Paired nasal and plasma metabolomics reveal distinct recovery trajectories during allergy immunotherapy

Allergic rhinitis involves inflammation at the nasal mucosa together with broader systemic immune activation, but most monitoring approaches measure only one compartment. A study published in iMetaMed used paired plasma and nasal secretion metabolomics to examine how local and circulating metabolism change from health to allergic disease and after subcutaneous immunotherapy (SCIT).

The researchers studied 28 patients with house dust mite-induced allergic rhinitis who completed six months of SCIT and achieved at least a 50% reduction in total visual analogue scale symptom score, together with 19 non-atopic healthy controls. Plasma and nasal secretion samples were collected before and after treatment from the patients and once from controls, yielding 150 biospecimens. Untargeted LC-MS metabolomics retained 831 plasma metabolites and 989 nasal secretion metabolites after quality control.

The disease-associated signal was broader in nasal secretions than in plasma. Using an FDR-adjusted threshold, the team identified 500 nasal secretion metabolites and 208 plasma metabolites associated with allergic rhinitis versus healthy controls. In paired pre- versus post-SCIT analyses, 98 nasal secretion metabolites and 40 plasma metabolites were treatment-responsive. Although treatment did not fully normalize either compartment, nasal secretions showed a more evident shift toward the healthy state.

Pathway and trajectory analyses indicated a division of labor between the two biological compartments. Plasma changes were enriched for nucleotide turnover, NAD+-related metabolism, and bioenergetic/redox pathways, consistent with systemic inflammatory and metabolic stress. Nasal secretions showed stronger lipid, steroid, and redox-related remodeling linked to the inflamed mucosal barrier and its recovery. Across the healthy-control, allergic-rhinitis, and post-SCIT continuum, these patterns suggested a plasma nucleotide/NAD+ axis and a nasal lipid-steroid-redox axis that changed in a coordinated but temporally asynchronous manner.

To quantify compartment preference, the researchers developed a Nasal-Plasma Contrast Index (NPCI) for 600 metabolites detected in both matrices. Integrating NPCI, temporal clustering, and differential analysis prioritized 30 exploratory candidate features. A nasal-enriched feature annotated as ATP was significant in both disease-state and treatment comparisons and followed a coherent temporal trajectory, making it a candidate readout for future validation. The study also examined associations with the magnitude of symptom improvement, but these response-intensity signals did not remain significant after multiple-testing correction and are therefore considered hypothesis-generating.

The authors emphasize that the study was not designed to establish a clinical response-prediction model. All enrolled patients improved during SCIT, and the cohort did not include non-responders, untreated longitudinal controls, or a placebo group. Untargeted metabolomics also provides relative feature abundance and, for some metabolites, putative rather than definitive structural identification. Prospective studies including responders and non-responders, targeted quantitative assays, and independent validation will be required before the candidate features can be used clinically.

Taken together, the findings support paired local and systemic sampling as a way to resolve complementary dimensions of allergic-rhinitis biology and to study how immunometabolic recovery unfolds during allergen immunotherapy.