In Search of Healthy Ageing: A Microbiome-Based Precision Nutrition Approach for Type 2 Diabetes Prevention

Abstract

Background/Objectives: Type 2 diabetes (T2D) is a leading cause of morbidity and mortality worldwide and in Spain, particularly in the elderly population, affecting healthy ageing. Nutritional strategies are key to its prevention. The gut microbiota is also implicated in T2D and can be modulated by nutrition. We hypothesize that precision nutrition through microbiota modulation may help prevent T2D. This article aims to (1) describe a gut microbiota bacterial profile associated with T2D prevention, (2) provide precision nutrition tools to optimize this profile, (3) analyze how overweight influences the microbiota composition and precision nutrition response, and (4) address the technical challenges of microbiome-based precision nutrition clinical implementation to prevent T2D. Methods: A review of gut microbiota associated with T2D prevention was conducted. 13 healthy Spanish participants over 62 with optimal blood glucose levels (7 normal weight and 6 overweight) underwent a 3-month precision nutrition intervention to optimize T2D-preventive gut microbiota using a bioinformatics food recommendation system, Phymofood (EP22382095). Fecal microbiota was analyzed pre- and post-intervention using full-length 16S rRNA gene amplification, MinION sequencing, and NCBI taxonomic classification. Results: 31 potentially preventive bacteria against T2D were selected. The intervention increased the relative abundance of beneficial genera (Butyrivibrio and Faecalibacterium) and species (Eshraghiella crossota, and Faecalibacterium prausnitzii). The overweight influenced microbiota composition and intervention response. Conclusions: A gut microbiota profile associated with T2D prevention was identified, and precision nutrition could increase the relative abundance of beneficial bacteria. Confounding factors such as overweight should be considered when designing microbiome-based precision nutrition interventions. These results contribute to a better understanding of the microbiota associated with T2D prevention and address technical challenges for clinical implementation in future healthy ageing strategies.

Keywords: 16S rRNA gene; MinION sequencing; NCBI taxonomic classification; T2D prevention; bioinformatics food recommendation system; gut microbiota; healthy ageing; microbiome-based precision nutrition; overweight; type 2 diabetes (T2D).

Figure 1

α-diversity of pre- and post-dietary intervention from normal weight samples. Comparison of community richness (number of OTUs, Chao1 index) and diversity and evenness (Shannon and Simpson indexes).

Figure 2

Venn diagram of the shared OTUs among pre- and post-dietary intervention from normal weight samples.

Figure 3

β-diversity of pre-dietary compared to post-dietary intervention from normal weight samples. The principal coordinates analysis (PCoA) plots were based on the following two metrics: Bray–Curtis dissimilarity and Jaccard index (ellipses represent 95% confidence intervals).

Figure 4

Different bacterial distribution in pre-dietary compared to post-dietary intervention in normal weight samples at the phylum level. (a) Stacked bar plots showing the distribution of bacterial taxa at the phylum level. (b) Violin plot illustrating the relative abundance of the phylum showing significant differences between groups pre- and post-intervention: Bacillota.

Figure 5

Different bacterial distribution in pre-dietary compared to post-dietary intervention in normal weight samples at the genus level. (a) Stacked bar plots showing the distribution of bacterial taxa at the genus level. (b) Violin plot illustrating the relative abundance of the genus showing significant differences between groups pre- and post-intervention: Gemmiger, Butyrivibrio, and Faecalibacterium.

Figure 6

Different bacterial distribution among pre- and post-dietary intervention normal weight samples at the species level. Violin plot representing the relative abundance of the species significantly different between pre- and post-intervention groups: Gemmiger formicilis, Coprococcus eutactus, Eshraghiella crossota, and Faecalibacterium prausnitzii.