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

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

dieting health insulin resistance lean mass lose fat lose weight muscle loss paleo paleolithic eating whole foods

Yes, these foods were "allowed" - Even nuts, albeit in limited amounts.

Ok, I have to admit that I have repeatedly made fun of "paleo" in the past. Its "cultish", sometimes even "sectarian" appeal is and will remain as hilarious in my eyes as the (for some people life-or-death-)question whether certain foods "are paleo" or not (who cares, as long as they are healthy?). If you happen to have seen my presentation at the Paleo Convention in Berlin, last year, you will know that, despite my apathy against the quasi-religious sides of "paleo", I do appreciate a certain set of "rules" or "principles" (or whatever you may call them) all iterations of "paleo" have in common.

These principles work! And they have just been shown to help middle-aged type II diabetics (age 59±8 years) shed a quite impressive 6.7 kg of body fat (w/out exercise "only 5.7kg) in 12 weeks - without dieting as in not eating, although you're hungry (Otten. 2016).

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As the headline already tells you, the subjects, individuals diagnosed with type 2 diabetes within the past 10 years, who had a BMI of 25–40 kg/m2 and were weight stable (i.e. <5% weight loss) for 6 months (that's important, because otherwise the data on the energy deficit in Figure 1, which was calculated as baseline vs. study intake would be inaccurate) were allowed to eat "as-libitum", which practically means "as much as they wanted", as long, as they adhered to (a) "paleo foods", i.e. lean meat, fish, seafood, eggs, vegetables, fruits, berries, and nuts, but no cereals, dairy products, legumes, refined fats, refined sugars, and (extra) salt (canned fish and cold cuts like ham were allowed) and (b) followed the following simple food-specific rules:

• 

  Paleo Goes "Real Science" - First Meta-Analysis of Available RCTs Shows Improvements in Health + Body Composition | learn more

  eggs - maximally 1–2/day, and no more than 5/week,
• potatoes - only 1 medium sized potato per day
• dried fruit - 130 g/day, not more,
• nuts - 60 g/day, so no snacking on almonds 24/7
• rapeseed or olive oil - maximum 15 g/day
• honey and vinegar - only small amounts as flavoring in cooking
• coffee & tea - max 300 ml/day (each, I assume)
• red wine - only one glass per week

Since the participants were also instructed to drink mainly still water, you will probably not be surprised that all subjects, irrespective of whether they had been randomly assigned to the no exercise or exercise group ended up in a significant energy deficit - in spite of being allowed to eat "ad-libitum" (see Figure 1 for the most relevant information about their diet(s)).

Figure 1: Energy and macronutrient intake; differences, rel. + abs. above bars (Otten. 2016).

It is also not surprising that the extra-exercise (1h of exercise, 3x per week | details see blue box below) that was done on top of the (at least) 30 min of moderate intensity exercise like brisk walking all patients had been prescribed as part of their regular T2DM treatment, almost doubled the energy deficit of the subjects in the paleo + exercise, i.e. the PD-EX group (remember: the subjects were allowed to eat more, as long as they stuck to the previously presented rules - since the intake of foods like steak or chicken breast was not limited, they would have been able - within certain limits - to significantly increase their energy intake and still did not fully compensate the energy expended during the workouts; this should remind you of previous articles of mine outlining that "exercise does not just make you hungry" | learn more)

What about compli-ance? Both groups increased their relative intake of protein and their intake of monounsatu-rated and polyunsatu-rated fatty acids. Both groups lowered their intake of carbohydrates and saturated fatty acids. The reduction of sodium intake was only significant in the PD-EX group. Nine of the 14 participants in the PD-EX group completed the 36 exercise sessions according to the study protocol. The remaining five participants completed between 27 and 35 workouts during the study period. The participants in the PD-EX group increased the cumulative weight load (weight × repetitions × sets) with the leg press during one exercise session from 1350 kg (900−1800) to 3000 kg (2700−4000) after 12 weeks.

What did the 1h workouts look like? The PD-EX group underwent a program comprising a combination of aerobic exercise and resistance training in 1-h sessions three times weekly at the Sports Medicine unit at Umeå University. The exercise sessions were performed on weekdays, with at least 1 day of rest between sessions. They were supervised by experienced personal trainers with bachelor’s degrees in Sports Medicine.

All exercise sessions started with aerobic exercise. The first session of each week consisted of low-intensity aerobic training at 70% of the maximum heart rate on a crosstrainer (Monark Prime, XT 50, Vansbro, Sweden). The second session of the week consisted of ten high-intensity sprint intervals at 100% of the maximal workload on a cycle-ergometer (Monark, Ergomedic 839E, Vansbro, Sweden), with low-intensity cycling between the sprints. The third session of each week comprised six moderate-intensity 5-min intervals between 45 and 60% of maximal workload on a cycle-ergometer. The duration/workload of the intervals increased every other week. When necessary, the intensity of the aerobic exercise sessions was adjusted in accordance with the participant’s performance.

After the aerobic exercise, the sessions progressed to resistance training with both upper and lower body exercises, including leg presses, seated leg extensions, leg curls, hip raises, flat and incline bench presses, seated rows, dumbbell rows, lat pull-downs, shoulder raises, back extensions, burpees, sit-ups, step-ups, and wall ball shots. At each training session, the participant performed 3–5 of the aforementioned resistance exercises, with 10–15 repetitions and 2–4 sets. Once participants could complete all repetitions, the workload was increased for the following session.

Still, the main advantage of exercise was not, as you may now falsely expect due to the ~100% increase in energy deficit, a significantly increased loss of body fat (the latter did not double and that must not surprise you!). Neither was it a powerful increase in insulin sensitivity (HOMA-IR), which increased in both groups similarly (45% | p<0.001). Yeah, and even the extra 0.2% decrease in HbA1c, the sugar coating on the subjects' red blood cells  (-0.9% in diet only, -1.1% in diet + exercise), is not the main reason you must not miss your workouts while dieting (paleo-style or not ;-).

Figure 2: Fat mass (a), insulin sensitivity (b), and cardiovascular fitness (c and d) during 12 weeks following either a Paleolithic diet with a supervised exercise program (PD-EX) or a Paleolithic diet combined with general exercise recommendations (PD). Boxes represent medians and IQRs, whiskers represent the most extreme values besides outliers, and filled circles represent outliers (>1.5 IQR); **p<0 .01="" 2016="" p="" td="" tten.="">

So why are workouts important, then? It's the increased fitness, as evidenced by the PD-EX exclusive increase in maximum oxygen uptake (0.2 L/min) and the conservation of lean mass, which reached statistical significance (1.2kg in PD-EX vs. 2.6 kg in PD) only in the male subjects (p<0.05 for the difference between intervention groups), however (it is well possible that this is due to a lack of protein in the women's diet, cf. bottom line), that made / makes exercise (esp. resistance training) so valuable while dieting... this and another thing, the abstract of the study does not appreciate, because it did not reach statistical significance: The increase in relative resting energy expenditure (REE), the scientists observed in the PD-EX group (this adds to the extra energy expenditure during workouts, by the way!). While the relative REE didn't change in the PD group, it increased by a(n over the long-term) potentially relevant (but statistically non-significant) 3% in the PD-EX group - an effect that more than countered the nasty reduction in REE scientists still hold responsible for the yoyo-effect most "biggest losers" experience after successfully losing weight.

Is this the first paleo study? Even though, the number is still low, this is not the first one. In 2009, already, Jönssen et al. reported that "a 3-month study period, a paleolithic diet improved glycemic control and several cardiovascular risk factors compared to a diabetes diet in patients with type 2 diabetes" (Jönssen. 2009). In 2013, the same authors found that a "Paleolithic diet is more satiating per calorie than a diabetes diet in patients with type 2 dia-betes [and that t]he Paleolithic diet was seen as instrumental in weight loss, albeit it was difficult to adhere to" (Jönssen. 2013) - a result they had previously observed in patients with heart disease, too, when they compared a paleo to a Mediterranean diet (Jönssen. 2010), which also improve glucose tolerance less effectively than the paleo diet in said subject group (Lindeberg. 2007). Furthermore, studies in healthy individiuals Frassetto et al. (2015) like Österdahl et al. report that even "a short-term intervention showed some favourable effects by the diet" (Österdahl. 2008) such as weight loss, waist reductions and an improved quality of the diet and improved "BP [blood pressure] and glucose tolerance, decreases insulin secretion, increases insulin sensitivity and improves lipid profiles without weight loss" (Frassetto. 2015). In view of the fact that the less than a handful of long-term (>1 year), studies similar benefits when comparing paleo to other recommended diets, such as the Nordic Nutrition Recommendations in Mellbert et al. (2014) also show "greater beneficial effects" (e.g. fat mass, abdominal obesity and triglyceride levels just as they were observed by Ryberg, et al. in 2013) for the paleo diet(s), one could argue that the evidence in favor of paleo dieting in health and disease is slowly accumulating.

Eventually, diet is king, ... and that, just like the fact that doubling the energy deficit you have on paper won't double the loss of fat mass, shouldn't be news to you. That doesn't mean that dieting with exercise would not increase the loss of fat mass, but what is more important is that it helped the subjects - at least the male ones - maintain significantly more lean mass (=muscle and organ mass, which also affects you REE!).

Whether the failure of the workout to produce significant lean mass maintenance in the women was due to their sex, their hormonal status (the females included in the study were postmenopausal) or the fact that they gravitated to eat less protein (this is speculative, since the study does not provides sex-specific intakes) cannot be said. Even in the men, the lean mass loss is yet large enough to speculate that we'd have seen sign. less muscle loss with higher protein intakes. After all, the 79g the subjects in the PED-EX group consumed on a daily basis amount to only 0,84g protein per kg of body weight. This has repeatedly been shown to be too little for older individuals - even if they were not dieting. A follow up to the study which includes (a) simply more protein or (b) an extra protein shake after the workouts that would bump the subjects' total protein intake into the ~1.6-2.0g/kg region would thus be something I'd like to see in the (not so distant) future.

As long as said study will not have been done, though (something tells me that it won't ;-), you can still reference Otten's study as evidence that you can effectively lose weight without cereals, dairy products, and legumes... I have to admit, though, that I suspect that especially the latter two of these "forbidden" foods would rather have augmented, not messed with the improvements in body composition Otten et al. observed in the study at hand | Comment!

References:

• Frassetto, Lynda A., et al. "Metabolic and physiologic improvements from consuming a paleolithic, hunter-gatherer type diet." European journal of clinical nutrition 63.8 (2009): 947-955.
• Jönsson, Tommy, et al. "Beneficial effects of a Paleolithic diet on cardiovascular risk factors in type 2 diabetes: a randomized cross-over pilot study." Cardiovasc Diabetol 8.35 (2009): 1-14.
• Jönsson, Tommy, et al. "A paleolithic diet is more satiating per calorie than a mediterranean-like diet in individuals with ischemic heart disease." Nutrition & metabolism 7.1 (2010): 1.
• Jönsson, Tommy, et al. "Subjective satiety and other experiences of a Paleolithic diet compared to a diabetes diet in patients with type 2 diabetes." Nutrition journal 12.1 (2013): 1.
• Mellberg, Caroline, et al. "Long-term effects of a palaeolithic-type diet in obese postmenopausal women: a two-year randomized trial." European journal of clinical nutrition 68.3 (2014): 350.
• Lindeberg, Staffan, et al. "A Palaeolithic diet improves glucose tolerance more than a Mediterranean-like diet in individuals with ischaemic heart disease." Diabetologia 50.9 (2007): 1795-1807.
• Österdahl, M., et al. "Effects of a short-term intervention with a paleolithic diet in healthy volunteers." European journal of clinical nutrition 62.5 (2008): 682-685.
• Otten, J, et al. "Effects of a Paleolithic diet with and without supervised exercise on fat mass, insulin sensitivity, and glycemic control: a randomized controlled trial in individuals with type 2 diabetes." Diabetes/Metabolism Research and Reviews (2016 |Accepted Article). doi: 10.1002/dmrr.2828
• Ryberg, Mats, et al. "A Palaeolithic‐type diet causes strong tissue‐specific effects on ectopic fat deposition in obese postmenopausal women." Journal of Internal medicine 274.1 (2013): 67-76.

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

adipocyte size fat cell fat cell sizes gain fat gylcemia health high protein homa-ir insulin insulin resistance lose fat overfeeding subcutaneous fat visceral fat visceral obesity

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

diabetes glucagon glucos management health high protein high protein diet insulin insulin resistance lose fat protein type I diabetes type II diabetes

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.

Baking Bread With ~100g Extra-Fat Reduces the Glycemic Response: Coconut Oil Beats Butter, Grapeseed & Olive Oil

Baking Bread With ~100g Extra-Fat Reduces the Glycemic Response: Coconut Oil Beats Butter, Grapeseed & Olive Oil

bread diabetes glucose management glycemia health high fat insulin resistance lose fat

No, adding fat to your bread's dough won't make you lose fat magically.

While fat no longer has the bad rep it still had a decade ago, the notion that baking bread with extra fat could have anti-diabetic effects, because it reduces the glucose peaks and the 2h area under the curve (AUC) is unconventional, to say the least; and thus SuppVersity news-worthy, because it is not broscience, but the result of a recent study.

In said study, the scientists tested (a) the effect of different types of fat / oil on the formation of amylose–lipid complexes (ALC) and, more importantly, (b) the effect of the ALCs on the glycemic response to a standardized amount of bread that was baked with the same amount of different fats / oils.

Don't forget that health and looking good naked require eating right and working out!

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The study was an acute, randomised, controlled, single-blinded trial that consisted of five types of bread, each tested on one occasion in a randomised order on separate days, with at least 3 washout days between test visits.

"Participants were recruited through advertisements and personal communications. Inclusion criteria were as follows: (1) males aged between 21 and 50 years, (2) BMI values between 18·0 and 24·9 kg/m2, (3) blood pressure≤120/80 mg/dl and (4) fasting blood glucose≤6·0 mmol/l. People who had metabolic diseases, were on prescribed medication, were smokers, took part in sports at competitive levels or were concurrently participating in other clinical trials were excluded from the study. Females were excluded from the study to prevent differences in menstrual cycles from affecting carbohydrate metabolism" (Lau. 2016).

On the day before a test session, no drinks, caffeine or physical activity were allowed. In addition, a standardised dinner was provided the evening before to reduce potential variations in GR that may arise because of the second meal effect. On the day after, participants had to report to the centre after a 10–12 h fast between 08.00 and 09.00 hours. There, they rested for at least 15 min before starting the test session, before the test meal was consumed "at a comfortable pace within 15 min" (Lau. 2016). Following consumption of test bread, participants were asked to rate their liking of the bread on a 100-mm liking scale. Blood samples (both venous and capillary) were collected at 15, 30, 45, 60, 90, 120, 150 and 180 min after test bread consumption. The same protocol was repeated until the completion of all the five test sessions.

Serving size, energy content and macronutrient composition of the test breads (per serving | Lau. 2016)

How was the bread prepared? This is what the scientists report: "The five types of bread used were as follows: control bread without any added fats (CTR) and breads baked with butter (BTR), coconut oil (COC), grapeseed oil (GRP) or olive oil (OLV). The ingredients used for test breads were as follows: 250 g bread flour (Prima), 125 g potable water, 10 g baker’s yeast (SAF), 40 g sugar (Fairprice) and 6 g salt (Fairprice). These ingredients were mixed at speed 1 for 8 min (Kitchenaid) to form base dough, of which 320 g was weighed and then fat/oil was added.

The fats/oils added were 96 g butter that contained predominantly SFA (Anchor), 87 g coconut oil that was rich in medium-chain TAG (Titi Ecofarm), 80 g grapeseed oil containing predominantly PUFA (Borges) and 76 g olive oil containing predominantly MUFA (Naturel). The amount of fats/oils added was calculated based on the percentage fat as stated on the nutritional panel on the packaging, and was added at 20 %, w/w of dough. Oil was not added into the control bread. The dough mixture was kneaded for a further 12 min, and was then allowed to rest at room temperature for 10 min. Following this, the dough was moulded into serving portions and proofed in the oven (EOB98000; Electrolux) at 40±1°C for 30 min in a fan-assisted mode. Baking was carried out in the same oven at 200°C for 18 min, and bread was allowed to stand for 10 min before being served warm" (Lau. 2016).

The results of the scientists' analysis of the ALC formation in the bread showed that the coconut (COC) and olive oil (OLV) had significantly higher amylose–lipid complex forming ability [reported wrong in the result section of the FT, but correct in the discussion] as compared with butter (BTR) and grapeseed oil (GRP | P<0·05).

Figure 1: Complexing index results for different types of bread. Values are means (n 6), with standard errors represented by vertical bars. a,b Mean values with unlike letters were significantly different (P < 0·05; one-way ANOVA with post hoc Tukey’s test). BTR = butter; COC = coconut oil; GRP = grapeseed oil; OLV = olive oil (Lau. 2016).

Interestingly, the increased ALC levels in the olive oil bread did not produce the same beneficial effects on the glucose response the scientists observed when the subjects consumed the bread that was baked with coconut oil.

Figure 2: (a) Postprandial response curves for change in blood glucose and (b) plasma insulin levels after consumption of 50 g available carbohydrate portion of test bread. Values are means (n 15), with standard errors represented by vertical bars. For glucose response, there were significant time (P < 0·001), bread (P < 0·001) and bread×time interaction effects (P=0·002) when analysed by two-way, repeated-measures ANOVA. For insulin response, two-way, repeated-measures ANOVA showed a significant time effect (P < 0·001) and bread×time interaction effect at near significant levels (P=0·074), but no effect of bread was seen (P=0·195). open circle, Control bread without oil; filled circle, bread with butter; open triangle, bread with coconut oil; filled triangle, bread with grapeseed oil; open square, bread with olive oil (Lau. 2016).

As you can see in Figure 2, all fat-enhanced breads improved the glycemia, but only the grapeseed (closed triangle) and coconut (open triangle) oils also rduced the insulin levels.

Can't I just add the coconut oil on top of the bread? No, you can't, because it has to be in the dough during baking - otherwise the amylose–lipid complexes won't form. What will happen though is that your insulin levels will rise sign. longer (see previous SV article). Edit: Elizabeth Alcott just posted this cool suggestion on Facebook: "Bake low carb coconut flour bread with coconut oil. Reduced calories and glycemic response at the same time." Not a bad idea, for sure.

What is interesting to see, though, is that the glucose AUC, i.e. the total amount of glucose that is released into the blood was still the lowest in those oils / fats with the highest ALC levels: coconut oil and olive oil.

Figure 3: Postprandial glycaemic and insulinaemic responses (AUCs for 180min) after consumption of test bread (Mean values with their standard errors for fifteen healthy young men | Lau. 2016)

As the authors point out in the discussion of the results of their study, their regression analysis "further confirmed that CI [=indicator of ALC formation] was a significant predictor of GR [glucose response], although it only accounted for 13·3 % of the observed variability" (Lau. 2016). Furthermore, the scientists highlight that ...

"[w]hen examined as IAUC, COC showed the greatest attenuation of GR [glucose response] in baked bread. A similar study by Clegg et al. (2012) showed that high-fat pancakes containing MCT had the slowest gastric emptying rate as compared with other fats/oils over a 4-h period. The low GR [glucose response] of COC in this study could be due to a combination of factors. These include delay in gastric emptying rates to MCT having a higher osmolarity (Clegg. 2012) and formation of ALC resulting in resistant starch (Kaur. 2000)" (Lau. 2016).

To assess the physiological significance of these observations, Lau et al. also investigated the surrogate measures of postprandial β-cell function (IGI30 and IGR) and the insulin response which did - in contrast to the glucose response (see Figure 3), not correlate with the ALC content of the breads. Instead, it appeared to be "partially due to rate of appearance of glucose as a result of carbohydrate digestibility" (Lau. 2016).

Will the additional butter on top of the potatoes reduce the insulin response? You can find the answer to this and the other questions in today's episode of "True or False?" | learn more!

So, what's the verdict? Well, adding ~25g of fat to bread increases its energy content significantly. Therefore, it is not clear how advantageous the improvements in glycaemia observed in the study at hand will actually be - after all, calories still count!

With that being said, the scientists' conclusion that "[t]he incorporation of fats during bread baking reduces GR, with the greatest attenuation seen in COC," is a significant result. One that can be partly explained by the reduction in carbohydrate digestibility via ALC formation, but not by any effects on the insulin response to the meal (if you fear insulin, adding fat is thus not going to cut it | learn more).

That the 'coconut advantage' is due to lauric acid and myristic acid in coconut oil is likely, but warrants further investigation; the same goes for the scientists' concluding remark that "[t]he use of simple dietary interventions (addition of functional lipids during cooking of carbohydrate-rich staple foods) may be an effective and practical strategy for improving glycaemic control, and may help in the prevention and management of [...T2DM] and CVD" (Lau. 2016) | Comment!.

References:

• Clegg, Miriam E., et al. "Addition of different fats to a carbohydrate food: Impact on gastric emptying, glycaemic and satiety responses and comparison with in vitro digestion." Food Research International 48.1 (2012): 91-97.
• Kaur, Kulwinder, and Narpinder Singh. "Amylose-lipid complex formation during cooking of rice flour." Food Chemistry 71.4 (2000): 511-517.
• Lau, et al. "Effect of fat type in baked bread on amylose–lipid complex formation and glycaemic response." British Journal of Nutrition, Published online: 22 April 2016.

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.