Protein Contradictions

Much of the popular press writes that we should eat more meals a day. As an example (How Much Protein for Strength and Mass Gains?):

total protein amount should be spread out over 5 to 6 intakes a day

They advise the amount of protein to be:

For males, who aim at increasing muscle mass and strength gains, if you only train once a day, 2 g a kg should be more than enough (for women 1.2g /kg of bodyweight).

Let’s do the math here. Suppose someone is 75 kg (about 165 lbs). At 2g/kg that would be 150 grams of protein per day. If they eat 5 meals a day that would be 30 grams of protein per meal. The problem is that they will probably not ever reach the Leucine threshold at any of the meals (Protein Gurus – Part 2). As a result they will never maximize muscle protein synthesis.

Also the timing between protein meals should be 5 hours and that would be 25 hours of eating in a day. Doesn’t quite fit.

My current optimized method is three protein meals a day spread out by five hours (Muscle Protein Synthesis Meal Spacing Maximum). This can be challenging and does require advance planning for meals.

 

Keto for the Win – Again

Here is another cross-over study showing the advantage of the keto diet over a medium carbohydrate diet (Johnstone AM, Horgan GW, Murison SD, Bremner DM, Lobley GE. Effects of a high-protein ketogenic diet on hunger, appetite, and weight loss in obese men feeding ad libitum. Am J Clin Nutr. 2008 Jan;87(1):44-55.).

Ad libitum energy intakes were lower with the LC diet than with the MC diet [P=0.02; SE of the difference (SED): 0.27] at 7.25 and 7.95 MJ/d, respectively. Over the 4-wk period, hunger was significantly lower (P=0.014; SED: 1.76) and weight loss was significantly greater (P=0.006; SED: 0.62) with the LC diet (6.34 kg) than with the MC diet (4.35 kg). The LC diet induced ketosis with mean 3-hydroxybutyrate concentrations of 1.52 mmol/L in plasma (P=0.036 from baseline; SED: 0.62) and 2.99 mmol/L in urine (P<0.001 from baseline; SED: 0.36).

These men were allowed to eat as much as they wanted but chose to eat less when they were given Low Carb food.

 

Maintenance Macros – The Data

In my last post I looked at the question of whether eating protein one day causes weight gain the next day (“Protein Makes Me Gain Weight The Next Day”).  Turned out protein didn’t make me gain weight all the way up to past 1 grams per pound of body weight.

This post looks at the maintenance macros I got from Ted Naiman’s website as described here (Maintenance Macros – Dr Ted Naiman). I have 150 days of Cronometer data and looked at the Protein to Non-Protein Energy ratio vs day-to-day weight gain.

Ted’s method sets the grams of protein starts to your body weight in lbs. Use the same number of grams of non-protein (carbs and fat) to maintain your weight. For me, that’s 170g of protein, 150g of fat, and 20g of carbs. With this method there’s no need to convert the number of grams to calories.

What does my own data show for Protein to non-protein ratio vs day-to-day ratio?

Here’s what I find interesting in the data:

  1. The vertical axis is weight change day-to-day. The zero line means that I did not change weight the next day.
  2. The day-to-day variance is pretty big.
  3. The horizontal axis is the ratio of Protein to Non-Protein grams. Most of my numbers were 1.0 or greater since I was trying to cut weight most of the time. That means I had more grams of protein than grams of fat+carbs.
  4. This graph looks at trendlines to generalize out the large day-to-day variances.
  5. The trendline is a 3rd order polynomial.
  6. There are only a couple of data points above a ratio of 2.0 to base the swing up after 2.25 on. It may not be a real swing.
  7. The best ratio for me to have the most loss the next day is around 1.75 grams of protein to grams of fat+carbs. Since I tend to hit my body weight or more in protein that would mean that I am eating relatively low fat on those days.
  8. There is a near zero crossing around 0.8:1 (which is more fat than protein). Ted’s numbers indicate the zero cross is around 1:1 which isn’t too far off from my numbers.
  9. Below the 0.8 I have a tendency to increase in weight.

All of this matches Ted’s concept. Here is Ted Naiman’s infographic:

Ted’s numbers for maintenance are pretty close to my actual measurements. I may have to eat a bit more fat to get to my goals of maintenance – which fits my experiences in the past month or so.

 

“Protein Makes Me Gain Weight The Next Day”

[This post replaces an earlier post which had an error in the spreadsheet data].

A common complaint I see on the Internet is

“When I eat more protein my weight goes up the next day.”

I rarely see anyone dig deeper into the question of whether higher consumption of protein actually causes greater weight gain the next day spread across time. A single day doesn’t say much about the effect of protein on weight.

A lot of days of eating protein

I have the last 150 days of data from Cronometer so I looked at how much my weight goes up the day after I eat protein. This is a chart of protein consumption compared to weight change on the following day.

A couple of things to note about this graph:

  1. The diagram is a scatter diagram.
  2. The Y axis is weight change between two adjacent days. Day to day weight fluctuations are all over the place.
  3. The X axis is grams of protein eaten the day before the weight change.
  4. The data is all over the place.
  5. The data appears to increase somewhat. More protein should on some level correspond to more weight gain (or less weight loss) since it’s more calories.
  6. The red line is the trendline that EXCEL determined.
  7. The trendline is a 3rd order polynomial.
  8. The deviation from the trendline is really big.
  9. The trendline shows that under 190 grams of protein (a whole lot of protein) results in weight loss (on the average).
  10. My weight is currently around 167 lbs.
  11. About 190 grams of protein results in weight stability (a bit over 1 gram of protein per lb of my weight).
  12. More protein than 190 grams results in a very small weight increases all the way to over 30 grams of protein.
  13. Very low protein (less than 100 grams) results in the most weight lost. It is not clear what the nature of this loss is from this chart. Is it more lean body mass lost? Is it just the low calorie amount for the day?
  14. I don’t have any very low protein data since I think it’s a serious mistake to undereat protein.

Conclusion

Protein, up more than 1 gram per lb of body weight did not result in average day-to-day weight gain.

Your mileage may vary. I am not sure how it could vary by much, but it may.

 

Low Carb Maintenance Plans

It seems like there are [at least] three basic ideas of how to implement maintenance on Low Carb. I picked representatives of each of three and look at their methodology for maintenance. Each of these three has their merits and downsides.

Dr Atkins – Titrate Carbs

Sometimes the first idea is the right idea and Dr Atkins sure did suffer a lot of slings and arrows in his day. He was tormented even in death by his opponents. I did his diet back in the 1990s and did well for over a year on it. I tapped out due to heart rhythm issues which seem to be recurring now (PVCs in particular). I think they are likely electrolyte imbalances. (Later note: See this post for my current strategy which seems to be working].

The Atkins diet starts with a 2 week induction period at 20g of carbs a day and then increases. At maintenance, the person is supposed to work up to their personal carb limit and remain at that level with an occasional adjustment if they go too far off the rails.

This approach might work well for some. It worked OK for me and at least taught me to avoid really stupid amounts of carbs but I never went below about 228 lbs before I transitioned over to maintenance carbs.

Later on, my Insulin pump data showed my average was 200 grams of carbs a day before I got off Insulin which is less than SAD and proof that I did learn something about carbs back when I was on Atkins. But, clearly 200 g of carbs was above my personal threshold. And there’s good evidence that 100 g is probably too much, too.

Carbs have a way of sneaking back in and this method is a way to get back on the blood sugar roller coaster for some. Above some point the benefits of the keto diet get lost because carb cravings get stoked. Plus it is difficult to add carbs and keep them clean carbs. It’s really easy to add back in junk carbs.

Dr Berstein – Titrate Protein

Dr Bernstein is a Diabetic Type 1 medical doctor who is also a pioneer. Bernstein recognizes the problems with titrating down carbs in his patients. Here is Bernstein’s own words (from 2015):

Bernstein – “When we want to halt weight loss we increase protein“.

I think Bernstein nicely addresses the problems with bringing back in carbs but I don’t think his method works for me. I’ve essentially done it when I did carnivore and I kept losing weight in spite of eating a lot more protein. It might work for others. My normal diet gives me enough substrate for GNG (which is demand driven) plus substrate for Muscle Protein Synthesis. Beyond some point Protein just gets eliminated via urea.

The reason this is ineffective may be that increasing protein over my normal diet significantly has a very marginal effect on FQ. Holding carbs constant (22.8g) and fat constant (191g) and taking my Protein from 179 to 300 only takes my FQ from 0.740 to 0.749 at the expense of a whole lot more protein.

Dr Ted Naiman – Balance Energy (P:NPE)

Dr Ted takes a unique approach which seeks to take into account the strengths of both of the previous methods. His method is to balance the energy from protein with the non-protein energy (in terms of grams of each not in terms of calories). His formula is essentially 30% of calories from Protein and 70% from fat and carbs. His method keeps ketogenic levels of carbs (20 or 30 or less a day) from good sources and avoids carb cravings that come with higher carb levels (no blood sugar roller coaster).

I Picked Dr. Ted

I am currently trying Dr Ted’s method. His method sets the Protein grams to body weight in lbs. I am currently 169 lbs, so if I want to stay in the 165-175 lbs range (nominal 170 lbs) I need to eat 170g of protein a day. That would be matched with 20 grams of carbs and 150g of fat a day. That is a FQ of 0.744-0.745. That is slightly more than my current 0.740 from last week.

P:NPE Balanced
FQ g cals portion cals Numer. Denom.
Carbs 20 80 0.032 0.0071 0.0071
Fat 145-155 1305-1395 0.52-.56 0.0817 0.1161
Protein 165-175 660-700 0.26-.28 0.0562 0.0708
Total 2045-2175 0.1450 0.1940
FQ 0.744-0.745

I am interested to see how this works out since that’s a lower total calorie count than I have been eating (and I was still losing weight). I was at an average of 2526 calories a day last week and my starting/ending weights were the same.

Dr. Ted’s method doesn’t adjust up or down for physical activity. I will do this for a month and see how it works out. If I drop below 165 lbs for any extended period I will evaluate whether I need to increase calories or not.

 

Insulin Resistance – Everything You Want to Know and Probably a Lot More

A great write-up on Insulin Resistance (Clin Biochem Rev. 2005 May; 26(2): 19–39. Insulin and Insulin Resistance. Gisela Wilcox).  It is not an understatement that the paper says:

…More than a century after scientists began to elucidate the role of the pancreas in diabetes, the study of insulin and insulin resistance remain in the forefront of medical research, relevant at all levels from bench to bedside and to public health policy

First some definitions:

Insulin resistance is defined where a normal or elevated insulin level produces an attenuated biological response; classically this refers to impaired sensitivity to insulin mediated glucose disposal.

Compensatory hyperinsulinaemia occurs when pancreatic β cell secretion increases to maintain normal blood glucose levels in the setting of peripheral insulin resistance in muscle and adipose tissue.

Insulin resistance syndrome refers to the cluster of abnormalities and related physical outcomes that occur more commonly in insulin resistant individuals. Given tissue differences in insulin dependence and sensitivity, manifestations of the insulin resistance syndrome are likely to reflect the composite effects of excess insulin and variable resistance to its actions.

Metabolic syndrome represents the clinical diagnostic entity identifying those individuals at high risk with respect to the (cardiovascular) morbidity associated with insulin resistance.

Interesting graphic (major pathways and influences on insulin secretion):

Here’s why Low Carb works so well:

Glucose is the principal stimulus for insulin secretion

Pancreatic β cells secrete 0.25–1.5 units of insulin per hour during the fasting (or basal) state, sufficient to enable glucose insulin-dependent entry into cells. This level prevents uncontrolled hydrolysis of triglycerides and limits gluconeogenesis, thereby maintaining normal fasting blood glucose levels. Basal insulin secretion accounts for over 50% of total 24 hour insulin secretion. … In healthy lean individuals circulating venous (or arterial) fasting insulin concentrations are about 3–15 mIU/L or 18–90 pmol/L

At rest we don’t need glucose for our muscles.

Muscle cells do not rely on glucose (or glycogen) for energy during the basal state, when insulin levels are low. Insulin suppresses protein catabolism while insulin deficiency promotes it, releasing amino acids for gluconeogenesis.

Perhaps of importance to low carb eaters a low level of glucose may produce a lower level of protein synthesis due to its similarity with starvation:

In starvation, protein synthesis is reduced by 50%. hilst data regarding a direct anabolic effect of insulin are inconsistent, it is clearly permissive, modulating the phosphorylation status of intermediates in the protein synthetic pathway.

In insulin resistance, muscle glycogen synthesis is impaired

We get fatter via:

Intracellular glucose transport into adipocytes in the postprandial state is insulin-dependent via GLUT 4; it is estimated that adipose tissue accounts for about 10% of insulin stimulated whole body glucose uptake.

As relates to low carb diets:

Insulin stimulates glucose uptake, promotes lipogenesis while suppressing lipolysis, and hence free fatty acid flux into the bloodstream.

As adipocytes are not dependent on glucose in the basal state, intracellular energy may be supplied by fatty acid oxidation in insulin-deficient states, whilst liberating free fatty acids into the circulation for direct utilization by other organs e.g. the heart, or in the liver where they are converted to ketone bodies.

Ketone bodies provide an alternative energy substrate for the brain during prolonged starvation.

Interesting:

…glucose uptake into the liver is not insulin-dependent

Another interesting section:

Whilst in insulin deficiency, e.g. starvation, these processes are more uniformly affected, this is not necessarily the case with insulin resistance. Compensatory hyperinsulinaemia, differential insulin resistance and differential tissue requirements may dissociate these processes. Resistance to insulin’s metabolic effects results in increased glucose output via increased gluconeogenesis (as in starvation), however, unlike starvation, compensatory hyperinsulinaemia depresses SHBG production and promotes insulin’s mitogenic effects. Alterations in lipoprotein metabolism represent a major hepatic manifestation of insulin resistance. Increased free fatty acid delivery, and reduced VLDL catabolism by insulin resistant adipocytes, results in increased hepatic triglyceride content and VLDL secretion. Hepatic synthesis of C-reactive protein, fibrinogen and PAI-1 is induced in response to adipocyte-derived pro-inflammatory cytokines such as TNFα and IL-6. Insulin may also increase factor VII gene expression.

Other factoids:

The insulin resistance syndrome describes the cluster of abnormalities which occur more frequently in insulin resistant individuals. These include glucose intolerance, dyslipidaemia, endothelial dysfunction and elevated procoagulant factors, haemodynamic changes, elevated inflammatory markers, abnormal uric acid metabolism, increased ovarian testosterone secretion and sleep-disordered breathing. Clinical syndromes associated with insulin resistance include type 2 diabetes, cardiovascular disease, essential hypertension, polycystic ovary syndrome, non-alcoholic fatty liver disease, certain forms of cancer and sleep apnoea.

Good write-up on Diabetes:

Compensatory hyperinsulinaemia develops initially, but the first phase of insulin secretion is lost early in the disorder. Additional environmental and physiological stresses such as pregnancy, weight gain, physical inactivity and medications may worsen the insulin resistance. As the β cells fail to compensate for the prevailing insulin resistance, impaired glucose tolerance and diabetes develops. As glucose levels rise, β cell function deteriorates further, with diminishing sensitivity to glucose and worsening hyperglycaemia. The pancreatic islet cell mass is reported to be reduced in size in diabetic patients; humoral and endocrine factors may be important in maintaining islet cell mass

 

 

What if the History of Diabetes Went Wrong?

In an interesting paper the question is asked what if the history of the development of our understanding of diabetes has it wrong? The paper (J. Denis McGarry. What If Minkowski Had Been Ageusic? An Alternative Angle on Diabetes. Science, Vol. 258, No. 5083 (Oct. 30, 1992), pp. 766-770).

Despite decades of intensive investigation, the basic pathophysiological mechanisms responsible for the metabolic derangements associated with diabetes mellitus have remained elusive. Explored here is the possibility that traditional concepts in this area might have carried the wrong emphasis. It is suggested that the phenomena of insulin resistance
and hyperglycemia might be more readily understood if viewed in the context of underlying abnormalities of lipid metabolism.
Some powerful food for thought in the paper. Another paper (Arius, Energy Metabolism) summarizes the argument as:
The author considers the possibility that the hyperinsulinemia of early non-insulin—dependent diabetes is coincident with hyperamylinemia, since insulin and amylin are cosecreted. Amylin would cause an increase in plasma lactate (Cori cycle); and lactate, a better precursor than glucose for fatty acid synthesis, would indirectly promote the production of very-low-density lipoproteins (VLDL). There would follow an increased flux of triglycerides from liver to muscle (and adipose tissue) and, as proposed and elaborated on, an increase in insulin resistance and production of many of the metabolic disturbances occurring in diabetes.
 The author of the paper draws heavily on the Randle Cycle.
The Randle cycle is a biochemical mechanism involving the competition between glucose and fatty acids for their oxidation and uptake in muscle and adipose tissue. The cycle controls fuel selection and adapts the substrate supply and demand in normal tissues. This cycle adds a nutrient-mediated fine tuning on top of the more coarse hormonal control on fuel metabolism. This adaptation to nutrient availability applies to the interaction between adipose tissue and muscle. Hormones that control adipose tissue lipolysis affect circulating concentrations of fatty acids, these in turn control the fuel selection in muscle. Mechanisms involved in the Randle Cycle include allosteric control, reversible phosphorylation and the expression of key enzymes.[5] The energy balance from meals composed of differing macronutrient composition is identical, but the glucose and fat balances that contribute to the overall energy balance change reciprocally with meal composition.
Interesting thoughts.
Fatty acids may act directly upon the pancreatic β-cell to regulate glucose-stimulated insulin secretion. This effect is biphasic. Initially fatty acids potentiate the effects of glucose. After prolonged exposure to high fatty acid concentrations this changes to an inhibition.[13] Randle suggested that the term fatty acid syndrome would be appropriate to apply to the biochemical syndrome resulting from the high concentration of fatty acids and the relationship to abnormalities of carbohydrate metabolism, including starvation, diabetes and Cushing’s syndrome.
My own weight had been in the 280 range for a long time. In the months before I was diagnosed as Type 2 Diabetic my weight dropped 50 lbs without any lifestyle changes. After I went on Metformin my weight was relatively lower for a while. When I eventually went on Insulin my weight went up 40+ lbs fairly quickly. It is well known that Insulin adds weight.
My own thought is that the Insulin is both the lock and the key. Increased levels of Insulin pushes glucose or fat into cells and decreased levels of Insulin allows fat to come out of cells. That’s why Intermittent Fasting is such a great bullet for Type 2 diabetics. It allows our fasting Insulin levels to drop. Add to that Low Carbohydrate diets and the perfect recipe for controlling Diabetes comes into play.
The problem never really was Insufficient Insulin. The problem was too much Insulin. And clearly it is a fat related problem.

A Second Protein Experiment

I did a previous Protein Experiment where I compared the response of my Blood Sugar to 50 grams of Whey Protein vs 50 grams of Casein Protein. Since both of those were “pure” Protein with very little fat, I was curious how those results would compare to animal protein which had fat.

For this experiment I chose Chicken Drumsticks. I weighed them amount of mean (total minus bones left at the end) and the nutritional information shows them to have been close to 50g of Protein:

Here is the Blood Glucose numbers (smoothed) over several hours added to the data from the original Whey/Casein test.  The chicken drumsticks are in yellow.

Accounting for Differences

  1. The drumsticks (in yellow) are lower overall because I have been on the PSMF longer and my blood sugar levels have dropped. This is evidence, at least to me, that the PSMF is doing good things for my metabolic health.
  2. There was a dip at the start of the chicken wing experiment which was due to exercise. In this case it was a particularly grueling Saturday morning routine with a lot of lifting and burpees, etc.  That explains the drop from 72 down to 64 at the start.
  3. The highest number was very comparable to the Whey and Casein numbers in terms of rise from the minimum. The max rise in Blood Sugar in all of these cases was no more than 20 units.
  4. The slope down with the animal Protein is longer and slower. That may explain less feelings of hunger as the consumption of the Protein ends.
  5. The curve is longer than either of the “pure” Proteins. The fat may extend that longer than the pure proteins. I’d like to repeat the experiment with low fat chicken breasts and see if it’s the fat or if it is the animal Protein vs Milk Protein of the Whey/Casein choices that makes a difference.

Conclusions

50 grams of Protein is a decent serving size. It is more than enough to stimulate Protein Muscle Synthesis.

All in all, I see nothing to worry about with eating Protein even for Type 2 Diabetics like myself. With all of the “Protein turns into candy bars” fear mongering out there, some sanity needs to be applied to the subject.

Disclaimer

Of course, I would encourage any diabetic to test to see where they are with this same test. At least this way they know what effect Protein would have on their body. If they are a Type 1 Diabetic this information could be helpful to determine what amount of Insulin they should add for Protein.

 

Protein-Sparing Modified Fast (PSMF) Weight Loss Studies

Here are some of the scientific studies concerning Protein Sparing Modified Fasts (PSMF) and high protein diets.

PSMF Diets

Muscle Protein Synthesis

Studies on Protein and Diabetes

Protein as a Macronutrient and Protein Requirements