Twenty male college basketball players from Hubei University of Technology were recruited in accordance with the principles outlined in the Helsinki Declaration. All participants were classified as second-level players in the Chinese University Basketball Association (CUBA) and provided written informed consent before the study. In this study, a double-blind methodology was employed to ensure that both the researchers and the participants were unaware of the group allocations. The participants were randomly assigned to either a high-dose group (n = 10) or a low-dose group (n = 10), with the sole difference between the groups being the dosage of the dietary fibre administered. Importantly, apart from this dosage variation, the two dietary fibre foods were identical in their composition, sensory characteristics, and nutritional profiles. The study was approved by the Ethics Committee of the Institute of Sports Medicine affiliated with the General Administration of Sport of China (Ethics Approval No. 202112).

The inclusion criteria were as follows: (1) healthy male college basketball players aged 17–25 years; (2) no history of diabetes, digestive or metabolic disorders, or other significant systemic diseases; (3) no prior or current sports-related injuries; (4) no use of medications or dietary supplements before the trial; (5) no allergies to oats, legumes, or dairy; and (6) no lactose intolerance. Participants were excluded if they failed to meet any single criterion of the inclusion standards.

Two dietary fibre supplements were developed. Both supplements from the same sources: oatmeal, rice flour, and inulin, the only difference between the groups was the quantity of fibre intake. The high-dose supplement contained 7.6 g of dietary fibre per 100 g, whereas the low-dose supplement contained 3.6 g of dietary fibre per 100 g. Apart from the fibre content, the supplements were identical in composition (Table 1).

The participants were instructed to avoid dairy products and high-fibre foods for five days before the experiment while maintaining their usual dietary habits. The study lasted for 8 weeks, during which each participant consumed 90 g of fibre-enriched food daily as breakfast. The food was dissolved in 180 mL of warm water at a temperature below 50 °C. The participants in the high-dose group received 6.84 g of dietary fibre per serving, whereas those in the low-dose group received 3.24 g. During the intervention, all the participants adhered to their regular training schedules, lifestyles, and dietary routines. They were instructed to cease training one day before testing and to fast after dinner, with only water permitted (Fig. 1).

Fig. 1.

Nutritional intervention protocol. (a) Implementation process of the trial protocol. (b) Subject allocation and dietary fibre supplementation protocol.

Throughout the intervention, nutritionists provided individualized meal plans, education, and monitoring to ensure compliance, including recording the types of products consumed and calories. The participants followed a standardized diet, with approximately 55%, 30%, and 15% total caloric intake derived from carbohydrates, fats, and proteins, respectively. Players maintain a daily energy balance by matching caloric intake to their basal metabolic rate, the thermic effect of food, and exercise energy expenditure, with no requirement to gain or lose weight.

During the experimental period, all participants maintained their regular training schedules and intensity. Training sessions were held Monday to Friday from 6:30 PM to 8:00 PM, lasting 90 minutes each, with weekends reserved for rest. Exercise intensity was categorized as moderate to high, with the maximum heart rate remaining below 190 beats per minute (See Appendix A).

The standardized meal components were carefully weighed and recorded via the “Athlete and General Population Dietary Analysis and Management System” software developed by the Sports Medicine Research Institute of the General Administration of Sport of China. This software was used to calculate participants’ average daily dietary fibre intake to ensure that athletes adhered to their dietary plan. Other recording methods are also applicable under the same principles. The detailed data are provided in Table 2.

Outcome data were collected at three time points: baseline (pre-experiment), mid-intervention (week 4), and postintervention (week 8). These included maximum strength tests, basketball-specific performance tests, and weekly subjective ratings of perceived exertion (RPE). Additionally, body composition, fatigue recovery indices, glycaemic and lipid metabolism markers, aerobic capacity, and anaerobic capacity were assessed. The participants were instructed to cease training one day prior to testing, fast after dinner, and consume only water. The specific testing protocols are as follows:

2.6.5 Athletic Performance Testing

A. Aerobic Capacity Testing

The participants performed treadmill exercises via a portable cardiopulmonary function testing system (Cortex Metamax 3B, Cortex Biophysik GmbH, Leipzig, Germany). The running speed ranged from 11.2 to 13.6 km/h, with a 2% increase every 2 minutes until exhaustion. The maximal oxygen uptake (VO₂max) was recorded at the point of fatigue, defined by a decrease in VO₂max despite continued effort.

B. Anaerobic Capacity Testing

Anaerobic performance was assessed via a 30-second Wingate test on a power bicycle (Monark 839E, Monark Exercise AB, Vansbro, Sweden). The maximum and average anaerobic powers were calculated.

C. Maximum Strength Testing (1RM Squat)

The participants warmed up with 5–10 repetitions using an unloaded barbell, followed by a 1-minute rest. Additional sets of 3–5 repetitions were completed with progressively increasing loads, followed by a 2-minute rest. The one-repetition maximum (1RM) squat was determined after a final attempt.

D. Basketball-Specific Performance Testing

The basketball-specific tests included jumping tests (single-leg vertical jump, double-leg vertical jump, standing double-touch height, and triple frog jump), speed tests (half-court triangle run), and agility tests (variable-distance shuttle run) (Fig. 2).

Fig. 2.

Basketball-specific performance testing. (a) Half-court triangle running. (b) Variable-distance shuttle run.

Statistical analyses were conducted via SPSS 27.0 (IBM Inc., Chicago, IL, USA). The data are presented as the means $\pm$ standard deviations (M $\pm$ SDs). Only baseline and postintervention data were included in the analysis. Prior to performing t tests, all variables were tested for normality and confirmed to meet normal distribution assumptions.

Independent-samples t tests were used to compare the baseline characteristics of the two groups, as well as intergroup differences in preintervention body composition, fatigue recovery indicators, glucose and lipid metabolism markers, RPE, and physical performance. Postintervention intergroup differences were assessed by analysing changes (postintervention minus baseline) via independent-samples t tests. Within-group changes were evaluated via paired samples t tests. A p value of 0.05 was considered to indicate statistical significance.

Spearman correlation analysis was used to examine the relationships between postintervention gut microbiome composition and selected biochemical markers. The correlation strength (r) was categorized as follows: $>$0.8, very strong; 0.6–0.8, moderately strong; 0.3–0.5, moderate; and 0.3, weak. A p value of 0.05 was considered to indicate statistical significance [24].

Baseline characteristics were compared between the two groups. No significant differences were observed, confirming comparable baseline conditions (Table 2).

Both groups were provided with standardized meals during the intervention. The average dietary fibre intake from these meals was 16.3 $\pm$ 7.8 g/day in both groups. When dietary fibre supplements were included, the high-dose group consumed a total of 23.1 $\pm$ 7.8 g/day, whereas the low-dose group consumed 19.5 $\pm$ 7.8 g/day (Table 2).

At baseline, there were no significant differences in body weight, body fat percentage, fat mass, or lean body mass between the high-dose group and the low-dose group. After the intervention, no significant differences in these indicators were observed between the two groups overall (Table 3).

In both the high-dose and low-dose groups, body weight, body fat percentage, fat mass, and lean body mass were significantly lower after the intervention.

At baseline, testosterone, cortisol, and the testosterone/cortisol ratio were similar between the high-dose and low-dose groups (p = 0.368, 0.357, and 0.540, respectively). After the intervention, no significant differences in these indicators were observed between the two groups. In the high-dose group, testosterone and cortisol levels significantly decreased after the intervention (p = 0.040, 0.001), whereas the testosterone/cortisol ratio significantly increased (p = 0.048). In the low-dose group, only cortisol significantly decreased (p = 0.016), whereas the other indicators did not significantly change (testosterone and testosterone/cortisol ratio: p = 0.077 and 0.107, respectively) (Table 4).

At baseline, CK levels were similar between the high-dose and low-dose groups (p = 0.230). After the intervention, there were no significant differences in creatine kinase levels between the two groups. Additionally, no significant changes in creatine kinase levels were observed between the high-dose and low-dose groups (p = 0.857 and 0.601, respectively).

At baseline, RPE was similar between the high-dose and low-dose groups (p = 0.678). After the intervention, there were no significant differences in RPE between the groups. However, in both the high-dose and low-dose groups, RPE significantly decreased after the intervention (p 0.001).

At baseline, fasting blood glucose and fasting insulin levels were similar between the high-dose and low-dose groups (p = 0.348, 0.562). After the intervention, no significant differences in these indicators were observed between the two groups. In the high-dose group, fasting blood glucose significantly decreased after the intervention (p = 0.001), whereas fasting insulin did not significantly change (p = 0.509). In the low-dose group, neither fasting blood glucose nor fasting insulin levels significantly changed (p = 0.115, 0.121) (Table 5).

Similarly, at baseline, total cholesterol, triglyceride, HDL, LDL, and NEFA levels were similar between the high-dose and low-dose groups (p = 0.669, 0.288, 0.320, 0.348, 0.907, respectively). After the intervention, no significant differences in these indicators were observed between the two groups. Within the high-dose group, no significant changes were observed in total cholesterol, triglyceride, HDL, LDL, or NEFA levels after the intervention (p = 0.839, 0.072, 0.209, 0.892, 0.080, respectively). Similarly, in the low-dose group, these indicators also showed no significant changes except for cortisol (p = 0.611, 0.622, 0.455, 0.892, 0.068).

The correlations between gut microbiota at the species level and fatigue recovery/metabolic indicators after intervention are shown in Fig. 7. According to our previous results, the only biochemical index that significantly changed between the two groups after the intervention was cortisol, and the abundances of Faecalibacterium prausnitzii, Lachnospira eligens significantly changed at the species level.

Fig. 7.

Correlation analysis for the relationships of gut microbiota species with fatigue recovery indicators and glucose and lipid metabolism indicators after the intervention. TG, triglyceride; CK, creatine kinase; GLU-HK, blood glucose; INS, insulin; LDL, low-density lipoprotein; CHO, total cholesterol; HDL, high-density lipoprotein; T, testosterone; C, cortisol; NEFA, nonesterified fatty acid. Red indicates positive correlations, blue indicates negative correlations, and * indicates statistical significance, * indicates p 0.05, ** indicates p 0.01.

The changes in the abundances of Faecalibacterium prausnitzii (r = –0.266, p = 0.048), Lachnospira eligens (r = –0.318, p = 0.017) were negatively correlated with changes in cortisol levels.

The changes in athletic performance indicators before and after nutritional intervention in both groups are shown in Table 6.

At baseline, no significant differences were observed between the high-dose and low-dose groups. Postintervention, the average power in the low-dose group was significantly greater than that in the high-dose group, whereas the other indicators were not significantly different between the two groups.

The high-dose group showed significant improvements in maximum strength, single-leg vertical jump, triple frog jump, half-court triangle run, and variable-distance shuttle run. The low-dose group demonstrated significant improvements in single-leg vertical jump, double-leg vertical jump, standing double-touch height, and half-court triangle run. Both groups presented significant increases in maximum power, average power, and relative oxygen uptake after intervention.

Changes in the gut microbiome were assessed through species diversity and composition. After 8 weeks of dietary fibre supplementation, the low-dose group presented greater shifts in species diversity, whereas the high-dose group presented more stable and convergent changes in microbiota. In both groups, the abundance of beneficial bacteria increased, whereas that of harmful or conditionally pathogenic bacteria tended to decrease, suggesting an overall positive trend.

Postintervention, alpha diversity (as measured by the Chao1 index) increased significantly in the low-dose group, indicating an increase in species richness. However, the unchanged Shannon and Simpson indices suggest that species evenness remained stable. Beta diversity analysis revealed significant shifts in community composition in the low-dose group, whereas the high-dose group presented more stable, convergent microbial changes.

These outcomes may be influenced by the specific type and dosage of dietary fibre. The gut microbiome has a remarkable ability to metabolize diverse fibre types due to its extensive repertoire of carbohydrate-active enzymes; however, microbial responses are highly individualized and depend on host-specific factors and the microbiome composition [25]. Studies suggest that higher fibre intake (e.g., $>$50 g/day) may yield more pronounced health benefits, although most human interventions use smaller doses [3, 26, 27]. Importantly, changes in microbiota diversity need to be evaluated in the context of species composition, as increasing diversity alone does not inherently signify improved gut health.

At the phylum level, both groups presented an increase in Firmicutes and a decrease in Bacteroidetes, with Firmicutes linked to increased energy absorption and calorie acquisition [28]. An increased ratio of Firmicutes to Bacteroidetes (F/B) is associated with obesity in nonathletic populations, but we did not observe an increase in weight or body fat in athletes, which may indicate that athletes are different from the general population [29, 30]. Athletes undergo prolonged high-intensity training, leading to certain physiological adaptations that increase energy utilization efficiency [31].

At the species level, both groups presented increases in beneficial bacteria such as Faecalibacterium prausnitzii, Lachnospira eligens. These species are key producers of SCFAs, particularly butyrate, which support colonic health, reduce inflammation, and protect against intestinal pathogens [32, 33]. The abundance of harmful bacteria, such as Escherichia coli, decreased in both groups. Escherichia coli, a common bacterium in the human gut that is conditionally pathogenic [34] and, under certain conditions, can cause gastrointestinal or urinary infections in humans and other animals [35].

These findings underscore the usefulness of dietary fibre supplementation for creating a healthier and more stable gut microbiome, which may alleviate gastrointestinal disturbances caused by exercise and indirectly benefit athletic performance.

Both the high-dose and low-dose groups experienced significant reductions in body weight, body fat percentage, and fat mass after 8 weeks of supplementation, with smaller reductions in lean body mass than fat mass.

Epidemiological studies have consistently shown that higher dietary fibre intake is associated with lower body weight and fat mass [36]. While most studies have focused on overweight populations, this study demonstrated similar benefits in healthy athletes [37]. Moreover, combining dietary fibre supplementation with exercise may amplify reductions in body weight and fat mass, as seen in prior studies involving aerobic training and fibre intake [38]. The observed decrease in Bacteroidetes, which is positively associated with body fat percentage, may also contribute to improved body composition [39].

Additionally, because this study involved college athletes whose training intensity, lifestyle management, and dietary balance were less stringent than those of professional athletes were, it is plausible that, after eight weeks of standardized dietary planning, systematic training, and maintenance of healthy daily habits, participants experienced significant reductions in both body weight and body fat.

However, the lack of significant intergroup differences in weight and fat mass changes suggests that the relatively small dosage difference between the two groups may have limited the observed effects. Future studies should explore higher supplementation doses to better assess dose-dependent effects.

Fatigue recovery was assessed via testosterone, cortisol, the testosterone-to-cortisol ratio, creatine kinase, and RPE. Both groups showed significant decreases in cortisol and RPE after the intervention, indicating improved recovery and reduced perceived fatigue. In the high-dose group, testosterone levels decreased significantly, but the testosterone-to-cortisol ratio increased, suggesting favourable adaptation to training loads.

Correlation analysis revealed that increases in beneficial bacteria such as Faecalibacterium prausnitzii, Lachnospira eligens were negatively associated with cortisol levels. Cortisol can also reflect stress responses—such as anxiety or depression—triggered by exercise or training. Previous studies have demonstrated that increased Faecalibacterium prausnitzii abundance is correlated with reduced cortisol levels and improved stress regulation, reinforcing the link between gut health and psychological recovery [40, 41]. Additionally, the abundance of Lachnospira eligens was found to be correlated with changes in cortisol levels [42] and may affect human sleep [43]. These bacteria are known to produce SCFAs, which have anti-inflammatory properties and may reduce stress-induced inflammatory responses [44].

These findings highlight the role of dietary fibre in promoting both physical and psychological recovery, contributing to players’ overall training adaptation and performance.

Over an 8-week period of dietary fibre supplementation at various doses, only the high-dose group exhibited a significant decrease in fasting blood glucose levels, all of which remained within the normal range. Indicators related to lipid metabolism showed no significant changes. Although lipid metabolism markers remained stable in this study, animal studies suggest that Faecalibacterium prausnitzii may improve lipid profiles, potentially benefiting athletes with high energy demands [45].

Dietary fibre improves glucose metabolism through SCFA production during fermentation, which stimulates the release of glucagon-like peptide-1 (GLP-1), promoting insulin secretion and glucose regulation [46, 47, 48]. Additionally, viscous fibres can delay gastric emptying and reduce glucose absorption in the small intestine [49, 50]. These processes ultimately contribute to improved blood glucose levels.

Furthermore, SCFAs produced from fibre fermentation can influence hepatic lipid metabolism through GPR41 and GPR43 [51]. SCFAs activate the key fatty acid receptor peroxisome proliferator-activated receptor gamma (PPAR-$\gamma$), thereby enhancing lipid metabolism [52]. Acetate, an SCFA, can be utilized by the liver for lipogenesis [53], whereas propionate enhances the activity of lipoprotein lipase in adipose tissue [54].

Because the participants were healthy with normal baseline glucose and lipid levels, the observed changes were limited. Future studies may focus on athletes with metabolic abnormalities to better evaluate the effects of dietary fibre on glucolipid metabolism.

Both groups demonstrated significant improvements in aerobic capacity, anaerobic capacity, lower-limb strength, and basketball-specific performance metrics, including vertical jumps, triple frog jumps, and sprint times. These improvements are likely attributable to the combination of training and dietary fibre supplementation.

In a study by Sugiyama et al. [55], university athletes consumed 6 g of supplemental dietary fibre daily for four weeks and performed more sit-up and side jump repetitions after extreme fatigue. The authors hypothesized that the athletes’ habitual fibre intake was insufficient and that supplementation improved their gut microbiome, thereby enhancing physical performance. The role of dietary fibre in enhancing performance may involve SCFA-mediated improvements in skeletal muscle metabolism and energy utilization [56]. SCFAs, particularly butyrate, enhance mitochondrial function and reduce exercise-induced inflammation, thereby improving endurance and recovery.

Furthermore, the reductions in body weight and fat mass observed in this study may have improved explosive power and sprint efficiency, contributing to better basketball-specific performance [57, 58]. Maximum lower-limb strength also contributes to enhanced vertical jump performance [59]. However, Hadžić et al. [57] conducted a study with adolescent basketball players and reported that supplementation with 6 grams of soluble dietary fibre over four weeks did not result in reduced body weight or fat and had a minimal impact on explosive lower-limb performance.

Although dietary fibre supplementation alone may not directly improve performance, the optimization of the gut microbiome, improved body composition, faster fatigue recovery, and stabilized metabolism observed in this study indirectly benefit athletic performance.

Currently, there are no dietary fibre supplements specifically tailored for athletes. As dietary fibre is an indigestible nutrient, large-scale supplementation over a short period is likely to cause gastrointestinal symptoms, which can adversely affect athletes’ training and competitive performance. Consequently, in the present study, we used relatively small supplementation doses to evaluate the feasibility and tolerability of fibre intake in athletes. The low-dose group consumed approximately 3.24 g/day of fibre, while the high-dose group consumed 6.84 g/day. This limited difference was intentional, as our primary aim was to introduce fibre supplementation gradually and in a manner that would not disrupt athletes’ daily routines or cause significant gastrointestinal discomfort. However, this approach may have limited our ability to detect substantial differences in gut microbiota composition and performance outcomes between the groups.

In this experiment, we aimed to observe the effects of additional dietary fibre supplementation while maintaining athletes’ daily routines as much as possible. Despite this, neither group of athletes reached the recommended intake levels, which is an important consideration for future research. Additionally, the insufficient adaptation period for dietary management may have influenced the results, as both dietary restriction and fibre supplementation were introduced simultaneously without allowing adequate time for physiological adaptation. Future studies should prioritize implementing a prolonged adaptation phase (e.g., 2–4 weeks) for dietary modifications prior to initiating fibre interventions, thereby allowing clearer differentiation between the effects of dietary restriction and fibre supplementation. This limitation complicates our ability to attribute observed changes solely to fibre supplementation.

The primary aim of this exploratory trial was to evaluate potential between-group differences in body composition changes following high- versus low-dose interventions. While no statistically significant differences were detected, this finding must be interpreted in light of the study’s limited statistical power. A post hoc power analysis revealed a power of less than 80%, indicating a high probability of type II error. This is related to the small sample size (n = 19), which was insufficient to reliably detect anything less than very large between-group effects. To achieve 80% power for detecting large effects (d = 0.7), a minimum of 34 participants per group would be needed ($\alpha$ = 0.05), underscoring the need for larger-scale validation. Despite these limitations, this study provides critical preliminary data for future trials.