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Abstract

Purpose

The aim of this study was to analyze the incidence of retinal vein occlusion (RVO) in patients with and without diabetes in the population and compare the influencing factors.

Method

The community-based Kailuan Eye Study included 14,440 participants (9835 male, 4605 female) with a mean age of 54.0 ± 13.3 years (range, 20–110 years). They underwent a systemic and ophthalmologic examination. RVO were diagnosed on fundus photographs.

Result

By matching for age and gender, we included a total of 2767 patients each with diabetes and non-diabetes. The prevalence of RVO among patients with and without diabetes was 1.5% and 0.8%, respectively. The prevalence of RVO was higher in patients with diabetes than in patients without diabetes in all age groups. Multifactorial regression analysis showed that only fasting blood glucose levels were significantly different between patients with RVO with or without DM. The occurrence of RVO in the group with diabetes was mainly associated with higher fasting glucose and systolic blood pressure; in the group without diabetes, RVO was mainly associated with higher diastolic blood pressure, Body Mass Index, and lower low-density lipoprotein cholesterol levels.

Conclusion

We found that patients with diabetes have increased risks of RVO. In addition to blood pressure control, we recommend educating patients with diabetes about RVO, to prevent its subsequent occurrence.

Keywords

Retinal vein occlusion diabetes mellitus risk factors

Introduction

Retinal vein occlusion (RVO) stands as a distinctive vascular disorder affecting the retina’s microcirculation, often resulting in significant visual impairment. This condition arises from the occlusion of retinal veins, disrupting the delicate balance of blood flow within the retina. The pathophysiology of RVO is complex and multifactorial, involving intricate interactions between systemic and local factors. RVO has garnered attention as a leading cause of vision loss globally, with prevalence rates varying across different populations and age groups. The Beaver Dam Eye Study¹ reported an overall RVO prevalence of 0.5% in a predominantly Caucasian population aged 43–84 years, while studies in Asian populations have reported varying prevalence rates, underscoring the need for region-specific investigations.^2–4

Diabetes mellitus (DM), a chronic metabolic disorder characterized by hyperglycemia, has emerged as a significant risk factor for various microvascular complications, including RVO. The microangiopathy associated with diabetes leads to alterations in the retinal microvasculature, predisposing individuals to retinopathy and potentially contributing to the development of RVO. The prevalence of diabetes has been steadily increasing on a global scale, further emphasizing the need to explore its implications on ocular health. Studies have shown that individuals with diabetes are more prone to developing RVO compared to their non-diabetic counterparts.^5–7 While the association between diabetes and RVO is established, the nuanced differences in the occurrence and influencing factors of RVO in population with and without diabetes remain under explored.^7–9 Understanding these distinctions is crucial for tailoring preventive strategies and interventions based on the unique risk profiles of these subgroups.

Despite existing literature highlighting the association between diabetes and RVO, several gaps in knowledge persist. The specific systemic factors within the population with diabetes that contribute to RVO development, and how these factors differ from individuals without diabetes, remain areas of active investigation. Additionally, the role of glycemic control and other systemic parameters in modulating the risk of RVO requires further exploration. The Kailuan Eye Study, on which our research is based, provides a comprehensive platform for investigating the epidemiology of RVO in a diverse community-based cohort. By encompassing a broad age range and including a substantial number of both patients with and without diabetes, this study offers a unique opportunity to elucidate the intricate relationship between diabetes and RVO in a real-world setting. Our study aims to address these gaps by providing a detailed analysis of RVO incidence, focusing on the subgroups with and without diabetes. By identifying influencing factors, we seek to contribute to the evolving understanding of the complex interplay between diabetes and retinal vascular health, ultimately informing targeted interventions for the prevention and management of RVO in diverse populations.

Methods

The Kailuan Eye Study is a cross-sectional study which included participants of a longitudinal community-based cohort study. The community of Kailuan is located in the city of Tangshan with approximately 7.2 million inhabitants. The study population included employees and retirees of a coal mining company (Kailuan Group Company). At baseline between year 2006 and 2007, 101,510 participants from Kailuan community with an age ranging between 18 and 98 years were included in the cohort that repeated at 2-year intervals. Based on a unit-based cluster random sampling method, 14,440 individuals were randomly selected and agreed to participate the Kailuan Eye Study out of the Kailuan cohort. All participants underwent an interview with standardized questions on their socioeconomic background, educational level, psychic depression, physical activity, known major systemic diseases such as arterial hypertension and diabetes mellitus (DM), living habit (including sleep condition, alcohol consumption and smoking. The Kailuan Eye Study was approved by the Medical Ethics Committee of the Kailuan General Hospital, Beijing Tongren Hospital and Peking University First Hospital, and conducted adhered to the tenets of the Declaration of Helsinki. Written informed consent was signed from all the subjects.

Two groups of subjects were included in this study: a DM group and a matched non-DM (control) group. Subjects in the diabetic group were selected from all patients with a diagnosis of diabetes mellitus and complete clinical data from the Kailuan Eye Disease Study; subjects in the nondiabetic group were randomly selected from the Kailuan Eye Disease Study’s database and matched to each participant in the diabetic group by age and sex. We finally included 2767 subjects in the diabetes group and randomly matched 2767 control subjects of the same age and sex.

Clinical data and personal information of all participants was collected by trained interviewers through a standardized and systematic examination. In-person questionnaires were administered by research doctors. Information obtained included demographic and socioeconomic information, lifestyle habits (smoking, alcohol, and exercise), and self-reported medical history (diabetes, hypertension, dyslipidemia, cardiovascular disease, thyroid diseases, stroke, family history, current medication intake, etc.). Blood pressure (BP) was measured with a mercury sphygmomanometer following the standard procedures. Two readings of blood pressure were taken at a 5-min interval when participants had been in the sitting position for at least 5 min. The average was recorded for data analysis. Waist circumstance (WC), hip circumstance (HC) and neck circumstance (NC) were measured directly. Under overnight fasting condition, blood samples were collected from all participants to test fasting plasma glucose (FPG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglyceride (TG), total cholesterol (TC), uric acid, hypersensitive C-reactive protein (hsCRP), glutamate pyruvate transaminase (GPT), total serum bilirubin, serum creatinine, urea, red blood count (RBC), white blood count (WBC), blood platelet count (BPC), hemoglobin (Hb), neutrophil count at the clinical laboratory of the Kailuan General Hospital. The diagnostic criteria for diabetes were the measurement of FPG concentration ≥7.0 mmol/l during the 10-year follow-up period, a self-reported history of diabetes or a history of taking hypoglycaemic agents.

Ocular examinations included measurements of visual acuity (VA), tonometry, slit-lamp assisted biomicroscopy of the anterior segment, and ocular biometry consisting of the central corneal thickness (CCT), corneal curvature, anterior chamber depth (ACD), lens thickness (LT), and axial length (AL) by applying optical low-coherence reflectometry (Lenstar 900 Optical Biometer; Haag-Streit, Koeniz, Switzerland) and optical coherence tomography angiography (OCTA) (AngioPlex; Carl Zeiss Meditec, Dublin, CA, USA). Two 45° fundus photographs was obtained on the optic nerve head and macula by a nonmydriatic fundus camera (CR6-45NM; Canon, Inc., Osta, Tokyo, Japan). Pupils were dilated medically by applying eye drops containing 0.5% tropicamide and 0.5% phenylephrine hydrochloride.

Retinal vein occlusions (RVOs) have been defined as retinal vascular disorders characterized by engorgement and dilatation of the retinal veins due to increased retinal venous blood pressure, with secondary (mostly) intraretinal hemorrhages; (mostly) intraretinal (and partially subretinal) edema which can also include the foveal region and which can lead to hard retinal exudates as deposits of lipids; and a varying degree of retinal ischemia including cotton wool spots as signs of it. The diagnosis of retinal vein occlusion typically relies solely on clinical examination. Nonperfused central retinal vein occlusion is indicated by visual acuity worse than 20/200, a relative afferent pupillary defect, and the presence of cotton-wool spots and large, confluent hemorrhages.^10,11 Central RVO manifests as retinal edema, optic disc hyperemia or edema, venous dilatation, scattered superficial and deep retinal hemorrhages, or occluded and sheathed retinal veins. Branch RVO exhibits similar signs but is confined to a retinal sector by an arteriovenous crossing at the apex of an obstructed vein.^5,12 Fundus fluorescein angiography, although better able to visualize RVO perfusion and macular edema, was not used in this study due to its invasive limitations. Two experienced graders (YY, WQ) initially assessed the fundus photographs. They were masked to the history, and the clinical data and medical records were not available during the grading process. Image enhancement tools were not used and any manipulation of the images was not allowed for the detail grading. The intrarater and interater reliability were evaluated by assessing the kappa coefficient (κ = 0.96). All eyes with the diagnosis of RVO were rechecked and the diagnosis was verified by a senior grader (WYX). In unclear situations, a panel of ophthalmologists (WYX, JBJ) reassessed the fundus photographs and the OCT images to arrive at a final consensus.

All statistical analyses were performed using a commercially available statistical software program (SPSS, version 27.0; IBM/SPSS, Chicago, IL, USA) and plotting software (Graphpad Prism 9, USA). Continuous variables were demonstrated as mean ± standard deviation or as median (quartiles), and categorical variables were presented as number (proportions). The Pearson chi-square test was used to compare the demographic characteristics and comorbid disorders between the DM and control groups. Binary univariate and multivariate logistic regression were applied to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) of each risk factor for the development of RVO. A two-tailed p value of less than 0.05 was defined as statistically significant at the 95% CI level.

Results

A total of 2767 of the 14,440 participants had diabetes, and after excluding ineligible subjects, we simultaneously matched 2767 controls. The demographic information of the diabetes group and the sex-age matched control group and the comparison of systemic factors including biochemical indicators are shown in Table 1. The mean age of all participants of two groups was 59.87 ± 9.7 years, of which 22.33% were females. A total of 728 subjects (26.31%) were above 65 years of age, 1633 subjects (59.02%) were between 50 and 65 years of age, while 406 subjects (14.67%) were below 50 years of age in the DM group. Under age- and sex-matched conditions, subjects in the DM group had significantly higher levels of blood pressure, lipids, and creatinine and a more complex systemic profile than subjects in the control group.

Table 1.

Descriptive information of the systemic and ocular variables.

	Non-DM group		DM group		p-value
	Mean	SD	Mean	SD	p-value
Age, years	59.87	9.70	59.87	9.70	1.000
Systolic blood pressure, mmHg	141.63	18.99	147.98	19.48	<0.001
Diastolic blood pressure, mmHg	80.11	10.57	82.24	10.80	<0.001
Height, m	1.69	0.07	1.68	0.07	0.914
Weight, kg	70.75	10.68	73.62	10.82	<0.001
Body Mass index, kg/m2	24.87	3.11	25.90	3.19	<0.001
Waist circumference, cm	87.78	9.07	91.27	10.41	<0.001
Hip circumference, cm	98.83	10.24	99.47	10.98	0.060
Neck circumference, cm	38.85	4.06	39.13	2.81	0.015
Heart rate	74.78	11.54	79.03	12.43	<0.001
Fasting plasma glucose, mmol/L	5.56	0.61	9.24	2.72	<0.001
High-density lipoprotein cholesterol, mmol/L	1.50	0.63	1.48	0.52	0.264
Low-density lipoprotein cholesterol, mmol/L	3.04	0.93	3.08	1.01	0.203
Triglyceride, mmol/L	2.00	2.73	2.68	2.70	<0.001
Total cholesterol, mmol/L	4.83	2.15	4.74	2.02	0.162
Uric acid, mmol/L	330.49	86.11	322.46	89.07	0.003
Hypersensitive C-reactive protein, mmol/L	2.03	9.67	2.25	4.14	0.361
Total protein, mmol/L	72.94	5.04	75.07	6.04	<0.001
Albumin, mmol/L	47.52	14.20	46.90	3.57	0.071
Glutamate pyruvate transaminase, mmol/L	22.34	16.79	26.25	28.71	<0.001
Total bilirubin, mmol/L	15.32	10.51	15.85	7.43	0.063
Creatinine, mmol/L	76.63	32.50	71.48	33.15	<0.001
Urea, mmol/L	6.04	7.79	6.09	3.24	0.769
Red blood count (RBC)	4.86	0.45	4.94	0.48	<0.001
White blood count (WBC)	5.94	1.69	6.17	1.92	<0.001
Blood platelet count (BPC)	230.84	53.49	232.79	57.99	0.271
Hemoglobin (Hb), mmol/L	149.03	14.64	150.43	15.23	0.003
Neutrophil count	3.72	2.68	4.12	2.70	<0.001
Waist-to-hip ratio	0.89	0.08	0.92	0.07	<0.001

A total of 66 subjects were found to have RVO in the cross sectional. A total of 43 patients (1.5%) with RVO were found in the diabetic group, which was significantly higher (p = .01, X² = 6.15) than the control group (23, 0.8%). The majority of RVO cases in both groups were BRVO, in which only one CRVO was detected in the control group and two participants had binocular BRVO in the DM group. After grouping the patients by age (Figure 1), we found that there were more patients with RVO in the DM group than in the control group in every age group, but only the 50-65 age group showed a statistically significant difference (p = .03, X² = 4.88). Differences in systemic parameters among RVO subjects with and without DM were compared by independent samples t-tests. The results showed that RVO patients with DM had higher systolic blood pressure (SBP, p = .017), larger waist circumference (p = .009) and waist-to-hip ratio (p = .048), faster heart rate (p = .048), and higher blood concentrations of FPG (p < .001) and triglyceride (p = .061), compared to RVO patients without DM. Incorporating the above statistically significant variables into a multivariate logistic regression model, only blood concentration of FPG was found to be statistically different between DM and non-DM RVO patients (OR = 118.59, 95%CI: 8.88, 1583.29).

Figure 1.

The distribution of RVO patients by age in the DM and non-DM group.

Univariate logistic regression analysis was applied to explore the effects of systemic factors on the prevalence of RVO in patients with diabetes. The results of the analysis revealed that the occurrence of RVO in patients suffering from diabetes mellitus was mainly associated with high blood pressure (p = .002) and high FPG levels (p = .003). After correcting for age, multivariate logistic regression showed that for every 1 mmHg increase in systolic blood pressure and every 1 mmol/L increase in FPG, the risk of RVO increased by 2.5% and 154% in patients with diabetes, respectively. (Table 2, Figure 2)

Table 2.

Associations (univariate and multivariate logistic regression) between retinal vein occlusion and systemic parameters in diabetic group.

Parameters	Unstandardized coefficient	p value	Odds ratio	95% CI
Univariate
Age, years	0.01	0.502	1.01	0.98	,	1.05
Gender	0.34	0.446	1.41	0.58	,	3.40
Anterior chamber depth, mm	0.54	0.247	1.72	0.69	,	4.29
Lens thickness, mm	−0.02	0.949	0.98	0.50	,	1.92
Axial length, mm	0.15	0.449	1.17	0.78	,	1.74
Systolic blood pressure, mmHg	0.03	0.002	1.03	1.01	,	1.04
Diastolic blood pressure, mmHg	0.03	0.061	1.03	1.00	,	1.06
Height, m	−1.27	0.605	0.28	0.00	,	34.47
Weight, kg	0.00	0.866	1.00	0.97	,	1.03
Body Mass index, kg/m2	0.03	0.568	1.03	0.93	,	1.14
Waist circumference, cm	0.03	0.061	1.03	1.00	,	1.06
Hip circumference, cm	0.01	0.422	1.01	0.98	,	1.04
Neck circumference, cm	−0.06	0.426	0.94	0.81	,	1.09
Heart rate	0.00	0.843	1.00	0.97	,	1.02
Fasting plasma glucose, mmol/L	0.14	0.003	1.15	1.05	,	1.26
High-density lipoprotein cholesterol, mmol/L	−0.69	0.123	0.50	0.21	,	1.20
Low-density lipoprotein cholesterol, mmol/L	−0.12	0.56	0.89	0.60	,	1.32
Triglyceride, mmol/L	0.01	0.793	1.01	0.92	,	1.12
Total cholesterol, mmol/L	−0.06	0.554	0.95	0.79	,	1.13
Uric acid, mmol/L	0.00	0.898	1.00	1.00	,	1.00
Hypersensitive C-reactive protein, mmol/L	−0.10	0.211	0.91	0.77	,	1.06
Total protein, mmol/L	−0.01	0.64	0.99	0.93	,	1.04
Albumin, mmol/L	−0.05	0.124	0.95	0.89	,	1.02
Glutamate pyruvate transaminase, mmol/L	0.00	0.838	1.00	0.99	,	1.01
Total bilirubin, mmol/L	−0.02	0.468	0.98	0.92	,	1.04
Creatinine, mmol/L	0.00	0.572	1.00	1.00	,	1.01
Urea, mmol/L	−0.05	0.643	0.96	0.79	,	1.16
Red blood count (RBC)	0.08	0.829	1.08	0.54	,	2.16
White blood count (WBC)	−0.04	0.674	0.96	0.81	,	1.15
Blood platelet count (BPC)	0.00	0.683	1.00	0.99	,	1.01
Hemoglobin (Hb), mmol/L	0.00	0.97	1.00	0.98	,	1.02
Neutrophil count	−0.03	0.764	0.97	0.81	,	1.17
Waist-to-hip ratio	2.44	0.13	11.48	0.49	,	270.31
History of smoking	−0.17	0.498	0.85	0.52	,	1.37
History of stroke	0.94	0.367	2.56	0.33	,	19.72
History of hyperlipidemia	0.15	0.787	1.16	0.40	,	3.34
Multivariate
Age, years	0.01	0.512	1.01	0.98	,	1.05
Systolic blood pressure, mmHg	0.02	0.011	1.02	1.01	,	1.04
Fasting plasma glucose, mmol/L	0.13	0.006	1.14	1.04	,	1.25
Gender	0.37	0.417	1.45	0.59	,	3.52

Figure 2.

Forest plot of the main associated factors for RVO patients of the DM group.

In contrast, among those without diabetes, univariate logistic regression analyses showed a higher incidence of RVO was associated with higher blood pressure, weight and BMI, higher blood concentration level of uric acid, lower level of LDL-C and TC. In addition, a previous history of heart failure or heart attack significantly increased the incidence of RVO. Weight (covariance with BMI), and systolic blood pressure (covariance with diastolic blood pressure) were excluded from multivariate analyses, taking into account the covariance of the variables. In the final model analyzed we found that the prevalence of RVO in the population without diabetes was mainly associated with diastolic blood pressure, BMI and LDL-C, correcting for age. For every 1 mmHg increase in diastolic blood pressure, every 1 increase in BMI, and every 1 mmol/L drop in LDL-C, the risk of RVO increased by 5%, 9%, and 39% respectively. (Table 3, Figure 3)

Table 3.

Associations (univariate and multivariate logistic regression) between retinal vein occlusion and systemic parameters in non-diabetic group.

Parameters	Unstandardized coefficient	p value	Odds ratio	95% CI
Univariate
Age, years	0.01	0.258	1.01	0.99	,	1.04
Gender	0.16	0.586	1.17	0.66	,	2.08
Anterior chamber depth, mm	−0.55	0.140	0.58	0.28	,	1.20
Lens thickness, mm	0.06	0.854	1.06	0.55	,	2.04
Axial length, mm	−0.22	0.183	0.80	0.58	,	1.11
Systolic blood pressure, mmHg	0.02	0.010	1.02	1.00	,	1.03
Diastolic blood pressure, mmHg	0.05	0.000	1.05	1.03	,	1.07
Height, m	−1.14	0.508	0.32	0.01	,	9.26
Weight, kg	0.02	0.033	1.02	1.00	,	1.05
Body Mass index, kg/m2	0.11	0.003	1.11	1.04	,	1.19
Waist circumference, cm	0.01	0.279	1.01	0.99	,	1.04
Hip circumference, cm	0.00	0.809	1.00	0.98	,	1.02
Neck circumference, cm	0.02	0.490	1.02	0.97	,	1.06
Heart rate	−0.01	0.381	0.99	0.97	,	1.01
Fasting plasma glucose, mmol/L	0.10	0.592	1.11	0.76	,	1.62
High-density lipoprotein cholesterol, mmol/L	−0.03	0.888	0.97	0.65	,	1.45
Low-density lipoprotein cholesterol, mmol/L	−0.44	0.004	0.64	0.48	,	0.87
Triglyceride, mmol/L	0.01	0.748	1.01	0.95	,	1.08
Total cholesterol, mmol/L	−0.20	0.003	0.82	0.71	,	0.93
Uric acid, mmol/L	0.00	0.003	1.00	1.00	,	1.01
Hypersensitive C-reactive protein, mmol/L	0.00	0.935	1.00	0.97	,	1.03
Total protein, mmol/L	0.02	0.346	1.02	0.97	,	1.08
Albumin, mmol/L	−0.05	0.146	0.95	0.89	,	1.02
Glutamate pyruvate transaminase, mmol/L	0.00	0.860	1.00	0.98	,	1.01
Total bilirubin, mmol/L	0.00	0.558	1.00	0.99	,	1.02
Creatinine, mmol/L	0.00	0.470	1.00	1.00	,	1.01
Urea, mmol/L	0.00	0.886	1.00	0.95	,	1.04
Red blood count (RBC)	0.48	0.073	1.62	0.96	,	2.74
White blood count (WBC)	0.03	0.625	1.03	0.90	,	1.18
Blood platelet count (BPC)	0.00	0.539	1.00	0.99	,	1.00
Hemoglobin (Hb), mmol/L	−0.01	0.401	0.99	0.98	,	1.01
Neutrophil count	0.01	0.832	1.01	0.94	,	1.09
History of heart failure	2.18	0.053	8.80	0.97	,	79.97
History of cardio infarction	1.74	0.024	5.71	1.26	,	25.74
History of stroke	1.15	0.271	3.17	0.41	,	24.78
History of smoking	−0.09	0.642	0.92	0.64	,	1.31
Multivariate
Age, years	0.04	0.008	1.04	1.01	,	1.07
Diastolic blood pressure, mmHg	0.05	<0.001	1.05	1.03	,	1.07
Body Mass index, kg/m2	0.09	0.016	1.09	1.02	,	1.18
Low-density lipoprotein cholesterol, mmol/L	−0.49	0.002	0.62	0.45	,	0.84

Figure 3.

Forest plot of the main associated factors for RVO patients of the non-DM group.

Discussion

In the present community-based cross-sectional study, we found a prevalence of RVO of 1.5% in subjects with diabetes mellitus, all of whom had BRVO. The occurrence of RVO in subjects with diabetes mellitus was significantly associated with higher fasting glucose levels and higher systolic blood pressure. The prevalence of RVO in subjects without diabetes was 0.8% and only three of them were CRVO. The occurrence of RVO in subjects without diabetes was significantly associated with higher age, higher DBP and higher BMI. In contrast, higher FPG and SBP levels showed significantly correlated to the prevalence of RVO in patients with diabetes.

The prevalence of RVO found in this study population was higher compared to the Beaver Dam Eye Study (0.6%),¹ the Gutenberg Health Study (0.4%),⁵ the Singapore Epidemiology of Eye Disease Study¹³ and the Singapore Malay Eye Study⁴ (0.7%). The prevalence of RVO in the present study was lower than that of The Hisayama Study (2.1%),¹⁴ The Blue Mountains Eye Study (1.6%),¹² and the Bhaktapur retina study (2.95%).¹⁵ In the Beijing Eye Study with a similar population sample, the prevalence of RVO was about 1.9%, which was slightly higher than in the present study.¹⁶ In multiethnic studies, the RVO incidence of 1.1% was similar across diverse ethnic groups, including people of European, African, Hispanic, and Chinese descent.^17,18 The methodological and population characteristics may vary across studies. Considering that this study was a community-based population study, there may have been some degree of selection bias affecting the prevalence. This may also be related to genetic and environmental factors.

In this study, we grouped subjects with and without diabetes and explored the effect of diabetes on retinal vein occlusion and intervention. The association between diabetes and RVO has been explored in several articles. These studies evaluated risk factors in patients with RVO rather than the incidence in patients with DM. Similar to the findings of the Diabetes Patients database in Taiwan,⁶ our study also found an increased risk of RVO in patients with diabetes mellitus. The meta-analysis by O'Mahoney et al.¹⁹ included 2,916 cases and 28,646 controls and showed that DM was significantly associated with RVO (Odds Ratio, 1.5; 95% CI, 1.1-2.0). The results of the Korean RVO study²⁰ showed a stronger correlation between DM and CRVO among Korean patients with RVO. However, the results of the Blue Mountains Eye Study¹² (Blue Mountains Eye Study) and the Hisayama Study (Hisayama Study)¹⁴ were inconsistent, demonstrating that DM is not a significant risk factor for RVO. This is inconsistent with the findings of our study. This disagreement could be explained by differences in race, study population, or study methodology in each report.

This study suggests that diabetes is one of the independent risk factors for RVO. Microvascular retinopathy is a common pathogenic mechanism for both diseases. Hyperglycemia triggers a series of events, such as the production of advanced glycation end products and nonenzymatic glycated proteins, that lead to vascular endothelial dysfunction. These events alter the structure and function of the extracellular matrix, basement membrane, and vessel wall, resulting in focal or generalized stiffness, stenosis, and arteriovenous stenosis of retinal vessels in patients with DM.^21,22 These retinal microvascular signs are also common in patients with RVO.^12,23 Retinal microvascular stiffness or stenosis may make the vessels more susceptible to occlusion, ultimately leading to RVO formation. In addition, diabetes is a significant predictor of systemic atherosclerosis, including carotid and retinal atherosclerosis.^24,25 Atherosclerosis may result in reduced blood flow leading to thrombosis, downstream venous occlusion and RVO. The role of inflammation in the development of diabetes and diabetic retinopathy has been much demonstrated. The levels of inflammatory factors such as high-sensitivity C-reactive protein and tumor necrosis factor TNF-α are often elevated in patients with diabetes.^26–28 And inflammation is also thought to be involved in the pathogenesis of RVO. In both BRVO and CRVO,^29,30 the levels of inflammatory factors play an important role in the development and prognosis of the disease. This suggests that inflammation may be an important association between DM and RVO.

In the control group we found that the development of RVO was associated with many systemic factors, including higher diastolic blood pressure, BMI, as well as lower LDL-C and total cholesterol levels, and a previous history of heart failure, but was not significantly associated with fasting blood glucose levels. While the occurrence of RVO in patients with diabetes was significantly associated only with elevated systolic blood pressure and blood glucose levels. Hypertension is an important independent risk factor for RVO has been reported in many studies.^5,13,31 Hypertension leads to retinal micro-vessel wall damage, atherosclerosis, and thromboembolism,^32,33 which ultimately leads to RVO. Even early elevation of blood pressure may increase the risk of RVO.³⁴ The increased systemic pressure may cause damage to the delicate walls of retinal veins, leading to structural changes and making them more susceptible to occlusion. The narrowing and stiffening of arteries may create an environment conducive to the formation of clots and thrombosis. Elevated blood pressure can lead to venous stasis, a condition where blood flow in the veins slows down. This sluggish blood flow increases the risk of thrombus formation, especially in the retinal veins. In addition, chronic hypertension can cause dysfunction of the endothelium, the inner lining of blood vessels. Endothelial dysfunction is a key factor in the development of vascular diseases, including RVO. Impaired endothelial function may contribute to inflammation and thrombosis in the retinal veins. Addressing hypertension as part of a holistic approach to vascular health is key in reducing the risk of RVO and its potential vision-threatening consequences.

In prior RVO guidelines and literature,^35–38 diabetes and hypertension, along with associated atherosclerosis, can impair vascular endothelial function, thereby reducing vessel elasticity and elevating thrombosis risk.³⁹ Both diabetes and hypertension foster thrombosis through the activation of inflammatory pathways and heightened oxidative stress, culminating in vascular wall damage and disruptions in coagulation and fibrinolytic systems, thereby increasing blood viscosity. Hypertension can also induce retinal vein compression, further escalating thrombotic risk. Stringent control of blood glucose and pressure levels constitutes crucial intervention to mitigate vascular damage and lower RVO risk. Additionally, routine ophthalmologic assessments and multidisciplinary collaboration for comprehensive management of high-risk patients are pivotal in preventing RVO development.

Consistent findings from studies underscore a higher RVO prevalence among older demographics, implicating age as a significant risk determinant. Concerning sex disparities in RVO, some studies indicate a marginally higher incidence among males, while others find no notable gender-based distinction.^40–42 In our study, neither age nor gender exhibited a significant impact on RVO occurrence in logistic regression analyses. However, RVO incidence was notably elevated in the age group above 50 years. Given the broad age range within our sample, accurately reflecting overall trends in correlation analysis may pose challenges. Nevertheless, these findings align with prior research outcomes. Gender did not emerge as a significant factor, suggesting that systemic influences may supersede gender in RVO development.

There are some limitations of this study. The study was conducted in a specific community, and the findings may not be directly applicable to other geographical or ethnic populations. To address this, future research endeavors could aim to incorporate a more diverse participant pool, allowing for a more comprehensive understanding of how different populations may be affected by RVO. Factors such as genetic predispositions and lifestyle variations should be considered when generalizing the results. While the study delves into systemic factors, it may not comprehensively explore other potential risk factors for RVO, such as genetic factors, hyperlipidemia, and lifestyle factors like smoking. Future investigations should integrate a wider array of variables to illuminate the multifaceted essence of RVO etiology. A comprehensive investigation encompassing a multidisciplinary approach could provide a more holistic understanding of RVO development. By collaborating with experts from various medical specialties and employing diverse research methodologies, future studies can endeavor to unravel the complex interplay between genetic, lifestyle, and systemic factors contributing to RVO.

Despite the limitations, the research offers valuable insights into the prevalence and influencing factors of RVO, particularly in the context of diabetes. As a large-sample, community-based cross-sectional study, this approach allows for a more representative sample and provides insights into the prevalence and influencing factors of retinal vein occlusion (RVO) in a real-world setting. In addition, a detailed systemic examination and multifactorial analysis ensured a comprehensive assessment of potentially influential factors, providing clinicians with valuable insights to tailor interventions for patients with diabetes at higher risk for RVO.

In conclusion, our community-based cross-sectional study provides a comprehensive analysis of RVO incidence in both populations with and without diabetes. The heightened prevalence of RVO in individuals with diabetes, coupled with the identified influencing factors, elucidates the complex interplay between diabetes and retinal vascular health. The significance of glycemic control and blood pressure management in reducing the incidence of RVO underscores the potential for targeted interventions to preserve ocular health in patients with diabetes. As we delve deeper into the intricate mechanisms governing RVO, our findings pave the way for a more nuanced understanding of this visually debilitating complication and inform strategies for its prevention and management in diverse populations.

Footnotes

Acknowledgements

The authors thank the staff and participants in the Kailuan Eye Study for their help and support.

Authors’ contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yao Yao, Qian Wang, Jingyan Yang and Yanni Yan. The first draft of the manuscript was written by Yao Yao and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Supported by the National Natural Science Foundation of China (82101146, 82220108017, 82141128); The Capital Health Research and Development of Special (2024-1-2052); Science& Technology Project of Beijing Municipal Science & Technology Commission (Z201100005520045); Sanming Project of Medicine in Shenzhen (No. SZSM202311018); The Beijing Nova Program (Z211100002121052).

ORCID iD

Wenbin Wei

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Prevalence and risk factors of retinal vein occlusion in individuals with diabetes: The kailuan eye study

Abstract

Purpose

Method

Result

Conclusion

Keywords

Introduction

Methods

Results

Discussion

Footnotes

Acknowledgements

Authors’ contributions

Declaration of conflicting interests

Funding

ORCID iD

References