Background: The pathogenesis of non-alcoholic fatty liver disease (NAFLD) with a global prevalence of 30% is multifactorial and the involvement of gut bacteria has been recently proposed. However, finding robust bacterial signatures of NAFLD has been a great challenge, mainly due to its co-occurrence with other metabolic diseases.

Results: Here, we collected public metagenomic data and integrated the taxonomy profiles with in silico generated community metabolic outputs, and detailed clinical data, of 1206 Chinese subjects w/wo metabolic diseases, including NAFLD (obese and lean), obesity, T2D, hypertension, and atherosclerosis. We identified highly specific microbiome signatures through building accurate machine learning models (accuracy = 0.845-0.917) for NAFLD with high portability (generalizable) and low prediction rate (specific) when applied to other metabolic diseases, as well as through a community approach involving differential co-abundance ecological networks. Moreover, using these signatures coupled with further mediation analysis and metabolic dependency modeling, we propose synergistic defined microbial consortia associated with NAFLD phenotype in overweight and lean individuals, respectively.