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What affects natural killer cell activity: a cross-sectional study
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Moon, Junhyung | - |
| dc.contributor.author | Oh, Hyoju | - |
| dc.contributor.author | Yu, Yechan | - |
| dc.contributor.author | Suh, Eunkyung | - |
| dc.contributor.author | Cho, A-Ra | - |
| dc.contributor.author | Kim, Moon Jong | - |
| dc.contributor.author | Lee, Soo Hyun | - |
| dc.contributor.author | Park, Jin Hun | - |
| dc.contributor.author | Cho, Baek Hwan | - |
| dc.contributor.author | Lee, Yun-Kyong | - |
| dc.date.accessioned | 2026-06-17T06:56:22Z | - |
| dc.date.available | 2026-06-17T06:56:22Z | - |
| dc.date.created | 2026-06-04 | - |
| dc.date.issued | 2026-04 | - |
| dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/212695 | - |
| dc.description.abstract | Introduction Natural killer (NK) cells are key effectors of innate immunity, mediating rapid defense against infections and malignancies while maintaining tissue homeostasis. Despite its clinical relevance, few large-scale studies have comprehensively analyzed the diverse factors influencing NK cell activity (NKA).Methods This study analyzed factors associated with NKA in 11,007 health-screening records integrating clinical, hematologic, metabolic, inflammatory, lifestyle, and self-reported symptom data.Results 46 check-up variables and 14 questionnaire items were significantly associated with NKA, including inflammatory markers such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), platelet count, and monocyte count; metabolic indices including albumin, total protein, alkaline phosphatase (ALP), low-density lipoprotein (LDL)-cholesterol, calcium, and phosphorus; tumor markers such as carcinoembryonic antigen (CEA); lifestyle factors including smoking, exercise, and sleep quality; and subjective symptoms such as fatigue, dizziness, nocturia, myalgia, and heat intolerance. Artificial intelligence models captured nonlinear interactions among heterogeneous variables, achieving the highest area under the receiver operating characteristic curve of 0.716 +/- 0.014 for identifying low-NKA individuals. SHAP analysis identified neutrophil count, PLR, and platelet count as top predictors.Discussion Lower NKA was associated with elevated inflammatory markers and altered metabolic profiles, highlighting the interplay between systemic inflammation, metabolic dysregulation, and innate immune function. This study provides the first integrated, population-scale mapping of NKA association from routine health checkup data, demonstrating the feasibility of AI-driven immune profiling. | - |
| dc.language | English | - |
| dc.publisher | Frontiers Research Foundation | - |
| dc.relation.isPartOf | FRONTIERS IN IMMUNOLOGY | - |
| dc.relation.isPartOf | FRONTIERS IN IMMUNOLOGY | - |
| dc.subject.MESH | Adult | - |
| dc.subject.MESH | Aged | - |
| dc.subject.MESH | Biomarkers | - |
| dc.subject.MESH | Cross-Sectional Studies | - |
| dc.subject.MESH | Female | - |
| dc.subject.MESH | Humans | - |
| dc.subject.MESH | Killer Cells, Natural* / immunology | - |
| dc.subject.MESH | Killer Cells, Natural* / metabolism | - |
| dc.subject.MESH | Male | - |
| dc.subject.MESH | Middle Aged | - |
| dc.subject.MESH | Neutrophils / immunology | - |
| dc.title | What affects natural killer cell activity: a cross-sectional study | - |
| dc.type | Article | - |
| dc.contributor.googleauthor | Moon, Junhyung | - |
| dc.contributor.googleauthor | Oh, Hyoju | - |
| dc.contributor.googleauthor | Yu, Yechan | - |
| dc.contributor.googleauthor | Suh, Eunkyung | - |
| dc.contributor.googleauthor | Cho, A-Ra | - |
| dc.contributor.googleauthor | Kim, Moon Jong | - |
| dc.contributor.googleauthor | Lee, Soo Hyun | - |
| dc.contributor.googleauthor | Park, Jin Hun | - |
| dc.contributor.googleauthor | Cho, Baek Hwan | - |
| dc.contributor.googleauthor | Lee, Yun-Kyong | - |
| dc.identifier.doi | 10.3389/fimmu.2026.1751240 | - |
| dc.relation.journalcode | J03075 | - |
| dc.identifier.eissn | 1664-3224 | - |
| dc.identifier.pmid | 42136637 | - |
| dc.subject.keyword | artificial intelligence | - |
| dc.subject.keyword | health-screening data | - |
| dc.subject.keyword | immunity | - |
| dc.subject.keyword | natural killer cell activity | - |
| dc.subject.keyword | statistical analysis | - |
| dc.contributor.affiliatedAuthor | Cho, A-Ra | - |
| dc.identifier.scopusid | 2-s2.0-105038927490 | - |
| dc.identifier.wosid | 001763205900001 | - |
| dc.citation.volume | 17 | - |
| dc.identifier.bibliographicCitation | FRONTIERS IN IMMUNOLOGY, Vol.17, 2026-04 | - |
| dc.identifier.rimsid | 93182 | - |
| dc.type.rims | ART | - |
| dc.description.journalClass | 1 | - |
| dc.description.journalClass | 1 | - |
| dc.subject.keywordAuthor | artificial intelligence | - |
| dc.subject.keywordAuthor | health-screening data | - |
| dc.subject.keywordAuthor | immunity | - |
| dc.subject.keywordAuthor | natural killer cell activity | - |
| dc.subject.keywordAuthor | statistical analysis | - |
| dc.subject.keywordPlus | CARCINOEMBRYONIC ANTIGEN | - |
| dc.subject.keywordPlus | ACTIVATION | - |
| dc.subject.keywordPlus | BLOOD | - |
| dc.type.docType | Article | - |
| dc.description.isOpenAccess | Y | - |
| dc.description.journalRegisteredClass | scie | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.relation.journalWebOfScienceCategory | Immunology | - |
| dc.relation.journalResearchArea | Immunology | - |
| dc.identifier.articleno | 1751240 | - |
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