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Ning-Shan Chang

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Open access Sep 2026

Longitudinal associations of one-carbon metabolism biomarkers with biological and epigenetic aging in older adults in India.

Circulating folate, vitamin B12, and homocysteine reflect one-carbon (1-C) metabolism, a pathway central to methylation and cellular maintenance, but longitudinal evidence linking these biomarkers to biological aging remains limited, particularly in low- and middle-income populations. We examined associations of 1-C biomarkers with clinical biomarker-based Phenotypic Age and DNA methylation-based measures of accelerated biological aging among adults aged ≥ 60 years in the Longitudinal Aging Study in India Diagnostic Assessment of Dementia (LASI-DAD), with repeated measures over 4.5 years. Higher serum folate level was associated with slower biological aging, whereas higher homocysteine was associated with faster aging. Longitudinally, each doubling in homocysteine was associated with ~ 3.2 years faster Phenotypic Age acceleration, whereas each doubling in folate was associated with ~ 1.1 years slower aging. Transition to folate deficiency and persistent hyperhomocysteinemia were associated with accelerated aging across Phenotypic Age and DNA methylation-based aging measures, with folate exhibiting the strongest inverse associations with DunedinPACE and SystemsAge. These findings provide longitudinal evidence that 1-C metabolism, particularly folate status and homocysteine, is associated with trajectories of biological aging in older adults in India.

Jian-Feng Wang, Eileen M. Crimmins, Jung Ki Kim et al. · GeroScience · 0 citations

Open access Aug 2026

An Open Benchmark for Systems Vaccinology: Insights from the CMI-PB Challenges

Systems vaccinology approaches have identified factors affecting vaccine responses in multiple studies, but the ability of computational models to generalize these findings to unseen data remains unclear. We established a community resource to create and compare models predicting B. pertussis booster vaccination responses and put such modeling approaches to the test. We compiled multi-modal experimental training data from three independent cohorts (n=117 individuals), and asked investigators to predict vaccine responses in a cohort of 54 newly recruited individuals using only their pre-booster vaccination data. We benchmarked a total of 107 computational models. Top-performing models were characterized by workflows that prioritized rigorous data preprocessing, robust imputation of missing data, and the use of multi-omics integration or non-linear machine learning. We identified pre-existing antigen-specific antibody titers and baseline monocyte frequencies as the most consistent predictors of post-vaccination immunity, highlighting the dominant role of individual immune setpoints. We established the resulting datasets and evaluation framework as a community resource to advance predictive immunology and facilitate personalized vaccination strategies.

Pramod Shinde, Lisa Willemsen, Jiyeun Lee et al. · bioRxiv · 0 citations