REF-826 · Knechtle & Nikolaidis, 2018 · Frontiers in Physiology, Vol. 9, article · Paper · PubMed

Physiology and Pathophysiology in Ultra-Marathon Running

C+thin evidenceMeasured on a mixed groupWritten before October 2026, being re-checked

An overview of ultra-marathon physiology and pathophysiology. A typical ultra-marathoner is male, married, well-educated and ~45 years old; women account for ~20% of finishers. Ultra-marathoners are older and train with a larger weekly volume, but more slowly, than marathoners.

Previous experience (finishes in ultras, personal best marathon time) is the most important predictor of ultra performance, followed by anthropometry (low BMI, low body fat) and training (high volume and running speed in training). The sex difference has narrowed to ~10-20% depending on race length, and peak performance comes at 35-45 years or older, later for longer races.

An ultra causes an energy deficit that reduces body fat and skeletal muscle mass; with other risk factors (heat or cold, the host country) it can lead to exercise-associated hyponatremia. Biomarkers of muscle, heart, liver, kidney, immune and endocrine stress change, usually temporarily.

In longer ultras ~50-60% of participants have musculoskeletal problems, mostly at the ankle and knee; creatine kinase can reach 100,000-200,000 U/l. Digestive problems, gastrointestinal bleeding, a temporary fall in renal function and upper respiratory infections after the race are common.

Participants

Not applicable (review). The abstract does not give the number of studies reviewed.

Protocol

Narrative overview of the literature on ultra-marathon participation, performance predictors and the acute effects of the race on organ systems.
No search strategy, inclusion criteria or pooling described in the abstract.

Key Results

TopicWhat the review reports
Typical participantMale, married, well-educated, ~45 years old; women ~20% of finishers
Training vs marathonersOlder, larger weekly training volume, slower training pace
Strongest performance predictorPrevious experience (ultra finishes, personal best marathon time), then anthropometry (low BMI, low body fat) and training (high volume and speed)
Sex differenceNarrowed to ~10-20%, depending on race length
Age of fastest times35-45 years or older; rises with race distance or duration
Musculoskeletal problems (longer ultras)~50-60% of participants; mostly ankle and knee
Creatine kinaseCan reach 100,000-200,000 U/l, depending on fitness and race length
Other acute effectsEnergy deficit (fat and muscle loss), hyponatremia risk, cardiac biomarker and imaging changes, liver enzyme rises, temporary fall in renal function, digestive problems and GI bleeding, upper respiratory infections
Most biomarker changes are temporary and usually normalise after the race.

Practical Application

This narrative review of ultra-marathon research finds that experience — previous ultra finishes and a fast marathon personal best — predicts ultra performance best, and that in longer ultras ~50-60% of runners develop musculoskeletal problems, mostly at the ankle and knee. Build ultra distance over several races rather than jumping straight to the longest one, and plan for leg problems as the likeliest thing to go wrong.
Blood tests after an ultra can look alarming: creatine kinase of 100,000-200,000 U/l, liver enzymes and cardiac biomarkers raised, renal function temporarily down. The review says these usually normalise after the race. Inference, not tested here: a raised post-race value on its own is expected, but dark urine, little urine or chest symptoms are a reason to see a doctor, not to wait.
Hyponatremia risk rises with the ultra plus heat or cold; the prevention rules are in REF-776 (drink to thirst).
Upper respiratory infections are common after an ultra. Not from this paper; the base's own practice: keep the week after an ultra easy and treat a post-race cold as a reason for more rest.
The review's numbers are approximate summaries; cite them as base rates, not as study results.

Limitations

Narrative overview; study selection is not described and figures are given as approximations.
Mixes road, trail, multi-day and timed ultras; the rates differ by format and are not separated in the abstract.
Performance predictors come from cross-sectional correlations, not training trials.
Written from the abstract and record metadata only (full text not read).
How this entry was checked and changed
Why this level

narrative overview without systematic method; its figures are approximate ("~") and not traced to named studies in the abstract