Gut microbiome predicts personalized responses to dietary fiber in prediabetes: a randomized, open-label trial

Domain: biology
Published: 2025-12-13
Editorial outcome: minor_revision

Abstract
Gut microbiota contributes to prediabetes progression, however, whether microbiota features can guide targeted prevention and treatment for diabetes requires validation through large-scale clinical trials. Here, in a randomized, open-label trial, we randomly assigned 802 prediabetic subjects to a usual care control group (patient education and dietary recommendations, n = 393) or a dietary fiber intervention group (n = 409) for 6 months. The primary outcome was the percentage change in whole-blood HbA1c, and secondary outcomes were the changes in other glucose, insulin, lipid, liver and kidney function, and anthropometric parameters. There were no statistically significant differences in the primary and secondary outcomes between groups. In post-hoc analysis, we reclassified subjects into four clusters using a multivariate clustering model based on age, BMI, HbA1c, HOMA2-IR and HOMA2-B. These clusters differed in metabolic status, risks of diabetes and its complications, gut microbiome and serum metabolites. Notably, dietary fiber improved glycemic control in Clusters 3 and 4, but not in Clusters 1 and 2, consistent with observed gut microbiota alleviations. By using a LightGBM machine learning model, we calculated a microbiome-based clinical decision score to predict personalized fiber intervention responses and identified individuals who can get glycemic benefits. In conclusion, our study suggests that the gut microbiota response influences the effectiveness of dietary fiber intervention and provides a clinically applicable model to guide microbiome-targeted personalized medicine for prediabetes. Clinical Trial Registry: ChiCTR1900027663.

Introduction
Prediabetes is an intermediate stage of glucose homeostasis disturbance, affecting approximately 720 million individuals worldwide in 2021 and projected to affect 1 billion people by 20451. Without clinical intervention, up to 65% of prediabetics eventually develop type 2 diabetes mellitus (T2DM)2. Individuals with prediabetes have a higher risk of developing cardiovascular disease, retinopathy, neuropathy, and nephropathy compared to those with normal glucose levels3. Therefore, the clinical objective for prediabetes management is to restore normoglycemia or, at minimum, to delay further deterioration of glycemia. Emerging evidence shows that lifestyle interventions for individuals with prediabetes at the early stage of their glucose dysregulation can decrease the rates of progression from prediabetes to diabetes and development of complications4.

While randomized clinical trials can demonstrate the effectiveness of treatments to prediabetes, individual responses to these treatments vary5. One important reason for this variability is that the current criteria for defining prediabetes, fasting glucose, hemoglobin A1c (HbA1c) and 2-h glucose after a 75g oral glucose load, do not capture a homogeneous population; individuals within the same blood glucose range can exhibit different metabolic status. Insulin resistance and islet β-cell dysfunction, the two primary mechanisms underlying abnormal glycometabolism, can vary widely among individuals and often emerge well before the onset of hyperglycemia6. The metabolic heterogeneity observed in prediabetes is associated with multifactorial determinants, including genetic predisposition, lifestyle factors, and gut microbiota7. New classifications of prediabetes and diabetes that include additional variables, especially insulin secretion and insulin sensitivity, have been proposed to better predict the risk of progression to diabetes-related complications in cohort studies8–10. These multivariable-driven subclassifications have the potential to enhance the effectiveness of targeted prevention and treatment for diabetes and related complications. However, there is still a lack of large-scale randomized clinical intervention studies to provide persuasive and compelling evidence for these approaches.

Disruptions in gut microbiome, particularly through changes in the production of specific metabolites like short-chain fatty acids, have been shown to play a causative role in the progression of insulin resistance and reduction of insulin secretion in both mouse models and human studies11,12. These alterations are associated with the pathophysiology of diabetes and prediabetes13. While many studies have characterized changes in gut microbiome among patients with prediabetes and diabetes14,15, recent study showed that microbiome features identified through unsupervised stratification of patients, using more comprehensive clinical parameters, may better reflect microbial alterations linked with the progression of abnormal glycometabolism16. The relationship between gut bacteria and metabolic phenotypes necessitates further validation in large-scale cohorts.

The gut microbiota is recognized as a target for high-fiber diets, which are crucial for diabetes prevention and management17. Since humans lack the genetic capacity to digest and utilize dietary fiber, it is exclusively fermented and utilized by gut bacteria18. A specific group of bacteria in the gut microbiota utilizes dietary fiber to produce acetate and butyrate, leading to increased levels of GLP-1 and enhanced insulin secretion19. This mechanism underlies the improvement of glycemic control observed in patients with diabetes19. However, animal experiments and small human cohort studies have shown varied responses to high-fiber diets, which appear to be associated with the composition and function of gut microbiome in individuals with impaired glucose metabolism20. For chronic metabolic diseases that require long-term intervention and management, accurately evaluating individual effectiveness before intervention is crucial in clinical practice. Due to limited cohort sizes, the gut microbiota members determining whether subjects with impaired glucose metabolism achieve glycemic benefits from dietary fiber interventions vary across studies, and a widely applicable predictive model based on gut microbiota to predict individual benefit has yet to be established21,22.

In the current study, to evaluate the relationship of gut microbiota and serum metabolic profiles among homogeneous metabolic phenotypes of prediabetes, we recruited 802 subjects from 8 medical centers and classified them into four clusters using a multivariate clustering model based on comprehensive glycometabolism-related parameters. The four clusters exhibit distinguished features in metabolic disorder and significant differences in gut microbiome serum metabolic profiles. To explore the association between responses to fiber intervention and microbiota, all the participants were randomly assigned to receive either the usual care as control group or dietary fiber supplement intervention for 6 months. We found that dietary fiber supplements significantly altered the gut microbiome and improved glycometabolism in Clusters 3 and 4, but not in Clusters 1 and 2. Finally, we used a machine learning model and generated a score based on baseline microbiome characteristics to predict and quantify the responsiveness of individuals to dietary fiber intervention. This model was validated in two independent fiber intervention trials. Our findings indicate that gut microbiota and serum metabolites correlate with the systemic physiological and metabolic states of their host, and gut microbiota profiles of individuals influence the effectiveness of dietary fiber interventions. This underscores the importance of individual microbiome features in guiding personalized interventions to improve health.

Results
Subjects reclassified into four clusters using glucose metabolism related variables
We recruited 802 subjects with prediabetes out of 2076 individuals from 8 medical centers in eastern China for our open-label, parallel-group study (GPD study) from December 4th, 2019 to August 1st, 2021 (Supplementary Fig. 1 and Supplementary Tables 1 and 2). According to criteria of WHO23 and ADA24, 87 subjects were impaired fasting glycaemia (IFG), 260 were impaired glucose tolerance (IGT), 150 were both impaired fasting glycaemia and glucose intolerance (IFG-IGT), and 305 were severe impaired glucose metabolism (SIGM). We observed considerable variation in fasting and postprandial insulin levels, anthropometry parameters, blood lipid parameters, liver and kidney function parameters among subjects within the same glycemic subtype (Supplementary Fig. 2 and Supplementary Table 3). Additionally, no significant differences were observed in dietary patterns and histories of metabolic diseases among the glycemic subtypes (Supplementary Fig. 3 and Supplementary Table 4). These findings indicated that individuals in same WHO/ADA-based subgroup exhibit a high degree of heterogeneity in their physiological and metabolic states.

As post hoc analysis, we reclassified our subjects based on multiple baseline variables, including age, BMI, HbA1c, HOMA2-IR and HOMA2-B8. The multivariate clustering was conducted by using the K-means method and identified four clusters based on the dendrogram inflection point and a high silhouette width, which assesses the cluster stability (Fig. 1a). Clusters 1 through 4 included 197, 189, 248, and 163 subjects, respectively. We found that the coefficients of variation for most clinical parameters within the four multivariate clusters were lower than those within the glycemic subtypes (Fig. 1b), suggesting that the subjects in the same multivariate clusters shared similar physiological and metabolic states.

Among these clusters, subjects in Cluster 1 exhibited the poorest islet β-cell function and the lowest degree of lipid metabolism disorder (Fig. 1c and Supplementary Table 5 and Supplementary Fig. 4). Cluster 2 was characterized by the highest levels of insulin resistance and lipid metabolism disorder, with increased risks of cardiovascular, liver and kidney diseases. Subjects in Cluster 3 were the oldest and had a higher degree of glucose and lipid metabolism disorders. Cluster 4 was distinguished by the lowest age and HbA1c level than the other clusters, along with higher optimal kidney function but a high degree of liver damage.

Compared to the other clusters, Cluster 4 demonstrated the healthiest dietary pattern, with the highest diet scores in modified Mediterranean Diet Score (mMED), food-based Lifelines Diet Score (LLDS), and Chinese Healthy Eating Index (CHEI). This was primarily due to significantly higher intake of fresh vegetables and fruits, seafood and fermented dairy among the subjects in Cluster 4 (Fig. 1d and Supplementary Table 6 and Supplementary Fig. 5). However, Cluster 4 also had the highest proportion of subjects with a family history of diabetes (Fig. 1e and Supplementary Table 6). Clusters 2 and 3 exhibited higher proportions of subjects with a history of hypertension, hyperlipidemia and fatty liver compared to the other clusters. In contrast, Cluster 1 showed a high proportion of subjects with a history of hypertension, but lower proportions of other metabolic disease histories.

Overall, by utilizing multiple variates related to glucose and insulin metabolism, we reclassified subjects with prediabetes into four sub-clusters. These sub-clusters provide a more accurate reflection of physiological states compared to traditional glycemic subtypes.

The characteristics of gut microbiota and serum metabolites across the four clusters at the baseline
We profiled the gut microbiota of the subjects by 16S rRNA gene sequencing on baseline fecal samples. We calculated the proportion of variance in the gut microbiota explained by possible influencing factors (Fig. 2a). The variance explained by glycemic subtypes was not significant, and there were no significant differences in microbiota structure among the subtypes (Supplementary Fig. 3). In contrast, multivariate clusters showed a significant proportion of explained variance in the gut microbiota (Fig. 2a). The clinical center exhibited the highest proportion of explained variance in the gut microbiota, prompting us to adjust for the clinical center in subsequent gut microbiota analyses.

We found that subjects in Cluster 2 had significantly lower richness in their gut microbiota compared to Clusters 1 and 3 (Fig. 2b). Moreover, a PCoA and PERMANOVA tests based on Aitchison distance revealed significant differences in the overall structure of gut microbiota among Clusters 1, 2, and 4, while Cluster 3 was only significantly different from Cluster 2 (Fig. 2c and Supplementary Fig. 5). The number of edges, density, degree and betweenness centrality were used to assess the connectivity and complexity of co-abundance networks of gut microbiota for each cluster (Fig. 2d and Supplementary Table 7). Compared to Clusters 1 and 2, Clusters 3 and 4 exhibited lower values for edges and significantly lower betweenness centrality, with Cluster 4 also showing a significantly lower degree. Additionally, Cluster 3 had the lowest robustness among the clusters, while Cluster 4 showed significantly lower robustness than Cluster 2. The low complexity and stability of the microbial networks in Clusters 3 and 4 might suggest that their gut microbiota is more susceptible to external interventions. We identified 49 ASVs that differed among four clusters. Notably, ASVs from the families Lachnospiraceae and Oscillospiraceae were enriched in Cluster 1 compared to other clusters (Fig. 2e and Supplementary Fig. 6). The abundance of ASVs in the genera Bifidobacterium and Bacteroides was lower in Cluster 2 than that in Clusters 1 and 4. Meanwhile, ASVs in the genera Alistipes, Dorea and Eubacterium were increased in both Clusters 3 and 4.

We then analyzed the serum metabolome using untargeted metabolomics. Consistent with our findings in gut microbiota, the clinical center exhibited the highest proportion of explained variance in the serum metabolome (Fig. 2f) There were no significant differences in metabolome among the glycemic subtypes based on a PCoA and PERMANOVA tests (adjust for clinical center, Supplementary Fig. 3). However, we found that the significant differences among clusters based on multivariate clustering, except for Cluster 2 and 3 (Fig. 2g and Supplementary Fig. 5). We identified 29 differential metabolites across the four clusters, which showed completely opposite levels between Clusters 1 and 4 (Fig. 2h). Notably, metabolites associated with amino acid metabolism and purine metabolism were enriched in Cluster 2 and 4. This finding aligns with the higher protein intake observed in participants of these two clusters (Supplementary Fig. 5).

Taken together, the gut microbiota and serum metabolite profiles in individuals with prediabetes showed significantly distinguished features among the four clusters based on multivariate clustering.

Dietary fiber improved glycemic control in subjects from Cluster 3 and 4
In this study, the above 802 subjects with prediabetes were randomly assigned to either the control group (U group, n = 393), which received usual care including patient education and dietary recommendations based on the 2014 Chinese Diabetes Society guidelines for T2DM, or the intervention group (W group, n = 409), which received dietary fiber supplements. At baseline, all clinical parameters were comparable between the U and W groups (Supplementary Table 2).

The intervention was conducted from August 10th, 2020 to February 16th, 2022. 331 subjects in W group and 328 subjects in U group were completed the 6-month intervention (Supplementary Fig. 1). No adverse events were reported in either group or no subjects were diagnosed with diabetes at the end of 6-month intervention. During the intervention period, there were no significant differences in dietary patterns (excluding the dietary fiber supplement) between the U group and W group, either overall or within the four multivariate clusters (Supplementary Tables 8-10). No significant differences in HbA1c and other glycemic-related parameters were found between W and U groups after intervention (Fig. 3 and Supplementary Table 11). We observed significant decrease in FPG and PBG but increase in HbA1c in both the U and W groups at the end of the trial compared to their baseline levels. Moreover, no significant differences in glycemic-related parameters were found between the W and U groups after the intervention in the four glycemic subtypes (Supplementary Tables 12 and 13).

We further analyzed the intervention effects in the different multivariate clusters. In Clusters 1 and 2, the levels of HbA1c, FPG and PBG did not show significant differences between the U and W groups after the intervention (Fig. 3 and Supplementary Tables 14–17). However, in Cluster 3, the FPG level was significantly lower in the W group compared to the U group after the intervention (statistic = 6773, P = 0.004, effect size statistic = 0.201, 95% CI = 1.227–6.374). In Cluster 4, HbA1c level at 6 months was significantly lower in the W group than in the U group (statistic = 2987, P = 0.005, effect size statistic = 0.233, 95% CI = 1.525–7.189), suggesting that the fiber intervention significantly alleviated the deterioration of HbA1c in Cluster 4. Additionally, in Cluster 4, levels of ALT (statistic = 2901, P = 0.030, effect size statistic = 0.185, 95% CI = 1.111–29.156) and AST (statistic = 3005, P = 0.009, effect size statistic = 0.222, 95% CI = 2.727–18.266) were significantly lower in the W group than in the U group after the intervention.

Overall, after the dietary fiber intervention, improvement of glycometabolism was observed in the W group of Clusters 3 and 4, but not in Clusters 1 and 2, when compared to their respective control groups.

The improvement of glycometabolism of the subjects with prediabetes is related to whether gut microbiota can be changed by dietary fiber
Compared to baseline (0M), gut microbiome structure was significantly changed in the W group after intervention (6M), but not in the U group (Supplementary Fig. 7). The distance change distribution curves of microbiota revealed that this difference in the W group was driven by subjects with large change distances in microbiota after intervention. Across the glycemic subtypes, no significant changes in microbiota structure were observed in the W and U groups after intervention. It suggests that glycemic subtypes of prediabetes are unable to distinguish individuals with differing responses of gut microbiome to dietary fiber.

We further analyzed the changes of microbiota in the different multivariate clusters. After the intervention, the diversity of microbiome, indicated by Shannon index, significantly decreased in Cluster 3 (Fig. 4a). While the richness of the microbiome, indicated by observed numbers of ASVs, significantly decreased in Cluster 3 and 4. In contrast, the richness of the microbiome in the U group significantly increased in Cluster 3. The U group of Cluster 1 exhibited a significant increase in microbial diversity at 6M, while no significant change was observed in the W group of Cluster 1 and both groups of Cluster 2 (Fig. 4a). The aPCoA scores plot and PERMANOVA test showed significant changes in gut microbiota structure only in the W group of Clusters 3 and 4, leading to a significant separation from the U group at the end of intervention (Fig. 4b and Supplementary Fig. 7). However, no changes in gut microbiota were observed in either the W or U groups in Cluster 2, while significant shifts were observed in the U group but not in the W group of Cluster 1.

Our previous study proposed the core gut microbiome signature of two antagonistic bacterial guilds as a potential holistic health indicator and a common target for health enhancement25. Using the similar strategy, we constructed co-abundance networks of gut microbiota in the W group at 0M and 6M and 55 ASVs exhibited consistent correlations across these two time points, which formed a “Two Competing Guilds” with 33 ASVs in Guild 1 and 22 ASVs in Guild 2 (Fig. 4c and Supplementary Fig. 8 and Supplementary Table 18). A similar network structure was observed in the U group (Supplementary Fig. 9). By using PICRUSt2 to predict the functions of microbiome, Guild 2 significantly enriched with genes related to carbohydrate utilization and shorty-chain fatty acids production, but Guild 1 tended to have higher copies of genes related to drug resistance and bacterial infectious diseases (Supplementary Fig. 9). This might suggest that Guild 2 was potentially beneficial, and Guild 1 was detrimental. During the 6-month intervention, the ratios of Guild 2 to Guild 1 remained decreased trends in both the W and U groups of Clusters 1 and 2, as well as in the U groups of Clusters 3 and 4. However, this trend was altered by dietary fiber intervention in the W group of Clusters 3 and 4. These alterations were due to that the abundance change value of Guild 2 was significantly higher in the W group compared to the U group in Cluster 3; In cluster 4, the abundance change value of Guild 1 was significantly lower in the W group than in the U group, while that of Guild 2 was significantly higher in the W group (Fig. 4d).

Then we identified the key gut microbiota members that responded to dietary fiber in the W group of Clusters 3 and 4, which showed improvements in glycometabolism and changes in gut microbiota (Fig. 4e, f). Three ASVs in Cluster 3 and six ASVs in Cluster 4 belonging to Parabacteroides distasonis, which has been reported to ameliorates insulin resistance in obese mice26, were significantly enriched. Furthermore, other potential beneficial bacteria were enriched, including Bacteroides uniformis, Bifidobacterium longum and P. goldsteinii in Cluster 3, as well as Lactobacillus mucosae in Cluster 4. Consistent with previous studies, the bacteria showed strain/specie-specific responses to the dietary fiber.

We subsequently compared the serum metabolome before and after intervention across multivariate clusters (Fig. 4g and Supplementary Fig. 10). Significant changes in metabolome were observed in the W group of Cluster 3, leading to significant separation from the U group at the end of intervention. In Cluster 4, significant changes in metabolome were observed in both W and U groups but with opposite direction. However, no changes in metabolome were observed in either the W or U groups in Cluster 2, while significant changes were observed in the U group but not in the W group of Cluster 1. We identified 49 and 61 metabolites with significant abundance changes after the intervention only in the W group of Clusters 3 and 4, respectively (Supplementary Table 19). KEGG enrichment analysis revealed significant changes in the microbiota-related metabolism pathways as aromatic amino acid metabolism and branched-chain amino acid metabolism pathways, following the intervention in Clusters 3 and 4 (Fig. 4h).

Taken together, our dietary fiber supplements induced significant changes in gut microbiota and serum metabolites only in subjects from Clusters 3 and 4, but not in Clusters 1 and 2. This suggests that the metabolic states and gut microbiota of individuals with prediabetes may determine the effectiveness of dietary interventions.

Baseline microbiota could predict and quantify the personalized responsiveness to dietary fiber intervention
The above analysis indicated that not all subjects with prediabetes benefited from dietary fiber intervention, which was mediated by their gut microbiota. We then tested whether the baseline gut microbial features could be used to predict responsiveness.

FPG, PBG and HbA1c can predict individual progression to diabetes and improve in any single parameter alone was insufficient to delay diabetes development27. Then we calculated a glycemic benefit score for each subject in the W group based on whether FPG, PBG and HbA1c could improvement from 0M to 6M. Subjects were then categorized into low (score = 0 and 1, alleviation of no or only one parameter) and high (score = 2 and 3, alleviation at least two parameters) responders (Fig. 5a and Supplementary Table 20). Using the baseline abundance of ASVs, we used a LightGBM classification model to distinction between high and low responders and calculate importance of each ASV. The highest ROC-AUC value would archive by using the top 44 ASVs (rank by importance) and decrease by including additional ASVs (Supplementary Fig. 11). Then these 44 ASVs were identified as microbiome features for training the LightGBM classification model to distinguish the high and low responders, and the ROC-AUC value was 0.81 (95% CI: 0.74–0.88, Fig. 5b, c). We also used classification models based on the baseline serum metabolome and combined metabolome-microbiome to distinction between high and low responders. Comparable predictive power among models with microbiome data, metabolome data and combined metabolome-microbiome data (microbiome vs. metabolome, P = 0.754; microbiome vs. combined, P = 0.528; metabolome vs. combined, P = 0.631; Delong test), suggested that using microbiome features alone would be sufficient (Fig. 5c and Supplementary Figs. 11, 12).

Next, we calculated the Shapley additive explanations (SHAP) values for each of these 44 ASVs in each subject to represent their average contribution to the LightGBM model predictions and generated a microbiome-fiber score (MFS) based on the SHAP values in each subject. The high responders had a significantly higher MFS compared to low responders, and the MFS showed a significantly positive association with the glycemic benefit score (Fig. 5d, e). Through bootstrap resampling, we identified the cutoff value of MFS ensuring ≥ 99% accuracy in predicting low or high responders (Fig. 5f). When MFS of subjects were ≤ 17.6, the glycemic benefit score had a 99% probability of being 0 or 1, indicating that limited or no glycemic benefit from dietary fiber intervention, while MFS of subjects were ≥ 24.5, the glycemic benefit score had a 99% probability of being 2 or 3, indicating the glycemic benefit from dietary fiber intervention (Fig. 5f). Since the MFS was constructed as an integer-only data, the final cutoff values were set to 17 and 25. Notably, in the subjects with MFS ≥ 25, HbA1c, FPG and PBG were significantly lower in W than U group after intervention (Fig. 5g and Supplementary Table 21). Conversely, in the subjects with MFS ≤ 17, the W group showed no glycemic improvement after intervention. This suggests that the MFS, based on gut microbiota features, could be used to predict and quantify the individual glycemic benefit of prediabetic subjects from dietary fiber intervention.

Subsequently, we need to verify the efficacies of classification models and MFS in independent cohorts possessing gut microbiota, FPG, PBG, and HbA1c data. Because we could not find any available datasets from published fiber intervention with prediabetes, two independent cohorts from our previously published studies with T2DM were served as validation cohorts. The clinical data of pre- and post-intervention from GLC study28, which was a 2-week dietary fiber intervention for subjects with obesity and T2DM, was used to calculate the glycemic benefit score for each subject and classify low and high responders using the same criteria as the GPD cohort (Supplementary Fig. 13). All the above 44 ASVs as microbiome features for training the LightGBM classification model to distinguish the high and low responders were detected in microbiome data from GLC study. The established LightGBM classification model based on the baseline microbiome features had satisfactory capability to distinguish between high and low responders, with MFS ≥ 25 and ≤ 17 identifying 100% of high and low responders, respectively (Fig. 5h). To further validate the predictive capacity of microbiome model for long-term glycemic benefits of subjects after dietary fiber intervention, the clinical data of 1-year after-intervention follow-up from QD study25, which was a 3-month dietary fiber intervention and 1-year after-intervention follow-up for subjects with T2DM, was used to define high and low responders. The trained LightGBM classification model based on baseline microbiome features also could distinguish between high and low responders after 1 year withdrawal of fiber intervention, with MFS ≥ 25 and ≤ 17 identifying 100% of high and low responders, respectively (Fig. 5i and Supplementary Fig. 14).

In summary, the features of baseline microbiota in subjects can effectively predict and quantify their personalized responsiveness to dietary fiber intervention. This clinically actionable model and MFS may guide personalized microbiome-targeted prevention and treatment for prediabetes.

Methods
Clinical study
GPD study. The Gut-Prediabetes (GPD) study is an open-label, multicenter, parallel-group, randomized clinical trial in subjects with Prediabetes. The study protocol was approved by the Ethics Committee at Shanghai General Hospital Institutional Review Board ([2019] 296). All subjects provided written informed consent and received compensation (consisted of monetary allowance and gifts) for their time and participation in the study. The trial was registered in the Chinese Clinical Trial Registry: ChiCTR1900027663 (https://www.chictr.org.cn/).

Study design. The study is designed as an open-label, multicenter, parallel-group, randomized clinical trial (Supplementary Fig. 1) with two aims: usual care control and the dietary fiber supplement intervention lasted 6 months, followed by 18 months follow-up. At baseline, the end of intervention and 18 months follow-up, clinical parameters of subjects were measured. Fecal and urine samples were collected, and food frequency and medical diseases history questionnaires were performed.

Primary outcomes: the change of hemoglobin A1c (HbA1c) after-intervention; the annual progression rate of type 2 diabetes mellitus (T2DM). Secondary outcomes: the changes of other glucose, insulin, lipid, liver and kidney function, and anthropometric parameters at after-intervention and 18-month follow-up. Details were shown in the supplementary method. Exploratory outcomes: the changes of gut microbiota and serum metabolome after-intervention and 18-month follow-up.

Five of our eight clinical centers are located in Shanghai. During the 18-month follow-up period, from March to October 2022, Shanghai implemented a city-wide lockdown due to the COVID-19 pandemic. Subjects at these centers were confined either at home or in makeshift hospitals, experiencing significant disruptions to their diet and daily lives. Approximately 42.06% of subjects reported contracting COVID-19 during this period. Consequently, this introduced substantial, unforeseen, and uncontrollable interference to the entire study. At the end of the 18-month follow-up, we conducted physical examinations and data collection according to the protocol. However, the data for the annual progression rate of type 2 diabetes was deemed highly unreliable. Therefore, we have presented these results in the Supplementary materials (Supplementary Table 20).

Sample size estimation. Sample size estimation was performed in NCSS PASS (version 11.0). The HbA1c changes in 6 months showed a mean reduction of −0.6% ± 1% in the intervention group versus −0.3% ± 1% in controls35. The annual T2DM progression rate was projected at 6% (intervention) versus 11% (control) based on prior evidence36. Sample size calculation (α = 0.05, power = 90%, 1:1 allocation, 10% dropout rate) yielded 235 subjects per group based on HbA1c changes and 369 subjects per group based on annual progression rate. Therefore, 400 subjects in each group were planned to be enrolled.

Inclusion and exclusion criteria. Inclusion criteria: Age between 30 and 75 years. Meeting diagnostic criteria for prediabetes of WHO23 and ADA24. Exclusion criteria: diagnosed with T2DM, severe organic diseases, infectious diseases, mental diseases; limb disabilities; used antidiabetic, weight management, gut microbiota-modulating drugs within 3 months before enrollment; women who were pregnant, lactating; alcoholic intemperance; anemia; allergic to intervention food. Details were shown in the Supplementary method.

Randomization. The central randomization system used to issue random numbers, and subjects randomly assigned to intervention and control groups with 1:1 ratio. Randomization was done using dynamic minimization method and stratified according to BMI (<25 kg/m2, ≥25 kg/m2) and 2-h post-OGTT blood glucose (<7.8 mol/L, ≥7.8 mol/L). The randomization was performed separately at each clinical center.

Intervention. The subjects received either usual care (U group) or the dietary fiber supplement (W group) for 6 months. The usual care consisted of standard dietary and exercise advice according to the 2014 Chinese Diabetes Society guidelines for T2DM. The commercially available dietary fiber supplement (Adfontes Co., Ltd, Shanghai, China; Catalog: 400001) consists of wheat bran, oat bran, Coix lachrymal-jobi L., Fagopyrum tataricum, resistant starch, inulin, fructo-oligosaccharides, and xylo-oligosaccharides. The nutritional composition of the dietary fiber supplement is shown in Supplementary Table 1. Subjects were required to consume 15 g dietary fiber for each meal (45 g dietary fiber/day)19,37.

Statistics and reproducibility
Statistical analysis was performed in the R environment (version 4.2.1). Comparisons across multiple groups using Kruskal–Wallis test with Dunn’s test. Comparisons of metabolic diseases history using Pearson’s Chi-squared test (two-sided). Inter-group comparisons at same time point used Mann–Whitney U test (two-sided). Intra-group comparisons at different time points used Wilcoxon matched-pairs signed-rank test (two-sided). At baseline, generalized linear model was used to assess the relationships between gender and other variables. When performing pairwise comparisons among subgroups, ANCOVA (two-sided) was used to adjust for gender for the variables that significantly correlated with gender. All experiments were performed based on biological replicates to confirm the reproducibility of the results. The investigators were not blinded to allocation during experiments and outcome assessment.

Multivariate clustering of subjects (post hoc analysis)
Analyses were performed in R environment (version 4.2.1) using package cluster (version 2.1.4)38. The variables considered for clustering included age, BMI, HbA1c, HOMA2-B and HOMA2-IR8. Five subjects was outliers (greater than mean ± 5 standard deviation) and excluded from analysis. The data of non-normally distributed variables (skewness ≥ 1.0) were log-transformed, and then the data of above 5 variables were z-scored. Optimal cluster number was determined through hierarchical clustering (Based on Euclidean distance) and silhouette widths. Multivariate clustering was performed using k-means method. The clustering is considered stable when the Within-Cluster Sum of Squares (WCSS)—defined as the sum of squared distances between each data point and its centroid—is minimized. We conducted 100 clustering runs with random initiation values, computed the WCSS for each clustering, and selected the clustering with minimal WCSS as the final clustering outcome. Cluster-wise stability was assessed by Jaccard value greater than 0.7539.

Gut microbiota analysis
Fecal DNA extraction and sequencing. Fecal DNA was extracted40 using the DNeasy® 96 PowerSoil® Pro QIAcube® HT Kit (QIAGEN, Hilden, Germany) and a QIAGEN QIAcube HT workstation (QIAGEN, Hilden, Germany). A sequencing library of the V3-V4 region of the 16S rRNA gene was constructed, and then shotgun sequencing was performed on an Illumina MiSeq platform (Illumina, Inc., San Diego, CA, USA) following the manufacturer’s instructions. Details were shown in the Supplementary method. Both preprocessing and detection procedures for all samples were conducted at the central laboratory (Shanghai Jiao Tong University).

Gut microbiota diversity analyses. The QIIME2 pipeline (version 2021.4)41 was employed to obtain Amplicon Sequence Variants (ASVs). The following analyses were performed in the R environment (version 4.2.1), unless otherwise stated. The abundance of ASVs was transformed by relative log expression (RLE) using package edgeR package (version 3.32.1)42. α-diversity was calculated and adjusted clinical center via linear regression (package stats version 4.2.1)43.

Adjusted principal coordinates analysis (aPCoA) based on Aitchison distance was performed with package aPCoA (version 1.3)44. Permutational Multivariate Analysis of Variance (PERMANOVA) was conducted using package pairwiseAdonis (version 0.4)45 with 999 permutations. Clinical center adjusted in aPCoA and PERMANOVA.

Co-abundance network of gut microbiota. Fastspar (version 1.0.0)46 calculated ASVs correlations (retaining ASVs with |r| ≥ 0.3 and P ≤ 0.001) based on the abundance of ASVs that shared by more than 20% of the samples. Network robustness analysis was referred to Yuan M et al.47

Guild analysis. The details of guild analysis were described in our prior study25, and shown in the Supplementary method. Functional genes of ASVs of guilds were predicted, using phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt2; version 2.4.1)48 implemented in python environment (version 3.7.6).

Differentially abundant ASVs identification. MaAslin2 model with adjusting clinical center was used to identify baseline different ASVs across clusters (package Maaslin2 version 1.10.0)49. The different ASVs required Benjamini-Hochberg method adjusted P-values < 0.25 (MaAslin2) and P < 0.05 (Kruskal-Wallis test).

Light Gradient Boosting Machine (lightGBM) model was used to calculate feature importance of each ASV for distinguishing pre- and pro- intervention in W group of Clusters 3 and 4, using package lightgbm (version 3.3.5)50. ASVs with feature importance > 0 underwent Wilcoxon matched-pair signed-rank test to compare intra-group changes of W group or U group. The ASVs that only showed significant changes (P < 0.05) in the W group were identified as fiber-response bacteria.

Serum metabolome analysis
Serum samples were sent to Panomix Biomedical Tech Co., Ltd. (Suzhou, China) for untargeted metabolome profiling. LC analysis was performed on a Vanquish UHPLC System (Thermo Fisher Scientific). Mass spectrometric detection of metabolites was carried out using a Q Exactive (Thermo Fisher Scientific, USA) equipped with an ESI ion source. Details were shown in the Supplementary method. The multivariate statistical analysis of metabolites data was same as microbiome data. Enrichment analysis was performed in MetaboAnalyst analysis platform (version 5.0; https://www.metaboanalyst.ca/)51 based on the KEGG database.

Using machine learning model to predict the responsiveness to dietary fiber
Glycemic benefit score. W group subjects were classified as low and high responders based on % changes of FPG, PBG, HbA1c after intervention. Glucose parameters with % change < 0 were assigned a score of 1; otherwise, scored 0. The glycemic benefit score (sum of scores across three glucose parameters) defined high-responders (score ≥ 2) and low-responders (score < 2).

Classification model training. The following analyses were performed in the R environment (version 4.2.1). Based on baseline ASV abundances, a LightGBM classification model with stratified 10-fold cross-validation was used to calculate the feature importance of each ASVs for distinguishing low and high responders. Hyperparameter tuning was performed via grid search, according to the LightGBM documentation guidelines (https://lightgbm.readthedocs.io/en/latest/index.html). Feature importance-ranked ASVs underwent the sequential forward selection to find the optimal ASV combination achieving the maximum area under the receiver operating characteristic curve (ROC-AUC) with package pROC (version 1.18.5)52. Shapley additive explanations (SHAP) method was used to quantify the contribution of each ASV to predict outcomes at individual level using package shapviz (version 0.9.5)53. Metabolome model and Combined metabolome-microbiome model were trained by the same strategy as above microbiome model. Delong tests compared performance across microbiome, metabolome, and combined models.

Microbiome-fibre score (MFS). We constructed microbiome-fiber scores (MFS) based on the SHAP value of each ASV of optimal ASV combination, which is defined by

MFSi = \sum_{j=1}^{n} S_{ij}

Where, MFS is an MFS value for individual i, S_{ij} = 0, if X_{shap,ij} ≤ 0; 1, if X_{shap,ij} > 0, S_{ij} is the MFS value for the jth ASV in ith individual. n is the sum of the ASVs, and X_{shap,ij} is the SHAP value for the jth ASV in ith individual.

50% samples were randomly selected with 99 repetition to perform sequential accumulation of MFS values in descending and ascending orders was performed until a classification accuracy of 99% was achieved for low and high responders, respectively (package cutpointr version 1.1.2)54.

Validation in external validation cohorts
Validation cohort 1. Validation Cohort 1 was a 2-weeks dietary fiber intervention for 19 subjects with T2DM or obesity from our published study (GLC study)28. FPG, HbA1c, continuous glucose monitoring data (CGM) at baseline and after-intervention was used to calculate glycemic benefit score. We performed DNA extraction, 16S rRNA gene sequencing and analysis of baseline fecal samples using same methodology as the GPD study.

Validation cohort 2. Validation Cohort 2 was a 3-month dietary fiber intervention with 1-year follow-up for 67 T2DM subjects from our published study (QD study)25. FPG, PBG, HbA1c at baseline and 1-year after-intervention follow-up was used to calculate glycemic benefit score. We performed DNA extraction, 16S rRNA gene sequencing and analysis of baseline fecal samples using same methodology as the GPD study.

Validation of the classification model and MFS. Based on the abundance of ASVs or serum metabolites in subjects from validation cohorts, LightGBM model established in GPD study was used to distinguish low and high responders in validation cohorts and ROC-AUC was used to evaluate the accuracy of classification. The MFS of every subject in validation cohorts was calculated and the percentages of low and high responders within the two MFS cutoff values were used to evaluate whether MFS can represent glycemic benefit of individual.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study provides useful findings related to the role of dietary fiber supplements and the gut microbiome in prediabetes management, which is a significant public health concern. The clinical trials procedure and study design appear sound, reliable, and valid. The figures and tables provided are thorough and organized well. Below are general comments I have.\n1. Why were 45 g fiber/day chosen? Are there any side effects for consuming the high dose of fibers? As for dietary interventions, adherence and gastrointestinal side effects are important, especially for high dose and long-term applicability.\n2. For the analysis, I noticed that there were fewer males than females in the clusters 2, 3, and 4. This imbalance should be controlled/accounted for statistically, but the authors did not mention if this was done. The exact statistical procedures utilized need to be mentioned for the entire study. If this group imbalance in the males and females was not accounted for statistically, then the results were probably invalid. In that case, the data need to be re-analyzed.\n3. The results and discussion parts need to be rewritten. In some cases, the authors were attempting to discuss the results in the results section, which would be more suitable in the discussion section. For the discussion section, authors need to provide justifications and/or additional information instead of simply describing the findings. For example, the first paragraph of the discussion section should be in the results section. The manuscript in current version looks repetitive and is difficult to follow.\n4. The authors shown that dietary fiber improved glycemic control in cluster 3 and 4, but lacked mechanistic discussion. It seems that participants in cluster 4 consumes significantly higher dietary fiber, fresh vegetables and fruits, sea food and fermented dairy, but their gut microbiota is still more susceptible to external interventions. The results shown in the manuscript are interesting, but causality between microbiota and glycemic improvements remains unproven.\n5. Lastly, I would like to suggest the authors adopt a neutral, academic tone in their phrasings. For example, in lines 518-520, While the intervention showed short-term benefits of dietary fiber supplements, it’s risky to make strong claims about long-term impact or precision medicine without the 18-month progression data. Suggest to soften the language to reflect that these are promising early findings but need further validation.", "reviewer_2": "This study is a post-hoc analysis of a multi-center open-label randomized trial comparing the effect of dietary fiber supplement intervention to usual care control on blood sugar level among prediabetic adults in East region of China (n=802). Intervention/follow up lasted 6 mo. Overall, the intervention did not affect HbA1c (primary outcome), however, the investigators found that some sub-groups of the population did benefit from the fiber intervention. These subgroups were identified using machine learning algorithms that included baseline covariates, and, importantly, microbiome data. The results highlight the potential role of baseline gut microbiome in predicting responses to fiber supplement intervention. However, there was some clarification we needed from the authors for better understanding and interpreting the results.\nMajor\nThere are many comparisons between post hoc, data driven subgroups, which may reflect overfitting of the data from the trial. To test whether the data is overfit to this trial/these participants, it is critical that the investigators attempt to either: 1) split their participants into a discovery cohort and validation cohort, and then test whether the sub-group findings in the discovery cohort are replicagted in the validation cohort; or 2) use external RCTs to validate the findings. The investigators indeed appear to take approach #2, however, the details of the cohorts used to validate the findings, and exactly what findings were validated needs additional information for the readers/reviewers to be able to determine whether findings are indeed transportable.\nFor randomization, the authors mentioned stratifying by BMI and 2-hour post-OGTT blood glucose. How about clinical center? What is the distribution of intervention and control by clinical center?\nBased on Figure 2, clinical center has the largest impact on baseline microbiome and gut metabolome. Could you describe the batch in which the samples were processed and comment on the potential impact of batch effect?\nFor the multi-variate clustering, I assume the linkage for the hierarchical clustering was Euclidean (If is, please add it in the methods). Given the k-means clustering is sensitive to the choice of initiation values, what is your initiation values for the k-means clustering? Have you tried randomized initiation for the k-means clustering? Please add more details for the k-means clustering for reproducibility.\nFor the ASV, please include the sequence for each ASV in a supplemental table.\nFor all packages or algorithms, please add citations.\nI noticed that in the microbiome PCoA, the PC1, PC2 showed the same number of explained variance for Fig2 (baseline), Supp Fig5 (baseline) and Supp Fig 7 (baseline + M6). Do authors put the wrong figures here? Please double check the figures and data.\nSupp Fig 5 showed the distribution of clinical centers was different across the multi-variate clusters. Could the difference in patients characteristics explain the difference in the responses to interventions? For example: Cluster 4 was dominated by site 7, which is a liver cancer institute according to the supplemental methods. We saw changes in ALT and AST in this cluster only. Could it be because of there were more patients with liver problems in cluster 4?\nFor the comparison of W and U group in each multi-variate cluster, have you adjusted for any co-variates (e.g., clinical center)? For post-hoc analysis of clinical trials, you should adjust for any potential confounders given the balance of co-variates between the intervention and control group may not hold in each cluster.\nHave you run any statistical test for comparing the change of Guild2 to Guild 1 ratio in the W and U group in each clusters?\nIn the MFS analysis, what about patients with scores between 17 and 25? Could the author add a cut point x between 17 and 25? There was lower probability of responses (<50%) in (17, x), higher probability of responses in (x - 25).\nBoth validation studies included T2DM patients instead of prediabetic adults. Is it possible to find validation studies with prediabetic adults? It not, please acknowledge this as a limitation.\nGiven this study and both validation studies were done in East region of China, please discuss about the generalization and need for external validation in the limitation.\nInvestigators should note in the limitations that PiCrust may have low validity for predicting microbial function\nMinor\nIntroduction (line 93): Citation 8 and 9 are the same, please correct\nIntroduction (line 113-115): Add citation for “A specific group of bacteria in the gut microbiota utilizes dietary fiber to produce acetate and butyrate, leading to increased levels of GLP-1 and enhanced insulin secretion”.\nResults (line 160): By saying ‘similar glucose levels’, do you mean the WHO/ADA-based participant categories? Please be more accurate in wording.\nResults (line 241): ‘accurately’ seems too vague here. What do you mean by ‘accurately classifying’\nResults (line 286): By saying ‘in the part of subjects’, do authors mean a specific part of subjects?\nResults (line 360): Change ‘improvement’ to ‘improve’\nResults (line 370-374): Report Delong test p value.\nFigure 2 and Supp Figure 5: Is it possible to combine Supp Figure 5d with Fig 2c (Supp Figure 5e with Fig 2g)? Just lay out all the data points on the centroid plot. Having both seems redundant.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "We sincerely thank the reviewer for the thorough evaluation of our manuscript. We are particularly grateful for the reviewer's acknowledgment of the significance of our research and the effort invested in this study. Please find our detailed responses to comments and suggestions below.\nPlease note that the Version 1 means original version, Version 2 means revised version in our reply below.\n1. Why were 45 g fiber/day chosen? Are there any side effects for consuming the high dose of fibers? As for dietary interventions, adherence and gastrointestinal side effects are important, especially for high dose and long-term applicability.\nReply: We thank the Reviewer for raising this important point. According to Dietary reference intakes for China (2023), the recommended daily dietary fiber intake for healthy adults is 25–30 g. Given that the purpose of our clinical trial is alleviation of abnormal glucose metabolism in the subjects with prediabetes through changing their dysbiosis gut microbiota, the amount of dietary fiber intake should be higher than recommended levels for healthy individuals. The initial clinical study that utilized a daily fiber to glycemic control suggested that 25-50 g fiber per day (Chandalia M et al., N Engl J Med. 2000, 342: 1392-1398). Moreover, our previous research showed that employed a daily intervention of 45 g dietary fiber per person improved glycemic control in type 2 diabetes patients (Zhao L, et al., Science. 2018, 359:1151-1156). Then we used 45 g fiber/day in the current study. We have added the above two citations to the method section (Line 605, Page 22 in Version 2).\nAs the Reviewer mentioned, adherence and gastrointestinal side effects are important for high dose and long-term dietary intervention. To supervise potential adverse effects of sustained high-fiber intake and ensure intervention adherence, trained medical personnel supervised the intake of the dietary fiber supplement and any side effects throughout the intervention period via phone call, with daily supervision during the first week, weekly supervision from the second week to the first month, fortnightly supervision from the first month to three months, and monthly supervision thereafter. As we mentioned in the Supplementary method section (Line 137-140). During the intervention, we did not receive any reports of adverse events, as mentioned in the results section: “No adverse events were reported in either group or no subjects were diagnosed with diabetes at the end of 6-month intervention.” (Line 251-253, Page 10 in Version 2).\n2. For the analysis, I noticed that there were fewer males than females in the clusters 2, 3, and 4. This imbalance should be controlled/accounted for statistically, but the authors did not mention if this was done. The exact statistical procedures utilized need to be mentioned for the entire study. If this group imbalance in the males and females was not accounted for statistically, then the results were probably invalid. In that case, the data need to be re-analyzed.\nReply: Thanks for the valuable suggestion from Reviewer. Within our recruited cohort, males comprised 38% and females comprised 62% of subjects. For multivariate clustering, significant differences in gender ratio were found between Cluster 1 and other 3 clusters, Cluster 2 and Cluster 3 (Supplementary Table 5, Version 2). We used generalized linear models (GLM) to assess the relationships between gender and other variables and re-analyzed the variables that significantly correlated with gender by ANCOVA adjusted for gender in pairwise comparisons among four clusters. The gender-adjusted results are presented in Supplementary Tables 3-6 of Version 2.\nAccording to the re-analysis results, the clinical characteristics of the four clusters are unaffected by adjusting the gender: Cluster 1 exhibited the poorest islet β-cell function and the lowest degree of lipid metabolism disorder. Cluster 2 was characterized by the highest levels of insulin resistance and lipid metabolism disorder, with increased risks of cardiovascular, liver and kidney diseases. Cluster 3 were the oldest and had a higher degree of glucose and lipid metabolism disorder. Cluster 4 was distinguished by the lowest age and HbA1c level than the other clusters, along with higher optimal kidney function but a high degree of liver damage. The characteristics of the four subtypes regarding diet and disease history are also unaffected and were as follows: Cluster 4 demonstrated the healthiest dietary pattern, with the highest diet scores in modified Mediterranean Diet Score (mMED), food-based lifelines diet score (LLDS), and Chinese Healthy Eating Index (CHEI). This was primarily due to significantly higher intake of fresh vegetables and fruits, sea food and fermented dairy among the subjects in Cluster 4. However, Cluster 4 also had the highest proportion of subjects within a family history of diabetes. Clusters 2 and 3 exhibited higher proportions of subjects with a history of hypertension, hyperlipidemia and fatty liver compared to the other clusters. In contrast, Cluster 1 showed a high proportion of subjects with a history of hypertension, but lower proportions of other metabolic diseases histories.\nNo significant difference in gender ratio was observed between W and U groups (P=0.6669, Chi-squared test; Supplementary Table 2, Version 2). Moreover, there were no significant differences observed in the gender between W and U groups in each subcluster (Supplementary Table 8, Version 2). In the analysis of post-intervention effects for each cluster, comparisons were restricted to differences between the U and W groups within each cluster, without cross-cluster comparisons. Therefore, the analysis of post-intervention effects for each cluster was not influenced by gender ratio.\n3. The results and discussion parts need to be rewritten. In some cases, the authors were attempting to discuss the results in the results section, which would be more suitable in the discussion section. For the discussion section, authors need to provide justifications and/or additional information instead of simply describing the findings. For example, the first paragraph of the discussion section should be in the results section. The manuscript in current version looks repetitive and is difficult to follow.\nReply: We thank this valuable comment from Reviewer. In the revised manuscript, we removed the sentences that were used to discuss the results in the Result section as follows:\n(1) Removed the sentence “As a community of microorganisms, the ecological network can also reflect important features of the gut microbiota.” (Line 211-212, Page 8 in Version 1).\n(2) Revised the sentence “Taken together, our results suggested that gut microbiota and serum metabolites are ...... in individuals with prediabetes.” (Line 239-244 of Page 9 in Version 1) as “Taken together, the gut microbiota and serum metabolite profiles in individuals with prediabetes showed significantly distinguished features among the four clusters based on multivariate clustering.” (Line 238-240, Page 9 in Version 2).\n(3) Revised the sentence “KEGG enrichment analysis revealed significant changes in the aromatic amino acid metabolism ...... following the intervention in Clusters 3 and 4.” (Line 339-343, Page 13 in Version 1) as “KEGG enrichment analysis revealed significant changes in the microbiota related metabolism pathways as aromatic amino acid metabolism and branched-chain amino acid metabolism pathways, following the intervention in Clusters 3 and 4.” (Line 339-342, Page 13 in Version 2).\n(4) Removed the sentence “Based on the U.S. Diabetes Prevention Program cohort study” (Line 356, Page 13 in Version 1).\nAdditionally, we have modified the repetitive descriptions of the results in discussion section. The specific modifications are as follows:\n(1) Revised the first paragraph “In the current study, we recruited 802 subjects ...... from dietary fiber intervention in both the GPD and validation studies.” (Line 420-431, Page 16 in Version 1) as “In the current study, we recruited 802 subjects with prediabetes to conduct the GPD study, which represents the largest randomized, parallel-groups, multicenter clinical intervention involving dietary fiber supplement in prediabetes to date. Improvements in glucometabolism across the four subclusters with distinct metabolic statuses and microbiomes were closely associated with the responsiveness of their gut microbiome to dietary fiber. Using gut microbiome features to predict whether individuals with prediabetes could benefit from dietary fiber intervention may help improve targeted prevention and treatment for prediabetes.” (Line 424-431, Page 16 in Version 2).\n(2) Revised the sentence “By performing a large-scale intervention cohort, we built ...... at an early stage through a non-invasive, microbiota-targeted method.” (Line 491-497, Page 18 in Version 1) as “Therefore, evaluating individual effectiveness before intervention is crucial in clinical practice. The prediction model based on baseline gut microbiota from our work could assist individuals in selecting effective therapeutic strategies at an early stage through a non-invasive, microbiota-targeted method.” (Line 501-505, Page 18 in Version 2)\n4. The authors shown that dietary fiber improved glycemic control in cluster 3 and 4, but lacked mechanistic discussion. It seems that participants in cluster 4 consumes significantly higher dietary fiber, fresh vegetables and fruits, sea food and fermented dairy, but their gut microbiota is still more susceptible to external interventions. The results shown in the manuscript are interesting, but causality between microbiota and glycemic improvements remains unproven.\nReply: We thank Reviewer raising this comment. Since humans lack the genetic capacity to digest and utilize dietary fiber, the improvement in glycemic metabolism induced by dietary fiber is mediated through the gut microbiota. Our previous study has demonstrated that the dietary fiber significantly enriches the specific strains of acetate and butyrate-producing bacteria and increase the levels of acetic and butyric acids in the intestine, which modify the gut environment to inhibit the detrimental bacteria and their metabolites such as indole and hydrogen sulfide. These changes promote intestinal glucagon-like peptide-1 (GLP-1) secretion cascade that improves glucose regulation (Zhao L, et al., Science. 2018, 359:1151-1156). Additionally, in our recent work about identifying core microbiome signature as an indicator of health and potential target for health enhancement, we suggested that dietary fiber promotes the growth of the foundation guild and confirms a balance favoring the foundation guild over the pathobiont guild. Notably, the growth of the foundation guild also produces beneficial metabolites beyond short-chain fatty acids, thereby further promoting host health (Wu G, et al., Cell, 2024, 187:6550-6565). Then, the mechanism of improvement in glycemic control by dietary fiber is dependent on alleviation of the gut microbiota. In the current study, following dietary fiber intervention, only Clusters 3 and 4 exhibited significant changes in overall structure of gut microbiota and increase in the abundance of foundation guild. The responsiveness of gut microbiota in the subjects from Clusters 3 and 4 to dietary fiber intervention is the reason for improved glycemic control.\nAs for why only the microbiota of Clusters 3 and 4 responded to dietary fiber intervention, the reason might be different microbiota compositions exhibit varying capacities to respond to dietary fiber. We showed that distinct baseline gut microbiota compositions across clusters (Figure 2c, Version 2), potentially influenced by their dietary patterns and metabolic states. As noted by the reviewer, subjects in Cluster 4 consumed significantly higher amounts of dietary fiber, fresh vegetables and fruits, seafood, and fermented dairy products. The abundance and diversity of fiber-degrading bacteria in Cluster 4 might be higher, predisposing its microbiota to easy response to fiber intervention. Conversely, Clusters 1 and 2 may lack fiber-degrading bacteria within their microbiota, or the inability of their microbiota to metabolize the specific type of dietary fiber used in the current study. However, the mechanisms of the differential responses of microbiota to dietary fiber among the four clusters require further experimental investigation.\nIn response to the reviewer's suggestion, we have expanded the discussion section to include the discussion of the mechanisms of dietary fiber improves glycemic control and the potential reasons for the differential responses observed among the gut microbiota of 4 clusters. The specific modifications are as follows (Line 479-496, Page 18 in Version 2):\n“Since humans lack the genetic capacity to digest and utilize dietary fiber, the improvement in glycemic metabolism induced by dietary fiber is mediated through the gut microbiota. Our previous study has demonstrated that dietary fiber selectively promotes a specific group of bacteria to produce acetate and butyrate, leading to increased levels of GLP-1 and enhanced insulin secretion, which is the mechanisms of dietary fiber improves glycemic control (Zhao L, et al., Science. 2018, 359:1151-1156). Additionally, in our recent work about identifying core microbiome signature as an indicator of health and potential target for health enhancement, we suggested that dietary fiber promotes the growth of the foundation guild and confirms a balance favoring the foundation guild over the pathobiont guild, thereby further promoting host health (Wu G, et al., Cell. 2024, 187:6550-6565). The mechanism of improvement in glycemic control by dietary fiber is dependent on alleviation of the gut microbiota. The responsiveness of gut microbiota in the subjects from Clusters 3 and 4 to dietary fiber intervention is the reason for improved glycemic control. The gut microbiota in the subjects from Clusters 1 and 2 may lack fiber-degrading members, or the inability of their microbiota to metabolize the specific type of dietary fiber used in the current study. However, the mechanisms of the differential responses of microbiota to dietary fiber among the four clusters require further experimental investigation.”\n5. Lastly, I would like to suggest the authors adopt a neutral, academic tone in their phrasings. For example, in lines 518-520, While the intervention showed short-term benefits of dietary fiber supplements, it’s risky to make strong claims about long-term impact or precision medicine without the 18-month progression data. Suggest to soften the language to reflect that these are promising early findings but need further validation.\nReply: Thanks for this valuable suggestion. We have modified the assertive statements and soften the language to reflect that these are promising early findings but need further validation. All modifications have been marked in the manuscript. The issue about “risky to make strong claims about long-term impact or precision medicine without the 18-month progression data”, we have rewritten the related part in limitation (Line 509-518, Page 19 in Version 2) as “One limitation of this study is that the follow-up period was not long enough to observe the influence of our dietary fiber supplement on the rates of progression from prediabetes to diabetes. The GPD study was designed as a 6-month intervention with an additional 18-month follow-up, with one of the primary outcomes being the annual rates of progression from prediabetes to diabetes. However, due to the impact of the COVID-19 pandemic on our 18-month follow-up, we relied on data from 6-month intervention, using changes in HbA1c as the primary outcome. Therefore, the long-term impact of dietary fiber interventions on prediabetic populations and the predictive efficacy of gut microbiota models for such effects warrant further investigation through longitudinal trials.”\nWe sincerely thank the reviewer for the meticulous review of this manuscript. In response to the valuable suggestions and questions provided by reviewer, we have provided detailed responses to each point below.\nPlease note that the Version 1 means original version, Version 2 means revised version in our reply below.\nMajor\n1. There are many comparisons between post hoc, data driven subgroups, which may reflect overfitting of the data from the trial. To test whether the data is overfit to this trial/these participants, it is critical that the investigators attempt to either: 1) split their participants into a discovery cohort and validation cohort, and then test whether the sub-group findings in the discovery cohort are replicagted in the validation cohort; or 2) use external RCTs to validate the findings. The investigators indeed appear to take approach #2, however, the details of the cohorts used to validate the findings, and exactly what findings were validated needs additional information for the readers/reviewers to be able to determine whether findings are indeed transportable.\nReply: Thanks for the valuable suggestion. The two validation cohorts were derived from our previously published studies. Detailed information regarding cohort recruitment, sample collection procedures, and intervention protocols is available in the original publications. We provide citations for these articles below. For your convenience, detailed information regarding the validation cohorts is provided below.\nValidation cohort 1: Lin X, Wang C, Liu B, Zhu Y, Zhai R, Zhang C. Temporal response patterns of human gut microbiota to dietary fiber. iMeta, e70046 (2025). Doi:10.1002/imt2.70046.\nGenerally, validation cohort 1 is a 2-week dietary fiber intervention study for subjects with T2DM or obesity. The study was registered in the Chinese Clinical Trial Registry: ChiCTR2100046478. All enrolled subjects (n=20) underwent a 14-day baseline follow‐up and then underwent a 2‐week dietary fiber intervention, consuming 18 g/day in the low‐dose phase (Day 14–Day 20) and 36 g/day in high‐dose phase (Day 21–Day 27) before meals, while maintaining their usual diet. During the low‐dose phase, participants took 6 g of insoluble fiber before breakfast and 6 g of mixed fibers (3 g insoluble, 3 g soluble) before both lunch and dinner. Fiber dose doubled in the high‐dose phase. Finally, 19 subjects completed 14 days intervention. Blood samples were collected on the first day of baseline and last day of intervention to determined FPG and HbA1c. Additionally, subjects began wearing a continuous glucose monitoring device (Abbott FreeStyle Libre, Chicago, USA) for continuous blood glucose (CBG) monitoring on the first day of baseline and continued until the end of fiber intervention. Fecal samples were collected every day during the experiment, we employed baseline fecal samples for DNA extraction, followed by 16S rRNA gene sequencing.\nValidation cohort 2: Wu G, et al. A core microbiome signature as an indicator of health. Cell 187, 6550-6565. e6511 (2024). Doi: 10.1016/j.cell.2024.09.019.\nGenerally, validation cohort 2 is a 3-month dietary fiber intervention study with 1-year follow-up for T2DM subjects. The study was registered in the Chinese Clinical Trial Registry: ChiCTRIPC-14005346. The intervention group enrolled 79 subjects who received a high dietary fiber diet (WTP diet) intervention differing from the GPD study for 3 months. The WTP diet, based on whole grains, traditional Chinese medicinal foods, and prebiotics, included three ready-to-consume pre-prepared foods. Intervention group stopped WTP diet intervention at the end of the third month. Then, continued a 1-year follow-up. The 67 subjects completed both the 3-month intervention and the 1-year follow-up. Blood glucose was measured at baseline, 3-month intervention, and 1-year follow-up. The fasting venous blood was used to measure HbA1c, fasting blood glucose, a 3-h meal tolerance test (Chinese buns containing 75 g of available carbohydrates; MTT test) was conducted, and the postprandial venous blood samples at 30, 60, 120, and 180 min were collected. Fecal samples were collected at baseline, 3-month intervention, and 1-year follow-up.\n2. For randomization, the authors mentioned stratifying by BMI and 2-hour post-OGTT blood glucose. How about clinical center? What is the distribution of intervention and control by clinical center? Based on Figure 2, clinical center has the largest impact on baseline microbiome and gut metabolome.\nReply: We apologize for the absence of this detail in the methods section. The randomization of our GPD cohort was performed separately at each clinical center, with an allocation ratio of 1:1 between the intervention and control groups at each clinical center, and this information has now been added to the Methods section (Line 591-595, Page 21-22 in Version 2). “The central randomization system used to issue random numbers, and subjects randomly assigned to intervention and control groups with 1:1 ratio. Randomization was done using dynamic minimization method and stratified according to BMI (<25kg/m2, ≥25kg/m2) and 2-hour post-OGTT blood glucose (<7.8mol/L, ≥7.8mol/L). The randomization was performed separately at each clinical center.”\nThe distribution of intervention (W) and control (U) groups in each clinical center has been provided below. As shown in Figure 1 (Supplementary Figure 1d in Version 2), the number of subjects in U and W groups was approximately 1:1 at each clinical center. This figure has been added to the Supplementary Figure 1d.\nFigure 1 The distribution of intervention (W) and control (U) groups in each clinical center (Supplementary Figure 1d in Version 2).\n3. Could you describe the batch in which the samples were processed and comment on the potential impact of batch effect? For the multi-variate clustering, I assume the linkage for the hierarchical clustering was Euclidean (If is, please add it in the methods).\nReply: We thank the Reviewer for raising this important point. On the day of follow-up at each clinical center, subjects collected fecal sample using same sampling protocol and sample collection kits uniformly provided by the central laboratory (Shanghai Jiao Tong University). The medical personnel at each center collected blood samples using same sampling protocol. After collecting, all samples were uniformly transported to the central laboratory for cryopreservation. After completion of all samples collection, the central laboratory personnel uniformly performed DNA extraction, amplification, and sequencing for all fecal samples using the same batch of DNA extraction kits. Similarly, serum samples underwent uniform preprocessing and metabolomic analysis by the central laboratory personnel. Therefore, the observed differences in microbiota and metabolomic profiles are not attributable to batch effects. We have supplemented the details that all samples were uniformly processed at the central laboratory in Methods section (Line 636-638, Page 23 in Version 2). “Both preprocessing and detection procedures for all samples were conducted at the central laboratory (Shanghai Jiao Tong University).”\nFor the multi-variate clustering, the hierarchical clustering was based on Euclidean distance, and we have added it to the Method section (Line 622-623, Page 23 in Version 2). “Optimal cluster number was determined through hierarchical clustering (Based on Euclidean distance) and silhouette widths.”\n4. Given the k-means clustering is sensitive to the choice of initiation values, what is your initiation values for the k-means clustering? Have you tried randomized initiation for the k-means clustering? Please add more details for the k-means clustering for reproducibility.\nReply: We appreciate the Reviewer’s identification of the omission in our methodology description. As the Reviewer mentioned, the k-means clustering is sensitive to the choice of initiation values. The clustering is considered stable when the Within-Cluster Sum of Squares (WCSS) – defined as the sum of squared distances between each data point and its centroid – is minimized. We conducted 100 clustering runs with random initiation values, computed the WCSS for each clustering, and selected the clustering with minimal WCSS as the final clustering outcome. We have added the details about this issue in the Methods section (Line 624-629, Page 23 in Version 2).\n5. For the ASV, please include the sequence for each ASV in a supplemental table.\nReply: Thanks for the valuable suggestion. We have provided a file maned as “ASV Sequences” containing the sequences of all ASVs in the Supplemental data.\n6. For all packages or algorithms, please add citations.\nReply: Thanks for the valuable suggestion. We have added citations of all packages or algorithms in the method section. These citations have been shown as follows.\nPackages or algorithms References\nMaechler M. Cluster: cluster analysis basics and extensions. R package version cluster (version 2.1.4)\n2.0. 7–1 (2018).\nR Core Team. R: A language and environment for statistical computing. R stats (version 4.2.1)\nFoundation for Statistical Computing (2013).\nQIIME2 pipeline Hall M, Beiko RG. 16S rRNA gene analysis with QIIME2. Microbiome analysis: (version 2021.4) methods and protocols. Springer (2018).\nRobinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for edgeR (version 3.32.1) differential expression analysis of digital gene expression data. Bioinformatics 26, 139-140 (2010).\nShi Y, Zhang L, Do K-A, Peterson CB, Jenq RR. aPCoA: covariate adjusted aPCoA (version 1.3)\nprincipal coordinates analysis. Bioinformatics 36, 4099-4101 (2020).\npairwiseAdonis (version Martinez Arbizu P. pairwiseAdonis: Pairwise multilevel comparison using 0.4) adonis. R package version 0.4 (2020).\nWatts SC, Ritchie SC, Inouye M, Holt KE. FastSpar: rapid and scalable Fastspar (version 1.0.0)\ncorrelation estimation for compositional data. Bioinformatics 35, 1064-1066 (2019).\nPICRUSt2 (version Douglas GM, et al. PICRUSt2 for prediction of metagenome functions. Nature 2.4.1) biotechnology 38, 685-688 (2020).\nMaaslin2 (version Mallick H, et al. Multivariable association discovery in population-scale meta- 1.10.0) omics studies. PLoS computational biology 17, e1009442 (2021).\nKe G, et al. Lightgbm: A highly efficient gradient boosting decision tree. lightgbm (version 3.3.5)\nAdvances in neural information processing systems 30, (2017).\nMetaboAnalyst analysis Pang Z, et al. MetaboAnalyst 5.0: narrowing the gap between raw spectra and platform (version 5.0) functional insights. Nucleic acids research 49, W388-W396 (2021).\nRobin X, et al. pROC: an open-source package for R and S+ to analyze and pROC (version 1.18.5)\ncompare ROC curves. BMC bioinformatics 12, 77 (2011).\nshapviz (version 0.9.5) Mayer M. shapviz: SHAP Visualizations. R package version 0.9.5 (2024).\nThiele C, Hirschfeld G. cutpointr: improved estimation and validation of optimal cutpointr (version 1.1.2)\ncutpoints in R. Journal of Statistical Software 98, 1-27 (2021).\n7. I noticed that in the microbiome PCoA, the PC1, PC2 showed the same number of explained variance for Fig2 (baseline), Supp Fig5 (baseline) and Supp Fig 7 (baseline + M6). Do authors put the wrong figures here? Please double check the figures and data.\nReply: We apologize for our mistake. Both Figure 2c and Supplementary Figure 5 display aPCoA results of baseline microbiota, therefore, the explained variance of PC1 and PC2 of the two figures should be same. Supplementary Figure 7 represents aPCoA results of baseline and post-intervention microbiota. We mistake the explained variance of Figure 2c and Supplementary Figure 5 and have changed to the correct explained variance in revised figures. The correct figures are shown as follows Figures 2 and 3 (Figure 2c and Supplementary Figure 5d in Version 2).\nFigure 2 aPCoA scores plot of baseline gut microbiota (Figure 2c in Version 2).\nDifferences in β-diversity of baseline gut microbiota across the clusters were assessed using the adjusted principal coordinates analysis (aPCoA) of Aitchison distance. Data are shown with mean ± SEM of each cluster.\nFigure 3 aPCoA scores plot based on baseline gut microbiota (Supplementary Figure 5d in Version 2).\nDifferences in β-diversity of baseline gut microbiota across the clusters were assessed using the adjusted principal coordinates analysis (aPCoA) of Aitchison distance. Data are shown with all sample points of each cluster.\nTo avoid similar mistakes, we have double checked all figures, and no additional errors were found.\n8. Supp Fig 5 showed the distribution of clinical centers was different across the multi-variate clusters. Could the difference in patients characteristics explain the difference in the responses to interventions? For example: Cluster 4 was dominated by site 7, which is a liver cancer institute according to the supplemental methods. We saw changes in ALT and AST in this cluster only. Could it be because of there were more patients with liver problems in cluster 4?\nReply: We thank the Reviewer for raising this important point. The number of subjects recruited varied across the eight clinical centers, with clinical center 7 enrolling the most subjects (n=215). Aside from Cluster 4, clinical center 7 also contributed the largest proportion of subjects within Cluster 2. Nevertheless, significant differences were observed between Cluster 2 and Cluster 4 in physiological states, gut microbiota, and metabolomic profiles. The observed distinctions between clusters are not attributable to variations among clinical centers, but rather to inherent differences in the metabolic states of the clusters.\nThe \"Liver Cancer Institute\" is one of the official institutional names of the clinical center 7. The prediabetic subjects were recruited from residents within catchment areas of clinical center 7. These subjects have no association with liver cancer, and individuals exhibiting abnormal liver function indicators were excluded. Concentrations of alanine transaminase or aspartate transaminase >2.0 times of the upper normal limit (50U/L for alanine transaminase; 35U/L for aspartate transaminase) is one of the exclusion criteria (Line 118-120 of Supplementary Methods, Version 2). Among the four clusters, Cluster 2 exhibited the highest levels of ALT and AST (Figure 1c and Supplementary Table 5, Version 2). In Cluster 2, 8.5% and 11.6% of subjects exhibited elevated ALT and AST levels exceeding the upper normal limit, respectively. In Cluster 4, 6.1% and 6.7% of subjects exhibited elevated ALT and AST levels exceeding the upper normal limit, respectively. This indicates that the improvement in ALT and AST levels in Cluster 4 is not attributable to it having the highest ALT and AST levels or more subjects with liver problems.\n9. For the comparison of W and U group in each multi-variate cluster, have you adjusted for any co-variates (e.g., clinical center)? For post-hoc analysis of clinical trials, you should adjust for any potential confounders given the balance of co-variates between the intervention and control group may not hold in each cluster.\nReply: Thanks for the valuable suggestion. At baseline, no significant differences were observed between U and W groups in clinical parameters, clinical center, dietary patterns, and disease history (Supplementary Table 2, Version 2).\nSimilarly, at baseline, no significant differences were found in clinical parameters (Supplementary Tables 14 and 15, Version 2), clinical center (Supplementary Table 8, Version 2) and dietary patterns (Supplementary Table 8, Version 2) between U and W groups in each subcluster. Therefore, we did not adjust for any co-variates for the comparison of W and U groups in each cluster.\n10. Have you run any statistical test for comparing the change of Guild2 to Guild 1 ratio in the W and U group in each clusters?\nReply: We apologize for confusion may have caused. We compared the ratios of Guild 2 to Guild 1 between the W and U groups in each cluster using the Mann-Whitney U test and compared the ratios of Guild 2 to Guild 1 between baseline and after intervention in W or U group using Wilcoxon paired signed-rank test. To better illustrate the changes in abundance of Guild 1 and Guild 2 in the U and W groups within each cluster after the intervention, we also compared the abundance change values (%change) of Guild 1 and Guild 2 between the W and U groups within each cluster using Mann-Whitney U test. In U group (without fiber intervention), the ratio of Guild 2 to Guild 1 in Cluster 1 was significantly decreased. In U groups of other 3 clusters, the ratios of Guild 2 to Guild 1 exhibited decreased trends (Figure 4). After dietary fiber intervention, in the W groups of Cluster 1 and Cluster 2, the ratios of Guild 2 to Guild 1 remained decreased trends. However, this trend was altered by dietary fiber intervention in the W group of Clusters 3 and 4 (Figure 4). These alterations were due to that the abundance change value of Guild 2 was significantly higher in the W group compared to the U group in Cluster 3; In cluster 4, the abundance change value of Guild 1 was significantly lower in the W group than in the U group, while that of Guild 2 was significantly higher in the W group (Figure 4). This result indicates that dietary fiber only confirmed a balance favoring Guild 2 over Guild 1 in W groups of Clusters 3 and 4. We have added the figure showing the abundance change values of Guild 1 and Guild 2 to Figure 4d in Version 2, meanwhile, we have modified the description regarding the changes in the abundances of Guild1 and Guild2 in Version 2 (Line 313-320, Page 12).\n“During the 6-month intervention, the ratios of Guild 2 to Guild 1 remained decreased trends in both the W and U groups of Clusters 1 and 2, as well as in the U groups of Clusters 3 and 4. However, this trend was altered by dietary fiber intervention in the W group of Clusters 3 and 4. These alterations were due to that the abundance change value of Guild 2 was significantly higher in the W group compared to the U group in Cluster 3; In cluster 4, the abundance change value of Guild 1 was significantly lower in the W group than in the U group, while that of Guild 2 was significantly higher in the W group (Fig. 4d).”\nFigure 4 The change of Guild 2 to Guild 1 abundance ratio and the abundance change values of Guild 1 and Guild 2 (Figure 4d in Version 2).\n%Change = (abundance after-intervention - baseline abundance) / baseline abundance * 100%.\nData was shown with mean ± SEM. Intra-group comparations used Mann-Whitney U test and inter-group comparation between time points used Wilcoxon matched-pair signed-rank test. *P < 0.05, ***P < 0.001.\n11. In the MFS analysis, what about patients with scores between 17 and 25? Could the author add a cut point x between 17 and 25? There was lower probability of responses (<50%) in (17, x), higher probability of responses in (x - 25).\nReply: Thanks for the valuable suggestion. The glycemic benefit score for each subject was calculated based on whether FPG, PBG and HbA1c could improve after intervention, a score of 1 was assigned for each parameter that showed improvement, resulting in glycemic benefit score ranging from 0 to 3. When MFS of subjects were ≤17, the glycemic benefit score had a 99% probability of being 0 or 1. While MFS of subjects were ≥ 25, the glycemic benefit score had a 99% probability of being 2 or 3. Subjects with MFS values between 17 and 25, encompassed subjects with glycemic benefit scores from 0 to 3, comprising 10 subjects with a score of 0, 33 subjects with a score of 1, 132 subjects with a score of 2, and 14 subjects with a score of 3. The cutoff value between 17 and 25 was calculated using the maximize metric method (R package “cutpointr”), the value is 20.4, which is the cut point suggested by Reviewer. We have added this cutoff value in Figure 5f of Version 2 (shown as Figure 5 below), which is the MFS value corresponding to the intersection point of the two lines in Figure 5f. When MFS of subjects were > 20.4, the glycemic benefit score had more than 50% probability of being 2 or 3. The objective of developing the predictive model and identifying the MFS cutoff was to guide precision interventions for prediabetes; therefore, we hope that the probability of glycemic benefit represented by the cutoff value is above 99%.\nFigure 5 The MFS cutoff values that enable accurate distinction between low and high responders (Figure 5f in Version 2).\nThe solid line and shaded region in the corresponding color represents the mean value and standard deviation across 99 iterations, respectively.\n12. Both validation studies included T2DM patients instead of prediabetic adults. Is it possible to find validation studies with prediabetic adults? It not, please acknowledge this as a limitation.\nReply: Thanks for the valuable suggestion. In order to find the validation cohort for our prediction model, we conducted a search on PubMed for all studies on dietary fiber intervention in prediabetes from 1980 to 2025 using the key words including Fiber, Prebiotics, Prediabetes, Impaired glucose tolerance, Impaired fasting glucose, and identified a total of 15 studies (shown in the summary table below). To validate our microbiota-based model for predicting glycemic benefits, the validation cohort was required to include both gut microbiota data and measurements of fasting plasma glucose, OGTT 2-h plasma glucose, and HbA1c. However, none of the studies met our requirements. Thus, we used the two dietary fiber intervention cohorts for T2DM subjects that we previously published as the validation cohorts.\nOur validation results demonstrated that the predictive model, constructed based on the gut microbiota of prediabetes, remained effective in predicting glycemic benefits from fiber interventions in diabetes. This is because of the mechanisms by which gut microbiota respond to dietary fiber and improve glycemic control is consistent in both prediabetic and diabetic individuals. We have elucidated the reason for using the two cohorts from our previously published studies as the validation cohorts in the Result section (Line 395-399, Page 15 in Version 2). “Subsequently, we need to verify the efficacies of classification models and MFS in independent cohorts possessing gut microbiota, FPG, PBG, and HbA1c data. Because we could not find any available datasets from published fiber intervention with prediabetes, two independent cohorts from our previously published studies with T2DM were served as validation cohorts.” Meanwhile, as suggested by the Reviewer, we have acknowledged this as a limitation (Line 518-524, Page 18 in Version 2). “Secondly, given that the gut microbiome is influenced by region, ethnicity, dietary pattern, and disease factors (Andreu-Sánchez S, et al., Cell. 2025, 188:3942-3959.e9), both the training and validation cohorts for our prediction model were derived from Eastern China, and the validation cohorts primarily consisted of T2DM patients. Thus, further validation in larger cohorts encompassing diverse regions, ethnicities, and including more prediabetic individuals is required to confirm the generalizability of our model.”\nGut microbiota Blood glucose\nSubjects Intervention food References (doi)\ndata data\nCombined Galacto-\nOligosaccharides and\n16S rRNA gene\nPrediabetes Bifidobacterium FPG; HbA1c 10.3390/nu16142205.\nsequencing\nbreve-Derived\nPostbiotic\nPrediabetes Fiber-Rich Diet None FPG; PBG 10.1210/jc.2016-3722.\nObese Galacto- 16S rRNA gene\nFPG; PBG 10.1053/j.gastro.2017.03.051.\nPrediabetes Oligosaccharides sequencing\nPrediabetes Resistant starch None FPG; PBG 10.1093/ajcn/nqy121.\nIsomaltulose and FPG; PBG;\nPrediabetes None 10.3390/nu14122386.\nSoluble Dietary Fiber HbA1c\nElderly FPG; PBG;\nMixed dietary fiber None 10.1016/j.nut.2024.112580.\nPrediabetes HbA1c\nLactobacillus\nrhamnosus HN001\nPrediabetes and cereal enriched None FPG; HbA1c 10.1136/bmjopen-2021-055214.\nwith oat-derived beta-\nglucan\nObese Fibre-rich diet or\nNone FPG; PBG 10.1007/s00394-015-0831-3.\nresistant starch\nPurified fiber extract FPG; PBG;\nPrediabetes None 10.3390/nu11102385\nderived from oat hulls HbA1c\nChitosan FPG; PBG;\nPrediabetes None 10.1039/c4fo00469h\noligosaccharide HbA1c\nFPG; PBG;\nPrediabetes Inulin None 10.3390/nu13093235\nHbA1c\nPrediabetes Resistant starch None FPG; HbA1c 10.1093/ajcn/nqy121\nPrediabetes and 16S rRNA gene\nXylooligosaccharide FPG; PBG 10.3389/fphys.2015.00216\nhealthy subjects sequencing\nRed Raspberry and\nImpaired fasting Metagenomic\nFructo- FPG; PBG 10.1093/jn/nxac037\nglucose sequencing\nOligosaccharide\nPrediabetes Raw bran None FPG 10.2337/diacare.3.1.46.\nFPG, fasting plasma glucose. PBG, OGTT 2-h plasma glucose. HbA1c, hemoglobin A1c.\n13. Given this study and both validation studies were done in East region of China, please discuss about the generalization and need for external validation in the limitation. Investigators should note in the limitations that PiCrust may have low validity for predicting microbial function.\nReply: Thanks for the valuable suggestion. The gut microbiota varies significantly among different regions (Andreu-Sánchez S, et al., Cell. 2025, 188:3942-3959.e9). Even within Guangdong Province in China, regional factors can still be observed to have an impact on the gut microbiota (He Y, et al., Nat Med. 2018. 24:1532-1535). In our study, the glycemic improvement by dietary fiber and the predictive model for predicting glycemic benefits were all based on the microbiota. Therefore, whether our conclusions are also valid in different regions requires further verification through cohorts from different regions. As the Reviewer' suggestions, we have added the discussion about the generalizability of our prediction model and need for external validation in the Limitation section (Line 518-524, Page 18 in Version 2). “Secondly, given that the gut microbiome is influenced by region, ethnicity, dietary pattern, and disease factors (Andreu-Sánchez S, et al., Cell. 2025, 188:3942-3959.e9), both the training and validation cohorts for our prediction model were derived from Eastern China, and the validation cohorts primarily consisted of T2DM patients. Thus, further validation in larger cohorts encompassing diverse regions, ethnicities, and including more prediabetic individuals is required to confirm the generalizability of our model.”\nThe approach of predicting bacterial functions based on 16S rRNA sequencing data does have limitations, for instance, the predictions are biased toward existing reference genomes, which means that rare environment-specific functions are less likely to be identified, and predictions cannot provide resolution to distinguish strain-specific functionality (Douglas GM, et al., Nat Biotechnol. 2020. 38:685-688). Nevertheless, the guild functions predicted by PICRUSt2 in our study are consistent with those identified by Wu G, et al. (Cell. 2024. 187:6550-6565) through metagenomic data analysis, which indicates that our results based on PICRUSt2 predictions are reasonable. Of course, a more precise analysis of the microbiome functions is still needed. Thus, as the Reviewer' suggestions, we added a discussion on the limitations of PICRUSt2 in predicting bacterial functions in the Limitations section (Line 524-526, Page 19 in Version 2). “Furthermore, the approach of predicting bacterial functions based on 16S rRNA sequencing data exhibit constrained accuracy and more precise analysis of the microbiome functions is still needed.”\nMinor\n14. Introduction (line 93): Citation 8 and 9 are the same, please correct\nReply: Thanks for pointing out the error. We have corrected citation 9.\nCitation 8: Ahlqvist E, et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol 6, 361-369 (2018).\nCitation 9: Zaharia OP, et al. Risk of diabetes-associated diseases in subgroups of patients with recent-onset diabetes: a 5-year follow-up study. Lancet Diabetes Endocrinol 7, 684-694 (2019).\n15. Introduction (line 113-115): Add citation for “A specific group of bacteria in the gut microbiota utilizes dietary fiber to produce acetate and butyrate, leading to increased levels of GLP-1 and enhanced insulin secretion”.\nReply: We have added the citation in Version 2 (Line 115, Page 5) as citation 19. The citation is presented below.\nZhao L, et al. Gut bacteria selectively promoted by dietary fibers alleviate type 2 diabetes. Science 359, 1151-1156 (2018).\n16. Results (line 160): By saying ‘similar glucose levels’, do you mean the WHO/ADA-based participant categories? Please be more accurate in wording.\nReply: We apologize for our confusing wording. Subjects with similar glucose levels mean the those who fall into the same glycemic subtype according to the criteria of WHO and ADA. We have changed ‘similar glucose levels’ to ‘same WHO/ADA-based subgroup’ in Version 2 (Line 160, Page 7).\n17. Results (line 241): ‘accurately’ seems too vague here. What do you mean by ‘accurately classifying’\nReply: We apologize for our confusing wording. Accurately classifying means multivariate clusters. We have modified the paragraph in Version 2 (Line 238-240, Page 9). “Taken together, the gut microbiota and serum metabolite profiles in individuals with prediabetes showed significantly distinguished features among the four clusters based on multivariate clustering.”\n18. Results (line 286): By saying ‘in the part of subjects’, do authors mean a specific part of subjects?\nReply: We apologize for our confusing wording. “in the part of subjects” means subjects with large change distances in microbiota after-intervention. We revised this sentence as “Subjects with large change distances in microbiota after-intervention” in Version 2 (Line 282-283, Page 11).\n19. Results (line 360): Change ‘improvement’ to ‘improve’\nReply: We apologize for this spelling error. We have changed ‘improvement’ to ‘improve’ in Version 2 (Line 356, Page 13).\n20. Results (line 370-374): Report Delong test p value.\nReply: We have reported the P values of Delong test in Version 2 (Line 370-373, Page 14). “Comparable predictive power among models with microbiome data, metabolome data and combined metabolome-microbiome data (microbiome vs. metabolome, P =0.754; microbiome vs. combined, P= 0.528; metabolome vs. combined, P = 0.631; Delong test)”\n21. Figure 2 and Supp Figure 5: Is it possible to combine Supp Figure 5d with Fig 2c (Supp Figure 5e with Fig 2g)? Just lay out all the data points on the centroid plot. Having both seems redundant.\nReply: Thanks for your valuable suggestion. Figure 2c and 2g display the aPCoA scores plot with the mean ± SEM for each cluster, which reflects the inter-cluster differences of microbiota. Supplementary Figure 5d and 5e show the aPCoA scores plot with all sample points included, which reflects the distribution of different individuals within each cluster. These two figures show the results of the aPCoA analysis from different perspectives; therefore, we have retained the two figures.\nWe sincerely appreciate your time and effort in reviewing our manuscript."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The revised manuscript by the authors reads well and is significantly improved from the last submission. I would recommend it accepted for publication.", "reviewer_2": "Excellent job in responding to all reviewer comments. No further comments.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

Decision letter