The study drew on the Shanghai Suburban Adult Cohort and Biobank, or SSACB, a population-based research platform that enrolled adults between 2016 and 2019. The analysis focused on 7,614 participants aged 20 to 74 years who had valid biological age measurements at both baseline and follow-up. Rather than relying on self-reported illness, the team identified infection-related episodes by linking participants to local health information systems, then summarized the burden of infection quarterly between baseline and the first follow-up visit. This linkage gave the researchers a longitudinal, clinically grounded record of infection exposure across the community, an approach that distinguishes the work from earlier studies that examined only isolated severe infections such as sepsis or pneumonia hospitalizations.

To capture how infection burden unfolds over time, the researchers applied group-based trajectory modeling, a statistical technique that sorts individuals into latent groups sharing similar patterns of an outcome across repeated time intervals. Four distinct infection trajectories emerged from the data. The vast majority of participants, 71.15 percent, fell into an infrequent trajectory, while 18.72 percent followed a decreasing pattern of infections over the observation window. A smaller but notable group, 8.18 percent, showed an increasing trajectory, and 1.96 percent of participants followed a frequent trajectory, experiencing infections repeatedly throughout the follow-up period. This stratification allowed the team to ask not simply whether infections matter, but whether the temporal pattern of exposure carries its own signal.

The central outcome of the study was biological age acceleration, a metric intended to quantify how much older or younger a person’s physiology is compared with their chronological age. Biological age was estimated using the Klemera-Doubal method, or KDM, an algorithm that combines multiple clinical biomarkers into a single aging estimate and has been validated against morbidity and mortality risk in large datasets such as the US National Health and Nutrition Examination Survey. Biological age acceleration was then defined as the residual from regressing biological age on chronological age, so that a positive residual indicates a person aging faster than the calendar would predict. By measuring this residual at baseline and follow-up, the researchers could model the annual change in biological age acceleration using linear mixed-effects models, a framework well suited to repeated-measures data.

The results pointed squarely at the frequent-infection group. Compared with participants in the infrequent trajectory, those in the frequent group showed the largest estimate of accelerated biological aging in models adjusted for age and sex, with a beta coefficient of 0.67 and a 95 percent confidence interval spanning 0.04 to 1.31. While the confidence interval is wide, reflecting the small size of the frequent group, the estimate represents the strongest association among the trajectory categories. Beyond frequency, severity mattered as well: a greater proportion of severe infection episodes was associated with faster change in biological age acceleration, indicating that the intensity of illness, not just its recurrence, contributes to the aging signal.

The associations proved durable. In long-term analyses, the link between infection trajectories and biological age acceleration persisted, suggesting that the effect is not a transient perturbation of blood biomarkers during or shortly after acute illness. Intriguingly, the relationship appeared stronger among individuals with higher polygenic susceptibility to infection. The team constructed polygenic risk scores using genome-wide association data and standard methods including stacked clumping and thresholding and PRS-continuous shrinkage, then tested whether genetic liability modified the infection-aging association. The observation that genetically susceptible individuals showed amplified effects hints at a gene-environment interplay in which inherited vulnerability to infection and cumulative pathogen exposure jointly shape the pace of biological aging.

Perhaps the most consequential finding concerns survival. Frequent infection trajectories were associated not only with faster biological aging but also with higher all-cause mortality and greater years of life lost, as assessed with Cox proportional hazards models. This connects the biological aging metric to a clinically meaningful endpoint: if infection burden accelerates the underlying aging process, and that acceleration tracks with earlier death, then infections may represent a modifiable pathway toward healthier longevity. The result also echoes prior work by some of the same investigators, who previously reported associations between biological age acceleration and the burden of hospitalization for community-acquired pneumonia, as well as a Taiwanese nationwide study linking distinct infection trajectories to mortality in older adults.

Mechanistically, several plausible pathways could explain how repeated infections erode physiological resilience. Acute infections can trigger cytokine storms and systemic inflammation, and recurrent inflammatory insults may promote cellular senescence, a hallmark of aging in which damaged cells accumulate and secrete pro-inflammatory signals. Persistent herpesviruses such as cytomegalovirus are known to drive T-cell immunosenescence, reshaping the immune system in ways that resemble accelerated aging, and studies have linked herpesvirus antibodies to greater expression of p16, a senescence marker, in T cells. Mitochondrial dysfunction following viral infections, oxidative stress, and even infection-associated clonal hematopoiesis, an age-related expansion of mutated blood stem cells, have all been proposed as routes by which pathogens could leave lasting biological fingerprints. A recent transcriptome-based aging clock study similarly reported that bacterial or viral infections accelerate aging as read out from peripheral blood leukocytes.

The study’s strengths lie in its prospective design, its use of linked health records rather than self-report, and its attention to longitudinal patterns rather than single severe events. Still, the authors and readers alike should interpret the findings with appropriate caution. The cohort is drawn from suburban Shanghai, and generalizability to other populations and health systems remains to be established. As with any observational study, residual confounding cannot be excluded; people who experience frequent infections may differ in unmeasured ways, from underlying chronic conditions to socioeconomic circumstances, that independently accelerate aging. The Klemera-Doubal biological age measure, while validated, is one of several competing aging metrics, and different clocks can yield divergent results, as illustrated by conflicting findings on epigenetic aging in COVID-19 patients. Reverse causation is also possible, since biologically older individuals may simply be more susceptible to infections in the first place.

Even with those caveats, the study carries a clear public health message: infections deserve attention as a potential contributor to unhealthy aging, particularly among vulnerable populations. The authors suggest that cumulative infection burden, especially frequent and severe patterns, warrants greater focus in efforts to promote healthy longevity. If confirmed in diverse cohorts and with experimental or quasi-experimental designs, the findings could reframe routine infections as more than short-term inconveniences, positioning vaccination, infection prevention, and prompt treatment as strategies not only for immediate health but for slowing the biological clock itself.

Subject of Research: Longitudinal infection trajectories and their association with biological age acceleration and mortality in adults

Article Title: Longitudinal infection trajectories and biological aging acceleration in adults: a prospective cohort study

Article References: Wang, B., Shen, K., Qian, C., Yi, L., Zhang, Y., Yu, H., Liu, X., Jiang, Y., Zhang, T., & Zhao, G. (2026). Longitudinal infection trajectories and biological aging acceleration in adults: a prospective cohort study. GeroScience. https://doi.org/10.1007/s11357-026-02514-2

Image Credits: AI Generated

DOI: 10.1007/s11357-026-02514-2

Keywords: biological aging, infection trajectories, GeroScience, cohort study, Klemera-Doubal method, polygenic risk score, immunosenescence, mortality, Shanghai Suburban Adult Cohort and Biobank, cellular senescence, inflammation, public health