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Insulin load index / most ketogenic foods

It's good to be eating the big green leafies. They have low energy density and high micronutrient and phytonutrient density.

I'm not aware of anyone having specific amino acid deficiencies unless they are vegan or at least vegetarian. Otherwise that just takes care of itself.
 
I guess they must be right since some guy survived for two years on his own body fat.

Looking at the biochemistry I wasn't convinced that the glycerol group released in lipolysis is actually net available to the body, because it needs to be reused to build the triglyceride framework to transport the next load of fat out of the adipose cells to wherever it's going to be metabolised. But maybe I am wrong.

But anyway, glucose needs are overstated by many multiples. The brain runs fine on mostly ketones and the heart muscle prefers ketones.
 
Hard to review via Dropbox as it opens in the forum browser. Some quick feedback

You make repeated statements about correlation but you need to preface that by saying what data sets you are correlating and what your methodology is, up front. And you need to deploy better statistical measures than R^2 as that really is basic.

Fat

Don't agree that your first bullet point is proved by your argument. Eg what if eating fat didn't in any way diminish our eating of carbs and protein? It does, but you need to make the point about satiety. See Taubes.

I just noticed the value for Beer and it's insanely low. That value is just plain wrong. Possibly they are seeing the suppressing effect of alcohol on GNG. What time period do they sample FII at?

I think calling protein a Black Swan is hyperbole. Black Swans are something that almost never happen. Protein is mundane. You'll need a better simile for your title I'm afraid.

I think you are missing a point about financial modelling and back fits. While it is not a sufficient condition for a model to back fit the historical data, it is still one of the necessary conditions.

You need to incorporate the prompt response to protein in your discussion which only mentions the slower GNG response.

Based on the discussions here I was expecting you to make the point more that certain refined and purified foods are highly glycemic whether they are protein or carbs. Maybe that's a more important variable to consider than carbs vs protein.

Your correlation data is across a very arbitrary data set. How do you weight the component foods in the data set? It doesn't make sense to weight them equally. Not if you are using the equation that you are testing the correlation for, to apply to real world decisions on injecting insulin. It's a good start to look at the correlation on aggregate, but you need to look at specific cases. What is the worst case deviation of your formula from FII? What's the worst quartile, the SD, etc? How many predictions would be off, and how far off? What's the area under that error curve? And crucially, is it larger or smaller than ignoring protein?

I think it is overstating the case to say that the FII data "proves" fat never requires insulin. It's evidence I guess, but there is also evidence to the contrary.

I would suggest you not say multiple times that people who don't get dosing for protein "have a problem" etc. Straw man, ad hominem, etc. Sure some are not aware, and many find it's not the case for them, but there isn't a cabal of GNG-deniers out there. The truth is more prosaic than that. Just people disagreeing about the relative importance of protein, either personally or in general. It does not strengthen your argument to make ad hominem statements about your opponents, real or imagined
 

Thanks so much Spiker for the quick review and commentary. I will update and refine accordingly.
 
Just some more detailed comments about R^2. You are seeing a value of around 0.50 for 0.56 x protein + carbs predicting FII. The plain language interpretation of R^2 = 0.50 is that your formula explains half the variation and does not explain the other half of the variation. Is that good enough? In many areas of statistics, getting the answer half right, or right half of the time, hence wrong half of the time, would be considered a null hypothesis rather than any useful result.

The second point is that R^2 almost always improves when you add an extra variable (protein in your case), even when that variable is clearly irrelevant. This is called the kitchen sink effect. So while I agree protein is important, be cautious about what your analysis with R^2 actually proves.
 
@martykendall there are some major claims in the following literature that seem to reflect what you see:
http://www.mangomannutrition.com
Probably worth some follow up and comparison in your manifesto. The "high real carb" diet seems to have a lot of traction amongst US and more sporty diabetics, and these guys are countering directly a lot of the received knowledge that we see on here.

All fascinating stuff!
 
@martykendall I've had a chance to review your paper now. I have to agree with @Spiker on the black swans title. With this stuff I don't believe there are such things.

In reviewing the correlation data, I'm not clear why you've chosen R^2 in place of r. As someone with a quantitative background, I'd like to understand your reason for that?

You have a lot of questions dressed up as statements, such as that about protein. You should probably attempt to provide a number of answers to these or at least discuss the reason for the uncertainty.

Reading it, I'm not sure whether you are trying to present conclusions or discuss the data. The conclusions are not clearly spelt out. I think it suggests that it is a scientific paper but doesn't fit the form, ie, an abstract, introduction, data presentation and conclusions, followed by any and all references, which should always be included. Presented in that way, it is will read more like a research paper.
 
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Had a look at MangoMan...

Seems that he's promoting a high fibre nutrient dense diet which make a lot of sense on a number of levels as the high fibre will cancel out insulin demand of total carbohydrates and the low calorie density will also help to reduce the insulin load and total calories.

He links to a Dr Greger (Nutritionfacts.org) video which uses psysiological insulin resistance as a case against low carb. If you've got a low average blood sugar and low insulin but your blood sugar spikes if you ever have a carb binge then I don't see a problem. The problems come from prolonged high blood sugars and high insulin levels. The Greger video also infers that dietary fat causes fat in the blood which is not robust. Responding to the misinformation in this video would be a major blog post in itself!

Lots more thoughts from scanning through his site, but overall a whole foods fruits and vegetables approach would be a great improvement on the standard diet. Not sure about teaching diabetics to fear fat though.
 
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tim2000s said:
@martykendallIn reviewing the correlation data, I'm not clear why you've chosen R^2 in place of r. As someone with a quantitative background, I'd like to understand your reason for that?
Click to expand...

Just lazy. Pearson's r seems to be used more in medical analysis. R2 just simply pops out of Excel. I have updated with R2, r and p.


My real aim is to get people thinking and start discussion (as has occurred here).

I've been pretty up front about not being a medical professional, just someone with a passion and a person interest trying to make sense of the data.

The intention is to make this into a blog post which people can respond to. It will be part of the discussion on the blog which will include a more rigorous treatment of the different ways to look at the glucogenic potential of protein.

Thanks heaps for taking the time to have a look and provide feedback.
 

I have added the following comment near the end:

"There is still quite a degree of scatter in this real life data. This could be due to measurement error in the macronutrients, food quantity, the characteristics of the people that the food was tested on, real life measurement error or something else. However, it helps better predict insulin demand than carbohydrate alone.

The fact that there is still a high degree of variability in the data and hence limited ability to accurately predict the insulin response to food can be mitigated by keeping the overall insulin load of the diet reasonably low. Dr Richard Bernstein talks about the ‘law of small numbers’ whereby the compounding errors in the calculation of insulin requirement and the mismatch of insulin response with the rate of digestion misalign means that it is impossible to accurately calculate insulin dose. The only way to meaningfully address this is to keep the overall insulin demand low."

I appreciate what you're saying about over emphasising the R2. While there is an improvement in the correlation, the main aim was to deal with the obvious issue of protein causing an insulin and blood sugar reaction that was not dealt with by the carbohydrates alone.
 

> You make repeated statements about correlation but you need to preface that by saying what data sets you are correlating and what your methodology is, up front.


Noted. I have fleshed out the background a little better.


> And you need to deploy better statistical measures than R^2 as that really is basic.


I have added Person’s r and p values


Fat


> Don't agree that your first bullet point is proved by your argument. Eg what if eating fat didn't in any way diminish our eating of carbs and protein? It does, but you need to make the point about satiety. See Taubes.


Noted. Have elaborated. Not the main point of this article, but good to clarify.


> I think calling protein a Black Swan is hyperbole. Black Swans are something that almost never happen. Protein is mundane. You'll need a better simile for your title I'm afraid.


Hyperbole noted. Have pulled back the terminology to ‘outlier’.


> I think you are missing a point about financial modelling and back fits. While it is not a sufficient condition for a model to back fit the historical data, it is still one of the necessary conditions.


Yes, but a simpler system is often more robust compared to one that uses the ‘kitchen sink’.


You need to incorporate the prompt response to protein in your discussion which only mentions the slower GNG response.


> Based on the discussions here I was expecting you to make the point more that certain refined and purified foods are highly glycemic whether they are protein or carbs. Maybe that's a more important variable to consider than carbs vs protein.


This is somewhat addressed by considering fibre, however I’ve highlighted it as an issue.


> Your correlation data is across a very arbitrary data set. How do you weight the component foods in the data set?


I’ve just tried to look at weighting based on fibre (1x), carbs (1x) and protein (0.54x). I have looked separately at dairy and meat and there doesn’t appear to be anything special about these products that can’t be explained by these variables, at least with the data that we have.


> It doesn't make sense to weight them equally. Not if you are using the equation that you are testing the correlation for, to apply to real world decisions on injecting insulin. It's a good start to look at the correlation on aggregate, but you need to look at specific cases.


This is where we come back to designing a system that is too complex to be robust or viable to be applied by people in real life. Going beyond carbs is already a stretch for most.


> What is the worst case deviation of your formula from FII? What's the worst quartile, the SD, etc? How many predictions would be off, and how far off? What's the area under that error curve? And crucially, is it larger or smaller than ignoring protein?


The reality is that calculating dosages for insulin will always be inaccurate to some degree. Insert Bernstein’s ‘law of small numbers’ argument. The way to manage this conundrum is to keep the insulin load low to try to stay off the roller coaster.


> I think it is overstating the case to say that the FII data "proves" fat never requires insulin. It's evidence I guess, but there is also evidence to the contrary.


I have intentionally used “indicates” rather than “proves”. What I’m doing in this article is eliminating variables that have limited use in application to design a robust but still simple system.


> I would suggest you not say multiple times that people who don't get dosing for protein "have a problem" etc. Straw man, ad hominem, etc. Sure some are not aware, and many find it's not the case for them, but there isn't a cabal of GNG-deniers out there. The truth is more prosaic than that. Just people disagreeing about the relative importance of protein, either personally or in general. It does not strengthen your argument to make ad hominem statements about your opponents, real or imagined.


I’m on the ketogains Facebook page where many seem to think that you just can’t overdo protein. One person said “You’re wrong! If there was a book Gluconeogenis for Dummies I’d buy it for you.” I’m over it now… I’ve taken out the strawman argument of the article. Hopefully the data stands on its own without me having to over emphasis the point.


Thanks again Spiker for the feedback.


https://www.dropbox.com/s/cpvh2cmrmezmuko/Black swans, outliers and reliable trends.docx?dl=0
 
Warning argument from authority (because I haven't time or inclination to write a long answer either) but you might like to look at where Mangoman is coming from. His Phd research, should have given him a good knowledge of carbohydrate and fatty acid metabolism . .
http://www.ncbi.nlm.nih.gov/pubmed/19887594
I noticed that a co-author on all his papers and therefore presumably his supervisor, was Mark Hellerstein ,normally considered as a leading expert in the field
http://nst.berkeley.edu/faculty/marc-hellerstein
 

Thanks Phoenix. You always pull out some great references. I'll check them out. I'm learning heaps from your guys.

I don't have too many issues with MangoMan's approach, from the reading that I've done of it today. I struggled though with the Dr Greger's video that he had embedded in this post - http://www.diabetesdaily.com/blog/2...ique-approach-without-cutting-all-your-carbs/. I though there were a number of strawmen arguments (as Spiker would call them) to support he high carb / vegan agenda.

What Mangoman is saying makes sense if his definition of carbs includes fibre. Increasing fibre will improve insulin sensitivity and gut health and usually come with extra nutrition that you don't get with just fats, which is all good. If you're an active type 1 burning off the carbs it's probably going to work well. If you are more sedentary then less carbs and a bit more fat and protein will probably give you a better HbA1c.
 
Is anyone able to get a full text copy of this paper from June's Diabetes Care
Impact of Fat, Protein, and Glycemic Index on Postprandial Glucose Control in Type 1 Diabetes: Implications for Intensive Diabetes Management in the Continuous Glucose Monitoring Era

Kirstine J. Bell1,2, Carmel E. Smart3,4, Garry M. Steil5,6, Jennie C. Brand-Miller1, Bruce King3,4 and HowardA.Wolpert2,

http://care.diabetesjournals.org/content/38/6/1008.short?rss=1
(Kirstin Bell and Jennie Brand Miller of the Insulin index and Wolpert who suggests fat also plays an part )
 
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