Microbially derived short-chain fatty acids (SCFAs) in the human gut are tightly coupled to host metabolism, immune regulation and integrity of the intestinal epithelium. However, the production of SCFAs can vary widely between individuals consuming the same diet, with lower levels often associated with disease. A systems-scale mechanistic understanding of this heterogeneity is lacking. Here we use a microbial community-scale metabolic modelling (MCMM) approach to predict individual-specific SCFA production profiles to assess the impact of different dietary, prebiotic and probiotic inputs. We evaluate the quantitative accuracy of our MCMMs using in vitro and ex vivo data, plus published human cohort data. We find that MCMM SCFA predictions are significantly associated with blood-derived clinical chemistries, including cardiometabolic and immunological health markers, across a large human cohort. Finally, we demonstrate how MCMMs can be leveraged to design personalized dietary, prebiotic and probiotic interventions aimed at optimizing SCFA production in the gut. Our model represents an approach to direct gut microbiome engineering for precision health and nutrition.

人体肠道中微生物衍生的短链脂肪酸 (SCFA) 与宿主代谢、免疫调节和肠上皮的完整性紧密相关。然而，食用相同饮食的个体之间 SCFA 的产生可能存在很大差异，较低的水平通常与疾病有关。缺乏对这种异质性的系统规模机械理解。在这里，我们使用微生物群落规模代谢模型 (MCMM) 方法来预测个体特定的 SCFA 生产概况，以评估不同饮食、益生元和益生菌输入的影响。我们使用体外和离体数据以及已发表的人类队列数据来评估 MCMM 的定量准确性。我们发现，在大型人类队列中，MCMM SCFA 预测与血液来源的临床化学物质（包括心脏代谢和免疫健康标志物）显着相关。最后，我们展示了如何利用 MCMM 设计个性化饮食、益生元和益生菌干预措施，旨在优化肠道中 SCFA 的产生。我们的模型代表了一种直接肠道微生物组工程的方法，以实现精准的健康和营养。