Tag Archives: glucose load

the intimate relationship between carbohydrates, protein, insulin, fibre, fat, sugar and nutrient density exposed!

  • The food insulin index data demonstrates that the carbohydrate content of your food does not accurately predict insulin response.
  • Protein requires about half as much insulin as carbohydrate.
  • Indigestible fibre from whole foods tends to have a minimal influence on our glucose and insulin response.
  • Dietary fat does not require a significant amount of insulin.
  • Net carbohydrates plus approximately half protein correlates well with our insulin response.
  • This understanding can help select foods that will cause a lower insulin response and enable more accurate calculation of insulin dosing for people with diabetes.

background

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

When the Global Financial Crisis hit in 2009 things got too volatile, and I got out of the market.  It was no longer fun.  However, the skills I learned as a quantitative trader, along with my day job as an engineering running multi-criteria analyses to identify motorway alignments and prioritise road investments and the like have given me a unique perspective on nutrition that people seem to have found useful.

On the Optimising Nutrition blog, I have tried to describe a system to manage nutrition that makes sense to me.  In these articles, I try to document the things that I wish someone had shown to Monica and me 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, 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 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, to better manage blood glucose and insulin demand.

carbohydrate

The amount of carbohydrate does an excellent job of explaining the amount our glucose levels increase.

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

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.

The importance of dietary fibre should not be discounted, especially when trying to reduce insulin demand.  Some recommend that people with diabetes limit total carbohydrates, rather than considering net carbohydrates, or non-fibre carbohydrates.  The danger with a total carbohydrates approach is that people will avoid non-starchy fibrous 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 the highest fat foods have the lowest insulin response (R2 = 0.38, r = 0.631, p < 0.001).

 

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).  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).

Now, while getting more of your energy from fat will help to reduce your insulin requirements and stabilise your blood sugar, you should keep in mind:

  1.  The glycerol backbone in fat can be converted to glucose if necessary via gluconeogenesis, so there can still be some insulin and glucose response to refined fat.
  2. Refined fat typically does not contain a broad spectrum of micronutrients.
  3. While type 2 diabetes appears on the surface to be a condition of glucose intolerance, it is fundamentally an issue with your adipose tissue being full.  Once you exceed your personal fat threshold your body fat is no longer able to hold excess energy and it spills over into the bloodstream.  Reducing the carbohydrates in your diet will stabilise the blood glucose swings, however, you will need to reduce your overall energy intake to enable the excess energy to flow from your body fat stores before you become truly insulin sensitive and lower your blood glucose levels.

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.  When we zoom in on the bottom left corner of the carbohydrate vs insulin response chart we see that high-fat foods such as butter, bacon, avocado, olive oil and walnuts do not have a significant insulin response.  However, high protein foods such as fish, steak and tuna still have a significant insulin response.

As a general rule, as we increase the protein content of our food our insulin requirements come down.  High protein foods force out the processed carbohydrates which require the greatest amount of insulin.  Choosing higher protein foods will generally reduce insulin (R2 = 0.10, r = 0.47, p < 0.001).

Increasing protein will also typically lead to a spontaneous reduction in intake due to the thermic and satiety effects of protein. [3] [4]   It is vital to eat adequate protein, but it is hard to overeat protein due to the strong satiety response.

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 people with diabetes 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 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.

For most people, transitioning to a reduced carbohydrate whole foods diet will give them most of the results they are after.  However, for people who require a therapeutic ketogenic diet, consideration of protein may be necessary 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.

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). Most people struggling with diabetes will need to consider the total sugar in their diet to optimise blood.

insulin load vs food insulin index

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).

nutrient density

One of the shortcomings of the insulin load concept is that extremes of insulin load can lead to a nutrient-poor outcome.  As shown in the chart below, nutrient density seems to peak at about 40% insulinogenic calories.  If you are insulin resistant, you will want to choose food that has less than 40% insulinogenic calories.

If you have diabetes, you may want to tweak your diet to less than 25% insulinogenic calories.  Meanwhile, if you are chasing therapeutic keto, then you will want less than 15% insulinogenic calories.  But keep in mind that this will have negative impacts on your ability to get the essential nutrients you need.

the Nutrient Optimiser

We’ve been working hard to build a tool that will make all this info easier to understand and apply.    If you head on over to NutrientOptimiser.com and tell us some details about yourself this exciting new free tool will give you target macronutrient ranges, optimal food choices and suggested meals that will help you reach your goals.

We’re very excited to have this tool now available for you to use.  We’d love your feedback on how we can improve it to help more people.

 

last updated January 2018

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