Britton Needham wrestled competitively from the age of eight, becoming a four-time kids’ state champion, four-time high school state placer, and an All-American wrestler – all while managing type-1 diabetes. How? In part, by adjusting his diet and competition strategy based on the hundreds of blood sugar measurements he would take on tournament days.
“Eventually, my fingers became so calloused that I would have to take the needle out of the lancet and manually prick them,” he says. “It was not especially enjoyable – but it worked.”
Britton’s father Shane describes the process as a scientific experiment. “We documented what Britton ate, how he trained, how he wrestled, and gradually fine-tuned it,” he says. “But there were times when his blood sugar was too high and there wasn’t much he could do about it, so he had to score as many points as possible, as quickly as possible, before he started getting really tired in the third period.”
Britton’s success on the mat demonstrates that with disciplined monitoring and a scientific eye – it no doubt helped that Shane is himself an analytical scientist, serial bioanalytical entrepreneur, and bodybuilding competitor – it is possible for someone with type-1 diabetes to compete in elite sport (read our interview with Britton and Shane Needham here). But it may also offer a glimpse into a more precise, bioanalytically-informed approach to sport performance that could soon become part of the lives of many athletes – if it isn’t already.
Athletes and coaches are no strangers to using data to improve performance. Indeed, the increasingly widespread use of GPS systems, heart-rate monitors, and other performance- and recovery-tracking technologies have been offered as one explanation for the rise in the number of sportspeople performing at the elite level into their late thirties and early forties, such as Christiano Ronaldo – one of seven players at this year’s World Cup over the age of 40 – Venus Williams, LeBron James, and Tom Brady.
And with advances in sampling technologies, measurements can be taken – even during exercise – without athletes having to endure hundreds of pin pricks. Those samples can be sent to a lab – now via dried blood spot, eliminating the need for cold-chain logistics – for a full omic workup. Wearable devices too are now able to continuously measure analytes of interest – providing athletes and coaches with real-time information. These data could then be used by coaches to fine-tune training, diet, and recovery.
The chemistry of pushing the limits
Travis Nemkov and Angelo D’Alessandro, researchers at the University of Colorado School of Medicine (read our interview with D'Alessandro and Nemkov here), started collecting samples from elite cyclists during training camps in 2016 using touch-activated fingertip or shoulder-based microsampling devices. They analyzed the samples using mass spectrometry – measuring 3,000-4,000 molecules, spanning metabolites, lipids, proteins, and their modifications – before and after different training tests. They found that they could distinguish not just good performers from poor performers, but even good performers from the very best performers.
Using metabolomic signatures linked to lactate threshold, the researchers identified differences in amino acid metabolism, fat metabolism, and coenzyme A metabolism that were correlated with the differences in performance. “It was striking how diagnostic these molecular signatures turned out to be,” says Nemkov.
They then extended that work into real competition settings. They sampled riders before and after stages of a World Tour race and compared those profiles with their training camp data. They found that the best-performing cyclists were able to maintain their fat-burning capacity throughout the race, even as fatigue accumulated. While some molecular features progressively deviated over time, the top performers consistently recovered much closer to baseline between stages.
“That kind of information could eventually be used to personalize training or nutrition strategies,” says Nemkov. “If certain depleted metabolites relate to vitamin pathways or recovery mechanisms, for example, those could potentially be targeted directly.”
More recently, the researchers expanded the work into ultra-endurance running. In a study involving runners competing in the UTMB – a 171-kilometre ultra-marathon around Mont Blanc – they combined metabolomics, lipidomics, proteomics, and metallomics with hemodynamic and hematological measurements. The athletes were running continuously for roughly 24 to 45 hours, and they observed an enormous inflammatory response together with clear evidence of red blood cell damage and turnover.
“Interestingly, the red blood cells themselves began to resemble cells aged in stored blood bags used for transfusion medicine, which gave us insight into how extreme endurance stress alters blood physiology,” says Nemkov. They also analyzed samples from runners in the Trans Europe Foot Race, a 4,700-kilometre ultra-marathon stretching from southern Italy to northern Norway over 64 consecutive days. There, they saw even more pronounced inflammatory and vascular effects, including arterial stiffening. Plasma from those runners impaired vascular endothelial cell function in ex vivo experiments and increased oxidative stress markers.
“What this work is really helping us understand is where the boundary lies between adaptive training and physiological overload – when exercise shifts from being beneficial to potentially harmful,” says Nemkov.
From prevention to precognition
If researchers could identify a biochemical marker, or a group of metabolites, that can reveal when an athlete is approaching the limits of adaptive training, that data could be used to have the athlete pull back in training or take a rest from competition.
The thing I’d most love to know is: do I actually need to train today or not?” says Nathan Lawler, physiologist and metabolomics researcher at Murdoch University’s National Phenome Centre, and sports scientist with the West Coast Eagles Australian Football League Women’s team, and athlete himself – he was recently selected for the Australian Masters team for the over-35s hockey World Cup (read our interview with Lawler here). “Sport has always had this mentality of pushing harder, adding more sessions, doing more work. But there has to be a balance point somewhere where the body is effectively saying: it doesn’t matter today – you can back off and still get the adaptation you need.”
Lawler imagines something similar to the athlete biological passport used in anti-doping – but for broader athlete monitoring.
“I think it could be useful in the space between competitions, particularly in team sports,” he says. “If a team plays once a week, you’ve got a seven-day window between matches. Metabolomics won’t do much on game day, but it could be valuable during that recovery and preparation period – to help monitor fatigue, identify metabolites or amino acids that are depleted, and give dietitians information they can use to adjust nutrition. It could also support broader health and wellness monitoring, providing biochemical data alongside more subjective athlete management systems.”
The key question is: do we have good chemical markers of fatigue and the kind of longitudinal stability data that people can trust and know how to use? “Yes and no,” says Lawler. “A lot of the knowledge we currently draw on actually comes from the clinical space. So we often start with a clinical condition and then try to translate that back into a sporting context.”
At the National Phenome Centre, Lawler is involved in several large population clinical studies looking at chronic fatigue syndrome and long COVID. “We already have a fairly good idea of what fatigue-associated metabolic panels can look like in those patient populations – during flare-ups, day-to-day fatigue, and different disease states,” he says. “There actually appears to be quite a lot of crossover.”
But in terms of applied research, the work is yet to be done. “Now that I’m working more with accessible sampling approaches like dried blood spots, the next question becomes whether those metabolites are measurable and stable in that format,” he says. “If they are, then there’s no reason not to take that into the sporting environment and start testing those ideas.”
Nemkov and his team have also observed similar overlapping molecular signatures between the blood of depleted athletes and blood from clinical populations. “Some of the metabolites associated with extreme fatigue in athletes mirror markers we observe in patients with long COVID or in cancer patients experiencing severe cancer-associated fatigue,” he says.
“That’s important because it reinforces the idea that these molecular signatures are genuinely informative biomarkers of fatigue and physiological stress.”
Nemkov also believes that we have the analytical tools and biological understanding needed to start applying these approaches in real sporting environments. “We can already measure biomarkers linked to fatigue, oxidative stress, incomplete fat oxidation, amino acid metabolism, and a range of other exercise-relevant pathways,” he says. “I think we already have enough to begin applying these approaches in practice.”
But Liam Heaney, Reader in Bioanalytical Chemistry at Loughborough University, UK, whose work also sits at the intersection of analytical chemistry, exercise physiology, sport, and health, is more skeptical – at least for now (read our interview with Heaney here).
“If you look through the literature, there are a number of experiments exploring changes in the metabolome across seasons or training periods,” he says. “Researchers do identify molecules – either known or unknown – that change over time or appear to associate with particular athlete outcomes. The challenge is that those findings don’t often match the next study, or the study after that. They tend to be quite individualized between experiments.
“We don’t have a mature body of knowledge around using analytical science for things like performance monitoring, injury susceptibility, fatigue monitoring, or understanding training load.
“Precision medicine approaches sound fantastic in theory, but in elite sport they may simply involve too much complexity and workload to be practical on a routine basis.”
Lawler agrees that the key piece is the information itself: knowing exactly what to measure and what it means. “But that will come with more research,” he says. “Over the past few years, I’ve seen a real increase in studies using exercise models, sports teams, metabolomics, microsampling, and dried blood spots. People are starting to trust the technology as more data emerges around stability and reproducibility.”
A Gut Feeling
Heaney’s work focuses on microbial metabolites. In particular, his team wondered whether short-chain fatty acids produced by gut bacteria could serve as positive indicators – or even positive drivers – of athletic performance. Rather than trying to manipulate the gut microbiome itself to increase production of these metabolites, his team is bypassing the “middleman” and supplementing the compounds directly.
Normally, gut bacteria produce short-chain fatty acids through the fermentation of fibres and other complex carbohydrates that we can’t digest ourselves. “Even if you increase production in the gut, a lot of those metabolites are used locally within the gut environment and never make it into circulation,” he says. “So our thinking was: why not supplement them chemically and see what happens?”
The first phase of that work looked at pharmacokinetics. “Essentially, if we administer these compounds as fatty acid salts, how much actually enters the system, and how dose-dependent are those changes?” The next study then involved four weeks of supplementation with acetate, butyrate, and propionate – a combination of the three main short-chain fatty acids. The researchers tested participants using a self-paced 5 km treadmill run, where runners controlled the speed themselves but couldn’t see their pace or elapsed time – only the distance completed. “Interestingly, trained runners are incredibly good at reproducing their pacing under those conditions,” he says.
After the supplementation period and performance testing, they also put participants through a “fairly gruelling” – as Heaney puts it – muscle damage protocol involving 100 drop jumps from a 60 cm platform. “The idea there was that while any direct performance improvements from supplementation might be relatively small, there’s substantial evidence suggesting these compounds may have anti-inflammatory or immune-related effects,” he says.
The paper is due to be published later this year. “There were definitely some interesting findings that suggest directions worth pursuing further,” he says.
Alongside that work, Heaney’s team has been looking at short-chain fatty acids outside the sports context. One paper recently published examined whether levels detected in breath correlate with blood concentrations. “Interestingly, they don’t seem to match up in the way we expected,” he says.
“The assumption had been that these compounds could cross fairly easily from blood into breath via the lungs. But what we’re seeing suggests that at least some of the short-chain fatty acids detected in breath may actually be produced locally within the lungs themselves.”
That opens up another area Heaney’s team are becoming increasingly interested in – whether short-chain fatty acids could be used to support respiratory health. “That has obvious relevance in sport, especially for athletes who travel frequently, spend time on flights, or undergo heavy training loads, all of which increase susceptibility to respiratory infections and illness,” he says. “There’s also interest in sports like swimming, where breathing mechanics are tightly regulated, and in other physically demanding professions such as the military or firefighting. In many ways, those occupations resemble elite sport – just in heavy protective clothing.”
“Although this whole area is still relatively new for us, we think there’s real translational potential there, both in athletic performance and broader health applications,” he adds.
The continuous question
A key question when assessing the usefulness of any biomarker-based athlete intervention is turnaround time. “Right now, even with technologies like dried blood spots, you still have to collect the sample, send it to a lab, run the analysis, process the data, and return the results,” says Lawler. “By that point, the moment where that information might have influenced training or recovery decisions may already have passed.”
“The issue with metabolomics is that it’s still largely exploratory,” he adds. “Coaches don’t really want exploratory data; they want clear answers. They want to know: ‘If we measure this marker, what does it actually tell us, and what action should we take because of it?’ At the moment, metabolomics often can’t provide that level of direct interpretation.”
“The problem with the current approach is that you may not hear back on that result for a month,” Britton Needham says. “Whenever you are measuring something – even a single metabolite, such as glucose, ketones, or anything else – there is always a trade-off. Do you want the result quickly, or do you want it to be accurate? Right now, there is not really a method on the market that does both at the same time: something quick and accurate enough to make real-time treatment decisions based on the result.”
“I have been involved in dried blood spot technology,” says Shane Needham. “But the issue is that they are still not really real-time, and there are also sampling issues from a personal perspective. How does somebody collect the sample? Do they smear the spot? How much blood do they collect?”
If metabolomics researchers do eventually identify reliable markers, Heaney believes the next step would be to hand things over to experts in wearables, biosensors, and other sensing technologies – “to translate those discoveries into practical athlete monitoring tools,” he says.
D’Alessandro agrees. “Ultimately, we may find that, within this deluge of data, the relevant information is limited to a subset of structured data,” he says. “From there, a more targeted lab-on-a-chip approach – perhaps a wearable device that can sense a handful of relevant features, such as lactate, succinate, and others, in a similar fashion to the continuous glucose monitors we already have – may replace mass spectrometry with a more targeted and precise approach.”
In fact, this is exactly what Britton Needham – now a third-year PhD student in analytical chemistry – following in his father’s footsteps – is working on. While his PhD research is focused on building custom ion mobility spectrometers, he also recently launched a company to develop a spectroscopy-based wearable platform that could, in theory, be tuned to different analytes of interest.
“My interest starts with glucose, for personal reasons, but it could also be ketones, lipids, potentially something from the lactic acid cycle, or even myostatin,” he says. “But the main goal is to develop a wearable device to monitor blood glucose in a much better way than is currently possible.” Britton has a patent pending and is hoping to have a prototype finished by the end of the year.
Shane Needham also raises a chicken-and-egg problem with identifying important metabolites for athletes and coaches. “You do not measure what you cannot measure,” he says. “Glucose was relatively easy to measure – or at least we have turned it into something easy to measure. Because it is not currently convenient to put a wearable on an athlete and measure certain biomarkers, we are not doing it.”
His point is that if we had an analytical device that is wearable, real time, and gives feedback on biomarkers of interest, that would become data researchers – and individual coaches – could correlate and learn from. “That may help us identify other metabolites, lipids, sugars, or other markers worth measuring,” he adds.
Nemkov also sees the application and discovery happening in parallel – even with laboratory analysis. “Every time we collect these large-scale datasets, we’re also learning more about biochemical pathways and uncovering new markers that may become useful in the future,” he says.
D’Alessandro believes that AI could help researchers overcome the problem of finding well-validated markers that work across individuals by generating meaning from big data. He and his team are applying machine learning approaches such as elastic net models, random forests, large language models, to integrate these datasets with real-world performance measurements. “That allows us to identify molecular signatures that predict performance, fatigue, and physiological stress, and eventually to create digital twins of athletes that can model how somebody might respond to training, competition, or recovery protocols over an entire season,” he says.
“It’s akin to polygenic risk scores, where you’re trying to understand someone’s genetic propensity to develop a certain disease,” says Nemkov. “You might measure specific variation across 200 to 400 different genes, but collectively that gets consolidated into a single polygenic risk score. I think that’s where this is going.”
There may also be a role for a level of testing in between continuous monitoring devices and lab testing: namely, in-field tests using handheld and portable analytical systems.
“The main market for those systems will probably be clinical, but there’s no reason sports teams couldn’t eventually adopt them too,” says Lawler. “Especially if they had in-house expertise to run consistent assays and generate the information they need.”
“I remember when you could go into a pharmacy and get your blood pressure tested using a machine,” says Shane Needham. “I think we may eventually see something similar with technologies such as ion mobility spectrometers. Manchester United, for example, might have a spectrometer in the locker room to test players after a match, before a match, maybe even at halftime.”
“I think we could see those kinds of instruments in pharmacies or walk-in clinics. They could give us a lot of biological data. And, perhaps with AI, that data could even help with diagnosis.”
Can Analytical Science Help Build Muscle?
Building muscle is important for athletes of all stripes – and for everyone else, especially to ward off age-related sarcopenia. Could analytical measurements help athletes, coaches, or hypertrophy researchers understand how to build muscle as effectively and efficiently as possible?
“There has been quite a lot of research using mass spectrometry to study hypertrophy, particularly in relation to protein supplementation and muscle protein synthesis,” says Liam Heaney. A number of studies have used isotope-labeled amino acids to track their uptake into muscle tissue, then applied isotope ratio mass spectrometry to understand whether particular dietary strategies – such as protein dose, protein type, timing of ingestion, or co-ingestion with carbohydrate – lead to improved muscle protein synthesis rates.
“The main focus was never really the mass spectrometry itself, but rather the nutritional and physiological questions: when should protein be consumed, how much is optimal, what type is best, and does combining it with carbohydrate make a difference?” He says.
Much of that work was led by Stuart Phillips, Professor in the Department of Kinesiology at McMaster University, Canada.
“Hypertrophy is the end result, but the biology is the accumulated consequence of repeated changes in protein turnover,” says Phillips. “Tracer methodologies and modern mass spectrometry have enabled us to quantify muscle protein synthesis and related pathways with much greater precision, clarifying many debates that previously relied on indirect inference.”
“Better measurement has made us more humble about individual variability. Earlier work often treated ‘the average response’ as the main story,” he adds. “Now we know that two athletes can do the same program, eat similar diets, and show very different trajectories.”
However, there is only so much protein synthesis can tell an athlete about the effectiveness of their training for hypertrophy, according to Eric Helms, Co-director of the Sport Research Institute New Zealand (SPRINZ), Senior Research Fellow at Auckland University of Technology, and professional natural bodybuilder (read our interview with Helms here). “Right now, muscle protein synthesis – at least in the short term – has to be manipulated a lot before it predicts hypertrophy,” he says. “Gross muscle protein synthesis can increase because of muscle regeneration and damage, which does not necessarily mean hypertrophy. You might see an acute response, if that person does not eat enough protein for the next four days, starts dieting, or does not train again, you won’t necessarily see hypertrophy. So many other factors predict long-term hypertrophy.”
There have been other proposed proxies for hypertrophy, which researchers could use to measure the effectiveness of a training stimulus. For example, researchers can measure oxygenated and deoxygenated hemoglobin through changes in near-infrared spectroscopy – a measure of metabolic stress, which has been hypothesised to contribute to hypertrophy; but this is debated in the field.
“One theory is that metabolic stress directly drives hypertrophy,” says Helms. “Another idea is that it acts as an indirect contributor, where the metabolic status of the muscle changes motor unit recruitment – which may help explain why high-rep training to failure can produce similar growth to heavy, low-rep training or moderate-rep training. And the third possibility is that metabolic stress is just correlated with hypertrophy-related processes.”
But the prevailing theory for why muscles grow in response to resistance training is that tension stimulus is sensed by mechanosensors, which initiate hypertrophy-causing signaling cascades, involving molecules such as mTOR.
“In cases where we have seen something that could be interpreted as metabolic-stress-induced hypertrophy, there has usually been another explanatory mechanism,” says Helms – which is why he is skeptical about the utility of NIRS for hypertrophy. “You are measuring a questionable mechanism, and only one aspect of that mechanism, which may or may not be causative.”
Nevertheless, Helms and PhD student Takahiro Itagaki are using NIRS to understand the relationship between someone’s subjective feeling of the “pump” – the temporary swelling of muscles that occurs during and immediately after a workout – and how closely, and how reliably, that maps onto constructs such as circulating lactate, immediate increases in muscle thickness, and changes in hemoglobin or deoxygenated hemoglobin in the muscle. “Whether any of that predicts hypertrophy remains questionable – but it is a neat study,” he says.
But in an ideal world, what would lifters and coaches measure to understand whether a muscle is growing? “Something that measures how many grams of protein are being accreted or delivered to your muscles on a regular basis would be fascinating,” says Helms. “At-home muscle protein synthesis would be very interesting: which workouts produced higher or lower levels?
“But if you had a continuous analysis of muscle protein synthesis, and you also knew someone’s maximum rate and rate of breakdown, then you could know more about how effective the training was. A constant measure of mTOR activation would also be interesting too. In theory, it could tell us whether we were in an anabolic state and whether the training was effective.
“Hypothetically, there are things we could measure, but it would almost require a ‘God machine’ measuring all the muscles in your body.”
Helms believes that on one hand, our ability to measure hormones and signaling factors has moved beyond our mechanistic understanding in hypertrophy research. “Acute responses to training do not reliably map onto long-term hypertrophy,” he says. “That is why we are in this strange space where hormone coaches and biohackers can measure these things, but many of the measurements may not actually matter – continuous glucose monitoring is a good example, at least in a hypertrophy context. Our technology has advanced past our theoretical understanding of the mechanisms.” But on the other hand, understanding what is happening at the individual muscle-level using in-field or wearable analytical devices is currently out of reach.
“Hypertrophy is difficult because it is a physiological process rather than a performance adaptation,” he says. “It might be surprising to some readers to realize that we still do not fully understand how hypertrophy occurs.
“We have identified some mechanosensors that we think stimulate hypertrophy, and we have some understanding at the signaling level. But if you really try to pin all of this down, there are still disconnects between the most mechanistic understanding, the training variables, and the observations from applied research.”
Travis Nemkov, however, is more optimistic about the potential role metabolomics could play in hypertrophy research.
“The MoTrPAC [Molecular Transducers of Physical Activity Consortium] studies have really laid the groundwork for this type of research by sampling plasma and skeletal muscle before and after resistance training over the course of months for multi-omics analysis,” he says. “As these data are analyzed, altered biochemical networks that occur in parallel to strength improvements, for example, will help to molecularly characterize these physiological adaptations.”
He also thinks those measurements could be compared with established markers of muscle damage. “One of the classic examples is creatine kinase – particularly the muscle isoform – which we often see increasing in plasma as physiological stress accumulates during long endurance competitions,” he says. “When you see creatine kinase rising in plasma, that’s essentially telling you there’s muscle breakdown occurring.”
The idea is to stress the system and then measure the recovery trajectory. “That’s where a lot of exercise metabolomics has become powerful – measuring immediately after exercise, then again at six hours, 24 hours, 48 hours, and 72 hours later,” he says. “Once you do that, you can start mapping what returns to baseline quickly, what remains disrupted, and what reflects recovery versus overload. Those kinds of time-course studies could absolutely help determine whether a training stimulus is sufficient, excessive, or perhaps not enough from an anabolic perspective.
From precision sport to precision medicine
Shane Needham’s point underscores a thread that runs through work in analytical sport science; namely, the close connection with clinical medicine.
“We’re using elite athletes as a model system for understanding optimal human physiology,” says Nemkov. “The long-term objective is to apply those insights to patient populations, particularly in areas like exercise oncology.
“We want to understand whether multiomic profiling can help identify which patients will benefit most from post-treatment exercise programs, and how to tailor exercise interventions more precisely.”
Angelo D’Alessandro is of a similar view. “In the future, you could imagine monitoring an elite sports team longitudinally throughout a season, using repeated microsampling to identify who is approaching injury risk, who may need reduced workload, or who is recovering optimally,” he says. “But equally, the same principles could apply to aging, chronic disease, rehabilitation, or exercise prescriptions for the general public.”
Could elite athletes become an early adopter population for analytical monitoring approaches that are eventually rolled out by clinicians as part of a transition towards a more precise approach to healthcare?
“That is our vision,” says D’Alessandro. “We want these tools to become accessible to the general population as part of preventive and personalized healthcare.”
“I think athletes could start a little bit of a revolution, where others see it and think, ‘Wow, that is insane,’ and then begin measuring those biomarkers themselves,” says Britton Needham.
Stuart Phillips imagines a three-tiered system for elite sports monitoring: continuous or near-continuous monitoring for workload, movement, heart rate metrics, sleep proxies, and sometimes glucose; periodic lab-quality checks (blood panels, metabolic testing, imaging) to anchor the interpretation and keep the day-to-day signals honest; and targeted deep dives when something changes: unexpected fatigue, repeated soft-tissue issues, unexplained performance drift, or body composition shifts. “Some version of this is already happening in elite environments, but it is uneven and not ready – nowhere near – for prime time,” he says.
Interestingly, this outlook parallels Mike Snyder’s (Stanford W. Ascherman Professor of Genetics, Director of the Center for Genomics and Personalized Medicine, Stanford School of Medicine, USA) vision for the future of precision medicine from our 2025 cover feature: “When it comes to biochemical testing, I see a combination of different sampling frequencies emerging. There will be continuous monitoring – this is useful for tracking specific biomarkers in real-time, like glucose, cytokines, or lactate. Then there’s frequent but less invasive sampling – finger-prick devices that instantly analyze a small panel of key markers (maybe half a dozen or up to 20 analytes), done weekly to track overall health trends. And finally, deep profiling at longer intervals will be key – micro-sampling done every few months to get a very comprehensive biochemical profile, measuring thousands of molecules at once.”
“Elite sport is a natural early-adopter ecosystem because the incentives are strong and the support infrastructure exists,” says Phillips. “Athletes also tend to be highly engaged with their bodies, which makes them good ‘systems’ for testing measurement-plus-feedback approaches” – as Britton Needham’s experience demonstrates.
“The caveat is that elite sport is not a generalizable human model,” he adds. “Athletes are not typical, and what is optimal for performance is not always optimal for long-term health. In fact, most athletes train and run their ‘show’ at near pathological levels. Still, the methods developed in sport can translate into better ways to monitor recovery, detect low energy availability, preserve lean mass during injury, and tailor resistance training prescriptions. That is where the healthspan link becomes interesting. If we can learn how to preserve muscle function and resilience in extreme training settings, we can often adapt the principles to aging populations where the goal is independence and quality of life.”
Heaney believes that, in many ways, the situation in sports science mirrors precision medicine itself: “scientifically exciting, but incredibly complex,” he says. “The big difference is funding. Precision medicine attracts huge investment because it’s tied directly to health outcomes, lifespan, and disease. Sport doesn’t receive that same level of support. Governments and major funding bodies generally don’t view athlete monitoring experiments as critical public health priorities, so the resources needed for truly large-scale precision sport studies are rarely available.”
Lawler concurs. “From a funding perspective, I think a lot of the progress may come from the clinical side first and then work its way up into sport,” he says. “Sports teams can be their own economy – some do have money – but the question is whether they believe in the value enough to invest. And unless you get buy-in from everyone – coaches, athletes, support staff – it’s hard to push forward.
“From my own experience, it’s difficult to go to a team and say, ‘I can take your blood and make your training better,’ because you can’t guarantee that. Nothing in this space is guaranteed yet.
“That said, I’m optimistic that athlete monitoring could realistically become commonplace in the next five to ten years – and I hope we can contribute to making it happen.
“The bigger question is whether high-performance sport will actually push it forward. And even if they do, there’s no guarantee we’d hear about it publicly. Sport can be very secretive. Teams don’t necessarily want competitors knowing what they’re doing, so a lot of innovation can stay behind closed doors.”
“We really can distinguish athletes and performance based on these technologies, so I do think it’s going to be adopted,” says Nemkov. “It’s such a good prognostic measurement for how training and competition are going that I would be surprised if, in 10 years, this wasn’t being used.
“In a way, it’s already being used, if you consider lactate measurement as a way of guiding training and performance. This is just measuring multiple pathways that are tangential to lactate accumulation, which could potentially provide more precision when prescribing a different training regimen or nutritional protocol.”
“My optimistic view is that athletes will spend less time guessing and more time doing high-quality work with better recovery,” says Phillips. “Training may become slightly lower in junk volume and higher in precision: fewer sessions that are ‘hard for the sake of hard,’ and more sessions targeted to the intended adaptation.
“My pessimistic view is also worth stating: the risk is over-surveillance and data anxiety. If every minor fluctuation is treated as a crisis, you end up with athletes who feel ‘monitored’ rather than supported. The best environments will use analytics to reduce stress, not add it.
“My very pessimistic answer is that nothing really changes and all we have is more (and more) data that nobody knows what to do with!”
Teaser image credit: Images for collage sourced from Adobe Stock
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