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

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.

Ketogenic diet but not free-sugar restriction alters glucose tolerance, lipid metabolism, peripheral tissue phenotype, and gut microbiome: RCT

Restricted sugar and ketogenic diets can alter energy balance/metabolism, but decreased energy intake may be compensated by reduced expenditure. In healthy adults, randomization to restricting free sugars or overall carbohydrates (ketogenic diet) for 12 weeks reduces fat mass without changing energy expenditure versus control. Free-sugar restriction minimally affects metabolism or gut microbiome but decreases low-density lipoprotein cholesterol (LDL-C). In contrast, a ketogenic diet decreases glucose tolerance, increases skeletal muscle PDK4, and reduces AMPK and GLUT4 levels. By week 4, the ketogenic diet reduces fasting glucose and increases apolipoprotein B, C-reactive protein, and postprandial glycerol concentrations. However, despite sustained ketosis, these effects are no longer apparent by week 12, when gut microbial beta diversity is altered, possibly reflective of longer-term adjustments to the ketogenic diet and/or energy balance. These data demonstrate that restricting free sugars or overall carbohydrates reduces energy intake without altering physical activity, but with divergent effects on glucose tolerance, lipoprotein profiles, and gut microbiome.

Creation and validation of a NOVA scored picture set to evaluate ultra-processed foods

There has been a rapid shift in the modern food environment towards increased processing in foods consumed in the United States (US) and globally. The NOVA system (not an acronym) for classifying food on degree of processing currently has the most empirical support. Consumption of foods in the NOVA 4 category, ultra-processed foods (UPF), is a risk factor for a host of poor health outcomes including heart disease, stroke, and cancer. Despite these poor health outcomes, UPF make up 58% of calories consumed in the US. Methodologies for assessing the reinforcing and rewarding properties of these foods are necessary tools. The Becker-DeGroot-Marschak auction paradigm (BDM) is a well validated tool for measuring value and is amenable to neuromonitoring environments. To allow for the testing of hypotheses based on level of food processing, we present a picture set of 14 UPF and 14 minimally-processed foods (MPF) matched on visual properties, food characteristics (fat, carbohydrate, cost, etc.), and rated perceptual properties. Further, we report our scoring of these foods using the NOVA classification system and provide additional data from credentialed nutrition professionals and on inter-rater reliability using NOVA, a critique of the system. Finally, we provide all pictures, data, and code used to create this picture set as a tool for researchers.

The hedonic overdrive model best explains high-fat diet-induced obesity in C57BL/6 mice

OBJECTIVE

High-fat diets cause obesity in male mice; however, the underlying mechanisms remain controversial. Here, three contrasting ideas were assessed: hedonic overdrive, reverse causality, and passive overconsumption models.

METHODS

A total of 12 groups of 20 individually housed 12-week-old C57BL/6 male mice were exposed to 12 high-fat diets with varying fat content from 40% to 80% (by calories), protein content from 5% to 30%, and carbohydrate content from 8.4% to 40%. Body weight and food intake were monitored for 30 days after 7 days at baseline on a standard low-fat diet.

RESULTS

After exposure to the diets, energy intake increased first, and body weight followed later. Intake then declined. The peak energy intake was dependent on both dietary protein and carbohydrate, but not the dietary fat and energy density, whereas the rate of decrease in intake was only related to dietary protein. On high-fat diets, the weight of food intake declined, but despite this average reduction of 14.4 g in food intake, they consumed, on average, 357 kJ more energy than at baseline.

CONCLUSIONS

The hedonic overdrive model fit the data best. The other two models were not supported.

Ultra-processed Food and Obesity: What Is the Evidence?

PURPOSE OF REVIEW

Obesity is a growing global healthcare concern. A proposed driver is the recent increase in ultra-processed food (UPF) intake. However, disagreement surrounds the concept of UPF, the strength of evidence, and suggested mechanisms. Therefore, this review aimed to critically appraise the evidence on UPF and obesity.

RECENT FINDINGS

Observational studies demonstrate positive associations between UPF intake, weight gain, and overweight/obesity, more clearly in adults than children/adolescents. This is supported by high-quality clinical data. Several mechanisms are proposed, but current understanding is inconclusive. Greater UPF consumption has been a key driver of obesity. There is a need to change the obesogenic environment to support individuals to reduce their UPF intake. The UPF concept is a novel approach that is not explained with existing nutrient- and food-based frameworks. Critical analysis of methodologies provides confidence, but future observational and experimental research outputs with greater methodological rigor will strengthen findings, which are outlined.