Trimethylamine N-oxide (TMAO), a gut microbiota-derived metabolite of dietary choline and L-carnitine, has emerged as a leading molecular mediator of the gut-heart-kidney axis in heart failure (HF). Evidence from 14 prospective cohort studies demonstrates consistent associations between elevated circulating TMAO and adverse outcomes including mortality, rehospitalization, and major adverse cardiovascular events. The prognostic independence of TMAO is attenuated after renal function adjustment, reflecting both obligate renal clearance and a bidirectional relationship with kidney injury. Critically, TMAO is not modulated by conventional neurohormonal guideline-directed medical therapy (GDMT), suggesting that gut microbiome dysbiosis represents a pathophysiological axis not reached by current HF treatment. The field has evolved through three conceptual eras: TMAO as a single prognostic biomarker, TMAO as a cardiorenal mediator, and most recently, TMAO as the anchor of a multi-metabolite gut-heart signature encompassing trimethyllysine, choline, crotonobetaine, and acetyl-L-carnitine. Beyond the TMAO pathway, emerging evidence for phenylacetylglutamine, short-chain fatty acids, and protein-bound uremic toxins as parallel gut-derived cardiovascular mediators supports a multidimensional metabolite profiling approach. Panel approaches consistently outperform single-marker TMAO. Ethnic and dietary heterogeneity complicate universal threshold application. Without human interventional data demonstrating that lowering TMAO improves cardiovascular outcomes, TMAO remains a prognostic risk marker rather than a validated clinical target. Clinical translation requires resolving the renal confounding problem, validating multi-metabolite panels across diverse populations, and conducting intervention trials targeting the gut-heart-kidney axis.

Background Acute myeloid leukemia (AML) remains the most lethal acute leukemia in adults, with 5-year overall survival below 32% despite recent advances including venetoclax-, FLT3-, IDH1/2-, and Menin-targeted therapies. Clinical outcomes remain highly heterogeneous across patients, highlighting the need for robust molecular biomarkers capable of improving prognostic precision. MicroRNAs (miRNAs) are critical regulators of hematopoietic differentiation, apoptosis, and therapeutic resistance and are differentially expressed across AML subtypes. However, their clinical translation has been limited by high dimensionality, feature redundancy, and relatively small cohort sizes. Methods We developed and evaluated the AML Survival Estimator (AMLS), an inheritable bi-objective combinatorial genetic algorithm integrated with support vector regression (SVR), using TCGA-LAML miRNA expression profiles (n = 156). AMLS was benchmarked against ten widely used machine-learning approaches, including penalized regression, tree-based ensembles, support-vector regression, k-nearest neighbors, and multilayer perceptron models. Performance was assessed using stratified cross-validation with Pearson correlation (R), Harrell’s concordance index (C-index), and mean absolute error (MAE). Functional characterization of the derived miRNA signature was performed through consensus target integration followed by pathway enrichment, gene ontology analysis, network reconstruction, and Kaplan–Meier risk stratification. Results AMLS achieved superior prognostic performance with pooled out-of-fold metrics of Pearson R = 0.86, C-index = 0.788, and MAE = 7.49 months, substantially outperforming all comparator models. Restricting analyses to the AMLS-derived 28-miRNA signature improved all baseline learners by approximately 2–4-fold, with the multilayer perceptron achieving R = 0.674; however, none matched the native AMLS framework, indicating that the evolutionary optimization strategy contributes predictive information beyond feature selection alone. The prognostic signature included biologically established AML-associated miRNAs, including hsa-miR-191, hsa-miR-29c, hsa-miR-125b, hsa-miR-148a, hsa-miR-15b, hsa-miR-10b, and hsa-miR-30c, linked to DNA methylation, apoptosis, cell-cycle regulation, and oncogenic Wnt/MAPK signaling pathways. Functional analyses demonstrated significant enrichment of canonical AML-associated pathways, including p53, PI3K–AKT, TGF-β, JAK–STAT, FoxO, and hematopoietic lineage signaling. Conclusions Our findings demonstrate that evolutionary learning integrated with SVR can recover a compact and biologically interpretable miRNA prognostic signature that substantially outperforms conventional machine-learning approaches for AML survival prediction. The identified miRNA network converged on key leukemogenic pathways involved in apoptosis, cell-cycle regulation, and oncogenic signaling, supporting both the biological relevance and prognostic utility of the framework. Given the minimally invasive and quantitatively scalable nature of miRNA profiling, this approach may provide a practical molecular adjunct for improving prognostic assessment and precision medicine strategies in AML. Abstract Figure Schematic overview of the AMLS framework. Left: acute myeloid leukemia, a clonal hematological malignancy with persistent prognostic heterogeneity. Middle: AMLS couples an evolutionary learning-based feature selection algorithms to support vector regression for miRNA-based survival modeling. Right: AMLS recovers a 28-miRNA prognostic signature that predicts overall survival with Pearson R = 0.86 and MAE = 7.5 months.