Publications by authors named "S Nusinovici"
Objectives: To determine the association between telomere length (TL) and age-related macular degeneration (AMD) and examine the potential variations with sex and ethnicity.
Methods: Population-based, cross-sectional study. A total of 52,083 participants from the UK Biobank were included.
We investigated whether the effect of lipid-lowering drugs (LLDs) on age-related macular degeneration (AMD) differs according to the main complement genetic variants in Singapore Epidemiology of Eye Diseases (SEED) ( = 5,579) and UK Biobank studies ( = 445,727). The effect of LLD was determined for each stratum of 20 complement genetic variants. In SEED, 484 individuals developed AMD and 216 showed progression over 6 years.
View Article and Find Full Text PDFObjective: Our objectives were to identify correlation patterns between complement and lipid pathways using a multiomics data integration approach and to determine how these interconnections affect age-related macular degeneration (AMD).
Design: Nested case-control study.
Subjects And Controls: The analyses were performed in a subset of the Singapore Indian Eye Study.
Article Synopsis
- The study develops a new biological ageing marker called RetiPhenoAge using deep learning algorithms that analyze retinal images to predict phenotypic age, surpassing traditional chronological age evaluations.
- Researchers trained a convolutional neural network on retinal photographs from the UK Biobank to identify patterns linked to various health biomarkers and assess the marker’s effectiveness in predicting morbidity and mortality across three independent cohorts.
- The study also compares RetiPhenoAge with other ageing markers and investigates its relationship with systemic health conditions and genetic factors, employing various statistical models to evaluate risks associated with mortality and illness.
Objective: Our objective was to determine the effects of lipids and complement proteins on early and intermediate age-related macular degeneration (AMD) stages using machine learning models by integrating metabolomics and proteomic data.
Design: Nested case-control study.
Subjects And Controls: The analyses were performed in a subset of the Singapore Indian Chinese Cohort (SICC) Eye Study.