Abstract
This retrospective case–control study examined the association between the rs2071559 (-604T/C) single nucleotide polymorphism (SNP) in the vascular endothelial growth factor receptor (VEGFR)-2 gene and the risk of diabetic retinopathy (DR) in Northern Han Chinese. This study included patients diagnosed with diabetes mellitus (DM) in Shijiazhuang between 07/2014 and 07/2016. The healthy controls were unrelated individuals who received routine physical examinations. The diabetic patients were grouped as DM (diabetes but no fundus examination abnormalities), proliferative DR (PDR), and non-proliferative DR (NPDR). Finally, 438 patients were included: 114 controls and 123, 105, and 96 patients in the DM, NPDR, and PDR groups, respectively. In the multivariable analyses and all genetic models, the VEGFR-2 rs2071559 SNP was not associated with DR (among all diabetic patients) or with PDR (among the patients with DR) after adjustment for age, sex, duration of DM, blood glucose, systolic blood pressure, diastolic blood pressure, and body mass index (all P > 0.05). In conclusion, the VEGFR-2- 604T/C rs2071559 SNP is not associated with DR or PDR in the Han Chinese population of Shijiazhuang (China).
Keywords: type 2 diabetes, diabetic retinopathy, vascular endothelial growth factor receptor, gene polymorphism, VEGFR-2
1. Introduction
Diabetic retinopathy (DR) is a microvascular complication of type 2 diabetes mellitus (T2DM) and a leading cause of preventable blindness in adults [1–3]. DR is a spectrum that starts with non-proliferative DR (NPDR) that eventually progresses to proliferative DR (PDR) [1–3]. The worldwide prevalence of DR is 24.6% in patients with diabetes [4], the highest incidence rates are observed in African Americans, Hispanics, and South Asians [3]. In China, among patients with diabetes, the prevalence rates of DR and vision-threatening DR are 27.9 and 12.6%, respectively [5]. The management of DR includes glycemic control, lipidemia control, blood pressure control, laser therapy, intravitreal anti-vascular endothelial growth factor (VEGF) therapy, vitrectomy, and intravitreal steroids [2,6].
VEGF is a key molecule in the development of DR [7,8]. Intravitreal VEGF levels are high in diabetic patients and correlate with neovascularization [9], especially in patients with PDR [3,10]. In addition to VEGF levels, the expression and distribution of the VEGF receptor (VEGFR)-2 are also crucial in developing DR and progressing from NPDR to PDR [11–13]. Indeed, VEGFR-2 controls endothelial cell proliferation, migration, survival, and osmosis via its activation by VEGF [8].
Single nucleotide polymorphisms (SNPs) in the VEGFR-2 gene can affect the development and progression of multiple diseases by affecting the binding efficiency between VEGFR-2 with VEGF or by reducing the transcriptional activity of VEGFR-2 [14–16]. The involvement of some VEGF SNPs in the development of DR and response to treatment are well-known [17–20], but the role of VEGFR-2 SNPs in the development of DR is poorly known. The rs2071559 SNP is located in the promoter of VEGFR-2 and decreases the transcriptional activity of VEGFR-2 [14,21,22]. A previous study revealed that four SNPs in the VEGF gene and one (rs2071559 or -604T/C) in the VEGFR-2 gene were associated with the development of DR [23], but it did not examine the association of the rs2071559 SNP with PDR. There are several studies on the polymorphisms of VEGF in DR [17,23–25], but only one study of the rs2071559 SNP in VEGFR-2 was found [23]. Since DR is a complex trait affected by multiple genes and the environment, exploring the role of VEGFR-2 SNPs in various populations is relevant.
Therefore, the present study aimed to examine the association between the -604T/C locus polymorphism of the VEGFR-2 gene and the risk of DR among T2DM patients and then of PDR among DR patients in a Han population in North China.
2. Materials and methods
2.1. Study design and subjects
This retrospective case–control study included patients with T2DM who visited the Department of Ophthalmology and the Department of Endocrinology of the Fourth Hospital of Hebei Medical University between July 2014 and July 2016. T2DM was diagnosed according to the International World Health Organization (WHO) T2DM diagnostic criteria (1999) [26]. The healthy controls were unrelated individuals who received routine physical examinations between July 2014 and July 2016. All subjects were of Han ethnicity and from families living in Shijiazhuang, Hebei Province, China, for at least three generations.
The subjects were divided into four groups (one control group and three patient groups) according to the 2002 International Clinical Classification Criteria for DR [27]. The DM group included the T2DM patients without fundus examination abnormalities. The PDR group included the patients with one or more of the following: neovascularization, vitreous hemorrhage, and preretinal hemorrhage. The NPDR group included the patients with microangioma, intraretinal hemorrhage, and microvascular abnormalities, but without the PDR-specific findings.
Informed consent: Informed consent has been obtained from all individuals included in this study.
Ethical approval: The research related to human use has been complied with all the relevant national regulations and institutional policies and in accordance with the tenets of the Helsinki Declaration, and has been approved by the ethics committee of The Fourth Hospital of Hebei Medical University.
2.2. Data collection
The data were obtained from the medical records and included sex, age, fasting blood glucose levels, body height, body weight, systolic blood pressure, diastolic blood pressure, and T2DM duration. The body mass index (BMI) was calculated as BMI = body weight (kg)/height (m2).
2.3. VEGFR-2 -604T/C polymorphism
DNA was routinely extracted from venous blood collected in sodium citrate tubes and stored at 4°C. Peripheral blood leukocyte DNA was extracted using the proteinase K digestion-saturated sodium chloride salting-out method within 1 week of blood collection [28].
The polymerase chain reaction-ligation detection reaction (PCR-LDR) method was used to determine the VEGFR-2 -604T/C polymorphism. The forward primer for the amplification of rs2071559 was 5′-AAA TAT TTT GGG AAA TAG CGG GAA TG-3′, and the reverse primer was 5′-TGG CGA ACT GGG CAA GTG CGT TTT C-3′. The forward and reverse primers for -604T/C were 5′-GAG CAC GAT GGA CAA AAG CCT-3′ and 5′-GGG AGA AGC GGA TAC TCA GCC-3′, respectively. The PCR product length was 255 bp, and the GC content was 47.5%. The LDR probe design included two probes. The sequences of the probes for rs2071559 were TC: 5′-TTT TTT TTA TAT TTT GGG AAA TAG CGG GAA TGC-3′, TT: 5′-TTT TTT TTT TTA TAT TTT GGG AAA TAG CGG GAA TGT-3′, and TR: 5′-P-TGG CGA ACT GGG CAA GTG CGT TTT CTT TTT T-FAM-3′.
The size of the ligated products was 64/C and 67/T. The LDR reaction consisted of cycling through the following conditions for 25 cycles: 94°C for 30 s and 56°C for 1 min.
Sterile distilled water was used as a negative control. In addition, 10% of the DNA samples were randomly selected for duplicate experiments. All repeated results were consistent with the original ones.
2.4. Statistical analysis
Statistical analysis was performed using SPSS 13.0 (SPSS, Chicago, IL, USA). The Hardy–Weinberg equilibrium was tested in all groups using the chi-square test. Normally distributed continuous variables were expressed as means ± standard deviations (and analyzed using one-way analysis of variance and the Bonferroni post hoc test. Non-normally distributed continuous variables were presented as medians (ranges) and compared using the Kruskal–Wallis test. Non-conditional logistic regression analysis was performed to calculate the odds ratios (ORs) and 95% confidence intervals (95% CIs). P-values < 0.05 were considered statistically significant.
3. Results
3.1. Characteristics of the subjects
Finally, 438 subjects were included: 114 controls, 123 patients in the DM group, 105 in the NPDR group, and 96 in the PDR group. There were no significant differences in age and sex among the four groups (all P > 0.05) (Table A1).
3.2. Association between VEGFR-2 gene rs2071559 and DR
The three genotypes (CC, CT, and TT) were present in all four groups and met the Hardy–Weinberg equilibrium (all P > 0.05) (Table A2). The frequencies of CC, CT, and TT were 17.6, 45.6, and 36.8% in the control group, 12.2, 50.4, and 37.4% in the DM group, 7.6, 53.3, and 39.1% in the NPDR group, and 7.3, 46.9, and 45.8% in the PDR group. There were no significant differences among the four groups (P = 0.191) (Table 1).
Table 1.
Distribution of genotype and allele frequencies of VEGFR-2 -604T/C
| Control group N = 114 | DM group N = 123 | NPDR group N = 105 | PDR group N = 96 | P | |
|---|---|---|---|---|---|
| Genotype, n (%) | 0.191 | ||||
| CC | 20 (17.6%) | 15 (12.2%) | 8 (7.6%) | 7 (7.3%) | |
| CT | 52 (45.6%) | 62 (50.4%) | 56 (53.3%) | 45 (46.9%) | |
| TT | 42 (36.8%) | 46 (37.4%) | 41 (39.1%) | 44 (45.8%) | |
| Allele, n (%) | 0.198 | ||||
| C | 92 (40.4%) | 92 (37.4%) | 72 (34.3%) | 59 (30.7%) | |
| T | 136 (59.6%) | 154 (62.6%) | 138 (65.7%) | 133 (69.3%) |
DM, diabetes mellitus without DR group; NPDR, non-proliferative DR group; PDR, PDR group.
The C and T allele frequencies were 40.4 and 59.6% in the control group, 37.4 and 62.6% in the DM group, 34.3 and 65.7% in the NPDR group, and 30.7 and 69.3% in the PDR group (P = 0.198) (Table 1).
3.3. Univariable and multivariable analyses in the T2DM patients
The rs2071559 genotypes were not associated with DR in the univariable analysis (P > 0.05) (Table 2). The duration of T2DM (OR = 1.064, 95% CI: 1.024–1.105, P = 0.002), blood glucose (OR = 1.801, 95% CI: 1.057–3.067, P = 0.030), systolic blood pressure (OR = 1.023, 95% CI: 1.009–1.038, P = 0.001), and diastolic blood pressure (OR = 1.047, 95% CI: 1.021–1.075, P < 0.001) were associated with DR. The rs2071559 SNP of the VEGFR-2 gene was not associated with DR after adjustment for age, sex, duration of DM, blood glucose, systolic blood pressure, diastolic blood pressure, and BMI (Table 3).
Table 2.
Univariable logistic regression analysis for factors associated with DR among type 2 diabetic patients
| OR | 95% CI | P | ||
|---|---|---|---|---|
| Risk factor | ||||
| Age | >60 vs ≤60 | 1.008 | (0.643, 1.58) | 0.971 |
| Sex | Female vs male | 1.073 | (0.684, 1.685) | 0.759 |
| Duration of DM | Per one year | 1.064 | (1.024, 1.105) | 0.002 |
| Blood glucose | >6.1 vs ≤6.1 mmol/L | 1.801 | (1.057, 3.067) | 0.030 |
| SBP | Per one mmHg | 1.023 | (1.009, 1.038) | 0.001 |
| DBP | Per one mmHg | 1.047 | (1.021, 1.075) | <0.001 |
| BMI | 18.5–24 | Ref. | — | — |
| ≤18.5 | 1.063 | (0.187, 6.047) | 0.945 | |
| >24 vs 18.5-24 | 0.794 | (0.496, 1.271) | 0.337 | |
| Genotype | ||||
| Dominant model | (TC + CC) vs TT | 0.815 | (0.515, 1.292) | 0.384 |
| Recessive model | (TC + TT) vs CC | 0.581 | (0.273, 1.234) | 0.158 |
| Co-dominant model | TC vs TT | 0.541 | (0.243, 1.205) | 0.133 |
| CC vs TT | 0.882 | (0.546, 1.422) | 0.606 | |
| Over-dominant model | (TT + TC) vs TC | 1.006 | (0.642, 1.576) | 0.978 |
DM, diabetes mellitus; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body weight index; OR, odds ratio; CI, confidence interval.
Table 3.
Multivariable logistic regression analysis of the factors associated with DR among type 2 diabetic patients using different genetic models
| Genetic models | Genotype | Adjusted OR | 95% CI | P |
|---|---|---|---|---|
| Dominant model | TT | Ref. | ||
| TC/CC | 0.929 | (0.567, 1.521) | 0.769 | |
| Recessive model | TT/TC | Ref. | ||
| CC | 0.495 | (0.218, 1.121) | 0.092 | |
| Co-dominant model | TT | Ref. | ||
| CC | 0.506 | (0.213, 1.204) | 0.123 | |
| TC | 1.041 | (0.623, 1.74) | 0.877 | |
| Over-dominant model | TC | Ref. | ||
| TT/CC | 0.841 | (0.519, 1.363) | 0.483 |
Adjusted for age, sex, duration of diabetes mellitus, blood glucose, SBP, DBP, and BMI.
OR, odds ratio; CI, confidence interval.
3.4. Univariable and multivariable analyses among patients with DR
The rs2071559 genotypes were not associated with PDR in the univariable analysis (P > 0.05) (Table 4). Female sex (OR = 1.776, 95% CI: 1.010–3.125, P = 0.046), duration of T2DM (OR = 1.082, 95% CI: 1.032–1.134, P = 0.001), systolic blood pressure (OR = 1.018, 95% CI: 1.002–1.034, P = 0.032), and BMI > 24.0 kg/m2 (OR = 0.358, 95% CI: 0.198–0.645, P = 0.001) were associated with the development of PDR. The rs2071559 SNP of the VEGFR-2 gene was not associated with PDR in any genetic model after adjustment for age, sex, duration of DM, blood glucose, systolic blood pressure, diastolic blood pressure, and BMI (Table 5).
Table 4.
Univariable logistic regression analysis for factors associated with PDR among DR patients
| OR | 95% CI | P | ||
|---|---|---|---|---|
| Risk factor | ||||
| Age | >60 vs ≤60 | 0.842 | (0.484, 1.466) | 0.544 |
| Sex | Female vs male | 1.776 | (1.01, 3.125) | 0.046 |
| Duration of DM | Per one year | 1.082 | (1.032, 1.134) | 0.001 |
| Blood glucose | >6.1 vs ≤6.1 mmol/L | 1.552 | (0.743, 3.244) | 0.242 |
| SBP | Per one mmHg | 1.018 | (1.002, 1.034) | 0.032 |
| DBP | Per one mmHg | 1.013 | (0.986, 1.041) | 0.349 |
| BMI | 18.5–24 | Ref. | — | — |
| ≤18.5 | 0.193 | (0.019, 1.946) | 0.163 | |
| >24 vs 18.5–24 | 0.358 | (0.198, 0.645) | 0.001 | |
| Genotype | ||||
| Dominant model | (TC + CC) vs TT | 0.757 | (0.432, 1.327) | 0.331 |
| Recessive model | (TC + TT) vs CC | 0.954 | (0.332, 2.737) | 0.930 |
| Co-dominant model | TC vs TT | 0.815 | (0.271, 2.449) | 0.716 |
| CC vs TT | 0.749 | (0.42, 1.336) | 0.327 | |
| Over-dominant model | (TT + TC) vs TC | 1.295 | (0.744, 2.256) | 0.361 |
PDR, proliferative DR; DM, diabetes mellitus; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body weight index; OR, odds ratio; CI, confidence interval.
Table 5.
Multivariable logistic regression analysis of the factors associated with PDR among DR patients in different genetic models
| Genetic models | Genotype | Adjusted OR | 95% CI | P |
|---|---|---|---|---|
| Dominant model | TT | Ref. | ||
| TC/CC | 0.868 | (0.456, 1.651) | 0.665 | |
| Recessive model | TT/TC | Ref. | ||
| CC | 1.207 | (0.355, 4.102) | 0.763 | |
| Co-dominant model | TT | Ref. | ||
| CC | 1.092 | (0.303, 3.931) | 0.893 | |
| TC | 0.842 | (0.435, 1.629) | 0.609 | |
| Over-dominant | TC | Ref. | ||
| TT/CC | 1.205 | (0.641, 2.262) | 0.563 |
Adjusted for age, sex, duration of diabetes mellitus, blood glucose, SBP, DBP, and BMI.
PDR, PDR; OR, odds ratio; CI, confidence interval.
4. Discussion
While the role of VEGF SNPs in the development of DR and response to treatment has been described [18,20], the role of VEGFR-2 SNPs in the development of DR is poorly known. Therefore, this study aimed to examine the association between the rs2071559 SNP of the VEGFR-2 gene and the risk of DR in a Han population in North China. The results suggest that the VEGFR-2 -604T/C polymorphism (rs2071559) is not associated with DR among T2DM patients or PDR among patients with DR in the Han population of Shijiazhuang (China).
DR is a complex trait affected by numerous genes and environmental factors [29,30], and the pathogenesis is still unclear. Of note, DR is a polygenic trait affected by numerous genes, and any polymorphisms within each of these genes will influence the development of DR [29,30]. VEGF is a key molecule in the development of DR [7,8]. Intravitreal VEGF levels are high in diabetic patients and correlate with neovascularization [9], especially in patients with PDR [3,10]. In addition to VEGF levels, the expression and distribution of the VEGF receptor (VEGFR)-2 are also important in developing DR and progressing from NPDR to PDR [11–13]. Indeed, VEGFR-2 controls endothelial cell proliferation, migration, survival, and osmosis via its activation by VEGF [8]. Still, other receptors are involved in DR. VEGFR belongs to the receptor tyrosine kinase superfamily and is a membrane mosaic protein. It contains three subtypes: VEGFR-1 (Flt-1), VEGFR-2 (KDR/Flk-1), and VEGFR-3 (Flt-4) [31,32]. In addition, there are some related receptors, such as neuropilin 1 and 2 [33]. Among them, VEGFR-1 is mainly expressed in monocytes, trophoblast cells, renal mesangial cells, and vascular endothelial cells [31,32]. It is known that VEGF-A, VEGF-B, and PIGF can be combined with it, which is mainly related to the growth regulation of hematopoietic stem cells. VEGFR-2 is mainly distributed on the surface of endothelial cells [31,32]. Other cells include hematopoietic stem cells, retinal precursor cells, and megakaryocytes, which bind to VEGF-A, VEGF-C, VEGF-D, and VEGF-E [31,32]. Combined with VEGF, it stimulates endothelial cell proliferation, increases vascular permeability, and promotes angiogenesis [31,32]. VEGFR-3 is mainly expressed in lymphatic endothelial cells [31,32]. It is known that VEGF-C and VEGF-D combine with VEGFR-3 to regulate the survival and function of lymphatic endothelial cells [34]. NP-1 and NP-2 mainly exist in nerve axons and vascular endothelial cells. They can regulate the signal transduction of VEGFR-2 [35]. They are specific receptors of VEGF165.
At present, it has been recognized that the effect of VEGF on the proliferation and differentiation of endothelial cells is mediated by VEGFR-2. VEGFR-2 regulates the survival of endothelial cells. VEGF-A depends on VEGFR-2 and its subsequently activated PI3K (phosphatidylinositol 3-kinase) to regulate the survival of human umbilical vein endothelial cells, which is conducive to angiogenesis [31,32]. Second, VEGFR-2 regulates the migration of endothelial cells. The migration of endothelial cells is of great significance to angiogenesis. Endothelial cells can migrate along the concentration gradient of VEGF and other growth factors through the basement membrane hydrolyzed by protease, which plays a key role in metastasis [31,32]. Third, VEGFR-2 regulates the proliferation of endothelial cells. VEGF-A is the VEGF level of many endothelial cells. VEGFR-2 on the cell membrane combines with it to transmit extracellular signals to cells and activate a series of downstream signal pathways to regulate the proliferation of endothelial cells [31,32]. Fourth, VEGFR-2 regulates the permeability of endothelial cells. After VEGFR-2 is activated by VEGF binding, it stimulates vascular endothelial cells to release nitric oxide and causes changes in vascular permeability. The above effects are helpful to promote the survival, proliferation, and migration of endothelial cells and induce neovascularization [31,32].
Besides VEGFR-2, a previous genome-wide study revealed that the rs918519 SNP in LOC285626, rs1158314 in NRXN3, rs8004963 in NRXN3, rs11159428 in NRXN3, and rs918520 in LOC285626 are associated with PDR [36]. Another study revealed that the -634G>C SNP in VEGF is associated with PDR [19]. Churchill et al. [17] showed that the -160C, rs13207351, and rs1570360 SNPs in VEGF are associated with PDR. So far, no SNPs in VEGFR-2 have been associated with PDR. A previous study showed that the VEGFR-2 rs2071559 SNP was associated with DR [23], but that previous study did not examine the association of the rs2071559 SNP with PDR. The present study examined the rs2071559 SNP with PDR, and it was not associated with DR in all subjects or with PDR among patients with DR. DR is a complex trait affected by the clinical characteristics of the T2DM patient, genetic background, and living environment [30]. The impact of the rs2071559 on DR can be affected by various other SNPs in the VEGF pathway, as well as in other pathways interacting with the VEGF pathway since DR is a polygenic disorder [29,30]. The relationship between the rs2071559 SNP and the development of PDR in Chinese may be weakened by the influence of other polymorphisms found in the Chinese population, but it will have to be examined. A Spanish study reported no association between the rs2071559 SNP and age-related macular degeneration [37]. Nevertheless, this SNP has been associated with various conditions, including age-related macular degeneration, stroke, endometriosis, hepatocellular carcinoma, and recurrent spontaneous abortion [15,16,20,38–40], but not with lymphoma [41]. In patients with T2DM, the rs2071559 SNP is associated with the extent of carotid atherosclerosis and with myocardial infarction [42,43].
Anti-VEGF drugs have become the first-line treatment for many retinal neovascularization diseases, including age-related macular degeneration and PDR [44,45]. Anti-VEGFR-2 drugs, including the monoclonal antibody ramucirumab and a variety of small-molecule tyrosine kinase inhibitors, remain solely used for the systemic treatment of malignant tumors (e.g., gastric and gastroesophageal adenocarcinoma in the second-line setting) [46], and are only in early trials for eventual ophthalmologic applications [47,48]. Therefore, in the context of future applications of VEGFR-2-targeting drugs in ophthalmic diseases, understanding the factors related to VEGFR-2 that can influence the course of the diseases is of importance. Future studies should examine all SNPs in VEGFR-2 and the various proteins involved in its pathway.
Indeed, reliable SNPs could serve as predictive markers to make informed decisions about the management of specific diseases, the effectiveness of various drugs, and adverse reactions to specific drugs. The plasma sVEGFR-2 levels are elevated in severe DR, while plasma levels of sVEGFR-1 and sVEGFR-3 remain unaltered between patients and controls [49]. In this study, there were no associations between the VEGFR-2 rs2071559 SNP and DR of PDR. VEGFR-2 controls endothelial cell proliferation, migration, survival, and osmosis via its activation by VEGF [8]. VEGFR-2 expression is higher in diabetic eyes than in controls [50]. The activation of VEGFR-2 by VEGF leads to nitric oxide production, blood vessel permeability, and endothelial proliferation [50]. An animal study showed that the specific knockdown of VEGFR-2 in the retina led to decreased neovascularization and improved the morphology of the endothelial cells, highlighting the central role of VEGFR-2 in retinal diseases [51]. Hence, in the context of future anti-VEGFR-2 therapies, a better understanding of the potential effects of VEGFR-2 in DR is essential.
This study has limitations. The sample size was small and from a single center. In addition, only one SNP was analyzed in this study because it was not studied before. Other VEGFR-2 SNPs might yield different results.
This study showed no associations between the rs2071559 SNP and DR (among T2DM patients) or PDR (among DR patients). In the Han population in the Shijiazhuang area of China, the rs2071559 SNP of VEGFR-2 is not associated with the risk of developing DR or PDR.
Appendix
Table A1.
Baseline characteristics of the subjects
| Controls N = 114 | DM N = 123 | NPDR N = 105 | PDR N = 96 | |
|---|---|---|---|---|
| Age, years, median (range) | 56.5 (43, 76) | 61 (38, 82)a | 62 (31, 81)a | 60.5 (38, 79) |
| Male, n (%) | 61 (53.5%) | 56 (45.5%) | 53 (50.5%) | 35 (36.5%) |
| BMI, kg/m2, median (range) | 24.5 (16.7, 35.8) | 24.8 (16.8, 34.9) | 25.4 (17.3, 48.9) | 23.9 (18.3, 30.7)c |
| Systolic pressure, mmHg, median (range) | 129.5 (95, 213) | 128 (96, 180) | 130 (80, 200) | 140 (100, 200)a,b |
| Diastolic pressure, mmHg, median (range) | 80 (40, 132) | 80 (60, 100) | 80 (50, 120) | 80 (60, 110)b |
| Duration of DM, years, median (range) | — | 7 (0.5, 24) | 8 (1, 30) | 10 (0, 28)b,c |
| Fasting blood glucose, mmol/L, median (range) | — | 7 (4, 24) | 7.5 (4.1, 19) | 8 (3.52, 24)b |
DM, diabetes mellitus without DR group; NPDR, non-proliferative DR group; PDR, proliferative DR group; BMI, body weight index.
Bonferroni post hoc test.
aCompared with control, P < 0.05.
bCompared with DM, P < 0.05.
cCompared with NPDR, P < 0.05.
Table A2.
Hardy–Weinberg equilibrium
| Group | X 2 | P |
|---|---|---|
| PDR | 0.98 | 0.32 |
| NPDR | 3.54 | 0.06 |
| DM | 0.72 | 0.40 |
| Control | 0.31 | 0.58 |
DM, diabetes mellitus without DR group; NPDR, non-proliferative DR group; PDR, proliferative DR group.
Footnotes
Funding information: Authors state no funding involved.
Author contributions: Y.Z.Y. and Y.Q.G. carried out the studies, participated in collecting data, and drafted the manuscript. D.D.W. and S.M.Z. performed the statistical analysis and participated in its design. C.J.S. and H.W.L. helped to draft the manuscript. All authors read and approved the final manuscript.
Conflict of interest: Authors state no conflict of interest.
Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
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