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.

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.

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.

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.

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.

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.

Associations of inner retinal layers with risk of incident dementia: An individual participant data analysis of four prospective cohort studies.

INTRODUCTION: Our main objective was to investigate whether retinal neurodegeneration, estimated from lower thickness of inner retinal layers, was associated with incident all-cause dementia and Alzheimer's disease (AD). METHODS: We performed an individual participant data meta-analysis using unpublished data from four prospective cohort studies with a total of 69,955 participants (n = 1087 cases of incident all-cause dementia; n = 520 cases incident AD; follow-up time median [interquartile range] 11.3 [8.8-11.5] years). RESULTS: General baseline characteristics of the study population were mean (standard deviation) age, 58.1 (8.8) years; 47% women. After adjustment, lower baseline macular retinal nerve fiber layer thickness was significantly associated with a 10% and 11% higher incidence of all-cause dementia and AD, respectively. Lower baseline macular ganglion cell-inner plexiform layer thickness was not significantly associated with these outcomes. DISCUSSION: These findings suggest that retinal neurodegeneration precedes the onset of clinical dementia. Retinal imaging tools may be informative biomarkers for the study of the early pathophysiology of dementia.

Association Between Retinal Nerve Fiber Layer Thickness and Incident Dementia in the European Prospective Investigation into Cancer in Norfolk Cohort.

BACKGROUND: Retinal nerve fiber layer (RNFL) thickness may reflect cerebral status. OBJECTIVE: This study assessed the relationship between RNFL thickness and incident all-cause dementia in the European Prospective Investigation into Cancer in Norfolk (EPIC-Norfolk) Eye Study. METHODS: Glaucoma detection with variable corneal compensation (GDx-VCC) and Heidelberg Retinal Tomograph II (HRT II) derived global mean RNFL thickness from dementia-free participants at baseline within the EPIC-Norfolk Eye Study were analyzed. Incident dementia was identified through linkage to electronic medical records. Cox proportional hazard mixed-effects regression models adjusted for key confounders were used to examine the associations between RNFL thickness and incident dementia in four separate models. RESULTS: 6,239 participants were included with 322 cases of incident dementia and mean age of 67.5-years old, with 49.7% women (median follow-up 13.2-years, interquartile range (11.7 to 14.6 years). Greater RNFL thickness (GDx-VCC) was not significantly associated with a lower risk of incident dementia in the full adjusted model [HR per quartile increase 0.95; 95% CI 0.82-1.10]. Similarly, RNFL thickness assessed with HRT II was also not associated with incident dementia in any model (full adjusted model; HR per quartile increase: 1.06; [95% CI 0.93-1.19]. Gender did not modify any associations under study. CONCLUSION: GDx-VCC and HRT II derived RNFL thickness are unlikely to be useful predictors of incident dementia. Higher resolution optical imaging technologies may clarify whether there are useful relationships between neuro-retinal morphology and brain measures.

Ethnicity is not biology: retinal pigment score to evaluate biological variability from ophthalmic imaging using machine learning.

Background: Few metrics exist to describe phenotypic diversity within ophthalmic imaging datasets, with researchers often using ethnicity as an inappropriate marker for biological variability. Methods: We derived a continuous, measured metric, the retinal pigment score (RPS), that quantifies the degree of pigmentation from a colour fundus photograph of the eye. RPS was validated using two large epidemiological studies with demographic and genetic data (UK Biobank and EPIC-Norfolk Study). Findings: A genome-wide association study (GWAS) of RPS from UK Biobank identified 20 loci with known associations with skin, iris and hair pigmentation, of which 8 were replicated in the EPIC-Norfolk cohort. There was a strong association between RPS and ethnicity, however, there was substantial overlap between each ethnicity and the respective distributions of RPS scores. Interpretation: RPS serves to decouple traditional demographic variables, such as ethnicity, from clinical imaging characteristics. RPS may serve as a useful metric to quantify the diversity of the training, validation, and testing datasets used in the development of AI algorithms to ensure adequate inclusion and explainability of the model performance, critical in evaluating all currently deployed AI models. The code to derive RPS is publicly available at: https://github.com/uw-biomedical-ml/retinal-pigmentation-score. Funding: The authors did not receive support from any organisation for the submitted work.

The Association of Physical Activity with Glaucoma and Related Traits in the UK Biobank.

PURPOSE: To examine the association of physical activity (PA) with glaucoma and related traits, to assess whether genetic predisposition to glaucoma modified these associations, and to probe causal relationships using Mendelian randomization (MR). DESIGN: Cross-sectional observational and gene-environment interaction analyses in the UK Biobank. Two-sample MR experiments using summary statistics from large genetic consortia. PARTICIPANTS: UK Biobank participants with data on self-reported or accelerometer-derived PA and intraocular pressure (IOP; n = 94 206 and n = 27 777, respectively), macular inner retinal OCT measurements (n = 36 274 and n = 9991, respectively), and glaucoma status (n = 86 803 and n = 23 556, respectively). METHODS: We evaluated multivariable-adjusted associations of self-reported (International Physical Activity Questionnaire) and accelerometer-derived PA with IOP and macular inner retinal OCT parameters using linear regression and with glaucoma status using logistic regression. For all outcomes, we examined gene-PA interactions using a polygenic risk score (PRS) that combined the effects of 2673 genetic variants associated with glaucoma. MAIN OUTCOME MEASURES: Intraocular pressure, macular retinal nerve fiber layer (mRNFL) thickness, macular ganglion cell-inner plexiform layer (mGCIPL) thickness, and glaucoma status. RESULTS: In multivariable-adjusted regression models, we found no association of PA level or time spent in PA with glaucoma status. Higher overall levels and greater time spent in higher levels of both self-reported and accelerometer-derived PA were associated positively with thicker mGCIPL (P < 0.001 for trend for each). Compared with the lowest quartile of PA, participants in the highest quartiles of accelerometer-derived moderate- and vigorous-intensity PA showed a thicker mGCIPL by +0.57 µm (P < 0.001) and +0.42 µm (P = 0.005). No association was found with mRNFL thickness. High overall level of self-reported PA was associated with a modestly higher IOP of +0.08 mmHg (P = 0.01), but this was not replicated in the accelerometry data. No associations were modified by a glaucoma PRS, and MR analyses did not support a causal relationship between PA and any glaucoma-related outcome. CONCLUSIONS: Higher overall PA level and greater time spent in moderate and vigorous PA were not associated with glaucoma status but were associated with thicker mGCIPL. Associations with IOP were modest and inconsistent. Despite the well-documented acute reduction in IOP after PA, we found no evidence that high levels of habitual PA are associated with glaucoma status or IOP in the general population. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Unsupervised representation learning improves genomic discovery and risk prediction for respiratory and circulatory functions and diseases.

High-dimensional clinical data are becoming more accessible in biobank-scale datasets. However, effectively utilizing high-dimensional clinical data for genetic discovery remains challenging. Here we introduce a general deep learning-based framework, REpresentation learning for Genetic discovery on Low-dimensional Embeddings (REGLE), for discovering associations between genetic variants and high-dimensional clinical data. REGLE uses convolutional variational autoencoders to compute a non-linear, low-dimensional, disentangled embedding of the data with highly heritable individual components. REGLE can incorporate expert-defined or clinical features and provides a framework to create accurate disease-specific polygenic risk scores (PRS) in datasets which have minimal expert phenotyping. We apply REGLE to both respiratory and circulatory systems: spirograms which measure lung function and photoplethysmograms (PPG) which measure blood volume changes. Genome-wide association studies on REGLE embeddings identify more genome-wide significant loci than existing methods and replicate known loci for both spirograms and PPG, demonstrating the generality of the framework. Furthermore, these embeddings are associated with overall survival. Finally, we construct a set of PRSs that improve predictive performance of asthma, chronic obstructive pulmonary disease, hypertension, and systolic blood pressure in multiple biobanks. Thus, REGLE embeddings can quantify clinically relevant features that are not currently captured in a standardized or automated way.

Association of Systemic Medication Use with Glaucoma and Intraocular Pressure: The European Eye Epidemiology Consortium.

PURPOSE: To investigate the association of commonly used systemic medications with glaucoma and intraocular pressure (IOP) in the European population. DESIGN: Meta-analysis of 11 population-based cohort studies of the European Eye Epidemiology Consortium. PARTICIPANTS: The glaucoma analyses included 143 240 participants and the IOP analyses included 47 177 participants. METHODS: We examined associations of 4 categories of systemic medications-antihypertensive medications (ß-blockers, diuretics, calcium channel blockers [CCBs], α-agonists, angiotensin-converting enzyme inhibitors, and angiotensin II receptor blockers), lipid-lowering medications, antidepressants, and antidiabetic medications-with glaucoma prevalence and IOP. Glaucoma ascertainment and IOP measurement method were according to individual study protocols. Results of multivariable regression analyses of each study were pooled using random effects meta-analyses. Associations with antidiabetic medications were examined in participants with diabetes only. MAIN OUTCOME MEASURES: Glaucoma prevalence and IOP. RESULTS: In the meta-analyses of our maximally adjusted multivariable models, use of CCBs was associated with a higher prevalence of glaucoma (odds ratio [OR], 1.23; 95% confidence interval [CI], 1.08 to 1.39). This association was stronger for monotherapy of CCBs with direct cardiac effects (OR, 1.96; 95% CI, 1.23 to 3.12). No other antihypertensive medications, lipid-lowering medications, antidepressants, or antidiabetic medications were associated with glaucoma. Use of systemic ß-blockers was associated with a lower IOP (ß coefficient, -0.33 mmHg; 95% CI, -0.57 to -0.08 mmHg). Monotherapy of both selective systemic ß-blockers (ß coefficient, -0.45 mmHg; 95% CI -0.74 to -0.16 mmHg) and nonselective systemic ß-blockers (ß coefficient, -0.54 mmHg; 95% CI, -0.94 to -0.15 mmHg) was associated with lower IOP. A suggestive association was found between use of high-ceiling diuretics and lower IOP (ß coefficient, -0.30 mmHg; 95% CI, -0.47 to -0.14 mmHg) but not when used as monotherapy. No other antihypertensive medications, lipid-lowering medications, antidepressants, or antidiabetic medications were associated with IOP. CONCLUSIONS: We identified a potentially harmful association between use of CCBs and glaucoma prevalence. Additionally, we observed and quantified the association of lower IOP with systemic ß-blocker use. Both findings potentially are important, given that patients with glaucoma frequently use systemic antihypertensive medications. Determining causality of the CCB association should be a research priority. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Rare variant analyses across multiethnic cohorts identify novel genes for refractive error.

Refractive error, measured here as mean spherical equivalent (SER), is a complex eye condition caused by both genetic and environmental factors. Individuals with strong positive or negative values of SER require spectacles or other approaches for vision correction. Common genetic risk factors have been identified by genome-wide association studies (GWAS), but a great part of the refractive error heritability is still missing. Some of this heritability may be explained by rare variants (minor allele frequency [MAF] ≤ 0.01.). We performed multiple gene-based association tests of mean Spherical Equivalent with rare variants in exome array data from the Consortium for Refractive Error and Myopia (CREAM). The dataset consisted of over 27,000 total subjects from five cohorts of Indo-European and Eastern Asian ethnicity. We identified 129 unique genes associated with refractive error, many of which were replicated in multiple cohorts. Our best novel candidates included the retina expressed PDCD6IP, the circadian rhythm gene PER3, and P4HTM, which affects eye morphology. Future work will include functional studies and validation. Identification of genes contributing to refractive error and future understanding of their function may lead to better treatment and prevention of refractive errors, which themselves are important risk factors for various blinding conditions.

The Association of Alcohol Consumption with Glaucoma and Related Traits: Findings from the UK Biobank.

PURPOSE: To examine the associations of alcohol consumption with glaucoma and related traits, to assess whether a genetic predisposition to glaucoma modified these associations, and to perform Mendelian randomization (MR) experiments to probe causal effects. DESIGN: Cross-sectional observational and gene-environment interaction analyses in the UK Biobank. Two-sample MR experiments using summary statistics from large genetic consortia. PARTICIPANTS: UK Biobank participants with data on intraocular pressure (IOP) (n = 109 097), OCT-derived macular inner retinal layer thickness measures (n = 46 236) and glaucoma status (n = 173 407). METHODS: Participants were categorized according to self-reported drinking behaviors. Quantitative estimates of alcohol intake were derived from touchscreen questionnaires and food composition tables. We performed a 2-step analysis, first comparing categories of alcohol consumption (never, infrequent, regular, and former drinkers) before assessing for a dose-response effect in regular drinkers only. Multivariable linear, logistic, and restricted cubic spline regression, adjusted for key sociodemographic, medical, anthropometric, and lifestyle factors, were used to examine associations. We assessed whether any association was modified by a multitrait glaucoma polygenic risk score. The inverse-variance weighted method was used for the main MR analyses. MAIN OUTCOME MEASURES: Intraocular pressure, macular retinal nerve fiber layer (mRNFL) thickness, macular ganglion cell-inner plexiform layer (mGCIPL) thickness, and prevalent glaucoma. RESULTS: Compared with infrequent drinkers, regular drinkers had higher IOP (+0.17 mmHg; P < 0.001) and thinner mGCIPL (-0.17 µm; P = 0.049), whereas former drinkers had a higher prevalence of glaucoma (odds ratio, 1.53; P = 0.002). In regular drinkers, alcohol intake was adversely associated with all outcomes in a dose-dependent manner (all P < 0.001). Restricted cubic spline regression analyses suggested nonlinear associations, with apparent threshold effects at approximately 50 g (∼6 UK or 4 US alcoholic units)/week for mRNFL and mGCIPL thickness. Significantly stronger alcohol-IOP associations were observed in participants at higher genetic susceptibility to glaucoma (Pinteraction < 0.001). Mendelian randomization analyses provided evidence for a causal association with mGCIPL thickness. CONCLUSIONS: Alcohol intake was consistently and adversely associated with glaucoma and related traits, and at levels below current United Kingdom (< 112 g/week) and United States (women, < 98 g/week; men, < 196 g/week) guidelines. Although we cannot infer causality definitively, these results will be of interest to people with or at risk of glaucoma and their advising physicians. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.

Body Fat Percentage and the Long-term Risk of Fractures. The EPIC-Norfolk Prospective Population Cohort Study.

BACKGROUND: This cohort study aimed to determine the association between body fat percentage (BF%), incident fractures and calcaneal broadband ultrasound attenuation (BUA). METHODS: Participants were drawn from the EPIC-Norfolk Prospective Population Cohort Study (median follow-up = 16.4 years). Cox models analysed the relationship between BF% and incident fractures (all and hip). Linear and restricted cubic spline (RCS) regressions modelled the relationship between BF% and BUA. RESULTS: 14,129 participants (56.2 % women) were included. There were 1283 and 537 incident all and hip fractures respectively. The participants had a mean (standard deviation) age of 61.5 (9.0) years for women and 62.9 (9.0) years for men. Amongst men, BF% was not associated with incident all fractures. While BF% < 23 % (median) was not associated with hip fractures, BF% > 23 % was associated with increased risk of hip fractures by up to 50 % (hazard ratio (95 % confidence interval) = 1.49 (1.06-2.12)). In women, BF% < 39 % (median) was associated with up to 32 % higher risk of all fractures (1.32 (1.13-1.44)), while BF% > 35 % was not associated with this outcome. Higher BF% was associated with lower risk of incident hip fractures in women. Higher BF% was associated with higher BUA amongst women. Higher BF% up to ~23 % was associated with higher BUA amongst men. CONCLUSIONS: Higher BF% is associated with lower risk of fractures in women. While there was no association between BF% and all fractures in men, increasing BF% >23 % was associated with higher risk of hip fractures in men. This appears to be independent of estimated bone mineral density. Fracture prevention efforts need to consider wider physical, clinical, and environmental factors.

Food Processing and Risk of Crohn's Disease and Ulcerative Colitis: A European Prospective Cohort Study.

BACKGROUND & AIMS: Industrial foods have been associated with increased risks of several chronic conditions. We investigated the relationship between the degree of food processing and risks of Crohn's disease (CD) and ulcerative colitis (UC) in the European Prospective Investigation into Cancer and Nutrition cohort. METHODS: Analyses included 413,590 participants (68.6% women; mean baseline age, 51.7 y) from 8 European countries. Dietary data were collected at baseline from validated country-specific dietary questionnaires. Associations between proportions of unprocessed/minimally processed and ultraprocessed food intake and CD and UC risks were estimated using Cox models to obtain hazard ratios (HRs) and 95% CIs. Models were stratified by center, age, and sex, and adjusted for smoking status, body mass index, physical activity, energy intake, educational level, and alcohol consumption. RESULTS: During a mean follow-up period of 13.2 years, 179 incident cases of CD and 431 incident cases of UC were identified. The risk of CD was lower in people consuming high proportions of unprocessed/minimally processed foods (adjusted HR for the highest vs lowest quartile: 0.57; 95% CI, 0.35-0.93; P trend < .01), particularly fruits and vegetables (adjusted HRs, 0.54; 95% CI, 0.34-0.87 and 0.55; 95% CI, 0.34-0.91, respectively). There was no association between unprocessed/minimally processed food intake and the risk of UC. No association was detected between ultraprocessed food consumption and CD or UC risks. CONCLUSIONS: In the European Prospective Investigation into Cancer and Nutrition cohort, consumption of unprocessed/minimally processed foods was associated with a lower risk of CD. No association between UC risk and food processing was found.

Association of lipid-lowering drugs and antidiabetic drugs with age-related macular degeneration: a meta-analysis in Europeans.

BACKGROUND/AIMS: To investigate the association of commonly used systemic medications with prevalent age-related macular degeneration (AMD) in the general population. METHODS: We included 38 694 adults from 14 population-based and hospital-based studies from the European Eye Epidemiology consortium. We examined associations between the use of systemic medications and any prevalent AMD as well as any late AMD using multivariable logistic regression modelling per study and pooled results using random effects meta-analysis. RESULTS: Between studies, mean age ranged from 61.5±7.1 to 82.6±3.8 years and prevalence ranged from 12.1% to 64.5% and from 0.5% to 35.5% for any and late AMD, respectively. In the meta-analysis of fully adjusted multivariable models, lipid-lowering drugs (LLD) and antidiabetic drugs were associated with lower prevalent any AMD (OR 0.85, 95% CI=0.79 to 0.91 and OR 0.78, 95% CI=0.66 to 0.91). We found no association with late AMD or with any other medication. CONCLUSION: Our study indicates a potential beneficial effect of LLD and antidiabetic drug use on prevalence of AMD across multiple European cohorts. Our findings support the importance of metabolic processes in the multifactorial aetiology of AMD.

Artificial intelligence-enabled retinal vasculometry for prediction of circulatory mortality, myocardial infarction and stroke.

AIMS: We examine whether inclusion of artificial intelligence (AI)-enabled retinal vasculometry (RV) improves existing risk algorithms for incident stroke, myocardial infarction (MI) and circulatory mortality. METHODS: AI-enabled retinal vessel image analysis processed images from 88 052 UK Biobank (UKB) participants (aged 40-69 years at image capture) and 7411 European Prospective Investigation into Cancer (EPIC)-Norfolk participants (aged 48-92). Retinal arteriolar and venular width, tortuosity and area were extracted. Prediction models were developed in UKB using multivariable Cox proportional hazards regression for circulatory mortality, incident stroke and MI, and externally validated in EPIC-Norfolk. Model performance was assessed using optimism adjusted calibration, C-statistics and R2 statistics. Performance of Framingham risk scores (FRS) for incident stroke and incident MI, with addition of RV to FRS, were compared with a simpler model based on RV, age, smoking status and medical history (antihypertensive/cholesterol lowering medication, diabetes, prevalent stroke/MI). RESULTS: UKB prognostic models were developed on 65 144 participants (mean age 56.8; median follow-up 7.7 years) and validated in 5862 EPIC-Norfolk participants (67.6, 9.1 years, respectively). Prediction models for circulatory mortality in men and women had optimism adjusted C-statistics and R2 statistics between 0.75-0.77 and 0.33-0.44, respectively. For incident stroke and MI, addition of RV to FRS did not improve model performance in either cohort. However, the simpler RV model performed equally or better than FRS. CONCLUSION: RV offers an alternative predictive biomarker to traditional risk-scores for vascular health, without the need for blood sampling or blood pressure measurement. Further work is needed to examine RV in population screening to triage individuals at high-risk.