Introduction

T2D has become one of the tough challenges of the twenty-first century’s prominent health issues; it affected more than 463 million people globally in 2019 and may swell to over 700 million by 2045 [1]. T2D is diagnosed by hyperglycemia that comes from both insulin resistance in body tissues and from insufficient insulin production in pancreatic β-cells [2]. Conventional risks include hereditary factors, lack of exercise, high body weight, and unhealthful diet.

Nevertheless, the earlier grey literature has brought light to the new ‘player,’ known as gut microbiome—the community of microorganisms inhabiting the gastrointestinal tract, in the pathogenesis of T2D and metabolic dysfunction [3].

Known effects of gut microbiota are on nutrient digestion, including absorption of nutrients, energy metabolism, regulation of immunity and the production of short-chain fatty acids such as butyrate, propionate, and acetate [4]. In addition to obesity, dysbiosis, an abnormal distribution of the gut microbiome, has associations with metabolic syndrome and T2D [5]. T2D patients also possess different stool microbiota composition that afford less bacterial diversity and different Firmicutes/Bacteroidetes ratios, and Bel bringen weniger sauerstoff-bindende Bakterien wie Bifidobacterium und Akkermansia [6,7].

From a mechanistic point of view, the gut microbiota has been shown to modulate some aspects of the development of IR and β-cell dysfunction. These could be including modulation of inflammation processes, changes in the production of SCFAs, disruption of gut barrier function, and changes within bile acid signalling [8]. SCFAs: the action mechanisms of acetate and propionate on glucose metabolism and the insulin sensitivity-lowering effects are related to the activation of the G-protein-coupled receptors in the SCFAs and the inhibition of histone deacetylase 1/2 that alters the expression of genes related to glucose homeostasis [9]. Further, microbial-derived LPSs can also move into the causing systemic inflammation, which further accelerates the insulin resistance [10].

Due to the close connection between gut microbiome and metabolic pathways, intervention on the gut microbiota holds a therapeutic opportunity to treat or even to prevent the T2D. Prebiotics have frequently been administered in the form of probiotics, where live bacteria—‘the good bugs’—are’ introduced into the digestive system with the hope that they will enable the rebalancing process and enhance metabolic results (51). The present work will assess the beneficial impact of the specific treatment with probiotics on gut microbiota and metabolic features in subjects with prediabetes and investigate the role of the gut microbiome in T2D and its implications for treatment.

Methods

Study Design

The present study was a double blind, randomized placebocontrolled clinical trial and was performed over a period of twelve weeks. The main aim focused on the evaluation of the relationship of these concentration with the gut microbiota and other metabolic indices in patients with prediabetes to unravel the role of gut microbiome in the pathogenesis of T2D.

Participants

A total of 200 participants diagnosed with prediabetes (based on American Diabetes Association criteria: Patients with prediabetes (HbA1c between 5.7% and 6.4%) were contacted by outpatient clinics and selected for the study. The eligibility criteria includedparticipants from 30 to 65 years with BMI between 25 and 35 kg/m² and without use of antbiotics or probiotics in three months prior to the study. These excluded participants with type 1 diabetes, gastrointestinal diseases, acute infection and those who were taking medications that altered glucose tolerance.

Randomization and Blinding

Participants were randomly assigned in a 1:A 1:1 match was assigned to either the probiotic group or the placebo group according to a computer generated block randomization schedule. To reduce detection bias, allocation to the study group and control group was also concealed to both the participants and the researchers.

Intervention

The probiotic group took a probiotic cocktail every day in the amount of 1×10^9 in Lactobacillus acidophilus, 1×10^9 in Bifidobacterium lactis, and 1×10^8 in Akkermansia muciniphila. The control group administered a similar capsule which was composed of the inactive substance microcrystalline cellulose. The subjects of both groups were asked to adhere to their usual eating habits and physical exercise routine during the duration of the trial.

Sample Size Calculation

The sample size was determined based on detecting a moderate effect size (Cohen’s d = 0.5) in insulin sensitivity (HOMA-IR) between the probiotic and placebo groups, with a significance level (α) of 0.05 and power (1-β) of 0.80. Using the formula for two independent means:

n = ((Zα/2 + Zβ)² ⋅ 2σ²Δ²) n = \left( \frac{(Z_{\alpha/2} + Z_ {\beta})^2 \cdot 2\sigma^2}{\Delta^2} \right)n = (Δ2(Zα/2 + Zβ)2⋅2σ2)

Where:

• Zα/2 = 1.96Z_{\alpha/2} = 1.96Zα/2 = 1.96 (for α = 0.05)
• Zβ=0.84Z_{\beta} = 0.84Zβ=0.84 (for 80% power)
• σ=1\sigma = 1σ=1 (standard deviation)
• Δ=0.5\Delta = 0.5Δ=0.5 (effect size)

n = (1.96 + 0.84) 2⋅2(1)2(0.5)2)n = \left( \frac{(1.96 + 0.84)^2 \cdot 2(1)^2}{(0.5)^2} \right)n = (0.5) 2(1.96+0.84)2⋅2(1)2) n = ((2.8)2⋅20.25) n = \left( \frac{(2.8)^2 \cdot 2}{0.25} \rin = (0.25(2.8)2⋅2) ⋅2) n = (7.84 ⋅ 20.25) n = \left( \frac{7.84 \ cdot 2}{0.25} \right)n = (0.257.84⋅2) n = (15.680.25) n = \left( \frac{15.68}{0.25} \right)n = (0.2515.68) n = 62.72n = 62.72n = 62.72

Thus, a minimum of 63 participants per group was required. Accounting for a 20% dropout rate, the final sample size was set at 100 participants per group.

Data Collection

Baseline and Post-Intervention Assessments: Anthropometric Measurements: Stature, Body Mass, Body Mass Index, Waist circumference. Blood Tests: Blood glucose during the fasting, HbA1c, insulin, lipids, CRP, IL-6.

Insulin Sensitivity and β-Cell Function: HOMA-IR and insulin secretory assays were used to calculate it.

Gut Microbiome Analysis: Fecal swabs obtained and used for sequencing of the 16S rRNA in order to assess the microbial richness and density.

Statistical Analysis

Data were analyzed in Statistical Package for Social Science version 25.0. Basic quantitative data were expressed in terms of mean ± standard deviation for normal variables and frequencies for categorical variables. The normality test for the estimates was conducted via the Shapiro-Wilk test. Mixed between-group comparisons were conducted using independent samples t-tests or U tests for normally and non-normally distributed continuous variables, respectively, and Chi-square tests for categorical variables. When making comparisons within groups, t-tests for paired variables or Wilcoxon rates were used for analysis. The microbial data were processed and analyzed through pipeline (QIIME 2) in terms of alpha and beta diversities and LEfSe. If p was less than 0. Cutoff point for significance, the result was considered statistically significant.

Results

Participant Characteristics

Out of the 200 participants enrolled, 180 completed the study (90 in each group), yielding a retention rate of 90%. Baseline characteristics were comparable between the probiotic and placebo groups (Table 1).

Table 1: demonstrates that there were no significant differences in baseline characteristics between the probiotic and placebo groups, ensuring comparability for subsequent analyses.

Impact of Probiotic Supplementation on Gut Microbiota Composition

Post-intervention analysis revealed significant alterations in the gut microbiota of the probiotic group compared to the placebo group (Figure 1, Table 2). The probiotic group showed a notable increase in beneficial bacteria such as Akkermansia muciniphila and Faecalibacterium prausnitzii, alongside a reduction in the Firmicutes/Bacteroidetes (F/B) ratio.

Figure 1: Relative Abundance of Key Gut Microbial Phyla in Probiotic vs. Placebo Groups

Table 2: Changes in Gut Microbiota Composition

Metabolic Outcomes

• Insulin Sensitivity and β-Cell Function
• Probiotic supplementation led to significant improvements in insulin sensitivity and β-cell function (Table 3).

Table 3: Metabolic Parameters Pre- and Post-Intervention

Relationship Between Gut Bacteria and metabolic indexes

Thus, Spearman correlation analysis showed that some of the identified bacteria are associated with certain metabolic effects. There were significant inverse correlations between AM and HOMA-IR (r = -0.38, p < 0.001) and serum CRP levels (r = -0.35, p < 0.001). Faecalibacterium prausnitzii had a significant direct relationship with insulin secretion (r = 0.32, p < 0.001) and an inverse relationship, albeit moderate, with IL-6 (r = -0.29, p = 0.002) (Figure 2).

Figure 2: Correlation Matrix Between Gut Microbiota and Metabolic Parameters