We enrolled 26 healthy infants from three communes in Abidjan, Côte d’Ivoire: Abobo, Cocody, and Marcory ( Table 1 ). The infants were divided into six groups: BF: Breastfed only; BF + FD: Breastfed + Food Diversification; FF: Formula-fed only; FF + FD: Formula-fed + Food Diversification; BF + FF: Breastfed + Formula-fed; BF + FF + FD: Breastfed + Formula-fed + Food Diversification. All infants were 0 - 200 days of age; exclusive breast-feeding, formula-feeding, mixed-feeding and infants received additional foods were established throughout the research period. In addition to the feeding pattern, there were no significant differences in gestational age and there was no use of antibiotics for infants in the six groups. The infants did not have any gastrointestinal diseases. Infants were not born by caesarean section. We grouped the infant based on the different diet categories identified during the surveys. The infants were divided into six feeding groups based on their feeding practices:

1) BF: Breastfed only

2) BF + FD: Breastfed + Food Diversification

3) FF: Formula-fed only

4) FF + FD: Formula-fed + Food Diversification

5) BF + FF: Breastfed + Formula-fed

6) BF + FF + FD: Breastfed + Formula-fed + Food Diversification

The infants were also categorized into three age groups:

Table 1 provides an overview of the characteristics of the studied samples.

Legend: M: Male; F: Female.

In all populations, fecal samples were collected from each individual in the morning and kept at −80˚C until extraction. DNA was extracted according to the protocol described by the manufacturer of the QIAamp PowerFecal Pro DNA KIT. The extracted DNA was stored at −20˚C until processed. To access the faecal microbiota, the V3 - V4 hypervariable region of the 16S rRNA gene was amplified using the primers (V3 - V4): 341F->CCTACGGGNGGCWGCAG 805R->GACTACHVGGGTATCTAATCC [10] . The PCR was done in two (2) steps, with Illumina Nextera adapters. The conditions of the first PCR: Enzyme Phusion (Thermofisher) Concentration of the primers: 0.4 μM final. The first PCR amplification was carried by 33 cycles at 55˚C for 30 s. The use of positive (+) and negative (−) controls for the PCR was carried out and the PCR products were visualized on an agarose gel. The amplicons are diluted to 1/50 and used as a template for the 2nd PCR (also called barcoding or indexing since this step is used to attach the indexes and the Illumina adapters necessary for Miseq sequencing).

Amplicons were pooled (5 µL each) to generate the library. The pooled amplicons were subsequently purified using Ampure beads (Beckman) at a 0.7 ratio. Quality control of the library was conducted using Qubit HS dsDNA (Invitrogen) and quantitative PCR (qPCR) with the NEBNext® Library Quant Kit for Illumina (New England Biolabs). The library profile was analyzed on a Bioanalyzer using a DNA HS chip (Agilent Technologies). The library was sequenced on a MiSeq (Illumina) using a MiSeq V3 kit, PE300, with a 12% Phix spike-in. The sequencing reads were processed by the MiSeq, and the resulting FASTQ files were analyzed using the bioinformatics platform at CERMOFC (Montreal, Quebec, Canada).

Raw sequences from all analyzed samples were demultiplexed, and adapters and barcodes were removed. The quality of the sequences was then assessed using FastQC, following a previously established protocol [11] [12] . The resulting data files were imported and processed within the R environment using various scripts and built-in functions available in different R packages. The sequences were subsequently trimmed using Trimmomatic [13] . Quality control post-trimming was performed with FastQC [11] and MultiQC [12] . Finally, the filtered and trimmed files were processed using the “filterAndTrim” function from the DADA2 package [14] .

Low-quality bases were discarded prior to analysis. The DADA2 (Divisive Amplicon Denoising Algorithm) pipeline was employed to process the filtered data files. Steps including dereplication, core denoising algorithm application, and base pair fusion were performed to obtain fully denoised sequences. The “removeBimeraDenovo” function from the DADA2 package was then utilized to eliminate chimeric sequences. Data cleaning was conducted through decontamination, denoising, and rarefaction, employing the decontam [15] [16] and vegan [17] packages. Amplicon sequence variant (ASV) sequences were assigned taxonomy using the Silva database (Silva_nr_v132) [18] with the “assignTaxonomy” function from the DADA2 package, and a phyloseq data object was created. A phylogenetic tree for taxa was constructed using the R package ape, leveraging the prune_taxa functions from the phyloseq package [15] . Differential analyses were conducted to identify differentially abundant taxa for each parameter using DESeq2 [19] .

Illumina MiSeq high-throughput sequencing of the V3 - V4 hypervariable regions of the 16S rRNA gene was conducted to characterize the bacterial lineages present in the fecal microbiota of 26 infants. A total of 1,564,001 filtered taxonomic 16S rRNA gene sequences were generated, yielding a mean (±SD) of 62.55 ± 8.35 sequences per sample. The Chao1, ACE, Simpson, and Shannon diversity indices were employed to assess species richness and evenness of amplicon sequence variant (ASV) distribution within the bacterial community. The richness and diversity indices of the observed bacterial community are reported. The ACE index ranged from 174 to 580, while Chao1 richness varied between 174 and 590.

The Shannon and Simpson indices ranged from 1.49 to 4.55 and from 2.77 to 60.35, respectively. Notably, the fecal samples, particularly sample A24, exhibited the highest values compared to the others. The analysis of alpha diversity, as indicated by the Shannon species richness, revealed a significant microbial diversity in relation to dietary types (Kruskal-Wallis test, p = 0.05) and the age of the infants (Kruskal-Wallis test, p < 0.05). The majority of tag sequences had a length of approximately 50 bp, which aligns with the expected length of most bacterial V3 - V4 regions.

This study was approved by the Ethics Committee of Côte d’Ivoire. Informed consent was obtained from the mothers of all enrolled infants.

Differences between populations were analyzed using both parametric (ANOVA) and nonparametric statistical methods. All results are presented as mean values (± SE), and differences between groups were considered significant at p < 0.05.

To estimate microbial richness based on observed Amplicon Sequence Variants, the Chao1 index was used, while biodiversity was assessed using the Shannon index across the three groups. Significance was determined at a threshold of 0.05 ( Table 2 ). No significant differences in richness or biodiversity were observed between the BF + FD, FF + DA, and BF + FF + FD samples at this threshold.

The results indicate that sequences from all samples belonged to four bacterial phyla: Actinobacteria, Proteobacteria, Firmicutes, and Bacteroidetes. These findings align with previous studies, which identify these phyla as the primary contributors to the establishment of the intestinal microbiota in infants [20] - [22] . This concordance may be due to the focus on infants of a similar age group as those in the referenced studies.

In general, among infants who received complementary foods (BF + FD, FF + FD, BF + FF + FD), Actinobacteria was the most dominant phylum (55%), followed closely by Bacteroidetes (54%). Firmicutes/Bacteroidetes Ratio as an Indicator of Gut Microbiota State. Since Bacteroidetes and Firmicutes are the dominant phyla in the intestine, the ratio between these groups is often considered an informative indicator of the overall state of the intestinal microbiota. Studies have shown that the Firmicutes/Bacteroidetes ratio is lower in the elderly compared to young adults [23] . In this study, the Firmicutes/Bacteroidetes ratio was similarly low, which may be attributed to the early introduction of complementary foods. The intake of these foods produced a gut microbiota profile closer to that of adults, as the addition of complementary foods in infant diets reduced the abundance of Firmicutes.

These results indicate that the composition of an individual’s gut microbiota is influenced by their diet. Numerous studies have examined the timing of complementary food introduction during the first year of life [24] . Findings from these studies often show a dominance of Firmicutes, followed by Actinobacteria, after food diversification. However, in the present study, diversification resulted in a dominance of Actinobacteria and Bacteroidetes. This difference may be due to the varied ages at which diversification occurred and the specific diets introduced in this study. Additionally, infants in this study received only semiliquid foods, which may have had a milder impact on the gut microbiota compared to more solid or varied diets.

In the FF + FD diet, Actinobacteria is the dominant phylum at 7%, highlighting its prevalence as diet-dependent. Although these infants began dietary diversification early and were never breastfed, it appears that foods rich in Bifidobacterium are frequently provided to them.

In the BF + FD diets, Actinobacteria dominates at 23%, followed closely by Bacteroidetes (22%) and Firmicutes (21%). The predominance of Actinobacteria suggests that, despite the introduction of complementary foods, the intestinal microbiota of these infants was not profoundly altered. This may be due to the semi-liquid nature of these early foods, their irregular introduction, and the continued practice of breastfeeding.

In the BF + FF + FD diets, Bacteroidetes and Firmicutes predominate with 28% each, followed by Actinobacteria at 25%. This balance indicates a notable shift in the gut microbiota composition, likely due to the combined effects of mixed feeding practices and diverse dietary inputs.

The four phyla—Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria—are more prevalent in infants on a milk-based diet combined with complementary foods.

A comparative study of infants who began early food introduction (FF + FD, BF + FD, BF + FF + FD) reveals a diverse range of bacterial families. In BF + FD diets, the dominant gut microbiota families are Bifidobacteriaceae (17.38%) and Enterobacteriaceae (9.13%). This dominance is likely due to breastfeeding, as breast milk supplies oligosaccharides to the intestine, promoting the growth of Bifidobacterium species in the ecosystem as long as breastfeeding remains the primary food source. Additionally, breast milk intake is associated with Bifidobacterium presence [25] - [27] .

Another possible reason for this composition is that complementary foods had a limited impact on the gut microbiota in BF + FD infants, likely due to the types of foods introduced and their infrequent administration.

In the BF + FD diet, Streptococcaceae represents 7.21%. However, in the feces of infants on this diet, the families Bacteroidaceae, Coriobacteriaceae, Lachnospiraceae, and Lactobacillaceae show higher prevalence than others. The presence of this diverse microbiota suggests a high level of diversity and complexity in infants receiving both breast milk and complementary foods, likely due to the added dietary variety. This complementary feeding appears to reduce the bifidogenic population, and early complementary feeding has been associated with an increased risk of gastrointestinal and respiratory infections [28] .

In the FF + FD diet, the proportions of these bacteria decrease, except for Bifidobacteriaceae (23.83%) and Enterobacteriaceae (14.06%), which show an increase. Notably, in the feces of infants on the FF + FD diet, Bacteroidaceae, Coriobacteriaceae, Lachnospiraceae, and Lactobacillaceae are less dominant. These infants, who have not been breastfed, may be consuming formula containing probiotics, explaining the prevalence of the bifidogenic population despite the absence of breast milk.

In the feces of infants on BF + FF + FD diets, families such as Akkermansiaceae, Atopobiaceae, Bacteroidaceae, Veillonellaceae, Streptococcaceae, Coriobacteriaceae, and Lachnospiraceae are more dominant. This composition reflects the diversity and complexity of the gut microbiota when supplementary foods are introduced, resembling an adult-like microbiota profile.

The abundance of Lachnospiraceae, in particular, is a predictive factor for overweight risk, especially following early dietary diversification between three and four months [29] [30] . Early introduction of complementary foods accelerates the maturation of the gut microbiota in children, aligning it more closely with an adult microbiota profile [31] [32] . However, inadequate microbiota maturation during this period is linked to suboptimal growth and development in early life [33] .

Moreover, early food introduction is associated with a higher relative abundance of Akkermansiaceae in infants’ stools. While a greater presence of Akkermansiaceae has been linked to improved metabolic health in adults [34] [35] , this bacterial family could potentially have adverse effects in infants.

Furthermore, the families Desulfovibrionaceae and Eggerthellaceae are nearly absent in the feces of children subjected to the different diets (BF + FD, FF + FD, BF + FF + FD). Infants receiving complementary foods exhibit a more complex and diverse intestinal microbiota. Notably, the microbiota of infants on the BF + FF + FD diet is more complex and diverse compared to those on the BF + FD and FF + FD diets.

In each diet group (BF + FD, FF + FD, and BF + FF + FD), 19 bacterial genera were detected, with each representing less than 1% abundance. The genus Escherichia-Shigella is more prevalent in the feces of infants on the FF + FD diet (14.35%) compared to those on the BF + FD (9.37%) and BF + FF + FD diets (9.17%). This indicates a higher presence of pathogenic flora in infants receiving diets supplemented with complementary foods.

The genus Lactobacillus is more abundant in infants on the BF + FD diet (6.63%) than in those on the BF + FF + FD (6.55%) and FF + FD (5.59%) diets. In contrast, the genus Streptococcus is more prevalent in infants on the BF + FF + FD diet (7.68%) compared to BF + FD (7.4%) and FF + FD (6.4%).

Additionally, new bacterial genera, such as Aquamonas, Libanicoccus, Megasphaera, and Eggerthella, were identified, though they are less well-known. The analysis of the intestinal microbiota composition in infants who received different diets revealed a higher abundance of Bifidobacterium in FF + FD infants (24.33%) compared to BF + FD infants (17.84%) and BF + FF + FD infants (14.99%). Moreover, Escherichia-Shigella is significantly more abundant in FF + FD infants (14.35%) than in BF + FD (9.37%) and BF + FF + FD (9.17%) infants. The presence of this pathogenic flora may be linked to the intake of complementary foods and the use of infant formula, particularly in unsanitary conditions ( Figures 1-3 ).

This figure illustrates the relative abundance of various genera of intestinal flora in infants subjected to and BF + FD diets. The data highlights the differences in microbiota composition between the two dietary groups, showcasing specific genera that are more prevalent in each diet. This comparative analysis provides insights into how dietary choices influence the gut microbiota of infants.

This figure illustrates the relative abundance of various genera of intestinal flora in infants subjected to and FF + FD diets.

This figure presents the relative abundance of various genera of intestinal flora in infants following the BF + FF + FD diet. It illustrates the composition of the gut microbiota within this dietary group, highlighting the specific genera that are predominant. The data provides valuable insights into how the BF + FF + FD diet influences the diversity and richness of the intestinal microbiota in infants.

Infants were classified into three age groups: 0 - 119 days, 120 - 179 days, and 180 - 200 days. The analysis of intestinal microbiota composition reveals that Bifidobacterium is more dominant in infants aged 120 - 179 days (20.62%) compared to those aged 0 - 119 days (15.7%) and 180 - 200 days (14%). These results suggest that the frequency of breastfeeding may decrease as complementary foods are introduced, leading to a reduction in Bifidobacterium abundance.

Enterobacteriaceae levels were highest in infants aged 0 - 119 days, followed by those aged 180 - 200 days, and lowest in the 120 - 179 days group. This variation in abundance is linked to dietary differences among the age groups, aligning with findings from previous studies indicating higher Enterobacteriaceae presence in infants at three months of age 0 - 119 days age group, the intestinal microbiota was characterized by a richness of Bacteroidaceae (6.81%), Bifidobacteriaceae (15.7%), Enterobacteriaceae (11.28%), Enterococcaceae (6.09%), Lactobacillaceae (6.63%), and Streptococcaceae (7.32%). In contrast, less abundant families included Veillonellaceae (4.62%), Ruminococcaceae (2.67%), Prevotellaceae (3.02%), Desulfovibrionaceae (0.1%), and Akkermansiaceae (2.16%). This microbiota composition indicates that early diversification may influence the gut microbiota of infants in this age range.

Breastfed infants typically exhibit a microbiota rich in Bifidobacterium and Lactobacillus, with lower levels of Escherichia coli and Clostridium difficile. However the study found that 60% of infants in the 0 - 119 days age group received complementary foods, suggesting that early food introduction can complicate the expected milk-only microbiota, leading to similarities with an adult-like microbiota.

In the 120 - 179 days age group, the intestinal microbiota showed increased levels of Bifidobacteriaceae (20.62%), Enterobacteriaceae (8.95%), and Streptococcaceae (7.15%), but decreased abundance of Veillonellaceae (4.3%), Enterococcaceae (5.41%), and Clostridiaceae (5.34%). These findings confirm that early introduction of complementary foods is more prevalent among infants aged 0 - 119 days.

The Lactobacillaceae family was predominant in infants aged 180 - 200 days (6.69%) compared to those aged 0 - 119 days (6.63%) and 120 - 179 days (5.6%). The presence of Lactobacillus in this age group suggests an association with formula feeding. The higher prevalence of Lactobacillus could also indicate increased consumption of probiotic-rich foods or a return to exclusive breastfeeding practices by mothers. Overall, the gut microbiota of infants receiving early complementary foods tends to resemble that of adults, characterized by a dominance of Bacteroidetes, Firmicutes (including Enterococcus and Streptococcus), Actinobacteria (primarily Bifidobacterium), and Proteobacteria. This microbiota exhibits considerable interpersonal and intrapersonal variability. The relationship between microbiota composition and age is complex, with studies showing an increase in facultative anaerobes such as Enterococcus and Streptococcus with aging. Conversely, some studies report a decline with age, while others show no significant reduction until the elderly population. A significant difference in gut microbiota composition was observed across age groups (Wallis test, p < 0.05).

Figure 4 visually represents the diversity indexes of the gut microbiota across different infant age groups, highlighting significant variations in microbial composition based on age.

The early period is crucial for establishing a diverse and balanced gut microbiota, which plays a central role in the maturation of the immune system. Early alterations in microbiota composition (e.g., due to inadequate nutrition or excessive antibiotic use) can increase the risk of chronic inflammatory diseases, such as asthma, allergies, and autoimmune disorders. Metabolism and Obesity: An imbalanced gut microbiota (dysbiosis) in young children has been linked to an increased risk of obesity and metabolic syndrome in adulthood. Specific bacteria influence nutrient absorption and fat storage. Mental Health: The gut-brain axis indicates that the gut microbiota influences neurological and behavioral development. Early disruptions could be associated with neurodevelopmental disorders such as autism or mood disorders. Predisposition to Chronic Diseases: Altered gut microbiota can affect susceptibility to cardiovascular diseases, type 2 diabetes, and even certain cancers.

This study could not only contribute to a better understanding of the impacts of early dietary diversification on the gut microbiota but also inform concrete interventions to improve public health.