Your gut bacteria shape your health from childhood through old age, study shows

Your gut bacteria shape your health from childhood through old age, study shows

Scientists are discovering how your gut bacteria evolve over time - weight, diabetes and heart health - providing new insights into preventing metabolic diseases.

Did you know that over 1 billion people worldwide suffer from metabolic disorders such as obesity and type 2 diabetes mellitus (T2DM)? These conditions contribute significantly to global health burdens, increasing the risk of cardiovascular disease and reducing life expectancy.

The gut microbiome plays a critical role in metabolic health, but its influence evolves from infancy through old age, shaped by diet, lifestyle and genetics.

While previous studies focus on specific age groups, understanding these associations across the lifespan is essential for targeted prevention strategies. Further research is needed to determine the long-term metabolic effects of microbiome changes and potential interventions. However, the ability to translate these findings into clinical recommendations is limited by differences in assay methods and the dynamic nature of gut microbiome composition over time.

About the study

Microbiome diversity declines with age - the study found that gut microbiome diversity remains relatively stable throughout adulthood but declines after 65 years, with an even sharper decline after 80, potentially affecting metabolic health.

In a recently published study inThe Lancet Regional Health – EuropeA population-based study was conducted using three Dutch cohorts representing different life stages: pre-adolescents from Generation-R age 62.7 years, n = 1265) and an adult validation cohort from the Lifelong Study (LLD) (mean age 45.0 years, n = 1117).

Stool samples were collected and bacterial deoxyribonucleic acid (DNA) was extracted and sequenced using 16S ribosomal RRRNA gene sequencing. Microbiome clustering was performed using the K-means algorithm to identify patterns associated with metabolic health.

Anthropometric measurements, blood biomarkers (glucose, insulin, triglycerides, cholesterol) and lifestyle factors (diet, physical activity, smoking) were assessed. Logistic regression models were used to analyze the association between microbiome clusters and metabolic health, adjusting for confounding factors such as age, gender, and medications.

Longitudinal follow-up (median 6.5 years) was performed in the RS to assess the relationship between microbiome clusters and atherosclerotic cardiovascular disease (ASCVD). Multiple imputation was used for missing data. Statistical analyzes were performed using R software. Ethical approval was obtained and participants provided written informed consent.

It is important to note that dietary data for some participants were collected years before stool sampling, which could influence the interpretation of microbiome-diet interactions.

Study results

Education level influences gut health – lower maternal education in childhood and personal education in adulthood were both associated with an unhealthy gut microbiome, suggesting that socioeconomic factors play a role in microbial diversity.

Two distinct microbiome clusters were identified in each cohort, labeled cluster U (unhealthy) and cluster H (healthy). Cluster U was characterized by lower microbial diversity and increased abundance of Streptococcus and Fusicatenibacter, while cluster H had higher diversity with larger scales of Christensenellaceae_R-7_Group and Prevotella_9.

In pre-adolescents, those assigned to cluster U had higher body fat percentage, triglyceride levels and C-reactive protein (CRP), indicating a higher inflammatory state. In older adults, Cluster U was associated with increased waist-to-hip ratio (WHR), insulin resistance, and hypertension.

Similar patterns were observed in the LLD validation cohort, with individuals in cluster U having higher obesity prevalence and lower high-density lipoprotein cholesterol (HDL-C).

Logistic regression analysis showed that individuals in cluster U were between 1.10 and 1.65 times more likely to be metabolically unhealthy than those in cluster H. This association was strongest in older adults. This gut microbiome composition becomes a more significant determinant of metabolic health with age.

A key finding was the association between microbiome clusters and future cardiovascular risk. In the RS, individuals in cluster U had a significantly higher average 5-year ASCVD risk (mean 0.059 ± 0.071) than those in cluster H (mean 0.047 ± 0.042, p < 0.001). However, survival analysis did not find this difference to be statistically significant (hazard ratio [HR] = 1.52, 95% CI: 0.83–2.80, p > 0.05), meaning that the observed trend requires further investigation in larger studies.

Factors affecting microbiome cluster assignment included socioeconomic status (SES), smoking, and proton pump inhibitor (PPI). Lower maternal education levels were associated with an unhealthy microbiome in children, while lower personal education levels influenced clustering in adults. While specific bacterial taxa have been associated with metabolic health between cohorts, the overall microbiome composition showed some variability between groups, likely due to differences in age, lifestyle and sequencing methods.

These findings have far-reaching implications for individuals and communities. A deeper understanding of microbiome-driven metabolic health could lead to personalized diet and lifestyle recommendations to prevent obesity and metabolic disorders. However, due to the complexity of gut microbiome interactions, translating these findings into clinical interventions remains difficult.

On a global scale, addressing gut microbiome imbalances could significantly reduce healthcare costs and disease burden.

Study limitations

This study provides valuable evidence on a life-course relationship between gut microbiome composition and metabolic health. However, some limitations should be taken into account:

• Die Studie verwendete 16S -rRNA -Sequenzierung, die eine begrenzte taxonomische Auflösung aufweist, was bedeutet, dass bestimmte Bakterienarten oder funktionelle Merkmale nicht unterscheiden können.
• Während das ASCVD-Risiko bewertet wurde, war die Nachbeobachtungszeit (6,5 Jahre) relativ kurz, und der Zusammenhang zwischen mikrobiomen Clustern und kardiovaskulären Ergebnissen erreichte keine statistische Signifikanz.
• Die Studienpopulation bestand hauptsächlich aus niederländischen Personen, was die Verallgemeinerbarkeit auf ethnisch vielfältigere Bevölkerungsgruppen einschränken kann.
• Die Ernährungsdaten wurden Jahre vor Stuhlproben gesammelt, die die Schlussfolgerungen hinsichtlich der Interaktionen von Ernährungsmikrobiomen beeinflussen können.

Conclusions

Certain gut bacteria are consistently linked to metabolic health - across all age groups, Christensenellaceae_R-7_group was linked to better metabolic health, while Streptococcus and Fusicatenibacter were linked to obesity and inflammation.

This study provides evidence for a life-course relationship between gut microbiome composition and metabolic health. Individuals with an unhealthy microbiome profile had higher body fat, insulin resistance and triglyceride levels and were at higher risk of developing cardiovascular disease.

These associations were stronger in older adults, suggesting that gut microbiome diversity plays an increasing role in metabolic health over time. Given that gut microbiome composition is modifiable by diet and lifestyle, early life interventions targeting microbial health provide a unique opportunity to prevent metabolic disorders later in life.

However, further research is needed to determine whether microbiome-derived interventions such as probiotics, prebiotics, or dietary changes can have a meaningful impact on long-term metabolic health outcomes.

Final thoughts

With growing evidence supporting the role of the gut microbiome in metabolic health, scientists continue to explore its potential as a biomarker for disease prediction and a target for personalized interventions. While the results of this study highlight strong associations, the implementation of microbiome science into everyday healthcare continues to require further clinical validation and understanding of the underlying mechanisms.

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