Pro-inflammatory and Hyperinsulinaemic Dietary Patterns are Associated with Specific Gut Microbiome Profiles: A TwinsUK Cohort Study

Endocrinology Gastroenterology Medicine Biochemistry Life Sciences Microbiology
pro-inflammatory diet hyperinsulinaemic diet gut microbiome insulin resistance chronic inflammation

In recent years, the role of the gut microbiome in human health and disease has garnered significant attention. Research indicates that gut microbial dysbiosis is closely associated with various chronic diseases, such as obesity, inflammatory bowel disease, cancer, and neurodegenerative disorders. Diet, as a critical factor influencing the gut microbiome, may impact host metabolic health by modulating microbial composition and function. However, the specific mechanisms linking dietary patterns to the gut microbiome remain unclear.

This study aims to explore the relationship between two metabolic dietary patterns—the Empirical Dietary Index for Hyperinsulinaemia (EDIH) and the Empirical Dietary Inflammatory Pattern (EDIP)—and the gut microbiome. EDIH and EDIP are dietary indices developed based on Food Frequency Questionnaires (FFQs), designed to assess the impact of diet on insulin secretion and chronic inflammation, respectively. Previous studies have shown that these dietary patterns are associated with the risk of various metabolic diseases, such as type 2 diabetes, cardiovascular disease, and cancer. However, the mediating role of the gut microbiome in these associations has not been thoroughly investigated.

Source of the Paper

This paper was jointly authored by Ni Shi, Sushma Nepal, Rachel Hoobler, and others from The Ohio State University, University of Utah, and King’s College London. The paper was accepted on October 14, 2024, and published in the journal Gut Microbiome, titled “Pro-inflammatory and hyperinsulinaemic dietary patterns are associated with specific gut microbiome profiles: A TwinsUK cohort study.”

Research Process

1. Study Population and Data Collection

The study was based on the TwinsUK cohort, a UK twin registry database comprising over 15,000 twin participants aged 18 to 100. The research team selected 1,610 adult participants from this cohort who provided fecal 16S rRNA sequencing data. Additionally, the study collected participants’ dietary habits (via FFQs), serum biomarkers (such as insulin, glucose, and C-reactive protein), as well as lifestyle and demographic information.

2. Dietary Assessment and EDIH/EDIP Calculation

Dietary data were assessed using a 131-item FFQ, which recorded the types and quantities of foods consumed weekly by participants. To ensure data comparability, the research team standardized food intake portions based on the Nutrition Data System for Research (NDSR) recommendations. Using these data, the team calculated each participant’s EDIH and EDIP scores, which reflect the potential impact of diet on insulin secretion and chronic inflammation, respectively.

3. Gut Microbiome Analysis

Fecal 16S rRNA sequencing data were generated using the Illumina MiSeq platform and analyzed using the DADA2 pipeline. The research team classified the sequencing data at the genus level and calculated the alpha diversity of the microbiome (Shannon index and Pielou evenness). To identify microbiome features associated with EDIH and EDIP, the team employed an Elastic Net Regression model, validated through 10-fold cross-validation.

4. Functional Pathway Prediction

The research team used the PICRUSt2 tool to predict microbiome functional pathways associated with EDIH and EDIP. Through multivariate linear regression analysis of metabolic pathways, significant biosynthesis and degradation pathways linked to dietary patterns were identified.

5. Statistical Analysis

The study employed multivariate linear regression models to analyze the relationship between EDIH and EDIP scores and serum biomarkers (such as insulin, glucose, and C-reactive protein). Additionally, sensitivity analyses were conducted on twin pairs to account for genetic influences.

Key Findings

1. Dietary Patterns and Microbiome Diversity

The study found that higher EDIH and EDIP scores (indicating diets more prone to hyperinsulinemia or pro-inflammation) were significantly associated with lower gut microbiome alpha diversity. Specifically, compared to the lowest quintile, the highest quintile of EDIH and EDIP scores led to a 3.2% and 2.3% decrease in the Shannon index, and a 2.1% and 1.4% decrease in the Pielou evenness index, respectively.

2. Microbiota Genera Associated with EDIH and EDIP

The Elastic Net Regression model identified 32 microbiota genera associated with EDIH and 15 genera associated with EDIP. Among them, genera such as Ruminococcaceae_UCG-008, Lachnospiraceae_UCG-008, and Defluviitaleaceae_UCG-011 were linked to lower insulin and inflammation levels, while genera like Negativibacillus and Streptococcus were associated with higher insulin and inflammation levels.

3. Functional Pathway Analysis

Functional pathway analysis revealed that EDIH and EDIP scores were significantly associated with various metabolic pathways. For example, participants with higher EDIH scores exhibited reduced fatty acid biosynthesis pathways, while those with higher EDIP scores showed alterations in nucleotide sugar and amino acid metabolism pathways. Additionally, the Mevalonate Pathway was significantly upregulated in both dietary patterns, a pathway closely related to cholesterol synthesis and cellular metabolism regulation.

4. Serum Biomarker Analysis

The study found that higher EDIH and EDIP scores were significantly associated with elevated insulin levels and insulin resistance (HOMA-IR). Moreover, EDIP scores were also linked to higher C-reactive protein levels, suggesting that pro-inflammatory dietary patterns may contribute to systemic inflammation.

Conclusions and Significance

This study demonstrates that hyperinsulinemic and pro-inflammatory dietary patterns may influence host metabolic health and chronic disease risk by altering the composition and function of the gut microbiome. Specifically, these dietary patterns reduce microbiome diversity and modulate biosynthesis pathways related to fatty acids, amino acids, and cholesterol metabolism. These findings provide new directions for future dietary intervention studies, aiming to reduce insulin resistance and chronic inflammation by modulating the gut microbiome, thereby improving metabolic health.

Research Highlights

  1. First Validation of EDIH and EDIP in a Non-US Population: This study marks the first application of EDIH and EDIP in the UK TwinsUK cohort, enhancing data comparability through standardized food portions.
  2. Identification of Microbiota Genera Associated with Dietary Patterns: Using the Elastic Net Regression model, the study identified microbiota genera significantly associated with hyperinsulinemic and pro-inflammatory dietary patterns, offering potential targets for microbiome interventions.
  3. Functional Pathway Analysis Reveals Metabolic Mechanisms: Through the PICRUSt2 tool, the study uncovered the impact of EDIH and EDIP on fatty acid, amino acid, and cholesterol metabolism pathways, providing new insights into the mechanisms linking dietary patterns to metabolic health.

Additional Valuable Information

The research team also conducted sensitivity analyses on twin pairs, showing that the relationship between microbiome diversity and dietary patterns remained consistent among twins, further supporting the robustness of the findings. Additionally, the team emphasized the need for future studies to integrate metagenomic and metatranscriptomic data to comprehensively elucidate the complex interactions between dietary patterns and the microbiome.