Comment on: Association of eGFR with stages of diabetic retinopathy and age-related macular degeneration in Indian population
Year: 2024
Type: letter
Abstract: Dear Editor, Neelamegam et al.[1] recently conducted a comprehensive study investigating the correlation between estimated glomerular filtration rate (eGFR) and the progression of diabetic retinopathy (DR) as well as age-related macular degeneration (AMD) within an Indian population cohort. The study analyzed the relationship between renal function, as measured by eGFR, and the severity of these ocular conditions, revealing a significant association. Specifically, the research demonstrated that lower eGFR levels, indicative of compromised kidney function, are closely linked with advanced stages of DR and AMD. This association suggests that eGFR could serve as a potential biomarker for the early detection and monitoring of the progression of these retinal diseases. The findings highlight the critical need for integrated healthcare approaches that emphasize monitoring renal function alongside routine ophthalmic evaluations in patients with diabetes and older adults. Such an approach could enhance early intervention strategies and improve outcomes for individuals at risk of developing severe retinal complications. Furthermore, these results contribute to the growing body of evidence supporting the interrelation of systemic and ocular health, underscoring the importance of a multidisciplinary approach in managing chronic diseases such as diabetes and age-related conditions. While the study provides valuable insights into the potential link between renal function and retinal diseases, there are several aspects that warrant further discussion and clarification. First, the description provided in this study[1] raises concerns regarding the interpretation of the findings. The abstract of this study states, “Age and high blood urea level, diastolic blood pressure, mild and moderate DR were the risk factors associated with CKD.” While these associations are indeed noteworthy, it is crucial to recognize the limitations inherent in a cross-sectional study design. Such a design only captures data at a single point in time, making it impossible to establish temporal relationships or causality. Therefore, the statement that age, high blood urea level, diastolic blood pressure, and mild-to-moderate DR are definitive risk factors for CKD is premature and may be misleading. Risk factors, by definition, imply a temporal and causal relationship that cannot be confirmed without longitudinal data. To draw robust conclusions about the role of these variables as risk factors for CKD, it is essential to conduct further research using prospective or retrospective cohort studies. These types of studies would allow for the observation of the progression of CKD over time in relation to these variables, thereby providing stronger evidence to support or refute the claims made in this study. Such rigorous investigation is necessary to inform clinical practice and guide effective risk assessment and management strategies for patients at risk of CKD. Second, the statistical methods employed in this study[1] raise significant concerns regarding their appropriateness of the results. As outlined in Table 4 of the study,[1] the systolic blood pressure in the “No to mild CKD” group is reported as 128.2 ± 179.95 mmHg. The fact that the mean value of 128.2 is considerably smaller than the standard deviation of 179.95 suggests that the data are not normally distributed and are likely to exhibit a positive skew. This observation is further supported by the reported duration of diabetes, which is 4.88 ± 5.93 years in the “No to mild CKD” group and 6.29 ± 7.94 years in the “Moderate to severe CKD” group. In both instances, the standard deviations exceed the means, which again indicates that the data do not conform to a normal distribution. Given these non-normal distributions, the use of parametric tests, such as the t-test, for comparing group differences is inappropriate. Parametric tests assume normality in the data, and their application in non-normally distributed data can lead to misleading conclusions. In this case, the Mann-Whitney U test, a non-parametric alternative, would have been more suitable for analyzing differences between groups as it does not assume normality and is better equipped to handle skewed data distributions. While we acknowledge that the final conclusions of the study might remain largely unchanged, the application of appropriate statistical methods is critical for ensuring the robustness, reliability, and reproducibility of the findings. Proper statistical analysis not only strengthens the credibility of the study but also enhances its scientific integrity by providing more accurate and defensible results. Financial support and sponsorship: Nil. Conflicts of interest: There are no conflicts of interest.
Source: Indian Journal of Ophthalmology
Authors: Rui Yao, Tao Tian, Xiaoxi Yao
Language: English
Subfield: Ophthalmology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4405737026