Evidence map›Paper›PMID 42136637›Full record

ArticleFrontiers in immunology2026

What affects natural killer cell activity: a cross-sectional study.

Junhyung Moon, Hyoju Oh, Yechan Yu, Eunkyung Suh, A-Ra Cho, Moon Jong Kim, Soo Hyun Lee, Jin Hun Park, Baek Hwan Cho, Yun-Kyong Lee

Abstract read
In one paragraph

Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Junhyung Moon *Department of Software Convergence, School of Healthcare Convergence, CHA University, Pocheon, Republic of Korea.
Hyoju Oh *Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam, Republic of Korea.
Yechan YuDepartment of AI Healthcare Convergence, CHA University, Pocheon, Republic of Korea.
Eunkyung SuhChaum Life Center, Department of Family Medicine, CHA University, Seoul, Republic of Korea.
A-Ra ChoDepartment of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
Moon Jong KimDepartment of Family Medicine, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea.
Soo Hyun LeeDepartment of Family Medicine, Chaum Medical Checkup Center Samseongdong Branch, CHA University, Seoul, Republic of Korea.
Jin Hun ParkDepartment of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam, Republic of Korea.
Baek Hwan ChoDepartment of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam, Republic of Korea.
Yun-Kyong LeeDepartment of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Killer Cells, NaturalAdultBiomarkersCross-Sectional StudiesFemaleHumansMaleMiddle AgedBiomarkersartificial intelligencehealth-screening dataimmunitynatural killer cell activitystatistical analysis

Identifiers

PMID42136637
PMCPMC13167445

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.