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Genetics/Genomes/Proteomics/Metabolomics

Genetically Determined Plasma Lipid Levels and Risk of Diabetic Retinopathy: A Mendelian Randomization Study

  1. Lucia Sobrin1,
  2. Yong He Chong2,3,
  3. Qiao Fan2,
  4. Alfred Gan3,
  5. Lynn K. Stanwyck1,
  6. Georgia Kaidonis4,
  7. Jamie E. Craig4,
  8. Jihye Kim5,
  9. Wen-Ling Liao6,7,
  10. Yu-Chuen Huang8,9,
  11. Wen-Jane Lee10,
  12. Yi-Jen Hung11,
  13. Xiuqing Guo12,
  14. Yang Hai12,
  15. Eli Ipp13,
  16. Samuela Pollack14,
  17. Heather Hancock15,
  18. Alkes Price14,
  19. Alan Penman16,
  20. Paul Mitchell17,
  21. Gerald Liew17,
  22. Albert V. Smith18,19,
  23. Vilmundur Gudnason18,19,
  24. Gavin Tan3,
  25. Barbara E.K. Klein20,
  26. Jane Kuo12,21,
  27. Xiaohui Li12,
  28. Mark W. Christiansen22,
  29. Bruce M. Psaty22,23,
  30. Kevin Sandow12,
  31. Asian Genetic Epidemiology Network Consortium*,
  32. Richard A. Jensen22,
  33. Ronald Klein20,
  34. Mary Frances Cotch24,
  35. Jie Jin Wang2,17,
  36. Yucheng Jia12,
  37. Ching J. Chen15,
  38. Yii-Der Ida Chen12,
  39. Jerome I. Rotter12,
  40. Fuu-Jen Tsai8,25,
  41. Craig L. Hanis5,
  42. Kathryn P. Burdon26,
  43. Tien Yin Wong2,3,27 and
  44. Ching-Yu Cheng2,3,27⇑
  1. 1Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear, Boston, MA
  2. 2Duke-NUS Medical School, National University of Singapore, Singapore
  3. 3Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
  4. 4Department of Ophthalmology, Flinders University, Adelaide, South Australia, Australia
  5. 5Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, TX
  6. 6Graduate Institute of Integrated Medicine, China Medical University, Taichung, Taiwan
  7. 7Center for Personalized Medicine, China Medical University Hospital, Taichung, Taiwan
  8. 8School of Chinese Medicine, China Medical University, Taichung, Taiwan
  9. 9Genetic Center, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
  10. 10Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
  11. 11Department of Internal Medicine, Tri-Service General Hospital, Taipei City, Taiwan
  12. 12Institute for Translational Genomics and Population Sciences, LA BioMed, and Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA
  13. 13Department of Medicine, LA BioMed, Harbor-UCLA Medical Center, Torrance, CA
  14. 14Department of Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA
  15. 15Department of Ophthalmology, The University of Mississippi Medical Center, Jackson, MS
  16. 16Department of Medicine, The University of Mississippi Medical Center, Jackson, MS
  17. 17Centre for Vision Research, The Westmead Institute for Medical Research, University of Sydney, Sydney, New South Wales, Australia
  18. 18Faculty of Medicine, University of Iceland, Reykjavík, Iceland
  19. 19Icelandic Heart Association, Kópavogur, Iceland
  20. 20Department of Ophthalmology and Visual Sciences, University of Wisconsin-Madison, Madison, WI
  21. 21Clinical and Medical Affairs, CardioDx, Inc., Redwood City, CA
  22. 22Cardiovascular Health Research Unit, Division of General Internal Medicine, University of Washington, Seattle, WA
  23. 23Kaiser Permanente Washington Health Research Institute, Seattle, WA
  24. 24Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, MD
  25. 25Departments of Medical Genetics, Pediatrics, and Medical Research, China Medical University Hospital, Tiachung, Tiawan
  26. 26Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
  27. 27Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
  1. Corresponding author: Ching-Yu Cheng, chingyu.cheng{at}duke-nus.edu.sg.
    1. L.S. and Y.H.C. contributed equally to this work.

    2. T.Y.W. and C.-Y.C. contributed equally to this work.

    Diabetes 2017 Dec; 66(12): 3130-3141. https://doi.org/10.2337/db17-0398
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    Abstract

    Results from observational studies examining dyslipidemia as a risk factor for diabetic retinopathy (DR) have been inconsistent. We evaluated the causal relationship between plasma lipids and DR using a Mendelian randomization approach. We pooled genome-wide association studies summary statistics from 18 studies for two DR phenotypes: any DR (N = 2,969 case and 4,096 control subjects) and severe DR (N = 1,277 case and 3,980 control subjects). Previously identified lipid-associated single nucleotide polymorphisms served as instrumental variables. Meta-analysis to combine the Mendelian randomization estimates from different cohorts was conducted. There was no statistically significant change in odds ratios of having any DR or severe DR for any of the lipid fractions in the primary analysis that used single nucleotide polymorphisms that did not have a pleiotropic effect on another lipid fraction. Similarly, there was no significant association in the Caucasian and Chinese subgroup analyses. This study did not show evidence of a causal role of the four lipid fractions on DR. However, the study had limited power to detect odds ratios less than 1.23 per SD in genetically induced increase in plasma lipid levels, thus we cannot exclude that causal relationships with more modest effect sizes exist.

    Introduction

    Diabetic retinopathy (DR) is a major microvascular complication of diabetes and is the leading cause of blindness in working-aged adults (1). It has been estimated that the global prevalence for any DR and proliferative DR (PDR) to be 34.6% and 7.0%, respectively (2).

    Dyslipidemia is a major cardiovascular risk factor and has been suggested also as a potential risk factor for DR, in particular the more severe end points such as PDR and diabetic macular edema (DME) (2,3). However, in contrast to tight glycemic and blood pressure control, which have been shown in clinical trials to reduce DR progression (4,5), therapies targeted at dyslipidemia have not shown similar results (6,7). In this regard, fenofibrate, a peroxisome proliferator–activated receptor α (PPAR-α) agonist, has shown benefits in reducing requirements for laser treatment of DR and DME (8), but the therapeutic effects of fenofibrate may not be lipid dependent. The association of dyslipidemia with DR has been inconsistent among observational studies (9–12). Possible reasons for this include confounding (e.g., with obesity), reverse causation, and measurement biases. As such, there is difficulty in establishing a causal relationship between plasma lipids and DR.

    Mendelian randomization (MR) is a study design using genetic variants as instrumental variables (IVs) to evaluate the causal relationship between a biomarker and an outcome of interest (13). Because it takes advantage of the natural randomization of genetic variants inherited independent of confounding factors such as lifestyle and environmental factors (14,15), MR avoids the issues of confounders and reverse causality and serves as a practical approach to evaluate the relationship between plasma lipids and DR.

    In this study, we used an MR approach pooling multiple studies to evaluate the causal relationship between plasma lipids and two DR phenotypes, 1) any DR and 2) severe DR, by using genetic variants associated with plasma lipids as IVs.

    Research Design and Methods

    Study Participants

    We included a total of 18 genome-wide association studies (GWAS) on DR: African American Proliferative Diabetic Retinopathy Study (AAPDR); Age Gene/Environment Susceptibility–Reykjavik Study (AGES Reykjavik); Australian Genetics of Diabetic Retinopathy Study (AUST); Blue Mountains Eye Study (BMES); Cardiovascular Health Study-African American (CHS-AA); Cardiovascular Health Study-Whites (CHS-Whites); Genetic Center, China Medical University Hospital, Taiwan; Genetics of Latinos Diabetic Retinopathy (GOLDR); Jackson Heart Study (JHS); Multi-Ethnic Study of Atherosclerosis-African American (MESA-AA); Multi-Ethnic Study of Atherosclerosis-Chinese (MESA-CHN); Multi-Ethnic Study of Atherosclerosis-European (MESA-EU); Multi-Ethnic Study of Atherosclerosis-Hispanic (MESA-HIS); Singapore Chinese Eye Study (SCES); Singapore Malay Eye Study (SiMES); Singapore Indian Eye Study (SINDI); Starr County Health Studies; and Taiwan–US Diabetic Retinopathy Study (TUDR). Details of the individual studies have been previously described (16–31). Of them, 17 had phenotype information on any DR and 11 on severe DR. Genotyping was performed on either the Illumina (San Diego, CA) or Affymetrix (Santa Clara, CA) platforms. Imputation was done using the Markov Chain Haplotyping software IMPUTE2 or MaCH with 1000 Genomes or HapMap Phase II as reference panels (Table 1). Details about imputation quality control and adjustment are provided in Table 1. Informed consent was obtained from all participants, ethics approval was obtained from the local ethics committee, and recommendations of the Declaration of Helsinki were adhered to.

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    Table 1

    Details of each study population

    DR Assessment and Definition

    DR was either assessed through retinal photography or clinical diagnosis in the studies involved. DR was graded using the Early Treatment of Diabetic Retinopathy Study (ETDRS) adaptation of the modified Airlie House classification system or the American Academy of Ophthalmology (AAO) International Clinical Diabetic Retinopathy Disease Severity Scale. On the ETDRS scale, grade 10 represents no DR, grades ≥20 indicates any DR, and grades ≥53 indicates severe nonproliferative DR (NPDR) and PDR. On the AAO scale, the category of no DR indicates absence of DR, the remaining four categories together indicate any DR, and the two highest categories together capture severe NPDR and PDR. As all the studies were graded by one of these two scales and it is straightforward to harmonize DR phenotypes across these two scales, it was possible to easily harmonize the DR phenotype across all the studies.

    Two DR phenotypes were assessed in MR analyses: 1) any DR referred to participants with evidence of presence of DR and 2) severe DR referred to participants with severe NPDR and/or PDR (Table 1). Control subjects in the GWAS analyses were defined as subjects with type 2 diabetes without DR; case subjects were subjects with type 2 diabetes with either of the defined DR phenotypes.

    Genetic IVs

    We selected lipid-associated single nucleotide polymorphisms (SNPs) at 157 loci, including 60 for HDL cholesterol, 30 for LDL cholesterol, 28 for triglycerides, and 39 for total cholesterol, previously identified by the Global Lipids Genetic Consortium (GLGC) (32) in individuals of European ancestry. Summary statistics data for the association between these 157 SNPs and plasma lipids were used as genetic IVs for MR analyses in all ethnicities and for Caucasian cohorts. The SNPs used as IVs were not in linkage disequilibrium (R2 <0.2) with each other as reported by the original report (32). We then tested the effects of these 157 SNPs on plasma lipid levels in East Asian populations from the Asian Genetic Epidemiology Network (AGEN) Consortium and identified 51 SNPs (28 for HDL cholesterol, 10 for LDL cholesterol, and 13 for triglycerides) associated with plasma lipids (P < 0.05) in East Asians and used them for MR analysis in Chinese groups.

    As the goal was to estimate the unconfounded association of specific lipid fractions with the DR outcomes, any of the 157 SNPs that were also associated with another fraction by definition violates the MR assumption that each SNP IV has no pleiotropic effect and only acts on the outcome via the specific lipid fraction exposure. Therefore, for the primary analysis, we selected the subset of SNPs that were unique (independent) to each lipid fraction (i.e., did not also have pleiotropic effect on another lipid fraction) as reported by the GLGC (32). Using the Type 2 Diabetes Knowledge Portal (www.type2diabetesgenetics.org), we also examined whether any of these SNPs were significantly associated (P < 5 × 10−8) with other risk factors for DR (type 2 diabetes itself, related glycemic traits, and hypertension). We also eliminated those SNPs from the primary analysis (Supplementary Table 1). However, we were also concerned that the primary analysis would suffer from a significant loss of power and might overcorrect for pleiotropy among the different lipid fractions. Therefore, we also performed a secondary analysis with the entire set of 157 SNPs. Of note, the 157 SNPs were chosen such that each SNP was only assigned to be the IV for the lipid fraction for which it was most strongly associated. That is, if a SNP was significantly associated with both HDL and total cholesterol levels but the association with HDL levels was stronger, then it was only chosen as an IV for HDL levels. This eliminated some pleiotropic SNPs from the analysis, although it was not as conservative as the primary analysis that eliminated SNPs with any pleiotropic effects completely, e.g., they were not assigned as IVs for any lipid fraction.

    Statistical Analysis

    We obtained GWAS summary statistics data from individual studies for either or both DR phenotypes for the SNPs where genotype and imputed data were available. We then performed inverse variance–weighted, fixed-effect meta-analyses with METAL software to pool available GWAS summary data for each SNP for both DR phenotypes from individual studies. Individual SNP data were pooled from all studies, as well as studies from Caucasian and Chinese cohorts separately.

    Next, the association between plasma lipids and DR at each SNP was calculated as β(lipid-DR) = β(SNP-lipid)/β(SNP-DR) (33), where β(lipid-DR) represents the estimated effect size (logarithm of the odds ratio [OR]) of 1 SD of genetically determined plasma lipid levels on DR. To assess the association between each lipid trait and DR, we combined the β(lipid-DR) estimates across multiple SNPs using fixed-effect meta-analysis. Cochran Q test was applied to assess heterogeneity across SNPs. Heterogeneity across SNPs was found to be low (I2 <40%) among studies (Supplementary Table 2), hence random-effect meta-analysis was not carried out.

    We performed the same analysis for two subgroups of studies for each DR phenotype where the IVs were presumed to be stronger on account of similar ancestry backgrounds: 1) among studies of Caucasian ancestry using the SNPs identified by the GLGC as IVs and 2) among studies of Chinese ancestry using SNPs from the AGEN Consortium as IVs. Of note, β(SNP-lipid) estimates differed between GLGC and AGEN Consortium, thus supporting the separate analyses in these two populations. All statistical analyses were performed using Stata 14 (StataCorp LP, College Station, TX).

    Results

    The baseline characteristics of the participants in each study are shown in Table 2. A total of 2,969 case and 4,096 control subjects were included in the analysis of the any DR phenotype and 1,277 case and 3,980 control subjects were included in the analysis of the severe DR phenotype. A summary of the 157 lipid-associated SNPs used as IVs for MR analysis and the SNP pooled association with DR are shown in Supplementary Tables 3 and 4.

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    Table 2

    Baseline characteristics of participants in each study

    Tables 3 and 4 show the results of the MR analysis for the any DR phenotype in all cohorts and the subgroup Caucasian and Chinese cohort analyses. We did not find any significant association between plasma lipids and DR. In the primary analysis (Table 3), for each 1-SD increase in genetically induced increase in plasma lipid profiles, the OR of having any DR was 0.91 (95% CI 0.67–1.23) for HDL, 2.50 (0.91–6.87) for LDL, 1.00 (0.86–1.15) for triglycerides, and 0.83 (0.53–1.31) for total cholesterol in the all ethnicities analysis.

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    Table 3

    MR estimate of the association between lipids and any DR using SNPs unique to each lipid fraction and independent of glycemic traits# (primary analysis)

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    Table 4

    MR estimate of the association between lipids and any DR for all SNPs (secondary analysis)

    In the secondary analysis (Table 4), for each 1-SD increase in genetically induced increase in plasma lipid profiles, the OR of having DR was 0.94 (95% CI 0.79–1.14) for HDL, 0.95 (0.75–1.20) for LDL, 1.08 (0.96–1.22) for triglycerides, and 0.92 (0.74–1.14) for total cholesterol in the all ethnicities analysis.

    Tables 5 and 6 show the results of the MR analysis for the severe DR phenotype. For the primary analysis (Table 5), the OR (95% CI) for the association between plasma lipids and severe DR was 0.98 (0.74–1.31) for HDL, 0.95 (0.39–2.36) for LDL, 0.84 (0.33–2.12) for triglycerides, and 0.68 (0.25–1.87) for total cholesterol. In the secondary analysis (Table 6), the OR (95% CI) for the association between plasma lipids and severe DR was 1.02 (0.81–1.29) for HDL, 0.94 (0.80–1.10) for LDL, and 0.69 (0.41–1.16) for total cholesterol, respectively. In the secondary analysis, there was stronger evidence that genetically determined plasma triglycerides levels conferred an increased risk of having severe DR (OR 1.37 [95% CI 0.99–1.88]), although the results did not achieve statistical significance (P = 0.056). We did not find any association between plasma lipids and severe DR in the subgroup Caucasian and Chinese cohort analyses.

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    Table 5

    MR estimate of the association between lipids and severe DR using SNPs unique to each lipid fraction and independent of glycemic traits# (primary analysis)

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    Table 6

    MR estimate of the association between lipids and severe DR for all SNPs (secondary analysis)

    Of note, in the primary analysis using only strictly defined independent IVs, the risk of genetically determined plasma triglycerides levels on having severe DR was greatly reduced (OR 0.84 [95% CI 0.33–2.12]), suggesting that the association in the secondary analysis was due to pleiotropic triglyceride-related SNPs. Given this finding, we also repeated the analysis for triglycerides and severe DR using the 12 SNPs that have effects on triglycerides and at least one other lipid fraction (Table 6). The risk of genetically determined plasma triglycerides levels on having severe DR was strengthened (OR 1.42 [95% CI 1.01–2.00], P = 0.044) when only these 12 pleiotropic SNPs were used. Because the PPAR-α agonist fenofibrate has shown benefits in reducing requirements for laser treatment of DR and DME (8) that are not explained by its therapeutic effects on triglyceride levels, we examined whether any of these 12 SNPs were in or near PPAR-α target genes (34). We found that 3 of these 12 SNPs are near PPAR-α target genes involved in lipoprotein uptake/metabolism and lipogenesis (Supplementary Table 5).

    We calculated the power for this study using all 157 SNPs. We determined power for varying ORs for DR per SD of the exposure variable (plasma lipid), with the assumption that the proportion of lipid variance explained by SNP IVs is R2 ∼10% and with a type 1 error of 0.05 (Supplementary Table 6) (35). The minimum OR for which the study has 80% power is 1.23 for the any DR outcome and approximately 1.3 for the severe DR outcome.

    Discussion

    To the best of our knowledge, our study is the most comprehensive MR study to evaluate the causal role of plasma lipids in DR development by combining multiethnic cohorts from different countries. We did not see clear evidence of a causal relationship between lipid measures and DR in the group as a whole or in the subgroup analyses in Caucasian and Chinese cohorts using stronger IVs. Our findings may help shed light on the considerable variability in previous observational studies exploring the association between plasma lipids and DR (36). In previous studies, HDL (37,38), LDL (39,40), triglycerides (41), and total cholesterol (38) have been inconsistently shown to be associated with DR. Our findings suggest that these associations previously observed may overall be noncausal, partially due to residual confounders. Our findings were generally consistent throughout the subgroup analyses and across populations as we found no heterogeneity across different populations. However, this study was not powered to detect modest (OR <1.23) effect sizes, and thus we cannot exclude the possibility that more modest causal associations between lipid levels and DR may exist.

    Our findings did suggest a possible causal relationship between a pleiotropic pathway that includes the triglyceride pathway and severe DR. In a subanalysis examining the SNPs that have effects on triglycerides and at least one other lipid fraction, there was a marginally significant (P = 0.044) association between the genetically determined plasma lipid levels and severe DR risk. This finding must be interpreted cautiously given the multiple hypotheses tested in this study, but it is an interesting finding that should be followed up in future studies.

    Previous studies have shown an association between dyslipidemia and severe DR (2), as well as beneficial effects of fenofibrate treatment on DR (42). Fenofibrate acts mainly to lower plasma triglycerides levels, but the mechanism of its effect on DR is unclear (43). In the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study, treatment with fenofibrate reduced the need for laser treatment for DR and showed a reduction in two-step progression in DR among those with preexisting DR (8). The Action to Control Cardiovascular Risk in Diabetes (ACCORD) study similarly showed that fenofibrate reduced DR progression in combination with statins, although this effect could not be entirely explained based on plasma lipid–altering effects (5). Our data suggest that the SNPs that influence triglyceride levels but also influence other plasma lipid fractions may have the strongest influence on DR risk, suggesting pleiotropic effects of SNPs may be important. In particular, further examination of the effects of the three triglyceride SNPs near PPAR-α target genes (Supplementary Table 5) may help to further explain how fenofibrate reduces DR progression with a mechanism other than change in plasma lipid profile.

    It is possible that the traditional lipid measures of total, HDL, and LDL cholesterol and triglycerides may not accurately measure the effects of dyslipidemia on DR. Previous studies have suggested a more direct relationship between apolipoprotein AI (ApoAI) and apolipoprotein B (ApoB) with DR compared with traditional lipid measures. ApoAI can be found in HDL and is overexpressed in the retina of patients with diabetes (44). ApoB is a structural protein for VLDL, IDL, and LDL (45) and may reflect the atherogenic potential of lipid metabolism (46). Observational studies have found ApoAI, ApoB, and ApoB-to-ApoAI ratio to be significantly associated with DR with higher discriminating abilities for DR compared with traditional lipid measures (47). Our study did not evaluate genetically determined apolipoprotein levels as IVs for MR analysis, which may yet reveal possible causal relationships between dyslipidemia and DR.

    The strengths of this study include pooled data from multiple population-based studies, allowing us to increase sample size and thus statistical power. Despite this, our study is still limited by sample size. It is possible that a larger, better powered study in the future could reveal a positive finding. We also used multiple lipid-associated SNPs to increase the ability to detect an association between each lipid trait and DR, as effects of individual SNPs on DR may be modest. The IVs used for the European analysis (all genome-wide significant SNPs) were quite strong with an estimated F-statistic of greater than 10, given the R2 ∼10% in the original report (32). For Asian subanalysis, the IVs were weaker, but the sensitivity analysis using the strong IVs (genome-wide significant SNPs) did not change the results materially (Supplementary Tables 2 and 7).

    Limitations to this study include differing DR grading methodologies among pooled studies, but harmonization was straightforward because all studies were graded on one of two widely accepted scales. Another limitation is that the traditional meta-analysis techniques used do not completely take into account the variability in allelic effects between ethnic groups. Fixed-effects meta-analysis assumes the allelic effect to be the same in all populations. Conversely, random-effects meta-analysis assumes that each population has a different underlying allelic effect, which is also suboptimal as populations from the same ethnic group tend to be more homogenous that those that are more distantly related. We found little evidence of heterogeneity, and therefore we feel that the fixed-effect meta-analysis approach is justified and that heterogeneity is not a likely explanation for the negative results. However, we cannot exclude the possibility that some trans-ethnic heterogeneity may decrease the power of this study slightly. The variation in imputation thresholds and adjustment among the cohorts is another limitation of the study, as whether a SNP was imputed and imputation accuracy can affect the precision, variance explained, and power of the study. In addition, our study did not explore the relationship between plasma lipids and DME, which has been suggested in previous studies (48).

    The SNPs chosen as IVs for MR analysis in all ethnicities were identified from a previous study of individuals from European ancestry, which explained only 10–15% of total lipid trait variance (32), and this might also have weakened the IV strength in our non-European cohorts. However, when we compare findings from that in the largest European GWAS for lipid levels to the findings from genetic association studies performed in African Americans, Hispanics, and Asians, we find great consistency with regard to effect size and direction among ethnicities (Supplementary Tables 8–11). Although there may be some loss of power from potential interancestry differences in SNPs affecting lipid levels, it is likely outweighed by the gain in power by using the larger number of SNPs from the European lipid GWAS, which explains a greater amount of lipid level variation.

    In addition, the SNPs chosen as IVs from MR analysis were derived from a study of mainly subjects without diabetes, which may also decrease the validity of the measures in our study. However, a recent GWAS of lipid levels performed exclusively in patients with type 2 diabetes identified all of the top findings that had been previously found in populations without diabetes, indicating that there is significant alignment of the genetic architecture of lipid levels between populations with and without diabetes (Supplementary Table 12) (49). We did not establish the association of the SNPs with lipid levels directly in our own cohorts because we only had lipid level data on a subset of patients. This is a limitation, but we note that other MR studies of lipid SNPs have also used the approach we used here with positive results (50), and so we do not think this methodologic limitation is likely to explain our negative results.

    One final limitation of this study is the inability to convert risk estimates into more clinically meaningful estimates. This is a limitation of all MR studies using the summary statistics from large GWAS studies, but it does not invalidate the main aim of these studies, which is to garner evidence for causality (50). In the GLGC GWAS, the statistical analysis was a linear regression with the inverse normal transformed lipid trait as the dependent variable (32). The effect estimates were provided in SD units. Unfortunately, the raw lipid value data from this study are not available. Therefore, we are not able to convert our findings to a more clinically meaningful outcome such as SD of raw plasma lipid levels. The GLGC GWAS does provide the average SD for LDL (36.8 mg/dL), HDL (14.7 mg/dL), triglycerides (92.3 mg/dL), and total cholesterol (42.7 mg/dL) in its Supplementary Table 1 (32). But the SD of the raw plasma lipid values cannot be derived directly from the SD of the inverse normalized values without access to raw data.

    In conclusion, our findings did not find clear evidence of a causal role of dyslipidemia on the risk for DR, suggesting that the inconsistently observed associations from previous studies were noncausal and may also have been affected by confounders. We did find a nominal association between pleiotropic triglyceride IVs and severe retinopathy, which should be explored in further studies, particularly given that some of these IVs are in loci near genes that are targets for PPAR-α and that fenofibrate, a PPAR-α agonist, has been shown to decrease DR progression. Our study provides further understanding of the relative contribution of plasma lipids to the pathogenesis of diabetic complications.

    Article Information

    Acknowledgments and Funding. The authors acknowledge the support from the following organizations for this research: Research to Prevent Blindness, Inc. (Career Development Grant, Special Scholar Award), National Eye Institute (EY16335, EY22302), Massachusetts Lions Eye Research Fund, Alcon Research Institute (Young Investigator Award), American Diabetes Association (1-11-CT-51), and Harvard Catalyst (Faculty Fellowship Award).

    The Age, Gene/Environment Susceptibility–Reykjavik Study (AGES Reykjavik) was supported by the U.S. National Institutes of Health (NIH) through the Intramural Research Program of the National Institute on Aging (ZIAAG007380) and the National Eye Institute (ZIAEY00401, N01-AG-1-2100), Hjartavernd (the Icelandic Heart Association), the Althingi (Icelandic Parliament), and the University of Iceland Research Fund. The authors are indebted to the staff at the Icelandic Heart Association and to the AGES Reykjavik participants who volunteered their time and allowed us to contribute their data to this international project. The funders had no role in collection, management, analysis, or interpretation of data nor were funders involved in the preparation, writing, or approval of the article or the decision to submit the article for publication.

    The Australian Genetics of Diabetic Retinopathy Study (AUST) was supported by the National Health and Medical Research Council of Australia (NHMRC) (595918) and the Ophthalmic Research Institute of Australia. K.P.B. is supported by a Senior Research Fellowship from the NHMRC, and J.E.C. is supported by a Practitioner Fellowship from the NHMRC.

    The Blue Mountains Eye Study (BMES) was supported by the NHMRC, Canberra, Australia (NHMRC project grants 974159, 211069, 302068, 529923 [Centre for Clinical Research Excellence in Translational Clinical Research in Eye Diseases]). The BMES GWAS and genotyping costs was supported by NHMRC, Canberra, Australia (NHMRC project grants 512423, 475604, 529912), and the Wellcome Trust as part of Wellcome Trust Case Control Consortium 2 (085475/B/08/Z, 085475/08/Z).

    The Cardiovascular Health Study (CHS) was supported by the NIH National Heart, Lung, and Blood Institute (NHLBI) (HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, N01HC75150, U01HL080295, U01HL130114) and the CHARGE infrastructure grant HL105756, with additional contribution from the National Institute of Neurological Disorders and Stroke. Additional support was provided by the National Institute on Aging (R01AG023629). A full list of principal CHS investigators and institutions can be found at https://chs-nhlbi.org. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

    The study of Genetic Center, China Medical University Hospital, Taiwan, was supported by research grants from Academia Sinica, Taiwan (Biosignature Project). The funding organization had no role in the design or conduct of this research.

    The Jackson Heart Study (JHS) is supported and conducted in collaboration with Jackson State University (HHSN268201300049C, HHSN268201300050C), Tougaloo College (HHSN268201300048C), and the University of Mississippi Medical Center (HHSN268201300046C, HHSN268201300047C) contracts from the NHLBI and the National Institute on Minority Health and Health Disparities. The authors also wish to thank the staffs and participants of the JHS. The views expressed in the manuscript are those of the authors and do not necessarily represent the views of the NHLBI, the NIH, or the U.S. Department of Health and Human Services.

    The Multi-Ethnic Study of Atherosclerosis (MESA) and the MESA SNP Health Association Resource (SHARe) project are conducted and supported by the NHLBI in collaboration with MESA investigators. Support for MESA is provided by NHLBI contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079, UL1-TR-001881, and DK063491. Funding for SHARe genotyping was provided by NHLBI contract N02-HL-64278. Genotyping was performed at Affymetrix (Santa Clara, CA) and the Broad Institute (Boston, MA) using the Affymetrix Genome-Wide Human SNP Array 6.0. The authors thank the other investigators, the staff, and the participants of the MESA study for their valuable contributions. A full list of participating MESA investigators and institutions can be found at http://www.mesa-nhlbi.org.

    The Singapore Epidemiology of Eye Diseases (SEED) study was supported by the National Medical Research Council, Singapore (0796/2003, 1176/2008, 1149/2008, STaR/0003/2008, 1249/2010, CG/SERI/2010, CIRG/1371/2013, CIRG/1417/2015) and Biomedical Research Council, Singapore (08/1/35/19/550, 09/1/35/19/616). C.-Y.C is supported by an award from National Medical Research Council (CSA/033/2012). The funding organization had no role in the design or conduct of this research.

    Duality of Interest. No potential conflicts of interest relevant to this article were reported.

    Author Contributions. L.S., Y.H.C., L.K.S., and C.-Y.C. contributed to the writing of the manuscript. Q.F., A.G., G.K., J.E.C., J.Ki., W.-L.L., Y.-C.H., W.-J.L., Y.-J.H., X.G., Y.H., E.I., S.P., H.H., A.Pr., A.Pe., P.M., G.L., A.V.S., V.G., G.T., B.E.K.K., J.Ku., X.L., M.W.C., B.M.P., K.S., Asian Genetic Epidemiology Network Consortium, R.A.J., R.K., M.F.C., J.J.W., Y.J., C.J.C., Y.-D.I.C., J.I.R., F.-J.T., C.L.H., K.P.B., and T.Y.W. reviewed and edited the manuscript. All authors collected and researched data. L.S., Y.H.C., Q.F., A.G., L.K.S., G.K., J.E.C., J.Ki., W.-L.L., Y.-C.H., W.-J.L., Y.-J.H., X.G., Y.H., E.I., S.P., A.Pr., P.M., G.L., A.V.S., V.G., G.T., B.E.K.K., J.Ku., X.L., M.W.C., B.M.P., K.S., Asian Genetic Epidemiology Network Consortium, R.A.J., R.K., M.F.C., J.J.W., Y.J., Y.-D.I.C., J.I.R., F.-J.T., C.L.H., K.P.B., T.Y.W., and C.-Y.C. performed the analysis. C.-Y.C. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    Footnotes

    • This article contains Supplementary Data online at http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db17-0398/-/DC1.

    • * A complete list of the collaborators from the Asian Genetic Epidemiology Network Consortium can be found in the Supplementary Data online.

    • Received March 30, 2017.
    • Accepted September 22, 2017.
    • © 2017 by the American Diabetes Association.
    http://www.diabetesjournals.org/content/license

    Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at http://www.diabetesjournals.org/content/license.

    References

    1. ↵
      1. Cheung N,
      2. Mitchell P,
      3. Wong TY
      . Diabetic retinopathy. Lancet 2010;376:124–136pmid:20580421
      OpenUrlCrossRefPubMedWeb of Science
    2. ↵
      1. Yau JWY,
      2. Rogers SL,
      3. Kawasaki R, et al.; Meta-Analysis for Eye Disease (META-EYE) Study Group
      . Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care 2012;35:556–564pmid:22301125
      OpenUrlAbstract/FREE Full Text
    3. ↵
      1. Miljanovic B,
      2. Glynn RJ,
      3. Nathan DM,
      4. Manson JE,
      5. Schaumberg DA
      . A prospective study of serum lipids and risk of diabetic macular edema in type 1 diabetes. Diabetes 2004;53:2883–2892pmid:15504969
      OpenUrlAbstract/FREE Full Text
    4. ↵
      1. UK Prospective Diabetes Study (UKPDS) Group
      . Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet 1998;352:837–853pmid:9742976
      OpenUrlCrossRefPubMedWeb of Science
    5. ↵
      1. Chew EY,
      2. Ambrosius WT,
      3. Davis MD, et al.; ACCORD Study Group; ACCORD Eye Study Group
      . Effects of medical therapies on retinopathy progression in type 2 diabetes. N Engl J Med 2010;363:233–244pmid:20587587
      OpenUrlCrossRefPubMedWeb of Science
    6. ↵
      1. Lim LS,
      2. Wong TY
      . Lipids and diabetic retinopathy. Expert Opin Biol Ther 2012;12:93–105pmid:22122357
      OpenUrlCrossRefPubMed
    7. ↵
      1. Mohamed Q,
      2. Gillies MC,
      3. Wong TY
      . Management of diabetic retinopathy: a systematic review. JAMA 2007;298:902–916pmid:17712074
      OpenUrlCrossRefPubMedWeb of Science
    8. ↵
      1. Keech AC,
      2. Mitchell P,
      3. Summanen PA, et al.; FIELD Study Investigators
      . Effect of fenofibrate on the need for laser treatment for diabetic retinopathy (FIELD study): a randomised controlled trial. Lancet 2007;370:1687–1697pmid:17988728
      OpenUrlCrossRefPubMedWeb of Science
    9. ↵
      1. Klein R,
      2. Klein BE,
      3. Moss SE,
      4. Cruickshanks KJ
      . Relationship of hyperglycemia to the long-term incidence and progression of diabetic retinopathy. Arch Intern Med 1994;154:2169–2178pmid:7944837
      OpenUrlCrossRefPubMedWeb of Science
      1. Klein BE,
      2. Myers CE,
      3. Howard KP,
      4. Klein R
      . Serum lipids and proliferative diabetic retinopathy and macular edema in persons with long-term type 1 diabetes mellitus: the Wisconsin Epidemiologic Study of Diabetic Retinopathy. JAMA Ophthalmol 2015;133:503–510pmid:25502808
      OpenUrlPubMed
      1. Wong TY,
      2. Klein R,
      3. Islam FM, et al
      . Diabetic retinopathy in a multi-ethnic cohort in the United States. Am J Ophthalmol 2006;141:446–455pmid:16490489
      OpenUrlCrossRefPubMedWeb of Science
    10. ↵
      1. Wang S,
      2. Xu L,
      3. Jonas JB,
      4. You QS,
      5. Wang YX,
      6. Yang H
      . Dyslipidemia and eye diseases in the adult Chinese population: the Beijing eye study. PLoS One 2012;7:e26871pmid:22128290
      OpenUrlPubMed
    11. ↵
      1. Davey Smith G,
      2. Hemani G
      . Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet 2014;23:R89–R98pmid:25064373
      OpenUrlCrossRefPubMedWeb of Science
    12. ↵
      1. Bull CJ,
      2. Bonilla C,
      3. Holly JM, et al
      .; PRACTICAL Consortium. Blood lipids and prostate cancer: a Mendelian randomization analysis. Cancer Med 2016;5:1125–1136
    13. ↵
      1. Huang Y,
      2. Xu M,
      3. Xie L, et al
      . Obesity and peripheral arterial disease: a Mendelian randomization analysis. Atherosclerosis 2016;247:218–224pmid:26945778
      OpenUrlPubMed
    14. ↵
      1. Foong AW,
      2. Saw SM,
      3. Loo JL, et al
      . Rationale and methodology for a population-based study of eye diseases in Malay people: the Singapore Malay Eye Study (SiMES). Ophthalmic Epidemiol 2007;14:25–35pmid:17365815
      OpenUrlCrossRefPubMedWeb of Science
      1. Lavanya R,
      2. Jeganathan VS,
      3. Zheng Y, et al
      . Methodology of the Singapore Indian Chinese Cohort (SICC) eye study: quantifying ethnic variations in the epidemiology of eye diseases in Asians. Ophthalmic Epidemiol 2009;16:325–336pmid:19995197
      OpenUrlCrossRefPubMedWeb of Science
    15. Fu YP, Hallman DM, Gonzalez VH, et al. Identification of diabetic retinopathy genes through a genome-wide association study among Mexican-Americans from Starr County, Texas. J Ophthalmol 2010;2010:861291
      1. Tsai FJ,
      2. Yang CF,
      3. Chen CC, et al
      . A genome-wide association study identifies susceptibility variants for type 2 diabetes in Han Chinese. PLoS Genet 2010;6:e1000847pmid:20174558
      OpenUrlCrossRefPubMed
      1. Huang YC,
      2. Lin JM,
      3. Lin HJ, et al
      . Genome-wide association study of diabetic retinopathy in a Taiwanese population. Ophthalmology 2011;118:642–648pmid:21310492
      OpenUrlCrossRefPubMedWeb of Science
      1. Fried LP,
      2. Borhani NO,
      3. Enright P, et al
      . The Cardiovascular Health Study: design and rationale. Ann Epidemiol 1991;1:263–276pmid:1669507
      OpenUrlCrossRefPubMed
      1. Penman A,
      2. Hoadley S,
      3. Wilson JG,
      4. Taylor HA,
      5. Chen CJ,
      6. Sobrin L
      . P-selectin plasma levels and genetic variant associated with diabetic retinopathy in African Americans. Am J Ophthalmol 2015;159:1152–1160.e2pmid:25794792
      OpenUrlPubMed
      1. Tandon A,
      2. Chen CJ,
      3. Penman A, et al
      . African Ancestry analysis and admixture genetic mapping for proliferative diabetic retinopathy in African Americans. Invest Ophthalmol Vis Sci 2015;56:3999–4005pmid:26098467
      OpenUrlPubMed
      1. Mitchell P,
      2. Smith W,
      3. Attebo K,
      4. Wang JJ
      . Prevalence of age-related maculopathy in Australia. The Blue Mountains Eye Study. Ophthalmology 1995;102:1450–1460pmid:9097791
      OpenUrlPubMedWeb of Science
      1. Mitchell P,
      2. Smith W,
      3. Wang JJ,
      4. Attebo K
      . Prevalence of diabetic retinopathy in an older community. The Blue Mountains Eye Study. Ophthalmology 1998;105:406–411pmid:9499768
      OpenUrlCrossRefPubMedWeb of Science
      1. Burdon KP,
      2. Fogarty RD,
      3. Shen W, et al
      . Genome-wide association study for sight-threatening diabetic retinopathy reveals association with genetic variation near the GRB2 gene. Diabetologia 2015;58:2288–2297pmid:26188370
      OpenUrlPubMed
      1. Kuo JZ,
      2. Guo X,
      3. Klein R, et al
      . Systemic soluble tumor necrosis factor receptors 1 and 2 are associated with severity of diabetic retinopathy in Hispanics. Ophthalmology 2012;119:1041–1046pmid:22330960
      OpenUrlCrossRefPubMedWeb of Science
      1. Sheu WH,
      2. Kuo JZ,
      3. Lee IT, et al
      . Genome-wide association study in a Chinese population with diabetic retinopathy. Hum Mol Genet 2013;22:3165–3173pmid:23562823
      OpenUrlCrossRefPubMedWeb of Science
      1. Bild DE,
      2. Bluemke DA,
      3. Burke GL, et al
      . Multi-Ethnic Study of Atherosclerosis: objectives and design. Am J Epidemiol 2002;156:871–881pmid:12397006
      OpenUrlCrossRefPubMedWeb of Science
      1. Harris TB,
      2. Launer LJ,
      3. Eiriksdottir G, et al
      . Age, Gene/Environment Susceptibility-Reykjavik Study: multidisciplinary applied phenomics. Am J Epidemiol 2007;165:1076–1087pmid:17351290
      OpenUrlCrossRefPubMedWeb of Science
    16. ↵
      1. Gunnlaugsdottir E,
      2. Halldorsdottir S,
      3. Klein R, et al
      . Retinopathy in old persons with and without diabetes mellitus: the Age, Gene/Environment Susceptibility--Reykjavik Study (AGES-R). Diabetologia 2012;55:671–680pmid:22134840
      OpenUrlPubMed
    17. ↵
      1. Willer CJ,
      2. Schmidt EM,
      3. Sengupta S, et al.; Global Lipids Genetics Consortium
      . Discovery and refinement of loci associated with lipid levels. Nat Genet 2013;45:1274–1283pmid:24097068
      OpenUrlCrossRefPubMed
    18. ↵
      1. Nelson CP,
      2. Hamby SE,
      3. Saleheen D, et al.; CARDIoGRAM+C4D Consortium
      . Genetically determined height and coronary artery disease. N Engl J Med 2015;372:1608–1618pmid:25853659
      OpenUrlCrossRefPubMed
    19. ↵
      Rakhshandehroo M, Knoch B, Muller M, Kersten S. Peroxisome proliferator-activated receptor alpha target genes. PPAR Res 2010;2010:612089
    20. ↵
      1. Brion MJ,
      2. Shakhbazov K,
      3. Visscher PM
      . Calculating statistical power in Mendelian randomization studies. Int J Epidemiol 2013;42:1497–1501pmid:24159078
      OpenUrlCrossRefPubMedWeb of Science
    21. ↵
      1. Chang YC,
      2. Wu WC
      . Dyslipidemia and diabetic retinopathy. Rev Diabet Stud 2013;10:121–132pmid:24380088
      OpenUrlCrossRefPubMed
    22. ↵
      Kohner EM, Aldington SJ, Stratton IM, et al. United Kingdom Prospective Diabetes Study, 30: diabetic retinopathy at diagnosis of non-insulin-dependent diabetes mellitus and associated risk factors. Arch Ophthalmol 1998;116:297–303
    23. ↵
      1. Popescu T,
      2. Mota M
      . Dyslipidemia and hypertension in patients with type 2 diabetes and retinopathy. Rev J Intern Med 2009;47:235–241
      OpenUrl
    24. ↵
      1. Klein R,
      2. Marino EK,
      3. Kuller LH, et al
      . The relation of atherosclerotic cardiovascular disease to retinopathy in people with diabetes in the Cardiovascular Health Study. Br J Ophthalmol 2002;86:84–90pmid:11801510
      OpenUrlAbstract/FREE Full Text
    25. ↵
      1. Wong TY,
      2. Cheung N,
      3. Tay WT, et al
      . Prevalence and risk factors for diabetic retinopathy: the Singapore Malay Eye Study. Ophthalmology 2008;115:1869–1875pmid:18584872
      OpenUrlCrossRefPubMedWeb of Science
    26. ↵
      1. Rema M,
      2. Srivastava BK,
      3. Anitha B,
      4. Deepa R,
      5. Mohan V
      . Association of serum lipids with diabetic retinopathy in urban South Indians--the Chennai Urban Rural Epidemiology Study (CURES) Eye Study--2. Diabet Med 2006;23:1029–1036pmid:16922712
      OpenUrlCrossRefPubMed
    27. ↵
      1. Wong TY,
      2. Simó R,
      3. Mitchell P
      . Fenofibrate - a potential systemic treatment for diabetic retinopathy? Am J Ophthalmol 2012;154:6–12pmid:22709833
      OpenUrlCrossRefPubMed
    28. ↵
      1. Simó R,
      2. Roy S,
      3. Behar-Cohen F,
      4. Keech A,
      5. Mitchell P,
      6. Wong TY
      . Fenofibrate: a new treatment for diabetic retinopathy. Molecular mechanisms and future perspectives. Curr Med Chem 2013;20:3258–3266pmid:23745548
      OpenUrlCrossRefPubMed
    29. ↵
      1. Simó R,
      2. García-Ramírez M,
      3. Higuera M,
      4. Hernández C
      . Apolipoprotein A1 is overexpressed in the retina of diabetic patients. Am J Ophthalmol 2009;147:319–325.e1pmid:18848320
      OpenUrlCrossRefPubMed
    30. ↵
      1. Davidson MH
      . Apolipoprotein measurements: is more widespread use clinically indicated? Clin Cardiol 2009;32:482–486pmid:19743499
      OpenUrlCrossRefPubMedWeb of Science
    31. ↵
      1. Walldius G,
      2. Jungner I
      . The apoB/apoA-I ratio: a strong, new risk factor for cardiovascular disease and a target for lipid-lowering therapy--a review of the evidence. J Intern Med 2006;259:493–519pmid:16629855
      OpenUrlCrossRefPubMedWeb of Science
    32. ↵
      1. Sasongko MB,
      2. Wong TY,
      3. Nguyen TT, et al
      . Serum apolipoprotein AI and B are stronger biomarkers of diabetic retinopathy than traditional lipids. Diabetes Care 2011;34:474–479pmid:21270203
      OpenUrlAbstract/FREE Full Text
    33. ↵
      1. Das R,
      2. Kerr R,
      3. Chakravarthy U,
      4. Hogg RE
      . Dyslipidemia and diabetic macular edema: a systematic review and meta-analysis. Ophthalmology 2015;122:1820–1827pmid:26150053
      OpenUrlPubMed
    34. ↵
      1. Marvel SW,
      2. Rotroff DM,
      3. Wagner MJ, et al.; The ACCORD/ACCORDion Investigators
      . Common and rare genetic markers of lipid variation in subjects with type 2 diabetes from the ACCORD clinical trial. PeerJ 2017;5:e3187pmid:28480134
      OpenUrlPubMed
    35. ↵
      1. Burgess S,
      2. Davey Smith G
      . mendelian randomization implicates high-density lipoprotein cholesterol-associated mechanisms in etiology of age-related macular degeneration. Ophthalmology 2017;124:1165–1174pmid:28456421
      OpenUrlPubMed
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    Genetically Determined Plasma Lipid Levels and Risk of Diabetic Retinopathy: A Mendelian Randomization Study
    Lucia Sobrin, Yong He Chong, Qiao Fan, Alfred Gan, Lynn K. Stanwyck, Georgia Kaidonis, Jamie E. Craig, Jihye Kim, Wen-Ling Liao, Yu-Chuen Huang, Wen-Jane Lee, Yi-Jen Hung, Xiuqing Guo, Yang Hai, Eli Ipp, Samuela Pollack, Heather Hancock, Alkes Price, Alan Penman, Paul Mitchell, Gerald Liew, Albert V. Smith, Vilmundur Gudnason, Gavin Tan, Barbara E.K. Klein, Jane Kuo, Xiaohui Li, Mark W. Christiansen, Bruce M. Psaty, Kevin Sandow, Asian Genetic Epidemiology Network Consortium, Richard A. Jensen, Ronald Klein, Mary Frances Cotch, Jie Jin Wang, Yucheng Jia, Ching J. Chen, Yii-Der Ida Chen, Jerome I. Rotter, Fuu-Jen Tsai, Craig L. Hanis, Kathryn P. Burdon, Tien Yin Wong, Ching-Yu Cheng
    Diabetes Dec 2017, 66 (12) 3130-3141; DOI: 10.2337/db17-0398

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    Genetically Determined Plasma Lipid Levels and Risk of Diabetic Retinopathy: A Mendelian Randomization Study
    Lucia Sobrin, Yong He Chong, Qiao Fan, Alfred Gan, Lynn K. Stanwyck, Georgia Kaidonis, Jamie E. Craig, Jihye Kim, Wen-Ling Liao, Yu-Chuen Huang, Wen-Jane Lee, Yi-Jen Hung, Xiuqing Guo, Yang Hai, Eli Ipp, Samuela Pollack, Heather Hancock, Alkes Price, Alan Penman, Paul Mitchell, Gerald Liew, Albert V. Smith, Vilmundur Gudnason, Gavin Tan, Barbara E.K. Klein, Jane Kuo, Xiaohui Li, Mark W. Christiansen, Bruce M. Psaty, Kevin Sandow, Asian Genetic Epidemiology Network Consortium, Richard A. Jensen, Ronald Klein, Mary Frances Cotch, Jie Jin Wang, Yucheng Jia, Ching J. Chen, Yii-Der Ida Chen, Jerome I. Rotter, Fuu-Jen Tsai, Craig L. Hanis, Kathryn P. Burdon, Tien Yin Wong, Ching-Yu Cheng
    Diabetes Dec 2017, 66 (12) 3130-3141; DOI: 10.2337/db17-0398
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