Uncategorized Archives - Nutritional Psychology

Can Our Facial Attractiveness Depend on What We Just Ate?

Posted on April 22, 2024April 23, 2024 by CNP Super Admin

A study in France published in PLOS One found that facial attractiveness can change depending on what a person eats.
Opposite-sex raters evaluated study participants’ facial attractiveness using pictures taken two hours after breakfast.
Facial attractiveness of both men and women was reduced after a breakfast rich in carbohydrates.
The effects were possible due to an increase in age appearance in women and a decrease in perceived masculinity in males.

We all know that our attractiveness can change depending on the circumstances. For example, when we’re tired or after sleepless nights, we might not appear as attractive as when fresh and well-rested. This difference will primarily reflect on our face, one of the most important parts of the human body for evaluating attractiveness.

Facial attractiveness
Facial attractiveness is a highly valued trait in many cultures. It is often associated with health, youth, and symmetry, which are genetic fitness markers. From an evolutionary perspective, facial attractiveness is thought to signal good genes and reproductive potential, leading individuals to prefer mates with appealing facial features. In various cultures, attractive faces tend to be linked to social status, success, and desirability. They influence mate selection and social interactions (Little, 2021; Rhodes, 2006).

Research suggests that certain universal features, such as clear skin, balanced facial proportions, and symmetry, are consistently rated more attractive across different cultures. However, cultural differences exist, with preferences for specific facial traits varying based on societal norms and environmental factors (Zhan et al., 2021).

Facial beauty is one of the first things we notice when observing someone. Studies show that our brains need just one-tenth of a second to recognize a face and evaluate the aggressiveness and trustworthiness of a person (Shavlokhova et al., 2024; Willis & Todorov, 2006).

The beauty premium
Researchers also discuss the “beauty premium,” the phenomenon where individuals perceived as more attractive tend to receive various advantages in life. These include higher earnings, better job opportunities, and preferential treatment in social situations. This advantage is not limited to employment but can extend to other areas such as education, politics, and legal outcomes (Dossinger et al., 2019).

Researchers suggest that the beauty premium may stem from a combination of factors, including societal biases that associate physical attractiveness with positive traits and the confidence and social skills that attractive individuals may develop as a result of favorable treatment (Borland & Leigh, 2014; Mobius & Rosenblat, 2006).

But does facial beauty depend on what we have just eaten?

The current study
Study author Amandine Visine and her colleagues wanted to investigate whether refined carbohydrate intake affects facial attractiveness in young men and women. They note that Western populations started consuming much more refined carbohydrates in several previous decades than they did in earlier centuries. This shift was associated with various adverse health consequences. Some researchers proposed that it also affected facial attractiveness, but the results were inconclusive (Visine et al., 2024). This study aimed to clarify those effects.

The study procedure
The study participants were 52 males and 52 females, young adults between 20 and 30 years of age, heterosexual, and with four grandparents of European origin. The study authors invited them for breakfast in their lab and asked them to ensure they came on an empty stomach.

In the lab, researchers randomly assigned participants to eat a breakfast containing only unrefined carbohydrates or a breakfast containing only refined carbohydrates. Both breakfasts had 500 calories. Approximately 2 hours after breakfast, the study authors took participants’ photos. On this occasion, participants also completed a dietary habits index. The study authors used these data to calculate participants’ glycemic load, which estimates the impact of food eaten on blood sugar levels. They estimated this parameter and the total energy intake for breakfast, afternoon snacks, and participants’ between-meal food intake.

After this, the study authors recruited independent groups of raters in a public place in Montpellier, France, to rate femininity/masculinity, apparent age, and attractiveness of study participants’ faces using the photos the study authors took. Raters rated the facial attractiveness of participants of the opposite sex. Seventy-seven raters rated apparent age, 150 rated perceived masculinity/femininity, and 252 raters rated attractiveness. Independent of this, the study authors used computer software to produce estimates of the masculinity/femininity of a face based on its morphology (see Figure 1).

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

Eating breakfast comprised of refined carbohydrates reduced facial attractiveness in both men and women
Raters rated the faces of participants who ate breakfast consisting of refined carbohydrates as less attractive than those who ate unrefined carbohydrate breakfast. Raters saw men and women from the refined carbohydrate breakfast group as less attractive.

Regarding chronic food intake, raters saw participants with the highest total energy intake for breakfast as the most attractive. They tended to rate both men and women with higher refined carbohydrate intake between meals as less attractive. Women, but not men, eating more refined carbohydrates for breakfast and for the afternoon snack were seen as less attractive. Raters saw men eating more refined carbohydrates for afternoon snacks as more attractive. Men preferred women with lower breakfast and afternoon snack glycemic load, while women preferred men with a higher afternoon snack glycemic load and a lower energy intake (see Figure 2).

Figure 2. Results of refined vs. unrefined carbohydrate breakfast on attractiveness

In general, higher age was associated with lower attractiveness ratings. Physical activity and perceived masculinity/femininity were associated with better attractiveness ratings.

Further analysis showed that it is possible that breakfast contents and dietary habits modified one’s age and masculine/feminine appearance. This, in turn, affected the attractiveness ratings.

Conclusion
The study shows that the immediate contents of a meal and long-term dietary habits can affect one’s attractiveness to individuals of the opposite sex. It also indicated that higher refined carbohydrate consumption reduces perceived attractiveness.

This indicates that to maintain facial beauty, one needs to consider diet, aside from other factors.

The paper “Chronic and immediate refined carbohydrate consumption and facial attractiveness” was authored by Amandine Visine, Valerie Durand, Leonard GuillouI, Michel Raymond, and Claire Berticat.

References

Borland, J., & Leigh, A. (2014). Unpacking the Beauty Premium: What Channels Does It Operate Through, and Has It Changed Over Time? Economic Record, 90(288), 17–32. https://doi.org/10.1111/1475-4932.12091

Dossinger, K., Wanberg, C. R., Choi, Y., & Leslie, L. M. (2019). The beauty premium: The role of organizational sponsorship in the relationship between physical attractiveness and early career salaries. Journal of Vocational Behavior, 112, 109–121. https://doi.org/10.1016/j.jvb.2019.01.007

Little, A. C. (2021). Facial Attractiveness. In T. K. Shackelford & V. A. Weekes-Shackelford (Eds.), Encyclopedia of Evolutionary Psychological Science (pp. 2887–2891). Springer International Publishing. https://doi.org/10.1007/978-3-319-19650-3_1881

Mobius, M. M., & Rosenblat, T. S. (2006). Why Beauty Matters. American Economic Review, 96(1), 222–235. https://doi.org/10.1257/000282806776157515

Rhodes, G. (2006). The Evolutionary Psychology of Facial Beauty. Annual Review of Psychology, 57(1), 199–226. https://doi.org/10.1146/annurev.psych.57.102904.190208

Shavlokhova, V., Vollmer, A., Stoll, C., Vollmer, M., Lang, G. M., & Saravi, B. (2024). Assessing the Role of Facial Symmetry and Asymmetry between Partners in Predicting Relationship Duration: A Pilot Deep Learning Analysis of Celebrity Couples. Symmetry, 16(2), 176. https://doi.org/10.3390/sym16020176

Visine, A., Durand, V., Guillou, L., Raymond, M., & Berticat, C. (2024). Chronic and immediate refined carbohydrate consumption and facial attractiveness. PLOS ONE, 19(3), e0298984. https://doi.org/10.1371/journal.pone.0298984

Willis, J., & Todorov, A. (2006). First Impressions: Making Up Your Mind After a 100-Ms Exposure to a Face. Psychological Science, 17(7), 592–598. https://doi.org/10.1111/j.1467-9280.2006.01750.x

Zhan, J., Liu, M., Garrod, O. G. B., Daube, C., Ince, R. A. A., Jack, R. E., & Schyns, P. G. (2021). Modeling individual preferences reveals that face beauty is not universally perceived across cultures. Current Biology, 31(10), 2243-2252.e6. https://doi.org/10.1016/j.cub.2021.03.013

Can Dietary Prebiotics Reduce the Rewarding Effects of High-Calorie Foods?

Posted on November 30, 2023January 12, 2024 by CNP Super Admin

An experimental study of overweight young adults published in Gut demonstrated that dietary prebiotics can reduce the rewarding effects of high-calorie food
After 14 days of using prebiotics, fMRI scans showed reduced reward-related brain activation in response to high-calorie food compared to the placebo group
Occurrence of short-chain-fatty-acid-producing Bifidobacteriaceae increased in participants taking prebiotics

We are all familiar with the intense feelings of pleasure we get after consuming highly tasty and palatable food – a piece of chocolate cake, for example. We will remember these feelings and associate them with this food. When we decide what to eat in the future, we will recall the memories of that food and the feelings we experienced when eating it —and likely develop a desire to eat the same food again. That is an important part of how we create our food preferences.

Consuming highly palatable foods, especially those high in sugar and fat, can increase dopamine in the brain, creating a sense of pleasure and satisfaction

Rewarding effects of food
The feelings of pleasure we experience when eating highly palatable foods are created by our brain’s reward system. The brain’s reward system comprises a complex network of neural circuits that play a fundamental role in motivation, reinforcement, and pleasure. This process involves the release of neurotransmitters, particularly dopamine, in response to rewarding stimuli, such as food, sex, or positive social interactions. This dopamine release reinforces behaviors associated with the reward, creating a drive to seek out and repeat those behaviors (Lewis et al., 2021) (see Figure 1).

Consuming highly palatable foods, especially those high in sugar and fat, can increase dopamine in the brain, creating a sense of pleasure and satisfaction. This activation of reward neural pathways reinforces the desire to eat, making us more likely to seek those foods in the future.

Figure 1. Food and the brain’s reward system

How is the brain’s reward system linked to obesity?
Repeated exposure to highly rewarding foods, especially in excess, can lead to desensitization of the reward system. When this happens, an individual will need more and more food to achieve the expected pleasant feelings, i.e., the rewarding effects of food. This might make the individual increase the intake of the food in question, potentially leading to overeating and an increase in body weight in the long run (see Figure 2).

Figure 2. Exposure to highly rewarding foods can lead to increased food intake

The described mechanism is similar to that which occurs with addictions. This is the reason why some authors propose that an individual can develop an addiction to certain foods and that food addiction should be recognized as a distinct mental health condition. Studies these researchers carried out indicate that consumption of foods rich in both sugar and fats, such as (many types of) ultra-processed foods, is particularly likely to result in food addiction (Gearhardt et al., 2011, 2023; Hedrih, 2023; Thanarajah et al., 2023).

Studies of these neural mechanisms in humans found functional alterations in the areas of the brain involved in the reward system in overweight individuals (e.g., Pujol et al., 2021; Seabrook et al., 2023), while studies on mice clearly show that high-fat diets can indeed induce obesity by disrupting the functioning of neural mechanisms regulating food intake (e.g., Ikemoto et al., 1996).

This leads many researchers to search for methods of influencing the brain’s reward system to prevent obesity or help individuals regain healthy body mass.

The current study
Study author Evelyn Medawar and her colleagues hypothesized that consuming high doses of prebiotic fiber as a supplement would change the patterns of neural activity related to food in the brain reward system in individuals at risk for weight gain and developing diabetes. Prebiotic fiber is a type of dietary fiber that promotes the growth and activity of beneficial bacteria in the gut, supporting a healthy microbiota. While it is indigestible for humans, it serves as food for various bacteria living in the gut, promoting their growth.

The authors of this study expected that an intervention using prebiotics would alter the composition of the gut microbiome, which would, in turn, alter the food-related patterns of brain activation (Medawar et al., 2023).

The gut microbiome or gut microbiota is the community of trillions of bacteria, fungi, viruses, and other microorganisms that live in the human gut. These microorganisms are critical for digestion, allowing the human digestive system to digest substances it would not be able to digest without them. Still, they also serve a number of other metabolic roles.

High doses of prebiotic fiber as a supplement would change the patterns of neural activity related to food in the brain reward system

How can gut bacteria affect the brain reward system?
Scientists studying human life processes in recent decades discovered a system in the human body that allows communication between gut microorganisms and the brain. This bidirectional communication system is called the microbiota-gut-brain axis (MGBA). It links the gut microbiota, the gastrointestinal tract, and the central nervous system (CNS).

Aside from their role in digestion, gut microorganisms produce various signaling molecules that can influence neural, endocrine, and immune functions. These molecules enable communication between the gut and the brain through neural, hormonal, and immunological pathways. Studies have already identified various ways in which processes in the gut can affect brain functioning, but much still remains to be discovered (Qu et al., 2023; Valles-Colomer et al., 2019; Zhu et al., 2023).

Recent findings indicate that the microbiota-gut-brain axis MGBA can affect and regulate the reward system of the brain as well (Carbia et al., 2023; García-Cabrerizo et al., 2021). Given this, it is possible that gut microbiota changes could also affect the brain’s reward system (see Figure 3).

Figure 3. Signaling molecules enabling communication between the gut and brain

Recent findings indicate that the microbiota-gut-brain axis (MGBA) can affect and regulate the reward system of the brain

The procedure
Study participants were 59 overweight adults (body mass index values between 25 and 30). Their ages ranged between 18 and 42 years. Nineteen of them were females. Participants received 9-10 EUR per hour for their activities during testing days and another 30 EUR for completing the study.

The study’s authors prepared two different treatment substances for participants: a prebiotic fiber supplement and a placebo. The daily dose of prebiotic fiber consisted of 30 grams of inulin, a carbohydrate found in plants such as chicory root. Humans lack the enzymes needed to digest inulin, but many beneficial bacteria in the gut can ferment it. The product of this fermentation is short-chain fatty acids. Among other things, these substances are involved in the functioning of the MGBA (learn more about short-chain fatty acids in NP 120 Parts I and II through CNP) (see Figure 4).

Figure 4. Research procedure using prebiotic fiber vs. placebo

A daily placebo treatment substance consisted of 16g of maltodextrin, a carbohydrate used as a food additive and a thickening agent derived from starch. The placebo treatment was matched on calories with the prebiotic fiber treatment. Both substances were packed in identical sachets, so participants could not tell which substance a sachet contained. In both cases, participants were supposed to take two sachets of the treatment substance daily.

Each participant was supposed to take each of the two treatment substances for 14 days in a row, with two weeks between the treatments. The order in which each participant would take the treatments was randomized – some participants first took the prebiotic fiber for 14 days, paused for 14 days, and then took a placebo for another 14 days. Other participants would start with 14 days of placebo and finish with 14 days of using the prebiotic fiber. This was a blind study, so study participants and staff working with them did not know when they were taking a placebo and when it was prebiotic fiber.

Neuroimaging and measurements
Before and after each 14-day treatment, participants underwent functional magnetic resonance imaging (fMRI). During imaging, they viewed pictures of food and art and rated how much they wanted the item shown in the picture. Researchers told them they would receive a reward based on their ratings – either a food item that could be eaten immediately after fMRI or a carton print of an art piece.

Participants gave blood samples to assess short-chain fatty acid concentrations, gastrointestinal hormones, glucose, lipids, and inflammatory markers. They also gave stool samples for measuring gut microbiota composition and short-chain fatty acids.

Brain reactions to high-calorie foods decreased after prebiotic treatment

On average, during fMRI scans, participants wanted food more than they wanted art. The prebiotic treatment did not change the relationship between these wanting scores. However, after 14 days of taking prebiotics, participants had lower overall wanting scores than after 14 days of taking a placebo. In particular, after prebiotics treatment, participants expressed very little desire for very low and very high-calorie foods and plants (see Figure 5).

Figure 5. Brain reactions to high-calorie foods decreased after prebiotic treatment

When brain activity was examined, regional brain activity in reaction to food and art pictures remained the same. Evaluation of food pictures activated large parts of the reward network, including the ventral tegmental area, hypothalamus, nucleus accumbens, basal ganglia, and ventromedial thalamus, as well as anterior insula, amygdala, cingulate, ventromedial prefrontal cortex, and distinct parts of the orbitofrontal cortex.

However, brain reactions to pictures of wanted, high-calorie food decreased after prebiotics (compared to placebo) in three brain regions – ventral tegmental area, right and right medial the orbitofrontal cortex. Additionally, participants reported feeling less subjective hunger during fMRI after the prebiotics treatment.

Brain reactions to pictures of wanted, high-calorie food decreased after prebiotics compared to placebo

Prebiotic treatment altered gut microbiota composition
Analysis of stool samples indicated that the treatment with prebiotics led to significant shifts in gut microbiota composition. Overall richness, evenness, and diversity (the number of different species of microorganisms) decreased. The amounts of Bifidobacteria in samples increased. Increases in Bifidobacteria were associated with increases in the activity of a number of metabolic pathways.

Participants who had a more substantial decrease in Subdoligranulum bacteria after prebiotics treatment tended to show a decreased activation of the brain’s ventral tegmental area in response to high-calorie food. Participants who had a decrease in Lactiplantibacillus, a lactic acid-producing bacteria, after prebiotics treatment tended to have decreased activation in the right medial orbitofrontal cortex.

Conclusion
The study showed that intervention with a common prebiotic attenuated reactions of the brain’s reward network in response to high-calorie food. It also induced changes to the gut microbiota, through which this effect was likely achieved.

The study showed that intervention with a common prebiotic attenuated reactions of the brain’s reward network in response to high-calorie food

Since the discovery of the microbiota-gut-brain axis, scientists have considered using prebiotics or other interventions aimed at gut microbiota to achieve beneficial changes in brain activity and behavior. The results of this study indicate that this idea holds merit. While it used a common type of prebiotic, it proved that brain functioning can be affected in this way. This opens a path for future researchers to develop more effective and specific ways to achieve mental health outcomes through interventions aimed at gut microbiota.

The paper “Prebiotic diet changes neural correlates of food decision-making in overweight adults: a randomized controlled within-subject cross-over trial” was authored by Evelyn Medawar, Frauke Beyer, Ronja Thieleking, Sven-Bastiaan Haange, Ulrike Rolle-Kampczyk, Madlen Reinicke, Rima Chakaroun, Martin von Bergen, Michael Stumvoll, Arno Villringer, A Veronica Witte.

References

Carbia, C., Bastiaanssen, T. F. S., Iannone, F., García-cabrerizo, R., Boscaini, S., Berding, K., Strain, C. R., Clarke, G., Stanton, C., Dinan, T. G., & Cryan, J. F. (2023). The Microbiome-Gut-Brain axis regulates social cognition & craving in young binge drinkers. EBioMedicine, (In press), 104442. https://doi.org/10.1016/j.ebiom.2023.104442

García-Cabrerizo, R., Carbia, C., O´Riordan, K. J., Schellekens, H., & Cryan, J. F. (2021). Microbiota-gut-brain axis as a regulator of reward processes. Journal of Neurochemistry, 157(5), 1495–1524. https://doi.org/10.1111/JNC.15284

Gearhardt, A. N., Bueno, N. B., DiFeliceantonio, A. G., Roberto, C. A., Jiménez-Murcia, S., & Fernandez-Aranda, F. (2023). Social, clinical, and policy implications of ultra-processed food addiction. BMJ, e075354. https://doi.org/10.1136/bmj-2023-075354

Gearhardt, A. N., Yokum, S., Orr, P. T., Stice, E., Corbin, W. R., & Brownell, K. D. (2011). Neural Correlates of Food Addiction. Archives of General Psychiatry, 68(8), 808–816. https://doi.org/10.1001/ARCHGENPSYCHIATRY.2011.32

Hedrih, V. (2023). 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

Lewis, R. G., Florio, E., Punzo, D., & Borrelli, E. (2021). The Brain’s Reward System in Health and Disease. In Advances in Experimental Medicine and Biology (Vol. 1344, pp. 57–69). Springer. https://doi.org/10.1007/978-3-030-81147-1_4

Medawar, E., Beyer, F., Thieleking, R., Haange, S.-B., Rolle-Kampczyk, U., Reinicke, M., Chakaroun, R., von Bergen, M., Stumvoll, M., Villringer, A., & Witte, A. V. (2023). Prebiotic diet changes neural correlates of food decision-making in overweight adults: a randomised controlled within-subject cross-over trial. Gut, gutjnl-2023-330365. https://doi.org/10.1136/gutjnl-2023-330365

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

Qu, Y., Eguchi, A., Wan, X., Ma, L., Chang, L., Shan, J., Yang, Y., Mori, C., & Hashimoto, K. (2023). Repeated use of 3,4-methylenedioxymethamphetamine is associated with the resilience in mice after chronic social defeat stress: A role of gut–microbiota–brain axis. Psychiatry Research, 320(October 2022), 0–9. https://doi.org/10.1016/j.psychres.2022.115020

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

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

Valles-Colomer, M., Falony, G., Darzi, Y., Tigchelaar, E. F., Wang, J., Tito, R. Y., Schiweck, C., Kurilshikov, A., Joossens, M., Wijmenga, C., Claes, S., Van Oudenhove, L., Zhernakova, A., Vieira-Silva, S., & Raes, J. (2019). The neuroactive potential of the human gut microbiota in quality of life and depression. Nature Microbiology, 4(4), 623–632. https://doi.org/10.1038/s41564-018-0337-x

Zhu, X., Sakamoto, S., Ishii, C., Smith, M. D., Ito, K., Obayashi, M., Unger, L., Hasegawa, Y., Kurokawa, S., Kishimoto, T., Li, H., Hatano, S., Wang, T. H., Yoshikai, Y., Kano, S. ichi, Fukuda, S., Sanada, K., Calabresi, P. A., & Kamiya, A. (2023). Dectin-1 signaling on colonic γδ T cells promotes psychosocial stress responses. Nature Immunology. https://doi.org/10.1038/s41590-023-01447-8

Children Prone to Overeating are More Likely to be Overweight as Adults

Posted on September 30, 2023October 3, 2023 by CNP Super Admin

Body weight and the body mass index
When we want to track whether our body has accumulated (unwanted) body fat, we usually assess this by weighing ourselves. This is adequate because, after we stop growing, changes in our body mass will most likely be due to changes in our body composition (e.g., accumulating or losing body fat). However, when we need to compare different persons, their differences in height come into play. People with very different weights can have similar body compositions if they are of different height. To solve this issue, scientists use the body mass index.

Body Mass Index (BMI) is a statistical indicator calculated by dividing an individual’s weight (in kilograms) by the square of their height (in meters). It serves as a straightforward and widely accepted method of estimating body fat and determining whether an individual falls into categories such as underweight, normal weight, overweight, or obese. BMI provides a convenient way to screen for potential weight-related health issues. However, it has limitations, notably because it does not account for factors like muscle mass, body composition, or fat distribution. An individual with a BMI above 30 is considered obese, while values between 25 and 30 indicate that a person is overweight (Bhatt et al., 2023).

Food intake and obesity
Most obviously, obesity is caused by excessive food intake over a prolonged period. However, the causes of such excessive intake are much more complex. Our body uses a complex mechanism to regulate our food-related behavior. We shift between states of hunger and satiety multiple times every day. While hunger motivates us to seek and eat food, satiety tells us that we should stop eating. Both are linked to our emotions in very complex ways (Swami et al., 2022).

Hunger motivates us to seek and eat food; satiety tells us we should stop eating

However, recent studies indicate that neither hunger nor satiety strictly depend on our body’s nutritional needs. Scientists differentiate between two processes of hunger. One is triggered by a lack of specific nutrients (homeostatic hunger), while the other arises from learned associations between various cues for food (appetite) (Hedrih, 2023) (see Figure 1).

Figure 1. Mechanisms to regulate food-related behavior.

Causes of obesity
Food intake can often not result in satiety (feeling full or satisfied). Studies showed that mice that became obese because they were fed high-fat diets* do not seem to experience satiety and decreased motivation to consume food after eating.

Food intake can often not result in satiety

Researchers have tracked the cause of this disruption to the lateral orbitofrontal cortex region of the brain. They found that reduced inhibition of neurons produces this effect (Seabrook et al., 2023) (see Figure 2).

Figure 2. Prolonged high-fat diet disrupts the food intake regulation mechanism

Due to all this, disruption of the hunger-satiety mechanism in the brain is now seen as one of the primary causes of obesity. However, the exact ways this disruption develops in humans are unknown. Scientists have linked the functioning of this mechanism to various genetic and environmental factors, but also to diet (Changizi et al., 2002; Dubois et al., 2022; Stevenson et al., 2023). Of these, diet is the most easily modifiable.

Disruption of the hunger-satiety mechanism in the brain is now seen as one of the primary causes of obesity

The current study
Study author Lise Dubois and her colleagues wanted to explore the links between young adults’ eating behaviors, dietary patterns, and weight status. They also wanted to know whether eating behaviors in childhood are associated with eating habits and weight status in adulthood.

Their study participants were young adults in the Quebec Longitudinal Study of Child Development. The Quebec Longitudinal Study of Child Development is an ongoing study that has been running since 1998. It started with 2,120 singleton babies who entered the study at 5 months old. Researchers conducting the study periodically collect various data from these individuals, now adults.

At the time of the last data collection for the current study, these individuals were 22 years of age, and 698 participated.

The study procedure
Participants completed assessments of eating behaviors (the Adult Eating Behavior Questionnaire) and reported how often they ate items from each of the 60 food groups. They also reported their height and weight and several other pieces of demographic data. The study authors used data on height and weight to calculate participants’ body mass index values.

These researchers also used data on participants’ eating behaviors that persons “most knowledgeable” about the participant (who was a child at that time) provided when participants were 2, 3, 4, 5, and 6 years old. These persons were most often mothers. From these data, researchers derived information on whether the child was prone to overeating and fussy/picky eating (see Figure 3).

Figure 3. Research Procedure

The Eating behavior assessment
The Adult Eating Behavior Questionnaire measures multiple aspects of adult eating behavior. Hunger assesses an individual’s perception of their hunger levels. Questions inquire about how often one feels hungry and how strong that hunger is. Food responsiveness assesses an individual’s sensitivity and responsiveness to the sight and smell of palatable foods and how much these influence eating behaviors. Enjoyment of food evaluates how much an individual enjoys the taste and experience of consuming food.

Emotional overeating and undereating assess the extent to which an individual tends to overeat or undereat as a coping mechanism for emotional distress or negative emotions. Satiety responsiveness examines how often and how strongly an individual experiences the feeling of fullness during and after meals. Food fussiness is the extent to which a respondent is picky about what foods to eat. Slowness in eating assesses the tendency to eat slowly.

Associations between eating behaviors
Results showed that all food approach behaviors and tendencies to enjoy and eat lots of food were associated. The same was the case with different food avoidance behaviors. Food approach measures were negatively correlated with food avoidance measures. For example, individuals who reported enjoying the taste of food more also reported feeling stronger hunger and being more responsive to the sight and smell of food (food responsiveness).

Individuals who reported enjoying the taste of food more also reported feeling stronger hunger and being more responsive to the sight and smell of food

Participants who were slow eaters tended to experience satiety more easily and more strongly. They were also more prone to emotional undereating. Participants who experienced satiety more easily were more likely to be picky about their food (food fussiness). On the other hand, people who responded more easily to the sight and smell of food were less likely to be slow eaters (see Figure 4).

Figure 4. Food approach and avoidance behaviors

Overweight and obese individuals tend to be more positive about food
Results showed that overweight and obese individuals were more prone to emotional overeating and enjoyed the taste of food and the experience of eating more (Enjoyment of food) than their normal-weight peers. On the other hand, they were less prone to emotional undereating, did not experience satiety as easily, and were less likely to be slow eaters.

Males had higher average values on all food behavior measures than females, except food fussiness. In other words, men tended to report experiencing every food behavior, both positive and negative, more strongly than females, except for picky eating. Men and women, on average, reported being picky eaters equally often.

Four food consumption patterns
Analysis of data on food item consumption revealed 4 different dietary patterns. Study authors named them healthy (eats legumes, nuts & seeds, whole-grain products, vegetables, and fruit), beverage-rich (a tendency to drink sugar-sweetened beverages and unsweetened milk & plant-based drinks), high energy density (a tendency to eat processed meat, alcohol, cheese and fatty/salty snacks), and protein-rich (a tendency to eat meat, poultry, fish, shellfish, and eggs).

These patterns indicate groups of food items associated in the sense that individuals who consume one food item from the pattern are also more likely to consume other items in that pattern. For example, people who report eating meat are more likely to consume poultry, fish, shellfish, and eggs. Patterns are not different groups of people but tendencies that each individual can have differently (see Figure 5).

Figure 5. Four food consumption dietary patterns

Participants more prone to the healthy dietary pattern tended to be less picky about their food, enjoyed food more, and had a slightly stronger feeling of hunger than those less prone to this pattern. Individuals more often drinking drinks from the beverage-rich pattern tended to report being somewhat fussier about their food and enjoying food a bit less. The protein-rich pattern was very weakly associated with the enjoyment of food. Still, individuals prone to this pattern experienced satiety a bit less easily. Finally, those consuming more foods from the high energy density pattern also experienced satiety less easily. They tended to be more fussy about their food (see Figure 6).

Figure 6. Food preferences and eating habits

Individuals who were prone to overeating as children were more likely to be overweight or obese as adults
Participants who were prone to overeating in childhood were 1.44 times more likely to be overweight or obese as adults than individuals who were not prone to overeating as children. These individuals also tended to experience satiety less easily than adults compared to those who were not overeating as children.

Individuals who were fussy eaters in childhood tended to be slightly more prone to emotional undereating as adults. They were also more prone to be fussy about their food and were slightly less likely to consume the foods constituting the healthy dietary pattern. There was no association between picky eating in childhood and weight status in adulthood.

Women, but not men, who were fussy eaters in childhood, were more prone to emotional overeating and enjoyed the sight and smell of food as adults. In contrast, men who were picky eaters in childhood tended to be enjoying food a bit less than adults (see Figure 7).

Figure 7. Eating habits in childhood and adulthood

Conclusion
Overall, the study reported several associations between eating behaviors and dietary patterns. Individuals more prone to one positive behavior towards food tended to be likelier to show other positive-approaching behaviors (enjoyment of food, responsiveness…). Such individuals were less likely to manifest negative behaviors towards food (be picky or slow eaters, feel satiety easily). Results also showed numerous associations between eating behaviors and dietary patterns, i.e., the food individuals eat.

Most importantly, they showed that individuals prone to overeating in childhood were more likely to be obese or overweight as adults. Additionally, associations, although very weak, were found between eating behaviors in childhood and current eating behaviors over a time gap spanning more than a decade.

These links support the notion that eating behaviors take root in early childhood

These links support the notion that eating behaviors take root in early childhood. Still, the fact that they are generally weak indicates that eating behaviors are very modifiable. Given this, early interventions to establish healthy eating habits in childhood can make it more likely that a person will continue with such habits into adulthood. However, they also indicate that it is never too late to change unhealthy eating behaviors as the repercussions of childhood tendencies on adult eating behaviors appear limited.

The paper “Eating behaviors, dietary patterns and weight status in emerging adulthood and longitudinal associations with eating behaviors in early childhood” was authored by Lise Dubois, Brigitte Bédard, Danick Goulet, Denis Prud’homme, Richard E. Tremblay, and Michel Boivin.

*The study authors do not specify their definition of types of fats in the “high-fat diet” described in this paper (for example, polyunsaturated vs. monounsaturated vs. trans fats).

References

Bhatt, R. R., Todorov, S., Sood, R., Ravichandran, S., Kilpatrick, L. A., Peng, N., Liu, C., Vora, P. P., Jahanshad, N., Gupta, A., & Bhatt, R. R. (2023). Integrated multi-modal brain signatures predict sex-specific obesity status. Brain Communications, 5(2), 1–14. https://doi.org/10.1093/BRAINCOMMS/FCAD098

Changizi, M. A., McGehee, R. M. F., & Hall, W. G. (2002). Evidence that appetitive responses to dehydration and food deprivation are learned. Physiology and Behavior, 75(3), 295–304. https://doi.org/10.1016/S0031-9384(01)00660-6

Dubois, L., Bédard, B., Goulet, D., Prud’homme, D., Tremblay, R. E., & Boivin, M. (2022). Eating behaviors, dietary patterns and weight status in emerging adulthood and longitudinal associations with eating behaviors in early childhood. International Journal of Behavioral Nutrition and Physical Activity, 19(1). https://doi.org/10.1186/s12966-022-01376-z

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

Stevenson, R. J., Bartlett, J., Wright, M., Hughes, A., Hill, B. J., Saluja, S., & Francis, H. M. (2023). The development of interoceptive hunger signals. Developmental Psychobiology, 65(2), 1–11. https://doi.org/10.1002/dev.22374

Swami, V., Hochstöger, S., Kargl, E., & Stieger, S. (2022). Hangry in the field: An experience sampling study on the impact of hunger on anger, irritability, and affect. PLOS ONE, 17(7), e0269629. https://doi.org/10.1371/JOURNAL.PONE.0269629

Are Hunger Cues Learned in Childhood?

Posted on September 16, 2023November 10, 2023 by CNP Super Admin

A study on a group of Australian students and their caregivers examined whether hunger cues our body uses to create the subjective feeling of hunger, might be something that is learned in childhood. Results showed a substantial association between how students and their primary caregivers experience hunger. This might indicate that how we experience hunger is indeed learned in childhood by caregivers (Stevenson et al., 2023). The study was published in Developmental Psychobiology.

The way we experience hunger is learned in childhood from caregivers

When do we get hungry?
People eat when they feel hungry. The sensation of hunger motivates individuals to seek food and ingest it. For a long time, scientists and the general public believed that we feel hungry when our brain detects that certain nutrients are lacking in the body. Views such as these are called the energy-deficit models of hunger (Stevenson et al., 2023).

However, studies in the past century, particularly those in the last decades, revealed that experiences of hunger need not be a consequence of lacking nutrients. Humans and many animals can experience hunger when they smell or see tasty food, when they feel bored or desire sensory stimulation (McKiernan et al., 2008), or when they are under stress (Levine & Morley, 1981) (see Figure 1).

Figure 1. Energy-deficit model of hunger vs. other reasons for hunger

Hunger can be learned
Studies indicate that individuals (but also animals) can learn to expect food at certain places and at certain times. Human daily rhythms of psychological processes (circadian rhythms) are typically adjusted to having three meals per day. However, our body can also learn to expect a different number of daily meals at different times. This expectation will then trigger hunger at those times without much link to energy needs (Isherwood et al., 2023).

Our body can learn to expect a different number of daily meals and at different times

Two processes of hunger
Some scientists propose that there might be two different processes responsible for hunger. According to this concept, one of these processes is centered around acquiring nutrients the body requires. Lack of specific nutrients triggers specific signals leading to the experience of hunger that motivates the organism to seek and eat foods containing those nutrients. This process is called homeostatic hunger. It describes the traditional view about how hunger develops (see Figure 2).

Figure 2. Homeostatic hunger (Process 1)

The other process is appetite (see Figure 3). It arises from the learned associations between various cues for food and their consequences. For example, I see a package of chocolate. I know from previous experience that if I open it, there will be chocolate inside. I also know that if I eat the chocolate, I will feel its pleasant taste in my mouth. Due to all this, when I see chocolate, I start feeling an appetite for chocolate.

Figure 3. Appetite (Process 2)

Is homeostatic hunger real?
While scientific studies thoroughly explored and documented the processes of appetite, several authors in recent decades expressed skepticism about the workings of homeostatic hunger and its very existence. These authors note that the energy intake of a single meal is practically negligible compared to the body’s total energy reserves.

The body utilizes a complex regulation system that ensures body tissues receive enough nutrients even if a meal or several meals are missed. Hunger regulation mechanisms, on the other hand, often seem to be more about accounting for the limited capacity of the gut and adapting to the physiological challenges and hindrance of other activities that digesting food represents than about the provision of energy (Rogers & Brunstrom, 2016; Stevenson et al., 2023). For example, we will likely not feel hungry while doing intense physical work. The feeling of hunger will come only after we take a break or reduce the activity level. With these and other arguments in mind, these authors claim that the feeling of hunger might not have anything to do with the body’s short-term energy needs (Rogers & Brunstrom, 2016).

The feeling of hunger might not have anything to do with the body’s short-term energy needs at all (Rogers & Brunstrom, 2016)

The current study
Study author Richard J. Stevenson and his colleagues wanted to test the hypothesis that food sensations are learned. A recent study found that rat pups cannot respond adequately to food deprivation until they have encountered food and eaten in that state (Changizi et al., 2002). In other words, rat pups learn that eating food will produce rewarding consequences when they feel the sensations we interpret as hunger.
But does that work similarly in humans? Study authors believe so. They state that parenting might be important for teaching children the meaning of hunger. This likely happens during the weaning period and onward.

Parenting might be important for teaching children the meaning of hunger

The weaning period
The weaning period is when caregivers gradually introduce solid foods into a baby’s diet and reduce its dependency on breast milk or formula as the primary source of nutrition. This transition typically begins when a baby is around six months old, although the timing can vary depending on the baby’s development and the recommendations of healthcare professionals.

During the weaning period, parents and caregivers start offering the baby a variety of soft, mashed, or pureed foods in addition to breast milk or formula. The goal is to expose the baby to different tastes and textures while ensuring it receives the nutrients needed for healthy growth and development. Over time, solid foods gradually replace some of the milk feeds.

Learning to interpret hunger
It is in this period that children likely learn the meaning of hunger. Occasionally, they experience hunger-related feelings (e.g., a tummy rumble). On some occasions, this feeling will be followed by food. On others, it would not be. The child will note that when these sensations are followed by food, the food will taste good, and they will feel good after eating it (see Figure 4).

Figure 4. Weaning and learning to interpret hunger

Once they have learned this, they will likely be resistant to change because of the nature of the learning process. This learning would likely persist into adulthood as a pattern of internal signals linked to eating. These patterns of signals would differ between individuals but are likely more similar to the pattern of one’s primary caregiver (the person they learned it from) than to that of a stranger.

The procedure
To test this, the study authors asked 116 caregiver-offspring pairs to complete a hunger survey. The “offspring” were first-year psychology students, and the “caregiver” was the person who primarily cared for the student when he/she was a child. The study authors excluded five pairs of participants because they failed the check questions in the survey or suffered from an eating disorder. Data from a total of 111 pairs remained for the analysis. The average age of student participants (“the offspring”) was 22 years. It was 52 for the primary caregivers. One hundred and five primary caregivers were mothers, and 6 were fathers.
After they agreed to participate in the study, researchers asked both offspring and their caregivers to complete a 30-minute online survey. They instructed them to do this 30 minutes before eating a main meal. The survey first asked respondents about their current level of hunger and continued with questions focusing on different internal states that participants associated with hunger.
Additionally, participants completed assessments of hunger beliefs (a questionnaire created by study authors) and eating attitudes (the Three-Factor Eating Questionnaire). Before taking the survey, students reported their age, height, weight, gender, and whether they were currently dieting. Students completed the survey before their caregivers (See Figure 5).

Figure 5. Research Procedure

Participants were moderately hungry while they were doing the survey
The median time since the last meal during the survey was 2-4 hours. This was the case both with offspring and their caregivers. Both groups of participants reported feeling a moderate urge to eat at the time of the survey. This urge was a bit more pronounced in offspring (students). Both groups reported that they could eat moderate food at that point. Overall, offspring and their caregivers were similarly hungry when they were doing the survey.

Caregivers and offspring tended to give similar responses to the hunger survey
The study authors found a medium-strength association between the responses of offspring and their caregivers on the food survey. While their responses were far from identical, there was quite a bit of similarity—much more than could be expected based on random chance.

Offspring report experiencing hunger signals more intensely than caregivers
The researchers used statistical procedures to divide hunger survey questions into several groups based on hunger signals participants tended to report similarly. They then calculated the reported intensity of those groups of signals. Results showed that the rankings of intensities of these signals were the same for offspring and caregivers.

However, on average, offspring reported experiencing the same hunger signals more intensely than their caregivers. The most intensely experienced hunger signals were empty stomachs and fatigue. Full stomach and positive anticipation were the least often seen as signaling hunger.

Offspring reported being more prone to uncontrolled and emotional eating and less prone to restrained eating than their caregivers. Additional analysis showed that when offspring and their caregivers believed more in homeostatic hunger (i.e., hunger being an indicator that the body needs energy), when offspring was more prone to uncontrolled eating, and when the caregiver had a greater body mass index, the responses of offspring and their caregiver to the hunger survey tended to be more similar (see Figure 6).

Figure 6. Different parental beliefs about hunger

Conclusion
Overall, the study supported the hypothesis that hunger sensations, what sensations to interpret as hunger, are indeed learned. The fact that responses about the sensations one interprets as hunger were similar between offspring and their caregiver shows that this might indeed be something that individuals learn from their caregivers in childhood and persists without much change into adulthood.

Parent beliefs about the cause of hunger influenced what they teach their children, implying that genetics are not the sole driver of parent-child similarity

These findings may have important implications for preventing eating disorders and obesity. If interpreting sensations from the body as hunger is learned in early childhood along with what to do about those sensations, there might be a link between these learnings and later eating disorders, including the current worldwide obesity pandemic (Wong et al., 2022). It might also turn out that the development of these disorders can be mitigated or even completely prevented by simply changing what the current and future children learn about interpreting hunger sensations and how to deal with them.

The paper “The development of interoceptive hunger signals” was authored by Richard J. Stevenson, Johanna Bartlett, Madeline Wright, Alannah Hughes, Brayson J. Hill, Supreet Saluja, and Heather M. Francis.

References
Changizi, M. A., McGehee, R. M. F., & Hall, W. G. (2002). Evidence that appetitive responses for dehydration and food-deprivation are learned. Physiology and Behavior, 75(3), 295–304. https://doi.org/10.1016/S0031-9384(01)00660-6\

Isherwood, C. M., van der Veen, D. R., Hassanin, H., Skene, D. J., & Johnston, J. D. (2023). Human glucose rhythms and subjective hunger anticipate meal timing. Current Biology, 33(7), 1321-1326.e3. https://doi.org/10.1016/j.cub.2023.02.005

Levine, A. S., & Morley, J. E. (1981). Stress-induced eating in rats. American Journal of Physiology – Regulatory, Integrative and Comparative Physiology, 241(1), R72–R76.

McKiernan, F., Houchins, J. A., & Mattes, R. D. (2008). Relationships between human thirst, hunger, drinking, and feeding. Physiology & Behavior, 94(5), 700. https://doi.org/10.1016/J.PHYSBEH.2008.04.007

Rogers, P. J., & Brunstrom, J. M. (2016). Appetite and energy balancing. Physiology & Behavior, 164, 465–471. https://doi.org/10.1016/J.PHYSBEH.2016.03.038

Stevenson, R. J., Bartlett, J., Wright, M., Hughes, A., Hill, B. J., Saluja, S., & Francis, H. M. (2023). The development of interoceptive hunger signals. Developmental Psychobiology, 65(2), 1–11. https://doi.org/10.1002/dev.22374

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/NQAC00

How Your Gut Microbiota is Linked to Both Positive and Negative Aspects of Mental Health

Posted on September 2, 2023November 10, 2023 by CNP Super Admin

Microbiota composition is linked to both positive and negative aspects of mental health

A large-scale study in Belgium and the Netherlands found links between the abundance of certain groups of gut bacteria species and mental health outcomes. Faecalibacterium and Coprococcus bacteria that produce a short-chain fatty acid called butyrate were consistently more abundant in individuals with higher quality of life. In contrast, Dialister, Coprococcus spp, tended to be depleted in individuals with depression. Social functioning tended to be better in individuals with many bacteria capable of producing 3,4-dihydroxyphenylacetic acid in their gut. 3,4-dihydroxyphenylacetic acid is a substance our body produces when processing dopamine, a neurotransmitter associated with experiencing good feelings (Valles-Colomer et al., 2019). The study was published in Nature Microbiology.

Social functioning was better in those with bacteria capable of producing a substance our body produces (3,4-dihydroxyphenylacetic acid) when processing dopamine

Humans have known for centuries that there is a link between how our digestive system works and how we feel. Everyone senses from experience that our mental state also deteriorates when our digestive system doesn’t work well. However, in the past century, medical and biological science has advanced enough to allow scientists to examine the gut microbiota in our digestive system and study the interaction between them and the human body in detail.

A large-scale study found links between the abundance of certain gut bacteria species and mental health outcomes

What is gut microbiota?

The human gut microbiome, often called gut microbiota or gut flora, is a complex community of trillions of microorganisms that reside in the digestive tract, primarily in the colon. These microorganisms include bacteria, viruses, fungi, and other microbes. Gur microbiota is critical in digesting food, absorbing nutrients, and aiding our metabolic activity.

Humans have known for centuries there is a link between our digestive system and how we feel

Gut microbiota helps maintain a balanced and healthy immune system. The composition and diversity of gut microbiota can vary significantly among individuals and can be influenced by factors such as diet, genetics, and lifestyle. It is increasingly recognized as a crucial factor in overall health and well-being (Heiss et al., 2021; Zhu et al., 2023).

Microbiota-gut-brain-axis

A key pathway through which the link between gut microbiota and well-being is achieved is the microbiota-gut-brain axis (MGBA). The microbiota-gut-brain axis is a bidirectional communication system connecting the gut, microbiota, and brain. This axis regulates physiological and psychological processes (Carbia et al., 2023; Zhu et al., 2023).

Gut microbiota can vary among individuals and is recognized as a crucial factor in overall health and well-being (Heiss et al., 2021; Zhu et al., 2023)

The microbiota-gut-brain axis (MGBA) is based on small proteins called cytokines and several other biomolecules, including the hormone cortisol, short-chain fatty acids (SCFAs), tryptophan, neurotransmitters, and others (see Figure 1).

Figure 1. Some of the Biomolecules involved in MGBA

Emerging studies reveal that the gut microbiota produces substances that can influence the brain’s activity and its responses to stress and emotions. Additionally, the microbiota-gut-brain axis is closely tied to the immune system, influencing the body’s inflammatory responses and potentially contributing to neuroinflammation (Zhu et al., 2023).

Gut microbiota produces substances that influence the brain’s activity and its responses to stress and emotions

These scientific findings suggest that interventions targeting the gut microbiota, such as probiotics and dietary changes, may positively impact mental health and neurological disorders. This can open a new avenue of treatment for mental health issues and possibly other disorders.

The current study

Study author Mireia Valles-Colomer and her colleagues wanted to examine the association between gut microbiota composition and quality of life indicators in the general population. They also wanted to examine links between gut microbiota composition and depression (Valles-Colomer et al., 2019).

They note that recent advances in genetic sequencing technology allowed researchers to start studying the role of the gut microbiota in a broad range of neurological and psychiatric disorders and diseases. Advancements in the field of metagenomics are a particularly important part of this as it allows relatively easy and noninvasive exploration of human gut microbiota composition.

Recent advances in genetic sequencing technology allows researchers to study the role of microbiota in neurological and psychiatric disorders

Metagenomics

Metagenomics is a field of genetics and microbiology that involves the study of genetic material collected directly from environmental samples, like soil, water, or the human gut, without the need for isolating and cultivating individual organisms. It employs advanced DNA sequencing techniques to analyze and characterize collective genomes of microorganisms in studied samples and their genetic diversity.

In the case of human gut microbiota studies, researchers typically collect stool samples for this purpose. They then use metagenomics techniques to determine the presence, absence, and abundance of different species of microorganisms in the gut microbiota (see Figure 2).

Figure 2. Metagenomics

The procedure – the Belgian Flemish Gut Flora project data

The authors of this study analyzed data from two large-group longitudinal studies in Europe. The first set came from the Belgian Flemish Gut Flora Project (FGFP). It contained data from 1054 individuals on gut microbiota and depression as reported by general medical practitioners. The quality of life of these study participants was assessed using the RAND 36-Item Health Survey 1.0. This assessment covers eight health concepts – role limitations caused by emotional health problems, social functioning, emotional well-being, vitality, physical functioning, role limitations caused by physical health, body pain, and general health perception. Participants who were using antidepressants but were not diagnosed with depression were excluded from the analysis.

From this group, study authors selected 80 participants with clinically diagnosed depression (40 were using antidepressants) and 70 healthy participants as controls, matched with them on age, sex, body mass index, and stool consistency for in-depth analysis using shotgun metagenomic sequencing. Shotgun metagenomic sequencing is a method that involves sequencing all the genetic material present in a microbial community sample, providing a comprehensive view of the genes and organisms within that community.

The Lifelines Cohort data and controls

Researchers used another sample to verify their findings – the Lifelines Cohort. The Lifelines Cohort is a large-scale, three-generation longitudinal study in the Netherlands. It contains a large amount of medical and psychological data from over 167,000 participants so far. The Lifelines cohort study was started in 2006 and aimed to include 10% of the northern population of the Netherlands of all ages. The authors of the Lifelines Cohort study hope to be able to follow these individuals for 30 years and collect follow-up data during this time.

In this study, the authors used data from 1063 individuals from the Lifeline Cohort. The quality of life of this group was assessed in the same way as in the Belgian sample. Participants self-reported depression. Researchers asked participants to indicate the disorders they have or have had, and depression was on the list. Participants also reported on their use of antidepressants in the last three months.

The study authors used gas chromatography-mass spectrometry (GC-MS) to determine butyrate levels in stool samples from this dataset. Butyrate is a short-chain fatty acid produced by certain species of bacteria in the gut during the fermentation of dietary fiber (see Figure 3). It is an important energy source for the cells lining the colon and helps maintain their integrity and function. Additionally, it has anti-inflammatory properties and has been associated with various health benefits. Butyrate potentially reduces the risk of inflammatory bowel diseases and promotes overall gut health.

Figure 3. The Short-Chain Fatty Acid Butyrate

Additionally, study authors collected and sequenced seven stool samples from patients suffering from major depressive disorder resistant to treatment. Participants in this sample were diagnosed with moderate-to-severe depression and inadequate response to at least two therapies with antidepressants. Inadequate response means that symptoms of depression persist after treatment.

Gut microbiota composition was related to quality of life

Results revealed multiple associations between microbiome characteristics and all aspects of quality of life (see Figure 4). Study participants with better quality of life indicators tended to have more Faecalibacterium and Coprococcus bacteria in their guts. Those with better physical functioning tended to have fewer bacteria from the Flavonifractor group of species (genus). This group of bacterial species was also increased in individuals suffering from major depressive disorder (MDD).

Figure 4. Associations between microbiome characteristics and all aspects of quality of life (as outlined earlier)

Study authors note that Faecalibacterium and Coprococcus bacteria produce the short-chain fatty acid butyrate. Butyrate levels in the gut are generally reduced in individuals with inflammatory bowel disease and those with depression. They examined the Lifelines cohort data to verify this finding, and the results showed that the abundance of these bacteria is indeed associated with butyrate concentrations in the stool.

Butyrate levels in the gut are reduced in those with inflammatory bowel disease and depression

Coprococcus and Dialister bacteria are depleted in the guts of individuals suffering from depression

Study authors identified 4 groups of bacterial species that were consistently depleted in individuals suffering from depression (depleted in this case, means that they are present in numbers significantly lower than those found in typical healthy individuals).

However, further analyses revealed that antidepressants can substantially affect the composition of gut bacteria. When study authors controlled for the use of antidepressants, only Coprococcus and Dialister groups of species remained associated with depression. There were significantly fewer bacteria from these groups in the guts of individuals suffering from depression than healthy individuals (see Figure 5). This finding was held in the Flemish Gut Flora and the Lifeline Cohort data.

Figure 5. Microorganism abundances linked to Quality of Life and Depression

Bacteria producing 3,4-dihydroxyphenylacetic acid are more abundant in individuals with better social functioning

Next, the study authors examined the gut-brain modules, i.e., they looked for groups of bacteria that produce substances that could affect mental states and their links to quality-of-life indicators. These analyses showed that bacteria producing 3,4-dihydroxyphenylacetic acid (DOPAC) were more abundant in participants with better social functioning scores.

The potential for producing this substance was the most strongly associated with Coprococcus group of bacteria. DOPAC is produced from dopamine, an important neurotransmitter, and researchers believe it can reduce the proliferation of colon cancer cells. Reduced DOPAC levels are a potential biomarker of Parkinson’s disease (Valles-Colomer et al., 2019).

Bacteria involved in the degradation of glutamate and production of GABA tended to be depleted in participants with depression

Additionally, bacteria involved in glutamate degradation tended to be depleted in participants with depression. Glutamate is an amino acid that plays a role in various metabolic and signaling pathways in the body. However, it is also the primary excitatory neurotransmitter in the central nervous system. This means that it increases the likelihood of neurons generating a nerve impulse.

Microorganisms involved in synthesizing gamma-aminobutyric acid (GABA) also tended to be depleted in participants with depression. GABA is an important inhibitory neurotransmitter. It makes neurons less likely to fire a nerve impulse (see Figure 6).

Figure 6. Link of microbial substance to the quality of life and depression

Conclusion

Overall, the analysis of two large sets of gut microbiome samples from two different (although neighboring) countries confirmed specific links between gut microbiota composition and mental health indicators. Individuals with better quality of life indicators tended to have more Faecalibacterium and Coprococcus bacteria in their gut. Those with better physical functioning tended to have fewer bacteria from the Flavonifractor species group. Bacteria from Coprococcus and Dialister groups of species tended to be much less present in the guts of individuals suffering from depression.

Bacteria capable of producing 3,4-dihydroxyphenylacetic acid or DOPAC were more abundant in participants with better social functioning scores. DOPAC is produced from dopamine, an important neurotransmitter in the human body, and it plays various important roles in the body’s functioning. Bacteria involved in the degradation of glutamate and the production of GABA, two important neurotransmitters, tended to be depleted in individuals with depression (see Figure 7).

Figure 7. Summary

While these findings are only correlational and do not allow for cause-and-effect conclusions, future research can be expected to map causal pathways responsible for the observed associations. This could open a new avenue of mental health treatments to achieve improved mental outcomes by affecting the gut or adjusting gut microbiota composition. It is also not hard to imagine scientists in the future using genetic techniques to create microorganisms that could influence mental health or mental states when placed in the gut.

The paper “The neuroactive potential of the human gut microbiota in quality of life and depression” was authored by Mireia Valles-Colomer, Gwen Falony, Youssef Darzi, Ettje F. Tigchelaar, Jun Wang , Raul Y. Tito, Carmen Schiweck, Alexander Kurilshikov , Marie Joossens, Cisca Wijmenga, Stephan Claes, Lukas Van Oudenhove, Alexandra Zhernakova, Sara Vieira-Silva , and Jeroen Raes.

To learn more about this topic,, CNP has developed two university-level continuing education courses exploring the evidence based interconnections in the microbiota-gut-brain axis diet-mental health relationship (MGBA-DMHR). See our course pages here.

References

Heiss, C. N., Mannerås-Holm, L., Lee, Y. S., Serrano-Lobo, J., Håkansson Gladh, A., Seeley, R. J., Drucker, D. J., Bäckhed, F., & Olofsson, L. E. (2021). The gut microbiota regulates hypothalamic inflammation and leptin sensitivity in Western diet-fed mice via a GLP-1R-dependent mechanism. Cell Reports, 35(8). https://doi.org/10.1016/j.celrep.2021.109163

Restrained Eating Leads to Hunger and Food Craving

Posted on August 15, 2023January 10, 2024 by CNP Super Admin

Study Overview

A study in Israel followed changes in food craving, restrained eating, hunger, and negative emotions within a representative sample of the population over a span of 10 days. The findings revealed that restrained eating is the central link between food-related sensations and negative emotions. The participants who engaged in restrained eating exhibited heightened food cravings and increased hunger at later times. Notably, stress emerged as a pivotal factor in the correlation between eating behaviors and negative emotional states, as outlined by Dicker-Oren and colleagues (2022). The study was published in Appetite.

Introduction

Food is one of the most fundamental needs of all living organisms. In the classical hierarchy of human needs proposed in the mid-20th century by Abraham Maslov, food is one of the primary needs that must be satisfied if other, higher-level needs are to be activated (Lester, 2013). This is why humans (and most other organisms) have evolved several psychological and behavioral mechanisms to ensure that securing enough food is given top priority.

Humans have evolved psychological and behavioral mechanisms to ensure that securing enough food is given top priority

These mechanisms induce changes to perception, attention, emotions, and various physiological parameters when we are hungry (Swami et al., 2022) (see Figure 1).

Figure 1. Psychological and behavioral mechanisms activated in hunger (Swami et al., 2022)

These mechanisms aim to focus us on food and prepare us for food intake, allowing us to survive and flourish for millennia of human existence. However, around the mid-20th century, a crucial change in the human dietary landscape happened – industrially processed foods became widely available and cheap. While the widespread availability of high-calorie and addictive processed foods has addressed food insecurity in many parts of the world, it has also given rise to a new challenge — obesity.

The obesity pandemic

Obesity is a medical condition characterized by excessive body fat accumulation, negatively affecting host health and well-being. The share of obese individuals in the population has been on the rise in recent decades in most world countries (Wong et al., 2022), reaching levels where many are talking about a global obesity pandemic.

While it is fairly obvious that excessive intake of food is a necessary component in the development of obesity, this excessive intake of food is caused by a combination of various biological, psychological, and physiological factors (Ulrich-Lai et al., 2015). Among these, emotions and food-related sensations are thought to be important.

While it is fairly obvious that excessive intake of food is a necessary component in the development of obesity, this excessive intake of food is caused by a combination of various biological, psychological, and physiological factors

Food craving and hunger

Food craving is a strong desire to consume specific foods, such as chocolate or potato chips (van Kleef et al., 2013). It differs from hunger by its specificity and intensity. People can crave food even when they are not hungry (Reichenberger et al., 2018). On the other hand, the sensation of hunger makes one motivated to eat, but not necessarily a specific type of food.

Food craving is a strong desire to consume specific foods or food types. It differs from hunger by its specificity and intensity.

Food craving may be a response to physiological deficiencies or disturbances in the body, but they may also be initiated through psychological mechanisms without deficiencies of nutrients (Weingarten & Elston, 1990). Food cravings are considered to be one of the main factors of overeating.

Food craving may be a response to physiological deficiencies or disturbances in the body, but they may also be initiated through psychological mechanisms without deficiencies in nutrients

When individuals notice they are gaining excessive weight or if they are worried they may be eating too much, many will try to limit their food intake consciously. Rather than rely on the sensation of satiety to signal that they have eaten enough, or if the over-consumption of hyperpalatable foods distorts these cues, these individuals may consciously decide to limit how much food they will eat. The amount will often be such that the person still feels hungry after eating it.

This type of behavior is referred to in science as restrained eating. People who engage in restrained eating often set rules or restrictions on what and how much they can eat, and they may closely monitor their calorie intake. This is usually done in an effort to achieve a specific body image or weight goal. However, restrained eating can sometimes lead to disordered eating patterns and psychological distress (Dicker-Oren et al., 2022).

The current study

Study author S.D. Dicker-Oren and colleagues aimed to examine the dynamic associations between food craving, restrained eating, hunger, and negative emotions. They wanted to know how these factors vary in the same individual over time. These researchers were particularly interested in determining whether negative emotions associated with food craving, restrained eating, and hunger differed (see Figure 2).

Figure 2. The study objective examined associations between food craving, restrained eating, hunger, and negative emotions.

They applied an analytic technique called network analysis to map the network of links between these factors and identify those that are central that can best predict others. The study authors also wanted to know which factors were most strongly associated with food cravings simultaneously and in subsequent assessments. They conducted a study using ecological momentary assessment.

Ecological momentary assessment

Ecological Momentary Assessment (EMA) is a research method to gather real-time data about individuals’ behaviors, thoughts, and emotions in their natural environments. It involves repeatedly sampling participants’ experiences over time, often through mobile devices like smartphones, to capture their moment-to-moment fluctuations and responses to various stimuli. Ecological momentary assessment provides valuable insights into how individuals’ psychological states and behaviors vary daily.

In this study, researchers wanted to observe whether food craving, hunger, restrained eating, and related emotions change within the same person. This procedure allowed them to observe, for example, what other sensations or emotions appear when a person experiences a craving for a certain type of food or whether participants tend to experience some specific emotions at the same time when they experience hunger or when they intentionally try to reduce their food intake, even though they still feel hungry.

Researchers wanted to observe whether food craving, hunger, restrained eating, and related emotions change within the same person

The procedure

Participants were 123 individuals from the general population of Israel. The study authors recruited them using social media posts and advertisements. To be included in the study, participants needed to be at least 18 years of age, with no severe psychiatric illness, not be underweight, have no eating disorders, and not have undergone obesity-related surgery. Female participants could not be pregnant or breastfeeding at the time of the study.

In the scope of the ecological momentary assessment procedure, participants answered questionnaires in the form of online surveys on the Qualtrics platform three times per day – in the morning, afternoon, and evening, over the course of 10 days. For each survey, the data collection software sent them a personal email message with the link to the survey and a WhatsApp notification on their smartphone. If a respondent missed four surveys, researchers would call him/her by phone to encourage him/her to continue participating in the study.

Participants answered questionnaires in the form of online surveys three times per day – in the morning, afternoon, and evening, over the course of 10 days

The surveys

At the start of the study, participants completed the baseline questionnaire. This questionnaire contained questions about various demographic characteristics of participants and about their weight and height. The study authors combined weight and height data to calculate body mass index values for each participant.

The questionnaires participants completed in the scope of the ecological momentary assessment asked them to indicate the extent to which they experienced each of the sensations and emotions from a list “since you woke up,” in the morning version of the questionnaire, or “over the last six hours” in the afternoon and evening versions. The questions were about experiencing seven different negative emotions – guilt, sadness, fear/ being afraid, stress, loneliness, boredom, and anger. It also asked about experiences of nervousness and anxiety and about restrained eating (“Did you deliberately try to limit the amount of food you eat?”) (see Figure 3). Additionally, participants completed assessments of food craving and hunger (“intense desire to eat” and “hunger” subscales from the Food Cravings Questionnaire-state, FCQ-S) in the scope of these surveys.

Figure 3. Sensations and emotions and restrained eating, assessments of food craving and hunger

Observed factors showed a degree of stability over time

Most of the factors measured tended to show certain stability over time. In later surveys, people who reported higher levels of negative emotions, hunger, craving, or restrained eating at one time point were also more likely to report these emotions and sensations.

Participants who reported consciously limiting their food intake were more likely to be hungry and crave food later

Individuals who reported restrained eating and hunger earlier were more likely to report food craving at the subsequent time. Individuals who reported restrained eating at one time were likelier to report being hungry later. Participants who reported sadness were likely to report loneliness and anger later. They were also more likely to report feeling stressed, afraid, and angry at later time points. When participants reported feeling stressed at one time, they were more likely to report craving food the next time.

When participants reported feeling stressed at one time, they were more likely to report craving food the next time

Certain participants experienced a frequent interplay of restrained eating, hunger, and food craving

Across all time points/surveys, participants who more often reported food craving were also more likely to report experiencing hunger and practicing restrained eating. Stress was the central emotion – participants who more often reported feeling stressed were more likely to report experiencing all other negative emotions as well. However, researchers found no association between mean levels of hunger, food craving, or restrained eating and average levels of negative emotions.

Individuals practiced restrained eating less when they were angry. They tended to crave food more when they were feeling bored.

Conclusion

The study revealed one piece of insight into the complex interplay between food-related sensations, negative emotions, and restrained eating. Restrained eating seemed to drive hunger and food craving. Individuals who exerted effort to consciously limit their food intake also more often experienced hunger and food craving. These two experiences often followed restrained eating. Hunger appeared to trigger food craving, but food craving did not seem to predict any overeating-related variables at later times (see Figure 4).

Figure 4. Study findings

The study results have important implications for beginning to understand some of the psychological dynamics of eating disorders and for planning interventions. The finding that restrained eating, which in itself represents an attempt by an individual to prevent excessive food intake (and perhaps the upregulation in brain and gut reward-based systems in the body), can, in this instance, actually have a reverse effect in the long run (by increased hunger and food craving) indicating that interventions aiming to reduce restrained eating might paradoxically have a positive effect on regulating food intake. The finding that stress can drive food craving indicates that it might also be useful for eating disorder interventions to address stress.

The paper “The dynamic network associations of food craving, restrained eating, hunger and negative emotions” was authored by S.D. Dicker-Oren, M. Gelkopf, and T. Greene.

More about dietary intake behavior, hedonic eating, and reward and gut-based mechanisms can be found in NP 110: Introduction to Nutritional Psychology Methods, and 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

Adams, R. C., Chambers, C. D., & Lawrence, N. S. (2019). Do restrained eaters show increased BMI, food craving and disinhibited eating? A comparison of the restraint scale and the restrained eating scale of the Dutch Eating Behaviour Questionnaire. Royal Society open science, 6(6), 190174. https://doi.org/10.1098/rsos.190174

Cortés-García, L., Rodríguez-Cano, R., & von Soest, T. (2022). Prospective associations between loneliness and disordered eating from early adolescence to adulthood. The International journal of eating disorders, 55(12), 1678–1689. https://doi.org/10.1002/eat.23793

de Macedo, I. C., de Freitas, J. S., & da Silva Torres, I. L. (2016). The influence of palatable diets in reward system activation: A mini-review. Advances in pharmacological sciences, 2016, 7238679. https://doi.org/10.1155/2016/7238679

Demeke, S., Rohde, K., Chollet-Hinton, L., Sutton, C., Kong, K. L., & Fazzino, T. L. (2023). Change in hyper-palatable food availability in the US food system over 30 years: 1988-2018. Public health nutrition, 26(1), 182–189. https://doi.org/10.1017/S1368980022001227

Dicker-Oren, S. D., Gelkopf, M., & Greene, T. (2022). The dynamic network associations of food craving, restrained eating, hunger and negative emotions. Appetite, 175(March), 106019. https://doi.org/10.1016/j.appet.2022.106019

Gupta, A. R., Osadchiy, V., & Mayer, E. A. (2020). Brain–gut–microbiome interactions in obesity and food addiction. Nature Reviews Gastroenterology & Hepatology, 17(11), 655–672. https://doi.org/10.1038/s41575-020-0341-5

Hazzard, V. M., Loth, K. A., Crosby, R. D., Wonderlich, S. A., Engel, S. G., Larson, N., & Neumark-Sztainer, D. (2023). Relative food abundance predicts greater binge-eating symptoms in subsequent hours among young adults experiencing food insecurity: Support for the “feast-or-famine” cycle hypothesis from an ecological momentary assessment study. Appetite, 180, 106316. https://doi.org/10.1016/j.appet.2022.106316

Lester, D. (2013). Measuring Maslow’s hierarchy of needs. Psychological Reports, 113(1), 1027–1029. https://doi.org/10.2466/02.20.PR0.113x16z1

Lutter, M., & Nestler, E. J. (2009). Homeostatic and hedonic signals interact in the regulation of food intake. The Journal of nutrition, 139(3), 629–632. https://doi.org/10.3945/jn.108.097618

Reichenberger, J., Richard, A., Smyth, J. M., Fischer, D., Pollatos, O., & Blechert, J. (2018). It’s craving time: Time of day effects on momentary hunger and food craving in daily life. Nutrition, 55-56, 15-20. https://doi.org/10.1016/j.nut.2018.03.048

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 Clinical and Translational Report 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

Ulrich-Lai, Y. M., Fulton, S., Wilson, M., Petrovich, G., & Rinaman, L. (2015). Stress exposure, food intake and emotional state. Stress, 18(4), 381–399.

van Kleef, E., Shimizu, M., & Wansink, B. (2013). Just a bite: Considerably smaller snack portions satisfy delayed hunger and craving. Food Quality and Preference, 27(1), 96-100. https://doi.org/10.1016/j.foodqual.2012.06.008

Weingarten, H. P., & Elston, D. (1990). The phenomenology of food cravings. Appetite, 15(3), 231–246. https://doi.org/10.1016/0195-6663(90)90023-2

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

PsychoNutritional Education Within Nutritional Psychology: A Study Linking Sugar Consumption with Adverse Health Outcomes

Posted on June 6, 2023November 10, 2023 by CNP Super Admin

Within nutritional psychology, we explore the dietary and nutritional elements influencing the diet-mental health relationship. This involves identifying the processes and mechanisms through which our diet and dietary intake patterns influence our brain, behavior, and experience. Sugar intake is a substantial and increasing part of the human diet. Understanding its influence on our diet-mental health relationship is important within nutritional psychology. Let’s look at a recent study published in the BMJ that explores the sugar-health connection.

This umbrella review by Huang and colleagues (2023) reviewed 73 meta-analytic articles exploring the connection between sugar consumption and adverse health outcomes. In this study, findings reported that sugar consumption is associated with many adverse health outcomes. Specifically, this study identified associations between sugar consumption and eighteen harmful metabolic issues, ten harmful cardiovascular conditions, seven types of cancer, and ten other types of adverse medical conditions. The best quality evidence was for links between sugar consumption and increased body weight (Huang et al., 2023).

Studies have shown an association between sugar consumption and certain metabolic and cardiovascular issues, cancer, obesity, and other adverse medical conditions.

Sugars and carbohydrates

Carbohydrates are a type of molecule that provides the body with energy. Alongside protein and fat, carbohydrates are considered macronutrients, nutrients the human body requires in large quantities. The three main types of carbohydrates are sugars, starches, and fibers (Slavin, 2013) (see Figure 1).

Figure 1. Three types of carbohydrates

Fibers and starches are called complex carbohydrates, made of many simple carbohydrates strung together to form large molecules. They need to be broken down by the body into simple carbohydrates to be used. The human body can break down and digest starches, such as those found in bread, cereals, or pasta, but it cannot digest most fibers. However, eating foods containing fiber can help an individual feel full (satiated) and be less likely to overeat (Cho et al., 2010; Samra & Anderson, 2007).

Eating foods containing fiber can help one feel satiated and be less likely to overeat.

Sugars

Sugars are called simple carbohydrates because their molecules are the smallest and the simplest. The three most common sugars are glucose (the primary source of energy for the cells of our body), fructose (the naturally occurring sugar found in fruits, honey, and vegetables), and sucrose (the substance sold in supermarkets as “sugar,” a combination of glucose and fructose, also naturally occurring).

Sugars are naturally found in various fruits, vegetables, and animal products (e.g., honey, milk, meat). However, sugars are also very often added to food and drinks, either by the cook or the manufacturer, during food production and preparation, or directly by the consumer, before consumption. This is done to increase the sweetness of food, thus making it tastier. Researchers refer to sugars added to food and beverages in this way as “added sugars” (Huang et al., 2023).

Added sugars

Generally, sugar-sweetened beverages are the largest source of added sugars. These include soft drinks (both carbonated and non-carbonated), fruit drinks, and sports and energy drinks. Milkshakes, smoothies, and similar sweetened drinks also contain added sugar.

Foods with lots of added sugar include various desserts, candies, cakes, sweet snacks, ice cream, granola and energy bars, milk chocolate, candied dried fruits, canned fruits, sweetened flavored yogurts, many condiments (such as ketchup), most cereals, cocoa spreads, creamers, jams, and many others (see Figure 2).

Figure 2. Foods containing lots of added sugars

What are the health consequences of high sugar consumption?

Although the Dietary Guidelines for Americans by the United States Department of Agriculture recommend that between 45% and 65% of calories in our diet should come from carbohydrates, they also suggest that the intake of added sugars should be very limited or altogether avoided. Sources such as the Harvard T.H. Chan School of Public Health website specify that the body needs no carbohydrates from added sugar. Despite this, foods and drinks with added sugars represent a large share of people’s dietary intake worldwide, with consumption increasing in many developing countries. This is particularly true with sugar-sweetened beverages such as soft drinks (Huang et al., 2023).

The body does not need carbohydrates from added sugar. Yet, foods and drinks with added sugars represent a large share of the dietary intake of people worldwide.

Numerous studies have linked a high intake of sugars with an increased risk of various adverse health outcomes. These include obesity, diabetes, cardiovascular disease, dental caries, and abnormal fat accumulation in tissues where fat is not normally stored, such as the liver, muscles, pancreas, heart, and others.

The current study

Study author Yin Huang from the Sichuan University in Chengdu, China, and his colleagues wanted to evaluate the quality of evidence, potential biases, and validity of all available studies on dietary sugar consumption and health outcomes.

The number of scientific studies exploring links between the consumption of sugars and health runs in hundreds of thousands worldwide, and new ones are conducted and published daily. These researchers conducted an umbrella review.

The number of scientific studies exploring links between the consumption of sugars and health runs in hundreds of thousands worldwide.

What is an umbrella review?

When a certain topic attracts a lot of research interest, many studies are conducted to explore that topic further. Studies are conducted by different researchers. They use more or less different methodologies and can also conceptualize key factors they explore in ways that are not completely identical.

Scientists need ways to integrate the multitudes of findings produced in this way. To do this, they can conduct systematic reviews, evaluating the findings of different studies and discussing similarities and differences between their methodologies and findings. When there are many studies (e.g., more than a dozen) that explore the same link between certain factors or the same aspect of some phenomenon, a meta-analysis can be conducted to map the strength of the links observed and make quantitative inferences about tendencies in the findings of these studies.

Finally, when the number of studies on a topic is really large, and their study methodologies and factors are diverse (i.e., there are many systematic reviews and meta-analyses), the type of study that can integrate their results is called the umbrella review. An umbrella review is a type of systematic review that combines evidence from a multitude of systematic reviews and meta-analyses on a certain topic (see Figure 3).

It starts with a rigorous process of searching and selecting relevant systematic reviews and meta-analyses to include in the study. Assessing the quality of the included studies and integrating the findings of different reviews follows. Its very name indicates a study incorporating the results of many integrating studies (see Figure 3).

Figure 3. Set-up of an umbrella review

The procedure

The authors searched publicly available databases of research publications for systematic reviews and meta-analyses that studied the health outcomes of sugar consumption. They considered studies of sugar consumption that measured the specific proportion of sugars in foods, the percentage of total energy derived from sugars, or combinations of these two. They searched PubMed, Embase, Web of Science, and the Cochrane Database of Systematic Reviews from inception through January 2022. They looked for papers written in English.

They used “sugars” or “sugar” and “systematic review” or “meta-analysis” as keywords for their searches. Two of the authors conducted the searches separately. A third author resolved any discrepancies between their literature screening results.

Search results

The search yielded 8601 research papers—2184 of those turned out to be duplicates and were excluded. Of the remaining 6417 papers, 6221 were excluded from further analysis for various reasons. This left 196 papers with eligible titles and abstracts. Of these, researchers excluded a further 124 because data on sugar could not be extracted from them, because they were reviewed without meta-analyses, or due to various methodological issues. 4 turned out not to be in English and were thus excluded.

In the end, 73 scientific articles contained results of meta-analyses and met all the inclusion criteria set by the researchers. These papers were included in the umbrella analysis.

The quality of evidence

An important aspect these researchers considered when analyzing was the quality of evidence presented for the claims made in the analyzed papers. Not all scientific findings are the same. Some findings are obtained on large samples that are likely to be representative of studied populations. They describe strong and clear effects of one studied factor on another or strong associations between phenomena. Such studies are considered to provide strong evidence. When the physical mechanism creating the claimed effects is known, or at least when researchers have a plausible idea of what it might be, the evidence is even stronger.

However, studies are sometimes done on small samples that are or might be quite different from the population. They might report associations between studied phenomena without any further knowledge or idea about the physical mechanism creating the reported link. And the links reported might be very weak, sometimes so weak that one might suspect they are simply due to chance. The evidence quality provided by such studies is considered to be much worse.

In the case of this study, researchers classified meta-analytic studies they analyzed into five categories based on the strength of the evidence. The highest evidence strength was assigned to studies conducted on very large samples (over 1000 cases or over 20,000 participants, depending on the features of the studied phenomena) and reported strong effects or associations similar across different studies covered by the meta-analysis analyzed. On the other hand, studies that reported results that were near zero or were very weak were considered to provide weak evidence for the associations reported. Sample sizes were taken into account because it is much, much easier to obtain small samples that are unusual and thus provide unusual results than large samples (Hedrih & Hedrih, 2022).

They assigned these evidence classes numbers ranging from 1 (best quality evidence) to 4 (weakest evidence) and N.S. (no association at all, N.S. stands for non-significance).

Figure 4. Quality of evidence

High sugar consumption is associated with four beneficial and 45 harmful health outcomes.

Results showed that most of the meta-analyses included in the study studied associations between the consumption of sugar and endocrine/metabolic diseases (such as diabetes, 28 such studies), cancer (25 studies), and cardiovascular diseases (17) (see Figure 5).

Figure 5. The contents of this umbrella review

These studies reported four beneficial associations between high sugar consumption and medical outcomes and 45 harmful ones. Thirty-four studies reported no associations between sugar consumption and the health outcomes they examined.

Children who consume more sweetened beverages have a 55% higher chance of being obese.

Higher consumption of sugar-sweetened beverages was associated with higher body weight and a higher risk of gout (class 4 evidence). Gout is arthritis characterized by a buildup of uric acid crystals in the joints that cause inflammation and pain.

Children who consumed high quantities of sugar-sweetened beverages had a 55% higher likelihood of being obese compared to children who did not consume such beverages (class 2 evidence). The more frequently children consumed such beverages, the higher their body mass was.

Children who consumed high quantities of sugar-sweetened beverages had a 55% higher likelihood of being obese than children who did not.

Sugar consumption, coronary heart disease, and cancer

One study linked a higher intake of sugar-sweetened beverages with the risk of coronary heart disease. They reported that every additional glass of sugar-sweetened beverages (250 mL) per day was associated with a 17% increase in the risk of coronary heart disease (class 2 evidence). Another study linked higher consumption of these beverages with an increased likelihood of myocardial infarction (class 3 evidence).

Every additional glass of sugar-sweetened beverages (250 mL) per day was associated with a 17% increase in the risk of coronary heart disease.

One meta-analysis reported that participants who consumed sugar-sweetened beverages the most had a two times higher risk of developing hepatocellular carcinoma (a type of liver cancer, class 4 evidence). Another meta-analysis linked higher fructose intake with a higher risk of pancreatic cancer (class 3 evidence).

Other health outcomes

A recent meta-analysis reported that every additional glass (250 mL) of sugar-sweetened beverages per day was associated with a 4% increase in death from all causes (class 3 evidence). Another study reported a link between higher consumption of these drinks and depression (class 2 evidence).

Researchers also reported several other health outcomes linked to sugar consumption, but the evidence for those associations was very weak (class 4). These included links between increased consumption of sugar-sweetened beverages and hypertension, death from cardiovascular disease, prostate cancer, death from breast cancer, overall risk of death from cancer, risk of asthma, fatty liver disease, bone mineral density, and others.

Health outcomes not associated with sugar include the risk of colorectal cancer, ovarian cancer, and death from cardiovascular disease. Fructose intake was not associated with type 2 diabetes, risk of hypertension, and uric acid concentrations in the serum. Consumption of sugar of sugar-sweetened beverages was not found to be associated with the risk of chronic kidney disease. Do remember that all the studies conducted are meta-analyses.

Conclusion

The analysis showed that high dietary sugar intake is associated with various harmful health outcomes. Although evidence was strongest for the effects of high intake of sugars on increased body weight, particularly in children, the evidence for other adverse health effects is also considerable. These findings come from a series of meta-analyses, each of which integrated findings of multitudes of individual studies conducted in different places and on different people worldwide, giving extra weight to the reported results.

The insights from these studies can help better educate the general population about health outcomes related to nutritional choices, particularly consuming food and beverages with extreme quantities of added sugars. They can also be useful to manufacturers of food and beverages as a guide to make future food products healthier.

The paper “Dietary sugar consumption and health: umbrella review” was authored by Yin Huang, Zeyu Chen, Bo Chen, Jinze Li, Xiang Yuan, Jin Li, Wen Wang, Tingting Dai, Hongying Chen, Yan Wang, Ruyi Wang, Puze Wang, Jianbing Guo, Qiang Dong, Chengfei Liu, Qiang Wei, Dehong Cao, and Liangren Liu.

Figure 6. Study findings of the umbrella review

References

Cho, S., Case, I., & Nishi, S. (2010). Fiber and Satiety. In Weight Control and Slimming Ingredients in Food Technology (pp. 227–276). Blackwell Publishing Ltd.

Hedrih, V., & Hedrih, A. (2022). Interpreting statistics for beginners : a guide for behavioural and social scientists. Routledge, Taylor&Francis Group. https://doi.org/10.4324/9781003107712

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

Samra, R. A., & Anderson, G. H. (2007). Insoluble cereal fiber reduces appetite and short-term food intake and glycemic response to food consumed 75 min later by healthy men. The American Journal of Clinical Nutrition, 86(4), 972–979. https://doi.org/10.1093/AJCN/86.4.972

Slavin, J. L. (2013). Carbohydrates, dietary fiber, and resistant starch in white vegetables: Links to health outcomes. Advances in Nutrition, 4(3), 3515–3555. https://doi.org/10.3945/AN.112.003491

The Western Diet and How it Weakens the Interoceptive Signals that Control our Appetitive Behavior

Posted on May 9, 2022November 9, 2023 by CNP Super Admin

In terms of the global dietary landscape, the Western diet provides a rich diversity and abundance of highly processed foods and beverages that are easy to consume. Although highly palatable, this availability also provides us with a steady stream of external cues that can heighten feeding behaviors. Obesogenic environments (those prevalent with cues that promote excessive weight gain) are pervasive across Western societies and are hypothesized to overwhelm the internal cues that normally regulate our dietary intake (Sample et al., 2016).

Obesogenic environments (those prevalent with cues that promote excessive weight gain) are pervasive across Western societies and are hypothesized to overwhelm the internal cues that normally regulate our dietary intake.

These internal cues, which are perceived by the brain through neural circuits in a process known as interoception, are critical in maintaining proper appetites and, ultimately, a healthy physical and mental body. Interoception refers to the process of identifying and listening to internal bodily signals, which may be a modifiable determinant of appetite regulation and weight gain (Robinson et al., 2018). Research is exploring the mechanisms by which interoceptive sensations mediate our nutritional health.

Interoception: our body’s communication network to eating regulation

Interoception is considered one of the six foundational elements within nutritional psychology and is defined as “our internal awareness of the body’s state.” Encompassing conscious and subconscious physiological signals, we sense a variety of internal cues that inform our brain about our heartbeat, breathing, and hunger. A growling stomach, a desire for certain foods, fatigue from a lack of food, and even emotions associated with eating are all examples of internal sensations that can further drive feeding and appetitive behaviors.

The study of interoception is increasing in the field of neuroscience, and there is compelling evidence demonstrating the links between awareness of one’s internal state (called interoceptive awareness) and the regulation of emotion (Price & Hooven, 2018).

Considering the diet-mental health relationship (DMHR), the regulation of food intake—and, perhaps, bodyweight—partially depends on the brain’s capability to respond to internal physiological signals arising from external food-related cues in our environment (Sample et al., 2016). In other words, the ability of environmental food cues to evoke appetitive and dietary intake behavior is held in check by interoceptive satiety signals that inhibit those behaviors (Sample et al., 2016).

There is compelling evidence demonstrating the links between awareness of one’s internal state (called interoceptive awareness) and the regulation of emotion.

Western diets impair the interoceptive signals that control our eating.

In this 2016 study, Sample and her colleagues conducted experiments to investigate how food-related external cues may overwhelm interoceptive internal cues that normally regulate dietary intake. They hypothesized that food-related external cues could promote overeating by attenuating the brain’s hippocampal function, which processes interoceptive satiety signals.

The authors examined the role of a Western diet (WD) (consisting of highly processed, low nutrient foods) and its obesogenic effect on the internal sensations that mediate our appetite. Rodents were trained to use internal cues (i.e., feelings of hunger) and external cues (i.e., food) and then assigned to two distinct groups: a regular chow or WD chow. Both groups then removed external cues to assess the different diets’ direct impact on interoceptive signals that mediate feeding. Interestingly, the study found that the WD group failed to distinguish between internal and external cues (Figure 1).

Figure 1. Western diets (WD) impact the internal interoceptive signals used by the hippocampus to regulate appetite and dietary intake. The positive sign indicates the addition of either regular or WD food and the negative sign indicates the removal of food-related external cues.

The inability to parse between interoceptive deprivation (i.e. “I am hungry”) and external signals (i.e. “the food looks delicious”) is a function that has been characterized by the hippocampus, with previous studies demonstrating how the WD could interfere with homeostatic feeding behaviors (Jacka, 2015; Attuquayefio, 2016; Stevenson, 2020).

Indeed, the authors noticed that inhibiting the hippocampus (through surgical injections of a neurotoxin) in a cohort of rodents led to increased weight gain and reduced sensitivity to satiety hormones like cholecystokinin, which normally tells the body you are full and slows digestion (Figure 1). Ultimately, the study suggests that the WD impairs the interoceptive energy state signals that the hippocampus relies on to regulate feeding behavior.

Current research further validates this connection. In a recent study by Robinson, Marty, Higgs, and Jones (2022) exploring the connection between interoception, eating behavior, and body weight in over a thousand human adults, these researchers found that poorer self-reported ability to detect interoceptive signals (deficits in interoceptive accuracy) was, in fact, predictive of higher BMI.

The findings are certainly food for thought as we navigate the choices of food available to us in our day-to-day lives—especially for those living in regions characterized by a high intake of the Western diet. Knowing the options and alternatives of nutritious foods is essential to maintaining proper feeding behaviors that will keep one healthy.

References

Attuquayefio, T., Stevenson, R. J., Boakes, R. A., Oaten, M. J., Yeomans, M. R., Mahmut, M., & Francis, H. M. (2016). A high-fat high-sugar diet predicts poorer hippocampal-related memory and a reduced ability to suppress wanting under satiety. Journal of experimental psychology. Animal learning and cognition, 42(4), 415–428. https://doi.org/10.1037/xan0000118

Jacka, F. N., Cherbuin, N., Anstey, K. J., Sachdev, P., & Butterworth, P. (2015). Western diet is associated with a smaller hippocampus: a longitudinal investigation. BMC medicine, 13, 215. https://doi.org/10.1186/s12916-015-0461-x

Price, C. J., & Hooven, C. (2018). Interoceptive Awareness Skills for Emotion Regulation: Theory and Approach of Mindful Awareness in Body-Oriented Therapy (MABT). Frontiers in psychology, 9, 798. https://doi.org/10.3389/fpsyg.2018.00798

Robinson, E., Marty, L., Higgs, S., & Jones, A. (2021). Interoception, eating behaviour and body weight. Physiology & behavior, 237, 113434. https://doi.org/10.1016/j.physbeh.2021.113434

Sample, C. H., Jones, S., Hargrave, S. L., Jarrard, L. E., & Davidson, T. L. (2016). Western diet and the weakening of the interoceptive stimulus control of appetitive behavior. Behavioural brain research, 312, 219–230. https://doi.org/10.1016/j.bbr.2016.06.020

Stevenson, R. J., Francis, H. M., Attuquayefio, T., Gupta, D., Yeomans, M. R., Oaten, M. J., & Davidson, T. (2020). Hippocampal-dependent appetitive control is impaired by experimental exposure to a Western-style diet. Royal Society open science, 7(2), 191338. https://doi.org/10.1098/rsos.191338

The “Diet-Sleep” Relationship: Is there A Connection?

Posted on May 13, 2022November 9, 2023 by CNP Super Admin

With growing recognition of the link between mental health and sleep, many of us want to know if making lifestyle changes, including what we eat, can impact our sleep habits and potentially improve our overall psychological well-being.

Recent research by Rostami et al. (2022) examines the link between sleep quality and sleep-related outcomes and a newly proposed hybrid diet, referred to as the Mediterranean-DASH Diet Intervention for Neurodegenerative Delay, or MIND diet. The MIND diet combines the Mediterranean and Dietary Approaches to Stop Hypertension (DASH) diets, which have both been previously studied for their impact on various aspects of psychological health (Bayes et al., 2022; Salari-Moghaddam et al., 2019) respectively).

Rostami and colleagues set out to explore the relationship between the MIND diet and psychological function, including depression, anxiety/stress, and sleep.

The MIND diet includes 10 “brain-healthy food groups” (green leafy vegetables, other vegetables, nuts, berries, beans, whole grains, fish, poultry, olive oil, and wine) and identifies 5 “brain-unhealthy food groups” (red meats and processed red meat products, butter and stick margarine, cheese, pastries, fast fried foods, and sweets).

In this study, Rostami and colleagues set out to explore the relationship between the MIND diet and psychological function, including depression, anxiety/stress, and sleep.

This is the first study to explore the relationship between adherence to the MIND diet and sleep.

400 Iranian adult males with a mean age of 38.67 years working in healthcare centers were randomly selected to participate. They had no history of chronic disease. Using a food frequency questionnaire (FFQ) consisting of 168 foods and their standard serving sizes, participants reported how often they consumed each food. The research team then calculated an overall MIND diet score of 0-14 for participants based on intake of both the “brain-healthy food groups” of the MIND diet and the specified “brain-unhealthy food groups” (Note: wine was not included in the calculated MIND diet score since it was not on the FFQ). A higher MIND diet score indicated greater adherence to the MIND diet.

Additional information obtained from participants consisted of demographics (i.e., age, smoking status, marital status, education), height and weight measurements for calculation of Body Mass Index (BMI), and frequency of physical activity. The researchers used the Depression Anxiety and Stress Scale (DASS-21) to look for depression, anxiety, and stress and included questionnaires to examine sleep quality (Pittsburgh Sleep Quality Index), daytime sleepiness (Epworth Sleepiness Scale), and insomnia (Insomnia Severity Index).

Greater adherence to the MIND diet was linked to better sleep quality and fewer reports of daytime sleepiness and insomnia.

According to Rostami et al., this is the first study to explore the relationship between adherence to the MIND diet and sleep. They found that greater adherence to the MIND diet was linked to better sleep quality and fewer reports of daytime sleepiness and insomnia. The authors suggest that the anti-inflammatory and antioxidant properties of the MIND diet likely contribute to the observed positive impact on sleep.

The anti-inflammatory and antioxidant properties of the MIND diet likely contribute to the observed positive impact on sleep.

No significant effect between MIND-diet adherence and depression, anxiety, or stress was observed in this study.

Limitations of these findings include lack of generalizability of results to other populations and possible misreporting from participants on self-report measures. The nature of the study design (cross-sectional) prevents findings related to causality. The authors recommend further investigation to address these factors and verify their results.

References:

Bayes, J., Schloss, J., & Sibbritt, D. (2022). The effect of a Mediterranean diet on the symptoms of depression in young males (the “AMMEND” study): A Randomized Control Trial. The American journal of clinical nutrition, nqac106. Advance online publication. https://doi.org/10.1093/ajcn/nqac106

Rostami, H., Parastouei, K., Samadi, M., Taghdir, M., & Eskandari, E. (2022). Adherence to the MIND dietary pattern and sleep quality, sleep related outcomes and mental health in male adults: A cross-sectional study. BMC Psychiatry, 22(167). https://doi.org/10.1186/s12888-022-03816-3

Salari-Moghaddam, A., Keshteli, A. H., Mousavi, S. M., Afshar, H., Esmaillzadeh, A., & Adibi,(2019). Adherence to the MIND diet and prevalence of psychological disorders in adults. Journal of affective disorders, 256, 96–102. https://doi.org/10.1016/j.jad.2019.05.056

Are You What Your Gut-Microbiome Wants You To Eat?

Posted on May 3, 2022November 9, 2023 by CNP Super Admin

We’ve all heard the saying “you are what you eat,” but new microbiome research is shedding light on this old adage, with a more modern-day update being “you are what your gut-microbiome wants you to eat.” Let’s look at why this is the case.

First, we know that our food choices significantly impact our physical and mental health. As far back as the 1800s and 1900s, scientists hypothesized an apparent correlation between our food intake and the subsequent effects on appetite, body image, and brain function (Tzameli, 2013). Though biomedical research has already established the endocrine responses that regulate hunger and satiety in the gut-brain axis signaling, little attention has been paid to the mechanisms that influence an individual’s choice of food and nutrition.

Microorganisms that live in our gut may influence what we eat!

A growing body of evidence indicates that our gut microbiome may be one of the factors influencing our food choices. From Nutritional Psychology conceptualization, we are beginning to understand that eating behavior and food preferences are dependent on many aspects of the diet-mental health relationship (DMHR), such as our psychosocial environment, interoceptive experiences, sensory perception, cognitive processes, and psychological state. However, emerging research in the Microbiota-Gut Brain Axis (MGBA) suggests that the microorganisms residing within our gut may also influence what we eat. Therefore, the classic expression, “you are what you eat,” may soon be reframed as “you are [also] what your microbiome wants you to eat.”

A feedback loop between our gut microbiome, brain, and food choices.

To explore the influence of the gut microbiome on diet selection behavior, Trevelline and Kohl conducted an experiment in 2022 to study the influence of gut microbes on the diet selection behaviors in mice.

The classic expression, “you are what you eat,” may soon be reframed as “you are [also] what your microbiome wants you to eat.”

To achieve this, intestinal microbiota from three “donor” mouse species, each with distinct foraging behavior, were transplanted into germ-free “host” mice to colonize their intestinal tracts.

Following that, the donor germ-free mice were randomly divided into three treatment groups, each based on the donor species:

Carnivore (i.e., predatory-based)
Herbivore (i.e., plant-based)
Omnivore (i.e., inclusive-based)

The mice were then given a choice between a low protein-carbohydrate (LPC) diet and a high protein-carbohydrate (HPC) diet, and their diet preferences were tracked for 11 days. To assess the impact of the donor microbiome on host diet selection behavior, the researchers compared the microbiomes of mice in three treatment groups: predatory (carnivores), inclusive (omnivores), and plant-based (herbivores) (Fig 1A).

Figure 1A. Experimental design to assess host diet selection behaviors across different microbiomes. From Trevelline and Kohl, Proceedings of the National Academy of Sciences, 2022.

Strikingly, the authors discovered that when mice have given a choice of selected diets varying in macronutrient composition, each microbiome had a distinct effect on food choice behavior (Fig 1B). For example, host mice that received microbiota from herbivorous donors voluntarily ate fewer carbohydrates, evidenced by a higher protein:carbohydrate (P:C) ratio diet intake. On the other hand, omnivore and carnivore treatment groups chose a lower P:C ratio diet intake.

Given that these host mice had no microbiome prior to transplantation, the change in diet selection behavior is evidence of the microbiome influencing food choice (Alcock, 2014). Moreover, through an in-depth analysis of blood and fecal samples, the authors discovered the microbial release of essential amino acids (EAAs) from the gut microbiome of host mice, including tryptophan. Tryptophan is an important food choice driver because it is a precursor to serotonin, the happiness hormone that has been shown to regulate feeding behavior, metabolism, and diet selection (Harrold, 2012; Cryan, 2019; Kaur & Bose, 2019; Yabut, 2019; Gao, 2020; Trevelline & Kohl, 2022). Together, these findings show that the gut microbiome can influence host diet selection behavior by mediating the availability of EAAs.

The gut microbiome can influence host diet selection behavior by mediating the availability of Essential Amino Acids (EAAs).

Finally, the findings discussed here are of great interest to Nutritional Psychology. Together with other studies, they show us that what we eat can be influenced by our microbiota’s ‘bottom up’ connection. And in turn, this connection affects our food choices and dietary intake, which cycles back to influence our microbiota.

Figure 1B. Gut microbiome of donor mice altering feeding choices in host mice.

This reciprocal feedback loop is partly caused by the gut microbiome’s ability to synthesize EAAs, which interact with the gut-brain axis and, in turn, influence dietary habits. Depending on the food choices made, the body’s response to those choices can be beneficial or detrimental. Therefore, increasing awareness of the factors influencing dietary intake may help us to impact both our physical and mental health positively.

References

Alcock, J., Maley, C. C., & Aktipis, C. A. (2014). Is eating behavior manipulated by the gastrointestinal microbiota? Evolutionary pressures and potential mechanisms. BioEssays : news and reviews in molecular, cellular and developmental biology, 36(10), 940–949. https://doi.org/10.1002/bies.201400071

Cryan, J. F., O’Riordan, K. J., Cowan, C., Sandhu, K. V., Bastiaanssen, T., Boehme, M., Codagnone, M. G., Cussotto, S., Fulling, C., Golubeva, A. V., Guzzetta, K. E., Jaggar, M., Long-Smith, C. M., Lyte, J. M., Martin, J. A., Molinero-Perez, A., Moloney, G., Morelli, E., Morillas, E., O’Connor, R., … Dinan, T. G. (2019). The microbiota-gut-brain axis. Physiological reviews, 99(4), 1877–2013. https://doi.org/10.1152/physrev.00018.2018

Gao, K., Mu, C. L., Farzi, A., & Zhu, W. Y. (2020). Tryptophan metabolism: A link between the gut microbiota and brain. Advances in nutrition (Bethesda, Md.), 11(3), 709–723. https://doi.org/10.1093/advances/nmz127

Harrold, J. A., Dovey, T. M., Blundell, J. E., & Halford, J. C. (2012). CNS regulation of appetite. Neuropharmacology, 63(1), 3–17. https://doi.org/10.1016/j.neuropharm.2012.01.007

Kaur, H., Bose, C., & Mande, S. S. (2019). Tryptophan metabolism by gut microbiome and gut-brain-axis: An in silico analysis. Frontiers in Neuroscience, 13, 1365. https://doi.org/10.3389/fnins.2019.01365

Trevelline, B. K., & Kohl, K. D. (2022). The gut microbiome influences host diet selection behavior. Proceedings of the National Academy of Sciences of the United States of America, 119(17), e2117537119. https://doi.org/10.1073/pnas.2117537119

Tzameli I. (2013). Appetite and the brain: You are what you eat. Trends in Endocrinology and Metabolism: TEM, 24(2), 59–60. https://doi.org/10.1016/j.tem.2012.12.001

Yabut, J. M., Crane, J. D., Green, A. E., Keating, D. J., Khan, W. I., & Steinberg, G. R. (2019). Emerging roles for serotonin in regulating metabolism: New implications for an ancient molecule. Endocrine reviews, 40(4), 1092–1107. https://doi.org/10.1210/er.2018-00283

Consuming highly palatable foods, especially those high in sugar and fat, can increase dopamine in the brain, creating a sense of pleasure and satisfaction

High doses of prebiotic fiber as a supplement would change the patterns of neural activity related to food in the brain reward system

Recent findings indicate that the microbiota-gut-brain axis (MGBA) can affect and regulate the reward system of the brain

Brain reactions to high-calorie foods decreased after prebiotic treatment

Brain reactions to pictures of wanted, high-calorie food decreased after prebiotics compared to placebo

The study showed that intervention with a common prebiotic attenuated reactions of the brain’s reward network in response to high-calorie food

Hunger motivates us to seek and eat food; satiety tells us we should stop eating

Food intake can often not result in satiety

Disruption of the hunger-satiety mechanism in the brain is now seen as one of the primary causes of obesity

Individuals who reported enjoying the taste of food more also reported feeling stronger hunger and being more responsive to the sight and smell of food

These links support the notion that eating behaviors take root in early childhood

The way we experience hunger is learned in childhood from caregivers

Our body can learn to expect a different number of daily meals and at different times

The feeling of hunger might not have anything to do with the body’s short-term energy needs at all (Rogers & Brunstrom, 2016)

Parenting might be important for teaching children the meaning of hunger

Parent beliefs about the cause of hunger influenced what they teach their children, implying that genetics are not the sole driver of parent-child similarity

Microbiota composition is linked to both positive and negative aspects of mental health

Social functioning was better in those with bacteria capable of producing a substance our body produces (3,4-dihydroxyphenylacetic acid) when processing dopamine

A large-scale study found links between the abundance of certain gut bacteria species and mental health outcomes

Humans have known for centuries there is a link between our digestive system and how we feel

Gut microbiota can vary among individuals and is recognized as a crucial factor in overall health and well-being (Heiss et al., 2021; Zhu et al., 2023)

Gut microbiota produces substances that influence the brain’s activity and its responses to stress and emotions

Recent advances in genetic sequencing technology allows researchers to study the role of microbiota in neurological and psychiatric disorders

Butyrate levels in the gut are reduced in those with inflammatory bowel disease and depression

Humans have evolved psychological and behavioral mechanisms to ensure that securing enough food is given top priority

While it is fairly obvious that excessive intake of food is a necessary component in the development of obesity, this excessive intake of food is caused by a combination of various biological, psychological, and physiological factors

Food craving is a strong desire to consume specific foods or food types. It differs from hunger by its specificity and intensity.

Food craving may be a response to physiological deficiencies or disturbances in the body, but they may also be initiated through psychological mechanisms without deficiencies in nutrients

Researchers wanted to observe whether food craving, hunger, restrained eating, and related emotions change within the same person

Participants answered questionnaires in the form of online surveys three times per day – in the morning, afternoon, and evening, over the course of 10 days

Participants who reported consciously limiting their food intake were more likely to be hungry and crave food later

When participants reported feeling stressed at one time, they were more likely to report craving food the next time

Studies have shown an association between sugar consumption and certain metabolic and cardiovascular issues, cancer, obesity, and other adverse medical conditions.

Eating foods containing fiber can help one feel satiated and be less likely to overeat.

The body does not need carbohydrates from added sugar. Yet, foods and drinks with added sugars represent a large share of the dietary intake of people worldwide.

The number of scientific studies exploring links between the consumption of sugars and health runs in hundreds of thousands worldwide.

High sugar consumption is associated with four beneficial and 45 harmful health outcomes.

Children who consume more sweetened beverages have a 55% higher chance of being obese.

Children who consumed high quantities of sugar-sweetened beverages had a 55% higher likelihood of being obese than children who did not.

Every additional glass of sugar-sweetened beverages (250 mL) per day was associated with a 17% increase in the risk of coronary heart disease.

Obesogenic environments (those prevalent with cues that promote excessive weight gain) are pervasive across Western societies and are hypothesized to overwhelm the internal cues that normally regulate our dietary intake.

There is compelling evidence demonstrating the links between awareness of one’s internal state (called interoceptive awareness) and the regulation of emotion.

Rostami and colleagues set out to explore the relationship between the MIND diet and psychological function, including depression, anxiety/stress, and sleep.

This is the first study to explore the relationship between adherence to the MIND diet and sleep.

Greater adherence to the MIND diet was linked to better sleep quality and fewer reports of daytime sleepiness and insomnia.

The anti-inflammatory and antioxidant properties of the MIND diet likely contribute to the observed positive impact on sleep.

Microorganisms that live in our gut may influence what we eat!

A feedback loop between our gut microbiome, brain, and food choices.

The classic expression, “you are what you eat,” may soon be reframed as “you are [also] what your microbiome wants you to eat.”

The gut microbiome can influence host diet selection behavior by mediating the availability of Essential Amino Acids (EAAs).

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