Research suggests microbiome and genetic testing could ‘revolutionise’ personalised nutrition

The authors of the single-arm, uncontrolled-pilot, prospective study from Italy and Belgium utilised genetic profiles, microbiome composition, and physiological parameters to create personalised dietary plans for seven volunteers.

They also investigated the mechanisms that govern the impacts of nutrients and foods on the gut microbiome, aiming to establish correlations between dietary interventions and changes in the microbiota and host responses.

Results suggested that analysing genetic profiles, microbiome composition, and various physiological parameters could allow for the development of more effective personalised dietary plans.

The authors conclude: “The proposed digitalised approach offers cost advantages through efficiency, scalability, and data analysis, as well as the benefits of personalisation, real-time monitoring, continuous support, and behaviour change, making it more advantageous compared to traditional methods.  

“Its potential to revolutionise personalised nutrition interventions, offering individuals a more engaging, effective, and accessible way to optimise their dietary choices and overall health, combined with the obtained findings, highlights the importance of personalised nutrition in optimising health and well-being, as well as the role of the gut microbiome in dietary interventions.” 

Background​

The authors argue that, given the availability of advanced sequencing technologies, it is increasingly important to complement microbiome data with robust dietary data. 

The authors note there are limitations of current methods​ used to assess dietary intake in microbiome studies, such as food frequency questionnaires (FFQs) and 24-hour dietary recalls, emphasising the need for better methods that consider food preferences rather than just nutrient intake.

They add that there is potential of using machine learning (ML) in this field, as ML has proven valuable in diagnosing and predicting risk of various health conditions​.

Recent research​ has shown that personalised nutrition interventions have the potential to directly influence and modify the composition of the gut microbiome, with precision nutrition emerging as a tool to provide personalised dietary recommendations​ based on an individual’s unique characteristics. 

However, there is a high degree of variability​ in how individuals respond to diet, making longitudinal studies essential to uncover long-term effects and factors influencing responses.

The authors note that collecting precise dietary data and using machine learning can enhance these studies, and collaborations across disciplines​ are crucial in addressing challenges.

This new research aimed to investigate how diet affects the microbiota and its relation to host physiology, to further establish cause-and-effect relationships and develop personalised nutrition strategies for disease management.

The authors say the integration of microbiome and host-microbial metabolome analyses promises to illuminate the intricate metabolic interplay between the gut microbiota and the host in both health and disease. 

The study ​

The study included seven volunteers (four females = 57%, and three males = 43%, age = 40.9 ± 10.3 years, Body Mass Index (BMI) = 23.2 ± 2.9 kg/m2), who were required to self-monitor their weight, diet, and activities between March and July 2022. 

Faecal samples were taken for microbiome sampling in April 2022 and May 2022 and an average reading was used to indicate the pre-intervention microbiome.  

A third faecal sample was taken in July 2022, a month after the tailored plan was put in place, for analysis of the post-intervention microbiome.

Saliva samples were taken before and after intervention for nutrigenomics analysis.

Participants used an at-home app ArmOnIA which collected data on the following parameters: anthropometric and physiological;microbiome and nutritional; age, weight, metabolic rate in kilocalories, body mass index (BMI), percentage of body fat, muscle mass, bone mass, percentage of body water, daily physical activities, resting heart rate, average heart rate, duration of deep sleep, duration of shallow sleep, and rapid eye movement (REM) sleep.

Using the participants’ data, the authors devised personalised plans tailored to the specific needs and goals of each patient using software known as “Terapia Alimentare”, developed by Dietosystem, a division of DS MediGroup S.p.A..

After one month of intervention, positive changes were observed in food and nutrient intake, body composition, physiological parameters, and gut microbiome composition.

Discussing benefits of their personalised diet, the authors say an innovative aspect was the incorporation of the genomic profile and microbiome of each participant.  

“The integration of genomic data, specifically genetic variations such as single nucleotide polymorphisms (SNPs), allowed us to gain insights into potential gene expression patterns that may influence an individual’s metabolism and response to dietary components.

"These genetic variations can serve as proxies for understanding how certain genes may be expressed or regulated in a person’s body. In this sense, to prescribe diets to the participants, the nutritionists applied a two-pronged approach, drawing insights from both scientific literature and their extensive expertise in the field of nutrigenomics." 

Changes in the microbiome​

They authors note the lack of definition for a “healthy core gut microbiota” remains an ongoing challenge, so their study primarily aimed to comprehend the impact of dietary interventions on participants’ gut microbiota and their potential repercussions on overall health. 

“Our approach to identifying beneficial and detrimental bacteria draws on established literature and empirical observations, considering broader taxonomic categories as indicators of potential microbial imbalances with functional significance. 

“Rather than assuming that individual species or genus-level taxa would singularly transform microbiome function, we leveraged existing research to pinpoint specific bacterial taxa or patterns linked to various health outcomes or microbial community imbalances. These dietary recommendations targeted observed imbalances, striving to foster a more favourable gut microbial ecosystem. 

“Furthermore, it is noteworthy that our study revealed substantial improvements in various physiological and anthropometric parameters, such as resting heart rate and BMI. These findings align with well-documented indicators of enhanced health status, underscoring the potential advantages of our dietary interventions for overall well-being.”

Specific microbial species, like Acinetobacter junii, ​involved in the metabolism of fats​, and Alistipes finegoldii​, a bile-tolerant bacteria constituting a biomarker of the healthy gut​, showed changes related to dietary interventions.

Additionally, Lachnospiraceae​, known for its involvement in carbohydrate catabolic pathways leading to the production of acetate and butyrate​, as well as metabolic pathways of aromatic amino acids resulting in the release of beneficial compounds like indole-propionic acid, indole, phenol, and p-cresol, increased.

Conversely, decreases were noted in Bacteroides plebeius, ​linked to dysbiosis-associated rheumatoid arthritis​, which the authors credit to increased intake of omega-3.

The authors note: “Although these interesting results pave the way for the integration of nutritional approaches in the modulation of gut health, further research is needed to understand the underlying mechanisms and long-term implications. 

“Achieving consensus in this field remains challenging due to various influencing factors. Advanced analytical tools and future advances in microbiome-wide association studies may provide more insights into these relationships.”

“Future advances in microbiome-wide association studies, supported by bioinformatic algorithms and correlation coefficients, will enable further categorization of microbial genes into specific groups, such as metagenomic linkage groups, metagenomic species, co-abundance gene groups, or metagenomic species pan-genomes​. The resulting microbial gene catalogue represents a rich data source to establish associations and predictions regarding health or disease status, leveraging the power of advanced machine learning technologies.” 

Journal: Nutrients

https://www.mdpi.com/2072-6643/15/18/3931#app1-nutrients-15-03931​

“Unraveling the Gut Microbiome–Diet Connection: Exploring the Impact of Digital Precision and Personalized Nutrition on Microbiota Composition and Host Physiology.”

Authors: Giada Bianchetti, Flavio De Maio, Alessio Abeltino, Cassandra Serantoni, Alessia Riente, Giulia Santarelli, Maurizio Sanguinetti, Giovanni Deluge, Roberta Martinoli, Silvia Barbaresi, Marco De Spirito, and Giuseppe Maulucci.