Tag Archives: carbohydrate counting

energy density, food hyper-palatability and reverse engineering optimal foraging theory

I’m looking forward to Robb Wolf’s new book Wired to Eat in which he talks about the dilemma of optimal foraging theory (OFT) and how it’s a miracle in our modern environment that even more of us aren’t fat, sick and nearly dead.[1]


[yes, I may be a Robb Wolf fan boy.]

But what is  optimal foraging theory[2]?   In essence it is the concept that we’re programmed to hunt and gather and ingest as much energy us we can with the least amount of energy expenditure or order to maximise survival of the species.

In engineering or economics this is akin to a cost : benefit analysis.  Essentially we want maximum benefit for minimum investment.


In a hunter gatherer / paleo / evolutionary context this would mean that we would make an investment (i.e. effort / time / hassle that we could have otherwise spent having fun, procreating or looking after our family) to travel to new places where food was plentiful and easier to obtain.

In these new areas we could spend as little time as possible hunting and gathering and more time relaxing.  Once the food became scarce again we would move on to find another land of plenty.

The people who were good at obtaining the maximum amount of food with the minimum amount of effort survived and thrived and populated the world, and thus became our ancestors.  Those that didnt’ didn’t.

So you can see how the OFT paradigm would be well imprinted on our psyche.

OFT in the wild

In the wild, OFT means that native hunter gatherers would have gone bananas for bananas when they were available…


… gone to extraordinary lengths to obtain energy dense honey …


… and eaten the fattiest cuts of meat and offal, giving the muscle meat to the dogs.


OFT in captivity

But what happens when we translate OFT into a modern context?


Until recently we have never had the situation where nutrition and energy could be separated.

In nature, if something tastes good it is generally good for you.

Our ancestors, at least the ones that survived, grew to understand that as a general rule:

 sweet = good = energy to survive winter

But now we have entered a brave new world.


These days we have are surrounded by energy dense hyperpalatable foods that are designed to taste good without providing substantial levels of nutrients.


When these foods are available our primal programming leaves us defenceless.

Our willpower or our calorie counting apps are no match for engineered foods with an optimised bliss point.


These days diabetes is becoming a bigger problem than starvation in the developing world due to a lack of nutritional value in the the foods they are eating.[3]

The recent industrialisation of the world food system has resulted in a nutritional transition in which developing nations are simultaneously experiencing undernutrition and obesity.

In addition, an abundance of inexpensive, high-density foods laden with sugar and fats is available to a population that expends little energy to obtain such large numbers of calories.

Furthermore, the abundant variety of ultra processed foods overrides the sensory-specific satiety mechanism, thus leading to overconsumption.”[4]

what happens when we go low fat?

So if the problem is simply that we eat too many calories, one solution is to reduce the energy density of our food by avoiding fat, which is the most energy dense of the macronutrients.

Sounds logical, right?

The research into the satiety index demonstrates that there is some basis to the concept that we feel more full with lower energy density, high fibre, high protein foods.[5] [6]   The chart below shows how hungry people report being in the two hours after being fed 1000kJ of different foods (see the low energy density high nutrient density foods for weight loss article for more on this complex and intriguing topic).


However the problem comes when we focus on reducing fat (along with perhaps reduced cost, increased shelf life and palatability combined with an attempt to reach that optimal bliss point[7]), we end up with cheap manufactured food like products that have little nutritional value.


Grain subsidies were brought in to establish and promote cheap ways to feed people to prevent starvation.[8]  It seems now they’ve achieved that goal.[9]


Maybe a little too well.


The foods lowest in fat however are not necessarily the most nutrient dense.     Nutritional excellence and macronutrients are are not necessarily related.

In his blog post Overeating and Brain Evolution: The Omnivore’s REAL Dilemma Robb Wolf says:

I am pretty burned out on the protein, carbs, fat shindig. I’m starting to think that framework creates more confusion than answers.

Thinking about optimum foraging theory, palate novelty and a few related topics will (hopefully) provide a much better framework for folks to affect positive change. 

The chart below shows a comparison of the micronutrients provided by the least nutrient dense 10% of foods versus the most nutrient dense foods compared to the average of all foods available in the USDA foods database.


The quantity of essential nutrients you can get with the same amount of energy is massive!  If eating is about obtaining adequate nutrients then the quality of our food, not just macronutrients or calories matters greatly!

Another problem with simply avoiding fat is that the foods lowest in fat are also the most insulinogenic so we’re left with foods that don’t satiate us with nutrients and also raise our insulin levels.  The chart below shows that the least nutrient dense food are also the most insulinogenic.

what happens when we go low carb?

So the obvious thing to do is to rebel and eliminate all carbohydrates because low fat was such a failure.  Right?


So we swing to the other extreme and avoid all carbohydrates and enjoy fat ad libitum to make up for lost time.

The problem again is that at the other extreme of the macronutrient pendulum we may find that we have limited nutrients.

The chart below shows a comparison of the nutrient density of different dietary approaches showing that a super high fat therapeutic ketogenic approach may not be ideal for everyone, at least in terms of nutrient density.  High fat foods are not always the most nutrient dense and can also, just like low fat foods, be engineered to be hyperpalatable to help us to eat more of them.


The chart below shows the relationship (or lack thereof) between the percentage of fat in our food and the nutrient density.   Simply avoiding or binging on fat does not ensure we are optimising our nutrition.


While many people find that their appetite is normalised whey they reduce the insulin load of their diet high fat foods are more energy dense so it can be easy to overdo the high fat dairy and nuts if you’re one of the unlucky people whose appetite doesn’t disappear.


what happens when we go paleo?

So if ‘paleo foods’ worked so well for paleo peeps then maybe we should retreat back there?  Back to the plantains, the honey and the fattiest cuts of meat?


Well, maybe.  Maybe not.


For some people ‘going paleo’ works really well.  Particularly if you’re really active.

Nutrient dense, energy dense whole foods work really well if you’re also going to the CrossFit Box to hang out with your best buds five times a week.


But for the rest of us that aren’t insanely active, then maybe simply ‘going paleo’ is not the best option…


… particularly if we start tucking into the energy dense ‘paleo comfort foods’.


If we’re not so active, then intentionally limiting our exposure to highly energy dense hyperpalatable foods can be a useful way to manage our OFT programming.

enter nutrient density

A lot of people find that nutrient dense non-starchy veggies, or even simply going “plant based”, works really well, particularly if you have some excess body fat (and maybe even stored protein) that you want to contribute to your daily energy expenditure.


Limiting ourselves to the most nutrient dense foods (in terms of nutrients per calorie) enables us to sidestep the trap of modern foods which have separated nutrients and energy.  Nutrient dense foods also boost our mitochondrial function, and fuel the fat burning Krebs cycle so we can be less dependent on a sugar hit for energy (Cori cycle).

Limiting yourself to nutrient dense foods (i.e. nutrients per calorie) is a great way to reverse engineer optimal foraging theory.


If your problem is that energy dense low nutrient density hyperpalatable foods are just too easy to overeat, then actively constraining your foods to those that have the highest nutrients per calorie could help manage the negative effects of OFT that are engrained in our system by imposing an external constraint.


But if you’re a lean Ironman triathlete these foods are probably not going to get you through.  You will need more energy than you can get from nutrient dense spinach and broccoli.

optimal rehabilitation plan?

So while there is no one size fits all solution, it seems that we have some useful principles that we can use to shortlist our food selection.

  1. We are hardwired to get the maximum amount of energy with the least amount of effort (i.e. optimal foraging theory).
  2. Commercialised manufactured foods have separated nutrients from food and made it very easy to obtain a lot of energy with a small investment.
  3. Eliminating fat can leave us with cheap hyperpalatable grain-based fat free highly insulinogenic foods that will leave us with spiralling insulin and blood glucose levels.
  4. Eating nutrient dense whole foods is a great discipline, but we still need to tailor our energy density to our situation (i.e. weight loss vs athlete).

the solution

So I think we have three useful quantitative parameters with which to optimise our food choices to suit our current situation:

  1. insulin load (which helps as to normalise our blood glucose levels),
  2. nutrient density (which helps us make sure we are getting the most nutrients per calorie possible), and
  3. energy density (helps us to manage the impulses of OFT in the modern world).


I have used a multi criteria analysis to rank the foods for each goal.  The chart below shows the weightings used for each approach.


The lists of optimal foods below have been developed to help you manage your primal impulses.  The table below contains links to seperate blog posts and printable .pdfs for a range of dietary approaches that may be of interest depending on your goals and situation.

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 you identify your optimal dietary approach.


I hope this helps.

Good luck out there!


[1] http://ketosummit.com/

[2] https://en.wikipedia.org/wiki/Optimal_foraging_theory

[3] http://www.hoajonline.com/obesity/2052-5966/2/2

[4] https://www.ncbi.nlm.nih.gov/pubmed/24564590

[5] http://nutritiondata.self.com/topics/fullness-factor

[6] https://www.ncbi.nlm.nih.gov/pubmed/7498104

[7] https://www.nextnature.net/2013/02/how-food-scientists-engineer-the-bliss-point-in-junk-food/

[8] https://en.wikipedia.org/wiki/Agricultural_subsidy

[9] http://blog.diabeticcare.com/diabetes-obesity-growth-trend-u-s/

insulin dosing options for type 1 diabetes

  • This article reviews a range of approaches to calculating insulin requirements for people with type 1 diabetes.
  • The simplest approach is standard carbohydrate counting, which may be ideal for someone whose diet is dominated by carbohydrates.
  • Bernstein recommends standardised meals for which the insulin dose is refined based on ongoing testing and refinement.
  • Stephen Ponder’s ‘sugar surfing’ builds on carbohydrate counting, with correcting insulin given when blood glucose levels rise above a threshold due to gluconeogenesis.
  • The food insulin index approach predicts insulin requirements based testing in healthy people of the insulin response to popular foods.
  • The total available glucose (TAG) advocates a ‘dual wave bolus’ where insulin for the carbohydrates is given with the meal, with a second square wave bolus given for the protein which is typically slower to digest and metabolise.


In the article Standing on the Shoulders of Giants we met a handful of people who have achieved excellent blood sugar control in spite of having type 1 diabetes.  Common elements of their success include:

  • keeping carbohydrates low to prevent the blood sugar roller coaster,
  • accurately dosing for a controlled amount of dietary carbohydrate,
  • targeting normal blood sugar ranges (i.e. 83mg/dL or 4.6mmol/L) with regular correcting doses,
  • regular exercise and / or intermittent fasting to improve insulin sensitivity, and
  • having a reliable method to account for the insulinogenic effect of protein.

Everyone’s diabetes management regimen is going to be different.  There will be a degree of trial and error to find what will work best for you.  This article reviews a number of approaches that you can learn from to see what suits you.

carbohydrate counting

In the 1970s Dr Richard Bernstein got hold of a blood glucose meter (long before they were easily available) and started experimenting on himself to understand how much a certain amount of carbohydrate raised his blood glucose levels and how much insulin he required to bring his blood glucose back down.

From this style of experimentation we can determine the “carbohydrate to insulin ratio” (i.e. how much insulin is required for a certain amount of carbohydrates).

Carbohydrate counting is now the standard approach that most people with type 1 diabetes are taught.  This approach involves estimating the grams of carbohydrate in your food for each meal.  With this knowledge you can then program the amount of carbohydrates eaten into the insulin pump which calculates the insulin dose based on a pre-set carbohydrate to insulin ratio.

The advantage of this approach is that it is relatively simple.  However it does not consider the insulin required for protein which is typically addressed with correcting doses or sometimes basal insulin.

Carbohydrate counting is a good starting point, but it is by no means perfect. [1] [2]

Bernstein’s own approach

Dr Richard Bernstein advises that his patients, in addition to restricting carbohydrates (i.e. no more than 6g of carbohydrates for breakfast, 12g for lunch and 12g dinner), to have the same meals every day which allows insulin doses to be refined to optimise for blood sugars.  If your blood glucose levels run high one day you can add a little more insulin the next day or a little less if they are running low.

Bernstein also advises targeting an average blood glucose of 83mg/dL (4.6mmol/L), and that you correct if blood glucose levels go outside a ten point range from this target (i.e. 73mg/dL to 93mg/dL or 4.0mmol/L to 5.2mmol/L).  A small bolus of insulin is given to bring blood glucose levels down and a certain portion of a glucose tablet is used to bring blood sugars back up precisely.

Bernstein advises that people with type 1 diabetes modulate the quantity of protein to manage their weight.  If you’re a growing child, protein is essentially unrestricted.  If you’re trying to lose weight protein can be reduced to further reduce insulin.

Bernstein says that protein requires about half as much insulin as carbohydrates and outlines how to dose for it in this video from Dr Bernstein’s Diabetes University.  In practice, though, his approach to dosing for protein requires consistent meals and fine tuning of insulin dose.

sugar surfing

Another popular method is ‘sugar surfing’ which is effectively a ‘bolt-on’ to carbohydrate counting developed by Dr Stephen Ponder to manage the glucose response to things other than carbohydrates.

This approach involves dosing for carbohydrates with the meal, and then watching the continuous glucose monitor (CGM) and giving regular ‘micro doses’ of insulin to keep blood sugar under around 93mmol/L (5.2mmol/L).  Typically two or three separate doses will be required to bring a ‘protein spike’ under control for someone with type 1 diabetes.

Injected insulin works over a period of up to eight hours and it is difficult to match the timing of the insulin action with digestion.  The advantages of this approach are that it allows for the variability in the time of protein digestion which varies from person to person and is different for different foods and hence difficult to predict accurately.


I think the secret to making ‘sugar surfing’ work is to turn the waves that you’re surfing into more manageable ripples by following a diet with a reduced insulin load.

food Insulin Index

“It is possible that other methods of matching insulin with food are not being studied because of the belief that carbohydrate counting is a well-founded, evidence-based therapy,” the researchers concluded. “Indeed, this meta-analysis shows the scarcity of high-level evidence.” [3]

One of the experiments documented in Clinical Application of the Food Insulin Index to Diabetes Mellitus (Bell, 2014) [4] demonstrated that type 1 diabetics calculating their insulin requirement using the food insulin index approach achieved significantly improved blood glucose control compared with those using standard carbohydrate counting.

Estimating the insulin required for the meal from carbohydrate and protein rather than carbohydrate alone is potentially a massive step forward in improving blood sugar control for people with type 1 diabetes.

The limitation of using the approach practiced in the study is that the food selection tested is limited to the one hundred or so popular processed supermarket foods.

If you’ve read my blog you’ll know that I’ve tried to develop a robust method for calculating the insulin requirement for foods based on their macronutrients without having to test them in vivo (i.e. in real, living people). [5] [6] [7]  The chart below shows how we can use the food insulin index test data to more accurately predict the insulin demand of a particular food using this formula.



Rather than separating doses for carbohydrates and protein, the food insulin index approach assumes that all of the insulin is given with the meal.  The risk with this is that the insulin will take action before the protein digests which will lead to low blood glucose.

Total Available Glucose (TAG)

The TAG (Total Available Glucose) approach is based on a book by Mary Joan Oexmann published in 1989. [8]  This method calculates the insulin required for carbohydrate, protein (54% of carbohydrate) and fat (10% of carbohydrate).

If we gave all the insulin calculated for both carbohydrates and protein when we sat down to eat a high protein meal it is possible that the insulin would take effect before the protein digested, leading to low blood glucose before the gluconeogenesis from the protein had time to kick in.

To deal with the fact that glucose from protein can take longer to show up in the blood stream people who follow the TAG approach typically use a ‘dual wave bolus’.  The insulin for the carbohydrates is dosed with the meal, while the insulin for the protein is infused slowly as a separate “square wave bolus” over a period of three hours or so.  The need for the carbohydrate and protein boluses to be split will depend on the amount of protein in your meal and how quickly protein raises your blood sugars.


separate boluses for carbohydrates and protein

A few people who achieve excellent blood sugar control simply use two separate boluses – one for carbohydrates before the meal with another one for protein around an hour after the meal.

This approach requires that the insulin for the protein and carbohydrate are calculated separately with the protein bolus being given a number of hours after the meal.

This approach can be refined using a CGM to confirm when the blood sugar response to the protein kicks in and hence when the bolus for protein is required.

insulin calculator

To assist in calculating the bolus for carbohydrates and protein Ted Naiman of Burn Fat Not Sugar has created this insulin calculator.

You can run it on your computer or phone, enter the properties of the food that you are about to eat and it will calculate the appropriate dose for carbohydrate and protein.  The outputs from this calculator will give you all the required data to follow any of the insulin dosing strategies above.

People who have used it so far have found it beneficial.



An example screen grab from the calculator is shown below with the explanation of inputs and outputs following.



  • If you are using a manufactured food product in a packet you can simply use the nutritional details per serve (protein, fat, carbohydrates) and then factor for the number of serves.
  • If you are in the US you will need to enter the total carbohydrates and fibre values. If you’re in the UK or Australia you don’t need to enter the fibre as it is already subtracted from the carbohydrate count.
  • The protein multiplier is based on the food insulin index testing in non-diabetic people, [9] however you can modify this if you want, based on your own trial and error testing. The analysis of the quantity of amino acids (as detailed in the article the insulin index v2) suggests that this value is unlikely to exceed 80 to 90%.
  • A default carb to insulin ratio of 22 has been used, however you can enter yours from your pump or calculate it based on this article.  [10]
  • You can enter your bolus insulin on board (BoB) which will be subtracted from the carbohydrate insulin dose.


  • The first line of the outputs is the percentage of insulinogenic calories. As a reference keep in mind that a whole egg is about 25% insulinogenic calories.  A high percentage of insulinogenic calories is not ideal for people with diabetes and insulin resistance.  It may be helpful to select nutritious foods with a low insulin load from this list.
  • The insulin load is calculated using the following formula (i.e. for both carbohydrates and protein).


  • The percentage of glucose from carbohydrate could be used if you were going to split your dose into an initial bolus for carbohydrates with a separate bolus for protein. This value can be entered into your pump if you’re using a dual wave bolus, with the protein bolus typically given over a longer period.
  • You can use the grams of carbohydrates or the quantity of insulin for the carbohydrates and the protein if you’re using separate boluses.


If you are following the meals from the blog you will notice that the net carbs, total insulin load and percentage of insulin for carbohydrates have been included in a table at the end of each recipe assuming a standardised 500 calorie meal.

our experience

Moni has experimented with dosing separately for the protein component of the meal, however it typically turns out that her blood glucose has risen by the time she doses for the protein.  It seems that for her, the glucose from the protein (via gluconeogenesis) hits her bloodstream quickly and hence delaying the dose for protein is not appropriate for her.

We are also trying to focus on a handful of nutrient dense meals with pre-determined insulin doses.  The table below shows the insulin required for a 500 calorie serving for a range of meals.  All of these have limited carbohydrates and the insulin dose for the protein is greater than the carbohydrate dose.  I have provided hyperlinks to some of the meals that are already published on the blog.

If you can’t handle the thought of weighing and measuring and then calculating the insulin dose for everything you eat, as a ‘rule of thumb’ all of these meals require dosing as if they were about 20 to 25 grams of carbs.  If you are choosing meals with a low insulin load then the insulin dosing for food ends up representing only about 20% of the daily dose.


carbs (g)

protein (g)

 insulin load (g)

bacon, eggs, avocado and spinach




spinach and cheddar scrambled eggs




steak, broccoli, spinach, haloumi




coffee with cream and stevia




chia seed pudding – no fruit




broccoli, bacon, cream and mozzarella




sausages, avocado, sour cream, tomato




bacon, eggs, spinach and pesto




bacon and eggs




bacon, eggs, avocado and sauerkraut




fathead pizza with anchovies and pesto




bacon asparagus and eggs




For us, this approach combines a number of aspects from the various approaches discussed above:

  • all of these meals having less than 12g of carbs as recommended by Bernstein,
  • calculated insulin dose for both protein and carbohydrates,
  • nutrient dense meals, and
  • relatively low percentage of insulinogenic calories (i.e. high fat meals) meaning that the overall insulin dose stays relatively low.

We’re all on a journey.  I hope this helps you move towards finding a strategy that is optimal for you.


[1] http://www.thelancet.com/journals/landia/article/PIIS2213-8587(13)70144-X/abstract

[2] http://www.medpagetoday.com/Endocrinology/Diabetes/42610

[3] http://www.thelancet.com/journals/landia/article/PIIS2213-8587(13)70144-X/abstract

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

[5] https://optimisingnutrition.wordpress.com/2015/03/23/most-ketogenic-diet-foods/

[6] https://optimisingnutrition.wordpress.com/the-insulin-index/

[7] https://optimisingnutrition.wordpress.com/?p=2637

[8] http://www.amazon.com/T-A-G-A-Diabetic-Food-System/dp/0688084583

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

[10] http://www.bd.com/us/diabetes/page.aspx?cat=7001&id=7303

putting it all together… protein and net carbs

So far we’ve learned that carbohydrate alone isn’t a fantastic predictor of insulin requirement.


The observation that protein requires about half as much insulin as carbohydrate improves our estimation of insulin demand.

Then understanding that fibre neutralises the insulin effect of carbohydrates also helps us predict the amount the amount of insulin required by a particular food.

Microsoft Word Document 25032015 40944 AM.bmp

Using net carbohydrates with an allowance for about half the protein gives us a better way to estimate insulin requirement of food compared to using carbohydrates alone.


In order to help us compare various food options we can calculate the proportion of insulinogenic calories of our foods using this formula:


And if we want to keep track of the insulinogenic load of our diet too keep our blood sugars under control or to maintain or achieve nutritional ketosis we can use this formula:


This deeper understanding of the impact of the influence of carbohydrates, protein and fibre may also be useful when it comes to choosing foods with a lower insulin load or even more accurately calculating insulin dosages for diabetics.

[next article…  how long does it take to digest protein?]

[this post is part of the insulin index series]

[Like what you’re reading?  Skip to the full story here.]

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).

food insulin index table - fructose analysis v2 21122015 44912 PM.bmp

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.


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


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


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


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



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


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.