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Personalized Microbiome Features Found in Study Spanning Multiple Body Sites

NEW YORK – A longitudinal study of the human microbiome spanning several body sites has uncovered stable, individualized microbial community composition features, including ones associated with insulin resistance, according to an international team led by investigators at Stanford University School of Medicine.

"Our results underscore the idea that we each have individualized microbiomes across our body that are special to us," Michael Snyder, director of the Stanford Center for Genomics and Personalized Medicine, said in a statement. "Your genetics, your diet, and your immune system are all shaping this ecosystem."

Using targeted 16S ribosomal RNA gene sequencing data generated at the Jackson Laboratory for Genomic Medicine or by the now-defunct firm uBiome, Snyder and colleagues at Stanford University, the Stanford Center for Genomics, and elsewhere profiled microbial communities in skin, oral, nasal, and stool samples collected quarterly for up to six years in 86 individuals between the ages of 29 and 75 years old — work they outlined in the journal Cell Host & Microbe on Tuesday.

With the help of a "degree of microbial individuality" (DMI) metric, the team was able to distinguish between shared- and individual-specific microbes found at each of the body sites under the range of health, disease, and environmental circumstances the participants encountered over time.

"We argue that the differences between individuals alone do not provide significant biological insights unless they are analyzed in conjunction with within-individual differences, using standardized metrics," co-first author Xin Zhou, a genetics researcher affiliated with Stanford University, the Stanford Center for Genomics and Personalized Medicine, and the Stanford Diabetes Research Center, explained in an email.

In particular, the DMI metric "allows us to correlate individuality with various factors, such as stability, and to assess individuality in the context of disease states," Zhou added. "This helps determine whether individuals with certain conditions exhibit a highly personalized microbiome that is, in many cases, related to disease."

Together with corresponding blood proteomic, metabolomic, lipidomic, cytokine, and other clinical lab test data, the microbial community data also revealed microbiome patterns corresponding with insulin sensitivity, insulin resistance, and forms of stress ranging from respiratory illness to antibiotic use or vaccination.

In participants with insulin resistance, for example, the investigators detected higher-than-usual levels of microbiome personalization, along with microbiome dysregulation that resembled the short-term microbial community changes found in individuals experiencing a fleeting illness.

"[W]e have discovered common dysbiosis signatures shared between short-term respiratory viral infections and long-term insulin resistance," Zhou said, adding that the work further pointed to "specific upper respiratory microbiome features associated with insulin resistance that correlate with low-grade inflammation."

He noted that the team is continuing to use the date- and host-matched metabolomic, lipidomic, proteomics, immunomic, and clinical data they now have on hand to dig into host-microbiome interactions contributing to the signature and to other health or disease-related states.

In addition to identifying individual-specific microbiome features, which tended to remain more stable over time than their common microbe counterparts, the researchers got a closer look at microbiome stability differences by body site. In particular, they found that the stability of the microbiomes found in stool and oral samples exceed that of the skin and nasal microbiomes, potentially owing to distinct environmental exposures and interactions at each site.

Together, Zhou suggested, the resulting dataset "represents a pioneering effort to compile microbiome profiles from various body sites along with host multiomics data longitudinally."

"This approach not only facilitates the comparison of microbial metrics — such as longitudinal stability or microbial richness — using other body sites as references," he explained, "but also allows for an in-depth analysis of factors influencing these metrics, especially in relation to diseases."