Frequently Asked Questions About the 2018 WCRF/AICR Score

The standardized 2018 WCRF/AICR Score focuses on eight key lifestyle components but it does not address all major risk factors that should be considered when examining cancer-related outcomes. One key example is tobacco use. The 2018 WCRF/AICR Cancer Prevention Recommendations include not smoking and avoiding other tobacco exposures in an overall statement along with other cancer risk-related behaviors, such as sun exposure practices, but does not address tobacco use as a specific recommendation. We do strongly recommend that researchers include tobacco use and other risk factors (for example, sun exposure, sodium intake) in their models when appropriate. Researchers should carefully consider the complexities of the appropriate tobacco use variables according to their specific research question and data availability (e.g., smoker versus non-smoker or former smoker, years smoking, number of cigarettes).

The decisions regarding which sub-recommendations to operationalize were based on factors such as the limitations of the existing literature, lack of specificity in the sub-recommendations, or redundancy with another recommendation (that would create unequal weighting if incorporated). For example, the 2018 WCRF/AICR Score does not operationalize the physical activity sub-recommendation on sedentary time because there is limited evidence to propose specific cut-points; this limitation has been acknowledged in both the 2007 and 2018 WCRF/AICR Expert Reports and the 2018 Physical Activity Guidelines for Americans.

The plant-based foods component is another example where we only operationalized two of the four sub-recommendations. This latter limitation is mitigated in the current Score, as the fiber sub-score captures fiber intake from legume and whole-grain sources and we retained refined grains in the aUPF definition. Nevertheless, future efforts in studies that collect additional data may inform how to better capture these constructs (e.g., through cohorts that assess weight patterns from childhood, objective measures of physical activity and sedentary time, and the consideration of different food-based constructs).

The Score can be used in any adult population 18 years of age and older. For example, the Score may be used in healthy adult populations, adults with comorbid conditions, and in adult cancer survivor populations. The main benefit of this standardized Score is that it will allow researchers to compare findings across populations and countries. Past studies created a variety of different scoring systems, which limited comparability across studies. The new Score was created to address this limitation. Additionally, it can also be used across different study designs, including cohort studies, interventions, and surveillance studies.

It was important to include BMI and waist circumference, since both are addressed in the 2018 WCRF/AICR’s Third Expert Report and both capture different aspects of healthy weight. Evidence suggests that increased waist circumference is associated with increased disease risk independent of BMI.

Fast foods are a diverse group of foods characterized by high levels of fat, starches, and sugars. There is a lack of established standards to define and instruments to capture fast foods, particularly in the cancer-related literature. UPFs are not explicitly specified in the 2018 WCRF/AICR Third Expert Report but the foods addressed in this recommendation are typically considered to be UPFs. An adaptation of the NOVA classification system provides an adapted ultra-processed foods (aUPFs) variable to represent fast foods.

We conducted an extensive literature search, consulted the CUP Expert Panel, and reviewed the distribution of percent of total energy from UPFs from past studies. We examined the distribution of percent of energy from aUPF within the National Institutes of Health-AARP (formally known as the American Association of Retired Persons) Diet and Health Study and found that it was comparable to the percent of total energy from added sugars and saturated fat. Furthermore, although the fast foods component is the only one defining cut-points via data-driven tertiles, this may also be viewed as a strength; the cut-points help to address the variation of the aUPF sources of a given population’s food system and the variation in measurements by researchers (e.g., grams versus % kilocalories), thereby providing a more adaptable and comparable component Score across future studies. It is anticipated that the aUPF-based framework will be refined by future researchers to enhance the utility and practicality of this construct.

The UPFs variable is calculated based on data collected from dietary assessment instruments such as a food frequency questionnaire (FFQ) or 24-hour recalls. Foods considered UPFs are based on guidance from the NOVA classification system. The list of foods considered as aUPFs in a given study may vary by country (e.g., if the white bread is most likely made fresh in a bakery vs. ultra-processed to enhance preservation in the grocery store) or dietary instrument (e.g., if plain yogurt is distinguished from sweetened yogurt). More details on NOVA can be found here).

Once the foods contributing to the aUPF variable are identified, researchers can take the summed kilocalories (or grams, depending on your dietary instrument) for each individual and divide it by their total kilocalories (or total grams), then multiply by 100 to estimate the % of each participant’s diet comprised of aUPFs (i.e., [kcal from aUPF/total kcal] x 100 = % UPF in diet). This is then repeated for all participants and the tertiles are created based on the participants’ distribution.

Previous research with the 2007-based scores suggests that the relationship between the Score and cancer-related risks may not be linear: a one-point increment in the Score from 0 to 1 may be different from a Score increase from 1 to 2 or 7 to 8. It is thus important for researchers to consider if there are linear or non-linear associations between the Score and their health outcomes of interest. For linear effect estimates, we recommend that researchers report 1-point increments to promote uniformity across studies. Additionally, it is important to note that at this time, we do not have guidance regarding a priori categorizations of the total Score, given that it may depend on the distribution of the Score in a certain population. We encourage researchers who apply their own categories to provide their rationale and full range of the Score within the selected categories to enhance the interpretation of results and enable future comparisons.

The use of the standardized 2018 WCRF/AICR Score by researchers is strongly encouraged to maximize the ability to make comparisons across studies; however, exploratory efforts to adapt and improve the utility of the standardized Score are also encouraged. Researchers should clearly document where they depart from the published Score (e.g., change cut-points due to national guidelines, add additional constructs, or abbreviate the Score due to data limitations) to support methodological transparency. Incomplete reporting of modifications to the standardized scoring system may introduce heterogeneity in observed relationships that are due to methodological issues rather than adherence to the Score components.

Three examples where modifications can be explored are operationalizing additional goals and recommendations; examining different sub-score cut-points; and weighting components. Additional guidance can be found in a 2020 commentary by Shams-White et al.

Our goal was to create a simple overall scoring system, weighting each recommendation equally. It is debatable as to whether weighting should be equal within components (e.g., for calculating the alcohol sub-score in women, considering if two servings of ethanol/d should be treated the same as 10 servings of ethanol/d) and between components (e.g., giving equal weight to breastfeeding and body weight), and if the weighting should vary based on outcome. Regarding the latter, there is strong evidence linking body fatness to at least twelve types of cancer and several of the Score components may operate through obesity to increase cancer risk. However, this recommendation is not weighted in this simple Score. Additionally, there is also a stronger dose-response relationship between alcohol intake versus sugar-sweetened drink intake and cancer risk that is not currently reflected in the Score. Finally, there are five diet-related components in the 2018 WCRF/AICR Score, giving greater weight to diet than body weight, physical activity, and breastfeeding. To address these questions, in addition to reporting results using the unweighted, standardized scoring system, future research can examine the implications of reweighting the components in the aforementioned scenarios (e.g., within components, between components, and between lifestyle factors) and test the predictive validity of the Score.

The 2018 WCRF/AICR Score can be derived from most commonly used, detailed lifestyle assessment data collection methods. For example, dietary components can be assessed based on data from FFQs, 24-hour recalls, and even food records; physical activity data can be collected via self-report physical activity questionnaires or accelerometers. However, some studies may include fewer items on a given component compared to others or may collect frequency per week but not quantity. Questionnaires may also include specific fast foods questions (e.g., focusing on frequency of eating out at restaurants and/or fast food establishments) or, conversely, be missing measures for aUPF data. The latter may be due to the use of older assessment instruments or the analysis of older data when there was a lower prevalence of UPFs in a given food system.

In instances where studies do not include adequate measures that align with the standardized scoring system, the 2018 WCRF/AICR Score may be adapted to assess adherence. For example, if the standardized Score’s red and processed meat intake or physical activity cut-points are not feasible for a study given that only frequency per week was assessed and not dosage (e.g., missing grams or minutes/week, respectively), subjective tertiles may be used similar to the fast foods component sub-score to stratify participants. However, all modifications should be fully reported.

There is much that we do not know about the impact of modifications to the Score measures; implications of such changes are an area for future research. Regardless of the alternate path chosen, we recommend that researchers apply the Score definitions as closely as possible and explicitly state all deviations from the standard Score.

We are currently compiling publications utilizing the 2018 WCRF/AICR Score. We encourage researchers to send updates about their publications to RFAB@mail.nih.gov.