Macronutrients: how to get the right carbohydrate, protein and fat balance

In our previous post, we revealed our secrets of how we calculate the calories that you should eat. But we also said that it isn’t just HOW MUCH you eat that matters, but WHAT you eat too.

Mammoth Hunters recommends a diet based on the proportions of macronutrients (that’s protein, carbohydrates and fats), and today we’re going to explain how we work that out. But before we start, because we approach this problem scientifically, we’re going to give you a summary of the current state of play in the world cup of food and nutrition.

Macronutrients percentages: quantity or quality?

There are currently two theories on why some diets make people grow fat, and/or unhealthy:

  • Traditional theory (A): they eat too much
  • Alternative theory (B): as well as quantity, the effect what we eat has on our metabolism is also important.

Although it might seem at first sight that the two theories complement each other, these two viewpoints have become polarized into two opposing battle lines.

Hypothesis A: all calories in macronutrients are equal

This theory is staightforward: the obesity problem arises because something is wrong with our energy balance. In other words, because we eat too much.

One consequence of this viewpoint has been the way fats have been demonised over the last few years. A gram of fat contains 9 Kcal while a gram of carbohydrate contains 4 Kcal. Besides, foods that contain carbohydrates do so in a relatively low proportion. For example, vegetables have about 15% carbohydrates, while oil or butter are basically 100% fats. So it’s easy to understand that, under this paradigm, eating fat is the worst thing you could do.

A big supporter of this hypothesis is Dr. Kevin Hall of the US National Institute for Diabetes and Liver Disease. Throughout his career, Dr. Hall has developed mathematical models to study macronutrient metabolism. His results seem to indicate that big changes in different types of macronutrients don’t affect our bodies’ hormonal balance too much(2). Here we leave you a video (in English) introduced by the great man in person:

Hypothesis B: counting macronutrients is what matters

This theory is more recent, and it’s the one supported by authors such as Loren Cordain (one of the big names in the Paleo diet), Dr. Peter Attia or the science journalist  Gary Taubes,  among many others.  According to this theory of obesity, particular foods in our current diet bring about a malfunction of the hormones and enzymes in our body. For example, the expression of hormones such as insulin, glucagon or leptin that control the storage or release of fats is related to the type of macronutrients we eat. This second theory suggests the opposite of eating too much; that is to say, it puts the whole emphasis on the macronutrients and not the food itself.

The main argument in favour of those who support this alternative theory, is that studies done so far on nutrition and obesity are, to put it mildly, not nearly rigorous enough at the scientific level and they mostly don’t give sufficient consideration to the relevant question as to how diet affects our health.

The good news is, there is a big movement towards unification. The Nutrition Science Initiative organisation has initiated a several million dollar program to use proper science to find the ultimate answers to the nutritional problems we are now experiencing.

How to calculate macronutrients

Now that we’ve given you this optimistic introduction as to how things stand in the nutrition world, we’re going to tell you how we’ve made our calculations and made a “macronutrients app” for Mammoth Hunters.

Going back to what we put in our post about calculating calories, we can say that the energy a person expends is:


This formula tells us that the calories an individual must take in depends on the calories they expend on a rest day plus the energy used up in exercise.


So we start by calculating the macronutrients ratio in a rest day, and then adjust it to a training day.


The first macronutrient to adjust is protein. The WHO recommends that we should be a minimum of 0.66 g protein per Kg weight for an adult. This seems quite low isn’t it? The function of the WHO is to establish minimum standards to avoid nutritional deficiencies and thus diseases. Remember, though, that health does not mean absence of disease.

Our aim is to provide dietary advice towards health, not to avoid diseases, and thus we recommend protein quantities that are substantially higher (as you can see from the following table). To arrive at these proportions we reject the myth that a diet moderately high in proteins is bad for health (see many references, 2,3,4,5,6,7).  By this we don’t mean that we have to have a high protein diet to stay healthy, thus falling into excessive nutritionism (Noooooo way!). Current population studies of hunter gatherers have shown how both high and low protein diets can be equally healthy. 8,9,10,11,12,13.

Activity level Weight loss Staying healthy Muscle building
Sedentary 1.1 1.1 1.3
Active 1.5 1.5 1.7
Very active 1.8 1.8 2
Athletic 2.2 2.2 2.5
Super athletic 2.6 2.6 2.8

(Units are grams of protein per Kg body weight)

It isn’t difficult to work out the amounts of protein from this table, since Mammoth Hunters supporters give us the data we need.


Calculating carbohydrates is a bit more complicated. Most of all because of the variety of ‘scientific’ data that we have come across.  Strangely enough, most nutritionists use carbohydrate tables that don’t depend on body weight.

The following table gives us an example of this method:

Objective Glycaemic intake (g/day)
ketogenic diet 60
sedentary low carbohydrate diet 100
sedentary person 116
person with low activity level 132
active person 141
very active person 150

We think this is not a very accurate approximation for two reasons:

  • The calories that a person uses up largely depend on that person’s weight. To separate carbohydrate consumption from this variable will cause big fluctuations between different people in the share of calories that come from carbohydrates (a girl weighing 50 Kg uses up 1500 kCal whereas a big man of 100 Kg can use up 2,800 Kcal). So, if we don’t take body mass into account, these two people with the same level of activity would take in the same amount of carbohydrates.
  • If we calculate proteins as a function of body weight, it seems very inconsistent not to do the same with carbohydrates.

The second obstacle we cover is that the only studies we found that looked at carbohydrate consumption as a function of weight were in the context of high performance sportsmen. In those cases, the proportion of carbohydrates was extremely high (a minimum of 3g carbohydrate/Kg body weight on rest days and up to 12 g/Kg on training days)14; levels that represent 70% of the diet. In addition, these articles recommend taking in carbohydrates with a high glycemic load that are full of anti-nutrients: (pasta, bread etc …). This goes very much against our philosophy.

We believe that these proportions are recommended  because these studies were made with the premise that fat is bad for you. Furthermore, they were working with individuals who, because of their levels of physical activity, needed a diet that was way above the norm. And that isn’t the case for most of our users.

Finally, to get out of this jam, we use a different approach: paleontology and the paleo diet.

In a study published in the British Journal of Nutrition in 2010, Dr. Remko S. Kuipers suggests various scenarios of the Paleolithic diet, with a range of animal/vegetable protein contributing from 30%/70% to 70%/30%, and a proportion of carbohydrates of around 40%. In Mammoth Hunters we use these parameters to work out the amounts of carbohydrate per Kg of body weight that we suggest.

We make the following assumptions regarding macronutrients origin:

  • The protein comes from animals
  • The carbohydrates come from  vegetables
  • The fat has a mixed origin.
  Protein Fat Carbs
Meat 22% 4.0%  
Eggs 14% 7.0%  
Fish 20% 7.5%  
Vegetables 0.5%   15%
Root vegetables 0.5%   15%
Nuts   50% 20%
Olive oil   100%  

To make the diet more sustainable, we have chosen a calorie ratio of around 70/30% animal/vegetable origin (70% vegetable, 30% animal). If we believe in living in accordance with nature, we have to look after it as well.

In weight loss diets we reduce carbohydrate intake, in order to reprogram the metabolism towards using up fats efficiently (a.k.a ketogenic macronutrients diet).

Taking all these variables on board, our proposal for grams of carbohydrate/Kg of body weight is:

Activity level Weight loss Staying healthy Muscle building
Sedentary 1 1.5 2
Active 1.5 2 2.5
Very active 2 2.5 3
Athletic 2.5 3 3.5
Super athletic 3 3.5 4


By this time you’ll be getting tired of all these formulae!

Luckily, calculating the fat is very easy.

We just look at the energy we need and take away the sum of the proteins and carbohydrates … and voila, now we know how much fat we need!

FAT INTAKE = (TDEE – g protein * 4 kCal/g protein – g carbohydrates * kCal/g carbohydrate) / 9  kCal/g fat

For the perfectionist, part of this fat will come from eating protein (0.35 g of fat/g protein approx), so in our recommended intake we subtract this portion:


And that’s it!

We now know the macronutrients ratio for a resting day…

But that’s not all!

And we warn you that the rest isn’t easy.

Calculation for a training day

Calculating macronutrients for a training day has been the hardest bone to pick, although, as you’ll see, the solution is very simple in the end.

As we said at the beginning, a training day is like a rest day; then add the energy used up in the session, then add the increase in oxygen consumption after exercise, or EPOC.

Let’s imagine a scenario with a person who uses up 2000 kCal while resting and 2500 kCal on a training day.  This person has used up some 25% more energy. If you remember the explanation in our previous post, to calculate TDEE we use BMR multiplied by a factor determined by the level of activity.

Each of these factors results in a leap of about 20% in relation to BMR.

Activity level Index Calories Increase in relation to BMR
BMR    1667  
Sedentary 1.20 2000 20%
Active 1.37 2284 37%
Very active 1.55 2584 55%
Athlete 1.73 2884 73%
Super athlete 1.90 3167 90%

We could say that a sedentary person who increases his physical activity level by up to 40% on a training day is, to all intents and purposes, an active person. Logically, our first step was to recalculate macronutrients pèrcentage for this new level of activity on the training day. Things looked good, but when we started doing tests, we realised that the more energy our supporter used up, the less fat the model offered.

Evidently this wasn’t the right solution!

Our second approach was to go a step further and look for the correlation between protein (or carbohydrate) consumption and the increase in energy expended. We realized we had an almost perfect correlation!  This regression told us that for each 1% more energy expended, we had to increase the amount of protein by 2.1.

Protein = (% calories in relation to the resting activity level * 2.1 + 1) * protein on a rest day

We must admit that when we worked out this formula we thought we were super geniuses!

But the results were even more unfortunate.  Now we not only had cases with less fat as more energy was consumed, but we were also getting negative fat values!  Once we had got over our initial frustration we recognized the big mistake we had made, or rather, actually,  two big mistakes we had made.

  • The amount of protein (and carbohydrates) is linked to the person’s body weight and not to their level of consumption. This means that, in jumping from one activity level to another, the ratio is not kept, and in many cases the increase in calories provided is greater than the caloric increase, so the model compensates by lowering the fats.
  • It’s a mistake to use the percentage increase as this isn’t a standardized measure. For a person who consumes 2500 kCal, 10% comes to 250 kCal, whereas for a person who consumes 1500 kCal that only comes to 150kCal. In our model, however, we linked the same increase in the amount of protein and carbohydrates in both cases.

Conclusions: we are not geniuses!

But maybe we were making things too complicated …

This is one of those cases where our enthusiasm for making  a super model blinded us to the simplest option: if we increase calorie expenditure by 20% we increase macronutrients by 20%. The advantage of this model is that, as well as being much simpler and more robust, it allows us to keep the proportion of macronutrients that we think is ideal.

In the chart below you can see an example of counting macronutrients for an active person who wants to keep healthy, with different levels of activity. You can see not just the amount of macronutrients but also how this is reflected in different meals. We have also included the % in grams and energy obtained from animal and vegetable sources.

Tabla de Macros


At best you will be wondering, why such a fuss over a few nuts?

We could have avoided this long discussion and  just told you the way we worked it out. Once more our answer is, that we care about the science. We want to share with you the whole process we followed so the logic behind our model will be clear.

We suppose that by this time you will have realized that there is no suitable, ideal standard method of working out calories and macronutrients. In reality all the models are approximate.  We’ve done the best we can to adapt as much to scientific rigor as to the guidelines of the evolutionary diet.

Any comments, suggestions or criticisms you have will be very gratefully received.  Our intention is, as always, to offer the most rigorous model and the one that  makes life easiest for our supporters.

Did you like this article?

Feel inspired to visit  Mammoth Hunters and try it out!


1.            Joint WHO/FAO/UNU Expert Consultation. Protein and amino acid requirements in human nutrition. World Health Organ. Tech. Rep. Ser. 1–265, back cover (2007).

2.            Skov, A. R., Toubro, S., Rønn, B., Holm, L. & Astrup, A. Randomized trial on protein vs carbohydrate in ad libitum fat reduced diet for the treatment of obesity. Int. J. Obes. Relat. Metab. Disord. J. Int. Assoc. Study Obes. 23, 528–536 (1999).

3.            Poortmans, J. R. & Dellalieux, O. Do regular high protein diets have potential health risks on kidney function in athletes? Int. J. Sport Nutr. Exerc. Metab. 10, 28–38 (2000).

4.            Knight, E. L., Stampfer, M. J., Hankinson, S. E., Spiegelman, D. & Curhan, G. C. The impact of protein intake on renal function decline in women with normal renal function or mild renal insufficiency. Ann. Intern. Med. 138, 460–467 (2003).

5.            Martin, W. F., Armstrong, L. E. & Rodriguez, N. R. Dietary protein intake and renal function. Nutr. Metab. 2, 25 (2005).

6.            Gross, J. L. et al. Effect of a chicken-based diet on renal function and lipid profile in patients with type 2 diabetes: a randomized crossover trial. Diabetes Care 25, 645–651 (2002).

7.            Pecis, M., de Azevedo, M. J. & Gross, J. L. Chicken and fish diet reduces glomerular hyperfiltration in IDDM patients. Diabetes Care 17, 665–672 (1994).

8.            Kuipers, R. S. et al. Estimated macronutrient and fatty acid intakes from an East African Paleolithic diet. Br. J. Nutr. 104, 1666–1687 (2010).

9.            Cordain, L., Miller, J. B., Eaton, S. B. & Mann, N. Macronutrient estimations in hunter-gatherer diets. Am. J. Clin. Nutr. 72, 1589–1592 (2000).

10.           Brinkworth, G. D., Noakes, M., Buckley, J. D., Keogh, J. B. & Clifton, P. M. Long-term effects of a very-low-carbohydrate weight loss diet compared with an isocaloric low-fat diet after 12 mo. Am. J. Clin. Nutr. 90, 23–32 (2009).

11.           Cordain, L., Eades, M. R. & Eades, M. D. Hyperinsulinemic diseases of civilization: more than just Syndrome X. Comp. Biochem. Physiol. A. Mol. Integr. Physiol. 136, 95–112 (2003).

12.           Lindeberg, S. & Lundh, B. Apparent absence of stroke and ischaemic heart disease in a traditional Melanesian island: a clinical study in Kitava. J. Intern. Med. 233, 269–275 (1993).

13.           Lindeberg, S., Nilsson-Ehle, P., Terént, A., Vessby, B. & Scherstén, B. Cardiovascular risk factors in a Melanesian population apparently free from stroke and ischaemic heart disease: the Kitava study. J. Intern. Med. 236, 331–340 (1994).

14.           Burke, L. M., Hawley, J. A., Wong, S. H. S. & Jeukendrup, A. E. Carbohydrates for training and competition. J. Sports Sci. 29 Suppl 1, S17–27 (2011).

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