reference Garnier-Sagne, Leblanc and Verger 59 ), 7 d and 14 d( Reference Leclercq, Berardi and Sorbillo 60 ). Some studies used a combination of retrospective and prospective intake data in order to first identify potential high consumers and then to further investigate these high consumers( Reference Arcella, Le Donne and Piccinelli 61 , 62 ). It is apparent therefore that many of the published studies do not satisfy these criteria, potentially making it difficult to make direct comparisons.

Data on LCS concentrations in products can be obtained from a number of sources; the least resource intensive, yet least accurate, is to use maximum permitted use levels. This source of information is commonly used in initial, conservative estimates of exposure, or when it is not possible to obtain more refined data( Reference Renwick 48 ). However, the actual levels used in products are unlikely to meet these values, particularly when a blend of two or more LCS is used within a particular product, as is frequently done. Therefore using maximum permitted use levels in the calculation will result in an overestimate of exposure. Concentrations in specific products have been obtained from manufacturers( Reference Devitt, Daveman and Buccino 49 , Reference Serra-Majem, Bassas and Garcia-Glosas 56 , Reference Leclercq, Berardi and Sorbillo 60 64 ), providing a more accurate measure; however, difficulties in obtaining such information in the past have been highlighted( 65 , 66 ). Furthermore, with product innovations and changing tastes among consumers, the concentrations of LCS within products are likely to evolve over time, potentially introducing error into subsequent estimates of exposure unless ongoing data are received from manufacturers. Another method for determining LCS concentrations in products is to directly measure them analytically and to this end, numerous methodologies for the determination of LCS in foods and beverages have been published( Reference Zygler, Wasik and Namiesnik 10 ). Recent studies investigating LCS exposure have adopted this approach( Reference Chung, Suh and Yoo 50 Reference Ha, Ha and Choi 53 , Reference Lino, Costa and Pena 57 , Reference van Rooij-van den Bos, Konings and Heida 58 ) and, although it allows for a more accurate and objective measure of LCS concentrations in products, it is also likely to be costly. Furthermore, with the ubiquity of LCS in today's market and the trend towards more widespread use, adopting such an approach as part of future assessments may prove unfeasible.

Recent developments in low-calorie sweeteners exposure assessment

A desire to harmonise food additive exposure assessment across the EU led to guidelines on how Member States should collect intake data for exposure assessments( 40 ) and this was further enhanced through the recent implementation of the Flavourings, additives and food contact exposure tool (FACET)( 66 ). As a result of the FACET project, a publicly available exposure software package has been released (available at: http://expofacts.jrc.ec.europa.eu/facet/login.php). One of the strengths of the FACET project is that cooperation was obtained from FoodDrinkEurope, an alliance of national food and drink industries in the EU, for the provision of information on the concentrations of targeted food additives in products, including aspartame and acesulfame-K and this will result in a more sustainable system of monitoring exposure over time.

Alternatively, the Monte Carlo risk assessment software tool, developed as part of the EU-wide ACROPOLIS (Aggregate and cumulative risk of pesticides: an on-line integrated strategy) project, may also be used for the assessment of exposure to chemicals in foods (available at: https://mcra.rivm.nl/Account/Login). This tool is designed to allow a cumulative assessment of exposure to multiple chemicals from multiple sources( Reference van Klaveren, Kennedy and Moretto 67 ) and has been successfully applied for the assessment of acute and chronic exposure to a group of pesticides in a number of European countries( Reference Boon, van Donkersgoed and Christodolou 68 ). Like the FACET tool, the ACROPOLIS tool is freely accessible.

Actual low-calorie sweeteners exposure

European based studies carried out over the last 20 years have largely reported that the intakes of LCS fall well within the ADI (Table 2), with only the intake of cyclamate potentially exceeding the ADI in some population sub-groups. Similar results were reported in studies conducted in Korea, Australia and New Zealand and Brazil( Reference Chung, Suh and Yoo 50 Reference Ha, Ha and Choi 52 , Reference Leclercq, Berardi and Sorbillo 60 ). A review by Renwick( Reference Renwick 48 ) reported that the overall intakes of LCS had not increased significantly during the preceding decade, although it has been reported elsewhere that the numbers of people consuming LCS were increasing( Reference Sylvetsky, Welsh and Brown 69 ).

Table 2. Exposure estimates of low-calorie sweeteners in Europe over the past 20 years

No data reported for other LCS. ‘–’ indicates no data reported.

Ace-k, acesulfame-K; ADI, acceptable daily intake; Asp, aspartame; Cyc, cyclamate; Sac, saccharin; Suc, sucralose; FSA, Food Standards Agency.

* Mean % ADI presented with high consumers (% ADI) where available.

Nutritional biomarkers: concepts and considerations

The focus of this review will be on nutritional biomarkers of exposure and within this specific context, biomarkers will be defined as cellular, biochemical, analytical, or molecular measures that are obtained from biological media such as tissues, cells or fluids and are indicative of exposure to an agent( 39 ). As such, nutritional biomarkers of exposure, by their very nature, are independent of the sources of bias associated with self-reported dietary intake data and can therefore provide a more objective measure of intake( Reference Kaaks, Riboli and Sinha 70 , Reference Bingham 71 ). Such biomarkers can be used as measures of intake, for the assessment of nutritional status or in order to validate more traditional dietary assessment tools( Reference Potischman and Freudenheim 72 ). Although the application of a biomarker approach is not new in nutrition research, it has been suggested that many existing nutritional biomarkers have not been properly validated and the field of biomarkers is yet to be fully exploited( Reference Jenab, Slimani and Bictash 73 ). A number of considerations are important for properly implementing such an approach; the target biomarker must be specific to the food or food component, be reproducible and be sensitive to changes in intakes over time( Reference Hedrick, Dietrich and Estabrooks 74 ). Furthermore, the relevant biomarker should be obtained in a minimally invasive way( Reference Crews, Alink and Anderson 75 ) and the biological sample should be collected, processed and stored in an appropriate manner, so that a true reflection of intake can be obtained( Reference Wild, Andersson and O'Brien 76 ). Factors that may affect the validity of a biomarker include genetic variability, physiologic factors, dietary factors, the biological sample of choice and the analytical method used to measure it( Reference Jenab, Slimani and Bictash 73 ).

Four classes of nutritional biomarkers, namely recovery, concentration, replacement and predictive biomarkers, have been described according to the relationship between the biomarker and intake of the component of interest( Reference Jenab, Slimani and Bictash 73 ). Recovery biomarkers exhibit a strong time defined relationship between intake and excretion and can therefore be used to estimate absolute intakes. Examples of such biomarkers are doubly labelled water for energy intake( Reference Livingstone and Black 77 ) and urinary nitrogen( Reference Day, McKeown and Wong 78 , Reference Bingham 79 ) and potassium( Reference Day, McKeown and Wong 78 ) for protein and potassium intakes, respectively. Concentration and replacement biomarkers differ from recovery biomarkers in that they exhibit a lower correlation with absolute intake( Reference Jenab, Slimani and Bictash 73 ); however, they are useful for the purposes of ranking individuals according to intake and therefore can be used in the investigation of the relationship between the food or food component and disease( Reference Potischman and Freudenheim 72 ). Examples of such biomarkers are carotenoids and aflatoxins( Reference Jenab, Slimani and Bictash 73 ). The class of predictive biomarkers was first proposed by Tasevska et al.( Reference Tasevska, Runswick and McTaggart 80 ) when describing the use of urinary fructose and sucrose as markers for sugar intake. Although relatively small amounts of a dose were recovered in the form of urinary sucrose and fructose, it was demonstrated that a higher level of correlation (R > 0·6) with dietary intake existed than with concentration or replacement biomarkers and therefore this class of biomarker would fall between recovery and concentration biomarkers. As part of the validation process, it is important to characterise the relationship between the target biomarker and intake of the food or food component of interest( Reference Kuhnle 81 ), as such information will inform the application of the biomarker (e.g. to estimate absolute intakes, rank individuals or monitor compliance).

Two broad strategies for the development of a biomarker approach have been described; discovery- and hypothesis-driven( Reference Kuhnle 81 ). Discovery-driven approaches have become more prominent recently with the use of metabolomics to identify previously unknown biomarkers or panels of biomarkers that are associated with dietary patterns or the consumption of specific foods or food components( Reference O'Sullivan, Gibney and Brennan 82 ). Hypothesis-driven approaches differ in that prior knowledge of the component of interest and its metabolic fate are required and this information subsequently informs a more targeted approach to biomarker development( Reference Kuhnle 81 ).

Potential application of a biomarker approach for investigating low-calorie sweeteners intakes

The metabolic fates of LCS are well known (Table 3) and therefore a hypothesis-driven approach would appear to be the most appropriate for the implementation of a biomarker approach for investigating exposure. Following ingestion, aspartame is hydrolysed to aspartic acid, phenylalanine and methanol, each of which commonly occur in a normal diet( Reference Butchko, Stargel and Comer 83 , Reference Magnuson, Burdock and Doull 84 ), while thaumatin undergoes normal protein digestion( 85 ). Neohesperidine dihydrochalcone, although not known to exist in nature, is structurally similar to naturally occurring flavonoid glycosides and undergoes a similar metabolic fate to these analogues with the same or similar metabolites( Reference Borrego, Montijano and O'Brien Nabors 86 ). This finding would indicate that no obvious specific candidate biomarkers exist for these compounds. Acesulfame-K( Reference Christ and Rupp 87 ) and saccharin are almost completely absorbed and excreted unchanged via the urine,( Reference Byard, McChesney and Golberg 88 Reference Sweatman, Renwick and Burgess 90 ) while cyclamate (30–50 %)( Reference Renwick and Williams 91 ) and sucralose (10–15 %)( Reference Grice and Goldsmith 92 , Reference Roberts, Renwick and Sims 93 ) undergo partial absorption and the absorbed proportion is excreted unchanged via the urine with the unabsorbed proportions excreted via the faeces. In about 20 % of the population, cyclamate can be converted to cyclohexylamine via bacterial hydrolysis in the gut, which is absorbed and also excreted via the urine( Reference Bopp, Sonders and Kesterson 94 ). Furthermore, the extent of cyclamate conversion to cyclohexylamine can be variable during chronic exposure( Reference Renwick, Thompson and O'Shaughnessy 95 ). Steviol glycosides also undergo bacterial hydrolysis to steviol which is then absorbed and excreted via the urine as steviol glucuronide( Reference Geuns, Buyse and Vankeirsbilck 96 Reference Wheeler, Boileau and Winkler 98 ). Advantame is converted to advantame acid and a small proportion is absorbed (about 6 %) and excreted via the urine while about 90 % of a dose is excreted via the faeces( Reference Ubukata, Nakayama and Mihara 99 ).

Table 3. Metabolic fates and routes of excretion of low-calorie sweeteners approved in Europe

CAS, Chemical Abstract Service; NHDC, neohesperidine dihydrochalcone; N/A, not applicable as broken down to normal dietary components; JECFA, Joint FAO/WHO Expert Committee on Food Additives.

* No CAS Number.

Principal route of excretion listed first.

Saccharin, acesulfame-K, cyclamate and sucralose undergo no or limited metabolism following absorption into the body; therefore candidate biomarkers, in the form of the parent compounds, exist for these LCS. For steviol glycosides and advantame, the excretory products may act as suitable biomarkers for intakes. A high level of specificity of the candidate biomarkers exists as they are not found elsewhere in the diet or formed endogenously and given that at least a proportion of each of these compounds is excreted via the urine, a urinary biomarker approach may be feasible. Indeed, such an approach for assessing exposure to acesulfame-K and saccharin was previously described by Wilson et al.( Reference Wilson, Wilkinson and Crews 63 ) who measured levels of excretion in 24-h urine samples and found excellent levels of correlation in an intake/excretion study (R 2 > 0·99 for both compounds), demonstrating a clear dose–response relationship for both compounds. Slightly lower correlations were observed when validated against an FFQ which is probably more indicative of a limitation with the FFQ rather than the biomarker as the FFQ did not account for non-dietary sources of the LCS. The dose–response relationships for cyclamate, sucralose, steviol glycosides and advantame, however, are less clear so future work will be required to elucidate these relationships before the usefulness of measuring urinary concentrations for investigating intakes is established.

To fully characterise the relationship between urinary excretion and intakes of these LCS, a suitable and validated analytical method is first required( Reference Wild, Andersson and O'Brien 76 ). To this end, a liquid chromatography, tandem-MS/MS method of simultaneously determining urinary levels of acesulfame-K, cyclamate, saccharin, steviol glucuronide and sucralose has recently been developed and validated( Reference Logue, Dowey and Strain 100 ) and allows for investigations of the feasibility of implementing a biomarker approach for assessing intakes of these compounds.

Potential limitations of a urinary biomarker approach for assessing low-calorie sweetener intakes

Although a biomarker approach would potentially offer many advantages over more traditional methods of investigating exposure to LCS, potential limitations should also be acknowledged. As urinary biomarkers, only short-term exposure (previous 24–48 h) can be investigated( Reference Kuhnle 81 ). The mode of sampling is also an important factor to consider for the implementation of a biomarker approach and if 24-h urine samples are required, a lack of compliance could also represent a limitation. To mitigate this limitation, however, methods of assessing compliance have been developed such as the paraminobenzoic acid method( Reference Bingham and Cummings 101 ). It must also be acknowledged that a biomarker approach, utilised on its own, will not provide information on the source of exposure. Therefore in the event that the ADI of a particular LCS is being exceeded, more traditional methods would be required to identify the main sources of exposure( 39 ). As such, for the purposes of monitoring exposure in the population, a biomarker approach would likely be most useful when used as an adjunct to more traditional methods.

In summary, knowledge of the intakes of LCS is a legislative requirement in the EU and although the recent development and implementation of the FACET project will help harmonise methods among EU Member States and improve knowledge with regard to exposure to food additives, a successfully implemented biomarker approach for investigating LCS intake would undoubtedly be a useful adjunct to such monitoring activities. Furthermore, with ongoing interest and debate around the efficacy, as well as safety, of long-term LCS use, a biomarker approach would help elucidate the intakes of specific and combinations of LCS, and thereby address a limitation in the evidence to date, as highlighted by the French Agency for Food, Environmental and Occupational Health and Safety.

Financial Support

This work was principally funded by the National Institute for Public Health and the Environment (RIVM), The Netherlands as part of a PhD studentship for C. L. Part of this work was also funded by the JPI-HDHL project Foodball (Project no. 50-52905-98-12).

Conflicts of Interest

None.

Authorship

C. L. conducted a literature search and drafted the manuscript. A. M. G., L. C. D., J. J. S. and H. V. reviewed and approved the final version.

References

1. World Health Organisation (2015) WHO Media Centre. Obesity and Overweight Fact sheet No 311. http://www.who.int/mediacentre/factsheets/fs311/en/ (accessed May 2015).Google Scholar
2. Butland, B, Jebb, S, Kopelman, P et al. (2007) Foresight, Tackling Obesities: Future Choices – Project Report, 2nd ed.; UK Government for Science. http://www.foresight.gov.uk/OurWork/ActiveProjects/Obesity/Obesity.asp (accessed March 2015).Google Scholar
3. Malik, VS, Schulze, MB & Hu, FB (2006) Intake of sugar-sweetened beverages and weight gain: a systematic review. Am J Clin Nutr 84, 274288.CrossRefGoogle ScholarPubMed
4. Malik, VS, Popkin, BM, Bray, GA et al. (2010) Sugar-sweetened beverages and risk of metabolic syndrome and type 2 diabetes. Diab Care 33, 24772483.Google Scholar
5. Vartanian, LR, Schwartz, MTB & Brownell, KD (2007) Effects of soft drink consumption on nutrition and health: a systematic review and meta-analysis. Am J Public Health 97, 667675.Google Scholar
6. Morenga, T, Mallard, S & Mann, J (2012) Dietary sugars and body weight: systematic review and meta-analyses of randomised controlled trials and cohort studies. BMJ 346, e7492.Google Scholar
7. Te Morenga, LA, Howatson, AJ, Jones, RM et al. (2014) Dietary sugars and cardiometabolic risk: systematic review and meat-analyses of randomised controlled trials of the effects on blood pressure and lipids. Am J Clin Nutr 100, 6579.CrossRefGoogle ScholarPubMed
8. Xi, B, Huang, Y, Reilly, KH et al. (2015) Sugar-sweetened beverages and risk of hypertension and CVD: a dose–response meta-analysis. Br J Nutr 113, 709717.CrossRefGoogle ScholarPubMed
9. Drewnowski, A, Mennella, JA, Johnson, SL et al. (2012) Sweetness and food preference. J Nutr 142, 1142S1148S.Google Scholar
10. Zygler, A, Wasik, A & Namiesnik, J (2009) Analytical methodologies for determination of artificial sweeteners in foodstuffs. Trends Anal Chem 28, 10821102.Google Scholar
11. Mortensen, A (2006) Sweeteners permitted in the European Union: safety aspects. Scand J Food Nutr 50, 104116.CrossRefGoogle Scholar
12. Ghosh, S & Sudha, ML (2012) A review on polyols: new frontiers for health-based bakery products. Int J Food Sci Nutr 63, 372379.Google Scholar
13. Renwick, AG (1990) Acceptable daily intake and the regulation of intense sweeteners. Food Addit Contam 7, 463475.Google Scholar
14. Herrman, JL & Younes, M (1999) Background to the ADI/TDI/PTWI. Regul Toxicol Pharmacol 30, S109S113.Google Scholar
15. Logue, C, Peters, JAC, Gallagher, AM et al. (2015a) Perspectives on low calorie intense sweeteners with a focus on aspartame and stevia. Eur J Nutr Food Saf 5, 104112.Google Scholar
16. World Health Organization (1987) Principles for the Safety Assessment of Food Additives and Contaminants in Food. Environmental Health Criteria, Volume 70. http://www.inchem.org/documents/ehc/ehc/ehc70.htm#SectionNumber:5.5 (accessed May 2015).Google Scholar
17. Kroger, M, Meister, K & Kava, R (2006) Low-calorie sweeteners and other sugar substitutes: a review of the safety issues. Compr Rev Food Sci Food Saf 5, 3547.Google Scholar
18. Marinovich, M, Galli, CL, Bosetti, C et al. (2013) Aspartame, low-calorie sweeteners and disease: regulatory safety and epidemiological issues. Food Chem Toxicol 60, 109115.CrossRefGoogle ScholarPubMed
19. de la Hunty, A, Gibson, S & Ashwell, M (2006) A review of the effectiveness of aspartame in helping with weight control. Nutr Bull 31, 115128.Google Scholar
20. Bellisle, F & Drewnowski, A (2007) Intense sweeteners, energy intake and the control of body weight. Eur J Clin Nutr 61, 691700.Google Scholar
21. Anderson, GH, Foreyt, J, Sigman-Grant, M et al. (2012) The use of low-calorie sweeteners by adults: impact on weight management. J Nutr 142, 1163S1169S.Google Scholar
22. de Ruyter, JC, Olthof, MR, Seidell, JC et al. (2012) A trial of sugar-free or sugar sweetened beverages and body weight in children. N Engl J Med 367, 13971406.Google Scholar
23. Verhagen, H, Andersen, R, Antoine, JM et al. (2012) Application of the BRAFO tiered approach for benefit-risk assessment to case studies on dietary interventions. Food Chem Toxicol 50, Suppl. 4, S710S723.CrossRefGoogle ScholarPubMed
24. Pereira, MA (2013) Diet beverages and the risk of obesity, diabetes and cardiovascular disease: a review of the evidence. Nutr Rev 71, 433440.CrossRefGoogle ScholarPubMed
25. Swithers, SE (2013) Artificial sweeteners produce the counterintuitive effect of inducing metabolic derangements. Trends Endocrinol Metab 24, 431441.CrossRefGoogle ScholarPubMed
26. Miller, PE & Perez, V (2014) Low-calorie sweeteners and body weight and composition: a meta-analysis of randomised controlled trials and prospective cohort studies. Am J Clin Nutr 100, 765777.Google Scholar
27. Mattes, RD & Popkin, BM (2009) Nonnutritive sweetener consumption in humans: effects on appetite and food intake and their putative mechanisms. Am J Clin Nutr 89, 114.Google Scholar
28. Weihrauch, MR & Diehl, V (2004) Artificial sweeteners- do they bear a carcinogenic risk? Ann Oncol 15, 14601465.Google Scholar
29. Schernhammer, ES, Bertrand, KA, Birmann, BM et al. (2012) Consumption of artificial sweetener- and sugar-containing soda and risk of lymphoma and leukemia in men and women. Am J Clin Nutr 96, 14191428.Google Scholar
30. Duffey, KJ, Steffen, LM, Van Horn, L et al. (2012) Dietary patterns matter: diet beverages and cardiometabolic risks in the longitudinal Coronary Artery Risk Development in Young Adults (CARDIA) Study. Am J Clin Nutr 95, 909915.CrossRefGoogle ScholarPubMed
31. Pereira, MA & Odegaard, AO (2013) Artificially sweetened beverages- do they influence cardiometabolic risk? Curr Atheroscler Rep 15, 375.Google Scholar
32. Wiebe, N, Padwal, R, Field, C et al. (2011) A systematic review on the effect of sweeteners on glycaemic response and clinically relevant outcomes. BMC Med 9, 123.Google Scholar
33. Imamura, F, O'Connor, L, Ye, Z et al. (2015) Consumption of sugar sweetened beverages, artificially sweetened beverages and fruit juice and incidence of type 2 diabetes: systematic review, meta-analysis and estimation of population attributable fraction. BMJ 351, h3576.Google Scholar
34. La Vecchia, C (2013) Low-calorie sweeteners and the risk of preterm delivery: results from two studies and a meta-analysis. J Fam Plann Reprod Health Care 39, 1313.Google Scholar
35. French Agency for Food, Environmental and Occupational Health and Safety (ANSES) (2015) Opinion on the assessment of the nutritional benefits and risks related to intense sweeteners. https://www.anses.fr/en/system/files/NUT2011sa0161RaEN.pdf (accessed February 2015).Google Scholar
36. Harricharan, M, Wills, J, Metzger, N et al. (2014) Dietitian perceptions of low-calorie sweeteners. Eur J Public Health 25, 472476.Google Scholar
37. Academy of Nutrition and Dietetics (2012) Position of the Academy of Nutrition and Dietetics: use of nutritive and nonnutritive sweeteners. J Acad Nutr Diet 112, 739758.CrossRefGoogle Scholar
38. European Parliament and Council (2008) Regulation (EC) No. 1333/2008 of 16 December 2008 on food additives. J Eur Union L237, 312.Google Scholar
39. International Programme on Chemical Safety (IPCS) (2004) IPCS Risk Assessment Terminology. World Health Organization: Geneva, Switzerland, International Programme on Chemical Safety Harmonization Project Document No. 1. http://www.who.int/ipcs/methods/harmonization/areas/ipcsterminologyparts1and2.pdf (accessed May 2015).Google Scholar
40. European Commission (2001) Report from the Commission on dietary food additive intake in the European Union. COM 542 Final, pp. 1–27.Google Scholar
41. Renwick, AG (2008) The use of a sweetener substitution method to predict dietary exposure for the intense sweetener rebaudioside A. Food Chem Toxicol 46, S61S69.Google Scholar
42. Hansen, SC (1979) Conditions of use of food additives based on a budget for an acceptable daily intake. J Food Prot 42, 429432.Google Scholar
43. Thompson, FE & Subar, AF (2013) Dietary Assessment Methodology. In Nutrition in the Prevention and Treatment of Disease, 3rd ed., pp. 546 [Coulston, AM, Boushey, CJ, Ferruzzi, MG, editors]. London: Elsevier.Google Scholar
44. Livingstone, MBE, Prentice, AM, Strain, JJ et al. (1990) Accuracy of weighed dietary records in studies of diet and health. BMJ 300, 708712.CrossRefGoogle ScholarPubMed
45. Bingham, SA (1991) Limitations of the various methods for collecting dietary intake data. Ann Nutr Metab 35, 117127.Google Scholar
46. Horner, NK, Patterson, RE, Neuhouser, ML et al. (2002) Participant characteristics associated with errors in self-reported energy intake from the Women's Health Initiative food-frequency questionnaire. Am J Clin Nutr 76, 766773.Google Scholar
47. Kipnis, V, Midthune, D, Freedman, L et al. (2002) Bias in dietary-report instruments and its implications for nutritional epidemiology. Public Health Nutr 5, 915923.Google Scholar
48. Renwick, AG (2006) The intake of intense sweeteners- an update review. Food Addit Contam 23, 327338.CrossRefGoogle ScholarPubMed
49. Devitt, L, Daveman, D & Buccino, J (2004) Assessment of intakes of artificial sweeteners in children with Type 1 diabetes mellitus. Can J Diab 28, 15.Google Scholar
50. Chung, M-S, Suh, H-J, Yoo, W et al. (2005) Daily intake assessment of saccharin, stevioside, D-sorbitol and aspartame from various processed foods in Korea. Food Addit Contam 22, 10871097.Google Scholar
51. Huvaere, K, Vandevijvere, S, Hasni, M et al. (2012) Dietary intake of artificial sweeteners by the Belgian population. Food Addit Contam A 29, 5465.Google Scholar
52. Ha, MS, Ha, SD, Choi, SH et al. (2013) Assessment of Korean exposure to sodium saccharin, aspartame and stevioside. Food Addit Contam A 30, 12381247.Google Scholar
53. Ha, MS, Ha, SD, Choi, SH et al. (2013) Assessment of exposure of Korean consumers to acesulfame K and sucralose using a stepwise approach. Int J Food Sci Nutr 64, 715723.Google Scholar
54. Toledo, MCF & Ioshi, SH (1995) Potential intake of intense sweeteners in Brazil. Food Addit Contam 12, 799808.Google Scholar
55. Ilback, NG, Alzin, M, Jahri, S et al. (2003) Estimated intake of the artificial sweeteners acesulfame-k, aspartame, cyclamate and saccharin in a group of Swedish diabetics. Food Addit Contam 20, 99114.Google Scholar
56. Serra-Majem, L, Bassas, L, Garcia-Glosas, R et al. (2003) Cyclamate intake and cyclohexylamine excretion are not related to male fertility in humans. Food Addit Contam 20, 10971104.Google Scholar
57. Lino, CM, Costa, IM, Pena, A et al. (2008) Estimated intake of the sweeteners, acesulfame-k and aspartame from soft drinks, soft drinks based on mineral waters and nectars for a group of Portuguese teenage students. Food Addit Contam 25, 12911296.Google Scholar
58. van Rooij-van den Bos, L, Konings, EJM, Heida, P et al. (2004) Investigations on the artificial sweeteners saccharin, aspartame, acesulfame-K and cyclamate in food products. Project Number ZD 03K120. Utrecht: Dutch Food Safety Authority.Google Scholar
59. Garnier-Sagne, I, Leblanc, JC & Verger, Ph (2001) Calculation of the intake of three intense sweeteners in young insulin-dependent diabetics. Food Chem Toxicol 39, 745749.Google Scholar
60. Leclercq, C, Berardi, D, Sorbillo, MR et al. (1999) Intake of saccharin, aspartame, acesulfame k and cyclamate in Italian teenagers: present levels and projections. Food Addit Contam 16, 99109.Google Scholar
61. Arcella, D, Le Donne, C, Piccinelli, R et al. (2004) Dietary estimated intake of intense sweeteners by Italian teenagers. Present levels and projections derived from the INRAN-RM-2001 food survey. Food Chem Toxicol 42, 677685.Google Scholar
62. Food Standards Australia New Zealand (2004) Consumption of intense sweeteners in Australia and New Zealand: Benchmark Survey 2003. Evaluation Report Series No. 8.Google Scholar
63. Wilson, LA, Wilkinson, K, Crews, HM et al. (1999) Urinary monitoring of saccharin and acesulfame-k as biomarkers of exposure to these additives. Food Addit Contam 16, 227238.Google Scholar
64. Food Standards Agency (UK) (2003) Diary survey of the intake of intense sweeteners by young children from soft drinks (No. 36/03) http://tna.europarchive.org/20110116113217/http://www.food.gov.uk/science/surveillance/fsis2003/fsis-200336softdrink Google Scholar
65. National Institute for Public Health and the Environment (RIVM) (2014) Post-Launch Monitoring of Novel Foods/Ingredients (revised version), Methodology Applied to Additive Stevia. The Netherlands: Bilthoven.Google Scholar
66. European Commission (2012) Community Research and Development Information Service (CORDIS). Final Report – FACET (flavours, additives and food contact material exposure task). http://cordis.europa.eu/publication/rcn/10506_en.html (accessed March 2015).Google Scholar
67. van Klaveren, JD, Kennedy, MC, Moretto, A et al. (2015) The ACROPOLIS project: its aims, achievements and way forward. Food Chem Toxicol 79, 14.Google Scholar
68. Boon, PE, van Donkersgoed, G, Christodolou, D et al. (2015) Cumulative dietary exposure to a selected group of pesticides of the triazole group in different European countries according to the EFSA guidance on probabilistic modelling. Food Chem Toxicol 79, 1331.Google Scholar
69. Sylvetsky, AC, Welsh, JA, Brown, RJ et al. (2012) Low-calorie sweetener consumption is increasing in the United States. Am J Clin Nutr 96, 640646.Google Scholar
70. Kaaks, R, Riboli, E & Sinha, R (1997) Biochemical markers of dietary intake. IARC Sci Publ 142, 103126.Google Scholar
71. Bingham, SA (2002) Biomarkers in nutritional epidemiology. Public Health Nutr 5, 821827.Google Scholar
72. Potischman, N & Freudenheim, JL (2003) Biomarkers of nutritional exposure and nutritional status: an overview. J Nutr 133, 873S874S.CrossRefGoogle ScholarPubMed
73. Jenab, M, Slimani, N, Bictash, M et al. (2009) Biomarkers in nutritional epidemiology: applications, needs and new horizons. Hum Genet 125, 507525.Google Scholar
74. Hedrick, VE, Dietrich, AM, Estabrooks, PA et al. (2012) Dietary biomarkers: advances, limitations and future directions. Nutr J 11, 109.Google Scholar
75. Crews, H, Alink, G, Anderson, R et al. (2001) A Critical assessment of some biomarker approaches linked with dietary intake. Br J Nutr 86, Suppl. 1, S5S35.Google Scholar
76. Wild, CP, Andersson, C, O'Brien, NM et al. (2001) A critical evaluation of the application of biomarkers in epidemiological studies on diet and health. Br J Nutr 86, Suppl. 1, S37S53.Google Scholar
77. Livingstone, MBE & Black, AE (2003) Markers of the validity of reported energy intake. J Nutr 133, 895S920S.Google Scholar
78. Day, N, McKeown, N, Wong, M et al. (2001) Epidemiological assessment of diet: a comparison of a 7-day diary with a food frequency questionnaire using urinary markers of nitrogen, potassium and sodium. Int J Epidemiol 30, 309317.Google Scholar
79. Bingham, SA (2003) Urine nitrogen as a biomarker for the validation of dietary protein intake. J Nutr 133, 921S924S.Google Scholar
80. Tasevska, N, Runswick, SA, McTaggart, A et al. (2005) Urinary sucrose and fructose as biomarkers for sugar consumption. Cancer Epidemiol Biomarkers Prev 14, 12871294.Google Scholar
81. Kuhnle, GGC (2012) Nutritional biomarkers for objective dietary assessment. J Sci Food Agric 92, 11451149.Google Scholar
82. O'Sullivan, A, Gibney, MJ & Brennan, L (2011) Dietary intake patterns are reflected in metabolomic profiles: potential role in dietary assessment studies. Am J Clin Nutr 93, 314321.Google Scholar
83. Butchko, HH, Stargel, WW, Comer, CP et al. (2002) Aspartame: review of safety. Regul Toxicol Pharmacol 35, S1S93.CrossRefGoogle ScholarPubMed
84. Magnuson, BA, Burdock, GA, Doull, J et al. (2007) Aspartame: a safety evaluation based on current use levels, regulations, and toxicological and epidemiological studies. Crit Rev Toxicol 37, 629727.Google Scholar
85. Joint FAO/WHO Expert Committee on Food Additives (JECFA) (1985) Thaumatin. WHO Food Additives Series 20. http://www.inchem.org/documents/jecfa/jecmono/v20je15.htm (accessed June 2015).Google Scholar
86. Borrego, F & Montijano, H (2001) Neohesperidine dihydrochalcone. In Alternative Sweeteners, 3rd ed., pp. 87105 [O'Brien Nabors, L, editor]. New York: Marcel Dekker.Google Scholar
87. Christ, O & Rupp, W (1976) Human experiments with Acetosulfam-14C. Pharmacokinetics after oral administration of 30 mg to three healthy male probands. In Acesulfame Potassium. WHO Food Additives Series 28. http://www.inchem.org/documents/jecfa/jecmono/v28je13.htm (accessed June 2015).Google Scholar
88. Byard, JL, McChesney, EW, Golberg, L et al. (1974) Excretion and metabolism of saccharin in man. II. Studies with 14C-labelled and unlabelled saccharin. Food Cosmet Toxicol 12, 175184.Google Scholar
89. Ball, LM, Renwick, AG & Williams, AG (1977) The fate of [14C]saccharin in man, rat and rabbit and of 2-sulphamoyl[14C]benzoic acid in the rat. Xenobiotica 7, 189203.Google Scholar
90. Sweatman, TW, Renwick, AG & Burgess, CD (1981) The pharmacokinetics of saccharin in man. Xenobiotica 11, 531540.Google Scholar
91. Renwick, AG & Williams, RT (1972) The fate of cyclamate in man and other species. Biochem J 129, 869879.Google Scholar
92. Grice, HC & Goldsmith, LA (2000) Sucralose- An overview of the toxicity data. Food Chem Toxicol 38, Suppl. 2, S1S6.Google Scholar
93. Roberts, A, Renwick, AG, Sims, J et al. (2000) Sucralose metabolism and pharmacokinetics in man. Food Chem Toxicol 38, S31S41.Google Scholar
94. Bopp, BA, Sonders, RC & Kesterson, JW (1986) Toxicological aspects of cyclamate and cyclohexylamine. Crit Rev Toxicol 16, 213306.Google Scholar
95. Renwick, AG, Thompson, JP, O'Shaughnessy, M et al. (2004) The metabolism of cyclamate to cyclohexylamine in humans during long-term administration. Toxicol Appl Pharmacol 196, 367380.Google Scholar
96. Geuns, JMC, Buyse, J, Vankeirsbilck, A et al. (2006) Identification of steviol glucuronide in human urine. J Agric Food Chem 54, 27942798.CrossRefGoogle ScholarPubMed
97. Geuns, JMC, Buyse, J, Vankeirsbilck, A et al. (2007) Metabolism of stevioside be healthy subjects. Exp Biol Med 232, 164173.Google Scholar
98. Wheeler, A, Boileau, AC, Winkler, PC et al. (2008) Pharmacokinetics of rebaudioside A and stevioside after single oral doses in healthy men. Food Chem Toxicol 46, S54S60.Google Scholar
99. Ubukata, K, Nakayama, A & Mihara, R (2011) Pharmacokinetics and metabolism of N-[N-[3-(3-hydroxy-4-methoxyphenyl)propyl]-α-aspartyl]-L-phenylalanine 1-methyl ester, monohydrate (advantame) in the rat, dog, and man. Food Chem Toxicol 49, S8S29.Google Scholar
100. Logue, C, Dowey, LC, Strain, JJ et al. (2015b) A novel method for the simultaneous determination of five low calorie sweeteners in human urine. Proc Nutr Soc 74(OCE1), E72.Google Scholar
101. Bingham, S & Cummings, JH (1983) The use of 4-aminobenzoic acid as a marker to validate the completeness of 24 h urine collections in man. Clin Sci 64, 629635.Google Scholar
102. Vin, K, Connolly, A, McCaffrey, T et al. (2013) Estimation of the dietary intake of 13 priority additives in France, Italy, the UK and Ireland as part of the FACET project. Food Addit Contam A 30, 20502080.Google Scholar
103. European Commission (2000) Minutes of the 120th Meeting of the Scientific Committee on Food held on 8–9 March 2000 in Brussels. http://ec.europa.eu/food/fs/sc/scf/out57_en.html (accessed March 2015).Google Scholar
Figure 0

Table 1. Intense sweeteners approved for use in Europe

Figure 1

Fig. 1. (Colour online) Safety factors applied to the no observed adverse effect level to establish the acceptable daily intake (Source: Logue et al.(15)). ADI, acceptable daily intake; NOAEL, no observed adverse effect level.

Figure 2

Fig. 2. Tiered approach for food additive exposure estimates (adapted from EC(40)). MPLs, maximum permitted levels; ADI, acceptable daily intake.

Figure 3

Table 2. Exposure estimates of low-calorie sweeteners in Europe over the past 20 years

Figure 4

Table 3. Metabolic fates and routes of excretion of low-calorie sweeteners approved in Europe