"Nearly half of our chronic fatigue subjects had some disorder of breathing -- a totally unappreciated issue, probably involved in making symptoms worse," said Dr. Benjamin Natelson of the Icahn School of Medicine, senior author of the study published in Frontiers in Medicine. "Identifying these abnormalities will lead researchers to new strategies to treat them, with the ultimate goal of reducing symptoms."

Breathe easy

The study included 57 people diagnosed with chronic fatigue syndrome and 25 healthy individuals of similar age and activity level. All participants completed two days of cardiopulmonary exercise tests. During these sessions, the researchers monitored heart rate, blood pressure, oxygen uptake efficiency, blood oxygen saturation, and how much effort participants used to breathe. They also analyzed breathing rate and patterns to detect signs of hyperventilation and dysfunctional breathing.

Dysfunctional breathing is often seen in asthma patients, but it can develop for many different reasons. Typical features include frequent deep sighs, rapid breathing, forceful exhalation from the abdomen, or chest breathing without proper diaphragm use, which prevents the lungs from fully expanding. It can also involve a lack of coordination between chest and abdominal movements, meaning the muscles that support breathing are no longer working smoothly together.

"While we know the symptoms generated by hyperventilation, we remain unsure what symptoms may be worse with dysfunctional breathing," said Dr. Donna Mancini of the Icahn School of Medicine, first author of the study. "But we are sure patients can have dysfunctional breathing without being aware of it. Dysfunctional breathing can occur in a resting state."

Catching your breath

Results showed that people with chronic fatigue syndrome took in roughly the same amount of oxygen as the control group -- their peak VO2 max was similar. However, 71% of the chronic fatigue group showed breathing abnormalities, such as hyperventilation, dysfunctional breathing, or both.

Almost half of the chronic fatigue participants breathed irregularly during the tests, compared to only four people in the control group. About one-third of the fatigue patients hyperventilated, while just one person in the control group did. Nine patients had both hyperventilation and dysfunctional breathing, a combination not seen in any of the controls.

Both of these breathing disorders can produce symptoms similar to those of chronic fatigue, including dizziness, difficulty concentrating, shortness of breath, and exhaustion. When both occur together, they can also cause chest pain, palpitations, fatigue, and (unsurprisingly) anxiety. The researchers believe that these breathing problems may worsen the effects of chronic fatigue or even play a direct role in post-exertional malaise.

"Possibly dysautonomia could trigger more rapid and irregular breathing," said Mancini. "It is well known that chronic fatigue syndrome patients often have dysautonomia in the form of orthostatic intolerance, which means you feel worse when upright and not moving. This raises the heart rate and leads to hyperventilation."

Pulmonary physiotherapy?

These findings suggest that addressing dysfunctional breathing could help relieve some symptoms of chronic fatigue. The researchers plan to continue investigating how dysfunctional breathing and hyperventilation interact. Although more studies are needed before any official treatments are recommended, they already have several promising ideas.

"Breathing exercises via yoga could potentially help, or gentle physical conditioning where breath control is important, as with swimming," suggested Natelson. "Or biofeedback, with assessment of breathing while encouraging gentle continuous breath use. If a patient is hyperventilating, this can be seen by a device that measures exhaled CO2. If this value is low, then the patient can try to reduce the depth of breathing to raise it to more normal values."

While the human body contains roughly 30 to 40 trillion human cells, the intestines alone harbor about 100 trillion bacterial cells. In other words, we carry more bacterial cells than our own. These microbes aren't just involved in digestion; they also produce and modify thousands of compounds called metabolites. These small molecules act as chemical messengers, circulating through the body and influencing metabolism, immunity, and even brain function. Understanding how specific bacteria produce particular metabolites could unlock new ways to support overall health.

Mapping the Microbial Puzzle

"The problem is that we're only beginning to understand which bacteria produce which human metabolites and how these relationships change in different diseases," explained Project Researcher Tung Dang from the Tsunoda lab in the Department of Biological Sciences. "By accurately mapping these bacteria-chemical relationships, we could potentially develop personalized treatments. Imagine being able to grow a specific bacterium to produce beneficial human metabolites or designing targeted therapies that modify these metabolites to treat diseases."

The main challenge lies in the sheer scale of the data. With countless bacteria and metabolites interacting in complex ways, identifying meaningful patterns is extremely difficult. To tackle this, Dang and his team turned to advanced artificial intelligence (AI) methods.

Their system, called VBayesMM, uses a Bayesian approach to detect which bacterial groups significantly influence particular metabolites. It also measures uncertainty in its predictions, helping prevent overconfident but incorrect conclusions. "When tested on real data from sleep disorder, obesity and cancer studies, our approach consistently outperformed existing methods and identified specific bacterial families that align with known biological processes," said Dang. "[This gives] confidence that it discovers real biological relationships rather than meaningless statistical patterns."

Understanding the System's Strengths and Limits

Because VBayesMM can recognize and communicate uncertainty, it provides researchers with more trustworthy insights than earlier tools. Although it's optimized for large-scale data, analyzing massive microbiome datasets remains computationally demanding. Over time, however, these costs are expected to decrease as processing power improves. The system also performs best when there is extensive bacterial data compared to metabolite data; otherwise, accuracy can drop. Another limitation is that VBayesMM treats bacteria as independent actors, even though they often interact in complex, interdependent networks.

"We plan to work with more comprehensive chemical datasets that capture the complete range of bacterial products, though this creates new challenges in determining whether chemicals come from bacteria, the human body or external sources like diet," said Dang. "We also aim to make VBayesMM more robust when analyzing diverse patient populations, incorporating bacterial 'family tree' relationships to make better predictions, and further reducing the computational time needed for analysis. For clinical applications, the ultimate goal is identifying specific bacterial targets for treatments or dietary interventions that could actually help patients, moving from basic research toward practical medical applications."

By using AI to navigate the vast and intricate world of gut microbes, researchers are moving closer to unlocking the microbiome's potential to transform personalized medicine.