James wrote:Very cool, but I seriously doubt you'll get meaningful data. Digestive, postprandial, and general metabolic physiology takes some time to adapt to dietary intake. If you continually change your intake, and it looks like you have quite a long list of experiments, you won't get an idea of how you would respond to a habitual diet with these experimental variables. At best you'll get an idea of how you'd respond if you never ate a consistent diet.
Perhaps 1mo will be too short of a window into my consistently inconsistent diet.
For a few days, I plan to just let the data run and eat my standard 2x meal 40g net carb HFLC diet. I'm anticipating a relatively stable 80-100mg/dL range (my last A1C was 4.9%) -- there shouldn't be any surprises here. This will set the baseline and let me see what glucose does overnight and before my meals via gluconeogenesis. I am curious to see if there are times in the day while practicing IF when serum glucose might experience brief periods of rising -- over the course of a month, I should notice this phenomenon if it's there.
I'd like to try a few low carb days using a large meal in a small eating window (maybe closer to the 80-100g net carb range, which I was doing for a while) and just see what happens. These would be very high fiber, very high fat meals, with a low amount of daily carbs put into a small window (eg. for a 3 meal a day diet, that might be the "per meal carbohydrate equivalent" as a 3-meal 300g+ total carbohydrate day.) The different foods here might have a big impact on the overall effect, although I tend to reach for the same foods when assembling these sorts of large "big-ass salad" type meals (eg. cauliflower, broccoli, onions, peppers, fennel, leeks, nuts, asparagus, avocados, tomato, zucchini, leafy greens.) The data should be clear enough to comparatively see how high glucose might rise when I eat a
ton of low-GI veg covered in fat to weigh that sort of eating strategy against smaller meals or lower carb intakes. I can experiment a bit with different foods (eg. some fat covered legumes, and maybe some sweet potato or cooked+cooled potato with olive oil) to see where the boundaries are within that context and which alterations to that basic formula work better than others. Some sort of trend or overall peak glucose range should be visible in the data. I'm curious to see if some sort of post-meal low-level activity might help to attenuate the post-prandial glucose elevation.
I'm hoping to keep exercise set around 4-5d/wk and play with the post-workout meals to see what I can get away with -- what happens if I follow up with a few ripe bananas with a bowl of blueberries, freshly cooked beans, or whey protein + sprouted brown rice, vs a green banana + almond butter or cooked + cooled beans with fat. I'll have to play with it.
When the results aren't clear, I can re-run the same foods with permutations on the variables until I see some sort of trend. I probably won't get as much out of it as I'm hoping, but maybe a few interesting things or tricks will be gleaned.
I want to pick up some liquid Skin Tac adhesive before applying a sensor to see if I can improve my odds for keeping the thing glued on.
Live continuous insulin data would be great. I would love a monitor for continuous serum triglycerides (or serum sdLDL/LDL-P.) I have a nagging feeling that by trading carbs for fats, I'm trading post-prandial glucose elevations for post-prandial triglyceride elevations. If I recall from Attia's post, serum Ketone monitoring might be feasible, but there's no market for the technology.
Any ideas for things to try out while plugged in?
