Few metrics exist to describe phenotypic diversity within ophthalmic imaging datasets, with researchers often using ethnicity as a surrogate marker for biological variability. 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) and reproduced in a Tanzanian, an Australian, and a Chinese dataset. 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 eight 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. RPS decouples traditional demographic variables 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 .
\n \n\n \n \nPURPOSE: Periodontitis, a ubiquitous severe gum disease affecting the teeth and surrounding alveolar bone, can heighten systemic inflammation. We investigated the association between very severe periodontitis and early biomarkers of age-related macular degeneration (AMD), in individuals with no eye disease. DESIGN: Cross-sectional analysis of the prospective community-based cohort United Kingdom (UK) Biobank. PARTICIPANTS: Sixty-seven thousand three hundred eleven UK residents aged 40 to 70 years recruited between 2006 and 2010 underwent retinal imaging. METHODS: Macular-centered OCT images acquired at the baseline visit were segmented for retinal sublayer thicknesses. Very severe periodontitis was ascertained through a touchscreen questionnaire. Linear mixed effects regression modeled the association between very severe periodontitis and retinal sublayer thicknesses, adjusting for age, sex, ethnicity, socioeconomic status, alcohol consumption, smoking status, diabetes mellitus, hypertension, refractive error, and previous cataract surgery. MAIN OUTCOME MEASURES: Photoreceptor layer (PRL) and retinal pigment epithelium-Bruch's membrane (RPE-BM) thicknesses. RESULTS: Among 36 897 participants included in the analysis, 1571 (4.3%) reported very severe periodontitis. Affected individuals were older, lived in areas of greater socioeconomic deprivation, and were more likely to be hypertensive, diabetic, and current smokers (all P < 0.001). On average, those with very severe periodontitis were hyperopic (0.05 \u00b1 2.27 diopters) while those unaffected were myopic (-0.29 \u00b1 2.40 diopters, P < 0.001). Following adjusted analysis, very severe periodontitis was associated with thinner PRL (-0.55 \u03bcm, 95% confidence interval [CI],\u00a0-0.97 to\u00a0-0.12; P\u00a0= 0.022) but there was no difference in RPE-BM thickness (0.00 \u03bcm, 95% CI,\u00a0-0.12 to 0.13; P\u00a0= 0.97). The association between PRL thickness and very severe periodontitis was modified by age (P < 0.001). Stratifying individuals by age, thinner PRL was seen among those aged 60 to 69 years with disease (-1.19 \u03bcm, 95% CI,\u00a0-1.85 to\u00a0-0.53; P < 0.001) but not among those aged < 60 years. CONCLUSIONS: Among those with no known eye disease, very severe periodontitis is statistically associated with a thinner PRL, consistent with incipient AMD. Optimizing oral hygiene may hold additional relevance for people at risk of degenerative retinal disease. FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
\n \n\n \n \nMOTIVATION: Genome-wide association studies (GWAS) have been remarkably successful in identifying associations between genetic variants and imaging-derived phenotypes. To date, the main focus of these analyses has been on established, clinically-used imaging features. We sought to investigate if deep learning approaches can detect more nuanced patterns of image variability. RESULTS: We used an autoencoder to represent retinal optical coherence tomography (OCT) images from 31\u2002135 UK Biobank participants. For each subject, we obtained a 64-dimensional vector representing features of retinal structure. GWAS of these autoencoder-derived imaging parameters identified 118 statistically significant loci; 41 of these associations were also significant in a replication study. These loci encompassed variants previously linked with retinal thickness measurements, ophthalmic disorders, and/or neurodegenerative conditions. Notably, the generated retinal phenotypes were found to contribute to predictive models for glaucoma and cardiovascular disorders. Overall, we demonstrate that self-supervised phenotyping of OCT images enhances the discoverability of genetic factors influencing retinal morphology and provides epidemiologically informative biomarkers. AVAILABILITY AND IMPLEMENTATION: Code and data links available at https://github.com/tf2/autoencoder-oct.
\n \n\n \n \nBACKGROUND: Amblyopia is a common neurodevelopmental condition and leading cause of childhood visual impairment. Given the known association between neurodevelopmental impairment and cardiometabolic dysfunction in later life, we investigated whether children with amblyopia have increased risk of cardiometabolic disorders in adult life. METHODS: This was a cross-sectional and longitudinal analysis of 126,399 United Kingdom Biobank cohort participants who underwent ocular examination. A subset of 67,321 of these received retinal imaging. Data analysis was conducted between November 1st 2021 and October 15th 2022. Our primary objective was to investigate the association between amblyopia and a number of components of metabolic syndrome and individual cardiometabolic diseases. Childhood amblyopia, dichotomised as resolved or persisting by adulthood, cardiometabolic disease and mortality were defined using ophthalmic assessment, self-reported, hospital admissions and death records. Morphological features of the optic nerve and retinal vasculature and sublayers were extracted from retinal photography and optical coherence tomography. Associations between amblyopia and cardiometabolic disorders as well as retinal markers were investigated in multivariable-adjusted regression models. FINDINGS: Individuals with persisting amblyopia (n\u00a0=\u00a02647) were more likely to be obese (adjusted odds ratio (95% confidence interval): 1.16 (1.05; 1.28)), hypertensive (1.25 (1.13; 1.38)) and diabetic (1.29 (1.04; 1.59)) than individuals without amblyopia (controls, (n\u00a0=\u00a018,481)). Amblyopia was also associated with an increased risk of myocardial infarction (adjusted hazard ratio: 1.38 (1.11; 1.72)) and death (1.36 (1.15; 1.60)). On retinal imaging, amblyopic eyes had significantly increased venular caliber (0.29 units (0.21; 0.36)), increased tortuosity (0.11 units (0.03; 0.19)), but lower fractal dimension (-0.23 units (-0.30;\u00a0-0.16)) and thinner ganglion cell-inner plexiform layer (mGC-IPL,\u00a0-2.85 microns (-3.47;\u00a0-2.22)). Unaffected fellow eyes of individuals with amblyopia also had significantly lower retinal fractal dimension (-0.08 units (-0.15;\u00a0-0.01)) and thinner mGC-IPL (-1.14 microns (-1.74;\u00a0-0.54)). Amblyopic eyes with a persisting visual deficit had smaller optic nerve disc height (-0.17 units (-0.25;\u00a0-0.08)) and width (-0.13 units (-0.21;\u00a0-0.04)) compared to control eyes. INTERPRETATION: Although further research is needed to understand the basis of the observed associations, healthcare professionals should be cognisant of greater cardiometabolic dysfunction in adults who had childhood amblyopia. Differences in retinal features in both the amblyopic eye and the unaffected non-amblyopic suggest generalised versus local processes. FUNDING: Medical Research Council (MR/T000953/1) and the National Institute for Health and Care Research.
\n \n\n \n \nBACKGROUND: Sensory changes due to aging or disease can impact brain tissue. This study aims to investigate the link between glaucoma, a leading cause of blindness, and alterations in brain connections. METHODS: We analyzed diffusion MRI measurements of white matter tissue in a large group, consisting of 905 glaucoma patients (aged 49-80) and 5292 healthy individuals (aged 45-80) from the UK Biobank. Confounds due to group differences were mitigated by matching a sub-sample of controls to glaucoma subjects. We compared classification of glaucoma using convolutional neural networks (CNNs) focusing on the optic radiations, which are the primary visual connection to the cortex, against those analyzing non-visual brain connections. As a control, we evaluated the performance of regularized linear regression models. RESULTS: We showed that CNNs using information from the optic radiations exhibited higher accuracy in classifying subjects with glaucoma when contrasted with CNNs relying on information from non-visual brain connections. Regularized linear regression models were also tested, and showed significantly weaker classification performance. Additionally, the CNN was unable to generalize to the classification of age-group or of age-related macular degeneration. CONCLUSIONS: Our findings indicate a distinct and potentially non-linear signature of glaucoma in the tissue properties of optic radiations. This study enhances our understanding of how glaucoma affects brain tissue and opens avenues for further research into how diseases that affect sensory input may also affect brain aging.
\n \n\n \n \nThe association between residential greenness and allostatic load (AL), a marker of composite physiological burden and predictor of chronic disease, remains understudied. This study comprised 212,600 UK Biobank participants recruited over 2007 and 2010 at the baseline. Residential greenness was modeled as the normalized difference vegetation index (NDVI) from high spatial resolution (0.50 m) color infrared imagery and measured within a 0.5 km radial catchment. AL was measured as a composite index from 13 biomarkers comprising three physiological systems (metabolic, cardiovascular, and inflammatory systems) and two organ systems (liver and kidney). Multilevel mixed-effects generalized linear models with a random intercept for UK Biobank assessment centers were employed to examine the association between residential greenness and AL. Each interquartile range (IQR = 0.24) increment in NDVI greenness was associated with lower AL (beta (\u03b2) = -0.28, 95% confidence interval (CI) = -0.55, -0.01). Consistently, relative to the lowest NDVI greenness quintile, participants in the highest quintile had lower AL (\u03b2 = -0.64, 95% CI = -1.02, -0.26). The proportion of the association between greenness and AL mediated by the physical activity was 3.2%. In conclusion, residential greenness was protectively associated with AL, a composite marker of wear and tear and general health.
\n \n\n \n \nBACKGROUND: Randomised trials of vitamin D supplementation for cardiovascular disease and all-cause mortality have generally reported null findings. However, generalisability of results to individuals with low vitamin D status is unclear. We aimed to characterise dose-response relationships between 25-hydroxyvitamin D (25[OH]D) concentrations and risk of coronary heart disease, stroke, and all-cause mortality in observational and Mendelian randomisation frameworks. METHODS: Observational analyses were undertaken using data from 33 prospective studies comprising 500\u2008962 individuals with no known history of coronary heart disease or stroke at baseline. Mendelian randomisation analyses were performed in four population-based cohort studies (UK Biobank, EPIC-CVD, and two Copenhagen population-based studies) comprising 386\u2008406 middle-aged individuals of European ancestries, including 33\u2008546 people who developed coronary heart disease, 18\u2008166 people who had a stroke, and 27\u2008885 people who died. Primary outcomes were coronary heart disease, defined as fatal ischaemic heart disease (International Classification of Diseases 10th revision code I20-I25) or non-fatal myocardial infarction (I21-I23); stroke, defined as any cerebrovascular disease (I60-I69); and all-cause mortality. FINDINGS: Observational analyses suggested inverse associations between incident coronary heart disease, stroke, and all-cause mortality outcomes with 25(OH)D concentration at low 25(OH)D concentrations. In population-wide genetic analyses, there were no associations of genetically predicted 25(OH)D with coronary heart disease (odds ratio [OR] per 10 nmol/L higher genetically-predicted 25(OH)D concentration 0\u00b798, 95% CI 0\u00b795-1\u00b701), stroke (1\u00b701, [0\u00b797-1\u00b705]), or all-cause mortality (0\u00b799, 0\u00b795-1\u00b702). Null findings were also observed in genetic analyses for cause-specific mortality outcomes, and in stratified genetic analyses for all outcomes at all observed levels of 25(OH)D concentrations. INTERPRETATION: Stratified Mendelian randomisation analyses suggest a lack of causal relationship for 25(OH)D concentrations with both cardiovascular and mortality outcomes for individuals at all levels of 25(OH)D. Our findings suggest that substantial reductions in mortality and cardiovascular morbidity due to long-term low-dose vitamin D supplementation are unlikely even if targeted at individuals with low vitamin D status. FUNDING: British Heart Foundation, Medical Research Council, National Institute for Health Research, Health Data Research UK, Cancer Research UK, and International Agency for Research on Cancer.
\n \n\n \n \nAlthough dementia research has been dominated by Alzheimer's disease (AD), most dementia in older people is now recognised to be due to mixed pathologies, usually combining vascular and AD brain pathology. Vascular cognitive impairment (VCI), which encompasses vascular dementia (VaD) is the second most common type of dementia. Models of VCI have been delayed by limited understanding of the underlying aetiology and pathogenesis. This review by a multidisciplinary, diverse (in terms of sex, geography and career stage), cross-institute team provides a perspective on limitations to current VCI models and recommendations for improving translation and reproducibility. We discuss reproducibility, clinical features of VCI and corresponding assessments in models, human pathology, bioinformatics approaches, and data sharing. We offer recommendations for future research, particularly focusing on small vessel disease as a main underpinning disorder.
\n \n\n \n \nBACKGROUND: Early detection of \u03b2-amyloid (A\u03b2) accumulation, a major biomarker for Alzheimer's disease (AD), has become important. As fluid biomarkers, the accuracy of cerebrospinal fluid (CSF) A\u03b2 for predicting A\u03b2 deposition on positron emission tomography (PET) has been extensively studied, and the development of plasma A\u03b2 is beginning to receive increased attention recently. In the present study, we aimed to determine whether APOE genotypes, age, and cognitive status increase the predictive performance of plasma A\u03b2 and CSF A\u03b2 levels for A\u03b2 PET positivity. METHODS: We recruited 488 participants who underwent both plasma A\u03b2 and A\u03b2 PET studies (Cohort 1) and 217 participants who underwent both cerebrospinal fluid (CSF) A\u03b2 and A\u03b2 PET studies (Cohort 2). Plasma and CSF samples were analyzed using ABtest-MS, an antibody-free liquid chromatography-differential mobility spectrometry-triple quadrupole mass spectrometry method and INNOTEST enzyme-linked immunosorbent assay kits, respectively. To evaluate the predictive performance of plasma A\u03b2 and CSF A\u03b2, respectively, logistic regression and receiver operating characteristic analyses were performed. RESULTS: When predicting A\u03b2 PET status, both plasma A\u03b242/40 ratio and CSF A\u03b242 showed high accuracy (plasma A\u03b2 area under the curve (AUC) 0.814; CSF A\u03b2 AUC 0.848). In the plasma A\u03b2 models, the AUC values were higher than plasma A\u03b2 alone model, when the models were combined with either cognitive stage (p\u2009
\n \n\n \n \nPURPOSE: 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\u00a0=\u00a0109\u00a0097), OCT-derived macular inner retinal layer thickness measures (n\u00a0=\u00a046\u00a0236) and glaucoma status (n\u00a0=\u00a0173\u00a0407). 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\u00a0
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