Risk Factors for Age-Related Macular Degeneration: Findings from the Andhra Pradesh Eye Disease Study in South India

Krishnaiah, Sannapaneni; Das, Taraprasad; Nirmalan, Praveen K; Nutheti, Rishita; Shamanna, B R; Rao, Gullapalli N; Thomas, Ravi

doi:10.1167/iovs.05-0853

Cited by 142 publications

(141 citation statements)

References 68 publications

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“…247 A south Indian study found a prevalence of AMD in its population similar to other developed countries. 96 A Japanese population study reported similar prevalence of ARM to the white population of the Blue Mountains eye study.…”

Section: Ethnicitymentioning

confidence: 80%

“…95 Cigar smoking in India has also been linked with a higher risk for developing AMD (OR 3.29). 96 A study of Latino subjects also demonstrated an association between smoking and AMD in the Hispanic population (OR 2.4). 97 The effects of passive smoking on AMD risk have been examined in a UK study comparing 435 people with end-stage AMD with 280 healthy controls.…”

Section: -86mentioning

confidence: 94%

“…It is well reported that increasing age is strongly linked with a higher risk of developing ARMD 92,96,128,204,[215][216][217][218][219][220][221] and certain genes have been recognised for their association with disease development, but there are other non-modifiable risk factors for developing the disease that are inconsistent in the literature.…”

Section: Non-modifiable Risk Factorsmentioning

confidence: 99%

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Risk Factors for Age-related Macular Degeneration

Berrow¹,

Bartlett²,

Eperjesi³

et al. 2011

European Ophthalmic Review

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Age-related macular disease (ARMD) is a degenerative disease of the macula, most common over the age of 50 years.1 It is the leading cause of visual loss within western industrialised countries. [2][3][4] The number of blind registrations attributable to the disease increased by 30-40 % between 1950 and 1990 3 and the number of cases each year is continuing to rise 4-6 as these populations have an increasing longevity. Definition of Age-related Macular DiseaseThe international age-related maculopathy group has defined an international classification system for quantifying and defining the different subgroups of ARMD in an attempt to permit easier comparison of research findings between groups.1 Age-related maculopathy (ARM) is a disorder of the macular area most apparent after age 50 and is characterised by the following:• Areas of drusen which are external to the neuroretina and retinal pigment epithelium (RPE). They are soft and distinct or soft and indistinct. Hard drusen are not characteristic of ARM. Drusen are discrete white-yellow spots containing abnormal extracellular lipoprotein deposits that accumulate between the RPE basal lamina and the inner collagenous layer of Bruch's membrane. 7• Hyperpigmentation in the outer retina or choroid with drusen.• Hypopigmentation of the RPE with drusen.This early stage of the condition may not affect vision, but can predispose patients to visual loss (see Figure 1).Later stages of the condition are classified as 'wet' or 'dry' age-related macular degeneration (AMD). These forms of the disease can occur with or without the involvement of new blood vessel growth. If new vessels are not involved (dry AMD), clinical presentation is a sharply defined round or oval area of hypopigmentation where choroidal vessels are more visible than the surrounding area, with a diameter greater than 175 μm 1 (see Figure 2). This is also known as geographic atrophy (GA).The term 'wet AMD', also known as disciform AMD, exudative AMD or neovascular AMD refers to the development of choroidal neovascularisation (see Figure 3) and has numerous manifestations, including:• choroidal neovascularisation (CNV);• RPE detachment(s);• subretinal or sub-RPE neovascular membrane(s);• deposition of scarring, glial tissue or fibrin-like material within the epiretinal, intraretinal, subretinal or sub-RPE layers;• subretinal haemorrhages (without other retinal vascular cause); and• hard exudates (formed from lipid) associated with the above manifestations (without other retinal vascular cause).This article uses the terms ARM, AMD and ARMD as per the international classification system described. Physiology of Age-related Macular DiseaseThe RPE rests on Bruch's membrane and separates the neural retina from the choriocapillaris. The RPE phagocytoses the outer segment discs of the photoreceptors and is a point of metabolite and waste exchange, which is considered crucial to retinal function. 8 The initial signs of ARMD are variations within and below the RPE, seen as alterations in the pigmentation of the ...

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Section: Ethnicitymentioning

confidence: 80%

Section: -86mentioning

confidence: 94%

Section: Non-modifiable Risk Factorsmentioning

confidence: 99%

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Risk Factors for Age-related Macular Degeneration

Berrow¹,

Bartlett²,

Eperjesi³

et al. 2011

European Ophthalmic Review

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“…While AMD has generally been a greater issue in developed countries, studies from India suggest similar prevalence (1.4-1.8%) for late AMD as the population continues to age in developing countries (10,11) . China, on the other hand, has a considerably lower prevalence as confirmed by the Beijing Eye Study (12) .…”

Section: Introductionmentioning

confidence: 99%

Risk factors of age-related macular degeneration in Argentina

Nano

Lansingh²,

Pighin³

et al. 2013

Arq. Bras. Oftalmol.

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Purposes: To assess the risk factors of age-related macular degeneration in Argentina using a case-control study. Methods: Surveys were used for subjects' antioxidant intake, age/gender, race, body mass index, hypertension, diabetes (and type of treatment), smoking, sunlight exposure, red meat consumption, fish consumption, presence of age-related macular degeneration and family history of age-related macular degeneration. Main effects models for logistic regression and ordinal logistic regression were used to analyze the results. Results: There were 175 cases and 175 controls with a mean age of 75.4 years and 75.5 years, respectively, of whom 236 (67.4%) were female. Of the cases with age-related macular degeneration, 159 (45.4%) had age-related macular degeneration in their left eyes, 154 (44.0%) in their right eyes, and 138 (39.4%) in both eyes. Of the cases with age-related macular degeneration in their left eyes, 47.8% had the dry type, 40.3% had the wet type, and the type was unknown for 11.9%. The comparable figures for right eyes were: 51.9%, 34.4%, and 13.7%, respectively. The main effects model was dominated by higher sunlight exposure (OR [odds ratio]: 3.3) and a family history of age-related macular degeneration (OR: 4.3). Other factors included hypertension (OR: 2.1), smoking (OR: 2.2), and being of the Mestizo race, which lowered the risk of age-related macular degeneration (OR: 0.40). Red meat/fish consumption, body mass index, and iris color did not have an effect. Higher age was associated with progression to more severe age-related macular degeneration. Conclusion: Sunlight exposure, family history of age-related macular degenera tion, and an older age were the significant risk factors. There may be other variables, as the risk was not explained very well by the existing factors. A larger sample may produce different and better results.

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“…2 To the best of our knowledge, ANN models have not been established till date for predictions in the areas of ophthalmology. Therefore, the specific purpose of the present report is to train and test the ANN model including developing and validating LR risk score on persons aged 40 to 102 years in a total population (n=3723) and sub-populations drawn using random under sampling technique (n=213) and combination of random under sampling and random over sampling technique (n=1420) for the accurate prediction of AMD.…”

Section: Introductionmentioning

confidence: 99%

Modeling the risk of age-related macular degeneration and its predictive comparisons in a population in South India

Surampudi

Keeffe

2015

Int J Community Med Public Health

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INTRODUCTIONAge-related Macular Degeneration (AMD) is one of the leading causes of irreversible vision loss among the elderly and is becoming a major public health problem in the developing world due to an increased size of the older population resulting from increased life expectancy. An estimated 25 to 30 million people around the world are believed to have been affected by AMD, and this number is expected to triple within 25 years. The etiology of AMD is not fully understood but is assumed to be of multi-factorial origin. Modeling the risks of AMD using the new approach is one way of finding the nonmodifiable and modifiable risk factors that suggests ways of prevention strategies for delaying the onset of AMD or early diagnosis of AMD, thereby preventing the further deterioration of the visual loss due to AMD.Many techniques have been used to derive clinical algorithms to infer the presence or severity of disease. The accuracy of a statistical method is finally reflected by its ability to predict outcome from training as well as test samples. Modeling a validated risk score based on a set of risk factors that cause AMD in the population is necessary for several reasons. The validated risk score yields a threshold score which can potentially identify ABSTRACT Background: Objective of current study was to develop and cross-validate the prediction models for Age-related Macular Degeneration (AMD) by using Logistic Regression (LR) and Artificial Neural Networks (ANN). Methods: A population based cross-sectional epidemiologic study. The data (n=3723) were analyzed on participants aged ≥40 years in Andhra Pradesh, South India. Sub-population data from this sample was drawn by using random under sampling and random over sampling techniques to derive a risk score from the LR model. The models were compared for their predictive abilities by an Area under the Receiver Operating Characteristic Curve (AUROC). Results: The LR risk score was built with a score ranging from 0 to 60 for a sub-population dataset (n=213). A cutoff score of ≥30 had a sensitivity of 79% and a specificity of 69%. The predictive performance of ANN and LR was statistically equivalent (76% vs. 78%; P = 0.624). Both the models were stable and consistently obtained the same predictive accuracies in a 30-fold split-sample cross validation. Conclusions:The sensitivity analysis of the ANN model indicated the relative importance of prioritizing modifiable risk factors for AMD in order to base preventive interventions to reduce the impact of the modifiable factors on AMD.

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Risk Factors for Age-Related Macular Degeneration: Findings from the Andhra Pradesh Eye Disease Study in South India

Abstract: The prevalence of AMD in this south Indian population is similar to those reported in other developed countries. Abstinence from smoking may reduce the risk of AMD in this population.

Cited by 142 publications

References 68 publications

Risk Factors for Age-related Macular Degeneration

Risk Factors for Age-related Macular Degeneration

Risk factors of age-related macular degeneration in Argentina

Modeling the risk of age-related macular degeneration and its predictive comparisons in a population in South India

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