Huang YC, Liao WL, Lin HJ, Huang YT, Chang YW, Yang JS, Weng AL, Tsai FJ. Polygenic risk score for predicting diabetic retinopathy in patients with type 2 diabetes: A twenty-year follow-up study. World J Diabetes 2025; 16(10): 110138 [PMID: 41113490 DOI: 10.4239/wjd.v16.i10.110138]
Corresponding Author of This Article
Fuu-Jen Tsai, MD, PhD, Department of Medical Research, China Medical University Hospital, China Medical University, No. 2 Yuh-Der Road, Taichung 404, Taiwan. 000704@tool.caaumed.org.tw
Research Domain of This Article
Genetics & Heredity
Article-Type of This Article
Retrospective Cohort Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Yu-Chuen Huang, Hui-Ju Lin, Fuu-Jen Tsai, School of Chinese Medicine, China Medical University, Taichung 404, Taiwan
Yu-Chuen Huang, Ya-Wen Chang, Jai-Sing Yang, Fuu-Jen Tsai, Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 404, Taiwan
Wen-Ling Liao, Center for Personalized Medicine, China Medical University Hospital and Graduate Institute of Integrated Medicine, China Medical University, Taichung 404, Taiwan
Hui-Ju Lin, Yu-Te Huang, Department of Ophthalmology, China Medical University Hospital, Taichung 404, Taiwan
Angel L Weng, American School in Taichung, Taichung 406, Taiwan
Angel L Weng, Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, United States
Fuu-Jen Tsai, Department of Medical Genetics, China Medical University Hospital, Taichung 404, Taiwan
Fuu-Jen Tsai, Division of Pediatric Genetics, Endocrinology and Metabolism, Children’s Hospital of China Medical University, Taichung 404, Taiwan
Co-first authors: Yu-Chuen Huang and Wen-Ling Liao.
Author contributions: Huang YC and Liao WL wrote the manuscript, and made equal contributions as co-first authors; Lin HJ, Huang YT, Chang YW, Yang JS, and Weng AL performed the research; Tsai FJ edited the manuscript. All authors have read and agreed to the published version of the manuscript.
Supported by China Medical University Hospital, No. DMR-113-105.
Institutional review board statement: The study was reviewed and approved by the Ethics Committee of China Medical University Hospital, No. CMUH111-REC1-176.
Informed consent statement: Written informed consent was obtained from all participants.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: No additional data are available.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Fuu-Jen Tsai, MD, PhD, Department of Medical Research, China Medical University Hospital, China Medical University, No. 2 Yuh-Der Road, Taichung 404, Taiwan. 000704@tool.caaumed.org.tw
Received: May 30, 2025 Revised: June 26, 2025 Accepted: September 18, 2025 Published online: October 15, 2025 Processing time: 139 Days and 0.9 Hours
Abstract
BACKGROUND
Diabetic retinopathy (DR) is the leading cause of blindness among working-age adults, with an increasing prevalence due to the global burden of diabetes.
AIM
To develop a polygenic risk score (PRS) to identify high-risk groups for DR and evaluate its severity in patients with type 2 diabetes (T2D).
METHODS
This population-based study included 13335 patients with T2D, comprising 7295 patients with DR and 6040 without DR. Genetic data, duration of DR diagnosis, body mass index, systolic blood pressure, diastolic blood pressure, and glycated hemoglobin A1c levels were obtained from the study population. The PRS was constructed from a genome-wide association study conducted in a Taiwanese Han population. Electronic medical records were used to track patients with T2D and analyze the associations between PRS, timing of DR diagnosis, and therapeutic interventions. The hazard ratio (HR) of PRS for DR development and severity was estimated using multivariate Cox proportional hazards regression.
RESULTS
The results demonstrated that patients with T2D in the top PRS decile had a 1.21-fold greater risk of developing DR [HR = 1.21; 95% confidence interval (CI): 1.01-1.45; P = 0.041] over a 20-year follow-up period. Among patients with DR, those in the highest PRS decile exhibited a 4.81-fold increased risk of requiring more than four laser treatments (HR = 4.81; 95%CI: 1.40-16.5; P = 0.012) and a 1.38-fold increased risk of undergoing vitreoretinal surgery (HR = 1.38; 95%CI: 1.01-1.90; P = 0.044).
CONCLUSION
Patients with T2D with a higher PRS are at increased risk of developing DR and may experience more severe forms of the disease.
Core Tip: This population-based study developed a polygenic risk score (PRS) to assess diabetic retinopathy (DR) risk in patients with type 2 diabetes. Findings show that individuals with higher PRS are more likely to develop DR, including severe forms, over a 20-year period. Our study highlights the genetic basis of DR and introduces a potential tool for risk stratification and personalized screening in patients with type 2 diabetes. The PRS may help identify high-risk patients who could benefit from more frequent ophthalmological assessments and earlier intervention.
Citation: Huang YC, Liao WL, Lin HJ, Huang YT, Chang YW, Yang JS, Weng AL, Tsai FJ. Polygenic risk score for predicting diabetic retinopathy in patients with type 2 diabetes: A twenty-year follow-up study. World J Diabetes 2025; 16(10): 110138
Diabetes mellitus is a major and rapidly growing global public health concern, characterized by elevated blood glucose levels due to insulin deficiency or resistance[1,2]. More than 95% of diabetes cases worldwide are attributed to type 2 diabetes (T2D), a global epidemic primarily driven by obesity, lifestyle factors, aging, and genetic predisposition[3]. Effective disease management requires a combination of public health interventions, lifestyle modifications, and personalized medical treatments to reduce the burden of diabetes and its complications[3,4]. Among the most serious complications of diabetes are macrovascular and microvascular diseases, which are the leading causes of morbidity and premature mortality in individuals with diabetes[5].
Diabetic retinopathy (DR) is the leading cause of blindness among working-age adults worldwide, with its incidence rising alongside increasing global prevalence of diabetes[6,7]. While several risk factors for DR, such as poor glycemic control, long duration of diabetes, hypertension, hyperlipidemia, and albuminuria, have been identified[8-12], the underlying mechanisms remain incompletely understood. Emerging evidence suggests that low-grade inflammation plays a critical role in the development of DR[13]. Elevated levels of inflammatory mediators, including interleukin-1β, monocyte chemotactic protein-1, and tumor necrosis factor-α, have been detected in the vitreous and retina of affected individuals[13]. These cytokines, along with vascular endothelial growth factor, contribute to blood-retinal barrier disruption, vascular damage, neuroinflammation, and pathological angiogenesis[13-15]. These processes contribute to complications such as diabetic macular edema and proliferative DR. Given the complex and multifactorial nature of DR, a deeper understanding of its molecular mechanisms is essential for the development of effective therapeutic strategies.
Genetic factors play a significant role in DR susceptibility and severity, as demonstrated in family-based studies[16,17]. However, the specific genetic factors underlying DR remain unclear. Despite numerous linkage analyses, candidate gene association studies, and genome-wide association studies (GWAS) on DR, reproducible findings for the identified risk loci are limited, even among populations with the same ancestry[18,19]. GWAS in European, Asian, and African populations have identified multiple loci and single nucleotide polymorphisms (SNPs) associated with DR risk; however, these findings have been inconsistent across studies[20-26]. A recent meta-analysis of published GWASs on DR confirmed the significant association of two SNPs, rs4462262 (ZWINT-MRPS35P3) and rs7903146 (TCF7 L2), with DR in replication cohorts[19]. However, the SNP rs4462262 Lacks functional relevance in DR pathogenesis. TCF7 L2 may stimulate retinal neovascularization and influence DR development by modifying the response to glycemic control therapies[19].
DR is a complex disease with a polygenic genetic architecture[27]. To bridge the gap between GWAS findings and clinical application, polygenic risk scores (PRS) have been developed to quantify the cumulative genetic risk based on multiple loci in an individual. However, studies examining the role of PRS in DR development are limited, with most studies conducted in individuals of European ancestry or involving genetic risk scores (GRS) calculated using a limited selection of SNPs. One PRS model developed in Europe demonstrated that patients with T2D in the highest decile of the PRS had a 1.8-fold higher risk of developing DR than those in the lowest decile[28]. Additionally, patients in the top PRS decile exhibited an increased risk of retinal hemorrhage [odds ratio = 1.44; 95% confidence interval (CI): 1.03-2.02] and diplopia (odds ratio = 1.31; 95%CI: 1.02-1.70), both of which are serious manifestations of DR[28]. Our previous research utilized data from 58 significant SNPs across 44 susceptibility loci identified in prior GWAS and meta-analyses to construct the GRS. This GRS was integrated into a predictive model that included conventional DR risk factors [age, glycated hemoglobin A1c (HbA1c), duration of diabetes, and systolic blood pressure] to predict DR risk in the Taiwanese Han population[29]. The results showed that T2D patients in the highest tertile of GRS had a 1.63-fold increased risk of developing DR compared to those in the lowest tertile (95%CI: 1.03-2.58)[29]. These findings underscore the potential value of PRS for the early identification of DR in high-risk groups, which may help prevent vision loss in individuals with diabetes mellitus.
This study aimed to conduct a large-scale investigation involving 13335 patients with T2D to assess genetic predisposition to DR by developing and evaluating a genome-wide PRS in the Taiwanese Han population. To assess the clinical validity and utility of PRS, we investigated the potential biological mechanisms underlying the DR-associated loci identified in this study. Additionally, we aimed to incorporate an electronic medical record system to longitudinally monitor these patients over a 20-year period and use PRS to identify high-risk groups and assess DR severity over time.
MATERIALS AND METHODS
Source of data
The genetic data and electronic medical records for the study cohort were sourced from the Precision Medicine Project at China Medical University Hospital in Taichung, Taiwan. This project enrolled over 300000 participants who provided written informed consent after receiving a full explanation of the purpose and nature of all procedures related to genome-wide genotyping and clinical data extraction. Details of the project have been previously published[30].
Study population
Patients with T2D, aged ≥ 20 years at diagnosis, were identified using the International Classification of Diseases Ninth Revision, Clinical Modification (ICD-9-CM) codes 250.xx, except for 250.x1/x3, and ICD-10-CM codes E11.xx. DR was defined using ICD-9-CM codes 250.50 and 250.52 and ICD-10-CM codes E11.3xx, excluding codes E11.36 and E11.39. Treatment information, including laser therapy, eye injections, and eye surgery, was retrieved from electronic medical records. Diabetes duration was measured from the date of T2D diagnosis to the time of study assessment. The duration of DR was determined based on the interval between the diagnosis of T2D and the diagnosis of DR. Non-DR controls were defined as T2D patients with a diabetes duration of more than five years and no diagnosis of DR. All diabetic patients are enrolled in the Diabetes Shared Care Program in Taiwan[31,32], which provides regular follow-ups for managing diabetes and monitoring complications such as DR. Thus, any timing bias in DR diagnosis is likely minimal. Other risk factors for DR, including body mass index (BMI), systolic blood pressure, diastolic blood pressure, and HbA1c, were collected from the Precision Medicine Project at China Medical University Hospital. All participants were of Taiwanese Han ancestry[33].
Genotyping and discovery GWAS
Genotyping was performed using the Axiom Taiwan Precision Medicine v1 custom SNP array (Thermo Fisher Scientific, Waltham, MA, United States). This array, specifically designed to maximize the capture of genetic variation in samples from the Taiwanese Han population, includes approximately 740000 SNPs distributed across the human genome. Detailed methodologies for genotyping, quality control, and imputation are available in previous publications[34-36]. The study population was randomly assigned in a 7:3 ratio to two cohorts: One consisting of 9670 individuals (4892 with DR and 4778 without DR) for the discovery GWAS and 3665 individuals (2403 with DR and 1262 without DR) for subsequent replication.
PRS construction
The PRS model was constructed using summary statistics derived from the discovery GWAS of DR. We applied the clumping and thresholding (C + T) method with PRSice-2 v2.3.5.[37]. In brief, the algorithm progressively selects a set of SNPs to form clumps around an index SNP. Each clump is defined by a pairwise threshold and includes SNPs within 250 kb of the index SNP that are in linkage disequilibrium with it. After completing linkage disequilibrium clumping and P value thresholding, the estimated β-coefficients of the effect alleles were employed as weights to calculate the PRS using PLINK v2.0[38].
Molecular network analysis of DR-associated SNPs
The molecular network of DR-associated SNPs identified by GWAS was built using core analysis in ingenuity pathway analysis software (https://digitalinsights.qiagen.com/product-login/; version: 111725566; Qiagen Sciences, Inc., MD, United States). Fisher’s exact test was used to determine the significant differences in the available networks (P < 0.05).
Statistical analysis
Student’s t-tests were employed to compare continuous variables, while χ2 tests were utilized for categorical variables to assess differences in characteristics and clinical data between the DR and non-DR groups. Cox proportional hazards models and Kaplan-Meier curves were used to estimate the associations between PRS, time to DR diagnosis, and DR severity. Statistical analyses were performed using SPSS version 22 (SPSS Inc., Chicago, IL, United States) and R Statistical Software (v3.6.1, R Core Team). Statistical significance was set at P < 0.05.
RESULTS
Characteristics of the study population
We identified 4892 patients with T2D in the discovery cohort and 2403 patients with T2D in the replication cohort as having DR. Patients with DR had a significantly lower mean age at T2D diagnosis than those without DR in both the discovery (61.8 ± 12.2 vs 64.7 ± 13.6, P < 0.001) and replication (59.2 ± 11.9 vs 65.1 ± 13.3, P < 0.001) cohorts. The proportion of males was significantly lower among patients with DR compared to those without DR (53.4% vs 57.7%, P < 0.001 in the discovery group; 51.5% vs 56.4%, P = 0.005 in the replication group). In the discovery DR group, a higher mean HbA1c level and lower BMI were observed, while in the replication DR group, a higher mean HbA1c level and higher BMI were noted. The median times to DR diagnosis were 24 months and 36 months in the discovery and replication cohorts, respectively. The follow-up duration ranged from 0-228 months for patients with DR and 60-240 months for patients without DR. Table 1 provides a summary of the characteristics of the study population.
Table 1 Characteristics of the patients with type 2 diabetes in the discovery and replication cohorts, mean SD/n (%).
A GWAS was conducted involving 4892 T2D patients with DR and 4778 patients without DR in the discovery dataset. As illustrated in the Manhattan plot in Figure 1, 43 SNPs showing evidence of association with DR were identified at a genome-wide suggestive significance level (P < 1 × 10-5), located near LOC101927967 (chromosome 2), Kruppel-like factor 15 (KLF15) (chromosome 3), slit guidance ligand 3 (chromosome 5), cGMP-dependent protein kinase 1 (PRKG1) (chromosome 10), polymerase II-associated protein 3 (chromosome 12), and TATA-box binding protein associated factor, RNA polymerase I subunit C (TAF1C) (chromosome 16).
Figure 1 Manhattan plot of the genome-wide association study of diabetic retinopathy in the discovery dataset.
The bottom blue line indicates the genome-wide suggestive significance threshold of P < 1 × 10-5. KLF15: Kruppel-like factor 15; SLIT3: Slit guidance ligand 3; PRKG1: CGMP-dependent protein kinase 1; RPAP3: RNA polymerase II-associated protein 3; TAF1C: TATA-box binding protein associated factor, RNA polymerase I subunit C.
Figure 2 Kaplan-Meier curves depicting the probability over time in the bottom 25% (blue) and top 10% (green) groups of polygenic risk score.
A: Patients with type 2 diabetes remaining diabetic retinopathy (DR)-free; B: Patients with DR not requiring more than four laser treatments; C: Patients with DR not requiring vitreoretinal surgery. Hazard ratios for patients in the top 10% of polygenic risk score were estimated using a Cox regression model adjusted for diagnosis age and glycated hemoglobin A1c. T2D: Type 2 diabetes; DR: Diabetic retinopathy; PRS: Polygenic risk score; HR: Hazard ratio; CI: Confidence interval; HbA1c: Glycated hemoglobin A1c.
PRS construction and assessment of DR risk
The PRS for DR was generated using summary statistics from the GWAS of the discovery dataset and calculated with the PRSice-2 software. Additionally, the PRS was evaluated for its association with the development and severity of DR in T2D patients during a 20-year follow-up period. The results indicated that the median times to develop DR in the top decile and bottom quartile PRS among T2D patients were 108 and 96 months, respectively. T2D patients in the top decile of PRS had a 1.21-fold greater risk of developing DR [hazard ratio (HR) = 1.21; 95%CI: 1.01-1.45; P = 0.041] than those in the bottom quartile (Figure 2A). Furthermore, T2D patients with DR in the top PRS decile had a 4.81-fold greater risk of requiring more than four laser treatments (HR = 4.81; 95%CI: 1.40-16.5; P = 0.012; Figure 2B) and a 1.38-fold increased risk of undergoing vitreoretinal surgery (HR = 1.38; 95%CI: 1.01-1.90; P = 0.044) than those in the bottom quartile (Figure 2C).
Molecular networks of DR-associated SNPs
The potential molecular pathways associated with the 43 SNPs linked to DR were investigated using a significance threshold of P < 1 × 10-5. Ingenuity pathway analysis revealed the ten most significantly affected biological pathways and molecules related to DR. The primary biological pathways identified included RNA polymerase I complex assembly, Ras-associated protein 1 (Rap1) signaling, and brain and muscle ARNT-like 1 (BMAL1): CLOCK and neuronal PAS domain protein 2 activation of circadian gene expression. Key molecules identified were TAF1C, PRKG1, and KLF15 (Table 2).
Table 2 The ten most significantly affected biological pathways and molecules related to diabetic retinopathy identified through ingenuity pathway analysis.
In this study, we identified six novel loci associated with DR-LOC101927967, KLF15, slit guidance ligand 3, PRKG1, polymerase II-associated protein 3, and TAF1C-using a GWAS in Taiwanese Han patients with T2D. Furthermore, we established a PRS based on our GWAS findings to identify patients with T2D at high risk of DR using 20 years of follow-up data from a large Taiwanese Han hospital database[39]. T2D patients in the top PRS decile showed a 21% increased risk of developing DR and experienced more severe symptoms requiring more frequent laser treatments and vitreoretinal surgery. To the best of our knowledge, this is the first genome-wide PRS study to use Asian genetic data to evaluate DR in patients with T2D. These findings highlight the potential of PRS to screen T2D patients at high risk for DR, facilitating targeted interventions and more frequent retinal screening. While our study primarily focused on understanding the association between PRS and DR, we acknowledge that the practical application of PRS in clinical decision-making and screening tools remains an important question to be assessed by future studies. Therefore, future research should focus on the added value of PRS in enhancing existing prediction models, its effect on clinical outcomes, and practical approaches for its integration into routine clinical decision-making. However, a prospective study design is necessary to definitively determine the clinical utility of the PRS for DR prediction.
Previously, we developed a DR prediction model using four DR-associated SNPs based on published GWAS and meta-analysis results to calculate the GRS along with conventional DR risk factors[29]. Although promising, the study had several limitations, including its cross-sectional design, relatively small sample size, and limited gene composition. Therefore, in the present study, we performed a new GWAS, used the results to develop a PRS for DR, and conducted long-term follow-up of non-DR T2D patients to assess their risk of developing DR. Our results demonstrate that PRS can effectively predict the risk of DR development in patients with T2D and identify patients with DR who are likely to experience more severe symptoms. The four SNPs selected in our previous prediction model were located in the plexin domain-containing 2, Rho GTPase-activating protein 22 (ARHGAP22), contactin 5, and formin 1 genes[29]. Plexin domain-containing 2 and ARHGAP22 are located on chromosome 10 and were first reported as DR risk genes for T2D patients in our previous study[20]. Although these two genes were not included in the present GWAS results, we identified PRKG1 on chromosome 10q11.23-21.1, which is implicated in the Rap1 signaling pathway. PRKG1 may play a role in maintaining the basal barrier properties of the retinal vascular endothelium and activating the cAMP-dependent guanine nucleotide exchange factor-Rap1 pathway[40]. However, it is worth noting that ARHGAP22 is located on chromosome 10q11.22-11.23, close to PRKG1. Another meta-analysis validated the significant association between two SNPs, rs4462262 (ZWINT-MRPS35P3) and rs7903146 (TCF7 L2), and DR in replication cohorts[19]. Both ZWINT-MRPS35P3 (10q21.1) and TCF7 L2 (10q25.2-25.3) genes are located on chromosome 10q, indicating that the 10q region may harbor multiple genes associated with DR.
This study also identified KLF15, located on chromosome 3, which has not been previously associated with DR development. KLF15 is involved in the circadian clock pathway, with period circadian regulator 3 regulating its expression during adipogenesis via Bmal1[41]. In retinal cells, KLF15 represses photoreceptor-specific genes such as rhodopsin and interphotoreceptor retinoid-binding protein, maintaining cellular identity by preventing inappropriate gene activation in non-photoreceptor cells[42]. Bmal1, which regulates KLF15, is crucial for retinal development and cell cycle regulation, and its loss delays cell cycle exit during retinal neurogenesis[43]. Additionally, alterations in angiogenesis-regulating signaling pathways can disrupt endothelial homeostasis, contributing to diseases such as DR[44]. Bmal1 plays a key role in the regulation of endothelial cell physiology, and genetic changes in Bmal1 can affect angiogenesis in both developmental and pathological contexts[45]. Another gene identified in this study, TAF1C, was found to be significantly elevated in T2D patients, indicating its potential role in increased protein synthesis in the disease[46].
Several limitations were identified in this study. First, the replication cohorts were confined to the Taiwanese Han population, which potentially limits the generalizability of our findings to other populations or ethnic groups. The PRS should be independently validated in diverse cohorts using multi-ethnic datasets. Furthermore, until rigorous validation studies are conducted, conclusions regarding its applicability to non-Taiwanese Han populations should be drawn with caution. Second, we used diagnostic codes to define DR cases, which may have introduced bias in the classification of the DR subtypes. To address this issue, we used the medical order code for DR treatment in electronic medical data as a measure of disease severity. Patients who underwent laser treatment and vitreoretinal surgery were considered to have proliferative DR. Although we used the number of laser treatments and vitreoretinal surgeries as indicators of DR severity, we recognize that this may not fully reflect the entire spectrum of severity. For example, it is possible that some patients with advanced DR may not have opted for these treatments due to personal preference or differing treatment thresholds preferred by different retinal specialists. Nevertheless, Taiwan’s insurance system provides coverage for over 99% of the population, and our hospital offers direct referral access to retinal subspecialists. Therefore, we believe that treatment availability and affordability were unlikely to be major limiting factors in this study. Thus, we still consider these to be valid and practical indicators of disease severity. In addition, factors such as lipid levels, smoking status, socioeconomic status, and treatment adherence may also influence the risk of DR. Omitting these variables could have introduced bias in estimating the associations between PRS and DR outcomes. Therefore, future studies should consider incorporating these factors to better understand the relationship between genetic risk and DR. Finally, our study was retrospective and focused on the relationship between PRS and DR prevalence rather than its incidence. Therefore, future studies should evaluate PRS prospectively and longitudinally.
CONCLUSION
In this study, we demonstrated that PRS is an independent predictor for DR development in the Taiwanese Han population and is associated with earlier onset and greater severity of DR. Further investigation is warranted to evaluate the potential of PRS as a valuable tool for decision-making in determining the optimal timing for ophthalmological evaluation and the frequency of subsequent follow-up in patients with T2D.
ACKNOWLEDGEMENTS
We thank the iHi Clinical Research Platform from the Big Data Center of China Medical University Hospital for data exploration, and administrative support. We also thank GGA Corporation and the Molecular Science and Digital Innovation Center, Taiwan, for their support in data analysis, which was partly performed using ingenuity pathway analysis at China Medical University, Taichung, Taiwan.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Endocrinology and metabolism
Country of origin: Taiwan
Peer-review report’s classification
Scientific Quality: Grade B, Grade B, Grade B, Grade B
Novelty: Grade B, Grade B, Grade B
Creativity or Innovation: Grade B, Grade B, Grade B
Scientific Significance: Grade A, Grade B, Grade B
P-Reviewer: Aktas G, MD, PhD, Chief Physician, Professor, Türkiye; Horowitz M, DSc, MD, PhD, FRACP, Professor, Australia; Nemr MTM, Associate Professor, FAHA, Egypt S-Editor: Wu S L-Editor: A P-Editor: Wang WB
Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, Stein C, Basit A, Chan JCN, Mbanya JC, Pavkov ME, Ramachandaran A, Wild SH, James S, Herman WH, Zhang P, Bommer C, Kuo S, Boyko EJ, Magliano DJ. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045.Diabetes Res Clin Pract. 2022;183:109119.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 3033][Cited by in RCA: 5090][Article Influence: 1696.7][Reference Citation Analysis (36)]
Stratton IM, Adler AI, Neil HA, Matthews DR, Manley SE, Cull CA, Hadden D, Turner RC, Holman RR. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study.BMJ. 2000;321:405-412.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 5819][Cited by in RCA: 5995][Article Influence: 239.8][Reference Citation Analysis (0)]
Clustering of long-term complications in families with diabetes in the diabetes control and complications trial. The Diabetes Control and Complications Trial Research Group.Diabetes. 1997;46:1829-1839.
[PubMed] [DOI]
Awata T, Yamashita H, Kurihara S, Morita-Ohkubo T, Miyashita Y, Katayama S, Mori K, Yoneya S, Kohda M, Okazaki Y, Maruyama T, Shimada A, Yasuda K, Nishida N, Tokunaga K, Koike A. A genome-wide association study for diabetic retinopathy in a Japanese population: potential association with a long intergenic non-coding RNA.PLoS One. 2014;9:e111715.
[RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)][Cited by in Crossref: 55][Cited by in RCA: 65][Article Influence: 5.9][Reference Citation Analysis (0)]
Pollack S, Igo RP Jr, Jensen RA, Christiansen M, Li X, Cheng CY, Ng MCY, Smith AV, Rossin EJ, Segrè AV, Davoudi S, Tan GS, Chen YI, Kuo JZ, Dimitrov LM, Stanwyck LK, Meng W, Hosseini SM, Imamura M, Nousome D, Kim J, Hai Y, Jia Y, Ahn J, Leong A, Shah K, Park KH, Guo X, Ipp E, Taylor KD, Adler SG, Sedor JR, Freedman BI; Family Investigation of Nephropathy and Diabetes-Eye Research Group, DCCT/EDIC Research Group, Lee IT, Sheu WH, Kubo M, Takahashi A, Hadjadj S, Marre M, Tregouet DA, Mckean-Cowdin R, Varma R, McCarthy MI, Groop L, Ahlqvist E, Lyssenko V, Agardh E, Morris A, Doney ASF, Colhoun HM, Toppila I, Sandholm N, Groop PH, Maeda S, Hanis CL, Penman A, Chen CJ, Hancock H, Mitchell P, Craig JE, Chew EY, Paterson AD, Grassi MA, Palmer C, Bowden DW, Yaspan BL, Siscovick D, Cotch MF, Wang JJ, Burdon KP, Wong TY, Klein BEK, Klein R, Rotter JI, Iyengar SK, Price AL, Sobrin L. Multiethnic Genome-Wide Association Study of Diabetic Retinopathy Using Liability Threshold Modeling of Duration of Diabetes and Glycemic Control.Diabetes. 2019;68:441-456.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 61][Cited by in RCA: 58][Article Influence: 9.7][Reference Citation Analysis (0)]
Imamura M, Takahashi A, Matsunami M, Horikoshi M, Iwata M, Araki SI, Toyoda M, Susarla G, Ahn J, Park KH, Kong J, Moon S, Sobrin L; International Diabetic Retinopathy and Genetics CONsortium (iDRAGON), Yamauchi T, Tobe K, Maegawa H, Kadowaki T, Maeda S. Genome-wide association studies identify two novel loci conferring susceptibility to diabetic retinopathy in Japanese patients with type 2 diabetes.Hum Mol Genet. 2021;30:716-726.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 18][Cited by in RCA: 26][Article Influence: 6.5][Reference Citation Analysis (0)]
Sudmant PH, Rausch T, Gardner EJ, Handsaker RE, Abyzov A, Huddleston J, Zhang Y, Ye K, Jun G, Fritz MH, Konkel MK, Malhotra A, Stütz AM, Shi X, Casale FP, Chen J, Hormozdiari F, Dayama G, Chen K, Malig M, Chaisson MJP, Walter K, Meiers S, Kashin S, Garrison E, Auton A, Lam HYK, Mu XJ, Alkan C, Antaki D, Bae T, Cerveira E, Chines P, Chong Z, Clarke L, Dal E, Ding L, Emery S, Fan X, Gujral M, Kahveci F, Kidd JM, Kong Y, Lameijer EW, McCarthy S, Flicek P, Gibbs RA, Marth G, Mason CE, Menelaou A, Muzny DM, Nelson BJ, Noor A, Parrish NF, Pendleton M, Quitadamo A, Raeder B, Schadt EE, Romanovitch M, Schlattl A, Sebra R, Shabalin AA, Untergasser A, Walker JA, Wang M, Yu F, Zhang C, Zhang J, Zheng-Bradley X, Zhou W, Zichner T, Sebat J, Batzer MA, McCarroll SA; 1000 Genomes Project Consortium, Mills RE, Gerstein MB, Bashir A, Stegle O, Devine SE, Lee C, Eichler EE, Korbel JO. An integrated map of structural variation in 2,504 human genomes.Nature. 2015;526:75-81.
[RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)][Cited by in Crossref: 1847][Cited by in RCA: 1583][Article Influence: 158.3][Reference Citation Analysis (0)]
Liu TY, Lu HF, Chen YC, Liao CC, Lin YJ, Yang JS, Liao WL, Lin WD, Chen SY, Huang YC, Lin WY, Liu YH, Hsu KC, Chang SS, Chen HD, Chou YP, Chang JG, Wang CH, Chang CT, Huang CM, Yeo KJ, Wang TY, Yeh CC, Chen JH, Huang CP, Lai HC, Chen RH, Lin HJ, Wu PY, Wang JY, Kuo CC, Cho DY, Tsai CH, Tsai FJ. Diversity and longitudinal records: Genetic architecture of disease associations and polygenic risk in the Taiwanese Han population.Sci Adv. 2025;11:eadt0539.
[RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)][Cited by in Crossref: 2][Cited by in RCA: 4][Article Influence: 4.0][Reference Citation Analysis (0)]
