Microbiome in improving and personalizing treatment

Your unique microbiome may be used to improve and personalize your future therapeutic experience

In a study recently published in the journal Nature Reviews Microbiology, researchers summarized more than 200 articles relating microbiomes to clinical diagnostic and precision therapeutic interventions.

Gut microbiota and its potential in personalized therapy

The gut microbiome, also known as gut microbiota or gut flora, is a collective term for all microorganisms inhabiting the digestive tract of higher animals.

In stark contrast to the human host genome, the gut microbial metagenome exhibits considerable diversity and flexibility and is constantly evolving in response to host physiology and environment. Gut microbial assemblages are unique in both host characteristics, as they are often derived from maternal and environmental microbiota.

The composition of the microbiome varies significantly among individuals, and may also change within the same individual, reflecting dynamic changes that occur throughout life as a result of age, geographic location, daily rhythms, and exposure to environmental changes. It occurs nutritionally and medicinally.

Growing evidence emphasizes the importance of gut microbiota in conferring nutritional, immune, and psychological benefits to their host. Consequently, significant disturbances in the gut microbial ecosystem, termed "dysbiosis," are associated with metabolic, gastrointestinal, neurological, and inflammatory consequences.

Characterizing an individual's gut microflora may help better understand their current health and help develop optimal clinical interventions. Current research on treatment personalization often focuses on chronic conditions, particularly cancer.

These studies typically include biochemical and genetic phenotyping to inform interventions for patients. However, these methods have certain limitations

For example, biochemical phenotyping uses standardized methods, which can lead to binary results with a small range to accurately understand the dynamic health of an individual. Similarly, genetic phenotyping cannot account for temporal changes in health or the phenotypic consequences of gene-environment interactions. Personalization based on patient microbiome community composition overcomes the limitations of current personalization approaches and ensures intra-individual consistency, a critical requirement in diagnostic testing.

Diagnostic advances

Metagenomic sequencing, which is the process of analyzing the genetic composition and diversity of the gut microbiome, has been successfully investigated as a biomarker of patients' overall health and specific disease prevalence. These studies have led to the identification of trimethylamine N-oxide (TMAO), a microbiome-modulated metabolite, and its role in predicting cardiovascular disease (CVD) risk, as well as branched-chain amino acids to predict type 2 diabetes (T2D).

Further studies have paired metagenomic sequencing with machine learning (ML) artificial intelligence algorithms to differentiate between glucose intolerance and type 2 diabetes and normal glucose metabolism with diagnostic accuracy greater than currently used diagnostic tools. These findings show how microbiome analyzes can not only replace current diagnostic tools, but also, together with artificial intelligence, dramatically reduce the burden of overworked human doctors.

The use of microbiome-targeted interventions as a means of modifying disease risk in disease-susceptible populations may complement and optimize current primary prevention approaches.

One person's food is another's poison

Health behaviors have been identified as the easiest risk inhibitors in weight, age, cardiovascular health, and other chronic noncommunicable diseases, with studies suggesting "optimal" behaviors to improve overall health.

Unfortunately, a growing body of research suggests that different people may respond differently to behavioral interventions. High-intensity physical exercise, while beneficial in weight loss, raises blood glucose levels, which is harmful for people with type 1 diabetes (T1D). Similarly, individual-specific gut microbial communities can process and assimilate dietary nutrients with significant differences in host health outcomes.

Phenotyping the patient's gut microbiota can help personalize behavioral and clinical interventions against common and specific health conditions. In addition, the recurrent phenotype can be used as a marker of treatment response and intervention effectiveness. Artificial intelligence models based on these concepts have been shown to outperform current gold standards in predicting and monitoring patient responses to clinical interventions.