Food and Chemical Toxicology

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1 Food and Chemical Toxicology 50 (2012) 5 25 Contents lists available at ScienceDirect Food and Chemical Toxicology journal homepage: Review State of the art in benefit risk analysis: Food and nutrition M.J. Tijhuis a,b,, N. de Jong a, M.V. Pohjola c, H. Gunnlaugsdóttir d, M. Hendriksen a, J. Hoekstra a, F. Holm e, N. Kalogeras b, O. Leino c, F.X.R. van Leeuwen a, J.M. Luteijn f, S.H. Magnússon d, G. Odekerken b, C. Rompelberg a, J.T. Tuomisto c, Ø. Ueland g, B.C. White h, H. Verhagen a,i,j a National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720 BA Bilthoven, The Netherlands b Maastricht University, School of Business and Economics, P.O. Box 616, 6200 MD Maastricht, The Netherlands c National Institute for Health and Welfare (THL), P.O. Box 95, FI Kuopio, Finland d Matís, Icelandic Food and Biotech R&D, Vínlandsleið 12, 113 Reykjavík, Iceland e FoodGroup Denmark & Nordic NutriScience, Rugaardsvej 14, A2, Rugaard, DK-8400 Ebeltoft, Denmark f University of Ulster, School of Nursing, Shore Road, Newtownabbey (Jordanstown), BT37 0QB Northern Ireland, United Kingdom g Nofima, Osloveien 1, N-1430 Ås, Norway h University of Ulster, Dept. of Pharmacy & Pharmaceutical Sciences, School of Biomedical Sciences, Cromore Road, Coleraine, BT52 1SA Northern Ireland, United Kingdom i Maastricht University, NUTRIM School for Nutrition, Toxicology and Metabolism, P.O. Box 616, 6200 MD Maastricht, The Netherlands j University of Ulster, Northern Ireland Centre for Food and Health (NICHE), Cromore Road, Coleraine, BT52 1SA, Northern Ireland, United Kingdom article info abstract Article history: Available online 12 June 2011 Keywords: Benefit risk Benefit Risk Food Nutrition Benefit risk assessment in food and nutrition is relatively new. It weighs the beneficial and adverse effects that a food (component) may have, in order to facilitate more informed management decisions regarding public health issues. It is rooted in the recognition that good food and nutrition can improve health and that some risk may be acceptable if benefit is expected to outweigh it. This paper presents an overview of current concepts and practices in benefit risk analysis for food and nutrition. It aims to facilitate scientists and policy makers in performing, interpreting and evaluating benefit risk assessments. Historically, the assessments of risks and benefits have been separate processes. Risk assessment is mainly addressed by toxicology, as demanded by regulation. It traditionally assumes that a maximum safe dose can be determined from experimental studies (usually in animals) and that applying appropriate uncertainty factors then defines the safe intake for human populations. There is a minor role for other research traditions in risk assessment, such as epidemiology, which quantifies associations between determinants and health effects in humans. These effects can be both adverse and beneficial. Benefit assessment is newly developing in regulatory terms, but has been the subject of research for a long time within nutrition and epidemiology. The exact scope is yet to be defined. Reductions in risk can be termed benefits, but also states rising above the average health are explored as benefits. In nutrition, current interest is in optimal intake; from a population perspective, but also from a more individualised perspective. In current approaches to combine benefit and risk assessment, benefit assessment mirrors the traditional risk assessment paradigm of hazard identification, hazard characterization, exposure assessment and risk characterization. Benefit risk comparison can be qualitative and quantitative. In a quantitative comparison, benefits and risks are expressed in a common currency, for which the input may be deterministic or (increasingly more) probabilistic. A tiered approach is advocated, as this allows for transpar- Abbreviations: ADI, acceptable daily intake; AICR, Association for International Cancer Research; ALARA, as low as reasonably achievable; AR, Average Requirement; ATBC, alpha-tocopherol, beta carotene cancer prevention (trial); BCRR, Benefit Cancer Risk Ratio; BENERIS, benefit risk assessment for food: an iterative value-of-information approach; BMD, benchmark dose; BMDL, lower one-sided confidence limit on the BMD; BNRR, Benefit Noncancer Risk Ratio; BRA, Benefit risk analysis; BRAFO, benefit risk analysis of foods; CARET, beta-carotene and retinol efficacy trial; DALY, disability adjusted life years; EAR, Estimated Average Requirement; EFSA, European Food Safety Authority; FAO, [United Nations] Food and Agriculture Organization; FOSIE, food safety in Europe: risk assessment of chemicals in the food and diet; FUFOSE, Functional Food Science in Europe; ILSI, International Life Sciences Institute; JECFA, Joint FAO/WHO Expert Committee on Food Additives; LLAB, lower level of additional benefit; LOAEL, Lowest Observed Adverse Effect Level; MOE, margin of exposure; NOAEL, No Observed Adverse Effect Level; OECD, Organisation for Economic Co-operation and Development; PAR, population attributable risk; PASSCLAIM, process for the assessment of scientific support for claims on foods; POD, point of departure; PRI, population reference intake; RDA, recommended dietary allowance; RfD, Reference Dose; RIVM, [Dutch] National Institute for Public Health and the Environment; RR, relative risk; SCF, Scientific Committee on Food; (P)TDI/WI/MI, (provisional) tolerable daily/weekly/monthly intake; UL, tolerable upper intake level; ULAB, upper level of additional benefit; WHO, World Health Organization; QALIBRA, quality of life integrated benefit and risk analysis; QALY, quality adjusted life years; WCRF, World Cancer Research Fund. Corresponding author at: National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720 BA Bilthoven, The Netherlands. Tel.: ; fax: address: mariken.tijhuis@rivm.nl (M.J. Tijhuis) /$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi: /j.fct

2 6 M.J. Tijhuis et al. / Food and Chemical Toxicology 50 (2012) 5 25 ency, an early stop in the analysis and interim interaction with the decision-maker. A general problem in the disciplines underlying benefit risk assessment is that good dose response data, i.e. at relevant intake levels and suitable for the target population, are scarce. It is concluded that, provided it is clearly explained, benefit risk assessment is a valuable approach to systematically show current knowledge and its gaps and to transparently provide the best possible science-based answer to complicated questions with a large potential impact on public health. Ó 2011 Elsevier Ltd. All rights reserved. Contents 1. Introduction to the scope of this paper Key terms in benefit risk assessment of food and nutrition Risk assessment Food toxicology Basic principles Identification of adverse effects Characterization of adverse effects and risk Micronutrients Nutritional epidemiology Basic principles Study design and dose response characterization in epidemiology Proposed frameworks for use of epidemiologic data in risk assessment Scope of risk assessment Benefit assessment Basic principles and concepts Study design and strength of evidence Proposed framework Scope of benefit assessment Benefit risk assessment Integration of disciplines Benefit risk assessment approaches Generalities in approaches Specific approaches Integrated measures Dealing with uncertainties Issues yet to be solved Evaluation of evidence Animal to human translation Integrated measures Exposure assessment Lack of data Alternative actions and scenarios Benefit risk assessment in food and nutrition: case studies Benefit risk management of food and nutrition Benefit risk communication of food and nutrition Conclusions and recommendations Conflict of Interest Acknowledgements References Introduction to the scope of this paper Food and nutrition are in essence necessary and beneficial to, but may also have adverse effects on, human health. Public health professionals have realized, while acknowledging the success of the current food safety system, that the health loss due to unhealthy food and nutrition is many times greater than that attributable to unsafe food; and that the health gains to be made through the consumption of certain foods are many times greater than the health risks involved (van Kreijl et al., 2006). The beneficial and adverse effects may occur concurrently through a single food item (for example fish or whole grain products) or a single food component (for example folic acid or phytosterols), within the same population range of dietary intake. This means that any policy action directed at the adverse effects also affects the degree of beneficial effects. The challenge in the relatively new field of benefit risk assessment in food and nutrition is to scientifically measure and weigh these two sides. Historically, the assessments of benefits and risks have been separate processes, where by far most attention has been directed at the assessment of risks, as required by regulation (EU, 2000, 2002b). Focus has been on food safety, with precaution as a risk

3 M.J. Tijhuis et al. / Food and Chemical Toxicology 50 (2012) management option (EC, 2000a) surrounded by much debate as to when and how it should be applied. Worded differently, it is a political decision whether or not to include benefits. From a public health perspective, however, a decision-making process which strives for the lowest risk does not by definition lead to the optimal population health outcome. If the benefits are high enough, some risk may be acceptable. As such, there is a broader picture to be assessed. The benefit risk assessment approach entails a paradigm shift from traditional risk analysis, as known from toxicology, to benefit risk analysis. Essentially, it brings together different scientific disciplines, which have their own history, perspective, tools and uncertainties: Toxicology, the discipline that traditionally investigates the risk of too much (acute or chronic adverse effects), as required by the regulatory process. Nutrition, a younger discipline that investigates the risk of too little (nutritional deficiencies), the risk of too much (nutritional intoxications or affluence-associated problems) and the benefits of optimal nutrient intakes. In benefit risk assessment, there is an important role for epidemiology, the methodology-oriented discipline that investigates associations between (real-life human) exposures and health outcomes, adverse or beneficial. In order to weigh the risks and benefits of foods or their components, they must be evaluated and expressed in a comparable way. Methodologies have been developed and applied to actual cases. New grounds have been explored, yet consensus on the general principles or approaches for conducting benefit risk analysis for foods and food components still needs to be reached. This paper presents an overview of current approaches to an integrated weighing of benefits and risks, a state of the art in benefit risk analysis, in the field of food and nutrition. In this, most attention is directed at the assessment phase, but benefit risk management and benefit risk communication are also recognized as an integral part of the benefit risk paradigm. The perspective is broad, rather than technical. The aim is to facilitate scientists and policy makers in carrying out and judging benefit risk assessments, to eventually come to better informed decisions about food-related health issues. We start with an overview of key terms in the field. We then describe risk assessment and benefit assessment separately to build up to a description of current approaches to combine the two. This is illustrated by an overview of benefit risk case studies and followed by a brief description of benefit risk management and communication. We end with conclusions and recommendations. 2. Key terms in benefit risk assessment of food and nutrition As benefit risk assessment of food and nutrition involves different disciplines using different scientific jargon, we first define the key terms as they are used in this review: food, nutrition, risk, benefit and benefit risk assessment. In this review, food is defined as any substance or product, whether processed, partially processed or unprocessed, intended to be, or reasonably expected to be ingested by humans (EU, 2002a). Foods are made up of many different components affecting body functions. Food and food component are viewed as a broad concept: macronutrients, micronutrients, bioactive nonnutrients, additives, contaminants, residues, phyto/phyco/mycotoxins, micro-organisms, allergens, supplements, whole foods and novel foods. The total of these components that are ingested via food, naturally occurring or added, can be seen as nutrition. Notably, nutritious has the connotation of healthy. As a science, nutrition studies the total of processes by which food or its components are ingested, digested, absorbed, metabolized, utilized, needed and excreted, and the interactions between these components and between these processes, and their effect on human health and disease. In the food and nutrition context, we see risk as the probability and severity of an adverse health effect occurring to humans following exposure (or lack of exposure) to a food or food component. In risk assessment, a distinction is made between risk and hazard. In this context, the intrinsic potential of a food (component) to result in adverse health effects is called a hazard. A hazard, then, becomes a risk if there is sufficient exposure. Risk can result from both action and lack of action and both can be analyzed (Wilson and Crouch, 2001). An adverse health effect is defined by the WHO as a change in morphology, physiology, growth, development or life span of an organism, which results in impairment of functional capacity or impairment of capacity to compensate for additional stress or increase in susceptibility to the harmful effects of environmental influences (WHO, 1994). A beneficial health effect can analogously be seen as an improvement of functional capacity or improvement of capacity to deal with stress or decrease in susceptibility to the harmful effects of environmental influences in the above definition (Palou et al., 2009). On the benefit side, no parallel words exist for hazard and risk. In order to make the parallel distinction on the benefit side but also have similarity in nomenclature, we will use adverse health effects and beneficial health effects instead of hazards and benefits. In this review we use benefit analogously to risk, as the probability and degree of a beneficial health effect occurring to humans following exposure (or lack of exposure) to a food (component). Beneficial health effects may postpone the onset of disease and thus benefit can also be measured as a reduction of risk. Benefit risk assessment is seen here as a science-based process intended to estimate the benefits and risks for humans following exposure (or lack of exposure) to a particular food or food component and to integrate them in comparable measures, thus facilitating better informed decisions by decision-makers. 3. Risk assessment Risk assessment is an established field, that is mainly addressed by toxicology (Faustman and Omenn, 2008). The traditional risk assessment paradigm (EC, 2000b) consists of hazard identification (what effect?), hazard characterization (at what dose? how?), exposure assessment (how much is taken in?) and risk characterization (what is the probability and severity of the effect?). A risk assessment framework specifically for food has been developed in the context of the FOSIE project (Food Safety in Europe, (Smith, 2002). The basic concepts and approaches to risk assessment in toxicology will be addressed in Section 3.1. A separate paragraph is dedicated to micronutrients, which is placed under toxicology for practical reasons. There is an additional (not yet clearly defined) role for epidemiology, which will be addressed in Section 3.2. Focus will be on those characteristics where the most pronounced differences exist between toxicology, epidemiology and later benefit assessment, and which need to be considered when integrating the different disciplines. Table 1 presents a summary of some of the characteristics of toxicological and epidemiologic risk assessment.

4 8 M.J. Tijhuis et al. / Food and Chemical Toxicology 50 (2012) Food toxicology Classical food toxicology focuses on the absence of risks, viz. on safety (EU, 2000, 2002b). Risk assessment may be initiated for a variety of purposes, most commonly it is triggered by a legal requirement (EC, 2000b) Basic principles The basic tenet of quantitative risk assessment is that data on health effects detected in small populations of animals exposed to relatively high concentrations of an agent can be used to predict health effects in large human populations exposed to lower concentrations of the same agent. Generally, risk is measured as the fraction of the population exceeding a defined upper intake or guidance level. This level is established by working down from the most sensitive hazardous effect (the so-called critical effect) in animals and applying uncertainty factors for extrapolation to the most sensitive human subgroup. The toxicological approach differs between compounds that are assumed to show a threshold effect and those that are not, between compounds that are avoidable in the diet and those that are not, and depends on the amount of scientific information available and the policy of decision-makers towards acceptance of risk. First we briefly address the identification of adverse health effects, then we address their characterization and translation into risk Identification of adverse effects The better toxicity studies have an appropriate number of dose levels, sufficient sample sizes and focus on relevant endpoints in a relevant species (WHO-Harmonization-Project, ). These are mostly animal toxicity tests, with an experimental design following OECD guidelines for the testing of chemicals (OECD). Tests of acute toxicity, i.e. the toxicity that manifests itself immediately or within 14 days after exposure to a single administration of a chemical by ingestion, inhalation or dermal application, are often not useful for hazard identification and risk assessment in relation to foods and food chemicals (Barlow et al., 2002), because human exposure is generally of chronic nature and is usually much lower than the dose that is identified in these tests. Tests of subacute or subchronic toxicity, i.e. repeated dose toxicity studies mostly performed in rodents for a period of 28 or 90 days, should reveal the major toxic effects. The effects may include changes in body and organ weight, organ histopathology, hematological parameters, serum and urine clinical chemistry and sometimes extensions to screen for neurotoxicity or immunotoxicity. Tests of chronic toxicity cover a larger part of the animal s life span, e.g. 12 or 24 months in rodents. There are specific tests and procedures for establishing reproductive and developmental toxicity, for neurotoxicity, for genotoxicity, for carcinogenicity, for immunitoxicity and for food allergies (Barlow et al., 2002) Characterization of adverse effects and risk The standard approach to characterize a dose response relationship, for substances assumed to show non-carcinogenic threshold effects, is the derivation of the No Observed Adverse Effect Level (NOAEL). This is the largest amount of a substance that the most sensitive animal model can consume without adverse health effects (see Fig. 1a). Usually, the most robust datasets are chosen with adverse effects occurring at the lowest levels of exposure from studies using the most relevant exposure routes (Faustman and Omenn, 2008). If there are no adequate data demonstrating a NOAEL, then a LOAEL (the lowest amount at which adverse effect are observed) may be used. Based on the NOAEL (or LOAEL), health-based guidance levels are established, under the rationale that these will ensure protection against all other adverse health effects which may be caused by the compound considered. This means that although different endpoints may yield different NOAEL s, one NOAEL is chosen as a point of departure from the experimental dose response range for setting acceptable exposure levels and this is the one for the most relevant sensitive endpoint in the most sensitive species; Based on this NOAEL, a health based guidance value is established by means of Table 1 Characteristics of toxicological and epidemiological risk assessment a, that need to be integrated, adapted from van den Brandt et al., Characteristic Tox Epi Initiation Mainly regulatory Mainly academic Starting point Usually suspicious substance Both substance and endpoint Study design Mainly experimental Mainly observational Formal procedures Yes No defined Study population Selected, genetically homogeneous animals Mainly free-living, genetically heterogeneous humans Exposure under study Controlled (administered), mostly one agent, supraphysiological Uncontrolled b (measured), multiple agents likely, physiological doses doses Extrapolation To lower doses ( actual exposure ) using default value of 10 or No, actual exposure chemical specific adjustment value Presentation of Usually absolute Usually relative (ranking of study subjects) exposure and risk Quantification of effect (with chronic exposure) Incidence (proportion of animals with adverse effect over duration of study; usually lifetime risk;) or continuous measure Incidence rate (proportion with adverse effect per person-year, standardized to a particular age distribution; lifetime risk rare) or continous measure Comparison Difference Usually ratio c a b c Evaluation of evidence Some sources of uncertainty Selection of high-quality study with the most sensitive effect in the most sensitive subgroup (worst-case scenario or positive evidence approach) Threshold assumption, low-dose extrapolation Interspecies differences Human interindividual variation Imprecision With focus on chronic exposures. Unless randomized clinical trial (RCT). Necessary for case-control study; for cohort studies calculation of risk differences is also possible. All high-quality studies (weight of evidence approach, calculating overall effect) Exposure measurement error Residual confounding Selection bias Human interindividual variation Imprecision

5 M.J. Tijhuis et al. / Food and Chemical Toxicology 50 (2012) uncertainty factors. By default (i.e. unless specified data are available to do otherwise) the NOAEL is divided by 10 to take into account differences between animals and humans (interspecies), and again by 10 to take into account differences in sensitivity between humans (interindividual) (Dorne and Renwick, 2005; Renwick, 1993). The guidance level for nutrients (see also Section 3.1.4) is the tolerable upper intake level (UL), for additives is termed the ADI (acceptable daily intake) and for contaminants in food the terminology in Europe is tolerable daily (or weekly/monthly) intake (TDI/TWI/TMI). In the USA, Reference Dose (RfD) is preferred as a less value-laden term for the latter. All these terms refer to an estimate of the intake of a substance over a lifetime that is considered to be without appreciable health risk (WHO, 1994). They are meant to protect 98% of the population. To most toxicologists, guidance levels are soft estimates and not a matter of marking acceptability from non-acceptability (EPA, 1993). Exposures somewhat higher than the guidance level are associated with increased probability of adverse effects, but that probability is not a certainty. Similarly, the absence of a risk to all people cannot be assured at the level below the guidance level (EPA, 1993). As a consequence, more and more dose response modeling is being advocated as a more science-based way to go. Different mathematical and physiological approaches are being explored. Edler et al. (2002) describe a number of statistical and mechanistic modeling approaches with differing data requirements, degree of complexity, applicability and type and quality of resulting risk estimates, including: structure activity relationships and the threshold of toxicological concern; the benchmark dose (BMD); probabilistic risk assessment; and physiologicallybased pharmacokinetic modeling. The latter fits in the development towards a systems toxicology ; in vivo dose response curves could be predicted by combining in vitro toxicity data and in silico kinetic modeling (Louisse et al., 2010; Punt et al., 2008). Other initiatives exist, e.g. the Key Events Dose Response Framework (KEDRF) incorporates information on the substance s mode of action and examines critical events that occur between the initial dose of a bioactive agent and the effect of concern (Julien et al., 2009). By default, the threshold approach is not applied to genotoxic carcinogens, because theoretically (although the organism is equipped with some degree of cytoprotection) one molecule can initiate the cancer process. The Joint FAO/WHO Expert Committee on Food Additives (JECFA) and in Europe the SCF (the predecessor Fig. 1a. Theoretical description of adverse health effects of a nutrient or foreign non-genotoxic compound as a function of excessive intake. Note: The NOAEL is a point observation of the most critical effect. Uncertainty factors (UF s) are applied to the NOAEL to derive the guidance level (UL, ADI, TDI). The default UF is 10 to allow for species differences and 10 to allow for human variability. Increased UF s may be used if the study does not detect a NOAEL and the guidance level has to be based on the LOAEL. Lower UF s may be used with higher quality data or extremely mild and reversible effects. Source: Renwick et al., Reprinted by permission of the publisher (Elsevier). of EFSA, the European Food Safety Authority) advised that the presence of carcinogenic contaminants in the diet should be reduced to irreducible levels by current technological standards (Edler et al., 2002), which is complicated by the fact that detection limits are increasingly more sensitive. This approach is captured in the ALAR- A (as low as reasonably achievable) management option to regulate avoidable genotoxic carcinogens. These committees considered that there was no adequate science basis for low-dose extrapolation to quantify a virtual safe dose, an acceptable additional cancer risk for lifetime exposure to the compound of interest which can be set at, for example, one additional cancer case in a population of one million people. Current methods that do quantify a virtual safe dose are based on linear extrapolation from a point of departure on the dose response curve (Boobis et al., 2009). It is argued that for genotoxic carcinogens, both the threshold concept and the low-dose linearity concept are in need of refinement (Boobis et al., 2009; Rietjens and Alink, 2006; Waddell, 2006). This will require better mechanistic insight and risk benefit analysis (Boobis et al., 2009; Rietjens and Alink, 2006; Waddell, 2006). It can help overcome the critique with regard to the precautionary principle that it does not provide a transparent way to weigh and account for different risks, or in other words, that it does not help regulators to decide which risks to regulate (EFSA, 2005; Post, 2006). EFSA recommends using the margin of exposure (MOE) approach as a harmonised methodology for assessing the risk of substances in food and feed, which are genotoxic and carcinogenic (EFSA, 2005). The MOE approach uses a reference point, that is often taken from an animal study and that corresponds to a dose that causes a low but measurable response in animals. This reference point is then compared with various dietary intake estimates in humans, taking into account differences in consumption patterns in the population (EFSA, 2005). The MOE essentially shows whether the human levels of exposure are close to effect levels and enables the comparison of the risks posed by different genotoxic and carcinogenic compounds. It is for the risk manager to decide if the magnitude of the MOE is acceptable, and if further action is needed taking into consideration additional aspects, such as the feasibility of removing the substance from the food supply. In general a MOE of 10,000 or higher, if it is based on a bench mark dose lower confidence limit for a 10% change in effect (BMDL10, see below) from an animal study, might be considered of low concern from a public health point of view and as a low priority for risk management actions. However, this number is arbitrary and the judgment is ultimately a matter for decision-makers. Moreover an MOE of that magnitude in itself should not preclude the application of risk management measures to reduce human exposure. Recently, an outline framework for calculating an MOE was proposed in order to help to ensure transparency in the results (Benford et al., 2010). The MOE approach could also be applied for non-carcinogenic substances. EFSA recommends the use of the benchmark dose (BMD) to estimate the MOE, for instance where the human exposure is close to the ADI (EFSA, 2005, 2009b). The BMD approach estimates the dose that causes a low but measurable critical effect, e.g. a 5% increase in the incidence of kidney toxicity or a 10% percent change in the level of liver weight. By calculating the lower confidence limit of the estimated dose (BMDL), the uncertainty and variability in the data is taken into account (EFSA, 2009b). In this BMD approach more information is used than in the NOAEL approach Micronutrients For micronutrients, a somewhat different approach needs to be followed, as risk can come from intakes that are too high, but also from intakes that are too low. Micronutrients, home in the field of

6 10 M.J. Tijhuis et al. / Food and Chemical Toxicology 50 (2012) 5 25 nutrition, are addressed here because toxicology-related measures are used in their risk assessment. The higher end guidance level, the UL (see Fig. 1a), differs in two principal aspects from the ADI and TDI. First, micronutrients, as opposed to additives or contaminants, are subject to homeostatic control. This means that the body may adapt its functioning to intake and deal with some level of excess or shortage. Second, UL s may be set for different life stage groups. The other often used guidance level, the recommended dietary allowance (RDA, see Fig. 1b) (also known as population reference intake, PRI) (EFSA, 2010a), is by default set for different life stage groups and by sex and represents the nutritional needs of most healthy individuals (thereby exceeding the minimal requirements for almost all). It is calculated from the Estimated Average Requirement (EAR, see Fig. 1b) or Average Requirement (AR) (EFSA, 2010a), which is the daily intake value that is estimated to meet the requirement, as defined by a specified indicator of adequacy, in 50% of the individuals in a life stage or sex group (Renwick et al., 2004). Often, a normal distribution of requirements and a 10 15% coefficient of variation (CV) for the EAR are assumed. The RDA must be considered when deriving the UL, as it is possible that default uncertainty factors would yield a UL below the RDA. The intake range between deficiency and toxicity varies greatly between nutrients. For example, it is large for vitamin C and narrow for zinc. In addition, it may overlap between different populations. Related to this given, there is critique on current methods of establishment of the upper level for micronutrients and maximum permitted levels of vitamins and minerals in food supplements working from precaution, and a call for using decision science is made (Verkerk, 2010). Here, the point is that dosages that induce risks in a few might actually at the same time induce benefits in many (Verkerk, 2010) Nutritional epidemiology In the traditional risk assessment of foods, the use of epidemiologic data has been limited. There is no mandatory role (and there are no clear criteria) for epidemiologic data in risk assessment for regulatory purposes, despite the fact that (nutritional) epidemiologists are conceptually and practically already used to describe effects at physiological intake levels and despite the limitations of animal studies due to the requirement of both interspecies and high-to-low dose extrapolations. Fig. 1b. Theoretical description of beneficial health effects of a nutrient as a function of level of intake. Note: The solid line shows the risk of inadequacy for the effect used to establish the recommended dietary allowance (RDA); as intake decreases below the RDA there is an increase in the risk of adverse effects due to inadequate intake. At an intake equal to the EAR (also known as AR), 50% of the population will have intakes that are adequate, but for 50% of the population this intake will be inadequate for the prevention of adverse effects or maintenance of benefit. The dashed line represents the risk of inadequacy of nutrient intake for a health benefit (e.g. disease risk reduction) not included in the derivation of the RDA. Source: Renwick et al., Reprinted by permission of the publisher (Elsevier) Basic principles Epidemiology is about identifying and quantifying associations between (real-life) human exposures and health outcomes, adverse or beneficial. The exposure and the health outcome, usually a disease or marker for a disease, are most often a priori specified and hypothesis-driven. If the association between exposure and outcome is found to be inverse, i.e. higher exposure leads to lower disease occurrence, this signifies a reduction in risk. In other words, in epidemiology, an exposure is a factor or condition that may increase or decrease the risk of disease (WCRF/AICR, 2007). This work often serves the quest for knowledge and understanding more than a practical regulatory goal, but it is also possible to quantify the effect on (public) health to directly aid in decision making. Reflecting the above, three types of measures can broadly be distinguished in epidemiology: measures of disease occurrence, measures of association and measures of effect (Rothman et al., 2008). Disease occurrence can be expressed as cases that are prevalent (current) or incident (new). The measures of association are most often expressed as relative differences (or ratio measures), for example: the relative risk (RR) to acquire disease X among those consuming food (component) Y in the 5th quintile of the intake distribution is 1.5 compared to those in the 1st quintile (the reference, set to 1); but often absolute difference measures, such as the excess risk that consuming food (component) Y contributes to acquiring disease X, can also be calculated. The measures of effect have impact on those exposed or on the population. An example of the latter is the population attributable risk (PAR), which can be seen as the reduction in occurrence of disease X that would be achieved if the population had been entirely unexposed to food (component) Y, compared with its current exposure pattern (Rothman et al., 2008). In the risk assessment context, epidemiologic studies are mainly observational in design Study design and dose response characterization in epidemiology The great advantage of epidemiology is the fact that it actually studies the species it wants to make inferences about. Yet this is also its hurdle, as humans are free-living and subject to many influences. Both exposure and outcome are difficult to measure accurately and subject to systemic bias and measurement error (Kroes et al., 2002; Rothman et al., 2008; Willett, 1998). These are not insurmountable problems, however. The possibility of bias occurring can be reduced through good study design. Study design can be experimental or observational. Experimental design in humans, encompassing randomized controlled trials (RCT s), is not ethical or feasible in case of (severe) risks or long latency (van den Brandt et al., 2002). In some cases, unintended risks turn up in trials, such as in the ATBC and CARET trials (ATBC-Study-Group, 1994; Omenn et al., 1996) and in the Norwegian Vitamin Trial and Western Norway B Vitamin Intervention Trial for folate (Ebbing et al., 2009), where an increased percentage of cancer cases was seen where a decrease was expected. These trials were, however, limited to one dose, and thus dose response relationships could not be characterized. Observational studies include cohort studies (starting with exposure assessment and following up on disease development) and case-control studies (starting with diseased and non-diseased and assessing past exposure). They allow for long latencies between exposure and health outcome of interest. These studies do not provide direct evidence for causal relationships, but a number of methodological considerations have been proposed to judge whether an observed association is a causal relationship and to help in making decisions based on epidemiologic evidence (Hill, 1965; Phillips and Goodman, 2004; Rothman et al., 2008). All factors suspected to be related to the factor of interest need to be measured accurately and in unbiased samples of the target population. Statistical modeling techniques can then control for their

7 M.J. Tijhuis et al. / Food and Chemical Toxicology 50 (2012) influence on the outcome (confounding) or detect associations that are different in subgroups (effect modification or interaction). In studies that do not show an effect, it is often not clear whether the effect is null or simply smaller than can be detected by the studies (insufficient power). Combining data from different studies will increase the power and estimate dose response functions with more precision. However, gathering the appropriate dose response data will not always be possible. An example of successful use of epidemiologic studies is the case of aflatoxins and liver cancer (van den Brandt et al., 2002). Animal models had been found to be inappropriate, showing a large degree of variability in the carcinogenic potential of aflatoxins across species. From human studies looking into Hepatitis B and liver cancer, which included aflatoxin intake, the aflatoxin potency to induce liver cancer (cancers/year/ people/ng aflatoxin/kg body weight/day) could be calculated. Another example, in which dose response relations have been estimated from human observational studies, is the relation between methyl mercury in fish and cognitive development, as recently summarized by the FAO/WHO expert consultation (FAO/WHO, 2010) Proposed frameworks for use of epidemiologic data in risk assessment The need for a more structured use and presentation of epidemiologic data is risk assessment is long recognized (Hertz-Picciotto, 1995). Hertz-Picciotto (1995) has proposed a classification framework for the use of individual epidemiologic studies in dietary quantitative risk assessment, with special focus on dose response assessment, and this has been modified by van den Brandt et al. (2002). They identified three classification categories for epidemiologic studies: (1) the study can be used to derive a dose response relationship; (2) the study can be used to check on plausibility of an animal-based risk assessment; (3) the study can contribute to the weight-of-evidence determination of whether the agent is a health hazard. For the classification of a study into the three categories a number of criteria are used addressing its validity and utility. Recently, a three tier framework has been proposed through which human observational studies can be selected for use in quantitative risk assessment, with specific focus on exposure assessment (Vlaanderen et al., 2008). General recommendations for reporting of observational studies have been provided by STROBE, which stands for Strengthening the Reporting of Observational Studies in Epidemiology ( (Vandenbroucke et al., 2007)) Scope of risk assessment In contrast to benefit assessment, the scope of risk assessment is fairly well established (see Section 4.4). It deals with the assessment of adverse health effects caused by physical or chemical agents. These can be occurring naturally in foods, resulting from food preparation or manufacturing processes or environmental contaminants. At the moment there is further development towards a better understanding of the nature and mechanisms of toxic effects caused by these agents, the inclusion of data other than the traditional toxicological data (Eisenbrand et al., 2002; van den Brandt et al., 2002), and consideration of the shape of the dose response relationships (linear, threshold, J-shape, U- shape) (Calabrese et al., 2007; Zapponi and Marcello, 2006). 4. Benefit assessment Benefit assessment is not an established field. Although beneficial effects conferred by food have been the topic of investigation for a long time (for example within nutritional epidemiology, see Section 3.2), the structured characterization of benefits has only recently begun to develop. Benefit assessment has recently gained more attention because of the developments in the form of health claims on foods. In December 2006, the European Union published its Regulation 1924/2006 on nutrition and health claims made on foods, which, several decades after risk assessment, places a legal perspective also on benefit assessment. Below we will address some concepts and approaches in benefit assessment Basic principles and concepts In the past decades, the goal of adequate nutrition, as defined by the absence of nutritional deficiencies, has shifted to the ambitious goal of optimal nutrition. This change goes beyond the concept of risk reduction and does not only apply to traditional foods and food components, but has also led to the concept of functional foods. Food is seen as a possibility to improve health. Taking beneficial effects into account, may also uncover a discrepancy. The RDA (see Section micronutrients) allows a small percentage of the population to be at inadequate intake for the specified indicator of adequacy; however, if the intake needed to gain a theoretical benefit (as indicated by the dashed line in Fig. 1b) is higher than this level, then a substantial part of the population may not have the advantage of this theoretical benefit. In the benefit assessment context, epidemiologic studies can be both observational and experimental, but experimental ( intervention ) studies are preferred Study design and strength of evidence As described in Section 3.2 both observational and experimental methodologies can be used to assess risks and benefits. From observational studies, there is ample experience in describing associations between human exposure and health outcomes. The level of evidence they provide is most commonly assessed by using the WCRF/AICR criteria, resulting in the judgment convincing, probable, limited-suggestive, limited-no conclusion or substantial effect on risk unlikely (WCRF/AICR, 2007); or the WHO criteria, resulting in the judgment convincing, probable, possible or insufficient (WHO, 2003). For ethical reasons, human experiments (or intervention studies ) are generally pre-eminently carried out to study beneficial and not adverse health effects, once observational data have shown that the benefit is reasonably probable and/or toxicological screening has given reasonable proof of safety. However, it is not uncommon that these trials are short-term and limited to only one or two doses, and these doses are generally rather high to yield the best chance of success (van den Brandt et al., 2002). This hampers the construction of dose response curves that are relevant for long-term human exposure. Short-term trials may not always be appropriate to characterize effects of food and nutrition, because after long-term exposure response mechanisms may adapt to a different state (tolerance) or wear out (threshold). Guidelines for the design and reporting of human intervention studies and for a standardized approach to prove the efficacy of foods and food constituents are being developed (ILSI, 2010). The created knowledge and methodology can be applied in a broader context, and this includes answering a benefit risk question Proposed framework There is agreement in the field to consider benefits according to standardized assessment procedures and specific definitions

8 12 M.J. Tijhuis et al. / Food and Chemical Toxicology 50 (2012) 5 25 (Palou et al., 2009). Benefits should be scientifically proven. It is advocated to structure benefit assessment in the same framework as risk assessment; For example, the EU advisory working group on harmonization of risk assessment procedures identifies a 4-stage approach consisting of value identification, value characterization, use assessment, and benefit characterization (EC, 2000b). EFSA uses the terminology identification of positive / reduced adverse health effect, characterization of positive / reduced adverse health effect, exposure assessment, and benefit characterization (EFSA, 2010b). Examples of initiatives specifically focusing on benefit assessment, in the form of claims made by industry, are the FUFOSE and PASSCLAIM projects, coordinated by ILSI Europe (www. ilsi.org/europe). One of the goals for the FUFOSE project was to assess critically the science base required to provide evidence that specific nutrients and food components beneficially affect target functions in the body (Bellisle and et al., 1998; Diplock et al., 1999). Its follow-up, the PASSCLAIM project, aimed to define a set of generally applicable criteria for the scientific substantiation of claims on foods (Aggett et al., 2005; Asp and Bryngelsson, 2008). The PASSCLAIM criteria cover: (1) the characterization of the food or food ingredients for the health claim, (2) the necessity of human data for the substantiation of a health claim (by preference data coming from human intervention studies but also accepting human observational data), (3) the use of valid endpoints or biomarkers as well as statistically significant and biological relevant changes therein, and (4) the requirement for the totality of the data and weighing of the evidence before making a judgment whether a health claim is or is not substantiated (Aggett et al., 2005; Asp and Bryngelsson, 2008). Addressed in the claims regulation 1924/2006 is the particular beneficial nutritional property of a food (in so-called nutrition claims) or the relationship between a food or food category and health (in so-called health claims) (Verhagen et al., 2010). Examples of the latter are the relationship between calcium and development of bones and teeth, the relationship between plant sterols and coronary heart disease through reduced blood cholesterol and the relationship between iron and cognition in children. A state of the art in nutrition and health claims in Europe is given by Verhagen et al. (2010) Scope of benefit assessment Claims are a driver of, but not the only, development in benefit assessment. There are other initiatives to increase benefit from food, such as voluntary food reformulation by industry (for example reducing the amount of salt, sugar or trans fatty acids) (van Raaij et al., 2009). With respect to traditional foods there has been a development in research to look at whole foods or whole diets (dietary patterns), for example the Mediterranean diet. Benefits are often measured on a disease scale, either directly as a reduction in disease risk or indirectly as a change in disease marker. Currently, markers of optimal health are also being explored (Elliott et al., 2007). This is a fundamentally different process, as subjects are healthy individuals and effects are expected to typically be small. Two bottlenecks have been identified in the development of the newly explored biomarkers to quantify health optimization (van Ommen et al., 2009): the robustness of homeostasis and inter-individual differences in what appear to be normal values. Many markers are maintained within a limited range and effects are masked unless homeostasis is challenged. Nutritional challenge tests (e.g., a high-fat diet) are being developed in this context. These will bring imbalance to normal physiology. Quicker recovery then signifies more optimal health. A combination of perturbation of homeostasis and systems biology and the -omics technologies have been proposed as a means to quantify the system s robustness and to measure all the relevant components that describe the system and discriminate between individuals (Elliott et al., 2007; Hesketh et al., 2006; Keijer et al., 2010; van Ommen et al., 2009). This development also signifies a shift from a population orientation to a more individual based orientation of benefit risk analysis. So far, benefits are based in the biological health domain. Decision-makers are bound by a limited amount of (public) money and (thus) require management options based on physiological mechanisms, not on for example individual preferences. However, certain perceptions and behaviors may well influence the quality of life in such a way that they are relevant for benefit risk weighing (for a detailed description of consumer perception, see Ueland et al., 2011). Also, long-term benefits, in the form of ecological considerations, could be relevant. Thus, it might be reasonable to accept a broad scope of what entails a benefit. 5. Benefit risk assessment From a legal perspective, benefit assessment is about the permission to carry a claim on a product and risk assessment is about defining a maximum intake dose below which the population intake is safe (without appreciable adverse effects). From a public health perspective, benefit assessment and risk assessment are not yes/no or safe/unsafe decisions, but describe a range and continuum of doses and likelihoods of effect. Benefit risk assessment is about evaluating the whole scope of human health and disease. It involves more than adding an assessment of benefits to an assessment of risks; the scientific processes to be integrated are not symmetric. Below, we will first briefly address the integration of the science. Next, we will discuss a number of frameworks for performing a benefit risk analysis, the integration of outcome measures, uncertainty in the assessment and other (problematic) issues Integration of disciplines Until recently, benefit assessment and risk assessment were approached as separate processes and the domain of different underlying scientific disciplines. In benefit risk analysis, all inevitably comes together. There are differences in the process and in the output which need to be integrated. Table 2 presents some of these differences. The integration requires adaptations from all disciplines involved. There is increasing interest to know which effects occur on the entire range of human exposure to be able to characterize benefits and risks. Toxicologists have suggested that toxicology should redirect its focus from looking at adverse effects at high levels of experimental exposure, to characterizing the complex biological effects, both adverse and beneficial, at low levels of exposure, the so-called low-dose toxicology (Rietjens and Alink, 2006). This will help risk quantification of realistic intakes and is also interesting from the idea that some low (physiologic) doses actually turn out to be beneficial (Son et al., 2008). Epidemiologists and nutritionists have urged all who are generating human data to improve the quality of their data presentation and expand it, by presenting more detailed information focusing on dose response necessities, and/or by sharing primary data (de Jong et al., submitted for publication). Some also stress the important role for early, quantifiable

9 M.J. Tijhuis et al. / Food and Chemical Toxicology 50 (2012) Table 2 Characteristics of (regulatory) benefit and risk assessment a, that need to be integrated. Characteristic Benefit assessment Risk assessment Study design Experimental (observational may also be accepted) Experimental (observational may also be accepted) Study population Human Animal (Human is possible) Widely accepted In development Yes methodology Widely accepted endpoints In development; also measured as reduction in adverse endpoint Yes Exposure Usually few (supraphysiological) doses in experimental study; Usually few (supraphysiological) doses in experimental study physiological doses/observational design may also be accepted Study duration Usually short (days/weeks) in experimental design, years/decades can be covered in observational design Usually lifetime of animal until adverse endpoint occurs, years/decades can be covered in observational design Evidence required Grading convincing (sometimes probable admitted) in weight of Strongest effect/steepest slope in positive evidence approach evidence approach Desired outcome in benefit risk assessment setting Benefit characterization, including low dose response information Risk characterization, including low dose response information Desired outcome in regulatory setting Benefit/no benefit Safe/unsafe or acceptable/non-acceptable a With focus on chronic exposures. biomarkers of exposure and of effect as well as for the -omics technologies (Palou et al., 2009). This is expected to improve exposure assessments and provide early response profiles. Ideally the (old and new) disciplines could complement each other within the benefit risk assessment process, with their roles differing depending on e.g. the type of food (component) and its history of use, but always from the aim to end up with data most relevant to humans Benefit risk assessment approaches A number of research efforts have been dedicated specifically to the development of benefit risk methodology in the field of food and nutrition. There have been three recent Europe-based projects, each with its own focus: BRAFO ( ), QALIBRA ( ) and BENERIS ( ); in addition, the topic has been subject of investigation by the European Food Safety Authority (EFSA, 2007, 2010b), by national food and health related institutes (Fransen et al., 2010; van Kreijl et al., 2006) and by individual groups Generalities in approaches Conceptually, those exploring the field of benefit risk assessment in food and nutrition agree that much health gain can come from investing in a better diet, more than from further improving food safety (see Table 3). Practically, there is consensus about the importance of a well formulated and well described benefit risk question. This prior narrative needs address the exposure, target population, health effects and scenarios. As there is more experience with risk assessment than with benefit assessment, it is generally suggested that the steps taken in benefit risk assessment should mirror the traditional risk assessment paradigm of hazard identification, hazard characterization, exposure assessment and risk characterization, with a risk arm and a benefit arm running parallel until comparison (see Fig. 2). A fully quantitative benefit risk analysis requires a large amount of data (see Fig. 3) and time. Assumptions about missing or incomplete data are inevitable. A tiered (stepwise) approach is advocated, allowing transparency and an early stop in the analysis in case the question is answered or in case there is substantial lack of data. In most approaches there is not only room for in-between scientific evaluation, but also for interim consultation with the decision-maker. This means that if a risk is found which is acceptable to the decision-maker, then the benefit risk assessment may also come to a stop (i.e., a management judgement). In fact, policy makers are most often the intended user and initiator of the benefit risk assessment. In general, a distinction can be made between a qualitative comparison and a quantitative comparison of benefits and risks. In a qualitative comparison the benefits and risks are compared in their own currency. This is sufficient when there is clear dominance of either the benefits or the risks, e.g. low gastritis incidence versus high cancer incidence, or when comparison with guidance values indicates that either the benefits or the risks are negligible and there is no true benefit risk question left. In cases where benefits or risks do not clearly dominate, quantitative comparison may answer the benefit risk question. Quantitative comparison of benefits and risks is seen as an (optional) end stage and can be achieved by expressing the effect of each endpoint in Table 3 Comparing estimated health loss and potential health gain by healthy diet and unsafe food in the Netherlands (van Kreijl et al., 2006; see Section 6). Factor Number of deaths/year DALYs a /year Health gain through improvement of food safety versus improvement of diet (composition, amount) Diet composition b 13, ,000 Bodyweight ,000 Diet in total c > Foodborne infections by known pathogens Chemical constituents Food safety in total a DALYs = disability adjusted life years; important here is the message, not the exact numbers: annual health gain to be achieved through improvements to the diet is many times greater than that possible through further improving food safety. b Diet composition including five factors (fruits, vegetables, fish, saturated fatty acids and trans fatty acids). c Counting diet composition and part of bodyweight linked to diet.