Determinants of diabetic retinopathy in Ethiopia: A systematic review and meta-analysis

Abstract

Introduction Diabetic retinopathy (DR) is the primary retinal vascular complication of diabetes mellitus and a leading cause of visual impairment and blindness. It affects the global diabetic population. In Ethiopia, about one-fifth of diabetic patients were affected by DR, but there were inconsistent finding across studies about the determinants factors of DR. Therefore, we aimed to identify the risk factors for DR among diabetic patients. Methods We have accessed previous studies through an electronic web-based search strategy using PubMed, Google (Scholar), the Web of Science, and the Cochrane Library with a combination of search terms. The quality of each included article was assessed using the Newcastle Ottawa Assessment Scale. All statistical analyses were carried out using Stata version 14 software. The odds ratios of risk factors were pooled using a fixed-effect meta-analysis model. Heterogeneity was assessed using the Cochrane Q statistics and I-Square (I2). Furthermore, publication bias was detected based on the graphic asymmetry test of the funnel plot and/or Egger’s test (p< 0.05). Results The search strategy retrieved 1285 articles. After the removal of duplicate articles, 249 articles remained. Following further screening, about 18 articles were assessed for eligibility, of which three articles were excluded because of reporting without the outcome of interest, poor quality, and not full text. Finally, fifteen studies were reviewed for the final analysis. Co-morbid hypertension (HTN) (AOR 2.04, 95%CI: 1.07, 3.89), poor glycemic control (AOR = 4.36, 95%CI: 1.47, 12.90), and duration of diabetes illness (AOR = 3.83, 95%CI: 1.17, 12.55) were found to be confirmed associated factors of diabetic retinopathy. Conclusion In this study, co-morbid HTN, poor glycemic control, and longer duration of diabetes illness were found to be the determinant factors of DR. Aggressive treatment of co-morbid HTN and blood glucose, and regular eye screening should be implemented to reduce the occurrence of DR. Trial registration The review protocol was registered in the international prospective register of systematic reviews (PROSPERO) with registration number PROSPERO: CRD42023416724.

Diabetes-Related Microvascular Complications in Primary Health Care Settings in the West Bank, Palestine

“…The likelihood of individuals developing DR seems to increase with advancing age [62], particularly in cases where individuals have been living with diabetes for an extended duration [63]. Prolonged hyperglycemia has been observed to induce retinal vascular impairment, resulting in the development of several stages of diabetic retinopathy [64]. Furthermore, our study revealed that individuals aged 60 years or older exhibited a probability of having a diabetic foot that was more than double that of younger patients.…”

The causal effect of hypertension, intraocular pressure, and diabetic retinopathy: a Mendelian randomization study

“…Some RCTs have proved that good regular blood pressure control is advantageous for the emergence of DR. Higher blood pressure level may be associated with the occurrence and progress of DR; however, mildly elevated hypertension does not significantly impact the development of DR (23). Meta-analysis based on clinical trials has demonstrated that low blood pressure can effectively reduce the risk of microvascular and macrovascular complications in DM (24,25).…”

“…Some RCTs have proved that good regular blood pressure control is advantageous for the emergence of DR. Higher blood pressure level may be associated with the occurrence and progress of DR; however, mildly elevated hypertension does not significantly impact the development of DR ( 23 ). Meta-analysis based on clinical trials has demonstrated that low blood pressure can effectively reduce the risk of microvascular and macrovascular complications in DM ( 24 , 25 ). Recently, an increasing number of researchers have revealed the close relationship between hypertension and DR via deep machine learning, the construction of risk prediction models, and the phenotypic and genetic analysis of large sample banks such as the UK Biobank ( 26 – 28 ).…”