{"doi":"10.64898/2026.02.17.706440","title":"Insulin resistance modifies longitudinal multi-omics responses to habitual diet","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>How habitual diet influences the gut microbiome and plasma metabolome across insulin resistance states remains unclear. We conducted year-long multi-omics profiling of 71 deeply phenotyped adults, integrating repeated assessments of diet, metabolome, gut microbiome, clinical laboratory measures, and inflammatory markers. Using gold-standard insulin suppression tests and machine learning-derived dietary patterns, we examined how dietary patterns relate to metabolic and microbial landscapes by insulin resistance status. Insulin-sensitive individuals exhibited stronger and more numerous diet-omics associations than insulin-resistant individuals, identifying metabolic flexibility as a central determinant of dietary responsiveness. Parabacteroides emerged as a candidate microbial mediator between refined carbohydrate-rich dietary patterns and host metabolic signatures. Integrated into a cardiovascular risk prediction model, diet, metabolites, microbial taxa, and immune markers each contributed to 10-year atherosclerotic cardiovascular disease risk. These findings show that inter-individual variation in cardiometabolic risk partly reflects differences in molecular responsiveness to habitual diet, informing precision nutrition and cardiovascular prevention.</jats:p>","journal":null,"year":null,"id":636993,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":808048,"name":"Xiaotao Shen","orcid":"0000-0002-9608-9964","position":1,"is_corresponding":false},{"id":5637,"name":"Dalia Perelman","orcid":"0000-0003-3335-1950","position":2,"is_corresponding":false},{"id":1653688,"name":"Pranav Berry","orcid":null,"position":3,"is_corresponding":false},{"id":705473,"name":"Yingzhou Lu","orcid":"0009-0008-7774-6018","position":4,"is_corresponding":false},{"id":1653689,"name":"Rachel Battersby","orcid":null,"position":5,"is_corresponding":false},{"id":5627,"name":"Sophia Miryam Schüssler-Fiorenza Rose","orcid":"0000-0002-6311-6671","position":6,"is_corresponding":false},{"id":227632,"name":"Alessandra Celli","orcid":"0009-0000-2186-0892","position":7,"is_corresponding":false},{"id":629366,"name":"Caroline Bejikian","orcid":null,"position":8,"is_corresponding":false},{"id":110673,"name":"Michael Snyder","orcid":null,"position":9,"is_corresponding":false},{"id":848069,"name":"Heyjun Park","orcid":"0000-0003-3724-9590","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Insulin resistance modifies longitudinal multi-omics responses to habitual diet","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>How habitual diet influences the gut microbiome and plasma metabolome across insulin resistance states remains unclear. We conducted year-long multi-omics profiling of 71 deeply phenotyped adults, integrating repeated assessments of diet, metabolome, gut microbiome, clinical laboratory measures, and inflammatory markers. Using gold-standard insulin suppression tests and machine learning-derived dietary patterns, we examined how dietary patterns relate to metabolic and microbial landscapes by insulin resistance status. Insulin-sensitive individuals exhibited stronger and more numerous diet-omics associations than insulin-resistant individuals, identifying metabolic flexibility as a central determinant of dietary responsiveness. Parabacteroides emerged as a candidate microbial mediator between refined carbohydrate-rich dietary patterns and host metabolic signatures. Integrated into a cardiovascular risk prediction model, diet, metabolites, microbial taxa, and immune markers each contributed to 10-year atherosclerotic cardiovascular disease risk. These findings show that inter-individual variation in cardiometabolic risk partly reflects differences in molecular responsiveness to habitual diet, informing precision nutrition and cardiovascular prevention.</jats:p>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19767382","pmcid":null,"openalex_id":null,"authors":[],"funders":[{"funder_name":"National Institute of Health","grant_id":"U54DK102556","title":null},{"funder_name":"National Institute of Health","grant_id":"R01DK110186","title":null},{"funder_name":"National Institute of Health","grant_id":"R01HG008164","title":null},{"funder_name":"National Institute of Health","grant_id":"S10OD020141","title":null},{"funder_name":"National Institute of Health","grant_id":"UL1TR001085","title":null},{"funder_name":"National Institute of Health","grant_id":"P30DK116074","title":null},{"funder_name":"National Institute of Health","grant_id":"T32HL098049","title":null},{"funder_name":"National Institute of Health","grant_id":"K08 ES028825","title":null}],"total_grants":8,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"green","license":"https://www.biorxiv.org/about/FAQ#license","oa_locations":[{"url":"https://www.biorxiv.org/content/biorxiv/early/2026/02/18/2026.02.17.706440.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.64898/2026.02.17.706440","host_type":"publisher"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12934625/","host_type":"repository"}],"fields_of_study":[],"mesh_terms":[],"keywords":[],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-06T18:06:15.866890Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}