RESUMO
Cupping of the optic nerve head, a highly heritable trait, is a hallmark of glaucomatous optic neuropathy. Two key parameters are vertical cup-to-disc ratio (VCDR) and vertical disc diameter (VDD). However, manual assessment often suffers from poor accuracy and is time intensive. Here, we show convolutional neural network models can accurately estimate VCDR and VDD for 282,100 images from both UK Biobank and an independent study (Canadian Longitudinal Study on Aging), enabling cross-ancestry epidemiological studies and new genetic discovery for these optic nerve head parameters. Using the AI approach, we perform a systematic comparison of the distribution of VCDR and VDD and compare these with intraocular pressure and glaucoma diagnoses across various genetically determined ancestries, which provides an explanation for the high rates of normal tension glaucoma in East Asia. We then used the large number of AI gradings to conduct a more powerful genome-wide association study (GWAS) of optic nerve head parameters. Using the AI-based gradings increased estimates of heritability by â¼50% for VCDR and VDD. Our GWAS identified more than 200 loci associated with both VCDR and VDD (double the number of loci from previous studies) and uncovered dozens of biological pathways; many of the loci we discovered also confer risk for glaucoma.
Assuntos
Inteligência Artificial , Glaucoma/genética , Disco Óptico/diagnóstico por imagem , Adulto , Idoso , Algoritmos , Feminino , Estudo de Associação Genômica Ampla , Glaucoma/diagnóstico , Glaucoma/patologia , Humanos , Processamento de Imagem Assistida por Computador , Padrões de Herança , Pressão Intraocular , Masculino , Pessoa de Meia-Idade , Rede Nervosa , Disco Óptico/patologia , Fotografação , Polimorfismo de Nucleotídeo Único , Fatores de RiscoRESUMO
BACKGROUND: Previous observational studies have suggested that coffee intake may be associated with a reduction in cancer risk. Mendelian randomization (MR) studies can help clarify whether the observed associations are likely to be causal. Here we evaluated whether coffee intake is associated with: (i) overall risk of being diagnosed with/dying from any cancer; and (ii) risk of individual cancers. METHODS: We identified 46 155 cases (of which 6998 were fatal) and 270 342 controls of White British ancestry from the UK Biobank cohort (UKB), based on ICD10 diagnoses. Individuals with benign tumours were excluded. Coffee intake was self-reported and recorded based on cup/day consumption. We conducted both observational and summary data MR analyses. RESULTS: There was no observational association between coffee intake and overall cancer risk [odds ratio (OR) per one cup/day increase = 0.99, 95% confidence interval (CI) 0.98, 1.00] or cancer death (OR = 1.01, 0.99, 1.03); the estimated OR from MR is 1.01 (0.94, 1.08) for overall cancer risk and 1.11 (0.95, 1.31) for cancer death. The relationship between coffee intake and individual cancer risks were consistent with a null effect, with most cancers showing little or no associations with coffee. Meta-analysis of our MR findings with publicly available summary data on various cancers do not support a strong causal relationship between coffee and risk of breast, ovarian, lung or prostate cancer, upon correction for multiple testing. CONCLUSIONS: Taken together, coffee intake is not associated with overall risk of being diagnosed with or dying from cancer in UKB. For individual cancers, our findings were not statistically inconsistent with earlier observational studies, although for these we were unable to rule out a small effect on specific types of cancer.