Dataset Information

Multiethnic Genome-Wide Association Study of Diabetic Retinopathy Using Liability Threshold Modeling of Duration of Diabetes and Glycemic Control.

ABSTRACT: To identify genetic variants associated with diabetic retinopathy (DR), we performed a large multiethnic genome-wide association study. Discovery included eight European cohorts (n = 3,246) and seven African American cohorts (n = 2,611). We meta-analyzed across cohorts using inverse-variance weighting, with and without liability threshold modeling of glycemic control and duration of diabetes. Variants with a P value <1 × 10-5 were investigated in replication cohorts that included 18,545 European, 16,453 Asian, and 2,710 Hispanic subjects. After correction for multiple testing, the C allele of rs142293996 in an intron of nuclear VCP-like (NVL) was associated with DR in European discovery cohorts (P = 2.1 × 10-9), but did not reach genome-wide significance after meta-analysis with replication cohorts. We applied the Disease Association Protein-Protein Link Evaluator (DAPPLE) to our discovery results to test for evidence of risk being spread across underlying molecular pathways. One protein-protein interaction network built from genes in regions associated with proliferative DR was found to have significant connectivity (P = 0.0009) and corroborated with gene set enrichment analyses. These findings suggest that genetic variation in NVL, as well as variation within a protein-protein interaction network that includes genes implicated in inflammation, may influence risk for DR.

SUBMITTER: Pollack S

PROVIDER: S-EPMC6341299 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Multiethnic Genome-Wide Association Study of Diabetic Retinopathy Using Liability Threshold Modeling of Duration of Diabetes and Glycemic Control.

Pollack Samuela S Igo Robert P RP Jensen Richard A RA Christiansen Mark M Li Xiaohui X Cheng Ching-Yu CY Ng Maggie C Y MCY Smith Albert V AV Rossin Elizabeth J EJ Segrè Ayellet V AV Davoudi Samaneh S Tan Gavin S GS Chen Yii-Der Ida YI Kuo Jane Z JZ Dimitrov Latchezar M LM Stanwyck Lynn K LK Meng Weihua W Hosseini S Mohsen SM Hosseini S Mohsen SM Imamura Minako M Nousome Darryl D Kim Jihye J Hai Yang Y Jia Yucheng Y Ahn Jeeyun J Leong Aaron A Shah Kaanan K Park Kyu Hyung KH Guo Xiuqing X Ipp Eli E Taylor Kent D KD Adler Sharon G SG Sedor John R JR Freedman Barry I BI Lee I-Te IT Sheu Wayne H-H WH Kubo Michiaki M Takahashi Atsushi A Hadjadj Samy S Marre Michel M Tregouet David-Alexandre DA Mckean-Cowdin Roberta R Varma Rohit R McCarthy Mark I MI Groop Leif L Ahlqvist Emma E Lyssenko Valeriya V Agardh Elisabet E Morris Andrew A Doney Alex S F ASF Colhoun Helen M HM Toppila Iiro I Sandholm Niina N Groop Per-Henrik PH Maeda Shiro S Hanis Craig L CL Penman Alan A Chen Ching J CJ Hancock Heather H Mitchell Paul P Craig Jamie E JE Chew Emily Y EY Paterson Andrew D AD Grassi Michael A MA Palmer Colin C Bowden Donald W DW Yaspan Brian L BL Siscovick David D Cotch Mary Frances MF Wang Jie Jin JJ Burdon Kathryn P KP Wong Tien Y TY Klein Barbara E K BEK Klein Ronald R Rotter Jerome I JI Iyengar Sudha K SK Price Alkes L AL Sobrin Lucia L

Diabetes 20181128 2

To identify genetic variants associated with diabetic retinopathy (DR), we performed a large multiethnic genome-wide association study. Discovery included eight European cohorts (n = 3,246) and seven African American cohorts (n = 2,611). We meta-analyzed across cohorts using inverse-variance weighting, with and without liability threshold modeling of glycemic control and duration of diabetes. Variants with a P value <1 × 10-5 were investigated in replication cohor ...[more]

PMID: 30487263

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Project description:Importance:The association of diabetic microvascular complications such as diabetic retinopathy (DR) and diabetic kidney disease (DKD) with mortality in populations is not clear. Objective:To examine the association of DR and DKD separately and jointly with all-cause and cardiovascular disease (CVD) mortality in a multiethnic Asian population. Design, Setting, and Participants:A population-based cohort study was conducted including 2964 adults between the ages of 40 and 80 years with diabetes who participated in the Singapore Epidemiology of Eye Diseases study (baseline, 2004-2011). Data analysis was performed from January to May 2018. Exposures:Diabetic retinopathy ascertained from retinal photographs and DKD from estimated glomerular filtration rate. Main Outcomes and Measures:All-cause and CVD mortality obtained by linkage with the National Registry of Births and Deaths until May 2017. Results:Of the 2964 adults (mean [SD] age, 61.8 [10.0] years; 1464 [49.4%] female; 592 Chinese, 1052 Malay, and 1320 Indian), 29.9% of the participants had DR, while 20.7% had DKD. Over a median (interquartile range) follow-up of 8.8 (7.2-11.0) years, 610 deaths occurred (20.6%), of which 267 (9.0%) were due to CVD. In separate models, the multivariable hazard ratios for all-cause and CVD mortality were 1.54 (95% CI, 1.24-1.91) and 1.74 (95% CI, 1.27-2.40), respectively, for DR and 2.04 (95% CI, 1.64-2.56) and 2.29 (95% CI, 1.64-3.19), respectively, for DKD. In models including both DR and DKD, the subgroup with DKD alone (27.1% and 12.6%) followed by DR alone (6.5% and 5.2%) contributed substantially to the excess risk of all-cause and CVD mortality. Compared with those with no DR and DKD, the hazard ratios of all-cause and CVD mortality were 1.89 (95% CI, 1.40-2.57) and 2.26 (95% CI, 1.42-3.61), respectively, for DKD alone and 1.38 (95% CI, 1.03-1.86) and 1.64 (95% CI, 1.06-2.56), respectively, for DR alone. Hazard ratios for all-cause and CVD mortality were 2.76 (95% CI, 2.05-3.72) and 3.41 (95% CI, 2.19-5.32), respectively, for those with both DKD and DR. The relative excess risk associated with the interaction was 0.49 (95% CI, -0.29 to 1.27; P = .20) for all-cause mortality and 0.51 (95% CI, -0.83 to 1.85; P = .50) for CVD mortality. Conclusions and Relevance:These results suggest that risks of all-cause and CVD mortality were significantly higher in those with DKD and DR, but DKD was more strongly associated with excess risk. The findings underscore the importance of early identification and close monitoring and management of patients with DR and DKD to reduce the risk of death.

Project description:Importance:A deep learning system (DLS) is a machine learning technology with potential for screening diabetic retinopathy and related eye diseases. Objective:To evaluate the performance of a DLS in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, possible glaucoma, and age-related macular degeneration (AMD) in community and clinic-based multiethnic populations with diabetes. Design, Setting, and Participants:Diagnostic performance of a DLS for diabetic retinopathy and related eye diseases was evaluated using 494?661 retinal images. A DLS was trained for detecting diabetic retinopathy (using 76?370 images), possible glaucoma (125?189 images), and AMD (72?610 images), and performance of DLS was evaluated for detecting diabetic retinopathy (using 112?648 images), possible glaucoma (71?896 images), and AMD (35?948 images). Training of the DLS was completed in May 2016, and validation of the DLS was completed in May 2017 for detection of referable diabetic retinopathy (moderate nonproliferative diabetic retinopathy or worse) and vision-threatening diabetic retinopathy (severe nonproliferative diabetic retinopathy or worse) using a primary validation data set in the Singapore National Diabetic Retinopathy Screening Program and 10 multiethnic cohorts with diabetes. Exposures:Use of a deep learning system. Main Outcomes and Measures:Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity of the DLS with professional graders (retinal specialists, general ophthalmologists, trained graders, or optometrists) as the reference standard. Results:In the primary validation dataset (n?=?14?880 patients; 71?896 images; mean [SD] age, 60.2 [2.2] years; 54.6% men), the prevalence of referable diabetic retinopathy was 3.0%; vision-threatening diabetic retinopathy, 0.6%; possible glaucoma, 0.1%; and AMD, 2.5%. The AUC of the DLS for referable diabetic retinopathy was 0.936 (95% CI, 0.925-0.943), sensitivity was 90.5% (95% CI, 87.3%-93.0%), and specificity was 91.6% (95% CI, 91.0%-92.2%). For vision-threatening diabetic retinopathy, AUC was 0.958 (95% CI, 0.956-0.961), sensitivity was 100% (95% CI, 94.1%-100.0%), and specificity was 91.1% (95% CI, 90.7%-91.4%). For possible glaucoma, AUC was 0.942 (95% CI, 0.929-0.954), sensitivity was 96.4% (95% CI, 81.7%-99.9%), and specificity was 87.2% (95% CI, 86.8%-87.5%). For AMD, AUC was 0.931 (95% CI, 0.928-0.935), sensitivity was 93.2% (95% CI, 91.1%-99.8%), and specificity was 88.7% (95% CI, 88.3%-89.0%). For referable diabetic retinopathy in the 10 additional datasets, AUC range was 0.889 to 0.983 (n?=?40?752 images). Conclusions and Relevance:In this evaluation of retinal images from multiethnic cohorts of patients with diabetes, the DLS had high sensitivity and specificity for identifying diabetic retinopathy and related eye diseases. Further research is necessary to evaluate the applicability of the DLS in health care settings and the utility of the DLS to improve vision outcomes.

Dataset Information

Multiethnic Genome-Wide Association Study of Diabetic Retinopathy Using Liability Threshold Modeling of Duration of Diabetes and Glycemic Control.

Publications

Multiethnic Genome-Wide Association Study of Diabetic Retinopathy Using Liability Threshold Modeling of Duration of Diabetes and Glycemic Control.

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