Society: AGA

Background:
We and others have demonstrated a tight coupling between dietary intake, gut microbial ecology, and near-term alterations in cardiometabolic biomarkers in the ZOE PREDICT-1 study (Zeevi 2015; Asnicar & Berry 2021). However, it is unknown whether adherence to a de novo data-driven microbially-informed diet derived from the PREDICT-1 is associated with long-term weight change and its complications.
Methods:
To evaluate the impact of a “Metabolic Microbiome Score” (MMS) on long-term weight change, we pooled data from 3 ongoing U.S.-based prospective cohorts with lifestyle and dietary data collected through biennial questionnaires and validated food frequency questionnaires. MMS was calculated by summing reported food intake multiplied by food quality scores derived from the PREDICT-1, while normalizing for the servings of foods consumed. To validate prior findings, we assessed the association between MMS, food groups, and gut microbial features using partial Spearman correlations adjusted for age, sex, and body mass index. We used a multivariable generalized linear regression model to examine the association between 4-year changes in MMS and 4-year weight change after adjusting for relevant clinical and lifestyle confounders. We fitted Cox proportional hazards models to examine the associations between MMS and incident diabetes, cardiovascular disease (CVD), and mortality.
Results:
Among 186,661 participants with more than 20 years of follow-up, we observed that compared to the lowest quartile, those in the highest quartile of MMS were associated with 1.36 kg less weight gain (95% CI, -1.40 to -1.32; Table). A 1-SD increase in MMS was associated with 0.54 kg less weight gain over 4-year study intervals (95% CI, -0.55 to -0.52). There was a modest correlation between MMS and other established dietary patterns, such as the Western (Spearman rho = -0.43) and prudent diets (rho = 0.54). We also confirmed previously observed links between MMS, higher quality foods, and the relative enrichment of health-associated gut microbial features, such as Eubacterium eligens (Fig. A). The link between MMS and weight change appeared to differ according to age, sex, and body mass index, suggesting that those younger, female, or with obesity would derive greater benefit from adhering to diet high in MMS (P for interactions <0.05). In addition, adherence to MMS diet was associated with statistically-significant reductions in obesity-related complications including type 2 diabetes, CVD, and mortality (Fig. B).
Conclusion:
A data-driven dietary score corresponding with better near-term cardiometabolic markers was associated with less long-term weight gain and reduced risk of obesity-related complications in 3 independent U.S. cohorts, suggesting a role for population-wide public health initiatives geared towards personalized improvement of diet quality.

<b>Table. Weight change over 4-year periods according to per 1-SD increase and quartiles of change in Metabolic Microbiome Score </b><br /> Abbreviations: SD, standard deviation; MV, multivariable<br /> <sup>a</sup>Additionally adjusted for sex, questionnaire cycle (4-year intervals), White (yes/no), height (continuous), body mass index (continuous), smoking pack-years (continuous), physical activity (continuous), change in physical activity (continuous), and post-menopausal hormone use (yes/no [for women])<br /> <sup>b</sup>Additionally adjusted for total energy intake (continuous)

Table. Weight change over 4-year periods according to per 1-SD increase and quartiles of change in Metabolic Microbiome Score
Abbreviations: SD, standard deviation; MV, multivariable
^aAdditionally adjusted for sex, questionnaire cycle (4-year intervals), White (yes/no), height (continuous), body mass index (continuous), smoking pack-years (continuous), physical activity (continuous), change in physical activity (continuous), and post-menopausal hormone use (yes/no [for women])
^bAdditionally adjusted for total energy intake (continuous)

<b>Figure A. Metabolic Microbiome Score (MMS) validation. </b>Partial Spearman correlation heatmap for food groups and microbiome adjusted for sex, age, and body mass index. Red color represents positive correlation while blue color represents negative correlation. Asterisks represent significance. ***: p<sub>FDR </sub>≤ 0.001; **: 0.001 < p<sub>FDR </sub>≤ 0.01; *: 0.01 < p<sub>FDR </sub>≤ 0.05.<br /> <b>Figure B. Metabolic Microbiome Score and risk of obesity-related complications.</b> Adjusted for age, sex, White (yes/no), body mass index (continuous), smoking pack-years (continuous), physical activity (continuous), alcohol intake (continuous), post-menopausal hormone use (yes/no [for women]), and disease-specific confounders.

Figure A. Metabolic Microbiome Score (MMS) validation. Partial Spearman correlation heatmap for food groups and microbiome adjusted for sex, age, and body mass index. Red color represents positive correlation while blue color represents negative correlation. Asterisks represent significance. ***: p_FDR≤ 0.001; **: 0.001 < p_FDR≤ 0.01; *: 0.01 < p_FDR≤ 0.05.
Figure B. Metabolic Microbiome Score and risk of obesity-related complications. Adjusted for age, sex, White (yes/no), body mass index (continuous), smoking pack-years (continuous), physical activity (continuous), alcohol intake (continuous), post-menopausal hormone use (yes/no [for women]), and disease-specific confounders.

Daniel Kim

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