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Xie X, Wang W, Wang H, Zhang Z, Yuan X, Shi Y, Liu Y, Zhou Q, Liu T. Artificial Intelligence-Assisted Perfusion Density as Biomarker for Screening Diabetic Nephropathy. Transl Vis Sci Technol 2024; 13:19. [PMID: 39388177 PMCID: PMC11472892 DOI: 10.1167/tvst.13.10.19] [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: 12/06/2023] [Accepted: 07/30/2024] [Indexed: 10/12/2024] Open
Abstract
Purpose To identify a reliable biomarker for screening diabetic nephropathy (DN) using artificial intelligence (AI)-assisted ultra-widefield swept-source optical coherence tomography angiography (UWF SS-OCTA). Methods This study analyzed data from 169 patients (287 eyes) with type 2 diabetes mellitus (T2DM), resulting in 15,211 individual data points. These data points included basic demographic information, clinical data, and retinal and choroidal data obtained through UWF SS-OCTA for each eye. Statistical analysis, 10-fold cross-validation, and the random forest approach were employed for data processing. Results The degree of retinal microvascular damage in the diabetic retinopathy (DR) with the DN group was significantly greater than in the DR without DN group, as measured by SS-OCTA parameters. There were strong associations between perfusion density (PD) and DN diagnosis in both the T2DM population (r = -0.562 to -0.481, P < 0.001) and the DR population (r = -0.397 to -0.357, P < 0.001). The random forest model showed an average classification accuracy of 85.8442% for identifying DN patients based on perfusion density in the T2DM population and 82.5739% in the DR population. Conclusions Quantitative analysis of microvasculature reveals a correlation between DR and DN. UWF PD may serve as a significant and noninvasive biomarker for evaluating DN in patients through deep learning. AI-assisted SS-OCTA could be a rapid and reliable tool for screening DN. Translational Relevance We aim to study the pathological processes of DR and DN and determine the correspondence between their clinical and pathological manifestations to further clarify the potential of screening DN using AI-assisted UWF PD.
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Affiliation(s)
- Xiao Xie
- Eye Institute of Shandong First Medical University, Eye Hospital of Shandong First Medical University (Shandong Eye Hospital), Jinan, China
- State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Qingdao, China
- School of Ophthalmology, Shandong First Medical University, Jinan, China
| | - Wenqi Wang
- Department of Chinese Medicine Ophthalmology, The First Affiliated Hospital of Shandong First Medical University (Shandong Provincial Qianfoshan Hospital), Jinan, China
| | - Hongyan Wang
- Eye Institute of Shandong First Medical University, Eye Hospital of Shandong First Medical University (Shandong Eye Hospital), Jinan, China
- State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Qingdao, China
- School of Ophthalmology, Shandong First Medical University, Jinan, China
| | - Zhiping Zhang
- First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Xiaomeng Yuan
- Eye Institute of Shandong First Medical University, Eye Hospital of Shandong First Medical University (Shandong Eye Hospital), Jinan, China
- State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Qingdao, China
- School of Ophthalmology, Shandong First Medical University, Jinan, China
| | - Yanmei Shi
- First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yanfeng Liu
- Jinan Health Care Center for Women and Children, Jinan, China
| | - Qingjun Zhou
- State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Qingdao, China
- Qingdao Eye Hospital of Shandong First Medical University, Qingdao, China
| | - Tingting Liu
- Eye Institute of Shandong First Medical University, Eye Hospital of Shandong First Medical University (Shandong Eye Hospital), Jinan, China
- State Key Laboratory Cultivation Base, Shandong Provincial Key Laboratory of Ophthalmology, Qingdao, China
- School of Ophthalmology, Shandong First Medical University, Jinan, China
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Nusinovici S, Rim TH, Li H, Yu M, Deshmukh M, Quek TC, Lee G, Chong CCY, Peng Q, Xue CC, Zhu Z, Chew EY, Sabanayagam C, Wong TY, Tham YC, Cheng CY. Application of a deep-learning marker for morbidity and mortality prediction derived from retinal photographs: a cohort development and validation study. THE LANCET. HEALTHY LONGEVITY 2024:100593. [PMID: 39362226 DOI: 10.1016/s2666-7568(24)00089-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 05/03/2024] [Accepted: 05/03/2024] [Indexed: 10/05/2024] Open
Abstract
BACKGROUND Biological ageing markers are useful to risk stratify morbidity and mortality more precisely than chronological age. In this study, we aimed to develop a novel deep-learning-based biological ageing marker (referred to as RetiPhenoAge hereafter) using retinal images and PhenoAge, a composite biomarker of phenotypic age. METHODS We used retinal photographs from the UK Biobank dataset to train a deep-learning algorithm to predict the composite score of PhenoAge. We used a deep convolutional neural network architecture with multiple layers to develop our deep-learning-based biological ageing marker, as RetiPhenoAge, with the aim of identifying patterns and features in the retina associated with variations of blood biomarkers related to renal, immune, liver functions, inflammation, and energy metabolism, and chronological age. We determined the performance of this biological ageing marker for the prediction of morbidity (cardiovascular disease and cancer events) and mortality (all-cause, cardiovascular disease, and cancer) in three independent cohorts (UK Biobank, the Singapore Epidemiology of Eye Diseases [SEED], and the Age-Related Eye Disease Study [AREDS] from the USA). We also compared the performance of RetiPhenoAge with two other known ageing biomarkers (hand grip strength and adjusted leukocyte telomere length) and one lifestyle factor (physical activity) for risk stratification of mortality and morbidity. We explored the underlying biology of RetiPhenoAge by assessing its associations with different systemic characteristics (eg, diabetes or hypertension) and blood metabolite levels. We also did a genome-wide association study to identify genetic variants associated with RetiPhenoAge, followed by expression quantitative trait loci mapping, a gene-based analysis, and a gene-set analysis. Cox proportional hazards models were used to estimate the hazard ratios (HRs) and corresponding 95% CIs for the associations between RetiPhenoAge and the different morbidity and mortality outcomes. FINDINGS Retinal photographs for 34 061 UK Biobank participants were used to train the model, and data for 9429 participants from the SEED cohort and for 3986 participants from the AREDS cohort were included in the study. RetiPhenoAge was associated with all-cause mortality (HR 1·92 [95% CI 1·42-2·61]), cardiovascular disease mortality (1·97 [1·02-3·82]), cancer mortality (2·07 [1·29-3·33]), and cardiovascular disease events (1·70 [1·17-2·47]), independent of PhenoAge and other possible confounders. Similar findings were found in the two independent cohorts (HR 1·67 [1·21-2·31] for cardiovascular disease mortality in SEED and 2·07 [1·10-3·92] in AREDS). RetiPhenoAge had stronger associations with mortality and morbidity than did hand grip strength, telomere length, and physical activity. We identified two genetic variants that were significantly associated with RetiPhenoAge (single nucleotide polymorphisms rs3791224 and rs8001273), and were linked to expression quantitative trait locis in various tissues, including the heart, kidneys, and the brain. INTERPRETATION Our new deep-learning-derived biological ageing marker is a robust predictor of mortality and morbidity outcomes and could be used as a novel non-invasive method to measure ageing. FUNDING Singapore National Medical Research Council and Agency for Science, Technology and Research, Singapore.
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Affiliation(s)
- Simon Nusinovici
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Tyler Hyungtaek Rim
- Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore; MediWhale, Seoul, South Korea
| | - Hengtong Li
- Department of Ophthalmology and Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Marco Yu
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Mihir Deshmukh
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Ten Cheer Quek
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | | | | | - Qingsheng Peng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Can Can Xue
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Zhuoting Zhu
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China; Centre for Eye Research Australia, Ophthalmology, University of Melbourne, Melbourne, VIC, Australia
| | - Emily Y Chew
- National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Tien-Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Tsinghua Medicine, Tsinghua University, Beijing, China
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore; Department of Ophthalmology and Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore; Department of Ophthalmology and Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
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Amir Hamzah NA, Wan Zaki WMD, Wan Abdul Halim WH, Mustafar R, Saad AH. Evaluating the potential of retinal photography in chronic kidney disease detection: a review. PeerJ 2024; 12:e17786. [PMID: 39104365 PMCID: PMC11299532 DOI: 10.7717/peerj.17786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 06/30/2024] [Indexed: 08/07/2024] Open
Abstract
Background Chronic kidney disease (CKD) is a significant global health concern, emphasizing the necessity of early detection to facilitate prompt clinical intervention. Leveraging the unique ability of the retina to offer insights into systemic vascular health, it emerges as an interesting, non-invasive option for early CKD detection. Integrating this approach with existing invasive methods could provide a comprehensive understanding of patient health, enhancing diagnostic accuracy and treatment effectiveness. Objectives The purpose of this review is to critically assess the potential of retinal imaging to serve as a diagnostic tool for CKD detection based on retinal vascular changes. The review tracks the evolution from conventional manual evaluations to the latest state-of-the-art in deep learning. Survey Methodology A comprehensive examination of the literature was carried out, using targeted database searches and a three-step methodology for article evaluation: identification, screening, and inclusion based on Prisma guidelines. Priority was given to unique and new research concerning the detection of CKD with retinal imaging. A total of 70 publications from 457 that were initially discovered satisfied our inclusion criteria and were thus subjected to analysis. Out of the 70 studies included, 35 investigated the correlation between diabetic retinopathy and CKD, 23 centered on the detection of CKD via retinal imaging, and four attempted to automate the detection through the combination of artificial intelligence and retinal imaging. Results Significant retinal features such as arteriolar narrowing, venular widening, specific retinopathy markers (like microaneurysms, hemorrhages, and exudates), and changes in arteriovenous ratio (AVR) have shown strong correlations with CKD progression. We also found that the combination of deep learning with retinal imaging for CKD detection could provide a very promising pathway. Accordingly, leveraging retinal imaging through this technique is expected to enhance the precision and prognostic capacity of the CKD detection system, offering a non-invasive diagnostic alternative that could transform patient care practices. Conclusion In summary, retinal imaging holds high potential as a diagnostic tool for CKD because it is non-invasive, facilitates early detection through observable microvascular changes, offers predictive insights into renal health, and, when paired with deep learning algorithms, enhances the accuracy and effectiveness of CKD screening.
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Affiliation(s)
- Nur Asyiqin Amir Hamzah
- Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor, Malaysia
- Faculty of Engineering and Technology, Multimedia University, Ayer Keroh, Melaka, Malaysia
| | - Wan Mimi Diyana Wan Zaki
- Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor, Malaysia
| | | | - Ruslinda Mustafar
- Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras, Kuala Lumpur, Malaysia
| | - Assyareefah Hudaibah Saad
- Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor, Malaysia
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Jeeyavudeen MS, Crosby M, Pappachan JM. Continuous glucose monitoring metrics in pregnancy with type 1 diabetes mellitus. World J Methodol 2024; 14:90316. [PMID: 38577196 PMCID: PMC10989406 DOI: 10.5662/wjm.v14.i1.90316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 12/17/2023] [Accepted: 01/16/2024] [Indexed: 03/07/2024] Open
Abstract
Managing diabetes during pregnancy is challenging, given the significant risk it poses for both maternal and foetal health outcomes. While traditional methods involve capillary self-monitoring of blood glucose level monitoring and periodic HbA1c tests, the advent of continuous glucose monitoring (CGM) systems has revolutionized the approach. These devices offer a safe and reliable means of tracking glucose levels in real-time, benefiting both women with diabetes during pregnancy and the healthcare providers. Moreover, CGM systems have shown a low rate of side effects and high feasibility when used in pregnancies complicated by diabetes, especially when paired with continuous subcutaneous insulin infusion pump as hybrid closed loop device. Such a combined approach has been demonstrated to improve overall blood sugar control, lessen the occurrence of preeclampsia and neonatal hypoglycaemia, and minimize the duration of neonatal intensive care unit stays. This paper aims to offer a comprehensive evaluation of CGM metrics specifically tailored for pregnancies impacted by type 1 diabetes mellitus.
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Affiliation(s)
| | - Mairi Crosby
- Department of Endocrinology and Metabolism, University Hospitals of Edinburgh, Edinburgh EH16 4SA, United Kingdom
| | - Joseph M Pappachan
- Department of Endocrinology and Metabolism, Lancashire Teaching Hospitals NHS Trust, Preston PR2 9HT, United Kingdom
- Faculty of Science, Manchester Metropolitan University, Manchester M15 6BH, United Kingdom
- Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PL, United Kingdom
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Betzler BK, Chee EYL, He F, Lim CC, Ho J, Hamzah H, Tan NC, Liew G, McKay GJ, Hogg RE, Young IS, Cheng CY, Lim SC, Lee AY, Wong TY, Lee ML, Hsu W, Tan GSW, Sabanayagam C. Deep learning algorithms to detect diabetic kidney disease from retinal photographs in multiethnic populations with diabetes. J Am Med Inform Assoc 2023; 30:1904-1914. [PMID: 37659103 PMCID: PMC10654858 DOI: 10.1093/jamia/ocad179] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 08/17/2023] [Accepted: 08/21/2023] [Indexed: 09/04/2023] Open
Abstract
OBJECTIVE To develop a deep learning algorithm (DLA) to detect diabetic kideny disease (DKD) from retinal photographs of patients with diabetes, and evaluate performance in multiethnic populations. MATERIALS AND METHODS We trained 3 models: (1) image-only; (2) risk factor (RF)-only multivariable logistic regression (LR) model adjusted for age, sex, ethnicity, diabetes duration, HbA1c, systolic blood pressure; (3) hybrid multivariable LR model combining RF data and standardized z-scores from image-only model. Data from Singapore Integrated Diabetic Retinopathy Program (SiDRP) were used to develop (6066 participants with diabetes, primary-care-based) and internally validate (5-fold cross-validation) the models. External testing on 2 independent datasets: (1) Singapore Epidemiology of Eye Diseases (SEED) study (1885 participants with diabetes, population-based); (2) Singapore Macroangiopathy and Microvascular Reactivity in Type 2 Diabetes (SMART2D) (439 participants with diabetes, cross-sectional) in Singapore. Supplementary external testing on 2 Caucasian cohorts: (3) Australian Eye and Heart Study (AHES) (460 participants with diabetes, cross-sectional) and (4) Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA) (265 participants with diabetes, cross-sectional). RESULTS In SiDRP validation, area under the curve (AUC) was 0.826(95% CI 0.818-0.833) for image-only, 0.847(0.840-0.854) for RF-only, and 0.866(0.859-0.872) for hybrid. Estimates with SEED were 0.764(0.743-0.785) for image-only, 0.802(0.783-0.822) for RF-only, and 0.828(0.810-0.846) for hybrid. In SMART2D, AUC was 0.726(0.686-0.765) for image-only, 0.701(0.660-0.741) in RF-only, 0.761(0.724-0.797) for hybrid. DISCUSSION AND CONCLUSION There is potential for DLA using retinal images as a screening adjunct for DKD among individuals with diabetes. This can value-add to existing DLA systems which diagnose diabetic retinopathy from retinal images, facilitating primary screening for DKD.
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Affiliation(s)
- Bjorn Kaijun Betzler
- Yong Loo Lin School of Medicine, National University of Singapore, 117597, Singapore
| | - Evelyn Yi Lyn Chee
- School of Computing, National University of Singapore, 117417, Singapore
| | - Feng He
- Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore
| | - Cynthia Ciwei Lim
- Department of Renal Medicine, Singapore General Hospital, 168753, Singapore
| | - Jinyi Ho
- Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore
| | - Haslina Hamzah
- Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore
| | - Ngiap Chuan Tan
- SingHealth Polyclinics, Singapore Health Services, 168582, Singapore
| | - Gerald Liew
- Westmead Institute for Medical Research, University of Sydney, NSW 2145, Australia
| | - Gareth J McKay
- Centre for Public Health, Queen’s University Belfast, Belfast BT12 6BA, United Kingdom
| | - Ruth E Hogg
- Centre for Public Health, Queen’s University Belfast, Belfast BT12 6BA, United Kingdom
| | - Ian S Young
- Centre for Public Health, Queen’s University Belfast, Belfast BT12 6BA, United Kingdom
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore
- Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, 169857, Singapore
| | - Su Chi Lim
- Khoo Teck Puat Hospital, 768828, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, 117549, Singapore
| | - Aaron Y Lee
- Department of Ophthalmology, University of Washington, Seattle, WA 98104, United States
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore
- Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, 169857, Singapore
| | - Mong Li Lee
- School of Computing, National University of Singapore, 117417, Singapore
| | - Wynne Hsu
- School of Computing, National University of Singapore, 117417, Singapore
| | - Gavin Siew Wei Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore
- Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, 169857, Singapore
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore
- Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, 169857, Singapore
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Das AK, Kalra S, Joshi S, Mithal A, Kumar KMP, Unnikrishnan AG, Thacker H, Sethi B, Chowdhury S, Sugumaran A, Satpathy A, Gadekar A, Menon SK, Neogi R, Chodankar D, Trivedi C, Wangnoo SK, Zargar AH, Rais N. The LongitudinAl Nationwide stuDy on Management And Real-world outComes of diabetes in India over 3 years (LANDMARC trial). Endocrinol Diabetes Metab 2023; 6:e422. [PMID: 37392036 PMCID: PMC10495555 DOI: 10.1002/edm2.422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 03/22/2023] [Accepted: 03/25/2023] [Indexed: 07/02/2023] Open
Abstract
INTRODUCTION LANDMARC (CTRI/2017/05/008452), a prospective, observational real-world study, evaluated the occurrence of diabetes complications, glycemic control and treatment patterns in people with type 2 diabetes mellitus (T2DM) from pan-India regions over a period of 3 years. METHODS Participants with T2DM (≥25 to ≤60 years old at diagnosis, diabetes duration ≥2 years at the time of enrollment, with/without glycemic control and on ≥2 antidiabetic therapies) were included. The proportion of participants with macrovascular and microvascular complications, glycemic control and time to treatment adaptation over 36 months were assessed. RESULTS Of the 6234 participants enrolled, 5273 completed 3 years follow-up. At the end of 3-years, 205 (3.3%) and 1121 (18.0%) participants reported macrovascular and microvascular complications, respectively. Nonfatal myocardial infarction (40.0%) and neuropathy (82.0%) were the most common complications. At baseline and 3-years, 25.1% (1119/4466) and 36.6% (1356/3700) of participants had HbA1c <7%, respectively. At 3-years, population with macrovascular and microvascular complications had higher proportion of participants with uncontrolled glycemia (78.2% [79/101] and 70.3% [463/659], respectively) than those without complications (61.6% [1839/2985]). Over 3-years, majority (67.7%-73.9%) of the participants were taking only OADs (biguanides [92.2%], sulfonylureas [77.2%] and DPP-IV inhibitors [62.4%]). Addition of insulin was preferred in participants who were only on OADs at baseline, and insulin use gradually increased from 25.5% to 36.7% at the end of 3 years. CONCLUSION These 3-year trends highlight the burden of uncontrolled glycemia and cumulative diabetes-related complications, emphasizing the importance of optimizing diabetes management in India.
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Affiliation(s)
- Ashok K. Das
- Mahatma Gandhi Medical College and Research InstituteSri Balaji VidyapeetPuducherryIndia
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - S. K. Wangnoo
- Apollo Hospital Education and Research FoundationNew DelhiIndia
| | - A. H. Zargar
- Center for Diabetes & Endocrine CareSrinagarIndia
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Rende U, Guller A, Goldys EM, Pollock C, Saad S. Diagnostic and prognostic biomarkers for tubulointerstitial fibrosis. J Physiol 2023; 601:2801-2826. [PMID: 37227074 DOI: 10.1113/jp284289] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 05/23/2023] [Indexed: 05/26/2023] Open
Abstract
Renal fibrosis is the final common pathophysiological pathway in chronic kidney disease (CKD) regardless of the underlying cause of kidney injury. Tubulointerstitial fibrosis (TIF) is considered to be the key pathological predictor of CKD progression. Currently, the gold-standard tool to identify TIF is kidney biopsy, an invasive method that carries risks. Non-invasive diagnostics rely on an estimation of glomerular filtration rate and albuminuria to assess kidney function, but these fail to diagnose early CKD accurately or to predict progressive decline in kidney function. In this review, we summarize the current and emerging molecular biomarkers that have been studied in various clinical settings and in animal models of kidney disease and that are correlated with the degree of TIF. We examine the potential of these biomarkers to diagnose TIF non-invasively and to predict disease progression. We also examine the potential of new technologies and non-invasive diagnostic approaches in assessing TIF. Limitations of current and potential biomarkers are discussed and knowledge gaps identified.
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Affiliation(s)
- Umut Rende
- School of Biomedical Engineering, The University of New South Wales, Sydney, NSW, Australia
| | - Anna Guller
- Macquarie Medical School, Faculty of Medicine, Health & Human Sciences, Macquarie University, NSW, Australia
| | - Ewa M Goldys
- School of Biomedical Engineering, The University of New South Wales, Sydney, NSW, Australia
| | - Carol Pollock
- Kolling Institute of Medical Research, Royal North Shore Hospital, St Leonards, NSW, Australia
| | - Sonia Saad
- Kolling Institute of Medical Research, Royal North Shore Hospital, St Leonards, NSW, Australia
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Li Y, Liu Y, Liu S, Gao M, Wang W, Chen K, Huang L, Liu Y. Diabetic vascular diseases: molecular mechanisms and therapeutic strategies. Signal Transduct Target Ther 2023; 8:152. [PMID: 37037849 PMCID: PMC10086073 DOI: 10.1038/s41392-023-01400-z] [Citation(s) in RCA: 72] [Impact Index Per Article: 72.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 02/19/2023] [Accepted: 02/28/2023] [Indexed: 04/12/2023] Open
Abstract
Vascular complications of diabetes pose a severe threat to human health. Prevention and treatment protocols based on a single vascular complication are no longer suitable for the long-term management of patients with diabetes. Diabetic panvascular disease (DPD) is a clinical syndrome in which vessels of various sizes, including macrovessels and microvessels in the cardiac, cerebral, renal, ophthalmic, and peripheral systems of patients with diabetes, develop atherosclerosis as a common pathology. Pathological manifestations of DPDs usually manifest macrovascular atherosclerosis, as well as microvascular endothelial function impairment, basement membrane thickening, and microthrombosis. Cardiac, cerebral, and peripheral microangiopathy coexist with microangiopathy, while renal and retinal are predominantly microangiopathic. The following associations exist between DPDs: numerous similar molecular mechanisms, and risk-predictive relationships between diseases. Aggressive glycemic control combined with early comprehensive vascular intervention is the key to prevention and treatment. In addition to the widely recommended metformin, glucagon-like peptide-1 agonist, and sodium-glucose cotransporter-2 inhibitors, for the latest molecular mechanisms, aldose reductase inhibitors, peroxisome proliferator-activated receptor-γ agonizts, glucokinases agonizts, mitochondrial energy modulators, etc. are under active development. DPDs are proposed for patients to obtain more systematic clinical care requires a comprehensive diabetes care center focusing on panvascular diseases. This would leverage the advantages of a cross-disciplinary approach to achieve better integration of the pathogenesis and therapeutic evidence. Such a strategy would confer more clinical benefits to patients and promote the comprehensive development of DPD as a discipline.
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Affiliation(s)
- Yiwen Li
- National Clinical Research Center for Chinese Medicine Cardiology, Xiyuan Hospital, Chinese Academy of Chinese Medical Sciences, Beijing, 100091, China
| | - Yanfei Liu
- National Clinical Research Center for Chinese Medicine Cardiology, Xiyuan Hospital, Chinese Academy of Chinese Medical Sciences, Beijing, 100091, China
- The Second Department of Gerontology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, 100091, China
| | - Shiwei Liu
- Department of Nephrology and Endocrinology, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, 100102, China
| | - Mengqi Gao
- Department of Nephrology and Endocrinology, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, 100102, China
| | - Wenting Wang
- National Clinical Research Center for Chinese Medicine Cardiology, Xiyuan Hospital, Chinese Academy of Chinese Medical Sciences, Beijing, 100091, China
| | - Keji Chen
- National Clinical Research Center for Chinese Medicine Cardiology, Xiyuan Hospital, Chinese Academy of Chinese Medical Sciences, Beijing, 100091, China.
| | - Luqi Huang
- China Center for Evidence-based Medicine of TCM, China Academy of Chinese Medical Sciences, Beijing, 100010, China.
| | - Yue Liu
- National Clinical Research Center for Chinese Medicine Cardiology, Xiyuan Hospital, Chinese Academy of Chinese Medical Sciences, Beijing, 100091, China.
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Broadening horizons in mechanisms, management, and treatment of diabetic kidney disease. Pharmacol Res 2023; 190:106710. [PMID: 36871895 DOI: 10.1016/j.phrs.2023.106710] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/23/2023] [Accepted: 02/24/2023] [Indexed: 03/06/2023]
Abstract
Diabetic kidney disease (DKD) is the first cause of end-stage kidney disease in patients with diabetes and its prevalence is increasing worldwide. It encompasses histological alterations that mainly affect the glomerular filtration unit, which include thickening of the basement membrane, mesangial cell proliferation, endothelial alteration, and podocyte injury. These morphological abnormalities further result in a persistent increase of urinary albumin-to-creatinine ratio and in a reduction of the estimated glomerular filtration rate. Several molecular and cellular mechanisms have been recognized, up to date, as major players in mediating such clinical and histological features and many more are being under investigation. This review summarizes the most recent advances in understanding cell death mechanisms, intracellular signaling pathways and molecular effectors that play a role in the onset and progression of diabetic kidney damage. Some of those molecular and cellular mechanisms have been already successfully targeted in preclinical models of DKD and, in some cases, strategies have been tested in clinical trials. Finally, this report sheds light on the relevance of novel pathways that may become therapeutic targets for future applications in DKD.
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Identifying myoglobin as a mediator of diabetic kidney disease: a machine learning-based cross-sectional study. Sci Rep 2022; 12:21411. [PMID: 36496504 PMCID: PMC9741614 DOI: 10.1038/s41598-022-25299-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 11/28/2022] [Indexed: 12/13/2022] Open
Abstract
In view of the alarming increase in the burden of diabetes mellitus (DM) today, a rising number of patients with diabetic kidney disease (DKD) is forecasted. Current DKD predictive models often lack reliable biomarkers and perform poorly. In this regard, serum myoglobin (Mb) identified by machine learning (ML) may become a potential DKD indicator. We aimed to elucidate the significance of serum Mb in the pathogenesis of DKD. Electronic health record data from a total of 728 hospitalized patients with DM (286 DKD vs. 442 non-DKD) were used. We developed DKD ML models incorporating serum Mb and metabolic syndrome (MetS) components (insulin resistance and β-cell function, glucose, lipid) while using SHapley Additive exPlanation (SHAP) to interpret features. Restricted cubic spline (RCS) models were applied to evaluate the relationship between serum Mb and DKD. Serum Mb-mediated renal function impairment induced by MetS components was verified by causal mediation effect analysis. The area under the receiver operating characteristic curve of the DKD machine learning models incorporating serum Mb and MetS components reached 0.85. Feature importance analysis and SHAP showed that serum Mb and MetS components were important features. Further RCS models of DKD showed that the odds ratio was greater than 1 when serum Mb was > 80. Serum Mb showed a significant indirect effect in renal function impairment when using MetS components such as HOMA-IR, HGI and HDL-C/TC as a reason. Moderately elevated serum Mb is associated with the risk of DKD. Serum Mb may mediate MetS component-caused renal function impairment.
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Lin W, Luo Y, Liu F, Li H, Wang Q, Dong Z, Chen X. Status and Trends of the Association Between Diabetic Nephropathy and Diabetic Retinopathy From 2000 to 2021: Bibliometric and Visual Analysis. Front Pharmacol 2022; 13:937759. [PMID: 35795563 PMCID: PMC9251414 DOI: 10.3389/fphar.2022.937759] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 06/06/2022] [Indexed: 12/14/2022] Open
Abstract
Background: Diabetic nephropathy (DN) and diabetic retinopathy (DR) are microvascular complications of diabetes that share a similar pathogenesis and clinical relevance. The study aimed to visually analyze the research status and development trend of the relationship between DN and DR by means of bibliometrics and knowledge mapping. Methods: Publications were collected from the Science Citation Index-Expanded of the Web of Science Core Collection between 2000 and 2021. CiteSpace, Alluvial Generator, and Microsoft Excel were used to analyze and present the data. Results: A total of 3,348 publications were retrieved and 3,285 were included in the analysis after deduplication. The publications demonstrated an annually increasing trend. The results of the collaborative network analysis showed that the United States, Steno Diabetes Center, and Tien Y. Wong were the most influential country, institution and author, in this field of research, respectively. The analysis of references and keywords showed that the pathogenesis of DN and DR and their relationship with cardiovascular disease are research hotspots. The clinical relevance and drug therapy for DN and DR will become frontiers of future research in this field. Conclusion: This study is the first to visualize the correlation between DN and DR using a bibliometric approach. This study provides a reference of research trends for scholars.
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Affiliation(s)
- Wenwen Lin
- School of Clinical Medicine, Guangdong Pharmaceutical University, Guangzhou, China
- National Clinical Research Center for Kidney Diseases, State Key Laboratory of Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People’s Liberation Army, Beijing, China
| | - Yayong Luo
- School of Clinical Medicine, Guangdong Pharmaceutical University, Guangzhou, China
- National Clinical Research Center for Kidney Diseases, State Key Laboratory of Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People’s Liberation Army, Beijing, China
| | - Fang Liu
- School of Clinical Medicine, Guangdong Pharmaceutical University, Guangzhou, China
- National Clinical Research Center for Kidney Diseases, State Key Laboratory of Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People’s Liberation Army, Beijing, China
| | - Hangtian Li
- School of Clinical Medicine, Guangdong Pharmaceutical University, Guangzhou, China
- National Clinical Research Center for Kidney Diseases, State Key Laboratory of Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People’s Liberation Army, Beijing, China
| | - Qian Wang
- National Clinical Research Center for Kidney Diseases, State Key Laboratory of Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People’s Liberation Army, Beijing, China
| | - Zheyi Dong
- National Clinical Research Center for Kidney Diseases, State Key Laboratory of Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People’s Liberation Army, Beijing, China
- *Correspondence: Zheyi Dong, ; Xiangmei Chen,
| | - Xiangmei Chen
- School of Clinical Medicine, Guangdong Pharmaceutical University, Guangzhou, China
- National Clinical Research Center for Kidney Diseases, State Key Laboratory of Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People’s Liberation Army, Beijing, China
- *Correspondence: Zheyi Dong, ; Xiangmei Chen,
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Betzler BK, Sabanayagam C, Tham YC, Cheung CY, Cheng CY, Wong TY, Nusinovici S. Retinal Vascular Profile in Predicting Incident Cardiometabolic Diseases among Individuals with Diabetes. Microcirculation 2022; 29:e12772. [PMID: 35652745 DOI: 10.1111/micc.12772] [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: 12/02/2021] [Revised: 04/12/2022] [Accepted: 05/25/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVE To determine the longitudinal associations between retinal vascular profile (RVP) and four major cardiometabolic diseases; and to quantify the predictive improvements when adding RVP beyond traditional risk factors in individuals with diabetes. METHODS Subjects were enrolled from the Singapore Epidemiology of Eye Disease (SEED) study, a multi-ethnic population-based cohort. Four incident cardiometabolic diseases, calculated over a ~6-year period, were considered: cardiovascular disease (CVD), hypertension (HTN), diabetic kidney disease (DKD) and hyperlipidaemia (HLD). The RVP - vessel tortuosity, branching angle, branching coefficient, fractal dimension, vessel calibre, and DR status - was characterized at baseline using a computer-assisted program. Traditional risk factors at baseline included age, gender, ethnicity, smoking, blood pressure (BP), HbA1c, estimated glomerular filtration rate (eGFR) or cholesterol. The improvements in predictive performance when adding RVP (compared to only traditional risk factors) was calculated using several metrics including area under the receiver operating characteristics curve (AUC) and Net Reclassification Improvement (NRI). RESULTS Among 1,770 individuals with diabetes, incidences were 6.3% (n=79/1259) for CVD, 48.7% (n=166/341) for HTN, 14.6% (n=175/1199) for DKD, and 59.4% (n=336/566) for HLD. DR preceded the onset of CVD (RR 1.85[1.14;3.00]) and DKD (1.44 [1.06;1.96]). Narrower arteriolar calibre preceding the onset of HTN (0.84 [0.72;0.99]), and changes in arteriolar branching angle preceded the onset of CVD (0.78 [0.62;0.98]) and HTN (1.15 [1.03;1.29]). The largest predictive improvement was found for HTN with AUC increment of 3.4% (p=0.027) and better reclassification of 11.4% of the cases and 4.6% of the controls (p=0.008). CONCLUSION We found that RVPs improved the prediction of HTN in individuals with diabetes, but add limited information for CVD, DKD and HLD predictions.
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Affiliation(s)
- Bjorn Kaijun Betzler
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.,Ophthalmology and Visual Sciences Academic Clinical Programme, Duke-NUS Medical School, National University of Singapore, Singapore
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.,Ophthalmology and Visual Sciences Academic Clinical Programme, Duke-NUS Medical School, National University of Singapore, Singapore
| | - Carol Y Cheung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.,Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.,Ophthalmology and Visual Sciences Academic Clinical Programme, Duke-NUS Medical School, National University of Singapore, Singapore.,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Tien-Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.,Ophthalmology and Visual Sciences Academic Clinical Programme, Duke-NUS Medical School, National University of Singapore, Singapore.,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Simon Nusinovici
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.,Ophthalmology and Visual Sciences Academic Clinical Programme, Duke-NUS Medical School, National University of Singapore, Singapore
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Theuerle JD, Al-Fiadh AH, Wong E, Patel SK, Ashraf G, Nguyen T, Wong TY, Ierino FL, Burrell LM, Farouque O. Retinal microvascular function predicts chronic kidney disease in patients with cardiovascular risk factors. Atherosclerosis 2021; 341:63-70. [PMID: 34756728 DOI: 10.1016/j.atherosclerosis.2021.10.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 09/27/2021] [Accepted: 10/13/2021] [Indexed: 11/26/2022]
Abstract
BACKGROUND AND AIMS Endothelial dysfunction is a precursor to atherosclerosis and is implicated in the coexistence between cardiovascular disease (CVD) and chronic kidney disease (CKD). We examined whether retinal microvascular dysfunction is present in subjects with renal impairment and predictive of long-term CKD progression in patients with CVD. METHODS In a single centre prospective observational study, 253 subjects with coronary artery disease and CVD risk factors underwent dynamic retinal vessel analysis. Retinal microvascular dysfunction was quantified by measuring retinal arteriolar and venular dilatation in response to flicker light stimulation. Serial renal function assessment was performed over a median period of 9.3 years using estimated GFR (eGFR). RESULTS Flicker light-induced retinal arteriolar dilatation (FI-RAD) was attenuated in patients with baseline eGFR <90 mL/min/1.73 m2, compared to those with normal renal function (eGFR ≥90 mL/min/1.73 m2) (1.0 [0.4-2.1]% vs. 2.0 [0.8-3.6]%; p < 0.01). In patients with normal renal function, subjects with the lowest FI-RAD responses exhibited the greatest annual decline in eGFR. In uni- and multivariable analysis, among subjects with normal renal function, a 1% decrease in FI-RAD was associated with an accelerated decline in eGFR of 0.10 (0.01, 0.15; p = 0.03) and 0.07 mL/min/1.73 m2 per year (0.00, 0.14; p = 0.06), respectively. FI-RAD was not predictive of CKD progression in subjects with baseline eGFR <90 mL/min/1.73 m2. CONCLUSIONS Retinal arteriolar endothelial dysfunction is present in patients with CVD who have early-stage CKD, and serves as an indicator of long-term CKD progression in those with normal renal function.
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Affiliation(s)
- James D Theuerle
- Department of Cardiology, Austin Health, Melbourne, Australia; Department of Medicine, Austin Health, The University of Melbourne, Melbourne, Australia
| | - Ali H Al-Fiadh
- Department of Cardiology, Austin Health, Melbourne, Australia; Department of Medicine, Austin Health, The University of Melbourne, Melbourne, Australia
| | - Edmond Wong
- Department of Cardiology, Austin Health, Melbourne, Australia
| | - Sheila K Patel
- Department of Medicine, Austin Health, The University of Melbourne, Melbourne, Australia
| | - Gizem Ashraf
- Department of Cardiology, Austin Health, Melbourne, Australia
| | - Thanh Nguyen
- The Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, Australia; Ophthalmology, Department of Surgery, The University of Melbourne, Melbourne, Australia
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Duke-NUS Medical School, National University of Singapore, Singapore
| | | | - Louise M Burrell
- Department of Cardiology, Austin Health, Melbourne, Australia; Department of Medicine, Austin Health, The University of Melbourne, Melbourne, Australia
| | - Omar Farouque
- Department of Cardiology, Austin Health, Melbourne, Australia; Department of Medicine, Austin Health, The University of Melbourne, Melbourne, Australia.
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