Muralidharan Sargurupremraj

Background and ObjectivesStroke and dementia are leading causes of death and disability worldwide, requiring enhanced preventive strategies. Glucagon-like peptide-1 receptor agonists (GLP-1RA), wid...

Cerebral small vessel disease (cSVD) is a leading cause of stroke and dementia with no specific treatment, of which molecular mechanisms remain poorly understood. To identify potential biomarkers and therapeutic targets, we applied Mendelian randomization to examine over 2,500 proteins measured in plasma and, uniquely, cerebrospinal fluid, in relation to magnetic resonance imaging (MRI) markers of cSVD in more than 40,000 individuals. Here we show that 49 proteins are associated with MRI markers of cSVD, most prominently in cerebrospinal fluid. We highlight associations that are consistent across platforms and ancestries, and supported by complementary observational analyses, and we explore differences between fluids. The proteins are enriched in pathways related to the extracellular matrix, immune response and microglial activity. Many also associate with stroke and dementia, and several correspond to existing drug targets. Together, these findings reveal a robust biological fingerprint of cSVD and highlight opportunities for biomarker and drug discovery and repositioning.

Cerebral small vessel disease (cSVD), characterized by pathologic changes in the structure and function of small brain vessels, is detectable on brain MRI in the absence of clinical symptoms. However, imaging cerebral small vessels themselves in vivo remains costly and challenging. There is growing interest in investigating whether retinal microvascular imaging features could be proxies for changes in the brain microvasculature. Using a multipronged approach, we explored the relation of retinal microvascular characteristics with MRI markers of cSVD (MRI-cSVD). First, we explored this relationship in older community persons from the population-based 3C-Dijon cohort. MRI-cSVD was assessed on a 1.5-Tesla MRI at baseline, comprising white matter hyperintensity volume (WMHV), lacunes, and a composite extreme cSVD phenotype (WMHV extreme distributions and presence/absence of lacunes). At 10-year follow-up, participants underwent measurements of retinal microvascular features on fundus using the Singapore "I" Vessel Assessment software. To support 3C-Dijon findings, we conducted a comprehensive literature review up to July 2024 from PubMed/EMBASE and used 2-sample Mendelian randomization (MR) leveraging large-scale genome-wide association studies, to assess causality and directionality. In 670 3C-Dijon participants (median age 70.7, 65.7% women), multivariable analyses (adjusted for age, sex, axial length, and cardiovascular risk factors) showed a significant association of lower arteriolar fractal dimension (FDa) with extreme cSVD (odds ratio [OR] 1.68, 95% CI 1.20-2.34) after multiple testing correction (p < 0.0042), and at p < 0.05, associations of lower FDa and smaller arteriolar caliber with larger WMHV (β = 0.0534 [95% CI 0.0075-0.0569] and 0.0519 [95% CI 0.0045-0.0993]), and of greater venular tortuosity (TORTv) with lacunes (OR 1.45, 95% CI 1.05-2.00). Lower FDa was also associated with poorer executive function. The systematic review of the literature identified 12 studies (N = 7,796) that showed mostly consistent direction of effects for FDa (5/6 studies), TORTv (4/6), and arteriolar caliber (10/11), although statistical significance was observed in 5 individual studies only. Two-sample MR based on large genome-wide association studies (N = 5,292-52,798) showed evidence for a potentially causal association of greater TORTv with extreme cSVD and larger WMHV (p = 0.0017 and 0.049), with no evidence for reverse causation. We provide multimodal evidence that geometric characteristics of the retinal microvasculature are associated with increased burden of MRI-cSVD and possibly worse executive function.

Background: While All-cause dementia (ACD) may often be characterized by the abnormal deposition of extracellular β-amyloid (Aβ) in the brain cortex and hyperphosphorylated tau (p-tau) as neurofibrillary tangles intracellularly, there is a need to identify early metabolic changes that may accompany these pathological changes. Objective: This study evaluated the predictive value of fatty acids in cerebrospinal fluid (CSF) fractions in differentiating cognitively unimpaired (CU), mild cognitive impairment (MCI), and ACD participants. Methods: CSF fatty acid profiles were analyzed from CU (n=68), MCI (n=38), and ACD (n=37) individuals aged 77.3 ± 7.7 years, sourced from the Huntington Medical Research Institutes (HMRI). Multivariable binary logistic regression identified the most effective CSF fatty acid biomarkers for distinguishing CU, MCI, and ACD groups. The top-performing CSF fatty acid biomarkers were combined with Aβ ₄₂ , tau, and the Aβ ₄₂ /tau ratio to evaluate their collective diagnostic performance. The model was adjusted for covariates, including age, sex, smoking status, hypertension, diabetes, and APOE genotype. Receiver operating characteristic (ROC) curves were generated for the top 10 CSF fatty acids ranked by area under the curve (AUC), sensitivity, and specificity, and their significance was assessed using the DeLong test. Results: The top 10 fatty acids in CSF fractions demonstrated superior discrimination between CU individuals and those with MCI compared to traditional markers such as Aβ ₄₂ , tau, and the Aβ ₄₂ /tau ratio. Furthermore, incorporating a panel of these fatty acid biomarkers alongside Aβ ₄₂ /tau significantly improved diagnostic accuracy. Age, sex, smoking, hypertension, APOE genotype, and diabetes did not significantly influence the model’s performance. Conclusion: This study suggests that changes in fatty acid metabolism occur in early ACD pathology. Thus, strategies that regulate fatty acid metabolism may prevent cognitive decline in an older population. Highlights: This study revealed that CSF fractions’ fatty acids have a better discriminatory power for CU from MCI than CSF Aβ ₄₂ , tau, and Aβ ₄₂ /tau ratio. A panel of CSF fraction fatty acids combined with Aβ ₄₂ /tau remarkably improved its diagnostic performance for differential diagnosis of CU, MCI, and ACD. Clinical evaluation of these fatty acids will strengthen the detection of early cognitive impairment. A prospective large cohort multicenter study of the diagnostic utility of CSF fractions’ fatty acids will provide robust evidence before extensive clinical usage of these fatty acids.

Cerebral small vessel disease (cSVD) is a major contributor to stroke, dementia, and cognitive decline. Despite significant progress through large-scale genome-wide association studies (GWAS) for cSVD and stroke, the genetic architecture underlying these conditions remains poorly understood. This review highlights recent advancements in statistical tools and provides a comprehensive overview of current insights into the genetic underpinnings of cSVD and stroke. We focus on the relevance of non-additive effects, local heritability, and polygenicity in shaping these traits. While single nucleotide polymorphism (SNP)-based heritability estimates for stroke and cSVD traits remain lower than pedigree-based estimates, we explore challenges and opportunities in addressing this “missing heritability.” In particular, we emphasize the importance of investigating both common and rare variants, to better characterize the genetic basis of cSVD. Furthermore, we discuss the role of negative selection in shaping complex disease traits and the relevance of the “omnigenic” model in the context of cSVD traits. In summary, we aim to provide a more nuanced understanding of cSVD and stroke genetics, paving the way for future research into their molecular mechanisms.

Genome-wide association studies (GWAS) have identified numerous genetic loci influencing human disease risk. However, linking these loci to causal genes remains challenging, limiting opportunities for drug target discovery. Transcriptome-wide association studies (TWAS) address this by linking variants to gene expression, but typically rely on bulk RNA sequencing, which lacks cell-specific resolution. Here, we present a single-cell TWAS pipeline combining cis -Mendelian randomization (MR) with colocalization analyses at the single-cell level. As a case study, we examined how genetically proxied gene expression in immune cells influences atherosclerotic cardiovascular disease (ASCVD) risk. We integrated single-cell expression quantitative trait loci (sc-eQTL) for 14 immune cell types with GWAS for coronary artery disease, large artery atherosclerotic stroke, and peripheral artery disease. Single-cell cis -MR analyses revealed 440 gene-outcome associations across cell types, 84% of which were missed by bulk TWAS, despite a considerably smaller sample size of the sc-eQTL dataset. Of these associations, 17 were replicated with external cis -eQTLs and demonstrated colocalization with ASCVD GWAS signals. Notably, genetically proxied expression of LIPA in monocytes was associated with coronary artery disease, large artery atherosclerotic stroke, and subclinical atherosclerosis traits. These findings were confirmed in a phenome-wide association study without evidence of associations with unexpected clinical outcomes. Single-cell RNA sequencing and immunohistochemistry of human carotid plaques revealed high LIPA expression in plaque macrophages. Our pipeline provides a solution for the discovery of cell-specific expression patterns that drive genetic predisposition to human disease, potentially impacting target selection for cell-tailored therapeutics.

Subcortical brain structures are involved in developmental, psychiatric and neurological disorders. Here we performed genome-wide association studies meta-analyses of intracranial and nine subcortical brain volumes (brainstem, caudate nucleus, putamen, hippocampus, globus pallidus, thalamus, nucleus accumbens, amygdala and the ventral diencephalon) in 74,898 participants of European ancestry. We identified 254 independent loci associated with these brain volumes, explaining up to 35% of phenotypic variance. We observed gene expression in specific neural cell types across differentiation time points, including genes involved in intracellular signaling and brain aging-related processes. Polygenic scores for brain volumes showed predictive ability when applied to individuals of diverse ancestries. We observed causal genetic effects of brain volumes with Parkinson’s disease and attention-deficit/hyperactivity disorder. Findings implicate specific gene expression patterns in brain development and genetic variants in comorbid neuropsychiatric disorders, which could point to a brain substrate and region of action for risk genes implicated in brain diseases.

The Mendelian randomization (MR) paradigm allows for causal inferences to be drawn using genetic data. In recent years, the expansion of well-powered publicly available genetic association data related to phenotypes such as brain tissue gene expression, brain imaging, and neurologic diseases offers exciting opportunities for the application of MR in the field of neurology. In this review, we discuss the basic principles of MR, its myriad applications to research in neurology, and potential pitfalls of injudicious applications. Throughout, we provide examples where MR-informed findings have shed light on long-standing epidemiologic controversies, provided insights into the pathophysiology of neurologic conditions, prioritized drug targets, and informed drug repurposing opportunities. With the ever-expanding availability of genome-wide association data, we project MR to become a key driver of progress in the field of neurology. It is therefore paramount that academics and clinicians within the field are familiar with the approach.