Tag Archives: glycemic index

trends, outliers, insulin and protein

  • The carbohydrate content of a food alone does not accurately predict insulin response.  Protein and fibre content of food also influence in insulin response.
  • The food insulin index data indicates that dietary fat is the one macronutrient that does not does not require a significant amount of insulin.
  • Net carbohydrates plus approximately half protein correlates well with observed insulin response.
  • This knowledge can be used to help select low insulin foods and more accurately calculate insulin doses for diabetics.

background

Back before the GFC I used to dabble in share trading.  I don’t know much about financial systems, but I spent a good deal of time designing and testing “trend following” trading systems.

One of the pitfalls for newbies is to design a system with excessive “curve fitting”.  That is, to design a complex system that would work fantastically on a specific set of historical data.  If you ran an overly curve fitted system on another set of data or tried to trade it in real time it would fail because it was too finely tuned to the discrete set of historical data.

“Everything should be as simple as possible, but no simpler.”

Albert Einstein

Another lesson from trading is that you should be able to describe simply why a good system works.  My trading system scanned the market for stocks that were moving up quickly over a number of time periods with minimal volatility so that I could place a close ‘stop loss’ that would take me out of the trade quickly if the trend turned.

When the GFC hit things got too volatile and I got out of the market.  It was no longer fun.  However the skills I learned as an amatuer a quantitative trader (along with my day job running multi criteria analyses to identify motorway alignments, road investments and the like) have given me an interesting angle on nutrition that I hope people find useful.

On the Optimising Nutrition blog I have tried to describe a system to manage nutrition that makes sense to me.  I want to document the things that I wish someone had shown us when we started out trying to understand diabetes and nutrition.

If we want to understand and predict the behaviour of insulin, the master regulator hormone of the human body, we need to first determine what we know that is accurate, significant and useful that we can use.

Kirstine Bell’s PhD thesis Clinical Application of the Food Insulin Index to Diabetes Mellitus[1] (Sept 2014) details the results of the latest food insulin test data for more than one hundred foods.  It also evaluates the relationship between insulin demand and protein, fat, carbohydrates, glycaemic index, glycaemic load, indigestible fibre, individual amino acids and blood glucose.

Previously I have discussed in a moderate amount of detail how to calculate how much insulin may be required based on the carbohydrate, protein and fibre ingested.  Given the importance of this issue, this article looks in more detail at what can be learned from the test data included in this thesis about the relationship between these parameters, with a view to better manage blood glucose and insulin demand.  You will see that I have tried to look at the issue from a number of different directions and have also included a more rigorous statistical analysis.

carbohydrate

Most people know that carbohydrates require insulin.  As shown in the chart below, carbohydrates goes some way to explaining insulin response.  However it is far from a perfect relationship (R2 = 0.44, r = 0.67, p < 0.05).

image001

indigestible fibre

Taking indigestible fibre into account (i.e. net carbohydrates) improves the relationship (R2 = 0.48, r = 0.69, p < 0.05).  The best correlation is achieved when we subtract all the indigestible fibre from the total carbohydrate value.  However we can see from the cluster of data points on the vertical axis there is something going on that is not explained by carbohydrates alone.

image002

The importance of dietary fibre should not be discounted, especially when trying to reduce insulin demand.  Some recommend that diabetics limit total carbohydrates, rather than considering net carbohydrates, or non-fibre carbohydrates.  The danger with a total carbohydrates approach is that people will avoid fibrous non-starchy vegetables that provide vitamins and minerals that cannot be obtained from other foods (unless you’re consuming a significant amount of organ meats), as well as feeding the gut bacteria which is also important to help improve insulin sensitivity and the body’s ability to digest fats. [2]

fat

The food insulin index data indicates that foods that are largely comprised of fat have a negligible insulin response (R2 = 0.38, r = 0.631, p < 0.001).

image003

To put this another way, the chart below shows the sum of carbohydrate plus protein (i.e. the non-fat content of foods) versus the insulin index (R2 = 0.38, r = 0.62, p < 0.001) indicating that:

  • the greater the proportion of fat in a particular food the less insulin is required; and
  • the more carbohydrates and / or protein ingested the more insulin is required.

image004

Hence, it appears that to reduce insulin demand we need to reduce carbs and / or protein!

The figure below shows a similar chart for the glucose score (i.e. the area under the curve of the blood glucose rise over three hours after ingestion of the food).  Again, this indicates that the blood glucose response is lowest for foods that contain a higher proportion of calories from fat (R2 = 0.45, r = 0.68, p < 0.001).

image005

While it appears that insulin demand is triggered by carbohydrates and protein, what is not clear is the relative degree to which carbohydrates and protein contribute to insulin demand.  Are they equivalent or does protein cause a smaller insulin  response?

protein

Another observation from trading is that you can learn a lot by considering outliers.  You have to decide whether the data points that don’t quite fit the trend are garbage or ‘black swans’ need to be accounted for in the system.

In the carbohydrate vs insulin relationship the outliers are the high protein foods that trigger a higher insulin response than can be explained by considering carbohydrates alone.

As shown in this plot, high protein foods are typically lower in carbohydrates which produce the greatest amount of glucose.  Choosing higher protein foods will generally reduce insulin (R2 = 0.10, r = 0.47, p < 0.001).

image006

Increasing protein will also typically lead to a spontaneous reduction in intake due to the thermic and satiety effects of protein. [3] [4]   Protein is critically important for many bodily functions.  It is vital to eat adequate protein.

However protein in excess of the body’s needs for growth and repair can be converted to glucose.  The fact that protein can turn to glucose represents a potential ‘hack’ for diabetics trying to manage their blood glucose as they can get the glucose required for brain function without spiking blood glucose as much as carbohydrates.

Choosing higher protein foods will generally lead to better blood glucose control.  Although high protein foods still raise the blood glucose somewhat, particularly if you are not insulin sensitive, however the blood glucose response is gentler and hence the pancreas can secrete enough insulin to balance blood glucose.

image007

For most people, transitioning to a reduced carbohydrate whole foods diet will give them most of the results they are after.  However for people with Type 1 Diabetes or people trying to design a therapeutic ketogenic diet, consideration of protein may be important to further refine the process to achieve the desired outcomes.

For a healthy bodybuilder the glucogenic and insulinogenic effect of protein might be an anabolic advantage, with the post workout protein shake providing an insulin spike to help build muscle.

However for someone struggling to lose weight on a low carb diet, considering the insulinogenic effect of protein might just be what they need to reduce insulin and normalise blood sugars and thus enable them to reach their goals.

glycaemic index

The glycaemic index is a reasonable predictor of insulin demand in terms of correlation (R2 = 0.54, r = 73, p < 0.01), however the ‘elephant in the room’ again is the high protein low carbohydrate foods (e.g. white fish, low fat cheese, lean beef etc).

image009

The other issue is that the glycaemic index is an empirical measurement that has to be measured in humans “in vivo” and can’t easily be calculated based on commonly available food properties.  And again, the glycaemic index does not deal with the insulin response from high protein foods.

glycaemic load

The same issues apply to glycaemic load.  There is a reasonable correlation between glycaemic load and insulin demand.  However it still does not explain the insulin effect of high protein foods (R2 = 0.57, r = 0.75, p < 0.01).  And you have to run these tests in real people “in vivo”.

image010

glucose score

Like the food insulin index, the glucose score is measured “in vivo” based on the area under the curve of a healthy person’s glucose rise due to a particular food.

Glucose score is interesting in that it actually achieves an excellent correlation with insulin demand (R2 = 0.75, r = 0.87, p < 0.001), however there is still a disconnect when it comes to high protein foods.

image010

It seems that some foods that do not raise blood glucose significantly over three hours still elicit an insulin response.  High protein foods digest slowly although they do still require insulin to metabolise.  In a normal healthy person the body’s insulin response to protein is balanced by release of glycogen from the liver, with blood glucose being kept in balance by insulin and glycogen. [5]

In a normal person the insulin keeps up with this slow blood glucose rise and hence we do not see a pronounced blood glucose spike due to high protein foods.

The interesting outliers here are processed low fat milk products that seem to require more insulin than would be anticipated by the blood glucose response.  On the other side of the trend line we have brown rice, pasta and other less processed whole foods which raises the blood glucose but does not require as much insulin as might be expected.

Accounting for fibre (i.e. net carbs rather than total carbs) goes some way to help anticipate the effect of processing.  However the effect of processed foods is an interesting area for future study that is beyond the capacity of this dataset to address.

I ran a number of correlation analysis and could not find an explanation of why a certain food sat above or below the trend line, whether it be carbohydrates, sugar, fibre or protein.

sugar

The sugar content of a food is not a particularly useful predictor of insulin demand (R2 = 0.10, r = 0.32, p = 0.001) compared with net carbohydrates (R2 = 0.48, r = 0.69, p < 0.05).  Quitting sugar is only part of the solution.  Most people struggling with diabetes or obesity should ideally consider their total carbohydrate intake.

image011

curve fitting

Kirstine Bells’ Clinical Application of the Food Insulin Index to Diabetes Mellitus[6] documents the development of a number of formula to explain the relationship between food properties and the food insulin index response.  The aim of this her thesis was essentially to build an improved glycemic index to predict insulin response rather than only considering changes in blood glucose.

The chart below shows the best relationship developed using a stepwise multiple linear regression analysis of the various parameters to forecast insulin demand documented in Clinical Application of the Food Insulin Index to Diabetes Mellitus. [7]

The correlation is excellent (R2 = 0.78, r = 0.89, p < 0.001).  However this relationship relies heavily on the glucose score (GS) which has to be tested “in vivo”.

image012

If we strip out the glucose score then the best relationship achieved in the thesis is the one shown below using carbohydrates and protein with a correction factor (R2 = 0.46, r = 0.68, p < 0.001).

The problem with this approach is that it assumes that high fat foods have some insulinogenic effect.  However we have seen above that high fat foods have a negligible insulin response.  This formula also does not account for indigestible fibre which should be subtracted from the total carbohydrate count.  And according to this formula a food with zero carbohydrate and zero protein would still have a significant insulin index response of 10.4, which does not make sense.

image013

simple is true

If we take out indigestible fibre (net carbs), assume that fat has a negligible insulin response and refine the protein factor to maximise the correlation with the test data, we end up with this chart which has an improved correlation compared to the model above (R2 = 0.49, r = 0.70, p < 0.001).

image014

This approach also does a good job of predicting blood glucose (R2 = 0.59, r = 0.77, p < 0.001) as shown in the chart below.

image015

practical application

Individual foods can be ranked and prioritised based on their proportion of insulinogenic calories using the following formula:

image016

Foods with the lowest proportion of insulinogenic calories will have the gentlest impact on blood glucose and have the lowest insulin demand, a consideration which will be very useful for people who are insulin resistant (i.e. Type 2 Diabetes or Pre-Diabetes) or not able to produce adequate insulin themselves (i.e. Type 1 Diabetes).

You can find a detailed list of foods ranked by their proportion of insulinogenic calories here and with consideration of nutrients and other factors based on different goals here.

Diabetics and people wanting to reduce the insulin demand of their diet can track the total insulin load (as opposed to carbohydrate counting) using the following formula:

image017

The total insulin load can be reduced by decreasing carbohydrates, increasing fibre, moderating protein to the body’s optimum requirement and increasing fat until target blood glucose are achieved.

can we design a “perfect” system?

There is still quite a degree of in this real life data.  This could be due to measurement error in the macronutrients, food quantity, individual characteristics of the people that the food was tested on, or something else.

This approach considering the insulinogenic effect of protein and carbohydrates does however help to 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 manage the high level of variability is to reduce insulin demand to manageable levels.  This is especially beneficial for people who are injecting insulin, but also relevant for the rest of us.

summary

Building on the analysis of the food insulin index data, the key assumptions that underpin this system are:

  1. carbohydrates require insulin,
  2. indigestible fibre does not require insulin, and
  3. the glucogenic portion of protein that is not used for growth and repair and not lost in digestion also requires insulin.

In order to reduce our insulin load we should do the following, in order of priority:

  1. Reduce insulin load until you normalise blood glucose levels (i.e. reduce digestible carbohydrates and moderate protein if necessary),
  2. Increase nutrient density as much as you can while still maintaining good blood glucose levels (note: this will likely also include fibre from non-starchy veggies which will also increase fibre which reduces insulin and slows digestion),
  3. Reduce dietary fat if you still need to reduce body fat levels, and
  4. Implement an intermittent fasting routine to improve your insulin sensitivity and to kick-start ketosis.

 

references

[1] http://ses.library.usyd.edu.au/handle/2123/11945

[2] http://www.amazon.com/Brain-Maker-Power-Microbes-Protect/dp/0316380105

[3] http://wholehealthsource.blogspot.com.au/2013/04/glucagon-dietary-protein-and-low.html

[4] http://www.ncbi.nlm.nih.gov/pubmed/16002798

[5] http://wholehealthsource.blogspot.com.au/2013/04/glucagon-dietary-protein-and-low.html

[6] http://ses.library.usyd.edu.au/handle/2123/11945

[7] http://ses.library.usyd.edu.au/handle/2123/11945

[8] http://www.amazon.com/Brain-Maker-Power-Microbes-Protect-ebook/dp/B00MEMMS9I

superfoods for diabetes & nutritional ketosis

More than carbohydrate counting or the glycemic index, the food insulin index data suggests that our blood glucose and insulin response to food is better predicted by net carbohydrates plus about half the protein we eat.

The chart below show the relationship between carbohydrates  and our insulin response. There is some relationship between carbohydrate and insulin, but it is not that strong, particularly when it comes to high protein foods (e.g. white fish, steak or cheese) or high fibre foods (e.g. All Bran).

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Accounting for fibre and protein enables us to more accurately predict the amount of insulin that will be required for a particular food.  This knowledge can be  useful for someone with diabetes and / or a person who is insulin resistant to help them calculate their insulin dosage or to chose foods that will require less insulin.

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If your blood glucose levels are typically high you are likely insulin resistant (e.g.  type 2 diabetes) or not able to produce enough insulin (e.g. type 1 diabetes) it makes sense to reduce the insulin load of your food so your pancreas can keep up.

This list of foods has been optimised to reduce the insulin load while also maximising nutrient density.  These low insulin load, high nutrient density foods will lead to improved blood sugar control and normalised insulin levels.  Reduced insulin levels will allow body fat to be released and be used for energy to improve body composition and insulin resistance.

Also included in the table are the nutrient density score, percentage of insulinogenic calories, insulin load, energy density and the multicriteria analysis score score (MCA) that combines all these factors.

vegetables and fruit

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food ND % insulinogenic insulin load (g/100g) calories/100g MCA
broccoli 25 36% 3 22 1.66
endive 16 23% 1 17 1.65
chicory greens 15 23% 2 23 1.60
alfalfa 10 19% 1 23 1.52
coriander 16 30% 2 23 1.50
escarole 12 24% 1 19 1.45
zucchini 19 40% 2 17 1.33
avocado -2 8% 3 160 1.30
beet greens 14 35% 2 22 1.28
curry powder 4 13% 14 325 1.28
olives -7 3% 1 145 1.24
spinach 22 49% 4 23 1.23
basil 20 47% 3 23 1.16
paprika 9 27% 26 282 1.14
asparagus 19 50% 3 22 1.08
mustard greens 9 36% 3 27 1.05
banana pepper 8 36% 3 27 1.01
sage 6 26% 26 315 1.00
turnip greens 13 44% 4 29 0.97
cloves 10 35% 35 274 0.96
parsley 15 48% 5 36 0.96
collards 7 37% 4 33 0.95
lettuce 16 50% 2 15 0.95
watercress 26 65% 2 11 0.94
summer squash 12 45% 2 19 0.93
Chinese cabbage 18 54% 2 12 0.91
chard 16 51% 3 19 0.91
cauliflower 15 50% 4 25 0.91
portabella mushrooms 18 55% 5 29 0.89
chives 13 48% 4 30 0.88
okra 14 50% 3 22 0.88
eggplant 4 35% 3 25 0.87
cucumber 7 39% 1 12 0.86
pickles 7 39% 1 12 0.86
red peppers 7 40% 3 31 0.86
arugula 10 45% 3 25 0.84
sauerkraut 5 39% 2 19 0.83
blackberries -2 27% 3 43 0.83
poppy seeds -2 17% 23 525 0.82
jalapeno peppers 4 37% 3 27 0.81

eggs and dairy

dairy20and20eggs

food ND % insulinogenic insulin load (g/100g) calories/100g MCA
egg yolk 9 18% 12 275 1.34
cream 0 6% 5 340 1.32
sour cream 1 13% 6 198 1.25
whole egg 11 30% 10 143 1.22
cream cheese 1 11% 10 350 1.19
butter -1 2% 3 718 1.14
Swiss cheese 6 22% 22 393 1.08
cheddar cheese 5 20% 20 410 1.08
limburger cheese 1 19% 15 327 1.00
feta cheese 2 22% 15 264 0.99
camembert 1 21% 16 300 0.97
brie -0 19% 16 334 0.93
goat cheese -1 21% 14 264 0.90
blue cheese 0 21% 19 353 0.90
gruyere cheese 1 22% 23 413 0.87
Monterey cheese -1 20% 19 373 0.86
edam cheese 1 23% 21 357 0.85
gouda cheese 1 24% 21 356 0.85
muenster cheese -1 21% 19 368 0.85
mozzarella 7 34% 26 304 0.84
Colby -1 21% 20 394 0.82
ricotta -1 27% 12 174 0.81

nuts, seeds and legumes

image10

food ND % insulinogenic insulin load (g/100g) calories/100g MCA
coconut milk -5 8% 5 230 1.09
coconut cream -6 8% 7 330 1.01
sunflower seeds 1 15% 22 546 0.99
brazil nuts -1 9% 16 659 0.98
coconut meat -5 10% 9 354 0.98
flax seed -2 11% 16 534 0.97
macadamia nuts -2 6% 12 718 0.97
tofu 7 34% 8 83 0.95
sesame seeds -3 10% 17 631 0.92
hazelnuts -3 10% 17 629 0.88
peanut butter 0 17% 27 593 0.88
pumpkin seeds 1 19% 29 559 0.86
walnuts -3 13% 22 619 0.83
pecans -6 6% 12 691 0.83

seafood

seafood-salad-5616x3744-shrimp-scallop-greens-738

food ND % insulinogenic insulin load (g/100g) calories/100g MCA
mackerel 6 14% 10 305 1.35
caviar 15 33% 23 264 1.21
fish roe 22 47% 18 143 1.17
cisco 10 29% 13 177 1.17
trout 19 45% 18 168 1.09
salmon 23 52% 20 156 1.07
sardines 11 36% 16 185 1.00
herring 11 36% 19 217 1.00
anchovy 16 44% 22 210 0.98
sardine 11 37% 19 208 1.0
sturgeon 15 49% 16 135 0.87

animal products

7450703_orig

food ND % insulinogenic insulin load (g/100g) calories/100g MCA
beef brains 7 22% 8 151 1.27
lamb brains 7 27% 10 154 1.12
lamb liver 21 48% 20 168 1.10
lamb kidney 23 52% 15 112 1.09
beef tongue 0 16% 11 284 1.09
sweetbread -2 12% 9 318 1.07
bacon -2 11% 11 417 1.05
salami 2 18% 17 378 1.05
kielbasa -1 15% 12 325 1.03
bratwurst 0 16% 13 333 1.03
liver sausage -3 13% 10 331 1.02
turkey liver 18 47% 21 189 1.02
pepperoni 0 13% 16 504 1.02
pork ribs 1 18% 16 361 1.01
ground turkey 7 30% 19 258 0.98
park sausage 3 25% 13 217 0.98
chicken liver pate 8 34% 17 201 0.97
turkey bacon -1 19% 11 226 0.97
pork sausage 1 20% 16 325 0.97
meatballs -1 19% 14 286 0.95
T-bone steak 4 26% 19 294 0.94
chicken liver 18 50% 20 172 0.94
knackwurst -4 16% 12 307 0.92
beef sausage -2 18% 15 332 0.92
bologna -7 11% 9 310 0.91
liver pate -3 16% 13 319 0.91
turkey 1 20% 21 414 0.89
beef kidney 18 52% 20 157 0.88
roast beef 9 38% 21 219 0.86
duck -3 18% 15 337 0.86
blood sausage -5 14% 13 379 0.85
frankfurter -5 17% 12 290 0.85
lamb rib -2 19% 17 361 0.84

other dietary approaches

The table below contains links to separate blog posts and printable .pdfs detailing optimal foods for a range of dietary approaches (sorted from most to least nutrient dense) that may be of interest depending on your situation and goals.   You can print them out to stick to your fridge or take on your next shopping expedition for some inspiration.

dietary approach printable .pdf
weight loss (insulin sensitive) download
autoimmune (nutrient dense) download
alkaline foods download
nutrient dense bulking download
nutrient dense (maintenance) download
weight loss (insulin resistant) download
autoimmune (diabetes friendly) download
zero carb download
diabetes and nutritional ketosis download
vegan (nutrient dense) download
vegan (diabetic friendly) download
therapeutic ketosis download
avoid download

If you’re not sure which approach is right for you and whether you are insulin resistant, this survey may help identify the optimal dietary approach for you.

image02

latest food insulin index data

After some searching I came across a recent PhD thesis from the University Of Sydney titled Clinical Application of the Food Insulin Index to Diabetes Mellitus (Kirstine Bell, September 2014).

Kirstine Bell’s thesis demonstrated that the food insulin index data had the following practical applications:

  • calculating insulin dose using the food insulin index data provided better blood sugar control for type 1 diabetics compared to normal carbohydrate counting, and
  • type 2 diabetics improved their blood sugar control by choosing foods that caused a lower insulin secretion, independent of calories or carbohydrates.

Appendix 3 of the thesis also contained an extensive food insulin index database of foods that had now been tested.

I have plotted the relationship between carbohydrates and the food insulin index in the chart below.

image003

As you can see from this chart the relationship between carbohydrates and insulin is not straightforward.  Of particular interest is the fact that there are a number of high protein and low fat foods sitting quite high up on the vertical axis while there are a number of high fibre foods with a lower insulin response that you might expect.

However once we account for the effect of protein, fibre and fructose we can achieve a much better prediction of our body’s response to insulin as shown in the revised chart below.

image0232

This understanding of how to calculate our insulin response to food is a useful parameter, along with nutrient density and energy density, which enables us to prioritise our food choices to suit different goals.

image30

The table below contains links to separate blog posts and printable .pdfs detailing optimal foods for a range of dietary approaches (sorted from most to least nutrient dense) that may be of interest depending on your situation and goals.   You can print them out to stick to your fridge or take on your next shopping expedition for some inspiration.

dietary approach printable .pdf
weight loss (insulin sensitive) download
autoimmune (nutrient dense) download
alkaline foods download
nutrient dense bulking download
nutrient dense (maintenance) download
weight loss (insulin resistant) download
autoimmune (diabetes friendly) download
zero carb download
diabetes and nutritional ketosis download
vegan (nutrient dense) download
vegan (diabetic friendly) download
therapeutic ketosis download
avoid download

If you’re not sure which approach is right for you and whether you are insulin resistant, this survey may help identify the optimal dietary approach for you.

image02

ketosis… the cure for diabetes?

  • A reduced insulin load diet will lead to normalised blood sugars and improved insulin sensitivity.
  • A reduced insulin load diet can be achieved by reducing carbohydrates, moderating protein and choosing higher fibre foods.
  • Intermittent fasting also reduces insulin load.
  • Measuring your blood sugars is a simple and cost effective way to check that your metabolic health is on track.
  • A diet of nutrient dense, high fibre, high fat foods is the best way to optimise nutrition and minimise the risks associated with diabetes.

how to become diabetic…

In the “good old days” there were periods of feast and famine.  Food was typically eaten with the fibrous packing that it came with. In today’s modern food environment we are encouraged by the food industry (and those sponsored by it) to eat breakfast, lunch, dinner, snacks, pre-workout meals, post workout stacks, sports gels during exercise, and maybe some Gatorade to speed recovery.

Today’s food is plentiful, typically highly processed and low in fibre.  Carbohydrate and sugar based foods have a long shelf life, can be transported long distances and therefore cheap. Win, win?  Maybe not.

As we keep loading our bodies with simple sugars and carbohydrates our pancreas has to work overtime to produce insulin to shuttle excess sugar from the blood to your fat stores.

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Over time we become insulin resistant and the pancreas can’t keep up. Once your blood sugars get high enough you will be diagnosed with “type 2 diabetes” and put on medication to improve your insulin sensitivity, for a time. If nothing changes in your food intake your insulin sensitivity will continue to deteriorate until you reach a point when you’ll need to inject insulin to keep your blood sugars down.

Injecting excessive amounts of insulin will cause you gain even more body fat. Recently we have learned that it’s not just the high blood sugars that are diabolical for your health, high levels of insulin are also toxic. [1]

Doesn’t sound like much of a solution does it?

…and how to reverse it

While there are many aspects to managing diabetes including stress, sleep, food quality and environmental toxins, the simplest and most effective thing you can do to achieve optimal blood sugars is to do the opposite of what caused the problem in the first place.

Listed below are the main things that cause diabetes and what we can do to reverse it.

leads to diabetes reverses diabetes
Excessive sugar and simple carbohydrates in the diet generate high insulin load Reduce foods in your diet that require insulin [2]
Constant food with no significant periods between meals when insulin levels are reduced Create periods when your body does not have significant amounts of circulating insulin (i.e. intermittent fasting).

Sounds simple.  But it’s not easy or quick to reverse years of metabolic damage.   Your body is hard-wired to retain fat so it can survive the next famine.

Worth the effort?  People who have done it say yes.  That’s why they’re so annoyingly passionate about it!

Remember the type 1 diabetic roller coaster blood sugars in the last post?  The CGM plot shows the blood sugars of the same person a few months later on a low insulin load diet. [3] [4] [5]

image004 - Copy

foods that require insulin

You’re likely already aware that foods containing carbohydrates require your pancreas to produce insulin.

Recently I stumbled across some recent food insulin index test data [6] that indicates:

  • protein requires about half as much insulin as carbohydrates per gram on average, [7] and
  • carbohydrates in the form of indigestible fibre do not require insulin. [8]

So if you’re trying to reduce the insulin load of your diet you should:

  • limit simple processed carbohydrates that do not contain fibre,
  • choose high fibre foods (such as non-starchy vegetables) to obtain vitamins and minerals while keeping net carbohydrates low, and
  • back off on the protein if you’re not achieving the normalised blood sugars, weight loss or nutritional ketosis results you’re after.

insulin load

Rather than simply counting carbs, you could get a bit fancy and calculate your total insulin load using this formula:

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Most people will achieve nutritional ketosis with an insulin load of around 100 to 150 grams. Athletes and weight lifters will be able to tolerate more without messing up their blood sugars.  Inactive people aiming for weight loss may need to reduce their insulin load further. I don’t think that it’s ideal for most people to weigh and measure their food for extended periods.

If you’re not getting the results you want then tracking your food in MyFitnessPal or something similar can be a useful in the short term to retrain your dietary habits.

measuring for ketones versus measuring blood sugar

Once you get over seeing a little drop of your own blood, measuring your own blood sugar is pretty simple and painless, and is much cheaper than measuring blood ketones. In Australia and Canada blood sugar strips are about $0.16 compared to blood ketone strips which are about $0.80. [9]  In the US ketone strips are much more expensive, and basically unaffordable. Ketostix (which measure ketones in your urine) will typically only work for a little while until your body learns to use fat for fuel.

relationship between blood sugars and ketones

Blood sugar can be a useful way to see if you’re in ketosis. The chart below shows my blood sugars versus ketones over the last nine months or so that I’ve been trying to achieve nutritional ketosis.

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Based on my n=1 experience I’ve added the ketone levels which correlates HbA1c, average blood sugar and ketones.  This suggests that excellent blood sugar control for me is achieved when I’ve got ketone levels between 0.5 and 1.3mmol/L.

HbA1c average blood sugar ketones
 (%)  (mmol/L)  (mg/dL)  (mmol/L)
low normal 4.1 3.9 70 2.1
optimal 4.5 4.6 83 1.3
excellent < 5.0 < 5.4 < 97 > 0.5
good < 5.4 < 6 < 108 < 0.3
danger > 6.5 7.8 > 140 < 0.3

is more ketosis better?

The point way out to the right with a high ketone level of 2.1mmol/L and a blood sugar of 4.0mmol/L occurred after I cycled to work two days in a row on Bulletproof Coffee with a good amount of MCT oil.

In The Art and Science of Low Carbohydrate Performance [10] Volek and Phinney say that “light nutritional ketosis” occurs when blood ketones are between 0.5mmol/L and 1.0mmol/L and “optimal ketosis” is between 1.0mmol/L and 3.0mmol/L.

Based on the fact that an optimal blood sugar corresponds to a ketone reading of 1.3mmol/L and the low end of healthy normal blood sugars corresponds to a ketone reading of 2.1mmol/L I wonder if there is really any value in aiming for higher ketone values?

It’s interesting to note that Sami Inkenen, when rowing from the US to Hawaii on an 80% fat diet, [11] [12] was only getting ketones of around 0.6mmol/L [13]. If you’re striving for mental focus then loading up with butter, coconut oil and MCT oil to jack up your ketones might be for you.

If your aim is exercise performance or fat loss then ketones between 0.5mmol/L and 1.3mmol/L might be all you need to aim for. I also think loading up on dietary fat at the expense of getting adequate protein, vitamins and minerals may be counterproductive in the long term.

On the other end of the argument though, if you have good control of your blood sugars you should be showing some level of ketones in your blood.  If you consistently measure at a ketone value of less than 0.2mmol/L then it’s likely your blood sugar is not yet optimal.

what to do?

If you find this interesting and want to experiment I recommend that you buy a blood glucose metre and track your blood sugars for a while. I enter my results into a spreadsheet and look at the average of the past twenty results.

You can adjust your insulin load (i.e. less carbs, more fibre, moderate protein) until you achieve your target blood glucose level. As you test you’ll also notice that some foods cause your blood sugars to rise more than others.  Make sure you scratch those off your “do again” list.

You might also notice as you get your blood sugars under control you will get a metallic taste in your mouth, stronger smelling urine or a different body odour.  These are all signs that you’re transitioning into ketosis.  These symptoms typically don’t last for too long. If at first you don’t succeed, throw in some intermittent fasting.  I use bulletproof Coffee [16] to help me skip breakfast and sometimes lunch a couple of times a week.

Intermittent fasting is more effective than constant calorie restriction which can cause your metabolism to slow down due to conserve energy for the famine it thinks is coming. [17] [18] Having extended periods when insulin levels are low allows your body to learn to use body fat for fuel.

Once you begin to reset your insulin sensitivity you might start to notice a lack of inflammation and puffiness.  You may also find that you’re finally losing that stubborn weight and breaking through that dreaded plateau.  You may notice you feel great and your head is clearer than it’s been for a long time.  Or that that may just be my experience.

physiological insulin resistance

Some people find that as they reduce their carbohydrates that their fasting blood sugars will drift up.  This has been termed ‘physiological insulin resistance’ and is where the body develops a level of insulin resistance in the muscles to prioritise glucose for the brain. For some people this can be a transitionary phase on the way to stable ketosis.  It’s not thought to be something to be concerned about as it doesn’t cause elevated levels of insulin which is what can be really detrimental.

However some type 1 diabetics find it to be an issue long term and choose to increase the carbohydrates and protein in their food so they are just outside nutritional ketosis to reduce this effect.

My experience is that during this phase my post meal blood sugars were great even though the fasting blood sugars were higher than optimal.  As I continued to persist with more fat and added some intermittent fasting this went away and I was able to achieve lower fasting blood sugars.

Particularly during this time it is important to keep an eye on your average blood sugar (i.e. both fasting and after meals) and make sure it’s under 5.4mmol/L (100mg/dL).

can you eat too much fat?

It’s good to see medical researchers [19] and the media [20] coming out and admitting that the fear of fats over the past 30 years has led to diabolical health outcomes.

The fear of fat has forced people to eat more simple carbohydrates which has led to the diabetes epidemic. I analysed a number of dietary scenarios to see if there is any truth to the fear that low carbohydrate diets do not provide adequate nutrition and that you need your “heart healthy whole grains” to achieve optimal health, provide enough sugar for the brain, support growth in children etc. While a grain-based diet can be cheaper, my analysis suggest that a high fat diet that focuses on high fibre, high nutrient density, non-starchy vegetables is better in terms of the nutrition it provides and managing insulin demand.

The optimal diet to balance vitamins and minerals, amino acids and insulin load appears to contain between sixty and eighty percent calories from fat. It is possible to meet the recommended daily intake for most vitamins and minerals with 80% of calories coming from fat.

At the other end of the scale, higher levels of carbs may leave you storing more fat than you want to due to high insulin levels.

which foods are optimal?

What foods are optimal?  It all depends on your unique situation, goals and even finances.

I have developed a system to prioritise food choices based on the insulin properties of various foods as well as a range of other factors including:

  • nutrient density per calorie,
  • fibre per calorie,
  • nutrient density per dollar,
  • calorie density per weight, and
  • calories per dollar.

The list of foods below is a summary of the highest ranking foods using the weighting shown below in order to identify low insulin, high nutrient density food choices will lead to improved blood sugar control, mood, mental clarity, weight loss and overall health.

ND / calorie fibre / calorie ND / $ ND / weight insulinogenic (%) calorie / 100g $ / calorie
15% 5% 5% 10% 50% 10% 5%

Next time you’re wanting a nutritious meal that will push you into ketosis or lower your blood sugars you could consider some of these foods.

I’ve also developed this ‘cheat sheet using this approach to highlight optimal food choices depending, whether they be reducing insulin, weight loss or athletic performance.   Why not print it out and stick it to your fridge as a reminder of your optimal foods or to inspire your next shopping expedition?

vegetables

  • turnip greens
  • coriander (cilantro)
  • rosemary
  • spinach
  • parsley
  • peppers / capsicum
  • chives
  • mustard greens
  • collards
  • mushrooms
  • Swiss chard
  • artichokes
  • broccoli
  • Brussel sprouts
  • kale

fats and oils

  • butter
  • coconut oil
  • olive oil
  • fish oil
  • flaxseed oil

fruits

  • avocados
  • olives

eggs & dairy

  • whole egg
  • goat cheese
  • goat cheese
  • parmesan cheese
  • cheddar
  • cream
  • camembert
  • feta
  • cream cheese
  • blue cheese
  • Colby cheese
  • Swiss cheese
  • edam cheese
  • brie
  • gouda
  • mozzarella
  • ricotta
  • cottage cheese

nuts & seeds

  • brazil nuts
  • sunflower seeds
  • pecans
  • pumpkin seeds
  • almonds
  • macadamia nuts
  • pine nuts
  • coconut milk
  • coconut meat
  • pistachio nuts
  • cashews

animal products

  • organ means (liver, kidney, heart etc)
  • chorizo
  • bratwurst
  • herring
  • chicken
  • frankfurter
  • mackerel
  • duck
  • beef sausage
  • bacon
  • turkey
  • anchovy
  • ground beef
  • lamb
  • bologna
  • turkey
  • beef steak

In the next article we’ll look at which foods are optimal for weight loss by prioritising low calorie density, high fibre high nutrient density foods that will also help stabilise your blood sugars.

references

[1] https://www.youtube.com/watch?v=4oZ4UqtbB_g

[2] http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2716748/

[3] http://www.diabetes-book.com/

[4] https://www.youtube.com/channel/UCuJ11OJynsvHMsN48LG18Ag

[5] https://www.facebook.com/Type1Grit

[6] http://ses.library.usyd.edu.au/handle/2123/11945

[7] Some anecdotal evidence and studies such as http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4342171/pdf/IJE2015-216918.pdf indicate that it’s the protein in excess of the body’s needs for muscle growth and repair that gets turned to glucose and requires insulin.

[8] http://healthyeating.sfgate.com/indigestible-carbohydrates-1023.html

[9] http://www.ebay.com.au/itm/BEST-PRICE-10-X-ABBOTT-FREESTYLE-OPTIUM-KETONE-TEST-STRIPS-10-TOTAL-100-STRIPS/181527585627?_trksid=p2054897.c100204.m3164&_trkparms=aid%3D222007%26algo%3DSIC.MBE%26ao%3D1%26asc%3D20140407115239%26meid%3Db2cedda776824d9f8ed5d131a3232ea7%26pid%3D100204%26rk%3D3%26rkt%3D24%26sd%3D281508543955

[10] http://www.amazon.com/The-Art-Science-Carbohydrate-Performance/dp/0983490716

[11] https://gumroad.com/l/CK219

[12] http://www.fatchancerow.org/

[13] https://twitter.com/samiinkinen/status/451089012166385664

[14] https://www.facebook.com/ketogains

[15] https://www.facebook.com/ketogains

[16] https://www.bulletproofexec.com/bulletproof-fasting/

[17] https://www.youtube.com/watch?v=4oZ4UqtbB_g

[18] http://www.bodybuilding.com/fun/drsquat6.htm

[19] http://www.touchendocrinology.com/articles/nutrition-revolution-end-high-carbohydrates-era-diabetes-prevention-and-management [20] http://time.com/2863227/ending-the-war-on-fat/

[21] https://www.dropbox.com/s/h0zd5pjgw0gfqgq/Appendix%20D%20-%20Nutritional%20analysis%20of%20typical%20diets.docx?dl=0

[22] https://www.dropbox.com/s/ninuwyreda0epix/Optimising%20nutrition%2C%20managing%20insulin.docx?dl=0