The impact of a high-protein diet with strength training on the gastrointestinal microbiota in community-dwelling older adults: subanalysis of a randomized controlled trial

Background: A balanced gastrointestinal (GI) microbiota is essential for healthy aging. Although high-protein diets and strength training are recommended for older adults to maintain muscle mass, their effects on GI microbiota remain unclear.

Methods: This randomized controlled trial examined the effect of a habitual diet with recommended protein intake or high protein intake combined with strength training on the GI microbiota of 112 community-dwelling adults aged 65–85 years. The participants were divided into three groups: no intervention control (CON), recommended protein intake plus strength training (RP + T), and high protein intake plus strength training (HP + T). Over 17 weeks, protein intake increased significantly from 0.80 (IQR: 0.30–0.50) g/kg body weight at baseline, reaching 1.07 ± 0.25 g/kg in RP + T, and 1.62 ± 0.37 g/kg in HP + T groups. Stool samples collected at baseline, after dietary intervention, and after combined dietary and training intervention were analyzed using 16S rRNA gene amplicon sequencing.

Results: Despite increased protein intake, microbiota richness, diversity, and composition showed no significant changes within or between groups. Residual energy and inflammatory markers indicated that higher protein intake was well tolerated.

Conclusion: The findings suggest that increasing protein intake via food sources up to 1.6 g/kg body weight for more than 4 months, with or without strength training, does not adversely affect the GI microbiota composition in older adults.

1 Introduction

The population is steadily aging, with the number of people aged 80 years and older projected to triple by 2050 (1, 2). This increase in lifespan is associated with an increased risk of chronic diseases, such as type 2 diabetes, cancer, cardiovascular diseases, and dementia, as well as loss of muscle mass and independence (3). To preserve or enhance muscle mass in older adults, a combination of adequate protein intake and regular resistance training is recommended and has been substantiated in various studies (4).

Another important age-related modification involves the gastrointestinal (GI) microbiome, which is characterized by reduced microbiota diversity with increasing age. Age-related changes include a reduced abundance of beneficial bacteria such as Bifidobacterium and Lactobacillus, and the beneficial metabolites they produce, together with an increase in opportunistic pathogens and microorganisms related to chronic inflammation that might be associated with age-related chronic diseases (5, 6).

However, the link between high-protein diets and potential changes in GI microbiota in older adults has been barely investigated. Proteins have received increasing attention due to their role as key precursors in the formation of physiologically beneficial short-chain fatty acids (SCFAs) but also harmful putrefactive metabolites (such as ammonia, amines, sulfides, phenols, and indoles). The latter can be produced by the GI microbiota through proteolytic fermentation and may influence host health and contribute to the risk of diseases (7). Undigested proteins that reach the large intestine may lead to an increased abundance of opportunistic pathogenic microorganisms, potentially increasing the risk of metabolic diseases (8). Therefore, it is important to investigate whether the potential benefit of a high-protein diet on muscle mass or muscle quality might counteract GI health in older adults, particularly when the diet is provided via protein-rich foods rather than supplements, which might also change diet quality and composition. In an initial study involving 28 healthy elderly men who consumed a protein-rich diet—either at the level of the Recommended Daily Allowance (RDA) or twice that amount—for a duration of 10 weeks, no significant differences were observed in the composition of fecal microbiota or in the abundance of protein-associated volatile organic compounds. However, circulating trimethylamine-N-oxide (TMAO), a bacterial metabolite derived from choline and carnitine, and a potential novel biomarker for CVD, was increased by the 2x RDA diet (9).

Since no more data in humans are available, the goal of this randomized controlled trial was to compare the influence of a habitual diet with either a diet containing the recommended protein intake or a much higher protein intake, both with and without strength training, on the GI microbiota (as a secondary outcome analysis) in more than 110 older adults aged 65–85.

2 Materials and methods

2.1 Study design

This study was designed as a randomized controlled, observed-blind trial. After participants completed baseline assessments, they were allocated to one of three groups based on planned protein intake during the nutritional intervention (CON, control with no intervention; RP + T, recommended protein intake; HP + T, respectively, elevated high protein intake) as randomly permuted blocks via https://www.randomizer.at/ stratified by age groups [(65–70), (70–75), (75–80), (80–85) years] and sex (female; male) meeting the criteria for an observed-blind trial (10). The “+T”-groups additionally performed resistance training during the intervention. This resulted in eight different strata, and for every stratum block, randomization was performed. The randomization scheme was maintained by an independent researcher who was not involved in participant recruitment or baseline assessments, ensuring that allocation concealment was preserved throughout the enrollment process. A detailed description of the study with inclusion/exclusion criteria has been published previously (11).

This study evaluated the outcomes of the interventions on GI microbiota composition of stool samples collected during the study at three time points between July and December 2018 at the Center for Sport Science and University Sports, University of Vienna, Austria.

The study was approved by the Ethics Committee of the University of Vienna (Reference Number: 00322), registered at https://clinicaltrials.gov (NCT04023513), and performed in accordance with the 1975 Declaration of Helsinki.

2.2 Participant selection

The study population was defined as community-dwelling people aged between 65 and 85 years.

Regular resistance training in the past 6 months prior to the intervention was set as an exclusion criterion, as well as cognitive impairment evaluated by the Mini-Mental State Examination with a Score below 23 points, a frailty index ≥3, need for walking aids, and acute or chronic diseases contraindicating resistance training (11). None of the study participants reported antibiotic use at the time of recruitment; antibiotic use during the 17-week intervention was monitored.

A total of 137 of the 632 screened participants met the inclusion criteria and signed a written consent form before participation and allocation to the study groups (see Table 1 and Figure 1). Participants were only included in the final microbiota analysis if they met all inclusion criteria and provided stool samples at all three time points (T1, T2, and T3). This complete-case analysis approach was determined a priori to ensure robust longitudinal comparisons and to avoid potential biases from imputing missing microbiota data.

Table 1

Table 1. Baseline characteristics of study participants.

Figure 1

Figure 1. Flowchart of the study, adapted according to the dropouts in this microbiota-focused part (11).

2.3 Sports and dietary intervention

A detailed study description has been published by our group (11). In summary, the protein intake of the participants was monitored, and HP + T group participants were provided with protein-rich foods that are readily available in stores, such as high-protein bread, puddings, dairy products, soups, and pea protein sticks. The RP + T group received foods with medium protein content, such as milk products, homemade muffins, and bars, to compensate for the energy intake by the HP + T group. A protein intake of 1.07 g/kg body weight/day for the RP + T group and 1.62 g/kg body weight/day for the HP + T group for 17 weeks based on an initial nutritional assessment (T1) was achieved. At week 9 (T2), guided progressive resistance training was initiated for 8 weeks (T3). After each training session, the participants of the groups received a supplement (HP + T protein supplement; RP + T an isocaloric carbohydrate alternative). The total caloric intake was adapted according to sex-specific recommendations based on D-A-CH reference values (12). CON group participants did not receive any supplements or resistance training and remained on their baseline protein intake throughout the study period. Anthropometric data, body composition, physical function, muscle quality, and selected blood parameters were recorded at T1, T2, and T3, respectively.

2.4 Stool sampling

Stool sampling kits containing labeled sterile containers with a spatula, thermal bag, feces catcher, and instructions were provided to the participants at T0. Stool samples were collected at three time points (T1, T2, and T3) in exchange for a new sampling kit. The samples were collected within 24 h and stored below 4 °C until delivery. Samples were aliquoted on the same day of collection, shock-frozen in liquid nitrogen, and stored at −80 °C until further analysis.

2.5 DNA extraction and multiplex-barcoding PCR

Microbial DNA was extracted from stool samples by automated purification using a QIAamp® Fast DNA Stool Mini Kit on a QIAcube. The procedure was performed according to the handbook Protocol for Pathogen detection (version March 2014). After suspension and lysis of 200 mg of the sample, 200 μL of the supernatant was used for the extraction. The concentration and purity of the purified DNA were checked using a NanoDrop 1000 system (Thermo Fisher Scientific Inc.) before storage at −20 °C.

The microbiota community composition was analyzed by amplification and sequencing of the V4 region of the bacterial and archaeal 16S rRNA genes at the Joint Microbiome Facility (Medical University of Vienna and University of Vienna; Project ID JMF-1901-3) using an Illumina MiSeq-based highly multiplexed gene amplicon sequencing approach (13) with 515F (GTGYCAGCMGCCGCGGTAA) and 806R (GGACTACNVGGGTWTCTAAT) as primers (14, 15). Sequencing and raw data processing were performed as described previously (13). Amplicon pools were extracted from raw sequencing data using the FASTQ workflow in BaseSpace (Illumina) with default parameters.

Demultiplexing was performed using the Python package demultiplex (Laros JFJ)¹, allowing one mismatch for the barcodes and two mismatches for the linkers and primers.

Amplicon sequence variants (ASVs) were inferred using the DADA2 R package v1.12.33, applying the recommended workflow (16). FASTQ reads 1 and 2 were trimmed to 220/150 nt, with allowed expected errors of 2, respectively.

ASVs were classified according to the SSU rRNA gene sequences using SINA version 1.2.11, and the SILVA database SSU Ref NR 99 release 132 (16–19).

2.6 Monitoring protein absorption and digestion by residual stool energy

Protein absorption was monitored using a bomb-calorimetry-based method (20). Approximately 10 g of stool was transferred to a 50 mL falcon tube, and 10 mL of deionized water was added. The sample was homogenized using an Ultra-Turrax for approximately 10 s. After mixing, the residual sample on the Ultra-Turrax was homogenized with 5 mL of deionized water and added to the sample. The samples were stored in a sloping position to maximize their surfaces and frozen at −30 °C. The frozen samples were freeze-dried for approximately 60 h. After weighing, the samples were repeatedly freeze-dried for 2 h, until the change in weight was less than 1%. Dried samples were ground, and approximately 0.5 g was pressed into pellets for bomb calorimetry.

2.7 Monitoring GI inflammation by calprotectin ELISA

Calprotectin levels in stool samples were measured using a commercial ELISA Kit (21). Stool samples (100 mg) were aliquoted, extracted, and measured according to the kit manual for manual extraction using FLUOstar OPTIMA (22). The results were normalized to the weights of the samples.

2.8 Statistical analysis

Statistical analyses were performed using R version 4.2.1 (23) and RStudio version 2022.07.1 (24). Baseline data for the groups were expressed as mean ± SD or as median ± IQR according to the results of a Shapiro–Wilk test and tested for group differences using a chi-squared test or ANOVA.

Changes in the GI microbiota composition were tested for the three groups across the three time-points each with repeated-measures linear mixed-effect model for observed richness, estimated richness, estimated diversity, and Firmicutes/Bacteroidetes ratio based on rarefied subsamples. The subsample size was defined as 2,250 sequences per sample, calculated by 95% size of the smallest sample size. For PCoA, nMDS, and PermANOVA, ASVs below 0.1% relative abundance in any sample were excluded. Details of the packages and settings are shown in Figures 2–4. A total of 3,299 individual ASVs were tested with a repeated-measures linear mixed-effect model with intervention group, time point, and their interaction as fixed effects, random intercept for participants (≡ repeated measures) and multiple testing correction using the statistical method of false-discovery rate (FDR; Benjamini–Hochberg procedure) at a level of α = 0.05.

Figure 2

Figure 2. Changes in gastrointestinal microbiota (A) observed richness, (B) estimated richness, (C) estimated diversity, and (D) Firmicutes/Bacteroidetes ratio by intervention group (CON, control group = observation only; RP + T, recommended protein group + resistance training; HP + T, high protein group + resistance training) and timepoints (T1 represents baseline, T2 numbers after dietary intervention, and T3 after dietary and training intervention). The gray lines represent single participants, the dark blue lines represent the mean, and the light blue area represents the 95% CI. There were no significant changes within the groups throughout the time point tested using the repeated-measures linear mixed-effects model (α = 0.05); however, individual variation (gray lines) shows substantial fluctuation.

Figure 3

Figure 3. Principal coordinate (PCo) analysis with Bray–Curtis distance measure and untransformed species abundances was calculated using the ampvis2-package in R. The lines connect the three time points for each participant. Gastrointestinal microbiota-based ASVs below 0.1% in any stool sample were removed prior to analysis. The eigenvalues of PCo-Axis 1 and 2 are indicated as percentages in the axis titles. The groups showed complete overlap.

Figure 4

Figure 4. Non-metric multidimensional scaling analysis (nMDS) with the Bray–Curtis distance measure and untransformed species abundances was calculated using the ampvis2-package in R. The initial numerical factors used in the analysis were energy (kcal/day), carbohydrates (g/kg body weight/day), fat (g/kg body weight/day), protein (g/kg body weight/day), BMI (kg/m²), handgrip strength, maximum walking time 6 m, 6-min walking test, timed up and go test, age (years), and sex as nominal factors. Gastrointestinal microbiota-based ASVs with a relative abundance of less than 0.1% in all stool samples were removed. Goodness is shown in the upper-right corner as the stress value (not acceptable). A very short arrow length (energy) indicates a minimal correlation between the variable and ordination. The Mantel test revealed no significant correlation (α = 0.05). The groups showed complete overlap.

For all tests, α = 0.05 was set as the threshold of statistical significance. The FDR was used to correct for multiple testing.

3 Results

3.1 Study population

Of the 136 participants aged 65 and 85 years at baseline, 116 completed the study, and 112 were included in the analyses of the GI microbiota (see Figure 1). Sex was equally distributed among the three groups, and baseline data did not differ, as shown in Table 1. Anthropometric data, body composition, and comorbidities have been evaluated and published elsewhere (11).

3.2 Nutritional intervention

Baseline intake of macronutrients was evaluated for the subgroup included in GI microbiota analysis (n = 112) and showed no group differences when compared to the total study group (n = 116). Throughout the study, carbohydrate and fat intakes did not change significantly. After T1, protein intake increased in the RP + T groups (T1-T2: p = 0.003; T1-T3: p = 0.005) and HP + T (T1-T2: p < 0.0001; T1-T3: p < 0.0001) as planned, while it remained constant over the study period in the CON group. Based on protein intake related to body weight, the groups reached a stable protein intake of approximately 0.83 ± 0.24 g/kg body weight in CON, 1.07 ± 0.25 g/kg in RP + T, and 1.62 ± 0.37 g/kg in HP + T until the end of the intervention.

3.3 Monitoring of GI microbiota composition

One hundred and twelve participants provided stool samples at all three time points and met the inclusion criteria. 16S rRNA gene amplicon sequencing was used to analyze the composition of the GI microbiota. Interestingly, no significant changes were observed after intervention. As shown in Figure 5, microbiota richness, expressed as the number of unique amplicon sequence variants (ASV) on a rarefied depth dataset (n = 2,250 reads/sample), and markers of bacterial diversity did not change (tested using repeated-measures linear mixed-effects models with FDR correction, α = 0.05).

Figure 5

Figure 5. Heatmaps of relative ASV counts aggregated by (A) class level, (B) order level, (C) family level, and (D) genus level, grouped by time point, and facet by intervention group. A pairwise permANOVA (pairwise.adonis2) indicated no significant difference in the centroids of the time-point data within each intervention group. Therefore, it can be assumed that there were no differences or significant changes in the composition of gastrointestinal microbiota throughout the intervention within each intervention group.

Tests for changes in beta diversity in and between the microbiota communities (principal coordinates analysis) of the study groups showed no significant longitudinal shifts or clustering by subgroups (see Figure 2).

Non-metric multidimensional scaling analysis was used to evaluate the influence of single factors on microbiota. Based on the study design, we focused on the nutritional influence and impact of the strength training phase. The details are presented in Table 1. Only total energy intake was linked to shifts in the microbiota, but this correlation was not significant, as shown in Figure 3.

Based on this finding and with permANOVA testing of the counts of ASVs (see Figure 4), we did not observe shifts in the microbiota of the older adults by the intervention of increased protein intake.

3.4 Monitoring absorption and tolerance of increased protein intake

Protein absorption and tolerance were assessed using residual energy measurements and inflammatory markers in stool samples across all time points (T1, T2, and T3).

Bomb calorimetry analysis of freeze-dried stool samples showed no significant changes in residual energy between the baseline and the end of the intervention across all groups.

Calprotectin, a marker of GI inflammation, remained stable throughout the intervention period.

Statistical analysis using linear mixed-effects models confirmed that there were no significant changes in residual energy or inflammatory markers in the intervention groups compared to baseline.

These findings suggest that increased protein intake up to 1.62 ± 0.37 g/kg body weight was well-tolerated by the participants.

The data are summarized in the Supplementary material (Supplementary Figures 1, 2 and Supplementary Tables 1, 2).

4 Discussion

Human diet shapes the GI microbiota and influences overall health and aging. Diets rich in fiber, plant-based foods, fermented products, and polyphenols promote microbiota diversity and reduce chronic disease risk (25). Conversely, diets high in fat, sugar, processed foods, and red meat disrupt GI balance, thereby increasing dysbiosis and health risks (26).

For older adults, age-related GI microbiome changes—such as reduced diversity and beneficial bacteria—are linked to frailty and chronic diseases (2). A balanced, fiber-rich diet with diverse protein sources is crucial for maintaining GI health and supporting healthy aging (27).

In particular, adequate protein intake is considered crucial for maintaining or enhancing muscle quality and mass, thereby helping to prevent sarcopenia and its associated adverse health outcomes—especially when combined with resistance training (28, 29). However, a high dietary protein intake may exert adverse effects, particularly in older adults with compromised gastrointestinal efficiency or declining renal function (30). Therefore, we aimed to investigate the effects of 17 weeks of moderate and high protein intake, with and without strength training, on the GI microbiota of community-dwelling older adults.

We were successful in achieving a personalized protein intake of 1.07 ± 0.25 g/kg BW in the RP + T group, which is very close to the recommendation in the D-A-CH region, and we doubled the baseline protein intake to 1.62 ± 0.37 g/kg body weight in the HP + T group and followed this regimen for 17 weeks without a significant increase in carbohydrate and fat intake. One innovation of this study was that protein intake was not administered as a protein powder or supplementation, as in many other studies but was provided with commercially available food products of animal and plant origin, either rich in protein for the HP + T group (e.g., protein-rich milk products, bars, puddings, protein-rich bread, bacon crisps, protein-rich soups, pea protein sticks, and recipes for self-prepared foods) or similar foods with regular protein content for the RP + T group (e.g., milk products, bars, bread, soups, and self-made vegetable muffins). In total, we provided more than ten tons of food to the participants. Individual protein intake was recorded daily by the respective participants and monitored throughout the entire study period with nine 24-h recalls per participant.

Following this dietary modification, the protein content in the HP + T group increased from 14% at baseline to approximately 25% of total energy intake. Stool samples were collected at three time points throughout the study to monitor potential changes in GI microbiota composition, residual energy in stool, and the inflammation marker calprotectin.

However, it may also promote proteolytic fermentation, resulting in the formation of putrefactive metabolites (31, 32). Jantchou et al. (2010) demonstrated that a high-protein intake, especially from animal sources, is associated with an increased risk of inflammatory bowel disease (IBD) (33).

Stool microbiota diversity and composition were analyzed using 16S rRNA gene amplicon sequencing. Despite the dietary changes in protein intake, alpha and beta diversities did not change throughout the study in either intervention group. Figure 5 shows the development of observed richness, estimated richness, estimated diversity, and the Firmicutes/Bacteroidetes ratio. Furthermore, a principal coordinate analysis (see Figure 3) and a non-metric multidimensional scaling analysis (see Figure 4) showed a complete overlap between the groups and only fluctuations within individual participants.

To evaluate potential shifts at a finer taxonomic resolution, we examined the relative abundance of bacterial genera that are known to be associated with protein metabolism and health outcomes in older adults. The key genera examined included Bifidobacterium and Lactobacillus (beneficial bacteria that decline with age), Akkermansia (mucin-degrading bacteria associated with metabolic health), butyrate-producing genera (Faecalibacterium, Roseburia), and genera associated with protein fermentation. The repeated-measures linear mixed-effects model did not show significant changes in any of these measures. A pairwise permANOVA on relative ASV counts aggregated by different phyla (see Figure 5) showed sustained abundance of bacterial phyla. No statistically significant changes were observed in the relative abundance of these key genera across the time points or between the intervention groups.

Although our study confirms the structural stability of the GI microbiota, it should be noted that the structural composition alone does not reflect full functionality. High protein intake can increase proteolytic fermentation and potentially lead to putrid metabolites, such as phenols, indoles, sulfides, ammonia, amines, and trimethylamine N-oxide (TMAO) precursors. These metabolites may influence host health independently of compositional shifts. The observed stability in the microbiota composition indicated that increased protein intake did not disrupt the overall community structure.

This was an unexpected but very positive observation, proving that a shift to a high-protein diet via conventional food products over more than 4 months did not lead to negative outcomes in the GI microbiota of older adults.

Doubling protein intake through protein-rich foods, in alignment with current dietary recommendations, does not appear to adversely affect GI microbiota composition when predominantly provided via whole food sources. Adequate protein intake is the basis for preventing sarcopenia; however, only if the increased amount has no adverse effects on other health indicators such as GI health (34).

Similarly, residual energy and levels of the inflammatory marker fecal calprotectin did not differ significantly between or within the groups. There was no evidence of an increase in either of these markers. Residual energy can be used as a marker for digestion. An increase in residual energy based on the increased protein intake, which could be a sign of undigested protein reaching and passing through the large intestine (35), was not observed.

Taken together with the observed stability in GI microbiota diversity and composition, these findings support the feasibility of increasing protein intake in older adults to at least 1.6 g/kg body weight—a level shown to be effective in preserving and enhancing muscle mass with advancing age (36). This is needed, for example, to improve muscle protein synthesis or hand grip strength.

There is animal data that supplements our findings. The source of proteins and their quality appear to be a major driver of microbiota shifts and health outcomes, especially meat-based vs. non-meat-based proteins. Plant-based proteins, primarily soy and legume proteins, as well as dairy proteins such as casein, tend to be less obesogenic by affecting appetite, altering nitric oxide production, and have a high content of branched chain amino acids (BCAAs), and are more beneficial for microbiota balance (e.g., increased abundance of Akkermansia and Bifidobacterium, fewer sources of TMAO-metabolites) (37).

The application form is also important; whole-food diets with an increased number of unprocessed foods, as it was the focus of our study design, could mask macronutrient-specific effects. However, studies employing isolated macronutrients consistently demonstrate that high-fat, carbohydrate, and fiber content exert a more pronounced influence on microbiota composition than isocaloric high-protein diets (38–41).

First, the older adult microbiome exhibits inherent resilience and reduced plasticity compared to younger cohorts, as documented in aging research (42–44). Second, our whole-food intervention approach, which provides protein through diverse food matrices rather than isolated supplements, may have buffered macronutrient-specific effects. The complexity of whole foods, containing fiber, polyphenols, and other bioactive compounds, could mask direct protein effects on microbiota (45). Third, the food-based format ensured adequate co-ingestion of dietary fiber and other nutrients that may counteract potential negative effects of increased protein fermentation (46). Fourth, resistance training itself may have beneficial effects on GI health through improved intestinal transit time and systemic metabolic changes (47).

Fiber intake was assessed at all three time points to evaluate its potential role as a confounding variable. The average daily fiber intake remained stable in all groups throughout the study. However, due to missing product data for dietary fiber, the data were not fully sufficient to perform more detailed analyses and correlations in this subanalysis. To address this major limitation, the Healthy Eating Index by EPIC was calculated in a supplementary subanalysis (48). This showed that the increased intake of protein products had no particular influence on dietary quality.

The strength of our study is the two-phase study design, with an initial nutritional phase and a subsequent combined nutritional and exercise intervention, which has not been performed before in an older adult cohort. Furthermore, we achieved doubling of the baseline protein intake with a food-based approach and not by supplementation, which is an important aspect of dietary adherence, specifically in this age group. We individualized and tightly controlled the participants’ diet by performing 24-h recalls every 7–10 days; therefore, we believe that we kept the reporting error at a minimum. As the intensity of the strength training program was controlled by subjective experience (RPE scale), the resulting intensity was moderate, which we believe resulted in a low dropout rate during the strength training intervention. Microbiota analysis was performed in the stool samples and considered both nutritional and combined nutritional and training interventions, all of which potentially could have affected the GI microbiota. Our study was conducted exclusively on community-dwelling older adults (65–85 years) in Vienna, Austria, representing a relatively homogeneous Central European population. This limits the generalizability of our findings to populations with different genetic backgrounds, dietary traditions, baseline microbiota compositions, and environmental exposure. Future multicenter studies encompassing diverse geographic and ethnic populations would be valuable to confirm the generalizability of these findings. Another limitation is that we did not measure trimethylamine-N-oxide (TMAO) in this study, which is also currently under evaluation as a possible metabolic marker for cardiovascular risks.

5 Conclusion

A habitual diet with recommended or high protein intake, with or without strength training for over 17 weeks, showed no detectable adverse effects on the GI microbiota composition of 112 community-dwelling adults aged 65–85 years. Despite the increased protein intake of up to 1.62 g/kg body weight, GI microbiota richness, diversity, and composition, no significant changes within or between groups were observed. Residual energy and inflammatory markers indicated that a higher protein intake was well tolerated.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1275818/.

Ethics statement

The studies involving humans were approved by the Ethic Committee of the University of Vienna. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

PAZ: Formal analysis, Investigation, Software, Visualization, Writing – original draft. SU: Data curation, Investigation, Writing – review & editing. RA: Investigation, Writing – review & editing. AD: Investigation, Writing – review & editing. SS: Investigation, Writing – review & editing. MK: Investigation, Writing – review & editing. TB: Investigation, Writing – review & editing. CH: Investigation, Writing – review & editing. SR: Investigation, Writing – review & editing. BF: Conceptualization, Investigation, Methodology, Writing – review & editing. E-MS: Conceptualization, Methodology, Writing – review & editing. BH: Conceptualization, Investigation, Software, Writing – review & editing. PP: Conceptualization, Investigation, Software, Writing – review & editing. DB: Conceptualization, Investigation, Software, Writing – review & editing. BW: Conceptualization, Formal analysis, Funding acquisition, Validation, Writing – review & editing. K-HW: Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing – review & editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This study was supported by the University of Vienna, by funding from the Research Platform Active Ageing and the EU-program Interreg SK-AT (NutriAging). This study was supported by the Open Access Publishing Fund of the University of Vienna and the Vienna Doctoral School of Pharmaceutical, Nutritional, and Sport Sciences’ Completion Grant of the University of Vienna.

Generative AI statement

The author(s) declared that Generative AI was used in the creation of this manuscript. Perplexity AI was used for the literature search and writing assistance in the preparation of this study. All content generated or suggested by the AI was carefully reviewed, edited, and validated to ensure accuracy, originality, and compliance with ethical standards. The responsibility for the final content remains with the authors.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Acknowledgments

We would like to thank Gudrun Kohl and Jasmin Schwarz for laboratory sample processing at JMF. The computational results of the amplicon sequencing data presented in this study have been achieved using the Life Science Compute Cluster (LiSC) of the University of Vienna. The authors would like to express their gratitude to all participants and students who contributed to this project, as well as to Holmes Place Wien GmbH and Stars Fitness GmbH for providing training facilities. We also extend our thanks to Chiefs AG, Rudolf Ölz Meisterbäcker GmbH & Co KG, Neoh (Alpha Republic GmbH), AnovonA Medsupps GmbH, NÖM AG, Gittis Naturprodukte GmbH, GoVital (Findus Sverige AB), Nomad Foods Europe, Fleischwaren Berger GmbH & Co KG, and Handl Tyrol GmbH for supplying the protein-rich and standard food products.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2025.1712451/full#supplementary-material

Footnotes

1. ^github.com/jfjlaros/demultiplex.

References

1. World Health Organization. Ageing and health: World Health Organization (2024). Available online at: https://www.who.int/news-room/fact-sheets/detail/ageing-and-health (April 30, 2025).

Google Scholar

2. Ghosh, TS, Shanahan, F, and O’Toole, PW. The gut microbiome as a modulator of healthy ageing. Nat Rev Gastroenterol Hepatol. (2022) 19:565–84. doi: 10.1038/s41575-022-00605-x,

PubMed Abstract | Crossref Full Text | Google Scholar

3. Franzke, B, Bileck, A, Unterberger, S, Aschauer, R, Zöhrer, PA, Draxler, A, et al. The plasma proteome is favorably modified by a high protein diet but not by additional resistance training in older adults: a 17-week randomized controlled trial. Front Nutr. (2022) 9:925450. doi: 10.3389/fnut.2022.925450,

PubMed Abstract | Crossref Full Text | Google Scholar

4. Whaikid, P, and Piaseu, N. The effectiveness of protein supplementation combined with resistance exercise programs among community-dwelling older adults with sarcopenia: a systematic review and Meta-analysis. Epidemiol Health. (2024) 46:e2024030. doi: 10.4178/epih.e2024030,

PubMed Abstract | Crossref Full Text | Google Scholar

5. Salazar, J, Durán, P, Díaz, MP, Chacín, M, Santeliz, R, Mengual, E, et al. Exploring the relationship between the gut microbiota and ageing: a possible age modulator. Int J Environ Res Public Health. (2023) 20:5845. doi: 10.3390/ijerph20105845,

PubMed Abstract | Crossref Full Text | Google Scholar

6. Prokopidis, K, Cervo, MM, Gandham, A, and Scott, D. Impact of protein intake in older adults with sarcopenia and obesity: a gut microbiota perspective. Nutrients. (2020) 12:2285. doi: 10.3390/nu12082285,

PubMed Abstract | Crossref Full Text | Google Scholar

7. Wu, S, Bhat, ZF, Gounder, RS, Mohamed Ahmed, IA, Al-Juhaimi, FY, Ding, Y, et al. Effect of dietary protein and processing on gut microbiota-a systematic review. Nutrients. (2022) 14:453. doi: 10.3390/nu14030453,

PubMed Abstract | Crossref Full Text | Google Scholar

8. Duncan, SH, Iyer, A, and Russell, WR. Impact of protein on the composition and metabolism of the human gut microbiota and health. Proc Nutr Soc. (2021) 80:173–85. doi: 10.1017/S0029665120008022,

PubMed Abstract | Crossref Full Text | Google Scholar

9. Mitchell, SM, Milan, AM, Mitchell, CJ, Gillies, NA, D'Souza, RF, Zeng, N, et al. Protein intake at twice the RDA in older men increases circulatory concentrations of the microbiome metabolite trimethylamine-N-oxide (TMAO). Nutrients. (2019) 11:2207. doi: 10.3390/nu11092207,

PubMed Abstract | Crossref Full Text | Google Scholar

10. Medical University of Graz. Randomizer: Randomization Service for Multicenter Clinical Trials Graz, Austria: Medical University of Graz (2025).

Google Scholar

11. Unterberger, S, Aschauer, R, Zohrer, PA, Draxler, A, Franzke, B, Strasser, EM, et al. Effects of an increased habitual dietary protein intake followed by resistance training on fitness, muscle quality and body composition of seniors: a randomised controlled trial. Clin Nutr. (2022) 41:1034–45. doi: 10.1016/j.clnu.2022.02.017,

PubMed Abstract | Crossref Full Text | Google Scholar

12. Deutsche Gesellschaft für E, Österreichische Gesellschaft für E, Schweizerische Gesellschaft für E. Referenzwerte Für Die Nährstoffzufuhr. 2. Auflage, 8. aktualisierte Ausgabe ed. Bonn: Deutsche Gesellschaft für Ernährung, Österreichische Gesellschaft für Ernährung (2024).

Google Scholar

13. Pjevac, P, Hausmann, B, Schwarz, J, Kohl, G, Herbold, CW, Loy, A, et al. An economical and flexible dual barcoding, two-step Pcr approach for highly multiplexed amplicon sequencing. Front Microbiol. (2021) 12:669776. doi: 10.3389/fmicb.2021.669776,

PubMed Abstract | Crossref Full Text | Google Scholar

14. Parada, AE, Needham, DM, and Fuhrman, JA. Every base matters: assessing small subunit Rrna primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. (2016) 18:1403–14. doi: 10.1111/1462-2920.13023,

PubMed Abstract | Crossref Full Text | Google Scholar

15. Apprill, A, McNally, S, Parsons, R, and Weber, L. Minor revision to V4 region Ssu Rrna 806r gene primer greatly increases detection of Sar11 bacterioplankton. Aquat Microb Ecol. (2015) 75:129–37. doi: 10.3354/ame01753

Crossref Full Text | Google Scholar

16. Pruesse, E, Peplies, J, and Glockner, FO. Sina: accurate high-throughput multiple sequence alignment of ribosomal Rna genes. Bioinformatics. (2012) 28:1823–9. doi: 10.1093/bioinformatics/bts252,

PubMed Abstract | Crossref Full Text | Google Scholar

17. Quast, C, Pruesse, E, Yilmaz, P, Gerken, J, Schweer, T, Yarza, P, et al. The Silva ribosomal Rna gene database project: improved data processing and web-based tools. Nucleic Acids Res. (2013) 41:D590–6. doi: 10.1093/nar/gks1219

Crossref Full Text | Google Scholar

18. Callahan, BJ, McMurdie, PJ, Rosen, MJ, Han, AW, Johnson, AJ, and Holmes, SP. Dada2: high-resolution sample inference from Illumina amplicon data. Nat Methods. (2016) 13:581–3. doi: 10.1038/nmeth.3869,

PubMed Abstract | Crossref Full Text | Google Scholar

19. Callahan, BJ, Sankaran, K, Fukuyama, JA, McMurdie, PJ, and Holmes, SP. Bioconductor workflow for microbiome data analysis: from raw reads to community analyses. F1000Res. (2016) 5:1492. doi: 10.12688/f1000research.8986.2,

PubMed Abstract | Crossref Full Text | Google Scholar

20. Lovelady, HG, and Stork, EJ. An improved method for preparation of feces for bomb calorimetry. Clin Chem. (1970) 16:253–4. doi: 10.1093/clinchem/16.3.253,

PubMed Abstract | Crossref Full Text | Google Scholar

21. EUROIMMUN Medizinische Labordiagnostika AG. Calprotectin determination in stool. EQ6831–9601. Germany: W. Lübeck. (2019) 8.

Google Scholar

22. BMG Labtech GmbH. Fluostar Optima. Software Version 2.20R2, Firmware Version 1.26 (2022).

Google Scholar

23. R. Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing (2022).

Google Scholar

24. R. Studio Team. Rstudio: Integrated development for R. Boston, MA: RStudio, PBC (2020).

Google Scholar

25. Nemzer, BV, Al-Taher, F, Kalita, D, Yashin, AY, and Yashin, YI. Health-improving effects of polyphenols on the human intestinal microbiota: a review. Int J Mol Sci. (2025) 26:1335. doi: 10.3390/ijms26031335,

PubMed Abstract | Crossref Full Text | Google Scholar

26. Losso, JN. Food processing, Dysbiosis, gastrointestinal inflammatory diseases, and antiangiogenic functional foods or beverages. Annu Rev Food Sci Technol. (2021) 12:235–58. doi: 10.1146/annurev-food-062520-090235,

PubMed Abstract | Crossref Full Text | Google Scholar

27. Aziz, T, Hussain, N, Hameed, Z, and Lin, L. Elucidating the role of diet in maintaining gut health to reduce the risk of obesity, cardiovascular and other age-related inflammatory diseases: recent challenges and future recommendations. Gut Microbes. (2024) 16:2297864. doi: 10.1080/19490976.2023.2297864,

PubMed Abstract | Crossref Full Text | Google Scholar

28. Murphy, CH, McCarthy, SN, and Roche, HM. Nutrition strategies to counteract sarcopenia: a focus on protein, Lc N-3 Pufa and precision nutrition. Proc Nutr Soc. (2023) 82:419–31. doi: 10.1017/s0029665123003555,

PubMed Abstract | Crossref Full Text | Google Scholar

29. Nasso, R, D’Errico, A, Motti, ML, Masullo, M, and Arcone, R. Dietary protein and physical exercise for the treatment of sarcopenia. Clin Pract. (2024) 14:1451–67. doi: 10.3390/clinpract14040117,

PubMed Abstract | Crossref Full Text | Google Scholar

30. Narasaki, Y, Okuda, Y, Moore, LW, You, AS, Tantisattamo, E, Inrig, JK, et al. Dietary protein intake, kidney function, and survival in a nationally representative cohort. Am J Clin Nutr. (2021) 114:303–13. doi: 10.1093/ajcn/nqab011,

PubMed Abstract | Crossref Full Text | Google Scholar

31. Raimondi, S, Calvini, R, Candeliere, F, Leonardi, A, Ulrici, A, Rossi, M, et al. Multivariate analysis in microbiome description: correlation of human gut protein degraders, metabolites, and predicted metabolic functions. Front Microbiol. (2021) 12:723479. doi: 10.3389/fmicb.2021.723479,

PubMed Abstract | Crossref Full Text | Google Scholar

32. Amaretti, A, Gozzoli, C, Simone, M, Raimondi, S, Righini, L, Perez-Brocal, V, et al. Profiling of protein degraders in cultures of human gut microbiota. Front Microbiol. (2019) 10:2614. doi: 10.3389/fmicb.2019.02614,

PubMed Abstract | Crossref Full Text | Google Scholar

33. Jantchou, P, Morois, S, Clavel-Chapelon, F, Boutron-Ruault, MC, and Carbonnel, F. Animal protein intake and risk of inflammatory bowel disease: the E3n prospective study. Am J Gastroenterol. (2010) 105:2195–201. doi: 10.1038/ajg.2010.192,

PubMed Abstract | Crossref Full Text | Google Scholar

34. Beasley, JM, Shikany, JM, and Thomson, CA. The role of dietary protein intake in the prevention of sarcopenia of aging. Nutr Clin Pract. (2013) 28:684–90. doi: 10.1177/0884533613507607,

PubMed Abstract | Crossref Full Text | Google Scholar

35. Gilbert, MS, Ijssennagger, N, Kies, AK, and van Mil, SWC. Protein fermentation in the gut; implications for intestinal dysfunction in humans, pigs, and poultry. Am J Physiol Gastrointest Liver Physiol. (2018) 315:G159–70. doi: 10.1152/ajpgi.00319.2017,

PubMed Abstract | Crossref Full Text | Google Scholar

36. Morton, RW, Murphy, KT, McKellar, SR, Schoenfeld, BJ, Henselmans, M, Helms, E, et al. A systematic review, meta-analysis and meta-regression of the effect of protein supplementation on resistance training-induced gains in muscle mass and strength in healthy adults. Br J Sports Med. (2018) 52:376–84. doi: 10.1136/bjsports-2017-097608,

PubMed Abstract | Crossref Full Text | Google Scholar

37. Madsen, L, Myrmel, LS, Fjaere, E, Liaset, B, and Kristiansen, K. Links between dietary protein sources, the gut microbiota, and obesity. Front Physiol. (2017) 8:1047. doi: 10.3389/fphys.2017.01047,

PubMed Abstract | Crossref Full Text | Google Scholar

38. Ribeiro, RV, Senior, AM, Simpson, SJ, Tan, J, Raubenheimer, D, Le Couteur, D, et al. Rapid benefits in older age from transition to whole food diet regardless of protein source or fat to carbohydrate ratio: arandomised control trial. Aging Cell. (2024) 23:e14276. doi: 10.1111/acel.14276,

PubMed Abstract | Crossref Full Text | Google Scholar

39. Faraj, S, Sequeira-Bisson, IR, Lu, L, Miles-Chan, JL, Hoggard, M, Barnett, D, et al. Effect of a higher-protein nut versus higher-carbohydrate cereal enriched diet on the gut microbiomes of Chinese participants with overweight and normoglycaemia or prediabetes in the Tu Ora study. Nutrients. (2024) 16:1971. doi: 10.3390/nu16121971,

PubMed Abstract | Crossref Full Text | Google Scholar

40. Kiilerich, P, Myrmel, LS, Fjaere, E, Hao, Q, Hugenholtz, F, Sonne, SB, et al. Effect of a long-term high-protein diet on survival, obesity development, and gut microbiota in mice. Am J Physiol Endocrinol Metab. (2016) 310:E886–99. doi: 10.1152/ajpendo.00363.2015,

PubMed Abstract | Crossref Full Text | Google Scholar

41. Dong, TS, Luu, K, Lagishetty, V, Sedighian, F, Woo, SL, Dreskin, BW, et al. A high protein calorie restriction diet alters the gut microbiome in obesity. Nutrients. (2020) 12:3221. doi: 10.3390/nu12103221,

PubMed Abstract | Crossref Full Text | Google Scholar

42. Escudero-Bautista, S, Omana-Covarrubias, A, Nez-Castro, AT, Lopez-Pontigo, L, Pimentel-Perez, M, and Chavez-Mejia, A. Impact of gut microbiota on aging and frailty: a narrative review of the literature. Geriatrics (Basel). (2024) 9:110. doi: 10.3390/geriatrics9050110,

PubMed Abstract | Crossref Full Text | Google Scholar

43. Moya, A, and Ferrer, M. Functional redundancy-induced stability of gut microbiota subjected to disturbance. Trends Microbiol. (2016) 24:402–13. doi: 10.1016/j.tim.2016.02.002,

PubMed Abstract | Crossref Full Text | Google Scholar

44. Odamaki, T, Kato, K, Sugahara, H, Hashikura, N, Takahashi, S, Xiao, JZ, et al. Age-related changes in gut microbiota composition from newborn to centenarian: a cross-sectional study. BMC Microbiol. (2016) 16:90. doi: 10.1186/s12866-016-0708-5,

PubMed Abstract | Crossref Full Text | Google Scholar

45. Bartlett, A, and Kleiner, M. Dietary protein and the intestinal microbiota: an understudied relationship. iScience. (2022) 25:105313. doi: 10.1016/j.isci.2022.105313,

PubMed Abstract | Crossref Full Text | Google Scholar

46. Johnson, AJ, Vangay, P, Al-Ghalith, GA, Hillmann, BM, Ward, TL, Shields-Cutler, RR, et al. Daily sampling reveals personalized diet-microbiome associations in humans. Cell Host Microbe. (2019) 25:e5:789–802. doi: 10.1016/j.chom.2019.05.005,

PubMed Abstract | Crossref Full Text | Google Scholar

47. Monda, V, Villano, I, Messina, A, Valenzano, A, Esposito, T, Moscatelli, F, et al. Exercise modifies the gut microbiota with positive health effects. Oxidative Med Cell Longev. (2017) 2017:3831972. doi: 10.1155/2017/3831972,

PubMed Abstract | Crossref Full Text | Google Scholar

48. von Rüsten, A, Illner, A-K, Boeing, H, and Flothkötter, M. Die Bewertung Der Lebensmittelaufnahme Mittels Eines Healthy Eating Index‘ (Hei-Epic). Ernahrungs Umsch. (2009) 56:450–6. Available online at: https://www.ernaehrungs-umschau.de/fileadmin/Ernaehrungs-Umschau/pdfs/pfd_2009/08_09/EU08_450_456.qxd.pdf

Google Scholar