Carbohydrate Timing Boosts Training Effect: Cut Out Carbs After PM Glycogen Depleting HIT Workout ⇨ "Sleep Low" to Make Game-Changing Performance Gains in Only 3 Weeks

Carbohydrate Timing Boosts Training Effect: Cut Out Carbs After PM Glycogen Depleting HIT Workout ⇨ "Sleep Low" to Make Game-Changing Performance Gains in Only 3 Weeks

athletes carbohydrate timing CHO glycogen glycogen depletion high carbohydrate HIIT hit LISS LIT low carbohydrate performance triathlon

You are no triathlete or coach? That doesn't mean that this study isn't of interest for you. The figurative "extra wind" this training strategy can give you is relevant for almost every athlete.

In a recent study, scientists from the French National Institute of Sport investigated the effect of a chronic dietary periodization strategy in a group of twenty-one highly-trained male triathletes. Previous studies, in which "train-low" strategies, during which athletes are deliberately carbohydrate restricted over certain periods of their training cycle, have reported robust a up-regulation of selected markers of training adaptation (increased whole body fat oxidation, increased activities of oxidative enzymes) compared to training with normal glycogen stores and high CHO availability, however, the subjects experienced at best disappointing performance increases.

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Scientists have long speculated that the disconnect between the benefits "training low" offers on the level of cellular / mitochondrial adaptation, on the one hand, and the real-world performance increases, on the other hand, could be a consequence of the necessarily reduced high intensity training intensity during the low-carb phases (Yeo. 2008; Hulston. 2010). If we simply assume that this hypothesis is correct, the solution to the problem should be obvious: Train low when carbohydrates are not necessary and use them, whenever they promote maximal performance.

Marquet et al. implemented this principle in a way I tried to illustrated in Figure 1. More specifically, they tried to maximize the subjects' performance during PM high-intensity training (HIT) by providing copious amounts of carbohydrates before the session and restricted the carbohydrate intake to close to zero after this glycogen-depleting workout.To test the efficacy of this protocol, the scientists used a 2x3 week study design in which the first 3 weeks were used to standardize the volunteers training regimen (10-15 h·wk- 1 : 40% running, 35% cycling, 25% swimming), assess subjects' compliance to the study demands and ensure they all attained similar baseline fitness measures before study commencement.

Figure 1: Overview of important aspects of the dietary / supplemental aspects of the study.

During the decisive second 3-week phase, the subjects were instructed to follow identical diets (by prescribing exact menus, the scientists achieved a high degree of standardization) in combination with either the previously described "sleep low" carbohydrate intake strategy or their usual carbohydrate intake patterns. Unlike the diet / supplementation regimen, the training program the subjects followed was identical for all of them - it ...

Figure 2: Sample weekly protocol for training and CHO intake (g/kg) to achieve different CHO avail. around training (Marquet. 2016)

"consisted of six sessions over four consecutive days, including high intensity training (HIT) sessions in the afternoon and low intensity training (LIT) sessions the next morning. [...] LIT sessions consisted in 60 min cycling at 65% MAP (218.8 ± 20.4 W - 95% CI: 227.5 and 210.7), while HIT sessions consisted alternatively in 8 x 5 min cycling at 85% MAP (286 ± 26.7 W- 95% CI: 297.5 and 274.7) or 6x5 min running at their individual 10 km intensity with 1 min recovery between sets (37). [...] One LIT session per day was prescribed for the other days of the week for a total training volume of 10-15 h" (Marquet. 2016).

All subjects used their own training equipment to record their activity, the duration and intensity of exercise and heart rate. In conjunction with the volunteers' perceived exertion records, as well as VO2max tests, maximal and submaximal performance tests and the results of a simulation of the final leg of a triathlon race, the scientists got a pretty comprehensive set of data.

The effect of "training low" largely depends on the master regulator of mitochondrial adaptation PGC-1a. The latter is activated not just by the contraction induced calcium flux and exercise stress, but also by a lack of glycogen and increased levels of the (low) energy sensing protein AMPK.

How does "training low" work? By deliberately restricting the carbohydrate intake during certain phases of your training you will be able to train in a glyocogen-depleted state and thus with clearly suboptimal fuel availability. The lack of readily available glucose that can be derived from the glycogen stores in your muscle, whenever necessary, exerts profound effects on your overall resting fuel metabolism and patterns of fuel utilization during exercise and triggers acute regulatory processes underlying enzyme and gene expression, as well as cell signaling (signaling proteins, gene expression, transcription rate of several genes, enzymes activity) which regulate the adaptive response to exercise. The results are an increased capacity to oxidize fat, a reduced reliance on glucose as a preferred substrate, etc.

Data that tells us that the authors' hypothesis that they could get the benefits of training low while avoiding the negative sides by "sleeping low" was accurate:

• 

  Figure 3: Make no mistake about it! The total amount of CHO the subjects consumed was identical it was just timed differently. No difference existed for any of the other macronutrients, either (Marquet. 2016).

  There was a significant improvement in delta efficiency during submaximal cycling , i.e. the power output per calorie, a very important measure for endurance athletes, for the "sleep low" compared to the control group (CON: +1.4 ± 9.3 %, SL: +11 ± 15 %, P<0.05).
• A similarly pronounced, albeit due to inter-individual differences, which loom large in studies with relatively few participants, only borderline significant (P = 0.06) beneficial effect was observed during the supra-maximal cycling to exhaustion trial at 150% of peak aerobic power, where the control group saw improve-ments of only 1.63 ± 12.4 %, while the "sleep low" group improved by 12.5 ± 19.0 %.
• The "sleep low" protocol also triggered significantly higher (P < 0.05) improvements in 10k running performance, where the meager -0.10 ± 2.03 % increase in the control group was topped by a -2.9 ± 2.15 % performance increase in the "sleep low" group.

In the "sleep low" group, even the effects on the body composition were significantly more pronounced compared to the control group. To be precise, the subjects who "slept low" burned a whopping 8.7 ± 7.4 % body fat literally overnight, while the control group lost a likewise measurable, but significantly lower and overall non-significant -2.6 ± 7.4% of their body fat - don't be mislead by the size of the bars in Figure 4; the fat mass is on the right axis which starts at 8kg and ends at 10kg. So there was no significant inter-group difference at baseline. No significant inter-group differences were observed for the changes in lean and total mass, either.

Figure 4: Even if you're not training for performance, the improvements in body composition, or more specifically the significant reduction in body fat without sign. changes in lean or total mass, may be of interest for you | total and lean mass on the left axis, fat mass on the right axis; all values in kilograms; sign. changes in % above bars (Marquet. 2016).

Against that background, it is by no means an exaggeration to say that even in the short-term (and that's what I consider particularly impressive here) the "periodization of dietary CHO availability around selected training sessions" can promote "significant improvements" in several highly relevant performance marker of trained athletes" (Marquet. 2016).

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Drop the carbs pre-bed! No, that's not because carbohydrates in the evening would make you fat. As a SuppVersity reader you know that this is bogus (learn more). The reason why you should consider dropping carbs in the PM (or rather after intense workouts) is their "anti-adaptive" effect - an effect that occurs in response to their ability to replenish your glycogen-stores and thus shut down the "we need to adapt to use more fat" signal to your mitochondria...

Ok, that's not exactly the most scientific explanation (see red box for more), but it is one that highlights one of the most important and yet commonly overlooked principles of physiological adaptations: they occur in response to a need.

If you always provide more than enough carbohydrates, there's no need to increase your ability to use fat as a fuel. If, on the other hand, you (A) fuel yourself with carbs when your body really needs them (during HIT training) to perform at the crucial i + 1 level that will trigger an adaptive response at high intensities, and (B) cut yourself off of a readily available carbohydrate supply when you don't need them (during sleep and low intensity exercise) you maximize the adaptive response to both HIT and LIT (low intensity training) and boost your overall training results | Comment!

References:

• Hulston, Carl J., et al. "Training with low muscle glycogen enhances fat metabolism in well-trained cyclists." Medicine and science in sports and exercise 42 (2010): 2046-55.
• Marquet, et al. "Enhanced Endurance Performance by Periodization of CHO Intake: “Sleep Low” Strategy." Medicine & Science in Sports & Exercise (2015): Publish Ahead of Print.
• Yeo, Wee Kian, et al. "Skeletal muscle adaptation and performance responses to once a day versus twice every second day endurance training regimens." Journal of Applied Physiology 105.5 (2008): 1462-1470.

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, ...

become healthy dieting get healthy health lose fat lose weight low carb low carbohydrate low fat metabolic syndrome obesity TOF

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...

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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:

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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.

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.