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

Vitargo, Red Bull, Creatine & More | ISSN'15 #1

Pump Supps & Synephrine & X | ISSN'15 #2

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