True or False? 'Low Fat' for the Lean, 'Low Carb' for the Obese and Insulin Resistant - Pilot Study Confirms Often Heard Dieting Advise on a Surface Level , However, ...

True or False? 'Low Fat' for the Lean, 'Low Carb' for the Obese and Insulin Resistant - Pilot Study Confirms Often Heard Dieting Advise on a Surface Level , However, ...

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Low carb, or fat? Left or right?  Which one should you chose and why? Shall you go by your body weight, your insulin sensitivity or your personal food preferences?

"Low Fat for the Lean, Low Carb for the Obese and Insulin Resistant," this quote from the headline sounds like a reasonable advise if you look at the existing evidence on low carbohydrate dieting, which appears to excel whenever the subjects are significantly overweight and insulin resistant. Studies that would do a head-to-head comparison of the two to confirm the accuracy of the hypothesis that "because they are insulin resistant, avoiding carbohydrates will aid people with (pre-)diabetes in losing weight" are non-existent... well, I should probably say they "were" nonexistent; a recent pilot study by Gardner et al does after all just that: compare the weight loss response of insulin sensitive vs. resistant individuals who consume either a low fat or a low carbohydrate diet over 6 months.

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The results of Gardner's study have recently been published ahead of print in the venerable scientific journal  Obesity; and they are... interesting, but as it was to be expected for a pilot study with "only" 61 participants in four groups, more research will be needed to make definite conclusions.

Figure 1: Weight loss (kg) after 6 months on the respective in insulin resistant and sensitive subjects (Gardner. 2015).

If you look at the main study outcome, i.e. the weight loss in Figure 1, for example, it would appear as if the previously cited statement from the headline of today's SuppVersity article was confirmed. Due to the large intra-group (=between individuals in one group) differences in all four diet groups (see error bars in Figure 1, they are about as long as the mean difference, we may speak of a trend or a tendency, that appears to confirm the previously stated hypothesis that an insulin resistant individual is better off avoiding carbs when dieting while an insulin sensitive one should stick to the mainstream low-fat recommendations (remember all subjects were overweight, none of the was athletic).

Figure 2: Proportions of carbohydrates, fats, and proteins for each diet at baseline, 3 months, and 6 months (Gardner. 2015).

This wouldn't be a SuppVersity article, though, if a brief glimpse at the main result was everything it had to offer. Let's first take a look at the reported energy- and macronutrient intakes (Figure 2) of the healthy, premenopausal women and men (age 18-50) with stable (>2 months) BMIs and an age between 28 years and 40 years (aside from the increased number of subjects with metabolic syndrome in the insulin resistant group, there were no noteworthy inter-group differences at baseline).

Limbo-Titrate-Quality: The dietary strategy that was used is quite interesting and actually something worth copying for yourself or your clients. There was the "Limbo" phase where the fat or carb intake had to be cut back drastically to 20 g/day of total fat or digestible carbohydrates. The goal of this phase was, as the scientists point out "to achieve the lowest level of fat or carbohydrate intake within the first 8 weeks" (Gardner. 2015). In the second stage, the scientists labeled as the "Titrate" phase the subjects slowly added fat or carbohydrate back to their diet - in increments of 5 g/day (e.g., from 20 to 25 g/day). With each increase, the intake had to be maintained stable for at least 1, maximally 5 weeks before adding another 5 g/day. The (good) idea was to allow each of the participants (in what the researchers call stage 3 of the intervention, although 2 + 3 appear to depend on each other) to find his or her specific level of fat or carbohydrate intake he / sheh "could be maintained long term, potentially for the rest of their lives" (ibid). The fourth and last stage eventually focused on diet quality. In the "Quality" phase the subjects had to maximize the nutrient density of their foods by selecting whole foods, buying organic, grass-fed, etc.

As you can see significant inter-group differences were observed only for the macronutrient composition; and even though these differences were significant, we are nor talking about a ketogenic vs. no-fat diet. Rather than that, both diets had a relatively balanced macronutrient profile, albeit with different main energy sources (carbs vs. fat). What did not differ for the groups, though was the total energy intake (Figure 2). This is interesting, because, in theory, the subjects were allowed ot eat as much as they wanted; and still, the data in Figure 2 tells you that they restricted their energy intake by noteworthy 30% on average (the ~600kcal-deficit also explains why the subjects even lost weight), with no difference between the subjects on the allegedly more satiating low carb vs. low fat diet.

Figure 3: Fiber, added sugar and saturated fat intake in g/1000kcal (Gardner. 2015).

Visible differences existed, obviously, for the intake of fiber, added sugars and saturated fats (Figure 3). These differences, which are characteristic of low fat vs. low carb diets, may also be the reason a recent study by Mansoor et al. (see red box below) found differences in the effects on triglycerides (increases with high CHO and even more so sugar intake), HDL and LDL (both increase w/ high saturated fat intakes) when they compiled the results of the contemporary low fat vs. low carb trials.

So, what's healthier, then? Low carb or low fat? To answer this question, researchers from the University of Oslo have recently conducted a meta-analysis that yielded quite interesting results, when the individual findings from the studies were pooled as weighted mean difference (WMD) using a random effect model: Compared with participants on LF diets, participants on LC diets experienced a greater reduction in body weight (WMD –2·17 kg; 95 % CI –3·36, –0·99) and triglicerides (WMD –0·26 mmol/l; 95 % CI –0·37, –0·15), as well as a greater increase in HDL-cholesterol (WMD 0·14 mmol/l; 95 % CI 0·09, 0·19) - that's good. Unfortunately, they also saw signficant increases in LDL-cholesterol. With 0·16 mmol/l, the mean difference in LDL was larger than the mean HDL increase, which could suggest an increase in CVD risk and has the authors conclude that their "findings suggest that the beneficial changes of LC diets must be weighed against the possible detrimental effects of increased LDL-cholesterol" (Mansoor. 2015). Personally, I would say, though, that for the mostly obese subjects in the studies, the weight loss and reduction in triglycerides (likewise a marker of CVD risk) are more important than the increase in LDL-C - future studies should try to elucidate if the particle size and LDL oxidation worsened as well and what the actual long-term (years, not months) effects are.

It is thus no wonder that the data from  blood analysis of the study at hand (Figure 4) mirrors the results of the studies Mansoor et al. (2015) analyzed for their meta-analysis that is about to be published in the British Journal of Nutrition (see red box):

Figure 4: Changes in LDL-C, HDL-C, Trigs and fasting glucose after 3 & 6 months (Gardner. 2015).

With the most significant differences being observed for triglycerides and LDL, the situation is very similar to the one the Mansoor et al. describe in their review. In this regard, it is also worth mentioning that the differences between the groups were reduced, when the subjects started to increase their fat or carbohydrate intake by 5g on a 1-4 week basis to eventually end up at their individual "that's how I could eat for the rest of my life"-level (compare the 3 months with the 6 months data).

This doesn't solve the dilemma we're in, though: without further data on particle sizes and oxidative status of the LDL molecules, etc. it is virtually impossible to make a reliable prediction which of the two diets is going to have the higher long-term health benefits. What may be even more important, though, is that we must not forget that it is not debatable that both diets triggered significant weight loss and measurable health improvements, especially in the subjects with pre-existing insulin resistance (blue and orange bars in Figure 4).

Figure 5: Changes in the prevalence of metabolic syndrome after 3 and 6 months (Gardner. 2015).

Bottom Line: So, it doesn't matter how you diet as long as you diet? Well, as previously pointed out, the absolute weight-loss values in Figure 1 appear to confirm the hypothesis that "low carb" is for the sick, while "low fat" for the healthy overweight individuals.

The significant intra-group differences, however, tell us that whether you are or aren't insulin resistant is not the only determinant of your response to the different diets. Especially for healthy individuals experimentation and finding what suits you, your lifestyle and sports best does therefore still appear to be the way to go.

In those with pre-existing metabolic syndrome (which is more than just insulin resistance, by the way), the generally higher relative reduction in MetSyn prevalence Gardner et al. observed in their study (Figure 5) do yet appear to confirm the general trend towards low-carbohydrate diets for people with serious metabolic issue; and maybe that's actually the main take-home message of a study that must be seen as a first attempt to identify one of the variables that determine whether an individual thrives on a low carb, a low fat or maybe just a completely balanced diet | Comment!

References:

• Gardner, et al. "Weight Loss on Low-Fat vs. Low-Carbohydrate Diets by Insulin Resistance Status Among Overweight Adults and Adults with Obesity: A Randomized Pilot Trial" Obesity (2015): Ahead of print.
• Mansoor, et al. "Effects of low-carbohydrate diets v. low-fat diets on body weight and cardiovascular risk factors: a meta-analysis of randomised controlled trials." British Journal of Nutrition (2015): First view article.

Weight Loss, 'Metabolic Damage' and the Magic of Carbs? Human Study Probes Effects of Carbohydrate Content, GL & GI on Diet-Induced Suppression of Resting Metabolic Rate

Weight Loss, 'Metabolic Damage' and the Magic of Carbs? Human Study Probes Effects of Carbohydrate Content, GL & GI on Diet-Induced Suppression of Resting Metabolic Rate

dieting GI glycemic index glycemic load high carb high carbohydrate lose fat low carb low carbohydrate metabolic damage resting metablic rate RMR weight loss

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...

# Women appear to be particularly prone to # metabolic damage - more on # female fat loss:

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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:

Metabolic Damage in Biggest Losers: Will Diet & Intense Exercise Make You Fat, While Surgery Will Make You Lean? Plus: How to Avoid or Even Correct Diet-Induced REE Reductions | more

"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.

Figure 4: Changes in body composition (absolute value in kg) after 20 weeks and after weight loss phase 2 (Karl. 2015).

Bottom line: As Karl et al. point out, "neither low-GI relative to high-GI diets nor moderate-carbohydrate relative to high-carbohydrate diets showed differences with respect to effects on changes in body composition or resting metabolism during weight loss when confounding dietary factors were tightly controlled in a study providing all food for 22 weeks" (Karl. 2015).

This does not just go against the mainstream assumption that low GI and/or low(er) carbohydrate diets facilitate weight loss, fat loss and weight maintenance (see data in Figure 4 for an overview of these parameters, it also contradicts the initially mentioned broscientific assumption that carbohydrates, in general, and high GI carbs, in particular, have a protective effect against the unexpected diet-induced reduction of basal energy expenditure many people know as "metabolic damage". If there's anything of which the study at hand suggests that it could protect you from such unexpectedly large decrease in RMR, it's not high GI carby, but rather an already high(er) baseline RMR (see Figure 3).

And what does that tell us? Right! Since a high predicted RMR is a function of (a) being male, (b) being tall, and (c) being muscular, all three attributes may protect you from diet-induced "metabolic damage" | Let me know your thoughts and comment on Facebook!

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.

Calculated Energy Requirements ±15% & More Off of True Resting Energy Expenditure | Genotyping May Help Maintain Muscle While Dieting -- ISSN'15 Research Review Issue #5

Calculated Energy Requirements ±15% & More Off of True Resting Energy Expenditure | Genotyping May Help Maintain Muscle While Dieting -- ISSN'15 Research Review Issue #5

calculations energy requirements fat burner fatloss fatty acid oxidation genes genetics genotyping high carb ISSN15 ISSN2015 lose fat low carb

In view of the inaccuracy of the standard equations that are used to calculate our energy requirements we are approaching the age of the "misquantified self".

If I had to find a common theme in the studies discussed in this installment of the ISSN'15 Research Overview, I guess it would be "get lean and stay lean". If you asked for a common bottom line, it would be: It's complicated.

Complicated, because even though our genes appear to have a determining rule in what's the best diet for us, the "calories in vs. calories out" equation is eventually going to determine whether you are losing or gaining weight. That's problematic, not just because many of us have lost their inborn ability to match their energy intake to their individual requirements. It's also problematic in view of the increasing number of people who rely on the numbers of apps and fitness-trackers which are just as unreliable as the standard equations that were fed into the source code to calculate them.

Read more about ISSN and other studies at the SuppVersity

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High Protein, Body Comp & X | ISSN'15 #3

Keto Diet Re- search Update | ISSN'15 #4

The Misquantified Self & More | ISSN'15 #5

BCAA, Cholos-trum, Probiotics & Co | ISSN'15 #6

• Eating according to your genes may help you retain lean mass -- You will have noticed that the number of companies that do gene testing in order to tell you "exactly" what and how to eat is exploding lately. Unfortunately, the same cannot be said of research that would confirm that any of the test results people often pay several hundred dollars for are worth the paper they are printed on.
  
  Against that background, a recent study from the Texas A&M University comes quite handy for the shareholder value of said companies. The corresponding poster presentation at the ISSN Conference 2015 expanded on the results of a 2015 study by Coletta et al. (2015a), in which the researchers observed "that correctly matching diet type to some obesity-related genes promoted greater fat loss during the first 3 months of a diet and exercise intervention" (Coletta. 2015b). In the study at hand the researchers did now examine "whether these changes were observed following a 6-month diet and exercise training program" (ibid.).
  
  Over the course of the 6-months study, fifty sedentary, obese women (41.6 ± 12 yrs, 35.4 ± 8 kg/m²) who had been genotyped before the intervention, were either truly matched (T) to their diet group based on genotype (n = 28) or falsely matched (F) based on genotype (n = 22).
  

  

  Figure 1: Macronutrient composition of the diets (Coletta. 2015b).

  Irrespective of the group they were assigned to, all subjects consumed the same amount of energy (1,500 kcal/d), but with either a high or low ratio of carbohydrate:fat:protein percentages (see Figure 1). But the women didn't just eat less, they also had to perform a supervised circuit-style resistance-exercise program four days/week and followed a standardized walking program that consisted of 10,000 steps/day on three days/week. 

High carbohydrate diets for metabolic syndrome? A question of your genes - Study suggests that diets high in carbohydrate may not be appropriate for rs328 G carriers with the metabolic syndrome. In said study, two districts in Shanghai, China were randomly selected to be the intervention and control group, and patients (n=235) with metabolic syndrome within these two districts were selected based on a multistage sampling method.

"Three Days on Pasta, Muffin & Bread Diet (84% CHO) = 1kg Add. Lean Mass and a Sign. Trend for Decreased Fat Mass" - Probably the subjects in the study discussed in this SV Classic Article simply had the right genes ;-)

Fasting glucose was reduced in rs328 CC homozygotes (p=0.028) but not G carriers (p=0.686) within the intervention group.

Also an ancillary study with greater statistical power by combining the baseline measurements across both the intervention and control groups was conducted to test the cross-sectional statistical interactions between carbohydrate/fat and lipoprotein lipase genotypes for homeostasis model assessment of insulin resistance/insulin/fasting glucose. Increased carbohydrate intakes were positively associated with homeostasis model assessment of insulin resistance and insulin in rs328 G carriers but not CC homozygotes (p for interaction was 0.025).

"These results indicate that diet high in carbohydrate may not be suitable for metabolic syndrome rs328 G carriers, calling for the development of personalized dietary intervention for metabolic syndrome subjects," (Zhang. 2015) scientists say.

• Unsurprisingly, the combination of both, diet and exercise triggered significant reductions in weight and body fat in both diet groups (high and low carb). Nevertheless, both, the carbohydrate content of the diet and the genotype < > diet match, had significant effects on the study outcomes, as well. More specifically, ...
  • the participants following a more carbohydrate restricted diet experienced significantly greater weight loss and slightly greater body composition changes (the low-carb-fat-loss-advantage), and
  • matching diet based on gene-type exhibited better retention of fat free mass, albeit with no significant differences between groups in changes in weight or fat mass (the genotype-lean-mass-advantage).
  Now that sounds pretty much like genotyping your diet was always the way to go, right? Well, in view of the fact that the falsely assigned subjects experienced a slightly greater reduction in body fat percentage, the interpretation of the study results does actually depend on one's individual goals and is thus less obvious than it may appear to be when you read the abstract.
• How much should you eat, ladies? Study shows: No equation can answer this question exactly -- You may believe that your apps and fitness trackers were able to tell you "exactly" how much energy you need, but  eventually their recommendations are also based on equations like those Kisiolek et al. tested in their latest study.
  
  

  

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  For the experiment on which I base the above statement, the scientists recruited twenty-five recreationally active, college-aged women (20.72 ± 0.97 yrs; 163.04 ± 5.67 cm; 67.08 ± 10.40 kg; 29.04 ± 5.80% BF) who underwent a single day of testing, consisting of determination of REE by indirect calorimetry (TrueOne® 2400 Metabolic Measurement system, ParvoMedics, Sandy, UT) followed by body composition assessment.
  
  To avoid interferences by exercise or dietary factors, all subjects were instructed to refrain from strenuous exercise 48 hrs prior to testing in addition to fasting >8 hrs prior.
  
  During the actual testing, the participants laid motionless without falling asleep for 15-20 minutes during REE determination. Data were recorded during a period of time in which criterion variables (e.g., VO2 L/min) changed less than 5% every 5 minutes. In addition, the subjects' body composition was assessed using air displacement plethysmography (BODPOD, Cosmed, USA) via the Siri equatio and fed into the three equations the scientists tested, i.e. the (1) Nelson Equation the (2) Mifflin-St. Jeor Equation and the (3) Harris-Benedict Equation (with a moderate activity factor).
  

  

  Figure 2: Energy expenditure (kcal/24h) according to indirect calorimetry (measured) and the three tested equations (calculated); %-ages indicate differences to measured values (Kisiolek. 2015).

  For all three tested equations the results were significantly different than indirect calorimetry (p < 0.001; see Table 1). More specifically, ...
  • the Nelson and Mifflin-St. Jeor equations underestimated REE when compared to indirect calorimetry by 345.5 ± 51.5 and 220.6 ± 47.3 kcals, respectively,
  • while the Harris Benedict overestimated REE by 272.4 ± 49.3 kcals.
  Against that background it could be considered a "success" that all three equations were moderately correlated with the subjects' objectively measured resting energy expenditure (REE) as determined by indirect calorimetry.
  
  Well, "success" or not, the implications of Kisiolek's study should be clear: "Practitioners should exercise caution when providing dietary recommendations based upon predicted REE values as certain equations may over or underestimate energy requirements by several hundred kilocalories" (Kisiolek. 2015); and I would like to add: If you want to make sure you're nailing it, log your dietary intake during a weight stable week - that's the only way to know for sure how much energy you need.

So what didn't make the "cut"? Worth mentioning, but not discussing in detail are the observations Mullins et al. made when they investigated the effects of Iron Cuts®, a thermogenic supplement from MusclePharm, that appeared to improve the subjects strength, but had no significant effects on the health or body composition of 20 recreationally trained men who participated in Mullins' prospective, double-blind, placebo controlled randomized trial (Mullins. 2015).

Statistical significance was only observed for the small increase in fatty acid oxidation in response to Shred-Matrix® from -3h to pre-workout not for the post increase (Seijo. 2015). Neither of them means that the supplement will actively promote fat loss, though.

The increase in fatty acid oxidation Seijo et al. observed when they studies the acute effects of Shred-Matrix® on fat oxidation is certainly more impressive than the results of Mullins study, but eventually it is of even less practical relevance. As a SuppVersity reader you should now that the currently available research refutes the existence of a reliable mechanistic link between the acute increases in fatty acid oxidation the scientists observed before and after the workout and long-term fat loss (the post-workout increase in fatty acid oxidation was not even significantly higher than the increase in the placebo group, by the way).

In view of the lack of effect on mood state and perception of hunger it is thus very questionable, whether the supplement can actually do what the scientists say their results would "suggest" and augment "the weight-loss benefits at rest and during exercise" (Seijo. 2015) | Comment!

References:

• Coletta, A., et al. "Influence of Obesity-Related Genotype on Weight Loss Success and Body Composition Changes While Participating in an a 3-Month Exercise and Weight Loss Program: Preliminary Findings." The FASEB Journal 29.1 Supplement (2015a): LB241.
• Coletta, A., et al. "Effects of matching diet type to obesity-related genotype on body composition changes in women during a six-month resistance-exercise training and walking program." Journal of the International Society of Sports Nutrition 12.Suppl 1 (2015b): P16.
• Kisiolek, J., et al. "A comparison of resting energy prediction equations in young recreationally active women." Journal of the International Society of Sports Nutrition 12.Suppl 1 (2015): P50.
• Mullins, Jacy, et al. "Safety and efficacy of a proprietary thermogenic and cutting agent on measures of muscular strength and endurance, body composition, fat metabolism, and hormone levels." Journal of the International Society of Sports Nutrition 12.Suppl 1 (2015): P13.
• Seijo, Marcos, et al. "Effectiveness of multi-ingredient supplement on substrate utilisation, perception of hunger, mood state and rate of perceived exertion (RPE) at rest and during exercise." Journal of the International Society of Sports Nutrition 12.Suppl 1 (2015): P42.
• Zhang, Shixiu, et al. "Diets high in carbohydrate may not be appropriate for rs328 G carriers with the metabolic syndrome." Asia Pac J Clin Nutr 24.3 (2015): Ahead of print