Abstract
Background
The association between remnant cholesterol (RC) and diabetic retinopathy (DR) in type 2 diabetes mellitus (T2DM) remains unclear. Morphological changes in retinal vessels have been reported to predict vascular complications of diabetes, including DR.
Methods
This cross-sectional study included 6535 individuals with T2DM. The RC value was calculated using the recognized formula. The retinal vascular parameters were measured using fundus photography. The independent relationship between RC and DR was analyzed using binary logistic regression models. Multiple linear regression and subgroup analyses were employed to investigate the link between RC and vascular parameters, including the retinal arteriolar diameter (CRAE), venular diameter (CRVE), and fractal dimension (Df). Mediation analysis was performed to assess whether the vascular morphology could explain the association between RC and DR.
Results
RC was independently associated with DR in patients with a longer duration of T2DM (> 7 years). Patients with the highest quartile RC levels had larger CRAE (5.559 [4.093, 7.025] μm), CRVE (7.620 [5.298, 9.941] μm) and Df (0.013 [0.009, 0.017]) compared with patients with the lowest quartile RC levels. Results were robust across different subgroups. The association between RC and DR was mediated by CRVE (0.020 ± 0.005; 95% confidence interval: 0.012–0.032).
Conclusions
RC may be a risk factor for DR among those who have had T2DM for a longer period of time. Higher RC levels were correlated with wider retinal arterioles and venules as well as higher Df, and it may contribute to DR through the dilation of retinal venules.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12944-024-02064-6.
Keywords: Diabetic retinopathy, Remnant cholesterol, Retinal vessels, Type 2 diabetes mellitus
Background
Diabetes is a prevalent and significant chronic disease in contemporary society, the global prevalence of which is projected to reach 12.2% (783.2 million individuals) by 2045 [1]. The high morbidity and disability associated with diabetes have emerged as pressing public health concerns, posing a significant threat to human well-being and the global economy [2]. Diabetic retinopathy (DR) is a major contributor to diabetes-related disability [3]. However, known risk factors fail to explain the significant individual variations in the development and severity of DR, and the identification of additional risk factors for disease control is therefore required [4].
Dyslipidemia is a characteristic metabolic disorder in diabetes, typically manifesting at the early stages and hastening the onset of diabetic complications, including DR [5–7]. The role of remnant cholesterol (RC) has recently attracted growing attention. RC represents the cholesterol content in triglyceride-rich lipoproteins (TRLs), including very low- and intermediate-density lipoproteins as well as chylomicron remnants [8]. Large epidemiological studies have substantiated the close association between RC and the occurrence, as well as the progression of cardiovascular diseases (CVDs) in both individuals with and without diabetes, with a potential role in promoting atherosclerosis potentially surpassing that of low-density lipoprotein cholesterol (LDL-C) [9–14]. Unlike the well-established causal relationship between RC and CVDs, the connection between RC and microvascular disease remains uncertain. According to a multicenter cohort study conducted in Finland, the RC concentration was predictive of diabetic nephropathy and severe DR in patients with type 1 diabetes mellitus (T1DM) [15]. For those with type 2 diabetes mellitus (T2DM), a study in China revealed a positive association between RC and DR prevalence [16]. However, another cross-sectional study reported no significant correlation between the two [17]. To date, the relationship between RC and DR in the population with T2DM remains inconclusive.
This study attempted to clarify the impact of RC on DR by investigating the association between RC and retinal vascular morphology. The retinal microvasculature is the only directly observable deep microvascular system in the human body [18]. With the development of fundus photography technology and associated analysis software, the visuality and quantifiability of the structural patterns of the retinal microvasculature have enabled it to become a reliable research indicator [19]. Previous studies demonstrated that the retinal vascular diameter and geometric parameters (e.g., vascular tortuosity [VT], fractal dimension [Df]) are related to the risk of ocular and systemic diseases, such as DR, diabetic nephropathy, ischemic stroke, coronary heart disease, hypertension, and other cardio- and cerebrovascular diseases [20–24]. Structural and functional alterations in retinal blood vessels can predict micro- and macrovascular complications of diabetes, thereby facilitating early recognition of DR at a preclinical stage [25]. It is well accepted that the early morphological changes in retinal vessels in the context of diabetes are attributed to elevated blood glucose levels and the secondary persistent hypoxia and chronic inflammation [26–29]. However, minimal attention has been paid to the impact of abnormal metabolism of lipids (such as RC) on retinal vascular morphology.
This study targeted adult patients with T2DM, aiming to explore the associations between RC and retinal microvascular morphology, as well as DR, and to explore whether the connection between RC and DR is mediated by retinal microvascular morphology.
Methods
Participants
Patients were sourced from the Shanghai Cohort Study of Diabetic Eye Disease (SCODE). The inclusion and exclusion criteria for participants and the diagnostic criteria of T2DM and DR employed in the SCODE were previously described [30, 31]. According to the Declaration of Helsinki, this study was approved by the Ethics Committee of Shanghai General Hospital. Written informed consent was obtained from each participant.
Data collection
The basic information of patients was documented, including gender, date of birth, body mass index(BMI), blood pressure, time of diagnosis, and medical history. The mean arterial pressure (MAP) was computed as 1/3 of systolic blood pressure (SBP) plus 2/3 of diastolic blood pressure (DBP). Blood samples were collected for testing indicators of blood glucose (glycated hemoglobin A1c [HbA1c] and fasting plasma glucose [FPG]) and lipids (total cholesterol [TC], triglycerides [TG], high-density lipoprotein cholesterol [HDL-C], and LDL-C). RC = TC − HDL-C − LDL-C. Participants underwent routine ophthalmic examinations. Digital fundus photography (Topcon NW400; Topcon, Tokyo, Japan) was performed with or without mydriasis, and colored photographs centered on macula and optic disc were taken at a 45° angle for each eye.
Retinal vascular parameters
Optic disc-centered photos of the right eye were used for analyses; in cases where these were unavailable, photos of the left eye were used instead.
Computer vision and deep learning technology based on the bionic mechanism of human vision were integrated to automatically measure the vessel diameters, VT, and Df from retinal photographs. First, the fundus image was preprocessed, followed by extraction of the region of interest to obtain the target area. Subsequently, denoising, normalization and image enhancement were performed to enhance the significance of blood vessels (Fig. 1a) [32]. The preprocessed fundus image was fed into ResNet101-UNet for vascular segmentation. The segmented blood vessels were subjected to morphological erosion to extract their centerlines. A line perpendicular to a specific point on the centerline intersects the vascular edges at two points, the distance between which represents the vascular diameter corresponding to that specific point (Fig. 1b). Then, a deep learning network and an edge extraction algorithm were synergistically employed for precise localization of the optic disc. The papillary diameter (PD) was determined according to the smallest external circle within the segmentation area, and the diameter of all blood vessels within 0.5–1 PD from the optic disc edge was calculated. The six arteriolar and six venular vessels with the largest average diameters were selected, and the central retinal artery equivalent (CRAE) and central retinal vein equivalent (CRVE) were calculated according to established methodologies [33]. The ratio of CRAE to CRVE was denoted as arteriole-to-venule ratio (AVR). The measurement of VT and Df has been described elsewhere [34, 35].
Fig. 1.
Illustrations for analyses of retinal vascular parameters. a displayed the preprocessed fundus image. b depicted the retinal arterioles (red line) and venules (blue line) within 0.5–1 PD from the optic disc edge and the way to measure the vascular diameters
Statistical analysis
Continuous variables were reported as mean ± standard if distributed normally, or as median (inter-quartile range) if not. Differences between multiple independent groups were compared using ANOVA or the Kruskal–Wallis H test. Categorical variables were reported as frequency (percentage), and the chi-square test was employed to compare the proportions. According to the quartile, RC was converted into a categorical variable.
To investigate the relationship between RC and DR, the participants were stratified by the median duration of T2DM (≤ 7 years and > 7 years), and four distinct logistic regression models were constructed: crude model (without adjusting for any covariates), Model 1 (adjusting for age and gender), Model 2 (further adjusting for BMI, T2DM duration, HbA1c, and MAP) and Model 3 (further adjusting for TG, HDL-C, and LDL-C). The association between the vascular parameters and DR was also examined using the models. Multiple linear regression with four different models (as previously described) was conducted to explore the correlation between RC and vascular parameters. Participants were grouped based on age, gender, T2DM duration, BMI, HbA1c, MAP, and DR prevalence, allowing further investigation of the relationship between RC and vascular morphological parameters in each subgroup. The P-trend was calculated based on RC as a continuous variable containing the median of each quartile.
Multiple parallel mediation analyses adjusting for age, gender, BMI, T2DM duration, HbA1c, MAP, TG, HDL-C, and LDL-C were performed to quantify the extent to which the association between RC and DR was mediated by each retinal vascular morphological parameter. Bootstrapping with 5000 resamples was used to verify the indirect effects, where 95% confidence intervals (CIs) that did not cross zero were considered significant.
Data analysis was conducted with SPSS 26.0 and Mplus 8.3 software. P < 0.05 (two-tailed) was deemed statistically significant.
Results
A total of 6627 patients were initially enrolled, 67 and 25 of whom were excluded due to missing data and RC outliers, respectively. Consequently, 6535 patients were incorporated into the final analysis. The characteristics of the participants stratified by RC-level quartiles were summarized in Table 1. Individuals with higher RC levels were more likely to be women and younger individuals and to have had a shorter course of diabetes, lower HDL-C, and higher BMI, FPG, MAP, TC, and TG. The patients were then stratified according to median T2DM duration. Among those with a course > 7 years, the prevalence of DR significantly differed among the four groups. In terms of the retinal vascular morphological parameters, CRAE, CRVE, and Df were significantly higher in individuals with higher RC levels. Additionally, AVR and VT varied significantly among the different RC groups (Table 1).
Table 1.
Characteristics of the patients stratified by RC-level quartiles
| RC Quartile | Q1 | Q2 | Q3 | Q4 | All | P value |
|---|---|---|---|---|---|---|
| n | 1647 (≤ 0.35) | 1622 (0.36 − 0.58) | 1663 (0.59 − 0.90) | 1603 (0.91 − 10.89) | 6535 | |
| T2DM duration ≤ 7 years | 856 | 899 | 909 | 911 | 3575 | |
| T2DM duration > 7 years | 791 | 723 | 754 | 692 | 2960 | |
| RC (mmol/L) | 0.20 (0.10 − 0.30) | 0.47 (0.41 − 0.52) | 0.72 (0.65 − 0.80) | 1.23 (1.03 − 1.61) | 0.58 (0.35 − 0.90) | < 0.001 |
| Age (years) | 65 (61 − 70) | 64 (60 − 69) | 64 (60 − 68) | 64 (59 − 68) | 64 (60 − 69) | < 0.001 |
| Male, n (%) | 781 (47.4%) | 714 (44.0%) | 663 (39.9%) | 675 (42.1%) | 2833 (43.4%) | < 0.001 |
| T2DM duration (years) | 7 (4 − 12) | 7 (4 − 11) | 7 (4 − 12) | 6 (3 − 11) | 7 (4 − 11) | < 0.001 |
| BMI (kg/m2) | 24.09 (22.23 − 26.08) | 24.22 (22.48 − 26.26) | 24.54 (22.94 − 26.84) | 24.88 (23.01 − 26.90) | 24.44 (22.65 − 26.56) | < 0.001 |
| FPG (mmol/L) | 7.2 (6.3 − 8.3) | 7.3 (6.4 − 8.5) | 7.3 (6.4 − 8.7) | 7.5 (6.5 − 9.0) | 7.3 (6.4 − 8.6) | < 0.001 |
| HbA1c (%) | 7.4 (6.5 − 7.8) | 7.1 (6.4 − 7.8) | 7.2 (6.4 − 7.9) | 7.2 (6.6 − 8.0) | 7.2 (6.5 − 7.9) | 0.028 |
| SBP (mmHg) | 130 (124 − 134) | 130 (122 − 133) | 130 (123 − 134) | 130 (122 − 134) | 130 (122 − 134) | 0.242 |
| DBP (mmHg) | 78 (74 − 80) | 80 (74 − 82) | 78 (74 − 82) | 80 (75 − 82) | 78 (74 − 82) | < 0.001 |
| MAP (mmHg) | 94.7 (91.7 − 97.3) | 95.0 (92.0 − 98.7) | 95.0 (92.0 − 98.3) | 95.3 (92.0 − 98.7) | 95.0 (92.0 − 98.0) | < 0.001 |
| Hypertension, n (%) | 1058 (64.2%) | 1018 (62.8%) | 1118 (67.2%) | 1100 (68.6%) | 4294 (65.7%) | 0.001 |
| TC (mmol/L) | 4.43 (3.81 − 5.06) | 4.60 (4.02 − 5.21) | 4.90 (4.28 − 5.55) | 5.24 (4.58 − 5.99) | 4.79 (4.15 − 5.47) | < 0.001 |
| TG (mmol/L) | 1.10 (0.80 − 1.50) | 1.10 (0.90 − 1.50) | 1.60 (1.40 − 1.90) | 2.50 (2.10 − 3.60) | 1.50 (1.10 − 2.20) | < 0.001 |
| HDL-C (mmol/L) | 1.40 (1.21 − 1.71) | 1.40 (1.18 − 1.65) | 1.31 (1.11 − 1.54) | 1.14 (0.97 − 1.34) | 1.31 (1.10 − 1.57) | < 0.001 |
| LDL-C (mmol/L) | 2.68 (2.08 − 3.33) | 2.67 (2.15 − 3.21) | 2.81 (2.24 − 3.39) | 2.67 (2.09 − 3.30) | 2.70 (2.14 − 3.30) | < 0.001 |
| DR, n (%) | 201 (12.2%) | 217 (13.4%) | 213 (12.8%) | 223 (13.9%) | 854 (13.1%) | 0.508 |
| T2DM duration ≤ 7 years | 83 (9.7%) | 71 (7.9%) | 77 (8.5%) | 87 (9.5%) | 318 (8.9%) | 0.482 |
| T2DM duration > 7 years | 118 (14.9%) | 146 (20.2%) | 136 (18.0%) | 136 (19.7%) | 536 (18.1%) | 0.034 |
| NPDR, n (%) | 197 (12.0%) | 212 (13.1%) | 204 (12.3%) | 221 (13.8%) | 834 (12.8%) | 0.397 |
| PDR, n (%) | 4 (0.2%) | 5 (0.3%) | 9 (0.5%) | 2 (0.1%) | 20 (0.3%) | 0.179 |
| Insulin use, n (%) | 151 (9.2%) | 157 (9.7%) | 156 (9.4%) | 127 (7.9%) | 591 (9.0%) | 0.320 |
| CRAE (μm) | 137.77 (127.37 − 147.81) | 138.65 (129.33 − 148.54) | 138.50 (129.84 − 148.07) | 138.71 (129.59 − 149.16) | 138.39 (128.99 − 148.49) | 0.004 |
| CRVE (μm) | 228.49 (211.60 − 244.63) | 229.13 (213.62 − 245.51) | 231.99 (214.94 − 247.21) | 232.28 (215.36 − 250.36) | 230.31 (213.82 − 246.84) | < 0.001 |
| AVR | 0.60 (0.56 − 0.65) | 0.61 (0.56 − 0.65) | 0.60 (0.56 − 0.65) | 0.60 (0.56 − 0.65) | 0.60 (0.56 − 0.65) | 0.037 |
| VT, × 10–4 | 9.40 (8.23 − 10.61) | 9.36 (8.04 − 10.45) | 9.41 (8.24 − 10.51) | 9.48 (8.34 − 10.62) | 9.41 (8.23 − 10.53) | 0.033 |
| Df | 1.48 (1.45 − 1.52) | 1.50 (1.46 − 1.52) | 1.50 (1.46 − 1.53) | 1.50 (1.46 − 1.53) | 1.49 (1.46 − 1.53) | < 0.001 |
Relationship between RC and DR prevalence
No significant correlation between RC and DR was found among individuals with a T2DM duration of ≤ 7 years (Table 2). However, when the T2DM duration was > 7 years, RC could be a risk factor for DR. DR prevalence in Q2 and Q4 in all models and in Q3 in Model 3 was significantly higher than those in Q1. (Table 2).
Table 2.
Binary logistic regression analyses for correlation between RC and DR
| Variable | Crude model | Model 1 | Model 2 | Model 3 |
|---|---|---|---|---|
| T2DM duration ≤ 7 years | ||||
| RC (Quartile) | ||||
| Q1 | Ref | Ref | Ref | Ref |
| Q2 | 0.799 (0.573, 1.113) | 0.809 (0.580, 1.127) | 0.825 (0.591, 1.151) | 0.813 (0.581, 1.137) |
| Q3 | 0.862 (0.623, 1.193) | 0.881 (0.636, 1.221) | 0.878 (0.632, 1.220) | 0.847 (0.607, 1.183) |
| Q4 | 0.983 (0.717, 1.349) | 1.009 (0.734, 1.387) | 1.013 (0.735, 1.396) | 0.965 (0.667, 1.397) |
| P-trend | 0.790 | 0.671 | 0.687 | 0.983 |
| T2DM duration > 7 years | ||||
| RC (Quartile) | ||||
| Q1 | Ref | Ref | Ref | Ref |
| Q2 | 1.443 (1.105, 1.885) ** | 1.432 (1.096, 1.871) ** | 1.467 (1.117, 1.926) ** | 1.464 (1.114, 1.924) ** |
| Q3 | 1.255 (0.958, 1.644) | 1.242 (0.947, 1.629) | 1.276 (0.968, 1.683) | 1.345 (1.013, 1.786) * |
| Q4 | 1.395 (1.064, 1.829) * | 1.378 (1.049, 1.810) * | 1.427 (1.079, 1.888) * | 1.868 (1.339, 2.607) *** |
| P-trend | 0.061 | 0.078 | 0.051 | 0.001 |
Data were presented as odds ratio (95% CI). *P < 0.05, **P < 0.01, ***P < 0.001. Ref., reference
Crude model: unadjusted
Model 1: age and gender
Model 2: Model 1 + BMI, T2DM duration, HbA1c, and MAP
Model 3: Model 2 + TG, HDL-C, and LDL-C
Correlation between RC and retinal vascular morphological parameters
The effect of RC on retinal vascular morphological parameters was analyzed using a linear regression model (Table 3). In the crude model and Models 1, 2, and 3, the CRAE and Df of the participants in Q2, Q3, and Q4 and the CRVE of the participants in Q3 and Q4, were significantly higher than those in Q1. In the final model, the CRAE, CRVE, and Df exhibited an average increase of 5.559 (4.093, 7.025) μm, 7.620 (5.298, 9.941) μm, and 0.013 (0.009, 0.017), respectively, when comparing Q4 with Q1. There was no discernible linear correlation between RC and AVR or VT.
Table 3.
Multiple linear regression analyses for correlation between RC and retinal vasculature
| Variable | Crude model | Model 1 | Model 2 | Model 3 |
|---|---|---|---|---|
| CRAE | ||||
| RC (Quartile) | ||||
| Q1 | Ref | Ref | Ref | Ref |
| Q2 | 2.082 (0.814, 3.349) ** | 1.781 (0.519, 3.044) ** | 1.981 (0.721, 3.242) ** | 2.121 (0.861, 3.380) *** |
| Q3 | 2.588 (1.328, 3.847) *** | 2.156 (0.898, 3.414) ** | 2.477 (1.219, 3.735) *** | 3.044 (1.768, 4.320) *** |
| Q4 | 3.408 (2.137, 4.679) *** | 2.861 (1.591, 4.132) *** | 3.313 (2.039, 4.587) *** | 5.559 (4.093, 7.025) *** |
| P-trend | < 0.001 | < 0.001 | < 0.001 | < 0.001 |
| CRVE | ||||
| RC (Quartile) | ||||
| Q1 | Ref | Ref | Ref | Ref |
| Q2 | 1.402 (− 0.610, 3.414) | 0.901 (− 1.092, 2.894) | 1.011 (− 0.984, 3.006) | 1.195 (− 0.799, 3.190) |
| Q3 | 3.533 (1.534, 5.533) *** | 2.921 (0.935, 4.906) ** | 2.973 (0.981, 4.964) ** | 3.429 (1.409, 5.448) *** |
| Q4 | 6.001 (3.983, 8.019) *** | 5.054 (3.048, 7.060) *** | 5.113 (3.095, 7.131) *** | 7.620 (5.298, 9.941) *** |
| P-trend | < 0.001 | < 0.001 | < 0.001 | < 0.001 |
| AVR | ||||
| RC (Quartile) | ||||
| Q1 | Ref | Ref | Ref | Ref |
| Q2 | 0.006 (0.001, 0.011) * | 0.006 (0.001, 0.011) * | 0.007 (0.002, 0.012) ** | 0.007 (0.002, 0.012) ** |
| Q3 | 0.003 (− 0.002, 0.007) | 0.002 (− 0.003, 0.007) | 0.004 (− 0.001, 0.009) | 0.005 (− 0.000, 0.010) |
| Q4 | 0.000 (− 0.005, 0.005) | 0.000 (− 0.005, 0.005) | 0.002 (− 0.003, 0.007) | 0.005 (− 0.000, 0.011) |
| P-trend | 0.491 | 0.530 | 0.925 | 0.143 |
| VT, × 10–4 | ||||
| RC (Quartile) | ||||
| Q1 | Ref | Ref | Ref | Ref |
| Q2 | − 0.118 (− 0.246, 0.011) | − 0.119 (− 0.248, 0.009) | − 0.130 (− 0.259, -0.001) * | − 0.128 (− 0.257, 0.001) |
| Q3 | − 0.012 (− 0.139, 0.116) | − 0.016 (− 0.144, 0.113) | − 0.037 (− 0.166, 0.091) | − 0.044 (− 0.175, 0.086) |
| Q4 | 0.063 (− 0.066, 0.192) | 0.060 (− 0.069, 0.190) | 0.031 (− 0.100, 0.161) | − 0.029 (− 0.180, 0.121) |
| P-trend | 0.108 | 0.119 | 0.274 | 0.973 |
| Df | ||||
| RC (Quartile) | ||||
| Q1 | Ref | Ref | Ref | Ref |
| Q2 | 0.011 (0.007, 0.015) *** | 0.008 (0.005, 0.011) *** | 0.008 (0.005, 0.011) *** | 0.008 (0.005, 0.012) *** |
| Q3 | 0.013 (0.009, 0.017) *** | 0.009 (0.006, 0.013) *** | 0.009 (0.006, 0.013) *** | 0.011 (0.007, 0.014) *** |
| Q4 | 0.015 (0.011, 0.018) *** | 0.009 (0.006, 0.013) *** | 0.010 (0.006, 0.013) *** | 0.013 (0.009, 0.017) *** |
| P-trend | < 0.001 | < 0.001 | < 0.001 | < 0.001 |
Data were presented as coefficient (95% CI). *P < 0.05, **P < 0.01, ***P < 0.001. Ref., reference
Crude model: unadjusted
Model 1: age and gender
Model 2: Model 1 + BMI, T2DM duration, HbA1c, and MAP
Model 3: Model 2 + TG, HDL-C, and LDL-C
Subgroup analyses for the relationship between RC and retinal vascular morphological parameters
CRAE, CRVE, and Df were positively correlated with RC levels in distinct subgroups stratified by gender, age, T2DM duration, BMI, HbA1c, MAP, and DR status. This relationship was significant in most subgroups (Fig. S1, Fig. S2, and Fig. S3).
Association between retinal vascular morphology and DR
In all binary logistic regression models, the five vascular parameters were significantly associated with DR. Specifically, CRAE, CRVE, and VT were positively correlated with DR, whereas AVR and Df were negatively correlated with DR (Table S1).
Retinal vascular morphology mediates the relationship between RC and DR
Parallel mediation analysis was performed in participants with a duration of more than 7 years, including RC as the independent variable, DR as the dependent variable, and CRAE, CRVE, and Df as mediators (Fig. 2, Table 4). CRAE had no significant mediating effect on the relationship between RC and DR (0.003; 95% CI [−0.003, 0.011]). CRVE was a significant mediator of this association (0.020; 95% CI [0.012, 0.032]). Conversely, Df was a suppressor of this association (− 0.008; 95% CI [− 0.017, − 0.003]), but the suppressive effect was less pronounced than the mediating effect of CRVE. Therefore, the association between RC and DR is likely mediated by the diameter of the retinal venules.
Fig. 2.
Mediators of the relationship between RC and DR. Only statistically significant paths were shown. Standardized coefficient for each path was presented. *P < 0.05, **P < 0.01, ***P < 0.001
Table 4.
Standardized effects of the parallel mediation model
| Indirect path | Estimate (S.E.) | 95% CI | Z value | P value | |
|---|---|---|---|---|---|
| Lower | Upper | ||||
| CRAE | 0.003 (0.003) | − 0.003 | 0.011 | 0.950 | 0.342 |
| CRVE | 0.020 (0.005) | 0.012 | 0.032 | 3.961 | < 0.001 |
| Df, × 10–2 | − 0.008 (0.004) | − 0.017 | − 0.003 | − 2.352 | 0.019 |
Estimate represented the mediating effect value, calculated by multiplying the standardized coefficients of the two paths before and after the mediators. 95% CI was obtained by bootstrapping. Z value and P value were obtained by Sobel test
Discussion
The findings suggested that RC might be a risk factor for DR among individuals with T2DM with a duration of > 7 years. The retinal vascular morphological parameters, including CRAE, CRVE, and Df, were positively associated with RC in patients with T2DM. These vascular parameters also showed a significant correlation with DR. Moreover, the relationship between RC and DR may be mediated by the diameter of retinal venules (CRVE).
Few clinical studies have focused on the relationship between RC and DR against the background of T2DM. Only two cross-sectional studies have been conducted in China, and results were contradictory. In a study of 456 individuals in Harbin, Shan et al. discovered a positive correlation between RC levels and the prevalence and severity of DR [16]. Conversely, another study targeting 1956 participants in southern Taiwan found no significant association between RC and DR or proliferative DR (PDR) [17]. Herein, stratification based on T2DM duration revealed that higher levels of RC in patients with longer disease duration (> 7 years) were associated with a higher risk of DR. This implies that the influence of RC on the pathogenesis of DR may be subtle and slow, becoming more apparent as the disease progresses. Although substantial disagreements persist among the limited research findings, the potential impact of RC on DR cannot be ignored. Therefore, conducting more comprehensive longitudinal studies is imperative to prevent DR prevention in patients with T2DM.
Morphological and structural changes in the retinal microvascular system are considered preclinical signs of vascular complications of diabetes, including DR. In this study, five retinal vascular parameters were examined, among which AVR and VT showed no significant association with RC. Therefore, subsequent analyses focused on the other three vascular parameters: CRAE, CRVE, and Df. The two diameter parameters, CRAE and CRVE, were found to positively correlate with both RC and DR. However, the relationship between DR and CRAE, which characterizes the diameter of the retinal arterioles, remains controversial. Several prospective studies have shown that larger retinal arteriolar diameters can predict DR progression. Conversely, in young patients with T1DM in Denmark, a narrower retinal arteriolar diameter was associated with the onset of PDR [28, 36]. This divergence may be due to age, race, type of diabetes, or other factors, indicating the intricate nature of arteriole diameter regulation. In addition, a recent study found that owing to the central light reflex, CRAE could be overestimated in fundus camera images [37]. This measurement bias might partially explain the discrepancies reported in these studies. The association between DR and CRVE, which represents the diameter of the retinal venules, seemed clearer. Most studies demonstrated that a wider retinal venule diameter is associated with DR [26, 36, 38]. After stratification by gender, age, duration, BMI, HbA1c, MAP, and DR prevalence, a significant correlation remained between CRVE and RC in each subgroup, suggesting a strong association. Mediation analysis indicated that RC may contribute to the development of DR by inducing dilation of retinal venules. This finding emphasizes the role of alterations in retinal vascular morphology, particularly CRVE, in the pathological effects of RC on DR.
The parameter Df quantifies the geometric complexity of the retinal vascular branching patterns [39]. Limited research has indicated that Df may share a nonlinear relationship with the severity of DR, with an observed increase in Df among patients with mild nonproliferative DR (NPDR) and a decrease among those with moderate to severe NPDR [40]. Patients who progressed to PDR exhibited a lower Df than those who did not [26]. The increase in Df during the early stages of DR may be attributed to increased arteriovenous shunting induced by ischemia and hypoxia. With the loss of peripheral cells and appearance of avascular zones, a decline in Df may represent progression towards a more advanced stage of the disease. Herein, Df increased significantly with increasing RC levels, which may have resulted from the predominance of non-DR participants (86.9%). Subsequently, a subgroup analysis was performed based on the presence or absence of DR and showed that the positive correlation between Df and RC was no longer significant in the DR group. Mediation analysis and logistic regression indicated a negative association between Df and DR, which may be related to the specific type of DR in the participants.
TGs in TRLs are hydrolyzed in the circulation, generating residual particles rich in cholesterol esters, the cholesterol part of which is called RC [41]. These residual particles are sufficiently small enough to freely pass through the vascular endothelial barrier and reside in the connective tissue matrix, thereby facilitating ample interactions with the vascular wall structure [42]. Although RC is famous for promoting atherosclerosis, alterations in the microvasculature are unrelated to this characteristic. A large clinical study reported a substantial causal relationship between RC and systemic inflammation [43]. The RC particles that accumulate in the arterial wall may elicit inflammation, thereby inducing vascular endothelial damage [44]. RC may trigger inflammation and endothelial dysfunction, causing the expansion of the retinal arterioles and venules. Conversely, RC may be also involved in promoting retinal ischemia and hypoxia under high glucose conditions, leading to more arteriovenous shunts (manifested as an increase in Df) and compensatory vasodilation in response to chronic hypoxia, thereby accelerating the onset of early DR [45, 46]. Currently, robust basic research evidence regarding this issue is scarce. A more comprehensive and precise mechanism warrants further exploration to provide in-depth insight into the role of RC in microvascular diseases.
Study strengths and limitations
The present study elucidated the association between RC and retinal vascular morphology for the first time, and the findings revealed the intermediate role of morphological alterations in retinal vessels in DR induced by RC. Moreover, the large sample size enhances the validity of the results. However, the study has certain limitations. First, the RC value obtained indirectly through the calculation formula may lack precision compared with the direct measurement. Second, this study only examined a limited range of vascular parameters, thus failing to provide a comprehensive depiction of the morphological alterations in the fundus vessels. Third, the information on the utilization of lipid-lowering medications was not collected during the study, making it impossible to exclude their impact on the results. The TNT trial found that statin therapy reduced RC levels and cardiovascular risk in individuals with clinically evident CVDs [47]. In the FinnDiane study, the proportion of participants receiving lipid-lowering medications increased with increasing quartiles of RC, which indicated that, as discussed by the authors, the association between RC and outcome may be weakened by the efficacy of lipid-lowering drugs [15].
Conclusions
This study found that RC may be a risk factor for DR among people who have had T2DM for a longer period of time, implying a probable time-accumulative effect on DR. Concomitantly, higher RC levels were correlated with wider retinal arterioles and venules and higher Df values. Elevated RC levels could promote the onset and development of DR through the dilation of retinal venules. Therefore, individuals with long-standing T2DM must diligently monitor indicators of lipid metabolism. Controlling lipid levels is useful for mitigating the risk of microvascular complications. Additionally, the measurement and interpretation of retinal vascular parameters may be an effective combined preventive measure, especially for patients who do not show preclinical signs in fundus photographs but still possess risk factors (e.g., higher RC levels) for DR. More prospective studies are warranted to clarify whether RC can effectively predict DR, which is promising for opening new avenues for complication prevention.
Supplementary Information
Additional file 1: Fig. S1 Subgroup analyses for correlation between RC and CRAE. Fig. S2 Subgroup analyses for correlation between RC and CRVE. Fig. S3 Subgroup analyses for correlation between RC and Df. Table S1. Binary logistic regression analyses for correlation between retinal vasculature and DR.
Acknowledgements
The authors would like to express their gratitude to the participants and investigators of this study.
Abbreviations
- AVR
Arteriole to venule ratio
- BMI
Body mass index
- CI
Confidence interval
- CRAE
Central retinal artery equivalent
- CRVE
Central retinal vein equivalent
- CVD
Cardiovascular disease
- DBP
Diastolic blood pressure
- Df
Fractal dimension
- DR
Diabetic retinopathy
- FPG
Fasting plasma glucose
- HbA1c
Glycated hemoglobin A1c
- HDL-C
High-density lipoprotein cholesterol
- LDL-C
Low-density lipoprotein cholesterol
- MAP
Mean arterial pressure
- NPDR
Non-proliferative diabetic retinopathy
- PD
Papillary diameter
- PDR
Proliferative diabetic retinopathy
- RC
Remnant cholesterol
- Ref.
Reference
- SBP
Systolic blood pressure
- SCODE
Shanghai Cohort Study of Diabetic Eye Disease
- T1DM
Type 1 diabetes mellitus
- T2DM
Type 2 diabetes mellitus
- TC
Total cholesterol
- TG
Triglycerides
- TRL
Triglyceride-rich lipoprotein
- VT
Vascular tortuosity
Authors’ contributions
Chen S performed the data analysis and drafted the manuscript. Xu Y, Lu L, Cheng M, Wang Y, Yang Q, Shi Y, and Lin S contributed to the investigation, data collection and curation. Chen B provided the methodology guidance and revised the manuscript. Lin S provided the supervision and validation and revised the manuscript. Ling S and Zhou D were responsible for the software analysis and visualization. Zou H and Ma Y concepted and designed the study and revised the manuscript. All authors read and approved the final manuscript.
Funding
This work was supported by Chinese National Key Research and Development Program (Project No. 2021YFC2702100), Shanghai engineering research center of precise diagnosis and treatment of eye diseases, Shanghai, China (Project No. 19DZ2250100), Shanghai First People’s Hospital featured research projects (CCTR-2022C08), the Shanghai Public Health Three-Year Action Plan (GWVI-11.1–30, GWVI-11.1–22), Science and Technology Commission of Shanghai Municipality (23ZR1481000), Shanghai Municipal Health Commission (2022YQ051). The funding has no role in design, conduction or publication of the study.
Availability of data and materials
The data that support the findings of this study are available from Shanghai Municipal Centers for Disease Control and Prevention but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available.
Declarations
Ethics approval and consent to participate
The study was conducted in accordance with the ethical principles of the Helsinki Declaration and was approved by the Ethics Committee of Shanghai General Hospital (approval number: 2013KY023). Each participant signed an informed consent.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Senlin Lin, Email: woodylin@yeah.net.
Yan Shi, Email: shiyan@scdc.sh.cn.
Haidong Zou, Email: zouhaidong@sjtu.edu.cn.
Yingyan Ma, Email: mYy_29@163.com.
References
- 1.Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183:109119. doi: 10.1016/j.diabres.2021.109119. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Hu X, Liu Q, Guo X, Wang W, Yu B, Liang B, et al. The role of remnant cholesterol beyond low-density lipoprotein cholesterol in diabetes mellitus. Cardiovasc Diabetol. 2022;21(1):117. doi: 10.1186/s12933-022-01554-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Cheung N, Mitchell P, Wong TY. Diabetic retinopathy. Lancet. 2010;376(9735):124–136. doi: 10.1016/S0140-6736(09)62124-3. [DOI] [PubMed] [Google Scholar]
- 4.Lin KY, Hsih WH, Lin YB, Wen CY, Chang TJ. Update in the epidemiology, risk factors, screening, and treatment of diabetic retinopathy. J Diabetes Investig. 2021;12(8):1322–1325. doi: 10.1111/jdi.13480. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Wang J, Stančáková A, Soininen P, Kangas AJ, Paananen J, Kuusisto J, et al. Lipoprotein subclass profiles in individuals with varying degrees of glucose tolerance: a population-based study of 9399 Finnish men. J Intern Med. 2012;272(6):562–572. doi: 10.1111/j.1365-2796.2012.02562.x. [DOI] [PubMed] [Google Scholar]
- 6.Wu L, Parhofer KG. Diabetic dyslipidemia. Metabolism. 2014;63(12):1469–1479. doi: 10.1016/j.metabol.2014.08.010. [DOI] [PubMed] [Google Scholar]
- 7.Horton WB, Barrett EJ. Microvascular Dysfunction in Diabetes Mellitus and Cardiometabolic Disease. Endocr Rev. 2021;42(1):29–55. doi: 10.1210/endrev/bnaa025. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Jørgensen AB, Frikke-Schmidt R, West AS, Grande P, Nordestgaard BG, Tybjærg-Hansen A. Genetically elevated non-fasting triglycerides and calculated remnant cholesterol as causal risk factors for myocardial infarction. Eur Heart J. 2013;34(24):1826–1833. doi: 10.1093/eurheartj/ehs431. [DOI] [PubMed] [Google Scholar]
- 9.Varbo A, Benn M, Tybjærg-Hansen A, Jørgensen AB, Frikke-Schmidt R, Nordestgaard BG. Remnant cholesterol as a causal risk factor for ischemic heart disease. J Am Coll Cardiol. 2013;61(4):427–436. doi: 10.1016/j.jacc.2012.08.1026. [DOI] [PubMed] [Google Scholar]
- 10.Quispe R, Martin SS, Michos ED, Lamba I, Blumenthal RS, Saeed A, et al. Remnant cholesterol predicts cardiovascular disease beyond LDL and ApoB: a primary prevention study. Eur Heart J. 2021;42(42):4324–4332. doi: 10.1093/eurheartj/ehab432. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Castañer O, Pintó X, Subirana I, Amor AJ, Ros E, Hernáez Á, et al. Remnant Cholesterol, Not LDL Cholesterol, Is Associated With Incident Cardiovascular Disease. J Am Coll Cardiol. 2020;76(23):2712–2724. doi: 10.1016/j.jacc.2020.10.008. [DOI] [PubMed] [Google Scholar]
- 12.Sascău R, Clement A, Radu R, Prisacariu C, Stătescu C. Triglyceride-Rich Lipoproteins and Their Remnants as Silent Promoters of Atherosclerotic Cardiovascular Disease and Other Metabolic Disorders: A Review. Nutrients. 2021;13(6):1774. doi: 10.3390/nu13061774. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Fu L, Tai S, Sun J, Zhang N, Zhou Y, Xing Z, et al. Remnant Cholesterol and Its Visit-to-Visit Variability Predict Cardiovascular Outcomes in Patients With Type 2 Diabetes: Findings From the ACCORD Cohort. Diabetes Care. 2022;45(9):2136–2143. doi: 10.2337/dc21-2511. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Huh JH, Han KD, Cho YK, Roh E, Kang JG, Lee SJ, et al. Remnant cholesterol and the risk of cardiovascular disease in type 2 diabetes: a nationwide longitudinal cohort study. Cardiovasc Diabetol. 2022;21(1):228. doi: 10.1186/s12933-022-01667-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Jansson Sigfrids F, Dahlström EH, Forsblom C, Sandholm N, Harjutsalo V, Taskinen MR, et al. Remnant cholesterol predicts progression of diabetic nephropathy and retinopathy in type 1 diabetes. J Intern Med. 2021;290(3):632–645. doi: 10.1111/joim.13298. [DOI] [PubMed] [Google Scholar]
- 16.Shan Y, Wang Q, Zhang Y, Tong X, Pu S, Xu Y, et al. High remnant cholesterol level is relevant to diabetic retinopathy in type 2 diabetes mellitus. Lipids Health Dis. 2022;21(1):12. doi: 10.1186/s12944-021-01621-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Pan W, Han Y, Hu H, He Y. The non-linear link between remnant cholesterol and diabetic retinopathy: a cross-sectional study in patients with type 2 diabetic mellitus. BMC Endocr Disord. 2022;22(1):326. doi: 10.1186/s12902-022-01239-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Morris DS, Somner J, Donald MJ, McCormick IJ, Bourne RR, Huang SS, et al. The eye at altitude. Adv Exp Med Biol. 2006;588:249–270. doi: 10.1007/978-0-387-34817-9_21. [DOI] [PubMed] [Google Scholar]
- 19.Cheung CY, Xu D, Cheng CY, Sabanayagam C, Tham YC, Yu M, et al. A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre. Nat Biomed Eng. 2021;5(6):498–508. doi: 10.1038/s41551-020-00626-4. [DOI] [PubMed] [Google Scholar]
- 20.Klein R, Klein BE, Moss SE, Wong TY, Sharrett AR. Retinal vascular caliber in persons with type 2 diabetes: the Wisconsin Epidemiological Study of Diabetic Retinopathy: XX. Ophthalmology. 2006;113(9):1488–1498. doi: 10.1016/j.ophtha.2006.03.028. [DOI] [PubMed] [Google Scholar]
- 21.Xu X, Sun F, Wang Q, Zhang M, Ding W, Yang A, et al. Comprehensive retinal vascular measurements: a novel association with renal function in type 2 diabetic patients in China. Sci Rep. 2020;10(1):13737. doi: 10.1038/s41598-020-70408-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Cheung CY, Tay WT, Mitchell P, Wang JJ, Hsu W, Lee ML, et al. Quantitative and qualitative retinal microvascular characteristics and blood pressure. J Hypertens. 2011;29(7):1380–1391. doi: 10.1097/HJH.0b013e328347266c. [DOI] [PubMed] [Google Scholar]
- 23.Liew G, Mitchell P, Rochtchina E, Wong TY, Hsu W, Lee ML, et al. Fractal analysis of retinal microvasculature and coronary heart disease mortality. Eur Heart J. 2011;32(4):422–429. doi: 10.1093/eurheartj/ehq431. [DOI] [PubMed] [Google Scholar]
- 24.Cheung N, Liew G, Lindley RI, Liu EY, Wang JJ, Hand P, et al. Retinal fractals and acute lacunar stroke. Ann Neurol. 2010;68(1):107–111. doi: 10.1002/ana.22011. [DOI] [PubMed] [Google Scholar]
- 25.Cheung CY, Ikram MK, Klein R, Wong TY. The clinical implications of recent studies on the structure and function of the retinal microvasculature in diabetes. Diabetologia. 2015;58(5):871–885. doi: 10.1007/s00125-015-3511-1. [DOI] [PubMed] [Google Scholar]
- 26.Crosby-Nwaobi R, Heng LZ, Sivaprasad S. Retinal vascular calibre, geometry and progression of diabetic retinopathy in type 2 diabetes mellitus. Ophthalmologica. 2012;228(2):84–92. doi: 10.1159/000337252. [DOI] [PubMed] [Google Scholar]
- 27.Frydkjaer-Olsen U, Soegaard Hansen R, Simó R, Cunha-Vaz J, Peto T, Grauslund J. Correlation between Retinal Vessel Calibre and Neurodegeneration in Patients with Type 2 Diabetes Mellitus in the European Consortium for the Early Treatment of Diabetic Retinopathy (EUROCONDOR) Ophthalmic Res. 2016;56(1):10–16. doi: 10.1159/000444396. [DOI] [PubMed] [Google Scholar]
- 28.Cheung CY, Sabanayagam C, Law AK, Kumari N, Ting DS, Tan G, et al. Retinal vascular geometry and 6 year incidence and progression of diabetic retinopathy. Diabetologia. 2017;60(9):1770–1781. doi: 10.1007/s00125-017-4333-0. [DOI] [PubMed] [Google Scholar]
- 29.Lim LS, Chee ML, Cheung CY, Wong TY. Retinal Vessel Geometry and the Incidence and Progression of Diabetic Retinopathy. Invest Ophthalmol Vis Sci. 2017;58(6):Bio200–bio5. doi: 10.1167/iovs.17-21699. [DOI] [PubMed] [Google Scholar]
- 30.Lin Q, Jia Y, Li T, Wang S, Xu X, Xu Y, et al. Optic disc morphology and peripapillary atrophic changes in diabetic children and adults without diabetic retinopathy or visual impairment. Acta Ophthalmol. 2022;100(1):e157–e166. doi: 10.1111/aos.14885. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Xiang Z-Y, Chen S-L, Qin X-R, Lin S-L, Xu Y, Lu L-N, et al. Changes and related factors of blood CCN1 levels in diabetic patients. Front Endocrinol. 2023;14:1131993. doi: 10.3389/fendo.2023.1131993. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Long T, Xu Y, Zou H, Lu L, Yuan T, Dong Z, et al. A Generic Pixel Pitch Calibration Method for Fundus Camera via Automated ROI Extraction. Sensors (Basel). 2022;22(21):8565. doi: 10.3390/s22218565. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Knudtson MD, Lee KE, Hubbard LD, Wong TY, Klein R, Klein BE. Revised formulas for summarizing retinal vessel diameters. Curr Eye Res. 2003;27(3):143–149. doi: 10.1076/ceyr.27.3.143.16049. [DOI] [PubMed] [Google Scholar]
- 34.Shi XH, Dong L, Zhang RH, Zhou DJ, Ling SG, Shao L, et al. Relationships between quantitative retinal microvascular characteristics and cognitive function based on automated artificial intelligence measurements. Front Cell Dev Biol. 2023;11:1174984. doi: 10.3389/fcell.2023.1174984. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Jiang X, Dong L, Luo L, Zhou D, Ling S, Li D. Artificial Intelligence-based quantitative evaluation of retinal vascular parameters in thyroid-associated ophthalmopathy. Endocrine. Published online February 3, 2024. [DOI] [PubMed]
- 36.Broe R, Rasmussen ML, Frydkjaer-Olsen U, Olsen BS, Mortensen HB, Hodgson L, et al. Retinal vessel calibers predict long-term microvascular complications in type 1 diabetes: the Danish Cohort of Pediatric Diabetes 1987 (DCPD1987) Diabetes. 2014;63(11):3906–3914. doi: 10.2337/db14-0227. [DOI] [PubMed] [Google Scholar]
- 37.Pappelis K, Jansonius NM. Retinal Vessel Caliber Measurement Bias in Fundus Images in the Presence of the Central Light Reflex. Transl Vis Sci Technol. 2023;12(7):16. doi: 10.1167/tvst.12.7.16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Tsai AS, Wong TY, Lavanya R, Zhang R, Hamzah H, Tai ES, et al. Differential association of retinal arteriolar and venular caliber with diabetes and retinopathy. Diabetes Res Clin Pract. 2011;94(2):291–298. doi: 10.1016/j.diabres.2011.07.032. [DOI] [PubMed] [Google Scholar]
- 39.Cheung N, Donaghue KC, Liew G, Rogers SL, Wang JJ, Lim SW, et al. Quantitative assessment of early diabetic retinopathy using fractal analysis. Diabetes Care. 2009;32(1):106–110. doi: 10.2337/dc08-1233. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Ţălu Ş, Călugăru DM, Lupaşcu CA. Characterisation of human non-proliferative diabetic retinopathy using the fractal analysis. Int J Ophthalmol. 2015;8(4):770–776. doi: 10.3980/j.issn.2222-3959.2015.04.23. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Chait A, Ginsberg HN, Vaisar T, Heinecke JW, Goldberg IJ, Bornfeldt KE. Remnants of the Triglyceride-Rich Lipoproteins, Diabetes, and Cardiovascular Disease. Diabetes. 2020;69(4):508–516. doi: 10.2337/dbi19-0007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Borén J, Chapman MJ, Krauss RM, Packard CJ, Bentzon JF, Binder CJ, et al. Low-density lipoproteins cause atherosclerotic cardiovascular disease: pathophysiological, genetic, and therapeutic insights: a consensus statement from the European Atherosclerosis Society Consensus Panel. Eur Heart J. 2020;41(24):2313–2330. doi: 10.1093/eurheartj/ehz962. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Varbo A, Benn M, Tybjærg-Hansen A, Nordestgaard BG. Elevated remnant cholesterol causes both low-grade inflammation and ischemic heart disease, whereas elevated low-density lipoprotein cholesterol causes ischemic heart disease without inflammation. Circulation. 2013;128(12):1298–1309. doi: 10.1161/CIRCULATIONAHA.113.003008. [DOI] [PubMed] [Google Scholar]
- 44.Bernelot Moens SJ, Verweij SL, Schnitzler JG, Stiekema LCA, Bos M, Langsted A, et al. Remnant Cholesterol Elicits Arterial Wall Inflammation and a Multilevel Cellular Immune Response in Humans. Arterioscler Thromb Vasc Biol. 2017;37(5):969–975. doi: 10.1161/ATVBAHA.116.308834. [DOI] [PubMed] [Google Scholar]
- 45.Saldívar E, Cabrales P, Tsai AG, Intaglietta M. Microcirculatory changes during chronic adaptation to hypoxia. Am J Physiol Heart Circ Physiol. 2003;285(5):H2064–H2071. doi: 10.1152/ajpheart.00349.2003. [DOI] [PubMed] [Google Scholar]
- 46.Yim-Lui Cheung C, Wong TY, Lamoureux EL, Sabanayagam C, Li J, Lee J, et al. C-reactive protein and retinal microvascular caliber in a multiethnic asian population. Am J Epidemiol. 2010;171(2):206–213. doi: 10.1093/aje/kwp357. [DOI] [PubMed] [Google Scholar]
- 47.Vallejo-Vaz AJ, Fayyad R, Boekholdt SM, Hovingh GK, Kastelein JJ, Melamed S, et al. Triglyceride-Rich Lipoprotein Cholesterol and Risk of Cardiovascular Events Among Patients Receiving Statin Therapy in the TNT Trial. Circulation. 2018;138(8):770–781. doi: 10.1161/CIRCULATIONAHA.117.032318. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Additional file 1: Fig. S1 Subgroup analyses for correlation between RC and CRAE. Fig. S2 Subgroup analyses for correlation between RC and CRVE. Fig. S3 Subgroup analyses for correlation between RC and Df. Table S1. Binary logistic regression analyses for correlation between retinal vasculature and DR.
Data Availability Statement
The data that support the findings of this study are available from Shanghai Municipal Centers for Disease Control and Prevention but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available.