A method for predicting a response to different dietary fibres from nucleic acid sequences, said response to different dietary fibres being defined based on the fermentation or metabolic capabilities of a subject's gut microbiota assessed by its short chain fatty acid (SCFA) production rate in response to said different dietary fibres, the method being applied to abundance data of specific microbiota-derived nucleic acids obtained from prior collected stabilized biological sample of said subject, the method comprising the steps of: - isolating and extracting said specific microbiota-derived nucleic acids from said stabilized biological sample, wherein the specific sequences of said specific microbiota-derived nucleic acids are identified from a training database containing metagenomics sequencing data of the microbiota from healthy subjects, or subjects suffering from a particular disease, paired with in vitro measured data pertaining to the production rates of SCFAs and other fermentation metabolites from different dietary fibers in such subjects, said SCFA and other fermentation metabolites being selected from the list comprising propionate, butyrate, acetate, lactate, formate, succinate, iso-butyrate, valerate and iso-valerate; - determining and quantifying the relative abundance of said specific microbiota-derived nucleic acids from said stabilized biological sample to generate a first input, wherein said specific microbiota-derived nucleic acids consist in gene sequences specific to glycoside and/or polysaccharide hydrolases capable of hydrolyzing or cleaving complex polysaccharides of said different dietary fibres, as well as gene sequences specific to key enzymes from bacterial acetate, propionate and butyrate production pathways and/or other enzymes responsible for the degradation of said different dietary fibers; - applying a machine learning algorithm trained on the training database to predict from these specific microbiota-derived nucleic acid sequences the production rate of SCFAs and other fermentation metabolites in response to challenge with different dietary fibers so as to obtain a response to said different dietary fibres. - isolating and extracting said specific microbiota-derived nucleic acids from said stabilized biological sample, wherein the specific sequences of said specific microbiota-derived nucleic acids are identified from a training database containing metagenomics sequencing data of the microbiota from healthy subjects, or subjects suffering from a particular disease, paired with in vitro measured data pertaining to the production rates of SCFAs and other fermentation metabolites from different dietary fibers in such subjects, said SCFA and other fermentation metabolites being selected from the list comprising propionate, butyrate, acetate, lactate, formate, succinate, iso-butyrate, valerate and iso-valerate; - determining and quantifying the relative abundance of said specific microbiota-derived nucleic acids from said stabilized biological sample to generate a first input, wherein said specific microbiota-derived nucleic acids consist in gene sequences specific to glycoside and/or polysaccharide hydrolases capable of hydrolyzing or cleaving complex polysaccharides of said different dietary fibres, as well as gene sequences specific to key enzymes from bacterial acetate, propionate and butyrate production pathways and/or other enzymes responsible for the degradation of said different dietary fibers; - applying a machine learning algorithm trained on the training database to predict from these specific microbiota-derived nucleic acid sequences the production rate of SCFAs and other fermentation metabolites in response to challenge with different dietary fibers so as to obtain a response to said different dietary fibres.