How does previous diet impact the endocannabinoidome and microbiome response to a short-term Mediterranean diet intervention?

By Bhavana KunkalikarReviewed by Danielle Ellis, B.Sc.Feb 15 2023

In a recent study published in Microbiome, researchers assessed the different impacts of diet on the gut microbiome.

Background

Long-term food diets have a profound effect on the gut microbiome. In conjunction with certain food constituents, microbiome composition has been related to cardiometabolic risk. New research reveals that microbiome makeup influences the effectiveness of the Mediterranean diet (MedDiet) in protecting against cardiometabolic risk.

Additionally, long-term dietary trends comparable to the MedDiet have been linked to specific aspects of microbiome composition, such as the modification of keystone species and reduction of intestinal inflammation. Yet, studies related to the impact of the MedDiet on the gut flora exhibit no consensus.

About the study

In the present study, researchers determined the impact of a lead-in diet on the participants' response to a 48-hour MedDiet intervention.

For the first three days, the participants' food consumption consisted of a Mediterranean diet (MedDiet), followed by a Canadian diet (CanDiet) for 13 days, and then a MedDiet for three days. The CanDiet was developed to represent the current Canadian macronutrient consumption while preventing nutritional deficits in the short term.

The MedDiet featured increased consumption of vegetables and fruits, grains, and plant-based proteins. It contains higher quantities of monounsaturated fatty acids (MUFAs), omega-3 polyunsaturated fatty acids (PUFAs), and fibers and lesser quantities of red meat and saturated fatty acids (SFAs).

The participants were asked to consume the foods and beverages that the study provided, matching their calculated energy requirements. Each subject's energy requirements were estimated by averaging the energy demands assessed using a validated web-based 24-hour dietary recall (R24W). The R24W was performed on three separate events, and the energy expenditure was estimated using the Harris-Benedict formula. The healthy eating index (HEI) was also derived using the R24W.

A total of 21 healthy young adults aged between 20 years and 34 years, consisting of 11 females and ten males with a body mass index (BMI) ranging from 18.5 to 30 kg/ m2, successfully completed the study. On the morning of each dietary modification, blood and stool samples were collected after an overnight fast. After 48 hours of consuming the MedDiet, V2, and V4 samples were collected. High-performance liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) was employed to assess PUFAs, monoacyl-glycerols (MAGs), and N-acyl-ethanolamines (NAEs) levels in plasma samples.

Results

The plasma levels of polyunsaturated fatty acids rose in response to the two MedDiet interventions relative to the CanDiet and the baseline. Several endocannabinoidome (eCBome) mediators such as N-eicosapentaenoyl-ethanolamine (EPEA), NAEs, and N-docosahexaenoyl-ethanolamine (DHEA) along with 2-docosahexaenoyl-glycerol (2-DHG),  2-eicosapentaenoyl-glycerol (2-EPG), and 2-MAGs exhibited elevations following both MedDiet interventions.

The first MedDiet intervention did not have a significant impact on short-chain fatty acids (SCFAs) and branched CFAs (BCFAs). On the other hand, valerate, propionate, isobutyrate, and isovalerate increased significantly after the CanDiet and subsequently dropped after the second MedDiet. There was no significant difference between the amounts of bioactive lipids in MedDiet at V2 and V4. However, analysis of V4 suggests that diet stabilization enhanced the metabolite response reproducibility.

In general, the fold alteration of metabolites noted between the second MedDiet (V4) and the CanDiet (V3), as well as the CanDiet and the first MedDiet (V3/V2), were opposite. This demonstrated a direct effect of food on these metabolites. Altogether, the lead-in diet did not affect the reaction of bioactive lipids during MedDiet therapy since these metabolites reacted to short-term diet changes. Furthermore, the response of BCFAs to the second MedDiet intervention was stronger and less varied.

After the nutritional intervention, Simpson's and Shannon microbiota diversity were remarkably greater than at baseline. Except for Bacteroides, all significantly modified taxa exhibited baseline relative abundances of less than 5%. The team noted three microbiota response trends.

First, MedDiet interventions resulted in a reproducible boost of seven genera independent of the lead-in diet: Butyricoccus, Coprococcus.1,  Bacteroides, Lachnoclostridium, Parasutterella, Lachnospira, and Lachnospiraceae UCG 001. Secondly, the CanDiet modified certain genera such that it was reversible by MedDiet therapy, including Romboutsia, Roseburia, Ruminococcaceae UCG 004, Collinsella, and Subdoligranulum. Lastly, taxa whose relative abundance was altered by the CanDiet did not revert to their baseline levels following the second MedDiet.

Numerous taxa from the gut microbiota showed a consistent association with microbial diversity across multiple visits, revealing the durability of the relationship between microbiota content and diversity, even when the diet is altered. Only Lachnospira and Lachnoclostridium, among the taxa significantly related to baseline microbiome diversity, were significantly modified by diet. After false discovery rate (FDR) correction, no significant relationships were observed between plasma metabolites and microbial diversity.

Conclusion

The study findings showed that lipid mediators that played an essential role in the interaction between host metabolism and gut microbiota demonstrated a quick reaction to dietary changes. The lead-in diet influenced the gut flora and BCFA response to the MedDiet. The team also noted that a greater initial microbiome diversity enhanced gut microbiota stability in response to dietary alterations. The researchers believe that the study addressed the importance of considering prior diet in investigating the association between the gut microbiome and host metabolism.