A Cost-Effective Screening Inflammation Indicator for Atopic Dermatitis Suitable for Primary Care and Self-Assessment

<b>Background/Objectives</b>: Atopic dermatitis (AD), a chronic inflammatory skin condition, significantly impairs quality of life but remains underdiagnosed in primary care. Blood-cell-count-derived inflammatory indices are emerging as cost-effective biomarkers, but their pathological relevance to AD is limited and requires further discussion. <b>Methods</b>: We developed the Atopic Inflammation Index (AII), a novel blood-cell-based biomarker reflecting AD pathogenesis, and initially assessed its levels in AD patients and healthy controls using clinical samples from Shanghai, China. We then analyzed data from the NHANES (National Health and Nutrition Examination Survey) 2005-2006 cohort (<i>n</i> = 6855) to verify the AII-AD association and compared AII's diagnostic performance with IgE and eosinophils. <b>Results</b>: Clinical analysis showed a nonlinear association between AII and AD severity. AII effectively distinguished AD patients (including mild cases) from healthy controls (<i>p</i> < 0.001) without elevation in psoriasis or urticaria, unlike eosinophils. In NHANES 2005-2006 (<i>n</i> = 720 AD cases, 10.5%), AII levels were higher in AD compared to non-AD patients (2.33 [1.39-4.09] vs. 2.03 [1.19-3.49], <i>p</i> = 0.007) and remained independently associated after adjustment (OR = 1.03, 95%CI = 1.01-1.04, <i>p</i> = 0.003), while IgE/eosinophils showed non-significant trends. Restricted cubic splines confirmed linear prediction (<i>p</i> = 0.006), and subgroup analyses supported consistency (<i>P</i>-interaction > 0.05). AII outperformed eosinophils (AUC:0.568 vs. 0.546, <i>p</i> = 0.025) with improved detection (sensitivity 0.361→0.614). Sensitivity analysis confirmed robustness after excluding medications, chronic diseases and adult populations. <b>Conclusions</b>: AII is stable and reliable in screening and diagnosing AD, offering a low-cost, practical solution for primary care. This verifies the feasibility of integrating existing detection indicators into new biomarkers, providing valuable inspiration for precision medicine research.