Causal Roles of Lipids and Mediating Proteins in Diabetic Retinopathy: Insights from Metabolomic and Proteomic Mendelian Randomization

Causal Roles of Lipids and Mediating Proteins in Diabetic Retinopathy: Insights from Metabolomic and Proteomic Mendelian Randomization | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Causal Roles of Lipids and Mediating Proteins in Diabetic Retinopathy: Insights from Metabolomic and Proteomic Mendelian Randomization Jiawei Wang, Jing Su, Danyan Liu, Jingxue Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5904494/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Apr, 2025 Read the published version in Diabetology & Metabolic Syndrome → Version 1 posted 9 You are reading this latest preprint version Abstract Background: This study explores the causal relationships between five major lipids, 249 circulating metabolites, and four diabetic retinopathy (DR) outcomes: overall DR, background DR, severe background DR, and proliferative DR (PDR). We aim to identify plasma proteins that mediate these causal effects, offering insights into potential therapeutic targets. Methods: We conducted metabolome-wide Mendelian randomization (MR) analyses to assess associations between major lipids, metabolites, and DR outcomes. Multivariable MR (MVMR) and proteome-wide mediated MR (two-step MR) analyses were performed to ensure robust evaluation and identify mediating plasma proteins. Results: Triglycerides were identified as a significant risk factor for DR, mediated by proteins like Dickkopf-3 (DKK3), ST6 N-acetylglucosamine transferase 6 (ST4S6), and Neogenin (NEO1). For background DR, HDL-C, specific VLDL particles, and LDL triglycerides were protective, mediated by proteins like chloride intracellular channel 5 (CLIC5), basal cell adhesion molecule (BCAM), and Ribophorin I (RPN1). Additionally, polyunsaturated fatty acids (PUFAs) and total choline were protective against PDR, mediated by Radical Fringe Gene (RFNG). Conclusions: This study identifies specific plasma proteins that mediate the effects of lipids and metabolites on DR, establishing a direct molecular link between these biomarkers and disease progression. These findings enhance our understanding of the pathophysiological mechanisms underlying DR and highlight potential targets for therapeutic intervention. Diabetic retinopathy Major lipids Circulating metabolites Proteome causality Figures Figure 1 Figure 2 Figure 3 1. Introduction Diabetic retinopathy (DR) is the most common complication of diabetes mellitus (DM) and a leading cause of preventable blindness among adults, posing a significant public health challenge 1 , 2 . By 2030, it is projected that 129.84 million adults globally will suffer from DR, with this number rising to 160.50 million by 2045 3 . DR is characterized by progressive damage to the retinal microvasculature, with its development and progression are closely linked to metabolic dysregulation, including hyperglycemia and associated metabolic disturbances. Although elevated blood glucose levels has been recognized to contribute to the development of DR, differences in HbA1c levels explain only 6.6% of the variation in DR risk for the entire study cohort in a diabetes control and complications trial 4 . Dyslipidemia, defined by the presence of abnormal levels of lipids in the blood, is a common feature in individuals with diabetes and has been implicated in various diabetic complications, including DR 5 . Although epidemiological studies hint at a link between lipid irregularities and DR, establishing causality is complex, primarily due to the influence of confounding variables and the possibility of reverse causation 6 . Meta-analyses of DR research have identified associations between blood pressure, serum total cholesterol, and glycosylated hemoglobin levels with the prevalence of retinopathy. However, these factors collectively account for only a modest 9% of DR progression 7 . Metabolomics, a burgeoning field in the biomedical sciences, offers a novel perspective in the quest to understand complex diseases like DR. It is estimated that genetic variation accounts for approximately 50% of the observed phenotypic variance at the metabolite level, offering a unique opportunity for inferring causality from metabolite levels to disease risk 8 . Mendelian randomization (MR) represents a powerful approach in this context, utilizing genetic variants associated with a specific exposure to assess its causal impact on an outcome. By leveraging the principles of MR, researchers can navigate around the complexities of confounding factors and reverse causation, thereby enhancing the reliability of causality evidence in disease research. This method holds particular promise for elucidating the intricate interplay between metabolic pathways and DR pathogenesis. In this study, we aimed to explore the causal associations between five major lipids and 249 circulating metabolites with various DR outcomes, including overall DR, background DR, severe background DR, and proliferative DR (PDR). By employing metabolome-wide MR analyses, we assessed the relationships between these factors and DR outcomes. Additionally, we utilized proteome-wide mediated MR analyses to identify plasma proteins that mediate these causal effects. Our findings provide valuable insights into the roles of specific lipids, metabolites, and proteins in DR, highlighting potential targets for therapeutic intervention and guiding future research directions. 2. Method 2.1 Data sources The summary datasets used in this study are publicly available and can be accessed through the cited papers. All original GWAS studies included in this research were conducted with approval from their respective ethics committees, and informed consent was obtained from all participants involved in these studies. This metabolome-wide MR study utilized publicly accessible summary datasets, as detailed in Table S1 . In two-sample MR analyses, we obtained GWAS summary statistics for 254 circulating metabolites from the MRC-IEU OpenGWAS project ( https://gwas.mrcieu.ac.uk). These included data for five major non-fasted lipoprotein lipid traits measured using standard clinical chemistry assays in approximately 441,016 participants from the UK Biobank (UKBB) 9 , as well as 249 metabolic biomarkers using high-throughput NMR spectroscopy in over 114,000 participants of European ancestry from the UKBB 10 . Summary-level data for outcomes, including diabetic retinopathy (DR), background DR, severe background DR, and proliferative diabetic retinopathy (PDR), were sourced from the FinnGen Release 9, a comprehensive European consortium ( https://storage.googleapis.com/finngen-public-data-r9/summary_stats/) 11 . These datasets included 10,413 cases of DR compared to 308,633 control subjects, 4,011 background DR cases juxtaposed against 344,569 controls, 816 severe background DR cases compared to 344,569 controls, as well as 9,511 PDR cases against 362,581 controls. In the mediation analyses, we incorporated additional datasets beyond the previously described exposure and outcome data. Specifically, we utilized 4,489 GWAS summary statistics for available proteins sourced from the MR-Base NHGRI-EBI GWAS Catalog ( https://gwas.mrcieu.ac.uk/ ) as mediators, which included 3,282 plasma proteins from 3,301 healthy participants in the INTERVAL study 12 , 1,124 blood circulating proteins measured in 1,000 blood samples from the KORA study 13 , and 83 proteins from 3,394 individuals in the IMPROVE study 14 . 2.2 Identification of qualified genetic instrumental variables (IVs) To identify qualified genetic instruments, we selected single-nucleotide polymorphisms (SNPs) based on stringent criteria: we initially extracted SNPs that achieved genome-wide significance ( P < 5E-08) and then clumped them within a 1000 kb window to an LD threshold of R² < 0.1, using the 1000 Genomes European Ancestry reference panel 15 to ensure genetic independence. To avoid the influence of weak instrument bias, we calculated the F statistic with formula: F = \(\:{\text{R}}^{2}\times\:(\text{N}-\text{k}-1)/\text{k}\times\:(1-{\text{R}}^{2}\) ), and the genetic variation ( \(\:{\text{R}}^{2}\) ) with the formula \(\:{\text{R}}^{2}\) = \(\:2\times\:\text{E}\text{A}\text{F}\times\:(1-\text{E}\text{A}\text{F})\times\:{{\beta\:}}^{2}\) , where N represents the sample size, k represents the number of IVs, EAF is the effect allele frequency, and β is the estimated effect size. SNPs with an F statistic of ≥ 10 were retained to minimize weak instrument bias. Finally, we harmonized the exposure and outcome datasets to ensure that the effect of each variant on both the exposure and outcome aligned with the same allele. We inferred positive-strand alleles and systematically excluded palindromic SNPs with ambiguous allele frequencies and any incompatible alleles. 2.3 Univariable MR (UVMR) and multivariable MR (MVMR) In our study, we employed the R package "TwoSampleMR" (version: 0.5.8) for UVMR analyses to explore the relationship between circulating metabolites and DR outcomes. For single IV analyses, we utilized the Wald Ratio to estimate causal relationships. Under the assumption of valid IVs and no horizontal pleiotropy, we predominantly used the inverse-variance weighted (IVW) method as a robust MR approach to infer causality 16 . MVMR extends the standard MR framework by considering multiple potential risk factors within a single model 17 . To circumvent the suboptimal performance of traditional MVMR using standard linear regression in the presence of numerous risk factors, we employed MR-BMA. This Bayesian model averaging approach not only scales effectively to high-throughput experiments but also demonstrates robustness in detecting true causal risk factors, even when candidate factors are highly correlated 18 . The MVMR analyses were performed exclusively on causal circulating metabolites associated with the same DR outcome to ensure robust evaluation. Qualified IVs commonly related to causal circulating metabolites were extracted and processed using the same criteria as described previously ( P < 5E-08, clumped at R² < 0.1 within a 1000 kb window, based on the 1000 Genomes European Ancestry reference panel). Within a Bayesian framework, MR-BMA calculates the marginal inclusion probability (MIP) and the model-averaged causal effect (MACE) for each risk factor. MIP is the sum of the posterior probabilities (PP) of all models that include the risk factor, indicating the likelihood that the risk factor is a causal determinant of disease risk. MACE provides a conservative estimate of the average direct causal effect of the risk factor on the outcome, derived through weighted averaging, with the weights determined by the posterior probabilities of the respective models. 2.5 Mediation MR analysis We performed two-step MR analyses to explore RNA molecules that may mediate the link between circulating metabolites and DR outcomes. Initially, we applied UVMR to estimate the causal effect (β1) of circulating metabolites on each potential mediator. Subsequently, we also used UVMR to estimate the causal effect (β2) of each mediator on DR outcomes. If the results indicated that both β1 and β2 were significant, we used the "product of coefficients" method to calculate the mediation effect (β1 × β2) of circulating metabolites on DR outcomes through each mediator. We also calculated the direct effect of circulating metabolites on DR outcomes by excluding the mediator, which was derived by subtracting the mediation effect from the total effect. The standard errors for the mediation effects were calculated using the delta method formula: \(\:{SE}_{mediation}=\surd\:({\beta\:}1\times\:\text{S}\text{E}1)\:+({\beta\:}2\times\:\text{S}\text{E}2)\:\) . The z-score for the mediation effects was then calculated as: Z = \(\:{\text{m}\text{e}\text{d}\text{i}\text{a}\text{t}\text{i}\text{o}\text{n}\:\text{e}\text{f}\text{f}\text{e}\text{c}\text{t}\:({\beta\:}1\:\times\:\:{\beta\:}2)/SE}_{mediation}\) . Finally, the P -value for the mediation effects was calculated using the formula: \(\:P\:=\:2\times\:\text{p}\text{n}\text{o}\text{r}\text{m}(\text{q}=|\text{Z}|,lower.tail=FALSE)\) . Negative mediation proportions were truncated at a minimum threshold of 0%, as this is the lowest threshold to determine a mediation proportion. 2.6 Sensitivity analysis For the UVMR analyses, we conducted several sensitivity analyses to support the IVW estimates, including the weighted median, simple mode, weighted mode, and MR-Egger. The weighted median approach provides a reliable estimate of causality when at least 50% of the weight is derived from valid IVs 19 . The simple and weighted mode methods estimate the causal effect based on mode of the unweighted and IVW empirical density functions, respectively 20 . The MR-Egger regression method can detect directional horizontal pleiotropy and provide a corrected estimate 21 . The P -value < 0.05 for the MR-Egger intercept indicates the presence of directional pleiotropy. Cochran’s Q statistic was calculated to evaluate heterogeneity 22 . 2.7 Functional annotation for RNA mediators To annotate the RNA mediators implicated in mediating the causal relationship between metabolites and DR outcomes, we conducted functional annotation to uncover their biological significance. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the R packages "clusterProfiler" and "org.Hs.eg.db" to identify pathways enriched with the identified RNA mediators. Additionally, we constructed a Protein-Protein Interaction (PPI) network using data from the STRING database. 2.8 Statistical analysis To address multiple testing, we employed the Benjamini-Hochberg false discovery rate (FDR) procedure. IVW estimates with | \(\:{\beta\:}\) | > 0.1, P -value < 0.05, and FDR < 0.05, supported by at least one sensitivity analysis, were considered robust evidence of causality. IVW estimates with a P -value < 0.05 but FDR ≥ 0.05 or lacking support from sensitivity analyses were considered suggestive of potential causality. To clearly interpret the causal effect ( \(\:{\beta\:}\) ), we utilized the odds ratio (OR), an intuitive indicator for assessing risk, to exhibit the potential impact of lipid levels on DR outcomes. All MR analyses were performed using R software (version 4.3.1) with the following R packages: "TwoSampleMR" (version 0.5.8), "MVMR" (version 0.4), and "MendelianRandomization" (version 0.10.0). Additionally, R packages "clusterProfiler" (version 4.10.1) and "org.Hs.eg.db" (version 3.16.0) were utilized for GO and KEGG pathway investigations. The code for the MR-BMA method was obtained from the GitHub repository ( https://github.com/verena-zuber/demo_AMD ) referenced in the literature 18 . 3 Results 3.1 Two putative causal major lipids for DR outcomes with robust evidence In the UVMR analyses of five major lipids, including triglycerides, apolipoprotein B, LDL cholesterol, HDL cholesterol, and apolipoprotein A-I, we observed significant causal relationships between nearly all these lipids and one or more of the four DR outcomes ( P -value < 0.05, as shown in Fig. 1 , Table S2 ). Specifically, elevated triglyceride levels increased the risks for DR (OR [95% CI] = 1.11 [1.07–1.16]; P = 7.57e-06) with robust causal evidence, background DR (OR [95% CI] = 1.07[1.01–1.14]; P = 0.03), and severe background DR (OR [95% CI] = 1.18 [1.04–1.33]; P = 0.02) with suggestive causal evidence. Higher HDL cholesterol levels decreased the risk for all four DR outcomes, with robust evidence of causality for background DR (OR [95% CI] = 0.88[0.83–0.93]; P = 2.66e-06) and the strongest protective effect for severe background DR (OR [95% CI] = 0.82[0.70–0.93]; P = 5.20e-04). Additionally, apolipoprotein A-I exhibited varying degrees of protective effects across the four DR outcomes, with suggestive evidence of causality.. Both LDL cholesterol and apolipoprotein B exhibited slight protective effects against DR (OR = 0.92 and 0.95 respectively), LDL cholesterol showed suggestive causal effects on PDR (OR [95% CI] = 0.95[0.90–0.99]; P = 0.015). However, it is important to note that most results exhibited significant heterogeneity and pleiotropy ( P < 0.05). For results demonstrating pleiotropy, the MR-Egger method was employed to identify and exclude outlier SNPs with horizontal pleiotropy, ensuring the robustness of the conclusions (Table S3 ). For results indicating heterogeneity, the weighted median approach was utilized to facilitate causal inference (Table S4 ). 3.2 Eleven Plasma proteins mediated the causal effect of two causal major lipids on DR outcomes Our findings indicate that elevated triglyceride levels are associated with an increased risk of DR, whereas higher HDL cholesterol levels are linked to a reduced risk of background DR, substantiated by robust causal evidence. Mediation MR analysis identified ten plasma proteins (Fig. 2 A) that significantly mediate the causal effect of triglycerides on DR (β = 0.12). Among these, Dickkopf-related protein 3 (DKK3), Sulfotransferase Family 4A, Member 1 (ST4S6), and Neogenin 1 (NEO1) exhibit mediation effects consistent with the overall impact of triglycerides on DR (β = 0.03, 0.03, and 0.02 in mediation MR analyses, respectively). Additionally, PPI analysis revealed an interaction between Proto-Oncogene, Src Family Tyrosine Kinase (FYN) and NEO1 within the set of ten proteins (Fig. 2 B). Enrichment analyses demonstrated that Retinol Dehydrogenase 16 (RDH16), DKK3, NEO1, and FYN were significantly involved in biological processes such as Cellular Hormone Metabolic Process (GO: BP), Cellular Response to Transforming Growth Factor Beta Stimulus (GO: BP), Threonine Kinase Signaling Pathway (GO: BP), Postsynaptic Density (GO: CC), Co-receptor Binding (GO: MF), Axon Guidance (KEGG), and Retinol Metabolism (KEGG) (Fig. 2 C, adjusted P < 0.05). Furthermore, mediation MR analysis also revealed that the protein RPN1 significantly mediated the causal effect of HDL cholesterol on background DR (β = -0.10), with a mediation effect of -0.01 (Table S5 ). 3.3 Twenty-six putative causal metabolites for DR outcomes with robust evidence In the UVMR analyses, eleven metabolites, including seven associated with very low-density lipoprotein (VLDL), three with low-density lipoprotein (LDL), and one with intermediate-density lipoprotein (IDL), demonstrated robust causal associations with DR risk (Fig. 3 A and Table S2 ). Among these, the elevated triglycerides to total lipids ratio in large VLDL (OR [95% CI] = 1.11 [1.06–1.16]; P = 3.71e − 05) and increased phospholipids to total lipids ratio in large LDL (OR [95% CI] = 1.12 [1.06–1.17]; P = 2.66e-06) were found to significantly heighten the DR risk. The remaining nine metabolites exhibited negative causal associations with DR risk. However, when considering the combined effect of these eleven metabolites on DR risk through MR-BMA analysis, only six VLDL-related metabolites and two LDL-related metabolites exhibited significant negative associations with DR. Specifically, cholesteryl esters in very small VLDL, cholesterol in very small VLDL, concentration of very small VLDL particles, and triglycerides in large LDL showed higher MIPs of 0.918, 0.814, 0.744, and 0.631, respectively, with FDRs < 0.05. For background DR, UVMR analyses identified five metabolites with robust causal associations. Among these, two metabolites related to omega-3 fatty acids (OR = 0.89 and 0.91; P = 6.87e-05 and 8.97e-05) and docosahexaenoic acid (OR [95% CI] = 0.88 [0.82–0.94]; P = 5.38e-05) were observed to have significant negative associations with background DR risk. Conversely, an elevated ratio of omega-6 fatty acids to omega-3 fatty acids (OR [95% CI] = 1.12 [1.07–1.17]; P = 2.40e-05) and an increased phospholipids to total lipids ratio in large LDL (OR [95% CI] = 1.15 [1.09–1.22]; P = 3.83e-05) were associated with a heightened background DR risk. MR-BMA analysis, considering the combined impact of these five causal metabolites, found that only the phospholipids to total lipids ratio in large LDL had a significant effect on background DR risk with MIPs of 0.943 and FDR < 0.05(Fig. 3 B). For severe background DR, UVMR analyses identified that only elevated omega-3 fatty acid levels were significantly associated with a reduced risk of severe background DR (OR [95% CI] = 0.78 [0.66–0.90]; P = 7.00e-05). Regarding PDR, UVMR analyses identified nine metabolites with robust causal associations. Among these, increased tyrosine levels were significantly associated with an elevated risk of PDR (OR [95% CI] = 1.15 [1.08–1.21]; P = 5.50e-05). Additionally, two metabolites related to fatty acids (OR = 0.90 and 0.89; P = 1.29e-04 and 1.11e-05), two phospholipid-related metabolites (OR = 0.88 and 0.90; P = 9.50e-05 and 1.56e-05), two choline-related metabolites (OR = 0.89 and 0.90; P = 2.28e-05 and 1.59e-5), free cholesterol in small HDL (OR [95% CI] = 0.88 [0.81–0.94]; P = 9.55e-05), and phosphoglycerides (OR [95% CI] = 0.89 [0.84–0.94]; P = 1.08e-05) were observed to have significant negative associations with PDR. MR-BMA analysis, considering the combined impact of these nine causal metabolites, revealed that only elevated levels of total cholines and polyunsaturated fatty acids (PUFAs) were significantly associated with a reduced risk of PDR with MIPs of 0.164 and 0.184, respectively, and all FDR < 0.05(Fig. 3 B). 3.4 Thirteen plasma proteins mediated the effect of causal metabolites on DR outcomes Although our study initially identified 11 metabolites with robust causal associations with DR risk, we subsequently validated 8 of these metabolites through MVMR analysis. The proteome-wide mediation MR analysis identified 10 plasma proteins that mediate the causal effects of 7 of these metabolites on DR (Table 1 and Table S5 ). For the 5 very small VLDL-related metabolites, proteins Chloride Intracellular Channel 5 (CLIC5) and BCAM (Basal Cell Adhesion Molecule) significantly potentiated the protective effects of very small VLDL-related metabolites on DR (mediation β = -0.06 and − 0.02, total effect β = -0.11 to -0.15). Conversely, proteins (Signal Regulatory Protein Gamma) SIRPG, ST4S6, PGRC2 (Progestin and AdipoQ Receptor Family Member 2), DKK3, N-terminal pro-Brain Natriuretic Peptide (N-terminal pro-BNP), and (Thiamine Transporter) THTR significantly attenuated the protective effects of VLDL-related metabolites on DR (mediation β > 0). For the 2 LDL-related metabolites, proteins BCAM and Sushi, Von Willebrand Factor Type A, EGF And Pentraxin Domain Containing 1 (SVEP1) significantly augmented the protective effects of LDL-related metabolites on DR (mediation β = -0.02 and − 0.01, total effect β = -0.108 and − 0.119, respectively). Additionally, for background DR, proteome-wide mediation MR analysis revealed that NAD(P)H dehydrogenase mediates the risk effect of the phospholipid to total lipid ratio in large LDL on background DR (total effect β = 0.14, mediation effect = 0.01). For PDR, protein RFNG O-Fucosylpeptide 3-Beta-N-Acetylglucosaminyltransferase (RFNG) mitigates the protective effects of PUFAs and total cholines on PDR (mediation β = 0.07 and 0.06, total effect β = -0.12 and − 0.11, respectively), whereas protein PDE4D positively mediates the protective effects of PUFAs on PDR (mediation β = -0.03, total effect β = -0.12). Table 1 Summary of Significant Plasma Proteins from Mediation MR Analysis of causal metabolites on DR outcomes Exposure Mediate proteins Outcome Total Effect Effect of Exposure on Mediator Effect of Mediator on Outcome Mediation Effect Proportion Mediated Direct Effect P -value Triglycerides in large LDL BCAM DR -0.119 -0.194 0.122 -0.024 0.198 -0.096 1.39E-02 Triglycerides in large LDL TMEM2 DR -0.119 -0.122 -0.123 0.015 -0.126 -0.134 3.47E-02 Triglycerides in large LDL SVEP1 DR -0.119 -0.117 0.087 -0.01 0.085 -0.109 4.26E-02 Concentration of very small VLDL particles ST4S6 DR -0.136 -0.188 -0.16 0.03 -0.221 -0.166 6.46E-03 Concentration of very small VLDL particles CLIC5 DR -0.136 -0.22 0.287 -0.063 0.464 -0.073 3.07E-03 Concentration of very small VLDL particles BCAM DR -0.136 -0.129 0.122 -0.016 0.116 -0.12 4.24E-02 Concentration of very small VLDL particles DKK3 DR -0.136 -0.122 -0.11 0.013 -0.099 -0.15 4.80E-02 Phospholipids in very small VLDL N- terminal pro-BNP DR -0.126 -0.128 -0.123 0.016 -0.125 -0.142 3.02E-02 Phospholipids in very small VLDL ST4S6 DR -0.126 -0.17 -0.16 0.027 -0.215 -0.153 8.58E-03 Phospholipids in very small VLDL CLIC5 DR -0.126 -0.207 0.287 -0.059 0.469 -0.067 4.96E-03 Phospholipids in very small VLDL DKK3 DR -0.126 -0.138 -0.11 0.015 -0.121 -0.142 3.20E-02 Triglycerides in LDL BCAM DR -0.108 -0.186 0.122 -0.023 0.211 -0.085 1.87E-02 Triglycerides in LDL SVEP1 DR -0.108 -0.144 0.089 -0.013 0.119 -0.095 3.80E-02 Free cholesterol in very small VLDL BCAM DR -0.147 -0.13 0.122 -0.016 0.108 -0.131 4.49E-02 Free cholesterol in very small VLDL PGRC2 DR -0.147 -0.093 -0.173 0.016 -0.11 -0.163 1.76E-02 Total lipids in very small VLDL ST4S6 DR -0.134 -0.17 -0.146 0.025 -0.185 -0.158 1.01E-02 Total lipids in very small VLDL THTR DR -0.134 -0.08 -0.085 0.007 -0.051 -0.14 4.59E-02 Total lipids in very small VLDL BCAM DR -0.134 -0.161 0.122 -0.02 0.147 -0.114 2.27E-02 Total lipids in very small VLDL CLIC5 DR -0.134 -0.214 0.287 -0.061 0.458 -0.072 4.06E-03 Total lipids in very small VLDL SIRPG DR -0.134 0.133 0.212 0.028 -0.211 -0.162 2.91E-02 Total lipids in very small VLDL DKK3 DR -0.134 -0.129 -0.11 0.014 -0.106 -0.148 3.79E-02 Cholesteryl esters in very small VLDL ST4S6 DR -0.109 -0.12 -0.146 0.018 -0.162 -0.126 3.46E-02 Cholesteryl esters in very small VLDL SIRPG DR -0.109 0.14 0.212 0.03 -0.273 -0.138 2.38E-02 Cholesteryl esters in very small VLDL CLIC5 DR -0.109 -0.196 0.287 -0.056 0.516 -0.053 7.50E-03 Phospholipids to total lipids ratio in large LDL NAD(P)H dehydrogenase Background DR 0.135 0.099 0.14 0.014 0.103 0.121 1.68E-02 Polyunsaturated fatty acids RFNG PDR -0.118 0.382 0.194 0.074 -0.63 -0.192 1.80E-03 Polyunsaturated fatty acids PDE4D PDR -0.118 0.107 -0.234 -0.025 0.212 -0.093 3.55E-02 Total cholines RFNG PDR -0.107 0.312 0.194 0.061 -0.564 -0.168 8.24E-03 4. Discussion Among the major lipids analyzed, triglycerides emerged as the most significant risk factor, potentially contributing to disease risk through mediation by the proteins DKK3, ST4S6, and NEO1.Conversely, HDL cholesterol was identified as the most potent protective factor, potentially reducing the risk of background DR through mediation by the protein RPN1. Among the causal circulating metabolites, cholesteryl esters in very small VLDL exhibited the strongest protective effect against DR, with their influence mediated by the plasma proteins CLIC5 and BCAM.For background DR, the phospholipid-to-total lipid ratio in large LDL was identified as the most plausible causal metabolite, mediated by the plasma protein NAD(P)H dehydrogenase. Additionally, RFNG and PDE4D positively mediate the protective effects of PUFAs on PDR. Extensive evidence indicates that inadequate control of triglyceride levels is associated with the onset and progression of DR, whereas elevated HDL-C levels and the use of lipid-lowering medications significantly diminish the risk of DR 23 – 25 . Our results also revealed that the elevated triglyceride levels can distinctly increased DR risk, especially, we demonstrated that reduced HDL-C levels can increased background DR risk with robust causal evidence. Proteome-wide mediation MR analyses identified eleven plasma proteins that mediate the causal effects of triglyceride and HDL-C on DR outcomes, such as DKK3, ST4S6, NEO1, RDH16 and so on (Fig. 2 A). DKK3, a crucial member of the DKK family and an important modulator of Wnt signaling, is synthesized and secreted by Muller cells. A study involving 44 eyes from 39 patients with diabetic macular edema (DME) and 27 eyes from 27 controls identified significantly elevated levels of DKK3 in the aqueous humor of DME patients and in human Müller cells. This research suggests that excessive activation of Wnt signaling, mediated by elevated DKK3 levels, may contribute to neovascularization and the progression of DR 26 . Conversely, RDH16 is integral to retinal health due to its role in efficiently producing retinal reductase, which supports retinol metabolism 27 , 28 . Our research also indicated that RDH16 negatively mediated the risk impact of triglycerides on DR. Based on the analyses from both UVMR and MVMR, we have identified eight lipoprotein subclasses (including six very small VLDL particles and two LDL particles) that are protectively associated with DR. Very small VLDL particles have also reported to be inversely associated with incident diabetes 29 and age-related macular degeneration (AMD) 30 . Furthermore, a population-based study involving Chinese, Malay, and Indian adults used logistic regression to find that certain very small VLDL particles, consistent with our findings, such as cholesteryl esters in very small VLDL and LDL particles (including total lipids in large LDL), were protectively associated with moderate or more severe DR 31 . Additionally, the protein CLIC5 was found to significantly mediate the protective causal effect of very small VLDL particles on DR. While CLIC5 has also been reported to be significantly downregulated in glomerular tissues of diabetic nephropathy patients 32 . This suggests that the protective effect of very small VLDL particles on DR may, in part, be mediated through CLIC5. Our study elucidated that NAD(P)H dehydrogenase mediated the risk effect of the phospholipid-to-total lipid ratio in large LDL on background DR. NAD(P)H dehydrogenase is pivotal in modulating cellular redox balance and energy metabolism. Previous studies have shown that diabetic rats display elevated concentrations of free NAD(P)H, reflecting increased glycolytic activity, along with higher levels of bound NAD(P)H, suggesting enhanced oxidative phosphorylation in their retinas. 33 . These observations suggest that alterations in NAD(P)H dynamics, driven by modifications in lipid profiles, may exacerbate oxidative stress and metabolic dysregulation. Our study further revealed that PUFAs and total choline exhibited protective effects against PDR. PUFAs were generally considered beneficial 34 , 35 . Notably, two clinical studies conducted in Europe have identified an inverse relationship between omega-6 PUFAs and the incidence of DR 36 , 37 . Additionally, elevated phosphatidylcholine levels are associated with reduced risks of diabetes and cardiovascular diseases 38 . Moreover, our findings indicate that RFNG positively mediate the protective effects of PUFAs and total cholines on PDR. RFNG enhances NOTCH1 activity by modifying O-fucose residues on specific EGF-like domains, thereby promoting NOTCH1 activation through DLL1 and JAG1, which may contribute to neurogenesis 39 . The interaction between PUFAs, choline, and RFNG underscores a complex network of metabolic and signaling pathways that collectively influence retinal health. These insights could inform future therapeutic strategies aimed at leveraging these protective factors to prevent or mitigate the progression of DR. Limitations Despite the significant findings, our study has several limitations. First, although MR helps to infer causality by minimizing confounding and reverse causation, it relies on the assumption of no pleiotropy, meaning the genetic variants used as instruments should not affect the outcome through pathways other than the exposure of interest. Violations of this pleiotropy assumption could bias the results. Second, our analysis was based on data from European participants, which may limit the generalizability of our findings to other ethnic and demographic groups. Third, while we identified several proteins that potentially mediate the effects of lipids on DR, the exact biological mechanisms remain to be elucidated through experimental studies. Additionally, the use of plasma lipid measurements might not fully reflect lipid metabolism within retinal tissues, and tissue-specific studies are needed to confirm our findings. Furthermore, our study relies on summary-level GWAS data, which, while enabling large-scale causal inference, comes with inherent limitations. The use of summary statistics precludes individual-level data analyses, limiting our ability to assess potential confounding factors and effect modifications at a finer scale. Independent cohort validation is essential to confirm the robustness and replicability of our findings across diverse populations. Finally, the complexity of lipid metabolism and its interaction with various metabolic pathways necessitates further investigation to fully understand the causal relationships identified in this study. Future research should focus on longitudinal and tissue-specific analyses to validate and extend our findings. 5. Conclusion In conclusion, our metabolome-wide MR analysis has elucidated the complex relationships between lipid profiles and DR. We identified triglycerides as a significant risk factor for DR, mediated by the proteins DKK3, ST4S6, and NEO1, while HDL-C emerged as a potent protective factor, potentially reducing the risk of background DR through RPN1 mediation. Cholesteryl esters in very small VLDL exhibited the strongest protective effect against DR, mediated by CLIC5 and BCAM, while the phospholipid-to-total lipid ratio in large LDL was identified as a key causal metabolite for background DR, with its effects mediated by NAD(P)H dehydrogenase. Furthermore, the protective effects of PUFAs and total cholines on PDR were positively mediated by RFNG. These findings provide valuable insights into the metabolic pathways and potential therapeutic targets for DR, highlighting the importance of lipid metabolism in the pathogenesis of this condition. Future research should focus on validating these results in diverse populations and exploring the underlying mechanisms through experimental studies. Declarations Acknowledgements Funding: The work was supported by Medical Science Research Project of Hebei Province [20221109], and Hospital-level Foundation of the Second Hospital of Hebei Medical University [2HC202020]. Declarations of interest: none. Author contributions: Jiawei Wang: conceptualization; data curation; formal analysis and writing original draft; Jing Su: methodology; software; and validation; Danyan Liu: investigation; project administration; resources; supervision; visualization; and revision; Jingxue Ma: funding acquisition; investigation; project administration; resources; supervision; visualization; and revision. Ethics Approval and Consent to Participate The summary datasets utilized in this study are publicly accessible and available through the referenced publications. All original GWAS investigations incorporated in this research were conducted with the approval of their respective ethics committees, and informed consent was obtained from all participants involved in these studies. Data Availability The datasets employed in this study are publicly accessible summary datasets. They can be found in the cited publications, within the IEU OpenGWAS Project repository ([https://gwas.mrcieu.ac.uk/], or in the FinnGen repository ([https://storage.googleapis.com/finngen-public-data-r9/summary_stats/]. References Teo ZL, Tham YC, Yu M, et al. Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045: Systematic Review and Meta-analysis. Ophthalmology. 2021;128:1580–91. Leasher JL, Bourne RR, Flaxman SR, et al. Global Estimates on the Number of People Blind or Visually Impaired by Diabetic Retinopathy: A Meta-analysis From 1990 to 2010. Diabetes Care. 2016;39:1643–9. Global regional, national burden of diabetes. from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023;402:203–34. Yahyavi SK, Snorgaard O, Knop FK, et al. Prediabetes Defined by First Measured HbA(1c) Predicts Higher Cardiovascular Risk Compared With HbA(1c) in the Diabetes Range: A Cohort Study of Nationwide Registries. Diabetes Care. 2021;44:2767–74. Chapman MJ, Sposito AC. Hypertension and dyslipidaemia in obesity and insulin resistance: pathophysiology, impact on atherosclerotic disease and pharmacotherapy. Pharmacol Ther. 2008;117:354–73. Liu W, Yang C, Lei F, et al. Major lipids and lipoprotein levels and risk of blood pressure elevation: a Mendelian Randomisation study. EBioMedicine. 2024;100:104964. Li B, Zhao X, Xie W, et al. Causal association of circulating metabolites with diabetic retinopathy: a bidirectional Mendelian randomization analysis. Front Endocrinol (Lausanne). 2024;15:1359502. Hagenbeek FA, Pool R, van Dongen J, et al. Heritability estimates for 361 blood metabolites across 40 genome-wide association studies. Nat Commun. 2020;11:39. Richardson TG, Sanderson E, Palmer TM, et al. Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: A multivariable Mendelian randomisation analysis. PLoS Med. 2020;17:e1003062. Borges MC, Haycock PC, Zheng J, et al. Role of circulating polyunsaturated fatty acids on cardiovascular diseases risk: analysis using Mendelian randomization and fatty acid genetic association data from over 114,000 UK Biobank participants. BMC Med. 2022;20:210. Kurki MI, Karjalainen J, Palta P, et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. 2023;613:508–18. Sun BB, Maranville JC, Peters JE, et al. Genomic atlas of the human plasma proteome. Nature. 2018;558:73–9. Suhre K, Arnold M, Bhagwat AM, et al. Connecting genetic risk to disease end points through the human blood plasma proteome. Nat Commun. 2017;8:14357. Folkersen L, Fauman E, Sabater-Lleal M, et al. Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease. PLoS Genet. 2017;13:e1006706. Abecasis GR, Auton A, Brooks LD, et al. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491:56–65. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658–65. Sanderson E, Davey Smith G, Windmeijer F, Bowden J. An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. Int J Epidemiol. 2019;48:713–27. Zuber V, Colijn JM, Klaver C, Burgess S. Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization. Nat Commun. 2020;11:29. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40:304–14. Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol. 2017;46:1985–98. Bowden J, Del Greco MF, Minelli C, et al. Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic. Int J Epidemiol. 2016;45:1961–74. Bowden J, Del Greco MF, Minelli C, et al. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat Med. 2017;36:1783–802. Sen K, Misra A, Kumar A, Pandey RM. Simvastatin retards progression of retinopathy in diabetic patients with hypercholesterolemia. Diabetes Res Clin Pract. 2002;56:1–11. Chew EY, Ambrosius WT, Davis MD, et al. Effects of medical therapies on retinopathy progression in type 2 diabetes. N Engl J Med. 2010;363:233–44. Keech AC, Mitchell P, Summanen PA, et al. Effect of fenofibrate on the need for laser treatment for diabetic retinopathy (FIELD study): a randomised controlled trial. Lancet. 2007;370:1687–97. Ji B, Lim D, Kim J, et al. Increased Levels of Dickkopf 3 in the Aqueous Humor of Patients With Diabetic Macular Edema. Invest Ophthalmol Vis Sci. 2016;57:2296–304. Parés X, Farrés J, Kedishvili N, Duester G. Medium- and short-chain dehydrogenase/reductase gene and protein families: Medium-chain and short-chain dehydrogenases/reductases in retinoid metabolism. Cell Mol Life Sci. 2008;65:3936–49. Pavez Loriè E, Li H, Vahlquist A, Törmä H. The involvement of cytochrome p450 (CYP) 26 in the retinoic acid metabolism of human epidermal keratinocytes. Biochim Biophys Acta. 2009;1791:220–8. Dugani SB, Akinkuolie AO, Paynter N, et al. Association of Lipoproteins, Insulin Resistance, and Rosuvastatin With Incident Type 2 Diabetes Mellitus: Secondary Analysis of a Randomized Clinical Trial. JAMA Cardiol. 2016;1:136–45. Julian TH, Cooper-Knock J, MacGregor S, et al. Phenome-wide Mendelian randomisation analysis identifies causal factors for age-related macular degeneration. Elife. 2023;12:e82546. Quek D, He F, Sultana R, et al. Novel Serum and Urinary Metabolites Associated with Diabetic Retinopathy in Three Asian Cohorts. Metabolites. 2021;11:614. Fan C, Gao Y, Sun Y. Integrated multiple-microarray analysis and mendelian randomization to identify novel targets involved in diabetic nephropathy. Front Endocrinol (Lausanne). 2023;14:1191768. Su E, Kesavamoorthy N, Junge JA, et al. Comparison of Retinal Metabolic Activity and Structural Development between rd10 Mice and Normal Mice Using Multiphoton Fluorescence Lifetime Imaging Microscopy. Curr Issues Mol Biol. 2024;46:612–20. Vaidya H, Cheema SK. Arachidonic acid has a dominant effect to regulate lipogenic genes in 3T3-L1 adipocytes compared to omega-3 fatty acids. Food Nutr Res. 2015;59:25866. Marion-Letellier R, Savoye G, Ghosh S. Polyunsaturated fatty acids and inflammation. IUBMB Life. 2015;67:659–67. Houtsmuller AJ, Zahn KJ, Henkes HE. Unsaturated fats and progression of diabetic retinopathy. Doc Ophthalmol. 1980;48:363–71. Howard-Williams J, Patel P, Jelfs R, et al. Polyunsaturated fatty acids and diabetic retinopathy. Br J Ophthalmol. 1985;69:15–8. Morze J, Wittenbecher C, Schwingshackl L, et al. Metabolomics and Type 2 Diabetes Risk: An Updated Systematic Review and Meta-analysis of Prospective Cohort Studies. Diabetes Care. 2022;45:1013–24. Kakuda S, Haltiwanger RS. Deciphering the Fringe-Mediated Notch Code: Identification of Activating and Inhibiting Sites Allowing Discrimination between Ligands. Dev Cell. 2017;40:193–201. Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx TableS2.xlsx TableS3.xlsx TableS4.xlsx TableS5.xlsx Cite Share Download PDF Status: Published Journal Publication published 26 Apr, 2025 Read the published version in Diabetology & Metabolic Syndrome → Version 1 posted Editorial decision: Accepted 12 Apr, 2025 Reviews received at journal 03 Apr, 2025 Reviewers agreed at journal 02 Apr, 2025 Reviews received at journal 02 Apr, 2025 Reviewers agreed at journal 01 Apr, 2025 Reviewers agreed at journal 01 Apr, 2025 Reviewers invited by journal 01 Apr, 2025 Submission checks completed at journal 01 Apr, 2025 First submitted to journal 30 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5904494","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":437002233,"identity":"71af0cd7-e79a-4a83-a8b7-2845d17e63f4","order_by":0,"name":"Jiawei Wang","email":"","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiawei","middleName":"","lastName":"Wang","suffix":""},{"id":437002234,"identity":"ecc888f9-f7a5-4b8b-abf6-7efa88ae6d43","order_by":1,"name":"Jing Su","email":"","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Su","suffix":""},{"id":437002235,"identity":"768aca77-f3a6-441e-8151-4c7faba3622e","order_by":2,"name":"Danyan Liu","email":"","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Danyan","middleName":"","lastName":"Liu","suffix":""},{"id":437002236,"identity":"e1f35acd-48af-4aa1-970d-f26c68c26957","order_by":3,"name":"Jingxue Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYHCChANgir2x4eCHCgk5eeK18Bw++FjijIWxYQPRlkmkJRvwtlUkMhwgoFC3/cDDwwW/Dif2M+SYSUjOk0hgbGB++OgGHi1mZxISDs/sS0uc2XDGTKJwm0QeOwObsXEOPi0HgFp4e2wSNxzsAdqyTaKYsYGHTRqvlvMPQFokEvcf5jGT4J0jkdhwgJCWG0BbeH4AbWFjA3q/gSgtIFsa0oxnnGEGBvIxCWPDZkJ+OZ+T/Jnnz2HZ/vkPgVFZUycnz9788DE+LcAoTGBgbEMWYMarHATYDzAw/CGoahSMglEwCkYyAAAgnFU1g+C4tgAAAABJRU5ErkJggg==","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jingxue","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2025-01-26 04:38:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5904494/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5904494/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13098-025-01701-z","type":"published","date":"2025-04-26T15:58:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79773674,"identity":"db99ac4d-634d-427a-a9a1-f7e844407ca9","added_by":"auto","created_at":"2025-04-02 13:42:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":736628,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePutative causal relationships between 5 major lipids and four DR outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe forest plots delineated the causal impact of five major lipids on four diabetic retinopathy\u003c/p\u003e\n\u003cp\u003e(DR) outcomes through univariable Mendelian randomization analysis. An asterisk (*) indicates\u003c/p\u003e\n\u003cp\u003esuggestive causality, while three asterisks (***) denote robust causality.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5904494/v1/b2c132c50a2fe1c128478b39.png"},{"id":79773678,"identity":"52e50071-05f4-4ad9-869c-94c17d67f0be","added_by":"auto","created_at":"2025-04-02 13:42:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":717882,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProteins mediating the causal effect of triglycerides on DR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. The chord diagram illustrated that ten proteins significantly mediated the causal effect of triglycerides on diabetic retinopathy(DR).\u003c/p\u003e\n\u003cp\u003eB. Protein-protein interaction (PPI) analysis revealed that among these ten proteins, only Proto-Oncogene, Src Family Tyrosine Kinase (FYN), and Neogenin 1 (NEO1) exhibited interactions.\u003c/p\u003e\n\u003cp\u003eC. Enrichment analyses demonstrated that Retinol Dehydrogenase 16 (RDH16), Dickkopf-related protein 3 (DKK3), NEO1, and FYN were significantly implicated in various biological processes, including Cellular Hormone Metabolic Process (GO: BP), Cellular Response to Transforming Growth Factor Beta Stimulus (GO: BP), Threonine Kinase Signaling Pathway (GO: BP), Postsynaptic Density (GO: CC), Co-receptor Binding (GO: MF), Axon Guidance (KEGG), and Retinol Metabolism (KEGG) (Figure 2C, adjusted \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5904494/v1/8ab1d74abfdf4e13eb0b6f0f.png"},{"id":79774981,"identity":"f4b1a93c-09d8-465e-931d-2d84dbeaedb9","added_by":"auto","created_at":"2025-04-02 13:58:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1062954,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePutative causal circulating metabolites of four DR outcomes identified by univariable and multivariable Mendelian randomization analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. The heatmap illustrated the effects of 26 causal circulating metabolites with robust evidence on four diabetic retinopathy (DR) outcomes identified through univariable Mendelian randomization (UVMR) analyses. These included 11 metabolites for overall DR, 5 for background DR, 1 for severe background DR, and 9 for proliferative DR (PDR).\u003c/p\u003e\n\u003cp\u003eB. The forest plots depicted the results of multivariable Mendelian randomization (MVMR) analyses using the MR-BMA method. These analyses identified 8 out of the 11 causal metabolites for overall DR, 1 out of the 5 for background DR, and 2 out of the 9 for PDR, considering the combined impact of these metabolites.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5904494/v1/0bc35ad5065e480c2cfe92b4.png"},{"id":81569913,"identity":"be0b50f5-d699-4a16-aeb7-620b5496911d","added_by":"auto","created_at":"2025-04-28 16:12:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1787874,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5904494/v1/ecd98719-3f0e-4974-bfd9-4d80b2a0d26a.pdf"},{"id":79773673,"identity":"3fdc13a8-7b9e-4771-94de-cf8056bcf600","added_by":"auto","created_at":"2025-04-02 13:42:13","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10659,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5904494/v1/8a693f7d0f66aa18fe1665d0.xlsx"},{"id":79773677,"identity":"07c3f89f-2272-4e2d-ab81-f6ebebacdcd9","added_by":"auto","created_at":"2025-04-02 13:42:13","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":427032,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5904494/v1/4b5d143313b80a2604a0d962.xlsx"},{"id":79773675,"identity":"94ad8343-ef71-4daa-bbe7-27a216aef914","added_by":"auto","created_at":"2025-04-02 13:42:13","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":74969,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5904494/v1/8702972511bfb172c2031f84.xlsx"},{"id":79774037,"identity":"37e2bc69-c2dd-4b1c-b282-65c3708164cd","added_by":"auto","created_at":"2025-04-02 13:50:13","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":134647,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5904494/v1/77d683d6661d5aa1ce458525.xlsx"},{"id":79773682,"identity":"1496def7-548a-4777-9631-be03178a230a","added_by":"auto","created_at":"2025-04-02 13:42:13","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":56246,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5904494/v1/727faae5dde2fa96eb339918.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal Roles of Lipids and Mediating Proteins in Diabetic Retinopathy: Insights from Metabolomic and Proteomic Mendelian Randomization","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDiabetic retinopathy (DR) is the most common complication of diabetes mellitus (DM) and a leading cause of preventable blindness among adults, posing a significant public health challenge\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. By 2030, it is projected that 129.84\u0026nbsp;million adults globally will suffer from DR, with this number rising to 160.50\u0026nbsp;million by 2045\u003csup\u003e3\u003c/sup\u003e. DR is characterized by progressive damage to the retinal microvasculature, with its development and progression are closely linked to metabolic dysregulation, including hyperglycemia and associated metabolic disturbances. Although elevated blood glucose levels has been recognized to contribute to the development of DR, differences in HbA1c levels explain only 6.6% of the variation in DR risk for the entire study cohort in a diabetes control and complications trial\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDyslipidemia, defined by the presence of abnormal levels of lipids in the blood, is a common feature in individuals with diabetes and has been implicated in various diabetic complications, including DR\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Although epidemiological studies hint at a link between lipid irregularities and DR, establishing causality is complex, primarily due to the influence of confounding variables and the possibility of reverse causation\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Meta-analyses of DR research have identified associations between blood pressure, serum total cholesterol, and glycosylated hemoglobin levels with the prevalence of retinopathy. However, these factors collectively account for only a modest 9% of DR progression\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Metabolomics, a burgeoning field in the biomedical sciences, offers a novel perspective in the quest to understand complex diseases like DR. It is estimated that genetic variation accounts for approximately 50% of the observed phenotypic variance at the metabolite level, offering a unique opportunity for inferring causality from metabolite levels to disease risk\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Mendelian randomization (MR) represents a powerful approach in this context, utilizing genetic variants associated with a specific exposure to assess its causal impact on an outcome. By leveraging the principles of MR, researchers can navigate around the complexities of confounding factors and reverse causation, thereby enhancing the reliability of causality evidence in disease research. This method holds particular promise for elucidating the intricate interplay between metabolic pathways and DR pathogenesis.\u003c/p\u003e \u003cp\u003eIn this study, we aimed to explore the causal associations between five major lipids and 249 circulating metabolites with various DR outcomes, including overall DR, background DR, severe background DR, and proliferative DR (PDR). By employing metabolome-wide MR analyses, we assessed the relationships between these factors and DR outcomes. Additionally, we utilized proteome-wide mediated MR analyses to identify plasma proteins that mediate these causal effects. Our findings provide valuable insights into the roles of specific lipids, metabolites, and proteins in DR, highlighting potential targets for therapeutic intervention and guiding future research directions.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data sources\u003c/h2\u003e \u003cp\u003eThe summary datasets used in this study are publicly available and can be accessed through the cited papers. All original GWAS studies included in this research were conducted with approval from their respective ethics committees, and informed consent was obtained from all participants involved in these studies.\u003c/p\u003e \u003cp\u003eThis metabolome-wide MR study utilized publicly accessible summary datasets, as detailed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. In two-sample MR analyses, we obtained GWAS summary statistics for 254 circulating metabolites from the MRC-IEU OpenGWAS project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk). These included data for five major non-fasted lipoprotein lipid traits measured using standard clinical chemistry assays in approximately 441,016 participants from the UK Biobank (UKBB)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, as well as 249 metabolic biomarkers using high-throughput NMR spectroscopy in over 114,000 participants of European ancestry from the UKBB\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Summary-level data for outcomes, including diabetic retinopathy (DR), background DR, severe background DR, and proliferative diabetic retinopathy (PDR), were sourced from the FinnGen Release 9, a comprehensive European consortium (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://storage.googleapis.com/finngen-public-data-r9/summary_stats/)\u003csup\u003e11\u003c/sup\u003e. These datasets included 10,413 cases of DR compared to 308,633 control subjects, 4,011 background DR cases juxtaposed against 344,569 controls, 816 severe background DR cases compared to 344,569 controls, as well as 9,511 PDR cases against 362,581 controls. In the mediation analyses, we incorporated additional datasets beyond the previously described exposure and outcome data. Specifically, we utilized 4,489 GWAS summary statistics for available proteins sourced from the MR-Base NHGRI-EBI GWAS Catalog (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) as mediators, which included 3,282 plasma proteins from 3,301 healthy participants in the INTERVAL study\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, 1,124 blood circulating proteins measured in 1,000 blood samples from the KORA study\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, and 83 proteins from 3,394 individuals in the IMPROVE study\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.2 Identification of qualified genetic instrumental variables (IVs)\u003c/h3\u003e\n\u003cp\u003eTo identify qualified genetic instruments, we selected single-nucleotide polymorphisms (SNPs) based on stringent criteria: we initially extracted SNPs that achieved genome-wide significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5E-08) and then clumped them within a 1000 kb window to an LD threshold of R\u0026sup2; \u0026lt; 0.1, using the 1000 Genomes European Ancestry reference panel\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e to ensure genetic independence. To avoid the influence of weak instrument bias, we calculated the F statistic with formula: F = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{R}}^{2}\\times\\:(\\text{N}-\\text{k}-1)/\\text{k}\\times\\:(1-{\\text{R}}^{2}\\)\u003c/span\u003e\u003c/span\u003e), and the genetic variation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{R}}^{2}\\)\u003c/span\u003e\u003c/span\u003e) with the formula \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{R}}^{2}\\)\u003c/span\u003e\u003c/span\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:2\\times\\:\\text{E}\\text{A}\\text{F}\\times\\:(1-\\text{E}\\text{A}\\text{F})\\times\\:{{\\beta\\:}}^{2}\\)\u003c/span\u003e\u003c/span\u003e, where N represents the sample size, k represents the number of IVs, EAF is the effect allele frequency, and β is the estimated effect size. SNPs with an F statistic of \u0026ge;\u0026thinsp;10 were retained to minimize weak instrument bias. Finally, we harmonized the exposure and outcome datasets to ensure that the effect of each variant on both the exposure and outcome aligned with the same allele. We inferred positive-strand alleles and systematically excluded palindromic SNPs with ambiguous allele frequencies and any incompatible alleles.\u003c/p\u003e\n\u003ch3\u003e2.3 Univariable MR (UVMR) and multivariable MR (MVMR)\u003c/h3\u003e\n \u003cp\u003eIn our study, we employed the R package \"TwoSampleMR\" (version: 0.5.8) for UVMR analyses to explore the relationship between circulating metabolites and DR outcomes. For single IV analyses, we utilized the Wald Ratio to estimate causal relationships. Under the assumption of valid IVs and no horizontal pleiotropy, we predominantly used the inverse-variance weighted (IVW) method as a robust MR approach to infer causality\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. MVMR extends the standard MR framework by considering multiple potential risk factors within a single model\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. To circumvent the suboptimal performance of traditional MVMR using standard linear regression in the presence of numerous risk factors, we employed MR-BMA. This Bayesian model averaging approach not only scales effectively to high-throughput experiments but also demonstrates robustness in detecting true causal risk factors, even when candidate factors are highly correlated\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The MVMR analyses were performed exclusively on causal circulating metabolites associated with the same DR outcome to ensure robust evaluation. Qualified IVs commonly related to causal circulating metabolites were extracted and processed using the same criteria as described previously (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5E-08, clumped at R\u0026sup2; \u0026lt; 0.1 within a 1000 kb window, based on the 1000 Genomes European Ancestry reference panel). Within a Bayesian framework, MR-BMA calculates the marginal inclusion probability (MIP) and the model-averaged causal effect (MACE) for each risk factor. MIP is the sum of the posterior probabilities (PP) of all models that include the risk factor, indicating the likelihood that the risk factor is a causal determinant of disease risk. MACE provides a conservative estimate of the average direct causal effect of the risk factor on the outcome, derived through weighted averaging, with the weights determined by the posterior probabilities of the respective models.\u003c/p\u003e\n\u003ch3\u003e2.5 Mediation MR analysis\u003c/h3\u003e\n\u003cp\u003eWe performed two-step MR analyses to explore RNA molecules that may mediate the link between circulating metabolites and DR outcomes. Initially, we applied UVMR to estimate the causal effect (β1) of circulating metabolites on each potential mediator. Subsequently, we also used UVMR to estimate the causal effect (β2) of each mediator on DR outcomes. If the results indicated that both β1 and β2 were significant, we used the \"product of coefficients\" method to calculate the mediation effect (β1\u0026thinsp;\u0026times;\u0026thinsp;β2) of circulating metabolites on DR outcomes through each mediator. We also calculated the direct effect of circulating metabolites on DR outcomes by excluding the mediator, which was derived by subtracting the mediation effect from the total effect. The standard errors for the mediation effects were calculated using the delta method formula: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{SE}_{mediation}=\\surd\\:({\\beta\\:}1\\times\\:\\text{S}\\text{E}1)\\:+({\\beta\\:}2\\times\\:\\text{S}\\text{E}2)\\:\\)\u003c/span\u003e\u003c/span\u003e. The z-score for the mediation effects was then calculated as: Z = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{m}\\text{e}\\text{d}\\text{i}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}\\:\\text{e}\\text{f}\\text{f}\\text{e}\\text{c}\\text{t}\\:({\\beta\\:}1\\:\\times\\:\\:{\\beta\\:}2)/SE}_{mediation}\\)\u003c/span\u003e\u003c/span\u003e. Finally, the \u003cem\u003eP\u003c/em\u003e-value for the mediation effects was calculated using the formula: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:P\\:=\\:2\\times\\:\\text{p}\\text{n}\\text{o}\\text{r}\\text{m}(\\text{q}=|\\text{Z}|,lower.tail=FALSE)\\)\u003c/span\u003e\u003c/span\u003e. Negative mediation proportions were truncated at a minimum threshold of 0%, as this is the lowest threshold to determine a mediation proportion.\u003c/p\u003e\n\u003ch3\u003e2.6 Sensitivity analysis\u003c/h3\u003e\n\u003cp\u003eFor the UVMR analyses, we conducted several sensitivity analyses to support the IVW estimates, including the weighted median, simple mode, weighted mode, and MR-Egger. The weighted median approach provides a reliable estimate of causality when at least 50% of the weight is derived from valid IVs\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The simple and weighted mode methods estimate the causal effect based on mode of the unweighted and IVW empirical density functions, respectively\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The MR-Egger regression method can detect directional horizontal pleiotropy and provide a corrected estimate\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for the MR-Egger intercept indicates the presence of directional pleiotropy. Cochran\u0026rsquo;s Q statistic was calculated to evaluate heterogeneity\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Functional annotation for RNA mediators\u003c/h2\u003e \u003cp\u003eTo annotate the RNA mediators implicated in mediating the causal relationship between metabolites and DR outcomes, we conducted functional annotation to uncover their biological significance. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the R packages \"clusterProfiler\" and \"org.Hs.eg.db\" to identify pathways enriched with the identified RNA mediators. Additionally, we constructed a Protein-Protein Interaction (PPI) network using data from the STRING database.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.8 Statistical analysis\u003c/h3\u003e\n\u003cp\u003eTo address multiple testing, we employed the Benjamini-Hochberg false discovery rate (FDR) procedure. IVW estimates with |\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e| \u0026gt; 0.1, \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, supported by at least one sensitivity analysis, were considered robust evidence of causality. IVW estimates with a \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 but FDR\u0026thinsp;\u0026ge;\u0026thinsp;0.05 or lacking support from sensitivity analyses were considered suggestive of potential causality. To clearly interpret the causal effect (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e), we utilized the odds ratio (OR), an intuitive indicator for assessing risk, to exhibit the potential impact of lipid levels on DR outcomes. All MR analyses were performed using R software (version 4.3.1) with the following R packages: \"TwoSampleMR\" (version 0.5.8), \"MVMR\" (version 0.4), and \"MendelianRandomization\" (version 0.10.0). Additionally, R packages \"clusterProfiler\" (version 4.10.1) and \"org.Hs.eg.db\" (version 3.16.0) were utilized for GO and KEGG pathway investigations. The code for the MR-BMA method was obtained from the GitHub repository (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/verena-zuber/demo_AMD\u003c/span\u003e\u003cspan address=\"https://github.com/verena-zuber/demo_AMD\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) referenced in the literature \u003csup\u003e \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e \u003c/sup\u003e.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Two putative causal major lipids for DR outcomes with robust evidence\u003c/h2\u003e \u003cp\u003eIn the UVMR analyses of five major lipids, including triglycerides, apolipoprotein B, LDL cholesterol, HDL cholesterol, and apolipoprotein A-I, we observed significant causal relationships between nearly all these lipids and one or more of the four DR outcomes (\u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Specifically, elevated triglyceride levels increased the risks for DR (OR [95% CI]\u0026thinsp;=\u0026thinsp;1.11 [1.07\u0026ndash;1.16]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.57e-06) with robust causal evidence, background DR (OR [95% CI]\u0026thinsp;=\u0026thinsp;1.07[1.01\u0026ndash;1.14]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03), and severe background DR (OR [95% CI]\u0026thinsp;=\u0026thinsp;1.18 [1.04\u0026ndash;1.33]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) with suggestive causal evidence. Higher HDL cholesterol levels decreased the risk for all four DR outcomes, with robust evidence of causality for background DR (OR [95% CI]\u0026thinsp;=\u0026thinsp;0.88[0.83\u0026ndash;0.93]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.66e-06) and the strongest protective effect for severe background DR (OR [95% CI]\u0026thinsp;=\u0026thinsp;0.82[0.70\u0026ndash;0.93]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.20e-04). Additionally, apolipoprotein A-I exhibited varying degrees of protective effects across the four DR outcomes, with suggestive evidence of causality.. Both LDL cholesterol and apolipoprotein B exhibited slight protective effects against DR (OR\u0026thinsp;=\u0026thinsp;0.92 and 0.95 respectively), LDL cholesterol showed suggestive causal effects on PDR (OR [95% CI]\u0026thinsp;=\u0026thinsp;0.95[0.90\u0026ndash;0.99]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015). However, it is important to note that most results exhibited significant heterogeneity and pleiotropy (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For results demonstrating pleiotropy, the MR-Egger method was employed to identify and exclude outlier SNPs with horizontal pleiotropy, ensuring the robustness of the conclusions (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). For results indicating heterogeneity, the weighted median approach was utilized to facilitate causal inference (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.2 Eleven Plasma proteins mediated the causal effect of two causal major lipids on DR outcomes\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOur findings indicate that elevated triglyceride levels are associated with an increased risk of DR, whereas higher HDL cholesterol levels are linked to a reduced risk of background DR, substantiated by robust causal evidence. Mediation MR analysis identified ten plasma proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) that significantly mediate the causal effect of triglycerides on DR (β\u0026thinsp;=\u0026thinsp;0.12). Among these, Dickkopf-related protein 3 (DKK3), Sulfotransferase Family 4A, Member 1 (ST4S6), and Neogenin 1 (NEO1) exhibit mediation effects consistent with the overall impact of triglycerides on DR (β\u0026thinsp;=\u0026thinsp;0.03, 0.03, and 0.02 in mediation MR analyses, respectively). Additionally, PPI analysis revealed an interaction between Proto-Oncogene, Src Family Tyrosine Kinase (FYN) and NEO1 within the set of ten proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Enrichment analyses demonstrated that Retinol Dehydrogenase 16 (RDH16), DKK3, NEO1, and FYN were significantly involved in biological processes such as Cellular Hormone Metabolic Process (GO: BP), Cellular Response to Transforming Growth Factor Beta Stimulus (GO: BP), Threonine Kinase Signaling Pathway (GO: BP), Postsynaptic Density (GO: CC), Co-receptor Binding (GO: MF), Axon Guidance (KEGG), and Retinol Metabolism (KEGG) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, mediation MR analysis also revealed that the protein RPN1 significantly mediated the causal effect of HDL cholesterol on background DR (β = -0.10), with a mediation effect of -0.01 (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Twenty-six putative causal metabolites for DR outcomes with robust evidence\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eIn the UVMR analyses, eleven metabolites, including seven associated with very low-density lipoprotein (VLDL), three with low-density lipoprotein (LDL), and one with intermediate-density lipoprotein (IDL), demonstrated robust causal associations with DR risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Among these, the elevated triglycerides to total lipids ratio in large VLDL (OR [95% CI]\u0026thinsp;=\u0026thinsp;1.11 [1.06\u0026ndash;1.16]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.71e\u0026thinsp;\u0026minus;\u0026thinsp;05) and increased phospholipids to total lipids ratio in large LDL (OR [95% CI]\u0026thinsp;=\u0026thinsp;1.12 [1.06\u0026ndash;1.17]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.66e-06) were found to significantly heighten the DR risk. The remaining nine metabolites exhibited negative causal associations with DR risk. However, when considering the combined effect of these eleven metabolites on DR risk through MR-BMA analysis, only six VLDL-related metabolites and two LDL-related metabolites exhibited significant negative associations with DR. Specifically, cholesteryl esters in very small VLDL, cholesterol in very small VLDL, concentration of very small VLDL particles, and triglycerides in large LDL showed higher MIPs of 0.918, 0.814, 0.744, and 0.631, respectively, with FDRs\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor background DR, UVMR analyses identified five metabolites with robust causal associations. Among these, two metabolites related to omega-3 fatty acids (OR\u0026thinsp;=\u0026thinsp;0.89 and 0.91; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.87e-05 and 8.97e-05) and docosahexaenoic acid (OR [95% CI]\u0026thinsp;=\u0026thinsp;0.88 [0.82\u0026ndash;0.94]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.38e-05) were observed to have significant negative associations with background DR risk. Conversely, an elevated ratio of omega-6 fatty acids to omega-3 fatty acids (OR [95% CI]\u0026thinsp;=\u0026thinsp;1.12 [1.07\u0026ndash;1.17]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.40e-05) and an increased phospholipids to total lipids ratio in large LDL (OR [95% CI]\u0026thinsp;=\u0026thinsp;1.15 [1.09\u0026ndash;1.22]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.83e-05) were associated with a heightened background DR risk. MR-BMA analysis, considering the combined impact of these five causal metabolites, found that only the phospholipids to total lipids ratio in large LDL had a significant effect on background DR risk with MIPs of 0.943 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). For severe background DR, UVMR analyses identified that only elevated omega-3 fatty acid levels were significantly associated with a reduced risk of severe background DR (OR [95% CI]\u0026thinsp;=\u0026thinsp;0.78 [0.66\u0026ndash;0.90]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.00e-05).\u003c/p\u003e \u003cp\u003eRegarding PDR, UVMR analyses identified nine metabolites with robust causal associations. Among these, increased tyrosine levels were significantly associated with an elevated risk of PDR (OR [95% CI]\u0026thinsp;=\u0026thinsp;1.15 [1.08\u0026ndash;1.21]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.50e-05). Additionally, two metabolites related to fatty acids (OR\u0026thinsp;=\u0026thinsp;0.90 and 0.89; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.29e-04 and 1.11e-05), two phospholipid-related metabolites (OR\u0026thinsp;=\u0026thinsp;0.88 and 0.90; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.50e-05 and 1.56e-05), two choline-related metabolites (OR\u0026thinsp;=\u0026thinsp;0.89 and 0.90; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.28e-05 and 1.59e-5), free cholesterol in small HDL (OR [95% CI]\u0026thinsp;=\u0026thinsp;0.88 [0.81\u0026ndash;0.94]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.55e-05), and phosphoglycerides (OR [95% CI]\u0026thinsp;=\u0026thinsp;0.89 [0.84\u0026ndash;0.94]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.08e-05) were observed to have significant negative associations with PDR. MR-BMA analysis, considering the combined impact of these nine causal metabolites, revealed that only elevated levels of total cholines and polyunsaturated fatty acids (PUFAs) were significantly associated with a reduced risk of PDR with MIPs of 0.164 and 0.184, respectively, and all FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Thirteen plasma proteins mediated the effect of causal metabolites on DR outcomes\u003c/h2\u003e \u003cp\u003eAlthough our study initially identified 11 metabolites with robust causal associations with DR risk, we subsequently validated 8 of these metabolites through MVMR analysis. The proteome-wide mediation MR analysis identified 10 plasma proteins that mediate the causal effects of 7 of these metabolites on DR (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). For the 5 very small VLDL-related metabolites, proteins Chloride Intracellular Channel 5 (CLIC5) and BCAM (Basal Cell Adhesion Molecule) significantly potentiated the protective effects of very small VLDL-related metabolites on DR (mediation β = -0.06 and \u0026minus;\u0026thinsp;0.02, total effect β = -0.11 to -0.15). Conversely, proteins (Signal Regulatory Protein Gamma) SIRPG, ST4S6, PGRC2 (Progestin and AdipoQ Receptor Family Member 2), DKK3, N-terminal pro-Brain Natriuretic Peptide (N-terminal pro-BNP), and (Thiamine Transporter) THTR significantly attenuated the protective effects of VLDL-related metabolites on DR (mediation β\u0026thinsp;\u0026gt;\u0026thinsp;0). For the 2 LDL-related metabolites, proteins BCAM and Sushi, Von Willebrand Factor Type A, EGF And Pentraxin Domain Containing 1 (SVEP1) significantly augmented the protective effects of LDL-related metabolites on DR (mediation β = -0.02 and \u0026minus;\u0026thinsp;0.01, total effect β = -0.108 and \u0026minus;\u0026thinsp;0.119, respectively).\u003c/p\u003e \u003cp\u003eAdditionally, for background DR, proteome-wide mediation MR analysis revealed that NAD(P)H dehydrogenase mediates the risk effect of the phospholipid to total lipid ratio in large LDL on background DR (total effect β\u0026thinsp;=\u0026thinsp;0.14, mediation effect\u0026thinsp;=\u0026thinsp;0.01). For PDR, protein RFNG O-Fucosylpeptide 3-Beta-N-Acetylglucosaminyltransferase (RFNG) mitigates the protective effects of PUFAs and total cholines on PDR (mediation β\u0026thinsp;=\u0026thinsp;0.07 and 0.06, total effect β = -0.12 and \u0026minus;\u0026thinsp;0.11, respectively), whereas protein PDE4D positively mediates the protective effects of PUFAs on PDR (mediation β = -0.03, total effect β = -0.12).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Significant Plasma Proteins from Mediation MR Analysis of causal metabolites on DR outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMediate proteins\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEffect of Exposure on Mediator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEffect of Mediator on Outcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMediation Effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eProportion Mediated\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDirect Effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides in large LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBCAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.39E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides in large LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTMEM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.47E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides in large LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSVEP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.26E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcentration of very small VLDL particles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eST4S6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.46E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcentration of very small VLDL particles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCLIC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.07E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcentration of very small VLDL particles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBCAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.24E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcentration of very small VLDL particles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDKK3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.80E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhospholipids in very small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN- terminal\u003c/p\u003e \u003cp\u003epro-BNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.02E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhospholipids in very small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eST4S6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.58E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhospholipids in very small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCLIC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.96E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhospholipids in very small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDKK3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.20E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides in LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBCAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.87E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides in LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSVEP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.80E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree cholesterol in very small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBCAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.49E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree cholesterol in very small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePGRC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.76E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal lipids in very small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eST4S6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.01E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal lipids in very small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTHTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.59E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal lipids in very small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBCAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.27E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal lipids in very small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCLIC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.06E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal lipids in very small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSIRPG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.91E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal lipids in very small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDKK3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.79E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesteryl esters in very small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eST4S6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.46E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesteryl esters in very small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSIRPG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.38E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesteryl esters in very small VLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCLIC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.50E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhospholipids to total lipids ratio in large LDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNAD(P)H dehydrogenase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBackground DR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.68E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolyunsaturated fatty acids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRFNG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.80E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolyunsaturated fatty acids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePDE4D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.55E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRFNG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.24E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAmong the major lipids analyzed, triglycerides emerged as the most significant risk factor, potentially contributing to disease risk through mediation by the proteins DKK3, ST4S6, and NEO1.Conversely, HDL cholesterol was identified as the most potent protective factor, potentially reducing the risk of background DR through mediation by the protein RPN1. Among the causal circulating metabolites, cholesteryl esters in very small VLDL exhibited the strongest protective effect against DR, with their influence mediated by the plasma proteins CLIC5 and BCAM.For background DR, the phospholipid-to-total lipid ratio in large LDL was identified as the most plausible causal metabolite, mediated by the plasma protein NAD(P)H dehydrogenase. Additionally, RFNG and PDE4D positively mediate the protective effects of PUFAs on PDR.\u003c/p\u003e \u003cp\u003eExtensive evidence indicates that inadequate control of triglyceride levels is associated with the onset and progression of DR, whereas elevated HDL-C levels and the use of lipid-lowering medications significantly diminish the risk of DR\u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Our results also revealed that the elevated triglyceride levels can distinctly increased DR risk, especially, we demonstrated that reduced HDL-C levels can increased background DR risk with robust causal evidence. Proteome-wide mediation MR analyses identified eleven plasma proteins that mediate the causal effects of triglyceride and HDL-C on DR outcomes, such as DKK3, ST4S6, NEO1, RDH16 and so on (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). DKK3, a crucial member of the DKK family and an important modulator of Wnt signaling, is synthesized and secreted by Muller cells. A study involving 44 eyes from 39 patients with diabetic macular edema (DME) and 27 eyes from 27 controls identified significantly elevated levels of DKK3 in the aqueous humor of DME patients and in human M\u0026uuml;ller cells. This research suggests that excessive activation of Wnt signaling, mediated by elevated DKK3 levels, may contribute to neovascularization and the progression of DR\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Conversely, RDH16 is integral to retinal health due to its role in efficiently producing retinal reductase, which supports retinol metabolism\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Our research also indicated that RDH16 negatively mediated the risk impact of triglycerides on DR.\u003c/p\u003e \u003cp\u003eBased on the analyses from both UVMR and MVMR, we have identified eight lipoprotein subclasses (including six very small VLDL particles and two LDL particles) that are protectively associated with DR. Very small VLDL particles have also reported to be inversely associated with incident diabetes\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e and age-related macular degeneration (AMD)\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Furthermore, a population-based study involving Chinese, Malay, and Indian adults used logistic regression to find that certain very small VLDL particles, consistent with our findings, such as cholesteryl esters in very small VLDL and LDL particles (including total lipids in large LDL), were protectively associated with moderate or more severe DR\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Additionally, the protein CLIC5 was found to significantly mediate the protective causal effect of very small VLDL particles on DR. While CLIC5 has also been reported to be significantly downregulated in glomerular tissues of diabetic nephropathy patients\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. This suggests that the protective effect of very small VLDL particles on DR may, in part, be mediated through CLIC5. Our study elucidated that NAD(P)H dehydrogenase mediated the risk effect of the phospholipid-to-total lipid ratio in large LDL on background DR. NAD(P)H dehydrogenase is pivotal in modulating cellular redox balance and energy metabolism. Previous studies have shown that diabetic rats display elevated concentrations of free NAD(P)H, reflecting increased glycolytic activity, along with higher levels of bound NAD(P)H, suggesting enhanced oxidative phosphorylation in their retinas.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. These observations suggest that alterations in NAD(P)H dynamics, driven by modifications in lipid profiles, may exacerbate oxidative stress and metabolic dysregulation.\u003c/p\u003e \u003cp\u003eOur study further revealed that PUFAs and total choline exhibited protective effects against PDR. PUFAs were generally considered beneficial\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Notably, two clinical studies conducted in Europe have identified an inverse relationship between omega-6 PUFAs and the incidence of DR\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Additionally, elevated phosphatidylcholine levels are associated with reduced risks of diabetes and cardiovascular diseases\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Moreover, our findings indicate that RFNG positively mediate the protective effects of PUFAs and total cholines on PDR. RFNG enhances NOTCH1 activity by modifying O-fucose residues on specific EGF-like domains, thereby promoting NOTCH1 activation through DLL1 and JAG1, which may contribute to neurogenesis\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The interaction between PUFAs, choline, and RFNG underscores a complex network of metabolic and signaling pathways that collectively influence retinal health. These insights could inform future therapeutic strategies aimed at leveraging these protective factors to prevent or mitigate the progression of DR.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eDespite the significant findings, our study has several limitations. First, although MR helps to infer causality by minimizing confounding and reverse causation, it relies on the assumption of no pleiotropy, meaning the genetic variants used as instruments should not affect the outcome through pathways other than the exposure of interest. Violations of this pleiotropy assumption could bias the results. Second, our analysis was based on data from European participants, which may limit the generalizability of our findings to other ethnic and demographic groups. Third, while we identified several proteins that potentially mediate the effects of lipids on DR, the exact biological mechanisms remain to be elucidated through experimental studies. Additionally, the use of plasma lipid measurements might not fully reflect lipid metabolism within retinal tissues, and tissue-specific studies are needed to confirm our findings. Furthermore, our study relies on summary-level GWAS data, which, while enabling large-scale causal inference, comes with inherent limitations. The use of summary statistics precludes individual-level data analyses, limiting our ability to assess potential confounding factors and effect modifications at a finer scale. Independent cohort validation is essential to confirm the robustness and replicability of our findings across diverse populations. Finally, the complexity of lipid metabolism and its interaction with various metabolic pathways necessitates further investigation to fully understand the causal relationships identified in this study. Future research should focus on longitudinal and tissue-specific analyses to validate and extend our findings.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, our metabolome-wide MR analysis has elucidated the complex relationships between lipid profiles and DR. We identified triglycerides as a significant risk factor for DR, mediated by the proteins DKK3, ST4S6, and NEO1, while HDL-C emerged as a potent protective factor, potentially reducing the risk of background DR through RPN1 mediation. Cholesteryl esters in very small VLDL exhibited the strongest protective effect against DR, mediated by CLIC5 and BCAM, while the phospholipid-to-total lipid ratio in large LDL was identified as a key causal metabolite for background DR, with its effects mediated by NAD(P)H dehydrogenase. Furthermore, the protective effects of PUFAs and total cholines on PDR were positively mediated by RFNG. These findings provide valuable insights into the metabolic pathways and potential therapeutic targets for DR, highlighting the importance of lipid metabolism in the pathogenesis of this condition. Future research should focus on validating these results in diverse populations and exploring the underlying mechanisms through experimental studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe work was supported by Medical Science Research Project of Hebei Province [20221109], and Hospital-level Foundation of the Second Hospital of Hebei Medical University [2HC202020].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations of interest:\u003c/strong\u003e none.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJiawei Wang: conceptualization; data curation; formal analysis and writing original draft; Jing Su: methodology; software; and validation; Danyan Liu: investigation; project administration; resources; supervision; visualization; and revision; Jingxue Ma: funding acquisition; investigation; project administration; resources; supervision; visualization; and revision.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe summary datasets utilized in this study are publicly accessible and available through the referenced publications. All original GWAS investigations incorporated in this research were conducted with the approval of their respective ethics committees, and informed consent was obtained from all participants involved in these studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets employed in this study are publicly accessible summary datasets. They can be found in the cited publications, within the IEU OpenGWAS Project repository ([https://gwas.mrcieu.ac.uk/], or in the FinnGen repository ([https://storage.googleapis.com/finngen-public-data-r9/summary_stats/].\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTeo ZL, Tham YC, Yu M, et al. Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045: Systematic Review and Meta-analysis. Ophthalmology. 2021;128:1580\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeasher JL, Bourne RR, Flaxman SR, et al. Global Estimates on the Number of People Blind or Visually Impaired by Diabetic Retinopathy: A Meta-analysis From 1990 to 2010. Diabetes Care. 2016;39:1643\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal regional, national burden of diabetes. from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023;402:203\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYahyavi SK, Snorgaard O, Knop FK, et al. Prediabetes Defined by First Measured HbA(1c) Predicts Higher Cardiovascular Risk Compared With HbA(1c) in the Diabetes Range: A Cohort Study of Nationwide Registries. Diabetes Care. 2021;44:2767\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChapman MJ, Sposito AC. 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Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: A multivariable Mendelian randomisation analysis. PLoS Med. 2020;17:e1003062.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorges MC, Haycock PC, Zheng J, et al. Role of circulating polyunsaturated fatty acids on cardiovascular diseases risk: analysis using Mendelian randomization and fatty acid genetic association data from over 114,000 UK Biobank participants. BMC Med. 2022;20:210.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKurki MI, Karjalainen J, Palta P, et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. 2023;613:508\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun BB, Maranville JC, Peters JE, et al. Genomic atlas of the human plasma proteome. 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Genet Epidemiol. 2013;37:658\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanderson E, Davey Smith G, Windmeijer F, Bowden J. An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. Int J Epidemiol. 2019;48:713\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuber V, Colijn JM, Klaver C, Burgess S. Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization. Nat Commun. 2020;11:29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40:304\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHartwig FP, Davey Smith G, Bowden J. 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Curr Issues Mol Biol. 2024;46:612\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVaidya H, Cheema SK. Arachidonic acid has a dominant effect to regulate lipogenic genes in 3T3-L1 adipocytes compared to omega-3 fatty acids. Food Nutr Res. 2015;59:25866.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarion-Letellier R, Savoye G, Ghosh S. Polyunsaturated fatty acids and inflammation. IUBMB Life. 2015;67:659\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoutsmuller AJ, Zahn KJ, Henkes HE. Unsaturated fats and progression of diabetic retinopathy. Doc Ophthalmol. 1980;48:363\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoward-Williams J, Patel P, Jelfs R, et al. Polyunsaturated fatty acids and diabetic retinopathy. Br J Ophthalmol. 1985;69:15\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorze J, Wittenbecher C, Schwingshackl L, et al. 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Dev Cell. 2017;40:193\u0026ndash;201.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"diabetology-and-metabolic-syndrome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dims","sideBox":"Learn more about [Diabetology \u0026 Metabolic Syndrome](http://dmsjournal.biomedcentral.com/)","snPcode":"13098","submissionUrl":"https://submission.nature.com/new-submission/13098/3","title":"Diabetology \u0026 Metabolic Syndrome","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Diabetic retinopathy, Major lipids, Circulating metabolites, Proteome, causality","lastPublishedDoi":"10.21203/rs.3.rs-5904494/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5904494/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study explores the causal relationships between five major lipids, 249 circulating metabolites, and four diabetic retinopathy (DR) outcomes: overall DR, background DR, severe background DR, and proliferative DR (PDR). We aim to identify plasma proteins that mediate these causal effects, offering insights into potential therapeutic targets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted metabolome-wide Mendelian randomization (MR) analyses to assess associations between major lipids, metabolites, and DR outcomes. Multivariable MR (MVMR) and proteome-wide mediated MR (two-step MR) analyses were performed to ensure robust evaluation and identify mediating plasma proteins.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTriglycerides were identified as a significant risk factor for DR, mediated by proteins like Dickkopf-3 (DKK3), ST6 N-acetylglucosamine transferase 6 (ST4S6), and Neogenin (NEO1). For background DR, HDL-C, specific VLDL particles, and LDL triglycerides were protective, mediated by proteins like chloride intracellular channel 5 (CLIC5), basal cell adhesion molecule (BCAM), and Ribophorin I (RPN1). Additionally, polyunsaturated fatty acids (PUFAs) and total choline were protective against PDR, mediated by Radical Fringe Gene (RFNG).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study identifies specific plasma proteins that mediate the effects of lipids and metabolites on DR, establishing a direct molecular link between these biomarkers and disease progression. These findings enhance our understanding of the pathophysiological mechanisms underlying DR and highlight potential targets for therapeutic intervention.\u003c/p\u003e","manuscriptTitle":"Causal Roles of Lipids and Mediating Proteins in Diabetic Retinopathy: Insights from Metabolomic and Proteomic Mendelian Randomization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-02 13:42:08","doi":"10.21203/rs.3.rs-5904494/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-04-12T19:54:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-03T13:29:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"105296263267339686217345905248084258733","date":"2025-04-02T08:27:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-02T07:59:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"90844468902561685203223666753000568123","date":"2025-04-01T16:09:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103879418858601200306658741835579828848","date":"2025-04-01T09:53:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-01T09:19:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-01T07:47:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Diabetology \u0026 Metabolic Syndrome","date":"2025-03-30T12:46:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"diabetology-and-metabolic-syndrome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dims","sideBox":"Learn more about [Diabetology \u0026 Metabolic Syndrome](http://dmsjournal.biomedcentral.com/)","snPcode":"13098","submissionUrl":"https://submission.nature.com/new-submission/13098/3","title":"Diabetology \u0026 Metabolic Syndrome","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"42118671-4d43-46be-bfe9-a9f4cdd01329","owner":[],"postedDate":"April 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-28T16:07:35+00:00","versionOfRecord":{"articleIdentity":"rs-5904494","link":"https://doi.org/10.1186/s13098-025-01701-z","journal":{"identity":"diabetology-and-metabolic-syndrome","isVorOnly":false,"title":"Diabetology \u0026 Metabolic Syndrome"},"publishedOn":"2025-04-26 15:58:22","publishedOnDateReadable":"April 26th, 2025"},"versionCreatedAt":"2025-04-02 13:42:08","video":"","vorDoi":"10.1186/s13098-025-01701-z","vorDoiUrl":"https://doi.org/10.1186/s13098-025-01701-z","workflowStages":[]},"version":"v1","identity":"rs-5904494","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5904494","identity":"rs-5904494","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}