Nutrient clustering, NOVA classification, and nutrient profiling: How do they overlap, and what do they predict about food palatability?

We compared the performance of three food categorisation metrics in predicting palatability (taste pleasantness) using a dataset of 52 foods, each rated virtually (online) by 72-224 participants familiar with the foods in question, as described in Appetite 193 (2024) 107124. The metrics were nutrient clustering, NOVA, and nutrient profiling. The first two of these metrics were developed to identify, respectively: 'hyper-palatable' foods (HPFs); and ultra-processed foods (UPFs), which are claimed to be 'made to be hyper-palatable'. The third metric categorises foods as high fat, sugar, salt (HFSS) foods versus non-HFSS foods. There were overlaps, but also significant differences, in categorisation of the foods by the three metrics: of the 52 foods, 35 (67%) were categorised as HPF, and/or UPF, and/or HFSS, and 17 (33%) were categorised as none of these. There was no significant difference in measured palatability between HPFs and non-HPFs, nor between UPFs and non-UPFs (p ≥ 0.412). HFSS foods were significantly more palatable than non-HFSS foods (p = 0.049). None of the metrics significantly predicted food reward (desire to eat). These results do not support the use of hypothetical combinations of food ingredients as proxies for palatability, as done explicitly by the nutrient clustering and NOVA metrics. To discover what aspects of food composition predict palatability requires measuring the palatability of a wide range of foods that differ in composition, as we do here.

Development of Momentary Appetite Capture (MAC): A versatile tool for monitoring appetite over long periods.

Understanding how an intervention impacts appetite in real-life settings and over several days remains a challenging and under-explored research question. To this end, we developed Momentary Appetite Capture (MAC), a form of ecological momentary assessment that combines automated text messaging with an online platform. Participants report their appetite using visual analogue scales (hunger, desire to eat, and fullness) and a virtual portion-size selection task. In two separate studies, we assessed the feasibility and test-retest reliability of MAC. Participants were prompted every 2 hours over a 14-hour window, and they repeated this assessment over two consecutive weekdays. For each participant, we calculated a daily time-averaged area under the curve (AUC) for each appetite measure. In Study One (N = 25) time-averaged AUC was significantly positively correlated across test days for hunger (r = 0.563, p = .003), desire to eat (r = 0.515, p = .008) and prospective portion size (r = 0.914, p < .001), but not for fullness (r = 0.342, p = .094). Participants completed 95% of MACs (380 of 400), and we used participant feedback to improve the MAC tool and study protocol for Study Two. In Study Two (N = 31), 94% of MACs were completed (468 of 496). Across days, time-averaged AUC was significantly positively correlated for hunger (r = 0.595, p = < .001), fullness (r = 0.501, p = .004), desire to eat (r = 0.585, p < .001), and prospective portion size (r = 0.757, p < .001). Together, these studies suggest that MAC could be an acceptable and reliable tool to track appetite throughout the day. In the future, MAC could be used to explore the impact of weight-loss interventions on natural fluctuations in appetite.

Evidence that carbohydrate-to-fat ratio and taste, but not energy density or NOVA level of processing, are determinants of food liking and food reward.

This virtual (online) study tested the common but largely untested assumptions that food energy density, level of processing (NOVA categories), and carbohydrate-to-fat (CF) ratio are key determinants of food reward. Individual participants (224 women and men, mean age 35 y, 53% with healthy weight, 43% with overweight or obesity) were randomised to one of three, within-subjects, study arms: energy density (32 foods), or level of processing (24 foods), or CF ratio (24 foods). They rated the foods for taste pleasantness (liking), desire to eat (food reward), and sweetness, saltiness, and flavour intensity (for analysis averaged as taste intensity). Against our hypotheses, there was not a positive relationship between liking or food reward and either energy density or level of processing. As hypothesised, foods combining more equal energy amounts of carbohydrate and fat (combo foods), and foods tasting more intense, scored higher on both liking and food reward. Further results were that CF ratio, taste intensity, and food fibre content (negatively), independent of energy density, accounted for 56% and 43% of the variance in liking and food reward, respectively. We interpret the results for CF ratio and fibre in terms of food energy-to-satiety ratio (ESR), where ESR for combo foods is high, and ESR for high-fibre foods is low. We suggest that the metric of ESR should be considered when designing future studies of effects of food composition on food reward, preference, and intake.

Further evidence for sensitivity to energy density and a two-component model of meal size: Analysis of meal calorie intakes in Argentina and Malaysia.

Previously, we demonstrated a non-linear association between meal caloric intake and meal energy density (ED, kcal/g) in data from a controlled trial in the US and from free-living participants in the UK [1]. In both datasets, meal caloric intake increased with ED in lower energy-dense meals (below ∼1.75 kcal/g) and decreased in higher energy-dense meals (above ∼1.75 kcal/g). In the current study, we sought to explore whether this pattern extends to data from free-living participants in Argentina (N = 2738 meals) and Malaysia (N = 4658 meals). Again, a significant breakpoint was found in both the Argentinean (2.04 kcal/g (SE = 0.06)) and Malaysian (2.17 kcal/g (SE = 0.06)) datasets with mean centered meal caloric intake increasing with ED below the breakpoint and decreasing above the breakpoint. These results lend further support for our two-component theoretical model of meal size (g) in which a volume signal is dominant in lower energy-dense meals and a calorie-content signal is dominant in higher energy-dense meals. Together, our research adds to evidence supporting human sensitivity to calories and exposes a complexity in the correspondence between meal energy content and meal size in everyday (non-manipulated) meals. Further research is needed to provide causal evidence for this sensitivity and whether individual variation impacts meal size and energy balance.

Human nutritional intelligence underestimated? Exposing sensitivities to food composition in everyday dietary decisions.

The social and cultural significance of food is woven into every aspect of our dietary behaviour, and it contributes to our complex interaction with food. To find order within this complexity scientists often look for dietary 'universals' - phenomena or basic principles that guide our food choice and meal size, irrespective of wider context. One such idea is that taste characteristics provide a signal for dietary composition (e.g., sweet taste signals carbohydrate). Others have suggested that behaviour is guided by learning and is based on associations that form between the flavour of a food and its post-ingestive effects. Despite a large body of research, evidence supporting both processes is equivocal, leading some to conclude that humans are largely indifferent to food composition. Here, we argue that human abilities to gauge the nutritional composition or value of food have been underestimated, and that they can be exposed by embracing alternative methods, including cross-cultural comparisons, large nutrition surveys, and the use of virtual portion-selection tools. Our group has focused on assessments of food choice and expected satiety, and how comparisons across everyday foods can reveal non-linear relationships with food energy density, and even the potential for sensitivity to micronutrient composition. We suggest that these abilities might reflect a complex form of social learning, in which flavour-nutrient associations are not only formed but communicated and amplified across individuals in the form of a cuisine. Thus, rather than disregarding sociocultural influences as extraneous, we might reimagine their role as central to a process that creates and imbues a 'collective dietary wisdom.' In turn, this raises questions about whether rapid dietary, technological, and cultural change disrupts a fundamental process, such that it no longer guarantees a 'nutritional intelligence' that confers benefits for health.

The Effect of Intermittent Fasting on Appetite: A Systematic Review and Meta-Analysis.

Previously, narrative reviews have considered the effects of intermittent fasting on appetite. One suggestion is that intermittent fasting attenuates an increase in appetite that typically accompanies weight loss. Here, we conducted the first systematic review and meta-analysis to quantify the effects of intermittent fasting on appetite, when compared to a continuous energy restriction intervention. Five electronic databases and trial registers were searched in February 2021 and February 2022. Abstracts (N = 2800) were screened and 17 randomized controlled trials (RCTs), consisting of a variety of intermittent fasting regimes, met our inclusion criteria. The total number of participants allocated to interventions was 1111 and all RCTs were judged as having either some concerns or a high risk of bias (Cochrane RoB 2.0 tool). Random effects meta-analyses were conducted on change-from-baseline appetite ratings. There was no clear evidence that intermittent fasting affected hunger (WMD = -3.03; 95% CI [-8.13, 2.08]; p = 0.25; N = 13), fullness (WMD = 3.11; 95% CI [-1.46, 7.69]; p = 0.18; N = 10), desire to eat (WMD = -3.89; 95% CI [-12.62, 4.83]; p = 0.38; N = 6), or prospective food consumption (WMD = -2.82; 95% CI [-3.87, 9.03]; p = 0.43; N = 5), differently to continuous energy restriction interventions. Our results suggest that intermittent fasting does not mitigate an increase in our drive to eat that is often associated with continuous energy restriction.

Time to revisit the passive overconsumption hypothesis? Humans show sensitivity to calories in energy-rich meals.

BACKGROUND: A possible driver of obesity is insensitivity (passive overconsumption) to food energy density (ED, kcal/g); however, it is unclear whether this insensitivity applies to all meals. OBJECTIVES: We assessed the influence of ED on energy intake (kcal) across a broad and continuous range of EDs comprised of noncovertly manipulated, real-world meals. We also allowed for the possibility that the association between energy intake and ED is nonlinear. METHODS: We completed a secondary analysis of 1519 meals which occurred in a controlled environment as part of a study conducted by Hall and colleagues to assess the effects of food ultra-processing on energy intake. To establish the generalizability of the findings, the analyses were repeated in 32,162 meals collected from free-living humans using data from the UK National Diet and Nutrition Survey (NDNS). Segmented regressions were performed to establish ED "breakpoints" at which the association between consumed meal ED and mean centered meal caloric intake (kcal) changed. RESULTS: Significant breakpoints were found in both the Hall et al. data set (1.41 kcal/g) and the NDNS data set (1.75 and 2.94 kcal/g). Centered meal caloric intake did not increase linearly with consumed meal ED, and this pattern was captured by a 2-component ("volume" and "calorie content" [biologically derived from the sensing of fat, carbohydrate, and protein]) model of physical meal size (g), in which volume is the dominant signal with lower energy-dense foods and calorie content is the dominant signal with higher energy-dense foods. CONCLUSIONS: These analyses reveal that, on some level, humans are sensitive to the energy content of meals and adjust meal size to minimize the acute aversive effects of overconsumption. Future research should consider the relative importance of volume and calorie-content signals, and how individual differences impact everyday dietary behavior and energy balance.