Hypertension is common in ageing cats and may result in target organ damage (TOD) to the brain, eyes, heart or kidneys, which is associated with considerable morbidity. Early recognition of feline hypertension, prior to the development of TOD, is associated with increased survival time. Despite this, only a minority of cats at risk of developing hypertension have their systolic blood pressure (SBP) proactively monitored. Furthermore, even where SBP is measured a diagnosis of hypertension may be challenging, as situational hypertension is a major confounding factor. Documentation of TOD, such as hypertensive retinopathy, corroborates a diagnosis, however clinicians outside of specialist practice may not be confident in obtaining and interpreting retinal images. There is a need to identify the feline population most at risk of incident hypertension, and therefore those cats which would benefit most from regular monitoring. Ancillary tests to facilitate early identification of at-risk cats would mean anti-hypertensive treatment could be initiated to prevent TOD and enhance the welfare of ageing cats.
In human medicine, risk of hypertension may be stratified by traditional metrics such as lifestyle factors and urinary albumin excretion, alongside emerging tools including urinary biomarkers and artificial intelligence (AI) analysis of retinal imaging, which may predict onset and facilitate early diagnosis. The overall goal of this research is to determine whether these methods can be modified and applied to cats to improve veterinary practitioners’ ability to diagnose hypertension. This study aims to address this goal by:
1. Determining lifestyle risk factors associated with the rate of increase in SBP over time in cats aged >8 years
2. Investigating urinary albumin, epidermal growth factor and uromodulin as biomarkers of the presence of hypertension and as predictors of incident hypertension
3. Curating a database of retinal images annotated for quality and lesions specific to feline hypertension and incident hypertension, and evaluating these as predictors of incident hypertension in a pilot study using an AI application developed for human use.
To meet these objectives this study will utilise the database of longitudinal clinical data, residual stored urine and retinal images from cats attending the Royal Veterinary College Ageing Cat Clinic, as well as retinal images obtained from practices affiliated to the industrial partner, Boehringer Ingelheim (BI). The successful candidate will be required to participate in the Royal Veterinary College Ageing Cat Clinics and as such must be a qualified veterinary surgeon (MRCVS eligible) at the start of the studentship. Experience of retrospective epidemiological research, prospective clinical study design and/or laboratory-based research is desirable but not essential.
This project will provide a veterinary graduate with interdisciplinary training in clinical research, molecular biology, epidemiology, study design and training/validation of automated image analysis and the principles of AI deep learning models. The supervisory team includes Dr Jack Lawson (RVC), Prof. Rosanne Jepson (RVC), Prof. Jonathan Elliott (RVC), Prof. Cathy Egan (UCL), Christoph Schummer (BI) and Christian Troetschel (BI). All RVC supervisors are members of an established research group which has generated a large body of research with unparalleled impact in the field of feline nephrology and hypertension, and Prof. Egan is an internationally recognised expert on the implementation of AI for diagnosis of retinopathy, providing expertise to support this element of the proposal.
A 3-month placement with Boehringer Ingelheim is incorporated within the studentship where both technical and business strategy training will be provided. The successful candidate will work with the industry supervisors to gain experience in the development process of veterinary medicinal products, starting from the initial evaluation of a pre-project with proof-of-concept (POC) studies through to the marketing of the final product with accompanying technical support. This will equip the student with the training and skills necessary to understand how basic science is transformed into commercially viable products that meet regulatory requirements, and the economic viability of this translation.
Key Words: Feline; Hypertension; Animal Health; Urinary Biomarkers; Retinal Imaging
