PhD Program: Edmond and Lily Safra PhD Studentship in Parkinson's Disease

About the Project

Applications are invited for a 3-year PhD studentship funded by the Edmond J. Safra Foundation within Dr Cynthia Sandor’s group in the Department of Brain Sciences at Imperial College London.

This studentship will focus on:

Predicting Insulin Resistance from Blood Transcriptomes: A Peripheral Window onto Brain Insulin Resistance in Parkinson’s and Alzheimer’s Disease

The successful candidate will be based at Imperial’s White City Campus and will join an active and collaborative postgraduate community. The project will be supervised by Dr Cynthia Sandor, an Edmond and Lily Safra Assistant Professor in Parkinson's Disease in the Department of Brain Sciences, and a UK DRI Group Leader.

Background

Parkinson’s disease affects over 10 million people worldwide and its prevalence is increasing with ageing populations. Type 2 diabetes and insulin resistance are established risk factors for Parkinson’s disease. Increasingly, attention has turned to brain insulin resistance, a state in which neurons themselves respond poorly to insulin, with impaired signalling. Unlike peripheral insulin resistance, brain insulin resistance reflects blunted neuronal insulin action and has been linked to mitochondrial dysfunction, α-synuclein aggregation, and neuroinflammation, yet its genetic and biomarker basis in Parkinson’s disease remains poorly understood.

Recent work from Dr Sandor’s group and collaborators (Volpato et al. 2025) has shown that vulnerable dopamine-producing neurons show increased activity in renin–angiotensin system signalling and metabolic stress pathways, and that genetic risk for Parkinson’s overlaps with risk for type 2 diabetes and cardiometabolic traits. Epidemiological studies also suggest that certain blood pressure and diabetes medications may reduce Parkinson’s risk, but we do not yet know which patient subgroups benefit most.

A central obstacle is that brain insulin resistance cannot be measured directly at scale in patients, which motivates an accessible, blood-based readout that can stratify individuals and probe the metabolic dimension of neurodegeneration across large cohorts.

Project Overview

This PhD project will develop and validate a computational model that predicts insulin resistance from peripheral blood mononuclear cell (PBMC) transcriptomes, and apply it to existing Parkinson’s and Alzheimer’s disease PBMC datasets. Peripheral insulin resistance is directly predictable from blood and is only partially coupled to brain insulin resistance, so the PBMC-derived score is positioned as a scalable, blood-based proxy for the central process rather than a direct measure of it.

The project is entirely computational and combines machine learning with single-cell and bulk transcriptomics and rigorous cross-dataset validation.

Key objectives include:

• Signature derivation:Train a transferable insulin-resistance predictor on metabolically labelled reference cohorts using regression and classification models with per-cell-type pseudo bulk analysis to localise the signature to specific immune subsets.
• Data integration & validation:Harmonise heterogeneous single-cell and bulk PBMC datasets (using Harmony, scVI/scANVI and Com Bat), explicitly controlling for age, sex, BMI, medication and ancestry, and validate the signature with strict leave-one-dataset-out cross-validation and an external labelled cohort.
• Transfer to PD cohorts:Apply the validated signature to existing Parkinson’s PBMC datasets to impute per-subject and cell-type-resolved insulin-resistance scores, and test whether these scores are elevated relative to controls and track disease severity and cognition.
• Brain-IR alignment:Where central insulin-resistance anchors are available (for example intranasal-insulin…