The London Longevity Newsletter — Issue 12

Welcome to Issue 12 of the London Longevity Newsletter 🗞️

🎉 Announcing an upcoming London Longevity Network event:

Longevity Salon: The Skin Age — Measure. Repair. Reverse. featuring:

• ​Dr Mark Kotter, Scientific Founder & Executive Chairman of clock.bio

• ​Dr Cristiana Banila, Chief Scientific Officer at Mitra Bio

• ​Dr Oliver Zolman, Founder of Blueprint and Director of Collagen London Clinic

This brilliant panel will be sharing how innovation in skin biology is reshaping health and rejuvenation. After the panel, Collagen London Clinic will also run free skin-age scans, followed by drinks and connections.

Sign up here: https://luma.com/cd8hv4sf

Now diving in:

RESEARCH SPOTLIGHT 🧬

• Do somatic mutations in blood cells raise stroke risk?
  A massive analysis of more than 800,000 individuals across three biobanks reveals that clonal hematopoiesis of indeterminate potential (CHIP), an age-related expansion of mutated blood stem cells, substantially increases the risk of both ischemic and hemorrhagic stroke. Using genomic and clinical data from BioVU, All of Us, and the UK Biobank, the researchers found that CHIP carriers faced a 20% higher risk of stroke overall, with the strongest effects seen for JAK2(2.5× risk) and TET2 (1.4× risk) mutations. Strikingly, the association was present only in postmenopausal women, hinting that estrogen may buffer CHIP-related inflammation. The study also uncovered a gene–inflammation link: elevated genetically predicted levels of IL-1 receptor accessory protein (IL-1RAP) amplified stroke risk among CHIP carriers, nominating IL-1 signaling as a potential therapeutic target. Together, the findings position CHIP as a major, actionable determinant of cerebrovascular disease, bridging somatic genetics, inflammation, and stroke prevention. Read More

• Can everyday clinical data reveal biological age across the lifespan?
  This study introduces a “full life cycle biological clock” trained on over 1.1 billion routine clinical measurements spanning all age groups to estimate biological age and health trajectories from birth to old age. Using machine learning, the model integrates common lab tests and vital signs to derive an individualised biological age gap that predicts mortality, multimorbidity, and functional decline better than chronological age. The authors show that accelerated biological ageing signatures emerge decades before disease onset and differ by organ system and lifestyle factors such as smoking or BMI. Importantly, the clock generalises across populations and clinical settings, enabling large-scale, low-cost ageing assessment using existing medical records. This framework reframes biological ageing as a continuously measurable, clinically actionable phenotype, with potential for preventive health

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