Does mega-dose vitamin D blunt ultrarun bone stress?
Learn to train smart, run fast, and be strong with this endurance performance nerd alert from Thomas Solomon, PhD.
Single high-dose vitamin D supplementation impacts ultramarathon-induced changes in serum levels of bone turnover markers: a double-blind randomized controlled trial
Stankiewicz et al. (2025) J Int Soc Sports Nutr (click here to open the original paper)
What type of study is this?
◦ This study is a randomised controlled trialThe “gold standard” approach for determining whether a treatment has a causal effect on an outcome of interest. In such a study, a sample of people representing the population of interest is randomised to receive the treatment or a no-treatment placebo (control), and the outcome of interest is measured before and after exposure to the treatment and control..
What was the authors’ hypothesis or research question?
◦ The authors aimed to test whether a single high dose of vitamin D3, taken 24 hours before a mountain ultramarathon, would change post-race blood markers of bone formation and bone resorption.
What did the authors do to test the hypothesis or answer the research question?
◦ Thirty-five semiprofessional male ultramarathon runners (about 39–42 years old on average) completed a 240 km mountain ultramarathon. They were assigned by randomisation to receive vitamin D3 (150,000 IU; n = 16) or placebo (n = 19), taken 24 hours pre-race. Both the participants and researchers were blindedBlinding is when people in a study don’t know which treatment they’re getting. It stops expectations or beliefs (from patients or researchers) from skewing the results. “Single-blind” means participants don’t know; “double-blind” means participants and researchers don’t know; “triple-blind” means that the participants, researchers, and data analysts are kept in the dark. to the group assignments via numbered, sealed bottles. Blood was drawn 24 hours before, immediately after, and 24 hours after the race, and they measured 25(OH)D3 (25-hydroxyvitamin D3; the main circulating “storage” form of vitamin D and the standard blood test for vitamin D status) plus bone turnover and related markers, including: CTX (a bone resorption/breakdown marker), PINP (a bone formation marker), parathyroid hormone (which regulates calcium homeostasis), FGF23 (a hormone that regulates bone formation and breakdown), sclerostin (which suppresses bone formation), and procalcitonin (an inflammatory marker).
What did the authors find?
◦ Across all 35 finishers (n = 16 supplemented; n = 19 placebo), 25(OH)D3 (the main “storage” form of vitamin D) rose after the race in both groups, but the supplemented group’s rise was much larger (roughly doubling immediately, and 1.5× above baseline at 24 h), with clear statistical significanceEvidence that a result is unlikely to be due to chance under a “no effect” model (or null hypothesis). Statistical significance is often judged by a p-value below 0.05 to flag that “something” is going on, but not how big or important that “something” is. One statistically significant result doesn’t mean proof; replication is needed. And, a statistically significant result doesn’t necessarily indicate clinical significance..
◦ CTX (a marker of bone resorption/breakdown) dropped immediately after the ultramarathon overall, but the meaningful, sustained decrease was mainly seen in the vitamin D group (moderate-to-large effect sizeA standardised measure of the magnitude of an effect of an intervention. Unlike p-values, effect sizes show the size of the effect and how meaningful it might be. Common effect size measures include standardised mean difference (SMD), Cohen’s d, Hedges’ g, eta-squared, and correlation coefficients., reported via eta-squared (η2)A measure of effect size used in ANOVA that represents the proportion of the total variance in a dependent variable that is attributed to a factor or independent variable. Values range from 0 to 1, with higher values indicating a greater proportion of explained variance.); the placebo group showed no clear CTX change and tended to be higher by 24 hours.
◦ PINP (a bone formation marker) increased after the run, but mostly in the supplemented group.
◦ Parathyroid hormone (which regulates calcium homeostasis) and sclerostin (which suppresses bone formation) spiked sharply after the run in both groups, yet the placebo group had higher parathyroid hormone and higher sclerostin at 24 h (and higher procalcitonin, an inflammatory marker, as well), suggesting vitamin D blunted part of the post-race stress response.
◦ Clinically, though, we are playing biomarker chess: we do not know if these short-lived shifts translate to fewer stress fractures, better recovery, or better long-term bone outcomes. Consequently, clinical significanceReflects how meaningful a change is to a person’s health or performance. A small change can be statistically significant but not clinically significant; but, if a change is big enough to matter to people in real life, then it is clinically significant (and doesn’t just tick a statistical significance box). remains unclear).
◦ The authors concluded that a single 150,000 IU dose of vitamin D3 taken 24 hours before an ultramarathon raised 25(OH)D3 and appeared to blunt post-exercise signals of bone resorption and inflammation, potentially via calcium–PTH regulation.
What were the strengths?
◦ Methodologically, this was a double-blindBlinding is when people in a study don’t know which treatment they’re getting. It stops expectations or beliefs (from patients or researchers) from skewing the results. “Single-blind” means participants don’t know; “double-blind” means participants and researchers don’t know; “triple-blind” means that the participants, researchers, and data analysts are kept in the dark. The goal is simple: fair tests and trustworthy findings., placebo-controlled randomised controlled trialThe “gold standard” approach for determining whether a treatment has a causal effect on an outcome of interest. In such a study, a sample of people representing the population of interest is randomised to receive the treatment or a no-treatment placebo (control), and the outcome of interest is measured before and after exposure to the treatment and control. with protocol pre-registrationPreregistration is when a detailed description of a study plan is deposited in an open-access repository before collecting the study data. It promotes transparency and accountability, and boosts research integrity. Without preregistration, it is easier for scientists to change outcomes after seeing the data, selectively report “exciting” results, or run many analyses and only show the ones that work, which can introduce bias and weaken the trustworthiness of the findings., and repeated blood sampling at clearly defined timepoints around a real-world ultramarathon. The authors also report a power calculationA power calculation is a way to figure out how many people or data points you need in a study so you can reliably spot a real effect if it exists. It balances four things: the size of the effect you care about, how much random variation there is, how strict you are about false alarms, and how likely you want to be to detect the effect. In plain terms: it helps you avoid running a study that’s too small to be useful or so big that it wastes time and money., used objective lab outcomes, and reported effect sizesA standardised measure of the magnitude of an effect of an intervention. Unlike p-values, effect sizes show the size of the effect and how meaningful it might be. Common effect size measures include standardised mean difference (SMD), Cohen’s d, Hedges’ g, eta-squared, and correlation coefficients. to help interpret magnitude rather than relying only on statistical significanceEvidence that a result is unlikely to be due to chance under a “no effect” model (or null hypothesis). Statistical significance is often judged by a p-value below 0.05 to flag that “something” is going on, but not how big or important that “something” is. One statistically significant result doesn’t mean proof; replication is needed. And, a statistically significant result doesn’t necessarily indicate clinical significance. and P-valueA p-value is a statistical measure that indicates the probability that the result is at least as extreme as that observed if the null-hypothesis was true. If P is small, the observed difference is big enough to disprove (reject) the null hypothesis. In very basic terms, P equals the probability that the effect could be explained by random chance, and a P-value of less than 0.05 means the results look so promising that there’s only a 1-in-20 (or 5%) chance that they would have occurred if the treatment had no effect at all. Common thresholds for statistical significance are 0.05, 0.01, and 0.001..
What were the limitations?
◦ The biggest issue is interpretability: outcomes were short-term blood biomarkers, not patient-centred outcomes like injury rates, bone density, or performance, and follow-up stopped at 24 hours post-race. The cohort was also exclusively male and highly specific to 240 km mountain ultramarathoners, which limits generalisabilityGeneralisability is about how far you can confidently stretch a study’s findings beyond the specific people, place, and conditions that were tested. In simple terms, it asks: “If this result is true here, how likely is it to also be true in other groups or real-world settings?” It’s closely linked to external validity, which is the overall strength of those broader conclusions.. For example, would the findings hold in female athletes, shorter events, or recreational runners? Maybe, but this study cannot answer that.
◦ Furthermore, the authors did not measure serum calcium or dietary calcium intake, nor did they assess hydration, which is very important during prolonged exercise because dehydration can influence the concentration of metabolites and hormones in the blood.
How was the study funded, and are there any conflicts of interest that may influence the findings?
◦ The work was funded by the National Science Centre, Poland (grant 2020/37/B/NZ7/01794). The authors stated they had no commercial or financial conflicts of interest.
How can you apply these findings to your training or coaching practice?
◦ For coaching and real-world practice, this is interesting but not yet actionable. Vitamin D clearly boosted blood 25(OH)D levels and shifted post-race bone/inflammation biomarkers in a “less stressy” direction. However, the study did not measure outcomes that actually matter for training decisions (stress fractures, bone density, soreness, return-to-training, or performance), and it only followed runners for 24 hours after the race. Also, because they didn’t measure serum calcium or dietary calcium intake, it’s hard to know whether this is “vitamin D magic” or mostly a calcium–parathyroid hormone knock-on effect. So, treat this study’s findings as a hypothesis-generator, not a pre-race protocol. In the meantime, maintaining an adequate vitamin D status across training blocks is sensible. As for mega-dosing right before your A-race… there’s probably nothing to see here; move along, move along.
What is my Rating of Perceived scientific Enjoyment (RPsE)?
6 out of 10 → I experienced moderate scientific enjoyment because the design is properly controlled and registered, the stats are readable with effect sizes, and the physiology is kinda fun, but the outcomes are mostly biomarkers and the window is so short that the real-world meaning stays frustratingly blurry.
Important: Don’t make any major changes to your daily habits based on the findings of one study, especially if the study is small (e.g., less than 30 participants in a randomised controlled trial or less than 5 studies in a meta-analysis) or poor quality (e.g., high risk of biasRisk of bias in meta-analysis refers to the potential for systematic errors in the studies included in the analysis, which can lead to misleading or invalid results. Assessing this risk is crucial to ensure the conclusions drawn from the combined data are reliable. or low quality of evidenceA low quality of evidence means that, in general, studies in this field have several limitations. This could be due to inconsistency in effects between studies, a large range of effect sizes between studies, and/or a high risk of bias (caused by inappropriate controls, a small number of studies, small numbers of participants, poor/absent randomization processes, missing data, inappropriate methods/statistics). When the quality of evidence is low, there is more doubt and less confidence in the overall effect of an intervention, and future studies could easily change overall conclusions. The best way to improve the quality of evidence is for scientists to conduct large, well-controlled, high-quality randomized controlled trials.). What do other trials in this field show? (opens in new tab) Do they confirm the findings of this study or have mixed outcomes? Is there a high-quality systematic review and meta-analysis evaluating the entirety of the evidence in this field? (opens in new tab) If so, what does the analysis show? What is the risk of biasRisk of bias in a meta-analysis refers to the potential for systematic errors in the studies included in the analysis. Such errors can lead to misleading/invalid results and unreliable conclusions. This can arise because of issues with the way participants are selected (randomisation), how data is collected and analysed, and how the results are reported. or certainty of evidenceCertainty of evidence tells us how confident we are that the results reflect the true effect. It’s based on factors like study design, risk of bias, consistency, directness, and precision. Low certainty means more doubt and less confidence, and that future studies could easily change the conclusions. High certainty means that the current evidence is so strong and consistent that future studies are unlikely to change conclusions. across the included studies? I’ve written a deep-dive article on this topic; check it out at veohtu.com/vitamin-d-for-runners.
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