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Gut microbiota and metabolic responses to a 12-week caloric restriction combined with strength and HIIT training in patients with obesity: a randomized trial

BMC Sports Science, Medicine and Rehabilitation volume 16, Article number: 239 (2024) Cite this article

Abstract

Background

Nowadays, obesity has become a major health issue. In addition to negatively affecting body composition and metabolic health, recent evidence shows unfavorable shifts in gut microbiota in individuals with obesity. However, the effects of weight loss on gut microbes and metabolites remain controversial. Therefore, the purpose of this study was to investigate the effects of a 12-week program on gut microbiota and metabolic health in patients with obesity.

Methods

We conducted a controlled trial in 23 male and female patients with obesity. Twelve participants completed a 12-week program of caloric restriction combined with strength and HIIT training (INT, pre-BMI 37.33 ± 6.57 kg/m2), and eleven participants were designated as non-intervention controls (pre-BMI 38.65 ± 8.07 kg/m2). Metagenomic sequencing of the V3-V4 region of the 16S rDNA gene from fecal samples allowed for gut microbiota classification. Nuclear magnetic resonance spectroscopy characterized selected serum and fecal metabolite concentrations.

Results

Within INT, we observed a significant improvement in body composition; a significant decrease in liver enzymes (AST, ALT, and GMT); a significant increase in the relative abundance of the commensal bacteria (e.g., Akkermansia muciniphila, Parabacteroides merdae, and Phocaeicola vulgatus); and a significant decrease in the relative abundance of SCFA-producing bacteria (e.g., the genera Butyrivibrio, Coprococcus, and Blautia). In addition, significant correlations were found between gut microbes, body composition, metabolic health biomarkers, and SCFAs. Notably, the Random Forest Machine Learning analysis identified predictors (Butyrivibrio fibrisolvens, Blautia caecimuris, Coprococcus comes, and waist circumference) with a moderate ability to discriminate between INT subjects pre- and post-intervention.

Conclusions

Our results indicate that a 12-week caloric restriction combined with strength and HIIT training positively influences body composition, metabolic health biomarkers, gut microbiota, and microbial metabolites, demonstrating significant correlations among these variables. We observed a significant increase in the relative abundance of bacteria linked to obesity, e.g., Akkermansia muciniphila. Additionally, our study contributes to the ongoing debate about the role of SCFAs in obesity, as we observed a significant decrease in SCFA producers after a 12-week program.

Trial registration

The trial was registered on [05/12/2014] with ClinicalTrials.gov (No: NCT02325804).

Peer Review reports

Introduction

During the last few decades, obesity has become one of the most serious health issues, with an alarming rise in the prevalence of related comorbidities and overall mortality [1,2,3]. Combined with a sedentary lifestyle, it contributes to multiple components of the metabolic syndrome (MetS), including dyslipidemia, insulin resistance, and high blood pressure. These risk factors can further contribute to the development of type 2 diabetes mellitus (T2DM). Moreover, the average 31% prevalence of MetS increases the chance of coronary heart disease, cerebrovascular disease, and all-cause mortality by 1.5 times, twice, and three times, respectively [4].

In addition to negatively affecting body composition and metabolic health, recent evidence documents unfavorable shifts in gut microbiota composition in individuals with obesity and MetS [5, 6]. In comparison to non-obese individuals, obesity is characterized by the proliferation of potentially harmful bacteria and the inhibition of beneficial ones [6,7,8]. On the other hand, losing weight can increase microbial diversity, foster the growth of commensal bacteria (e.g., Akkermansia municiphila, Roseburia hominis, and Faecalibacterium prausnitzii), or affect the production of short-chain fatty acids (SCFAs) [9,10,11].

Lifestyle modifications, including a hypocaloric diet and increased physical activity, represent an easily available and essential component in the treatment of obesity [12,13,14]. However, the effect of weight loss on the gut microbiota and microbial-derived metabolites still shows controversial results. Some determinant factors, such as training level, intensity, and frequency of exercise, as well as the composition of the diet, have different impacts on the structure of the gut microbiota [15,16,17,18,19]. Moreover, the intervention’s impact is determined by its approach, whether it includes a change in dietary habits, improving physical activity, or a combination of both components [13, 20, 21]. Therefore, the aim of this prospective study was to investigate the effects of a 12-week combination of diet and guided physical activity on gut microbiota and metabolic health in male and female patients with obesity. Another goal of the study was to clarify the relationships between gut microbes, their metabolites, and other key components of the MetS. Therefore, we hypothesized that the weight loss program would have a favorable influence on body composition, metabolic health biomarkers, microbial α-diversity, relative abundance of beneficial bacteria associated with obesity and SCFA producers, and microbial-derived metabolites in patients with obesity.

Methods

Recruitment and study participants

Data for this study were obtained in a clinical trial with an intervention focused on weight loss (ClinicalTrials.gov, No: NCT02325804) conducted at the Biomedical Research Centre of the Slovak Academy of Sciences in Bratislava (BMC SAV). The study used a parallel design and was approved by Bratislava Self-Governing Region Ethics Committee No.05239/2016/HF. The study was conducted in accordance with the principles of the Declaration of Helsinki, which governs experiments involving human beings. All subjects signed their informed consent before participating in the study, read the written consent, were explained the study steps, and had a discussion with the investigators.

Study participants

The research group consisted of Caucasian participants, both males and females, with obesity from the Outpatient Clinic of Internal Medicine and Diabetes at the Institute of Clinical and Translational Research, BMC SAV. Participants were randomly divided into two groups: (a) the intervention group (INT) and (b) the non-intervention controls. During the study, the controls reported no structured or self-induced physical activity or caloric restriction. Figure 1 visualizes the study design.

Fig. 1
figure 1

Overview of study design

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Inclusion criteria were age 30–60 years and a BMI ≥ 30 kg/m2. Exclusion criteria included (1) taking antibiotics or extra probiotics for two weeks or in the last two months; (2) having an acute disease or infection within the last two months, such as an upper respiratory tract infection, fever, chronic inflammatory disorders, or autoimmune disorders; (3) having a chronic disease, such as diabetes treated with insulin or anti-diabetic drugs, chronic kidney disease, or use of corticosteroids; (4) having recently a major injury or surgery that would make it impossible to participate; or (5) being pregnant or breastfeeding.

Intervention

INT engaged in a 12-week program involving a calorie-reduced diet and increased physical activity. Each participant received detailed instructions and counseling on lifestyle compliance, a personalized nutrition plan (Planeat LLC, Bratislava, Slovakia), and a physical exercise plan. Participants met weekly with a dietitian for adjustments to their caloric intake and behavioral therapy.

Physical exercise

During the 12-week intervention, participants attended guided exercise sessions twice a week. Each 60-minute session included: (1) warm-up, (2) strength training (ST), (3) high-intensity interval training (HIIT), and (4) cool-down. The training program’s structure was based on previous research [22, 23]. ST involved 2–3 exercises targeting large muscle groups, with 2–3 sets of 8–12 repetitions and 1–2 min of rest between sets. HIIT consisted of 5–7 exercises performed in 3 circuits, with intervals of 30–45 s of work and 15–30 s of rest. Exercises incorporated body weight and various equipment, including dumbbells, kettlebells, TRX, resistance bands, balance balls, and fit balls. Additionally, the study encouraged participants to engage in at least 150 min of moderate-intensity physical activity per week [24].

Resting energy expenditure and diet

Energy intake was individually set, ranging from 1500 to 1900 kcal/day (6000–8000 kJ/day) for men and 1200 to 1600 kcal/day (5000–6700 kJ/day) for women, based on measured resting metabolic rate (RMR) using indirect calorimetry with continuous breath-by-breath analysis (Ganshorn PowerCube Ergo, Germany). Prior to measurement, subjects were instructed to sleep for seven to eight hours, avoid strenuous activity the day before, and fast overnight. The metabolic cart was calibrated with reference gas before each use, and flow meter calibration was performed. RMR was calculated using Weir’s formula [25] after reaching a steady state (5 min), with expired gases collected for 20 min and the final 10 min averaged. Total daily calorie intake comprised 30–50% carbohydrates, 25–30% fats, 20–30% proteins, and 14 g of fiber per 1000 kcal. Participants reported their food intake for monitoring and feedback. Nutrition software (Planeat LLC, Bratislava, Slovakia) collected and analyzed quantitative and qualitative data from two randomly selected weeks.

Physical characteristics

The examination was conducted at the Outpatient Clinic for Internal Medicine and Diabetes, Institute of Clinical and Translational Research, BMC SAV. Personal and family medical histories were collected. Body composition metrics were assessed before and after the intervention, including body weight, body fat percentage, muscle mass, and visceral fat using the InBody 230 (Serial, USB, LookingBody Basic 120). BMI was calculated as weight in kilograms divided by height in meters squared. Waist and hip circumferences were measured using a flexible tape, and blood pressure was recorded from the arm after a minimum of 5 min of rest (OMRON).

Biochemical assay

Venous blood was drawn before and after the intervention in the fasting state in polyethylene tubes containing ethylenediaminetetraacetic acid (EDTA) as an anticoagulant and immediately cooled in ice or in polyethylene tubes without anticoagulant (to obtain serum). After centrifugation at 4 °C, all plasma and serum aliquots were stored at -80 °C until assayed. The biochemical parameters were analyzed in a certified hospital laboratory (SYNLAB Bratislava, Slovakia). Serum glucose, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma-glutamyl transferase (GMT) levels were measured using an autoanalyzer Beckman Coulter AU (Beckman Coulter, Inc., 250 S. Kraemer Blvd. Brea, CA 92821, USA). Serum insulin concentrations were measured using a chemiluminescent microparticle immunoassay (CMIA; ARCHITECT Insulin: Abbott Laboratories Diagnostics Division Abbott Park, IL 60064, USA). The index of insulin resistance (HOMA-IR) was calculated from fasting glucose and insulin concentrations according to the equation: HOMA-IR = (fasting plasma glucose x fasting plasma insulin)/22.5 [26]. Serum fasting total cholesterol (T-chol), lipoproteins (LDL, HDL), and triglyceride (TG) levels were measured by an enzyme color assay for quantitative determination in a Beckman Coulter AU autoanalyzer (Beckman Coulter, Inc., 250 S. Kraemer Blvd. Brea, CA 92821, USA).

Plasma and fecal metabolite concentrations; NMR data acquisition

The concentrations of selected plasma and fecal metabolites were measured from serum aliquots and fecal samples using nuclear magnetic resonance (NMR) analysis, as detailed in our previous study by Hric et al. [16]. The concentration of the following 23 plasma metabolites was measured: lactate, alanine, valine, glucose, leucine, isoleucine, acetate, pyruvate, citrate, phenylalanine, tyrosine, glutamine, lysine, 3-OH-butyrate, creatine, creatinine, ketoleucine, ketoisoleucine, ketovaline, histidine, succinate, tryptophan, and lipoprotein fractions containing fractions of VLDL, LDL, IDL, and HDL. The concentration of the following 25 fecal metabolites was measured: propionate, acetate, butyrate, methanol, formate, threonine, leucine, isoleucine, valine, glycine, glycerol, alanine, pyruvate, glutamate, aspartate, fumarate, methionine, phenylalanine, tyrosine, uracil, tryptophan, valerate, proline, glucose, and nicotine derivate.

DNA extraction; NGS library preparation

Fecal samples were collected before and after the intervention from the participants in a DNA/RNA Shield-Fecal Collection Tube to maintain the stability of the nucleic acids in the fecal samples (ZymoResearch, Irvine, CA, USA). Participants were instructed on methods to prevent sample contamination during sample collection. Total DNA from fecal samples was extracted using the ZymoBIOMICS DNA/RNA kit (Zymo Research, Irvine, CA, United States), following the manufacturer’s protocol. NGS libraries were prepared using the 16S Microbiome NGS Assay (ViennaLab Diagnostics GmbH, Vienna, Austria). Locus-specific primers amplified the highly variable V3-V4 regions in the first PCR step. Agarose electrophoresis evaluated the first PCR products. During data demultiplexing, the second low-cycle PCR used dual index sequences to assign the reads to individual samples. Both PCR products were purified using Agencourt AMPure XP magnetic beads (Beckman Coulter, Brea, CA, United States). The Qubit 2.0 Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) quantified the DNA libraries, and an Agilent 2100 bioanalyzer (Agilent Technologies, Santa Clara, CA, United States) and a high-sensitivity DNA kit (Agilent Technologies) verified the DNA profiles. DNA libraries were diluted to 4 nM, pooled equimolarly for sequencing, and analyzed using the Illumina MiSeq platform (Illumina Inc., San Diego, CA, United States) through 300-bp paired-end reads.

Illumina data processing

The average number of sequences obtained after preprocessing the data was 65 861. Sequencing data were analyzed using the ViennaLab NGS Microbiome Assay software, which was included in the kit. The reads were preprocessed using BBMerge [27], Cutadapt [28], and SeqKit [29]. The read classification pipeline used the CLARK sequence classification system for species-level classification of reads [30]. The CLARK system was based on discriminative k-mers in a sequence database. The classification pipeline constructed the sequence database using sequences from the SILVA [31] and UNITE [32, 33] databases. The species taxonomy used by the read classification was downloaded from NCBI (https://www.ncbi.nlm.nih.gov/taxonomy/). Diversity statistics were calculated from species-level abundance results using MOTHUR [34].

Statistical analysis

Statistical analyses were performed using IBM SPSS Statistics version 19 (SPSS Inc., Chicago, IL, USA) and Python (3.8.10 version). The Shapiro-Wilk test checked the normality of the data. Parametric data were analyzed using a two-way ANOVA to account for within-subject dependencies arising from repeated measurements over time while concurrently assessing intervention effects between the intervention and control groups. Non-parametric data were analyzed using the Mann-Whitney test to compare effects between groups and the Wilcoxon signed-rank test to assess within-group impacts. The correlations between gut microbes, metabolites, and body composition were analyzed using the Spearman correlation coefficient. Correlations between significant changed gut bacteria and body composition variables were visualized by Heat Map using the library for statistical visualization in Python (https://seaborn.pydata.org/). The data were explored and analyzed by R ver. 4.0.3 [35] and by the use of the following packages: xdoParallel v. 1.0.17 [36], ggRandomForests v. 2.2.1 [37, 38], and randomForestSRC [39, 40]. Random Forest (RF) machine learning (ML) algorithm was trained to predict before/after status in the INT and CTRL group. In each RF, the feature selection was performed by nested cross-validation, with the graph depth as the objective function. Performance of RF with the selected features was assessed by the out-of-bag ROC curve and quantified by the Area under ROC (AUC). All values are expressed as the mean ± standard error of the mean, unless otherwise stated. The significance level of all statistical analyses was set at 0.05, unless otherwise stated. A Research Randomizer was used for the randomization of patients into groups [41]. To determine the appropriate sample size for this study, the Shannon index as a measure of alpha diversity was used. Based on data from a prior study [42], the mean Shannon index for individuals with obesity was reported as 5.11 with a standard deviation of 0.82. Our calculations indicate that to observe a statistically significant change of at least 20% in the Shannon index, approximately 10 participants per group would be needed.

Results

Body composition, cardiovascular characteristics, and calorimetric measurements

Of the 40 volunteers who underwent randomization, 23 (57.5%) completed the study: twelve participants (mean age 47.08 ± 10.48 years) in INT completed the program with at least 80% attendance in exercise training sessions, and eleven subjects as controls (mean age 41.27 ± 10.04 years). Unfortunately, eight patients from INT and nine from controls did not complete the final assessment, mainly due to antibiotic treatment for common infections or non-compliance with the study intervention. We observed a significant reduction in BMI, body fat percentage, waist circumference, hip circumference, visceral fat, and heart rate after a 12-week program. Table 1 reports the data analysis.

Table 1 Physical characteristics in the time × group interaction
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Nutrition and exercise reports

All the participants received a nutrition plan with food recipes for the duration of the intervention. Based on the measured RMR, prescribed daily calorie consumption was 1601.8 ± 219.8, containing 30–50% carbohydrates, 25–30% fats, 20–30% proteins, and 14 g.1000kcal-1 fiber. Total real calorie consumption and macronutrient intake were similar and included 48% carbohydrates, 21% proteins and 31% fats. We found a significant difference only between prescribed and self-reported protein intake (p = 0.002). Moreover, no significant differences were detected between prescribed and self-reported physical activity. The data analysis is summarized in Table 2.

Table 2 The food intake and physical activity in INT group
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Biochemical variables from serum

We compared the results in the biochemical analysis of plasma samples at baseline and after 12 weeks in the INT and controls. Within INT, we identified a significant decrease in serum fasting glucose levels (p = 0.020), uric acid (p = 0.034), and liver enzymes AST (p = 0.042), ALT (p = 0.034), and GMT (p = 0.027). No statistically significant differences were detected in the other observed parameters within the controls. Supplementary Table 1 reports the complete data analysis.

Microbial analysis of fecal samples

The 16S rRNA gene method identified the gut microbiota’s composition based on the relative abundance of microbes. The overall microbiota α-diversity defined by the Shannon, Simpson, and Chao1 indexes did not differ within the groups. The sequence analysis classified the majority of the bacteria into five phyla. The most prevalent were from the Firmicutes and Bacteroidetes phyla, followed by Proteobacteria, Actinobacteria, and Verrucomicrobia. Figure 2a shows the relative bacterial composition at the phylum level for each group. We identified a significant increase in the relative abundance of the phyla Bacteroidetes (p = 0.023) and Verrucomicrobia (p = 0.019) and a significant decrease in the relative abundance of the phylum Firmicutes (p = 0.005) in INT (Fig. 2b). Consequently, a 12-week program significantly reduced the Firmicutes/Bacteroidetes ratio (p = 0.031). Within the controls, there were no significant differences in the relative abundance of bacteria at the phylum level (Fig. 2b). Between the groups, there was a significant difference in Proteobacteria (p = 0.009) at the baseline. Supplementary Table 2 reports the complete data analysis.

Fig. 2
figure 2

Comparison of the phylotypes in the time × group interaction at the phylum level. a Bar graphs represent the relative abundance of the total bacterial population. b Box plot summarizing significant differences using the Wilcoxon signed-rank test to assess within-group effects and Mann-Whitney U test to assess between-group effects (p < 0.05). Abbreviations: INT intervention group; CTRL controls; ** p < 0.05; * p < 0.01; ns no significant

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Furthermore, we observed a significant altered post-intervention taxon at species level in INT, including an increase in the relative abundance of Akkermansia muciniphila, Parabacteroides merdae, or Phocaeicola vulgatus and a decrease in the relative abundance of Butyrivibrio fibrisolvens, Coprococcus comes, Blautia spp., or Erysipelatoclostridium ramosum (Fig. 3). Further differences in gut microbes between pre- and post-intervention in INT are summarized in Supplementary Table 2. No significant differences were detected in the aforementioned microbes within the controls or group interaction (Fig. 3, Supplementary Table 2).

Fig. 3
figure 3

Relative abundance of gut bacteria taxa in the time × group interaction. Bar graphs summarizing significant differences using the Wilcoxon signed-rank test to assess within-group effects and Mann-Whitney U test to assess between-group effects (p < 0.05). Values are presented as mean ± standard error of the mean. Abbreviations: INT intervention group; CTRL controls; * p < 0.05; ** p < 0.01; ns no significant

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The correlations between the aforementioned gut microbes and body composition characteristics were analyzed using the the differences between pre- and post-measurements (delta) in both groups. Spearman’s correlation coefficient (r) was computed for significant changed gut bacteria and significant changed body composition characteristics. The significant negative association was detected between the relative abundance of Akkermansia muciniphila and body weight and BMI; and the relative abundance of Phocaeicola vulgatus and body fat and visceral fat. The significant positive association was detected between the relative abundance of Blautia hydrogenotrophica and body weight, BMI, body fat, waist, and hip circumference; the relative abundance of Blautia massiliensis and hip circumference; the relative abundance of Coprococcus catus and body weight, BMI, body fat, and hip circumference; the relative abundance of Coprococcus comes and WHR; and the relative abundance of Parabacteroides merdae and WHR (Fig. 4). Supplementary Table 3 reports the complete data analysis of associations between gut microbes and body composition, biochemical analysis, plasma, and fecal metabolites.

Fig. 4
figure 4

Heat map of the correlations between significant changed gut bacteria and body composition variables. Each square represents the Spearman’s correlation coefficient. Shades of red cells specify positive or negative correlations. Abbreviations: BW Body weight; BMI Body mass index; BF Body fat; WHR Waist to Hip Ratio; WC Waist circumference; HC Hip circumference; VC Visceral fat; * p < 0.05 **; p < 0.01

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Serum and fecal metabolomics

NMR evaluated the concentrations of serum and fecal metabolites based on their relative concentrations. Within the INT, we observed a significant decrease in serum amino acids, including alanine (p = 0.050), phenylalanine (p = 0.019), valine (p = 0.014), leucine (p = 0.032), tyrosine (p = 0.009), and lysine (p = 0.050). Supplementary Table 4 reports the complete data analysis. Moreover, we identified a decreasing trend in the relative concentration of serum energy sources, e.g., SCFAs (propionate, butyrate, and acetate), glucose, or citrate. The time × group interaction did not reveal any statistically significant differences in the other observed parameters from serum samples. Regarding fecal metabolites, no significant differences were detected in the time × group interaction. Supplementary Table 4 reports the complete data analysis of plasma and fecal metabolites.

Random Forest Machine Learning

The Random Forest Machine Learning (RF-ML) analysis using Akkermansia muciniphila, Parabacteroides merdae, Phocaeicola vulgatus, Blautia caecimuris, Butyrivibrio fibrisolvens, Coprococcus comes, and Erysipelatoclostridium ramosum, along with BMI, body fat, waist circumference, and hip circumference as joint predictors of the before/after status in the INT group, resulted in a Receiver Operating Characteristic (ROC) curve with an Area Under the Curve (AUC) of 0.69. An AUC of 0.69 indicates a moderate level of discrimination by the model, suggesting that the identified microbial taxa (Butyrivibrio fibrisolvens, Blautia caecimuris, Coprococcus comes, and waist circumference) are capable of distinguishing between pre- and post-intervention states in INT subjects with a fair degree of accuracy. While not optimal, this AUC value reflects a reasonable predictive performance in this context (Fig. 5). Furthermore, the RF-ML analysis using the same variables in the CTRL group produced an ROC curve with an AUC of 0.52, indicating a poor set of variables for discriminating subjects in the time × group interaction (Fig. 6).

Fig. 5
figure 5

ROC (receiver operating characteristic) curves with an area under the ROC curve (AUC) for the RF-ML algorithm Butyrivibrio fibrisolvens, Blautia caecimuris, Coprococcus comes and waist circumference as joint predictors/discriminators between pre- and post-intervention in INT. Abbreviations: FPR false-positive rate; HIT high-intensity training group; RF-ML Random Forest Machine Learing; TPR true positive rate

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Fig. 6
figure 6

ROC (receiver operating characteristic) curves with an area under the ROC curve (AUC) for the RF-ML algorithm Butyrivibrio fibrisolvens joint predictor/discriminator between pre- and post-intervention in CTRL. Abbreviations: FPR false-positive rate; HIT high-intensity training group; RF-ML Random Forest Machine Learing; TPR true positive rate

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Discussion

As obesity remains a major public health issue linked to MetS and T2DM through gut microbiota alterations [1, 3, 4], recent research has increasingly focused on gut microbiota plasticity and intervention strategies. However, findings on the structure of gut microbiota in patients with obesity are often limited and contradictory, emphasizing the need for further investigation. This study aimed to evaluate the effects of the calorie-reduced diet combined with strength and HIIT training on the body composition, metabolic health biomarkers, gut microbiota composition and shift, and microbial-derived metabolites in patients with obesity. Consistent with our hypothesis, a 12-week weight loss program resulted in significant improvements in body composition (e.g., BMI, body fat percentage, waist circumference, hip circumference, and visceral fat) and liver enzymes (AST, ALT, GMT). Notably, we reported a significant increase in the relative abundance of Akkermansia muciniphila, a microbe associated with various health benefits and linked to several metabolic disorders, including obesity [43]. Furthermore, we detected a significant decrease in the relative abundance of important SCFA producers (e.g., Butyrivibrio, Coprococcus, and Blautia), as well as a decreasing trend, though not significantly, in fecal SCFAs (propionate, butyrate, and acetate). The RF-ML analyses have identified predictors (Butyrivibrio fibrisolvens, Blautia caecimuris, Coprococcus comes and waist circumference) with a moderate ability to discriminate between INT subjects pre- and post-intervention.

Consistent with previous studies [44,45,46], we reported several measures of obesity were significantly changed after a 12-week weight loss program, including BMI, body fat, waist circumference, and visceral fat. In addition to body composition, previous studies reported improvements in plasma lipids and lipoproteins, as well as other clinically relevant cardiometabolic risk factors (e.g., insulin sensitivity, enzyme livers) [47, 48]. Contrary to that, we did not observe a significant difference in the time × group interaction for any lipid profile variables. The latter may be explained by the near-optimal reference range lipid profile, which substantially limits further improvement by the weight loss program. However, we found a significant decrease in the concentration of liver enzymes (AST, ALT, and GMT) after the intervention in INT. It is believed that an imbalance between lipid storage and removal leads to increased fat deposition in the liver, resulting in elevated liver enzymes in obesity [49, 50]. Therefore, our findings may indicate positive changes in liver fat content after the intervention.

The impact of alterations in lifestyle and weight loss on microbiota diversity remains unclear, with several studies identifying an increase in bacterial richness after the diet-exercise intervention [11, 51,52,53], while others did not show an impact [54, 55]. Consistent with the latter two studies, we did not detect significant differences in bacterial α-diversity within either group. This suggests that changes in diet and exercise may not always lead to shifts in the gut microbiota diversity. Instead, certain groups of bacteria may play a more significant role in obesity and related disorders. Additionally, evidence on the shift in higher taxonomic rank (phylum) after the post-weight loss is conflicting: some studies report decreased Firmicutes and increased Bacteroidetes, others show the opposite, or no significant changes [56]. Our findings align with the first mentioned, revealing a significant increase in Bacteroidetes and a decrease in Firmicutes, leading to a reduced Firmicutes/Bacteroidetes ratio after a 12-week program. We assume there are differences in the severity of the ailment, participant age, comorbidities, food and exercise habits, environmental factors, methodology, or treatment regimens that account for discrepancies with other studies.

Consistent with previous studies [9, 52, 57], we observed a significant increase in the relative abundance of the obesity-related gut microbe Akkermansia muciniphila following weight loss [51]. Akkermansia muciniphila is an anaerobic, mucin-degrading bacterium residing in the mucus layer of intestinal epithelial cells, constituting about 3–4% of gut microbiota biomass [58]. It is considered beneficial for maintaining gut barrier integrity, modulating immune responses, and regulating body fat, insulin resistance, adipose tissue inflammation, and other metabolic disorders linked to poor dietary habits [59, 60]. The reduced abundance of A. muciniphila has been previously associated with several metabolic disorders, including obesity and T2DM [61]. Interestingly, we observed a significant negative association between this bacterium and body composition variables, including body weight, BMI, body fat percentage, waist, and hip circumference. Our previous study [62] reported similar findings, revealing a significantly reduced abundance of Akkermansia in individuals with class III obesity compared to normal-weight controls, along with significant negative associations between Akkermansia and body composition characteristics. As a result, our findings contribute to the growing body of evidence suggesting an association between Akkermansia and obesity, as well as metabolic-associated diseases [63].

Additionally, we have identified a significant increase in the relative abundance of benefical bacteria Phocaeicola vulgatus and Parabacteroides merdae in INT. Those results are in accordance with recent studies showing a lower relative abundance of these bacteria in obesity [64, 65]. According to the majority of research, it is beneficial for maintaining the intestinal barrier, reducing DSS-induced colitis, and preventing atherosclerosis [66, 67]. However, some studies suggest a favorable correlation between P. vulgatus and metabolic diseases, including T2DM [68]. P. merdae, another beneficial bacteria, protects against obesity-related atherosclerosis by enhancing essential amino acid catabolism [69], as evidenced by a significant decrease in serum amino acids in INT. Moreover, P. merdae was negatively associated with body composition characteristics, e.g., BMI, body fat percentage, and hip circumference. Therefore, our research suggests that P. vulgatus and P. merdae may be important indicators associated with obesity and related comorbidities, as the observed alterations in these species are consistent with their potential influence on host metabolism and body composition.

Conversely, we reported a significant decrease in the relative abundance of the pathogenic bacteria Erysipelatoclostridium ramosum in INT. Previous research indicates that E. ramosum contributes to obesity by increasing body fat through enhanced glucose and lipid absorption and intracellular lipid storage in the small intestine [8, 70]. Therefore, the reduction of this bacterium also supports the beneficial outcome of the weight-loss program on the structure of gut microbiota.

In this study, we detected a significant decrease in the relative abundance of important SCFA producers, including the genera Butyrivibrio, Coprococcus, and Blautia. These bacteria represent numerically significant components of the gut microbiota [8]. Several studies have established a link between changes in the relative abundance of SCFA-producing bacteria and obesity [8, 71, 72]. However, conflicting results still exist regarding the role of these bacteria and their metabolites in individuals with obesity. On one hand, some studies highlight the positive effects of SCFAs on appetite control, mediated by interactions with FFA2 and FFA3 receptors and the release of peptide hormones [73,74,75]. On the other hand, an excessive amount of SCFAs may promote lipogenesis in the liver and lead to the accumulation of TAGs in adipocytes [76]. Along with changes in SCFA producers, we detected a decreasing trend in fecal SCFAs after caloric restriction combined with strength and HIIT training, consistent with previous research [77,78,79]. Our study, therefore, supports the notion that individuals with obesity may produce more fecal SCFAs, which represent an additional energy source, potentially contributing to energy imbalance and obesity [80]. However, it is important to note that the individuals in this study were on a calorie-restricted diet, which can be a key factor that influences SCFAs levels.

Our study has several limitations. First, the cohort was relatively small. We strictly selected the patients, excluding typical chronic diseases like T2DM, to avoid the potential effects of confounders. Significant dropouts, primarily due to antibiotic treatment for common infections or non-compliance with the study intervention, further reduced our sample size. Second, our study lacks a detailed analysis of pre-intervention dietary and physical activity habits. Thus, we are unable to link the study-related intervention to the pre-intervention history of these factors. Furthermore, we did not include dietary and physical activity data from the control group. Lastly, there was a deficiency in data on stool consistency, such as the Bristol stool scale, which could serve as a proxy for gut transit time [81]. The change in gut transit time during the interventions could have significantly influenced the results.

Conclusions

Our results indicate that the 12-week caloric restriction combined with strength and HIIT training had favorable effects on body composition, metabolic health biomarkers, gut microbiota, and microbial-derived metabolites. Notably, there was an increase in the relative abundance of Akkermansia muciniphila, a bacterium associated with obesity and metabolic-associated diseases. Furthermore, we observed a significant decrease in the relative abundance of several SCFA producers, which adds to the controversy surrounding the role of SCFAs in obesity. Additionally, our findings underscored gut microbiota functionality, showing a favorable correlation between several gut microbes and body composition, metabolic health biomarkers, and SCFAs. These findings offer promising insights into the impact of caloric restriction combined with strength and HIIT training on various health markers, though their applicability to different populations and real-world settings warrants further investigation.

Data availability

Results of all analyses are included in this published article. The dataset supporting the conclusions of this article is available in the GenBank repository, accession number PRJNA1050790. The link to the database: https://dataview.ncbi.nlm.nih.gov/object/PRJNA1050790?reviewer=hjbbj68t8nstn1pm6fee715cqt.

Abbreviations

MetS:

Metabolic syndrome

T2DM :

Type 2 Diabetes mellitus

SCFAs:

Short-chain fatty acids

INT:

Intervention group

HIIT:

High-intensity interval training

NMR:

Nuclear magnetic resonance

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Acknowledgements

The authors are grateful to all the subjects for their participation in this study.

Funding

This study was supported by the Slovak Research under Grant No. APVV-17-0099 (VB), APVV-22-0047 (VB), and APVV-23-0028 (VB); by the Development Agency under Grant No. VEGA 2/0129/20 (AP) and VEGA 1/0260/21 (VB); and by the Comenius University Youth Grant No. UK/213/2022 (LN) and UK/203/2023 (LN).

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Authors and Affiliations

  1. Department of Biological and Medical Science, Faculty of Physical Education and Sport, Comenius University in Bratislava, Bratislava, 814 69, Slovakia

    Libuša Nechalová, Viktor Bielik, Ivan Hric, Miriam Babicová & Adela Penesová

  2. Biomedical Center, Institute of Clinical and Translational Research, Slovak Academy of Sciences, Bratislava, 845 05, Slovakia

    Libuša Nechalová, Ivan Hric & Adela Penesová

  3. Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, 036 01, Slovakia

    Eva Baranovičová & Marián Grendár

  4. Phoenix VA Health Care System, Phoenix, AZ, USA

    Juraj Koška

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  1. Libuša Nechalová
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Contributions

LN: writing the original draft; formal analysis; investigation; data curation; methodology; training program conception. VB: critical reading of the manuscript and supervision; conceptualisation; methodology; funding acquisition. IH: formal analysis; data curation. MB: data curation. EB: data curation. MG: data curation. JK: review and editing of the manuscript. AP: conceptualisation; investigation; project administration; funding acquisition. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Viktor Bielik.

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The study was approved by the Bratislava Self-Governing Region Ethics Committee (No. 05239/2016/HF) and conducted in accordance with the Declaration of Helsinki. The study adheres to CONSORT guidelines. All participants provided written informed consent.

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Not applicable.

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Nechalová, L., Bielik, V., Hric, I. et al. Gut microbiota and metabolic responses to a 12-week caloric restriction combined with strength and HIIT training in patients with obesity: a randomized trial. BMC Sports Sci Med Rehabil 16, 239 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13102-024-01029-7

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Keywords

BMC Sports Science, Medicine and Rehabilitation

ISSN: 2052-1847

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