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Metabolic characteristics of keto-adapted ultra-endurancerunners
Jeff S. Volek, Daniel J. Freidenreich, Catherine Saenz, Laura J. Kunces,
Brent C. Creighton, Jenna M. Bartley, Patrick M. Davitt, Colleen X. Munoz,
Jeffrey M. Anderson, Carl M. Maresh, Elaine C. Lee, Mark D. Schuenke,
Giselle Aerni, William J. Kraemer, Stephen D. Phinney
PII: S0026-0495(15)00334-0
DOI: doi: 10.1016/j.metabol.2015.10.028
Reference: YMETA 53329
To appear in: Metabolism
Received date: 26 June 2015
Revised date: 26 September 2015
Accepted date: 27 October 2015
Please cite this article as: Volek Jeff S., Freidenreich Daniel J., Saenz Catherine, Kunces
Laura J., Creighton Brent C., Bartley Jenna M., Davitt Patrick M., Munoz Colleen
X., Anderson Jeffrey M., Maresh Carl M., Lee Elaine C., Schuenke Mark D., Aerni
Giselle, Kraemer William J., Phinney Stephen D., Metabolic characteristics of keto-
adapted ultra-endurance runners, Metabolism (2015), doi: 10.1016/j.metabol.2015.10.028
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Metabolic characteristics of keto-adapted ultra-endurance runners
Jeff S. Voleka,b†, Daniel J. Freidenreicha,b, Catherine Saenza,b, Laura J. Kuncesa, Brent, C.
Creightona, Jenna M. Bartleya, Patrick M. Davitta, Colleen X. Munoza, Jeffrey M. Andersona,
Carl M. Maresha,b, Elaine C. Leea, Mark D. Schuenkec, Giselle Aernia, William J. Kraemera,b,
Stephen D. Phinneyd
aDepartment of Kinesiology, University of Connecticut, Storrs, CT, USA
bDepartment of Human Sciences, The Ohio State University, Columbus, OH, USA
cDepartment of Anatomy, University of New England, Biddeford, ME, USA
dSchool of Medicine (emeritus), University of California, Davis, Davis, CA, USA
Abbreviated Title: Keto-adaptation and ultra-endurance runners
Conflict of Interest: Dr. Volek and Dr. Phinney receive royalties from books on nutrition and
exercise.
†Corresponding Author:
Jeff S. Volek, PhD, RD
Professor
Department of Human Sciences
Ohio State University
305 W. 17th Ave
Columbus, OH 43210
Phone: 614-688-1701
E-mail: volek.1@osu.edu
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ABSTRACT
Background: Many successful ultra-endurance athletes have switched from a high-carbohydrate
to a low-carbohydrate diet; but they have not previously been studied to determine the extent of
metabolic adaptations.
Methods: Twenty elite ultra-marathoners and ironman distance triathletes performed a maximal
graded exercise test and a 180 min submaximal run at 64% VO2max on a treadmill to determine
metabolic responses. One group habitually consumed a traditional high-carbohydrate (HC: n=10,
%carbohydrate:protein:fat = 59:14:25) diet, and the other a low-carbohydrate (LC; n=10,
10:19:70) diet for an average of 20 mo (range 9 to 36 mo).
Results: Peak fat oxidation was 2.3-fold higher in the LC group (1.54 ± 0.18 vs 0.67 ± 0.14
g/min; P=0.000) and it occurred at a higher percentage of VO2max (70.3 ± 6.3 vs 54.9 ± 7.8%;
P=0.000). Mean fat oxidation during submaximal exercise was 59% higher in the LC group
(1.21 ± 0.02 vs 0.76 ± 0.11 g/min; P=0.000) corresponding to a greater relative contribution of
fat (88 ± 2 vs 56 ± 8%; P=0.000). Despite these marked differences in fuel use between LC and
HC athletes, there were no significant differences in resting muscle glycogen and the level of
depletion after 180 min of running (-64% from pre-exercise) and 120 min of recovery (-36%
from pre-exercise).
Conclusion: Compared to highly-trained ultra-endurance athletes consuming a HC diet, long-
term keto-adaptation results in extraordinarily high rates of fat oxidation, whereas muscle
glycogen utilization and repletion patterns during and after a 3 hr run are similar.
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Key Words: carbohydrate, fat, metabolism, exercise, glycogen
Abbreviations: VO2max = maximal oxygen consumption, HC = high-carbohydrate, LC = low-
carbohydrate, FASTER = Fat Adapted Substrate use in Trained Elite Runners, RPE = ratings of
perceived exertion, DXA = dual-energy X-ray absorptiometry, RER = respiratory exchange
ratio, USG = urine specific gravity, ELISA = enzyme-linked immunosorbent assay, NEFA =
non-esterified fatty acid, HOMA = homeostatic model assessment of insulin resistance, IP –
immediately post-exercise, PE-120 (120 min post-exercise)
Word Count (Abstract) = 240
Word Count (Manuscript) = 5567
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INTRODUCTION
The concept that a diet high in carbohydrate is necessary for optimizing exercise
performance gained credence in the late 1960s when it was discovered that muscle glycogen
depletion was associated with fatigue, and that a high-carbohydrate diet maintained muscle
glycogen and performance (1-3). Decades later, a great deal of evidence has accumulated in
support of consuming carbohydrate before, during and after exercise (4,5). Textbooks and
position statements (6) reinforce the supremacy of the high-carbohydrate fueling paradigm for
maximizing performance and recovery.
Less appreciated is the perspective that there is no essential requirement for dietary
carbohydrate because humans possess a robust capacity to adapt to low carbohydrate availability.
Around the same time the importance of glycogen was recognized in the 1960s, ground-breaking
work on the metabolic adaptations to starvation revealed an elegant mechanism by which
humans switch to using lipid-based fuels (7). After a few weeks of starvation when glycogen
levels significantly decrease, hepatic ketone production increases dramatically to displace
glucose as the brain’s primary energy source, while fatty acids supply the majority of energy for
skeletal muscle. Glucose production from non-carbohydrate sources via gluconeogenesis
supplies carbons for the few cells dependent on glycolysis. Similar metabolic adaptations
favoring near-exclusive reliance on lipid-based fuels occur after several weeks of a ketogenic
diet when carbohydrate is restricted to very low levels, protein is kept moderate, and dietary fat
is emphasized (8-10).
A critical mass of experimental data on ketogenic diets and ketone physiology has been
generated in the last decade demonstrating the safety and therapeutic efficacy in managing a
range of clinical conditions (11-13). Beyond the clinical applications, it was demonstrated more
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than three decades ago that consumption of a ketogenic diet with less than 10 g of carbohydrate
per day for 4 wk did not compromise endurance performance in elite cyclists (10). These keto-
adapted cyclists derived >90% of their fuel from the oxidation of fat while exercising at 64%
maximal oxygen consumption, corresponding to an average fat oxidation of 1.5 g/min.
Maximal rates of fat oxidation in humans are generally thought to be much less, even in
highly trained endurance athletes with very high aerobic capacity. In the largest study to date
that examined peak rates of fat oxidation during exercise, the highest reported value in any single
individual was 1.0 g/min (14), 50% lower than the mean value recorded for five keto-adapted
cyclists (10). A handful of other studies have examined diets lower in carbohydrate and higher
in fat on metabolism during exercise (15-18). Although these studies consistently show greater
fat oxidation, values are well below previously reported maximal rates (10). This is likely due to
a higher carbohydrate intake and insufficient time to allow full keto-adaptation; thus resulting in
less pronounced shifts in fuel use.
There is a distinct lack of research on keto-adapted athletes who have been restricting
carbohydrate for several consecutive months despite the fact that many endurance athletes have
adopted a low-carbohydrate lifestyle in the past few years. Metabolic studies in this group may
yield new insights into human capacities to adapt and thrive under different nutritional inputs.
Therefore, we designed the FASTER (Fat Adapted Substrate use in Trained Elite Runners) study
to compare metabolic differences between competitive ultra-marathoners and ironman distance
triathletes consuming low-carbohydrate (LC) and high-carbohydrate (HC) diets.
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METHODS
Experimental Approach
This was a cross-sectional study comparing two groups of elite ultra-endurance athletes
habitually consuming either a LC or HC diet. Athletes were carefully matched for age, physical
characteristics, primary competition distance, and competition times. With one exception, all
athletes lived in the United States and traveled via plane or car to our laboratory for two
consecutive days of testing. On Day 1, participants performed a maximal oxygen consumption
test during which peak fat oxidation was determined. Day 2 consisted of a treadmill run at 64%
of maximal oxygen consumption for 3 hr to determine metabolic responses before, during, and
after exercise. Subjects were informed of the purpose and possible risks of the investigation
prior to signing an informed consent document approved by the Institutional Review Board.
Participants
We targeted male ultra-endurance runners 21-45 years of age consuming a LC (n=10) or
a HC (n=10) diet who were in the top 10% of finalists competing in sanctioned running events
≥50 km and/or triathlons of at least half iron-man distance (113 km). Several athletes had
sponsors (55%), course records (30%), national records (10%), international records (10%), and
national/international-level appearance for Team USA (25%). Interested athletes completed
questionnaires to assess their medical, diet, training, and running competition histories. As a pre-
screening technique to determine if participants met dietary inclusion criteria, a registered
dietitian had at least one phone call and email communication with each athlete about their
habitual diet. Eligible participants were required to measure and weigh all foods consumed and
provide a detailed description for 3-days (2 weekdays and 1 weekend day). All subjects were
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contacted through phone calls and emails to obtain home recipes, local restaurant menus, and
nutritional information as necessary for determining accurate nutrient information. Habitual diet
information was entered into commercial nutrient analysis software (Nutritionist ProTM, Axxya
Systems, Stafford, TX). Subjects consuming a LC diet, defined as <20% energy from
carbohydrate and >60% from fat, consistently for at least 6 months were eligible for the LC
group. Subjects consistently consuming >55% energy from carbohydrate were considered for the
HC group. Athletes were excluded if they did not consume the appropriate diet for the allotted
amount of time or had any health issues, including, but not limited to diabetes, heart disease,
kidney, liver, or other metabolic or endocrine dysfunction, current injury, anti-inflammatory
medication use, anabolic drug use, or prone to excessive bleeding.
Exercise Protocols
Maximal Aerobic Capacity. Athletes were in racing condition but refrained from any
competitions for a minimum of 7 days prior to testing. They were instructed to maintain their
habitual diet leading up to testing and to record their diet for the day before and the day of travel
(Testing Day 1). Subjects arrived at the laboratory between 1600-1900 hours following a 4 hr
fast. Upon arrival, height and body weight were recorded, after which athletes were familiarized
with the equipment and procedures prior to completing a maximal oxygen consumption
(VO2max) test on a motorized treadmill (956i Treadmill, Precor, Woodinville, WA) using
indirect calorimetry (TrueOne 2400, ParvoMedics, Sandy, UT). Subjects were fitted with a
facemask and headgear (7450 Series Silicone V2TM Oro-Nasal Mask, Hans Rudolph, Shawnee,
KS) with an adaptor ring to attach the two-way air chamber and gas collection hose to the mask
(Adapter 7450 V2TM, Hans Rudolph, Shawnee, KS). The protocol involved a 3 min walking
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warm-up at 3.5 mph, followed by 2 min stages that incrementally increased speed and incline
linearly to elicit ~5% increase in VO2 at each stage. The test was considered complete when
subjects voluntarily stopped the treadmill, which occurred within 18 min. Ratings of perceived
exertion (RPE) on a 0-10 scale and heart rate were measured at each stage.
Breath-by-breath gas exchange measurements of VO2 and VCO2 were collected and
recorded every 30 sec and used to calculate oxygen uptake, minute ventilation, carbohydrate and
fat oxidation rates, and respiratory exchange ratio (RER) with the assumption that the urinary
nitrogen excretion was negligible. Peak fat oxidation was determined from the highest recorded
30 sec interval. Following testing, subjects consumed a meal (dinner) consistent with the
macronutrient percentages of their habitual diet. After dinner, all subjects were instructed to fast
overnight, restrict caffeine and abstain from taking over-the-counter medications. To ensure
hydration, liberal consumption of water was strongly encouraged for the rest of the evening.
Submaximal Exercise Protocol. Athletes returned to the laboratory for testing the
following morning at 0600 after a 10 hr overnight fast (Fig 1). Each subject provided a small
urine sample to assess specific gravity (Model A300CL, Spartan, Japan) as a measure of
hydration (all subjects had a USG >1.025). Body composition was determined via dual-energy
X-ray absorptiometry (DXA) (Prodigy, Lunar Corporation, Madison, WI), height was measured
to the nearest 0.1 cm and total body weight was recorded to the nearest 0.1 kg on a digital scale
(OHAUS Corp., Fordham Park, NJ). The facemask and headgear were used to determine resting
energy expenditure by indirect calorimetry for 10 min after which a seated blood sample was
obtained from a forearm vein. Subjects were then moved to a hospital bed for muscle biopsy
preparation and acquisition.
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After these baseline measures, subjects consumed a shake (5 kcal/kg body mass) with a
macronutrient distribution (%carbohydrate:fat:protein) of 5:81:14 in the LC group and 50:36:14
in the HC group. On average, this translated into shakes consisting of 343 kcal, 4.3 g
carbohydrate, 31.3 g fat, and 12.6 g protein for the LC group and 332 kcal, 42.7 g carbohydrate,
13.7 g fat, and 12.4 g protein in the HC group. Proportions varied, but both the LC and HC
shakes were made from heavy cream, olive oil, whey protein, walnut oil, and strawberries. The
HC shake also contained banana and agave syrup. Subjects rested quietly for 90 min after
ingestion of the shake. Indirect calorimetry measurements were taken during the last 10 min and
a second pre-exercise blood sample was taken.
Subjects then moved to the treadmill and began running at an intensity equivalent to 64%
VO2max for 180 min. Slight adjustments to the pace were made during the first 10 min based on
real-time measurements of oxygen uptake. During the endurance exercise test, subjects were
allowed to drink water ad libitum but no other nutritional supplementation was consumed. The
treadmill was stopped briefly at 60 and 120 min to obtain blood. Heart rate and RPE were
recorded every 30 min, and indirect calorimetry measurements were obtained for 10 min at the
following intervals: 50-60, 110-120, 140-150, and 170-180 min.
After 180 min of running, subjects moved to a wheel chair and blood was obtained
immediately. Subjects were moved to a bed and a second muscle biopsy was performed ~15 min
after completing the run. Subjects then consumed a shake identical to the pre-run shake. Indirect
calorimetry measurements and blood were taken at 30, 60, and 120 min post-exercise. A final
muscle biopsy was taken 120 min post-exercise. The entire day of testing lasted about 8 hr
finishing mid-afternoon (see Fig 1), which is the primary reason for including the shakes before
and after exercise since no other caloric intake was allowed during testing.
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Blood Draws and Muscle Biopsies
An indwelling Teflon cannula or a 21G butterfly needle was inserted into an antecubital
vein of the subject. The intravenous line was kept patent with normal saline solution flushes.
Prior to each blood draw, 3 mL of blood were extracted and discarded to avoid inadvertent saline
dilution of the blood. During each draw, blood was collected into appropriate tubes to allow for
whole blood, serum, and plasma analyses. Blood was centrifuged at 1500 x g for 15 min and 4ºC,
aliquoted into storage tubes, and stored in ultra-low freezers for batch analysis.
Muscle biopsies were obtained from the superficial portion of the vastus lateralis using
the percutaneous needle technique with suction. The biopsy site was prepped and the skin was
anesthetized by a local subcutaneous injection of 2% lidocaine hydrochloride. A small incision
(~1 cm) was made through the skin and muscle fascia and a 5 mm diameter sterile biopsy needle
(Surgical Instruments Engineering Ltd, Midlothain, United Kingdom) was introduced into the
muscle to a depth of 2 cm. To ensure adequate sample sizes a double-chop method combined
with suction was used. The muscle sample was removed from the needle and divided into
multiple pieces of roughly equal size. The specimens were cleaned of connective tissue and
blood, flash frozen in liquid nitrogen, and stored at -80ºC for later determination of glycogen.
The incision was covered with sterile gauze and compression was applied to prevent bleeding.
The incision was then closed with a single suture. In order to avoid possible impairment of
glycogen synthesis resulting from microtrauma in the area near the biopsy (19), we performed
the immediate post-exercise biopsy on the opposite leg, and the 2 hr post-exercise biopsy 3 cm
apart from the first incision site.
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Biochemical Analyses
Frozen samples were thawed only once before analysis. Plasma glucose and triglycerides
were measured using a Cobas C 111 analyzer (Roche Diagnostics, Indianapolis IN). Serum
insulin was analyzed by ELISA (Calbiotech, Spring Valley, CA). Serum glycerol (Cayman
Chemical, Ann Arbor, MI), ketones and total non-esterified fatty acids (NEFA) (Wako
Diagnostics, Mountain View, CA) were analyzed by enzymatic assays. Insulin, ketones, lactate,
NEFA, and glycerol had intra-assay CVs of 4.31, 6.40, 4.65, 4.51 and 5.31% respectively. All
samples were run in duplicate except for those measured on Cobas. Glucose and insulin values
were used to calculate an index of insulin resistance [HOMA-IR; calculated as Glucose
(mmol/L)∙Insulin (µIU/mL) /22.5] from the baseline resting sample (20). Glycogen was
determined from a portion of muscle (10 mg) that was hydrolyzed in 500 µl of 2N HCl by
heating at 99°C for 2 hr, with occasional vortexing. Any weight lost to heating was replaced
using ddH2O. The solution was neutralized with 500 µl 2N NaOH and 50 µl of Tris buffer (pH
6.5). 1 ml of glucose hexokinase reagent (ThermoFisher), including 20 µl of supernatant of the
hydrolysis product was prepared, and the free glycosyl units were then spectrophotometrically
assayed in triplicate at 340 nm on a Nanodrop 2000c spectrophotometer (21). Coefficient of
variation was 4.2%.
Statistical Analyses
Independent t-tests were used to examine differences between LC and HC groups for
dietary intake and physical characteristics. For biochemical responses to exercise, we used a
two-way repeated measures analysis of variance with group (HC and LC) as a between factor
and time (i.e., exercise-induced responses) as a within factor. Fisher’s least significant
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difference post hoc was used to examine pairwise comparisons when significant main or
interaction effects were observed. The alpha level for significance was set at p < 0.05.
RESULTS
Subject Characteristics and Habitual Diet. There were no significant differences
between groups in physical characteristics or aerobic capacity (Table 1). Two athletes in each
group were ironman distance triathletes while the remainder competed primarily in running
events ranging from 80 to 161 km (50 to 100 miles). The main difference between groups was
their habitual diet (Table 2). Average time on a LC diet was 20 mo (range 9 to 36 mo). In the
LC group, an overwhelming majority of energy intake was derived from fat (70%), primarily
saturated and monounsaturated fatty acids. Only ~10% of their energy intake was from
carbohydrate. Conversely, the HC group consumed over half their energy as carbohydrate
(59%). Absolute protein was not significantly different between groups, but LC athletes
consumed a greater relative amount than HC athletes (19 vs 14% of total energy). Daily nutrient
intake determined from food records the 2 days prior to testing showed similar dietary patterns as
the habitual diet and were not significantly different (Supplemental Table 1).
Peak Fat Oxidation. Peak fat oxidation was on average 2.3-fold higher in the LC group
(1.54 ± 0.18 vs 0.67 ± 0.14 g/min; P=0.000), with every subject in the LC group (range 1.15 to
1.74 g/min) exceeding the highest value in the HC group (range 0.40 to 0.87 g/min) (Fig 2A).
The percent of maximal aerobic capacity where peak fat oxidation occurred was also
significantly higher in the LC group (70.3 ± 6.3 vs 54.9 ± 7.8%; P=0.000) (Fig 2B).
Submaximal Substrate Oxidation. All 20 subjects completed 180 min of running. The
average percent maximum oxygen consumption during exercise was similar in the LC (64.7 ±
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0.0%) and HC (64.3 ± 0.0%) groups. Ratings of perceived exertion over the 3 hr run were also
similar between groups gradually increasing from 3.0 ± 1.3 at the start of exercise to 5.1 ± 1.9 at
the end of exercise in the LC group and from 2.9 ± 0.9 to 5.2 ± 2.9 in the HC group. Absolute
energy expenditure during the run was not different between the LC (12.4 ± 0.1 kcal/min) and
HC (12.2 ± 0.2) groups; however, substrate oxidation patterns at rest and during exercise were
significantly different (Fig 3). At rest prior to exercise, the RER was significantly (P=0.000)
lower in the LC (0.72 ± 0.05) than the HC (0.86 ± 0.08) group, indicating a contribution from fat
of 95 vs 47%, respectively. During 3 hr of exercise, RER fluctuated between 0.73 and 0.74
translating into relatively stable and higher fat oxidation rates of ~1.2 g/min in the LC group,
whereas fat oxidation values were significantly lower in the HC group at all time points (Fig
3A). The rate of carbohydrate oxidation in the LC group was stable during exercise and
significantly (P=0.000) lower than the HC group (Fig 3B). The average contribution of fat
during exercise in the LC and HC groups were 88 and 56%, respectively.
Circulating Metabolites. Circulating markers of lipid metabolism indicated a significantly
greater level of ketogenesis (Fig 4A) and lipolysis (Fig 4B) in the LC athletes. Serum non-
esterified fatty acids were higher at the start of exercise in LC athletes, but peak levels at the end
of exercise were not significantly different between groups (Fig 4C). Plasma triglycerides were
not different between groups (Fig 4D).
Plasma glucose and serum insulin were not significantly different between groups at rest
and during exercise but increased during the last hour of recovery in the HC athletes, likely due
to the greater amount of carbohydrate in the shake (Fig 5A and 5B). There was no significant
difference between groups in insulin resistance as determined by HOMA. Serum lactate
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responses were variable, but were significantly higher in LC athletes during the last hour of
exercise (Fig 5C).
Muscle Glycogen. Compared to baseline, muscle glycogen was significantly decreased by
62% immediately post-exercise and 38% at 2 hr post-exercise in the HC group. The LC group
exhibited a similar pattern; muscle glycogen was decreased by 66% immediately post-exercise
and 34% at 2 hr post-exercise (Fig 6A). There were no significant differences in pre-exercise or
post-exercise glycogen concentrations between groups. There was a high degree of variability in
muscle glycogen concentrations pre-exercise in both groups. In contrast, the depletion and
resynthesis patterns showed a more uniform response, especially the amount of glycogen
synthesized during the 2 hr recovery period in LC athletes (44.8 ± 7.5; 95% CI 40.2–49.4 mol/g
w.w.), which was one-third less variable than HC athletes (34.6 ± 23.9; 95% CI 19.8–49.4
mol/g w.w.) (Fig 6B). Interestingly, in all ten LC athletes the total amount of carbohydrate
oxidized during the 3 hr run as calculated from indirect calorimetry (mean±SD; 64 ± 25 g) was
lower than the total amount of glycogen disappearance (mean±SD; 168 ± 65 g), assuming 10 kg
of active tissue.
DISCUSSION
We studied two groups of highly-trained competitive ultra-endurance athletes who were
well matched in regards to training status and physical characteristics. The main difference was
that the LC athletes consumed 6-times less dietary carbohydrate than LC athletes (82 vs 684
g/day) for an average of 20 mo. The most notable findings were that compared to HC athletes,
the LC keto-adapted runners showed: 1) two-fold higher rates of peak fat oxidation during
graded exercise, 2) greater capacity to oxidize fat at higher exercise intensities, 3) two-fold
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higher rates of fat oxidation during sustained submaximal running, and 4) no differences in pre-
exercise muscle glycogen concentrations, the rate of glycogen utilization during exercise, and the
rate of glycogen synthesis during recovery. Thus, we show for the first time that chronic keto-
adaptation in elite ultra-endurance athletes is associated with a robust capacity to increase fat
oxidation during exercise while maintaining normal skeletal muscle glycogen concentrations.
The whole body maximal fat oxidation rates in the LC athletes were similar to those
observed in elite keto-adapted cyclists (10), which are noteworthy considering they are ~50%
higher than previously reported maximal rates of fat oxidation in the literature (14). During
submaximal exercise, fat contributed to 88 vs 56% of energy expended in LC and HC athletes,
respectively. Several studies have consistently reported that short-term consumption of a high-
fat diet in highly trained endurance athletes increases fat oxidation during submaximal exercise
(reviewed in 22). However, no studies have showed the same degree of shift towards fat
oxidation as observed in LC athletes reported here. In the LC ultra-endurance runners studied
here, a combination of greater degree of carbohydrate restriction and longer period of adaptation
likely resulted in a more robust capacity for fat oxidation during exercise.
Serum glycerol and non-esterified fatty acid concentrations increased rapidly during
exercise and decreased during recovery. Whereas fatty acids were similar between groups,
glycerol concentrations were ~2-fold higher in LC athletes. Phinney et al (10) also reported
similar circulating fatty acids concentrations before and after keto-adaptation. Serum glycerol is
a better indicator of adipose tissue lipolysis compared to fatty acids since adipose tissue and
skeletal muscles have lower glycerol kinase activity and thus are not able to use glycerol as
effectively. Still some reports suggest muscle can use circulating glycerol for intramuscular
triglyceride synthesis (23). Previous studies have indicated a greater capacity to transport
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circulating fatty acids into muscle after a short-term low-carbohydrate/high-fat diet (24,25).
Thus, these data indicate a higher rate of adipose tissue lipolysis in LC athletes resulting in a
greater release of both glycerol and fatty acids into the circulation, with greater overall uptake of
fatty acids into skeletal muscle.
Carbohydrate oxidation was significantly lower in the LC ultra-endurance runners, but
unexpectedly muscle glycogen concentrations were not different between groups. It was
previously reported that a 4 wk ketogenic diet in elite cyclists decreased resting muscle glycogen
by half and the rate of glycogen use during exercise by 4-fold (10). Other studies have shown
that a low-carbohydrate/high-fat diet decreases resting glycogen and the rate of glycogen use
during submaximal exercise (25,26). The duration of the LC diet was shorter (4 wk) in the work
by Phinney (10), suggesting that complete adaptations in glycogen homeostasis and kinetics may
take several months. The different glycogen responses could also be due to lower carbohydrate
intake, which was <10 g/day in cyclists (10) versus 86 g/day in the LC runners. A short-term
glycogen loading effect is unlikely since food logs were recorded for the two days leading up to
testing and indicated the average carbohydrate intake was 64 g/day in LC athletes
(Supplemental Table 1). The small relative contribution of carbohydrate to energy expenditure
in LC athletes, but similar use of glycogen as HC athletes, indicates a decreased reliance on
circulating glucose in the keto-adapted athlete.
The muscle glycogen responses in the LC athletes are provocative in light of data
previously reported in highly-trained Alaskan sled dogs (27,28). Sled dogs have an innately high
endurance capacity and often perform several hours of running at submaximal intensity while
consuming a high-fat/low-carbohydrate diet. Dogs running 160 km/day for 5 days showed no
cumulative muscle glycogen depletion despite eating a diet consisting of only 15% carbohydrate
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(27). In a subsequent study (28), dogs ran 140 km/day for 4 days and showed a 66% reduction in
muscle glycogen after the first 140 km run (similar to the 64% reduction in muscle glycogen in
the LC athletes) and a progressive increase in muscle glycogen over subsequent days of running.
A more recent study reported that trained Alaskan sled dogs eating a 16% carb diet showed an
unexpected high rate of carbohydrate oxidation during exercise that was associated with a
significant increase in gluconeogenesis from glycerol and increased lactate oxidation (29). Thus,
highly-trained high-fat adapted sled dogs show very different fuel utilization patters that
predicted based on studies done in trained humans eating a high-carbohydrate diet.
A provocative finding was that LC athletes appeared to break down substantially more
glycogen (>100 g) than the total amount of carbohydrate oxidized during the 3 hr run. This was
the case in all ten LC athletes. Why would athletes with high rates of acetyl CoA generation
from fatty acids bother breaking down muscle glycogen if those carbons are not terminally
oxidized? Although speculative, we believe the reason may be to provide a source of glucose for
the pentose phosphate pathway (PPP) and a source of pyruvate to form oxaloacetate. The PPP
generates important 5 carbon sugars and reducing power (i.e., NADPH) for biosynthetic
reactions such as ribonucleic acid synthesis and maintaining glutathione levels, respectively.
Glyceraldehyde-3-phosphate and fructose 1,6 bisphosphate generated from the PPP can also
enter glycolysis after the energy input stage, thereby generating ATP through substrate level
phosphorylation and increasing formation of pyruvate. Pyruvate may be necessary in a keto-
adapted athlete for two reasons. First, pyruvate can be used as an anaplerotic substrate by
pyruvate carboxylase to generate oxaloacetate. At the onset of exercise several TCA cycle
intermediates increase in concentration including fumarate, citrate and malate, however
oxaloacetate remains low. Thus, in the keto-adapted athlete glycogen breakdown may be
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necessary to ensure a constant source of oxaloacetate for optimal TCA functioning. Second,
pyruvate can be converted to lactate or alanine in muscle, which may then serve as
gluconeogenic substrate for the liver.
Rates of muscle glycogen synthesis in humans are highest when large amounts of
carbohydrate are consumed immediately post-exercise (30), yet the LC athletes had similar rates
of glycogen repletion compared to the HC athletes, despite receiving a negligible amount of
carbohydrate after exercise (4 vs 43 g) and more fat (31 vs 14 g). When no carbohydrate or
energy is provided after prolonged exercise, a small amount of muscle glycogen synthesis occurs
(31) presumably due to hepatic gluconeogenesis providing a source of glucose for glycogen.
Horses supplemented with fat after exercise showed impaired glycogen synthesis, but 3 wk of a
high-fat diet resulted in similar glycogen repletion as horses fed a high-carbohydrate diet (32).
An obvious question that arises is what is the carbon source for glycogen synthesis in the
absence of carbohydrate intake post-exercise? Although speculative, lactate and/or glycerol,
which were two-fold higher at the end of exercise in LC athletes and then sharply decreased
during recovery, may have provided a source of carbons for glycogen synthesis during recovery
(33). Lactate conversion to glycogen could occur directly (lactate glyconeogenesis) or indirectly
via the Cori cycle. Interestingly, lactate and ketones are both transported across cell membranes
by monocarboxylic acid transporters, which are upregulated after a ketogenic diet (34). Lactate
was shown to account for up to 18% of skeletal muscle glycogen synthesized after high-intensity
exercise (35). It could be that lactate rapidly replenished liver glycogen and an ability to maintain
hepatic glucose output in the face of limited exogenous carbohydrate intake. Lactate Regardless
of the mechanism, these results suggest that long-term consumption of a low-carbohydrate/high-
fat diet in highly-trained ultra-marathoners results in adaptations in the homeostatic regulation of
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muscle glycogen that act to preserve levels similar to those observed when exogenous
carbohydrate availability is high.
LC athletes had ~3-fold higher levels of circulating beta-hydroxybutyrate and total
ketones at rest, during exercise, and recovery. As expected, ketone levels increased progressively
during exercise and peaked 30 min into recovery consistent with the previously reported post-
exercise ketosis, also referred to as the Courtice-Douglas effect (36). When ketone levels exceed
0.5 mmol/L, they become a preferred fuel for the brain (37). Since ketones are derived from
fatty acids, the keto-adapted athlete has access to and an ability to utilize a stable, abundant
source of fuel in the form of beta-hydroxybutyrate. Beyond its role as a metabolite, beta-
hydroxybutyrate in the range of concentrations observed in the LC athletes has recently been
shown to be a potent inhibitor of histone deacetylases, which in turn upregulates expression of
antioxidant genes (38). It has also been observed that beta-hydroxybutyrate is associated with a
reduction in the generation of reactive oxygen species by mitochondria (39). It remains to be
demonstrated whether LC athletes derive benefit in terms of cognitive function or recovery from
these unique metabolic and signaling effects of beta-hydroxybutyrate.
Since this was a cross-sectional study it is possible that LC athletes had naturally higher
rates of fat oxidation or were somehow better suited to respond to a low-carbohydrate diet. Long-
term prospective diet intervention studies would be helpful to better understand the time course
and variability in adaptations to a low-carbohydrate diet. While we showed distinct differences
in metabolic responses between LC and HC athletes, we did not measure performance. It will be
important for future work to address a range of performance outcomes from endurance to
anaerobic capacity to strength/power, as well as sport and/or military-specific tasks. All the LC
athletes were initially high-carbohydrate athletes. Thus, they made the choice to switch to a very
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low-carbohydrate diet and had enough self-perceived benefit to continue this lifestyle. From a
practical standpoint, these observations broaden the dietary options available for endurance
athletes to include a very low level of carbohydrate.
In summary, these results provide the first documentation of the metabolic adaptations
associated with long-term consumption of a very low-carbohydrate/high-fat diet in highly trained
keto-adapted ultra-endurance athletes. The enhanced ability to oxidize fat during exercise across
a range of intensities is striking, as is the ability to maintain ‘normal’ glycogen concentrations in
the context of limited carbohydrate intake. Keto-adaptation provides an alternative to the
supremacy of the high-carbohydrate paradigm for endurance athletes.
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ACKNOWLEDGMENTS
The authors would like to thank Peter Defty for assistance in recruiting participants and the
extraordinary research participants for their enthusiasm to participate in this project.
FUNDING
This work was supported by contributions from Quest Nutrition and The Robert C. and Veronica
Atkins Foundation.
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FIGURE LEGENDS
Figure 1. Experimental protocol to determine metabolic responses to submaximal exercise.
Figure 2. Individual peak fat oxidation rates (A) and the exercise intensity eliciting peak
oxidation (B) during a maximal graded treadmill test. Mean responses between groups were
significantly different (Independent t-test, P=0.000). Circles indicate mean and 95% CI. LC =
low-carbohydrate diet group; HC = high-carbohydrate diet group.
Figure 3. Fat (A) and Carbohydrate (B) oxidation rate during 180 min of running at 65%
VO2max and 120 min of recovery. All time points were significantly different between groups.
LC = low-carbohydrate diet group; HC = high-carbohydrate diet group.
Figure 4. Circulating concentrations of ketones (A), glycerol (B), non-esterified fatty acids (C),
and triglycerides (D). LC = low-carbohydrate diet group; HC = high-carbohydrate diet group.
BOHB = beta-hydroxybutyrate. All variables showed significant main time and interaction
(group x time) effects. H and L = Indicates significant (P≤0.05) difference from the
corresponding baseline (BL) value for the HC and LC diet group, respectively. *Indicates
significant (P=0.000) difference between HC and LC values at that time point.
Figure 5. Circulating concentrations of glucose (A), insulin (B), and lactate (C). LC = low-
carbohydrate diet group; HC = high-carbohydrate diet group. All variables showed significant
main time and interaction (group x time) effects. H and L = Indicates significant (P≤0.05)
difference from the corresponding baseline (BL) value for the HC and LC diet group,
respectively. *Indicates significant (P=0.000) difference between HC and LC values at that time
point.
Figure 6. Mean (A) and individual (B) muscle glycogen concentrations at baseline (pre-
exercise), immediate post-exercise (IP), and 120 post-exercise (PE-120). Subjects ran on a
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treadmill at 65% VO2max for 180 min. *Indicates significant (P=0.000) difference from
baseline. †Indicates significant (P=0.000) difference from IP. No significant differences
between groups. LC = low-carbohydrate diet group; HC = high-carbohydrate diet group.
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Table 1. Subject characteristics.
High-Carbohydrate Diet
n=10
Low-Carbohydrate Diet
n=10
T-Test
Mean ± SD
Range
Mean ± SD
Range
P Value
Age, yr
32.9 ± 6.0
22.0–40.0
34.1 ± 7.1
21.0–45.0
0.689
Height, cm
173.9 ± 5.3
167.1–182.0
175.7 ± 7.8
165.1–189.4
0.555
Body mass, kg
66.5 ± 6.8
57.9–79.9
68.8 ± 8.2
55.5–81.6
0.491
Body fat, %
9.6 ± 4.3
4.7–15.5
7.8 ± 2.4
4.5–12.3
0.266
Lean mass, kg
57.3 ± 5.0
49.4–64.2
60.9 ± 7.1
50.2–71.7
0.387
Fat mass, kg
6.5 ± 3.3
2.8–12.1
5.5 ± 1.9
3.0–8.8
0.207
VO2max, L/min
4.25 ± 0.46
3.34–4.86
4.46 ± 0.39
3.78–4.95
0.299
VO2max,
mL/kg/min
64.3 ± 6.2
54.8–76.0
64.7 ± 3.7
59.6–71.1
0.850
Competitive
Running
Experience (yr)
9 ± 6
4–22
11 ± 8
1–25
0.583
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Table 2. Habitual daily nutrient intake.
High-Carbohydrate Diet
n=10
Low-Carbohydrate Diet
n=10
T-Test
Mean ± SD
Mean ± SD
P Value
Energy, kcal
3174 ± 611
2884 ± 814
0.380
Protein, g
118 ± 38
139 ± 32
0.186
Protein, %en
14.4 ± 3.5
19.4 ± 2.4
0.001
Protein, g/kg
1.7 ± 0.4
2.1 ± 0.6
0.192
Carbohydrate, g
486 ± 128
82 ± 62
0.000
Carbohydrate, %en
59.1 ± 10.2
10.4 ± 4.9
0.000
Fat, g
91 ± 31
226 ± 66
0.000
Fat, %en
25.0 ± 7.4
69.5 ± 6.0
0.000
Saturated fat, g
21 ± 10
86 ± 22
0.000
Monounsaturated fat, g
29 ± 14
82 ± 42
0.001
Polyunsaturated fat, g
18 ± 9
28 ± 17
0.106
Alcohol, %en
1.6 ± 2.4
0.7 ± 1.4
0.310
Cholesterol, mg
251 ± 249
844 ± 351
0.000
Fiber, g
57 ± 27
23 ± 17
0.000
Determined from 3 day dietary food records.
A
B
A
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Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
