Your Post-Workout Testosterone Levels Can Predict Your Gains - Study Takes Novel Approach to the T ↔ Muscle Link

Your Post-Workout Testosterone Levels Can Predict Your Gains - Study Takes Novel Approach to the T ↔ Muscle Link

build muscle gains gainz GH hormones IGF-1 insulin muscle gains testosterone

GainZ - Are they all about T and we just didn't do the right statistical tests in previous studies to realize that?

Only recently one of the longstanding "truths" of protein anabolism has been busted (learn why the acute muscle protein synthesis response matters more than prev. thought). And now, a new paper in the Journal of Strength and Conditioning Research (Mangine. 2016), appears to suggest that the lack of effect of exercise induced hormone elevations may have been misunderstood, too.

In the conclusion of their study, Mangine et al. point out that the previously used "[t]raditional statistical measures do not adequately assess the relationships between multiple variables that exist across time" (Mangine. 2016).

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In order to overcome this problem, their study used what the scientists call a "unique method for analyzing these types of relationships without the need for transforming data"; and - first things first - their the PLS-SEM analysis (details below) shows: "baseline muscle size and the hormonal response to resistance exercise are related to muscle hypertrophy following 8 wks of training  (Mangine. 2016).

Figure 1: The scientists ,odel for the relationship between changes in muscle size and the endocrine response to resistance exercise predicts influence of all hormones on muscle size and vice versa(!); RF_CSA = Rectus femoris cross-sectional area; RF_MT = Rectus femoris muscle thickness; VL_CSA = Vastus lateralis cross-sectional area; VL_MT = Vastus lateralis muscle thickness; WK1 = Week 1; WK8 = Week 8 (Mangine. 2016).

Moreover, the data from the Kennesaw State University, the University of Central Florida and the College of New Years appears to suggest that the often derided exercise-induced post-workout (PWO) increases in testosterone concentrations may be the most important agent in the hormone quintet of testosterone, cortisol, growth hormone, IGF-1, and insulin that is going to react to every intense resistance training study.

Figure 2: Sign. associations between PWO hormone levels and lean mass, as well as fiber size increases (West. 2012).

That's surprising in two ways: (A) the majority of previous studies refuted the existence of a practically relevant link between the amount of muscle you will gain and the change in hormone levels, altogether. And (B) you will remember that my hitherto favorite study on the "[a]sociations of exercise-induced hormone profiles and gains in strength and hypertrophy in a large cohort after weight training" by West and Phillips (2012) showed in a large cohort that - if there is any link between the PWO hormone response to resistance training and the changes in muscle size it would be a link to growth hormone (GH) and cortisol (see Figure 2).

Free testosterone (upper line) and cortisol (lower line) levels before and after exhaustive endurance exercise in trained young men (Anderson. 2016).

Excess cardio cannot, it will ruin your testosterone levels and (surprise) 24h post also your cortisol levels: The data in the figure on the left hand side was recorded in a recent study by Anderson et al. (2016) who observed that the full recovery of free testosterone and cortisol after an exhaustive endurance exercise session (prolonged exercise run on the treadmill until volitional fatigue, running at 100 % of ventilatory threshold (VT), within 3 % - 75 minutes) will take 48h - even in endurance trained fit, young men such as the 12 subjects (VO2max 66.3±4.8 ml/kg/min, age 22.8 ± 3.1 years, body fat 11.0 ± 1.4 %, training 7.1y) Anderson et al recruited.

That's obviously significantly different from what we see in the Magine study, at hand, where the likewise previously trained subjects completed at least 28 resistance training sessions (~90%) of an 8-wk resistance-training program (4 sessions/wk) that included six upper- and lower-body exercises during each session, under supervision of certified strength & conditioning specialists.

With the inclusion of potential influence of the initial muscle mass on the hormonal response to exercise Magine's study does now suggest what many trainees still believed, anyways: "When it comes to making gainz, the testosterone response to workouts counts." Furthermore, the scientists argue that the reason studies like McCall et al. (1999), Ahtianinen et al. (2003), and Walker et al. (2015), which used Pearson’s product moment correlation coefficients or Spearman’s rank correlation coefficients (Walker. 2014) were conflicting and not really reliable, because...

"significant amount of information [that] is lost when using either of these statistical procedures for assessing the relationships between concepts that exist across time (i.e. hypertrophy, multiple endocrine responses) because the statistics can only assess the relationship between two sets of values" (Mangine. 2016). 

With their approach, on the other hand, Mangine et al. (2016) transformed the correlation between hypertrophy and the endocrine response from baseline and post-testing into a single value (i.e. change score, average score). The method to do this is called "partial least squares structural equation modeling" (PLS-SEM) and it allows estimating complex cause-effect relationship models with latent variables. Since it is a component-based estimation approach, it differs from the covariance-based structural equation modeling you'd usually expect to be used and constitutes, as the scientists summarize

"[...] a variance based procedure that utilizes bootstrapping to statistically assess the relationships between multiple latent variables that are developed from several collected indicator variables [which has] been used to assess relationships within the biomedical sciences [already... even though] it has not yet been used to assess the relationships between the post-exercise endocrine response and muscle hypertrophy" (Mangine. 2016).

For it to work, the authors obviously have to assume that "the related variables were collected without systematic or random error" in their experiment that included pre-tests (PRE) of measures of muscle size (thickness and cross-sectional area) of the vastus lateralis and rectus femoris in 26 resistance-trained men who were randomly selected to complete a high-volume (VOL, n=13, 10–12RM, 1-min rest) or high-intensity (INT, n = 13, 3–5RM, 3-min rest) resistance training program while following a food-log controlled diet that was supplemented with a standardized supplement containing ~235 mL of chocolate milk (170 calories; 2.5g Fat; 29g Carbohydrate; 9g protein) or Lactaid® (150 calories; 2.5g Fat; 24g Carbohydrate; 8g protein) to each participant immediately following each workout.

A pre- vs post-workout salivary testosterone test could hold the clue to the perfect workout | more

Another argument that "testosterone may count" comes from a previously discussed, but in my humble opinion largely overlooked study by Beaven et al. (2008) whose study into the correlation between the individual testosterone response to a certain workout style and the subsequent gains subjects in a randomized cross-over design study made also suggests that "testosterone counts". Sounds intruiging? I know, but the corresponding SuppVersityarticle from 2013 went almost as unrecognized as the original paper that was published 5 years before in the Journal of Strength and Conditioning Research - the same journal in which Mangine et al. have now published the results of their study.

Blood samples were collected at baseline, immediately post-exercise, 30-min, and 60-min post-exercise during weeks 1 (WK1) and 8 (WK8) of training and testosterone, growth hormone [22 kD], insulin-like growth factor-1, cortisol, and insulin levels were evaluated using area-under-the-curve (AUC) analyses of the blood values, based on which the scientists were able to identify the relationships between muscle size (PRE), AUC values (WK1 + WK8) for each hormone, and muscle size (POST) "using a consistent PLS-SEM algorithm and tested for statistical significance (p<0.05) using a 1000 samples consistent bootstrapping analysis" (Mangine. 2016).

Figure 3: Actually significant was only the link between the effect of the muscle mass before the study and the testosterone response and the testosterone response on the muscle mass after the 8-wk study (Mangine. 2016).

The model the scientists developed was capable of explaining 73.4% (p<0.001) of variance in muscle size at POST and revealed "[s]ignificant pathways between testosterone and muscle size PRE (p=0.043) and muscle size at POST (p=0.032) were observed.

Table 1: In contrast to what you may have expected, there was no sign difference in the way the hormones effected the outcomes of the 8-wk resistance training study between the high intensity and volume arm (Magine. 2016).

And while the ability to explain muscle size at POST improved when the model was analyzed by group (INT: VOL: p<0.001), the data in Table 1 goes to show you that the researchers found no group differences between the intense low volume and the moderate intensity high volume training. This in turn suggests that the link between muscle size and post-exercise increases in hormone levels - especially the effects on testosterone - are universal and do not dependent on the training type (volume vs. high intensity), as previous studies that argued in favor of volume training based on its more pronounced effects on the hormone response to exercise suggested.

In view of the conflicting evidence and hitherto relatively conclusive evidence that endogenous T & co elevations do not matter, I would not begin to train "for testosterone elevations", now... you may after all still be barking up the wrong tree. Correlations and links are after all no causations | learn more

Bottom line: I would like to point out that the study at hand does not provide sufficiently reliable evidence to say that Mangine, et al. had 'proven that the post-workout testosterone increase had a mechanistic effect on your muscle gains'. What it does show, however, is, just as the scientists say, that "[e]xercise-induced testosterone elevations, independent of the training programs used in this study, appear to be related to muscle growth" (Magine. 2016, my emphasis in the quote).

This is in contrast to previous studies, where the pre-tfiraining correlation between muscle mass and thus testosterone levels had not been accounted for. The scientists' partial least squares regression structural equation modeling (PLS-SEM), however, is eventually just "statistical shenanigan". Therefore, its impressive explanatory power of 73.4% of the variance in muscle size following 8 wks of resistance training is a neat figure, but no proof of a mechanistic link.

Furthermore, one has to be careful to falsely single out testosterone among the five hormones that were assessed. After all, restricting the model to T, which was the only significant hormonal correlate of muscle gains, reduced the explanatory power of the model by only 30.8%. This leaves the rest of the hormones with an explanatory power of 42.6% (all statistics, obviously ;-). To say that GH, IGF-1, insulin and cortisol had 'no say' in skeletal muscle hypertrophy is thus just as unwarranted as the previously hinted at (false) conclusion that the study at hand would provide the long-sought definitive evidence of the muscle building effects of exercise-induced, natural testosterone surges, i.e. the temporary elevation of T-levels after a workout | Comment!

References:

• Ahtiainen, Juha P., et al. "Muscle hypertrophy, hormonal adaptations and strength development during strength training in strength-trained and untrained men." European journal of applied physiology 89.6 (2003): 555-563.
• Beaven CM, Cook CJ, Gill ND. Significant strength gains observed in rugby players after specific resistance exercise protocols based on individual salivary testosterone responses. J Strength Cond Res. 2008 Mar;22(2):419-25.
• Mangine, Gerald T., et al. "Exercise-Induced Hormone Elevations Are Related To Muscle Growth." The Journal of Strength & Conditioning Research (2016).
• McCall, Gary E., et al. "Acute and chronic hormonal responses to resistance training designed to promote muscle hypertrophy." Canadian Journal of Applied Physiology 24.1 (1999): 96-107.
• Walker, Simon, et al. "Effects of prolonged hypertrophic resistance training on acute endocrine responses in young and older men." Journal of Aging & Physical Activity 23.2 (2015).
• West, Daniel WD, and Stuart M. Phillips. "Associations of exercise-induced hormone profiles and gains in strength and hypertrophy in a large cohort after weight training." European journal of applied physiology 112.7 (2012): 2693-2702.

10 Days of 'Paleo Life in the Wilderness' Will Strip up to 18 cm off Your Waist and Boost Your Insulin Sensitivity by 53%

10 Days of 'Paleo Life in the Wilderness' Will Strip up to 18 cm off Your Waist and Boost Your Insulin Sensitivity by 53%

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Even though it may have been funny, this is not exactly how the scientists simulated the "paleo lifestyle" in the study at hand. Eventually, however, it came down to eating healthier, being active and even being stressed (within the limits of natural "paleo stress", though).

No, this science website is not going to turn into a paleo blog, ... don't worry. It's mere coincidence that this is the 2nd "paleo" study in 2 weeks that is interesting enough to get its own SuppVersity article devoted to it (last one).

Moreover, said study, which was published in the peer-reviewed scientific journal BioMed Research International, recently (Pruimboom. 2016), doesn't even have the world "paleo" in title of full-text and could still be called "the true paleo" study. It does, after all, revolve around a 10-day mimic of a "hunter-gatherer lifestyle" and its favorable effects on anthropometrics and clinical chemical indices such as the reductions in insulin, triglycerides, HDL, elevated liver health markers and other indices that are usually far from being optimal in the average student, scientist, physician, and other health professionals who participated in the study at hand.

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As the researchers point all, all subjects (n=10, n=32 and n=11) "were interested to experience the impact of ancient lifestyle on their own health and well-being and therefore jointly decided to engage in this study" (Pruimboom). In that, the term "this study" refers to three separate 10-day trips through the Spanish Pyrenees during the summers of 2011 (𝑛 = 10), 2012 (𝑛 = 32), and 2013 (𝑛 = 11), on which ...

"[t]he participants lived outdoors and walked from one watersource to another. Food was provided by the organization and with help of forest-guards from official institutes of the Catalan county. Food intake was planned before the trip, based on the average daily food intake by the traditionally living Hadzabe people in Tanzania. The use of mobile phones or other electronic devices was not allowed" (Pruimboom. 2016)

It is obviously debatable, if "mayonnaise" is a paleo food (not sure if they made it themselves) and how "paleo" the rest of the subjects' diet which may have been designed to mimic the macros, but probably not the foods of the Hadzabe (see Figure 1, tabular overview on the left) actually was.

Figure 1: Exemplary food intake (left) and changes in anthroprometrics (right); stat. sign. w/ p < 0.001 was observed for the median changes, not the minimal and maximal changes, obviously (Pruimboom. 2016).

What is undebatable, though, is the statistical significance and health relevance of the reductions in weight and body fat you can deduce from the sign. reduction in the median subjects' waist circumference.

Mind the statistics: There's a reason why I plotted more than one value in Figure 1. While I cannot tell you the reason why, I can tell you that at least one subject did not see the expected improvements in waist circumference. Possibly, he or she ate too much mayonnaise ;-), ... Why's that relevant? Well, it obviously goes to show you that calories still count. While the median subjects (with the low number of participants the scientists didn't calculate averages) obviously was in a caloric deficit, this one person probably just wasn't caught feasting secretly on the supply.

It should be noted, though, that these changes were certainly not attributable solely to the diet. Rather than that it should be obvious that the significant reduction in body and most certainly belly fat was the consequence of (a) what and how much the subjects ate while (b) experiencing what the Dutch scientists call "ancient stress factors" they were facing during a 10-day trip that revolved around the following four principles:

• Walking and limited manual work- providing the exercise / general physical activity stimulus modern humans lack: There were daily walking trips from waterhole to waterhole, with an average walking distance of about 14 km/day, including altitude differences up to approximately 1,000 m. The participants carried their own backpacks with an average weight of 8 kg. The trip took place in the part of the Pre-Pyrenees with a maximum altitude of 1,900 meters above sea level. In addition, some manual work was done to clean mountain trails as agreed upon with the Catalan Government.
• Intermittent fasting - leaving room to actually experience hunger and all its beneficial hormonal correlates (e.g. AMPK increase => mitochondrial housekeeping, etc.): Participants consumed two meals daily. The first meal was provided by the organization halfway and the second meal prepared on arrival at the camping site. Animals, including ducks, chickens, turkeys, rabbits, and fish, were delivered alive and killed by the participants. Fish were caught with nets in the Noguera river. All foods were prepared on the spot by the participants.
• 100% exposure to the elements - resynchronizing the internal clock: The participants slept outside in sleeping bags on small inflatable mattresses. Outside temperatures varied from 22 to 42∘C during daylight, whereas night temperatures varied from 12 to 21∘C. One group experienced a day of snow in the middle of July, which prompted the organization to provide hotel accommodations for a single night.
• Cyclic water intake - experiencing thirst to benefit from the anti-inflammatory release of oxytocin (Krause. 2011): Bulk (intermittent) drinking behavior was recommended by drinking as much as possible (up to satiety) after reaching a waterhole. The waterholes contained nonchloritized drinking water (Note: I would not suggest using "dehydration" as a means to improve your health; while it may have done this in the study at hand, it's simply stupid - and that's in the literal sense, as you've read in my article "Hydrated or Dumb").

Only in conjunction, with these "stress factors" did the diet do its body fat reducing and, as the data in Figure 2 shows, glucose and blood lipid reducing effects:

Figure 2: Changes in glucose and lipid metabolism over (I repeat) only 10 days; worth mentioning: all but the effect on HDL were statistically highly sign. with p < 0.001 (Pruimboom. 2016).

Effects of which the scientists say that they were the result of acute stress, which promotes release of stress hormones, including adrenaline, noradrenaline, and cortisol, all of which are bad in excess, but will "give rise to recovery from the reigning state of chronic low-grade inflammation and the return to homeostasis" (Pruimboom. 2016), when the stressors are hit the sweet spot of hormesis as it occurred in response to / corollary with the elevation in AST, ALT and hs-CRP of which I've explained previously that all of them can be natural reactions to (especially unaccustomed) physical activity (learn more about ALT, AST and exercise induced inflammation that may be misunderstood as a health problem).

In the study at hand, said "recovery from the reigning state of chronic low-grade inflammation" was characterized by "profound metabolic and immunologic adaptations", of which the scientists highlight that they relate to three classic features of the metabolic syndrome, i.e. body mass, glucose homeostasis, and circulating lipids. The fourth, i.e. blood pressure was - unfortunately - not recorded.

Ad-Libitum Paleo Diet W/ a Handful of Simple Rules Cuts 5-7 kg of Body Fat in 12 Weeks - Plus: Paleo Research Overview | more.

Bottom line: With the metabolic syndrome, also named the insulin resistance syndrome, being "a well-established risk factor for various diseases of affluence, including type 2 diabetes, cardiovascular disease, essential hypertension, polycystic ovary syndrome, nonalcoholic fattyliver disease, certain types of cancer (colon, breast, and pancreas), sleep apnea, and pregnancy complications, such as preeclampsia and gestational diabetes", the scientists are right to highlight in their conclusion that the subjects didn't just feel better subjectively (according to questionnaire), but returned from the "wilderness" in an objectively healthier state.

A state of which the scientists say that it has been promoted by the previously discussed consequences of the four pillars (see list) and related effects, such as the reduction of the postprandial inflammatory response (Holmer-Jensen. 2011; Klop. 2011; Peairs. 2011) and increased protection against pathogens (Fielding. 2000; MacEneaney. 2009) that occurs, when you are physically active before a meal. Even the presence of "cutaneous- and other body surface-directed danger signals" could, as Pruimboom et al. point out have been "hormetic triggers" | Comment!

References:

• Fielding, Roger A., et al. "Effects of prior exercise on eccentric exercise-induced neutrophilia and enzyme release." Medicine and science in sports and exercise 32.2 (2000): 359-364.
• Holmer-Jensen, Jens, et al. "Differential effects of dietary protein sources on postprandial low-grade inflammation after a single high fat meal in obese non-diabetic subjects." Nutrition journal 10.1 (2011): 1.
• Klop, Boudewijn, et al. "Understanding postprandial inflammation and its relationship to lifestyle behaviour and metabolic diseases." International journal of vascular medicine 2012 (2011).
• Krause, Eric G., et al. "Hydration state controls stress responsiveness and social behavior." The Journal of Neuroscience 31.14 (2011): 5470-5476.
• MacEneaney, Owen J., et al. "Effect of prior exercise on postprandial lipemia and markers of inflammation and endothelial activation in normal weight and overweight adolescent boys." European journal of applied physiology 106.5 (2009): 721-729.
• Peairs, Abigail D., Janet W. Rankin, and Yong Woo Lee. "Effects of acute ingestion of different fats on oxidative stress and inflammation in overweight and obese adults." Nutrition journal 10.1 (2011): 1.
• Pruimboom, Leo, et al. "Influence of a 10 days mimic of our ancient lifestyle on anthropometrics and parameters of metabolism and inflammation. The ‘Study of Origin’."

Interaction of Fat Cell Size, Protein Intake & Co. W/ Fat Gain + Insulin Res. in Overfed Men + Women in Metabolic Ward

Interaction of Fat Cell Size, Protein Intake & Co. W/ Fat Gain + Insulin Res. in Overfed Men + Women in Metabolic Ward

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That's rather the low protein variety of overfeeding... but wait, was the high protein diet even "high" in protein? Well high enough to affect liver fat, for sure.

You will probably remember José Antonio's high protein overfeeding study series (learn more) from the articles here and on the SuppVersity Facebook page. The results were quite impressive, but the number of controlled covariates were small and the dietary control was limited to food logs.

In a more recent study, George A. Bray and colleagues from the Pennington Biomedical Research Center of the Louisiana State University System, the George Mason University, and the FL Hospital & Sanford-Burnham Prebys Discovery Research Institute (Bray. 2016) determined the effect of overfeeding diets with 5%, 15% or 25% energy from protein on glycemia + body fat distribution in healthy men and women with add. covariates and in a metabolic ward.

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In total, 15 men and 5 women were overfed by 40% (extra calories above maintenance) for 56 days with diets containing

• 5% (LP) of the total energy as protein, 
• 15% (NP) of the total energy as protein, or 
• 25% (HP) of the total energy as protein

Insulin sensitivity was measured using a two-step insulin clamp at baseline and at 8 weeks. Body composition and fat distribution were measured by DXA and multi-slice CT scan ... so far not so different, but the subjects were contained in a metabolic ward, cheating on the diet was thus as impossible, as taking supplements or working out like maniacs.

Figure 1: Diagram that illustrates the 8-weekstudy design; N = 10 male, 5 female subjects (Bray. 2016).

In conjunction with the scientists' analysis of the subjects abdominal subcutaneous fat cell size, which was determined on osmium fixed fat cells, these are two strengths of a study, of which it is yet quite obvious that it also had its disadvantages:

• 

  Review the effects of different macronutrients in overfeeding studies | more

  the protein content of the diet is simply hilarious - that's not just because eating 5% protein, only is nothing but idiotic, but also because 25% of protein is far away from what can be considered "high protein" these days;
• the lack of exercise limits the significance of the results - at least for the majority of SuppVersity readers overeating in phases in which you don't exercise is probably nothing they would even remotely consider.

The scientists observations that neither the subjects' insulin sensitivity and free fatty acids during low and high levels of insulin infusion did not differ after 8 weeks of overfeeding.

Figure 2: Effect of 8 weeks of overfeeding on abdominal fat distribution, ectopic lipid; rel. changes (Bray. 2016).

What did differ, however, were the changes in body fat distribution according to DXA and how the latter depended on the protein content on fat cell size before the overfeeding period. More specifically, ...

• the fat free mass (FFM) and intrahepatic lipid increased more on the high protein, whereas 
• % BF and fasting free fatty acids (FFA) increased more on the low protein diet, while

In addition, the scientists observed that a high initial fat cell size predicted increased visceral fat gains and the FFA suppression during the high-dose insulin clamp.

Figure 3: Relation of Baseline Fat Cell Size to Change in Visceral Adipose Tissue Mass with Eight Weeks of
Overfeeding in heathy volunteers (VAT 0.040 +/- 0.70(FCS); P < .0063 | Bray. 2016)

The subjects' insulin levels at baseline, on the other hand, predicted the increase in subcutaneous but not visceral fat accumulation (see Figure 3) - most intriguingly with low fasting insulin
at baseline correlated predicting higher changes in % fat (for insulin the scientists observed a correlation with r = –0.43; P < .034), but not with other variables. It is thus not surprising that the most insulin sensitive subjects also gained the most subcutaneous fat... or, as the scientists put it: "HOMA IR predicted the increase in DSAT (r = 0.50; P <.016), but not other variables" (Bray. 2016).

Those are important insights of which the authors rightly point out that they clearly indicate that "an induction of insulin resistance with overfeeding is related to fat cell size and requires more than an expansion of adipose tissue stores" (Bray. 2016).

A surprising, but not debatable result of the study at hand is that the high protein diet increased liver fat (HUs;  measured with DXA, too).  The low protein diet, on the other hand, helped to decrease the subjects' liver fat significantly - remember: we are talking about a diet with 40% extra energy on top of the regular diet (Bray. 2016).

Bottom line: Yes, you've read all that in individual articles (albeit often about rodent studies) on SuppVersity before: (1) the more protein, the greater the lean mass gains; (2) the less protein, the greater the ratio of fat to lean mass gains; (3) the fuller your fat cells, the more likely you will gain metabolically unhealthy visceral fat; and (4) the more insulin sensitive you still are, the more metabolically healthier subcutaneous fat you will gain.

What is news, or at least has not been observed in Antonio's study in active individuals (also because they didn't look) is the surprisingly ill effect of high amounts of protein on liver fat (see Figure, right): while the low protein diet reduced the subjects' liver fat sign, the high protein diet triggered a small, but undesirable accumulation of liver fat during overfeeding in normal-weight subjects - not good, but not yet critical and hopefully something you'd not see w/ concomitant exercise or smaller calorie excess | Comment!

References:

• Bray, George A., et al. "Effect of three levels of dietary protein on metabolic phenotype of healthy individuals with 8 weeks of overfeeding." The Journal of Clinical Endocrinology & Metabolism (2016): jc-2016.

The Insulin / Glucagon Ratio and Why Diabetics and People W/ Severe Insulin Resistance Must be Careful With Protein

The Insulin / Glucagon Ratio and Why Diabetics and People W/ Severe Insulin Resistance Must be Careful With Protein

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You're insulin resistant and trying to lose weight with high protein intakes? Then you got to read this article carefully...

High protein diets can help you lose weight while maintaining muscle mass. This should make them the ideal choice of diabetic patients, many of whom are suffering from weight issues that are often not corollary, but rather causatively involved in the development of type II diabetes.

Unfortunately, studies in type I diabetics and preliminary evidence from type II diabetics and other insulin resistant individuals suggests that - if the disease has progressed significantly - eating too much protein can be a problem, as well, one that may worsen the ill effects of diabetes.

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The reason for the potentially detrimental effects of high protein intakes on glycemia is well-known, but rarely acknowledge: gluconeogensis. As early as in the 1970s, researchers observed that the administration of a high-protein diets to rats, can significantly elevate plasma glucose and insulin concentrations and reduce the sensitivity of fat cells to insulin (Blazquez. 1970).

Figure 1: Post-prandial insulin and glucose levels in rats after several weeks of high protein feeding (Blazquez. 1970).

Over the decades after the publication of the Blazquez study, evidence for both the beneficial (Tremblay. 2007) and potential ill effects (Unger. 1971; Eisenstein. 1974) of high protein diets on diabetes and insulin resistance has been accumulating (Linn. 2000).

Sign. increases in urea prod. are another consequence of protein-based gluconeogenesis (Gannon. 2001).

As usual you will find conflicting evidence: In 2001, for example, Gannon et al. found only a modest increase in serum glucose levels in type II diabetics in response to the ingestion of 50g of protein - in spite of the fact that ~20-23g of it were converted to glucose in the liver.

What is important to note, however, is the fact that the protein source in the Gannon study was lean beef - one of the slowest sources of protein you can have and thus not exactly the #1 candidate for being subjects to immediate and thus glucose raising gluconeogenesis.

In that, it has been know for almost as long that the degree of offset of the ratio of glucagon to insulin in type I and II diabetics may decide, whether the ingestion of high(er) protein diets will help or hinder glucose management. In the pertinent, seminal review, Unger observes that "the insulin:glucagon ratio (I/G) varies inversely with need for endogenous glucose production, being lowest in total starvation and highest during loading with exogenous carbohydrate" (Unger. 1971). It is thus not surprising that studies have observed that

• the infusion of the glucose precursor, alanine, in the fasting state causes a fall in I/G, a “catabolic response,” but increases I/G during a glucose infusion, an ”anabolic response, which spares alanine from the fate of being abused for gluconeogenesis, 
• similar effects have been observed after a protein load; normally after an overnight fast I/G rises in response to a beef meal, an anabolic response, while in the carbohydrate-deprived subject, the I/G does not rise, remaining at a catabolic level (cf. Chevalier. 2006)

Now, back in the day these observations were mainly used to support the concept of a "protein sparing action" of glucose. Today, the effect on gluconeogenesis, i.e. the production of glucose from proteins / amino acids in the liver, has moved to the center of attention of a number of scientists. Calbet and MacLean, for example, investigated how the plasma glucagon and insulin responses of humans would depend on the rate of appearance of amino acids after ingestion of very fast vs. fast protein sources.

Figure 2: Glucose and glucagon levels in the blood of healthy volunteers after ingesting either 25g glucose or protein solutions containing whey protein hydrolysate (WPH), pea peptide hydrolysate (PPH) or milk protein (MS | Calbet. 2002).

Their results (see Figure 2) indicate the obvious: Even in healthy individuals and even upon co-administering protein sparing and 25 g of anti-gluconeogenic glucose, the fastest protein sources (whey protein, WPI; pea peptide hydrolysate; PPH) produce the highest increase in glucagon, gluconeogenesis and thus serum glucose levels in the first 20 minutes after the ingestion of the 25 g of glucose plus ~30g of the different proteins.

Let's just be clear here: I am not saying that high protein diets cannot help with diabetes. I am just saying that bolus intakes of protein can be problematic for type I diabetics and people with severe insulin resistance and progressive type II diabetes.

What may not be a major problem for healthy individuals, though, can be a deal-breaker for diabetics, in whom studies into the inter-organ flux of substrates after a protein-rich meal (slow digesting beef 3g/kg body weight) show that the normally non-significant effect on glycemia (<5% in healthy subjects) was exuberant in the diabetic subjects in whom you will see a greater rise in blood glucose, and a three-to-fourfold increment in splanchnic glucose output at 30-90 min that was triggered by a doubling of arterial glucagon, which was not compensated for by an concomitant increase in insulin as it occurred in the healthy test subjects (Wahren. 1976).

Figure 3: Rel. changes in blood glucose after ingestion of 3g/kg lean meat in healthy and diabetic subjects (Wahren. 1976).

Whether an increase in protein intake will have beneficial or ill effects on your ability to control your glucose levels will thus clearly depend on the degree of hepatic insulin resistance / pancreatic dysfunction you expose.

• If you are severely diabetic and/or insulin resistance, i.e. you either don't produce enough or no insulin in response to the ingestion of protein or your body does not react to the insulin, as it would be the case in type I diabetes and progressive type II diabetes, your glycemia may be impaired by high protein meals.
• If you are only slightly insulin resistant, you will probably benefit from the insulinogenic effects of protein and the ability to replace carbohydrates in your meals with protein. You may nevertheless want to test your individual glucose response to fast-digesting proteins like whey or amino acid supplements, which may still result in an uncontrolled gluconeogenic response.
• If you are healthy and insulin sensitive, you won't have to worry about the gluconeogenic effects of high protein intakes - regardless of whether we are talking about fast or slow protein sources, because the former will spike insulin enough to blunt any pro-gluconeogenic effects of the concomitant increase in glucagon to keep the rates of gluconeogenesis and thus your glucose levels in check.

So, just as you've read it here at the SuppVersity before, what's good and what's bad for your cannot be generalized - even when it comes to something as popular as increasing your protein intake.

What do you have to remember? High protein intakes, especially in form of large bolus intakes of 30g or more protein per session can trigger unwanted glucose excursions. These problems with glucose management occur almost exclusively in diabetics, in whom the protein-induced increase in insulin and / or the effects of this increase in insulin is / are blunted.

Figure 1: GIP and GLP-1 response to whey and white bread (left, top & bottom); insulin release (%) per islet relative to glucose after incubation with different amino acids, amino acid mixtures and mixture + GIP (Salehi. 2012) | more

Due to the unavoidable protein induced increase in glucagon, diabetics and people with severe insulin resistance will fall into a catabolic state in which the lions share of the protein they ingest will be subject to gluconeogenesis, i.e. the production of glucose from proteins / their amino acids in the liver. The consequence of the skyrocketing rates of gluco-neogenesis is an increase in blood glucose that will only exacerbate the existing damaging effects of elevated glucose levels in diabetics and people with severe insulin resistance. Since the of gluco-neogenesis depends on the rate of appearance of amino acids in the blood, fast-digesting proteins like whey are more prone to trigger this effect than slow-digesting proteins like meat.

If you don't belong to the previously referred to group of people suffering from type I or severe type II diabetes and/or severe insulin resistance, though, you don't have to worry that high(er) protein diets could mess with your ability to manage your glucose levels | Comment on Facebook!

References:

• Blazquez, E., and C. Lopez Quijada. "The effect of a high-protein diet on plasma glucose concentration, insulin sensitivity and plasma insulin in rats." Journal of Endocrinology 46.4 (1970): 445-451.
• Calbet, Jose AL, and Dave A. MacLean. "Plasma glucagon and insulin responses depend on the rate of appearance of amino acids after ingestion of different protein solutions in humans." The Journal of nutrition 132.8 (2002): 2174-2182.
• Chevalier, Stéphanie, et al. "The greater contribution of gluconeogenesis to glucose production in obesity is related to increased whole-body protein catabolism." Diabetes 55.3 (2006): 675-681.
• Eisenstein, Albert B., Inge Strack, and Alton Steiner. "Glucagon stimulation of hepatic gluconeogenesis in rats fed a high-protein, carbohydrate-free diet." Metabolism 23.1 (1974): 15-23.
• Gannon, M. C., et al. "Effect of Protein Ingestion on the Glucose Appearance Rate in People with Type 2 Diabetes 1." The Journal of Clinical Endocrinology & Metabolism 86.3 (2001): 1040-1047.
• Linn, T., et al. "Effect of long-term dietary protein intake on glucose metabolism in humans." Diabetologia 43.10 (2000): 1257-1265.
• Tremblay, Frédéric, et al. "Role of dietary proteins and amino acids in the pathogenesis of insulin resistance." Annu. Rev. Nutr. 27 (2007): 293-310.
• Unger, Roger H. "Glucagon and the insulin: glucagon ratio in diabetes and other catabolic illnesses." Diabetes 20.12 (1971): 834-838.
• Wahren, J., P. H. I. P. Felig, and L. A. R. S. Hagenfeldt. "Effect of protein ingestion on splanchnic and leg metabolism in normal man and in patients with diabetes mellitus." Journal of Clinical Investigation 57.4 (1976): 987.

Sleep Science Update: New Insights into the Effect of a Lack of Quality Sleep on Glucose Control and Diabesity Risk

Sleep Science Update: New Insights into the Effect of a Lack of Quality Sleep on Glucose Control and Diabesity Risk

diabesity diabetes glucose management health insulin insulin resistance obesity on short notice sleep

Blue light is not the only enemy of sleep, but it's the most prevalent one, today.

Personally, I believe that sleep, "a condition of body and mind which typically recurs for several hours every night, in which the nervous system is inactive, the eyes closed, the postural muscles relaxed, and consciousness practically suspended" (that's what Google's "define"-feature will tell you about sleep), is still an under-appreciated determinant of optimal health and performance.

Evidence to support this assertion comes from a series of studies that were presented at the Winter Meeting of the British Nutrition Society on December 8-9, 2015 - a meeting with the telling title: "Roles of sleep and circadian rhythms in the origin and nutritional management of obesity and metabolic disease" (O'Sullivan. 2015).

Learn more about the health effects of correct / messed up circadian rhythms

Sunlight, Bluelight, Backlight and Your Clock

Sunlight a La Carte: "Hack" Your Rhythm

Breaking the Fast to Synchronize the Clock

Fasting (Re-)Sets the Peripheral Clock

Vitamin A & Caffeine Set the Clock

Pre-Workout Supps Could Ruin Your Sleep

• Circadian disruption in shift workers – the effects of insufficient sleep on dietary and lifestyle behaviours (Nea. 2016) - It will not surprise you that shift workers report more sleep problems compared to the general public. Studies estimate that 10–30 % of shift workers suffer from a circadian rhythm disorder known as “shift work disorder”(Gumenyuk. 2012). With their new quantitative study, a team of young researchers from the Dublin Institute of Technology and the University of Ulster provides some insights into the consequences of this problem.
  
  As the scientists point out, overall, just 34·3 % of the sample was achieving adequate sleep. A number of factors were associated with insufficient sleep – being male (p < 0·001), being 35–54 years of age (p < 0·001), having adult/child dependents (p < 0·001), working in larger organisations (p = 0·045), working in distribution/logistics, manufacturing or construction (p = 0·005), working night shifts (p = 0·042), and working longer shifts (p = 0·002).
  

  

  Factors that increased the subject's risk of not getting adequate sleep (Nea. 2016).

  Furthermore, the scientists observed that insufficient sleep had an effect on the diet of workers. Those who did not achieve adequate sleep were more likely to skip meals on working days and skipped meals significantly more frequently (p = 0·023).

  "Workers with insufficient sleep were also significantly less likely to consume the recommended 5 portions of fruit and vegetables per day (37·5 % vs 43·3 %, p = 0·045) and were less likely to consume the recommended 3 portions of milk/cheese/yoghurt per day (11·6 % vs 8·1 %, p = 0·050). In addition, those with insufficient sleep had higher prevalence of hypertension (10·2 % vs 5·7 %, p = 0·008) and depression/anxiety (7·3 % vs 3·4 %, p = 0·008)" (Nea. 2016)

  Participants were also questioned how they perceived shift work impacts on various aspects of their lives. Compared to those who achieve adequate sleep, those who had insufficient sleep were significantly more likely to report that shift work had a negative effect on their physical health (p < 0·001), mental health (p = 0·003), family life (p = 0·001), social life (p = 0·046), physical activity levels (p = 0·029) and overall quality of life (p = 0·002). Those with insufficient sleep were also significantly more likely to report that shift work increases how much alcohol they drink (p = 0·041).
• Oral glucose tolerance test results are affected by prior sleep duration: a randomised control crossover trial of normoglycaemic adults (Ellison. 2016) - As Ellison et al. rightly point out, "[o]ral glucose tolerance tests (OGTTs) remain the key clinical tool for assessing glucose control and diagnosing diabetes" (Ellison. 2016). In that, they criticize that "[c]urrent guidelines for administering such tests emphasise the importance of a preceding 8 hour fast (often undertaken overnight) but overlook the potential role that preceding sleeping patterns night might play in glucose control the following day" (ibid.). In view of the number of recent observational and experimental studies, which suggest that poor sleep is associated with an increased risk of diabetes, these tests may very well be messed up by a lack of sleep during the previous 8h fast. The aim of the latest study by scientists from the Sound Asleep Laboratory in Leeds was therefore "to explore the effect of early vs. late bedtimes on OGTT results using a cross-over randomised controlled trial" (ibid.).
  
  To this ends, the authors recruited 40 normoglycemic adults who were, after they had been stratification by self-reported pre-existing sleep patterns (as assessed using the Pittsburgh Sleep Quality Index; PSQI), allocated to either a ‘short’ (2·00am-7·00am) then ‘long’ (10·00pm−7·00am), or a ‘long’ then ‘short’ sleep duration, on two consecutive nights.

  "On each occasion, objective measures of sleep were obtained using the ‘SleepMeister’ application on an iPhone 4, with additional subjective assessments of sleep provided by subsequent completion of a version of the PSQI adapted to generate self-reports of sleep during the preceding night (as opposed to the preceding month). On each of the mornings following ‘short’ or ‘long’ sleep, participants again completed the PSQI and underwent a two-hour 75 g oral glucose tolerance test (OGTT), with blood glucose readings taken at 0, +30, +60, +90 and +120 minutes thereafter using finger-prick tests. Data were analysed using STATA v12. Ethical approval was granted by the University of Leeds REC (Ref:HSLTLM12075)" (Ellison. 2016).

  As it was to be expected, both the ‘SleepMeister’ application and the PSQI recorded significantly later bedtimes (SleepMeister: −19·9; 95 %CI: −20·1,−19·7; PSQI: −19·9; 95 %CI: −20·1,−19·7) and significantly shorter sleep durations (decimal hours: ‘SleepMeister’: −3·8;95 %CI: −4·3,−3·4; PSQI: −3·4; 95 %CI: −3·9,−2·9) following a 2am (vs.10pm) bedtime (i.e. ‘short’ and ‘long’ sleep duration, respectively) - a fact, the scientists consider evidence "that levels of compliance were high" (ibid.).
  
  In spite of that, there was no significant effect of sleep duration on fasting blood glucose levels prior to the OGTT after adjustment for sleep duration sequence (i.e. ‘short’ then ‘long’ vs. ‘long’ then ‘short’) and a modest imbalance in gender between the two intervention sequence group.
  

  

  Figure 2: Normal response (=expected response in OGGT, not the actual response of the subjects, because the absolute values are not disclosed in the abstract and an FT is not yet available) vs. calculated response as a consquence of insufficient sleep (normal + difference, rel. difference above bars | Ellison. 2016).

  What did differ, though, were the glucose levels recorded after the ingestion of 75 g glucose, which were consistently higher following a ‘short’ as opposed to a ‘long’ sleep duration, as well as the levels recorded at +60 and +90 minutes, which were likewise significantly higher by 1·18 mmol/l (95 %CI: 0·43,1·92; p = 0·003) and 0·55 mmol/l (95 %CI: 0·05,1·06; p = 0·032), respectively. These results, the scientists say, "indicate that short sleep duration the night before results in an immediate elevation in blood glucose levels the following morning in normoglycaemic adults" (ibid.). That this is a problem, should be obvious, after all it may falsely classify healthy individuals as pre-diabetics. Therefore, "further standardisation of pre-OGTT sleep duration, similar to that for an overnight fast," (ibid.) appears warranted.
• Less Sleep Duration and Poor Sleep Quality Lead to Obesity (Parvaneh. 2016) - In a recent cross-sectional study that was carried out to investigate the association of sleep deprivation and sleep quality with obesity, Malaysian scientists analyzed data from 225 Iranian adults (109 males and 116 females) aged 20–55 years.

  "Heart Questionnaire (SHHQ), International Physical Activity Questionnaire (IPAQ) and a 24-hour dietary recall were interview-administered to evaluate sleep pattern, physical activity and dietary intake of the subjects. Besides, anthropometric also were measured, then subjects were categorized into normal weight and over-weight/obese based on WHO (2000). Sleep duration and sleep quality were assessed based on 2 groups of normal weight and overweight/obese" (Parvaneh. 2016).

  The scientists' analysis of the data revealed that overweight/obese individuals have significantly shorter sleep duration (5·37 ± 1·1 hours) as compared to normal weight subjects (6·54 ± 1·06 hours).
  

  

  Figure 3: Overweight / obesity is linked to sign. sleep problems (Parvaneh. 2016).

  Sleep duration was yet not the parameter the scientists from the National University of Malaysia identified as a major risk factor for obesity - that was a poor sleep quality, which was associated with a 100% increased risk for being overweight or obese (OR: 2·0, 95 % CI: 1·18–3·37, p < 0·05). As a conclusion, the scientists state that "lower sleep quality and sleep duration increase the risk of being overweight and obese" and demand: "[S]trategies for the management of obesity should incorporate consideration on sleeping pattern" (Parvaneh. 2016). These strategies, by the way, may also help people keep their triglyceride levels in check. After all, another study that was presented at the same meeting of the Nutrition Society suggests that a high sleep efficiency shows a strong and negative correlation with triglycrides and another important marker of heart disease risk, the total cholesterol to HDL ratio (Al Khatib. 2016).
• Is insulin resistance associated with light at night in healthy sleep deprived individuals? (AlBreiki. 2016) - The simple answer to this question is "Yes!". The more complex one is that a recent study that was designed to investigate the impact of light and/or endogenous melatonin on plasma hormones and metabolites prior to and after a set meal in healthy sleep deprived subjects found that bright blunts the release of melatonin and the effects of insulin on glucose disposal.
  
  In the study, seventeen healthy participants, 8 females (22·2 years (SD 2·59) BMI 23·62 kg/m2 (SD 2·3)) 9 males (22·8 years (SD 3·5) BMI 23·8 kg/m2 (SD 2·06)) were randomised to a two way cross over design protocol; dim light condition (<5lux) and bright light condition (>500lux), separated by at least seven days.

  

  Melatonin promotes female weight loss - Suggested Read: "Trying to Lose Fat & Get "Toned"? Taking 1-3 mg Melatonin Helps Women Lose 7% Body Fat, Gain 3.5% Lean Mass".

  "Each session started at 18:00 h and finished at 06:00 h the next day. All participants were sleep deprived and semi-recumbent throughout the session. An isocalorific breakfast was consumed at 08:00 h and lunch was timed to be 10 hours before the evening meal. Each participant consumed an evening meal (1066 Kcal, 38 g protein, 104 g CHO, 54 g fat, 7 g fibre) at an individualised time based on estimated melatonin onset. Plasma and saliva samples were collected at specific time intervals to assess glucose, insulin and melatonin levels" (AlBreiki. 2016).

  As previously stated, the bright light reduced the salivary levels of melatonin significantly (p = 0·005). What is more relevant to the research question, however is that it also increased the postprandial glucose and insulin levels significantly compared to dim lights (p = 0·02, p = 0·001) respectively.
  
  

  

  Figure 3: Effect of light intensity on melatonin levels and glucose response of 8 female and 9 male normal-weight normoglycemic subjects to standardized meal consumed at night (AlBreiki. 2016).

  For the scientists this result is not exactly surprising. They had expected that the melatonin release would be suppressed due to the light intensity; that the increase in insulin was not able to compensate for the light-induced increased glucose resistance, however, shows that the ill effects of a  'night-shift-esque' bright light exposure at night on glucose metabolism are more severe than previously thought.

Redeem your sleep dept, now!

Bottom line: Along with studies highlighting the importance of sufficient hours of quality sleep on glucose control in pregnancy (Alghamdi. 2016; Alnaja. 2016) and the "largest study to-date to demonstrate a strong inverse association between late-onset diabetes and poor sleep, even after adjustment for potential confounding factors" (Alfazaw. 2016), the previously discussed studies highlight that sleep hygiene' is as important for your health as "clean eating" (whatever that maybe) and a sufficient amount of light and intense physical activity | Comment on Facebook!

References:

• AlBreiki, et al. "Is insulin resistance associated with light at night in healthy sleep deprived individuals?" Proceedings of the Nutrition Society, 75 (2016). 
• Alfazaw, et al. "Variation in sleep is associated with diagnosis of late-onset diabetes: a cross-sectional analysis of self-reported data from the first wave of ‘Understanding Society’ (the UK Household Longitudinal Study)." Proceedings of the Nutrition Society, 75 (2016). 
• Alghamdi, et al. "Short sleep duration is associated with an increased risk of gestational diabetes: Systematic review and meta-analysis." Proceedings of the Nutrition Society, 75 (2016). 
• Alnaja, et al. "Relationship between sleep quality, sleep duration and glucose control in pregnant women with gestational diabetes." Proceedings of the Nutrition Society, 75 (2016). 
• Al Khatib, et al. "The Sleep-E Study: An on-going cross-sectional study investigating associations of sleep quality and cardio-metabolic risk factors." Proceedings of the Nutrition Society, 75 (2016). 
• DeFronzo, Ralph A. "The triumvirate: β-cell, muscle, liver. A collusion responsible for NIDDM." Diabetes 37.6 (1988): 667-687.
• Ellison, et al. "Oral glucose tolerance test results are affected by prior sleep duration: a randomised control crossover trial of normoglycaemic adults." Proceedings of the Nutrition Society, 75 (2016). 
• Gumenyuk, Valentina, Thomas Roth, and Christopher L. Drake. "Circadian phase, sleepiness, and light exposure assessment in night workers with and without shift work disorder." Chronobiology international 29.7 (2012): 928-936.
• Nea et al. "Circadian disruption in shift workers – the effects of insufficient sleep on dietary and lifestyle behaviours." Proceedings of the Nutrition Society, 75 (2016). 
• O’Sullivan (ed.). "Roles of sleep and circadian rhythms in the origin and nutritional management of obesity and metabolic disease." Proceedings of the Nutrition Society. Volume 75 / Issue OCE1 - Winter Meeting, 8–9 December 2015. Published January 2016: E1-E42.
• Parvaneh, et al. "Less Sleep Duration and Poor Sleep Quality Lead to Obesity." Proceedings of the Nutrition Society, 75 (2016). 
• Peschke, Elmar. "Melatonin, endocrine pancreas and diabetes." Journal of pineal research 44.1 (2008): 26-40.