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Objective: To develop a deep learning algorithm (DLA) to detect diabetic kideny disease (DKD) from retinal photographs of patients with diabetes, and evaluate performance in multiethnic populations., Materials and Methods: We trained 3 models: (1) image-only; (2) risk factor (RF)-only multivariable logistic regression (LR) model adjusted for age, sex, ethnicity, diabetes duration, HbA1c, systolic blood pressure; (3) hybrid multivariable LR model combining RF data and standardized z-scores from image-only model. Data from Singapore Integrated Diabetic Retinopathy Program (SiDRP) were used to develop (6066 participants with diabetes, primary-care-based) and internally validate (5-fold cross-validation) the models. External testing on 2 independent datasets: (1) Singapore Epidemiology of Eye Diseases (SEED) study (1885 participants with diabetes, population-based); (2) Singapore Macroangiopathy and Microvascular Reactivity in Type 2 Diabetes (SMART2D) (439 participants with diabetes, cross-sectional) in Singapore. Supplementary external testing on 2 Caucasian cohorts: (3) Australian Eye and Heart Study (AHES) (460 participants with diabetes, cross-sectional) and (4) Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA) (265 participants with diabetes, cross-sectional)., Results: In SiDRP validation, area under the curve (AUC) was 0.826(95% CI 0.818-0.833) for image-only, 0.847(0.840-0.854) for RF-only, and 0.866(0.859-0.872) for hybrid. Estimates with SEED were 0.764(0.743-0.785) for image-only, 0.802(0.783-0.822) for RF-only, and 0.828(0.810-0.846) for hybrid. In SMART2D, AUC was 0.726(0.686-0.765) for image-only, 0.701(0.660-0.741) in RF-only, 0.761(0.724-0.797) for hybrid., Discussion and Conclusion: There is potential for DLA using retinal images as a screening adjunct for DKD among individuals with diabetes. This can value-add to existing DLA systems which diagnose diabetic retinopathy from retinal images, facilitating primary screening for DKD., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Purpose: gamma-Aminobutyric acid (GABA)-receptor genes are prime candidates for a role in seizure susceptibility. An association between the c.1465G-->A variant in the GABA(B) receptor 1 gene (GABBR1) and susceptibility to temporal lobe epilepsy (TLE) has been reported in an Italian cohort. We sought to replicate this association in an independent Australian cohort., Methods: The 234 patients with TLE and 164 healthy controls were genotyped for the c.1465G-->A variant, by using inclusion criteria identical to those of the first study., Results: The c.1465G-->A variant was found in one TLE patient and one control subject. Genotype and allele frequencies did not differ between groups., Conclusions: We did not replicate the reported associations between the c.1465G-->A variant and susceptibility to TLE. We suggest that the initial positive association may be due to undetected population stratification; the importance of genomic control is emphasized. Population-specific effects also may play a role, and we highlight the need to demonstrate an in vitro functional effect to give biologic meaning to any proposed association.