Causal inference to discover mechanisms and surrogate markers of retinal and brain disease from observational datasets

Background

Microvascular diseases are important causes of illness and death. Examples include diabetes mellitus, stroke, and vascular dementia. We need to understand how these diseases damage small blood vessels in order to improve diagnosis, prognostication, and predict response to treatments. Measuring microvascular disease within living human organs such as the brain is difficult, but retinal vessels are similar to the brain, and can be measured very precisely. Measuring the impact of systemic disease on retinal vessels, with reference to the impact of disease on brain vessels, could help us predict, diagnose, and understand brain disease mechanisms.

Studies assessing retinal biomarkers of brain disease have often relied on regression models associating a retinal variable (X) with some brain endpoint (Y). A significant X~Y association is taken as evidence that a retinal biomarker might be a “surrogate” of brain disease. However, this is known to be inadequate for assessing surrogate endpoints in clinical trials, and sophisticated methodology has been developed to assess the amount of information shared by a prospective surrogate X, and a true endpoint Y, about a randomized treatment (A)¹.

Retinal biomarkers are typically studied in observational datasets rather than clinical trials. Several large cohorts now combine eye and brain data, and there is a strong motivation to use these to develop retinal biomarkers of brain disease. However, at present we do not have sophisticated statistical approaches to assess retinal biomarkers in longitudinal datasets, analogous to those available for analysing surrogate endpoints in randomised clinical trials. These tools would describe the information shared by change in a retinal biomarker (delta_X), and a true endpoint (delta_Y), about a change in disease exposure over time (delta_A).

We suggest this is possible because we can use information about vascular biology to justify key topological elements of structural causal models (relating left eye, right eye, brain), instead of relying on randomization to justify these assumptions. If so, then adapting surrogate methods to handle longitudinal data about disease trajectory could allow us to understand the causes of microvascular disease in the eye and other organs, and from this, develop prognostic and surrogate markers for clinical use.

The student will work on simulated data and longitudinal datasets. For example, the Mild Stroke Study 3 (MSS3) – a landmark highly phenotyped longitudinal cohort study including retinal and brain imaging from people with cerebral small vessel disease; and the Scottish Collaborative Optometry-Ophthalmology Network e-research (SCONe) – which combines ocular and systemic health data from the general population and is a unique resource to help understand very early causes of eye and systemic health problems in people as they age.

New methodology can be applied to these to understand and prognosticate risk of stroke and vascular dementia, help demonstrate proof of concept, and facilitate external validation of prospective disease markers.

Aims

1) Review methods for testing retinal markers of brain disease; methods for individual-level surrogacy ²; and rationale for mapping structural causal models onto biological pathways ³.

2) Adapt individual-level surrogacy methods to longitudinal interventions

3) Test methods on data from SCONe and MSS3 to assess causes of microvascular disease in the retina, and brain

4) Evaluate prospective markers on additional (validation) longitudinal datasets, such as the UK Biobank, Lothian Birth Cohort, and PREVENT dementia cohort.

Training outcomes

1) Expertise in causal inference and surrogate outcome methodology, adapted for longitudinal datasets

2) Open-source analytical methods for the research community 

3) Proficiency in assessing the underlying assumptions of methodology, applying methods to solve problems, and communicating this to improve standards of research on retinal biomarkers

References

1. Ensor, H., Lee, R. J., Sudlow, C. & Weir, C. J. Statistical approaches for evaluating surrogate outcomes in clinical trials: A systematic review. J Biopharm Stat 26, 859–879 (2016).
2. Elst, W. V. D. et al. Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials. (2023).
3. Ross, L. N. & Bassett, D. S. Causation in neuroscience: keeping mechanism meaningful. Nat. Rev. Neurosci. 1–10 (2024) doi:10.1038/s41583-023-00778-7.