Unravelling genetic components of longevity

Exploring the Prospective Role of Propolis in Modifying Aging Hallmarks

Aging populations worldwide are placing age-related diseases at the forefront of the research agenda. The therapeutic potential of natural substances, especially propolis and its components, has led to these products being promising agents for alleviating several cellular and molecular-level changes associated with age-related diseases. With this in mind, scientists have introduced a contextual framework to guide future aging research, called the hallmarks of aging. This framework encompasses various mechanisms including genomic instability, epigenetic changes, mitochondrial dysfunction, inflammation, impaired nutrient sensing, and altered intercellular communication. Propolis, with its rich array of bioactive compounds, functions as a potent functional food, modulating metabolism, gut microbiota, inflammation, and immune response, offering significant health benefits. Studies emphasize propolis' properties, such as antitumor, cardioprotective, and neuroprotective effects, as well as its ability to mitigate inflammation, oxidative stress, DNA damage, and pathogenic gut bacteria growth. This article underscores current scientific evidence supporting propolis' role in controlling molecular and cellular characteristics linked to aging and its hallmarks, hypothesizing its potential in geroscience research. The aim is to discover novel therapeutic strategies to improve health and quality of life in older individuals, addressing existing deficits and perspectives in this research area.

Longitudinal machine learning uncouples healthy aging factors from chronic disease risks

To understand human longevity, inherent aging processes must be distinguished from known etiologies leading to age-related chronic diseases. Such deconvolution is difficult to achieve because it requires tracking patients throughout their entire lives. Here, we used machine learning to infer health trajectories over the entire adulthood age range using extrapolation from electronic medical records with partial longitudinal coverage. Using this approach, our model tracked the state of patients who were healthy and free from known chronic disease risk and distinguished individuals with higher or lower longevity potential using a multivariate score. We showed that the model and the markers it uses performed consistently on data from Israeli, British and US populations. For example, mildly low neutrophil counts and alkaline phosphatase levels serve as early indicators of healthy aging that are independent of risk for major chronic diseases. We characterize the heritability and genetic associations of our longevity score and demonstrate at least 1 year of extended lifespan for parents of high-scoring patients compared to matched controls. Longitudinal modeling of healthy individuals is thereby established as a tool for understanding healthy aging and longevity.

Polygenic prediction of human longevity on the supposition of pervasive pleiotropy

The highly polygenic nature of human longevity renders cross-trait pleiotropy an indispensable feature of its genetic architecture. Leveraging the genetic correlation between the aging-related traits (ARTs), we sought to model the additive variance in lifespan as a function of cumulative liability from pleiotropic segregating variants. We tracked allele frequency changes as a function of viability across different age bins and prioritized 34 variants with an immediate implication on lipid metabolism, body mass index (BMI), and cognitive performance, among other traits, revealed by PheWAS analysis in the UK Biobank. Given the highly complex and non-linear interactions between the genetic determinants of longevity, we reasoned that a composite polygenic score would approximate a substantial portion of the variance in lifespan and developed the integrated longevity genetic scores (iLGSs) for distinguishing exceptional survival. We showed that coefficients derived from our ensemble model could potentially reveal an interesting pattern of genomic pleiotropy specific to lifespan. We assessed the predictive performance of our model for distinguishing the enrichment of exceptional longevity among long-lived individuals in two replication cohorts and showed that the median lifespan in the highest decile of our composite prognostic index is up to 4.8 years longer. Finally, using the proteomic correlates of i L G S , we identified protein markers associated with exceptional longevity irrespective of chronological age and prioritized drugs with repurposing potentials for gerotherapeutics. Together, our approach demonstrates a promising framework for polygenic modeling of additive liability conferred by ARTs in defining exceptional longevity and assisting the identification of individuals at higher risk of mortality for targeted lifestyle modifications earlier in life. Furthermore, the proteomic signature associated with i L G S highlights the functional pathway upstream of the PI3K-Akt that can be effectively targeted to slow down aging and extend lifespan.