Anglia Ruskin University
GB
Researchers
Public research profiles associated with Anglia Ruskin University.
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A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010
Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010
Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010
Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study
BACKGROUND: Many causes of vision impairment can be prevented or treated. With an ageing global population, the demands for eye health services are increasing. We estimated the prevalence and relative contribution of avoidable causes of blindness and vision impairment globally from 1990 to 2020. We aimed to compare the results with the World Health Assembly Global Action Plan (WHA GAP) target of a 25% global reduction from 2010 to 2019 in avoidable vision impairment, defined as cataract and undercorrected refractive error. METHODS: We did a systematic review and meta-analysis of population-based surveys of eye disease from January, 1980, to October, 2018. We fitted hierarchical models to estimate prevalence (with 95% uncertainty intervals [UIs]) of moderate and severe vision impairment (MSVI; presenting visual acuity from
Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990–2013: quantifying the epidemiological transition
The Lancet Global Health Commission on Global Eye Health: vision beyond 2020
There is extensive evidence showing that improving eye health contributes directly and indirectly to achieving many Sustainable Development Goals, including reducing poverty and improving work productivity, general and mental health, and education and equity. Improving eye health is a practical and cost-effective way of unlocking human potential. Eye health needs to be reframed as an enabling, cross-cutting issue within the sustainable development framework.
Trends in prevalence of blindness and distance and near vision impairment over 30 years: an analysis for the Global Burden of Disease Study
Background To contribute to the WHO initiative, VISION 2020: The Right to Sight, an assessment of global vision impairment in 2020 and temporal change is needed. We aimed to extensively update estimates of global vision loss burden, presenting estimates for 2020, temporal change over three decades between 1990–2020, and forecasts for 2050. Methods We did a systematic review and meta-analysis of population-based surveys of eye disease from January, 1980, to October, 2018. Only studies with samples representative of the population and with clearly defined visual acuity testing protocols were included. We fitted hierarchical models to estimate 2020 prevalence (with 95% uncertainty intervals [UIs]) of mild vision impairment (presenting visual acuity ≥6/18 and
Gene-Environment Interactions and Epigenetic Regulation in Autism Etiology through Multi-Omics Integration and Computational Biology Approaches
Autism Spectrum Disorder (ASD) is a multifactorial neurodevelopmental condition characterized by substantial genetic heterogeneity and complex environmental influences. Emerging evidence suggests that gene-environment interactions, mediated through dynamic epigenetic mechanisms, play a critical role in modulating neurodevelopmental trajectories implicated in ASD. This review synthesizes current advances in understanding the etiological interplay between genetic variants, environmental exposures, and epigenetic regulation, with a focus on DNA methylation, histone modifications, and non-coding RNAs. We explore how these layers of molecular control intersect to dysregulate neurodevelopmental gene networks and contribute to ASD pathophysiology. Central to this investigation is the integration of multi-omics platforms— encompassing genomics, transcriptomics, epigenomics, proteomics, and metabolomics—supported by computational biology, machine learning, and systems-level modeling frameworks. These technologies facilitate the identification of molecular subtypes, predictive biomarkers, and regulatory circuits associated with ASD. Furthermore, we examine the translational implications of these findings in the context of precision medicine, including early diagnosis, patient stratification, and individualized therapeutic development. Despite the challenges of data heterogeneity, scalability, and interpretability, the integration of high-dimensional biological data holds transformative potential for elucidating ASD etiology and advancing targeted interventions.