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Mediation analyses of the impact of the <t>QASD</t> score on the gut microbiome and metabolic phenotypes. (A) Schematic representation of the two mediation hypotheses tested to evaluate the impact of the QASD score (independent variable) on the gut microbiome and clinical profiles of 1,643 individuals of MetaCardis study. In Direction 1, the QASD score impacts gut microbiome gene richness (GMGR; dependent variable), which is mediated by changes in clinical profiles (mediator). In Direction 2, the QASD score affects clinical profiles (dependent variable) mediated by alterations in the gut microbial gene richness (GMGR; mediator). The clinical variables tested included BMI, glycated hemoglobin, and HOMA-IR. Pairwise comparisons of the QASD score levels were conducted for low (0–1–2–3) vs. medium (4), low vs. high (5–6–7), and medium vs. high. Mediation analyses were adjusted for recruitment center, age, sex, and the use of metformin, statins, and PPIs (B) Confidence intervals of the beta coefficients representing the total effect (total), average direct effect (ADE), and average causal mediation effect (ACME) of the QASD score on the dependent variable in each direction (GMGR in Direction 1; clinical variables in Direction 2). Color and shape indicate the statistical significance of the mediation effects. Significant mediation in both directions ( p < 0.05 for total, ADE, and ACME) was observed only between the upper and lower levels of the QASD score (high vs. low) with glycated hemoglobin and HOMA-IR. (C) Decomposition of the significant mediations observed between high and low levels of the QASD score, presented as a bar plot showing the proportion of the total effect and confidence intervals attributable to the mediator (clinical variable in Direction 1; GMGR in Direction 2). (D) Bar plot representing the -log10 transformed P -values (x-axis) in PERMANOVA-based mediation analyses of the mediation effect of the gut microbiome composition (Bray‒Curtis distances derived <t>from</t> <t>MGS</t> abundance profiles), with QASD score as the exposure and clinical variables as outcomes. The dashed line represents the nominal significance level ( p -value = 0.05). (E) Barplots representing the F-statistic values (x-axis) for the exposure–microbiome association term and the microbiome–outcome association conditional on the exposure term in PERMANOVA-based mediation analyses (* = P -value < 0.05).
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Mediation analyses of the impact of the QASD score on the gut microbiome and metabolic phenotypes. (A) Schematic representation of the two mediation hypotheses tested to evaluate the impact of the QASD score (independent variable) on the gut microbiome and clinical profiles of 1,643 individuals of MetaCardis study. In Direction 1, the QASD score impacts gut microbiome gene richness (GMGR; dependent variable), which is mediated by changes in clinical profiles (mediator). In Direction 2, the QASD score affects clinical profiles (dependent variable) mediated by alterations in the gut microbial gene richness (GMGR; mediator). The clinical variables tested included BMI, glycated hemoglobin, and HOMA-IR. Pairwise comparisons of the QASD score levels were conducted for low (0–1–2–3) vs. medium (4), low vs. high (5–6–7), and medium vs. high. Mediation analyses were adjusted for recruitment center, age, sex, and the use of metformin, statins, and PPIs (B) Confidence intervals of the beta coefficients representing the total effect (total), average direct effect (ADE), and average causal mediation effect (ACME) of the QASD score on the dependent variable in each direction (GMGR in Direction 1; clinical variables in Direction 2). Color and shape indicate the statistical significance of the mediation effects. Significant mediation in both directions ( p < 0.05 for total, ADE, and ACME) was observed only between the upper and lower levels of the QASD score (high vs. low) with glycated hemoglobin and HOMA-IR. (C) Decomposition of the significant mediations observed between high and low levels of the QASD score, presented as a bar plot showing the proportion of the total effect and confidence intervals attributable to the mediator (clinical variable in Direction 1; GMGR in Direction 2). (D) Bar plot representing the -log10 transformed P -values (x-axis) in PERMANOVA-based mediation analyses of the mediation effect of the gut microbiome composition (Bray‒Curtis distances derived from MGS abundance profiles), with QASD score as the exposure and clinical variables as outcomes. The dashed line represents the nominal significance level ( p -value = 0.05). (E) Barplots representing the F-statistic values (x-axis) for the exposure–microbiome association term and the microbiome–outcome association conditional on the exposure term in PERMANOVA-based mediation analyses (* = P -value < 0.05).

Journal: Gut Microbes

Article Title: Prominent mediatory role of gut microbiome in the effect of lifestyle on host metabolic phenotypes

doi: 10.1080/19490976.2025.2599565

Figure Lengend Snippet: Mediation analyses of the impact of the QASD score on the gut microbiome and metabolic phenotypes. (A) Schematic representation of the two mediation hypotheses tested to evaluate the impact of the QASD score (independent variable) on the gut microbiome and clinical profiles of 1,643 individuals of MetaCardis study. In Direction 1, the QASD score impacts gut microbiome gene richness (GMGR; dependent variable), which is mediated by changes in clinical profiles (mediator). In Direction 2, the QASD score affects clinical profiles (dependent variable) mediated by alterations in the gut microbial gene richness (GMGR; mediator). The clinical variables tested included BMI, glycated hemoglobin, and HOMA-IR. Pairwise comparisons of the QASD score levels were conducted for low (0–1–2–3) vs. medium (4), low vs. high (5–6–7), and medium vs. high. Mediation analyses were adjusted for recruitment center, age, sex, and the use of metformin, statins, and PPIs (B) Confidence intervals of the beta coefficients representing the total effect (total), average direct effect (ADE), and average causal mediation effect (ACME) of the QASD score on the dependent variable in each direction (GMGR in Direction 1; clinical variables in Direction 2). Color and shape indicate the statistical significance of the mediation effects. Significant mediation in both directions ( p < 0.05 for total, ADE, and ACME) was observed only between the upper and lower levels of the QASD score (high vs. low) with glycated hemoglobin and HOMA-IR. (C) Decomposition of the significant mediations observed between high and low levels of the QASD score, presented as a bar plot showing the proportion of the total effect and confidence intervals attributable to the mediator (clinical variable in Direction 1; GMGR in Direction 2). (D) Bar plot representing the -log10 transformed P -values (x-axis) in PERMANOVA-based mediation analyses of the mediation effect of the gut microbiome composition (Bray‒Curtis distances derived from MGS abundance profiles), with QASD score as the exposure and clinical variables as outcomes. The dashed line represents the nominal significance level ( p -value = 0.05). (E) Barplots representing the F-statistic values (x-axis) for the exposure–microbiome association term and the microbiome–outcome association conditional on the exposure term in PERMANOVA-based mediation analyses (* = P -value < 0.05).

Article Snippet: Finally, when we examined the linear regression results for the MGS and serum metabolites involved in these mediations, we found a clear pattern: secondary bile acids and MGS that were increased in individuals with low QASD scores (such as CAG01263 : [Clostridium] bolteae ATCC BAA-613 and CAG00239 : Lachnospiraceae bacterium 7_1_58FAA ) were positively associated with HOMA-IR.

Techniques: Transformation Assay, Derivative Assay

Links between lifestyle factors and microbiome composition in the MetaCardis and GutInside cohorts. (A) Bar plot showing the cumulative effect sizes of multivariate models for non-redundant microbiome compositional variation (adjusted cumulative R² values) in individuals from the MetaCardis cohort ( n = 1,643). The results are presented for models including all individual components of the QASD score (“all QASD score items” panel; FDR < 0.01 in dbRDA analyses and p < 0.05 in stepwise model building, n = 9 variables) and for models excluding the four variables that define the QASD score (“no QASD score items” panel; FDR < 0.01 in dbRDA analyses and p < 0.05 in stepwise model building, n = 21 variables). (B) Bar plot showing cumulative effect sizes of multivariate models for non-redundant microbiome compositional variation (adjusted cumulative R² values) in individuals from the GutInside cohort ( n = 433). The results are shown for models including all individual components of the QASD score (“all QASD score items” panel; p < 0.1 in dbRDA analyses and p < 0.05 in stepwise model building, n = 5 variables) and for models excluding the four QASD score variables (“no QASD score items” panel; p < 0.1 in dbRDA analyses and p < 0.05 in stepwise model building, n = 5 variables). (C) Principal coordinate analysis (PCoA) of inter-individual differences in microbiome profiles (based on Bray–Curtis dissimilarity from MGS abundance data) in the MetaCardis cohort ( n = 1,643). The arrows in the main panel represent the effect sizes of a post hoc fit of 9 continuous nutritional covariates identified in the multivariate models from panel A. Boxplots represent the distribution of Metacardis individuals at different levels of the QASD score across 1 st and 2 nd ordination axis, with points coloured by GMGR. (D) Principal coordinate analysis (PCoA) of inter-individual differences in microbiome profiles (based on Bray–Curtis dissimilarity from MGS abundance data) in the GutInside cohort ( n = 433). The arrows in the main panel represent effect sizes of a post hoc fit of 12 continuous nutritional covariates identified in the dbRDA analyses ( p < 0.1). Boxplots represents the distribution of GutInside individuals at different levels of the QASD score across the 1 st and 2 nd ordination axis, with points coloured by GMGR. The full results from univariate and multivariate analyses are provided in Supplementary Tables S5 (MetaCardis data) and S6 (GutInside data). The common/specific legends in panels A and B corresponds to variables shared/unshared by models with/without the QASD score items.

Journal: Gut Microbes

Article Title: Prominent mediatory role of gut microbiome in the effect of lifestyle on host metabolic phenotypes

doi: 10.1080/19490976.2025.2599565

Figure Lengend Snippet: Links between lifestyle factors and microbiome composition in the MetaCardis and GutInside cohorts. (A) Bar plot showing the cumulative effect sizes of multivariate models for non-redundant microbiome compositional variation (adjusted cumulative R² values) in individuals from the MetaCardis cohort ( n = 1,643). The results are presented for models including all individual components of the QASD score (“all QASD score items” panel; FDR < 0.01 in dbRDA analyses and p < 0.05 in stepwise model building, n = 9 variables) and for models excluding the four variables that define the QASD score (“no QASD score items” panel; FDR < 0.01 in dbRDA analyses and p < 0.05 in stepwise model building, n = 21 variables). (B) Bar plot showing cumulative effect sizes of multivariate models for non-redundant microbiome compositional variation (adjusted cumulative R² values) in individuals from the GutInside cohort ( n = 433). The results are shown for models including all individual components of the QASD score (“all QASD score items” panel; p < 0.1 in dbRDA analyses and p < 0.05 in stepwise model building, n = 5 variables) and for models excluding the four QASD score variables (“no QASD score items” panel; p < 0.1 in dbRDA analyses and p < 0.05 in stepwise model building, n = 5 variables). (C) Principal coordinate analysis (PCoA) of inter-individual differences in microbiome profiles (based on Bray–Curtis dissimilarity from MGS abundance data) in the MetaCardis cohort ( n = 1,643). The arrows in the main panel represent the effect sizes of a post hoc fit of 9 continuous nutritional covariates identified in the multivariate models from panel A. Boxplots represent the distribution of Metacardis individuals at different levels of the QASD score across 1 st and 2 nd ordination axis, with points coloured by GMGR. (D) Principal coordinate analysis (PCoA) of inter-individual differences in microbiome profiles (based on Bray–Curtis dissimilarity from MGS abundance data) in the GutInside cohort ( n = 433). The arrows in the main panel represent effect sizes of a post hoc fit of 12 continuous nutritional covariates identified in the dbRDA analyses ( p < 0.1). Boxplots represents the distribution of GutInside individuals at different levels of the QASD score across the 1 st and 2 nd ordination axis, with points coloured by GMGR. The full results from univariate and multivariate analyses are provided in Supplementary Tables S5 (MetaCardis data) and S6 (GutInside data). The common/specific legends in panels A and B corresponds to variables shared/unshared by models with/without the QASD score items.

Article Snippet: Finally, when we examined the linear regression results for the MGS and serum metabolites involved in these mediations, we found a clear pattern: secondary bile acids and MGS that were increased in individuals with low QASD scores (such as CAG01263 : [Clostridium] bolteae ATCC BAA-613 and CAG00239 : Lachnospiraceae bacterium 7_1_58FAA ) were positively associated with HOMA-IR.

Techniques:

Microbiome and metabolome features associated with the QASD lifestyle score. (A) Heatmap representing the effect sizes (Cliff's delta) of 91 metagenomic species (MGS) showing significant differences between lifestyle score levels (high vs. low; high vs. medium; medium vs. low). Lifestyle score levels are categorized as low (QASD 0, 1, 2, 3), medium (4), and high (5, 6, 7). The effect sizes are strictly deconfounded by metformin, statin, PPI intake, and MetaCardis clinical groups, based on metadeconfoundR results (FDR < 0.1, OK_sd status meaning strictly deconfounded association, and absolute Cliff's delta > 0.1). Positive values indicate a higher abundance of the MGS at the reference level of the pairwise comparison (e.g., in HvsL, Cliff's delta > 0 denotes higher abundance at the “high” level of the lifestyle score compared to “low”, in red; Cliff's delta < 0 denotes lower abundance at the “high” level compared to “low”, in blue). The complete list of 122 MGS with significant differences between lifestyle score levels is available in Supplementary Table S8. (B) Same as panel A for 204 annotated serum metabolites with significant differences across lifestyle score levels (FDR < 0.1, OK_sd status, and absolute Cliff's delta > 0.1). The complete list of 319 metabolites with significant differences across lifestyle score levels is available in Supplementary Table S8. (C) Same as A-B for 6 gut metabolic modules (GMM) with significant differences across lifestyle score levels (FDR < 0.1, OK_sd status, and absolute Cliff's delta > 0.1). The text labels in heatmaps represent the significance level of the metadeconfoundR results (# = FDR < 0.001, ** = FDR < 0.01,* = FDR < 0.1).

Journal: Gut Microbes

Article Title: Prominent mediatory role of gut microbiome in the effect of lifestyle on host metabolic phenotypes

doi: 10.1080/19490976.2025.2599565

Figure Lengend Snippet: Microbiome and metabolome features associated with the QASD lifestyle score. (A) Heatmap representing the effect sizes (Cliff's delta) of 91 metagenomic species (MGS) showing significant differences between lifestyle score levels (high vs. low; high vs. medium; medium vs. low). Lifestyle score levels are categorized as low (QASD 0, 1, 2, 3), medium (4), and high (5, 6, 7). The effect sizes are strictly deconfounded by metformin, statin, PPI intake, and MetaCardis clinical groups, based on metadeconfoundR results (FDR < 0.1, OK_sd status meaning strictly deconfounded association, and absolute Cliff's delta > 0.1). Positive values indicate a higher abundance of the MGS at the reference level of the pairwise comparison (e.g., in HvsL, Cliff's delta > 0 denotes higher abundance at the “high” level of the lifestyle score compared to “low”, in red; Cliff's delta < 0 denotes lower abundance at the “high” level compared to “low”, in blue). The complete list of 122 MGS with significant differences between lifestyle score levels is available in Supplementary Table S8. (B) Same as panel A for 204 annotated serum metabolites with significant differences across lifestyle score levels (FDR < 0.1, OK_sd status, and absolute Cliff's delta > 0.1). The complete list of 319 metabolites with significant differences across lifestyle score levels is available in Supplementary Table S8. (C) Same as A-B for 6 gut metabolic modules (GMM) with significant differences across lifestyle score levels (FDR < 0.1, OK_sd status, and absolute Cliff's delta > 0.1). The text labels in heatmaps represent the significance level of the metadeconfoundR results (# = FDR < 0.001, ** = FDR < 0.01,* = FDR < 0.1).

Article Snippet: Finally, when we examined the linear regression results for the MGS and serum metabolites involved in these mediations, we found a clear pattern: secondary bile acids and MGS that were increased in individuals with low QASD scores (such as CAG01263 : [Clostridium] bolteae ATCC BAA-613 and CAG00239 : Lachnospiraceae bacterium 7_1_58FAA ) were positively associated with HOMA-IR.

Techniques: Comparison

Summary of bidirectional mediation analyses between the QASD score, metabolites, and metagenomic species (MGS). (A) Schematic representation of the two tested directions for causal mediation driven by the QASD score (independent variable). In Direction 1, the QASD score affects serum metabolites (outcome variable) mediated by the abundance of MGS (mediator). In Direction 2, the QASD score impacts the MGS (outcome variable) mediated by serum metabolites (mediator). Mediation tests were conducted for all pairwise combinations of MGS and serum metabolites that showed significant variations between the QASD score levels in the drug deconfounded analyses. All the mediation analyses were adjusted for recruitment center, age, sex, and the use of metformin, statins, and PPIs. (B) Venn diagrams summarizing the number of significant mediations (FDR < 0.05 for average causal mediation effect (ACME), average direct effect (ADE), and total effect) found in each direction across pairwise QASD score levels. (C) Density plots showing the proportion of mediation effects in shared significant mediations for Direction 1 (MGS as a mediator; serum metabolites as outcomes) and Direction 2 (serum metabolites as mediators; MGS as outcomes). (D) Density plots illustrating the proportion of mediation effects in specific significant mediations for Direction 1 and Direction 2. (E) Decomposition of the 2151 significant mediation relationships in Direction 1 (QASD → MGS → metabolite) observed between high and low QASD score levels, based on the proportion of the total effect of the QASD score on serum metabolites mediated by MGS abundances (y-axis). (F) Decomposition of the 1306 significant mediation relationships in Direction 2 (QASD → Metabolite → MGS) observed between high and low QASD score levels based on the proportion of the total effect of the QASD score on MGS abundances mediated by serum metabolite levels (y-axis).

Journal: Gut Microbes

Article Title: Prominent mediatory role of gut microbiome in the effect of lifestyle on host metabolic phenotypes

doi: 10.1080/19490976.2025.2599565

Figure Lengend Snippet: Summary of bidirectional mediation analyses between the QASD score, metabolites, and metagenomic species (MGS). (A) Schematic representation of the two tested directions for causal mediation driven by the QASD score (independent variable). In Direction 1, the QASD score affects serum metabolites (outcome variable) mediated by the abundance of MGS (mediator). In Direction 2, the QASD score impacts the MGS (outcome variable) mediated by serum metabolites (mediator). Mediation tests were conducted for all pairwise combinations of MGS and serum metabolites that showed significant variations between the QASD score levels in the drug deconfounded analyses. All the mediation analyses were adjusted for recruitment center, age, sex, and the use of metformin, statins, and PPIs. (B) Venn diagrams summarizing the number of significant mediations (FDR < 0.05 for average causal mediation effect (ACME), average direct effect (ADE), and total effect) found in each direction across pairwise QASD score levels. (C) Density plots showing the proportion of mediation effects in shared significant mediations for Direction 1 (MGS as a mediator; serum metabolites as outcomes) and Direction 2 (serum metabolites as mediators; MGS as outcomes). (D) Density plots illustrating the proportion of mediation effects in specific significant mediations for Direction 1 and Direction 2. (E) Decomposition of the 2151 significant mediation relationships in Direction 1 (QASD → MGS → metabolite) observed between high and low QASD score levels, based on the proportion of the total effect of the QASD score on serum metabolites mediated by MGS abundances (y-axis). (F) Decomposition of the 1306 significant mediation relationships in Direction 2 (QASD → Metabolite → MGS) observed between high and low QASD score levels based on the proportion of the total effect of the QASD score on MGS abundances mediated by serum metabolite levels (y-axis).

Article Snippet: Finally, when we examined the linear regression results for the MGS and serum metabolites involved in these mediations, we found a clear pattern: secondary bile acids and MGS that were increased in individuals with low QASD scores (such as CAG01263 : [Clostridium] bolteae ATCC BAA-613 and CAG00239 : Lachnospiraceae bacterium 7_1_58FAA ) were positively associated with HOMA-IR.

Techniques:

Overview of the strongest mediations between the QASD score-MGS-Serum metabolites in association with the HOMA-IR phenotype (A, B). Alluvial diagrams representing the 60 strongest significant mediation relationships (MGS-serum metabolites) in Direction 1 between high and low QASD score levels, categorized by the sign of the beta coefficients (increases/decreases in the mediator (MGS) and dependent variable (dv, serum metabolite) between high and low levels of the QASD score). (C, D) Alluvial diagrams representing the 41 strongest significant mediation relationships in Direction 2 between high and low QASD score levels, categorized by the sign of the beta coefficients (increases/decreases in the mediator (serum metabolite) and dependent variable (dv, MGS) between high and low levels of the QASD score). (E) Volcano plot representing the results of the linear regression analyses vs. HOMA-IR (dependent variable) of the 25 MGS included in the mediations represented in panels A–D. Dashed horizontal line represents the threshold for a significant association in multiple testing (FDR < 0.05 on 91 tested MGS). (F) Similar as E panel for the 28 serum metabolites included in mediations of panels A–D. Full results of mediation analysis and regressions vs. HOMA-IR are available in Supplementary Table S11.

Journal: Gut Microbes

Article Title: Prominent mediatory role of gut microbiome in the effect of lifestyle on host metabolic phenotypes

doi: 10.1080/19490976.2025.2599565

Figure Lengend Snippet: Overview of the strongest mediations between the QASD score-MGS-Serum metabolites in association with the HOMA-IR phenotype (A, B). Alluvial diagrams representing the 60 strongest significant mediation relationships (MGS-serum metabolites) in Direction 1 between high and low QASD score levels, categorized by the sign of the beta coefficients (increases/decreases in the mediator (MGS) and dependent variable (dv, serum metabolite) between high and low levels of the QASD score). (C, D) Alluvial diagrams representing the 41 strongest significant mediation relationships in Direction 2 between high and low QASD score levels, categorized by the sign of the beta coefficients (increases/decreases in the mediator (serum metabolite) and dependent variable (dv, MGS) between high and low levels of the QASD score). (E) Volcano plot representing the results of the linear regression analyses vs. HOMA-IR (dependent variable) of the 25 MGS included in the mediations represented in panels A–D. Dashed horizontal line represents the threshold for a significant association in multiple testing (FDR < 0.05 on 91 tested MGS). (F) Similar as E panel for the 28 serum metabolites included in mediations of panels A–D. Full results of mediation analysis and regressions vs. HOMA-IR are available in Supplementary Table S11.

Article Snippet: Finally, when we examined the linear regression results for the MGS and serum metabolites involved in these mediations, we found a clear pattern: secondary bile acids and MGS that were increased in individuals with low QASD scores (such as CAG01263 : [Clostridium] bolteae ATCC BAA-613 and CAG00239 : Lachnospiraceae bacterium 7_1_58FAA ) were positively associated with HOMA-IR.

Techniques: