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.

Fat Distribution Patterns and Future Type 2 Diabetes.

Fat accumulation in the liver, pancreas, skeletal muscle, and visceral bed relates to type 2 diabetes (T2D). However, the distribution of fat among these compartments is heterogenous and whether specific distribution patterns indicate high T2D risk is unclear. We therefore investigated fat distribution patterns and their link to future T2D. From 2,168 individuals without diabetes who underwent computed tomography in Japan, this case-cohort study included 658 randomly selected individuals and 146 incident cases of T2D over 6 years of follow-up. Using data-driven analysis (k-means) based on fat content in the liver, pancreas, muscle, and visceral bed, we identified four fat distribution clusters: hepatic steatosis, pancreatic steatosis, trunk myosteatosis, and steatopenia. In comparisons with the steatopenia cluster, the adjusted hazard ratios for incident T2D were 4.02 (95% CI 2.27-7.12) for the hepatic steatosis cluster, 3.38 (1.65-6.91) for the pancreatic steatosis cluster, and 1.95 (1.07-3.54) for the trunk myosteatosis cluster. The clusters were replicated in 319 German individuals without diabetes who underwent MRI and metabolic phenotyping. The distribution of the glucose area under the curve across the four clusters found in Germany was similar to the distribution of T2D risk across the four clusters in Japan. Insulin sensitivity and insulin secretion differed across the four clusters. Thus, we identified patterns of fat distribution with different T2D risks presumably due to differences in insulin sensitivity and insulin secretion.

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.

Inverse Association Between Fatty Liver at Baseline Ultrasonography and Remission of Type 2 Diabetes Over a 2-Year Follow-up Period.

BACKGROUND & AIMS: Improvement of fatty liver may be required for remission of type-2 diabetes. However, there is no longitudinal evidence on whether fatty liver reduces the chances for remission of type-2 diabetes. We investigated the association between fatty liver and remission of type-2 diabetes (the primary analysis), and also the association between improvement of fatty liver and remission of type-2 diabetes (the secondary analysis). METHODS: We collected data from 66961 people who underwent screening for type-2 diabetes from 2008 through 2016 at a single center in Japan. The primary analysis included 2567 patients with type-2 diabetes without chronic renal failure or a history of hemodialysis who underwent ultrasonography to detect fatty liver, all of whom had follow-up testing, including blood testing, for a median 24.5 months after the baseline ultrasonography. The secondary analysis included 1833 participants with fatty liver at baseline who underwent a second ultrasonography, and participants who had fatty liver at baseline but not at the second visit were considered to have had improvement of fatty liver. Remission of type-2 diabetes was defined as a fasting plasma glucose level below 126 mg/dL and an HbA1c level below 6.5% for more than 6 months without anti-diabetic drugs. Odds ratios (ORs) of remission of type-2 diabetes were estimated using logistic-regression models. RESULTS: A lower proportion of patients who had fatty liver detected by ultrasonography at baseline (8.7%, 167/1910) had remission of type-2 diabetes during the follow-up period than patients without fatty liver (13.1%, 86/657). Fatty liver at baseline was associated with a lower odds of remission of type-2 diabetes (multivariable-adjusted OR, 0.51; 95% CI, 0.37-0.72). A higher proportion of patients who had improvement of fatty liver had remission of type-2 diabetes (21.1%, 32/152) than patients with no improvement of fatty liver (7.7%, 129/1681). Improvement of fatty liver was associated with a higher odds of remission of type-2 diabetes (multivariable-adjusted OR, 3.08; 95% CI, 1.94-4.88). CONCLUSIONS: Over a follow-up period of approximate 2 years, remission of type-2 diabetes was less common in people with fatty liver detected by ultrasonography, and improvement of fatty liver was independently associated with type-2 diabetes remission.

Longitudinal association of fatty pancreas with the incidence of type-2 diabetes in lean individuals: a 6-year computed tomography-based cohort study.

BACKGROUND: Only a few studies have longitudinally evaluated whether fatty pancreas increases the risk of type-2 diabetes (T2D), and their results were inconsistent. Fatty pancreas is closely linked to overweight and obesity, but previous studies did not exclude overweight or obese individuals. Therefore, in this cohort study, we investigated the association between fatty pancreas and T2D incidence in lean individuals. METHODS: Between 2008 and 2013, 1478 nondiabetic lean individuals (i.e. body-mass index < 25 kg/m2) underwent health examinations including computed tomography (CT) and were followed for a median of 6.19 years. Fatty pancreas was evaluated by a histologically-validated method using pancreas attenuation (Hounsfield units [HU]) on CT at baseline; lower pancreas attenuation indicates more pancreatic fat. To detect incident T2D, we used fasting plasma glucose, HbA1c, and self-reports of prescribed anti-diabetes medications. Odds ratios (OR) for the association between pancreas attenuation and incident T2D were estimated using logistic regression models adjusted for likely confounders. RESULTS: T2D occurred in 61 participants (4.13%) during the follow-up period. Lower pancreas attenuation (i.e. more pancreatic fat) at baseline was associated with incident T2D (unadjusted OR per 10 HU lower attenuation: 1.56 [95% CI 1.28-1.91], p < 0.001). The multivariable-adjusted analysis revealed a similar association (adjusted OR per 10 HU lower attenuation: 1.32 [95% CI 1.06-1.63], p = 0.012). CONCLUSIONS: T2D was likely to develop in lean individuals with the fatty pancreas. Among people who are neither obese nor overweight, the fatty pancreas can be used to define a group at high risk for T2D.

Association between pancreatic fat and incidence of metabolic syndrome: a 5-year Japanese cohort study.

BACKGROUND AND AIM: Previous cross-sectional studies showed that pancreatic fat was associated with metabolic syndrome. However, no longitudinal study has evaluated whether people with high pancreatic fat are likely to develop future metabolic syndrome. This study investigated the association between baseline pancreatic fat and metabolic syndrome incidence. METHODS: In 2008-2009, 320 participants without metabolic syndrome underwent health checks, which included unenhanced computed tomography, and were followed up annually for 4-5 years. Baseline pancreatic fat amounts were evaluated using a histologically validated method that measured differences between pancreas and spleen attenuations on computed tomography. The participants were divided into low (reference), intermediate, and high pancreatic fat groups based on pancreas and spleen attenuation tertiles. Metabolic syndrome incidence was evaluated annually over a median follow-up period of 4.99 (interquartile range, 4.88-5.05) years, in accordance with the 2009 harmonized criteria. Risk ratios (RRs) for the association between baseline pancreatic fat amounts and metabolic syndrome incidence were estimated using Poisson regression models adjusted for age, sex, body mass index, liver fat, pre-metabolic syndrome, cigarette use, alcohol use, and physical activity. RESULTS: Metabolic syndrome incidence was 30.6% (98/320). Pancreatic fat was associated with an increased incidence of metabolic syndrome, based on a univariate analysis (RRs [95% confidence interval], 3.14 [1.74-5.67] and 3.96 [2.23-7.03] in the intermediate and high pancreatic fat groups, respectively). The association remained statistically significant in the multivariate analysis (RR [95% confidence interval], 2.04 [1.14-3.64] and 2.30 [1.28-4.14] for the same groups, respectively). CONCLUSIONS: Pancreatic fat predicts the future risk of metabolic syndrome.

Independent association between prediabetes and future pancreatic fat accumulation: a 5-year Japanese cohort study.

BACKGROUND: The association between pancreatic fat and glucose dysmetabolism has been reported in several cross-sectional studies; however, a recent longitudinal study showed that baseline pancreatic fat did not cause subsequent diabetes mellitus. We hypothesized that pancreatic fat is not a cause but a manifestation of glucose dysmetabolism and aimed to investigate the association between baseline prediabetes and future pancreatic fat accumulation. METHODS: Between 2008 and 2015, 198 nondiabetic participants, who underwent a health check-up via unenhanced computed tomography (CT) twice with CT intervals ≥ 5 years, were enrolled as prediabetes (n = 48) and non-prediabetes participants (n = 150). Prediabetes was defined as fasting plasma glucose of 100-125 mg/dl or hemoglobin A1c of 5.7-6.4%. Pancreatic fat was evaluated using a histologically validated method to measure the difference between pancreas and spleen attenuations (P-S) on CT. Pancreatic fat accumulation during follow-up was measured as P-S change from baseline. Multiple linear regression was used to evaluate the association between baseline prediabetes and future pancreatic fat accumulation with adjustment for age, sex, body mass index, physical activity, and liver fat at baseline. RESULTS: Mean pancreatic fat accumulation was 0.30 (SD, 5.8) Hounsfield units during follow-up. On univariate analysis, baseline prediabetes was associated with future pancreatic fat accumulation (ß = 3.73; 95% CI 1.91-5.55; P < 0.001). This association remained statistically significant on multivariate analysis (ß = 3.14; 95% CI 1.25-5.03; P = 0.001). CONCLUSIONS: Prediabetes is a risk factor for future pancreatic fat accumulation. Pancreatic fat may be a manifestation of glucose dysmetabolism.

Lack of Independent Association Between Fatty Pancreas and Incidence of Type 2 Diabetes: 5-Year Japanese Cohort Study.

OBJECTIVE: Previous cross-sectional studies have shown that attenuation in the pancreas seen on unenhanced computed tomography (CT) scans was inversely correlated with histologic pancreatic fat, and that fatty pancreas was associated with type 2 diabetes mellitus (T2DM). However, no longitudinal study has evaluated whether fatty pancreas increases the incidence of T2DM. We conducted a cohort study to investigate the association between fatty pancreas and the incidence of T2DM. RESEARCH DESIGN AND METHODS: A total of 813 participants without diabetes underwent health checks by unenhanced CT scanning in 2008 and 2009, and were observed for a median follow-up period of 5.06 (interquartile range 3.01-5.92) years. Attenuation in three regions of the pancreas seen on an unenhanced CT scan was measured, and the mean pancreatic attenuation was calculated to evaluate fatty pancreas at baseline; the more severe the fatty pancreas, the lower the mean pancreatic attenuation. The incident T2DM hazard ratios (HRs) for the association between fatty pancreas and T2DM incidence were estimated by Cox proportional hazards models adjusted for age, sex, BMI, liver attenuation seen on unenhanced CT scan, and alcohol intake of ≥20 g/day. RESULTS: T2DM occurred in 62 participants (7.6%) during the follow-up period. The higher pancreas attenuation (i.e., less pancreatic fat) at baseline was associated with decreased T2DM incidence in a univariate analysis (crude HR 0.97 [95% CI 0.96-0.99]); and fatty pancreas (lower pancreas attenuation) was positively associated with increased T2DM incidence. However, the association was substantially explained by the confounders (multivariate HR 1.00 [95% CI 0.98-1.02]). CONCLUSIONS: Fatty pancreas was not independently associated with future T2DM.

Independent Association Between Improvement of Nonalcoholic Fatty Liver Disease and Reduced Incidence of Type 2 Diabetes.

OBJECTIVE: Only a few studies have evaluated the long-term effects of nonalcoholic fatty liver disease (NAFLD) on type 2 diabetes mellitus (T2DM), and none have examined whether NAFLD improvement reduces T2DM incidence. We investigated the association between NAFLD improvement and T2DM incidence. RESEARCH DESIGN AND METHODS: Between 2000 and 2012, 4,604 participants who underwent a health check twice with >10 years between were enrolled. Exclusion criteria were positive hepatitis B surface antigen, positive hepatitis C antibody, ethanol intake >20 g/day, and diabetes. The 3,074 eligible participants were divided into an NAFLD group (n = 728) and a non-NAFLD group (n = 2,346) according to ultrasonography-detected fatty liver. The NAFLD group was categorized into an improved group (n = 110) and a sustained NAFLD group (n = 618) based on fatty liver disappearance at the second visit. Incident T2DM odds ratios (ORs) were estimated by logistic regression models adjusted for age, sex, BMI, impaired fasting glucose, family history of diabetes, dyslipidemia, hypertension, and physical exercise. RESULTS: T2DM occurred in 117 participants (16.1%) in the NAFLD group and 72 (3.1%) in the non-NAFLD group. NAFLD at baseline was associated with T2DM incidence (multivariate OR 2.37 [95% CI 1.60-3.52]). T2DM occurred in 7 participants (6.4%) in the improved group and in 110 (17.8%) in the sustained NAFLD group. NAFLD improvement was associated with reduced T2DM incidence (multivariate OR 0.27 [95% CI 0.12-0.61]). CONCLUSIONS: NAFLD improvement is associated with T2DM incidence reduction.