What influences fat metabolism during cycling? Our New Research

As we have discussed at length in previous blogs, and in our courses, carbohydrates and fats provide the energy to fuel metabolism during prolonged, endurance exercise (3). The carbohydrate energy we have stored in our muscles and liver as glycogen is limited, and can become depleted to low concentrations during long-duration exercises like Ironman triathlon (1, 6). In contrast, our fat energy stores, whilst metabolised more slowly, are effectively limitless in the context of exercise. For example, given that 1 g of fat provides ~9.75 kcal of energy (4), it can be estimated that a lean 70-kg individual with 10% body fat has ~68,250 kcal of endogenous fat energy. Theoretically at least, this is enough energy to complete more than six full-distance Ironman triathlons (5).

Accordingly, exercise physiologists have conducted huge amounts of research into the factors that influence the rates at which we metabolise fats and carbohydrates during exercise. This research has been particularly useful for those seeking to manipulate substrate utilisation responses to specific training sessions. My PhD student Jeff Rothschild and I recently published a paper that sought to synthesise the vast existing literature on this topic, to identify the factors that are most determining substrate utilisation responses to exercise.

What we did

To do this, we extracted data from 433 studies reporting the respiratory exchange ratio (RER) during continuous cycling exercise. The RER is the ratio of an individual’s carbon dioxide production to oxygen consumption during exercise and is measured by indirect calorimetry or the collection of expired gases. As the burning of fat produces an RER of ~0.7, and the burning of carbohydrates produces an RER of ~1.0, we can make inferences about an individual’s substrate utilisation through the measurement of RER. For example, if an individual has an RER of 0.85 during exercise, this suggests they are fuelling their metabolism through a 50:50 mix of fat and carbohydrate; lower values suggest a greater contribution from fat, and higher values suggest a greater contribution from carbohydrates.

We ran correlations between RER and various factors previously linked to substrate utilisation responses to exercise; some easily modifiable, some easily measured, some neither. We then produced linear mixed-effect models to examine the contributions made by various factors to RER responses to exercise.

Main findings

As I’m sure you can imagine, this work produced a lot of data. Below are the main findings of our initial correlational analyses:

• RER decreased with exercise duration, dietary fat intake, and when starting exercise with lowered muscle glycogen content. These results were as expected, as they either promote fat availability or reduce carbohydrate availability.
• Also, RER decreased with an increasing % of type I (slow twitch) muscle fibres and V̇O2max. These results were also in line with our expectations, as slow twitch fibres are more geared towards metabolising fat than fast twitch fibres, as are more aerobically fit individuals.
• RER increased with exercise intensity, dietary carbohydrate intake, and with carbohydrate intake before and during exercise. Again, these results aligned with our expectations. When exercise intensity increases, we need to produce energy to fuel metabolism more rapidly, and so shift our substrate use towards carbohydrate, which is more quickly broken down than fat. Dietary carbohydrate intake promotes muscle glycogen storage and availability, which likely contributes to the increased RER. When we ingest carbohydrates during exercise, we have an ‘extra’ fuel source (stored carbohydrate, stored fat, and incoming ingested carbohydrate). The ingested carbohydrate reduces the burden on both stored carbohydrate and stored fat, pushing up RER.
• Female athletes have lower RER than male athletes. There are a number of potential reasons for this, one of these being typical fibre-type profiles.

Now I’ll briefly outline some of the main findings from our modelling:

• Collectively, our overall model - containing exercise duration and intensity, daily fat and carbohydrate intake, carbohydrate intake within 4 hours of exercise and during exercise, age, sex, and muscle glycogen at the start of exercise - could explain ~61% of the variation in RER responses to exercise. This is an impressive amount of variation but does suggest other factors contribute significantly to substrate utilisation, likely skeletal muscle characteristics (2, 7–10).
• For example, we recently found, in a small sample (N = 17) of trained males, that whilst V̇O2max and the second ventilatory threshold power output explained ~41% of the variation in peak fat oxidation rates (PFO), this increased to ~88% when the activity of the enzyme citrate synthase (an indicator of mitochondrial protein content) and abundance of the fatty acid transport protein FAT/CD36 was added to the model (these models are not presented in our recent paper (7)). Whilst we are not comparing apples with apples here – PFO and RER are not the same measures – this does suggest skeletal muscle characteristics are important contributors to inter-individual variation in substrate utilisation responses to exercise. Unfortunately, they can’t be easily measured!
• Our model using only easily modified and measured factors (exercise duration and intensity, dietary intake before and during exercise), only explained ~34% of the variation in RER. This demonstrates that, at an individual level, it is not easy to precisely predict RER responses to exercise. This may therefore support – if at all possible – getting profiled in a laboratory if you are interested in what your substrate response to exercise is like.
• The factors that had the largest effects were sex, diet, and exercise duration; interestingly, daily fat and carbohydrate intake seemed to have much larger effects on RER than carbohydrate ingestion during exercise.
• Exercise duration appeared to have greater effects on RER than exercise intensity.

 Practical applications

So, as a practitioner or an athlete, what can we take away from all this analysis? Here are three key points:

1. If you are looking to maximise rates of fat oxidation during a training session, it seems more important to focus on daily fat and carbohydrate intake than how many carbohydrates you ingest during exercise, which had less influence.

2. Exercise duration has a very strong effect on RER; so, if you want to generate high rates of fat oxidation during a training session, make it long. As carbohydrate intake during exercise had a relatively modest influence on RER, it may be worth ingesting some carbohydrates during exercise, particularly in the latter stages of very long training sessions.

3. You can’t easily predict your RER response to exercise at an individual level, but we can be relatively confident that modifying these factors (diet and exercise duration in particular) will push it up or down.

And remember, as we like to say at SFuels, it's all about the "Right Fuel, Right Time". Taking in fat-based drinks (e.g. SFuels Train) in the first 90-120 min of endurance training, and then switching to a more carbohydrate-based drink (e.g. SFuels Race +) seems like the best option. 

Last, if you want to play with the numbers, check out this app. It uses the same predictive models featured in this study. See what influences your substrate oxidation! It's a real geek-out ;).....

References

1. Bergström J, Hermansen L, Hultman E, Saltin B. Diet, muscle glycogen and physical performance. Acta Physiol Scand 71: 140–150, 1967.

2. Dandanell S, Meinlid-Lundby A, Andersen AB, Lang PF, Oberholzer L, Keiser S, Robach P, Larsen S, Rønnestad BR, Lundby C. Determinants of maximal whole‐body fat oxidation in elite cross‐country skiers : Role of skeletal muscle mitochondria. Scand J Med Sci Sport 28: 2494–2504, 2018. doi: 10.1111/sms.13298.

3. Hargreaves M, Spriet LL. Skeletal muscle energy metabolism during exercise. Nat Metab 2: 817–828, 2020. doi: 10.1038/s42255-020-0251-4.

4. Jeukendrup AE, Wallis GA. Measurement of substrate oxidation during exercise by means of gas exchange measurements. Int J Sports Med 26: S28–S37, 2005. doi: 10.1055/s-2004-830512.

5. Kimber NE, Ross JJ, Mason SL, Speedy DB. Energy balance during an Ironman triathlon in male and female triathletes. Int J Sport Nutr Exerc Metab 12: 47–62, 2002. doi: 10.1123/ijsnem.12.1.47.

6. Maunder E, Kilding AE, Plews DJ. Substrate metabolism during Ironman Triathlon: Different horses on the same courses. Sports Med 48: 2219–2226, 2018. doi: 10.1007/s40279-018-0938-9.

7. Maunder E, Plews DJ, Wallis GA, Brick MJ, Leigh WB, Chang WL, Stewart T, Watkins CM, Kilding AE. Peak fat oxidation is positively associated with vastus lateralis CD36 content, fed‑state exercise fat oxidation, and endurance performance in trained males. Eur J Appl Physiol in press: 1–10, 2021. doi: 10.1007/s00421-021-04820-3.

8. Nordby P, Saltin B, Helge JW. Whole-body fat oxidation determined by graded exercise and indirect calorimetry: A role for muscle oxidative capacity? Scand J Med Sci Sport 16: 209–214, 2006. doi: 10.1111/j.1600-0838.2005.00480.x.

9. Shaw DM, Merien F, Braakhuis A, Keaney L, Dulson DK. Adaptation to a ketogenic diet modulates adaptive and mucosal immune markers in trained male endurance athletes.

10. Stisen AB, Stougaard O, Langfort J, Helge JW, Sahlin K, Madsen K. Maximal fat oxidation rates in endurance trained and untrained women. Eur J Appl Physiol 98: 497–506, 2006. doi: 10.1007/s00421-006-0290-x.