Cholesterol-lowering effects of oats induced by microbially produced phenolic metabolites in metabolic syndrome: a randomized controlled trial

Domain: chemistry
Published: 2026-01-14
Editorial outcome: major_revision

Abstract
Oats have various positive effects on human health, but the underlying mechanisms are not fully understood. To identify oat-microbiome-host interactions contributing to metabolic improvements, we conducted two randomized controlled dietary interventions in parallel-design in individuals with metabolic syndrome, comparing a short-term, high-dose and a six-week, moderate oat intake with respective controls (DRKS00022169). Both oat diets lead to an increase in plasma ferulic acid (0.64 [0.26, 1.02], P=0.002; 0.55 [0.21, 0.89], P=0.003), while the high-dose oat-diet also increased dihydroferulic acid (1.23 [0.44, 2.01], P=0.003). Here we show that microbial phenolic metabolites are driving factors for the cholesterol-lowering effect of oats, which might be of relevance since short-term, high-dose oat-diet is a suitable approach to alleviate obesity-related lipid disorders.

Introduction
Metabolic syndrome (MetS), characterized by the co-occurrence of central obesity, dyslipidemia, elevated blood pressure (BP), and dysglycemia1, is a significant risk factor for the development of type 2 diabetes mellitus (T2DM)2 and cardiovascular disease (CVD)3. In addition, MetS is linked to an aberrant gut microbiota4. The prevalence of MetS has increased dramatically in recent decades and is considered a major public health challenge in both developed and developing countries. Studies indicate that up to 31% of the world’s population is affected5. As the underlying pathophysiology of MetS is complex and cannot be attributed to a single mechanism, lifestyle changes offer a fundamental treatment option6. Among dietary modifications, fiber intake plays an important role in managing MetS, since the consumption of dietary fiber is associated with metabolic health7.

Oats offer an interesting and promising approach for treating MetS due to their unique composition characterized by a high fiber content, especially β-glucan, essential minerals and vitamins, and various bioactive substances, including phenols which exert antioxidant and anti-inflammatory effects that may improve metabolic function8. Furthermore, oats are an accessible and sustainable food item. The positive effects of oats on glucose metabolism were first described by Carl von Noorden, a German diabetologist who developed a special oat cure for the treatment of diabetes at the beginning of the 20th century9. A few years later, de Groot et al. observed that oats could also improve lipid metabolism by reducing serum cholesterol10. Since then, various clinical studies have confirmed these beneficial effects of oats, and health claims have been approved for the cholesterol-lowering properties and reduction in postprandial glycemic response of oat β-glucan11,12. However, the underlying mechanisms of oat-induced improvements in metabolic health are not fully understood. Recent studies suggest the ability of oats to modulate the gut microbiota as a mechanism for its health-promoting effects13. In particular, microbially produced metabolites were ascribed a decisive role in nutrition-induced metabolic improvement14. However, it is still unknown whether metabolites produced by the microbial degradation of oats, such as phenolic compounds, play an important health-promoting role alongside short-chain fatty acids (SCFAs)15 produced by the bacterial fermentation of β-glucan. We hypothesized that the beneficial effects of oats on metabolism might be influenced by host-microbiome interactions, leading to changes in phenolic compounds such as ferulic acid (FA), dihydroferulic acid (DHFA), 2-aminophenol sulfate, and 2-acetamidophenol sulfate, which have been indicated in recent studies as relevant (microbial) metabolites from whole grains such as oats with different biological functions16,17. The aim of this randomized controlled trial (RCT) was to investigate the effects of a short-term, high-dose oat diet (a modified version of the original oat cure by Carl von Noorden9) and a six-week, moderate oat diet compared with a macronutrient-adapted control diet and a Western diet, respectively, on lipid metabolism, the gut microbiota, and global metabolomic profiles, in particular the phenolic compounds FA and DHFA, in 68 individuals with MetS (n=17 participants/group) and to elucidate the underlying mechanism using an integrative multi-omics analysis.

In this work, our data demonstrate the superiority of the oat diets in increasing plasma levels of phenolic compounds, such as FA, DHFA, 2-aminophenol sulfate, and 2-acetamidophenol sulfate compared with the respective controls. In addition, the short-term, high-dose oat diet improves lipid metabolism by lowering serum cholesterol levels compared with the control diet. The observed associations between the cholesterol reduction and the alterations in metabolomic profiles indicate that phenolic compounds, especially their microbial degradation products, might be driving factors for the cholesterol-lowering effect of oats, besides the known mechanism of β-glucan.

Results
Impact of the two different oat diets on human metabolism
To determine the effects of a short-term, high-dose oat diet under hypocaloric conditions and a six-week, moderate oat diet under isocaloric conditions on metabolism, the gut microbiota, and global metabolomic profiles, in particular FA and DHFA, in individuals with MetS compared with corresponding oat-free control diets, two randomized controlled nutritional intervention studies were conducted in parallel-design with 34 subjects each (n=17 per group) (Fig. 1a, b) and completed in July 2022.

In the short-term intervention study, participants assigned to the oat group (OG) consumed three oat meals daily for two days instead of their habitual Western diet. Each oat meal comprised 100 g of rolled oat flakes (Demeterhof Schwab GmbH & Co. KG, Windsbach, Germany) boiled in water. To ascertain potential long-term effects, the two-day intervention period was followed by a six-week follow-up period during which the participants returned to their habitual diet without oats.

Subjects assigned to the control group (CG) consumed three standardized control meals without oats on each intervention day, which were macronutrient-adapted to the OG, instead of their habitual Western diet.

In the six-week intervention study, participants in the oat group (OG6w) replaced one habitual meal per day with an oat meal comprising 80 g of rolled oat flakes (Demeterhof Schwab GmbH & Co. KG), while maintaining their habitual Western diet. Participants in the corresponding control group (CG6w) maintained their habitual Western diet and remained abstinent from oats during the six-week study period according to the inclusion criteria.

During the short-term intervention study, two male participants withdrew from the study for personal reasons (Fig. 1c). Thus, 32 participants completed the study and were considered for the statistical analysis (OG: n=17, CG: n=15) which included 15 males and 17 females aged 58.9±7.4 years (mean±standard deviation [SD]) with a body mass index (BMI) of 32.0±3.3 kg/m² (Table 1). In the CG, 60% of participants were men, while in the OG 35% were men. All participants had central obesity (100%) and at least two further metabolic syndrome traits, including increased BP (100%), impaired glucose metabolism (75%), and dyslipidemia (63%).

In the six-week intervention study, all 34 participants, including 15 males and 19 females aged 59.7±7.6 years with a BMI of 31.6±3.2 kg/m² (Table 1), completed the study and were considered in the statistical analysis (Fig. 1d). In the CG6w, 47% of participants were men, while in the OG6w 41% were men. According to the inclusion criteria, all participants had central obesity (100%) and at least two further metabolic syndrome traits. Thus, at baseline, 97% of the subjects had elevated BP, 76% impaired glucose metabolism, and 59% dyslipidemia. Further baseline characteristics of the participants and detailed information on their habitual dietary behavior are provided in Supplementary Data 1.

Compliance and adverse events
Adherence to the study diets were recorded, showing a compliancy rate of 99% (short-term intervention study) and 89% (six-week intervention study). Tolerability assessment indicated that the diets were well tolerated overall, with no severe side effects related to the diets.

Oat diets increased plasma (dihydro)ferulic acid levels
According to our primary hypothesis, significant higher plasma levels of FA (0.64 [0.26, 1.02] (β estimate [95% confidence interval (CI)]), P=0.002; Fig. 2a) and DHFA (1.23 [0.44, 2.01], P=0.003; Fig. 2b), determined by liquid chromatography with tandem mass spectrometry (LC-MS/MS)18, were observed in OG compared to CG after the two-day intervention period, using linear regression adjusted for the baseline value of the response, age, sex, and BMI (LnRadj.). Consistent with this, an increase in plasma FA level was observed in OG6w compared with CG6w after the six-week intervention period (0.55 [0.21, 0.89], P=0.003; Fig. 2c); however, no difference in DHFA level was found (0.63 [−0.12, 1.37], P=0.096; Fig. 2d). Our results therefore suggest that both the short-term, high-dose and the six-week, moderate oat diet have a systemic effect. This effect is more pronounced and potentially stronger if related to the gut microbiota as shown in the short-term intervention due to the increase in DHFA, a major microbial metabolite of FA19.

Oat diet reduced serum cholesterol levels
Investigating the diet-induced modulation of the metabolism using sparse partial least squares-discriminant analysis (sPLS-DA)20,21, a significant difference between OG and CG was observed after the two-day intervention period (area under the curve (AUC)=0.88 (P=1.99 × 10−4), balanced error rate (BER)=0.154; Fig. 3a), with low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), and diastolic BP being the most important distinguishing features (Fig. 3b). A significant reduction in LDL-C (−16.26 [−23.60, −10.32] mg/dL, Padj.=0.011 (Bonferroni–Holm)) and TC (−15.61 [−24.17, −7.80] mg/dL, Padj.=0.02) levels in OG compared with CG were also identified using LnRadj. (Fig. 3b, c). Since cholesterol levels tended to remain below baseline during the six-week, oat-free follow-up period, persistent effects on lipid metabolism might be assumed (Fig. 3d). This assumption is further supported by the high compliance observed during the follow-up period, as all participants abstained from oat consumption and returned to their habitual Western diet, with no significant differences compared to their pre-study dietary patterns (Supplementary Data 2). Thus, our results indicate clearly that a high-dose oat diet improves lipid metabolism by decreasing serum TC and LDL-C levels, even after two days, which is consistent with the known cholesterol-lowering effect of oats22. In addition, beneficial effects on anthropometrics and glucose metabolism were observed within each diet group (Supplementary Data 2), which we attribute to the diet-related calorie restriction23.

In the six-week intervention study, sPLS-DA revealed no differences in the diet-induced modulation of metabolism between OG6w and CG6w (AUC=0.58 (P=0.399), BER=0.412; Fig. 3e, f). Participants’ metabolic status, including TC and LDL-C levels, remained largely stable (Fig. 3g, h and Supplementary Data 2), indicating that the consumption of a single oat meal daily, integrated into a Western diet under isocaloric conditions, has a rather mild effect on metabolism. Thus, along the known positive effects of oats on lipid metabolism22 observed in OG, the six-week, moderate oat diet stabilized metabolic markers.

Oat diet enhanced microbially produced phenols in plasma
After the two-day intervention period, a strong separation between OG and CG in the non-targeted global plasma metabolomic profile, which was generated by ultra-high-performance liquid chromatography coupled with tandem high-resolution mass spectrometry (UPLC-MS/MS)24,25, was observed using sPLS-DA (AUC=0.99 (P=1.47 × 10−6, BER=0.04; Fig. 4a). Phenolic compounds, including 2-acetamidophenol sulfate, 2-aminophenol sulfate, 4-hydroxyhippurate, 4-vinylphenol sulfate, and vanilloylglycine, were among the most distinguishing metabolites (Fig. 4b) and showed a significant increase in OG compared with CG (LnRadj.: false discovery rate (FDR) corrected q<0.05; Fig. 4b, c). Thus, our results suggest that a short-term, high-dose oat diet leads to a distinct shift in the plasma metabolomic profile, particularly characterized by an increase in microbially produced phenolic compounds.

A clear difference in the plasma metabolomic profile was also observed between OG6w and CG6w after the six-week intervention period (AUC=0.927 (P=2.1 × 10−5), BER=0.176; Fig. 4d). The phenolic compounds 2-acetamidophenol sulfate and 2-aminophenol sulfate were among the most important distinguishing metabolites and showed a significant increase in OG6w compared with CG6w (LnRadj.: q<0.05; Fig. 4e, f). Notably, an increase in these phenols was also detected in the OG, supporting our assumption that this increase is related to oats. Our results thus indicate that a six-week, moderate oat diet leads to shifts in the global plasma metabolomic profile that are consistent with those of a short-term, high-dose oat diet, although the effects are less pronounced.

Oat diets modulated fecal amino acid and lipid metabolites
When investigating the diet-induced modulation of the non-targeted global metabolomic profiles in feces24,25, sPLS-DA revealed a strong separation between OG and CG after the two-day intervention period (AUC=0.93 (P=1.16 × 10−4), BER=0.177; Fig. 5a). Amino acids and their (microbial) degradation products, including 8-methoxykynurenate, indolepropionate, 3-hydroxy-phenylacetate, and N-acetyltryptophan, as well as pyridoxate, a metabolite of the vitamin B6 metabolism, were among the most influential compounds for differentiating the diet groups (Fig. 5b). 8-methoxykynurenate and pyridoxate increased in OG compared to CG, while indolepropionate and 3-hydroxyphenylacetate decreased (LnRadj.: q<0.05; Fig. 5b, c). Shifts in the fecal metabolomic profile induced by the short-term, high-dose oat diet thus appear to be mainly linked to amino acid-related pathways, which have a significant relevance for cholesterol metabolism26.

After the six-week intervention period, different shifts in the fecal metabolomic profile were also revealed between OG6w and CG6w (AUC=0.75 (P=0.014), BER=0.353; Fig. 5d). The most influential metabolites contributing to group differentiation were lipid metabolites; however, these metabolites did not differ significantly between the diet groups according to LnRadj. (q>0.05; Fig. 5e). Thus, our results suggest that a six-week, moderate oat diet has less pronounced effects on the global fecal metabolomic profile compared with a short-term, high-dose oat diet and might lead to a shift in the lipid metabolite pattern rather than to changes in individual lipids.

Oat diets modulated gut microbiota composition
Investigating dietary modulation of the gut microbiota composition based on 16S rRNA gene sequencing using sPLS-DA, a significant difference was observed between OG and CG after the two-day intervention period (AUC=0.83 (P=2.96 × 10−3), BER=0.219; Fig. 6a). Erysipelotrichaceae UCG-003 was the most important genus for differentiating the diet groups (weight loading component 1: −1.0) and showed a significant increase in OG compared with CG according to LinDA27 (log fold change=1.01, q=0.025; Fig. 6b). This indicates that the increase is related to oats, which is further supported by the visible reduction during the oat-free follow-up (Fig. 6c).

After the six-week intervention period, differences in the modulation of the microbial composition were also observed between OG6w and CG6w (AUC=0.79 (P=0.004), BER=0.265; Fig. 6d). Ruminococcus torques group, a mucolytic bacterium that has been linked to negative health outcomes28, was most relevant for differentiating the diet groups (weight loading component 1: −1.0) and tended to decrease in OG6w compared with CG6w (log fold change=−1.58, q=0.07; Fig. 6e, f).

Our results suggest that both a short-term, high-dose and a six-week, moderate oat diet led to specific microbial shifts while maintaining the core microbiome composition with a stable overall ecological structure and diversity (Supplementary Fig. 1).

Modulated microbial functional capacity
Whether microbial functional capacity changed in response to the dietary interventions was investigated based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database29 by quantifying pathway abundance using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2 (PICRUSt2)30. According to sPLS-DA, OG and CG modulated the microbial functions differently but not statistically significantly (AUC=0.69 (P=0.09), BER=0.396; Fig. 6g); even though, several pathways seemed to be of relevance for differentiation, including carbohydrate digestion and absorption, aminobenzoate degradation, selenocompound metabolism, naphthalene degradation, and phosphotransferase system (PTS) (Fig. 6h). An increase in aminobenzoate degradation and PTS as well as a decrease in naphthalene degradation and carbohydrate digestion and absorption was identified using LMMadj. (P<0.05; Fig. 6h), suggesting that benzoate and related pathways might be of relevance and potentially associated with the observed increase in the phenolic metabolites in plasma31.

Furthermore, a different modulation of the microbial functional capacity between the diet groups of the six-week intervention study was revealed, although not statistically significant (AUC=0.68 (P=0.07), BER=0.353; Fig. 6i), with phenylalanine metabolism, proximal tubule bicarbonate reclamation, beta-lactam resistance, fructose and mannose metabolism, and PTS seemed to be the most relevant pathways for differentiation between OG6w and CG6w (Fig. 6j). An increase in OG compared to CG was shown in phenylalanine metabolism and proximal tubule bicarbonate reclamation, while a decrease was observed in the other pathways using LMMadj. (P<0.05; Fig. 6j). Thus, our results suggest that a six-week, moderate oat diet might modulate a variety of different microbial functions, which is in line with the heterogenous metabolic response.

Cholesterol reduction by oats linked to increase in phenols
Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO32) revealed strong correlations between the changes in metabolism, plasma and fecal metabolites, as well as microbial composition (model 1.1: AUC=0.94 (P=5.03 × 10−3), BER=0.07; Fig. 7a) and functional capacity (model 2.1: AUC=0.92 (P=5.11 × 10−3), BER=0.04; Fig. 7b) induced by the short-term, high-dose oat diet. Notably, the reduction in serum cholesterol levels was associated with an increase in several plasma phenolic compounds including FA, DHFA, 2-acetamidophenol sulfate, 2-aminophenol sulfate, 4-hydroxyhippurate, vanilloylglycine, 4-vinylphenol sulfate, 2-methoxy-hydroquinone sulfate (1), and 3-methoxycatechol sulfate (2). These inverse associations were additionally largely confirmed by pairwise correlation analyses (P<0.05; Fig. 7c). In addition, PLS regression models revealed that the change in plasma phenolic compounds solely predicted 13.6% of the variation in cholesterol levels (Q²=0.136) and explained 13.5% of the variance in TC (R²=0.135) and 19.3% of the variance in LDL-C (R²=0.193). Thus, our results suggest that phenolic compounds play an important role in the cholesterol-lowering effect of oats.

Moreover, associations of microbial phenol degradation products including DHFA and 4-hydroxyhippurate with genera such as Erysipelotrichaceae UCG-003 and microbial pathways such as aminobenzoate and naphthalene degradation were found (see below), indicating that the increase in plasma phenols is associated with oat-induced changes in the gut microbiota. Shifts in Erysipelotrichaceae UCG-003, Marvinbryantia and naphthalene degradation were associated with changes in circulating cholesterol levels (see below), with the inverse association between Erysipelotrichaceae UCG-003 and cholesterol levels also identified by pairwise correlation analysis (P<0.05; Fig. 7d). In addition, these changes in the gut microbiome solely predicted 13.5% of the variation in cholesterol levels (Q²=0.135) and explained 11.1% of the variance in TC (R²=0.111) and 19.6% of the variance in LDL-C (R²=0.196). This supports our assumption that alterations in microbial composition and functional capacity may contribute to lowering cholesterol levels.

Besides the phenolic compounds, various plasma amino acid metabolites such as 3-methylhistidine, N-acetyl-3-methylhistidine, and 1-methyl-5-imidazolelactate were positively associated with the oat-induced change in circulating cholesterol levels (see below, Supplementary Data 3), which was also shown by pairwise correlations (P<0.05; see below). These metabolites were additionally linked to shifts in Erysipelotrichaceae UCG-003 and microbial naphthalene degradation (see below, Supplementary Data 3), indicating that a decrease in histidine metabolites due to oat-induced shifts in microbial composition and functional capacity may contribute to lowering cholesterol levels; however, the observed associations were not as strong as for the phenolic compounds.

Moreover, the changes in cholesterol levels were positively associated with fatty acids, including plasma 2S,3R-dihydroxybutyrate, fecal branched-chain 14:0 dicarboxylic acid, and fecal isocaproate (i6:0), and negatively associated particularly with fecal endocannabinoids, which have been linked to anti-inflammatory effects33 (see below). These results were largely confirmed by additional pairwise correlation analyses (P<0.05; Fig. 7c, e). Notable, shifts in Erysipelotrichaceae UCG-003 and microbial naphthalene degradation were associated with many of the above-mentioned metabolites (see below; Supplementary Data 3). This suggests that changes in the lipidome contribute to the reduction in circulating cholesterol levels potentially via an oat-induced modulation of the gut microbiota; however, the correlations were also less pronounced compared to those with the phenolic compounds. Detailed information on the pairwise correlations is provided in Supplementary Data 4.

In the six-week intervention study, DIABLO revealed several correlations between changes in metabolism, plasma and fecal metabolites, as well as the microbial composition (model 1.2: AUC=0.85 (P=4.42 × 10−3), BER=0.32; Supplementary Fig. 2a) and functional capacity (model 2.2: AUC=0.81 (P=0.01), BER=0.32; Supplementary Fig. 2b). However, most of the selected features did not differ between OG6w and CG6w, indicating that the identified correlations might not be related to the moderate oat diet and should be interpreted with caution. Information on all correlations identified is provided in the supplement (Supplementary Data 3).

DHFA altered cholesterol metabolism in vitro
In peripheral blood mononuclear cells (PBMCs) fed with alkyne cholesterol, DHFA led to a significant decrease in the molar fraction of alkyne cholesterol (−15.71±6.33 mol% (mean±SEM), P=0.042), while the total lipidome remained stable (Fig. 8a). In addition, in metabolically healthy controls (MHCs), the molar fraction of alkyne cholesterol esters tended to be lower in the PBMCs treated with DHFA (−5.54±1.94 mol%, P=0.065; Fig. 8a). These results support that microbially produced phenolic compounds such as DHFA mediate the oats’ cholesterol-lowering effects by reducing alkyne cholesterol and cholesterol esters incorporation into PBMCs relative to the total lipidome.

In HuH7 cells fed with 13C labeled acetate and alkyne fatty acid 182, DHFA seemed to decrease the molar fraction of 13C labeled-alkyne cholesterol esters and alkyne cholesterol esters of the total lipidome, while the total lipidome itself increased by trend (Fig. 8b). Consistent with this, a decrease in the molar fraction of the unlabeled cholesterol esters tended to take place (Supplementary Fig. 3a). No differences in total lipidome, the molar fraction of alkyne cholesterol esters and alkyne cholesterol were observed between HuH7 cells either treated with or without DHFA and additionally fed with alkyne cholesterol (Supplementary Fig. 3b). Our results indicate that DHFA might impact different effects i) the totality of lipids, ii) the de novo synthesis of cholesterol esters and iii) the esterification of cholesterol in HuH7 cells, supporting that phenolic compounds such as DHFA might contribute to the cholesterol-lowering properties of oats.

Microbial oat metabolization produced phenolics in vitro
To prove whether the identified phenolic compounds stem from the microbial metabolization of oats, anaerobic fecal batch culture fermentations of in vitro digested oat flakes were conducted. This demonstrated that the gut microbes are capable of producing a variety of metabolites, including phenolic compounds, by directly interacting with the provided oats. In particular, a significant increase in 2-aminophenol was shown in both oat groups (physiological oat group (POG) vs. physiological control group (PCG): P<0.001 (6h, 36h), P<0.01 (24h), starving oat group (SOG) vs. starving control group (SCG): P<0.01 (24h, 36h); Fig. 8c), which is in line with the observed increase in the plasma level of 2-aminophenol sulfate in our RCTs. In addition, a rapid production of microbial products such as DHFA and vanillic acid were observed in both oat groups by a simultaneous reduction of oat phenolic compounds such as FA and vanillin mainly within 6h (POG vs. PCG: P<0.01 (DHFA, FA, vanillin), P<0.05 (vanillic acid); SOG vs. SCG: P<0.05 (DHFA, FA, vanillin); Supplementary Fig. 3c), emphasizing that the microbial activity results in the production of phenolic oat-derived metabolites.

Moreover, as expected, shifts in the concentrations of various SCFAs, in particular an increase in acetic acid (POG vs. PCG: all P<0.05) and succinate (SOG vs. SCG: P=0.033 (36h)) and a decrease in isobutyric acid (POG vs. PCG: P=0.002 (24h), SOG vs. SCG: P=0.001 (6h), P=0.047 (36h); Fig. 8c), were found. Furthermore, significantly higher abundances of known SCFA-producing bacteria such as Faecalibacterium, Fusicatenibacter, Bifidobacterium, Blautia, and Roseburia were observed in POG compared with PCG, especially after 36h of incubation (all P<0.05; Supplementary Fig. 3d). In addition, Erysipelotrichaceae UCG-003 seemed to increase in POG compared to PCG after 36h of incubation (P=0.172; Fig. 8d), supporting that the identified increase in the abundance of Erysipelotrichaceae UCG-003 in our RCT is induced by the short-term, high-dose oat intake.

Methods
Ethics approval and participants’ consent
This study complied with all relevant ethical regulations and was conducted in accordance with the principles of the Declaration of Helsinki and its subsequent amendments, approved by the ethics committee of the Medical Faculty, University of Bonn (Approval Number: 212/20, approval date: 03 June 2020), and prospectively registered at the German Clinical Trials Register under identifier DRKS00022169. Written informed consent was obtained from all the participants.

Study design
This study comprised two 1:1 randomized, controlled, prospective dietary interventions, each with a parallel design, conducted between September 2020 and July 2022 at the Department of Nutrition and Microbiota, University of Bonn, Germany. The short-term intervention study was completed on schedule, while the six-week intervention study was delayed due to the COVID-19 pandemic.

Potential subjects completed an initial telephone pre-screening followed by an in-person screening visit, which included anthropometric measurements and a detailed blood analysis to confirm eligibility and a detailed explanation of all study procedures and requirements. In the short-term intervention study, eligible participants were invited for two clinical visits, the first before (V1) and the second directly after the two-day intervention period (V2), followed by three clinic visits at two-week intervals during a six-week follow-up period (V3–V5) to determine potential long-term effects of the oat diet (Fig. 1a). Therefore, the follow-up period was conducted to evaluate the durability of the responses, allowing conclusions to be drawn about the optimal intervals at which a short-term, high-dose oat diet might be implemented on a regular basis. During the six-weeks, intervention study, eligible participants were invited for four clinical visits at two-weeks intervals with the first before (V1) and the last after the six-week intervention period (V4) (Fig. 1b).

After the first visit (V1), participants were randomly assigned to the experimental group (OG or OG6w) or the control group (CG or CG6w) of each study using computer-generated randomization tables in a block format with variable block length generated by a researcher not clinically involved in the study. These tables were concealed from the researchers and participants until the interventions were assigned and participants allocated to the respective intervention group by study personnel not involved in final data analysis. Blinding after allocation to the interventions was not feasible.

At V1 and the clinical visit directly after each intervention period (V2 or V4), anthropometric data, fecal samples as well as fasting and postprandial blood samples, taken during an oral glucose tolerance test (OGTT), were collected. Office BP and resting energy expenditure were measured using a semiautomatic BP measurement device and indirect calorimetry, respectively. In addition, an in-depth nutritional and lifestyle assessment was conducted at V1. At the clinical visits during the six-week intervention study (V2+V3), anthropometric and BP measurements were performed and fasting blood and fecal samples were collected. These also took place at the clinical visits during the follow-up period of the short-term intervention study (V3−V5). In addition, a fecal sample was collected after the first intervention day of the short-term intervention. Interim contact with the study coordinator (via telephone or e-mail) was made available to all participants on each intervention day.

Participants
Participants were recruited through newspaper advertisements, flyers, and social media in the surrounding area of Bonn, Germany (first participant enrolment: September 8, 2020; last participant enrolment: May 30, 2022). The study subjects included female and male adults of Caucasian descent with overweight or obesity (BMI 27–39.9 kg/m2) and MetS aged 45–70 years. Sex and gender of each participant were determined based on self-reported information (questionnaire). MetS was primarily diagnosed based on the global consensus definition of the International Diabetes Federation1 as central obesity measured by waist circumference (≥80 cm for women and ≥94 cm for men) and at least two of the following five criteria: (1) prehypertension (≥120–139 mmHg systolic and/or ≥80–89 mmHg diastolic)64 or stage 1 hypertension (≥140–159 mmHg systolic and/or ≥90–99 mmHg diastolic); (2) triglyceridemia (≥150 mg/dL); (3) decreased fasting serum HDL-C (<40 mg/dL for men and <50 mg/dl for women); (4) hyperglycemia (≥100 mg/dL); (5) indication of insulin resistance (HOMA-IR index >2.5) (adapted to the WHO definition65). Further inclusion criteria were (6) non-smoker (defined as abstinence during the past 3 years prior to study enrollment66,67) and (7) written informed consent for participation in the study. The specific threshold values for BP were chosen because prehypertension (≥120 mmHg systolic and/or ≥80 mmHg diastolic) has been associated with a significantly increased risk of cardiovascular disease68–71 and therefore represents a stage in which prevented dietary interventions may be more effective. The fifth criterion was added after the initial study registration in order to attach more importance to impaired glucose metabolism as a characteristic of MetS and to counteract recruitment difficulties due to the COVID-19 pandemic. In addition, the subjects’ habitual diet corresponded to a Western dietary pattern without regular oat consumption. Exclusion criteria were as follows: (1) regular oat consumption, defined as at least one usual portion per week; (2) chronic use of medication that affects glucose metabolism (e.g., cortisone); (3) diagnosed type 2 Diabetes mellitus, chronic liver diseases (transaminases > twice the normal value), gastrointestinal diseases, chronic inflammatory diseases (hsCRP > 30 mg/L) including rheumatoid arthritis, previous cardiovascular events, thyroid disease (e.g., untreated hypo- or hyperthyroidism), tumor diseases, acute illnesses or recent surgeries; (4) anemia; (5) pregnancy, lactation; (6) allergies and intolerance to components of the oat products; (7) antibiotic therapy within three months prior to study inclusion; (8) alcohol abuse (defined according to ICD-10/DSM-5 within the last three years, or self-reported problematic alcohol consumption requiring treatment with less than 12 months abstinence before study enrollment), medication abuse and/or drug abuse (BTMG or other psychotropic substances); (9) regular intake of dietary supplements (especially n-3 fatty acids, vitamin E, magnesium, calcium, iron, zinc); (10) planned change in lifestyle, especially participation in a weight loss program; (11) vegetarian or vegan diet; (12) participation in another clinical trial at the same time or within the last 30 days; (13) other exclusion criteria at the discretion of the doctor/investigator.

Dietary interventions
During the short-term intervention period, the participants followed a two-day, high-dose oat diet or a macronutrient-adapted control diet, both hypocaloric and high in fiber (1100–1200 kcal/d; carbohydrates (67 energy% (E%), of which fiber > 15%, β-glucan: 13.5 g vs. 0 g), protein (15 E%), and fat (17 E%)), depending on their allocation (OG vs. CG). Participants in the OG consumed three oat meals per day instead of their habitual diet. Each meal consisted of 100 g rolled oat flakes (Demeterhof Schwab GmbH & Co. KG, Windsbach, Germany) and has been consumed as porridge, prepared with water. Fruits restricted to apples, pears, and berries and vegetables restricted to spinach and leeks were used as additives and added to the meal plans of the CG in the same amount. No salt, sugar, or sweeteners were added. Participants in the CG consumed two meals per day comprising bread and raw vegetables (breakfast and dinner) and one warm meal per day (lunch) instead of their habitual diet. There was a time interval of four hours between meals. During this time, participants were required to consume only unsweetened beverages. For standardization, the participants received a recommendation for dinner the evening before the start of the intervention (carbohydrate-rich bread meal with raw vegetables). During the six-week follow-up period, participants of the OG adhered to their habitual diet without consuming oats.

During the six-week dietary intervention, participants in the OG6w replaced one meal per day with an oat meal consisting of 80 g oat flakes (Demeterhof Schwab GmbH & Co. KG) which corresponds to 8 g dietary fiber (3.5 g β-glucan), while maintaining their habitual diet under isocaloric conditions. For the preparation of the oat meal, the participants received a collection of recipes that included classic porridges, overnight oats, smoothies, and baked goods and was adapted to the daily calorie requirements of each participant individually, measured using indirect calorimetry. The participants in the CG6w maintained their habitual dietary pattern unchanged and did not consume any oats.

In both the short-term and six-week intervention period, the meals were prepared independently by the subjects at home using detailed instructions. Participants were instructed to avoid any other oat products (other than the oat flakes provided) for at least two weeks before and during the study. The nutrient composition of all test meals was calculated using the computer-based nutrient calculation program EBISpro, based on the German nutrient database Bundeslebensmittelschlüssel, version 2016 (Max Rubner-Institut, Karlsruhe, Germany).

Primary and secondary outcomes
All outcomes reported in the article are listed in the study protocol and registry and thus have been defined prior to the start of the study.

The primary outcome was the plasma concentration of (DH)FA and secondary outcomes were parameters of glucose (plasma glucose, serum insulin, HbA1c, glucagon) and lipid metabolism (lipids and lipoproteins (TC, HDL, triglycerides (TG)), free fatty acids, serum lipase); parameters of kidney (uric acid, urea, creatinine) and liver function (GOT, GPT, GGT, bilirubin) which, together with a small blood count, were also used as safety parameters; anthropometric parameters, resting energy expenditure and blood pressure which, together with energy and nutrient intake, were also used as control parameters; gut microbiota composition and diversity based on fecal samples; avenanthramides concentrations in plasma which served as a compliance marker; as well as global metabolomic profiles in plasma and feces which have been considered as optional secondary outcomes in the study protocol, with the objective to investigate the effects of a short-term, high-dose oat diet and a six-week, moderate oat diet compared with a macronutrient-adapted control diet and a Western diet, respectively, on lipid metabolism, the gut microbiota, and metabolomic profiles, in particular the phenolic compounds FA and DHFA, in individuals with MetS and to elucidate the underlying mechanism using an integrative multi-omics analysis.

Compliance
Adherence to the treatment protocol was assessed based on three independent criteria. Firstly, the concentration of the oat-specific biomarker AVAs72,73 was quantified as an objective blood marker. Plasma AVAs concentration was determined using LC-MS/MS by Metabolon Inc. (Morrisville) (Metabolon Method TAM223: “LC-MS/MS Quantitation of Dihydroferulic Acid and Three Avenanthramide Compounds in Human Plasma”) based on a previously described method73 (see below). Secondly, the participants received the exact number of oat packages required for each intervention period and were asked to return all empty and unemptied packages after completion of the respective intervention period. Thirdly, the participants who were assigned to the oat diets (OG or OG6w) or the control diet of the short-term intervention (CG) completed a detailed checklist on each intervention day to record precise information on the selection and preparation of the respective test meals.

During the six-week follow-up period in the oat group of the short-term intervention (OG), compliance with the study instruction (i.e., refraining from oat consumption and returning to habitual Western diet) was assessed using two complementary methods: (1) specific query by the study staff about abstinence of oat meals as consumed during intervention and any other oat products, and (2) 3-day dietary records completed by the participants at baseline (V1) and at the end of the follow-up period (V5).

Anthropometrics
Anthropometric measurements were performed following a standard operative procedure74. Body weight was measured in light clothing with an empty bladder, using electronic column scales with an accuracy of up to 100 g (seca scale 704, seca GmbH and Co. KG, Hamburg, Germany). Body height was determined to the nearest of 0.1 cm using a stadiometer (seca scale 704, seca GmbH and Co. KG, Hamburg, Germany). The BMI was calculated using the following formula: BMI = weight [kg] / (height [m])2. Waist circumference was measured midway between the lowest rib and iliac crest at maximal exhalation to the nearest of 0.1 cm in duplicates. Body composition (fat mass and fat-free mass) was determined by air-displacement plethysmography using a BOD-POD body composition system (Cosmed, Firdolfingen, Germany).

Office blood pressure and heart rate
Office blood pressure and heart rate were measured twice in a seated position using a semiautomatic blood pressure measurement device (Boso Carat Professional, Bosch+Son GmbH and Co. KG, Jungingen, Germany) under standardized conditions in accordance with European and American guidelines75,76.

Indirect calorimetry
For determination of whole-body resting energy expenditure (REE) and substrate oxidation via measurement of carbon dioxide production and oxygen consumption, indirect calorimetry was performed, using Quark-RMR® (Cosmed, Fridolfing, Germany) with a coefficient of variation of <10%77. REE was multiplied by 1.5 (physical activity level, PAL) to calculate the total energy expenditure.

Nutrition and lifestyle assessments
The habitual diet was characterized using a Food Frequency Questionnaire (FFQ) over 12 months. Physical activity (7-day total metabolic equivalent of task score) was assessed at baseline using the short version of the validated international physical activity questionnaire (IPAQ)78,79. For the evaluation of the sleeping behavior (circadian rhythm), the validated Munich chronotype questionnaire (MCTQ) was used80,81. Socio-economic status was obtained using a questionnaire based on the parent questionnaire to measure socioeconomic status in the study on the health of children and adolescents in Germany (KiGGS) Wave 282.

Collection of blood and stool samples
Fasting venous blood samples were collected in the morning between 8:00 a.m. and 10:00 a.m. after a 12-h overnight fast. Postprandial blood samples were collected during a 3-h OGTT. All samples were taken under standardized conditions using tubes containing EDTA, fluoride, or a coagulation activator (S-Monovette, Sarstedt, Germany). Plasma and serum supernatants were obtained by centrifugation at 3000 × g for 15 min at 8°C after complete coagulation (only serum) and immediately frozen in cryovials at −80°C until further analysis. Fecal samples were collected before each clinic visit according to a standard operation procedure and immediately stored at −80°C until further analysis83.

Oral glucose tolerance test
Oral glucose tolerance test was performed after an overnight fast with 75 g glucose and 300 ml water (Dextrose O.G-T.; Roche Diagnostics, GmbH, Mannheim, Germany). Venous blood samples were collected via a venous catheter before ingestion and at 15, 30, 45, 60, 120, and 180 min postprandially to analyze the time course of plasma glucose, serum insulin, serum TG and serum non-esterified fatty acid (NEFA) concentrations.

Various indices were calculated to assess the diet-induced effects on glucose control and insulin resistance. Insulin sensitivity was evaluated using the oral glucose insulin sensitivity index (OGIS)84,85 and the insulin sensitivity index (ISI), which provide a measurement of insulin-mediated glucose clearance86. β-Cell function was assessed using the insulinogenic index84,85, disposition index87–89 and HOMA-β90. In addition, insulin resistance was evaluated using the quantitative insulin sensitivity check index (QUICKI)91,92 and the triglyceride-glucose index (TyG)93,94 based on fasting concentrations. Moreover, the area under the curve (AUC) of the glucose and insulin concentration over 180 min, calculated using the trapezoidal rule and considering only complete datasets, was used to measure differences in glucose tolerance upon intervention. The AUCs of the NEFA and TG concentration were calculated in the same way over 120 min. Furthermore, the presence of metabolic dysfunction-associated steatotic liver disease (MASLD) was estimated using the fatty liver index (FLI ≥ 60)95,96.

Routine laboratory blood analyses
Routine laboratory analyses of serum included fasting and postprandial TG and insulin levels, fasting TC, LDL-C, HDL-C, and high-sensitivity C-reactive protein (hsCRP) levels, and clinical biochemistry. Routine laboratory analyses of plasma included fasting and postprandial glucose, fasting HbA1c, and hematology parameters. All parameters were measured in a certified medical laboratory (Central Laboratory of the Institute of Clinical Chemistry and Clinical Pharmacology at the University Hospital Bonn, Germany) within 4 h of blood sampling under standardized conditions using the Roche/Hitachi Cobas c system (Roche Diagnostics, Mannheim, Germany). Methods specifications are available online (https://www.ukbonn.de/ikckp/zentrallabor/leistungsverzeichnis/). The HOMA index was calculated as follows: HOMA-IR = [insulin (mU/L) × glucose (mg/dL)] ÷ 40590. Insulin resistance was assumed if the HOMA index was >2.597.

Analysis of non-esterified fatty acids
Fasting and postprandial serum NEFA concentrations were analyzed pre- and post-intervention using an in vitro enzymatic colorimetric method assay (NEFA-HR(2), Wako Diagnostics, Mountain View, CA, USA) following the manufacturer’s instructions and the recommended quality control procedure (inter-assay/intra-assay variability 5.4%/4.8%) at the Institute of Nutrition and Food Science, University Bonn, Germany98.

In vitro cell culture experiments
To confirm that the identified phenolic compounds mediate the beneficial effects of oats on cholesterol metabolism, we performed in vitro cell culture experiments. Briefly, blood was collected from participants with MetS (n=4; 65±9 y, 29.3±2.2 kg/m²) and MHCs (n=4; 33±11 y, 25.1±4.9 kg/m²) in EDTA-containing cuvettes to isolate peripheral blood mononuclear cells (PBMCs). After pre-processing, PBMCs were exposed to medium with or without 10 µM DHFA (Sigma-Aldrich, #17803-5G) that additionally contained 20 µg/mL alkyne cholesterol. In addition, HuH7 cells (JCRB0403) were treated with the same medium with or without 10 µM DHFA after 48 h of cultivation in growth medium. The medium was additionally supplemented either with 20 µg/mL alkyne cholesterol or 5 mM 1,2-13C sodium acetate (CIL, #CLM-440-1) and 100 µM alkyne fatty acid 182. After 24 h of incubation, the samples were subjected to lipid extraction and dissolved lipids were analyzed by MS.

In detail, PBMCs were isolated by density gradient centrifugation with Pancoll (density 1.077 g/mL, PAN Biotech, #P04-601000). After washing of the cells with PBS and removal of platelets, PBMCs were plated in RPMI Medium (PAN Biotech, # P04-22100) supplemented with Penicillin/Streptomycin and 10% Fetal Bovine Serum with a density of 2×106/mL into a 96 well round bottom plate (Sarstedt, # 83.3925). After 2 h incubation, PBMCs were exposed to medium with or without 10 µM DHFA (Sigma-Aldrich, #17803-5G). Additionally, the medium was supplemented with 20 µg/mL alkyne cholesterol. After 24 h, PBMCs were washed first with 200 µL ice-cold 0.5% DL-BSA (Carl Roth, #0052.3) in PBS and then with 200 µL ice-cold 155 mM Ammonium acetate in H2O. Between every washing step, plates were centrifuged at 610 × g for 5 min and supernatant was decanted. Subsequently, 250 µL of PBMC extraction mix (230 µl MeOH/CHCl3 5/1, 20 µl internal standard mix containing alkyne-labeled lipids: 103 pmol TG 48:3-d8, 14.6 pmol DG 32:3-d8, 26 pmol CE 17:2-d7, 15.8 pmol dh-alkyne-Cer 15:1-d8, 13 pmol PA 32:3-d8, 113 pmol PC 32:3-d8, 36 pmol PE 32:3-d8, 30 pmol PI 32:3-d8, 34 pmol PS 32:3-d8 and 20 pmol of double-labeled TG 49:5-d8 and nonalkyne labeled lipids: 250 pmol PE 33:1-d7, 472 pmol PC 33:1-d7, 98 pmol PS 31:1, 84 pmol PI 34:0, 56 pmol PA 31:1, 51 pmol PG 28:0, 39 pmol LPA 17:0, 35 pmol LPC 17:1, 38 pmol LPE 17:0, 32 pmol Cer 17:0, 240 pmol, SM 18:1-d9, 55 pmol GlcCer 12:0, 339 pmol TG 50:1-d4, 111 pmol, CE 18:1-d6 and 64 pmol DG 31:1) was added to each well and plates were placed into an ultrasound bath for 20 s. Samples were collected and then subjected to lipid extraction.

HuH-7 cells (JCRB0403) were cultivated in growth medium (DMEM 4.5 g l−1 glucose, 10% FCS plus penicillin/streptomycin), seeded into 12-well plates and allowed to grow for 48 h. HuH-7 cells were exposed to medium with or without 10 µM DHFA (Sigma-Aldrich, #17803-5G). Additionally, the medium contained either 20 µg/mL Alkyne cholesterol or 5 mM 1,2-13C2 sodium acetate (CIL, #CLM-440-1) and 100 µM alkyne fatty acid 182, and incubated for 24 h. Cells on 12-well plate were washed once with ice-cold 0.5% DL-BSA in PBS and quickly once with 155 mM ammonium acetate, taking care to remove the liquid after the last wash as complete as possible. To each well, 500 µl of extraction mix (480 µl MeOH/CHCl3 5/1, 20 µl internal standard mix containing alkyne-labeled lipids: 206 pmol TG 48:3-d8, 29.2 pmol DG 32:3-d8, 52 pmol CE 17:2-d7, 31.6 pmol dh-alkyne-Cer 15:1-d8, 26 pmol PA 32:3-d8, 226 pmol PC 32:3-d8, 72 pmol PE 32:3-d8, 60 pmol PI 32:3-d8, 68 pmol PS 32:3-d8 and 41 pmol of double-labeled TG 49:5-d8 and nonalkyne labeled lipids: 500 pmol PE 33:1-d7, 944 pmol PC 33:1-d7, 196 pmol PS 31:1, 168 pmol PI 34:0, 112 pmol PA 31:1, 102 pmol PG 28:0, 78 pmol LPA 17:0, 70 pmol LPC 17:1, 76 pmol LPE 17:0, 64 pmol Cer 17:0, 480 pmol, SM 18:1-d9, 110 pmol GlcCer 12:0, 678 pmol TG 50:1-d4, 222 pmol, CE 18:1-d6 and 128 pmol DG 31:1) was added and the entire plate was sonicated for 1 min in a bath sonicator. The extracts including the cell remnants were collected into 1.5-ml original Eppendorf tubes and centrifuged at 20,000 × g for 5 min to pellet protein. The supernatants were transferred into fresh tubes. After addition of 400 µl of CHCl3 and 600 µl of water, samples were shaken for 30 s and centrifuged for 5 min at 20,000 × g. The upper phase was removed, and the lower phase transferred into a fresh tube and dried for 20 min at 45°C in a speed-vak. CHCl3 (8 µl) was added and the tubes briefly vortexed. To each tube, 40 µl of Click mix was added (prepared by mixing 20 µl of 50 mM C171 in 50% MeOH (stored as aliquots at −80°C) with 200 µl of 5 mM Cu(I)AcCN BF4 in AcCN and 800 µl of ethanol), followed by sonication for 5 min and incubation at 40°C for 16 h. 250 µl of CHCl3 and 600 µl of water were added per sample and briefly shaken and centrifuged for 2 min at 20,000 × g. The upper phase was removed and the lower phase dried in a speed-vac as above. 1000 µl of spray buffer (2-propanol/methanol/water 8:5:1 + 10 mM ammonium acetate) was added, the tubes sonicated for 5 min and the dissolved lipids analyzed by MS.

Mass spectra were recorded on a Thermo Q-Exactive Plus spectrometer (Instrument Version 2.8 Build 280502, Software Version 2.13 SP1 Build 3170) equipped with a standard HESI ion source using direct injection from a Thermo Dionex AS-AP autosampler driven by an AXP-MS pump under the control of Xcalibur software (Version 4.7.69.37). Samples were sprayed at flow rates of 10 µl min−1 in spray buffer with the following parameters: sheath gas 6, aux gas 2, sweep gas 0, gas heating off, spray voltage positive mode 4.1 kV and ion transfer capillary temperature 280°C. MS1 spectra (resolution 280,000) were recorded in 100 m/z windows from 250 to 1200 m/z (positive mode), followed by recording MS/MS spectra (resolution 280,000) by data independent acquisition in 1 m/z windows from 250 to 1200 m/z (positive mode). Typical scan parameters were for MS1 scans: automatic gain control target 5×105, maximum ion time 200 ms, resolution 280,000, peak mode centroid, for MS2 scans: automatic gain control target 2×105, maximum ion time 100 ms, resolution 280,000, no spectral multiplexing, fixed first mass at 100 m/z, isolation window m/z 0.7, stepped normalized collision energy (NCE) 10, 35, 40, spectrum data type centroid. The raw data were converted to .mzML files using MSConvert (Version 3.0.24326-c89bdab) and analyzed with LipidXplorer software (Version 1.2.8.1). For identification and quantification of labeled lipids, molecular fragment query language (.mfql) files were written that identify the species by the presence of a peak corresponding to the expected masses of the lipid class. For further analysis, absolute amounts were calculated using internal standard intensities, followed by the calculation of the molar fraction for the identified lipids.

Fecal batch culture fermentation of in vitro digested oats
To evaluate if the metabolism of the microbiome results in the production of the identified oat-derived metabolites, in particular the phenolic compounds, we conducted an anaerobic in vitro fecal batch culture experiment. In brief, aliquots of homogenized baseline stool samples from four representative participants of the short-term intervention study were used to inoculate sterile anoxic basal medium either with or without in vitro digested oats in a starving group (anoxic basal buffer; SOG vs. SCG) or a physiological group (Brain Heart Infusion medium; POG vs. PCG). Samples for global metabolomic profiling including phenolic compounds, SCFAs and microbiome analysis were taken directly after inoculation, after 6, 24 h, and 36 h of incubation time.

In detail, oat flakes (Demeterhof Schwab GmbH & Co. KG, Windsbach, Germany) were digested in vitro according to a modified method described by Kristek et al.99. Oat flakes (60 g) were homogenized in 150 ml of sterile distilled water and microwaved for one minute at 950 W. Subsequently, 20 mg α-Amylase (from Aspergillus oryzae, Sigma-Aldrich; dissolved in 6,25 ml of 1 mM CaCl2) was added and the mixture incubated for 30 min at 37°C and 200 rpm on an incubation shaker. After incubation, the pH of the solution was adjusted to 2 using 6 M HCl, and 2.7 g Pepsin (Carl Roth) in 25 ml of 0.1 M HCl was added, followed by 2 h of incubation at 37°C and 200 rpm. From there, 560 mg of pancreatin (from porcine pancreas, Sigma-Aldrich), 3.5 g bile salts (Sigma-Aldrich), and 125 ml of 0.5 M NaHCO3 were added, and the pH was adjusted to 7.0 with 5 M NaOH. After 3 h of further incubation, the solution was transferred into a regenerated cellulose dialysis tubing (Spectra/Por® 7, Carl Roth) with 1 kDa MWCO and dialyzed against 0.01 M NaCl at 5°C for 15 h. Subsequently, the dialysis fluid was exchanged, and dialysis continued for 2 h. Finally, the dialyzed solution was lyophilized for 5 days to remove any fluid content.

For the fecal batch culture experiments, 500 mg of stool samples that were taken each from four representative participants prior to the short-term intervention study were homogenized in 5 ml of sterile, anoxic basal buffer (0.5 g/l bile salts (Sigma-Aldrich), 0.01 g/l CaCl2 × 2 H2O, 0.04 g/l K2HPO4, 0.01 g/l MgSO4 × 7H2O, 0.04 g/l KH2PO4, 0.1 g/l NaCl, 2 g/l NaHCO3, flushed with 90% N2 / 10% CO2). Subsequently, the slurry was divided into 2.5 ml aliquots until further use. To mimic a fasting period for the fecal microbiota in comparison to physiological conditions, one aliquot per participant was used to inoculate either 50 ml of sterile, anoxic basal buffer (starvation group) or 50 ml of sterile anoxic Brain Heart Infusion medium (BHI, flushed with 90% N2 / 10% CO2; physiological group) in serum flasks sealed with butyl rubber stoppers. After incubation overnight at 37°C, the starvation and the physiological groups were each used to inoculate (3% v/v) 50 ml of sterile, anoxic basal medium (see Kristek et al.99; flushed with 90% N2 / 10% CO2) either with or without 3.3 g of anoxic, sterile in vitro digested oat flakes (from 66 g/l stock solution in 5 mM potassium phosphate buffer, pH 7.5, flushed with N2 corresponding to an oat meal based on a colon volume of 1.5 l) in serum flasks sealed with butyl rubber stoppers. Finally, the cultures were incubated at 37°C over 36 h. Samples for global metabolomic profiling, including phenolic compounds, SCFAs, and microbiome analysis, were taken directly after inoculation, after 6, 24, and 36 h of incubation time.

SCFA profiling. The concentrations of SCFAs were determined using isocratic HPLC (Agilent 1260 Infinity II System equipped with a VWD dual wavelength detector, Agilent Technologies). Briefly, samples were centrifuged for 2 min at 12,000 × g and the supernatants were sterile filtered (Filtropur S, pore size: 0.2 µm, Sarstedt). The filtrate was diluted 1:5 with 5 mM TFA and finally analyzed on an Aminex HPX-87H 300 × 7.8 mm column (Bio-Rad Laboratories) at 60°C with 5 mM TFA as the mobile phase (flow rate of 0.5 ml/min). To determine the absolute concentrations of every SCFA species in each sample, UV absorption at 210 nm was quantified against respective standard curves using OpenLab CDS software version 2.6.

Targeted plasma metabolomic profile (primary outcome)
Plasma dihydroferulic acid concentration, the primary outcome of this study, and ferulic acid values (peak areas) before and after each intervention period were generated by Metabolon Inc. (Morrisville) using LC-MS/MS according to Metabolon Method TAM223 (“LC-MS/MS Quantitation of Dihydroferulic Acid and Three Avenanthramide Compounds in Human Plasma”)18.

In detail, human plasma samples were spiked with stable labeled internal standards and then incubated with glucuronidase and sulfatase enzymes to convert conjugated metabolites to their free form. After incubation, the samples were subjected to protein precipitation with acetonitrile. Next, the samples were centrifuged, and the supernatant dried down under nitrogen and reconstituted with water containing formic acid. The samples were mixed and injected onto an Agilent 1290/AB Sciex QTRAP 7500 LC-MS/MS system equipped with a BEH C18 reversed phase UHPLC column. The flow rate was 625 µL/min. The mass spectrometer was operated in negative mode using electrospray ionization (ESI).

The peak areas of the respective analyte productions were measured against the peak area of the corresponding internal standard product ions (e.g., dihydroferulic acid, avenanthramides). Quantitation was performed using a linear regression with a 1/x weighting generated from fortified calibration standards prepared immediately prior to each run. LC-MS/MS raw data were collected and processed using ABSCIEX software Analyst 1.6.3 and processed using SCIEX OS-MQ software v1.7. Data reduction was performed using Microsoft Excel for Office 365 v. 16.

Global metabolomic profiling
For both fasting plasma and fecal samples, non-targeted global metabolomic profiles before and after each intervention period were generated by Metabolon Inc. (Research Triangle) using UPLC-MS/MS24,25 (Supplementary Data 6). The metabolomic dataset of both intervention studies comprised a total of 1149 and 1082 metabolites in plasma and feces, respectively. In the supernatant of the batch culture experiment a total of 995 metabolites was identified. This includes amino acids, peptides, carbohydrates, energy intermediates, lipids, nucleotides, cofactors and vitamins, xenobiotics, and partially characterized molecules of both endogenous and an established microbial origin100. After removing drug-associated metabolites, 1128 plasma metabolites and 1054 fecal metabolites (applies to both interventions) were included in the analyses.

A detailed description of the procedure (Metabolon Platform) is provided below.

Sample accessioning. Following receipt, samples were inventoried and immediately stored at −80°C. Each sample received was accessioned into the Metabolon LIMS system and was assigned by the LIMS a unique identifier that was associated with the original source identifier only. This identifier was used to track all sample handling, tasks, results, etc. The samples (and all derived aliquots) were tracked by the LIMS system. All portions of any sample were automatically assigned their own unique identifiers by the LIMS when a new task was created; the relationship of these samples was also tracked. All samples were maintained at −80°C until processed.

Sample preparation. Samples were prepared using the automated MicroLab STAR® system from Hamilton Company. Several recovery standards were added prior to the first step in the extraction process for QC purposes. To remove protein, dissociate small molecules bound to protein or trapped in the precipitated protein matrix, and to recover chemically diverse metabolites, proteins were precipitated with methanol under vigorous shaking for 2 min (Glen Mills GenoGrinder 2000) followed by centrifugation. The resulting extract was divided into multiple fractions: two for analysis by two separate reverse phase (RP)/UPLC-MS/MS methods with positive ion mode electrospray ionization (ESI), one for analysis by RP/UPLC-MS/MS with negative ion mode ESI, one for analysis by HILIC/UPLC-MS/MS with negative ion mode ESI, while the remaining fractions were reserved for backup. Samples were placed briefly on a TurboVap® (Zymark) to remove the organic solvent. The sample extracts were stored overnight under nitrogen before preparation for analysis.

QA/QC. Several types of controls were analyzed in concert with the experimental samples: a pooled matrix sample generated by taking a small volume of each experimental sample (or alternatively, use of a pool of well-characterized human plasma) served as a technical replicate throughout the dataset; extracted water samples served as process blanks; and a cocktail of QC standards that were carefully chosen not to interfere with the measurement of endogenous compounds were spiked into every analyzed sample, allowed instrument performance monitoring and aided chromatographical alignment. Specifically, the Metabolon platform includes three distinct types of Metabolon QC samples and two different Metabolon QC standards: (i) the QC sample “MTRX” is a large pool of human plasma maintained by Metabolon that has been characterized extensively and used to assure that all aspects of the Metabolon process are operating within specifications; (ii) the QC sample “CMTRX” is a pool created by taking a small aliquot from every participant sample to assess the effect of a non-plasma matrix on the Metabolon process and distinguish biological variability from process variability; (iii) the QC sample “PRCS”, an aliquot of ultra-pure water, serves as a process blank to assess the contribution to compound signals from the process; (iv) the recovery standard (RS) is used to assess variability and verify performance of extraction and instrumentation; and v) the internal standard (IS) assesses variability and performance of instrument.

Instrument variability was determined by calculating the median relative standard deviation (RSD) for the standards that were added to each sample prior to injection into the mass spectrometers. Overall process variability for feces was determined by calculating the median RSD for all endogenous metabolites (i.e., non-instrument standards) present in 100% of the Client Matrix samples, which are technical replicates of pooled client samples. Overall process variability for plasma was determined by calculating the median RSD for all endogenous metabolites (i.e., non-instrument standards) present in the MTRX7 technical replicates. Values for instrument and process variability meet Metabolon’s acceptance criteria (median RSD internal standards: 6% (plasma) vs. 7% (feces), median RSD endogenous biochemicals: 11% (plasma) vs. 12% (feces)). Experimental samples were randomized across the platform run with QC samples spaced evenly among the injections.

Ultra high performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS). All methods utilized a Waters ACQUITY ultra-performance liquid chromatography (UPLC) and a Thermo Scientific Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution24. The dried sample extract was then reconstituted in solvents compatible to each of the four methods. Each reconstitution solvent contained a series of standards at fixed concentrations to ensure injection and chromatographic consistency. One aliquot was analyzed using acidic positive ion conditions, chromatographically optimized for more hydrophilic compounds (PosEarly). In this method, the extract was gradient eluted from a C18 column (Waters UPLC BEH C18-2.1×100 mm, 1.7 µm) using water and methanol, containing 0.05% perfluoropentanoic acid (PFPA) and 0.1% formic acid (FA). Another aliquot was also analyzed using acidic positive ion conditions; however, it was chromatographically optimized for more hydrophobic compounds (PosLate). In this method, the extract was gradient eluted from the same aforementioned C18 column using methanol, acetonitrile, water, 0.05% PFPA and 0.01% FA and was operated at an overall higher organic content. Another aliquot was analyzed using basic negative ion optimized conditions using a separate dedicated C18 column (Neg). The basic extracts were gradient eluted from the column using methanol and water, however, with 6.5 mM Ammonium Bicarbonate at pH 8. The fourth aliquot was analyzed via negative ionization following elution from a HILIC column (Waters UPLC BEH Amide 2.1×150 mm, 1.7 µm) using a gradient consisting of water and acetonitrile with 10 mM Ammonium Formate, pH 10.8 (HILIC). The MS analysis alternated between MS and data-dependent MSn scans using dynamic exclusion. The scan range varied slightly between methods but covered 70–1000 m/z. Raw data files are archived and extracted as described below.

Bioinformatics. The informatics system consisted of four major components, the Laboratory Information Management System (LIMS), the data extraction and peak-identification software, data processing tools for QC and compound identification, and a collection of information interpretation and visualization tools for use by data analysts. The hardware and software foundations for these informatics components were the LAN backbone, and a database server running Oracle 10.2.0.1 Enterprise Edition.

LIMS. The purpose of the Metabolon LIMS system was to enable fully auditable laboratory automation through a secure, easy to use, and highly specialized system. The scope of the Metabolon LIMS system encompasses sample accessioning, sample preparation and instrumental analysis and reporting and advanced data analysis. All of the subsequent software systems are grounded in the LIMS data structures. It has been modified to leverage and interface with the in-house information extraction and data visualization systems, as well as third party instrumentation and data analysis software.

Data extraction and compound identification. Raw data was extracted, peak-identified, and QC processed using a combination of Metabolon developed software services (applications). Each of these services perform a specific task independently, and they communicate/coordinate with each other using industry-standard protocols. Compounds were identified by comparison to library entries of purified standards or recurrent unknown entities. Metabolon maintain a library based on authenticated standards that contains the retention time/index (RI), mass to charge ratio (m/z), and fragmentation data on all molecules present in the library. Furthermore, biochemical identifications are based on three criteria: retention index within a narrow RI window of the proposed identification, accurate mass match to the library +/− 10 ppm, and the MS/MS forward and reverse scores between the experimental data and authentic standards. The MS/MS scores are based on a comparison of the ions present in the experimental spectrum to the ions present in the library spectrum. While there may be similarities between molecules based on one of these factors, the use of all three data points is utilized to distinguish and differentiate biochemicals. More than 5,400 commercially available purified or in-house synthesized standard compounds have been acquired and analyzed on all platforms for determination of their analytical characteristics. An additional 7000 mass spectral entries have been created for structurally unnamed biochemicals, which have been identified by virtue of their recurrent nature (both chromatographic and mass spectral). These compounds have the potential to be identified by future acquisition of a matching purified standard or by classical structural analysis. Metabolon continuously adds biologically-relevant compounds to its chemical library to further enhance its level of Tier 1 metabolite identifications.

Compound quality control. A variety of curation procedures were carried out to ensure that a high-quality dataset was made available for statistical analysis and data interpretation. The QC and curation processes were designed to ensure accurate and consistent identification of true chemical entities, and to remove or correct those representing system artifacts, mis-assignments, mis-integration, and background noise. Metabolon data analysts use proprietary visualization and interpretation software to confirm the consistency of peak identification and integration among the various samples.

Metabolite quantification and data normalization. Peaks were quantified using area-under-the-curve. For studies spanning multiple days, a data normalization step was performed to correct variation resulting from instrument inter-day tuning differences. Essentially, each compound was corrected in run-day blocks by registering the medians to equal one (1.00) and normalizing each data point proportionately (termed the “block correction”). For studies that did not require more than one day of analysis, no normalization is necessary, other than for purposes of data visualization. In certain instances, biochemical data may have been normalized to an additional factor (e.g., cell counts, total protein as determined by Bradford assay, osmolality, etc.) to account for differences in metabolite levels due to differences in the amount of material present in each sample.

Gut microbiome sample processing
Total genomic DNA was extracted from 120 mg fecal material or from pelleted material from 700 µl cell suspension using ZR BashingBead lysis tubes (0.1 and 0.5 mm, Zymo Research, Freiburg, Germany) in combination with the Chemagic DNA stool kit (PerkinElmer, Rodgau, Germany) following the manufacturer’s instructions101–103. After the addition of lysis buffer, mechanical lysis was performed using a Precellys 24 tissue homogeniser (Bertin Instruments, Frankfurt am Main, Germany). After extraction, the DNA was stored at −20°C until further analysis. High-throughput 16S rRNA amplicon sequencing of the fecal microbiome was performed at Life & Brain GmbH (Bonn, Germany). In brief, the V3V4 region of the 16S rRNA gene was amplified in a first PCR step using the Bakt_341F (5′-CCTACGGGNGGCWGCAG-3′) and Bakt_805R (5′-GACTACHVGGGTATCTAATCC-3′) primers combination. The 25 µL PCR reaction contained 2.5 µL of template (5 ng/µL), 12.5 µL of 2× KAPA HiFi HotStart ReadyMix (Roche, Mannheim, Germany), and 5 µL of primer mix (1 µM). Thermal cycling conditions comprised an initial denaturation at 95°C for 3 min, followed by 25 cycles of denaturation (30 s at 95°C), annealing (30 s at 55°C), and elongation (30 s at 72°C), with a final elongation step at 72°C for 5 min. In a subsequent PCR, dual indices and Illumina sequencing adapters were incorporated using the Nextera XT v2 Index Kit (Illumina, San Diego, CA, USA). The second PCR reactions consisted of 25 µL 2× KAPA HiFi HotStart ReadyMix, 5 µL Nextera XT index primers, and 10 µL PCR-grade water, in a total volume of 50 µL per sample. Cycling conditions were an initial denaturation at 95°C for 3 min, followed by 8 amplification cycles of denaturation (30 s at 95°C), annealing (30 s at 55°C), and elongation (30 s at 72°C), finalized by a 5 min elongation at 72°C. After each PCR step, amplicon libraries were quality-checked using an Agilent TapeStation 4200 with D1000 ScreenTape (Santa Clara, CA, USA) and purified with AMPure XP beads (Beckman Coulter, Krefeld, Germany). Samples were normalized to 4 nM and pooled in equimolar amounts104. The ZymoBIOMICS Gut Microbiome Standard (D6331, Zymo Research, Freiburg, Germany) was used as the positive control, while ultrapure water (rHO) served as the negative control. The final pool was quantified using the Qubit dsDNA HS assay kit (Thermo Fisher Scientific, Waltham, MA, USA), and the fragment size was checked using a D1000 ScreenTape (Agilent, Santa Clara, CA, USA). Sequencing was performed on an Illumina MiSeq system using the MiSeq reagent kit v3 with 2×300 cycles. Clustering was performed at 8 pM with a 20% spike-in of PhiX. Demultiplexing was performed using the MiSeq system. For the human intervention studies, the average number of paired-end (PE) reads was 76,504, with a minimum of 29,392 PE reads. In the batch culture fermentation experiment, the average number of PE reads was 137,854 (excluding blank and t0 samples), with a minimum of 31,045 PE reads. Blank samples yielded an average of 552 PE reads, with a minimum of 246 PE reads. t0 samples had an average of 21,587 PE reads, with a minimum of 4246 PE reads. The 16S rRNA sequencing data were processed using QIIME2 version 2021.4105. Sequence quality control and denoising were performed using DADA2106. The QC step included the filtering of PhiX reads and chimeric sequences. After denoising, sequences were classified using the SILVA database (138.1 SSU Ref NR 99) to identify amplicon sequencing variants (ASVs) for sequences with >99% sequence similarity.

Gut microbiome analysis
Diversity analyses were performed in QIIME2 based on a rarefied table with a sampling depth of 28,767 sequences (short-term intervention) and 21,876 sequences (six-week intervention), respectively, and were used for subsequent statistical analysis. We quantified alpha diversity using Shannon entropy, Pielou’s evenness, and Faith’s phylogenetic diversity (Faith-PD). Alpha diversity was assessed per sample, and a LMM was applied to evaluate the differences between the two diet groups in each study. The LMM was adjusted for the confounders sex, age, and BMI (LMMadj.), and estimated the diet effect relative to the baseline value and the control group (CG or CG6w). An individual-specific random intercept was included to account for differences in baseline alpha diversity. The model was defined as: alpha diversity ~ time*group+age+sex+BMI+(1|Person-ID), with the covariate of interest “time*group”.

We quantified beta diversity using Jaccard distance, Bray-Curtis dissimilarity, and UniFrac distance matrices. As beta diversity measures differences in community composition, we computed it with respect to each individual’s baseline microbiome. For the analysis of beta diversity—which considers the change with respect to baseline—we used a linear regression model (LnR), as baseline differences are accounted for by the metric itself. Beta diversity was modeled as a function of the diet groups, using the control group of each study (CG or CG6w) as the reference, and was adjusted for the confounders sex, age, and BMI (LnRadj.). The model was defined as: beta diversity ~ group+age+sex+BMI, with the covariate of interest ‘group’.

To identify differences between the diet groups of the RCTs at feature level, linear models for differential abundance analysis (LinDA)27, an established state-of-the-art method for analyzing microbiome compositional data, was applied. The control group of each study (CG or CG6w) and the baseline value were used as reference and the Person-ID was used as random effect. The model was defined as: taxon ~ time*group+age+sex+BMI+(1|Person-ID), with the covariate of interest “time*group”. The analyses were performed at genus level with a relative abundance threshold of 0.01%. Data were normalized using a centered-log-ratio (CLR) transformation with an offset of 1 prior to all analyses. Twenty-eight participants from the short-term intervention (n=4 dropouts [3 female, 1 male] due to an incomplete dataset) and all 34 participants from the six-week intervention were included in the respective analysis.

PICRUSt2 (version 2.5.1)30 was used to predict the functional potential of the fecal microbiota. Such functional prediction tools play a valuable role in facilitating preliminary hypothesis testing based on amplicon sequences; however, this method has inherent limitations in accurately reproducing functional potential. It should be acknowledged that the prediction model based on 16S or shotgun sequencing data does not confirm whether functional capacity is activated at the mRNA or protein level. Given the prohibitive cost of shotgun sequencing, functional prediction tools serve as a valid guide for subsequent studies of actual functional capacity. Predicted gene products in each bacterium were annotated using the KEGG database and classified into KEGG orthologous (KO) groups29, resulting in the identification of 6616 KO (short-term intervention: n=28) and 6424 KO (six-week intervention: n=34) across all samples. The ko2kegg_abundance function of the ggpicrust2 package107 was used for this task. In accordance with LinDA, a LMM was applied to the CLR-transformed count data with the control group of each study (CG or CG6w) and the baseline value as reference and the Person-ID as random effect (LMMadj.). The model was defined as: pathway ~ time*group+age+sex+BMI+(1|PersonID), with the covariate of interest “time*group”. The analyses were performed with a relative abundance threshold of 0.01%.

Sparse partial least squares-discriminant analysis
To identify the key features that differentiate the two groups in each intervention study in terms of diet-induced changes in clinical markers, gut microbiota composition and functional capacity, and global metabolomic profiles in plasma and feces, sPLS-DA was performed. sPLS-DA, implemented in the mixOmics v6.8.5 R package, enables the selection of the most discriminative features in the data to classify the samples by projecting the data into a lower dimensional space in a supervised manner20,21. This approach is optimal if the number of features is very high compared to the sample size, as in this present work. To account for the repeated-measures design of both intervention studies, the logarithmic fold change (log fold change) was used as the input to the models based on Lee et al.108. The performance of the sPLS-DA was assessed on the BER and AUC, which were calculated using a cross-validation approach. The results were visualized using two-component sample plots and loadings.

A detailed description of data processing and procedure is provided below.

Data processing. For the targeted plasma metabolomic profile, DHFA concentrations below the limit of quantitation, but above the peak area of the background DHFA peak in the blank samples, or concentrations above the limit of quantitation were extrapolated. Missing values in the global metabolomic profiles were imputed with the minimum observed value for each compound (% missing values short-term vs. six-week intervention study: 12% vs. 13% (plasma), 24% vs. 26% (feces)). This is in line with the literature, reporting that missing values are quite common in large untargeted metabolomics datasets and might comprise up to 50% of the dataset109. Metabolic and metabolomics data were transformed with the natural logarithm prior to the analysis. Microbiome data were normalized by using CLR transformation with an offset of 1. The log fold change was calculated for all datasets and used as the input to the models.

Procedure. The number of features and PLS components was chosen via stratified fivefold cross-validation with 25 random repeats, according to the best-balanced classification error rate (BER). The models were calibrated and assessed by fivefold-cross-validation. For each fold, absolute shrinkage and selection operator (LASSO)110 was used on the training data to minimize the number of features per component while maximizing class discrimination. BER and AUC were calculated for the final model on the test data. As result, the averages across the folds were presented.

The dataset of the clinical markers, which comprised 26 variables, was transformed into two components with three and 19 clinical markers in the short-term intervention study, and into one component with all 26 clinical markers in the six-week intervention study. The microbiome dataset, which comprised 143 or 146 genera (short-term vs. six-week), was transformed into two components with one and 41 genera in the short-term intervention study, and into one component with one microbe in the six-week intervention study. For the KEGG pathway data set, which comprised 145 pathways, two components with ten and five features were selected in the short-term intervention study. In the six-week intervention study, six components with 35, 13, two, two, five, and five pathways were selected from a total of 146 KEGG pathways. The model of the global plasma metabolomic profile comprised two components with 30 and 95 metabolites in the short-term intervention study and one component with 15 metabolites in the six-week intervention study, each selected from a total of 1128 metabolites. For the global fecal metabolomic profile, two components with 91 and 971 metabolites in the short-term intervention study and one component with 31 metabolites in the six-week intervention study, each selected from a total of 1054 metabolites, were selected as the best models. An overview of all selected features and their weight loadings are provided in the Supplementary Data 5. Further detailed information on the workflow can be found in the tutorials of mixOmics (http://mixomics.org/case-studies/).

Integrative multi-omics analysis
To determine the relationship between diet-induced changes in the clinical markers, gut microbiota, and targeted and global metabolomic profiles, DIABLO, an integrative multi-omics method that aims to identify common information across different types of data while distinguishing between different phenotypes32, in this study between the diet groups of each intervention trial, was applied for each study separately. Two different DIABLO evaluations (“models”) were performed. For model 1, clinical markers, metabolomic profiles including targeted plasma, global plasma, and global fecal profiles, and the microbial composition were used as inputs (short-term intervention: model 1.1; six-week intervention: model 1.2). Model 2 considered the functional potential of the microbiota instead of the composition (short-term intervention: model 2.1; six-week intervention: model 2.2). The log fold changes were used as inputs for the models108. The performance of the final models was assessed on the BER and AUC derived from internal cross-validation of the model. The results were presented using weight loadings and Circos plots (correlation cutoff r=±0.7).

In detail, DIABLO is a novel framework for the integration of multiple data sets in a supervised analysis that aims to identify key variables/common information across different datasets (e.g., clinical markers, metabolomic profiles, microbiome data) during integration process while distinguishing between different phenotypes (e.g., study groups)32. Thus, the correlated information between multiple datasets is maximized. DIABLO extends sparse generalized canonical correlation analysis (sGCCA)111 to the sPLS-DA framework for selecting co-expressed (correlated) variables from different datasets. To account for the repeated-measure design of the intervention studies, log fold change of the preprocessed data as described above was used as an input to the models. An initial partial least square analysis between the five datasets of model 1 including clinical marker, microbiome data (genera), targeted plasma metabolomic profile ((DH)FA), and global plasma and fecal metabolomic profiles showed correlations in the range of 0.66–0.96 (model 1.1: short-term intervention study) and 0.61–0.93 (model 1.2: six-week intervention study), therefore a square matrix filled with 0.75 and diagonal set to zeros was used as an input to the design parameter of the DIABLO model. The correlations for model 2, which included KEGG pathways instead of microbiome genera, ranged from 0.63–0.96 (model 2.1: short-term intervention study) and from 0.35–0.93 (model 2.2: six-week intervention study), so that the same input was used for the design parameters of the DIABLO model. Tuning of the DIABLO model was done similar to the tuning of the sPLS-DA model as described above. An overview of all selected features and their weight loadings are provided in the Supplementary Data 5. The final model 1.1 consisted of two components. Component one comprised one plasma metabolite of the targeted analysis (FA), three clinical markers (LDL, TC, DBP), four bacterial genera (Erysipelotrichaceae UCG-003, Marvinbryantia, Eggerthella, Intestinimonas), and 15 metabolites each of the global plasma and fecal metabolomic profile. Component two comprised one plasma metabolite of the targeted analysis (DHFA), 15 clinical markers, one bacterial genus ([Erysipelotrichaceae] uncultured), and 15 metabolites each of the global plasma and fecal metabolomic profile. The final model 2.1 consisted of two components. Component one comprised one plasma metabolite of the targeted analysis (FA), one clinical marker (LDL), five KEGG pathways (naphthalene degradation, carbohydrate digestion and absorption, novobiocin biosynthesis, aminobenzoate degradation, nitrogen metabolism), and 15 metabolites each of the global plasma and fecal metabolomic profile. Component two comprised one plasma metabolite of the targeted analysis (DHFA), three clinical markers (bilirubin, NEFAs, uric acid), two KEGG pathways (aminobenzoate degradation, riboflavin metabolism), and 15 metabolites each from of the global plasma and fecal metabolomic profile. The final model 1.2 consisted of two components. Component one comprised one plasma metabolite of the targeted analysis (FA), one clinical marker (HDL), 21 bacterial genera, one plasma metabolite (prolylglycine), and eleven fecal metabolites of the global metabolomic profile. Component two comprised one plasma metabolite of the targeted analysis (DHFA), one clinical marker (hsCRP), eleven bacterial genera, and one plasma metabolite (21-hydroxypregnenolone disulfate) and eleven fecal metabolites of the global metabolomic profiles. The final model 2.2 consisted of two components. Component one comprised one plasma metabolite of the targeted analysis (FA), one clinical marker (insulin), 31 KEGG pathways, and one plasma metabolite (prolylglycine) and 31 fecal metabolites of the global metabolomic profiles. Component two comprised one plasma metabolite of the targeted analysis (DHFA), one clinical marker (hsCRP), 21 KEGG pathways, and eleven plasma metabolites and one fecal metabolite (N-palmitoyltaurine) of the global metabolomic profiles. Further detailed information on the workflow can be found in the tutorials of mixOmics (http://mixomics.org/mixdiablo/diablo-tcga-case-study/).

Partial least square regression model
To indicate the predictive value of the microbial and phenolic changes in modulating cholesterol levels, partial least square (PLS) regression models were applied112. The models were implemented using the mixOmics package in R (version 4.2.2). The analyses were performed without sparse mode, i.e., all predictor variables identified in the previous DIABLO models (cutoff r=0.7) were included in the models without variable selection. R² and Q² total (cross-validated R²), calculated by leave-one-out cross-validation, were used to assess the fit and predictability of the model, respectively112. The log fold change of the identified features (nine phenolic compounds or two genera and one pathway) was used as predictor and the log fold change of total and LDL-cholesterol as response.

Calibration analysis
Out-of-fold (OOF) predictions for each DIABLO and sPLS-DA model were generated using fivefold cross-validations with 25 repeats. For each participant, the Oats–Control score margin were taken and converted it to a probability with Platt scaling (logistic regression fit only to the OOF data). Probabilities were grouped into quantile bins; the bin mean vs. the observed oats rate with 95% Wilson intervals and overlaid a loess smoother were visualized. AUC was derived from cross-validation. Since the dashed 45° line marks perfect calibration, the majority of the calibration plots did not indicate a bias and showed that the predicted probabilities match the observed outcomes well, in particular, the DIABLO models and the sPLS-DA model of the global plasma metabolomic profile, from which the main outcome of our study were derived, performed very well (Supplementary Fig. 4).

Statistics and sample size calculation
Statistical analyses were performed using SPSS (version 29.0; IBM Crop., Chicago, IL, USA), R (version 3.6.2; Boston, MA, USA), and Python (version 3.10). GraphPad Prism (version 10; GraphPad Software, San Diego, CA, USA), R, and Python were used for creating figures. For all analyses, a two-tailed significance level was set at P<0.05. For metabolic data analysis, family-wise error rate correction was applied via the block-wise Bonferroni–Holm method113 to correct for multiple testing (Padj.<0.05). This stringent multiple comparison method is particularly suitable for hypothesis-driven analyses as in the present case. For the microbiome and global metabolomic data with a high noise-to-signal ratio, FDR correction based on the Benjamini/Hochberg method114 was applied (q<0.05), which is frequently used for high-throughput data.

Prior to the analysis, the distribution of the data was visualized individually. Additionally, Shapiro-Wilk tests were performed to test for normality of the data and determine the appropriate approach for further analysis. Variables were analyzed either on their original scale or logarithmic transformed to approximate a normal distribution.

Postprandial results were described as the AUC for the 3-h OGTT, calculated using the trapezoidal method and only completed datasets.

To assess whether the data provided evidence of the superiority of the oat diets over the respective control in terms of our primary outcome and the metabolic and metabolomic outcomes, linear regression models were applied, including the baseline value and the confounding factors BMI, age, and sex (LnRadj.) as covariates. The control group of each study (CG or CG6w) was used as reference. The model was defined as: “post-intervention_value ~ baseline_value+group+age+sex+BMI”. Beta estimates of intervention (β) with 95% CI were reported to assess the magnitude of the effect size. In addition, a LMM adjusted for BMI, age, and sex (LMMadj.) was used to assess differences in clinical markers across all four time points of the six-week intervention study. The control group (CG6w) and the baseline value were used as reference, the Person-ID was used as random effect to account for repeating measurements. The model was defined as: clinical marker ~ time*group+age+sex+BMI+(1|Person-ID), with the covariate of interest “time*group”. Residuals obtained from the models were inspected for normality to control for the fit of the statistical model. If the residuals were not normally distributed, logarithmic transformation was applied.

To determine the changes over time within each study group of the RCTs (pre- vs. post-intervention), a paired Student’s t-test (or Wilcoxon rank test) was performed depending on the data distribution. In addition, to account for the changes within the OG, including the follow-up period, a LMM adjusted for BMI, age, and sex (LMMadj.) was build using the baseline value as reference and the Person-ID as random effect.

Pairwise correlations between diet-induced significantly altered clinical markers and changes in the microbiome and metabolomic profiles were assessed using Pearson’s and Spearman’s rank correlation coefficient. Only variables with at least five data points were included, using log fold change for analysis. Pairwise correlations were primarily intended to support the DIABLO results; therefore, no FDR correction was applied.

Sample size was calculated based on data from a previous intervention study that successfully assessed the effect of whole grain intake on blood DHFA concentration16, expecting a two fold change in the plasma DHFA concentration between the two diet groups in each intervention study using a two-sided t-test at a 5% significance level and with 95% power. Thus, a sample size of 14 people per group was required. Assuming a dropout rate of 20%, 17 people per group have been included (34 people per study). In line with the SAGER guidelines, sex-disaggregated analyses should be pursued; however, due to the limited sample size, stratification by sex was not conducted.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Key results: Please summarise what you consider to be the outstanding features of the work.\nMicrobial degradation products might be the driving factors for the oat-induced decrease in serum cholesterol levels.\nChanges in a number of metabolites of lipid and amino acid metabolism might contribute to the cholesterol-lowering effect in\nthe gut microbiome composition and function due to oats.\nValidity: Does the manuscript have flaws which should prohibit its publication? If so, please provide details.\nThe sections of the article do not align with CONSORT guidelines. Adjust sections to the following order Methods, Results,\nDiscussion. Include a specific and more detailed Conclusion.\nOriginality and significance: If the conclusions are not original, please provide relevant references. On a more subjective\nnote, do you feel that the results presented are of immediate interest to many people in your own discipline, and/or to people\nfrom several disciplines?\nThe conclusions are original but have indistinct links to previously conducted studies. It will be of interest to those working\nwithin Metabolic syndrome clinical trials specifically those who have investigated the properties of oats to improve lipid\nmetabolism.\nData & methodology: Please comment on the validity of the approach, quality of the data and quality of presentation. Please\nnote that we expect our reviewers to review all data, including any extended data and supplementary information. Is the\nreporting of data and methodology sufficiently detailed and transparent to enable reproducing the results?\nThe data analysis is very detailed in both supplementary tables and in the body of the text. Readers may find it hard to\nidentify the more notable significance of the trials main data points due to the dense description of each data point. The lack\nof description surrounding secondary and tertiary outcomes could subject the trials to being considered of more exploratory\nnature rather than controlled which is noticeable by the extensive description of the listing of data points. Authors might\ncondense findings from most significant to least significant as opposed to listing all findings and direct readers to\nsupplementary tables for specific but non-notable findings.\nAppropriate use of statistics and treatment of uncertainties: All error bars should be defined in the corresponding figure\nlegends; please comment if that’s not the case. Please include in your report a specific comment on the appropriateness of\nany statistical tests, and the accuracy of the description of any error bars and probability values.\nStatistical tests are appropriate and have been explained where used but with little justification for why a method was valid\nfor the particular data.\nConclusions: Do you find that the conclusions and data interpretation are robust, valid and reliable?\nThe conclusions are valid based on the data analysis presented in this paper.\nSuggested improvements: Please list additional experiments or data that could help strengthening the work in a revision.\nReferences: Does this manuscript reference previous literature appropriately? If not, what references should be included or\nexcluded?\nAppropriate references have been included\nClarity and context: Is the abstract clear, accessible? Are abstract, introduction and conclusions appropriate?\nThe abstract is clear however the statistics has been reduced to a percentage so could be more detailed.\nInflammatory material: Does the manuscript contain any language that is inappropriate or potentially libelous?\nThe manuscript does not contain any language that is inappropriate or potentially libelous", "reviewer_2": "This interesting study discusses the cholesterol-lowering effects of oats induced by microbially produced phenolic\nmetabolites in individuals with metabolic syndrome through dietary interventions and multi-omics analysis.\nThe message is that short term high oat diet induces pronounced effects on microbial composition and metabolism, whilst a\n6 wk moderate oat diet has very slight effects. This overall message has to be emphasized and discussed more. Diet\ncomposition at product/food items level has to be reported more extensively. When you add 100 g oat to one meal 3 times a\nday in the short term, how does that change overall diet composition and to what extent can differences be ascribed to oat?.\nWhat are the clinical implications of the current findings, how long do effects of high oat last and what would be the advise,\nto consume consistently a high oat diet or intermittently?.\nStatistical analyses like sPLS-DA and DIABLO were used for data interpretation. Many correlations were reported. Is it also\npossible to indicate the predictive value of microbial and phenolic changes in modulating cholesterol?\nIs it possible to add a figure with major outcomes both short-term and long-term?", "reviewer_3": "Linda et al conducted two randomized clinical trial in subjects with MetS, and demonstrated that gut microbiota-derived\nphenolic metabolites mediate the cholesterol-lowering effect of oats. The study is interesting and of significance, however,\nseveral major concerns preclude the publication.\nMajor\n1. Overall, this study is entirely based on bioinformatics analysis and all the data are correlative in nature. Given that 16S\nrRNA seq is not suitable for microbial function analysis, I don’t this the data, especially for microbial metabolites, provided in\nthe manuscript is convincing. Moreover, no in vitro or in vivo experiments have been carried out to validate or prove that\nphenolic compounds mediate the beneficial effect of oat on cholesterol metabolism.\n2. As is described by the authors, SCFAs are also important microbial products of oats. Then, do the authors detect SCFAs\nin plasma and fecal samples before and after the intervention> If yes, how about its change? Given the established\nbeneficial effect of SCFAs on cholesterol metabolism, how can you make sure that the modulating effect is exerted by\nphenolic compounds without detecting or adjusting for the effect of SCFAs?\n3. The design and description of the two trials are wired. For the short-term intervention, I am not sure the change in phenolic\ncompounds, gut microbiota, and clinical data refers to day 2 post intervention or the lasting effect by week 6 post\nintervention?\n4. When comparing the change of certain metabolites or clinical parameter between two intervention groups, the baseline\nlevel of this metabolite or clinical parameter should also be included in the model.\nMinor\n1. Why the authors restrict the age of study participants to 45-70 yrs?\n2. Why history of smoking or alcoholism is excluded? Ex-smokers are also excluded? The exclusion criteria seem to be too\nstrict, which would substantially affect the applicability of this study.\n3. For both untargeted and targeted metabolomics, more details should be provided. How plasma and fecal samples are\nextracted. What’s the detailed setup of the LC-MS/MS system? The flow rate, the column used and how features or peaks are\nidentified (including the database used) should be specified to ensure reproducibility.\n4. Prediction of microbial genes using 16s rRNA seq data is not accurate enough. The authors should at least acknowledge\nthis limitation clearly in the manuscript.\n5. Why do the authors choose different methods for multiple correction in metabolomics and microbiota analysis?"}, "author_response": "Dear Referees,\nThank you very much for all your valuable feedback. We have addressed all comments\nand suggestions in a point-by-point response (in blue), and your feedback has\nsignificantly improved the outcome of the study and strengthen the manuscript. Thus,\nwe hope that all concerns have been resolved and the paper is suitable for publication\nin the special issue Microbiome & Nutrition in Nature Communications.\nReviewer #1 (Remarks to the Author):\nKey results: Please summarise what you consider to be the outstanding features of the\nwork.\nMicrobial degradation products might be the driving factors for the oat-induced\ndecrease in serum cholesterol levels. Changes in a number of metabolites of lipid and\namino acid metabolism might contribute to the cholesterol-lowering effect in the gut\nmicrobiome composition and function due to oats.\nWe greatly appreciate your precise summary of our work. Thank you very much for\nyour efforts.\nValidity: Does the manuscript have flaws which should prohibit its publication? If so,\nplease provide details.\nThe sections of the article do not align with CONSORT guidelines. Adjust sections to\nthe following order Methods, Results, Discussion.\nThank you for pointing this out. The sections of the manuscript have been presented\naccording to the publishing guidelines of Nature Communications, i.e. Results,\nDiscussion, Methods. However, we are familiar with the CONSORT guidelines, which\nwe adhere to except for the order of the article sections. We have submitted the\nCONSORT 2010 checklist with this manuscript and updated it accordingly as part of\nthe revision.\nInclude a specific and more detailed Conclusion.\nThank you for this suggestion. We have rewritten the conclusion being more specific\nand detailed (lines 786-801):\n“In conclusion, oat consumption, particularly a short-term, high-dose oat diet, provides\nmetabolic health benefits for individuals with MetS by increasing circulating microbially\n1\nproduced phenolic metabolites, thereby lowering serum cholesterol levels. This\nidentified mechanism shows that phenolic metabolites besides SCFAs are driving\nfactors for the cholesterol-lowering properties of oats and emphasizes that interactions\nbetween oats and the gut microbiome play an important role in this health-promoting\neffect. The mild effects of the six-week oat diet indicate that a more personalized\nstrategy is needed to increase the health benefits of such moderate interventions,\nsince inter-individual differences in response to oats based on person-specific\ncharacteristics will be considered. The results offer great potential because oat-based\ninterventions, especially a short-term, high-dose oat diet, are a fast and effective\napproach to alleviate obesity-related lipid disorders, and they open new avenues for\nmicrobiota-targeted nutritional therapies, considering microbially produced phenolic\nmetabolites as potential agents to improve metabolic disorders linked to obesity.\nHowever, whether the identified phenolic metabolites from oats contribute to lowering\ncholesterol levels in a specific pattern needs to be further investigated.”\nOriginality and significance: If the conclusions are not original, please provide relevant\nreferences. On a more subjective note, do you feel that the results presented are of\nimmediate interest to many people in your own discipline, and/or to people from several\ndisciplines?\nThe conclusions are original but have indistinct links to previously conducted studies.\nIt will be of interest to those working within Metabolic syndrome clinical trials specifically\nthose who have investigated the properties of oats to improve lipid metabolism.\nThank you for your positive assignment. We agree that the results are of great interest\nto many people working with metabolic syndrome and in nutritional and biomolecular\nscience. Irrespective of this, we have improved our conclusions, highlighting the\noriginality and significance of our own work (lines 786-801). Linking of our results with\nstudies conducted previously is now solely part of the discussion.\nData & methodology: Please comment on the validity of the approach, quality of the\ndata and quality of presentation. Please note that we expect our reviewers to review\nall data, including any extended data and supplementary information. Is the reporting\nof data and methodology sufficiently detailed and transparent to enable reproducing\nthe results?\ntext. Readers may find it hard to identify the more notable significance of the trials main\ndata points due to the dense description of each data point. The lack of description\nsurrounding secondary and tertiary outcomes could subject the trials to being\nconsidered of more exploratory nature rather than controlled which is noticeable by the\nextensive description of the listing of data points. Authors might condense findings from\nmost significant to least significant as opposed to listing all findings and direct readers to\nsupplementary tables for specific but non-notable findings.\nThank you very much for this suggestion. Accordingly, we have adapted the structure\nof the result section so that the results are now presented depending on the\nphysiological impact and significance, starting with our primary outcome (lines 132-\n145) and completing with our complex DIABLO models (lines 407-465), which is\nsignificant and biologically relevant. Thus, we highlighted the predefined primary\noutcome of the conducted randomized controlled trail and reduced the description of\nthe secondary outcomes (lines 161-313). Our revision, following your suggestions,\nhopefully allows the reader to more easily identify the more notable significance of the\ntrails main data points and clearly separate the analysis from the primary and\nsecondary research questions.\nAppropriate use of statistics and treatment of uncertainties: All error bars should be\ndefined in the corresponding figure legends; please comment if that’s not the case.\nPlease include in your report a specific comment on the appropriateness of any\nstatistical tests, and the accuracy of the description of any error bars and probability\nvalues.\nStatistical tests are appropriate and have been explained where used but with little\njustification for why a method was valid for the particular data.\nThank you very much for pointing this out. We apologize that the description of why a\nmethod was valid for the particular data was not entirely clear.\nThe statistical methods employed in this study were carefully selected to match the\ncomplexity and nature of the data, ensuring the validity of the analysis. In accordance\nwith the principle of parsimony in statistical modelling, where the simplest adequate\nexplanation is preferred, linear regressions were applied for all data (except the\nmicrobiome data), as we were looking for a robust, linear relationship between the\nintervention and our primary and secondary parameters. This approach was chosen to\n3\navoid unnecessary complexity, which would not improve the analysis's explanatory\npower, and to avoid artificially inflating the analysis with overly complex models. In case\nwere more than two time points were included in the analysis (follow-up in the short-\nterm study, additional analysis in the six-week study considering all four clinical visits),\nlinear mixed models were used to better account for repeating measurements and a\nperson-specific offset.\nAll of the variables were checked for their distribution before analysis to justify how to\nproceed. In case of linear regressions and LMM, residuals were checked for deviation\nto normality. Variables were log-transformed, if major deviations from the normal\ndistribution were detected. Afterwards the distribution was checked again.\nFor the complex microbiome data, more advanced techniques, precisely linear models\nfor differential abundance analysis (LinDA, linear mixed model), were applied. This\nestablished state-of-the-art method handles the compositional nature of the\nmicrobiome data, accounts for zero-inflation, and has high statistical power in detecting\ndifferentially abundant taxa1. The count data used as the input to the model underwent\na center log ratio (CLR) transformation during the analysis steps, which is a\nstandardized processing step for microbiome data2,3. In accordance, linear (mixed)\nmodels were used to analyze diversity and functional capacity of the gut microbiome.\nWe have rewritten the corresponding section in the manuscript (lines 1052-1106, 1174-1203):\n“Gut microbiome analysis\n[…] A LMM was applied to assess the differences [in alpha diversity] between the two\ndiet groups in each study. This statistical method was used to address the natural\ncomplexity of microbiome data. […] The Person-ID was used as random effect to\naccount for repeating measurements and a person-specific offset. The model was\ndefined as: diversity matrix ~ time*group + age + sex + BMI + (1|Person-ID), with the\ncovariate of interest ‘time*group’. […] To determine the differences [in beta diversity]\nbetween the diet groups in each study, a linear regression analysis adjusted for\nconfounding factors was applied, using the control group of each study (CG or CG6w)\nas the reference (LnRadj.). As the distance metric for beta diversity is relative to the\noffset (baseline), the random effect was not applied. The model was defined as:\ndiversity matrix ~ group + age + sex + BMI, with the covariate of interest ‘group’.\nmodels for differential abundance analysis (LinDA)1, an established state-of-the-art\nmethod for analyzing microbiome data, was applied. […] The model was defined as:\ntaxon ~ time*group + age + sex + BMI + (1|Person-ID), with the covariate of interest\n‘time*group’. […] Data were normalized using a centered-log-ratio (CLR)\ntransformation with an offset of 1 prior to all analyses. […] In accordance with LinDA,\na LMM was applied to the CLR-transformed count data with the control group of each\nstudy (CG or CG6w) and the baseline value as reference, and the Person-ID as random\neffect (LMMadj.). The model was defined as: pathway ~ time*group + age + sex + BMI\n+ (1|Person ID), with the covariate of interest ‘time*group’..”\n“Statistics and sample size calculation\n[…] Prior to the analysis, the distribution of the data was visualized individually.\nAdditionally, Shapiro-Wilk tests were performed to test for normality of the data and\ndetermine the appropriate approach for further analysis. Variables were analyzed\neither on their original scale or logarithmic transformed to approximate a normal\ndistribution. […]\nTo assess whether the data provided evidence of the superiority of the oat diets over\nthe respective control in terms of our primary outcomes and the metabolic and\nmetabolomic outcomes, linear regression models were applied […]. The model was\ndefined as: “post-intervention_value ~ baseline_value + group + age + sex + BMI”. […]\nIn addition, a LMM was conducted adjusted for BMI, age, and sex (LMMadj.) to assess\ndifferences in clinical markers across all four time points of the six-week intervention\nstudy. The control group of each study (CG or CG6w) and the baseline value were used\nas reference, the Person-ID was used as random effect to account for repeating\nmeasurements. The model was defined as: clinical marker ~ time*group + age + sex\n+ BMI + (1IPerson-ID), with the covariate of interest ‘time*group’. Residuals obtained\nfrom the models were inspected for normality to control for the fit of the statistical\nmodel. If the residuals were not normally distributed, logarithmic transformation was\napplied.”\nand reliable?\nThe conclusions are valid based on the data analysis presented in this paper.\nThank you very much for your positive feedback.\nSuggested improvements: Please list additional experiments or data that could help\nstrengthening the work in a revision.\nReferences: Does this manuscript reference previous literature appropriately? If not,\nwhat references should be included or excluded?\nAppropriate references have been included.\nThank you very much for your overall positive assessment and that the literature we\nreferenced is appropriate.\nClarity and context: Is the abstract clear, accessible? Are abstract, introduction and\nconclusions appropriate?\nThe abstract is clear however the statistics has been reduced to a percentage so could\nbe more detailed.\nThank you very much for pointing this out. Due to the limited number of words in the\nabstract, we had to prioritized and highlighted the most important aspects, focusing on\nthe results, as they provide the key insights from the study. In the main text, we have\nrevised the statistics section accordingly and added more details about why a method\nwas valid for the particular data (lines 1052-1107, 1161-1215).\nInflammatory material: Does the manuscript contain any language that is inappropriate\nor potentially libelous?\nThe manuscript does not contain any language that is inappropriate or potentially\nlibelous.\nThank you very much for your overall positive feedback on the language of the\nmanuscript.\nThis interesting study discusses the cholesterol-lowering effects of oats induced by\nmicrobially produced phenolic metabolites in individuals with metabolic syndrome\nthrough dietary interventions and multi-omics analysis.\nThe message is that short term high oat diet induces pronounced effects on microbial\ncomposition and metabolism, whilst a 6 wk moderate oat diet has very slight effects.\nThis overall message has to be emphasized and discussed more.\nThank you very much for your overall positive feedback and for highlighting the\ndifferent outcomes of the two oat interventions. As suggested, we have discussed the\noverall message in more detail (lines 694-718):\n“The attenuated effect of the six-week oat intervention on plasma phenolic metabolite\nlevels may be attributed to the lower oat consumption (80 g/day) when integrating a\nsingle oat meal into the habitual diet, compared to the short-term high-dose oat diet\n(300 g/day) where three oat meals replaced the habitual diet entirely. The single meal\nleads to a relatively lower intake of oat bioactive compounds, including phenols, which\nmay impact their biological effects such as their hypolipidemic activity38. In addition,\nmaintaining the habitual Western dietary pattern alongside a single oat meal introduced\ngreater variability in foods and nutrient exposure, potentially covering intervention\nefficacy. We therefore assume that the impact of a single oat meal may not be strong\nenough to compensate for the inter-individual differences in habitual food intake during\nthe study period. Heterogeneity of oat meal preparation22 as recommended in the\nOG6w in combination with inter-individual differences in the response to oats based on\nthe gut microbiome and the host genetic phenotype may also contribute to the\nmoderate effect in the six-week intervention23. These observations suggest that\npersonalized nutrition strategies may optimize oat interventions at moderate doses,\nwhere individual parameters seem to exert greater influence compared to a high-dose\nprotocol. Despite the high individual variability, we could explain almost 20% of the\nvariance in LDL-cholesterol in the short-term study solely by the changes in the plasma\nphenolic compounds. Furthermore, it is worth noting that the participants in the short-\nterm intervention study additionally underwent calorie restriction, while the dietary\nintervention in the six-week study was isocaloric. The observed differences in the\nhealth effects of the two oat interventions may therefore be explained by different\n7\nunderlying mechanisms such as calorie restriction48, dose-dependent exposure to the\nbioactive compounds and potential synergistic effects.”\nDiet composition at product/food items level has to be reported more extensively. When\nyou add 100 g oat to one meal 3 times a day in the short term, how does that change\noverall diet composition and to what extent can differences be ascribed to oat?\nThank you for raising this question. We apologize that the description was not entirely\nclear and led to misunderstandings. Now we provided a concise and precise\ndescription of the intervention diets: During the short-term oat intervention, the\nparticipants consumed three oat meals per day instead of their habitual diet. Each meal\ncontained 100 g oat flakes and have been consumed as porridge prepared with 300-\n500 mL water. In the control group, the participants consumed three macronutrient-\nadapted control meals without oats (carbohydrates: 67 E%"}, {"round": 2, "reviewer_comments": {"reviewer_2": "The authors have addressed my comments sufficiently", "reviewer_3": "The authors have addressed most of my concerns and the manuscript has been substantially improved. However, several\nminor issues need to be addressed before being accepcted.\n1. Although the study is RCT in nature and follows good clinical research practice,I don't think you can claim that this is the\nhihhest level evidence. It should be note that only multi-center RCT with a large sample size can be regarded as a high\nlevel. The authors should clearly acknowledge this limitation.\n2. With a small sample size and strict inclusion criteria,the generalizability of this finding is inevitably influenced. Thefrefore,\nthe authors should be more careful when drawing the conclusion and clearly acknowledge this limitaton.", "reviewer_4": "The study by Klümpen et al goes deeper into examining the potential pathways related to the gut microbiome underlying a\nmore established association of oats consumption with lower cholesterol levels among people with metabolic syndrome (MetS).\nThey have conducted two well-designed parallel randomised controlled trials to look at both the short-term effects of a larger dose\nand the effects of a more consistent lower dose on several markers related to MetS, but also metabolic biomarkers including\nmicrobiotic end products. The thorough study design and statistical analyses, as well as minor deviations from the protocol despite\nthe challenges during the COVID-19 pandemic are commendable. The authors have made the most of their data by integrating\nmultiple sources of data to show how these may interact. The combination of two RCTs with in vitro experiments further strengthens\nthe presented conclusions. The authors also addressed most of the reviewers’ comments from the first round of review in detail.\nThere are four main comments. First, in order to put the conclusions into context, some important information is missing.\nAlthough compliance as measured with the biomarker FA (as well as DHFA) is presented for immediately after the\nintervention, there is no information on participant compliance during the follow-up. Ideally, if these biomarkers were\nmeasured during follow-up, the results should be presented, otherwise at least the results from the diet records should be\npresented. For example, participants might have liked the oat meals or thought that they are good for their health and might\nhave kept a higher intake during follow-up even if they were instructed not to do so. For the interpretation of the results as\n“persistent effects”, it needs to be clear that deviations from compliance did not happen.\nSecond, in some cases it looks like the randomisation may not have worked optimally as shown from the descriptive table\ne.g. the sex imbalance for the short-term intervention between the two groups. It is good that you have adjusted for sex in\nyour analyses, but such an observation along with any other observed imbalances needs to be addressed in the discussion\nfor possible implications on the interpretation of the results.\nThird, as discussed in more detail below, sPLS-DA may overfit the data so any metrics reported e.g. AUC should ideally\ncome from an internal validation. It is not clear so far whether this is what happens or the AUC is derived from the whole\nsample. Since this part of the analysis is related to prediction, relevant guidelines should be followed and ideally calibration\nshould also be assessed (see below for more details).\nFinally, the discussion does not include any strengths and most importantly limitations. It would be very important to highlight\nany limitations of these studies (e.g. not optimal randomisation etc) for the results to be interpreted more correctly, but also to\ninform future studies.\nPlease see some more detailed comments below.\nAbstract: It is missing some more quantitative measures. The previous reviewer 1’s comment has not been addressed. It is\nworth trying to reduce the number of words in other places in the abstract and include estimates with their 95% Cis\nrather than just the percentages.\nIntroduction: The rationale for why certain metabolites were chosen could be clearer – looks like mainly phenols were\nchosen. FA and AVAs are mentioned in line 30, then it switches to ‘phenols’ in line 44, then back to FA and DHFA in line 52\nthen two other phenols are introduced in the brief summary of results (line 56/57). Perhaps ‘phenols such as…’ to start would\nset this up to be clearer throughout the text.\nLine 51: ‘metabolism’ this early on in the paper is a bit vague – perhaps ‘components of metabolic syndrome’ or ‘lipid\nmetabolism’ would be clearer.\nResults\nLines 93 and 99: It is not clear what you mean by “showed a visceral fat distribution”. Do you mean high visceral fat?\nAbdominal or central obesity?\nLine 103: typo – Replace ‘tabel’ with table\nLines 89-103: As mentioned above, would it be worth highlighting some differences that may show that randomisation did\nnot happen optimally in some cases? For example, in the short-term intervention the CG has 60% men, while the OG has\n35% men. In addition, SBP and TG are higher in the short-term intervention compared with the long-term one. These may\nhave implications when comparing results between the CG and OG as well as the two interventions.\nLine 129-131: Is it correct that the oat biomarkers FA and DHFA were measured only at V1 and V2? Did you measure them\nat the rest of the visits? This would make it clearer whether the effects on lipids and BP at follow-up were indeed persistent\nor were because the participants changed their diet. If not then it would be good to include information from the dietary\nrecords to complement this result and state that based on the records participants did not change their diet, so indeed these\ncan be considered persistent effects.\nLine 135: Supplementary Figure 1 does not show any major differences for glucose, so it is probably not worth highlighting\nthis in your statements in lines 133-136.\nLine 217 (and 228): Could you add “but not statistically significantly” after “modulated the microbial functions differently” for\naccuracy?\nMethods\nLines 513-514: Do you think there are any implications from dropping the number of total visits from 9 as initially planned to\n4 or would the information you get still be sufficient?\nCan you clarify why one study is isocaloric but the other is hypocaloric? And why did you plan to look for persistent changes\nafter the intervention in the 48h intervention but not the 6 week one?\nLine 542: The International Diabetes Federation in the 2009 paper that you cite defines hypertension as Systolic ≥130\nand/or diastolic ≥85 mm Hg (or antihypertensive treatment with a history of hypertension). You have cited a different paper\nfor your blood pressure cut-offs and used their definition of elevated blood pressure rather than hypertension (≥120 and/or\ndiastolic ≥80 mm Hg). It is very helpful that you explained why you added the insulin resistance criterion, can you likewise\nclarify why you changed the blood pressure cut off from that suggested by the IDF?\nLine 557: In the rebuttal to reviewer 3, it states that ex-smokers were not excluded but in line 557, it states that one of the\nexclusion criteria is ‘history of smoking’. This might need re-wording to make clear whether those with a past history of\nsmoking were excluded or not.\nLines 731-744: From the description, it looks like the LMM and the LnR were used for the same purpose, but they contradict\neach other in terms of accounting for random effects. Could you clarify whether indeed the two methods were used for the\nsame purpose and if that is the case which one would be superior and what is the advantage of using both?\nsPLS-DA (775-788 and supplementary methods): You mention that “Missing values in the global metabolomics profiles\nwere imputed with the minimum observed value for each compound.”. How confident are you that all the values were\nmissing for example because they were below the low limit of quantification, which would indeed justify imputation with the\nminimum observed value? Could there be other reason of missing for some of the values that would justify other types of\nimputation (depending on the % missing this could be multiple imputation or imputation to the mean/median). In addition,\ncould you include the %missing? Finally, sPLS-DA is known for overfitting the data. You mention that you used 5-fold cross-\nvalidation to choose the number of features and components, which is a good start to partly account for the overfitting.\nCould you clarify whether the AUC was also derived from the cross-validation process or the whole dataset? To avoid\noverfitting, it may make sense to report the AUC from the cross-validation model that you selected based on BER. Finally,\nsince this is a prediction model, to comply with the guidelines of reporting prediction models (see for example TRIPOD\nhttps://pubmed.ncbi.nlm.nih.gov/25560730/ ) apart from presenting a measure of discrimination like the AUC, it would be\nmore complete to also evaluate calibration and the recommended approach is calibration plots (again derived through the\ncross-validation process to avoid overfitting).\nLines 802-804: Similarly to the previous comment, does the DIABLO workflow give an option to do any sort of internal\nvalidation to avoid overfitting of the AUCs especially since it is based on sPLS-DA as well?\nDiscussion\nIt would be helpful to have a limitations section of the discussion to address the potential impact of aspects of the study such\nas unequal sex distribution, waist circumference and baseline lipids between the arms of the studies etc.", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Dear Referees,\nThank you very much for all your valuable feedback. We have addressed all comments\nand suggestions in a point-by-point response (in blue), and your feedback has\nimproved our manuscript even more. Thus, we hope that all concerns have been\nresolved and the manuscript is now suitable for publication in the special issue\nMicrobiome & Nutrition in Nature Communications.\nReviewer #2 (Remarks to the Author):\nThe authors have addressed my comments sufficiently.\nThank you very much for your positive evaluation and for confirming that our revisions\nhave sufficiently addressed your previous comments.\nReviewer #3 (Remarks to the Author):\nThe authors have addressed most of my concerns and the manuscript has been\nsubstantially improved. However, several minor issues need to be addressed before\nbeing accepted.\nThank you very much for your overall positive assessment.\n1. Although the study is RCT in nature and follows good clinical research practice,\nI don't think you can claim that this is the highest-level evidence. It should be\nnote that only multi-center RCT with a large sample size can be regarded as a\nhigh level. The authors should clearly acknowledge this limitation.\nThank you for your comment. We agree that a multi-center RCT with a large\nsample size can be regarded as the highest level of evidence. As suggested,\nwe have acknowledged this limitation in the manuscript (lines 496-499):\n“To confirm our results based on an RCT, further studies are needed. For\nexample, a multicenter RCT with a sample size calculation may provide the next\nlevel of evidence in clinical research since it would allow better generalizability\nacross different populations and reduce a potential center-specific bias.”\n1\n2. With a small sample size and strict inclusion criteria, the generalizability of this\nfinding is inevitably influenced. Therefore, the authors should be more careful\nwhen drawing the conclusion and clearly acknowledge this limitation.\nThank you for pointing this out. We now have been more careful when drawing\nthe conclusion and acknowledged this limitation:\nStrengths and limitations (line 496-499): “To confirm our results based on an\nRCT, further studies are needed. For example, a multicenter RCT with a sample\nsize calculation may provide the next level of evidence in clinical research since\nit would allow better generalizability across different populations and reduce a\npotential center-specific bias.”\nConclusion (lines 515-517): “However, whether the identified phenolic\nmetabolites from oats contribute to lowering cholesterol levels in a specific\npattern in general and across different populations needs to be further\ninvestigated.”\nReviewer #4 (Remarks to the Author):\nThe study by Klümpen et al goes deeper into examining the potential pathways related\nto the gut microbiome underlying a more established association of oats consumption\nwith lower cholesterol levels among people with metabolic syndrome (MetS). They\nhave conducted two well-designed parallel randomised controlled trials to look at both\nthe short-term effects of a larger dose and the effects of a more consistent lower dose\non several markers related to MetS, but also metabolic biomarkers including\nmicrobiotic end products. The thorough study design and statistical analyses, as well\nas minor deviations from the protocol despite the challenges during the COVID-19\npandemic are commendable. The authors have made the most of their data by\nintegrating multiple sources of data to show how these may interact. The combination\nof two RCTs with in vitro experiments further strengthens the presented conclusions.\nThe authors also addressed most of the reviewers’ comments from the first round of\nreview in detail.\nThank you very much for your overall positive feedback and the precise summary of\nour work.\n• First, in order to put the conclusions into context, some important information is\nmissing. Although compliance as measured with the biomarker FA (as well as\nDHFA) is presented for immediately after the intervention, there is no\ninformation on participant compliance during the follow-up. Ideally, if these\nbiomarkers were measured during follow-up, the results should be presented,\notherwise at least the results from the diet records should be presented.\nFor example, participants might have liked the oat meals or thought that they are\ngood for their health and might have kept a higher intake during follow-up even\nif they were instructed not to do so. For the interpretation of the results as\n“persistent effects”, it needs to be clear that deviations from compliance did not\nhappen.\nThank you very much for pointing this out. To be clear that deviations form\ncompliance did not happen during the follow-up period, we recorded adherence\nto the study instructions in two different ways. First, we specifically asked the\nparticipants at each follow-up visit (V3-V5) whether they had consumed an oat\nmeal or even a specific oat product within the last two weeks since their last\nclinical visit at the study center and if so, documented the frequency. Second,\nat baseline (V1) and endline (V5), the participants completed an additional 3-day\ndietary record.\nBoth, the precise query and the dietary records clearly show that the participants\nadhered to the study instructions during the follow-up period by refraining from\neating oats and returning to their habitual Western diet.\nWe have included this information in the manuscript to supplement and\ncorroborate our findings on possible persistent effects:\nMethods (lines 640-645): “During the six-week follow-up period in the oat group\nof the short-term intervention (OG), compliance with the study instruction (i.e.,\nrefraining from oat consumption and returning to habitual Western diet) was\nassessed using two complementary methods: (1) specific query by the study\nstaff about abstinence of oat meals as consumed during intervention and any\nother oat products, and (2) 3-day dietary records completed by the participants\nat baseline (V1) and at the end of the follow-up period (V5).”\ncompliance observed during the follow-up period, as all participants abstained\nfrom eating oats and returned to their habitual Western diet with no significant\ndifferences compared to their pre-study dietary pattern (Supplementary Table\n2).”\nSupplementary Table 2: Energy and nutrient intake of the participants in the oat\ngroup (OG) at baseline and at the end of the follow-up period assessed by 3-day\ndietary record1\nBefore study End of the follow-up\nP-value\nparticipation (V1) period (V5)\nTotal energy, kcal/d 2,207.23 (886.25) 2,163.58 (505.38) 0.975\nTotal protein, g/d 92.78 (28.17) 88.37 (43.07) 0.949\nTotal fat, g/d 89.03 ± 6.37 91.74 ± 8.41 0.759\nTotal CH, g/d 202.39 (94.57) 190.71 (85.59) 0.711\nFiber, g/d 22.38 ± 1.21 22.61 ± 1.67 0.877\nEnergy%\nProtein 16.50 (4.36) 16.40 (4.19) 0.895\nFat 36.30 ± 1.62 36.67 ± 1.64 0.821\nCH 37.98 ± 1.79 38.70 ± 1.69 0.736\n1Data are presented as mean ± SEM or median (IQR) depending on data distribution. Absolute\nchange (∆) calculated as pre-intervention value subtracted from post-intervention value. T-\nvalues and P-values refer to paired Student's t-test. In case log-transformed data were used,\nthe P-value is labelled with a superscript a. Abbreviations: CH, carbohydrates.\n4\n• Second, in some cases it looks like the randomisation may not have worked\noptimally as shown from the descriptive table e.g. the sex imbalance for the\nshort-term intervention between the two groups. It is good that you have\nadjusted for sex in your analyses, but such an observation along with any other\nobserved imbalances needs to be addressed in the discussion for possible\nimplications on the interpretation of the results.\nThank you for bringing this up. Participants were randomly assigned to groups\nusing computer-generated randomization tables in a block format with variable\nblock length to provide statistical control over unknown confounding factors. In\ncontrast to a stratified approach, the random allocation does not produce an\nequal distribution of each variable, but rather ensures that they are allocated to\nthe groups at random. According to our internal validation, the variables in\nTable 1 were assigned to each group at random. In particular for the variable\nsex, a z-test yielded p = 0.1622, and a Fisher’s exact test (odds ratio = 2.75,\n95% CI [0.655, 11.538], p = 0.2872) similarly showed no statistically significant\ndifference, suggesting the observed variation is likely due to chance.\nNonetheless, we adjusted for sex, age, and BMI as prominent confounding\nfactors in our regression analyses, and we now discuss the potential\nimplications of baseline imbalances in the revised discussion section (lines 492-\n499).\n“However, our findings remain constrained by the limited sample size typical in\nclinical studies, making it susceptible to baseline imbalances, despite the\nrandomized group allocation. As a result, modest effects of the nutritional\ninterventions may have gone undetected due to large inter-individual variability\nand potential confounding factors. To confirm our results based on an RCT,\nfurther studies are needed. For example, a multicenter RCT with a sample size\ncalculation may provide the next level of evidence in clinical research since it\nwould allow better generalizability across different populations and reduce a\npotential center-specific bias.”\n5\n• Third, as discussed in more detail below, sPLS-DA may overfit the data so any\nmetrics reported e.g. AUC should ideally come from an internal validation. It is\nnot clear so far whether this is what happens or the AUC is derived from the\nwhole sample. Since this part of the analysis is related to prediction, relevant\nguidelines should be followed and ideally calibration should also be assessed\n(see below for more details).\nThank you for pointing this out. As recommended and in line with relevant\nguidelines, the AUC was derived from the internal cross-validation model.\nFurther details are provided below.\n• Finally, the discussion does not include any strengths and most importantly\nlimitations. It would be very important to highlight any limitations of these studies\n(e.g. not optimal randomisation etc) for the results to be interpreted more\ncorrectly, but also to inform future studies.\nMany thanks for your suggestion. We have now included the strengths and\nlimitations of our study in the discussion (lines 484-499):\n“Our study has several strengths. We conducted two well-designed parallel\nrandomized controlled trials and confirmed our results in three different in vitro\nexperiments. In addition, our study groups−including males and females−were\ncharacterized in depth and adherence to the respective diets was rigorously\nmonitored using three complementary methods. By integrating multiple data\nsets—including various clinical markers related to MetS, metabolomic profiles\nin plasma and feces, as well as the gut microbiome—we provide a\ncomprehensive understanding of the intervention effects and underlying\nmechanisms.\nHowever, our findings remain constrained by the limited sample size typical in\nclinical studies, making it susceptible to baseline imbalances, despite the\nrandomized group allocation. As a result, modest effects of the nutritional\ninterventions may have gone undetected due to large inter-individual variability\nand potential confounding factors. To confirm our results based on an RCT,\n6\nfurther studies are needed. For example, a multicenter RCT with a sample size\ncalculation may provide the next level of evidence in clinical research since it\nwould allow better generalizability across different populations and reduce a\npotential center-specific bias.”\nPlease see some more detailed comments below.\n• Abstract: It is missing some more quantitative measures. The previous reviewer\n1’s comment has not been addressed. It is worth trying to reduce the number of\nwords in other places in the abstract and include estimates with their 95% Cis\nrather than just the percentages.\nThank you for this recommendation. We have rewritten the abstract accordingly\nand provided the estimates with their 95% CIs as suggested (lines 1-17):\n“Oats have various positive effects on human health, but the underlying\nmechanisms are not fully understood. To identify oat-microbiome-host\ninteractions that contribute to metabolic improvements, we conducted two\nrandomized controlled dietary interventions in individuals with metabolic\nsyndrome, comparing a short-term, high-dose and a six-week, moderate oat\nintake with respective controls (DRKS00022169). While the moderate oat-diet\nleads to slight changes in metabolism, gut microbiota and metabolomic profiles,\nthe high-dose oat-diet decreases total cholesterol by 8% (-15.61\n[-24.17, -7.80] mg/dL, Padj. = 0.02, Bonferroni-Holm correction) and low-density\nlipoprotein cholesterol by 10% (-16.26 [-23.60, -10.32] mg/dL, Padj. = 0.011),\naccompanied by distinct changes in gut microbiota and metabolomic profiles,\nmost notably an increase in microbially produced phenolic compounds. Here we\nshow that phenolic metabolites are driving factors for the cholesterol-lowering\neffect of oats, which might be of relevance since a short-term, high-dose oat-\ndiet is a suitable approach to alleviate obesity-related lipid disorders.”\n7\n• Introduction: The rationale for why certain metabolites were chosen could be\nclearer – looks like mainly phenols were chosen. FA and AVAs are mentioned in\nline 30, then it switches to ‘phenols’ in line 44, then back to FA and DHFA in line\n52 then two other phenols are introduced in the brief summary of results (line\n56/57). Perhaps ‘phenols such as…’ to start would set this up to be clearer\nthroughout the text.\nThank you for this suggestion. We have rewritten the section as suggested (lines\n30-33 and line 50-57):\n“Oats offer an interesting and promising approach for treating MetS due to their\nunique composition characterized by a high fiber content, especially β-glucan,\nvarious bioactive substances including phenols such as ferulic acid (FA) and\navenanthramides (AVAs), and essential minerals and vitamins8. […] The aim of\nthis randomized controlled trail (RCT) was to investigate the effects of a short-\nterm, high-dose oat diet (a modified version of the original oat cure by Carl von\nNoorden9) and a six-week, moderate oat diet compared with a macronutrient-\nadapted control diet and a Western diet, respectively, on lipid metabolism, the\ngut microbiota, and global metabolomic profiles, in particular the phenolic\ncompounds FA and dihydroferulic acid (DHFA), in 68 individuals with MetS\n(n = 17 participants/group) and to elucidate the underlying mechanism using an\nintegrative multi-omics analysis.\nIn this work, our data demonstrate the superiority of the oat diets in increasing\nplasma levels of phenolic compounds, such as FA, DHFA, 2-aminophenol\nsulfate and 2-acetamidophenol sulfate compared with the respective controls.”\n• Line 51: ‘metabolism’ this early on in the paper is a bit vague – perhaps\n‘components of metabolic syndrome’ or ‘lipid metabolism’ would be clearer.\nThank you for pointing this out. We have rewritten this sentence to define our\naim more clearly (lines 50-57):\n“The aim of this randomized controlled trail (RCT) was to investigate the effects\nof a short-term, high-dose oat diet (a modified version of the original oat cure by\nCarl von Noorden9) and a six-week, moderate oat diet compared with a\n8\nmacronutrient-adapted control diet and a Western diet, respectively, on lipid\nmetabolism, the gut microbiota, and global metabolomic profiles, in particular\nthe phenolic compounds FA and dihydroferulic acid (DHFA), in 68 individuals\nwith MetS (n = 17 participants/group) and to elucidate the underlying\nmechanism using an integrative multi-omics analysis.”\n• Results\no Lines 93 and 99: It is not clear what you mean by “showed a visceral fat\ndistribution”. Do you mean high visceral fat?\nAbdominal or central\nobesity?\nThank you for bringing this up. We apologize that our description was not\nentirely clear. We have reworded the sentence as suggested and in line\nwith the IDF definition, as described in the methods (lines 97+104):\n“All participants had central obesity (100%) and at least two further\nmetabolic syndrome traits including increased BP (100%), impaired\nglucose metabolism (75%) and dyslipidemia (63%). [….] According to the\ninclusion criteria, all participants had central obesity (100%) and at least\ntwo further metabolic syndrome traits. Thus, at baseline, 97% of the\nsubjects had elevated BP, 76% impaired glucose metabolism, and 59%\ndyslipidemia.”\no Line 103: typo – Replace ‘tabel’ with table\nThank you for pointing this out. We have corrected the spelling mistake\naccordingly (line 108).\no Lines 89-103: As mentioned above, would it be worth highlighting some\ndifferences that may show that randomisation did not happen optimally\nin some cases? For example, in the short-term intervention the CG has\n60% men, while the OG has 35% men. In addition, SBP and TG are\nhigher in the short-term intervention compared with the long-term one.\nand OG as well as the two interventions.\nThank you for raising this question. We mentioned the differences in the\nsex distribution at baseline of the short-term diet as suggested (lines 96f).\nAs mentioned above, participants were randomly assigned to groups to\nprovide statistical control over unknown confounding factors; however,\nrandom allocation does not produce an equal distribution of each\nvariable. We agree that an imbalance in sex distribution between study\ngroups may have implications when comparing the results of these study\ngroups. Therefore, we adjusted our regression analyses for sex, age,\nand BMI as important confounding factors.\nFurthermore, nutritional interventions might potentially have a stronger\nimpact on higher or even pathological baseline level of clinical\nmarkers−such as SBP and TG−compared to physiological levels and this\nmay have implications when comparing and interpreting these results.\nAdditionally, we have discussed the potential implications of baseline\nimbalances in the revised discussion section (lines 492-499).\nResults (lines 96f): “In the CG, 60% of participants were men, while in\nthe OG 35% were men.”\nDiscussion (lines 492-499): “However, our findings remain constrained\nby the limited sample size typical in clinical studies, making it susceptible\nto baseline imbalances, despite the randomized group allocation. As a\nresult, modest effects of the nutritional interventions may have gone\nundetected due to large inter-individual variability and potential\nconfounding factors. To confirm our results based on an RCT, further\nstudies are needed. For example, a multicenter RCT with a sample size\ncalculation may provide the next level of evidence in clinical research\nsince it would allow better generalizability across different populations\nand reduce a potential center-specific bias.”\n10\no Lines 129-131: Is it correct that the oat biomarkers FA and DHFA were\nmeasured only at V1 and V2? Did you measure them at the rest of the\nvisits? This would make it clearer whether the effects on lipids and BP at\nfollow-up were indeed persistent or were because the participants\nchanged their diet. If not then it would be good to include information from\nthe dietary records to complement this result and state that based on the\nrecords participants did not change their diet, so indeed these can be\nconsidered persistent effects.\nThank you for raising this point again. As mentioned above, we recorded\nadherence to the study instructions during the follow-up period in two\ndifferent ways: (1) we specifically asked the participants at each follow-\nup visit (V3-V5) whether they had consumed an oat meal or even a\nspecific oat product within the last two weeks, and (2) participants\ncompleted an additional 3-day dietary record at baseline (V1) and endline\n(V5).\nBoth, the precise query and the dietary records clearly show that the\nparticipants adhered to the study instructions during the follow-up period\nby refraining from eating oats and returning to their habitual Western diet.\no Line 135: Supplementary Figure 1 does not show any major differences\nfor glucose, so it is probably not worth highlighting this in your statements\nin lines 133-136.\nThank you for pointing this out. We agree that Supplementary Figure 1\ndoes not show any major differences for glucose. Therefore, we have\nomitted the figure (as well as Supplementary Figure 2 for consistency)\n(lines 144 and 149).\no Line 217 (and 228): Could you add “but not statistically significantly” after\n“modulated the microbial functions differently” for accuracy?\nMany thanks for this question. To increase accuracy, we have rewritten\nthese sentences as suggested (lines 226-233 and 238-244):\n11\n“According to sPLS-DA, OG and CG modulated the microbial functions\ndifferently but not statistically significantly (AUC = 0.69 (P = 0.08),\nBER = 0.395; Fig. 6g); even though, several pathways seemed to be of\nrelevance for differentiation, including carbohydrate digestion and\nabsorption, aminobenzoate degradation, selenocompound metabolism,\nnaphthalene degradation, and phosphotransferase system (PTS)\n(Fig. 6h).”\n“Furthermore, a different modulation of the microbial functional capacity\nbetween the diet groups of the six-week intervention study was revealed,\nalthough not statistically significant (AUC = 0.68 (P = 0.07), BER = 0.353;\nFig. 6i), with phenylalanine metabolism, proximal tubule bicarbonate\nreclamation, beta-lactam resistance, fructose and mannose metabolism,\nand PTS seemed to be the most relevant pathways for differentiation\nOG6w and CG6w (Fig. 6j).”\n• Methods:\no Lines 513-514: Do you think there are any implications from dropping the\nnumber of total visits from 9 as initially planned to 4 or would the\ninformation you get still be sufficient?\nThank you for raising this important question. We agree that reducing the\nnumber of visits will result in a lower temporal resolution of data\ncollection. Thus, we carefully ensured that the main research questions\nwith all critical assessments and sample collections were maintained at\nstrategically relevant time points within the revised schedule. Thus, the\nmain endpoints of the study remain well covered. Additionally, we\nimplemented rigorous monitoring procedures to ensure participant\ncompliance and data completeness, although the practical requirements\nposed by the COVID-19 pandemic were challenging.\n12\no Can you clarify why one study is isocaloric but the other is hypocaloric?\nAnd why did you plan to look for persistent changes after the intervention\nin the 48h intervention but not the 6 week one?\nThank you for raising this question regarding our study design. Based on\nscientific objectives and due to practical considerations associated with\neach intervention (e.g., easy to implement), we decided to implement an\nisocaloric diet in one study and a hypocaloric diet in the other. For the\nsix-week intervention, an isocaloric protocol was chosen to isolate the\neffects of oat intake per se on metabolic and microbiome outcomes,\nindependent of caloric restriction or changes in energy balance. This\napproach allowed us to attribute observed physiological and microbiota\nchanges specifically to the dietary intervention rather than to weight loss\nor caloric deficit. Conversely, the 48h intervention was designed as a\nshort-term diet (metabolic challenge) based on a modified version of the\noriginal “oat cure” developed by Carl von Noorden and, for practical\nreasons related to participant compliance and the acute nature of the\nintervention, was conducted under hypocaloric conditions. This allowed\nus to detect rapid metabolic and microbiota responses to a defined oat\ndiet while controlling for dietary intake in a feasible time frame.\nDue to scientific rationale, we investigated persistent changes after the\n48h intervention study. Thus, it was important to assess whether the\nobserved effects were transient or persisted after cessation of the oat\nchallenge. Therefore, the follow-up period was conducted to evaluate the\ndurability of the responses, which allowed us to draw conclusions about\nthe optimal intervals at which a short-term, high-dose oat diet should be\nimplemented on a regular basis. The six-week intervention was\nspecifically designed to assess sustained changes induced by a\nprolonged and daily recurring dietary modification. Given the extended\nduration of the six-week intervention compared to the 48h intervention\nand the expected longer-lasting physiological and microbiota\nadaptations, our primary focus was on characterizing the outcomes at the\nend of the dietary period. We hope this clarifies the rationale underlying\nour study design choices.\n13\no Line 542: The International Diabetes Federation in the 2009 paper that\nyou cite defines hypertension as Systolic ≥130 and/or diastolic ≥85 mm\nHg (or antihypertensive treatment with a history of hypertension). You\nhave cited a different paper for your blood pressure cut-offs and used\ntheir definition of elevated blood pressure rather than hypertension (≥120\nand/or diastolic ≥80 mm Hg). It is very helpful that you explained why you\nadded the insulin resistance criterion, can you likewise clarify why you\nchanged the blood pressure cut off from that suggested by the IDF?\nThank you for raising this question. We appreciate the opportunity to\nexplain our rationale for using the different blood pressure (BP) cut-off.\nSeveral meta-analysis and systematic reviews have shown that blood\npressure values in the range classified as “prehypertension”\n(120-139 mmHg systolic and/or 80-89 mmHg diastolic) are associated\nwith a significantly increased risk of cardiovascular disease (Huang et al.\nJAHA. 2015. doi: 10.1161/JAHA.114.001519; Huang et al. BMC\nMedicine. 2013. doi: 10.1186/1741-7015-11-177; Guo et al. Curr\nHypertens Rep. 2013. doi: 10.1007/s11906-013-0403-y; Gupta et al.\nHypertension Research. 2010. doi: 10.1038/hr.2010.91). Thus,\nindividuals in this “high-normal” BP range are at increased risk but would\nnot be identified using the standard IDF definition for hypertension.\nChoosing a lower threshold additionally allows for earlier identification of\nindividuals at increased risk, enabling preventive dietary interventions to\nbe initiated at a stage when they may be more effective.\nFor clarification, we have included an explanation for changing the blood\npressure threshold from the value suggested by the IDF in the revised\nmanuscript (lines 574-578):\n“The specific threshold values for BP were chosen because\nprehypertension (≥120 mmHg systolic and/or ≥80 mmHg diastolic) has\nbeen associated with a significantly increased risk of cardiovascular\ndisease65-68, allowing preventive dietary interventions to be initiated at a\nstage where they may be more effective.”\n14\no Line 557: In the rebuttal to reviewer 3, it states that ex-smokers were not\nexcluded but in line 557, it states that one of the exclusion criteria is\n‘history of smoking’. This might need re-wording to make clear whether\nthose with a past history of smoking were excluded or not.\nThank you for pointing this out. We have reworded this exclusion criteria\naccordingly to make clear that only participants who are non-smokers\nwere included (line 588). However, ex-smokers included in this study\nreported an average duration of 26 years since quitting smoking, while\nparticipants with a recent history of smoking were excluded.\n“Exclusion criteria were as follows: (1) [….]; (6) smoking or alcoholism”\no Lines 731-744: From the description, it looks like the LMM and the LnR\nwere used for the same purpose, but they contradict each other in terms\nof accounting for random effects. Could you clarify whether indeed the\ntwo methods were used for the same purpose and if that is the case\nwhich one would be superior and what is the advantage of using both?\nMany thanks for raising this question. In the respective section, we\nconsider alpha and beta diversity. While alpha diversity is a sample\nspecific measure for microbial diversity, beta diversity provides a\nmeasure for the difference in diversity. We compute alpha diversity for all\nsamples separately, while beta diversity is always computed with respect\nto the baseline microbial profile for an individual. Accordingly, the\nmeasures are fundamentally different and required different statistical\nanalysis approaches.\nWe analyzed alpha diversity using a Linear mixed effect model (LMM)\nwith an individual specific random effect as the baseline diversity\nbetween individual may differ. For beta diversity – which considers the\nchange with respect to baseline – we used a linear regression as\ndifferences in baseline are accounted for by the metric itself. We have\nrewritten the corresponding section in the revised manuscript (lines\n767-791):\n15\n“We quantified alpha diversity using Shannon entropy, Pielou’s\nevenness, and Faith’s phylogenetic diversity (Faith-PD). Alpha diversity\nwas assessed per sample, and a linear mixed model (LMM) was applied\nto evaluate the differences between the two diet groups in each study.\nThe LMM was adjusted for the confounders sex, age and BMI (LMMadj.),\nand estimated the diet effect relative to the baseline value and the control\ngroup (CG or CG6w). An individual-specific random intercept was included\nto account for differences in baseline alpha diversity. The model was\ndefined as: alpha diversity ~ time*group + age + sex + BMI + (1|Person-\nID), with the covariate of interest ‘time*group’.\nWe quantified beta diversity using Jaccard distance, Bray-Curtis\ndissimilarity, and UniFrac distance matrices. As beta diversity measures\ndifferences in community composition, we computed it with respect to\neach individual’s baseline microbiome. For the analysis of beta diversity\n–which considers the change with respect to baseline – we used a linear\nregression model (LnR), as baseline differences are accounted for by the\nmetric itself. Beta diversity was modeled as a function of the diet groups,\nusing the control group of each study (CG or CG6w) as the reference\n(LnRadj), and was adjusted for the confounders sex, age and BMI. The\nmodel was defined as: beta diversity matrix ~ group + age + sex + BMI,\nwith the covariate of interest ‘group’."}, {"round": 3, "reviewer_comments": {"reviewer_3": "The authors have sufficiently addressed my concerns. One minor suggestion, the authors should avoid the use of \"through alterations of gut microbiota\". Given that no FMT experiments have been performed, it is strongly suggested to use \"associated/related\" instead.", "reviewer_4": "I would like to thank the authors for their responses. I have attached a document with a few additional comments, as we found it helpful to include tracked changes.", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Dear Referees,\nThank you very much for all your valuable feedback. We have addressed all remaining\ncomments and suggestions in a point-by-point response (in blue). Thus, we hope that\nthe manuscript is suitable for publication in the special issue Microbiome & Nutrition in\nNature Communications.\nReviewer #3 (Remarks to the Author):\nThe authors have sufficiently addressed my concerns. One minor suggestion, the\nauthors should avoid the use of \"through alterations of gut microbiota\". Given that no\nFMT experiments have been performed, it is strongly suggested to use\n\"associated/related\" instead.\nThank you very much for confirming that our revisions have sufficiently addressed your\nprevious comments, and for this valuable suggestion. We have adjusted the wording\nthroughout the manuscript as recommended.\nReviewer #4 (Remarks to the Author):\nI would like to thank the authors for their responses. I have attached a document with\na few additional comments, as we found it helpful to include tracked changes.\nThank you very much for the careful re-evaluation of our manuscript and for the\nadditional valuable comments. We have thoroughly addressed all new suggestions\nand implemented the recommended text changes accordingly. Details are provided in\nour responses below and in the revised manuscript.\nAdditional comments transferred from the document\nWe would like to thank the authors for their responses and revisions. Most comments\nhave been addressed, but there are a few additional comments below. We have\nincluded suggestions for text changes in the manuscript as tracked changes.\nThank you very much for your overall positive feedback and the additional comments,\nincluding the helpful suggestions for text changes.\n The rationale for why certain metabolites, particularly phenols, were chosen is\nstill not clear, especially for readers who are not very familiar with the biological\nfunctions/importance of phenols. Despite being in the title, comments about\nphenols are lost within lists of other components (line 30, line 44). It would be\nhelpful to have one sentence introducing or highlighting phenols and their\nbiological role earlier in the introduction and one sentence on why these specific\nones were chosen. Are they all the same type or have they been suggested in\nthe literature before?\nSuggestions:\no Perhaps highlight phenols and what they do earlier (lines 28-31):\nOats offer an interesting and promising approach for treating MetS due\nto their unique composition characterized by a high fiber content,\nespecially β-glucan, various bioactive substances including phenols\n(which… [insert something here to highlight what the biological\nrole/importance of phenols is] and essential minerals and vitamins.\no Then bring forward the ones you look at here somewhere (lines 39-48):\nHowever, the underlying mechanisms of oat-induced improvements in\nmetabolic health are not fully understood. Recent studies suggest the\nability of oats to modulate the gut microbiota as a mechanism for its\nhealth-promoting effects13. In particular, microbially produced\nmetabolites were ascribed a decisive role in nutrition-induced metabolic\nimprovement14. However, it is still unknown whether metabolites\nproduced by the microbial degradation of oats, such as phenolic\ncompounds, play an important health-promoting role alongside short-\nchain fatty acids (SCFAs)15 produced by the bacterial fermentation of β-\nglucan. Wee hypothesized that the beneficial effects of oats on\nmetabolism might be influenced by host-microbiome interactions, leading\nto changes in phenolic compounds such as FA, DHFA, 2-aminophenol\nsulfate and 2-acetamidophenol sulfate which have been indicated in…[\ninsert here something on why these ones have been picked].\nmetabolites should be explained more clearly by including a sentence\nintroducing phenols and their biological role earlier in the introduction and by\nadding an additional sentence on the specifically selected metabolites. We have\nincluded the additional information as suggested.\nLines 28-32: “Oats offer an interesting and promising approach for treating MetS\ndue to their unique composition characterized by a high fiber content, especially\nβ-glucan, essential minerals and vitamins, and various bioactive substances\nincluding phenols, which exert antioxidant and anti-inflammatory effects that\nmay improve metabolic function8.”\nLines 44-52: “However, the underlying mechanisms of oat-induced\nimprovements in metabolic health are not fully understood. Recent studies\nsuggest the ability of oats to modulate the gut microbiota as a mechanism for its\nhealth-promoting effects13. In particular, microbially produced metabolites were\nascribed a decisive role in nutrition-induced metabolic improvement14. However,\nit is still unknown whether metabolites produced by the microbial degradation of\noats, such as phenolic compounds, play an important health-promoting role\nalongside short-chain fatty acids (SCFAs)15 produced by the bacterial\nfermentation of β- glucan. We hypothesized that the beneficial effects of oats on\nmetabolism might be influenced by host-microbiome interactions, leading to\nchanges in phenolic compounds such as ferulic acid (FA), dihydroferulic acid\n(DHFA), 2-aminophenol sulfate and 2-acetamidophenol sulfate which have\nbeen indicated in recent studies as relevant (microbial) metabolites from whole\ngrains such as oats with different biological functions16,17.”\nMethods\n Thank you for clarifying compliance with the study instruction during follow-up.\nA brief comment in the limitations section that this is self-reported and not an\nobjective biomarker measure would be important, especially since you used\nbiomarkers to assess compliance during the interventions.\nthe limitations section (lines 482-486):\n“In addition, adherence to the study instruction during the follow-up period was\nself-reported, which is common practice in nutritional studies, though potential\noverestimation should be considered compared to the objective biomarker\nmeasures which were conducted before and after the short-term and six-week\nintervention.”\n Thank you for clarifying the rationale underlying the study design choices. The\nfollowing point from your response would be a good addition to the methods\nsection: Therefore, the follow-up period was conducted to evaluate the durability\nof the responses, which allowed us to draw conclusions about the optimal\nintervals at which a short-term, high-dose oat diet should be implemented on a\nregular basis.\nThank you for pointing this out. We have included this aspect in the methods\nsection as suggested (lines 530-532):\n“In the short-term intervention study, eligible participants were invited for two\nclinical visits, the first before (V1) and the second directly after the two-day\nintervention period (V2), followed by three clinic visits at two-week intervals\nduring a six-week follow-up period (V3 – V5) to determine potential long-term\neffects of the oat diet (Fig. 1a). Therefore, the follow-up period was conducted\nto evaluate the durability of the responses, allowing conclusions to be drawn\nabout the optimal intervals at which a short-term, high-dose oat diet might be\nimplemented on a regular basis.”\n To be clear where your definitions deviate from the IDF we suggest: Lines 565-576\n…coupled with two of the following five criteria: (1) elevated BP (≥120 mmHg\nsystolic and/or ≥80 mmHg diastolic), (adapted to include pre-hypertension)63;\n[….] (adapted to the WHO definition64). The specific threshold values for BP\nwere chosen because prehypertension (≥120 mmHg systolic and/or ≥80 mmHg\n4\ndiastolic) has been associated with a significantly increased risk of\ncardiovascular disease65–68 and therefore represents a stage in which\npreventive dietary interventions may be more effective. The fifth criterion was\nadded after the initial study registration in order to attach more importance to\nimpaired glucose metabolism as a characteristic of MetS and to counteract\nrecruitment difficulties due to the COVID-19 pandemic.\nThank you for this suggestion. To clarify where our definitions deviate from the\nIDF, we have revised the description of the inclusion criteria as recommended\n(lines 563-575).\n“…. coupled with two of the following five criteria: (1) elevated BP (≥120 mmHg\nsystolic and/or ≥80 mmHg diastolic) (adapted to include pre-hypertension63); (2)\nelevated fasting serum triglycerides (≥150 mg/dl); (3) decreased fasting serum\nHDL-C (<40 mg/dl for men and <50 mg/dl for women); (4) elevated fasting\nplasma glucose (≥ 100 mg/dl); (5) indication of insulin resistance (HOMA-IR\nindex > 2.5) (adapted to the WHO definition64). The specific threshold values for\nBP were chosen because prehypertension (≥120 mmHg systolic and/or ≥80\nmmHg diastolic) has been associated with a significantly increased risk of\ncardiovascular disease65-68 and therefore represents a stage in which\npreventive dietary interventions may be more effective. The fifth criterion was\nadded after the initial study registration in order to attach more importance to\nimpaired glucose metabolism as a characteristic of MetS and to counteract\nrecruitment difficulties due to the COVID-19 pandemic.”\n Line 583: “current or recent history of smoking or alcoholism (defined as…)”\nThank you for this suggestion. We defined smoking during the past 3 years prior\nto study enrollment as exclusion criteria since literature indicates that\ncardiometabolic effects of smoking decline after quitting within 2-3 years\n(Dobson et al. 1991"}]

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