Despite notable progress in metabolomics, particularly in experimental and clinical research contexts, its translation into routine clinical practice remains fraught with substantial challenges. A primary barrier lies in the lack of standardization across analytical workflows, which significantly hampers reproducibility and inter-study comparability. Variations in sample collection, preparation, instrument calibration, and data acquisition methods across laboratories introduce inconsistencies that undermine biomarker validation efforts and preclude the establishment of universally accepted metabolic signatures. The analytical complexity of metabolomics itself poses intrinsic challenges. Although thousands of features can be detected in biological samples, only a minority can be identified as known metabolites [50,69,110]. The vast majority remain unannotated, limiting biological interpretability and restricting their clinical utility. This issue is compounded by the inadequate standardization of data processing pipelines, which often lack robustness in feature detection, annotation, and integration with other omic datasets. As a result, metabolomic outputs, often consisting of high-dimensional molecular signatures with subtle abundance changes, remain difficult to distill into clinically actionable information. Moreover, the requirement for sophisticated, high-cost instrumentation further restricts accessibility, posing a major barrier to clinical implementation [50,69,110].

In the context of MBS, methodological limitations are further enhanced by the influence of comorbid conditions such as T2D and dyslipidemia, which modulate metabolic profiles both pre- and postoperatively. Additionally, inter-individual variability, shaped by genetic background, environmental exposures, lifestyle, and gut microbiome composition, necessitates a shift toward precision medicine strategies [69,193]. Integrating genomics, proteomics, microbiome, and metabolomic data through multi-omics platforms may enhance the resolution of metabolic adaptations following MBS and support individualized therapeutic approaches. Another limitation is the heterogeneity of study populations and the absence of appropriate stratification. Unstratified cohorts obscure the physiological effects attributable to MBS itself, diluting the specificity of metabolomic findings. Stratified or individualized analyses are therefore essential to delineate distinct metabolic responses and identify subpopulations that may derive differential benefit from surgical interventions. Additionally, dietary intake and concurrent pharmacological treatments are major confounders of metabolomic signatures and must be closely controlled to enhance data reliability and reproducibility [194].

Temporal aspects of metabolic remodeling post-MBS remain under-characterized due to the predominance of cross-sectional study designs, small sample sizes, and short follow-up durations. Such limitations constrain the ability to capture dynamic metabolic trajectories and obscure long-term effects [195,196,197,198]. Longitudinal, multicenter studies with ethnically and clinically diverse populations are needed to track metabolic adaptations over extended periods, enabling the identification of early predictive biomarkers and assessment of sustained therapeutic impact. From an analytical standpoint, the metabolome’s intrinsic dynamism and the bidirectional interplay between metabolites complicate data interpretation. Advanced multidimensional methodologies, including machine learning and systems biology approaches, are increasingly necessary to disentangle complex metabolic networks and uncover latent patterns. While several predictive models integrating metabolomic and clinical data are under development, their clinical validation is still limited. Enhancing interoperability, cost-efficiency, and integration into electronic health record systems will be critical for future clinical deployment [195,196,197,198].

There are also notable research gaps in the specific metabolite classes investigated in MBS studies [199,200,201]. Modified amino acids (e.g., gamma-glutamyl and methylated derivatives), kynurenine pathway metabolites, oxidized lipid species, bile acid subtypes, and gut-microbiota-derived SCFAs remain insufficiently characterized, despite their putative roles in metabolic reprogramming, immune modulation, and inflammation resolution [202,203]. Furthermore, emerging areas such as exosomal nucleic acids and trace-element-associated metabolomics (e.g., zinc, magnesium, iron) offer promising perspectives for biomarker discovery but have received limited attention [204,205,206].

Finally, the absence of functional correlation between metabolomic alterations and clinical endpoints remains a fundamental gap. Incorporating physiological assessments, such as hormonal profiling, insulin sensitivity tests, and imaging modalities, could bridge this divide and provide mechanistic insights into the metabolic benefits of surgery. Ultimately, translating metabolomic discoveries into clinically meaningful tools will require not only methodological refinement but also a concerted effort to align biomolecular data with functional and phenotypic outcomes [199,200,201].