Relationship between Brachial-Ankle Pulse Wave Velocity and Fundus Arteriolar Area Calculated Using a Deep-Learning Algorithm

Purpose Retinal vessels reflect alterations related to hypertension and arteriosclerosis in the physical status. Previously, we had reported a deep-learning algorithm for automatically detecting retinal vessels and measuring the total retinal vascular area in fundus photographs (VAFP). Herein, we investigated the relationship between VAFP and brachial-ankle pulse wave velocity (baPWV), which is the gold standard for arterial stiffness assessment in clinical practice. Methods Retinal photographs (n = 696) obtained from 372 individuals who visited the Keijinkai Maruyama Clinic for regular health checkups were used to analyze VAFP. Additionally, the baPWV was measured for each patient. Automatic retinal-vessel segmentation was performed using our deep-learning algorithm, and the total arteriolar area (AA) and total venular area (VA) were measured. Correlations between baPWV and several parameters, including AA and VA, were assessed. Results The baPWV was negatively correlated with AA (R = -0.40, n = 696, P < 2.2e-16) and VA (R = -0.36, n = 696, P < 2.2e-16). Independent variables (AA, sex, age, and systolic blood pressure) selected using the stepwise method showed a significant correlation with baPWV. The estimated baPWV, calculated using a regression equation with variables including AA, showed a better correlation with the measured baPWV (R = 0.70, n = 696, P < 2.2e-16) than the estimated value without AA (R = 0.68, n = 696, P < 2.2e-16). Conclusions AA and VA were significantly correlated with baPWV. Moreover, baPWV estimated using AA correlated well with the actual baPWV. VAFP may serve as an alternative biomarker for evaluating systemic arterial stiffness

Coexistence of Metabolic Dysfunction‐Associated Fatty Liver Disease and Chronic Kidney Disease Is a More Potent Risk Factor for Ischemic Heart Disease

Background Metabolic dysfunction–associated fatty liver disease (MAFLD), defined as fatty liver with overweight/obesity, type 2 diabetes, or metabolic abnormalities, is a newly proposed disease. However, it remains unclear whether the coexistence of MAFLD and chronic kidney disease (CKD) is a more potent risk factor for ischemic heart disease (IHD). Methods and Results We investigated the risk of the combination of MAFLD and CKD for development of IHD during a 10‐year follow‐up period in 28 990 Japanese subjects who received annual health examinations. After exclusion of subjects without data for abdominal ultrasonography or with the presence of IHD at baseline, a total of 14 141 subjects (men/women: 9195/4946; mean age, 48 years) were recruited. During the 10‐year period (mean, 6.9 years), 479 subjects (men/women, 397/82) had new onset of IHD. Kaplan–Meier survival curves showed significant differences in rates of the cumulative incidence of IHD in subjects with and those without MAFLD (n=4581) and CKD (n=990; stages 1/2/3/4–5, 198/398/375/19). Multivariable Cox proportional hazard model analyses showed that coexistence of MAFLD and CKD, but not MAFLD or CKD alone, was an independent predictor for development of IHD after adjustment for age, sex, current smoking habit, family history of IHD, overweight/obesity, diabetes, hypertension, and dyslipidemia (hazard ratio, 1.51 [95% CI, 1.02–2.22]). The addition of the combination of MAFLD and CKD to traditional risk factors for IHD significantly improved the discriminatory capability. Conclusions The coexistence of MAFLD and CKD predicts new onset of IHD better than does MAFLD or CKD alone

High level of fatty liver index predicts new onset of diabetes mellitus during a 10-year period in healthy subjects

Abstract Fatty liver index (FLI), a predictor of nonalcoholic fatty liver disease, has been reported to be associated with several metabolic disorders. This study aimed to evaluate the relationship between FLI and new onset of diabetes mellitus (DM). We investigated the association of FLI with new onset of DM during a 10-year period in subjects who received annual health examinations (n = 28,990). After exclusion of subjects with DM at baseline and those with missing data, a total of 12,290 subjects (male/female: 7925/4365) who received health examinations were recruited. FLI was significantly higher in males than in females. During the 10-year period, DM was developed in 533 males (6.7%) and 128 females (2.9%). Multivariable Cox proportional hazard models with a restricted cubic spline showed that the risk of new onset of DM increased with a higher FLI at baseline in both sexes after adjustment of age, fasting plasma glucose, habits of alcohol drinking and current smoking, family history of DM and diagnosis of hypertension and dyslipidemia at baseline. When the subjects were divided into subgroups according to tertiles of FLI level at baseline (T1–T3) in the absence and presence of impaired fasting glucose (IFG), hazard ratios after adjustment of the confounders gradually increased from T1 to T3 and from the absence to presence of IFG in both male and female subjects. In conclusion, a high level of FLI predicts new onset of DM in a general population of both male and female individuals

Relationship between Brachial-Ankle Pulse Wave Velocity and Fundus Arteriolar Area Calculated Using a Deep-Learning Algorithm

Purpose Retinal vessels reflect alterations related to hypertension and arteriosclerosis in the physical status. Previously, we had reported a deep-learning algorithm for automatically detecting retinal vessels and measuring the total retinal vascular area in fundus photographs (VAFP). Herein, we investigated the relationship between VAFP and brachial-ankle pulse wave velocity (baPWV), which is the gold standard for arterial stiffness assessment in clinical practice. Methods Retinal photographs (n = 696) obtained from 372 individuals who visited the Keijinkai Maruyama Clinic for regular health checkups were used to analyze VAFP. Additionally, the baPWV was measured for each patient. Automatic retinal-vessel segmentation was performed using our deep-learning algorithm, and the total arteriolar area (AA) and total venular area (VA) were measured. Correlations between baPWV and several parameters, including AA and VA, were assessed. Results The baPWV was negatively correlated with AA (R = -0.40, n = 696, P &lt; 2.2e-16) and VA (R = -0.36, n = 696, P &lt; 2.2e-16). Independent variables (AA, sex, age, and systolic blood pressure) selected using the stepwise method showed a significant correlation with baPWV. The estimated baPWV, calculated using a regression equation with variables including AA, showed a better correlation with the measured baPWV (R = 0.70, n = 696, P &lt; 2.2e-16) than the estimated value without AA (R = 0.68, n = 696, P &lt; 2.2e-16). Conclusions AA and VA were significantly correlated with baPWV. Moreover, baPWV estimated using AA correlated well with the actual baPWV. VAFP may serve as an alternative biomarker for evaluating systemic arterial stiffness

A Deep Learning Architecture for Vascular Area Measurement in Fundus Images

Purpose: To develop a novel evaluation system for retinal vessel alterations caused by hypertension using a deep learning algorithm. Design: Retrospective study. Participants: Fundus photographs (n = 10 571) of health-check participants (n = 5598). Methods: The participants were analyzed using a fully automatic architecture assisted by a deep learning system, and the total area of retinal arterioles and venules was assessed separately. The retinal vessels were extracted automatically from each photograph and categorized as arterioles or venules. Subsequently, the total arteriolar area (AA) and total venular area (VA) were measured. The correlations among AA, VA, age, systolic blood pressure (SBP), and diastolic blood pressure were analyzed. Six ophthalmologists manually evaluated the arteriovenous ratio (AVR) in fundus images (n = 102), and the correlation between the SBP and AVR was evaluated manually. Main Outcome Measures: Total arteriolar area and VA. Results: The deep learning algorithm demonstrated favorable properties of vessel segmentation and arteriovenous classification, comparable with pre-existing techniques. Using the algorithm, a significant positive correlation was found between AA and VA. Both AA and VA demonstrated negative correlations with age and blood pressure. Furthermore, the SBP showed a higher negative correlation with AA measured by the algorithm than with AVR. Conclusions: The current data demonstrated that the retinal vascular area measured with the deep learning system could be a novel index of hypertension-related vascular changes