Proteomic signals are not equal clinical phenotypes: redefining evidence standards in arbovirus-SARS-CoV-2...

The interaction between SARS-CoV-2 and dengue virus represents a biologically plausible yet clinically unresolved challenge in regions where both pathogens co-circulate. Emerging omics-based studies propose that prior SARS-CoV-2 exposure may shape a distinct dengue phenotype through immune imprinting and antibody-dependent mechanisms. However, whether these molecular signals translate into clinically meaningful outcomes remains unclear. This Perspective argues that current evidence remains preliminary and insufficient for clinically actionable interpretation, particularly regarding the conflation of group-level proteomic signals with patient-level clinical phenotypes. Using a recent pilot proteomic study as an illustrative example, we highlight key methodological constraints, including pooled sampling, limited sample size, inadequate control of confounding, and absence of longitudinal and clinical validation. We propose a framework for advancing from exploratory omics observations to clinically interpretable evidence, emphasizing patient-level resolution, temporal dynamics, statistical rigor, functional validation, and integration with standardized clinical endpoints. We also examine the clinical and public health consequences of premature inference, including the potential for premature clinical interpretation, overestimation of disease associations, and challenges in translational interpretation. We conclude that proteomic signals should be regarded as hypothesis-generating rather than predictive until supported by robust, reproducible, and clinically anchored evidence.