Key Trends Shaping the Future of Ageing Research: Insights from the UK Biobank Scientific Conference 2025

Lal Kabalak
Jan 6
2 min read

3 min read 508 words

We recently attended the UK Biobank Scientific Conference 2025 online. UK Biobank is a global resource, providing secure, ethical access to genomic, imaging, proteomic and health data from 500,000 participants — enabling researchers to study ageing not as a single process, but organ by organ, across the life course.

Across the conference, a consistent message emerged: our understanding of ageing is becoming more precise, predictive and personalised. Researchers showcased how large-scale datasets such as UK Biobank, combined with advances in imaging, molecular biology and artificial intelligence, are transforming ageing from a broad, abstract concept into something measurable across organs, systems and time. The following key trends reflect the most impactful directions highlighted during the conference and signal where ageing research is heading next.

 Organ-Specific Ageing via Imaging

Instead of treating ageing as a single global process, the field is moving toward measuring how individual organs age differently.

Key ideas:

Whole-body MRI & advanced imaging reveal that organs (e.g., brain, heart, liver) have distinct ageing trajectories.
Researchers are characterising what “normal” ageing looks like at scale, so deviations can signal early pathology.
These imaging data help define organ-specific age benchmarks and detect subtle changes long before disease emerges.

Trend: From global biomarkers of ageing → to fine-grained, organ-by-organ ageing signatures.

Proteomics as a Biological Age Signature

Proteomics — measuring levels of thousands of proteins in blood — is emerging as a powerful way to estimate biological age.

What’s happening:

Protein patterns change with age, and certain signatures correlate with health outcomes.
Researchers are building proteomic clocks that can estimate the biological age of specific organs or cell types.
These signatures can predict disease onset, resilience, and mortality better than chronological age alone.

Trend: From static biomarkers to dynamic, predictive proteomic age scores.

Digital Phenotyping & Cognitive Ageing

Wearables and digital tests (e.g., high-resolution cognitive assessments) are being paired with deep biological data.

This enables:

Detection of subtle cognitive shifts well before clinical symptoms.
Integration with imaging and other biomarkers to build multi-modal ageing profiles.
Continuous or remote monitoring of functional ageing in real life.

Trend: From episodic clinical assessments → to high-resolution, real-world digital ageing indicators.

Multi-Omics Integration

All of these data — imaging, proteomics, genomics, metabolomics, digital measures — are being integrated.

Why it matters:

Multi-modal data gives a 360° view of ageing processes.
It helps disentangle causal mechanisms vs. downstream effects.
Enables personalized ageing signatures (e.g., someone’s liver may age faster than their heart).

Trend: From siloed datasets → to harmonised, multi-layer models of ageing.

AI & Predictive Modelling

AI and machine learning are essential for making sense of these vast datasets.

Predicting who is at risk of disease decades before onset.
Identifying latent patterns that humans can’t see.
Generating biological ageing scores across organs and systems.

Trend: From hypothesis-driven analysis → to data-driven discovery at scale.

Overall Research Trajectory

Rather than ageing being a monolithic, uniform process:

 Ageing is becoming measurable, organ-specific, and predictive.
 Deep, population-wide data unlock earlier, finer signals of decline.
 Proteins, images, digital signals + AI = actionable ageing insights.