Mood was assessed using 3 validated questionnaires: 1 the POMS, 25 which measures 6 separate aspects of mood, including tension-anxiety, depression-dejection, anger-hostility, vigor-activity, fatigue-inertia, and confusion-bewilderment, and provides a global score of mood disturbance total mood disturbance score [TMDS] that is determined by subtracting the vigor-activity score from the sum of the 5 negative mood factors; 2 the Beck Depression Inventory BDI 26 ; and 3 the Spielberger State-Trait Anxiety Inventory SAI.

Before hypothesis testing, data were examined for normality. Data obtained from the mood scales were skewed and normalized by log transformation before analysis. Comparisons of baseline data between experimental conditions and between study dropouts and completers were performed using independent t tests and χ 2 tests for continuous and categorical variables, respectively.

The results indicated that there were no differences in baseline parameters between dropouts and completers. This result in conjunction with other considerations including assessment of reasons for dropout led us to believe that the assumption of the missing data being at random is reasonable.

To evaluate the outcomes of this study, 2 separate analyses were performed. First, mixed-effects models with repeated measures over time within participants were used to compare mean changes over time between the 2 treatment groups. In the models, participant sex and age were included as factors, and change in weight was included as a covariate to adjust for differences in weight loss.

The primary advantages of a mixed-model analysis are that participants are treated as random effects in the repeated measures model and that complete data are not required across the entire study period; ie, there is efficient use of all available data collected from the participants in the analysis.

Age and changes in body weight were included in the model as covariates. When a statistically significant main effect was found, post hoc comparisons were performed to determine differences between the 2 treatments at week Statistical analyses were performed with SPSS version Of the participants who were randomized, 11 2 on the LC diet and 9 on the LF diet withdrew from the study before it began and were not included in the analysis; a further 38 withdrew throughout the intervention, and an additional 3 participants did not complete the mood and cognitive function assessments at the end of the study at week 52 Figure 1.

Throughout the intervention, 1 participant in the LC group took more antidepressant medication, and 2 participants took less.

The results of the analysis did not change when the participants who were taking antidepressant medication were excluded.

On mixed-model analysis using data from all participants, both groups achieved substantial reductions in body weight over the course of the study, with no significant difference between the diets mean [SE] change in weight: LC group, There was an overall mean weight loss at 12 months of Over the study period, both groups also had similar reductions in plasma glucose levels mean [SE] change in plasma glucose levels [to convert glucose to milligrams per deciliter, divide by 0.

At baseline, there was no significant difference between groups on the BDI, SAI, or POMS subscale scores or on the TMDS score that was within 1 SD of the normal range for healthy adult populations. As previously reported, 13 both groups had an initial reduction in scores on the BDI, SAI, and POMS including the TMDS and the 6 subscales: tension-anxiety, depression-dejection, anger-hostility, vigor-activity, fatigue-inertia, and confusion-bewilderment that was of similar magnitude by week 8.

The overall trajectory ie, all time points of changes in the scores on the remaining POMS subscales—tension-anxiety, fatigue-inertia, and vigor-activity—showed no effect of diet. The completer's analysis ie, only those participants who completed the study using ANCOVA gave a similar pattern of results on the mixed-model analysis for all mood measures presented in Figure 2.

Values for the cognitive functioning tests are reported in the Table. At baseline, there was no significant difference between groups for working memory or speed of processing. The completer's analysis using ANCOVA gave similar results for these outcomes.

In this large, randomized, controlled study, we compared the long-term effects of a moderate energy-restricted LC diet with those of a conventional isocaloric LF diet on mood and cognitive function in overweight and obese individuals.

We previously reported marked improvements in mood with both diets over the shorter term of 8 weeks. The sustained improvements in mood in the LF group compared with the LC group are consistent with results from epidemiological studies showing that diets high in carbohydrate and low in fat and protein are associated with lower levels of anxiety and depression and have beneficial effects on psychological well-being.

The reason for this discrepancy in findings is not entirely clear, but it is important to note that unlike our study, which used validated mood and mental health assessment instruments, the study by McClernon and colleagues derived a measure of negative affect from a nonvalidated symptom checklist developed by practitioners for use in evaluating and treating patients undergoing an LC diet for weight loss.

This checklist may have biased the responses toward positive effects of an LC diet compared with an LF diet. Although attempts were made by McClernon and coauthors to control for weight loss in the statistical models because of the association between weight loss and psychological well-being, 1 , 37 , 38 the possibility cannot be dismissed that the greater weight loss with the LC diet accounted for the greater reductions in negative affect.

McClernon et al 14 also noted that in addition to dieting, individuals in the LC group received nutritional supplements while those in the LF group did not, raising the possibility that the group differences in symptoms observed could be attributed to a placebo effect of the nutritional supplements rather than to the LC diet per se.

Consistent with previous studies that have shown improvements in mood and emotional outcomes after weight loss, 1 , 37 , 38 and after participation in supervised weight-loss programs with social support, 1 , 39 , 40 improvements in mood were observed in the participants in the LF group in our study.

However, despite similar and substantial weight losses in the LC group, after improvements were realized over the short term, the mood state of the participants had returned to baseline levels at the end of 1 year.

This outcome suggests that some aspects of the LC diet may have had detrimental effects on mood that, over the term of 1 year, negated any positive effects of weight loss.

For mood scores that realized statistically significant differences, medium effect sizes 0. The present study was conducted as a controlled clinical trial in which participants were randomly assigned to the LC or LF diet rather than self-selecting a diet based on their food preference and eating habits.

In Western society, established eating patterns and the most common traditional dietary recommendations favor a high-carbohydrate dietary pattern, 4 , 5 with bread, pasta, rice, and fruit consumed in large quantities. Therefore, the LC diet being so far removed from normal dietary habits may have created a significant challenge for participants, leading to the possibility of food preoccupation, social eating impairment, and dysphoria.

Although, in the short term, participants may have been able to meet the challenges presented by this dietary pattern, over the longer term, it may have increased participant isolation, leading to the negative impact on mood state that may provide a possible explanation for the effects that were observed.

However, these social effects cannot be extrapolated from the current data, and future studies addressing this hypothesis are warranted. The possibility that the prescriptive nature of the dietary regimens had a negative impact on affect should also be considered.

Although a major strength of the current study was the use of highly prescriptive plans by which the dietary patterns were structured to include specific food quantities combined with regular dietary advice and counseling to ensure correct macronutrient and energy requirements, this highly prescriptive method may have alienated some participants.

However, the stringent structured approach did not appear to have a negative effect on mood in the LF diet; therefore, the observed response would suggest an interactive effect with diet, with the LC diet magnifying any psychological discomfort associated with the structure of the regimen.

Further research should test this hypothesis. In current practice, LC diets are typically followed ad libitum, without specific prescription of energy intake, promoting unlimited intake of protein and fat, with the only food restriction being to limit the intake of carbohydrate.

Mood among long-term consumers of LC diets may also be negatively affected by changes in serotogenic expression and neurotrophic factors. While a high-carbohydrate intake can increase serotonin synthesis, fat and protein intakes reduce serotonin concentrations in the brain.

Despite these results, it is important to note that mood state scores on average for both groups at baseline and throughout the study remained within the normal range for healthy adults.

This finding is consistent with previous studies showing that short-term decrements in cognitive performance were not sustained in persons on an LC diet.

Because it is well recognized that glucose is the brain's primary fuel 55 and that hypoglycemia impairs cognitive function, 56 , 57 it could have been hypothesized that the LC diet may have negatively affected cognitive performance relative to the LF diet. However, despite a restricted carbohydrate intake, the LC diet did not induce a hypoglycemic state, which is likely attributable to increased gluconeogenesis to maintain blood glucose levels, and after the intervention, participants in both groups had similar fasting plasma glucose levels that were within the normal range.

Although it is not possible to measure local changes in brain glucose levels because the level of glucose in the blood reflects the level of glucose in the brain, 58 maintenance of normal glucose levels may explain the lack of any difference in cognitive performance between the diets.

In the present study, the improvement in working memory, as assessed by DSB, that occurred after 8 weeks was sustained over 12 months in both diet groups. In contrast, previous studies have indicated that caloric restriction that leads to substantial weight loss has a minimal impact on cognitive performance, including working memory as assessed by DSB, in overweight and obese individuals.

In contrast to the present findings, a recent study by Witte et al 62 that did include a nondieting control group showed no change in a digit span task in overweight individuals after 3 months of energy restriction, suggesting that the improvements in DSB in the present study may not be explained by learning effects.

However, the possibility cannot be dismissed that the differences in study duration 3 months vs 12 months and the dietary regimens and delivery methods that were used also could have contributed to the discrepancies in the findings.

Alternatively, Witte and colleagues demonstrated substantial improvements in episodic memory performance as assessed by a word-recall task after energy restriction, which was correlated with decreases in fasting plasma levels of insulin.

We also observed a significant inverse correlation between the change in DSB a measure of working memory scores and the change in fasting plasma insulin levels; ie, the DSB scores increased with decreases in the plasma insulin levels. This association leads to the hypothesis that if episodic memory had been assessed in the present study, even greater improvements may have occurred compared with the study by Witte et al.

The current study focused on the assessment of 2 critical components of cognitive functioning: working memory and speed of processing. Although these 2 domains are fundamental components of cognition and measures that have demonstrated sensitivity to dietary change, 63 they do not constitute a comprehensive battery for the assessment of cognition.

For example, it is possible that aspects of cognition such as attention, short-term memory, long-term memory, and executive function might be influenced more by changes in the macronutrient content of a weight-loss diet.

This potential limitation affects the confidence with which conclusions can be drawn regarding the long-term effects of LC and LF diets on cognitive function. Therefore, further studies should be undertaken to assess a greater range of cognitive domains. Moreover, the current results highlight the importance of future research that recognizes the role of practice in changing cognitive performance.

Studies concerned with measuring intervention effects should undertake practice sessions until asymptotic levels of performance are achieved before randomization. Notwithstanding the increase in participant burden, it is important for future studies to perform repeated testing on a more frequent basis than was performed in our study to better characterize the time course of effects.

In conclusion, we found that despite similar weight loss after energy-restricted LC and LF diets for 12 months and rapid improvements in mood during the first 8 weeks with both diets, over the long term many of the benefits regressed in the LC diet group such that participants on the LF diet achieved better outcomes.

However, there was no evidence that the dietary macronutrient composition of LC and LF diets affected cognitive functioning over the long term, as changes in cognitive function were similar for both diets. Further studies are required to evaluate the effects of these diets on a wider range of cognitive domains.

Correspondence: Grant D. Brinkworth, PhD, Commonwealth Scientific and Industrial Research Organisation—Food and Nutritional Sciences, PO Box , Adelaide, BC, South Australia grant. brinkworth csiro. Author Contributions: Dr Brinkworth had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design : Brinkworth, Noakes, Clifton, and Wilson. Acquisition of data : Brinkworth. Analysis and interpretation of data : Brinkworth, Buckley, Noakes, Clifton, and Wilson.

Drafting of the manuscript : Brinkworth and Buckley. Critical revision of the manuscript for important intellectual content : Brinkworth, Buckley, Noakes, Clifton, and Wilson.

Statistical analysis : Brinkworth and Clifton. Obtained funding : Brinkworth, Buckley, and Noakes. Administrative, technical, and material support : Brinkworth, Buckley, Noakes, and Wilson.

Study supervision : Brinkworth. Simplot Australia, Mt Buffalo Hazelnuts Victoria, Webster Walnuts Victoria, Stahmann Farms Queensland, and Scalzo Food Industries Victoria donated foods for this study. Role of the Sponsor: The funding agencies had no role in the design and conduct of the study; in the collection, analysis, and interpretation of the data; or in the preparation, review, or approval of the manuscript.

Additional Contributions: We gratefully acknowledge Kathryn Bastiaans, Julia Weaver, Anne McGuffin, and Vanessa Courage for coordinating this trial; Angela Halyburton, Belinda Wyld, and Emily Brindal for the collection of the mood and cognitive function data; Xenia Cleanthous, Julianne McKeough, and Gemma Williams for implementation of the dietary intervention; Rosemary McArthur and Lindy Lawson for nursing expertise; Julie Syrette for data management; and Kylie Lange for statistical advice.

full text icon Full Text. Download PDF Top of Article Abstract Methods Results Comment Article Information References. Figure 1. View Large Download. Flow of participants through the trial. Mixed-Effects Model of the Effects of an Energy-Restricted, Low-Carbohydrate, High-Fat LC Diet and a High-Carbohydrate, Low-Fat LF Diet Over 12 Months on Working Memory Digit Span Backward and Inspection Time.

Miller-Kovach KHermann MWinick M The psychological ramifications of weight management. J Womens Health Gend Based Med ;8 4 PubMed Google Scholar Crossref. Wadden TAVogt RAAndersen RE et al. Exercise in the treatment of obesity. These effects have nothing to do with the ingredients of the foods themselves.

In addition, learned appetites can also influence our experience of foods. For example, our favorite foods usually trigger positive emotions. Even the smell of food can evoke a strong emotional experience. Furthermore, the situation in which food is consumed and our past experience with particular foods also affects our emotional response 6, 7.

For example, a person who thinks that drinking a cup of coffee will increase alertness might feel more alert even after drinking decaffeinated coffee.

The perfect diet to enhance mood and optimize performance and health remains unknown. Although abundant research exists on food-mood relationships, the findings of these studies are often generalized and subjective.

For example, the ability of carbohydrates to positively influence mood remains controversial. Therefore, it seems best to follow a well-balanced diet rich in protein, moderate in carbohydrates and low in fat since this could generally improve mood and energy levels.

This should also ensure the adequate supply of micronutrients such as omega-3 fatty acids, iron, folic acid and thiamine.

Furthermore, to avoid the sense of guilt evoked from overindulging in craved foods such as chocolate, the best way is to manage their intake such as including them in small amounts with meals and avoiding them when hungry.

In addition, reading the labels before consuming these comfort foods can also deter from overconsumption. Prasad, C. Food, mood and health: a neurobiological outlook.

Brazilian Journal of Medical and Biological Research, 31 12 : Rogers P. Nutrition and mental performance. Proceedings of the Nutrition Society, Spring, B et al. Journal of psychiatric research , 17 2 : Michaud C. Effects of breakfast size on short-term memory concentration and blood glucose.

Journal of Adolescent Health, Benon D. The effects of nutrients on mood. Public Heath Nutrition, 2 3A : Macht, M. Everyday mood and emotions after eating a chocolate bar or an apple. University of Pittsburgh Medical Center , March 4.

Omega 3 Fatty Acids Influence Mood, Impulsivity And Personality, Study Indicates. Pawels, E. Essential fatty acids as treatment for depression, or food for mood? Lang, Susan. Cornell Chronicle. Kudos to Sarah-Marie Hopf for the well-done article on food and mood. It sticks to the science and avoids the hype so often found on this topic.

Mary Saucier Choate, M. Food and Nutrition Educator The Co-op Food Stores PO Box Hanover, NH Your email address will not be published. Save my name, email, and website in this browser for the next time I comment. You Are What You Eat: How Food Affects Your Mood February 3, Fall Share on X Twitter Share on Facebook Share on Email.

Chocolate is a powerful mood enhancer. Bookmark the permalink. The Physiology of Stress: Cortisol and the Hypothalamic-Pituitary-Adrenal Axis ». Sarah Marie, Good review in content and great flowing writing style! Samir, Reply. So a meal like pasta or a snack of graham crackers will allow the brain to make serotonin, but eating chicken and potatoes or snacking on beef jerky will actually prevent serotonin from being made.

This can explain why people may still feel hungry even after they have eaten a ounce steak. Their stomachs are full but their brains may not be making enough serotonin to shut off their appetites. And what do protein dieters especially women miss most after the second week?

Women have much less serotonin in their brains than men, so a serotonin-depleting diet will make women feel irritable. And with this mood change comes a yearning to eat something sweet or starchy. Thus, it's not just a matter of will power or mind over matter; the brain is in control and sends out signals to eat carbohydrates.

According to Wurtman's clinical studies, if the carbohydrate craver eats protein instead, he or she will become grumpy, irritable or restless. Furthermore, filling up on fatty foods like bacon or cheese makes you tired, lethargic and apathetic.

Eating a lot of fat, she said, will make you an emotional zombie. Massachusetts Institute of Technology 77 Massachusetts Avenue, Cambridge, MA, USA. Massachusetts Institute of Technology. Search MIT.

These findings challenge the idea that CHOs can improve mood, and might be used to increase the public's awareness that the 'sugar rush' is a myth, inform health policies to decrease sugar consumption, and promote healthier alternatives. Keywords: Acute; Carbohydrates; Meta-analysis; Mood; Sugar.

Abstract The effect of carbohydrate CHO consumption on mood is much debated, with researchers reporting both mood improvements and decrements following CHO ingestion.

Publication types Meta-Analysis Research Support, Non-U. Hoyer SHenneberg NKnapp SLannert HMartin ML Brain glucose metabolism is controlled by amplification and desensitization of the neuronal insulin receptor. Park CR Cognitive effects of insulin in the central nervous system. Neurosci Biobehav Rev ;25 4 PubMed Google Scholar Crossref.

Brinkworth GDNoakes MBuckley JDKeogh JBClifton PM Long-term effects of a very-low-carbohydrate weight loss diet compared with an isocaloric low-fat diet after 12 mo.

Am J Clin Nutr ;90 1 32 PubMed Google Scholar Crossref. Grundy SMCleeman JIDaniels SR et al. Diagnosis and management of the metabolic syndrome. Curr Opin Cardiol ;21 1 1- 6 PubMed Google Scholar Crossref. McNair DMLorr MDroppleman FF Manual: Profile of Mood States.

San Diego, CA Eduational and Industrial Testing Service;. Beck ATWard CHMendelson MMock JErbaugh J An inventory for measuring depression. Arch Gen Psychiatry ; PubMed Google Scholar Crossref. Spielberger CDGorsuch RLLushene RVagg PRJacobs GA Manual for the State-Trait Anxiety Inventory.

Palo Alto, CA Consulting Psychologists Press Inc;. Wecshler D Wechsler Adult Intelligence Scale. San Antonio, TX Psychological Corporation and Harcourt Brace Jovanovich;.

Petrill AADeary IJ Inspection time and intelligence: celebrating 25 years of research. Intelligence ;29 6 Google Scholar Crossref. Cnaan ALaird NMSlasor P Using the general linear mixed model to analyse unbalanced repeated measures and longitudinal data.

Stat Med ;16 20 PubMed Google Scholar Crossref. Gadbury GLCoffey CSAllison DB Modern statistical methods for handling missing repeated measurements in obesity trial data: beyond LOCF. Obes Rev ;4 3 PubMed Google Scholar Crossref.

Heyting ATolboom JTEssers JG Statistical handling of drop-outs in longitudinal clinical trials. Stat Med ;11 16 PubMed Google Scholar Crossref. Beck ATSteer RABrown GK Beck Depression Inventory Manual. San Antonio, TX Psychological Corp;. Average daily nutrient intake and mood among obese women.

Nutr Res ;18 7 Google Scholar Crossref. de Castro JM Macronutrient relationships with meal patterns and mood in the spontaneous feeding behavior of humans.

Physiol Behav ;39 5 PubMed Google Scholar Crossref. Muñoz MAFíto MMarrugat JCovas MISchröder HREGICOR and HERMES investigators, Adherence to the Mediterranean diet is associated with better mental and physical health.

Br J Nutr ; 12 PubMed Google Scholar Crossref. Foster GDWadden TA Social and psychological effects of weight loss. brownell KDFairburn CG Eating Disorders and Obesity A Comprehensive Handbook. New York, NY: Guliford Press; Google Scholar. Geliebter AMaher MMGerace LGutin BHeymsfield SBHashim SA Effects of strength or aerobic training on body composition, resting metabolic rate, and peak oxygen consumption in obese dieting subjects.

Am J Clin Nutr ;66 3 PubMed Google Scholar. Wadden TASteen SNWingate BJFoster GD Psychosocial consequences of weight reduction. Am J Clin Nutr ;63 3 suppl S- S PubMed Google Scholar.

Wing RREpstein LHMarcus MDKupfer DJ Mood changes in behavioral weight loss programs. J Psychosom Res ;28 3 PubMed Google Scholar Crossref. Norman GRSloan JAWyrwich KW Interpretation of changes in health-related quality of life. Med Care ;41 5 PubMed Google Scholar.

Cohen J Statistical Power Analysis for the Behavioural Sciences. London, England Academic Press;. Freedman MRKing JKennedy E Popular diets: a scientific review. Obes Res ;9 suppl 1 1S- 40S PubMed Google Scholar Crossref.

Atkins RC Dr. Atkins' New Diet Revolution. New York, NY Avon Books;. Wurtman RJ Nutrients that modify brain function. Sci Am ; 4 59 PubMed Google Scholar Crossref. Rouch CMeile MJGerozissis K Persisting neural and endocrine modifications induced by a single fat meal.

Cell Mol Neurobiol ;25 6 PubMed Google Scholar Crossref. Benton D Carbohydrate ingestion, blood glucose and mood. Neurosci Biobehav Rev ;26 3 PubMed Google Scholar Crossref.

Ressler KJNemeroff CB Role of serotonergic and noradrenergic systems in the pathophysiology of depression and anxiety disorders. Depress Anxiety ;12 suppl 1 2- 19 PubMed Google Scholar Crossref. Young SN How to increase serotonin in the human brain without drugs. J Psychiatry Neurosci ;32 6 PubMed Google Scholar.

Castrén ERantamäki T Neurotrophins in depression and antidepressant effects. Novartis Found Symp ; 59, PubMed Google Scholar.

Molteni RBarnard RJYing ZRoberts CKGomez-Pinilla F A high-fat, refined sugar diet reduces hippocampal brain-derived neurotrophic factor, neuronal plasticity, and learning.

Neuroscience ; 4 PubMed Google Scholar Crossref. Schaaf MJHoetelmans RWde Kloet ERVreugdenhil E Corticosterone regulates expression of BDNF and trkB but not NT-3 and trkC mRNA in the rat hippocampus. J Neurosci Res ;48 4 PubMed Google Scholar Crossref.

Lindqvist AMohapel PBouter B et al. High-fat diet impairs hippocampal neurogenesis in male rats. Eur J Neurol ;13 12 PubMed Google Scholar Crossref. Dye LLluch ABlundell JE Macronutrients and mental performance.

Nutrition ;16 10 PubMed Google Scholar Crossref. Wenk GL An hypothesis on the role of glucose in the mechanism of action of cognitive enhancers. Psychopharmacology Berl ;99 4 PubMed Google Scholar Crossref.

Brody SKeller UDegen LCox DJSchachinger H Selective processing of food words during insulin-induced hypoglycemia in healthy humans. Psychopharmacology Berl ; PubMed Google Scholar Crossref. Schächinger HCox DLinder LBrody SKeller U Cognitive and psychomotor function in hypoglycemia.

Pharmacol Biochem Behav ;75 4 PubMed Google Scholar Crossref. Lund-Andersen H Transport of glucose from blood to brain. Physiol Rev ;59 2 PubMed Google Scholar. Kretsch MJGreen MWFong AKElliman NAJohnson HL Cognitive effects of a long-term weight reducing diet.

Int J Obes Relat Metab Disord ;21 1 21 PubMed Google Scholar Crossref. Martin CKAnton SDHan H et al. Examination of cognitive function during six months of calorie restriction. Rejuvenation Res ;10 2 PubMed Google Scholar Crossref. Bryan JTiggemann M The effect of weight-loss dieting on cognitive performance and psychological well-being in overweight women.

Appetite ;36 2 PubMed Google Scholar Crossref. Witte AVFobker MGellner RKnecht SFloel A Caloric restriction improves memory in elderly humans. Proc Natl Acad Sci U S A ; 4 PubMed Google Scholar Crossref. Osendarp SJBaghurst KIBryan J et al. NEMO Study Group, Effect of a mo micronutrient intervention on learning and memory in well-nourished and marginally nourished school-aged children.

Am J Clin Nutr ;86 4 PubMed Google Scholar. See More About Depressive Disorders Lifestyle Behaviors Obesity Psychiatry and Behavioral Health Diet. Select Your Interests Select Your Interests Customize your JAMA Network experience by selecting one or more topics from the list below. Save Preferences.

Privacy Policy Terms of Use. This Issue. Citations View Metrics. Just like fiber makes glucose in complex carbohydrates more difficult for the body to absorb, so does fat and protein. Dairy is an excellent example of a natural combination of carbohydrate lactose contains glucose , protein and fat.

Simple carbohydrates and carbohydrates that are easy to digest and absorb. Examples include sugar and flours made from refined grains. These carbohydrates provide the body and brain with LOTS of glucose fuel at once.

The brain gets the fuel it needs for a short amount of time. Due to the large influx of glucose into the bloodstream, the body is cued to store that glucose for later. This leaves the brain without fuel and susceptible to mood changes.

Like I said, our bodies are good at regulating themselves, however we still must listen. Though it can be easy to ignore hunger cues when you are busy, we get hunger cues because we need fuel. You may find that when you are truly listening to your hunger cues, you need more snacks.

Especially if those snacks include complex carbohydrates paired with a source of protein and or fat! So as the seasons change and you are meandering through your local grocery store, farmers market or harvesting your own produce, think about how you can incorporate this important energy source into your diet.

Fortunately, we can try to stabilize mood changes and ease anxiety caused by low blood glucose by providing the brain with the right kind of fuel. Stay Healthy!

Back to All Spread the Word.