Today was chest and arms with a little glutes for intermission

Bench

Incline bench

Incline dumbbell press

Assisted dips

Hip thrusters

Bicep curls (ez bar)

Head smashers (ez bar)

Cable flies

Cable raises arm curls

Sauna

Today was chest and arms with a little glutes for intermission

Bench

Incline bench

Incline dumbbell press

Assisted dips

Hip thrusters

Bicep curls (ez bar)

Head smashers (ez bar)

Cable flies

Cable raises arm curls

Sauna

My muscles have long heads and big bellies that take a long time to fill up

That’s why I think they’re meant to be much bigger. There’s a lot of room there. My dad was an amateur bodybuilder so I know what at least half of my genetics says I should be able to unlock.

But sometimes big ships turn very very slowly… so my body reacts differently than most.

I’ve always been “thick” for my 5’10” frame. Even when I weighed 255, most people thought I was 210 or something. Dense I guess (let the punnery begin).

I can usually lose fat a little faster than muscle so I can see how many seasons of bulking and trimming would work, but I think bulking (gaining fat and muscle together) is bad for my health and longevity.

So I lost 0.7lbs of scale fat and 0.1lbs of scale lean in 12 hours of daylight. Gotta love it!

I added the recent protein scale results to my DEXA results for comparison

Thanks. I was using that to show how long my muscle heads are. I have short tendons. I suspect that’s why I gain mass so slowly.

Or my lifting routine sucks…

Lean gain still eludes you. We’ve still no reason to think it’s even possible to not gain muscle while increasing strength.

For all you know you just have a lot of supporting tissues and fluids that are lost when you lose your fat.

I cook and eat pure unrendered beef fat that I get from the butcher daily. At first I tried frying it up to get a bit of that crispy, cooked fat taste. But to get it cooked to that state a lot of fat would render out, the same as happens when you try to crisp bacon. But the residual fat morsel was generally virtually inedible. It was just tough tissue with very little actual fat. I might as well have been chewing on tendons. This residual morsel was often a good sized fraction of the original hunk of pure seeming fat that I’d put in the pan.

Fat has a ton of supporting lean. If you’re losing fat, you’re losing lean. Just look at your graphs, they track each other. That doesn’t mean that you aren’t gaining muscle though. We need to find evidence that this is even possible while strength is increasing.

My graphs track but apparently every other guy on keto who lifts can increase lean and lose fat.

I may be in a different phase but I can’t do that right now.

Also… I understand the argument that muscle contains some fat but losing adipose tissue fat should dominate?

I still have a soft midsection…

“why am I soft in the middle when the rest of my life is so hard”

Yes but with regional breakdown it provides more info than many other body composition measures such as hydrostatic weight and bodpod. Changes in lean mass in legs and arms are pretty much going to be either muscle or glycogen. If one is consistent about diet, hydration and timing of scans one can minimize fluctuations in glycogen. And over a series of scans glycogen fluctuations will mostly average out. Karim’s rapid changes will complicate interpretation but it should still provide a useful complement to strength/performance data which also has limitations as it also fluctuates with factors other than muscle mass.

Karim,

Can you explain how your coming up with the coefficients you use and have you figured out how far you are from accessing the Cahill maximum energy from bodyfat per day?

Sure. I’m traveling but I’ll describe it as best as I can.

I basically have develop the formula with the missing coefficients- some for fat loss, some for lean gain. Clearly the lean gain formula is wrong (very wrong). The fat loss formula starts with a guess for the coefficients - max fat loss per day as a function of fat mass, RMR on lifting data, RMR change for resting days, etc…

I had an RMR component as a function of lean mass but I’ve since zero’d that out since the data didn’t align with any function… other variables must be more dominant.

For each datapoint and calculated point (via the formula), I calculate the error (the difference squared). This all happens in excel so there’s a column of “fat error”. I add up all the errors and take the square root of that sum. That’s my total “fit error”.

This is crude but it works well enough. I use excel’s solver to minimize the “fit error” by manipulating the cells where the coefficients are stored. It cranks for a few seconds and out comes the best fit coefficients.

I have to constrain the results sometimes. Things still have to make sense and the more data, the better the fit.

In my case, unconstrained, my Cahill coefficient of fat accessibility per day was outrageously high - like 50 cal / lb of fat mass. I force set that to a max of 35.

You think this is because of adding exercise to the equation? I don’t remember if Cahill used sedentary subjects, but could look it up…

[duh, I should have been looking for Alpert study, not Cahill]

LINK

I don’t know but I don’t trust the higher Cahill coefficient.

The different coefficients push and pull against each other to find a numerical minima. A close minima can be achieved by changing the RMR coefficients.

I discard numerical results that aren’t physically meaningful - or that I deem not meaningful

If you’re interested, I can run through a few iterations and see if you find something interesting.

It’s super nerdy but I’m game.

I found some old body circumference measures from many months ago. I’ll try to update and compare.

It would be something like 55 Cahill, 2200 RMR and 60% drop during rest days vs… 35 Cahill, 2800 and 70% and show the errors and fits

So… this:

…is part of it? Because, I’m so far away from diff-EQ now I can’t even see it from here.

A problem in the application of Eq. (5) is that we do not know the appropriate activity coefficient for the subjects of the Webb and Abrams work. These authors acknowledge that there was no way of knowing the energy expended on activity. We will estimate the value of the activity coefficient to be half way between the maximum and minimum values of the ME. This gives a value of d ¼ 40:6kJ=kg d

They picked a median value activity coefficient.

The value of the maximum transfer rate is derived from data for young, active male subjects studied by Keys et al. (1950).

…which study is of course is hard to find on the internet these days.