Lipid, Metabolomic and Gut Microbiome Profiles in Long-Term-Hospitalized Cardiac Patients—An Observational and Retrospective Study

<b>Background/Objectives</b>: Long-term hospitalization in cardiac patients is associated with significant metabolic and microbial alterations that may influence disease progression and prognosis. Although lipid imbalances, metabolomic shifts, and gut microbiome dysbiosis have each been linked individually to cardiovascular outcomes, their integrated evaluation in long-term-hospitalized patients remains underexplored. <b>Methods</b>: We conducted a retrospective observational study including 51 cardiac patients hospitalized for more than 25 days, compared with a control group of 41 patients hospitalized for short and intermediate durations (3-24 days). Clinical and demographic data were collected, alongside lipid profiling, metabolomic assessment through liquid chromatography-mass spectrometry (LC-MS), and gut microbiome analysis using GI360™ sequencing. Ethical approval was obtained, and all data were anonymized. Lipid-related findings are exploratory due to the small number of complete measurements. <b>Results</b>: Preliminary lipid trends were characterized by higher levels of LDL, triglycerides, and Lp(a), and lower HDL, in the long-term group. Metabolomic analyses revealed decreased energy-related metabolites (ATP, phosphocreatine ratio), altered amino acid patterns, and increased ketone utilization. Gut microbiome evaluation demonstrated a significant increase in dysbiosis index, with reduced diversity and dominance of potentially pathogenic taxa. These findings were correlated with clinical severity scores. Cross-domain relationships are exploratory and based on associative profiling rather than deep integrative modelling. <b>Conclusions</b>: Long-term hospitalization in cardiac patients is associated with distinct lipid, metabolomic, and gut microbiome profiles that may serve as predictive biomarkers of adverse outcomes. Future studies should validate these findings in larger cohorts and explore their integration into personalized management strategies.