Fruit and vegetables and health

Diets rich in fruit and vegetables (FV) have been linked with a reduced risk of chronic disease⁽ Reference Boeing, Bechthold and Bub ^{1 ,} Reference Wang, Ouyang and Liu ^{2 )}. The evidence is particularly strong for CVD⁽ Reference Boeing, Bechthold and Bub ^{1 –} Reference Crowe, Roddam and Key ^{4 )}, is weaker for both diabetes and cancer⁽ Reference Wang, Ouyang and Liu ^{2 ,} Reference Cooper, Sharp and Lentjes ^{5 – 10 )}, and is relatively consistent for specific cancer sites^{( 10 )}. FV are micronutrient and fibre-rich and therefore are recommended across all dietary guidelines^{( 11 – 14 )}.

Although the evidence linking increased FV intake with a reduced risk of CVD is consistent and relatively strong, it is largely based on observational studies⁽ Reference Boeing, Bechthold and Bub ^{1 –} Reference Woodside, Young and McKinley ^{3 )}, with few randomised controlled trials with clinically-relevant endpoints⁽ Reference Woodside, Young and McKinley ^{3 )}. This observational evidence relies on traditional dietary assessment of FV intake, with the majority of studies using a FFQ⁽ Reference Wang, Ouyang and Liu ^{2 ,} Reference Woodside, Young and McKinley ^{3 ,} Reference Boffetta, Couto and Wichmann ^{9 )}.

Assessment of fruit and vegetable intake

Accurate estimation of dietary intake can be challenging, and traditional methods have been shown to be prone to both random and systematic errors. In terms of the specific problems associated with measuring FV intake through traditional methods, FV have been shown to be particularly prone to overreporting, as the participants know that they are known to be health-promoting foods, and therefore tend to exaggerate usual intake⁽ Reference Jenab, Slimani and Bictash ^{15 ,} Reference Woodside, Young and McKinley ^{16 )}. A second reason, which may impact on the accuracy of reporting is that, to report consumption of a particular number of portions daily requires a knowledge of what constitutes a portion of a range of FV, and such knowledge of what constitutes a portion has been recently shown to be lacking amongst a population of low FV consumers⁽ Reference Rooney, McKinley and Appleton ^{17 )}.

Accurate dietary assessment is extremely important for confirming the associations between overall FV intake and chronic disease risk and to inform quantitative dietary guidelines. Indeed, the optimum level of FV intake for health protection is still a topic of debate⁽ Reference Woodside, Rooney and McKinley ^{18 ,} Reference Kypridemos, O'Flaherty and Capewell ^{19 )}. Accurate dietary assessment is also needed to help elucidate if different types of FV have different health-promoting properties. For example, while the evidence for an association between increased overall FV intake and diabetes risk is equivocal, specific FV might be associated with risk; for example, leafy green vegetables and diabetes risk⁽ Reference Carter, Gray and Troughton ^{6 –} Reference Li, Fan and Zhang ^{8 )}. There is also some debate over whether fruit juice has less benefit to health than other forms of fruit⁽ Reference Muraki, Imamura and Manson ^{20 )}. Furthermore, the effect of particular cooking and processing methods on micronutrient content, and micronutrient bioavailability and the resulting effects on health are still uncertain⁽ Reference Oude Griep and Geleijnse ^{21 )}. Finally, the concept of the need to consume a variety of FV, and the association between FV variety and health has been a focus of recent interest⁽ Reference Bhupathiraju and Tucker ^{22 –} Reference Cooper, Sharp and Lentjes ^{24 )}, but, again, to determine the true value of variety does rely on accurate dietary assessment methods.

The importance of dietary assessment method when determining the association between FV intake and disease risk is exemplified by the work of Bingham et al. ⁽ Reference Bingham, Luben and Welch ^{25 )}, who examined the association between FV intake and IHD risk, in a cross-sectional analysis of the EPIC Norfolk cohort study. Whilst there were strong associations between vitamin C intake assessed by food diary and plasma vitamin C status, coefficients were attenuated for vitamin C intake assessed by FFQ. Similarly, when examining risk of IHD, plasma vitamin C and FV intake assessed by food diary were associated with risk of IHD, but not FV intake assessed by FFQ⁽ Reference Bingham, Luben and Welch ^{25 )}. Therefore the choice of dietary assessment method can affect the observed association with disease risk, and selection of an appropriate method is vital. The fact that a food diary and plasma vitamin C reflect recent intake, whilst an FFQ will typically reflect intake over the previous year, is likely to have a bearing on diet–disease associations in observational studies and highlights the need to consider the timescale of the various intake or status assessment methods⁽ Reference Willett ^{26 )}.

Thus, there is a need to explore and develop new methods of accurately estimating FV in order to better capture intake and be able to answer important research questions, such as those mentioned earlier, by allowing better evaluation of the association between intake and disease risk, and the measurement of compliance in intervention studies.

Biomarkers of fruit and vegetable intake

As outlined earlier, traditional methods of assessing FV consumption have significant limitations, and an alternative, more objective way of estimating FV intake may be to measure the levels of compounds found in FV in biological samples, such as plasma, serum and urine. The use of biomarker methods in nutritional epidemiology in general has developed greatly in the past 20 years, with Bingham stating in 2002⁽ Reference Bingham ^{27 )} that ‘The collection of biological samples to improve and validate estimates of exposure, enhance the pursuit of scientific hypotheses, and enable gene–nutrient interactions to be studied, should become the routine in nutritional epidemiology.’ However, there are knowledge gaps, and in 2007 the Institute of Medicine recognised the lack of nutritional biomarkers, and confirmed a need for both biomarkers that can predict functional outcomes and chronic diseases, and those that can improve dietary assessment, but which are non-invasive, inexpensive and specific^{( 28 )}. Hedrick et al.⁽ Reference Hedrick, Dietrich and Estabrooks ^{29 )} reacted to this recommendation, suggesting a need to emphasise the development of biomarkers for evaluating adherence to national recommendations for specific food groups, e.g. wholegrains, fruit and vegetables.

Biomarkers are constituents in the blood, urine or saliva that can be used to indicate dietary exposure and compare this to intake estimated by dietary assessment. Depending on the food group and particular marker used, biomarkers can be classified into three main classes: recovery biomarkers are based on the total excretion of the marker over a specific time period and can estimate absolute intake, but only a few of these recovery biomarkers exist in nutrition, e.g. urinary potassium and urinary nitrogen⁽ Reference Kuhnle ^{30 )}. A further class of markers are predictive markers: these have incomplete recovery, but have a stable, time-dependent and strong association with intake, the main example being urinary sucrose and fructose as a marker of sugar intake⁽ Reference Tasevska, Runswick and McTaggart ^{31 )}. Concentration markers cannot estimate absolute intake, but are correlated with intake and therefore can rank intake of specific nutrients⁽ Reference Kuhnle ^{30 )}, while replacement biomarkers are closely related to concentration biomarkers, but are specifically where information from food databases is unsatisfactory or unavailable⁽ Reference Jenab, Slimani and Bictash ^{15 )}. A number of potential biomarkers of FV intake have been suggested, which are compounds found within FV, including a range of serum carotenoids (lutein, zeaxanthin, β-cryptoxanthin, α- and β-carotene and lycopene), and plasma vitamin C, and also urinary potassium, flavonoids in both urine and serum, and glucosinolates. All of these biomarkers of FV intake would be classified as concentration markers; therefore they will not reflect the exact dietary intake, but are likely to be highly correlated with intake.

Vitamin C and carotenoids are the most commonly used biomarkers, but the complexity of the FV food group makes these compounds potentially less useful as biomarkers of the overall food group, because of the variability of content within different fruit and vegetables⁽ Reference Kuhnle ^{30 )}. For example, the amount of vitamin C found within one portion of green pepper is equivalent to that found in about twenty portions of carrots and, conversely, the amount of total carotene found in one portions of carrots is equivalent to that found in more than forty-five portions of green pepper⁽ Reference Kuhnle ^{30 )}. Kuhnle concluded that, given this variability, it is important to use a combination of biomarkers or to develop new biomarkers; for example, total phenols have been suggested as a potential biomarker which, unlike vitamin C and carotenoids, has much lower variation across different types of FV⁽ Reference Kuhnle ^{30 )}. However, the use of total phenols as a biomarker of FV intake, while plausible based on food analysis, has yet to be explored in detail in human studies⁽ Reference Kuhnle ^{30 ,} Reference Medina-Remón, Barrionuevo-González and Zamora-Ros ^{32 )}.

Two separate systematic reviews (SR) have examined the use of FV biomarkers used in human intervention studies. The first, published in 2011 by Baldrick et al.⁽ Reference Baldrick, Woodside and Elborn ^{33 )}, aimed to examine the utility of the main biomarkers of FV intake to act as objective indicators of compliance in dietary intervention studies. Therefore, this review was particularly focused on identifying compliance markers for intervention studies and reviewed usual practice in this area. The search identified a total of ninety-five studies as suitable for inclusion according to pre-defined criteria and classified the interventions as being whole-diet interventions, individual FV intervention studies or mixed FV studies. Data were extracted and summarised for each study type. This review concluded that it was rarely possible to rely on assessment of a single biomarker as an indicator of dietary change in human intervention studies, but that single biomarkers could be good predictors of single classes of FV, e.g quercetin has been demonstrated to be a reasonable indicator of onion consumption. Similarly, for ‘fruit only’ intervention studies, assessment of vitamin C alone may suffice. However, the authors concluded that given the complexity of FV, and the large number of bioactive compounds they contain, a panel of biomarkers should be measured in FV trials, and this was likely to include a panel of carotenoids and vitamin C, but that further research should continue to explore more novel biomarker approaches⁽ Reference Baldrick, Woodside and Elborn ^{33 )}.

A more recent SR, in contrast to the more qualitative review of Baldrick et al.⁽ Reference Baldrick, Woodside and Elborn ^{33 )}, examined plasma vitamin C and serum carotenoids as indicators of FV intake, conducting both a SR and meta-analysis of randomised controlled trials and examining their comparative validity⁽ Reference Pennant, Steur and Moore ^{34 )}. Nineteen FV interventions, with 1382 participants in total, measures at least one biomarker, and nine trials, with n 667 participants, measured the five main carotenoids (lutein, β-cryptoxanthin, α-, β-carotene and lycopene) and vitamin C. Vitamin C and carotenoids (except lycopene) were responsive to general changes in FV intake at the group level, but there was no clear evidence of dose–response, so that those groups consuming higher number of portions of FV did not have more marked increases in these biomarkers. There was also no convincing evidence that any single biomarker was more responsive than others, with all CI overlapping, whilst there was high heterogeneity in responses, suggesting a lack of consistency in the size of response between studies. Owing to the high heterogeneity and lack of dose–response, the authors concluded that individual-level biomarker responses would be highly variable and could not be relied on⁽ Reference Pennant, Steur and Moore ^{34 )}. Moreover, the randomised controlled trials included in the SR were of low quality, as assessed using the GRADE (grades of recommendation, assessment, development and evaluation) system. This is not unexpected, as blinding is not possible in these whole-food studies, while many of the trials included were not originally designed to develop biomarkers and therefore included participants consuming nutritional supplements and those who smoked, or did not collect samples in the fasting state. Few trials stated whether there was allocation concealment, and the level of dietary control or monitoring of adherence was low, leading to uncertainty about actual FV intake, which is crucial for biomarker response. As with the previous SR, the authors concluded that further work is required to understand the determinants of biomarker variation among individuals⁽ Reference Pennant, Steur and Moore ^{34 )}.

Novel biomarker approaches

Given the challenges of the complexity of the FV food group, a number of novel biomarker approaches have been suggested. It is possible to consider the assessment of a range of biomarkers and statistically combining them to better predict overall FV intake. One approach to this is simply to sum individual biomarkers, e.g. carotenoids, to give a total carotenoid figure⁽ Reference Woodside, Young and Gilchrist ^{35 )}, but this leads to the total being dominated by the carotenoids present at the highest concentrations, e.g. lycopene. To overcome this potential issue, Cooper et al.⁽ Reference Cooper, Sharp and Luben ^{36 )} have recently summed the biomarkers identified within a previous SR as most likely to respond to increased FV intake⁽ Reference Baldrick, Woodside and Elborn ^{33 )}, and calculated the sum of standardised variables of vitamin C, β-carotene and lutein, examining resulting associations with type 2 diabetes risk in the EPIC-Norfolk study⁽ Reference Cooper, Sharp and Luben ^{36 )}.

McGrath et al.⁽ Reference McGrath, Hamill and Cardwell ^{37 )} have examined the effect of increased FV intake on biomarkers of FV consumption, both singly and in combination, but using data from dietary intervention studies and applying more complex statistics to combine the biomarkers. They conducted the BIOFAV study, a tightly controlled FV dietary intervention (all food provided and two meals daily on weekdays consumed under supervision) in low FV consumers. A total of thirty participants, who usually consumed fewer than two portions of FV daily, were randomised to either two, five or eight portions of FV daily for 4 weeks. Blood and urine samples were collected at baseline and 4 weeks, and plasma vitamin C and serum carotenoid analysis conducted. A combined model containing all carotenoids and vitamin C, when predicting allocated FV group, was a better fit than a model containing vitamin C only (P < 0·001) or lutein only (P = 0·006). The C-statistic was lower in the lutein only model (0·85) and the vitamin C model (0·68) than the full model (0·95)⁽ Reference McGrath, Hamill and Cardwell ^{37 )}.

The authors then applied this approach to three other previously conducted FV interventions. They observed a similar pattern of results, but the differences between the combined biomarker and individual biomarker models were less marked, perhaps due to the lower levels of dietary control in these other studies⁽ Reference McGrath, Hamill and Cardwell ^{37 )}. This approach needs to be replicated, and the effect of adding additional potential biomarkers, e.g. urinary flavonoid excretion, to the models to potentially increase the predictive capacity of the model needs to be explored. The utility of such an approach in observational studies also needs to be tested. An issue is that examining the potential of a combined biomarker panel in observational studies will require a ‘true’ measure of FV intake to compare the biomarker against, and most observational studies will have used FFQ-based data collection, which may not be accurate enough to reflect intake comparable with the timescale of the biomarker, i.e. reflect recent intake.

Other studies have also explored the combined biomarkers approach, and have similarly demonstrated an indication of its utility, although each study has used different biomarkers and approached the ‘combining’ in a different way. Analysis of the FLAVURS study, a study testing sequential increases of 2·3, 3·2 and 4·2 portions of FV every 6 weeks across 18 weeks in n 154 male and female participants at increased risk of CVD, suggested that an integrated plasma biomarker (including vitamin C, total cholesterol-adjusted carotenoids and FRAP (ferric reducing ability of plasma) values) was better correlated with FV intake (r = 0·47, P < 0·001) than individual biomarkers⁽ Reference Jin, Gordon and Alimbetov ^{38 )}. Inclusion of urinary potassium into the integrated biomarker panel did not further improve the correlation. This integrated plasma biomarker could therefore, the authors suggest, be used to distinguish between high and moderate FV consumers. No further indicators of model performance were included, which makes further comparisons with other studies difficult.

In another study, a prediction model was developed from twelve FV intervention studies⁽ Reference Souverein, de Vries and Freese ^{39 )}. The prediction model was developed based on a total of 526 male and female participants and was conducted as an individual participant data meta-analysis examining FV intake both including and then excluding FV juices. What was also important was that adjustments were included for important potential characteristics, such as age, BMI and smoking, that may have affected biomarker response, and this is the only study combining biomarkers to have explored the effect of such adjustment to date. Measures of performance for the prediction model were calculated using cross-validation. The final prediction model included carotenoids, folate and vitamin C, and these were positively correlated with FV intake⁽ Reference Souverein, de Vries and Freese ^{39 )}. For the prediction model of fruit, vegetable and juice intake, a reduced model which included only statistically significant predictors, selected using multivariable fractional polynomials performed best. For this model, a number of measures of performance were presented: the root-mean-squared error (258·0 g, the correlation between observed and predicted intake (0·78) and the mean difference between observed and predicted intake (−1·7 g limits of agreement: −466·3, 462·8 g). For the prediction of FV intake (excluding juices), the root-mean-squared error was 201·1 g, the correlation was 0·65 and the mean bias was 2·4 g (limits of agreement: −368·2, 373·0 g). The authors concluded that these models could be used to predict ranking of FV intake when validating questionnaires or to estimate FV intake at the group level. However, low levels of agreement meant that the prediction model should not be used to estimate individual intake⁽ Reference Souverein, de Vries and Freese ^{39 )}.

Therefore combining already known biomarkers of FV intake may be useful in improving the use of biomarkers to accurately estimate FV intake, but only a limited number of studies have, to date, examined this approach.

Metabolomics is an emerging analytical technique that identifies and quantifies small metabolites⁽ Reference Gibney, Walsh and Brennan ^{40 ,} Reference O'Gorman, Gibbons and Brennan ^{41 )}. Traditional biomarker approaches have assessed mainly the concentration in biofluids of phytochemicals measured previously in uncooked FV. In contrast, metabolomics has been used to identify biotransformation products (e.g., glucuronide and sulphate conjugates or colon microbiota fermentation products) of diet-derived chemicals that are both stable, more abundant and easily quantified by the standardised methods⁽ Reference Lloyd, Favé and Beckmann ^{42 ,} Reference Scalbert, Brennan and Manach ^{43 )}. The ability to comprehensively analyse metabolites in biological fluids to look for novel dietary exposure biomarkers in an untargeted way is likely to enhance the ability of researchers to characterise dietary exposure, with many potential applications in nutritional epidemiology. Challenges, however, exist, both in terms of the technology required to identify unknown metabolites and to deal with the large amounts of data produced during this type of analysis. Although a number of studies have examined specific FV classes and used metabolomics to identify potential novel biomarkers, e.g. proline betaine as a biomarker of citrus intake⁽ Reference Heinzmann, Brown and Chan ^{44 ,} Reference Lloyd, Beckmann and Favé ^{45 )}, and S-methyl-l-cysteine sulphoxide and metabolic derivatives as biomarkers of cruciferous vegetable intake⁽ Reference Edmands, Beckonert and Stella ^{46 )}, the use of metabolomics to assess overall FV intakes is, as yet, uncertain.

Another approach that has been suggested is the optical detection of carotenoids in the skin using a range of methods, including resonance Raman spectroscopy, reflection spectroscopy and pressure-mediated reflectance spectroscopy⁽ Reference Whigham and Redelfs ^{47 )}. Such a method would be non-invasive, simple and relatively inexpensive and would provide estimates on the spot without the need for collection of biological samples, which are then analysed in a laboratory. Whether such a technique is sensitive enough to pick up changes in FV intake within normal diet ranges remains to be established. However, a recent study has demonstrated a statistically significant association between carotenoid intake and skin carotenoids in 9–12-year-old children; hence, the authors suggest the potential for such a non-invasive method to measure FV intake in this population⁽ Reference Nguyen, Scherr and Linnell ^{48 )}.

Furthermore, the use of multiple dietary assessment methods and/or biomarker approaches in combination may strengthen the investigation of diet–disease relationships and increase statistical power⁽ Reference Freedman, Kipnis and Schatzkin ^{49 ,} Reference Freedman, Midthune and Carroll ^{50 )}. The approach has then been used in relation to the carotenoids lutein and zeaxanthin, the carotenoids, which are potential biomarkers of FV intake, and are of particular interest in eye disease as they are the only components of the macular pigment⁽ Reference Freedman, Tasevska and Kipnis ^{51 )}. In their study, Freedman et al.⁽ Reference Freedman, Tasevska and Kipnis ^{51 )} explored the difference in statistical power produced when examining either: (i) self-reported dietary intake of lutein and zeaxanthin from a FFQ; (ii) serum lutein and zeaxanthin concentration; or (iii) a combined method summing the ranking of participants from (i) and (ii). The combined measure, when examining the association between lutein and zeaxanthin and risk of nuclear cataracts, provided higher statistical significance that the dietary measure or serum measure alone. The authors suggest a saving of 8–53 % over analysis with dietary intake alone and 6–48 % for the serum level alone in terms of required sample size⁽ Reference Freedman, Tasevska and Kipnis ^{51 )}. Such an increase in power or reduction in required sample size is sizeable and indicates the potential utility of this approach.

Considerations when using biomarkers of fruit and vegetable intake

There are a number of important considerations when using biomarker approaches, and these will be common to all biomarkers. Consideration of the chronology of exposure is important for both traditional dietary assessment and biomarkers, with the likely time frame covered by different dietary assessment methods and biomarkers being considered when comparing methods (Fig. 1). There are a number of further factors, which will affect the ability of biomarkers to predict intake. These have been summarised by Jenab et al.⁽ Reference Jenab, Slimani and Bictash ^{15 )} for dietary assessment and biomarkers in general (adapted in Table 1), and will include a range of pre-analytical factors, which need to be considered⁽ Reference Lippi, Guidi and Mattiuzzi ^{52 ,} Reference Blanck, Bowman and Cooper ^{53 )}.

Fig. 1.

(Colour online) Timescale of nutritional biomarkers from different biological sources (adapted from Kuhnle⁽ Reference Kuhnle ^{30 )}).

Table 1.

Factors affecting nutritional biomarker response (adapted from Jenab et al.⁽ Reference Jenab, Slimani and Bictash ^{15 )}), with specific examples added for proposed fruit and vegetable (FV) biomarkers

Specifically, vitamin C is a particularly labile vitamin and therefore sample collection and stabilisation has to be conducted carefully, according to protocols, which involve the precipitation of proteins, usually with metaphosphoric or TCA⁽ Reference Vuilleumier and Keck ^{54 ,} Reference Salminen and Alfthan ^{55 )}. Such stabilisation is not commonly carried out within large-scale epidemiological studies. Similarly, carotenoids can be light-sensitive, and therefore exposure to light during processing and storage should be minimised⁽ Reference Craft, Wise and Soares ^{56 )}.

Genetic differences in biomarker responses have been observed, although to date these have only been analysed within observational studies⁽ Reference Timpson, Forouhi and Brion ^{57 ,} Reference Borel ^{58 )}. For example, Timpson et al.⁽ Reference Timpson, Forouhi and Brion ^{57 )} examined variation at the SLC23A1 locus in five independent population studies and found that each additional rare allele was associated with a reduction in circulating ascorbic acid concentrations (−5·98 (95 % CI −8·23, −3·73) μm/l, P = 2·0 × 10(−7) per minor allele). Similarly, carotenoid status has been suggested to depend on range of genotypes, including phase two enzyme glutathione S-transferase M1 and T1 polymorphisms, and this has been reviewed⁽ Reference Borel ^{58 )}. The effect of such polymorphisms on biomarker responses within FV intervention studies is not known, but to test this will require careful study design consideration and likely increase in required sample size.

Differences in biomarker responses have been observed based on baseline concentration⁽ Reference Jenab, Slimani and Bictash ^{15 )}, inflammation⁽ Reference Tomkins ^{59 )}, status of other nutrients, including other carotenoids⁽ Reference Reboul, Thap and Tourniaire ^{60 )}, BMI⁽ Reference Vioque, Weinbrenner and Asensio ^{61 )} and smoking⁽ Reference Alberg ^{62 )}. For example, plasma carotenoids and vitamin C were less strongly associated with dietary intake in obese older subjects than in those of normal weight⁽ Reference Vioque, Weinbrenner and Asensio ^{61 )}. Furthermore, plasma vitamin C tends to plateau at higher levels of intake (>120 mg/d), and therefore may not accurately reflect higher exposure⁽ Reference Padayatty and Levine ^{63 )}. A recent study examining carotenoids as biomarkers of FV intake in men and women, and using data from FV interventions, suggested that plasma β-cryptoxanthin and lutein concentrations were reliable biomarkers of FV consumption, but that there were significant sex differences in biomarker response following FV consumption⁽ Reference Couillard, Lemieux and Vohl ^{64 )}, suggesting that sex must be considered when monitoring biomarker responses. These factors are also considered in Table 1.

What has been less fully explored and which will be challenging, is whether biomarkers can ever be sensitive enough to pick up on differences in response by FV class, cultivar, production, processing and storage factors, which may impact on micronutrient content of the specific fruit or vegetable, and, affect health status. For example, cooking of FV leads to a reduction in vitamin C content⁽ Reference Zeng ^{65 )}, but the degree of loss will depend on the cooking procedure and length of cooking time. Miglio et al.⁽ Reference Miglio, Chiavaro and Visconti ^{66 )} examined the effect of different cooking methods on phytochemical properties, total antioxidant capacity and physicochemical properties of carrots, courgettes and broccoli, and highlighted that the modifications by cooking are strongly dependent on the vegetable species. Similarly the consumption of fat alongside carotenoid-rich foods increases bioavailability of the carotenoids⁽ Reference Priyadarshani ^{67 )}. While it is perhaps unlikely that FV biomarkers will ever be sensitive enough to measure the impact of some of these factors, what is likely is that there will be an improvement of accuracy in terms of global FV assessment.