#ESSW2025 session report: Session II brought together Dr. Shoamanesh, Dr. Strbian, Marios Georgakis & Dr. Gauberti to explore how stronger methodology can move #StrokeResearch closer to clinical practice.
🔗Read the #ESOBlog: https://ow.ly/kF9U50XHkGh
#Stroke #Neurology
Amazing work by Lingling Xu and the rest of the team!
🔗link to our preprint: www.medrxiv.org/content/10.1...
Rare variant burden testing further implicated damaging variants in COL21A1, LMNA, TP53BP2, RXRB, and FLOT2, also converging on extracellular matrix remodeling and fibrosis-related pathways.
TWAS using RNA-seq data from human aorta & coronary arteries identified 28 genes, incl, RSG19 and ULK4, whose expression likely drives these genetic associations. ScRNA-seq revealed strong enrichment of these genes in fibroblasts, implicating fibrotic remodeling mechanisms in arterial aging.
We then performed GWAS and rare variant burden testing.
We found 60 loci in GWAS and 5 genes in rare variant analyses. Of them, 34 loci were novel, not previously linked to vascular traits. The expression of nearby genes was strongly enriched in human arterial tissues.
Furthermore, it was predictive of future cardiovascular events (stroke, myocardial infarction, heart failure), incident hypertension, cardiovascular death, and all-cause mortality, independently of chronological age.
The difference of the derived predictions from chronological age was correlated with known features of arterial aging, as well as vascular risk factors.
Using PPG waveforms and blood pressure measurements, we developed an arterial aging clock by training a deep learning model to predict chronological age.
While aging of the large arteries, characterized by progressive stiffness, is a well-established risk factor for cardiovascular disease, the mechanisms driving this pathology remain poorly understood. We lack targeted interventions that could slow or prevent arterial aging.
In our new preprint, we present the largest genomic exploration of arterial aging to date, leveraging photoplethysmography (PPG)–derived pulse waveforms from 115,000 UK Biobank participants.
Our results provide insights into potential strategies to mitigate arterial aging👇
@mariosgeorgakis.bsky.social , not recently active on #Bluesky, on the methods for prioritizing drug targets for complex diseases using human genomic data
🔗link to the paper 🔓: www.deepvasc.com/s/georgakis-...
Stroke prevention is more nuanced than that of atherosclerosis in other vascular beds due to heterogeneity in underlying etiology.
In this paper we discuss the implications of recent trials for stroke prevention, as well as future perspectives for under development anti-inflammatory treatments
If you're up for a niche read on challenges in translating anti-inflammatory therapies for atherosclerotic stroke prevention, have a look at our review in Neurology
A great read👇
Some social science genetics papers are among the most interesting and methodologically rigorous I've read.
From biology to mating choices and inequalities, they deal with very fundamental concepts of what makes as humans.
Extremely proud to see this work led by @mihkeljesse.bsky.social now out on biorxiv!
Great work by Lanyue Zhang, Murad Omarov, and Lingling Xu.
Grateful for the partnership with @tourmalinebio.bsky.social (developing an anti-IL6 antibody) and the collaboration with
Pradeep Natarajan.
🔗link to study (open access🔓): www.nature.com/articles/s44...
However, each target and indication have unique nuances that necessitate individual tailored approaches for proper validation.
A scalable, one-size-fits-all approach risks overlooking important context and already acquired knowledge.
3⃣ Large-scale retrospective analyses of target-indication pairs (e.g. Minikel et al, Nature 2024) offer big picture evidence that genetically supported targets are more likely to lead to approved drugs.
2⃣ Nearly all data used in this study are publicly available.
In principle, this framework could be applied to genetically validate any drug target, particularly those developed for chronic, aging-related diseases.
We hope this work will provide prospective validation for the use of human genetics to predict trial-relevant clinical outcomes.
Thoughts for broader implications:
1⃣ Often, human genetic studies, replicate trial findings retrospectively.
The results of ZEUS, a phase 3 cardiovascular outcomes trial testing ziltivekimab in patients with ASCVD and high CRP, are expected in 2026.
Regarding side-effects, notable and consistent signals included:
👉 Glaucoma
👉 Perinatal & postpartum maternal hemorrhage
These may be relevant for specific populations and warrant further investigation.
The main risk-lowering effects of the IL-6 proxy were replicated across:
👉 cardiovascular & metabolic endpoints
👉 autoimmune disease outcomes
👉 respiratory infections (pneumonia, influenza, COPD-related)
We also observed novel protective associations with:
👉 depression
👉 gallstone disease
Finally, we performed a phenome-wide association study (PheWAS) in FinnGen to systematically assess whether our IL-6 genetic proxy is associated with any additional outcomes beyond those already studied.
Interestingly, our IL-6 genetic proxy showed no significant increase in the infection endpoints tested.
On the contrary, there was even evidence of risk-lowering effects on pneumonia hospitalization.
The primary safety concern for anti-IL-6 therapies is infection risk.
In previous work, we have shown that genetically proxied IL6R inhibition increases risk of bacterial infections, consistent with clinical experience with tocilizumab.
We also observed metabolic effects:
👉an association w/ lower risk of type 2 diabetes
👉significant increases in HDL particles
Across multiple metabolomic measurements, the effects were highly consistent with those of an IL6R instrument, indicating convergence downstream of IL-6 signaling
These findings were highly consistent in East Asian individuals from Biobank Japan.
We then tested associations with cardiovascular endpoints.
Like an IL6R instrument, IL6 perturbation was associated with lower risk of atherosclerotic outcomes:
👉 Coronary artery disease (CAD)
👉 Peripheral artery disease (PAD)
👉 Large artery atherosclerotic stroke
👉 Carotid atherosclerotic plaque
