Evidence map›Paper›PMID 42483183›Full record

ArticleFrontiers in immunology2026

A novel study to calculate immune-aging from peripheral blood T lymphocyte subsets and their mitochondrial parameters in healthy Chinese subjects.

Guofang Gan, Peng Guo, Xufan Li, Sihan Zhang, Wei Zhao, Yu Gao, Zhenchao Zhuang

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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.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Guofang Gan *Clinical Laboratory, Huzhou Maternity & Child Health Care Hospital, Huzhou, China.
Peng Guo *UBBio Technology (Zhejiang) Co., LTD, Hangzhou, China.
Xufan LiUBBio Technology (Zhejiang) Co., LTD, Hangzhou, China.
Sihan ZhangSchool of Computer Science, Wuhan University, Wuhan, China.
Wei ZhaoSu Zhou AI-For-Cure Company, Suzhou, China.
Yu GaoZhejiang Hospital, Department of Hematology, Hangzhou, China.
Zhenchao ZhuangAdicon Clinical Laboratories, Hangzhou, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Immunosenescence is a process in which the body's immune function declines with age, which is associated with the increased risk of infection, tumors, and other diseases. Traditional biological age assessment is difficult to fully reflect the changes in immune function. Flow cytometry can obtain multi-dimensional data such as immune cell subsets and mitochondrial function, and combined with machine learning, it is possible to quantify immune-aging. This study aimed to establish an accurate immune-aging prediction model based on peripheral blood indicators of healthy people in China. Methods: Peripheral blood samples were collected from healthy people aged 0.5 to 89 years old from September 2023 to December 2023, and 72 indicators (including blood routine, biochemistry, T/B/NK cell subsets, mitochondrial mass, and percentage of low membrane potential, etc.) were detected. A total of 11 core features were obtained by multi-stage screening of automatic and manual feature construction and mutual information-Boruta-forward selection. Random forest, LightGBM, and other algorithms were used, combined with the Bagging fusion strategy for modeling, and cancer patients were used as the external validation set. Results: The LightGBM model had the best performance, with an R² of 0.809 and an MAE of 5.687 in the test set of the healthy population. The immune-aging of cancer patients was significantly higher than the actual age. The model had a strong discrimination (AUC>0.900) in the adolescent group (0-19 years old) and the elderly group (≥60 years old), and a slightly weaker discrimination (AUC>0.780) in the middle-aged group. Conclusion: This model can accurately quantify the degree of immune senescence, provide reference for health management, vaccination, and anti-aging intervention, and also provide new ideas for the judgment of aging-related diseases.

Indexed as

AgingImmunosenescenceMitochondriaT-Lymphocyte SubsetsAdolescentAdultAgedAged, 80 and overChildChild, PreschoolChinaEast Asian PeopleFemaleHealthy VolunteersHumansInfantmachine learningmitochondrial massmitochondrial membrane potentialpotential markers of immunosenescence (PMIS)T lymphocyte subsets

Identifiers

PMID42483183
PMCPMC13386123

What Socratic holds

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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 Socratic graph.