Tag: diet changes for mental health

Elderly Individuals Eating a Balanced Diet have Better Mental Health and Cognitive Functioning

Posted on by CNP Staff
  • An analysis of UK Biobank data published in Nature Mental Health revealed that elderly individuals eating a balanced diet tended to have better mental health and cognitive abilities than individuals with other types of dietary preferences.
  • Study authors identified three additional dietary types – vegetarian, starch-free or reduced starch, and high protein-low fiber.
  • The study identified structural differences in specific areas of the brain between individuals eating a balanced diet and those preferring other diet types.

Food preferences are determined by many different factors. In modern societies, individual food choices are primarily defined by food preferences. In other words, people tend to eat the foods they like.

Dietary patterns and health


The link between food and health is very straightforward – if we do not eat, we will eventually die. Our body needs lots of different substances to continue functioning. We need some of them, such as proteins, fats, and carbohydrates, in large quantities. These substances are called macronutrients. Our bodies need many other types of substances in much smaller quantities. These micronutrients include vitamins, minerals, specific essential fatty acids, and many other substances.

 

In modern societies, individual food choices are primarily defined by food preferences. In other words, people tend to eat the foods they like.

 

However, a growing body of evidence indicates that specific patterns of consuming foods that our body needs can also lead to adverse health effects (for example, fats, sugars, and proteins are all substances our body needs, but eating too much or focusing on just one can produce adverse effects). Studies indicate that prolonged consumption of a diet rich in foods abundant both in fats and sugars can dysregulate our food intake control system, make us prone to overeating, and eventually lead to overweight and obesity (Hedrih, 2024; Ikemoto et al., 1996; Thanarajah et al., 2023; Wilding, 2001).

 

Prolonged consumption of a diet rich in foods abundant both in fats and sugars can dysregulate our food intake control system.

 

Studies also link prolonged consumption of foods rich in refined sugars with the development of type 2 diabetes, mental health conditions like depression and anxiety, and various other health issues (Hedrih, 2023; Huang et al., 2023).

The UK Biobank


Until recently, one of the challenges of studying the links between food intake and disease is that the effects of following specific dietary patterns often materialize after longer periods of time, sometimes years or even decades. Studies of diet-health relationships can be done on rodents in much shorter time periods, but the applicability of such findings to humans remains unknown without actual studies on humans. There are more and more experimental studies of the diet-mental health relationship in humans as well. Still, legal and ethical constraints usually make data collection techniques much less comprehensive.

Another issue with studying the diet-health relationship in humans is that human dietary patterns tend to be very diverse (unless experimentally controlled for research purposes). It is very difficult for a single group of researchers to have a large number of participants in a single research study. The UK Biobank helps solve this issue.

The UK Biobank is a large biomedical database containing in-depth genetic and health information from around half a million UK residents. It was established in 2006 with the aim of improving the prevention, diagnosis, and treatment of a wide range of serious and life-threatening illnesses. Data stored in the UK Biobank are available to researchers worldwide.

The current study


Study author Ruohan Zhang and his colleagues wanted to explore the associations between naturally developed dietary patterns and cognitive functions, mental health, blood and metabolic biomarkers, brain characteristics, and genetics (Zhang et al., 2024).

They analyzed data from the UK Biobank from participants who completed a food preference questionnaire. This included 182,990 elderly individuals, the average of whom was 71 years old and 57% female.

The food preference questionnaire consisted of 150 questions that assessed both the sensory qualities of food (e.g., taste) and food preferences. It also asked about health-related behaviors such as physical activity and watching TV. Participants rated how much they liked specific types of foods and beverages.

In addition, the study used data on participants’ mental health, cognitive functioning, blood and metabolic biomarkers, magnetic resonance imaging scans of participants’ brains, and genetic data related to mental disorders (see Figure 1).

 

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Figure 1. Study Procedure (Zhang et al., 2024)

 

The dietary subtypes


The study authors analyzed the structure of associations between participants’ food preference ratings to identify groups of food items that the same people tend to like or dislike. In other words, they looked for sets of food items such that if a person likes one of the items in a set, it is more likely that he/she will also like the other items and vice versa. They also looked for groups of people with similar food preferences.

In this way, they identified 4 dietary subtypes: 1) Starch-free or reduced starch, 2) vegetarian, 3) high protein and lower fiber, and 4) balanced subtype. Individuals in the first subtype showed a higher preference for fruits, vegetables, and protein foods but a lower preference for foods containing starches (e.g., grains, potatoes, cassava, beans, peas, etc.).  The vegetarian subtype showed a higher preference for fruits and vegetables but a lower preference for protein foods. The third subtype had a greater preference for snacks and protein foods but a lower preference for fruits and vegetables. The balanced subtype showed balanced preferences across all food categories (see Figure 2).

 

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Figure 2. Dietary subtypes

 

Individuals preferring a balanced diet had better mental health and cognitive function


Results showed that individuals with the balanced dietary subtype had better mental health results than the other 3 categories. They tended to have lower anxiety and depression symptoms, less mental distress and psychotic experiences, less prone to self-harm, reported fewer trauma experiences, and better well-being.

Participants with the balanced diet and those preferring a high protein-low fiber diet had better cognitive functioning compared to the other two groups. Magnetic resonance imaging scans showed certain differences in brain structures between the subtypes (see Figure 3).

 

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Figure 3. Results (Zhang et al., 2024)

 

Conclusion

 

The study shed light on the links between naturally developed dietary patterns and cognitive functioning in elderly individuals. It showed that a diet with balanced preferences for various food categories is associated with better mental health and cognitive abilities than diets focusing on specific types of food while avoiding others.

While the design of this study does not allow any definitive cause-and-effect inferences to be drawn from these results, it stands to reason that a balanced diet, a diet including many different types of food, would best provide all the nutrients needed for the optimal functioning of the human body, allowing for better cognitive functioning even in advanced age.

The paper “Associations of dietary patterns with brain health from behavioral, neuroimaging, biochemical and genetic analyses” was authored by Ruohan Zhang, Bei Zhang, Chun Shen, Barbara J. Sahakian, Zeyu Li, Wei Zhang, Yujie Zhao, Yuzhu Li, Jianfeng Feng, and Wei Cheng.

 

References

Hedrih, V. (2023, June 6). Health Consequences of High Sugar Consumption. CNP Articles in Nutritional Psychology. https://www.nutritional-psychology.org/health-consequences-of-high-sugar-consumption/

Hedrih, V. (2024, February 19). Consuming Fat and Sugar (At The Same Time) Promotes Overeating, Study Finds. CNP Articles in Nutritional Psychology. https://www.nutritional-psychology.org/16563-2/

Huang, Y., Chen, Z., Chen, B., Li, J., Yuan, X., Li, J., Wang, W., Dai, T., Chen, H., Wang, Y., Wang, R., Wang, P., Guo, J., Dong, Q., Liu, C., Wei, Q., Cao, D., & Liu, L. (2023). Dietary sugar consumption and health: Umbrella review. BMJ (Clinical Research Ed.), 381, e071609. https://doi.org/10.1136/bmj-2022-071609

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

Thanarajah, S. E., Difeliceantonio, A. G., Albus, K., Br, J. C., Tittgemeyer, M., Small, D. M., Thanarajah, S. E., Difeliceantonio, A. G., Albus, K., Kuzmanovic, B., & Rigoux, L. (2023). Habitual daily intake of a sweet and fatty snack modulates reward processing in humans. Cell Metabolism, 35, 1–14. https://doi.org/10.1016/j.cmet.2023.02.015

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

Zhang, R., Zhang, B., Shen, C., Sahakian, B. J., Li, Z., Zhang, W., Zhao, Y., Li, Y., Feng, J., & Cheng, W. (2024). Associations of dietary patterns with brain health from behavioral, neuroimaging, biochemical and genetic analyses. Nature Mental Health, 2(5), 535–552. https://doi.org/10.1038/s44220-024-00226-0

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

Posted on by CNP Staff
  • 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?

Posted on by CNP Staff
  • 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

 

 

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