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If you want to "hit your macros", you better hurry to find out what those macros are, before you start weighing everything. So, here are the three steps you got to take before you start ;-)

In view of the fact that I am getting questions like "how do I set up my macros" again and again... and did I say "again"? I would like to briefly outline a very easy you can use to create a baseline macronutrient template you can use for yourself or clients. The process itself involves some mathematics, but don't worry, it's easy and with the examples, I am going to give, even the math-haters out there will be able to come up with a new macro-template in ~2 minutes time. Before I go ahead I would yet briefly like to point out that I am a huge proponent of "food quality" and not a fan of "if it fits your macros" aka "I just want to eat all the shit I have always been eating!" So, for your own sake, please don't forget to check, where those "macros" are coming from.
Ok, let's get back to the two minutes: I have to admit, if you want to do it right and have no clue how much energy you want to consume on a daily basis, the whole process may take a bit longer than two minutes, but for the rest of you, it's going to be two minutes, I promise.

• Step 1: Find your / a client's daily energy requirements: This may be the fastest or longest-taking step. If you already know how much energy you want to consume or have a client consume on a daily basis, skip this step and go right to step 2.
  
  You're still there? Well, ok. This means you don't know how much energy you want to consume or prescribe and want me to tell you how to calculate that? Well, I will do that, but I personally don't recommend to calculate your requirements, and I am pretty sure that after you see my example calculation you will be similarly convinced that only Mr and Mrs Average will get good results with the most used equation to calculate your resting metabolic rate (RER) and total energy requirements (TEE) - the Harris-Benedict equation:
  

  Equation 1: The Harris-Benedict Equation in all its usless glory - don't rely on this or any other formula when you're trying to estimate your or a client's dietary requirements.

  That's it, the Harris-Benedict equation in all its useless glory. Well, then... let's take an example, not a totally extreme one, though, let's say you have two clients, a man and a woman:
  • Obese Man - obese at 175kg body mass, he is 27 years old, but looks like an overweight teenager, with his body size of only 179 cm he's having an extra-hard time moving his frame to the fridge and back, which is - as he freely admits - the only "exercise" he has gotten ever since he's lost his job 5 years ago
  • Active Woman - active, middle-aged, weighs 58 kg, you checked, because you know middle-aged women lie about their weight, her passport says age 47, her body size is 160 cm, and she says that she jogs regularly ("Roughly three times a week, I'd say"), no HIT, HIIT or strength training
  Now, in theory you can calculate our exemplary clients' resting metabolic rate (RER) using Equation 1. In other words, the equation is going to tell you the amount of energy our imaginary client would need if he or she was minimally physically active (that's  not 100% bed-rest, but close to). To go from the RER to the total energy their total energy requirements (TER) or total energy expenditure (TEE), you still have to multiply the value by ... for ...
  • 1.2 - non-active couch potato and office worker,
  • 1.3 - people who use the bike or walk or do other light physical exercise 1-2x/wk
  • 1.4 - people who jog or do similarly medium intense physically activity 3-5x/wk
  • 1.6 - the average recreational active gymrat who trains intensely at least 3x/wk
  • 1.7 - someone who lives in the gym and takes the stairs ;-) 
  For our two example clients, the obese man at 175 kg body mass (age 27, body size 179 cm) who moves to the fridge and back and our actually pretty active middle-aged women at 58 kg, who jogs regularly but hates high intensity workouts, the calculations and results are look like this:
  

  Equation 2: Calculation of the total daily energy requirement for the two example clients.

  I assume that you'd agree that that both values are way off what would make sense for the two, right? And that despite the fact I didn't chose the people on the really extreme ends of the "ripped female athlete to sign. obese male couch potato"-scale - for people on the edges, the results may well be even worse.
  
  So, I hope you see that you are way better off if you are having your clients log their food intakes for a week (at least three days) and determine their intake (or yours if you're doing this for yourself) by dividing the total energy intake by 7.

• Using the food log will not only allow you to avoid bogus predictions for people who are not "average", but it will also allow you to 
• estimate the previously mentioned food quality of your / your clients diet, 
• decide based on what you or he / she like whether a high carb or low carb approach is the one with a higher chance of 100% adherence and
• see that the current energy intake is at a level that would hardly sustain a toddler, due to years of dieting and meanwhile useless reductions in energy intake (if that's the case, click here)
• Now that you hopefully know how much energy you or your client need on daily basis, there's one last thing you have to do before you proceed to step #2: Determine if you, he or shee needs just the TER or 15-35% less (in very overweight clients even 40%-50% less) or 10-15% more in order to lose body fat or gain muscle, respectively.
• Step 2: Decide whether you want to go high or low carbohydrate: Now that you know "exactly" how much energy you or your client are going to consume on a daily basis, it's about time to decide which dietary prescription, i.e. high or low carbohydrate, you or your client are going to follow.
  
  Unless you're going to go keto which would require you to set a limit of ~10-15g of protein per meal to stay in full ketosis, you still have 30s to think about whether it should be high or low carb, while you're determining the protein baselines which are:
  • 1.2-2.2 g/kg (2.0g = suggested, unless lightweight) total weight for men
  • 1.2-1.8 g/kg (1.5g = suggested, unless lightweigh) total weight for women
  The lower recommendation for women is due to the fact women have a lower total body weight and less muscle on their frame. If you feed a tiny woman 2.0g protein per kg body weight, you may end up having her eat so much protein that there's no room for other nutrients, which is bad news in general, and very bad news for women who are much more susceptible to a lack of readily metabolizable energy that has not to go to the energy and time-consuming process of gluconeogenesis in the liver (and yes, you'd waste the protein anyways, because it would be turned into glucose).
  
  I think your 30s time for consideration are over and hope you've used the time wisely to decide if you want to go low or high carb? In general, the rule of thumb is: "The fatter you are, the greater the low-carb advantage is going to be." Now, as a waredownloadsoft reader you've read enough about low and high carbohydrate diets, their advantages and pitfalls on the waredownloadsoft to be able to decide, so (wo-)man up and take the responsibility and use
• 40-100g (80g suggested for men; 50g = suggested for women) as a minimum daily fat intake for yourself or clients on high carbohydrate diets or
• 50-120g (90g = suggested for men; 60g = suggested for women) as a minimum daily carbohydrate intake for yourself or clients on low carbohydrate diets and 
• <20g of carbohydrates (best divided across meals) on a truly ketogenic diet 
As you should have noticed you will always pick the macronutrient you're "avoiding" on your diet and determine a baseline for this. The reason is that the amount of carbs and fats that are appreciable baseline on high fat and high carbohydrate diets do not scale linearly with your or a clients weight. The idea is that you want to consume as little as it takes for optimal health and function. Accordingly, you never want to go to zero fat for endocrine reasons and you never want to cut the baseline carb intake so low that you force the body to convert a lion's share of protein into glucose. The latter is by the way something that happens if you follow a "almost only protein" diet and trust me, in the long term that's not good for either your health, or your looks and performance.
• Step 3: Calculate the amount of your primary energy source: The last step is actually the easiest one. You take the values from the previous steps, i.e. your total daily energy requirement (+/ - X% if dieting or bulking) and subtract the energy equivalents from Step #2 by filling your data into either the high or the low carbohydrate equation below:
  

  Equation 3: Use the results from steps 1-2 to calculate the amount of the main nutrient (in g/day).

  The values for TER are the ones you calculated in STEP 1, the ones for FAT and CHO are in grams per day from STEP 2. Since you've already determined the input values on an individual basis, the equations are identical for man and woman, fat or thin, athletic or sedentary people.
  
  So, assuming we'd chosen the standard approach for our two previously cited clients with a weight maintenance high carbohydrate diet (intake at TER) for the female and a restricted diet for the obese guy (TER - 30% = 2292.62 kcal/day), we would end up with...
  • Obese man - low carbohydrate approach, maximal carbohydrate intake 50g (because he's very insulin resistant), protein intake at 1.2 g/kg (=210g total per day | we use the lowest value, because due to his extreme body weight even that will get us way beyond the ~30g/meal rule).
    

    Figure 1: Results of the macro calculation for the obese man. Percentages expressed rel. to total macro intake (in g | left) and rel. to total energy intake (in kcal | right).

    With these values we would get a fat intake of 132.14 g/day and if we do the math on all macros we get a macro ratio of roughly 34% / 54% / 13% as FAT / PRO / CHO g/total macro intake per day. Usually, though, you see this expressed relative to the energy equivalents. In this case that's 53% / 38% / 9% as FAT / PRO / CHO and it gives you a much better idea of the fact that fat is the main energy source, here.
  • Active woman - high carbohydrate approach (because she doesn't like fat), maximal fat intake 45g (on the lower side, because her TER is relatively low, protein intake at 1.5g/kg (=87 g, that's minimally below the 30g/meal rule, but using a higher value would reduce her CHO intake to levels that have her run largely on gluconeogenesis which is bad for everyone and particularly nasty for women whose endocrine system tanks easily); in order not to starve her, we also assume a more realistic TER of 1,250kcal/day.
    

    Figure 1: Results of the macro calculation for the active women. Percentages expressed rel. to total macro intake (in g | left) and rel. to total energy intake (in kcal | right).

    If we enter these values into Equation 3, we get a carbohydrate intake of 124.25 g/day. If we do the math, just like we did for our other example client, that's a macro ratio of roughly 17.6% / 34.0% / 48.5% FAT / PRO / CHO in g/total macro intake per day and 32% / 28% / 40% FAT / PRO /CHO if the macronutrients are converted to energy equivalents and expressed relative to the total energy intake per day.
  That's it... well, you obviously you will have to tweak some of the input values, e.g. the protein intake or the baseline intakes for carbohydrates and fats if you are not happy with the results, but as I said this is an easy and proven way to get an idea of what your macros or the macros of one of your clients may look line. With some experience and a good knowledge of what works for you or a client you can optimize the result pretty quickly, though.

References:

• Antonio, Jose, et al. "The effects of consuming a high protein diet (4.4 g/kg/d) on body composition in resistance-trained individuals." Journal of the International Society of Sports Nutrition 11.1 (2014): 19.

Bodybuilders use carb-ups to look more muscular with a good reason.

Dual-energy X-ray Absorptiometry aka DEXA or DXA is the gold-standard for measuring one's body composition in both scientific studies and real-world scenarios like monitoring the training response in professional athletes.

Compared to other techniques such as body impedance analyses and the skin-fold method, which are highly susceptible to changes in the water balance and the expertise of the person taking the measurements, respectively, DXA data is considered a more reliable method to assess lean body mass (LBM) and body fat percentage in clinical research and athletic practice.
DXA is also one of the few technologies that can allegedly reliably determine total and segmental (i.e. trunk, leg and arm fat mass and LBM) body composition, a feature you should keep in mind, because it will be relevant to the evaluation of a recent study from the Université du Québec à Montréal (Rouillier. 2015), which found increases particularly in the legs, yet not in the trunk musculature. Irrespective of the location of the gains, it's a study that puts a question-mark behind the reliability of DXA data certain, not exactly uncommon scenarios.

It is common knowledge that the LBM values you get with DXA represent water, glycogen, proteins and minerals in the lean muscle. In view of the fact that the amount of water in muscle increases with each gram of glycogen that's stored (rule of thumb: 4g of water per 1g of glycogen), scientists have thus long speculated that significant changes in muscle glycogen and the subsequent increases in water may thwart the results you get when you access your own or your subjects's body composition by the means of DXA. Since the muscle glycogen concentrations and water content could markedly change in humans, during very short, acute phases of high-carbohydrate intakes, Pietrobelli et el. advocated research on the impact of changing muscle glycogen levels on body composition measurements, using DXA as early as in 1996 (Pietrobelli. 2015) - research that has not been done in the past ~20 years, though, until... well, until Rouillier et al. took care of it.
In their study, twenty non-obese young men (age 22.7 ± 2.6 years, BMI 23.5 ± 2.1 kg/m²) were subjected to two DXA tests. One before and after another one after being fed a high-carbohydrate diet for 3 days.

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"All participants followed a high-carbohydrate diet ( ≥ 75% of total kilocalories consumed from carbohydrates) for 3 days. Examples of detailed high-carbohydrate meals for the day as well as examples of foods high in carbohydrates, which included the amount of carbohydrate for each food in grams, were given to each subject based on their total energy expenditure.

Examples of foods that were recommended to the participants were pasta, cereals, rice, bread, muffins, potatoes, corn, juices and fruits. Participants were allowed to eat carbohydrate rich foods of their choice and no restrictions were given. It should be noted that each participant was instructed not to perform any strenuous exercises during the 3 days of energy expenditure estimation before the diet started and 3 days during the diet" (Rouillier. 2015)

To ensure that the subjects did not mess up the results by non-adherence, the participants completed a food diary during the 3-day high-carbohydrate diet to determine the mean percentage of carbohydrates consumed from total kilocalories. In that, the participants were asked to write as much information as possible about the foods they ate (i.e. brand names, percentage of carbohydrates, fats and proteins, how the food was cooked, etc.).

Figure 1: Let's be honest, you don't really believe that the subjects gained 5% substantial lean mass in the legs within just 3 days due to eating pasta, muffins and fruits, do you? No, well Rouillier et al. (2015) don't believe that either. Rather than that, they interpret their results as evidence in favor of the hypothesis that ignoring the glycogen levels makes DXA useless.

While the dietary adherence was, just as you'd expect it with a "muffins, cereals and pasta"-diet, high, the accuracy of the post DXA-test the scientists did was hilariously low. After all, it is quite unlikely that the young normal-weight men gained significant amounts of total and appendicular lean body mass after only three days on a 83.7 ± 8.4% high-carbohydrate diet (p < 0.01). What's even more unrealistic, though, is that they did that in the presence of a "strong tendency for lower body fat percentage values after the intervention (p = 0.05)" (Rouillier. 2015) - right?
References:

• Pietrobelli, Angelo, et al. "Dual-energy X-ray absorptiometry body composition model: review of physical concepts." American Journal of Physiology-Endocrinology And Metabolism 271.6 (1996): E941-E951.
• Rouillier, M-A., et al. "Effect of an Acute High Carbohydrate Diet on Body Composition Using DXA in Young Men." Annals of Nutrition and Metabolism 66.4 (2015): 233-236.

Will slimming down from a 120 cm to a 60 cm waist always ruin your metabolic rate and set you up for weight regain or can high GI protect you from yoyoing?

Broscience tells us: "Carb up to preserve your resting metabolic rate." And in fact, there is some scientific evidence that suggests a link between high(er) carbohydrate intakes and increased thyroid function. The same amount of T3 will trigger a sign. higher stimulation of lipolysis and fat oxidation, for example, on high vs. low carb diets (Mariash. 1980). Low carb diets, on the other hand, lead to significant reductions of the active thyroid hormone and increases in the 'thyroid receptor inhibitor' rT3 - even in healthy individuals and if the energy intake is standardizes (Serog. 1982; Ullrich. 1985). So, is broscience right? Well, overfeeding studies show a similar increase in T3 in response to protein, fat and carbohydrates (Danforth Jr. 1979). So refeeds should work, irrespective of their carbohydrate content...
As you can see, it is hardly possible to confirm or reject the "carb up to prevent metabolic damage" (=prevent the diet induced over-proportional reduction in resting energy expenditure) hypothesis based on the existing evidence. A recent study by J. Philip Karl and colleagues who tried to determine "the effects of diets varying in carbohydrate and glycemic index (GI) on changes in body composition, resting metabolic rate (RMR), and metabolic adaptation during and after weight
loss" (Karl. 2015), however, may yet take us one step further towards rejecting or confirming this commonly heard of idea.

Figure 1: Overview of the key parameters of the study design and dietary composition (Karl. 2015).

In said study, Karl et al. randomly assigned adults with obesity (n = 91) to one of four diet groups for 17 weeks. As you can see in Figure 1, the diets all subjects were provided with differed in percentage energy from carbohydrate (55% or 70% | Figure 1, top-right) and GI (low or high, Figure 1, bottom-right) but were matched for protein, fiber, and energy. The study design itself comprised 5 phases:

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"Phase 1 was a 5-week weight maintenance phase in which weight maintenance energy needs were determined by adjusting provided energy intake to maintain stable weight. Mean Phase 1 energy intake was 12.2 MJ/day with 48% energy provided as carbohydrate, 16% as protein, and 36% as fat. Following Phase 1, participants were randomized by the study statistician to their Phase 2 dietary assignment using computer-generated randomization. The four diets differed in carbohydrate content (55%, ModCarb or 70%, HighCarb of total energy) and dietary GI (less than 60, LowGI or 80, HighGI), and were provided for 12 weeks at 67% of the weight maintenance energy intake determined in Phase 1. 

Participants were allowed to increase their energy intake during Phase 2 by requesting additional, randomization-appropriate foods from the metabolic kitchen if too hungry to be adherent. Phase 3 was a 5-week weight maintenance phase during which food was provided according to randomization. Energy intake during Phase 3 was prescribed to support weight maintenance at the new, lower body weight, and was predicted from body weight and energy intake measured at the end of Phase 2, with adjustment for self-reported physical activity. Phase 4 was a 12- month follow-up period during which participants selected and pre pared their own meals after being provided with instructions on fol lowing the diet to which they were randomized" (Karl. 2015)

To assess the effects of this sequence of induction (weight maintenance), and weight stabilization phases, the body weight, body composition, RMR, and metabolic adaptation (measured RMR vs. predicted resting metabolic rate = RMR) of the middle aged study participants (49-64 years) were measured before and after all phases of the study.

Figure 2: (A) Weight loss and (B) percentage of total weight loss attributable to fat mass and fat free mass while consuming provided-food diets differing in glycemic index (GI) and percent energy from carbohydrate (55%, ModCarb and 70%, HighCarb) for 17 weeks (n = 79). Values are mean 6 SEM. Weight loss analyzed by repeated measures ANCOVA, body composition by two-factor ANOVA. a,bMain effect of time; asignificant decrease from baseline (P < 0.001), bsignificant difference from Phase 2 end (P < 0.001). No diet effects (main effects or interactions) for any comparisons. GI, glycemic index; HighCarb, 70% energy from carbohydrate; ModCarb, 55% energy from carbohydrate (Karl. 2015).

Interestingly, the analysis of this data revealed no significant inter-group differences in terms of any of the relevant study outcomes. Yes, you read me right: This means that neither the GI, nor the GL, nor the carbohydrate content of the diet had statistically significant effects on weight loss, body composition, RMR, or the metabolic adaptation aka "metabolic damage" due to weight loss.

Figure 3: Measured resting metabolic rate as a function of predicted metabolic rate (Karl. 2015). Note: If there was no "metabolic damage", the solid line which represents the ideal body-weight dependent decline of energy expenditure and the dashed line which represents the actual ratio of the measured to the predicted RMR should be congruent.

While there were no inter-group differences and neither the amount or the type of carbohydrates had an effect on the reduction of the metabolic rate, there is still one interesting result you can see in the right graph in Figure 3. Said graph depicts the ratio of the measured to the predicted metabolic rate during the 5-week weight maintenance phase. If you look closely, you will realize that it suggests that having a high predicted RMR, i.e. being heavier, being taller and being more muscular, is associated with a non-significant decline of the non-predicted reduction of the energy expenditure (=metabolic damage) and thus a narrowing of the gap between the solid and dashed line.

"Solid and dashed? I don't get it!"

You're asking how I can support this hypothesis? Well, the dashed line that represents the true ratio of the actual to the predicted RMR approaches the theoretical one (the solid line) for higher RMR values. If this was more than a trend, it would suggest that two things: (a) Losing less weight and thus maintaining a higher predicted metabolic rate protects against metabolic damage (that would be useless). And (b) being tall and muscular and thus having a naturally high(er) predicted RMR can protect you from suffering metabolic damage when you lose weight.

Unfortunately, it's not possible to tell which (if any) of the two options is correct. If I had to make an educated guess, though, I would say it's a combination of both: The weight change of an average 5.5 kg did not wary too much and was withing 95% confidence intervals of [-7.1 kg, -4.6 kg]. In conjunction with individual physiological qualities of people with higher baseline RMRs, it could still explain the narrowing of the gap between predicted and true RMR after dieting.
References:

• Danforth Jr, Elliot, et al. "Dietary-induced alterations in thyroid hormone metabolism during overnutrition." Journal of Clinical Investigation 64.5 (1979): 1336.
• Karl, J. Philip, et al. "Effects of carbohydrate quantity and glycemic index on resting metabolic rate and body composition during weight loss." Obesity 23.11 (2015): 2190-2198.
• Mariash, C. N., et al. "Synergism of thyroid hormone and high carbohydrate diet in the induction of lipogenic enzymes in the rat. Mechanisms and implications." Journal of Clinical Investigation 65.5 (1980): 1126.
• Serog, P., et al. "Effects of slimming and composition of diets on VO2 and thyroid hormones in healthy subjects." The American journal of clinical nutrition 35.1 (1982): 24-35.
• Ullrich, Irma H., Philip J. Peters, and M. J. Albrink. "Effect of low-carbohydrate diets high in either fat or protein on thyroid function, plasma insulin, glucose, and triglycerides in healthy young adults." Journal of the American College of Nutrition 4.4 (1985): 451-459.

After your workout, whey protein is fine. With a low carb + high fat breakfast, though, it may ruin the satiety inducing effects of the high fat + low CHO meal.

Today I am going to make the short news items actually short in order to be able to cover all the different studies investigating topics that range from the effects of TV watching and other distractions on your likelihood of snacking, the link between the amount of food kids consume when they are allowed to eat as much as they want at a buffet and their body fat levels, the differential effects of various macronutrients on appetite, satiety & co, the negative effects of believing in food addiction, the surprising insight that whey may be for low-carbers not and more.

I think this was a long enough preface. So, let's not waste anymore time, but rather dig into the papers I've found in the latest regular and special edition(s) [some are pre-release abstracts] of the scientific journal Appetite:

• Distraction during eating increases later snacking and reduced meal memory (Higgs. 2015) -  It is not actually surprising, but still interesting. If you are distracted by talking or watching TV or whatever else while you're eating this will have direct effects on your ability to recall what you've eaten and your snack choices (i.e. snacking vs. non-snacking).
  

  Figure 1: Mean snack (buiscits) when subjects were either focused on the meal or distracted (Higgs. 2015).

  In addition, Higgs et al. (2015) were able to show that the effect of distraction is larger when motivation to engage with the distracter was greater, i.e. someone who's watching a captivating TV series you will be more likely to snack later in the day than someone who more or less only "listens" to the charts on MTV. What's noteworthy, though, is that a "food related" TV program, will actually reduce the effect.
  
  Needless to say that focusing your attention on the actual eating process and the food you eat will decreased later snacking.
• Intake at a single, palatable buffet test meal is associated with total body fat and regional fat distribution in children (Fearnbach. 2015) - Eventually the results of this study from the Pennsylvania State University just confirm what we already know. People and in this case kids who tent do overeat are more likely to be obese.
  

  Figure 2: The fatter the kids are the more palatable foods are consumed at the buffet (Fernback. 2015).

  The reason I still mention this study is thus not the fact that the buffet pirates are more likely to be obese, but rather the fact that the relationship between food intake and body fat is particularly significantly for palatable buffets and valid only for savory, but not sweet, foods.
• Differential effect of fat and carbohydrate composition meals on food hedonics, satiation and satiety - The results of this investigation go against the current trend toward low carb diets, after all, Finlayson et al. clearly found that

  "When consumed in controlled amounts, LFHC [low fat high carb] meals induce a greater satiety (SQ) compared to isoenergetic HFLC [high fat low carb] meals. HFLC meals do not dampen the subsequent appeal bias for HFLC foods compared to matched LFHC meals" (Finlayson. 2015).

  What I personally find extra-surprising is that the study was conducted on forty-five overweight and obese adults who were studied on 2 separate days in which they were given access to HFLC or LFHC meals. Usually, overweight and obese people are the ones who best respond to low carb diets.

• Habitual high-fat consumers are not more susceptible to weight gain when exposed to increased portion sizes in a free-living setting (Dalton. 2005) - That's at least what the NIH worksite study suggests. In said study, two hundred thirty-three adults (67.7% women; age 42.6 ± 11.2; BMI 29.8 ± 6.4) were randomly allocated to one of four lunch size groups (400 kcal, 800 kcal, 1600 kcal, and no lunch control) for 6 months. Body weight and energy intake (assessed using 24 hr dietary recalls) were measured at baseline, and Months 1, 3 and 6. Of the 233 subjects, 75 were HF-C (40.2%E fat, SD: 3.0) and 74 were LF-C (26.6%E fat, SD: 3.2).
  
  

  Previous research from overfeeding studies yielded slightly different results with a minimal disadvantage for high fat intake | learn more

  Interestingly, there was no interaction between portion size exposure and fat intake according to habitual fat consumption. What the scientists did observe (and this may have skewed the results) was the fact that the subjects in the HF-C group had a higher BMI (M: 30.8, SD: 7.1) compared to LF-C (M: 28.1, SD: 5.5) and greater baseline total daily energy intake. It is nevertheless hard to debate that the weight change at months 1, 3 and 6 did not differ between HF-C and LF-C in response to portion size. Eventually it would thus appear as if the "habitual dietary energy density" was what actually determines ones obesity risk.
• Fiber-enriched (inulin) squash smoothie decreases ad-libitum food intake in subsequent meal (Slevin. 2015) - Now the fact that the BMI-adjusted (thus g/kg/m²) preload drink, which contained 0.67 g/kg/m² inulin, 0.67 g/kg/m² squash and 8.89 g/kg/m² water the 12 normal weight and 12 obese women consumed 30 min before going to a buffet for lunch did decrease the ad libitum intake (219.7 kcal, p = 0.008; 101.6 g, p = 0.032) and total food intake (187.9 kcal; p = 0.019; 102.3 g, p = 0.033) is not surprising. The fact that it did so only in the obese women, but not in the normal-weight subjects, on the other hand may be considered a surprise-
  

  Figure 3: The more different food items the subjects selected, the more they eat (exact numbers for the different food items were not mentioned in the abstract that is currently available | Slevin. 2015)

  Not surprising, but insightful was the revelation that (a) the subjects' food intake significantly increased with increasing items at lunch (242.7 g, 349.2 g, 431.1 g, p = 0.018; 554.1 kcal, 640.9 kcal, 747.4 kcal, p < 0.001) and that (b) the preload was most effective at reducing hunger (p = 0.005) and total food intake (296.3 kcal, p = 0.009) in the 5 item condition, i.e. if the subjects were told that they may chose only 5 items (overall, the scientists did 3 trials, a control trial, a pre-load trial and a pre-load trial in which the subjects were allowed to select only 5-items).
• Consumption of thylakoid-rich spinach extract reduces hunger, increases satiety and reduces cravings for palatable food in overweight women (Stenblom. 2015) - Thylakoids are contained in green-plant membranes like those of spinach (5g/100g spinach) and have been shown to reduce feelings of hunger as well as cravings for palatable food in human participants, during diet intervention and simultaneous weight loss in overweight women (Montelius. 2014M Stenblom. 2013 & 2014). What still had to be confirmed, though, is that these beneficial effects also affect the intake of such foods.
  
  

  Figure 4: Intake from snack buffet. The effect size is too low to trigger weightloss wonders (Stenblom. 2015).

  Stenblom's study in the course of which the thylakoids were served in a cold blueberry drink, mixed with 2.5 g rapeseed oil (Zeta, Di Luca & Di Luca AB, Stockholm, Sweden) and 92.5 g blueberry soup (Ekströms original, Procordia Food AB, Eslöv, Sweden), immediately before breakfast on treatment test days clearly shows that an effect can be observed with real foods, too, but if you take a look at the only really relevant parameter in Figure 4, i.e. the actual food intake on the breakfast buffet, the effect size is negligible.

• Whey is not always appetite suppressing! Addition of whey protein to a fat-based breakfast has detrimental effects on satiety following subsequent feeding (Allerton. 2015) - You, as a waredownloadsoft reader know: Previous studies have shown that whey protein consumed either before or alongside a meal will make the latter more satiating. What you probably didn't know (I didn't but maybe suspected it) is the fact that this is only the case if the meal is not a low-carb meal.
  
  In their latest study, scientists from the Northumbria University in Newcastle upon Tyne found that healthy males (age 24 ± 2 years, mass 79.7 ± 3.8 kg, BMI 24.5 ± 2.1 kg/m2) consume significantly more of a pasta-based lunch meal  (3427 kJ; 49%, 37%, 14% energy from carbohydrate, fat, protein)  that was served 180 minutes after a fat-based breakfast if the latter contained 20g of whey protein (FAT + WP) than after a "whey protein free" breakfast. In addition, the ...

  10+ Things You Probably Didn't Know Whey & Peptides That Form During its Digestion Can Do | more

  "[p]ostprandial ratings of perceived hunger, fullness, satisfaction and prospective consumption were similar after breakfast between trials (p > 0.05). Following lunch, levels of prospective consumption were higher under FAT+WP (time-averaged AUC; FAT+WP: 62 ± 7 vs FAT: 54 ± 6 mm, p = 0.024). Perceived satisfaction was significantly greater under FAT at 120 (FAT: 52 ± 5 vs FAT+WP: 38 ± 7 mm, p = 0.017) and 150 (FAT: 41 ± 5 vs FAT+WP: 30 ± 6 mm, p = 0.009) minutes post-lunch" (Allerton. 2015).

  Thus, it would appear as if people on a low-carb high fat diet, don't necessarily benefit to the same extend as people on a low(er) fat diet from adding whey protein to their meals. Or it may be even worse. As the scientists point out the "negative effects on responses to a subsequent meal were observed, with lower satisfaction and higher ratings of prospective consumption following prior ingestion of breakfast containing whey protein" - which would (emphasis on the conditional) mean nothing else than 'whey may do more harm than good for low carb high fat eaters who want to lose weight'... and yes, the effect may be mediated by the insulin spike that is higher for whey than for white bread (learn more)!