Introduction 

Protein is one of the three macronutrients (large nutrients), the others being fats and carbohydrates.  Originating from the Greek “proteios,” meaning primary or prime, protein is a fundamental component of human and animal tissue [7].  A protein is made up of amino acids (AA) linked together via peptide bonds [7].  Protein are built from various amounts of different amino acids, which are classified into “essential” or “non-essential” amino acids [3].  Non-essential amino acids can synthesised by the body, however essential amino acids cannot and must acquired through diet [3].  

Dietary protein serves multiple important functions.  They are the major structural component of all cells in the body (including skeletal muscle, bone, skin, hair) and used for the repair, growth, and maintenance of cells and tissue [3].  Proteins function as enzymes that power chemical reactions, and act as transport carriers and hormones [15].  Amino acids supplied by dietary protein also serve as precursors for nucleic acids (information-carrying molecules of cells), hormones, vitamins, and other molecules in addition to providing an energy source, supplying approximately 4 calories per gram, about the same as supplied by dietary carbohydrates [3, 15].  

This article will discuss the role of dietary protein in the context of muscle development and look at the current evidence to understand the optimal amount, type, and timing of protein intake to maximise muscle growth at rest and around workouts.    

Muscle Protein Synthesis 

Skeletal muscle is a highly adaptive organ where muscle tissue is constantly turning over, meaning it constantly breaks down and remodels to replace damaged proteins with new proteins [13].  Research into muscle protein turnover reveals that human muscle is dictated by daily changes in rates of muscle protein synthesis (MPS) (i.e. building of new muscle proteins) and muscle protein breakdown (MPB) (i.e. breaking down of old muscle proteins) [13].  The net balance between muscle protein breakdown and muscle protein synthesis ultimately determines whether muscle is grown or lost [13].  In the rested, fasted, or post-exercise state, muscle protein breakdown occurs to a larger extent than synthesis, therefore muscle is in a state of negative protein balance and suboptimal for muscle growth [13].  However, when dietary protein is consumed following exercise, there is a marked increase in muscle protein synthesis, transitioning muscle into a state of positive net protein balance [13].  

A single session of resistance exercise has been shown to increase muscle protein synthesis.  However, muscle protein balance following resistance exercise will remain negative in the absence of food intake.  Protein ingestion following resistance exercise is found to significantly increase muscle protein synthesis [2, 4, 6, 8, 9, 10, 11, 12].  Areta and colleagues (2014) showed that 15 and 30 grams of protein following a bout of resistance exercise increased muscle protein synthesis 16% and 34% above pre-exercise, resting levels.  As the summation of muscle protein synthesis over time results in muscle development (hypertrophy), the strategy of consuming protein when performing resistance exercise is widely used to increase muscle growth [6].

How Much Protein?

When quantifying the amount of protein to ingest to maximise muscle protein synthesis, research demonstrates a possible dose-response relationship between dietary protein and muscle protein synthesis [9, 10, 17].  Moore and colleagues (2009) were one of the first to identify this dose-response relationship following resistance exercise [10].  In their study, the authors tested 0, 5, 10, 20, or 40 grams of egg protein following a lower body resistance workout [10].  Muscle protein synthesis showed a dose-dependent response from 0 to 20 grams, whereby each subsequent protein amount yielded greater MPS response [10].  However, when doubling protein ingestion from 20 to 40 grams of protein, the authors found only a non-significant 10% increase in MPS [10].  The conclusion from this data was that protein intake of about 20 grams is sufficient to maximise MPS following resistance exercise.  These findings were supported by Witard et al (2013), who similarly found that 20 grams of whey protein following lower body resistance exercise (8 × 10 leg presses and leg extensions) maximised MPS following both resistance exercise and at rest, with an additional 10% benefit from increasing to 40 grams of protein [17].  

When performing full-body resistance exercise (i.e. upper and lower body), a more significant benefit has been found when consuming 40 grams of protein compared with 20 grams [9].  Macnaughton and colleagues (2016) performed a study where they randomised resistance trained men to receive either 20 grams of protein following total body resistance exercise or 40 grams of protein following training [9].  The authors found that myofibrillar fractional synthetic rate (a marker of muscle protein synthesis), was 20% greater when consuming 40 grams of protein following exercise compared with 20 grams of protein [9].  The authors suggested that the larger number of muscle activation from total body training may result in a greater demand for protein intake compared with less muscle activation (i.e. lower body training only) [9].  When taking the weight of the evidence together, the consumption of 20 grams of protein at rest and following exercise appears to maximise muscle protein synthesis, while 40 grams appears to provides an addition 10-20% benefit. 

What Type of Protein?

When comparing animal-based to plant-based protein sources for muscle development, research reveals that animal-based protein prove more efficient for stimulating muscle protein synthesis compared with plant-based [11, 16].  

Wilkinsion and colleagues (2007) showed that milk-based proteins led to greater stimulation of muscle protein synthesis compared with soy-based protein following resistance exercise [16].  In their study, subjects performed a leg workout (i,e. leg press, hamstring curl, and knee extension), completing 4 sets of 10 repetitions of each exercises [16].  Subjects then ingested either a 500 millilitre drink of either non-fat milk or a calorie-matched soy-beverage [16].  The authors found 34% greater rates of muscle protein synthesis in the the 3 hours following exercise when milk-protein was consumed compared with soy-protein [16].  A study by Tang and colleagues (1985) showed similar findings when they fed either whey, casein, or soy protein to subjects following lower body resistance exercise [11].  In their study, muscle protein synthesis was 31% higher when whey protein was consumed post-workout compared with soy protein [11].  

The inferior muscle protein synthetic response of plant-based protein compared with animal-based may be attributed to its lower total essential amino acid content (i.e. lysine, methionine) and lower content of specific, highly potent, muscle stimulating amino acids like leucine [14].  Also, it is suggested that, compared with animal-based proteins, plant-based proteins may have lower digestibility and absorbability, which would diminish the amino acids available in the body for protein synthesis [14].  Finally, it has been reported that plant-based proteins convert more quickly into urea (a waste product and main component of human urine) compared with animal-based proteins, which would decrease the potential for plant-based protein sources to stimulate muscle protein synthesis [14].  

Interestingly, research demonstrates that increasing the amount of plant-based protein may be effective for compensating for its lower anabolic properties [4, 8].  A few studies have shown similar increases in muscle between plant and animal-based proteins when plant-based protein amount was increased above 30 grams [4, 8].  For example, Brown and colleagues (2013) found that 33 grams of soy protein produced similar improvements in muscle mass compared with whey protein following prolonged (9 weeks) or resistance exercise [4].  Also, research reveals that plant-based blends of protein (i.e. soy, hemp, pea), may be more advantageous for stimulating muscle protein synthesis compared with a single-based plant source (i.e. pea) because, when combined, blends provide a more complete essential amino acid profile [4]. 

Best Time for Protein?

Timing protein intake is a strategy along with dose and type that can be used to enhance the adaptive response from exercise.  The great majority of studies that investigated the impact of protein intake on muscle protein synthesis have focused on protein ingestion directly after exercise [2, 4, 8, 9, 10, 11, 12].  It is well reported that consuming protein shortly following exercise successfully stimulates muscle protein synthesis and results in a positive net muscle protein balance [2, 4, 6, 8, 9].  The theoretical reasoning for ingesting protein shortly after exercise relates to further augmenting muscle protein synthesis [5].  As muscle protein synthesis rates peak within three hours of exercise and stay elevated for 24-72 hours, the aim is to then supply amino acids via dietary protein to the body to optimise MPS rates [5].  This time period would also encompass peak MPS sensitive, which is suggested to occur in the first five to six hours post-exercise [5]. 

A literature review published in the Journal of the International Society of Sports Nutrition in 2013 was completed to understand the time sensitivity of post-workout nutrient timing in the context of maximising the muscle protein synthetic response to exercise [1].  In this review, the authors found that the importance of timing post-workout nutrition (i.e. protein intake) may be more dependent on the timing of the pre-workout meal and the size of this meal [1].  The authors suggested that a pre-workout meal timed 1-2 hours prior to a bout of exercise would supply sufficient amino acids that would last for both during and following exercise creating less need for immediate post-workout protein [1].  If a larger pre-workout meal were consumed, it is conceivable that even more time can be given following a workout until protein consumption would be advocated [1].  Under circumstances where a meal is consumed 3-4 hours or more prior to a workout, immediate post-workout ingestion of protein would be recommended to prevent catabolism, which would compromise muscle growth and recovery [1].   

Summary

Protein serves multiple function in the body and, in the context of muscle growth, has been widely shown to augment muscle protein synthesis following exercise.  Optimising the amount, type, and timing of protein intake can help maximise the muscle protein synthetic response, thereby leading to faster muscle adaptation.  The consumption of 20 grams of animal-based protein (i.e whey) following a workout, or at rest, leads to near maximal MPS stimulation with 40 grams providing an additional 10-20% benefit.  While animal-based protein sources (i.e. soy, hemp, pea) stimulate MPS to a greater extent than plant-based, larger amounts (>30 grams) of plant-based proteins can be used to compensate for its lower anabolic properties.  Post-workout ingestion of protein timing is influenced in large part by pre-workout meal timing.  Due to the anabolic properties of a protein-rich meal, it is recommended that a pre- and post-workout meal should be separated by approximately 3-4 hours given a typical resistance exercise workout lasting 45-90 minutes [1].  When consuming a larger pre-workout meal, amino acids would be available for use for an extended period, therefore the pre- and post-workout could similarly be extended to to 5-6 hours [1].

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References

[1] Aragon, A. A. and Schoenfeld, B. J. 2013. Nutrient Timing Revisited: Is there a Post-Exercise Anabolic Window?  January. Vol. 10, No. 1, pp.1550-2783. Journal of the International Society of Sports Nutrition.

[2] Areta, J. L., et al. 2014. Reduced Resting Skeletal Muscle Protein Synthesis is Rescued by Resistance Exercise and Protein Ingestion Following Short-Term Energy Deficit. April. Vol. 306. No. 8. E989-997. Endocrinology and Metabolism.

[3] Bhattacharya, S. 2021. Computational Protein Structure Analysis : Kernel And Spectral Methods. 

[4] Brown, E. C. et al. 2004. Soy Versus Whey Protein Bars: Effects on Exercise Training Impact on Lean Body Mass and Antioxidant Status. December. Vol. 3, No. 22, pp.1475-2891. Nutrition Journal. 

[5] Burd, N.A. et al. 2011. Enhanced Amino Acid Sensitivity of Myofibrillar Protein Synthesis Persists for up to 24 h After Resistance Exercise in Young Men. April. Vo.141, No.4, pp.568–573. The Journal of Nutrition.

[6] Damas, F. et al. 2016. Resistance Training-Induced Changes in Integrated Myofibrillar Protein Synthesis are Related to Hypertrophy Only After Attenuation of Muscle Damage. September. Vol. 594. No. 18. pp.5209-5222. The Journal of Physiology.

[7] Guoyao, W.  2016. Dietary Protein Intake and Human Health. March. No. 7, Vol. 3, pp.1251-1265. Food and Function. 

[8] Joy, J. M et al. 2013. The Effects of 8 Weeks of Whey or Rice Protein Supplementation on Body Composition and Exercise Performance. June. Vol.12, No.86, pp.1475-2891. Nutrition Journal.

[9] Macnaughton, L. S. et al. 2016.  The Response of Muscle Protein Synthesis Following Whole-Body Resistance Exercise is Greater Following 40 g than 20 g of Ingested Whey Protein. August. Vol.4, No.15, Physiological Reports.

[10] Moore, D. et al. 2009. Ingested Protein Dose Response of Muscle and Albumin Protein Synthesis After Resistance Exercise in Young Men. January. Vol. 89, No. 1, pp.161–168. American Journal of Clinical Nutrition.

[11] Tang, E. T. at al. 1985. Ingestion of Whey Hydrolysate, Casein, or Soy Protein Isolate: Effects on Mixed Muscle Protein Synthesis at Rest and Following Resistance Exercise in Young Men. September. Vol.107, No.3, pp.987-992.  Journal of Applied Physiology.

[12] Tipton, K. D. et al. 1999. Postexercise Net Protein Synthesis in Human Muscle from Orally Administered Amino Acids. April. Vol. 276, No. 4, E628–34. American Journal of Physiology.

[13] Trommeleln, J. et al. 2019. The Muscle Protein Synthetic Response to Meal Ingestion Following Resistance-Type Exercise. January. Vol. 49, pp. 185-197. Sport Medicine.

[14] Van Vilet, S. et al. 2015. The Skeletal Muscle Anabolic Response to Plant- versus Animal-Based Protein Consumption. September. Vol.145, No.9, pp.1981-1991. The Journal of Nutrition.

[15] Wallace, T. C. 2019. Optimising Dietary Protein for Lifelong Bone Health: A Paradox Unraveled.  May. Vo. 54, No. 3. Nutrition Today. 

[16] Wilkinson, S. B. et al. 2007. Consumption of Fluid Skim Milk Promotes Greater Muscle Protein Accretion Following Resistance Exercise than an Isonitrogenous and Isoenergetic Soy Protein Beverage. Vol.85, No.4, pp.1031-1040. American Journal of Clinical Nutrition.

[17] Witard O. C, et al. 2013. Myofibrillar Muscle Protein Synthesis Rates Subsequent o a Meal in Response to Increasing Doses of Whey Protein at Rest and After Resistance Exercise. January. Vol.99, No.1, pp.86–95. American Journal of Clinical Nutrition.

Introduction

Carbohydrates are one of three macronutrients (protein and fat being the other two) that are found in food.  Broadly speaking, carbohydrates can be subdivided into three groups: sugars, starch, and dietary fiber.  These groups include carbohydrates foods like fruits and white sugar, potatoes, breads, and pasta, and fruits and vegetables, beans and lentils.  Carbohydrates have long been demonised by the media and both fitness and health professionals.  Popular low-carbohydrate diets like The Atkins diet labelled carbohydrates as “fattening” and made claims that carbohydrates themselves, not calories, are responsible for weight gain [4, 5].  Other low-carb diets like the ketogenic diet (high fat, low carb) claim to posses a significant metabolic advantage for body composition in accelerating weight loss compared with high-carbohydrate diets.  Despite the continued popularity of low-carbohydrate diets, experimental evidence does not support such claims [3, 4, 6, 10].  This article will discuss the “carbohydrate-insulin model” and examine the scientific literature to understand whether low-carbohydrate diets are associated with metabolic changes that would benefit fat loss.  

The Carbohydrate-Insulin Model of Obesity

When carbohydrates are eaten (i.e. fruits, vegetables, grains), a hormone called insulin is released from the pancreas, an organ located behind the stomach [5, 11].  Among other functions, insulin’s primary function is to regulate blood sugar levels [11].  It does this by transporting glucose from the blood into tissues such as muscle, fat, and liver, among others [11].  In healthy individuals, when carbohydrates are eaten, glucose (a simple sugar found mostly in plant foods) enters the blood and the pancreas senses this increase in glucose and releases insulin, which then brings blood glucose back down to normal levels [8, 11].  Once blood glucose goes back down, insulin also goes down [8, 11].  This is a normal cycle and happens every time we eat [8, 11].

In addition to the role insulin plays in controlling blood glucose, it also inhibits lipolysis, which is the process where triglycerides (the main components of fat) are broken down [7, 11].  Insulin also functions to stimulate lipogenesis, which is the building of fat [7, 11].  It is this observation that dietary carbohydrates increases insulin, which leads to decreased lipolysis (fat breakdown) and increased lipogenesis (fat creation), that have led to the hypothesis that carbohydrates are “fattening.”

Specifically, the ‘carbohydrate-insulin model’ has been used to argue that carbohydrates cause weight gain and obesity [5].  Proponents of the carbohydrate-insulin model suggest that consuming a high proportion of dietary carbohydrates elevate insulin and consequently “trap” free fatty acids from being released from fat cells (adipose tissue) and being used by metabolically active tissue like organs [4, 5]. The argument is that people then overeat because other tissues (i.e. muscles, heart, liver) cannot gain enough energy because of these “trapped” fats [4, 5, 6]. Therefore, according to this model, consuming excess calories is a result of fat tissue storage due to the increased insulin effect from carbohydrates.  

In line with this model, decreasing dietary carbohydrates will then reduce insulin secretion, increase fat mobilisation, and elevate the burning of free fatty acids [4, 5, 6].  It is proposed that a reduction in carbohydrates would lead to an increase in energy expenditure amounting to approximately 300-600 calories per day [4, 5].  This increase in energy expenditure would thereby result in accelerating fat loss efforts.  In contrast to the carbohydrate-insulin model, a more conventional model argues that, essentially, a calorie is a calorie, meaning that changes in dietary carbohydrates or fat will not significantly change energy expenditure or body fat provided the calories in both carbohydrates and fat are equal [4, 5, 6].  

A Look Into The Research 

If the carbohydrate-insulin hypothesis were true, and insulin was preventing fat breakdown, then all foods that produced a high insulin response would promote fat gain.  The macronutrient protein has been shown in many studies to increase insulin levels similar to carbohydrates [1, 2].  Yet, higher protein diets have been shown to be beneficial for body composition [2, 13]. Also, dairy foods are found to have a high insulin response [9].  Wennerserbery and colleagues (2009) published a study in the American Journal of Clinical Nutrition in 2009 that looked at impact of a increasing dairy intake on body composition [12].  In their study, the authors took 122 middle-aged, overweight individuals and randomly assigned them for 6-months to ether a normal diary group, or a high dairy group [12].  The normal dairy group were asked to continue with their current diet and not change their intake of dairy, while the high dairy group were instructed to include 3-5 portions of dairy products per day [12].  The increase in dairy primarily came from foods like milk, yogurt, and cheese [12]. The authors assessed body composition using dual-energy X-Ray absorptiometry (DEXA) and bioimpedenace [12]. 

Over the 6-month study period, the increase in dairy products in the high dairy group was associated with an increase in average calories as well as a higher intake of protein, fat (especially saturated fat), cholesterol, and calcium [12].  Despite these increases, there were no significant changes between groups in body weight, abdominal fat, or body fat [12]. 

Notably, over the study period, insulin levels between the normal and high dairy groups were not significantly different [12].  It is argued that a high carbohydrate leads to chronically high insulin levels, which results in more fat accumulation, because lipogenesis (fat creation) remains higher than lipogenesis (fat breakdown) [8].  However, insulin, and subsequently lipogenesis, only increase following meals [7, 8].  During the period between meals (fasting), lipogenesis (fat breakdown) will be greater than lipogenesis (fat creation) [8].  Therefore, over an entire day, the period of fat burning and gaining will balance out [8].  This could explain why the authors in the aforementioned study did not see increases in insulin levels in the high dairy group from baseline to post-study at 6-months.  

High Carbohydrates Versus Low Carbohydrate Diets

If carbohydrates are genuinely the cause of weight gain and obesity, then diets that are equal in calories and protein, should show advantages for fat loss in low-carb diets compared with higher-carb diets.  Dietary protein would need to be equated between diets because protein has been shown to increase energy expenditure, therefore offering an advantage for body composition [1, 13].  

A number of studies have examined whether a metabolic advantage exists with low carbohydrate diets, comparing high-carb with low-carb [3, 6, 10].  A meta-analysis conducted by obesity researcher and physicist Kevin Hall and Guo (2017) published in Gastroenterology, pooled together all studies that compared low-carbohydrate diets to relatively higher carbohydrate diets to understand the effects on energy expenditure and body fat [4].  In their study, the authors only accepted studies that matched calories and protein intake and varied their ratio of dietary carbohydrates and fats [4].  Also, to combat the issue of dietary non-adherence in their selected studies, the authors only included studies that controlled feeding where food was provided to subjects [4].  This meta-analysis included at total of 32 studies composed of 563 subjects [4].  

When examining energy expenditure differences between low-carb, high-fat diets compared with high-carb, low-fat diets, the authors found no significant differences between both diets [4].  Similarly, when examining whether an advantage exists with body fat with lower carbohydrate diets compared with lower fat diets, there was no significant difference [4].  In fact, there was a slight advantage in energy expenditure (26 calories more) and fat loss (16 grams more) with higher carbohydrate, lower fat diets compared with lower carb, higher fat [4].  The authors noted that “these results are in the opposite direction to the predictions of the carbohydrate-insulin model” and for all practical purposes “a calorie is a calorie [4].”

In an independent study by Hall and colleagues published in the American Journal of Clinical Nutrition in 2016, the authors completed a well controlled study to investigate whether a diet low in carbohydrates differed in energy expenditure and body composition compared with a calorie equated high-carbohydrate diet [6].  In this study, the authors took 17 obese or overweight men and admitted them to a metabolic ward where feeding could be tightly controlled [6].  The subjects were placed on a 7-week high-carbohydrate, low-fat diet (ketogenic diet) that contained 2,398 calories, 91 grams protein, 300 grams carbohydrates, and 93 grams of fat, followed by a 7-week low-carbohydrate, high-fat diet that contained 2,394 calories, 91 grams protein, 31 grams carbohydrates, and 212 grams of fat [6]. 

On the high-carb, low-fat diet, subjects lost 0.8kg of body weight with 0.5kg coming from body fat [6].  During the low-carb, high-fat diet, subjects lost a rapid 1.6kg of body weight, however only 0.2kg came from body fat [6].  Transitioning from the high-carb to the low-carb diet, subjects experienced a substantial decrease in insulin secretion, an increase in circulating free fatty acids, ketones, as well as a small increase in energy expenditure of about 100 calories per day [6].  Importantly, however, these changes were not accompanied by greater losses in body fat [6].  As each gram of stored carbohydrate, called glycogen, is attached to 3-4 grams of water, the authors noted that the initial, rapid loss of body weight subjects experienced on the low carb diet was primarily the result of water loss, as fat loss was slower than in the high carbohydrate diet [6].  These results directly oppose the carbohydrate-insulin model and are more in line with conventional views that manipulating carbohydrates or fat make little difference to energy expenditure or fat loss when calories and protein intake are equated.

Summary

The argument that low-carbohydrate diets result in body-fat gain relates to the carbohydrate-insulin model, which suggest that carbohydrate containing foods release high levels of insulin, which promote energy storage and prevent its release.  Although insulin plays an important role in regulating body fat, the carbohydrate-insulin model has been refuted in a number of studies.  Instead, studies that equate calories and protein intake show no advantage for fat loss whether carbohydrate intake is low or high.  The weight of the evidence support more of a conventional view that “a calorie is a calorie” in the context of body fat and energy expenditure.  In line with a conventional model, provided total calories are within what is required for an energy deficit and fat loss, the manipulation of carbohydrates or fat are inconsequential.  Priority should of be placed on creating an energy deficit where within, more personal preference can be applied for the proportion of carbohydrates and fats.

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References:

[1] Bray, G. A, et al. 2012. Effect of dietary protein content on weight gain, energy expenditure, and body composition during overeating: a randomized controlled trial. January. Vol. 307, No. 1, pp. 47–55. JAMA.

[2] Comerford, B. K. and Pasin, G. 2016. Emerging Evidence for the Importance of Dietary Protein Source on Glucoregulatory Markers and Type 2 Diabetes: Different Effects of Dairy, Meat, Fish, Egg, and Plant Protein Foods. August. Vol. 8, No. 8, p. 446. Nutrient.

[3] Dirlewanger, M. et al. 2000. Effects of short-term carbohydrate or fat overfeeding on energy expenditure and plasma leptin concentrations in healthy female subjects. November. Vol. 24, No. 11, pp. 1413-1418. International Journal of Obesity and Related Metabolic Disorders.

[4] Hall, D. K. and Guo, J. 2017. Obesity Energetics: Body Weight Regulation and the Effects of Diet Composition. May. Vol. 152, No. 7. pp. 1727-1727. Gastroenterology.

[5] Hall, D. K. 2017. A review of the carbohydrate-insulin model of obesity. March. Vol. 71, No. 3, pp. 323-326. European Journal of Clinical Nutrition. 

[6] Hall, D. K. et al. 2016. Energy expenditure and body composition changes after an isocaloric ketogenic diet in overweight and obese men. August. Vol. 104, No. 2, pp. 324-333. American Journal of Clinical Nutrition.

[7] Kersten, S. 2001. Mechanisms of nutritional and hormonal regulation of lipogenesis. April. Vol. 2, No. 4, pp. 282-286. EMBO Reports.

[8] Krieger, J. 2010. Insulin An Undeserved Bad Reputation. Weightololgy.  Taken From: https://weightology.net/insulin-an-undeserved-bad-reputation/.  

[9] Nilsson, M. et al. 2004. Glycemia and insulinemia in healthy subjects after lactose-equivalent meals of milk and other food proteins: the role of plasma amino acids and incretins. November. Vol. 80, No. 5, pp. 1246-1253. American Journal of Clinical Nutrition.

[10] Schrauwen, P. et al. 2000. Fat and carbohydrate balances during adaptation to a high-fat diet. November. Vol. 72, No. 5, pp.1239-1241. American Journal of Clinical Nutrition.

[11] Vargas, E. et al. 2021. Biochemistry, Insulin Metabolic Effects. January. StatPearls.

[12] Wennersbery, H. M. et al. 2009. Dairy products and metabolic effects in overweight men and women: results from a 6-mo intervention study. October. Vol. 90, No. 4, pp. 960-968. The American Journal of Clinical Nutrition. 

[13] Wycherley, T. P. et al. 2012. Effects of energy-restricted high-protein, low-fat compared with standard-protein, low-fat diets: a meta-analysis of randomized controlled trials. Vol. 96, No. 6, pp. 1281–1298. American Journal of Clinical Nutrition.

Dietary self-monitoring, which consists of recording all foods and beverages consumed along with portion sizes, is considered the “cornerstone” of weight loss treatment [1].  Self-monitoring has not received the rightful attention it deserves, and is typically viewed more as process that mediates weight loss, rather viewed as an important focus for weight loss intervention [5].  

As negative energy balance is required for weight loss, recording key aspects of dietary intake allows an individual to monitor his or her progress towards achieving negative balance and facilitate adjustment made in eating.  In a previous article, it was highlighted that individuals successful at maintaining weight loss over the long-term (i.e. at least one year), maintain heavy use of self-monitoring practices, which include tracking food intake.  

The use of dietary self-monitoring has strong theoretical foundation [1, 2, 8].  Self-regulation, which is an umbrella term used to describe the various processes by which individuals pursue and achieve goals, suggests that self-monitoring precedes self-evaluation towards the desired goal and the information gained from self-monitoring is used to determine one’s goal progress [2, 8].  According to self-regulation theories, self-monitoring should lead to sustained efforts to match the behaviours (i.e. nutrition tracking) to the goals (i.e. weight loss) [1].  Within self-monitoring, recoding food intake is central within this process, involving deliberate attention to one’s eating and recording specific details of eating behaviour [2]. 

This article will review the important role of dietary self-monitoring in weight loss interventions and explore the research to understand which methods of dietary monitoring demonstrate the greatest efficacy, as well as identifying the frequency of self-monitoring required to achieve meaningful weight loss and factors that affect self-monitoring adherence.

Method of Dietary Self-Monitoring 

A wide array of methods are used to perform dietary self-monitoring, including paper food diaries, meal photos, web-based food journalling, wearables, and mobile and tablet applications [11].  In the systematic review by Burke and colleagues (2011), over half the studies investigating the impact of dietary self-monitory on weight loss used paper diaries only [2, 5, 7].  In these studies, food monitoring varied from more general recordings of food eaten throughout each day, to more detailed recordings, including what time food was eaten, quality of food eaten, and grams of fat consumed [2, 5, 7].  In all studies, paper food diaries were associated with significant weight loss lending support to their use for effective self-monitoring [2, 5, 7]. 

The advent of nutritional digital applications like smartphone technologies (i.e. MyFitnessPal) have allowed for easier tabulation of daily calories, decreasing the burden of having to calculate calories manually and providing instant feedback on daily calorie consumption [6, 10, 11].  Having easy access to a database of foods, decreases the need to look up the nutritional values of foods and most nutritional applications allow for frequently consumed meals to be saved, therefore eliminating the hassle of repeated food searching and entry [6, 10, 11].  

In support, research has shown a positive association between electronic self-mentoring and weight loss, with some studies demonstrating an advantage in digital food recording over paper diary form [4].  A two-year randomised clinical trial that compared dietary self-monitoring using either paper or electronic diary found that, compared to the paper diary, the electronic groups were more adherent to self-monitoring and these improvements in adherence led to a greater percent of weight loss [4].  Other studies have shows similar weight loss success between electronic and paper diary self-monitoring, however participants tended to report more convenience and social acceptance using electronic forms like smart phones applications [4, 6, 9, 10].

Frequency of Dietary Self-Monitoring 

Research suggests that the accuracy and completeness of dietary self-monitoring are not as important as the frequency with which self-monitoring is performed [6, 9].  Even with app-based technology, daily recording of food intake is still perceived as effortful and can represent a significant time commitment [6].  Given that adherence to self-monitoring predicts weight loss success and self-monitoring overall tends to decline over time, it would be important to understand the threshold for dietary self-monitoring engagement needed to generate meaningful weight loss [6].  

Ideally, dietary self-monitoring is continuous and updated, e.g. an individual records what he or she eats throughout the day.  However, evidence shows that dietary self-monitoring is not always accurate and deteriorates over time [5].  Burke and colleagues (2008) found adherence to dietary self-monitoring declined in participants from 4.0 entries per day in the first 6 months, to 3.7 from months 6 to 12, then down to 2.2 entries per day from months 12 to 18 [5].  In their study, participants who exhibit less frequency of dietary recording were found to regain the most weight, consistent with other studies that looked at the impact of self-monitoring on weight loss [5].  

Similar findings were published by Harvey and colleagues in 2019 [6].  In an effort to quantify the amount of time required and daily frequency of self-monitoring necessary for successful weight loss, the authors randomised obese individuals to an 18-month, online, group-based weight loss intervention [6].  In their study, participants were randomised to either Behavioural Therapy alone or Behavioural Therapy plus Motivational Interviewing [6].  Participants met once per week in closed groups for the first 6 months, then monthly for the remaining 12 months [6].  Participants were placed on a calorie-reduced diet and asked to self-monitor their daily food intake using online food journalling for the first 6 months [6].  Instructions were given to participants to record food intake throughout the day immediately after consumption and to check the balance of calories to meet their daily goal [6].

The authors found that among individuals losing ≥ 5% or ≥ 10% of their baseline weight, these individuals were spending approximately 23 to 24 minutes per day (2.5 hours per week) recording dietary intake durning the initial month of the program [6].  However, this time reduced to 15 to 16 minutes per day by month 6 [6].  Data from this study also suggest that recording food intake about 3x/day is associated with successful weight loss, compared with fewer dietary recording frequencies [6].  This means that participants would self-monitor closer to the time of consumption (i.e. breakfast, lunch, dinner), in contrast to recording daily food intake in a single sitting [6].  The implications of more frequent self-monitoring would be improved recording accuracy and thus a better understanding of total calories consumed throughout the day.  

This study, as well as others, demonstrate that food journalling more times per day and more days in the month is significantly associated with greater weight loss [1, 6, 9].  However, with 34.5% of participants in this study not registering any food recordings by month 6, it should still remain the focus of weight loss interventions to identify strategies for sustaining higher frequencies of dietary self-monitoring over time [6].  

Factors Affecting Dietary Self-Monitoring 

Multiple factors are found to affect the efficacy of dietary self-monitoring.  Increased consistency has been identified as a powerful factor with studies showing the most consistent self-monitors lose the most weight [1, 2, 9].   

In support, Boutelle and colleagues (1998) found that increased self-monitoring consistency of food intake over an 8-week study significantly correlated with weight loss [1].  In their study, participants were asked to self-monitor daily food intake in booklets and researchers used a weekly rating scale to assess consistency [10].  Of the 59 individuals who participated in this study, 45.6% were found to monitor food intake on 75% or more of the days, while 10.5% of participants monitored food intake on fewer than 25% of the days [1].  By week 8, participants who demonstrated increased consistency with food monitoring more lost weight (mean weight change = -0.76kg) compared with less consistent self-monitors who actually regained weight (mean weight change = +0.48kg) [1].  

The evidence would suggest that individuals with weight loss objectives must learn to observe their eating behaviour systematically and highly consistently.  However, it still remains to be determined what causes certain individuals to sustain self-monitoring efforts?  It is possible that individuals who sustain their self-monitoring efforts maintain better adaptive strategies to counteract barriers to adherence.  Research has also suggested that individuals who had support from significant others (i.e. spouse) showed greater adherence with dietary self-monitoring [3].  Alternatively, these individuals may simply have higher motivations to self-monitor [3].  

As previously discussed, evidence shows the more frequent individuals monitored their food intake, the more weight loss was achieved [5, 6, 9].  Similar to consistency, increased frequencies of dietary self-monitoring facilitates weight loss by helping individuals enhance their awareness of food consumed throughout the day and week, which in turn improves adherence to calorie intake goals [6, 9].  Paterson and colleagues (2014) showed that individuals who showed increased frequency of dietary self-monitoring, assess by total number of food diaries submitted, lost more weight when combined with more consistent food recording (i.e. number of weeks completing ≥ 3 records per week) [9].   In their study, subjects who were not consistent at least 50% of the time regained at least 5% of their body weight from 6-month assessment [9]. These findings reinforce the importance of identifying strategies that can enhance both frequency and consistency of dietary self-monitoring.  

Practical Application 

Dietary self-monitoring, the systematic observation in recording of eating behaviour, is shown to be a critical aspect of weight loss interventions [1].  A systematic review by Burke and colleagues published in the Journal of the American Dietetics Association showed that, among the fifteen studies that used dietary self-monitoring for weight loss, all of the studies had clinically significant weight loss outcomes [2].  

Research shows that not just self-monitoring food intake, but highly consistent and frequent self-monitoring often correlates with significant weight loss and maintenance of weight control [6, 9].  Evidence has found that 23 to 24 minutes per day recording dietary intake may be required to generate meaningful weight loss (i.e. ≥ 5% or ≥ 10% weight loss), coupled with self-monitoring frequencies that aim to track each week, at least 3 or more times per day [6].  Aiming to self-monitor all foods eaten on at least 75% or more of the days are suggested to be a reasonable target for recording consistency [1].  

Among all the varying methods for tracking food intake, using mobile, smart phone apps has been suggested to enhance adherence to dietary self-monitoring potentially more than other tools [4, 6, 11].  Automated calorie calculations, food nutrition value databases, enhanced convenience, more social acceptance, and instant feedback are all reasons why mobile devices can help increase the timeliness and accuracy of dietary self-monitoring to improve adherence [2, 11]. 

Notably, evidence shows that the detail of food records are not as relevant as the frequency and consistency of self-monitoring for weight loss [9, 11].  In fact, abbreviated methods that use simple checkboxes to estimate fat and calorie content of meals are found to be more effective for improving self-monitoring consistency compared with more detailed recordings [7].  Given this, focus should be placed on strategies to improve frequency and consistency of dietary self-monitoring, not necessarily the detail of recordings [7, 9].

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References:

[1] Boutelle, K. N. et al. 1998. Further Support for Consistent Self-Monitoring as a Vital Component of Successful Weight Control. May. Vol. 6, No. 3. pp.219-223. Obesity Research.

[2] Burke, L. E. et al. 2011. Self-Monitoring in Weight Loss: A Systematic Review of the Literature. January. Vol. 111, No. 1, pp.92-102. Journal of the American Dietetics Association.

[3] Burke, L. E. 2009. Experiences of Self-Monitoring: Narratives of Success and Struggle During Treatment for Obesity. June. Vol. 19, No.6, pp.815-828. Qualitative Health Research.  

[4] Burke L. E. et al. 2009. Self-Monitoring in Behavioral Weight Loss Treatment: SMART Trial Short-term Results. Vol.17, S273. Obesity.

[5] Burke, L. E. et al. 2008. Using Instrumented Paper Diaries to Document Self-Monitoring Patterns in Weight-Loss.  Vol. 29, No. 2, pp. 182-193.  Contemporary Clinical Trials.

[6] Harvey, J. et al. 2019. Log Often, Lose More: Electronic Dietary Self-Monitoring for Weight Loss. March. Vol. 27, No. 3, pp. 380-384. Obesity.

[7] Helsel, D. L. et al. 2007. Comparison of Techniques for Self-Monitoring Eating and Exercise Behaviors on Weight Loss in a Correspondence-Based Intervention. October. Vol. 107, No. 10, pp.1807-1810. Journal of the American Dietetics Association. 

[8] Mann, T. et al. 2013. Self-Regulation of Health Behavior: Social Psychological Approaches to Goal Setting and Goal Striving. Vol. 32, No. 5, pp.487-498. Health Psychology. 

[9] Paterson, N. D. 2014. Dietary Self‐Monitoring and Long‐Term Success with Weight Management. September. Vol. 22, No. 9, pp.1962-1967. Obesity.

[10] Tate, D. F. 2001. Using Internet Technology to Deliver a Behavioral Weight Loss Program. March. Vol. 285, No. 9, pp.1172-1177. JAMA.

[11] Yu, Z. et al. 2015. Dietary Self-Monitoring in Weight Management: Current Evidence on Efficacy and Adherence. December. Vol. 115, No. 12, pp. 1931-1938. Journal of the Academy of Nutrition and Dietetics. 

Introduction

The food environment is a heavily under appreciated factor when it come to managing weight gain and refers to factors directly related to a way food is presented or delivered, such as its visibility, proximity, structure, packaging, portion size, or how it is served [10]. While people tend to acknowledge that environmental factors influence weight gain, they often wrongly believe they are unaffected.  People are also often surprised at how much food they consume, which implies they may lack important consumption awareness or fail to effectively self-monitor food intake [10}.  

For many individuals with weight loss objectives, self-monitoring (tracking) of food intake can be an inconvenience and annoyance.  Instead, people tend to rely on adjusting off consumption norms to determine appropriate food intake [10].  That is, how much one normally consumes [10].  The issue is that food consumption is greatly influenced by alternative norms and environmental cues, such as food proximity, variety, or portion size [8].  For example, large plate sizes may suggest, at least perceptionally, that it is appropriate to consume more food that smaller plate sizes [10].  Without food monitoring, the impact of environmental factors, such as plate size, on food consumption is magnified because they can easily distort one’s estimate of how much is eaten [8].  

Research shows that people eat more of a food when that food closer to them, a concept referred to as “proximity effect,” as well as when food is available in abundance and more visible [5. 9].  This suggests that the convenience and salience of foods can play an important roll in consumption frequency and ultimately influence weight gain.  The question is then how can someone with weight loss objectives modify his or her food environment to reduce or eliminate the unintended effects on consumption?  This article will identify three environmental traps within food salience, variety, and portion sizing that facilitate over food consumption and outline practical-based strategies within each area to alter the food environment to help control consumption. 

Salient Food Promotes Weight Gain

The visual cue of seeing a food can be a powerful stimulant for unintended overconsumption and weight gain.  Research demonstrates that higher food visibility of favourable foods can increase hunger and stimulate salivation, which can then result in greater food consumption [9, 10].  In support, Wansink and colleagues (2006) found that people consumed sweets 46% more quickly when the sweets were placed in clear containers, making them more visible, compared to when placed in opaque containers [9].  

In their study, the authors tested the influence on the visibility and proximity of chocolates on 40 female office workers [9].  The conditions tested included placing a bowl of chocolates (Hershey Kiss’s) on the desk of the participants in either covered bowls or opaque bowls [9].  The authors also tested proximity by manipulated the positioning the bowls, where the bowls were either positioned on the desk of participants, or 2 meters away from their desks [9].  After a 4-week study period, the authors found that participants ate more than double the amount of chocolates when the chocolates were placed on the desk and in a clear bowl, compared to when the chocolates were placed 2 meters away in an opaque bowl [9].   

Interestingly, when participants were asked to estimate how many chocolates they believed they ate, participants tended to underestimate how many chocolates they ate when the candies were most proximate (i.e. positioned on the desk) and overestimate how many they ate when less proximate (i.e. 2 meters away) [9].  These findings confirm the idea that people can lack consumption awareness under situations of strong visual food cues and underreport food eaten when favourable food is visible and in close proximity [9].  When asked to rate the influence of visibility and proximity, participants rated the candies as “more difficult to resist” and “attracting more attention” when the chocolates were more visible (i.e. in clear bowls), as well as when the chocolates were more proximal [9]. 

Although cognitive-based mechanisms can be used to explain why visible foods increase temptation for consumption, physiological components that can enhance hunger are also heavily involved [10].  Neuroscientist and obesity researcher, Stephan Guyenet, highlights that the visibility and smell of favourable foods enhance hunger and cravings by increasing the release of dopamine in the brain, a neurotransmitter involved in pleasure and learning [3].  Guyenet indicates that the sight and smell of favourable, calorie-rich foods stimulates a high motivation to eat them, or what is described as high “food reward” [3].  The process of learning to associate the sight and smell of foods, with the help of dopamine, makes these sensory cues very challenging to override [3]. 

Practical-Based Strategies

At home, or work environment, remove all tempting, high calorie-dense foods to eliminate exposure to food cues.  These include common confectionery snacks like cookies, crisps, and sweets, but also “healthier” food items like salted nuts.  If your family members consume these foods, you need to re-position them in inconvenient places in the house. For example, moving snacks from the kitchen counter to the highest shelf in the cupboard where they are completely out of sight and difficult to reach.  Alternatively, moving snacks to inconvenient locations in the house that require more effort to obtain.  By eliminating powerful visual cues to consume these foods, you will reduce your cravings. 

As you eliminate powerful visual cues to high calorie-dense foods, also reduce barriers to consuming foods that you want to eat more of.  For example, just the effort of peeling an orange  or pineapple, can become a barrier for consumption.  Instead, already have these foods prepared (ii.e. fruit peeled and sliced), ready to eat, and highly visible places (i.e. eye level in fridge).  The objective is to re-design your food environment to make more visible and easily accessible the food you need to eat more of, while doing the opposite for foods you need reduce consumption (you can still have them occasionally).

More Food Variety Promotes Weight Gain

A number of research studies demonstrate that simply increasing the variety of foods can increase its consumption and lead to weight gain.  A phenomenon researchers call the “buffet effect,” because of the overconsumption that is known to occur at buffets, increased food variety can increase consumption because it exposes the brain to new sensory stimuluses that consequently increase food reward and motivation to eat [3].  It is suggested that food variety relates to habituation [3].  Habituation refers to  the response where, the more we are exposed to particular stimulus, the less we respond to it and has been demonstrated in the context of food [3].  

Research by Rolls and colleagues (1981) found that when subjects were asked to rate the palatability of provided foods, their palatability ratings of a particular food decreased if they were  given that same food shortly after [6].  In that study, volunteers were asked to rate the palatability of eight different foods by tasting a small amount of each food [6].  Then, participants were given one of the eight food items again for lunch [6].   The authors found that the palatability rating of foods decreased more in the foods that were eaten previously compared to the other foods that were not [6].  Furthermore, when participants were given a second course containing all eight of the same foods they were given for lunch, they ate less [6].  This suggests habituation to certain foods in the study lowered the appeal to eat them, a term Rolls coined as “sensory-specific satiety [6].”

Habituation can also explain the “buffet effect.”  At a buffet, wide food variety allows us to select from different foods that all have different sensory properties (i.e. sweet, sour, fatty, salty) from each other [3].  Given this, the brain does not habituate to any particular food and can experience a new sensory experience with each food variety [3].  An individual may feel “full” or satiated after eating a large savoury meal, yet still desire a sweet dessert because it provides a novel stimulus [3].  This is how, with greater food variety, it is common for people to overeat.

Practical-Based Strategies

“Sensory-specific satiety” is a sensation of fullness applied to foods of similar sensory properties (i.e. sweet, salty, fatty) as to the one’s just eaten [7[.  To apply this phenomenon, select a food of similar property to your “trigger” food.  For example, eat two or more sweet tasting fruits prior to having dessert.  With similar sweet properties, the fruits will stimulate sensory-specific satiety and help to manage dessert cravings.  When it comes to high-calorie dense foods, limit the variety of these foods to naturally promote habituation.  Stick to one or two varieties only of a particular food and you will find it will better manage overconsumption. 

Large Portion Sizes Promotes Weight Gain

Larger food portions sizes have been studied extensively and consistently show significant increase in calorie intake and potential for greater weight gain.  A meta-analytic review by Zlatevska and colleagues (2014) showed that doubling a food portion size leads to about a 35% increase in calorie intake [11].  Furthermore, in a 2015 Cochrane review by Hollands and colleagues, the authors found that calories from food and non-alcoholic beverages attributable to differences in portions sizes fell between 215 and 275 calories per day [4].  

A study published by French and colleagues (2014) in the Journal of Obesity showed larger lunch portion sizes led to a significant increase in calorie intake and weight gain over 6 months in a real-life, work setting [2].  In their study, the authors randomly assigned participants to receive either a low portion size (400 calories), moderate portion size (800 calories), or high portion size (1,600 calories) lunch boxes for 6 months [2].  The study consisted of manipulating lunch menus with specific energy content according to each experimental condition [2].  Bodyweight and energy intake of participants were measured at 1, 3, and 6-months [1].  

The authors found that participants in the large box lunch group consumed significantly more calories compared to the low and moderate box lunch groups, which persisted up to the 6 months [2].  Also, the high portion size group experienced significant weight gain over the study period, while there was no change in bodyweight with the low and moderate box lunch groups from baseline to 6-months [2].  

Although many mechanisms have been used to explain this “portion-size effect,” a primary explantation as to why larger portion sizes stimulate overeating is that they create new consumption norms or level of food “appropriateness [8].”  As previously indicated, consumption norms refer to how much food an individual “normally” consumes or deems “appropriate [#].”  Within the potion-size effect concept, as opposed to hunger or satiety guiding intake, food portion size dictates the amount consumed and serves as the food norm [10].  With larger portion sizes, they implicitly suggest what is “normal” to an individual [10].  

Practical-Based Strategies

Manage your food portions by buying smaller portions of the foods you tend to over-consume.  Avoid buying calorie-dense foods in large portion / package sizes (i.e tubs of ice cream, large bags of crisps, bags of chocolate).  Instead, purchase only individual portion sizes.  For example, an individual bar of chocolate instead of a packet or box, or an individual serving of ice cream or crips instead of a tub or large bag.  Also, use smaller dishes (i.e. plates and bowls) when plating higher calorie foods.  For example, using a large plate with pasta will naturally encourage larger portions and tend to lead to overconsumption.  Whereas, using a smaller plate will encourage more reasonable size portioning, even if you have a second serving of the pasta.   

Summary

While much of the focus in weight loss is on food choice, food consumption plays a more important role in managing weight gain.  Food choice decisions determines what we eat (i.e. pasta, pizza, soup, salad), while food consumption decisions determines how much we eat (i.e. two plates of pasta, tub of ice cream).  As it is generally accepted that any food can fit into a healthy diet, it is more the overconsumption of foods, even those considered “healthy foods,” that lead to weight gain.  

The food environment, which relates to the way food is presented, has significant impact over food consumption.  Total food consumption, which includes how often a food is eaten (frequency) and how much is eaten at a time (volume), are greatly influenced by the food environment.  The frequency of food consumed is heavily influenced by salience (visibility and smell) and the ease or effort required to locate and eat the food.  The volume of foods is mediated by an one’s consumption norm, which is influenced by food variety and portion sizing.  Learning how food environment factors such as these influence consumption and how one can alter his or her food environment to help reduce consumption are both critical for managing short and long-term weight gain.

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References:

[1] Chandon, P. and Wansink, B. 2002. Does Stockpiling Accelerate Consumption. A Convenience-Salience Framework of Consumption Stockpiling. Vol. 39, pp.321–335. Journal of Marketing Research. 

[2] French, S. A. et al. 2014. Portion Size Effects on Weight Gain in a Free Living Setting. June. Vol. 22. No. 6. pp. 1400-1405. Obesity. 

[3] Guyenet, J. S. 2017. The Hungry Brain. Vermillion: London.

[4] Hollands, G. J. et al. 2015. Portion, Package or Tableware Size for Changing Selection and Consumption of Food, Alcohol and Tobacco. September.. Vol. 9. Cochrane Database Systematic Review. 

[5] Hunter, J. A. et al. 2016. Impact of Altering Proximity on Snack Food Intake in Individuals with High and Low Executive Function: Study Protocol. June. Vol 16. p. 504. BMC Public Health. 

[6] Rolls, B. J. et al. 1981. Variety In A Meal Enhances Food Intake In Man. February. Vol. 26, No. 2, pp. 215-221. Physiology & Behaviour.

[7] Rolls, B. J. et al. 1988. The specificity of satiety: The influence of foods of different macronutrient content on the development of satiety. Vol. 43, No. 2, pp.145-153. Physiology & Behaviour.

[8] Steenhuis, I. & Poelman, M. 2017. Portion Size: Latest Developments and Interventions. March. Vo. 6, No. 1, pp. 10-17. Current Obesity Reports.

[9] Wansink, B. 2016. The Office Candy Dish: Proximity’s Influence on Estimated and Actual Consumption. May. Vo. 30, No. 5, pp. 871-875. International Journal of Obesity.  

[10] Wansink, B. 2004. Environmental Factors that Increase the Food Intake and Consumption Volume of Unknowing Consumers. Vol. 24, pp. 455-479. Annual Review of Nutrition. 

[11] Zlatevska, N. et al. 2014. Sizing Up the Effect of Portion Size on Consumption: A Meta-Analytic Review. May. Vol. 78, No. 3, pp. 140–154. Journal of Marketing. 

Introduction

Fat loss follows the principles of thermodynamics in that energy expenditure needs to be lower than energy intake for fat loss to occur.  However, fat loss is clearly a result of a more complex set of biological, environmental and behavioural interactions, where changing any one of these factors can alter fat loss efforts [6].  For example, in the current obesogenic environment, the promotion of ultra-processed foods, like fast food and ready made meals, are ubiquitous [3].  

It is also well established that a number of biological adaptations occur with energy restriction that activate the body’s self defence systems to resist fat loss [4].  These include a slowing of metabolic rate, decrease in spontaneous activity, and hormonal alterations that increase hunger and lower metabolic rate [4].  A primary outcome from neural adaptations in response to energy restriction is elevated hunger [4].  Overeating as a result of increased hunger (or decrease in satiation) can close the energy gap, further resisting fat loss.  

Given the multiple factors that work against fat loss, identifying strategies that help create an energy deficit, increase satiety, and sustain fat lost over the long-term are highly relevant.  This article will review four simple nutritional strategies that have been shown in the scientific literature to aid in fat loss efforts.

Fat Loss Strategy 1: Eat Less Ultra-Processed Foods

Ultra-processed foods like potato chips, fast food, cookies, brownies and commercial cereals and breads, are foods that go through a number of processes that add chemicals, colourings, sweeteners and preservatives [3].  These foods are typically inexpensive, convenient, and carry long shelf-lives [3].  Also, ultra-processed foods tend to be much higher in calorie-dense combinations of fat, protein, sugar, starch, and salt, compared to unprocessed or minimally process foods, which make them more palatable and preferred [2]. 

It is suggested that these very food properties are most likely to cause food cravings and loss of control of eating [2].  Just as the dogs in Ivan Pavlov’s famous 1890’s study learned to associate a bell ring with food and would salivate at the sound of a bell alone, it is suggested that our brains similarly learn to create sensory cues to ultra-processed foods that stimulate pleasure (via dopamine) and a higher motivation to eat them [2].

The reinforcing, motivating and palatable features of these ultra-processed foods are said to carry high “food reward,” an umbrella term used to describe how our brains value certain foods more than others and create more motivation to eat them [2].  If ultra-processed foods are higher in calories and their food properties make them highly motivating to eat, it becomes much more challenging to control caloric intake for fat loss under these conditions.  

Reducing the amount of ultra-processed food and incorporating more unprocessed (i.e. fruits and vegetables) and minimally processed (i.e. potatoes, milk, grilled fish) foods in one’s nutrition can greatly help to minimise food reward and control calories consumed, making it easier to stay in a caloric deficit for fat loss.

Fat Loss Strategy 2: Eat More Low-Energy Dense Foods

Energy density describes the energy content of a food (calories), divided by its weight (grams) [6].  For example, for a medium size apple that contains 80 calories and weighs 154 grams, its energy density is 0.5 (80/154 = 0.5).  The energy density of foods can be divided into four categories as displayed below in Table 1 [8].  A food’s energy density depends both on its macronutrients (fats, proteins, carbohydrates) and its water content [7]. 

Research demonstrates that under ad libitum eating conditions (where people can eat as much as they want), people consistently eat more when given high energy dense foods compared with low energy dense foods [6].  Interestingly, when it comes to food intake, energy density appears to act independently of the amount of food consumed [7].  Rolls and colleagues (1998) demonstrated this when they fed subjects meals of varying energy densities (low, medium and high) [9].  In their study, they found that, over a two-day period, subjects in all three groups ate the same weight of food despite being provided with meals of different energy densities [9].  Activation of sensory nerves in the stomach that register food volume, not density, may explain why all three groups ate the same weight of food [9].  After the two days, the low calorie dense group ate approximately 400 calories per day less compared with the high calorie dense group [9].  With the low energy dense group consuming significantly fewer calories, fat loss would be more likely in this group over time compared with subjects in the high energy dense group. 

Therefore, consuming foods lower in energy density, such as fruits and vegetables, lean proteins, legumes, and low-fat dairy, can help greatly reduce calorie intake, thereby facilitating fat loss efforts.  

Fat Loss Strategy 3: Eat More High Fibre Foods

Fibre are complex carbohydrates that pass through the small intestine undigested and enter the large intestine [12].  In general, there are two main types of fibre: soluble and insoluble fibre [12].  Because high fibre foods are primarily made up of water, they typically supply fewer calories and have lower energy densities compared with low fibre foods [7].  For example, only 9 potato chips (Lay’s Classic) provide 100 calories, whereas, for the same amount of calories, this would equate to 3 cups of popcorn.  For fat loss, this means that larger amounts (greater volume) of popcorn can be consumed at relatively lower calorie content.

Nutritional scientist Barbara Rolls explains that consuming greater food volume can limit food intake via sensory specific satiety, which describes a decline in the pleasure response to the sensory properties of food as it is consumed [7].  In other words, as more of a particular food is consumed, the satisfaction of this food decreases, resulting in a desire to limit eating.  Given this, consuming greater amounts of popcorn, as opposed to potato chips, can help to activate sensory specific satiety because of the relatively larger volumes that can be consumed, at far less calories. 

Also, other physical and chemical characteristics of dietary fibre make it highly relevant to fat loss.  Dietary fibre has water-holding capacity, bulking and viscous properties that are shown to slow digestion and contribute to gastric (stomach) distention, which contribute to feelings of satiety and is shown to decrease food intake [12].  Mechanistically, gastric distention may help improve satiety by activating sensory receptors in the stomach that communicate feelings of fullness to the brain [7]. 

Eating more high fibre foods like different fruits, vegetables, whole grains and legumes can help to enhance satiety, making it easier to limit food intake as well as allowing for greater volumes of these fibrous foods to be consumed with the benefit of reduced calorie intake.

Fat Loss Strategy 4: Increase Daily Protein Intake

Dietary protein is a macronutrient found in animal and plant-based foods, and alternative sources such as algae, bacteria and fungi [11].  Although higher protein intakes are generally associated with maximising muscle development, research demonstrates that high protein diets, where protein represents 25% of more of total calories, can be effective for fat loss [5].  One potential mechanism for how protein facilitates fat loss is through diet-induced thermogenesis [5].  Thermic effect of food is defined as the increase in energy expenditure above baseline following consumption due to energy requirements for the digestion, absorption, and disposal of nutrients [5].  

Protein is found to have a thermic effect of 20-35% of energy consumed [5].  A study by Eistenstein and colleagues (2002) found that for a 2,000 calorie diet, increasing protein intake from 15% to 30% led to an energy expenditure increase of 22 calories per day due to thermic effect [1].  Other studies have shown increases of up to 90 calories per day from higher protein intakes [5].  Although this figure may appear relatively small, consumption over a longer period could become significant for helping fat loss, particularly when combined with other strategies. 

Also, a number of studies demonstrate the potential satiating effects of high protein diets, which, similar to high fibre foods, may have beneficial effects on limiting energy intake for fat loss [5].  Rolls and colleagues (1988) performed a study that provided five calorically matched preload meals (high protein (75% protein), high fat, high starch, high sucrose, and mixed preloads) to 10 females subjects on separate occasions, following which, subjects were asked to their rate feelings of hunger and satiety [10].  Two hours following preload meals, subjects who received the high protein and high starch meals reported the lowest hunger and highest satiety ratings compared to the other preload meals, lending support to the potential satiating effect of higher protein intake [10]

A potential mechanism to explain why higher protein intakes may enhance satiety is via changes in appetite hormones like appetite stimulating hormone ghrelin and satiety hormones GLP-1 and peptide YY [11].  It is suggested that protein-induced satiety is concomitant with a decrease in ghrelin and increase in both GLP-1 and peptide YY [11].

Increasing protein intake to represent at least 25% or more of total calories or 1.2-1.6 grams per kilogram can facilitate fat loss via increase thermic effect, while providing enhanced satiety along with high fibre foods. 

Summary

Under the first law of thermodynamics, calorie intake is required to be lower than calorie output for fat loss to occur.  On the surface, creating an energy deficit can appear simple.  However, biological, environmental and behavioural pressures make fat loss significantly more challenging.  Simple nutritional strategies that limit consumption of ultra-processed foods, while increasing consumption of low energy dense, high fibre foods, and increasing daily protein intake, have strong theoretical and clinical support for helping to facilitate fat loss efforts.

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References:

[1] Eistenstein, J. et al. 2002. High-protein Weight-loss Diets: Are They Safe and Do They Work? a Review of the Experimental and Epidemiologic Data. July. Vol. 60, No. 7, pp.189-200. Nutrition Reviews.

[2] Guyenet, S. 2017. The Hungry Brain. Vermillion: London.

[3] Hall, K. D., et al. 2019. Ultra-Processed Diets Cause Excess Calorie Intake and Weight Gain: An Inpatient Randomized Controlled Trial of Ad Libitum Food Intake. July. Vol. 30, No. 1, pp. 67-77. Cell Metabolism.  

[4] Hall, K. D. 2018. Metabolic Adaptations to Weight Loss. May. Vol. 26, No. 5, pp. 790-791. Obesity.

[5] Halton, T. L. and Frank, H, B. The Effects of High Protein Diets on Thermogenesis, Satiety and Weight Loss: A Critical Review. October. Vol. 23, No. 5, pp.373-385. Journal of the American College of Nutrition.

[6] MacLean, P. S. et al. 2011. Biology’s Response to Dieting: The Impetus for Weight Regain. September, Vol. 301, No. 3. American Journal of Physiology Regulatory, Integrative and Comparative Physiology. 

[6] Poppitt, S. D. and Prentice, A. M. 1996. Energy Density and Its Role in the Control of Food Intake: Evidence From Metabolic and Community Studies. April. Vol. 26, No. 2, pp.153-174. Appetite. 

[7] Rolls, B. J. 2017. Dietary Energy Density: Applying Behavioural Science to Weight Managements. September. Vol. 42, No. 3, pp. 246-253. Nutrition Bulletin.

[8] Rolls, B. J. 2012. The Ultimate Volumetrics Diet. William Morrow: New York.

[9] Rolls, B. J. et al. 1996. Energy Density of Foods Affects Energy Intake in Normal-weight Women. April. Vol. 67, No. 3, pp. 412-420. American Journal of Clinical Nutrition.

[10] Rolls, B. J. et al. 1988. The specificity of satiety: The influence of foods of different macronutrient content on the development of satiety. Vol. 43, No. 2, pp.145-153. Physiology & Behaviour.

[11] Wang, S. and Wang, C. 2019 The Effect of Dietary Protein on Weight Loss, Satiety and Appetite Hormone. February. Vol. 3, No. 2. ACTA Scientific Nutritional Health.

[12] Williams, B. A., et al. 2019. “Dietary Fibre”: Moving Beyond the “Soluble/Insoluble” Classification for Monogastric Nutrition, with an Emphasis on Humans and Pigs. May. Vol. 10, No. 45. Journal of Animal Science and Biotechnology.

Introduction 

The term diet brings about many different interpretations, feelings and associations. Those who diet typically associate the term with weight loss, food restriction, anxiety, and popular diets like low carbohydrate, low fat, vegan or a ketogenic diet.  The problem with the team “diet” is it is associated with a start and end point [1].  A start of a diet indicates the initiation of caloric restriction, and the end associated with a return back to “normal” eating [1].  This focus is clearly on a short-term result, rather than long-term sustainability.  A diet, regardless of the named type, can certainly be effective for weight reduction.  The research shows that six out of seven people successfully use dieting to lose weight [1].  The problem is that within three years of weight loss, approximately 95% of individuals will have regained all of the weight back [1].  With the success rate of diets being only about 5%, the problem then is not the weight loss, but rather the longer-term strategies, or behaviours, that individuals utilise to keep the weight off.  This article will look into three behavioural strategies successful dieters apply to ensure the weight stays off: self-monitoring, structure and flexible eating, and long-term thinking. 

Self-Monitoring 

Self-monitoring is the recording of the performance of a particular behaviour such as food intake, physical activity, or general movement [2].  Even self-weighing is considered a form of self-monitoring [2].  Self-monitoring is critical to the process of determining one’s progress towards a particular goal.  It brings about deliberate attention to an individuals behaviours and in recording one’s actions, self-mentoring provides the opportunity to not only evaluate progress, but modify the specific behaviour if required [2].  For example, if an individual records their food intake each day of their diet, he or she can easily determine whether their food intake is aligned with the deficit required for desired weight loss.  Furthermore, by placing deliberate attention on the behaviour of food intake, that individual can better modify their behaviour (i.e. regulate alcohol intake) to ensure the behaviour stays in line with their goal [2].  

The literature shows that individuals successful at maintaining weight loss over the long-term (i.e. at least one year), make heavy use of self-monitoring practices.  In a paper by Hindle and Carpenter (2011), the authors interviewed 10 female subjects who had lost 10% of their weight and kept it off for at least one year (i.e. weight maintainers) [4].   Among other interview questions, the subjects were asked “What do you think is different about how you have approached controlling your weight compared to others who have not had your success?” [4]  In response to this question, all subjects reported using self-monitoring practices, including regular bodyweight weighing, activity tracking and using diet records for food intake [4].  Subjects reported that these self-monitoring strategies enabled quick identification of any changes in all areas influencing weight loss and self-monitoring gave subjects the feeling of ownership and self-control over their weight [4].

Recording daily food and drink intake can bring about feelings of anxiety.  Often, bringing poor habits to the surface can be feel uneasy as most adopt an “out of sight, out of mind” mentality [1].  However, measuring food intake should be viewed as a self-regulation habit that allows for more control and mental engagement in the process of weight loss and management [1].  Similarly, bodyweight weighing, should deviate away from individual readings to assess from an average reading over multiple (i.e. three or four) days to account for daily fluctuations [1].  This approach helps to minimise feelings of poor self-image and neuroticism over bodyweight [1]. 

Diet Structure And Flexibility

Anyone who has worked with a personal trainer or coach, have probably benefited from some degree of structure.  This can take the form of a diet plan, workout program, sleep schedule, or general movement strategy.  In the context of weight loss, research shows that successful dieters utilise structure in their eating to provide direction, reduce deviation, and build confidence in their eating [3, 7].  This would explain why commercial diet programs, such as Weight Watchers have become popular over the years [3, 7].  Weight Watchers, and other diet programs, have been shown, at least in the short-term (up to 6 months), to generate superior weight loss compared to control, behavioural counselling, or educational intervention [3, 7].  It is likely that structured nutrition eliminates the guesswork in meals giving people confidence in knowing that their eating aligns with their weight loss goals.  

A recent study by Kruschitz and colleagues (2017) investigated the effects of a structured diet program on overweight and obese (body mass index >25) individuals [7].  In their study, the authors randomly sampled 70 male and female subjects and enrolled them in a standardised, meal replacement weight loss program for at least 24 weeks [7].  The program placed subjects on a very low calorie diet for 2 days, followed by a meal-replacement, energy-reduced diet for at least 10 weeks [7].  Following the 10 weeks, subjects were to maintain their weight without using meal replacements [7].  Behaviour therapy was also provided to subjects in the form of nutrition and activity tracking [7].  Results of this study showed a significant reduction in body weight and body fat among those who participated in the weight loss program [7].  From baseline to 12-months, 61% of subjects achieved between 5-15% weight loss from their initial starting weight [7].  However, it is relevant to note that subjects started to regain body weight and fat mass following 12 months, which questions the longer-term effects of structured weight loss programs [7].

For longer-term weight loss, a flexible approach to nutrition is found to be a key feature utilised by weight maintainers.  Where rigid eating is characterised by an “all or nothing” approach, flexible eating is a more graduated approach that moves away from a diet mentality to adopt a more flexible one [4, 5].  Within a flexible approach, there are no restricted foods, only parameters of calories and macronutrients (i.e. protein, fats, carbohydrates) [1].  Studies have shown that successful weight maintainers allow themselves to have the odd “treat” and avoid banning foods to eliminate feelings of deprivation and improve nutritional adherence [4].  A study by Stewart and colleagues (2002) showed that individuals who adopted a flexible approach to eating were less likely to report symptoms of eating disorders, including lower mood disturbances and anxiety [5].   Also, compared to individuals that used rigid control in eating, lower body mass indexes were found among individuals that applied a flexible approach [5].   

Think Long-Term

A common feature among individuals who achieved success in weight management is their mentality to view weight loss as a life-long venture not short-term fix [4].  Individuals describe their approach as being more of a way of life, rather than just a diet [4].  The mindset of successful weight maintainers is that their behaviours (i.e. increasing activity) would be practices they would keep forever [4].  In support, studies have found that successful dieters take long-term information into account when making decision, rather than relying on recent outcomes for information.  In other words, successful dieters make decisions based on long-term outcomes, as opposed to short-term, immediate outcomes [6].  For example, a unsuccessful dieter would give in to the pleasure of eating, whereas a successful dieter would exhibit self-control over eating purely for pleasure because of the potential for this behaviour to results in future disease or weight gain. 

A 2015 study by Koritzky and colleagues examined the differences in decision making between successful and unsuccessful dieters [6].  The authors recruited adults, primarily female (81% female), and enrolled them in a 16-week weight management program [6].  The subjects consisted of both successful and unsuccessful dieters, where successful weight loss was defined by losing at least 5% of one’s initial weight [6]. Subjects were asked to complete decision-making tasks at the beginning of the weight loss program followed by questionnaires [6].  Results of this study showed that compared to unsuccessful dieters, successful dieters had a greater tendency to use long-term information in decision making [6].  Individuals successful at weight loss tended to think about the long-term impact of one’s actions [6].  In the context of eating, these individuals would tend to adopt healthier eating habits rather than eat for satisfaction because they consider the long-term risks associated with poor eating habits [6].  This process of more effortful, long-term thinking may better facilitate the modification of habits and results in sustainable weight loss.

It is very common among dieters that struggle to lose weight and yo-yo diet to adopt short-term strategies [1].  These individuals are more focused on losing maximal weight without any consideration to longer-term consequences.  In contrast, successful weight loss individuals do not typically engage in fad diets, but instead focus on gradual, sustained weight loss [1].  Even goal setting among successful dieters is long-term with emphasis on consistent, sustainable behaviours such as regular exercise, controlled eating, and increased general movement.

Summary

With only 5% of diets being successful, attention should be placed on the strategies the small percentage of successful weight maintainers utilise to sustain weight loss long-term.  The literature demonstrates that weight maintainers apply heavy use of self-monitoring practices, including recording daily food intake, tracking activity and self-weighing.  Also, they use structure in their diet to provide stability and eliminate the guesswork in eating.  Even within a structured approach, successful weight maintainers use flexibility in their eating, allowing themselves to have any food within their caloric and macronutrient parameters and using self-monitoring to ensure they stay on track.  Finally, individuals who are successful at weight management think long-term.  They make decisions based on long-term outcomes and use this information to control their actions, such as not over consuming high-calorie dense foods as they would results in future weight gain.  These strategies or behaviours are consistently shown in the research to be utilised by successful dieters and should be heavily considered in one’s weight loss journey for long-term success. 

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References:

[1] Baker, P. and Norton, P. 2019. Fat Loss Forever. Characteristics of Successful Dieters. pp. 77-85.

[2] Burke et al. 2010. Self-Monitoring in Weight Loss: A Systematic Review of the Literature. January. Vo. 111, No. 1, pp.92-102. Journal of American Dietetics Association. 

[3] Gudzane, K. et al. 2015. Efficacy of Commercial Weight Loss Programs: An Updated Systematic Review. April. Vol. 162, No. 7, pp. 501-512. Annals of Internal Medicine.

[4] Hindle, L. and C. Carpenter. 2011. An Exploration of the Experiences and Perceptions of People Who Have Maintained Weight Loss. August. Vol. 24, No. 4, pp. 342-350. Journal of Human Nutrition Dietetics. 

[5] Stewart, T. et al. 2002. Rigid vs Flexible Dieting: Association with Eating Disorder Symptoms in Non-Obese Women. Vol. 38, pp. 39-44. Appetite. 

[6] Koritzky, G. et al. 2015. The Biggest Loser Thinks Long-Term: Recency as a Predictor of Success in Weight Management.  December. Vol. 6, pp. 1864. Frontiers in Psychology.

[7] Kruschitz, R. et al. 2017. Long-Term Weight-Loss Maintenance by a Meal Replacement Based Weight Management Program in Primary Care. April. Vol. 10, No. 2, pp. 76-84. Obesity Facts.

Introduction

The Personal Trainer Collective Conference – Bath, UK – April 23-24, 2016

The personal trainer collective (PTC) conference held in Bath, England on April 23-24, 2016 was an invaluable opportunity to listen to arguably the worlds leading minds in the area of resistance exercise, nutrition and evidence-based fitness.  The speakers included Brad Schoenfeld, Bret Contreras, James Krieger, and Alan Aragon.  The conference was held over a 2-day period, where each speaker presented once on each day.  This post will introduce two of the four speakers and highlight their subject matter presented with practical application.

Brad Schoenfeld – “The Hypertrophy Specialist”

Brad Schoenfeld is one of the most highly regarded professionals in the field of body composition training, specifically muscle hypertrophy (muscular development).  Schoenfeld is presently an assistant professor in the exercises science department at Lehman College in Bronx, NY and serves as their director of their human performance research team.  Also, Schoenfeld has written for many popular publications including Shape and Men’s Health in addition being a former natural bodybuilding competitor and personal trainer.

On day 1 of the conference, Schoenfeld discussed loading zones, specifically whether heavy or moderate loading in resistance training is better for gaining muscle or strength.  Shoenfeld and colleagues (2014) recently publish a paper investigating the differences in muscular adaptation (hypertrophy and strength) between bodybuilding training that uses a body part split routine versus powerlifting training utilising total body routine [7].  In this paper, 20 trained subjects (minimum of 1-year resistance training experience 3x/week) were randomly allocated to either a bodybuilding training group labelled “hypertrophy training” (HT) or a powerlifting training group labelled “strength training” (ST) [7].  The HT group worked each muscle once per week, with 3 exercises per session and performed 3 sets of 10 repetitions for each exercise with 90-second rest intervals [7].  The ST group trained complete body 3 times per week with 1 exercise per body part, per session performing 7 sets of 3 repetitions [7].

The authors from this study measured hypertrophy using ultrasound and strength using 1-repetition maximum (RM) for the squat and bench press [7].  The results of this study showed that both groups increased biceps thickness (muscle hypertrophy) by approximately 13% with no differences between groups, while both groups increased 1RM strength with the ST group showing greater strength increases in the bench press and squat [7].

This study suggests that for hypertrophy goals, as long volume is equated, both heavy to moderate-heavy weights can be used to generate hypertrophy gains, while using heavier weights appears best for increasing strength.  However, Schoenfeld was quick to indicate that using heavier loads, as used in ST group, greatly taxes the neural system and can easily lead to overtraining.  In contrast, lighter loads are able to spare the nervous system, which may prove more practical, particularly for a personal trainer that is working with an injury population and working with clients less experienced with resistance exercise .

In the later half of his talk, Schoenfeld transitioned to discuss the application of using low-loads for achieving muscle hypertrophy.  Traditional guidelines for resistance exercise indicate that loads of greater or equal to 65% of 1RM are required to generate hypertrophy gains.  Maximising muscle growth is dependent on recruiting as many motor units (a motor neuron and the muscle fibre it innervates) as possible in the targeted muscles. These guidelines are based on the theory that heavier weights are needed to fully recruit higher threshold motor units.  However, Shoenfeld points to research indicating that substantial hypertrophy can be achieved using lower-loads provided it is carried out to muscular failure.

The reasoning behind Schoenfeld’s suggestion for using low-loads to elicit hypertrophy is extracted from emerging research indicating a muscle fibre type specific response to training, whereby low-loads can activate type I muscle fibres.  There are three primary muscle fibre types in humans – type I, type IIa and type IIb.  Type I muscle fibres are deemed slow twitch fibres and are smaller compared to type II, more resistant to fatigue and endurance orientated compared to type II fibres.  An endurance activity like a 5000m run would be an example of a sport that would exhibit predominantly type I muscle fibre dominant athletes.

In contrast, type II muscle fibres, deemed fast-twitch, are larger and more anaerobic, therefore fatigue faster compared to type I fibres.  A explosive, power activity like the 100m sprint would be an example of a sport that would exhibit predominantly type II muscle fibre dominant athletes.  Schoenfeld notes that both type I and II muscle fibres have the capacity for muscle hypertrophy.  It is often claimed that only high intensities can recruit high threshold motor units.  However, muscular fatigue has a great role to play in hypertrophy stimulation and motor unit recruitment.

For example, if you were performing a set of dumbbell chest press and using a low-load of 30% of 1RM, your 1RM chest press (the maximum weight lifted for 1 repetition) was 100kg, then 30kg would represent 30% of 1RM.   As fatigue increases when performing a set of dumbbell chest press, the increased time-under-tension of the muscles stimulates these smaller, aerobic and more fatigue resistant type I muscle fibres.  Also, in reaching a point of muscular failure during the set, low-loads may provide a mechanism to further activate fast-twitch type II muscle fibres, stimulating growth of these muscle fibres as well.

Schoenfeld went on to describe several studies demonstrating greater muscle activation with higher weights (>70% 1RM), however these studies did not necessarily reflect long term muscle adaptation to resistance training.

Practical Application

The commonly held perspective that only heavier weights (65% 1 RM) are required to elicit hypertrophy gains is simply not true as evidenced by the scientific literature.  More accurately, a broad range of loads in resistance training (6 to 12 repetitions, 65 to 85% of 1RM) and volumes of 6-12 sets per muscle group, per session can be used to elicit hypertrophy gains.

Low-loads of approximately 30% of 1RM can be effective for stimulating growth of type I muscle fibres and if taken to failure, low-loads may provide a mechanism for stimulating type II muscle fibres.  Also, higher loads of >70% of 1RM may be required to activate the highest threshold motor units, further reinforcing the application of varying training loads in resistance training.

It is common for a personal trainer to adhere only to higher repetition ranges (i.e. 10-12, 12-15) to elicit hypertrophy gains.  However, varying repetitions and training loads can be a highly effective strategy for accelerating muscular adaptation.   One method of changing repetitions and training loads each workouts called daily undulating periodisation (DUP), has been been shown to be particularly effective for enhancing strength and muscle hypertrophy.  Periodising or planning the workouts of clients over a longer period (i.e. 3 – 4 months) can also help a personal trainer integrate varying training loads and repetition ranges according to the training goal of his or her clients.  

Alan Aragon – “The Nutrition Specialist”

Alan Aragon was another one of the four speakers presenting at the Personal Trainer Collective Conference.  Aragon is well respected in the field of evidenced-based fitness and is specifically recognised as a leader in the area of applied nutrition.  Aragon holds a Bachelor and Master of Science in Nutrition and maintains a position as continuing education provider for the Commission on Dietetic Registration and National Strength and Conditioning Association.  He has authored and co-authored on a number of professionally published journals in addition to acting as a contributing editor to popular publications like Men’s Health Magazine.

On the Saturday, Day 1, Aragon addressed the issue of nutrient timing and the proposed “anabolic window” theory.  This theory was first introduced by John Ivy and Robert Portman in 2004 and proposes that muscular adaptations are significantly compromised if key nutrients are not consumed within 45 minutes following exercise [4].  In their book “Nutrient Timing – The Future of Sports Nutrition,” Ivy & Portman explain that immediate, post-exercise co-ingestion of fast-absorbing protein and carbohydrates are critical for optimising muscular recovery and growth via reducing muscle protein breakdown and maximising net muscle protein synthesis  [4].  In other words, nutrients from either solid food or supplements must be consumed immediately following exercise to optimise muscle building or strength.  This theory was enlarged and popularised by supplement companies for financial gain and still remains a post-workout nutritional prescription for the average personal trainer.

However, there are some important imitations in Ivy & Portman’s guidelines for nutrient timing.  Specifically, the authors fail to indicate population specificity or who the guidelines are targeted towards.  Also, the authors focus on the acute effects rather than the chronic effects of research to inform their guidelines on nutrient timing.  Finally, the authors rely upon research using overnight-fasted subjects put through endurance/depletion protocols, which we will learn is different when compared to the response of non-fasted subjects.

The Origins of the “Anabolic Window”

The Study:

In 1988, Ivy and Colleagues published a research study in the Journal of Applied Physiology that set out to examine the effect of carbohydrate timing on muscle glycogen storage following exercise [3].

Methods:

The authors placed 12 male cyclists on a cycle ergometer for 70 continuous minutes at 68% VO2 max, interjected by six 2-minute cycle sprints at 88% VO2 max, on two separate occasions [3].  The subjects were either given a 25% carbohydrate solution (2g/kg) immediately following exercise or delayed consumption of this solution by 2 hours post-exercise.  Blood samples were obtained during exercise and at specific times following exercise [3].

Findings:

During the first 2-hours following exercise, glycogen storage for the group administered carbohydrates immediately post-exercise was 3 times greater (7.7 mumol.g) than the 2-hour delayed group (2.5 mumol.g) [3].  During the second 2-hour time period following exercise, glycogen was still roughly twice as fast in the immediate group compared to delayed group [3].  At the 4-hour mark,  the immediate group had 34.7% greater glycogen storage compared to the delayed group [3].

Conclusions:

Delaying the ingestion of a carbohydrate supplement post-exercise will result in a reduction in muscle glycogen storage [3].

The Fallacy of “Spiking” Insulin Levels Post-Exercise

Following a workout, it is proposed that insulin has anti-catabolic effects that reduce the breakdown of muscle protein [1].  A common strategy is for a personal trainer to prescribe high-glycemic (glucose or blood sugar), fast-absorbing proteins/amino acids or carbohydrates immediately following exercise to maximise insulin response and mitigate muscle protein breakdown and accelerate recovery.  This is often prescribed by a personal trainer in the form of sugar-rich foods like juice or supplements.

However, this post-exercise nutritional prescription is based on the assumption that exercise is performed under a fasted state (abstinence of food or drink).  Under a fasted state, there is compelling evidence indicating muscle protein breakdown is elevated following a workout [1].  Muscle protein breakdown is found to significantly increase 3-hours post exercise, up to 50% in some instances, then can persists further for up to 24-hours post-exercise under these conditions [1].

But, what if exercise is not performed under fasted conditions?  The research shows that a pre-workout meal (solid or supplemental) of either protein or mixed (protein, carbohydrate and fat) is more than sufficient to elevate insulin levels during and following a workout [1].  If this is the case, then the addition of insulin immediately post-workout would be redundant for reducing muscle protein breakdown or accelerating recovery.

In support of this argument, there is evidence showing that no greater elevation in net muscle protein balance, which is muscle protein synthesis (gaining) minus protein breakdown (losing), has been found beyond insulin elevation of 15-30 mU/L, which is approximately 3-5 times fasting levels [6].  This means that there is a “ceiling” on insulin intake and anything above these levels will not yield any additional benefit in the context of post-workout recovery.  This “ceiling.” otherwise knows as the “muscle full effect” or protein refraction was previously examined in a previous article of ours.

Also, the reality is that insulin elevations of 15-30 mU/L are easily attainable and even surpassed with normal-sized mixed meals.  Depending on the size of a meal, it takes approximately 1-2 hours for circulating nutrient levels to peak and another 3-6 hours (or more) for these levels to drop back down to baseline.  To demonstrate this metabolic response following a meal, Power and colleagues (2009) examined the effect of protein hydrolysis on the insulin response to the ingestion of whey protein supplement [5].  The authors showed that a 45 g dose of whey protein isolate takes approximately 50 minutes to cause blood amino acids to peak [5].  Insulin levels peaked at 40 minutes following ingestion of protein supplement [5].  Importantly, peaked insulin levels remained at elevated levels known to max-out net muscle protein balance (15-30 mU/L, or 104-2018 pmol/L) for about 2-hours [5].  The inclusion of carbohydrates to this protein dose would cause even greater elevations in peak insulin levels and sustain these levels even longer.

A study by Capaldo and colleagues (1999) measured the metabolic effects of ingesting a solid, mixed meal containing 75 g carbohydrate, 37 g protein, and 17 g fat [2].  The study showed that ingestion of this mixed meal raised insulin levels 3 times above fasting levels within 30 minutes, and 5 times greater than fasting at 60 minutes [2].  At the 5-hour mark, insulin levels still remained double that of fasting [2].  Therefore, recommendations from any personal trainer to spike insulin levels of his or her clients post-exercise to promote muscle recovery and growth fail to stand up against the evidence.

Only in the case of an overnight fast would immediate post-workout ingestion of nutrients be advocated to prevent muscle protein breakdown and enhance protein synthesis [1].  Instead, a personal trainer should focus closely on the pre-workout meal and timing.  If increasing strength and lean muscle mass is the objective, effort should made to consume a solid meal 1-2 hours prior to a workout to maximise performance and post-workout response [1].  A meal consisting of a protein drink supplement can be taken immediately prior to a workout as this form is metabolised more rapidly.

When considering nutrient intake following a workout, it is recommended that the duration between pre- and post-workout meals fall between 3 – 4 hours, depending on the size of the pre-workout meal and based on a workout lasting 45 – 90 minutes [1].  For example, if an individual trains at 5pm, a pre-workout meal at 3:30pm and post-workout meal at 7pm would be optimal for enhancing muscle protein synthesis and preventing muscle breakdown.

If a pre-workout meal is larger, which would increase its anti-catabolic effects, it is reasonable to suggest that the time period between pre- and post-workout meals would increase (5 – 6 hours) [1].  If a workout is performed more than 3 – 4 hours from a pre-workout meal, it is advisable that a protein or carbohydrate-protein mixed meal or supplement be taken immediately following the workout to enhance recovery and prevent a catabolic response [1].

Practical Application 

Nutrition within and surrounding a workout is typically an area a personal trainer will advise upon for his or her clients.  When examining the classical recommendation of a protein supplement drink or sugar food immediately following a workout, this recommendation holds little justification.

Only under a fasted state, or in the event of repeated bouts of intense exercise (i.e. cycling, running or weightlifting multiple times a day), would immediate, post-exercise nutrition be warranted.  Instead, it is a better strategy for a personal trainer to focus on pre-workout meal team, advising clients to consume a protein or protein-carbohydrate rich meal within 1-2 hours of initiating training.  This time period would allow circulating nutrient levels to peak and remain elevated (depending on meal size) for 1-2 hours (or longer) following a workout.   In the event of fasted exercise, which is often the case among those that exercise first thing in the morning, immediate, post-exercise nutrients are recommended.  This would also be advised with a pre-workout meal consumed more than 3 – 4 hours from the workout.

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References

1. Aragon, A. A. & Schoenfeld, B. J. 2013. Nutrient timing revisited: is there a post-exercise anabolic window? Journal of the International Society of Sports Nutrition. January. Vol. 10, No. 1:5, pp.186/1550-2783-10-5.

2. Capaldo, B. et al. 1999. Splanchnic and leg substrate exchange after ingestion of a natural mixed meal in humans. Diabetes. May. Vol. 48, No. 5, pp. 958-966.

3. Ivy, J. L., et al. 1988. Muscle glycogen synthesis after exercise: effect of time of carbohydrate ingestion. Journal of Applied Physiology. April. Vol. 64, No. 4, pp. 1480-1485.

4. Ivy, J. L. & Portman, R. Nutrient Timing: The Future of Sports Nutrition. 2004. North Bergen, NJ: Basic Health Publications.

5. Power et al. 2009. Human insulinotropic response to oral ingestion of native and hydrolysed whey protein. Amino Acids. July. Vol. 37, No. 2, pp. 333-339.

6. Sensi, S. & Capanni, F. Chronobiological aspects of weight loss in obesity: effects of different meal timing regimens. Chronobiology International. Vol. 4, No. 2, pp. 251-261.

7. Schoenfeld, B. J., et al. 2014. Effects of different volume-equated resistance training loading strategies on muscular adaptations in well-trained men. Journal of Strength and Conditioning Research.  October. Vol. 28, No. 10, pp.  2909-2918.

Introduction

Grains have emerged as a staple food in the modern day diet.  For some individuals, consumption of certain grains like rice, wheat germ, rye, and barley can result in adverse health reactions including constipation, gas, or bloating.  It is proposed that grains today are very different that what was cultivated before the industrial era and that the crossbreeding and hybridisation of grains contribute to the adverse health symptoms some experience.  This blog post will look into the evolution of grains to understand why some people may reactively negatively to their consumption.

Didn’t We Always Eat Grains?

Fossil records of tools used to grind and cook wild cereal grains provide a reliable indication of when and where cultures around the world began to use grains in their diet [9].  Archaeology records show that regular use of grains by any hunter-gatherer group appeared approximately 12,800 – 10,300 years ago in the Levant region (modern Palestine, Israel, Syria and Jordan), among a hunter-gatherer culture know as the Natufians [9, 21].  Prior to the Epi-paleolithic (10,000 –11,000 years ago) and Neolithic periods (10,000 – 5,500 years ago), it is suggested there was little or no evidence of cereal grain consumption [8].

There is evidence to suggest the first wild, cultivated wheat was called einkorn [8, 11].  Einkorn had the simplest genetic code of all subsequent wheat. Shortly after came the wheat called emmer [8, 11].

Natufians, a late Epi-Paleolithic hunter-gatherer population, are said to have harvested wild einkorn and emmer wheat that eventually became an essential part of their diet and reduced the need for hunting and gathering [8].  Domestication of emmer and einkorn wheat by Natufian descendants marked a fundamental shift to cultivating grains and the beginning of agriculture [11, 8].

Original einkorn and emmer are shown to be very different to our twenty-first century wheat.  Einkorn and Emmer wheat were “hulled,” meaning the seeds clung tightly to the stem [11].  Compare this to the modern day “naked” form of wheat where the seeds easily separate from the stem.  More importantly, modern wheat is crossbred and hybridized tens of thousands of times over with different wheat’s and grasses, which dramatically change the genetic makeup of wheat [11].  The mass yielding of modern wheat with its dramatic modifications and variations in enzyme, protein and gluten content have ultimately come at the cost of human health [11].

The industrial revolution (1760 – 1850) marked the advent of mechanized steel roller mills and automated sifting devices making grains available for mass production [8].  Prior to the industrial revolution, cereal grains were ground using stone milling tools, soaked, sprouted, fermented and roasted [1, 18].  They contained the entire contents of the cereal grain, including the germ, bran, and endosperm [18].  Today, the nutritional elements of grains are very different as the germ and bran are removed in the milling process, yielding flour composed of mainly endosperm [8].

Are There Potential Health Hazards With Grains?

Grains are the seeds of plants.  They are the reproductive material of each plant and in accordance with evolutionary strategy, plants, like animals, have mechanisms for protecting themselves [18].  As protective mechanisms to defend themselves against predators, some plants produce toxins that damage the gut.  It is proposed that these toxins bind to minerals in our body, possibly making them unavailable for the body to use and prevent the digestion and absorption of essential nutrients [18].

Gluten

One of these toxins found in plants is suggested to be gluten.  Gluten is a protein found most abundantly (80%) in wheat, but also present in:

• Wheat germ
• Rice (brown, white, wild)
• Rye
• Barley
• Bulgur
• Couscous
• Farina
• Graham flour
• Kamut Matzo
• Semolina
• Spelt
• Triticale

Although other non-grains such as amaranth, buckwheat, millet, quinoa, sorghum, teff, oats and corn do not naturally contain gluten, they are frequently contaminated with gluten as they are processed at mills that also handle wheat [18].  A 2014 article by Lee and colleagues showed that of the 78 randomly selected food samples labeled “gluten free,” 16 (20.5%) contained gluten levels equal to or above the defined gluten-free level (20 mg/kg) [19].  In particular, five of eight breakfast cereal samples showed gluten contents higher than 20 mg/kg [19].

In a previous article we discussed the potential immune responses to gluten proteins. In this article we highlight celiac disease, an autoimmune condition (where the body attacks its own cells and tissues) triggered by gluten and may lead to inflammation and damage to the tissues in the small intestine [18].  Also, we now know it is possible to be intolerant to gluten, independent of celiac disease, with a condition known as non-celiac gluten sensitivity (NCGS), a disorder that carries a multitude of symptoms within and outside the digestive tract [11].

In the last decade, NCGS has received growing attention as patients have reported more severe nonspecific symptoms with gluten intake than seen with classic celiac disease [2, 12, 20].  Specifically, non-celiac gluten sensitivity has been linked to allergies [20].  In 2011, Massari and colleagues biopsied 262 patients with both classical allergy (asthma, dermatitis) and gastrointestinal (stomach pain, diarrhoea, weight loss) symptoms [20].  In approximately 30% of allergic patients, the authors found lesions in the small intestines and villous atrophy (erosion of the tiny finger-like projections in the intestines that help to absorb nutrients) [20].  Interestingly, these authors observed a significant improvement in health state of the positive patients when they followed a gluten-free diet [20].

Also, gluten was found to worsen symptoms in NCGS individuals with irritable bowel Syndrome (IBS) [2].  A 2011 double-blinded, randomised controlled trial, randomly allocated 34 people to either a gluten or placebo group, where subjects received two bread slices and one muffin per day for 6-weeks [2].  Of the 19 patients in the gluten group, 13 reported worsening of their symptoms (pain, bloating, tiredness, stool inconsistency) within 1-week compared to 6 of the 15 patients in the placebo group [2].

A link between gluten and autism, schizophrenia and attention deficit/hyperactivity disorder has further been suggested [11, 12].  Increased intestinal permeability, referred to as “leaky gut syndrome” have been suggested in autism spectrum disorder (ASD) as part of the chain of events that allow peptides to cross the intestinal barrier, enter the bloodstream, then cross the blood-brain barrier (layer of cells separating the bloodstream from the brain) [12].  It has been suggested that wheat polypeptides bind to the brain’s morphine like receptor, the very same receptor to which opiate drugs bind, resulting in an excess of opioids thought to lead to ASD behaviors [11, 12].  A high percentage of abnormal intestinal permeability (IPT) was found among patients with autism compared with normal individuals [12].  

Gliadins

Gliadin is protein found in wheat gluten.  The group of gliadin proteins are those that are said to most strongly trigger the immune response in individuals with celiac disease [11].  Specifically, it has been suggested that gliadin increases intestinal permeability (“leaky gut”) by way of an intestinal peptide called zonulin [14].  A study by Drago and colleagues in 2006 showed that gliadin released zonulin causing disruption in the tight barrier that prevents toxins from entering the bloodstream leading to increased intestinal permeability [14].  High zonulin levels (together with increased intestinal permeability) have been observed in autoimmune and inflammatory diseases like celiac disease, multiple sclerosis, asthma and inflammatory bowel disease [11, 14].

                                         [Image: Fasano, A. 2009. Scientific America]

Gliadin may therefore induce the production of pro-inflammatory cytokines (messenger cells) in celiac or non-celiac individuals [2].  The activation of these pro-inflammatory cytokines may lead to low-grade inflammation in the body, said to be the root cause of all modern diseases like cardiovascular disease, obesity, and autoimmune diseases [2, 18].

Lectins

Similar to gliadins, lectins are proteins found in plants and are said to have evolved as a toxic defence mechanism to ward off predators [2, 17].  Plant lectins are usually at highest concentrations in plant parts because they are essential for plant reproduction [17].  Just like gliadins, most plant lectins are resistant to heat and digestive enzymes and are said to be capable of binding and damaging several tissues and organs in the body [2].  Lectin activity has been demonstrated in wheat, rye, barley, oats, corn, rice and most abundantly in wheat germ like in unprocessed muesli and many breakfast cereals [23].

A type of lectin known as wheat germ agglutinin (WGA), present in wheat seed in both the germ and the gluten part of endosperm, has been more extensively studied because of its potentially health damaging effects.  Each grain contains about one microgram of WGA and its function is to protect the plant against insects and fungi [17]. It is suggested that WGA may cause an inflammatory response by way of stimulating several pro-inflammatory cytokines in the body [13].  Higher WGA antibodies have been found in individuals with celiac disease compared to those with other intestinal disorders indicating a greater immune defence response among celiac sufferers [13].

After digesting foods with WGA like breakfast cereals, it is suggested that WGA is capable of crossing the intestinal barrier into the blood circulation, thereby allowing microbial and dietary toxins to interact with cells of the immune system [13].  The result may be enhanced symptoms within (diarrhoea, constipation, gas, bloating) and outside (headaches, lethargy) the gastrointestinal system in both celiac and non-celiac gluten sensitive individuals.

Research shows that WGA may also be toxic to the brain.  Though a process called “absorptive endocytosis,” WGA is able to travel among the tissues of the brain passing through the blood brain barrier (BBB) pulling bound substances with it disrupting neuronal membranes [13].  This disruption of neuronal membranes may be linked to neurodegenerative disorders like Parkinson’s, Alzheimer’s and Huntington’s disease.

Ingestion of WGA has been also been found to have an insulin-like effect, which may contribute to weight gain [25].  Specifically, lectin has been linked to obesity and “leptin resistance.”   In a previous article, we discussed leptin (not lectin, try not to confuse the two!) and identified leptin as a hormone made by adipose (fat) cells in the body that functions as our satiating hormone signalling to the brain to inhibit food intake [17, 23].  Leptin resistance is when this hormone is unable to function properly and the body wants to excessively eat because it thinks it’s starving [23]. Lectins ingested through grains can bind to the leptin receptor in the brain (hypothalamus) and negatively affect its function [17, 23].

Phytates

Phytates or phytic acid are compounds typically found in the outer layers of cereal grains and in the endosperm of legumes and seed oils [1, 3].  Phytates are the major storage form of phosphorus in all grains, nuts, seeds and beans and function as an essential energy source for the sprouting seed [1, 3].  Often labelled as “anti-nutrients,” the  phytates in grains are proposed to have an inhibiting effect potentially binding onto important minerals and trace elements like iron, zinc, calcium and manganese, the result of which may be malabsorption of nutrients in phytate containing grains and possible mineral deficiency [1, 3].

A 2004 study by Bohn and colleagues examined the effect of phytic acid on magnesium absorption among 20 (10 men and 10 women) healthy subjects [3].  The authors added phytic acid to 200g of white-wheat bread and whole-meal wheat bread and subject faecal samples were collected to test mineral absorption [3].  Magnesium absorption was shown to be approximately 60% lower when phytic acid was added to white-wheat bread and 25% lower when added at an amount similar to that in brown bread [3].

Phytic acid has also been shown to bind to iron in the body.  A 1992 study by Brune and collegues showed that iron content were reduced in five different types of rye or wheat bread compared to iron absorption from white bread [4].  The authors noted their findings “clearly indicate that the inhibition of iron absorption by wheat and rye bran could be ascribed to its content of phytate” [4].

There are several methods that traditional cultures like the Natufians would have used, which would remove or dramatically reduce the phytic acid in grains, therefore enhancing nutrient absorption.

• Milling and soaking milling
• Fermenting
• Germinating

However, important to note that more recent research suggests that phytic acid may actually exhibit protective health effects as an anti-cancer and anti-inflammatory compound [1, 22].  Ironically, it is suggested that phytic acid may act as an antioxidant by way of its iron and copper binding effects [1].  A number of studies have suggested the potential anti-cancer effects of phytic acid [1, 15, 22].  Studies by Shamsuddin et al. (2002) and Catherine and colleagues (2007) suggest phytic acid may lower blood glucose response by reducing the rate of starch digestion and slowing the gastric emptying, therefore exerting health benefits for diabetes patients [22, 7].  It is clear more research in this area is needed to clarify the potential health damaging or preserving effects of phytic acid.

Summary

It is suggested that dietary and lifestyle changes that began with the agricultural era approximately 11,000 years ago were too recent on an evolutionary time scale for the human genome to adapt [8, 5].  Prior to agricultural revolution and the domestication of animals, human diet would have been limited to minimally processed, wild plant and animal foods [5].  With the advent of agriculture and most certainly the industrial era, grains have accelerated to become a staple in the human diet.  As processed foods have become ubiquitous in the modern day diet (eg, cookies, cakes, bakery foods, breakfast cereals, bagels, rolls, muffins, crackers, chips, and other snack foods), it is suggested these foods are largely to blame for the global increase of non-communicable disease like obesity and cardiovascular disease. 

It is also theorised that toxins inherent in plants have the potential to cause substantial damage, altering the microorganism in the digestive system, increasing intestinal permeability and leading to low-grade inflammation [5].  If negative reactions are identified with grain consumption, substituting grains with alternatives like coconut and soaking or sprouting nuts and seeds to break down phytic acid has been advocated as a method to potentially reduce toxicity as well as the dietary integration of non-grains like quinoa and buckwheat.

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References:

1.  Admassu, S. 2009. Potential Health Benefits and Problems Associated with Phytochemicals in Food Legumes. East African Journal of Sciences. Vol. 3, No. 2, pp.116-133.

2.  Biesiekierski, J. R., et al. 2011. Gluten Causes Gastrointestinal Symptoms in Subjects without Celiac Disease: A Double-Blinded, Randomised, Placebo-Controlled Trial. The American Journal of Gastroenterology. March. Vol. 106, No. 3, pp. 508-514.

3.  Bohn et al. 2004. Phytic acid added to white-wheat bread inhibits fractional apparent magnesium absorption in humans. American Journal of Clinical Nutrition. March. Vol. 79, No. 3, Pp. 418-423.

4.  Brune, M. et al. 1992. Iron Absorption from Bread in Humans: Inhibiting Effects of Cereal Fiber, Phytate and Inositol Phosphates with Different Numbers of Phosphate Groups. The Journal of Nutrition. March. Vol. 122, No. 3, pp.442-449.

5.  Carrera-Bastos et al. 2011. The Western Diet and Lifestyle and Diseases of Civilization. Research Reports in Clinical Cardiology. March. Vol. 2. Pp.15-35.

6.  Catassi, C. et al. 2013. Non-Celiac Gluten Sensitivity: The New Frontier of Gluten Related Disorders. Nutrients. October. Vol.5, No.10, pp. 3839-3853.

7.  Catherine, C. N. 2007. Cranberry and its phytochemicals: A review of in vitro anti-cancer studies. Supplement: International research conference on food, nutrition, and cancer. Journal of Nutrition. January. Vol. 137, No. 1, pp. 186S-193S.

8.  Cordain, L. et al. 2005. Origins and evolution of the Western diet: health implications for the 21st century. American Journal of Clinical Nutrition. February. Vol. 81, No.2, pp.341-354.

9.  Cordain, L. 1999. Cereal Grains: Humanity’s Double-Edged Sword. World Review of Nutrition and Dietetics. Vol. 84, pp. 19 –73.

10. C3 Collaborating for Health. 2011. Non-communicable diseases in the UK: NCDs Worldwide [online]. London: C3 Collaborating for Health. [viewed 30 December 2015]. Available from: http://www.c3health.org/wp-content/uploads/2009/09/NCDs-briefing-paper-20111010.pdf.

11.  Davis, W. 2011. Wheat Belly. Canada: Collins.

12. de Margistris, L., et al. 2010. Alterations of the Intestinal Barrier in Patients With Autism Spectrum Disorders and in Their First-degree Relatives. Journal of Pediatric and Gastroenterology. October. Vol. 51, No.4, pp.418-424.

13. de Punder, K. & Pruimboom, L. 2013. The Dietary Intake of Wheat and other Cereal Grains and Their Role in Inflammation. Nutrients. March. Vol. 5, No. 3, pp. 771-787.

14. Drago, S. et al. 2006. Gliadin, zonulin and gut permeability: Effects on celiac and non-celiac intestinal mucosa and intestinal cell lines. Scandinavian Journal of Gastroenterology. April. Vol. 41, No. 4, pp. 408-419.

15. Fournier, D.B. and Gordon, G.B. 2000. Cyclooxygenase- 2 (COX-2) and colon cancer: potential targets for chemoprevention. Journal of Cell Biochemistry. Vol. 34, pp. 97-102.

16. Geoba., 2015. The World: Life Expectancy [online]. [Viewed 30 December 2015]. Available from: http://www.geoba.se/population.php?pc=world&type=15.

17. Jonsson, T. et al. 2005. Agrarian diet and diseases of affluence – Do evolutionary novel dietary lectins cause leptin resistance? BMC Endocrine Disorders. December. Vol. 5, No. 10.

18. Kresser, C. 2013. The Paleo Code. Grains and Pseudocereals. New York: Little, Brown and Company. pp.188-190.

19. Lee et. al. 2014. Gluten Contamination in Foods Labeled as ‘‘Gluten Free’’ in the United States. Journal of Food Protection. October. Vol. 77, No. 10, pp. 1830–1833.

20. Massari, S. et al. 2011. Occurrence of Nonceliac Gluten Sensitivity in Patients with Allergic Disease. International Archives of Allergy and Immunology. February. Vol.155, No.4, pp.389-394.

21. Ofer Bar, Y. & Lieberman, D. E. 1994. The Natufian Culture in the Levant. Journal of Field Archaeology. Autumn. Vol. 21, No. 3, pp. 376-378.

22. Shamsuddin, A. M. 2002. Anti-cancer function of phytic acid. International Journal of Food Science and Technology. October. Vol. 37, No. 7, pp. 769-782.

23. Unger, R. H. 2005. Longevity, lipotoxicity and leptin: the adipocyte defense against feasting and famine. Biochimie. January. Vol. 87, No. 1, pp. 57-64.

24. World Health Organisation. 2015. Chapter 1: Burden: mortality, morbidity and risk factors [viewed 31 December 2015]. Available from: http://www.who.int/nmh/publications/ncd_report_chapter1.pdf

25. Yevdokimova, N. Y. and Yefimov, A. S. 2001. Effects of Wheat Germ Agglutinin and Concanavalin A on the Accumulation of Glycosaminoglycans in Pericellular Matrix of Human Dermal Fibroblasts. A Comparison with Insulin.  Acta Biochimica Polonica. November. Vol 48, No. 2, pp. 563-572.

Organic Versus Conventional Farming

Organic farming is an agricultural production method that is becoming more prominent because of the growing popularity of organic food and products [4].  Certified organic farming produces food that adhere to a strict set of principles and standards that regulate the use of chemicals in crops, medicines for animals as well as their ethical treatment, and the impact farming has on the environment [4].

Previous research demonstrates higher nutrient content in foods that are organically grown, while others show no differences between organic and conventional food production.  Given these mixed responses, there is still much debate over whether organic farming produces food that offers superior health benefits compared with conventional farming.

A Look Into The Research

When we evaluate the available research in this area, it’s understandable why the organic versus non-organic debate exists.  In 2009, the Food Standards Agency, an independent Government department set up by an Act of Parliament to protect the public’s health and consumer interests in relation to food, showed that there are no important differences in the nutrition content, or any additional health benefits, of organic food when compared with conventionally produced food.

Other systematic literature reviews have recently analysed the available published information, with the aim of identifying the potential effects of organic and conventional production protocols on the nutritional quality of crops [2, 3, 4].  However, these systematic reviews present with multiple limitations including methodology flaws, failure to use more recently available publications, and poorly structured assessment of the gathered evidence.

As a result, there is still considerable controversy as to whether the use of organic production standards results in significant and consistent changes in the concentrations of potentially health-promoting and potentially harmful compounds in crops and crop-based foods.

Stronger Evidence Base

So, what do we have to support the health benefits of organically grown crops? A meta-analyses was published in the British Journal of Nutrition in 2014 that set out to examine the difference in composition between organic and conventionally produced crop based foods.  This Newcastle University led study analysed 343 papers where more than one-half of the publications analysed were published after 2006 [1].  A major strength of this study was that it systhesised a much larger evidence base that allowed the authors to use more appropriate statistical methods to draw more definitive conclusions between organic and conventional crops.

The findings from this study were significant. The authors showed that organically grown crops are up to 69% (18-69%) higher in important antioxidants like polyphenols versus conventionally grown crops, the equivalent of eating between 1-2 additional portions of fruits and vegetable a day! [1].

This study also found significantly lower levels of toxic heavy metals in organically grown crops [1].  Specifically, cadmium, which is one of only three metal contaminants along with lead and mercury for which the European Commission has set maximum permitted contamination levels in food, was found to be almost 50% (on average 48%) lower in organically crops than conventionally-grown ones [1]. Also, nitrogen concentrations were found to be significantly lower in organically grown crops and pesticide residues were four times more likely to be found in conventionally grown crops versus organic ones [1].

Image:  Soil Association. 2015.

Professor Charles Benbrook, one of the authors of the study and a leading scientist based at Washington State University, stated: “Our results are highly relevant and significant and will help both scientists and consumers sort through the often conflicting information currently available on the nutrient density of organic and conventional plant-based foods.”

Summary

Certainly, continued research needs to be conducted, particularly with controlled human dietary intervention to understand the health impact of switching to organically grow crops.  However, this recent meta-analysis suggests there are marked differences between organically grown crops and that of commercially grown crops.  If future research continues to identify superior health benefits with organically produced food, this would give greater consideration to food quality and help guide our food selection.

For more information on our personal training services please click here to read more.

References:

1.  Baranski, M. et al. British Journal of Nutrition (2014) Higher antioxidant concentrations and less cadmium and pesticide residues in organically-grown crops: a systematic literature review and meta-analyses.

2.  Dangour AD, et al. American Journal of Clinical Nutrition (2009) Nutritional quality of organic foods: a systematic review.

3.  Dangour AD. et al.. American Journal of Clinical Nutrition (2010) Nutrition-related health effects of organic foods: a systematic review.

4.  Dangour AD, et al. Indian Journal of Medical Research (2010) Nutritional composition & health benefits of organic foods – using systematic reviews to question the available evidence. pp 478-480.

Gluten can be defined as “the rubbery mass that remains when wheat dough is washed in water to remove starch granules and water soluble constituents” [8, 11].  Alternatively, in the context of gluten sensitivity, gluten can be defined as “a protein fraction from wheat, rye, barley, oats or their crossbred varieties and derivatives, to which some persons are intolerant and that is insoluble in water and 0.5 mol/L NaCl” [8].

Gluten is generally divided into protein fractions according to their solubility: alcohol-insoluble “glutenins” and alcohol-soluble “gliadin” proteins [4, 11].  Both proteins make important contributions to making dough.  Gliadins contribute mainly to the viscosity and extensibility of dough, while glutenins are responsible for the strength and elasticity in dough [11].

Historical Perspective

Archaeological and geological evaluation of pre-agricultural populations shows that human consumption of gluten-containing grains only occurred about 11,000 years ago with the advent of agriculture and prior to this time period, there was no evidence of regular grain consumption [3].  Furthermore, anthropological reports show traditional, pre-agricultural populations generally lived free of chronic degenerative diseases like obesity, type II diabetes, cancer and cardiovascular disease, which have now risen to epidemic proportions [3]. Importantly, 11,000 years marks only 0.5% of human history, simply not long for the human digestive system to have evolved to break down gluten proteins [3].

The agricultural and industrial era represented an evolutionary challenge for humans with widespread production and consumption of refined cereal grains, which created conditions for human disease.  These conditions have further led to a global increase in gluten exposure and gluten reactions like wheat allergies (WA), celiac disease (CD), and non-celiac gluten sensitivity (NCGS).

Wheat Allergies (WA)

What is a Wheat Allergy?

A wheat allergy is an adverse immune reaction to one or more of the proteins found in wheat [7]. WA can be classified into an (1) immediate food allergy, (2) wheat-dependent exercise induced anaphylaxis, (3) respiratory allergy, or (4) contact urticaria (hives) [7].

How Common are Wheat Allergies?

Zuidmeer and colleagues (2008) performed a systematic review of population-based studies from the UK and Germany and reported positive WA among children, with a prevalence as high as 0.5% [13].  Among adults, the prevalence of WA (assessed by IgE) was higher (> 3% in several studies) [13].  Multiple studies report that WA represents about 11% to 25% of the entire food allergy population [8].

Symptoms of Wheat Allergies

Symptoms of WA are similar to those of other classic food allergies, which include skin (e.g. swelling, itching, hives), respiratory (e.g. wheezing, impaired breathing, bronchial obstruction) and gastrointestinal (e.g., bloating, abdominal pain, diarrhoea) [6].  Also, anaphylaxis symptoms has been linked to WA [6].

Mechanisms Behind Wheat Allergies

Immunoglobulin E (IgE) antibodies play an important part in the onset of WA.  IgE antibodies function to bind to invaders (antigens), which create an inflammatory response intended to rid the body of infection or render the antigen inactive [1].  While IgE antibodies may combat the antigen, they also cause the release of histamines and other chemicals into the bloodstream that cause allergy symptoms like sneezing or abdominal pain [1].

The allergic response from the ingestion of wheat is caused by an immune reaction to the specific grain protein named gliadins [7].  In the case of respiratory allergies from wheat exposure, a group of wheat proteins called ?-amylase inhibitors are strongly associated with this response in addition to other proteins present in wheat like germ agglutinin and non-specific lipid transfer proteins (LTPs) [7].

Why Do Wheat Allergies Occur?

There is no single cause for the development of WA.  A specific class of antibodies known as secretory immunoglobulin A (SIgA), which help the gut to develop by enhancing intestinal barrier function, may increase the risk of WA if insufficient [1].

Also, immaturity of the intestinal barrier or digestive tract in addition to inadequate cultivation of healthy bacteria at birth can increase the risk of WA [1].  Even the absence of breast milk can increase the development of WA, as antibodies to gluten peptides from wheat are available in breast milk and breastfeeding has been shown to reduce gluten-triggered disorders in children [1].

Moreover, family history can play an important role in WA [1].  Children, who have a genetic predisposition to produce excess IgE, caused by the mother suffering from various allergies, are at least 8 times more likely to develop WA when delivered by caesarean section [1].

Celiac Disease (CD)

What is Celiac Disease?

Celiac Disease is an autoimmune condition (whereby the body’s immune system attacks its own healthy cells) triggered by the intake of gluten-containing wheat, rye and barley [8, 9].

How Common is Celiac Disease?

In the UK, there has been a 4-fold increase (9,087 cases) of CD between 1990 and 2011 [9].  Among children in Scotland, there has been a dramatic 6.4 fold increase (266 biopsy-positive children) in CD between 1990 and 2009 and similar trends have been reported in Europe and North America [10].  Also, CD is more common among females than males with a suggested ratio of 2:1 [8].

Symptoms of Celiac Disease

Symptoms of CD can present as classical intestinal features including different gastrointestinal symptoms like diarrhoea, abdominal pain and weight loss or non-classical extra-intestinal features like osteoporosis, anemia and neurological disturbances [7]

Mechanisms Behind Celiac Disease

When gluten-containing foods are ingested, the proteins in gluten are only partially broken down during digestion because of their high content of glutamine and proline residues [6].  Peptides (a molecule consisting of 2 or more amino acids) are then accumulated in the tubular opening in the intestines (intestinal lumen) and are responsible for an immune response characteristic of CD [6].

On exposure to gluten, inflammation is induced in the tissues that line the small intestine (mucosa), which leads to wasting of the villi (finger-like projects that line the small intestine) [5]. This disruption to the villi results in damage to the tight junction of cells that function like a wall protecting the internal tissues from environmental stresses, bacterial infection, and chemical damage [5].  Compromise to the tight junctions that form this protective intestinal barrier lead to an immune response and trigger CD symptoms [2].

Why Does Celiac Disease Occur?

Genetic predisposition plays an important role in CD as it is well established that CD is associated with specific genes called human leukocyte antigen (HLA) (HLA-DQ2 and HLA-DQ8) [ 5,7]. Carriers of the HLA genes account for about 40% of the genetic risk for CD [5].  However, it has been shown that only a small percentage of those that carry the HLA gene actually have CD [5]. Therefore, other factors must be involved in the onset of CD.

It has been suggested that imbalances in intestinal microorganisms (microbiota) may contribute to manifestations of CD [5].  Research shows that individuals with CD present with an imbalance of healthy bacteria in the small intestine with increased abundance of the bacteria called Bacteroides and a decreased in the bacteria called Bifidobacterium [5].  Even the initial development of the gastrointestinal tract in infants plays an important role in the microbiota and immune response later in life as an adult [5].   It has been demonstrated that events during infancy that may cause disturbances in gut microbiota such as infection and antibiotic treatment have been associated with CD in infants that are genetically susceptible [5].

Non-Celiac Gluten Sensitivity (NCGS)

What is Non-Celiac Gluten Sensitivity?

NCGS is a gluten (wheat) dependent disorder where neither allergic nor autoimmune mechanisms are involved [7].  In the literature, alternative names have been used to describe NCGS like gluten sensitivity, gluten hypersensitivity, or non-celiac gluten intolerance [2].  NCGS is distinct from CD in that it is not accompanied by anti-tissue transglutaminase 2 (anti-TG2) antibodies, which are proteins that defend against harmful substances in the body like bacteria or viruses [7, 8].  These anti-TGS antibodies are present in the small intestine in CD patients, but not in those with NCGS [8].

How Common is Non-Celiac Gluten Sensitivity?

Estimating the prevalence of NCGS is not accurately possible as many are self-diagnosed in addition to the lack of objective diagnostic criteria for NCGS assessment [4].  As a result, various estimates ranging broadly from 0.6% to around 6% to an alarming 63% of the general population have been made [2, 4].  NCGS appears to be more common in females and in young/middle age adults (17-63 years age range) [2, 4].

Symptoms of Non-Celiac Gluten Sensitivity

Symptoms of NCGS include irritable bowl (IBS)-like symptoms including abdominal pain, bloating, diarrhea and/or constipation, nausea, and flatulence [2, 4].  Symptoms outside the intestinal tract include headaches, joint and muscle pain, muscle contractions, leg or arm numbness, excessive tiredness, “foggy mind”, weight loss and anaemia [2, 4].  Also, symptoms can manifest themselves as behavioral disturbances like disturbances in attention and depression [2].

Mechanisms Behind Non-Celiac Gluten Sensitivity

Unlike patients with CD, research shows individuals with NCGS do not react adversely to the protein gliadin present in gluten [2].  Some studies suggest that a family of non-gluten proteins present in wheat called wheat amylase trypsin inhibitors (ATIs) may be responsible for NCGS [2, 12].  ATI proteins function to naturally protect wheat from pests [12].  And, as ATI content has dramatically increased in recent years to make wheat more pest-resistant, ATI content has similarly increases [12].  It is possible then, that an increase in ATIs might explain the growing number of NCGS [12].

Although, ATIs may not be the only cause of NCGS [2, 12].  Short-chain carbohydrates known as FODMAPs (fermented oligosaccharides, disaccharides, monosaccharides, and polyols) cause symptoms of NCGS, but not through an immune response as with ATIs.  Rather, FODMAPs cause symptoms through the indigestible nature of these carbohydrates that cause water retention and gas production in the small intestine [12].

Why Does Non-Celiac Gluten Sensitivity Occur?

Only 50% of people with NCGS have the human leukocyte antigen (HLA) (HLA-DQ2 and HLA-DQ8) genes, which are strong diagnostic markers in those with CD [2].  Therefore, as no adaptive immunity markers are present in NCGS, etiology is unknown [5].  However, it is clear that NCGS involves innate immune mechanisms.  The innate immune system functions to respond quickly to certain general signs of infection [6, 12].  Cells of the innate immune system have receptors called Toll Like Receptors (TLRs) that sense and defend against invading pathogens [2, 6].  Once pathogens are recognized, these TLRs trigger a rapid inflammatory response [6].  People with NCGS are found to have higher expressions of TLRs compared to controls, indicating involvement of the innate immune system [6].  Furthermore, imbalances in gut microorganisms (microbiota), which play an important role in regulating cells that protect the intestinal barrier, may also contribute to the onset of NCGS [5].

Summary

From an evolutionary perspective, the rapid increase in gluten related disorders is not surprising as it has not been long since humans began growing and preparing food rather acquiring it through hunting and gathering.  Until the agricultural era, humans were not exposed to cereal grains for hundreds of thousands of years.  Now, cereal grains like wheat, rice, barley, oats, rye, maize, millet and sorghum are staple foods of mankind around the world.  The consequences of mass cereal grain consumption are that humans and our digestive systems have not adapted to develop immunological tolerance to most cereal grain proteins [8].  A repeated number of studies demonstrate the harmful immune responses related to the gluten proteins in wheat.  Future posts will examine the gluten free diet (GFD) and its usefulness in combatting gluten-related disorders.

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References:

1. Brandtzaeg, P. 2007. Why We Develop Food Allergies. American Scientist. January-February. Vol. 95, No. 1, Pg. 28.

2. Czaja-Bulsa, G. 2015. Non-celiac gluten sensitivity: A new disease with gluten intolerance. Clinical Nutrition. April. Vol. 32, No. 2, pp. 189-194.

3. Carrera-Bastos et al. 2011. The Western Diet and Lifestyle and Diseases of Civilization. Research Reports in Clinical Cardiology. March. Vol. 2. Pp.15-35.

4. Guandalini, S. & Polanco, I. 2015. Nonceliac Gluten Sensitivity or Wheat Intolerance Syndrome? The Journal of Pediatrics. April. Vol. 166, No. 4, pp. 805-811.

5. Nylund, L. et al. 2016. The microbiota as a component of the celiac disease and non-celiac gluten sensitivity. Clinical Nutrition Experimental. January. pp. 1-8.

6. Sanz, Y. 2015. Gut microbiota influence the development and function of different host defence mechanisms of innate and adaptive immunity. Annals of Nutrition & Metabolism. Vol. 67, Sup. 2, pp. 28–41.

7. Sapone, A. et al. 2012. Spectrum of gluten-related disorders: consensus on new nomenclature and classification. BMC Medicine. February. Vol. 7, pp. 10-13.

8.  Scherf, K. A. et al. 2016. Gluten and wheat sensitivities: An overview. Journal of Cereal Science. Vol. 67, pp. 2-11.

9. West, J. et al. 2014. Incidence and prevalence of celiac disease and dermatitis herpetiformis in the UK over two decades: population-based study. The American Journal of Gastroenterology. May. Vol. 109, No. 5, pp. 757-768.

10. White, L. E. et al. 2013. The Rising Incidence of Celiac Disease in Scotland. Paediatrics. October Vol. 132, No. 4, pp. e924-31.

11. Wieser, H. 2007. Chemistry of Gluten Proteins. Food Microbiology. Vol 24, No. 2, pp. 115-119.

12. Wyrick, J. 2013. Gluten Sensitivity: What Does It Really Mean? Scientific American [Online] [Viewed March 03, 2016]. Avialable from: http://blogs.scientificamerican.com/guest-blog/gluten-sensitivity-what-does-it-really-mean/.

13.  Zuidmeer, L. et al. 2008. The Prevalence of Plant Food Allergies: A Systematic Review. The Journal of Allergy and Clinical Immunology. May. Vol. 121, No. 5, pp. 1210-1218.

For those individuals who have suffered from irritable bowl syndrome (IBS) or inflammatory bowel disease (IBD), the road to becoming healthier and fitter is immediately made more challenging. Nutrition has found to play an integral role in triggering symptoms of IBS and IBD with about 60% of patients reporting that certain foods exacerbate their symptoms [4]. Due to the symptomatic diversity of IBS, pharmacological treatments are often ineffective, targeting only primary symptoms while a host of other symptoms are present. Considering the potential role of food and trigger foods in symptomatic exacerbation of IBS and IBD, specific dietary alterations, like a low-FODMAP diet, have been found to greatly improve symptoms and overall quality of life [4, 5].

What Are IBS and IBD?

IBS is a chronic gastrointestinal disease that affects between 7% and 15% of the population [4]. It is characterized by symptoms like abdominal pain or discomfort, altered bowel habits, bloating and abdominal distention. IBD is inflammation of the small and large intestine and affects 1 in 250 people in the United Kingdom [3]. Crohn’s disease and ulcerative colitis together are the most common forms of inflammatory bowel diseases and are similarly characterized by abdominal pain, bloating (distension), constipation, diarrhoea and gas [8].

What Causes IBS and IBD?

Unfortunately, the cause of IBS and IBD are not completely understood and are thought to be a result of a combination of altered gut microbiota (reduced diversity of intestinal flora), visceral hypersensitivity (pain within the inner organs), and changes in gastrointestinal motility (movement of waste through the gastrointestinal tract) and low-grade inflammation [4].

What Can We Do About It?

More recently, researchers have investigated an elimination diet that aims to minimize symptoms of IBS called the low-FODMAP diet. FODMAP is an acronym used to define a group of fermentable, short-chain carbohydrates including fermented oligosaccharides (fructans and galactans), disaccharides (lactose), monosaccharides (fructose in excess of glucose), and polyols (sorbitol, mannitol etc.) [5].

The low-FODMAP eating pattern is based on the physiology that in humans, short-chain carbohydrates (FODMAPs) are poorly absorbed and broken down in the small intestine, which increases the fluid load in the colon and gas production [5]. Also, gas generated during the fermentation of FODMAP foods adds to the filling of gas in the colon, which as a result may cause bloating and abdominal distension. The open space in the colon called the lumen, fills with gas, which then activates gastrointestinal movement and the transit of waste contents within the colon, which may accelerate transit and contribute diarrhoea or subjectively perceived spasms [5].

Another mechanism whereby FODMAPs create gastrointestinal symptoms is through changes in intestinal fluid content within the lumen [5]. Diets rich in FODMAPs were shown to lead to increases in waste weight and water content leading to accelerated gut motility [5]. Also, subjects placed on high-FODMAP diets were found to have increased hydrogen concentrations in the lumen of the digestive tract, which correlated with increased gas in the colon and further suggests that fermentable foods may alter gut flora [7].

Low-FODMAP Diets: What’s The Evidence?

A randomized crossover study by Ong and colleagues in 2010, randomized 15 subjects with IBS and 15 healthy controls to a low or high-FODMAP diet for 2 days. The study used gastrointestinal symptoms and hydrogen gas samples (a marker of gas in the colon) as outcome measures. Subject on the high-FODMAP diet experienced worsening abdominal bloating and pain, gas, nausea, heartburn and fatigue, whereas subjects on low-FODMAP experienced an increase in only gas. Also, the subject on the high-FODMAP diet showed higher hydrogen levels compared to the low-FODMAP subjects [6].

A single-blinded randomized controlled trial by Staudacher and colleagues (2012) investigated the effects of a 4-week low-FODMAP diet on changes in microorganisms in the lumen of the digestive tract, short-chain fatty acids, and gastrointestinal symptoms in subjects with IBS. A total of 35 subjects were randomly allocated to either a low-FODMAP group or habitual diet (control) group. A stool sample was collected and analyzed for bacteria groups. At follow-up, the low-FODMAP group had a lower intake of short-chain fatty acids, the total bacteria in the lumen of the digestive tract did not differ between groups, and more patients in the intervention (low-FODMAP) group reported adequate control of symptoms (13/19, 68%) compared with controls (5/22, 23%) [7].

A 2013 randomized crossover study by Biesiekierski and colleagues looked at the impact of a 2-week low-FODMAP diet on 37 patients with IBS that fit the criteria for “non-celiac gluten sensitivity.” Subjects were placed on a low-FODMAP diet for 2-weeks then randomly allocated to high-gluten, low-gluten or whey protein diets for 1-week. The study used a 100-point visual analogue scale (VAS) and significant worsening of symptoms was indicated by a 20-point change in the VAS. All subjects that were placed on the low-FODMAP diet reported significant symptomatic improvement, which worsened with any addition of either gluten or whey protein to their diet [1].

A recent (2015) systematic review and meta-analysis by Marsh and colleagues, examined 6 randomized controlled trials and 16 non-randomized interventions aimed at determining the efficacy of a low-FODMAP diet on the treatment of functional gastrointestinal symptoms. Outcome measures included a reduction in the Symptoms Severity Score (SSS) and increases in the IBS Quality of Life (QOL) score. Analysis of the 22 studies showed a significant decrease in IBS SSS scores for those subjects on a low-FODMAP diet in both the randomised controlled and non-randomised intervention studies [4].

Summary

There is adequate evidence to suggest that adopting a low-FODMAP way of eating can significantly improve gastrointestinal symptoms such as abdominal pain, bloating, constipation, gas, and diarrhea in those individuals suffering from IBS or IBD. Individuals that have conditions like small intestinal bacterial overgrowth (SIBO) would also benefit from a low-FODMAP nutrition plan, as FODMAP foods need to pass through the small intestine and would feed already overgrown bacteria in small intestine potentially exacerbating the condition.

However, there is evidence that indicates that adherence to a low-FODMAP diet may lower concentrations of beneficial bacteria in the colon like bifidobacteria, which can be obtained from FODMAP foods [7]. The question would then be how long to practice a low-FODMAP diet as to not produce a damaging effect on the gastrointestinal microbiota (gut flora). Implications of a low-FODAMP diet on gut microbiota still need further investigation to accurately answer this question.

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References:

1.  Biesiekierski, et al. 2013. No effects of gluten in patients with self- reported non-celiac gluten sensitivity after dietary reduction of fermentable, poorly absorbed, short-chain carbohydrates. Gastroenterology. August. Vol. 145, No. 2, pp. 320-328.

2.  Halmos, E. P. et al. 2014. A diet low in FODMAPs reduces symptoms of irritable bowel syndrome. Gastroenterology. January, Vol. 146, No.1, pp. 67-75.

3.  IBD Scotland. 2015. What is IBD? [online] [Viewed 20 December 2015]. Available from: http://www.ibdscotland.org/content/what-ibd.

4.  Marsh, A., Eslick, E. M. & Eslick, G. D. 2015. Does a diet low in FODMAPs reduce symptoms associated with functional gastrointestinal disorders? A comprehensive systematic review and meta?analysis. European Journal of Nutrition. May.

5.  Muhammed, A. K. et al. 2015. Low-FODMAP Diet for Irritable Bowel Syndrome: Is It Ready for Prime Time? Digestive Diseases and Sciences. May, Vol. 60, No. 5, pp. 1167-1177.

6.  Ong, D. K. et al. 2010. Manipulation of dietary short chain carbohydrates alters the pattern of gas production and genesis of symptoms in irritable bowel syndrome. Journal of Gastroenterology and Hepatology. August, Vol. 25, No. 8, pp. 1366-1373.

7.  Staudacher, H. M. et al. 2012. Fermentable Carbohydrate Restriction Reduces Luminal Bifidobacteria and Gastrointestinal Symptoms in Patients with Irritable
Bowel Syndrome. The Journal of Nutrition. August, Vol. 142, No. 8, pp. 1510-1518.

8.  Stein, D. A. & Shaker, R. 2015. Inflammatory Bowel Disease: A Point of Care Clinical Guide [online]. Switzerland: Springer International Publishing [Viewed 20 December 2015]. Available from: http://www.springer.com/la/book/9783319140711.

9.  Wolf, W. A., Kiraly, L. N. & Ireton-Jones, C. 2015. Incorporating FODMAP Dietary Restrictions: Help or Hype? Gastroenterology and Nutrition. September, Vol. 4, No. 3, pp. 214-219.

10.  Yoon et al. 2015. Low-FODMAP formula improves diarrhea and nutritional status in hospitalized patients receiving enteral nutrition: a randomized, multicenter, double-blind clinical trial. Nutritional Journal. November, Vol. 14, No. 1, p. 116.

Post-Workout Nutrition Rationale

The primary rationale for immediate post-workout nutrition is to replenish glycogen stores [1].  Glycogen is a storage form of carbohydrates (i.e. glucose) and the primary fuel source during moderate-repetition resistance exercise.  Glycolysis, the breaking down of glucose, supplies as much as 80% of the energy during resistance exercise [1,13].  One study investigating muscle substrate utilisation during resistance exercise showed that just one set of arm curls performed to failure at 80% of 1 repetition-maximum (1RM) decreased muscle-glycogen stores by 12% [8].  Three sets at the same intensity reduced glycogen stores by 32% [8].

Glycogen has also been found to attenuate muscle protein breakdown [1].  This is accomplished through its insulin spiking effects, which is shown to reduce muscle degradation [5].  Following resistance exercise, muscle protein breakdown is elevated then rapidly rises thereafter lasting approximately 24 hours post-exercise [9].  Subjects performing resistance exercise under fasted conditions are found to exhibit peak muscle protein breakdown at just over 3 hours post-exercise [9]. Theses levels are further elevated by 30-50% three hours following strenuous exercise [6].

Finally, immediately post-workout nutrition is considered beneficial for elevating muscle protein synthesis [1].  Muscle protein synthesis refers to the building of new muscle protein, which forms the building blocks of muscle mass development [6].  The scientific literature reveals conflicting results when determining if immediate post-exercise consumption of protein, or carbohydrates, or a mixture of both enhances muscle protein synthesis.  Some studies have demonstrated an increase in muscle protein synthesis with the immediate post-exercise consumption of primarily protein and carbohydrates, while others found no difference when protein and carbohydrates were ingested 1 hour versus 3 hours post-exercise [7, 11].

Origins of the “Anabolic Window”

The “anabolic window” is commonly referred to as the short period following a workout whereby muscular adaptations are considered compromised if nutrients (i.e. carbohydrates and protein) are not ingested.  This theory was first brought to the mainstream by researchers John Ivy and Robert Portman.

Initiating the “faster is better” concept, Ivy and colleagues conducted a study in 1988 examining the effects of muscle glycogen synthesis following continuous cycling [3].  The authors exercised 12 male cyclists continuously for 70 minutes on a cycle ergometer at 68% VO2 max interspersing this with two, 2-minute bouts of higher intensity cycling at 88% VO2 max [3].  The subjects were fed a 25% (2g/kg) carbohydrate solution either immediately following exercise or 2 hours post-exercise [3].

The results of this study showed that glycogen synthesis was 3 times faster in the immediate consumption group compared to delayed intake [3].  At 4-hours post-exercise, glycogen synthesis was still approximately twice as fast in the immediate intake group than the delayed group [3].  As a result of these findings, Ivy and Portman suggested that immediate carbohydrate intake post-exercise was critical to replenish glycogen stores and maximise recovery [3, 4].

The same authors published a a book in 2004 called Nutrient Timing and in it provided guidelines that specific nutrients should be ingested within 45 minutes immediately post-exercise to prevent muscle breakdown and potential insulin resistance [4].

Limitations of Post-Workout Nutrition Guidelines

The book entitled Nutrient Timing (2004) by Ivy and Portman contains important limitations that should be considered when interpreting its recommendations for post-workout nutrient timing.  The following limitations include:

• The majority of research cited in the book relates to over-night fasted subjects.  Under circumstances of fasted, strenuous exercise, an increase in muscle protein breakdown does occur which extends net negative protein balance post-exercise [1].  However, in the majority of cases, exercise is not initiated in a fasted state.  It is more common for individuals to have a meal or supplement within 3 hours of exercise.

• Recommendations were based largely on studies that examined endurance or depletion protocols.  During endurance exercise (i.e. marathon, cycling) immediate post-workout nutrition to replenish glycogen is warranted given the requirement to perform for longer periods or multiple sessions in a day.  This requirement would be similar for a lifter performing multiple resistance training sessions in a day utilising the same muscle groups.  However, for the far majority of individuals, this requirement and the need for immediate glycogen replenishment does not exist.

• Studies used to inform recommendations were based primarily on acute study periods.  Subjects in many of these studies were put through single session protocols.  Longer study periods would be required to understand the true impact of immediate post-exercise ingestion of nutrients on glycogen synthesis.

Pre-Workout Nutrient Timing 

Traditional recommendation for post-workout nutrition timing is to consume protein, or carbohydrates, or a mixture of both to elevate insulin and create a positive net protein balance [1].  However, sufficient and consistent evidence indicates that once insulin has been elevated to peak levels, its effects will continue for several hours beyond consumption {2, 10].

A study by Capaldo and colleagues (1999) showed that a mixed meal of 37g of protein, 75g of carbohydrates and 17g of fat elevated insulin levels 3 times above fasting levels in 30 minutes [2].  At 1 hour post consumption insulin levels were 5 times above fasting levels and at 5 hours insulin was still twice that of fasting levels [2].

Moreover, the literature shows that insulin elevation will max out net muscle protein balance (protein synthesis minus protein breakdown) at 15-30 mU/L, which is approximately 3-4 times normal insulin levels [12].  A study by Power and colleagues (2009) showed that 45 grams of whey protein takes about 40 minutes to peak blood amino acids and keeps net muscle protein balance elevated at 15-30 mU/L for approximately two hours following [10].  In other words, attempting to increase insulin levels through ingestion of more protein or carbohydrates post-workout will not elevate levels beyond this limit.

Given a circumstance where an individual has consumed nutrients an hour or two prior to training, blood amino acid levels would remain elevated far beyond a typical 1-hour gym session, therefore making immediate post-workout nutrition completely redundant [1].  In this scenario, a meal consumed 1-2 hours pre-workout would be more than sufficient to ensure recovery and positive protein balance following a workout [1].

The “Muscle Full” Effect

There is a concept in the literature called the “muscle full” effect or protein refraction, which refers to the point where the ingestion of protein or amino acids cause muscle protein synthesis to peak, and no further increases in muscle protein synthesis can be achieved beyond this level [1].  Once peak amino acids levels are reached, muscle protein synthesis becomes refractory, or “full,” and additional amino acids are then relocated for oxidisation or alternative uses other than muscle protein synthesis [1].   Even though amino acids or protein may be available, muscle protein synthesis still returns to baseline levels within approximately 3 hours despite amino acid availability [1].

Based on this “muscle full” effect, it would be appropriate, under circumstances where a training session is undertaken 3 hours, or more, after a meal, to consume protein or a mixture of protein and carbohydrates immediately post-exercise to maximise recovery and muscle growth [1].

Summary

Traditional recommendations for immediate post-workout nutrition to maximise recovery and muscle growth is far from supported by the literature.  Only under circumstances where exercise is performed under a fasted state or multiple bouts of exercise are performed within a day would immediate post-workout nutrient be required.  Given the findings, emphasis should be placed on the timing and composition of the pre-workout meal.

A pre-workout protein supplement (20g-40g) or mixed meal consumed approximately 1-3 hours prior to a training session is sufficient to raise blood amino acid levels for at least 3-6 post-exercise making immediate post-workout nutritional intake redundant.  As an evidence-based guideline, the duration between pre- and post-workout meals should be about 3-4 hours [1].  The consumption of a larger pre-workout mixed meal would theoretically extend this interval even further (i.e. 5-6 hours) [1].

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References:

[1] Aaragon, A. A. & Schoenfeld, B. J. 2013. Nutrient timing revisited: is there a post-exercise anabolic window? Journal of the International Society of Sports Nutrition. January. Vol. 10, No. 1, pp. 1550-2783-10-5.

[2] Capaldo B. et al. 1999. Splanchnic and leg substrate exchange after ingestion of a natural mixed meal in humans. Diabetes. May. Vol. 48, No. 5, pp. 958–966.

[3] Ivy, J. L. et al. Muscle glycogen synthesis after exercise: effect of time of carbohydrate ingestion. Journal of Applied Physiology. April. Vol. 64, No. 4, pp. 1480-1485.

[4] Ivy, J. L. & Portman, R. 2004. Nutrient timing: The future of sports nutrition. California: Basic Health Publications, Inc.

[5] Kettelhut, J. C. et al. 1988. Endocrine regulation of protein breakdown in skeletal muscle. Diabetes/Metabolism Reviews. December. Vol. 4, No. 8, pp. 751-772.

[6] Kumar, V. et al. 1985. Human muscle protein synthesis and breakdown during and after exercise. Journal of Applied Physiology. June. Vol. 106, No. 6, pp. 2026-2039.

[7] Levenhagen, D. K. 2001. Postexercise nutrient intake timing in humans is critical to recovery of leg glucose and protein homeostasis. American Journal of Physiology, Endocrinology and Metabolism. June. Vol. 280, No. 6, E982-983.

[8] McDougall, J. D. et al. 1999. Muscle substrate utilization and lactate production. Canadian Journal of Applied Physiology. June. Vol. 24, No. 3, pp. 209-215.

[9] Pitkanen, H. T. et al. 2003. Free amino acid pool and muscle protein balance after resistance exercise. Medicine and Science in Sports and Exercise. May. Vol. 35, No. 5, pp. 784-792.

[10] Power, O. et al. 2009. Human insulinotropic response to oral ingestion of native and hydrolysed whey protein. Amino Acids. Vol. 37, No. 2, pp.333–339.

[11] Rasmussen, B. B. et al. 2000. An oral essential amino acid-carbohydrate supplement enhances muscle protein anabolism after resistance exercise. Journal of Applied Physiology. February. Vol. 88, No. 2, pp. 386-392.

[12] Rennie, M. J. et al. 2006. Branched-chain amino acids as fuels and anabolic signals in human muscle. The Journal of Nutrition. Vol. 136 (1 Suppl):264S–8S.

[13] Schoenfeld, B. J. 2013. The M.A.X. Muscle Plan. Leeds: Human Kinetics.

Our previous article discussed how to create an energy deficit for weight loss and how to adjust energy intake and/or expenditure to achieve continuous weight loss.  This article will explain how to calculate maintenance calories, which can be used as a starting point from which to make nutritional adjustments when starting with a personal trainer (i.e. calorie deficit).  Maintenance calories refers to the amount of calories required to maintain current weight [8].  There are four primary variables that influence maintenance calories and total daily energy expenditure (TDEE):

1. Basal metabolic rate (BMR)

BMR is defined as the energy expended at complete rest and is measured in the lying position in the morning after sleep [2, 9].  BMR is the largest component of daily energy expenditure and is largely determined by the amount of fat-free mass (muscle) and to a lesser extent fat mass [6].  Therefore, an individual with more muscle or fat will have a higher BMR.  In a sedentary lifestyle, BMR can represent as much as 70% of daily energy expenditure with individuals in sedentary occupations [1].   To differentiate, resting metabolic rate (RMR), is energy expended at rest, but at any point during the day [4].  RMR is generally within 10% of BMR [4].

1. Thermic effect of food (TEF)

TEF is defined as the energy expended after eating.  TEF generally represents about 10%-15% of total food intake [10]. Research shows a 6-7% increase in energy expenditure with carbohydrate meals, 3% increase with fats, and 25-40% energy increase with protein-based meals [10]. Therefore, those with diets higher in protein will expend significantly more from eating compared to fats or carbohydrates.

1. Thermic effect of activity  (TEA)

TEA refers to energy expended during formal activity (i.e. resistance exercise, cycling) and its value can vary greatly between chosen activity.  TEA can represent 10-20% over BMR among more sedentary individuals (i.e. desk workers) and up to 100% over BMR for heavily active people [7].

1. Non-exercise activity thermogenesis (NEAT)

NEAT is the energy expended during physical activity that excludes volitional-sporting-like exercise [5].  For example, fidgeting, walking and maintaining posture are all classified under NEAT [5].  NEAT represents approximately 15% of total daily energy expenditure and is found to vary considerably among people by up to 2000 calories/day with the greatest influence being occupation [3, 11].

Estimating Maintenance Calories

A number of different calculations, ranging from complex to simple, can be used to calculate maintenance calories.  Importantly, any calculation should be accepted as purely an estimate and a starting point for understanding caloric needs.

Based on the caloric values yielded from BMR, TEF and TEA, Lyle Macdonald suggests a starting point of 14-16 calories per pound of body weight [7].  For example, an individual weighing 165 lbs (75kg) would yield a maintenance calorie value of 2,301-2,640 calories/day.  Macdonald suggests that women, or those with slower metabolic rates, should use the lower value (14 cal/lb), while men, and individuals with faster metabolic rates, would apply the higher value (15 cal/lb) [7].

Alan Aragon provides a stepwise approach to calculating maintenance calories that takes into account both target body weight (TBW) and intensity of exercise [8].  Aragon defines TBW as a measure of lean muscle mass that also accounts for some variance [8].  Aragon’s method is described here:

The first step in how to calculate maintenance calories is determining TBW.

Step 1 – Calculate lean body mass (LBM)

A simple estimate of 25% of total body weight can be used to determine LBM.  For example, an individual weighing 165 lbs would yield 41 lbs of fat mass and 124 lbs of lean mass (165 x .25 = 41 lbs).  Alternatively, several online calculators are available that provide an estimate.

Step 2 – Determine target lean body mass and multiply this figure by 100

For example, an individual with 124 lbs of lean mass with the objective of gaining 2 lbs of lean muscle in 8 weeks would give a value of 12,600 (126 x 100 = 12,600).

Step 3 – Determine target body fat percentage and deducted this figure from 100

For example, an individual with 25% body fat with the objective of realistically losing 4-6% in 8 weeks would yield a value of 79 (100 – 21 = 79) to 81 (100 – 19 = 81).

Step 4 – Calculate TBW by dividing Step 2 by Step 3

For example, using the above figures, TBW for an 8 week period would be 159.5 lbs (12,600 / 79 = 159.5) to 155.5 lbs (12,600 / 81= 155.5).

Weekly training hours encompass any volitional type of activity [8].  For example, resistance training, running, cycling, hiking, swimming, etc.  An individual that resistance trains twice a week for 60 minutes and cycles to work for 30 minutes, three times a week, would total 3.5 hours of weekly training.

Weekly intensity is graded from 9 to 11 and is described using the following criteria [8}:

9 = low intensity training

10 = moderate intensity training

11 = high intensity training

This figure is highly subjective and can be problematic under circumstances where intensity levels vary among weekly activities (i.e. strength training versus cycling).  Once selected, weekly intensity is added to weekly training hours.  For example, an individual with 3.5 hours of moderate weekly training would give a weekly training intensity of 13.5 (10 + 3.5 = 13.5).

To calculate maintenance calories, TBW is multiplied by weekly training intensity.  For example, an individual with a TBW of 159.5 and weekly training intensity of 13.5 would yield 2,153 daily maintenance calories.

For individuals with a high metabolic rate or high NEAT, Aragon offers an additional formula:

TBW x (11 – 13 + Average Total Weekly Training Hours)

For example, an individual with a TBW of 159.5 that performs moderate intensity exercise (i.e. 60min), 3x/week and also has high NEAT would yield: 159.5 x (13 + 3) = 2,552 daily maintenance calories.

Adjusting Maintenance Calories

The reality of calorie calculations is that they may prove inaccurate.  After obtaining an estimate of daily maintenance calories, it becomes more important to compare this estimation against actual fluctuations in weight.  For example, if weight gain occurs under set maintenance calories, an adjustment should be made to lower caloric intake.  In contrast, losing weight under maintenance calories would require an increase in daily caloric intake. Tracking maintenance calories against weight changes over the course of 2-4 weeks is advisable for understanding the accuracy of caloric estimation.

Summary

As a baseline from which to make nutritional adjustments, calculating maintenance calories can prove highly beneficial.  For individuals with weight loss goals, determining maintenance calories is critical for setting an appropriate caloric deficit.  Actual changes in body weight may contradict estimated caloric values, therefore one must anticipate having to modify caloric intake to achieve maintenance levels.

For more information on our personal training services please click here to read more.

References:

[1] Ferro-Luzzi, A. 2005. The conceptual framework for estimating food energy requirement. Public Health Nutrition. October. Vol. 8, No. 7A, pp. 940-952.

[2] Kelly, M. P. 2017. The American Council on Exercise (ACE). Resting Metabolic Rate: Best Ways to Measure It—And Raise It, Too [Online]. [viewed February 02, 2017]. Available from: https://www.acefitness.org/certifiednewsarticle/2882/resting-metabolic-rate-best-ways-to-measure-it-and/

[3] Levine, J. A. et al. 2006. Non-Exercise Activity Thermogenesis. The Crouching Tiger Hidden Dragon of Societal Weight Gain. Arteriosclerosis, Thrombosis, and Vascular Biology. April. Vol. 26, No. 4, pp. 729-736.

[4] Levine, J. A. 2004. Nonexercise activity thermogenesis (NEAT): environment and biology. American Journal of Physiology, Endocrinology and Metabolism. May. Vol. 285. No. 5. E675-685.

[5] Levine, J. A. et al. 1999. Role of Nonexercise Activity Thermogenesis in Resistance to Fat Gain in Humans. Science. January. Vol. 283, No. 5399, pp. 212-214.

[6] Johnstone, A. M. et al. 2005. Factors influencing variation in basal metabolic rate include fat-free mass, fat mass, age, and circulating thyroxine but not sex, circulating leptin, or triiodothyronine. American Journal of Clinical Nutrition. November. Vol. 82, No. 5, pp. 941-948.

[7] Macdonald, L. 2008. Body Recomposition. How to Estimate Maintenance Calorie Intake – Q & A [Online]. [viewed February 02, 2017]. Available from: http://www.bodyrecomposition.com/fat-loss/how-to-estimate-maintenance-caloric-intake.html/.

[8] Schuler, L. & Aragon, A. 2014. The Lean Muscle Diet. How to Calculate Daily Calories. Rodale Inc. New York, NY.

[9] Sabounchi, N. S. et al. 2013. Best Fitting Prediction Equations for Basal Metabolic Rate: Informing Obesity Interventions in Diverse Populations. International Journal of Obesity (London). October. Vol. 37, No. 10, pp. 1364-1370.

[10] Scott, C. B. et al. 2007. Onset of the Thermic Effect of Feeding (TEF): a randomized cross-over trial. Journal of the International Society of Sports Nutrition. December. Vol. 4, No. 24. pp.

[11] Trexler, E. T. 2014. Metabolic Adaptations to Weight Loss Implications for the Athlete. Journal of the International Society of Sports Nutrition. February. Vol. 11, No. 1, pp. 1186/1550-278.

In part 1 of evidence-based weight loss, we discussed the metabolic adaptations with dieting that function to restore energy balance and resist weight lost.  It was identified that all components of total daily energy expenditure (TDEE) undergo adaptive changes that function to reduce the energy deficit [8, 9].  In dieting, the body reduces the calories burned when eating, at rest, and most notably during spontaneous activity, which attenuate further losses in fat mass.  This article will focus on strategies that can help overcome weight loss plateaus to achieve further weight loss.

Decreasing Energy Intake

The goal in weight loss, whether people acknowledge this or not, is to reduce fat mass while retaining lean muscle.  The retention of lean muscle is not only critical for achieving a toned and defined physique, but for many important bodily functions.  Lean muscle plays critical roles in maintaining strength for performance (i.e. gym workouts, sport) as well as for activities of daily living.  Also, lean muscle has a central function in protein metabolism [10].  It serves as the primary reservoir for amino acids to maintain protein synthesis in vital organs and tissues like the heart, skin, brain, and liver that are essential for survival [10].  Likewise, lean muscle has a great role in the prevention of many chronic diseases.  Diseases such as heart disease, obesity and cancer have all been associated with losses of lean muscle [10].

The assumption with dieters is that reductions in body weight are the result of loses of 100% fat mass.  Although this may be true if dieting is performed correctly, the likelihood is much greater that both lean muscle and connective tissue are being lost as well.  As muscle is more dense than fat (1.1 g/mL versus 0.9 g/mL), it weighs more [5].  Therefore, a combined loss of fat mass and muscle tissue could lead to a greater than expected loss in body weight.

Additionally, muscle and fat yield different energy amounts when broken down.  One pound of pure body fat generates about 3,500 kcals when metabolised, whereas muscle requires 600 kcal to be broken down for energy [2].  It is common strategy for personal trainers to set client caloric reductions of 500 kcal per day to achieve one pound of fat loss per week.  However, the danger is that the same 3,500 calorie reduction can result in significant body weight changes depending on the amount of muscle tissue lost.

From the chart below, the same caloric deficit of 500 calories per day (3,500 kcal/week) could results in even greater body weight loss reductions per week, but at the expense of valuable muscle loss.

Smart Dieting for Continuous Weight Loss

To understand how to accomplish continuous weight loss, it is important to reintroduce the energy balance equation: Energy intake – Energy Output = Energy Stored in Body [3].  This equation states that if energy intake from food is greater than energy expelled from TDEE, weight gain occurs [8].  Conversely, energy expenditure that exceeds intake results in weight loss [8].  Given this, there are three strategies to re-establish weight loss in the presence of weight plateauing [1].

1. Decrease energy intake from food
2. Increase energy expenditure within TDEE
3. Apply a combination of both

Decreasing Energy Intake From Food

As a starting point, an individual must first understand his or her maintenance calories.  Maintenance calories refers to a point where body weight is stable because energy expenditure is equivalent to energy intake.

Once maintenance calories are established, a caloric deficit is set to reduce energy intake.  Setting a caloric deficit is an individual decision, however caution must be taken with larger calorie deficits.   Larger energy deficits from dieting can generate more weight loss, although the risk of losing lean muscle mass becomes much greater [2].  Likewise, larger energy deficits lead to faster rates of adaptation that invariably resist further weight loss [2].  Given this, a gradual, stepwise approach to dieting is best to reduce lean muscle loses and the rate of metabolic adaptation [2].

Caloric deficits can be defined as:

1. Small: 10%-15% below maintenance calories
2. Moderate: 20%—25% below maintenance calories
3. Large: more than 25% below maintenance calories

Example:

Small caloric reductions are generally the easiest to create [4].  They may involve simple deductions in sauces or dressings used with food or very slight reductions in portion meal sizes.  Advantages to smaller caloric deficits include improved adherence and consistency as well as minimised risk of muscle loss and metabolic impact [4].  A drawback to smaller caloric deficits will be a slower rate of weight loss [4].

Moderate caloric deficits are more widely utilised for weight reduction [4].  Moderate deficits are still manageable and can be achieve with relatively few dietary alterations (i.e. reduced portion sizes, lower calorie foods).  This level of caloric reduction will potentially have greater metabolic consequences, however the rate of weight loss will be quicker [4].

Large caloric reductions can yield the greatest losses in fat mass [4].  However, adherence to larger calorie deficits can be challenging and difficult to manage, particularly long-term [4].  These larger reductions have the greatest metabolic impact with larger implications on appetite hormones like leptin [4].  Large caloric deficits are best applied over the short term (2-4 weeks) before an adjustment to moderate caloric deficit is made [4].

Adjusting For Metabolic Adaptation

Metabolic adaptation to dieting will occur at varying rates and its magnitude appears to be proportional to the size of energy deficit [2].  A study by Redman and colleagues (2009) demonstrated the varying rates of metabolic adaptations in their study using male subjects averaging 24.8% bodyfat and females averaging 36.7% bodyfat [6].  The subjects were randomised to four groups for 6-months described below in Figure 1.

  Figure 1

  Figure 2

The results displayed above in Figure 2 show the adaptation response pre- and post-study period.  The greatest adaptions occurred in the LCD group that were calorie restricted to a diet of 890 kcal/day in addition to increasing energy expenditure until the subjects reached a 15% weight reduction [6].

Knowing that an adaptive response to dieting in unavoidable, it will be the case that a portion of the initial calorie deficit created will become offset by adaptation [3].  This means that actual weight loss will be different to predicted [3].  For example, if the predicted caloric deficit is 500 kcal/day, the actual deficit may be 300 kcal/day due to adaptation.  Certainly, this discrepancy will have implications on predicted weekly weight loss.

To achieve weight loss beyond metabolic adaptation, dietary adjustments will need to be applied to re-open a caloric deficit [1].  As a general guideline, Eric Helms recommends a deficit of 100 calories if the predicted weight loss is less than 0.5% per week [3].  Similarly, Lyle Macdonald states that when fat loss slows below 1 lb per week, an additional calorie reduction of approximately 10% should be applied [3].   A more detailed approach to adjusting calories to account for adaption is provide by Lyle Macdonald below [3]:

For example, take a case of a female weighing 155 lbs and holding 26% bodyfat.  This individual that loses 3 lbs of fat mass (100% fat assumed), over the course of about 2 weeks would expect an adaptive drop in TDEE by 60-84 cal/day.  In other words, 60-84 cal/day will be offset from the initial caloric deficit.  To account for this adaptive response, the individual would need to create an additional caloric deficit of 60-84 cal per day to achieve continuous fat loss.  Nutritionally, this deficit would be created by reducing food intake (i.e. reducing portion sizes or number of meals) and/or selecting lower calorie foods.

Increasing Energy Expenditure Within TDEE

Apart from creating a caloric deficit through dieting, continuous fat loss can be achieved by increasing energy expenditure [3].  If TDEE exceeds energy intake, weight loss will be achieved.  It is more challenging to quantify energy expenditure, because of the various components that constitute TDEE.  However, it can be simplified by focusing on the two elements that can be controlled within TDEE, that is, volitional (thermic effect of activity/exercise (TEA/TEE) ) and spontaneous activity (non-exercise activity thermogenesis (NEAT) ).

Increasing volitional activity, such as resistance training, sport or aerobic activity, first requires careful assessment of one’s current exercise plan.  That is, understanding if increasing exercise levels are both available and appropriate. This decision will be influenced by a number of questions including time-sensitivity, current activity level, compliance, physical and psychological tolerance.

The chart below, adapted from McArdle and colleagues (2001), indicates the energy expenditure (cal/min/kg) for a number of activities [7].  For example a 65kg woman performing 60 minutes of light weight training would expend 195 calories (0.05 x 65kg x 60 = 195 calories) [7].  This list can be useful for estimating energy expended within certain forms of activity, however it is clearly not exhaustive and merely an estimate.

Non-volitional activity (i.e. NEAT) is even more challenging to quantify given that postural changes and fidgeting are included within NEAT.  Theoretically, one could could track (i.e. pedometer) distance in steps per day, estimate pace, then calculate using estimated energy expenditure.  However, this would only provide a broad estimation of steps taken per day and not account for other forms of NEAT.

A more appropriate method of assessing energy expenditure would be to gauge an estimate of TEA and NEAT, then assess over the period of 1-4 weeks whether weight changes occur.  If weight plateauing or regain occurs at a given caloric deficit, energy expenditure will need to be increased.

In the occurrence of metabolic adaptation, rather than creating an additional caloric deficit from dietary alterations, energy expenditure would be increased in accordance with the adaption.  Using the previous example of a female that loses 3 lbs of fat mass with an adaptive drop in TDEE of 60-84 cal/day, this individual would increase activity to expend 60-84 cal/day.  This may come in the form of walking an additional 20 minutes per day to expel 78 calories (0.06 x 65kg x 20 = 78 calories).

Combining Decreased Energy Intake & Increased Energy Expenditure

The final strategy to achieve continual weight loss is utilising a combination of both decreased energy intake and increased energy output [1].  This approach can take pressure off dieting by turning to activity to assist in creating an energy deficit.  To combine both approaches, an individual would select a caloric deficit (i.e. 500 kcal/day) and achieve a portion from dieting (i.e. 250 kcal/day) and the other portion from activity (i.e. 250 kacl/day from walking, resistance training).

The selected deficit from dieting and activity is entirely up to the individual.  Also, activity can come from a selection of volitional activity, such as cardiovascular exercise or sport, or from non-volitional activity such as walking.  Alternatively, a combination of both can be applied.

To maintain fat loss through metabolic adaptation, an energy deficit is re-opened using the above mentioned combination.  For example, take an individual dieting at 2000 calories/day with an adaptive response of 200 calories/day.  This person could reduce food intake to 1,900 calories/day in addition to increasing the number of steps taken/day to account for the remaining calories.  This combined approach is particularly useful for those where weight loss is time-sensitive.

Summary

In an effort to achieve continuous weight loss beyond metabolic adaptation, an energy deficit must be re-opened.  Three approaches to re-opening an energy deficit include reducing energy intake, increasing energy expenditure, or using a combination of both.  The scientific literature provides some useful insight into rate of adaptation for a given loss in fat mass.  However, adaption rates will vary between people.  The size of energy deficit will depend largely on the individual and his or her tolerance, urgency and compliance.  Large energy deficits created too quickly will impact strength and performance and it is best to apply a gradual energy deficit to minimise the rate of adaptation and muscle loss.

For more information on our personal training services please click here to read more.

References:

[1] Aragon, Alan. 2014. Alan Aragon Research Review (AARR): What Causes Weight Loss Plateaus, and How Can They Be Overcome? [online]. [viewed on January 17, 2017]. Available from: http://www.alanaragonblog.com/aarr/.

[2] Helms, R. H. Evidence-Based Recommendations for Natural Body Building Contest Preparation: Nutrition and Supplementation. Journal of the International Society for Sports Nutrition. May. Vol. 11, No.20, pp. 186/1550-2783.

[3] McDonald, L. 2016. Alan Aragon Research Review (AARR): How to Adjust Your Diet for Continuous Fat Loss [online]. [viewed on January 17, 2017] Available from: http://www.alanaragonblog.com/aarr/.

[4] McDonald, L. 2008. Body Recomposition: Setting the Deficit  – Small, Moderate or Large [online]. [viewed on January 17, 2017]. Available from: http://www.bodyrecomposition.com/fat-loss/setting-the-deficit-small-moderate-or-large.html/.

[5] McArdle, W. D. et al. 2009. Exercise Physiology: Nutrition, Energy and Human Performance. 7th Edition. Essex: Lippincott Williams & Wilkins.

[6] Redman, L.M. at al. 2009. Metabolic and behavioral compensations in response to caloric restriction: implications for the maintenance of weight loss. PloS One. Vol. 4, No. 2, e4377.

[7] The American Council on Exercise (ACE). 2017. Calorie Cost of Physical Activity. [Viewed on February 01, 2017]. Available from: https://www.acefitness.org/updateable/update_display.aspx?pageID=593.

[8] Thomas, D.M. et al. 2014. Time to Correctly Predict the Amount of Weight Loss with Dieting. Journal of the Academy of Nutrition and Dietetics. June. Vol. 114, No. 6, pp.857-861.

[9] Trexler, E. T. 2014. Metabolic Adaptations to Weight Loss Implications for the Athlete. Journal of the International Society of Sports Nutrition. February. Vol. 11, No. 1, pp. 1186/1550-278.

[10] Wolfe, R. R. 2006. The Underappreciated Role of Muscle in Health and Disease. American Journal of Clinical Nutrition. September. Vol. 84, No. 3, pp. 475-482.

Introduction

Weight gain and obesity continue to rapidly increase in the United Kingdom (UK) and the rest of the world.  This problem has risen to epidemic proportions and has paved the way for increased risk of mortality and morbidity.  In the UK, it is predicted that approximately half of adult men and more than a quarter of adult women will be obese by 2030, a figure that could jump to 50% by 2050 [4].

Obesity and being overweight are clearly associated with poor health and number of diseases such as type II diabetes, cardiovascular disease, hypertension and cancer [4].  Of equal importance is the detrimental impact of weight gain on self and social perception of body image and self-confidence.

Given the rise in weight gain, there has been a similar significant increase in weight loss strategies and interventions.  Estimates show that approximately 29 million (55%) of the UK population attempted weight loss in 2013 [3].  Of this population, 44% of UK men and 2 in 3 UK women have attempted weight loss [3].

The greater concern is that weight loss strategies appear to be only transiently effective for the great majority of people.  Research indicates that the vast majority of people who attempt weight loss fail to achieve or maintain a 10% reduction over 12 months [5].  A meta-analysis by Anderson and colleagues revealed that more than a third of weight loss is gained back within the first year and the majority of weight fully returning within 3-5 years [1].

The explanation for weight regain can be multi-factorial (i.e. psychological, social, motivational).  However, this article will choose to focus on the adaptive changes that occur with dieting, which are largely responsible for slowing or preventing weight loss and encouraging weight regain.

First, despite various claims and theories about how weight loss occurs, the basis of weight loss is dependent on energy balance, as in, energy going into the body versus energy being expelled [11].

Energy intake – Energy Output = Energy Stored in Body

Energy in simply comes from the food we digest and absorb.  Energy out is more complex and encompasses a number of different components that make up what is referred to as Total Daily Energy Expenditure (TDEE) [8].  TDEE describes the calories burned by the body over a 24-hour period and is composed of the following elements [8]:

1. Resting Metabolic Rate (RMR) – The number of calories burned at rest
2. Thermic Effect of Food (TEF) – The number of calories burned from eating
3. Non-Exercise Activity Thermogenesis (NEAT) – The number of calories burned from spontaneous, involuntary activity (i.e. fidgeting, walking, position changes)
4. Thermic Effect of Activity (TEA) – The number of calories burned from formal exercise

How do these components relate to weight loss?

From a survival standpoint, the body views dieting and calorie restriction as starvation and a threat to its survival [6].  In response to weight loss and dieting, the body is found to compensate and adapt as a self-preservation mechanism.  It is shown, that under weight loss conditions, the body will slow metabolism and promote energy conservation through a reduction in all elements of TDEE [6].

Thermic Effect of Food (TEF)

Thermic effect of food is found to decrease in response to dieting simply due to a reduction in the amount of food consumed [12].  Specifically, TEF is found to reduce approximately 10% of caloric deficit [8].  For example, if food is reduced by 300 calories per day, TEF drops by 30 calories per day.  This reduction will result in a change to energy balance, however, the impact is minimal.

Resting Metabolic Rate (RMR)

Resting metabolic rate goes down and its magnitude is largely related to reductions in body fat (BF) [9].  Adaptive reductions in RMR of 3-5% are shown in obese subjects defined as females ≥35% and males ≥26% BF [2]. This percentage of adaptation appears to increase with leaner individuals.  In men with ≤15% BF, RMR adaptations of 15% are shown, while among females with ≤25% BF, adaptations of 17-20% are found [2].

The adaptive drop in RMR is related primarily to changes in insulin, nervous system output and thyroid hormone (T3), all of which are associated with a decrease in leptin [2, 10].  The role of leptin in weight loss has attracted great attention in recent years because of its role in appetite regulation. Given leptin’s important role in weight loss, a closer look into its function is provided below.

What is Leptin?

Leptin is a protein that is considered the body’s appetite-controlling hormone.  It is primarily located in fat cells and is typically associated with body fat and appetite promotion [12].  Research indicates the higher the leptin levels, the greater the body fat percentage [8].  In response to dieting and weight loss, leptin levels can drop significantly over the short-term (50% or less within a week), then more gradually in relation to body fat reductions [8].

Among other important functions, leptin communicates with the hypothalamus, the part of the brain that controls eating, to feedback about how much energy is stored in the body and how much food is being consumed [9].  In dieting, leptin levels rapidly drop and there is increased brain activity in areas involved in sensory control of food intake [12].  This drop in leptin signals to the hypothalamus to stimulate appetite, reduce energy expenditure and slow metabolism, which in turn encourages weight regain [9, 12].  This was demonstrated in rodent studies, whereby low leptin levels increased appetite promoting neuropeptides while decreased appetite suppressing neuropeptides [12].  Leptin injections in the hypothalamus of the rodents reversed these alterations in neuropeptides as well restoring changes in metabolism, immunity and hormones [12].

Thermic Effect of Activity (TEA) & Non-Exercise Activity Thermogenesis (NEAT)

Dieting and weight loss create the greatest adaptation within activity, both volitional activity as in TEA (i.e. resistance training) and spontaneous activity as in NEAT (i.e. fidgeting, walking).  Adaptive drops of 60% of activity levels have been measured in both men and women that diet.  In the obese, this adaptation in activity can be as much as 95% [8].

The reason for this substantial reduction in activity is twofold.  Firstly, a leaner, smaller body simply becomes more efficient and burns fewer calories within a given activity [8, 13].  Rosenbaum and colleagues (2003) showed that during dieting and weight loss, skeletal muscle becomes 35% more efficient and fewer calories are burned, particularly at low workloads [13].  The authors speculated that decreasing levels of circulating thyroid hormone during dieting may lead to recruitment of slow-twitch muscle fibers, which consume fewer calories and produce less power per contraction compared to fast-twitch fibers [13].

Secondly, NEAT levels decrease in response to dieting because dieters tend to move around less compared to non-dieters [7].  Dieting can induce feelings of fatigue, which can discourage spontaneous type activity.  This was demonstrated by Martin and colleagues (1985) who showed that greater weight loss was associated with larger reductions in NEAT [7].  The authors suggested that even under conditions where food is readily available, dieters subconsciously decrease energy expenditure from activity [7].  This reduction in activity would preserve energy and restrict weight loss.  A study by Weigle and colleagues (1988) confirmed the adverse impact of NEAT following weight loss [15].  In their study,  obese subjects lost 23.2% of their body weight and NEAT contributed an adaptation of 582 calories per day or 71% of the decrease in total daily energy expenditure [15].

Summary

Weight loss is the result of energy expenditure exceeding energy intake.  However, in dieting, the body responds by adapting and reducing energy expenditure to resist weight loss.  The calories burned while eating (i.e. TEF) and at rest (i.e. RMR) both decrease with dieting.  However, it is reduced activity (i.e. NEAT) that contributes the greatest reduction in energy expenditure.  Subconsciously, people that diet reduce spontaneous activity and become more efficient performing the same movements and expend less calories doing so.  An individual dieting can burn over 400 calories per day less compared to a non-dieter [14].  It is this very combination of reduced NEAT and RMR levels that contribute to weight loss plateauing and weight regain.  The next post will focus on how to progress past these adaptive mechanisms to achieve further fat loss.

For more information on our personal training services please click here to read more.

References:

[1] Anderson J. W. 2001. Long-term weight-loss maintenance: a meta-analysis of US studies. American Journal of Clinical Nutrition. November. Vol. 74, No. 5, pp.:579-584.

[2] Astrup, A. 1999. Meta-Analysis of Resting Metabolic Rate in Formerly Obese Subjects. American Journal of Clinical Nutrition. June. Vol. 69, No. 6, pp. 1117-1122.

[3] BBC. 2004. BBC News: Many People Diet Most of the Time [Online]. [viewed on January 04, 2017]. Available from: http://news.bbc.co.uk/1/hi/health/3454099.stm

[4]  Gupta, H. Barriers to and Facilitators of Long Term Weight Loss Maintenance in Adult UK People: A Thematic Analysis. International Journal of Preventative Medicine. December. Vol. 5, No. 12, pp. 1512-1520.

[5] Kraschnewski, J.L et al. 2010. Long-term weight loss maintenance in the United States. International Journal of Obesity (London). November. No. 34, Vol. 11, pp. 1644–1654.

[6] MacLean, P. S. 2015. The Role of Adipose Tissue in Weight Regain After Weight Loss. Obesity Reviews. February. Supplement 1, pp. 45-54.

[7] Martin, C. K. et al. 1985. Effect of Calorie Restriction on the Free-Living Physical Activity Levels on Non-Obese Humans: Results of Three Randomised Trials. Journal of Applied Physiology. April. Vol. 110, No. 4, pp. 956-963.

[8] McDonald, L. 2016. How to Adjust Your Diet for Continuous Fat Loss. Alan Aragon Research Review (AARR). November. pp. 2-3.

[9] McDonald, L. 2013. The Ultimate Diet 2.0. Why is it So Hard: Part 1. Austin: Lyle McDomald.

[10] Morton, G. J. et al. 2006. Central nervous system control of food intake and body weight. Nature. September. Vol. 443, No. 7109, pp. 289-295.

[11] National Heart, Lung, and Blood Institute (NIH). 2013. Balance Food and Activity [Online]. [viewed on January 04, 2017]. Available from: https://www.nhlbi.nih.gov/health/educational/wecan/healthy-weight-basics/balance.htm

[12] Rexford, S. A. 2008. Revisiting Leptin’s Role in Obesity and Weight Loss. The Journal of Clinical Investigation. July. Vol. 118, No. 7, pp. 2380-2383.

[13] Rosenbaum, M. 2003. Effects of experimental weight perturbation on skeletal muscle work efficiency in human subjects. American Journal of Physiology. Regulatory, Integrative and Comparative Physiology. July. Vol. 285, No. 1, pp. 183-192.

[14] Weightology. 2016. Why is it so easy to regain weight? [Online]. [viewed on January 03, 2017]. Available from: http://weightology.net/weightologyweekly/index.php/free-content/free-content/volume-1-issue-9-why-is-it-so-easy-to-regain-weight/why-is-it-so-easy-to-regain-weight/

[15] Weigle et al. 1988. Weight Loss Leads to a Marked Decrease In Non-Resting Energy Expenditure in Ambulatory Human Subjects. Metabolism. October. Vol. 37, No. 10, pp. 930-936.