Evidence map›Paper›PMID 40075474›Full record

ArticleImmunity & ageing : I & A2025

Compositional analysis of lymphocytes and their relationship with health outcomes: findings from the health and retirement study.

Lantian Xu, Chihua Li, Allison E Aiello, Kenneth M Langa, Jennifer B Dowd, Rebecca C Stebbins, Helen C S Meier, Ziman Jiang, Grace A Noppert, Gen Li

Abstract read
In one paragraph

Article in Immunity & ageing : I & A, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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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.

Lantian Xu *Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
Chihua Li *Institute of Chinese Medical Sciences, University of Macau, Macao, SAR, China. chihuali@umich.edu.
Allison E AielloDepartment of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA.
Kenneth M LangaInstitute for Social Research, University of Michigan, Ann Arbor, MI, USA.
Jennifer B DowdLeverhulme Centre for Demographic Science, University of Oxford, Oxford, UK.
Rebecca C StebbinsDepartment of Internal Medicine, School of Medicine, University of Michigan, Ann Arbor, MI, USA.
Helen C S MeierLeverhulme Centre for Demographic Science, University of Oxford, Oxford, UK.
Ziman JiangDepartment of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
Grace A Noppert *Institute for Social Research, University of Michigan, Ann Arbor, MI, USA.
Gen Li *Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.

Funding

HRS Yrs29-34: Y33 SSA CoFundingU01AG009740 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Jessica Faul, KENNETH M LANGA · 1990 to 2026
$555.8M
Immunosenescence, socioeconomic disadvantage and dementia in the US aging populationR01AG075719 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Allison E Aiello, Grace A Noppert · 2022 to 2026
$3.6M
Novel Statistical Methods for Oral Microbiome Data AnalysisR03DE031296 · NIDCR · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DEMMER, RYAN T., LI, GEN · 2022 to 2023
$295k
NIA NIH HHS R01 AG075719NIA NIH HHS U01 AG009740NIDCR NIH HHS R03 DE031296US NIA R01AG075719
6 · The paper itself

Abstract

backgroundImmunosenescence, the gradual deterioration of the immune system, is critical for aging-related diseases. However, the lack of detailed population-level immune data has limited our understanding, underscoring the need for innovative analytical approaches. The Health and Retirement Study (HRS) in the United States provides a unique opportunity to examine T and B lymphocyte subsets using compositional data analysis and dimension reduction techniques.

methodsWe constructed a hierarchical tree structure to map relationships among T and B subset cells in HRS. Network analysis examined conditional dependence across 16 immune subset cells, while stepwise redundancy analysis (SRDA) identified a subset of pairwise logratio measures that capture main variance in immune composition. We conducted two sets of supervised learning analyses: first, linear penalized log-contrast models to examine the associations between subset cells and three health outcomes (chronic disease index, self-reported health, and frailty level); second, linear regressions to examine the associations between the top selected logratios and health outcomes.

findingsOur study included 6,250 participants from the HRS with a median age of 68. Network analysis showed some dependence among 16 immune subset cells, including associations between central memory CD4 + T cells and both other CD4 + T cells and other lymphocytes, as well as between central memory CD8 + T cells and other CD8 + T cells. SRDA identified nine key log-ratio measures, explaining over 90% of the variance in immune composition. Linear penalized log-contrast models showed that a lower proportion of naïve CD4 + T cells and higher proportions of other CD4 + and central memory CD8 + T cells were significantly associated with greater chronic disease burden, poorer self-reported health, and higher frailty levels. Linear regression models using log-ratios reinforced these patterns, showing that a higher ratio of other lymphocytes over naïve CD4 + T cells and terminally differentiated effector memory CD4 + T cells over other CD8 + T cells were associated with greater chronic disease burden, poorer self-reported health, and higher frailty levels. In contrast, a higher ratio of other lymphocytes over central memory CD4 + T cells was associated with better health outcomes.

interpretationOur findings highlight the value of a systems-based approach and compositional analysis in understanding immunosenescence and its impact on health. The identified subset cells and logratio measures provide meaningful insights into immune aging and warrant further investigation to explore their long-term relationships with health outcomes.

Identifiers

PMID40075474
PMCPMC11899731

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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.