FASTER10 – Ben Greenfield – Three Hour VO2 testing

The main test that Ben test was a three hour treadmill test in the FASTER study (Jeff S. Volek, Daniel J. Freidenreich, Catherine Saenz, Laura J. Kunces, Brent C. Creighton, Jenna M. Bartley, Patrick M. Davitt, Colleen X. Munoz, Jeffrey M. Anderson, Carl M. Maresh, Elaine C. Lee, Mark D. Schuenke, Giselle Aerni, William J. Kraemer, Stephen D. Phinney. Metabolic characteristics of keto-adapted ultra-endurance runners. Metabolism, Volume 65, Issue 3, March 2016, Pages 100-110.).

In Ben Greenfield’s case the activity was done at an average of 60% of VO2max. At that level of effort Ben got 85% of his energy from fat and 15% from carbohydrates. Here’s the data from the three hour treadmill test.

%VO2max FATcal CHOcal %cal-fat
58% 10.013 3.1007 76%
61% 12.394 1.2341 91%
59% 11.722 1.4985 89%
60% 11.645 1.8409 86%
61% 11.154 2.5325 81%
Average = 60% 11.386 2.046 85%

An interesting question is the trend of the oxidation over the three hours. Here the graph of the data is interesting. The blue line is energy from fat and the brown line is energy from carbs. The x-axis is time. Note that as time proceeds Ben is drawing less and less energy from fat and more and more from carbohydrates. The R^2 values show a strong significance.

Ben’s individual Fat Oxidation data is not too far off of the data from the study. The average makes it look as if a person could continue on seemingly forever but Ben’s data shows that is not the case.

Similarly, his carb oxidation rate is very similar to the average since it shows a steady climb up.

It should also be recalled that although fat provides 9 calories per gram and carbohydrates provide 4 calories per gram fat is burned less efficiently for energy – about 10% less efficient than carbohydrates.

 

Ultra-Endurance Walking and Running Events

A study looked at the studies on fueling ultra-endurance events. Ultra-endurance is defined as activities (walking and running) which take at least 6 hours. The study was (Eric Williams. Nutritional implications for ultra-endurance walking and running events. Extreme Physiol Med. 2016; 5: 13).

Given that the majority of an ultra-endurance athlete’s training is spent engaged in lengthy durations of aerobic activity, many of these athletes are well adapted to utilizing lipids via oxidative phosphorylation

Fat burners! But during the event itself how hard are they hitting it?

When the athlete is exercising at the standard marathon pace that requires 80–90% of maximal oxygen consumption (VO2 max) or above, carbohydrate will be his or her primary fuel source and could provide up to 96% of the energy being expended.

This is an issue with Low Carbohydrate diets since glycogen stores are reduced greatly. This is also why Phinney’s endurance tests are done at 62% of VO2max.

The paper had a nice graphic which shows the elements involved in performance in marathons.

Each of these would be interesting to look at in detail.

 

Exercise Intensity and Fat Burning

There is a common misconception that more exercise intensity burns more fat. It is true that more exercise intensity burns more calories but at some intensity level exercise burns more carbohydrates than fat. At even higher intensities there is no fat burned at all. Back to my chart of %VO2max (on the horizontal axis) vs calories of fat (blue) or carbs (brown).

The maximum amount of fat burning is at about 52% of VO2max. At around 78% of VO2max equal calories of fat and carbs are burned. After that point fat drops off quickly and carbs take over. At around 98% of VO2max all the energy comes from carbs. At this point the energy from carbs is over 20 kcals per minute which is nearly 2x the max energy that came from fat.

This is the reason that I have a hard time performing at high intensities. I just don’t have the carb stores to sustain longer high intensity efforts. This is why I changed my training to work more at the lower intensities where I can exclusively use fat as fuel.

It should be possible to push the blue curve over to the right farther through training. Zach Bitter’s numbers (Zach Bitter – Another FASTER participant) show that he uses fat for 98% of his energy at 75% of his VO2max. At that point I am nearly 50-50. I don’t think that this is just dietary fat adaptation but exercise adaptation. Zach can mobilize more fat energy than I can and training explains the difference.

 

Zach Bitter – Another FASTER participant

Zach Bitter was in the FASTER study (Jeff S. Volek, Daniel J. Freidenreich, Catherine Saenz, Laura J. Kunces, Brent C. Creighton, Jenna M. Bartley, Patrick M. Davitt, Colleen X. Munoz, Jeffrey M. Anderson, Carl M. Maresh, Elaine C. Lee, Mark D. Schuenke, Giselle Aerni, William J. Kraemer, Stephen D. Phinney. Metabolic characteristics of keto-adapted ultra-endurance runners. Metabolism, Volume 65, Issue 3, March 2016, Pages 100-110.).

Zach wrote about his experience in the FASTER study (Takeaways from the FASTER Study) where he gave some additional information about the FASTER tests. At least in Zach’s case (and probably in FASTER10 as well as FASTER43) the termination condition was inability to continue.

During the test, the researchers gradually increased both the speed and incline on the treadmill until I could no longer continue, and my rates of fat and carbohydrate metabolism at various intensities were measured.

Zach continued:

I can pinpoint where my fat metabolism and carbohydrate metabolism peak at varying intensities, and I can see the ratio between the two at any given percentage of my VO2 Max.

Zach had a very nice maximum fat oxidation rate and a very high rate of fat usage at a much higher % of his VO2max.

My fat metabolism peaked at 1.57 grams/minute. At this point in the test, my VO2 uptake was at 49.4. By dividing this number by my eventual VO2 Max of 66.1, I can calculate at what intensity I burn the most fat: 74.4%. At that intensity, I was burning 98% fat 2% carbohydrate (1.57 fat grams/minute and .07 carb grams/minute).

% VO2 Max
Fat Usage
Carb Usage
75%
98%
2%
84%
76%
24%
96%
23%
77%

What  Zach is saying is that at 74.4% of his VO2max he is burning nearly 98% fat (his chart shows that point as 75%). We don’t have the rest of the data to see where he was in lower intensities. That may well be a product of his level of training? Clearly he was very fat adapted as well.

Zach’s 50-50% cross-over point (where he burned 50% carbs/50% fat) is much higher than mine. My crossover point was at 75% of VO2max. By my calculations, Zach’s cross-over point was 89% of his VO2max.

My own max fat oxidation was at about 50% of my VO2max where at Zach’s was 50% higher.

Fat and Carb Burning

One interesting thing to look at in VO2max testing is how many grams of carbs and fat are oxidized vs heart rate. [Heart Rate is used as the surrogate for %VO2max]. My chart looks like:

The blue dots are fat oxidation. The brown dots are carbohydrate oxidation. Remember that carbs have 4 kcals per gram and fat has 9 kcals per gram.

Both curves are parabolas. The carbohydrate oxidation curve ends up being the limiting factor in exercise at high rare rates. My sweet spot is 120 bpm. My MAF number is 112-120 bpm. No coincidence there.

Normalizing the graph for calories is interesting since it shows the calories are somewhat comparable.

Here is Ben Greenfield’s data (in kcals also). It is quite similar to mine except Ben has more calories oxidized. Unfortunately I don’t have Ben’s heart rate data but VO2max is directly related to Heart Rate.

It is worth noting that Ben’s cross-over point is around 68% of his VO2max. Ben’s fat max oxidation is very close to my own rate (a little less actually).

Also, Ben barely goes to zero on his carb oxidation and it is at a very low 40% (approx) of his VO2max. Ben has said that his normal carbohydrate consumption was somewhere around 100 grams during the time preceding this test. Ben was relatively low carb for a high intensity athlete like himself.

Here is my data with the x-axis as %VO2max. Ben’s greater access to carb stores may help him.

How much fat was I burning?

It took some maths for me to figure out my own fat oxidation rate from my VO2max testing. Here’s the math (REE from VO2max).

At one point I got over 1.2 g/min of fat oxidation. Not bad considering that high numbers are often 1.6 g/min sort of numbers. Here’s the classical curve – an inverted parabola – of heart rate (x-axis) vs fat oxidation in g/min (y-axis).

The R^2 is 0.87 which is a pretty decent fit.

My sweet spot for maximum fat burning is about 120 bpm. My MAF range is 112-122 which nicely straddles this sweet spot of fat burning found during VO2max testing.

If you do the same maths on VO2max data you can find your sweet spot. But it won’t work well for fat burning if you are not fat adapted already. Or just use the MAF 180 formula.

 

Another Look at the Lambert Endurance Study

In this post I looked at a study on (Fat Adapted Athletes). I wrote the original post well before I had my own VO2max tests so I didn’t have a way to apply it to my own situation.

I found a link to a paper which is critical of the study (Asker E. Jeukendrup. High-carbohydrate versus high-fat diets in endurance sports.  Schweizerische Zeitschrift für «Sportmedizin und Sporttraumatologie 51 (1), 17–23, 2003).

The practical relevance of the improved endurance capacity at 62% VO2max reported by Lambert et al. [Lambert et al. 1994], however, is questionable since no endurance events are completed at these low exercise intensities.

I think this is a valid criticism of the work capacity of Low Carbohydrate athletes. There is a sweet spot where the diet is the most efficient.

However, in its favor the Low Carb diet lowers the RER and increases the VO2max point where carbs are oxidized. In my own VO2max testing, my RER crossed over the 0.7 line (From 100% fat burning just starting into burning carbs).

My own crossover point was about 59% of my VO2max which is quite close to the point noted above. That is a sweet spot since it has as high of possible fat oxidation with zero carbohydrate oxidation.

It is probably true that there are few competitive sports where operating at this point offers an advantage. Endurance activities of the Long/Slow sort may be an exception, particularly in long ultra-marathons.

In theory, a fat adapted person could perform very long activities at this level. This number is essentially the MAF number in my case. But that’s partly because I am already fat adapted.

What does this look like for the vegan FASTER study participant? His VO2max was an impressive 63.4. So, 63% of that is 39.9. At that value Damian’s %CHO was 88%.  That means 88% of Damian’s energy was coming from carbohydrates. That puts him at a performance advantage if the activity is within his carbohydrate stores but a disadvantage if the activity for a longer time where he needs to access his fat stores.

It’s also an interesting discontinuity in his data at the next point. There his VO2max dropped and his carbohydrate oxidation went way up. Not sure if this was a walk/run transition. Perhaps Damian is a much more efficient runner than fast walker?

I would say that it seems pretty clear to me Damian is not very good at burning fat. His RER never gets below 0.85 which is a mixed fuel mixture. Not sure how low a dietary fat level he is normally at?

Damian’s MAF Number

Damian was 32 years old. His MAF is 180-32 = 148. Unfortunately, there is no Heart Rate column in his data and I can’t figure out what the correlation to the RR BPM column means.

Bought a new toy

I just bought myself a Concept 2 Rower. I used one at Crossfit a couple of times.

I bought it with a Polar H7 Heart Rate strap.

This allows rowing with heart rate monitoring.

I will be using the same MAF heart rate number of 180 minus age (range of plus 0 minus 10). For me, at 58 years of age, that’s 112 – 122 bpm.

 

Gaining Muscle During a Cut

Leucine seems to be the central amino acid in muscle protein synthesis.

There’s plenty of interest in gaining muscle while cutting fat. Here’s an interesting item from a paper on that subject (Donald K. Layman, Jamie I. Baum. Dietary Protein Impact on Glycemic Control during Weight Loss. The Journal of Nutrition, Volume 134, Issue 4, 1 April 2004, Pages 968S–973S):

During catabolic periods such as energy restriction, supplementation with leucine or a complete mixture of the 3 BCAAs, leucine, isoleucine, and valine, stimulates muscle protein synthesis (35-37).

References

The three references (35-37) are:

35. Li, J. B. & Jefferson, L. S. (1978) Influence of amino acid availability on protein turnover in perfused skeletal muscle. Biochim. Biophys. Acta 544:351–359.
36. Buse, M. G. & Reid, S. S. (1975) Leucine. A possible regulator of protein turnover in muscle. J. Clin. Invest. 56:1250–1261.
37. Hong, S. C. & Layman, D. K. (1984) Effects of leucine on in vitro protein synthesis and degradation in rat skeletal muscle. J. Nutr. 114:1204–1212.

All three were rat studies. From 36.

The data presented indicate that leucine may act as a regulator of the turnover of protein in muscle cells. They are compatible with the hypothesis that leucine inhibits protein degradation and promotes protein synthesis in muscle.

Human Studies

Lundholm K, Edström S, Ekman L, Karlberg I, Walker P, Scherstén T. Protein Degradation in Human Skeletal Muscle Tissue: The Effect of Insulin, Leucine, Amino Acids. Clin Sci (Lond). 1981 Mar;60(3):319-26.

Satoshi Fujita,et.al. Effect of insulin on human skeletal muscle protein synthesis is modulated by insulin-induced changes in muscle blood flow and amino acid availability  Am J Physiol Endocrinol Metab. 2006 Oct; 291(4): E745–E754.

Changes in muscle protein synthesis were strongly associated with changes in muscle blood flow and phenylalanine delivery and availability. In conclusion, physiological hyperinsulinemia promotes muscle protein synthesis as long as it concomitantly increases muscle blood flow, amino acid delivery and availability.

 

Protein is not insulinogenic

In other words, Protein has minimal effect on your Insulin levels (Donald K. Layman Jamie I. Baum. Dietary Protein Impact on Glycemic Control during Weight Loss. The Journal of Nutrition, Volume 134, Issue 4, 1 April 2004, Pages 968S–973S.):

These data suggest that amino acids have minimal impact on plasma insulin concentrations when entering the body via the GI tract.

There’s data which shows a large effect of protein on Insulin but that protein was mainlined into the veins of the test subjects. Unless you are injecting your protein, you’ve got nothing to fear from protein.

Most of these studies used direct intravenous infusion of amino acids into the human forearm under fasted conditions and used euglycemic clamp techniques to measure glucose uptake and insulin resistance. Using these techniques, investigators found that acute increases in plasma amino acid concentrations resulted in higher plasma glucose concentrations, lower glucose uptake, and elevated plasma insulin levels.

Here’s one experiment cited which makes that point:

One of the first studies of the differences in amino acid metabolism between i.v. administration and oral intake was by Floyd et al. (51,52). These investigators evaluated the insulin response to i.v. infusion of amino acids or glucose (51) and also examined the insulin response to oral intake of protein (52). They found that infusion of 30 g of amino acids produced a 3-fold higher insulin response (∼180 μU/mL) than infusion of 30 g of glucose (∼50 μU/mL), suggesting a dramatic hyperinsulinemic effect of amino acids.

However, these investigators also examined the same measurements after subjects consumed a meal of 500 g of beef liver and found that the peak insulin response to the protein meal was only 30 μU/mL. Assuming that leucine is 1 of the most potent insulin secretagogues, the i.v. infusion provided <5 g of leucine while the beef meal provide >14 g of leucine (52). These data suggest that amino acids have minimal impact on plasma insulin concentrations when entering the body via the GI tract.

BCAAs may be the exception since the reach the bloodstream directly like carbohydrates…

The primary exceptions to this pattern of modifications are the BCAA, with over 80% of dietary content of leucine, valine, and isoleucine directly reaching blood circulation.

I wonder if that’s part of their popularity as a supplement?

Speed has an effect too:

For glucose, the postprandial handling occurs mostly within the first 2 h (43); however for amino acids the rate of disposal is much slower with <20% of the dietary amino acids degraded within the first 2 h (48). Thus, direct comparison of a high carbohydrate diet vs. a high protein diet is that the carbohydrate diet requires rapid equilibration of the glucose and insulin metabolic system with dramatic shifts between hepatic vs. peripheral regulations, while a high protein diet serves to stabilize the glycemic environment with delayed metabolism and less reliance on peripheral insulin actions.

And most relevantly to this page:

…diets with reduced carbohydrates and higher protein stabilize glycemic control during weight loss

This part gets really interesting since it describes metabolically broken folks like us…

As expected, as the subjects lost weight (∼6.3 kg) during the 10-wk energy restriction and they improved glycemic control as measured by reduced postprandial insulin response to the test meal. For the CHO Group, average values at wk 0 = 77 μU/mL and at wk 10 = 38 μU/mL. On the other hand, subjects consuming the moderate protein diet achieved normal values for 2-h insulin response after only 4 wk on the diet with average values at wk 0 = 75 μU/mL and at wk 10 = 12 μU/mL. These changes appear to be beneficial associated with the overall risk patterns of obesity and Metabolic Syndrome (57,58).

In summary:

In summary, use of diets with higher protein and reduced carbohydrates appears to enhance weight loss with greater loss of body fat and reduced loss of lean body mass. Beneficial effects of high protein diets may be increased satiety, increased thermogenesis, sparing of muscle protein loss, and enhanced glycemic control. Specific mechanisms to explain each of the observed outcomes remain to be fully elucidated. We suggest that a key to understanding the relationship between dietary protein and carbohydrates is the relationship between the intakes of leucine and glucose. Leucine is now known to interact with the insulin-signaling pathway with apparent modulation of the downstream signal for control of protein synthesis resulting in maintenance of muscle protein during periods of restricted energy intake. Leucine also appears to modulate glucose use by skeletal muscle. While total protein is important in providing substrates for gluconeogenesis, leucine appears to regulate oxidative use of glucose by skeletal muscle through stimulation of glucose recycling via the glucose-alanine cycle. These mechanisms appear to provide a stable glucose environment with low insulin responses during energy-restricted periods.