Longevity Papers

The vascular aging process accelerated by type 2 diabetes mellitus (T2DM) is responsible for the elevated risk of associated cardiovascular diseases. Metabolic disorder-induced immune senescence has been implicated in multi-organ/tissue damage. Herein, we sought to determine the role of immunosenescence in diabetic vascular aging and to investigate the underlying mechanisms.

Chronic, low-grade inflammation increases with aging, contributing to functional declines and diseases that reduce healthspan. Growing evidence suggests that transcripts from repetitive elements (RE) in the genome contribute to this "inflammaging" by stimulating innate immune activation, but evidence of RE-associated inflammation with aging in humans is limited. Here, we present transcriptomic and clinical data showing that RE transcript levels are positively related to gene expression of innate immune sensors, and to serum interleukin 6 (a marker of systemic inflammation), in a large group of middle-aged and older adults. We also: (1) use transcriptomics and whole-genome bisulfite (methylation) sequencing to show that many RE may be hypomethylated with aging, and that aerobic exercise, a healthspan-extending intervention, reduces RE transcript levels and increases RE methylation in older adults; and (2) extend our findings in a secondary dataset demonstrating age-related changes in RE chromatin accessibility. Collectively, our data support the idea that age-related RE transcript accumulation may play a role in inflammaging in humans, and that RE dysregulation with aging may be due in part to upstream epigenetic changes.

Telomeres are located at the ends of chromosomes and have specific sequences with a distinctive structure that safeguards genes. They possess capping structures that protect chromosome ends from fusion events and ensure chromosome stability. Telomeres shorten in length during each cycle of cell division. When this length reaches a certain threshold, it can lead to genomic instability, thus being implicated in various diseases, including cancer and neurodegenerative disorders. The possibility of telomeres serving as a biomarker for aging and age-related disease is being explored, and their significance is still under study. This is because post-mitotic cells, which are mature cells that do not undergo mitosis, do not experience telomere shortening due to age. Instead, other causes, for example, exposure to oxidative stress, can directly damage the telomeres, causing genomic instability. Nonetheless, a general agreement has been established that measuring telomere length offers valuable insights and forms a crucial foundation for analyzing gene expression and epigenetic data. Numerous approaches have been developed to accurately measure telomere lengths. In this review, we summarize various methods and their advantages and limitations for assessing telomere length.

Globally, the human population is aging, with an increased proportion of people in "old age" (over 60 years). This trend leads to a growing demand in aging research, stimulating studies in animal models such as mice, fish, and invertebrates. Recently, we published a research summary on the aging of hematopoietic stem cells (HSCs) in C57BL/6 mice based on 12 gene expression datasets. Here, I discuss in greater detail the added value of taking an integrated view, rather than considering each publication separately, to determine genes involved in aging. Considerable variation exists between lists of differentially expressed (DE) genes in HSCs, comparing young and old mice. This variation can result from factors such as inconsistent definitions of "young" and "old", technical variations and variations between laboratory mouse strains. We previously demonstrated that the variation between gene lists could be circumvented by forming a unified list of DE genes-the "aging list"-with citation indexes attached. The most frequently detected DE genes [approximately 200 most cited, which we named the "aging signature" (AS)] were highly consistent across publications. Gene Ontology classification of the AS list identified additional sources of variation between studies: one comes from the specifics of how the data are collected and analyzed; another comes from inconsistencies between how we define the gene categories. As discussed, overcoming these variations is the next challenge toward an integral approach to our systematic knowledge of the aging process.