What is “Food Noise” and How Does it Influence the DMHR?

  • Patients struggling with food intake regulation often report obsessively thinking about food for prolonged periods and spending lots of time doing things related to food.
  • A study published in Nutrients proposes that this phenomenon of heightened reactivity to food cues be termed “food noise.”
  • It proposes a conceptual model describing factors linking food cues and consequences of heightened food cue reactivity, including ways to regulate it.

Traditionally, people widely believed that individuals gain weight simply because they are not careful and eat too much. Religious teachings, for example, speak about gluttony, one of the deadly sins symbolizing primarily excessive or overindulgent eating. In this view, people become overweight more or less because their willpower is not strong enough to avoid the temptation to overeat. Similarly, to lose weight, they need to “tough it out” and show sufficient willpower to resist the urge to overeat.

 

In this view, people become overweight more or less because their willpower is not strong enough to avoid the temptation to overeat. They need to “tough it out” and show sufficient willpower to resist the urge to overeat

 

However, we are all aware of people who fail to lose weight or maintain healthy body weight in spite of significant efforts. Others maintain a healthy physique without paying much attention to their diets.

Given these observations, can being overweight or maintaining a healthy weight really be just a matter of willpower? Scientific discoveries made in recent decades say otherwise.

 

Can being overweight or maintaining a healthy weight really just be just a matter of willpower?

 

What causes obesity?
The obvious answer is that obesity results from consuming more calories than one expends. However, things are far from being so simple. For instance, our food intake is guided by processes in our brain that tell us when we need to eat and when to stop eating. This is the case in humans and most other complex species (Wilding, 2001). 

The mechanism of hunger creates a sensation of hunger when our body needs nutrients and a sensation of satiety when we eat enough. These sensations make us start or stop eating. However, studies show that this hunger-satiety regulation system is dysregulated in many individuals. This dysregulation can give rise to dysfunctional eating behaviors. When this happens, individuals may either consume less nutrients than they need, as observed in the case of anorexia, or more than their body needs, contributing to overweight and obesity (Pujol et al., 2021) (see Figure 1). 

 

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Figure 1. The Hunger-Satiety Regulation System

 

The food intake regulation system
The brain’s hypothalamus region regulates food intake through a complex system of neural circuits. However, this neural network in the hypothalamus interacts with many other systems of the body, such as the limbic system, which governs emotions and motivations related to eating. Higher cognitive processes, mediated by regions like the prefrontal cortex, play a crucial role in food choices and portion control decision-making. Furthermore, hormonal signals from the gastrointestinal tract, such as leptin and ghrelin, contribute to the body’s overall energy balance and influence the hypothalamus in modulating hunger and satiety cues. This intricate interplay among neural circuits, emotional centers, cognitive functions, and hormonal systems collectively orchestrates the complex regulation of food intake (see Figure 2).

 

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Figure 2. The brain’s food intake regulation system

 

As with any highly complex system, many things can lead to the dysregulation of the food intake control system. Studies show that genetic factors, such as those leading to the deficit of leptin, the hormone responsible for inhibiting feelings of hunger, can dysregulate this system and lead to obesity. Similar effects were observed in individuals with damage to the hypothalamus regions of the brain (Wilding, 2001). 

The strong increase in the share of obese individuals throughout the world in recent decades, the obesity pandemic, has pointed to additional factors leading to the dysregulation of the food intake control system. Studies find that diets based on certain types of food, such as highly processed foods or foods high in fat and sugars, dysregulate food intake, leading to obesity (Hedrih, 2023). Experiments show that feeding mice high-fat diets disrupts their food intake regulation and makes them develop obesity (Ikemoto et al., 1996), but also changes certain structures in the brains of their offspring (Lippert et al., 2020).

 

Diets based on highly processed foods or foods high in fat and sugars disrupt the regulation of food intake, consequently contributing to the development of obesity

 

Finally, research suggests that human food consumption is influenced not solely by a deficiency of nutrients in the body but frequently by learned food cues from childhood and throughout life  (Hedrih, 2023a; Schulte et al., 2019). These are the associations between our perceptions of food items and our experiences (taste, smell, etc.) of them. New studies indicate that people might differ in how reactive they are to these food queues, with some people being very much overwhelmed by them. This led researchers to coin the term “food noise” to describe this situation (Hayashi et al., 2023).

 

Humans consume food often in response to food cues learned in childhood and throughout life

 

What is ‘food noise’?
Our brains excel at triggering motivational responses when exposed to food cues (a concept involved with the term “availability” within nutritional psychology) (Morphew-Lu et al., 2021). Simply put, our brains are very good at making us desire the foods and beverages we see, smell, hear, or sense in another way (Hayashi et al., 2023). For example, when we smell the aroma of freshly baked pastries, hear the sizzle of bacon in a skillet, or see desserts at a party or in a grocery store, we often develop a desire to consume that food. This responsiveness to food cues constitutes our reactivity to them.

 

New studies indicate that people might differ in how reactive they are to food cues, with some people being very much overwhelmed by them

 

From an evolutionary perspective, being reactive to food cues has contributed to the survival of humans in times of food scarcity. It made them use opportunities to meet their nutritional needs whenever they arose, regardless of whether their body needed those nutrients at that very moment or not. However, in modern industrial societies, highly palatable and energy-dense foods are widely available, and the environment tends to be full of food cues. These include foods exposed for sale in grocery stores, food supplies kept at home, and many food advertisements found across various media channels.

People vary in their responsiveness to food cues. While some individuals can easily overlook the numerous food cues they encounter, others exhibit heightened reactivity. The latter group can be described as experiencing ‘food noise.’

 

People vary in their responsiveness to food cues

 

The authors of this paper, Daisuke Hayashi and his colleagues, define food noise as “heightened and/or persistent manifestations of food cue reactivity, often leading to food-related intrusive thoughts and maladaptive eating behaviors.” Individuals experiencing food noise find themselves constantly thinking about food, checking food ordering websites, and being obsessively preoccupied with food. This then easily leads them to act on these thoughts, resulting in overeating, binge eating, and weight gain as a consequence.

 

Food noise is defined as “heightened and/or persistent manifestations of food cue reactivity, often leading to food-related intrusive thoughts and maladaptive eating behaviors”

 

How was food noise discovered?
In recent decades, the global population has witnessed a significant increase in the prevalence of overweight and obese individuals (Wong et al., 2022).  This obesity epidemic has coincided with a surge in the number of people affected by type 2 diabetes, a chronic condition characterized by ineffective cell responses to insulin (the hormone that facilitates glucose uptake into cells of the body). This inefficiency leads to impaired glucose absorption and elevated blood sugar levels. Health professionals widely prescribe a type of medicine called GLP-1Ras or glucagon-like peptide-1 receptor agonists to treat type 2 diabetes. GLP-1RAs mimic the action of the glucagon-like peptide-1 hormone, helping to lower blood sugar levels. They do this by increasing insulin production and reducing glucagon secretion, a hormone that raises blood sugar levels, through several other mechanisms (see Figure 3).

 

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Figure 3. GLP-1Ras mechanism

 

Very soon after the use of GLP-1Ras became widespread medical practitioners noted that these medicines often also promote weight loss. Scientists identified multiple physiological mechanisms through which this effect can be achieved. However, many practitioners noted that patients using GLP-1Ras sometimes report that the “food noise” in their heads has decreased after using them. They reported that they stopped constantly thinking about foods or the next meal they planned to consume. Generally, the amount of thinking about food or food cue reactivity has been reduced (Hayashi et al., 2023).

 

The Cue–Influencer–Reactivity–Outcome (CIRO) model of food cue reactivity
Based on these and various other findings, Daisuke Hayashi and his colleagues proposed a conceptual model of factors influencing food cue reactivity. They called this model CIRO, which is short for the Cue–Influencer–Reactivity–Outcome. This model proposes that food cues can be internal, like hunger signals coming from the body or thoughts of food and eating, or external, like sensory cues (e.g., sight or smell of food), environmental (e.g., being in a place associated with eating like a restaurant or a cafeteria), or social (e.g., other people talking about food) (more about the Diet Sensory-Perceptual Relationship and the Diet-Interoceptive Relationship can be found in NP 110: Introduction to Nutritional Psychology Methods) (see Figure 4).

 

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Figure 4. Conceptual model of factors influencing food cue reactivity (Adapted from: https://doi.org/10.3390/nu15224809)

 

The presence of these food cues elicits different degrees of food cue reactivity. These degrees depend on various factors that modify food cue reactivity. Some of these factors are constant. These include the genetic makeup of the individual, weight status, appetitive traits, food preferences, and emotion regulation and coping skills. Others are transient. These include the time of day (e.g., a person will likely be more reactive to food cues at a time he/she usually eats), the environment, physical activity, sleep (lack of sleep tends to make one more prone to eat), stress, emotional state, or appetite-regulating hormones (e.g., level of leptin, ghrelin and other hormones in circulation in the body).

 

The presence of these food cues elicits different degrees of food cue reactivity 

 

Depending on the combination of present food cues and these modifying factors, the body will react more or less strongly (or not at all) to these cues. The manifestations of this reactivity can be biological or psychological. Biological manifestations include changes in heart rate, blood pressure, skin conductance, gastric activity, salivation, or region-specific brain activity. Psychological manifestations include increased attention to food (attention bias), food craving, anticipation of relief (if food is eaten), anticipation of positive reinforcement, preoccupation with food, and awareness of physiological hunger (the feeling of hunger).

Food cue reactivity consequently leads to a series of outcomes, some of which are short-term, while others are long-term. Short-term outcomes of heightened food reactivity include increased food intake and food-seeking behaviors. Long-term outcomes represent the results of repeated instances of exposure to food cues accompanied by heightened food cue reactivity (see Figure 5).

 

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Figure 5. Food cue reactivity outcomes (Adapted from: https://doi.org/10.3390/nu15224809)

 

This involves long-term behavioral outcomes that appear over longer periods. For example, it includes making food cues more powerful in encouraging overeating, a phenomenon known as ‘incentive sensitization’ (being extra sensitive to rewards). It also involves the direct connection between food cues and food intake, referred to as ‘Pavlovian conditioning’ (similar to how we associate a bell ringing with mealtime).

Moreover, food-seeking behaviors may intensify due to the rewarding nature of highly palatable foods, a process called ‘operant conditioning’ (like training ourselves to want certain things). Over time, heightened reactivity to food cues in environments with abundant food can lead to weight gain or regain, disordered eating, and a decline in overall quality of life, as illustrated in Figure 5 above.

 

Food-seeking behaviors can also become more pronounced due to the rewarding nature of highly palatable foods (operant conditioning)

 

Conclusion
This conceptual paper proposes the concept of “food noise,” defined as heightened and/or persistent manifestations of reactivity to food cues, often leading to food-related intrusive thoughts and maladaptive eating behaviors. In modern societies, where food abundance is prevalent , heightened food reactivity, i.e., food noise, may  induce  both biological and psychological changes that contribute to weight gain, disordered eating, and obesity.

 

Food noise may  induce  both biological and psychological changes that contribute to weight gain, disordered eating, and obesity

 

The paper’s authors also proposed a theoretical CIRO model of food cue reactivity that identifies factors that modify food cue reactivity. Controlling these factors can reduce food noise and thus help individuals maintain a healthy and balanced diet and a healthy weight.  Most notably, the model proposes that food noise can be reduced by modifying the environment to reduce people’s exposure to food cues and influencing the transient factors that modify food cue reactivity.

The review paper “What Is Food Noise? A Conceptual Model of Food Cue Reactivity” was authored by Daisuke Hayashi, Caitlyn Edwards, Jennifer A. Emond, Diane Gilbert-Diamond, Melissa Butt, Andrea Rigby, and Travis D. Masterson.

More about dietary intake behaviors and neural mechanisms can be found in online courses through CNP entitled NP 110: Introduction to Nutritional Psychology Methods, NP 120 Part I: Microbes in our Gut: An Evolutionary Journey into the World of the Microbiota Gut-Brain Axis and the DMHR, and NP 120 Part II: Gut-Brain Diet-Mental Health Connection: Exploring the Role of Microbiota from Neurodevelopment to Neurodegeneration. 

 

References
Hayashi, D., Edwards, C., Emond, J. A., Gilbert-Diamond, D., Butt, M., Rigby, A., & Masterson, T. D. (2023). What Is Food Noise? A Conceptual Model of Food Cue Reactivity. In Nutrients (Vol. 15, Issue 22). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/nu15224809

Hedrih, V. (2023a). Are Hunger Cues Learned in Childhood? CNP Articles. https://www.nutritional-psychology.org/are-hunger-cues-learned-in-childhood/

Hedrih, V. (2023b). Scientists Propose that Ultra-Processed Foods be Classified as Addictive Substances. CNP Articles in Nutritional Psychology. https://www.nutritional-psychology.org/scientists-propose-that-ultra-processed-foods-be-classified-as-addictive-substances/

Ikemoto, S., Takahashi, M., Tsunoda, N., Maruyama, K., Itakura, H., & Ezaki, O. (1996). High-fat diet-induced hyperglycemia and obesity in mice: Differential effects of dietary oils. Metabolism, 45(12), 1539–1546. https://doi.org/10.1016/S0026-0495(96)90185-7

Lippert, R. N., Hess, S., Klemm, P., Burgeno, L. M., Jahans-Price T, Walton, M. E., Kloppenburg, P., & Brüning, J. C. (2020). Maternal high-fat diet during lactation reprograms the dopaminergic circuitry in mice. Journal of Clinical Investigation, 130(7), 3761–3776.

Morphew-Lu, E., Lokken, K., Doswell, C., Protogerous, C., Greunke, S. (2021). Module 3: The Diet-Behavior Relationship. In E. Lu (Ed.), NP 110: Introduction to Nutritional Psychology. The Center for Nutritional Psychology. https://www.nutritional-psychology.org/np110/

Pujol, J., Blanco-Hinojo, L., Martínez-Vilavella, G., Deus, J., Pérez-Sola, V., & Sunyer, J. (2021). Dysfunctional Brain Reward System in Child Obesity. Cerebral Cortex, 31, 4376–4385. https://doi.org/10.1093/cercor/bhab092

Schulte, E. M., Yokum, S., Jahn, A., & Gearhardt, A. N. (2019). Food Cue Reactivity in Food Addiction: a Functional Magnetic Resonance Imaging Study HHS Public Access. Physiol Behav, 208, 112574. https://doi.org/10.1016/j.physbeh.2019.112574

Wilding, J. P. H. (2001). Causes of obesity. Practical Diabetes International, 18(8), 288–292. https://doi.org/10.1002/PDI.277

Wong, M. C., Mccarthy, C., Fearnbach, N., Yang, S., Shepherd, J., & Heymsfield, S. B. (2022). Emergence of the obesity epidemic: 6-decade visualization with humanoid avatars. The American Journal of Clinical Nutrition, 115(4), 1189–1193. https://doi.org/10.1093/AJCN/NQAC005

Does the Neighborhood You Live in Affect your Diet-Mental Health Relationship?

  • Individuals in disadvantaged neighborhoods tend to have higher body mass index and perceived stress
  • fMRI scans of these individuals’ brains indicate disruptions in information processing flexibility in brain regions involved in processing rewards, regulating emotions, and higher cognitive functions
  • The link between neighborhood characteristics and these neural changes may be partially mediated by obesity, i.e., the body mass index, but not by stress levels (Kilpatrick et al., 2023)

 

When traveling through towns and cities, it’s noticeable that different areas vary significantly in their appearance and available amenities. Some neighborhoods boast well-maintained, larger, and aesthetically pleasing buildings, while others are defined by smaller, older structures showing signs of disrepair and neglect.

Affluent and disadvantaged neighborhoods
The first type of neighborhood tends to be cleaner, safer, and have better maintained public spaces. It will also have access to upscale amenities such as boutique shops, gourmet restaurants, and cultural attractions. The second type of neighborhood likely has higher crime rates and may have issues with litter and graffiti. There will be fewer local businesses and may lack various amenities. We typically call the first group affluent neighborhoods, while researchers refer to the second group as disadvantaged.

Individuals in disadvantaged neighborhoods typically have lower income levels, limited access to quality education, healthcare, employment opportunities, and substandard living conditions. These individuals often encounter systemic barriers to social mobility, resulting in a lack of access to essential services and readily available resources in more affluent areas (Woolley et al., 2008).

Living in disadvantaged neighborhoods is linked to higher health risks
Living in a disadvantaged neighborhood is linked to various adverse outcomes in the diet-mental health relationship (DMHR). Individuals living in these areas are at a higher risk of obesity due to the poor quality of foods available to them and environments that hamper physical activity (Saelens et al., 2012; Zick et al., 2009). Lower income levels among residents make them more likely to consume ultra-processed foods, a known contributor to obesity (Monteiro et al., 2019). Additionally, chronic stressors linked with living in disadvantaged neighborhoods might increase the desire for highly palatable foods, which are often unhealthy, as a coping response.

 

Living in a disadvantaged neighborhood is linked to various adverse outcomes in the diet-mental health relationship (DMHR)

 

Consequently, these factors are associated with adverse neural changes such as reduced brain volume and unfavorable changes in the structure and functioning of specific brain regions. These changes can disrupt the brain’s mechanisms for regulating food intake, leading to obesity and contributing to mental health disorders, such as depression. (Samuthpongtorn et al., 2023; Seabrook et al., 2023). The risks of health problems related to obesity, such as cardiovascular diseases, diabetes, and certain forms of cancer, are increased with the consumption of ultra-processed foods.

 

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Figure 1. DMHR Risks of disadvantaged neighborhoods: Higher risk of obesity, increased desire, and consumption of ultra-processed foods/highly palatable foods. Changes in brain volume/structure/function, food intake regulation mechanisms, additional health problems: cardiovascular diseases, diabetes, cancer.

 

Area deprivation index
Whether a neighborhood is considered affluent or disadvantaged is a matter of degree. Some neighborhoods are more disadvantaged, and some are more affluent than others. It can be thought of as a continuum. Researchers use the area deprivation indices to assess a specific geographical area’s socioeconomic disadvantage or affluence (such as a neighborhood).

 

Whether a neighborhood is considered affluent or disadvantaged is a matter of degree

 

These indices can be constructed differently, but they typically consider factors such as income, education, employment, housing conditions, and essential services available in the area. Areas with wealthier, more educated residents, better employment, improved housing conditions, and good access to essential services would be considered more affluent. Those with opposite characteristics would be considered more disadvantaged (see Figure 2).

 

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Figure 2. Area Deprivation Index features

 

The current study
Study author Lisa A. Kilpatrick, and her colleagues aimed to investigate the relationship between the area deprivation index (ADI) and the microstructure of the brain cortex, assessed by the T1w/T2w ratio. They also explored how body mass index and stress affect that link.

They hypothesized that individuals living in areas with worse area deprivation index values would likely have higher body mass indexes, be more prone to diets conducive to obesity, and experience higher stress levels. Consequently, these factors would negatively impact the microstructure of their brain, particularly in the areas related to processing rewards, regulating emotions, and cognition.

T1- and T2-weighted images and T1w/T2w ratio
T1-weighted (T1w) and T2-weighted (T2w) images are two types of magnetic resonance imaging (MRI) sequences used to visualize and differentiate various tissues within the human body. In neuroimaging, T1-weighted images provide excellent structural details and are used to highlight distinctions between various brain tissues, making them useful for visualizing specific areas of the brain. T2-weighted images emphasize differences in water content within the brain, making them valuable for detecting abnormalities like edema, inflammation, and lesions—areas where the brain tissue is damaged. They are also useful for assessing regions filled with cerebrospinal fluid. (see Figure 3).

 

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Figure 3. Identifying tissue microstructure alterations on magnetic resonance imaging (MRI)

 

Researchers often compare signal intensities in these two types of images of the same brain area and calculate a measure called the T1w/T2w ratio. The T1w/T2w ratio can offer a more nuanced and quantitative understanding of the brain’s tissue properties, surpassing the insights provided by qualitative visual analysis alone. It can help researchers identify microstructural differences in the brain, areas where a certain disease is developing or present, regions with altered functionality, injuries, and other changes to the brain tissue.

In general, both a decrease and an increase in the T1w/T2w ratio in a brain region can indicate adverse developments in it, as it indicates that the tissue structure in that area differs from that observed in the brains of healthy individuals.

The procedure
The study involved 92 adults from the Los Angeles area, consisting of 27 men and 65 women. Between 2019 and 2022, participants underwent neuroimaging sessions encompassing T1w and T2w scanning.  Details about their place of residence were also gathered. Participants were recruited using flyers and emails distributed through various channels. The mean age of participants was 28 years.

Participants underwent a stress assessment using the Perceived Stress scale and provided dietary information through the VioScreen Graphical Food Frequency System. Researchers measured their weight and height to calculate body mass index values.

Findings
Area deprivation index was linked with microstructural alterations in brain regions responsible for reward processing, emotion regulation, and higher cognition.

Participants living in more deprived areas, i.e., areas with worse area deprivation index values, had increased T1w/T2w ratios in brain regions involved in reward-related processing, emotional regulation, and higher cognition. These were observed in the medial prefrontal and cingulate regions – mainly at middle/superficial cortical levels.

They also had decreased T1w/T2w ratios in regions of the neural system involved in social interaction. The affected areas were supramarginal, middle temporal, and primary motor regions in mainly middle/deep cortical levels. Both increased and decreased T1w/T2w ratios can be interpreted as indicators of adverse changes to the microstructure of neural tissue in the affected areas. Consequently, this suggests that the functioning of these areas is not as optimal as in a healthy brain. (see Figure 4).

 

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Figure 4. Area deprivation and brain microstructure

 

Body mass index mediates the link between area deprivation and altered brain microstructure
The study authors created and tested a statistical model suggesting that living in a more disadvantaged area correlates with higher body mass index values and increased stress. According to this model, these factors contribute to pronounced changes in the T1w/T2w ratios in the brain regions where alterations were observed.  In other words, they proposed that body mass index (i.e., being obese or overweight) and stress mediate the relationship between area disadvantage and the extent of changes to the microstructure of specific brain areas.

 

They proposed that body mass index (i.e., being obese or overweight) and stress mediate the relationship between area disadvantage and the extent of changes to the microstructure of specific brain areas

 

Analysis showed that although individuals living in more disadvantaged neighborhoods tend to experience higher stress levels, this does not lead to changes in the brain microstructure. On the other hand, this analysis confirmed that the link between area deprivation and the microstructure of specific brain areas is mediated by body mass index. However, the body mass index did not fully account for this link, indicating that additional factors likely contribute to the association between altered brain microstructure and area deprivation level.

Conclusion
Overall, the study showed that individuals living in more disadvantaged areas tend to have altered tissue structures in brain regions responsible for reward processing, emotion regulation, and cognition. These alterations to the tissue microstructure may disrupt the flexibility of information processing in these areas. Additionally, a significant portion of these brain changes is associated with obesity and likely connected to factors that contribute to obesity.

 

The study showed that individuals living in more disadvantaged areas tend to have altered tissue structures in brain regions responsible for reward processing, emotion regulation, and cognition

 

Due to the study’s design, it remains unclear whether life in disadvantaged neighborhoods leads to obesity and adverse changes in brain microstructure or if the altered brain microstructure restricts an individual’s ability to secure resources necessary for living in more affluent neighborhoods and avoid dietary behaviors that lead to obesity.  While this will have to be explored in future research, it is important for both policymakers and the general public to be aware of the connections between life in disadvantaged neighborhoods and brain health.

The paper “Mediation of the association between disadvantaged neighborhoods and cortical microstructure by body mass index” was authored by Lisa A. Kilpatrick, Keying Zhang, Tien S. Dong, Gilbert C. Gee, Hiram Beltran-Sanchez, May Wang, Jennifer S. Labus, Bruce D. Naliboff, Emeran A. Mayer, and Arpana Gupta.

 

References
Kilpatrick, L. A., Zhang, K., Dong, T. S., Gee, G. C., Beltran-Sanchez, H., Wang, M., Labus, J. S., Naliboff, B. D., Mayer, E. A., & Gupta, A. (2023). Mediation of the association between disadvantaged neighborhoods and cortical microstructure by body mass index. Communications Medicine, 3(1). https://doi.org/10.1038/s43856-023-00350-5

Monteiro, C. A., Cannon, G., Levy, R. B., Moubarac, J. C., Louzada, M. L. C., Rauber, F., Khandpur, N., Cediel, G., Neri, D., Martinez-Steele, E., Baraldi, L. G., & Jaime, P. C. (2019). Ultra-processed foods: What they are and how to identify them. In Public Health Nutrition (Vol. 22, Issue 5, pp. 936–941). Cambridge University Press. https://doi.org/10.1017/S1368980018003762

Saelens, B. E., Sallis, J. F., Frank, L. D., Couch, S. C., Zhou, C., Colburn, T., Cain, K. L., Chapman, J., & Glanz, K. (2012). Obesogenic Neighborhood Environments, Child and Parent Obesity: The Neighborhood Impact on Kids Study. American Journal of Preventive Medicine, 42(5), e57–e64. https://doi.org/10.1016/J.AMEPRE.2012.02.008

Samuthpongtorn, C., Nguyen, L. H., Okereke, O. I., Wang, D. D., Song, M., Chan, A. T., & Mehta, R. S. (2023). Consumption of Ultraprocessed Food and Risk of Depression. JAMA Network Open, 6(9), e2334770. https://doi.org/10.1001/jamanetworkopen.2023.34770

Seabrook, L. T., Naef, L., Baimel, C., Judge, A. K., Kenney, T., Ellis, M., Tayyab, T., Armstrong, M., Qiao, M., Floresco, S. B., & Borgland, S. L. (2023). Disinhibition of the orbitofrontal cortex biases decision-making in obesity. Nature Neuroscience, 26(1), 92–106. https://doi.org/10.1038/s41593-022-01210-6

Woolley, M. E., Grogan-Kaylor, A., Gilster, M. E., Karb, R. A., Gant, L. M., Reischl, T. M., & Alaimo, K. (2008). Neighborhood Social Capital, Poor Physical Conditions, and School Achievement. Children & Schools, 30(3), 133–145. https://doi.org/10.1093/CS/30.3.133

Zick, C. D., Smith, K. R., Fan, J. X., Brown, B. B., Yamada, I., & Kowaleski-Jones, L. (2009). Running to the Store? The relationship between neighborhood environments and the risk of obesity. Social Science & Medicine, 69(10), 1493–1500. https://doi.org/10.1016/J.SOCSCIMED.2009.08.032

 

 

Do Artificial Sweeteners Affect Eating Habits and The Diet-Mental Health Relationship?

Artificial sweeteners—sugar substitutes that satisfy our cravings for sugar but contain low calories—have become a popular alternative to reduce the risks associated with high-sugar consumption and means of bodyweight management. Yet, the long-term effects of these nonnutritive sweeteners (NNS) have yet to be determined, particularly how our brain responds differently to NNS and nutritive sweeteners (NSW), and consequently, the effects they have on our eating behaviors. While previous clinical trials have investigated the impact of NNS and NSW on neurobehavioral states, these studies were limited as they focused on mostly male cohorts within normal body mass index (BMI). 

The long-term effects of these NNS and their effects on eating behaviors have yet to be determined.

To create more generalizable data, Yunker et al. (2021), investigators from the University of Southern California, led a randomized crossover trial that aimed to elucidate the role of gender and BMI status on eating after ingesting NNS compared to NSW. 

The authors designed a longitudinal study, in which all participants received a complete sequence of interventions in random order across three separate visits and utilized functional MRI imaging (fMRI) and an ad libitum (Latin for “at one’s pleasure”) buffet meal for evaluation.

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Through fMRI, the authors measured the blood oxygen level-dependent (BOLD) signals, which reflect neural activity, in various brain areas as participants responded to different types of food cues after ingesting either the sucrose (an NSW), sucralose (an NSS), or water (a control) interventions. At defined intervals, participants also had their blood sampled to assess changes in endocrine response across these interventions (Fig. 1). Caloric intake was measured through the buffet meal for each participant to compare differences in appetitive and feeding behaviors across interventions.

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Figure 1. Study design from Yunker et al., JAMA Network Open, 2021.

 

Artificial Sweeteners May Cause Overeating

BOLD signals were greater in the medial frontal (MFC) and orbitofrontal cortices (OFC) in obese individuals when presented with food cues after ingesting sucralose compared to non-food cues. However, this difference was not observed in participants who’s BMIs were categorized as healthy or overweight, suggesting a distinct intersectional effect of BMI status on one’s neurobehavioral response to food upon ingesting artificial sweeteners. Furthermore, as opposed to male counterparts, BOLD signals were greater in the MFC and OFC of females and were heightened when the participants were females with obesity during the sucralose intervention in the food cue tasks.

The significant increase in BOLD signals within the MFC area/region of the brain is intriguing because previous studies have shown this brain region to be responsible for conditioned motivation for eating in mice (Petrovich, 2007). Likewise, as the region houses higher cognitive function, the higher BOLD signals suggest that participants may have thought more about eating when exposed to food cues after taking artificial sweeteners (Jobson, 2021). 

 

The higher BOLD signals suggest that participants may have thought more about eating when exposed to food cues after taking artificial sweeteners.

 

The increase in BOLD OFC signal is another interesting result as studies have correlated this brain area with processing the perception of food value, taste reward, and even smell in humans (Small, 2007; Seabrook, 2020). The primary concern concluded by this study is the possibility of overeating—and, in turn, obesity and its comorbidities—when individuals turn to sugar substitutes, especially for women who are already obese.

Although the authors conclude there was minimal effect on endocrine response between NSS and NSW, this study found reduced suppression of ghrelin–the “hunger hormone”–after ingesting sucralose, which suggests that artificial sweeteners may impair the normal homeostatic signaling that regulates feeding behaviors. As such, this would result in a longer period of “feeling hungry” which can contribute to overeating. This is evident in the study in which participants consumed more calories after ingesting sucralose, and this effect was more pronounced in females (no interaction/influence of BMI status found).

 

Artificial sweeteners may impair the normal homeostatic signaling that regulates feeding behaviors.

 

Taken together, the findings presented here emphasize the importance of considering biological factors when it comes to assessing the use and efficacy of artificial sweeteners for health-related concerns and body weight management.

 

References

Jobson, D. D., Hase, Y., Clarkson, A. N., & Kalaria, R. N. (2021). The role of the medial prefrontal cortex in cognition, ageing and dementia. Brain communications, 3(3), fcab125. https://doi.org/10.1093/braincomms/fcab125 

Petrovich, G. D., Ross, C. A., Holland, P. C., & Gallagher, M. (2007). Medial prefrontal cortex is necessary for an appetitive contextual conditioned stimulus to promote eating in sated rats. The Journal of neuroscience : the official journal of the Society for Neuroscience, 27(24), 6436–6441. https://doi.org/10.1523/JNEUROSCI.5001-06.2007

Small, D. M., Bender, G., Veldhuizen, M. G., Rudenga, K., Nachtigal, D., & Felsted, J. (2007). The role of the human orbitofrontal cortex in taste and flavor processing. Annals of the New York Academy of Sciences, 1121, 136–151. https://doi.org/10.1196/annals.1401.002 

Seabrook, L. T., & Borgland, S. L. (2020). The orbitofrontal cortex, food intake and obesity. Journal of psychiatry & neuroscience : JPN, 45(5), 304–312. https://doi.org/10.1503/jpn.190163 

Yunker, A. G., Alves, J. M., Luo, S., Angelo, B., DeFendis, A., Pickering, T. A., Monterosso, J. R., & Page, K. A. (2021). Obesity and Sex-Related Associations With Differential Effects of Sucralose vs Sucrose on Appetite and Reward Processing: A Randomized Crossover Trial. JAMA network open, 4(9), e2126313. https://doi.org/10.1001/jamanetworkopen.2021.26313 

What is Diet Diversity (DD) and How is it Related to Depression?

Adding various foods to our diet can significantly improve our dietary intake quality, aiding in the prevention of health issues ranging from chronic diseases to behavioral health conditions. The United States Department of Agriculture (USDA) guidelines and the Food Guide Pyramid suggest that eating a diversified diet is health-beneficial. Diet diversity (DD) is defined as the number of different foods or food groups consumed over a given period of time (Ruel, 2003). DD is recognized as a critical element of proper nutrition, as a range of nourishing foods provides a greater abundance of nutrients to support the brain and body.

 

A range of nourishing foods provides a greater abundance of nutrients to support the brain and body.

 

Diet diversity is increasingly associated with numerous factors relating to the diet-mental health relationship (DMHR), including psychological resilience (Yin et al., 2019), memory (Zhang et al., 2020), hippocampal volume (Otsuka et al., 2021), depression (Poorrezaeian et al., 2017), and anxiety (Alenko et al., 2021). 

One method used to measure an individual’s DD involves calculating a Diet Diversity Score (DDS). These scores measure the variability within a person’s diet. In terms of the DMHR, questionnaires can be used to assess the connection between DD with a variety of psychological health and well-being factors.

 

Diet diversity is increasingly associated with numerous factors relating to the diet-mental health relationship.

 

A cross-sectional study examining the connection between DD and mental health was conducted by Poorrezaeian et al. (2017). This study specifically focused on the association of dietary diversity with depression and stress among a sample of 360 randomly selected Iranian women. After conducting interviews to gather information about the participants’ dietary intake, responses were calculated as Diet Diversity Scores. Depression and stress levels were measured through the Depression, Anxiety, and Stress Scales (DASS-42), which is a validated questionnaire designed to capture self-reported scores on these dimensions. 

As expected, participants who ate diets that included a variety of vitamins and minerals ranked higher in terms of DDS. More critically, results showed that a 1 unit increase in DDS was associated with a 39% reduction in the risk of severe depression. In other words, women who had less varied diets had scored lower on DD, and these lower scores were associated with an increased risk of severe depression. 

 

Results showed a 1 unit increase in DDS was associated with a 39% reduction in the risk of severe depression.

 

Although this study found a significant relationship between DDS and severity of depression, no meaningful relationship between mild/moderate depression or stress with DDS was found. The authors note that since the current study findings indicate a relationship between DDS and eating a nutrient-dense diet, future studies examining the specific connection between stress and DDS are important and should be conducted. Some of the existing evidence demonstrating the relationship between stress and unhealthy eating habits can be found in CNP’s Diet and Stress research category). 

 

Thank you to CNP Intern Hashmin Sajjan for contributing this article!

 

References

Alenko, A., Agenagnew, L., Beressa, G., Tesfaye, Y., Woldesenbet, Y. M., & Girma, S. (2021). COVID-19-Related Anxiety and Its Association with Dietary Diversity Score Among Health Care Professionals in Ethiopia: A Web-Based Survey. Journal of Multidisciplinary Healthcare, 14987–996. https://doi.org/10.2147/JMDH.S305164

Otsuka, R., Nishita, Y., Nakamura, A., Kato, T., Iwata, K., Tange, C., Tomida, M., Kinoshita, K., Nakagawa, T., Ando, F., Shimokata, H., & Arai, H. (2021). Dietary diversity is associated with longitudinal changes in hippocampal volume among Japanese community dwellers. European Journal of Clinical Nutrition, 75(6), 946–953. https://doi.org/10.1038/s41430-020-00734-z

Poorrezaeian, M., Siassi, F., Milajerdi, A. et al. (2017). Depression is related to dietary diversity score in women: a cross-sectional study from a developing country. Ann Gen Psychiatry, 16, 39. https://doi.org/10.1186/s12991-017-0162-2

Ruel M. T. (2003). Operationalizing dietary diversity: A review of measurement issues and research priorities. The Journal of Nutrition, 133, (11 Suppl 2), 3911S–3926S. https://doi.org/10.1093/jn/133.11.3911S

Yin, Z., Brasher, M. S., Kraus, V. B., Lv, Y., Shi, X., & Zeng, Y. (2019). Dietary Diversity Was Positively Associated with Psychological Resilience among Elders: A Population-Based Study. Nutrients, 11(3), 650. https://doi.org/10.3390/nu11030650

Zhang, J., Zhao, A., Wu, W., Yang, C., Ren, Z., Wang, M., Wang, P., & Zhang, Y. (2020). Dietary Diversity Is Associated With Memory Status in Chinese Adults: A Prospective Study. Frontiers in Aging Neuroscience, 12, 580760. https://doi.org/10.3389/fnagi.2020.580760

 

Interoception in Nutritional Psychology

Is Interoception a missing Element in our understanding of an improved Diet-Mental Health Relationship?


What is Interoceptive Awareness (IA), how does it relate to the field of Nutritional Psychology, and how can it be used to improve our understanding of the Diet-Mental Health Relationship (DMHR)?

While you have likely heard of the five basic senses, there is one sense in particular that often escapes formal education. Brushed off as something that should be innate, Interoception refers to the senses we feel relating to the internal state of our body. Sometimes referred to as “the eighth sense,” Interoception provides awareness of the sensations arising inside our body – like from a growling stomach, an increased heart rate, or the physical feelings we experience inside our body when anticipating a super tasty treat. The field of Nutritional Psychology (NP) considers Interoception to be an important factor in understanding the effects that our dietary intake patterns can have on our mood, behavior, and mental health.

It can sometimes be difficult to discern exactly what we are sensing, feeling, and needing in relation to our dietary intake choices and patterns. When combined with the growing landscape of highly palatable foods available, we can find ourselves facing self-regulation challenges that cause us to crave more highly palatable foods, and less of the nutritious foods supportive of our brain, our psychological health, and our behavior.¹ In the end, reaching for these foods provides a somewhat satisfying “quick-fix,” but also affects the brain’s interoceptive functioning, which in turn, furthers our appetite for highly palatable foods.²

Building skills in Interoceptive Awareness (IA) is a Nutritional Psychology tool that can be used to help us feel less reactive when reaching for food. Approaching food from the perspective of what is needed to feel good, instead of in reaction to the need to satisfy a craving, can be a key element in supporting our mood, behavior and mental health, and improving our Diet-Mental Health Relationship (DMHR).  Less nutritious foods have their place in our diets when we’ve built cognitive, perceptual, behavioral, psychological and interoceptive skills that support us in making conscious choices in our daily dietary intake patterns. 

The field of Nutritional Psychology builds Interoceptive Awareness (IA) in individuals to increase their understanding of the bodily sensations they experience in response to their dietary intake patterns and habits. This awareness can lead us to a way of approaching food that is influenced more by internal awareness, choices and decisions, rather than impulses and reactions — which is what Nutritional Psychology is all about.  

Citations

  1. Diet – Mental Health Break #1 | The Center For Nutritional Psychology – CNP https://www.youtube.com/watch?v=n_FJmhTS7HQ&t=4s. Based on study by Richard J. Stevenson et al. (2020) Hippocampal-dependent appetite control is impaired by experimental exposure to a western-style diet. Royal Society Open Science. DOI: 10.1098/rsos.191338.

 

  1. A four-day Western-style dietary intervention causes reductions in hippocampal-dependent learning and memory and interoceptive sensitivity. https://pubmed.ncbi.nlm.nih.gov/28231304 or https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0172645&type=printable (full paper).

 

  1. CNP Research Library, Diet and Interoception Category: https://www.nutritional-psychology.org/research_studies/diet-and-interoception/

 

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