31 markers associated with autism identified in the intestinal microbiota

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An analysis of the intestinal microbiota of 1,627 children with and without autism spectrum disorder (ASD) has identified 31 biological markers associated with autism that could help in its early detection and evaluation of its treatment.

The gut microbiota includes microorganisms such as bacteria, viruses, fungi and archaea and is closely related to the health of the entire body, and even to our emotional well-being, which is why it is the subject of numerous scientific studies. The relationship between gut microbiota and autism spectrum disorders (ASD) is a well-known topic, but most research has focused on the variation in gut bacteria between people with ASD and those considered neurotypical.

Now, however, new research has identified 31 biological markers associated with autism that could have diagnostic value in the gut microbiota of children and adolescents. The researchers analysed faecal samples from 1,627 children (aged 1 to 13 years, 24.4% female) with or without ASD, living in China. The results have been published in Nature Microbiology and coincide with previous research in showing lower microbial diversity in people with autism.

The study was conducted by scientists at the Chinese University of Hong Kong, who carried out metagenomic sequencing of fecal samples from 1,627 children with and without ASD, aged 1 to 13 years, from five cohorts in China, and analysed these data together with additional factors such as diet, medication and comorbidity.

After evaluating all the data, the researchers identified 14 archaea, 51 bacteria, 7 fungi, 18 viruses, 27 microbial genes and 12 altered metabolic pathways, which represents a significant improvement compared to previous studies that only considered bacterial components.

“This study sheds light on how the gut microbiome, composed not only of bacteria but also fungi and viruses, can influence autism,” explains Qi Su, from the Chinese University of Hong Kong and lead author of the study, in statements to the SINC agency. “By identifying specific changes in children, possible early markers for diagnosing it are suggested,” he adds.

Analyze fecal samples to facilitate the diagnosis of ASD

In this study, machine learning was used, specifically a method called Random Forest that looks for patterns or differences between children to analyze the data of those with ASD and those without. This technique allowed the identification of specific microbial markers that help to understand the possible relationship between the intestinal microbiota and ASD and can facilitate the development of new diagnostic and therapeutic techniques.

The team developed a model based on a panel of 31 microbes and functions, which showed greater diagnostic accuracy in identifying children with ASD compared to models that only considered a single domain, such as bacteria or archaea. The authors suggest that these 31 markers could have clinical diagnostic potential because they can be reproduced across multiple cohorts.

“Simple tests such as analyzing fecal samples could help doctors diagnose ASD in the future,” says Su. “Understanding which microbes are out of balance in the affected child population could pave the way for developing noninvasive diagnostic tools and personalized treatments,” she adds.

“With further research on these microbial markers we could uncover more details about the mechanisms linking gut flora and ASD and develop therapies targeting the gut-brain axis.”

Furthermore, these markers could serve as indicators to monitor the efficacy of treatments. By following changes in the composition of the gut microbiome over time, doctors could assess whether specific interventions such as dietary changes or probiotic (beneficial bacteria) supplements effectively restore a healthy balance of the microbiota.

“With further research on these microbial markers, we may be able to uncover additional details about the mechanisms linking gut flora and ASD, leading to the development of novel therapies targeting the gut-brain axis. These advances promise earlier diagnosis, personalized treatments, and better outcome monitoring,” Su concludes.

Although these results represent a significant advance in autism diagnostic methods, the authors stress that no causal role of microbiota in the development of autism can be assessed and acknowledge that the study needs to be repeated in other groups and global populations to validate the results.

Toni Gabaldón, ICREA research professor and head of the Comparative Genomics group at the Institute for Research in Biomedicine (IRB Barcelona) and the Barcelona Supercomputing Centre (BSC-CNS), who was not involved in the study, explained in statements collected by SMC Spain: “It has been known for some time that children with autism spectrum disorders have a different intestinal microbiota, but most studies are based on the analysis of the bacterial component and at the taxonomic composition level. Using shotgun methodologies, where all the DNA of the sample is sequenced and not just marker genes, this study provides a more complete view, observing changes in archaea, fungi and viruses, and providing insight into potential metabolic changes associated with changes in microbiota. It also has a very particular focus on the discovery of biomarkers and proposes a panel of 31 species that discriminate quite well.”

“Current diagnosis is based on behavioral patterns that appear over time; adopting early biomarkers that could help detect autism earlier could facilitate the initiation of earlier therapies. If there are metabolic changes that influence the progression of symptoms and could be compensated by diet or the use of probiotics, modulating the microbiota would open the door to new treatments that improve some aspects.”

According to Mireia Vallès Colomer, head of the Microbiome Research Group at the Faculty of Medicine and Life Sciences at Pompeu Fabra University, in statements to the same media: “The study confirms that diet explains part of the alterations in the microbiome, but even after controlling for this factor, differences are still detected. To do this, they have analyzed new samples and reanalyzed data from previous studies. In addition, they did not limit themselves to examining the composition of the bacterial fraction of the microbiome (as many studies do), but also analyzed archaea, viruses and fungi. Although the methodology presents some debatable points, the data are publicly available, which will allow other teams to verify whether they reach the same conclusions.”

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