Structural correlations between brain magnetic resonance image-derived phenotypes and retinal neuroanatomy.

BACKGROUND AND PURPOSE: The eye is a well-established model of brain structure and function, yet region-specific structural correlations between the retina and the brain remain underexplored. Therefore, we aim to explore and describe the relationships between the retinal layer thicknesses and brain magnetic resonance image (MRI)-derived phenotypes in UK Biobank. METHODS: Participants with both quality-controlled optical coherence tomography (OCT) and brain MRI were included in this study. Retinal sublayer thicknesses and total macular thickness were derived from OCT scans. Brain image-derived phenotypes (IDPs) of 153 cortical and subcortical regions were processed from MRI scans. We utilized multivariable linear regression models to examine the association between retinal thickness and brain regional volumes. All analyses were corrected for multiple testing and adjusted for confounders. RESULTS: Data from 6446 participants were included in this study. We identified significant associations between volumetric brain MRI measures of subregions in the occipital lobe (intracalcarine cortex), parietal lobe (postcentral gyrus), cerebellum (lobules VI, VIIb, VIIIa, VIIIb, and IX), and deep brain structures (thalamus, hippocampus, caudate, putamen, pallidum, and accumbens) and the thickness of the innermost retinal sublayers and total macular thickness (all p < 3.3 × 10-5). We did not observe statistically significant associations between brain IDPs and the thickness of the outer retinal sublayers. CONCLUSIONS: Thinner inner and total retinal thicknesses are associated with smaller volumes of specific brain regions. Notably, these relationships extend beyond anatomically established retina-brain connections.

How many future dementia cases would be missed by a high-risk screening program? A retrospective cohort study in a population-based cohort.

INTRODUCTION: Risk prediction models aim to identify those at high risk to receive targeted interventions. We aimed to identify the proportion of future dementia cases that would be missed by a high-risk screening program. METHODS: We identified validated dementia risk prediction models from systematic reviews. We applied these to European Prospective Investigation of Cancer Norfolk, a large population-based cohort of 30,387 individuals with 29 years of linked healthcare data. RESULTS: A maximum of 16.0% (14.7,17.2) and 31.9% (30.2,33.5) of cases arose from the highest risk decile and quintiles, respectively. For every 1000 people considered to be at high risk, a maximum of 235 (215, 255) developed dementia. DISCUSSION: Seven in every 10 cases of dementia arose from people at normal risk, and eight in every 10 people at high risk did not develop dementia. Individual-level prevention approaches targeted at high-risk groups are unlikely to produce large reductions in disease incidence at the population level. HIGHLIGHTS: Dementia, a significant public health challenge, is not an inevitability of aging; risk reduction is possible. Several dementia risk prediction models have been validated in the general population, and these aim to identify people at high risk of the disease who can then be targeted with primary prevention interventions. An alternative prevention approach is to focus on interventions that reduce risk across the population, irrespective of risk status. In our study, seven out of every ten people who developed dementia during 29 year follow-up were classed as 'normal-risk' (rather than 'high risk') at baseline. Eight out of every ten people who were at high risk at baseline did not go on to develop dementia. Even if effective, dementia risk reduction efforts based upon targeted high-risk approaches are unlikely to reduce incidence of disease at the population level.

Smoking, Corneal Biomechanics, and Glaucoma: Results From Two Large Population-Based Cohorts.

Purpose: Smoking may influence measured IOP through an effect on corneal biomechanics, but it is unclear whether this factor translates into an increased risk for glaucoma. This study aimed to examine the association of cigarette smoking with corneal biomechanical properties and glaucoma-related traits, and to probe potential causal effects using Mendelian randomization (MR). Methods: Cross-sectional analyses within the UK Biobank (UKB) and Canadian Longitudinal Study on Aging (CLSA) cohorts. Multivariable linear and logistic regression models were used to assess associations of smoking (status, intensity, and duration) with corneal hysteresis (CH), corneal resistance factor, IOP, inner retinal thicknesses, and glaucoma. Two-sample MR analyses were performed. Results: Overall, 68,738 UKB (mean age, 56.7 years; 54.7% women) and 22 845 CLSA (mean age, 62.7 years; 49.1% women) participants were included. Compared with nonsmokers, smokers had a higher CH (UKB, +0.48 mm Hg; CLSA, +0.57 mm Hg; P < 0.001) and corneal resistance factor (UKB, +0.47 mm Hg; CLSA, +0.60 mm Hg; P < 0.001) with evidence of a dose-response effect in both studies. Differential associations with Goldmann-correlated IOP (UKB, +0.25 mm Hg; CLSA, +0.36 mm Hg; P < 0.001) and corneal-compensated IOP (UKB, -0.28 mm Hg; CLSA, -0.32 mm Hg; P ≤ 0.001) were observed. Smoking was not associated with inner retinal thicknesses or glaucoma status in either study. MR provided evidence for a causal effect of smoking on corneal biomechanics, especially higher CH. Conclusions: Cigarette smoking seems to increase corneal biomechanical resistance to deformation, but there was little evidence to support a relationship with glaucoma. This outcome may result in an artefactual association with measured IOP and could account for discordant results with glaucoma in previous epidemiological studies.

Utilizing multimodal AI to improve genetic analyses of cardiovascular traits.

Electronic health records, biobanks, and wearable biosensors contain multiple high-dimensional clinical data (HDCD) modalities (e.g., ECG, Photoplethysmography (PPG), and MRI) for each individual. Access to multimodal HDCD provides a unique opportunity for genetic studies of complex traits because different modalities relevant to a single physiological system (e.g., circulatory system) encode complementary and overlapping information. We propose a novel multimodal deep learning method, M-REGLE, for discovering genetic associations from a joint representation of multiple complementary HDCD modalities. We showcase the effectiveness of this model by applying it to several cardiovascular modalities. M-REGLE jointly learns a lower representation (i.e., latent factors) of multimodal HDCD using a convolutional variational autoencoder, performs genome wide association studies (GWAS) on each latent factor, then combines the results to study the genetics of the underlying system. To validate the advantages of M-REGLE and multimodal learning, we apply it to common cardiovascular modalities (PPG and ECG), and compare its results to unimodal learning methods in which representations are learned from each data modality separately, but the downstream genetic analyses are performed on the combined unimodal representations. M-REGLE identifies 19.3% more loci on the 12-lead ECG dataset, 13.0% more loci on the ECG lead I + PPG dataset, and its genetic risk score significantly outperforms the unimodal risk score at predicting cardiac phenotypes, such as atrial fibrillation (Afib), in multiple biobanks.

Unsupervised representation learning on high-dimensional clinical data improves genomic discovery and prediction.

Although high-dimensional clinical data (HDCD) are increasingly available in biobank-scale datasets, their use for genetic discovery remains challenging. Here we introduce an unsupervised deep learning model, Representation Learning for Genetic Discovery on Low-Dimensional Embeddings (REGLE), for discovering associations between genetic variants and HDCD. REGLE leverages variational autoencoders to compute nonlinear disentangled embeddings of HDCD, which become the inputs to genome-wide association studies (GWAS). REGLE can uncover features not captured by existing expert-defined features and enables the creation of accurate disease-specific polygenic risk scores (PRSs) in datasets with very few labeled data. We apply REGLE to perform GWAS on respiratory and circulatory HDCD-spirograms measuring lung function and photoplethysmograms measuring blood volume changes. REGLE replicates known loci while identifying others not previously detected. REGLE are predictive of overall survival, and PRSs constructed from REGLE loci improve disease prediction across multiple biobanks. Overall, REGLE contain clinically relevant information beyond that captured by existing expert-defined features, leading to improved genetic discovery and disease prediction.

The Association of Urinary Sodium Excretion with Glaucoma and Related Traits in a Large United Kingdom Population.

PURPOSE: Excessive dietary sodium intake has known adverse effects on intravascular fluid volume and systemic blood pressure, which may influence intraocular pressure (IOP) and glaucoma risk. This study aimed to assess the association of urinary sodium excretion, a biomarker of dietary intake, with glaucoma and related traits, and determine whether this relationship is modified by genetic susceptibility to disease. DESIGN: Cross-sectional observational and gene-environment interaction analyses in the population-based UK Biobank study. PARTICIPANTS: Up to 103 634 individuals (mean age: 57 years; 51% women) with complete urinary, ocular, and covariable data. METHODS: Urine sodium:creatinine ratio (UNa:Cr; mmol:mmol) was calculated from a midstream urine sample. Ocular parameters were measured as part of a comprehensive eye examination, and glaucoma case ascertainment was through a combination of self-report and linked national hospital records. Genetic susceptibility to glaucoma was calculated based on a glaucoma polygenic risk score comprising 2673 common genetic variants. Multivariable linear and logistic regression, adjusted for key sociodemographic, medical, anthropometric, and lifestyle factors, were used to model associations and gene-environment interactions. MAIN OUTCOME MEASURES: Corneal-compensated IOP, OCT derived macular retinal nerve fiber layer and ganglion cell-inner plexiform layer (GCIPL) thickness, and prevalent glaucoma. RESULTS: In maximally adjusted regression models, a 1 standard deviation increase in UNa:Cr was associated with higher IOP (0.14 mmHg; 95% confidence interval [CI], 0.12-0.17; P < 0.001) and greater prevalence of glaucoma (odds ratio, 1.11; 95% CI, 1.07-1.14; P < 0.001) but not macular retinal nerve fiber layer or ganglion cell-inner plexiform layer thickness. Compared with those with UNa:Cr in the lowest quintile, those in the highest quintile had significantly higher IOP (0.45 mmHg; 95% CI, 0.36-0.53, P < 0.001) and prevalence of glaucoma (odds ratio, 1.30; 95% CI, 1.17-1.45; P < 0.001). Stronger associations with glaucoma (P interaction = 0.001) were noted in participants with a higher glaucoma polygenic risk score. CONCLUSIONS: Urinary sodium excretion, a biomarker of dietary intake, may represent an important modifiable risk factor for glaucoma, especially in individuals at high underlying genetic risk. These findings warrant further investigation because they may have important clinical and public health implications. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.