Retinal image measurements and their association with chronic kidney disease in Chinese patients with type 2 diabetes: the NCD study

Xu, Xiayu; Gao, Bin; Ding, Wenxiang; Wang, Qiong; Zhang, Maiye; Tan, Tao; Sun, Fei; Lei, Jianqin; Ji, Qiuhe; Xu, Feng

doi:10.1007/s00592-020-01621-6

Cited by 11 publications

(9 citation statements)

References 28 publications

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“…with DKD and DR. As we know, DKD and DR are both microvascular complications of T2DM [41]. A close relationship between them has been reported in epidemiologic studies [4] since they share similar structural and physiological changes [6]. However, similar to some previous studies, our study showed that obesity and higher FCP were associated with an increased risk of DKD but tended to decrease the risk of DR [20,24,25].…”

Section: Comparison Of the Paradoxical Correlations Of Obesitysupporting

confidence: 84%

“…Diabetic microvascular complications, including diabetic kidney disease (DKD) and diabetic retinopathy (DR), are severely associated with reduced quality of life and increased mortality [ 3 ]. A close relationship between DKD and DR has been presented in epidemiologic studies [ 4 ] since they have similar structural and physiological changes [ 5 , 6 ]. However, in the real world, the presentations of DR and DKD are not always consistent, suggesting differences in their pathogenesis [ 4 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

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Association of Obesity Indices with Diabetic Kidney Disease and Diabetic Retinopathy in Type 2 Diabetes: A Real-World Study

Shi

Tian

et al. 2023

Journal of Diabetes Research

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Background. Diabetic microvascular complications mainly include diabetic kidney disease (DKD) and diabetic retinopathy (DR). Obesity was recognized as a risk factor for DKD, while the reported relationship between obesity and DR was inconsistent. Moreover, whether the associations can be attributed to C-peptide levels is unknown. Methods. Data from 1142 sequential inpatients with T2DM at Xiangyang Central Hospital between June 2019 and March 2022 were extracted retrospectively from the electronic medical record system. The associations between four obesity indices (body mass index (BMI), waist-hip circumference ratio (WHR), visceral fat tissue area (VFA), and subcutaneous fat tissue area (SFA)) and DKD and DR were evaluated. Whether the associations can be attributed to C-peptide levels was also explored. Results. Obesity was a risk factor for DKD after adjusting for sex, HbA1c, TG, TC, HDL, LDL, smoking history, education, duration of diabetes, and insulin use (obesity indices: BMI (OR 1.050: 95% CI: 1.008-1.094; P = 0.020 ); WHR (OR 10.97; 95% CI: 1.250-92.267; P = 0.031 ); VFA (OR 1.005; 95% CI: 1.001-1.008; P = 0.008 )), but it became insignificant after further adjusting for fasting C-peptide. The associations between BMI, WHR, VFA, and DKD might be U-shaped. Obesity and FCP tended to protect against DR; however, they became insignificant after adjusting for multiple potential confounders. C2/C0 (the ratio of the postprandial serum C-peptide to fasting C-peptide) was a protective factor for both DKD (OR 0.894, 95% CI: 0.833-0.959, P < 0.05 ) and DR (OR 0.851, 95% CI: 0.787-0.919; P < 0.05 ). Conclusions. Obesity was a risk factor for DKD, and the effect may be attributable to C-peptide, which represents insulin resistance. The protective effect of obesity or C-peptide on DR was not independent and could be confounded by multiple factors. Higher C2/C0 was associated with both decreased DKD and DR.

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Section: Comparison Of the Paradoxical Correlations Of Obesitysupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Association of Obesity Indices with Diabetic Kidney Disease and Diabetic Retinopathy in Type 2 Diabetes: A Real-World Study

Shi

Tian

et al. 2023

Journal of Diabetes Research

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“…Whereas regression and survival curve studies (nondeep learning studies) require identification of the biomarker a priori , deep learning studies allow a broader approach in which a convolutional neural network identifies biomarkers, but without specific identification beyond regions highlighted on saliency maps. Some deep learning studies have focused on cardiovascular risk [8 ▪▪ ,29–32], kidney disease [33,34 ▪ ,35] or neurovascular or neurodegenerative disease [5,6,28]. Others took a broader approach to look at biomarkers for variables including hypertension, diabetes mellitus, dyslipidaemia, body weight, muscle mass, creatinine and haematocrit [14,36–39].…”

Section: Types Of Systemic Retinal Biomarker Studiesmentioning

confidence: 99%

Systemic retinal biomarkers

Ranchod¹

2021

Current Opinion in Ophthalmology

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Purpose of review Systemic retinal biomarkers are biomarkers identified in the retina and related to evaluation and management of systemic disease. This review summarizes the background, categories and key findings from this body of research as well as potential applications to clinical care. Recent findingsPotential systemic retinal biomarkers for cardiovascular disease, kidney disease and neurodegenerative disease were identified using regression analysis as well as more sophisticated image processing techniques. Deep learning techniques were used in a number of studies predicting diseases including anaemia and chronic kidney disease. A virtual coronary artery calcium score performed well against other competing traditional models of event prediction. SummarySystemic retinal biomarker research has progressed rapidly using regression studies with clearly identified biomarkers such as retinal microvascular patterns, as well as using deep learning models. Future systemic retinal biomarker research may be able to boost performance using larger data sets, the addition of metadata and higher resolution image inputs.

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“…Changes in retinal vascular calibers reflect subclinical pathophysiologic responses to hyperglycemia, hypertension, inflammation, hypoxia, and endothelial dysfunction 10 . Consequently, retinal vascular calibers have been shown to be associated with several cardiometabolic diseases such as HTN, 11,12 CVD 13 and diabetic kidney disease (DKD) 14,15 . While newer quantitative retinal vascular geometric parameters (e.g., bifurcation and tortuosity of retinal vessels) have been developed, most existing studies investigate cross‐sectional associations, 16–19 resulting in a lack of information on the added value of RVPs to predict risk for future cardiometabolic disease.…”

Section: Introductionmentioning

confidence: 99%

“…10 Consequently, retinal vascular calibers have been shown to be associated with several cardiometabolic diseases such as HTN, 11,12 CVD 13 and diabetic kidney disease (DKD). 14,15 While newer quantitative retinal vascular geometric parameters (e.g., bifurcation and tortuosity of retinal vessels) have been developed, most existing studies investigate cross-sectional associations, [16][17][18][19] resulting in a lack of information on the added value of RVPs to predict risk for future cardiometabolic disease. Quantifying this added value is crucial to objectively assess the potential utility of RVPs in predicting incident cardiometabolic diseases over traditional risk factors.…”

Section: Introductionmentioning

confidence: 99%

Retinal vascular profile in predicting incident cardiometabolic diseases among individuals with diabetes

et al. 2022

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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 hyperlipidemia (HLD). The RVP—vessel tortuosity, branching angle, branching coefficient, fractal dimension, vessel caliber, 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 with 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 1770 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 caliber 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 = .027) and better reclassification of 11.4% of the cases and 4.6% of the controls (p = .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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Retinal image measurements and their association with chronic kidney disease in Chinese patients with type 2 diabetes: the NCD study

Cited by 11 publications

References 28 publications

Association of Obesity Indices with Diabetic Kidney Disease and Diabetic Retinopathy in Type 2 Diabetes: A Real-World Study

Association of Obesity Indices with Diabetic Kidney Disease and Diabetic Retinopathy in Type 2 Diabetes: A Real-World Study

Systemic retinal biomarkers

Retinal vascular profile in predicting incident cardiometabolic diseases among individuals with diabetes

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