Language barrier and its relationship to diabetes and diabetic retinopathy

10.1186/1471-2458-12-781BMC Public Health121

Impact of Migration and Acculturation on Prevalence of Type 2 Diabetes and Related Eye Complications in Indians Living in a Newly Urbanised Society

Background: Health of migrants is a major public health challenge faced by governments and policy makers. Asian Indians are among the fastest growing migration groups across Asia and the world, but the impact of migration and acculturation on diabetes and diabetes-related eye complications among Indians living in urban Asia remains unclear. Methodologies/Principal Findings: We evaluated the influence of migration and acculturation (i.e., migration status and length of residence) on the prevalence of type-2 diabetes mellitus (T2DM) and diabetes-related eye complications (diabetic retinopathy (DR) and cataract), among first-generation (defined as participant born in India with both parents born in India, n = 781) and second-generation (participants born in Singapore with both parents born in India, n = 1,112) Indian immigrants from a population-based study of Adult Indians in Singapore. Diabetes was defined as HbA1c≥6.5%, use of diabetic medication or a physician diagnosis of diabetes. Retinal and lens photographs were graded for the presence of DR and cataract. Compared to first generation immigrants, second generation immigrants had a higher age- and gender-standardized prevalence of T2DM (34.4% versus 29.0%, p<0.001), and, in those with T2DM, higher age- and gender-standardized prevalence of DR (31.7% versus 24.8%, p<0.001), nuclear cataract (13.6% versus 11.6%, p<0.001), and posterior sub-capsular cataract (6.4% versus 4.6%, p<0.001). Among first generation migrants, longer length of residence was associated with significantly younger age of diagnosis of diabetes and greater likelihood of having T2DM and diabetes-related eye complications. Conclusion: Second generation immigrant Indians and longer length of residence are associated with higher prevalence of diabetes and diabetes-related complications (i.e., DR and cataract) among migrant Indians living in Singapore. These data highlight potential worldwide impacts of migration patterns on the risk and burden of diabetes

Keratoconus with Corneal ‘Pips’

Keratoconus is a common ectatic corneal disorder. There are several causes of corneal opacity in keratoconic patients. This case illustrates two different causes, in the same patient, in either eye, and diagnostic imaging characteristics on Fourier-domain optical coherence tomography (FD-OCT) that aided surgical management

Developing an impartial game by mathematical approach

In this study, we developed an impartial game namely Nim by using a mathematical approach. The game consists of multiple ways of movement for player winning the game. A total of five players participating in the game testing. The experiment shows that the proposed impartial game for learning process is easy to use

Prevalence of obesity, type-2 diabetes, diabetic retinopathy, and cataract in Indian Immigrants and local Malays living in Singapore.

<p>Asterisk indicates statistical significance between groups in age and gender adjusted regression model (p<0.05). DR = Diabetic retinopathy; VTDR = vision-threatening diabetic retinopathy; NC = nuclear cataract; CC = cortical cataract; PSC = posterior sub-capsular cataract. Prevalence data are age and gender standardized using the 2010 Singapore Indian population census.</p

Non-linear relationships of duration of residence with prevalence of type-2 diabetes and its related complications in the first-generation Indian immigrants.

<p>Each plot is derived from a multivariate generalized additive model. The solid lines represent fitted lowess curves. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034829#pone-0034829-g002" target="_blank">Figure 2A</a> shows the nonlinear relationship with BMI, after controlling for the influences of age, gender, systolic blood pressure (SBP), high-density lipoprotein (HDL), and low-density lipoprotein (LDL); <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034829#pone-0034829-g002" target="_blank">Figure 2B</a> shows the nonlinear relationship with prevalence of diabetes, after controlling for the influences of age, gender, BMI, SBP, HDL, LDL, triglycerides, education, income and housing type; <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034829#pone-0034829-g002" target="_blank">Figure 2C</a> shows the linear relationship with age at diagnosis of diabetes, after controlling for the influences of age, gender, BMI, SBP, hba1c level, education, income and housing type; <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034829#pone-0034829-g002" target="_blank">Figure 2D</a> shows the nonlinear relationship with prevalence of DR, after controlling the influences of age, gender, diabetic duration, hba1c level, SBP, education, income and housing type; <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034829#pone-0034829-g002" target="_blank">Figures 2E to 2G</a> show the nonlinear relationships with prevalence of nuclear cataract (NC), cortical cataract (CC), posterior sub-capsular cataract (PSC) after controlling the influences of age, gender, diabetic duration, hba1c level, education, income and housing type.</p

Associations of type-2 diabetes and diabetes-related complications with migration status.

<p>OR = odds ratio; 95%CI = 95% confidence interval; PSC = posterior sub-capsular cataract. Asterisk indicates statistical significance in multivariate model (p<0.05).</p>a<p>: Multivariate logistic model adjusted for age, gender, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), triglyceride, education, income, and housing type.</p>b<p>: Multivariate logistic model adjusted for age, gender, BMI, SBP, DBP, duration of diabetes, hba1c level, education, income, and housing type.</p>c<p>: Multivariate logistic model adjusted for age, gender, BMI, duration of diabetes, hba1c level, education, income, and housing type.</p