1
|
Xue CC, Sim R, Chee ML, Yu M, Wang YX, Rim TH, Hyung PK, Woong KS, Song SJ, Nangia V, Panda-Jonas S, Wang NL, Hao J, Zhang Q, Cao K, Sasaki M, Harada S, Toru T, Ryo K, Raman R, Surya J, Khan R, Bikbov M, Wong IY, Cheung CMG, Jonas JB, Cheng CY, Tham YC. Is Kidney Function Associated with Age-Related Macular Degeneration?: Findings from the Asian Eye Epidemiology Consortium. Ophthalmology 2024; 131:692-699. [PMID: 38160880 DOI: 10.1016/j.ophtha.2023.12.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 12/06/2023] [Accepted: 12/26/2023] [Indexed: 01/03/2024] Open
Abstract
PURPOSE Chronic kidney disease (CKD) may elevate susceptibility to age-related macular degeneration (AMD) because of shared risk factors, pathogenic mechanisms, and genetic polymorphisms. Given the inconclusive findings in prior studies, we investigated this association using extensive datasets in the Asian Eye Epidemiology Consortium. DESIGN Cross-sectional study. PARTICIPANTS Fifty-one thousand two hundred fifty-three participants from 10 distinct population-based Asian studies. METHODS Age-related macular degeneration was defined using the Wisconsin Age-Related Maculopathy Grading System, the International Age-Related Maculopathy Epidemiological Study Group Classification, or the Beckman Clinical Classification. Chronic kidney disease was defined as estimated glomerular filtration rate (eGFR) of less than 60 ml/min per 1.73 m2. A pooled analysis using individual-level participant data was performed to examine the associations between CKD and eGFR with AMD (early and late), adjusting for age, sex, hypertension, diabetes, body mass index, smoking status, total cholesterol, and study groups. MAIN OUTCOME MEASURES Odds ratio (OR) of early and late AMD. RESULTS Among 51 253 participants (mean age, 54.1 ± 14.5 years), 5079 had CKD (9.9%). The prevalence of early AMD was 9.0%, and that of late AMD was 0.71%. After adjusting for confounders, individuals with CKD were associated with higher odds of late AMD (OR, 1.46; 95% confidence interval [CI], 1.11-1.93; P = 0.008). Similarly, poorer kidney function (per 10-unit eGFR decrease) was associated with late AMD (OR, 1.12; 95% CI, 1.05-1.19; P = 0.001). Nevertheless, CKD and eGFR were not associated significantly with early AMD (all P ≥ 0.149). CONCLUSIONS Pooled analysis from 10 distinct Asian population-based studies revealed that CKD and compromised kidney function are associated significantly with late AMD. This finding further underscores the importance of ocular examinations in patients with CKD. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
Collapse
Affiliation(s)
- Can Can Xue
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore
| | - Ralene Sim
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore
| | - Miao Li Chee
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore
| | - Marco Yu
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Tyler Hyungtaek Rim
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore
| | - Park Kyu Hyung
- Department of Ophthalmology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Kang Se Woong
- Department of Ophthalmology of Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Su Jeong Song
- Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
| | | | | | - Ning Li Wang
- Department of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Jie Hao
- Department of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Qing Zhang
- Department of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Kai Cao
- Department of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Mariko Sasaki
- Department of Ophthalmology, Keio University School of Medicine, Tokyo, Japan; Division of Vision Research, National Institute of Sensory Organs, National Hospital Organization Tokyo Medical Center, Tokyo, Japan
| | - Sei Harada
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Takebayashi Toru
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Kawasaki Ryo
- Public Health, Department of Social Medicine, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Rajiv Raman
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, India
| | - Janani Surya
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, India
| | - Rehana Khan
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, India
| | - Mukharram Bikbov
- Ufa Eye Research Institute, Ufa, Bashkortostan, Russian Federation
| | - Ian Y Wong
- Department of Ophthalmology, The Hong Kong Sanatorium & Hospital, Hong Kong SAR, China
| | - Chui Ming Gemmy Cheung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore; Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Republic of Singapore
| | - Jost B Jonas
- Department of Ophthalmology, Medical Faculty Mannheim Heidelberg University, Mannheim, Germany; Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore; Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Republic of Singapore; Centre for Innovation and Precision Eye Health & Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore; Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Republic of Singapore; Centre for Innovation and Precision Eye Health & Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore.
| |
Collapse
|
2
|
Wąż P, Zorena K, Murawska A, Bielińska-Wąż D. Classification Maps: A New Mathematical Tool Supporting the Diagnosis of Age-Related Macular Degeneration. J Pers Med 2023; 13:1074. [PMID: 37511686 PMCID: PMC10381320 DOI: 10.3390/jpm13071074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 06/18/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023] Open
Abstract
OBJECTIVE A new diagnostic graphical tool-classification maps-supporting the detection of Age-Related Macular Degeneration (AMD) has been constructed. METHODS The classification maps are constructed using the ordinal regression model. In the ordinal regression model, the ordinal variable (the dependent variable) is the degree of the advancement of AMD. The other variables, such as CRT (Central Retinal Thickness), GCC (Ganglion Cell Complex), MPOD (Macular Pigment Optical Density), ETDRS (Early Treatment Diabetic Retinopathy Study), Snellen and Age have also been used in the analysis and are represented on the axes of the maps. RESULTS Here, 132 eyes were examined and classified to the AMD advancement level according to the four-point Age-Related Eye Disease Scale (AREDS): AREDS 1, AREDS 2, AREDS 3 and AREDS 4. These data were used for the creation of two-dimensional classification maps for each of the four stages of AMD. CONCLUSIONS The maps allow us to perform the classification of the patient's eyes to particular stages of AMD. The pairs of the variables represented on the axes of the maps can be treated as diagnostic identifiers necessary for the classification to particular stages of AMD.
Collapse
Affiliation(s)
- Piotr Wąż
- Department of Nuclear Medicine, Medical University of Gdańsk, 80-210 Gdańsk, Poland
| | - Katarzyna Zorena
- Department of Immunobiology and Environment Microbiology, Medical University of Gdańsk, 80-210 Gdańsk, Poland
| | - Anna Murawska
- Department of Immunobiology and Environment Microbiology, Medical University of Gdańsk, 80-210 Gdańsk, Poland
| | - Dorota Bielińska-Wąż
- Department of Radiological Informatics and Statistics, Medical University of Gdańsk, 80-210 Gdańsk, Poland
| |
Collapse
|