Microbiome Age

This marker uses comparative algorithms to calculate the biological age of your gut based on its bacterial composition. Just as your skin or your heart can age faster or slower than your chronological age, your gut ecosystem has its own biological clock. A "young" microbiome is associated with greater resilience and metabolic health, while accelerated aging can be an early sign of inflammation or declining health.

From birth to old age, our microbiome follows a predictable developmental trajectory. In infancy, Bifidobacteria predominate; in adulthood, diversity reaches its peak; and in old age, this diversity tends to collapse, increasing inflammatory bacteria.

This analysis compares your microbial profile to a database of thousands of healthy individuals of all ages. The result tells you where your ecosystem falls on that timeline:

• Microbiome Age < Real Age: Generally positive. It indicates that you maintain a diversity and functionality typical of a younger person.
• Microbiome Age > Real Age: Suggests "premature aging" of the gut, often caused by poor diets, chronic stress, or frequent medication use.

The age of your microbiome is a powerful indicator of your overall vitality and potential longevity:

• Resilience and Recovery: A "young" microbiome has high plasticity; it recovers quickly after a course of antibiotics or an infection. An aged profile is more fragile and prone to remaining in states of chronic dysbiosis after a disturbance.
• Inflammation ("Inflammaging"): Accelerated microbiome aging correlates with the phenomenon of inflammaging (chronic low-grade inflammation associated with age). This manifests as a loss of butyrate-producing bacteria and an increase in opportunistic bacteria (Proteobacteria).
• Healthy Longevity: Studies in healthy centenarians show that, interestingly, they maintain a microbiome distinct from that of frail elderly individuals, retaining functional characteristics more typical of young adults.

Your goal is to keep your "gut age" stable, avoiding the degradation associated with premature aging.

The determination of Microbiome Age is performed using machine learning models, specifically regression algorithms such as Random Forest or Deep Neural Networks. The model has been trained with massive metagenomic sequencing datasets from age-stratified healthy control cohorts. The algorithm identifies "age-signatures": specific combinations of species and metabolic functions that change linearly or non-linearly over time. During the analysis of your sample, the model projects your taxonomic profile onto this reference curve to predict your biological age. The deviation between your chronological age and the predicted age is reported as an indicator of biological aging acceleration or deceleration.