Xu Lantian, Li Chihua, Aiello Allison E, Langa Kenneth M, Dowd Jennifer B, Stebbins Rebecca C, Meier Helen C S, Jiang Ziman, Noppert Grace A, Li Gen
Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
Institute of Chinese Medical Sciences, University of Macau, Macao, SAR, China.
Immun Ageing. 2025 Mar 12;22(1):12. doi: 10.1186/s12979-025-00505-z.
Immunosenescence, 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.
We 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.
Our 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.
Our 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.
免疫衰老，即免疫系统的逐渐衰退，对于与衰老相关的疾病至关重要。然而，缺乏详细的人群水平免疫数据限制了我们的理解，这凸显了创新分析方法的必要性。美国的健康与退休研究（HRS）提供了一个独特的机会，可使用成分数据分析和降维技术来研究T和B淋巴细胞亚群。
我们构建了一个层次树结构来描绘HRS中T和B亚群细胞之间的关系。网络分析检测了16种免疫亚群细胞之间的条件依赖性，而逐步冗余分析（SRDA）确定了一组成对对数比测量值，这些值捕获了免疫组成中的主要变异。我们进行了两组监督学习分析：第一，线性惩罚对数对比模型，以检验亚群细胞与三种健康结局（慢性病指数、自我报告的健康状况和虚弱程度）之间的关联；第二，线性回归，以检验所选的顶级对数比与健康结局之间的关联。
我们的研究纳入了6250名来自HRS的参与者，中位年龄为68岁。网络分析显示16种免疫亚群细胞之间存在一些依赖性，包括中央记忆CD4+T细胞与其他CD4+T细胞和其他淋巴细胞之间的关联，以及中央记忆CD8+T细胞与其他CD8+T细胞之间的关联。SRDA确定了9个关键的对数比测量值，解释了免疫组成中超过90%的变异。线性惩罚对数对比模型显示，初始CD4+T细胞比例较低以及其他CD4+和中央记忆CD8+T细胞比例较高与更大的慢性病负担、较差的自我报告健康状况和较高的虚弱水平显著相关。使用对数比的线性回归模型强化了这些模式，表明其他淋巴细胞与初始CD4+T细胞的比例较高以及终末分化效应记忆CD4+T细胞与其他CD8+T细胞的比例较高与更大的慢性病负担、较差的自我报告健康状况和较高的虚弱水平相关。相比之下，其他淋巴细胞与中央记忆CD4+T细胞的比例较高与更好的健康结局相关。
我们的研究结果突出了基于系统的方法和成分分析在理解免疫衰老及其对健康影响方面的价值。确定的亚群细胞和对数比测量值为免疫衰老提供了有意义的见解，值得进一步研究以探索它们与健康结局的长期关系。
