James Earl Jones died Monday at the age of 93. But long before he did, he gave Lucasfilm permission to recreate his iconic Darth Vader voice for shows like “Obi-Wan Kenobi.”
Despite the initial excitement and flashy headlines, all of these early ventures failed or switched focus away from aging. Most of these companies and their backers underestimated the complexity, costs, and time it would take to discover and develop a drug. Recent estimates suggest that developing a new drug takes over https://www.sciencedirect.com/science/article/abs/pii/S1359644623002428” rel=“noopener”>10 years and costs upwards of $6.1 billion and the failure rates exceed 90%. This figure reflects the immense difficulty of identifying therapeutic targets, conducting preclinical and clinical trials, and navigating the regulatory landscape. When it comes to developing a drug specifically for aging, the challenges multiply, making it much more difficult to design effective interventions and demonstrate their efficacy in clinical trials.
Fast forward to today, and a new generation of longevity biotechnology companies with a more conservative approach than their predecessors has emerged. Companies like http://www.bioagelabs.com” rel=“noopener”>BioAge Labs and http://www.insilico.com” rel=“noopener”>Insilico Medicine are using artificial intelligence (AI) to discover drugs that target specific chronic diseases or biological processes closely associated with aging. Instead of trying to develop therapies for aging directly, these companies focus on conditions that are closely linked to the aging process like obesity, muscle wasting, fibrosis, anemia, and even cancer… The strategy is to develop drugs for these diseases that could later be repurposed to address aging more broadly. And while in the technology industry we try to focus on moving very fast to win, here we prepare to play a very long game and focus on resilience and novelty rather than putting all eggs in one basket and failing miserably like dozens of companies in the past three decades.
A new study from the University of Copenhagen reveals that a specific gene plays a significant role in determining longevity, potentially opening the door to new treatments.
Sleep, fasting, exercise, green porridge, black coffee, a healthy social life…
There is an abundance of advice out there on how to live a good, long life. Researchers are working hard to determine why some people live longer than others, and how we get the most out of our increasingly long lives.
Researchers have discovered that T cells in the body can be reprogrammed to slow down and even reverse aging. Using a mouse model, scientists found T cells can be used to fight off another type of cell that contributes to the aging process.
Human lifespan is shaped by both genetic and environmental exposures and their interaction. To enable precision health, it is essential to understand how genetic variants contribute to earlier death or prolonged survival. In this study, we tested the association of common genetic variants and the burden of rare non-synonymous variants in a survival analysis, using age-at-death (N = 35,551, median [min, max] = 72.4 [40.9, 85.2]), and last-known-age (N = 358,282, median [min, max] = 71.9 [52.6, 88.7]), in European ancestry participants of the UK Biobank. The associations we identified seemed predominantly driven by cancer, likely due to the age range of the cohort. Common variant analysis highlighted three longevity-associated loci: APOE, ZSCAN23, and MUC5B. We identified six genes whose burden of loss-of-function variants is significantly associated with reduced lifespan: TET2, ATM, BRCA2, CKMT1B, BRCA1 and ASXL1. Additionally, in eight genes, the burden of pathogenic missense variants was associated with reduced lifespan: DNMT3A, SF3B1, CHL1, TET2, PTEN, SOX21, TP53 and SRSF2. Most of these genes have previously been linked to oncogenic-related pathways and some are linked to and are known to harbor somatic variants that predispose to clonal hematopoiesis. A direction-agnostic (SKAT-O) approach additionally identified significant associations with C1orf52, TERT, IDH2, and RLIM, highlighting a link between telomerase function and longevity as well as identifying additional oncogenic genes.
Our results emphasize the importance of understanding genetic factors driving the most prevalent causes of mortality at a population level, highlighting the potential of early genetic testing to identify germline and somatic variants increasing one’s susceptibility to cancer and/or early death.
The authors have declared no competing interest.
Knowledge of how centenarians’ biomarker profiles differ from those of non-centenarians at comparable ages already earlier in life is scarce. The lack of suitable, large prospective data with long follow-up is one likely reason for this. The Japanese cohort mentioned above included individuals aged 85+ only, and more than half of them were already centenarians at baseline enrollment. Since health selection likely starts even earlier than age 85, it is important to examine potential differences between long-lived individuals and those with average life spans already several years before—or during the process of—health deterioration.
Moreover, several studies have reported that centenarians are not such a homogeneous population as sometimes perceived. An Italian study based on 602 centenarians identified three subgroups with distinct health profiles [11]. It was found that 20% of the centenarians were in good health, 33% had intermediate health status, and 47% were in poor health. A Danish study also detected three distinct subgroups defined by health status: robust, intermediate, and frail centenarians [12]. About half of the Danish centenarians were in the “robust” group. A German study using health insurance data from 1,121 centenarians found four distinct comorbidity profiles, and only a small proportion of centenarians had a low morbidity burden [13]. These findings raise the question of whether such heterogeneity in centenarians’ health profiles is already visible earlier in life and, for example, reflected in their biomarker profiles. Uncovering potential heterogeneity in such profiles more than one decade ago may help us understand characteristics of health trajectories associated with exceptional longevity.
The AMORIS (Apolipoprotein MOrtality RISk) cohort offers a unique opportunity to compare biomarkers measured at similar ages but earlier in life between centenarians and their shorter-lived peers. The cohort contains a variety of biomarkers assessed approximately 30 years ago and was linked to several administrative health registers with data until 2020. Using these data, we aim to (i) describe biomarker profiles earlier in life among individuals eventually becoming centenarians and their shorter-lived peers, (ii) investigate the association between a set of biomarkers and the chance of reaching age 100 with up to 35 years of follow-up, and (iii) investigate differences in biomarker profiles within the centenarian population.
Aimée Parker shares how her childlike curiosity and collaborative spirit motivate her scientific pursuits.
Sleep, fasting, exercise, green porridge, black coffee, a healthy social life …
There is an abundance of advice out there on how to live a good, long life. Researchers are working hard to determine why some people live longer than others, and how we get the most out of our increasingly long lives.
A test based on an “epigenetic clock” is the latest basis for claims that calorie restriction slows down ageing – but the jury is still out on whether the dieting strategy really works.
Human lifespan is shaped by both genetic and environmental exposures and their interaction. To enable precision health, it is essential to understand how genetic variants contribute to earlier death or prolonged survival. In this study, we tested the association of common genetic variants and the burden of rare non-synonymous variants in a survival analysis, using age-at-death (N = 35,551, median [min, max] = 72.4 [40.9, 85.2]), and last-known-age (N = 358,282, median [min, max] = 71.9 [52.6, 88.7]), in European ancestry participants of the UK Biobank. The associations we identified seemed predominantly driven by cancer, likely due to the age range of the cohort. Common variant analysis highlighted three longevity-associated loci: APOE, ZSCAN23, and MUC5B. We identified six genes whose burden of loss-of-function variants is significantly associated with reduced lifespan: TET2, ATM, BRCA2, CKMT1B, BRCA1 and ASXL1. Additionally, in eight genes, the burden of pathogenic missense variants was associated with reduced lifespan: DNMT3A, SF3B1, CHL1, TET2, PTEN, SOX21, TP53 and SRSF2. Most of these genes have previously been linked to oncogenic-related pathways and some are linked to and are known to harbor somatic variants that predispose to clonal hematopoiesis. A direction-agnostic (SKAT-O) approach additionally identified significant associations with C1orf52, TERT, IDH2, and RLIM, highlighting a link between telomerase function and longevity as well as identifying additional oncogenic genes.
