Stool composition can predict a person’s imminent death: scientists have created a revolutionary analysis

A group of doctors led by Alexander de Porto from the University of Chicago and the University of Amsterdam have created an index of markers. They are contained in a patient's stool and are able to estimate the risk of his mortality within 30 days, writes Science alert.

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Scientists have dubbed the index the "metabolic dysbiosis score" (MDS). It could help save the lives of seriously ill patients in intensive care units.

The researchers note that their findings require further study and confirmation, but they represent a promising tool for future diagnostic medicine.

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"These findings suggest that fecal metabolic dysbiosis, as quantified by MDS, has potential as a biomarker to identify critically ill patients at increased risk of mortality," said de Porto and colleagues Eric Pamer and Bhakti Patel of the University of Chicago.

Seriously ill patients admitted to intensive care units often develop severe syndromes such as sepsis and acute respiratory distress syndrome, but these conditions do not always progress in the same way. This diversity makes treatment extremely challenging: two patients with the same syndrome may respond very differently to the same treatment.

One way to overcome this problem, the researchers say, is to identify individual traits that can be treated, rather than trying to tackle the entire syndrome at once. Scientists know that severely ill patients often have reduced diversity in their gut microbiota, as well as changes in the levels of metabolites produced by their microbiomes.

De Porto and colleagues looked at dysbiosis — an imbalance of gut microbiota — in seriously ill patients as a possible trait that could be treated. They examined stool samples from 196 patients with respiratory failure or shock, dividing them into two groups: a training group of 147 patients and a validation group of 49 patients.

Using these samples, they developed an MDS score based on the concentrations of 13 different fecal metabolites. The results showed a promising direction for further research.

“The MDS showed good results in predicting mortality in the training group of intensive care unit patients: accuracy was 84%, sensitivity 89%, specificity 71%,” the researchers stated. “However, the validation group, despite similar trends, did not reach statistical significance, likely due to the small sample. These data demonstrate the potential of the MDS and also highlight the need to confirm its predictive value and versatility in independent samples before widespread use.”

What was particularly interesting to the researchers was that while reduced microbiome diversity had previously been linked to poor outcomes in seriously ill patients, they found no such link. Instead, their results showed a strong association between dysbiosis and increased mortality risk, suggesting that it is the imbalance of the microbiome that plays a critical role in a patient’s health.

There is still much work to be done before the method is ready for clinical use. The negative result in a small sample of 47 patients shows that the method requires further improvement. However, there are some encouraging factors.

For example, the lab has already shown that fecal metabolites can identify liver transplant patients who are at higher risk of developing postoperative infection. In addition, although specific treatments have not yet been studied or developed, the MDS score points to possible avenues for further research.

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“The metabolites included in the index, such as short-chain fatty acids, bile acids, and tryptophan metabolites, point to biological pathways that could potentially be therapeutic targets,” the researchers explained. “Potential interventions could include dietary changes, probiotics, or direct supplementation of these metabolites.”

The next step is to work to confirm the effectiveness of MDS in new patient samples, as well as to study whether the association between the observed dysbiosis and increased mortality risk is causal or just a symptom of another process.

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