European Journal of Cardiovascular Medicine

India faces a substantial diabetes burden, with 77 million people affected, and forecasts suggest a 74% rise in diabetes cases in South East Asia. Approximately 10.3% of adults are affected by prediabetes, which carries the risk of developing into diabetes without intervention. Modifying lifestyle factors, such as losing weight and managing diet, can reverse prediabetes.

Diabetes is a collection of metabolic disorders marked by high levels of glucose in the blood due to issues with insulin secretion, insulin function, or both. Diabetes's persistent high blood sugar levels lead to lasting harm, impairment, and malfunction of various organs, particularly the eyes, kidneys, nerves, heart, and blood vessels [1], [2].

Various pathological mechanisms play a role in the development of diabetes. These factors encompass autoimmune destruction of the β-cells in the pancreas resulting in insulin deficiency, along with abnormalities that induce resistance to insulin. Diabetes is defined by reduced insulin effectiveness in tissues, resulting in irregularities in carbohydrate, fat, and protein metabolism. Inadequate insulin action can result from insufficient insulin secretion or decreased tissue sensitivity to insulin at different points in the complex pathways of hormone function. Insufficiency in insulin secretion and malfunctions in insulin function commonly occur together in a patient, making it difficult to determine which issue, if any, is the main reason for high blood sugar levels [3], [4], [5].

Diabetes can cause retinopathy, nephropathy, peripheral neuropathy, and autonomic neuropathy, leading to complications like “vision loss, renal failure, foot ulcers, amputations, Charcot joints, and symptoms affecting the gastrointestinal, genitourinary, cardiovascular systems, and sexual function. Diabetic patients have a higher likelihood of developing atherosclerotic cardiovascular, peripheral arterial and cerebrovascular diseases.” Individuals with diabetes frequently exhibit hypertension and lipoprotein metabolism abnormalities [6], [7].

There are two types of Diabetes of Disease:

Type 1 diabetes is an autoimmune disorder characterized by the immune system targeting and eliminating the beta cells in the pancreas responsible for producing insulin. This leads to an insufficiency of insulin, a hormone essential for controlling blood glucose levels. Usually diagnosed during childhood or adolescence, people with Type 1 diabetes need continuous insulin injections or an insulin pump to control their blood sugar levels. The precise ethology of Type 1 diabetes remains incompletely understood and is not avoidable.

Type 2 diabetes is a metabolic disorder marked by insulin resistance, causing the body's cells to not respond efficiently to insulin. At first, the pancreas increases insulin production to compensate, but eventually it may become unable to meet the increased demand. Type 2 diabetes is frequently linked to lifestyle factors like lack of physical activity, unhealthy eating habits, and being overweight. Although it can happen at any age, it is more prevalent in adults. Management includes lifestyle modifications, oral medications, and occasionally insulin treatment. Type 2 diabetes is preventable and strongly associated with lifestyle choices, unlike Type 1 diabetes [8], [9].

Utilizing ocular structural and functional markers can aid in the early detection of pre-diabetes and diabetes mellitus, prior to the manifestation of symptoms or complications, and in assessing the risk and seriousness of ocular conditions. Ocular structural markers are alterations in the anatomy, morphology, or biometry of the eye, including changes in the thickness, volume, or shape of the cornea, lens, retina, or optic nerve. Ocular functional markers are alterations in the physiology, biochemistry, or performance of the eye, including intraocular pressure, tear film composition, blood flow, and visual acuity. Various methods can be used to evaluate ocular structural and functional markers, including optical coherence tomography, fundus photography, tonometry, tear fluid analysis, and visual field testing [10], [11], [12].

AIMS AND OBJECTIVE

• Evaluate the prevalence of pre-diabetes within an ophthalmic outpatient setting.
• Categorize body fat mass values and examine the variations across normal, pre-diabetic, and diabetic groups.
• Examine and contrast changes in eye structure in individuals with pre-diabetes, as opposed to those who are healthy and those with diabetes. This involves assessing the thickness of the retinal nerve fiber layer, cornea, and corneal endothelial cell count.
• Evaluate alterations in eye function and compare them across healthy individuals, individuals with pre-diabetes, and individuals with diabetes. This includes evaluating accommodation, contrast sensitivity function, tear film condition, and intraocular pressure.
• Compare the parameters found in normal and diabetic subjects and suggest them as potential indicators for significant changes in the pre-diabetic group.

The study at Tertiary care centre aimed to examine ocular and systemic parameters in adults aged 25-45 years. The study utilized clear inclusion and exclusion criteria, along with a rigorous ethical approval process and thorough consent form procedures. The study utilized sophisticated measurement tools like the Omron Body Fat device and A1C Now+ point-of-care test by Bayer Healthcare to assess parameters such as body height, weight, waist circumference, total body fat, and HbA1c levels. Ocular parameters were thoroughly evaluated using advanced equipment including the Cochet Bonnet esthesiometer and Zeiss Optical Coherence Tomography. This assessment covered tear function, intraocular pressure, corneal sensitivity, Meibomian gland evaluation, endothelial cell density, corneal pachymetry, and retinal nerve fiber layer thickness.

Sample Size: 59

Criteria for inclusion

• Individuals aged 25-45 with no additional systemic or ocular conditions will be eligible for participation. Diabetic participants will be eligible for inclusion provided they do not have diabetic retinopathies.

Criteria for exclusion

• Exclusion criteria included subjects with other ocular morbidity and those with a history of previous ocular surgery.
• Additional exclusion criteria were employed for specific structural and functional tests. This was based on established confounding factors, such as decreased cerebrospinal fluid (CSF) and thinner nerve fiber layer thickness observed in individuals with high myopia. Therefore, individuals with severe myopia greater than -6.00D were not included in these tests.
• Subjects who had previously worn contact lenses were not included in the analysis of endothelial cell count, tear film assessment, and central corneal thickness data.
• Participants with glaucoma or a history of other eye diseases were not included in the study.
•  

Functional tests such as contrast sensitivity function, accommodation, and tear function tests were included in addition to structural tests like Meiboscore, endothelial cell density, corneal pachymetry, and retinal nerve fiber layer thickness. Statistical analysis utilized SPSS to compare mean values and standard deviations of systemic and ocular parameters. Participants were grouped based on HbA1c values, and a univariate linear correlation analysis was performed for specific data points.

The study showed a careful and precise method, following established standards, and using advanced equipment to measure both systemic and ocular parameters. The thorough methodology allowed for a detailed investigation of the specific age group, yielding valuable insights into the connection between eye and overall health in that population.

The above table discusses the Value of Mean and SD age, HbA1c, BMI, and BFM of 3 groups.

The test results showed a statistically significant association between increased body fat mass and prediabetes, with a p-value<0.001.

The above table discusses the Mean RNFL thickness in three groups.

The mean and standard deviation were computed using the IBM SPSS software for statistical analysis. The data distribution was assessed for normality, and it was found to be normally distributed based on Kolmogorov-Smirnov and Shapiro-Wilk values, both of which were greater than 0.05. An ANOVA test was used to compare the means of three groups within each segment. The results showed that the differences were not statistically significant, with a p-value greater than 0.05.

A normality test was performed on Central Corneal Thickness, indicating a non-normal distribution of data. Non-parametric independent samples Kruskal-Wallis tests were used to compare the variables. The difference in-group p-values was determined to be statistically significant. When comparing normal, diabetic, and prediabetic groups, the p-value was significant at <0.001. The Kruskal-Wallis test showed a non-significant p-value of 1.00 when comparing pachymetry values between the prediabetic and diabetic groups.

Endothelial cell density values were analyzed statistically using SPSS. Normality tests were performed, revealing a Gaussian distribution of the data. ANOVA was used to compare the means of endothelial cell density (ECD) among the three groups. The analysis showed a statistically significant difference in the data, with a p-value less than 0.001.

The statistical analysis was performed using IBM SPSS version 26.0. Following the evaluation of normal distribution, it was found that the data did not adhere to a normal distribution, prompting the use of a Kruskal-Wallis test. The Mann-Whitney U test was used to analyze differences between groups. The Chi-squared test of independence was used to compare proportions in the dataset.

The post hoc analysis revealed a significant age difference between the normal and prediabetes groups, while the differences in accommodation amplitude and add acceptance were also found to be significant.

The Contrast Sensitivity (CSF) and PSRT data were assessed for normal distribution, which showed a non-normal distribution. Non-parametric tests, such as the Kruskal-Wallis test and Mann-Whitney U test, were used to compare differences among the three study groups. Proportions were evaluated using a chi-squared test of independence, showing reduced proportions in the groups with prediabetes and diabetes in comparison to normal controls. Significant differences were observed in the CSF values among the three groups (p<0.001), as well as in the PSRT values (p<0.001). Intergroup comparisons consistently showed significant differences between any pair of groups.

Univariate analysis for independent samples was used to investigate the connection between HbA1c and CSF. The results showed a significant linear relationship between HbA1c and CSF (p<0.001), indicating that higher HbA1c levels were associated with reduced contrast sensitivity.

Tables 13 (one, two, three, 4) show the tear assessment test results, including the average values and standard deviations for all groups. The statistical analysis included all tear assessment data and involved tests to determine normality. Non-parametric tests were used due to the absence of a normal distribution in the data. The independent sample Kruskal-Wallis test showed a significant difference among groups in tear break-up time and Meiboscores, but not in tear prism height and ocular surface disease index scores. Intergroup differences were also noted in Meiboscores.

Variations in prediabetes prevalence among studies are attributed to discrepancies in the recommended thresholds for impaired fasting glucose and glycated hemoglobin by different organizations. HbA1c is regarded as a dependable test due to its consistency over time. Yip et al. and Sadikot et al. reported that the prevalence of prediabetes in the Asian population was 53.1% and 55.5%, respectively, according to ADA criteria. The present study indicates a nearly identical prevalence of prediabetes according to ADA criteria [13], [14].

Vanderwood et al. discovered that BMI has a low sensitivity of 68% and specificity when used to screen for prediabetes. Our study indicates body fat sensitivity of 82.9% and specificity of 17.6%, and BMI sensitivity of 41.06% with specificity of 75%. Dillon et al. found that an infrared breath test analyzer could detect prediabetes. However, our study proposes a more economical method using a body fat assessment tool, which is suitable for optometrists and primary care health personnel  [15], [16].

Fujiwara et al. [17] conducted a substantial population-based study on 2809 Japanese individuals, discovering a reduced circumpapillary retinal nerve fiber layer (RNFL) in individuals with diabetes and prediabetes. Our study found a thinner retinal nerve fiber layer (RNFL) in individuals with prediabetes and diabetes, but the difference was not statistically significant. Neriyanuri et al. [18] and Joltikov et al. [19] conducted studies that found similar results regarding retinal nerve fiber layer (RNFL) loss in diabetic individuals.

Xiao-Yang Luo and colleagues [20] researched central corneal thickness in a diverse Asian diabetic population, discovering a direct relationship between diabetes, high blood sugar levels, and increased central corneal thickness measurements. Our study also identified substantial variations in central corneal thickness among different groups.

The current research demonstrated a decrease in contrast sensitivity function (CSF) in individuals with prediabetes, in line with previous studies conducted by Safi et al. and other researchers [21]. Delayed photostress recovery time (PSRT) was noted in individuals with prediabetes and diabetes, consistent with research conducted by Zingirian et al.[22] and Khan et al.[23] regarding visual functional alterations in diabetes.

Studies by Spafford et al.,[24] Mäntyjärvi [25], found that the amplitude of accommodation was reduced in individuals with prediabetes and diabetes. Individuals with diabetes and prediabetes were willing to pay more for treatments for presbyopia.

Key study findings suggest incorporating opportunistic screening for prediabetes into regular eye exams, using non-invasive tests such as body fat measurement. The study recommends including extra evaluations, like waist-to-hip ratio, in regular eye exams to improve screening. Some structural changes were observed in individuals with prediabetes, such as decreased retinal nerve fiber layer thickness, endothelial cell count, and Meiboscores. However, these changes were not statistically significant and may warrant further investigation with a larger sample size.

Individuals with prediabetes showed significantly lower functional markers such as contrast sensitivity function and photo stress recovery test. The study suggests conducting additional research on contrast sensitivity function as a potential visual indicator for prediabetes, highlighting an inverse relationship with blood sugar levels. Delays in photo stress recovery test in prediabetes suggest early macular changes prior to diabetes diagnosis, emphasizing the importance of proactive screening. The study found that contrast sensitivity function and photo stress recovery test are useful indicators for prediabetes, while accommodation and higher additional acceptance may be risk factors. Structural markers consist of a thinner retinal nerve fiber layer, decreased endothelial cell counts, and lower Meiboscores.

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