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Background: Currently in the United Kingdom, cardiovascular disease (CVD) risk assessment is based on the QRISK3 score, in which 10% 10-year CVD risk indicates clinical intervention. However, this benchmark has limited efficacy in clinical practice and the need for a more simple, non-invasive risk stratification tool is necessary. Retinal photography is becoming increasingly acceptable as a non-invasive imaging tool for CVD. Previously, we developed a novel CVD risk stratification system based on retinal photographs predicting future CVD risk. This study aims to further validate our biomarker, Reti-CVD, (1) to detect risk group of ≥ 10% in 10-year CVD risk and (2) enhance risk assessment in individuals with QRISK3 of 7.5-10% (termed as borderline-QRISK3 group) using the UK Biobank., Methods: Reti-CVD scores were calculated and stratified into three risk groups based on optimized cut-off values from the UK Biobank. We used Cox proportional-hazards models to evaluate the ability of Reti-CVD to predict CVD events in the general population. C-statistics was used to assess the prognostic value of adding Reti-CVD to QRISK3 in borderline-QRISK3 group and three vulnerable subgroups., Results: Among 48,260 participants with no history of CVD, 6.3% had CVD events during the 11-year follow-up. Reti-CVD was associated with an increased risk of CVD (adjusted hazard ratio [HR] 1.41; 95% confidence interval [CI], 1.30-1.52) with a 13.1% (95% CI, 11.7-14.6%) 10-year CVD risk in Reti-CVD-high-risk group. The 10-year CVD risk of the borderline-QRISK3 group was greater than 10% in Reti-CVD-high-risk group (11.5% in non-statin cohort [n = 45,473], 11.5% in stage 1 hypertension cohort [n = 11,966], and 14.2% in middle-aged cohort [n = 38,941]). C statistics increased by 0.014 (0.010-0.017) in non-statin cohort, 0.013 (0.007-0.019) in stage 1 hypertension cohort, and 0.023 (0.018-0.029) in middle-aged cohort for CVD event prediction after adding Reti-CVD to QRISK3., Conclusions: Reti-CVD has the potential to identify individuals with ≥ 10% 10-year CVD risk who are likely to benefit from earlier preventative CVD interventions. For borderline-QRISK3 individuals with 10-year CVD risk between 7.5 and 10%, Reti-CVD could be used as a risk enhancer tool to help improve discernment accuracy, especially in adult groups that may be pre-disposed to CVD., (© 2023. The Author(s).)

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., (© 2022 John Wiley & Sons Ltd.)

Objective: To evaluate the performance of machine learning (ML) algorithms and to compare them with logistic regression for the prediction of risk of cardiovascular diseases (CVDs), chronic kidney disease (CKD), diabetes (DM), and hypertension (HTN) and in a prospective cohort study using simple clinical predictors., Study Design and Setting: We conducted analyses in a population-based cohort study in Asian adults (n = 6,762). Five different ML models were considered-single-hidden-layer neural network, support vector machine, random forest, gradient boosting machine, and k-nearest neighbor-and were compared with standard logistic regression., Results: The incidences at 6 years of CVD, CKD, DM, and HTN cases were 4.0%, 7.0%, 9.2%, and 34.6%, respectively. Logistic regression reached the highest area under the receiver operating characteristic curve for CKD (0.905 [0.88, 0.93]) and DM (0.768 [0.73, 0.81]) predictions. For CVD and HTN, the best models were neural network (0.753 [0.70, 0.81]) and support vector machine (0.780 [0.747, 0.812]), respectively. However, the differences with logistic regression were small (less than 1%) and nonsignificant. Logistic regression, gradient boosting machine, and neural network were systematically ranked among the best models., Conclusion: Logistic regression yields as good performance as ML models to predict the risk of major chronic diseases with low incidence and simple clinical predictors., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Acute aortic syndromes (AAS) have been related to significant circadian and seasonal conditions. We used time series analyses to study the impact of meteorological variations on AAS occurrence. We retrospectively assessed 140 patients presenting with AAS over a 6-year period in a French university hospital. Average daily temperature (T) and atmospheric pressure (AP) at the location of the event were collected within the previous 10 days, and their association with AAS investigated with generalized additive models. A decrease in temperature of more than 5 °C within the previous seven days was significantly associated with an increased risk of AAS occurrence (OR equal to 1.86 [1.06; 3.44]). Subgroup analysis revealed that the risk was only significant among normotensive individuals (n = 41) free from blood pressure lowering medication (OR equal to 2.3 [1.05; 5.37]), but not among hypertensive individuals under blood pressure lowering medication despite a larger patient number (n = 99). Similarly, only among the subgroup of normotensive individuals a decrease of AP between 2 and 4 kPa within the previous 3 days was associated with an increased risk of AAS (OR equal to 2.93 [1.1; 8.15]) and an increased between 2 and 4 kPa was associated with a decreased risk (OR equal to 0.59 [0.36; 1.00]). Variations of meteorological conditions (temperature and AP) within the previous week seem to have effects on triggering AAS especially among the population free from blood pressure lowering medication.

Objectives: The few studies examining the risk of circulatory system diseases (CSD) associated with ionizing radiation have reported inconsistent results. Radon, a known pulmonary carcinogen, emits ionizing radiation. The aim of this study was to examine CSD mortality in a French cohort of uranium miners and evaluate the plausibility of an association with radon exposure., Methods: The cohort included men employed as uranium miners for >/=1 year between 1946-1990. We obtained vital status and cause of death from national registers and reconstructed radon exposure for each year. Exposure--risk relations were estimated with a linear excess relative risk (ERR) model using a 5-year lag time., Results: The cohort comprised 5086 miners, followed up for a mean duration of 30.1 years. The average cumulative exposure of the radon-exposed miners was 36.6 working level months (WLM). A total of 1411 deaths were observed, including 319 deaths due to CSD. No excess risk was found for this overall cause of death. A significant positive trend was observed between deaths from cerebrovascular diseases (CeVD) and cumulative radon exposure, together with a significant ERR per 100 WLM [ERR per 100 WLM 0.49, 95% confidence interval (95% CI) 0.07-1.23)]. Hard physical activity was identified as a potential modifying factor of the exposure-risk relation., Conclusions: For the first time in a cohort of uranium miners, our results suggest an association between CeVD mortality and cumulative radon exposure. Due to a lack of data, which limited our ability to assess possible confounding by cardiovascular risk factors, these findings should be interpreted with caution.

Background ageing is an important risk factor for a variety of human pathologies. Biological age (BA) may better capture ageing-related physiological changes compared with chronological age (CA). Objective we developed a deep learning (DL) algorithm to predict BA based on retinal photographs and evaluated the performance of our new ageing marker in the risk stratification of mortality and major morbidity in general populations. Methods we first trained a DL algorithm using 129,236 retinal photographs from 40,480 participants in the Korean Health Screening study to predict the probability of age being ≥65 years ('RetiAGE') and then evaluated the ability of RetiAGE to stratify the risk of mortality and major morbidity among 56,301 participants in the UK Biobank. Cox proportional hazards model was used to estimate the hazard ratios (HRs). Results in the UK Biobank, over a 10-year follow up, 2,236 (4.0%) died; of them, 636 (28.4%) were due to cardiovascular diseases (CVDs) and 1,276 (57.1%) due to cancers. Compared with the participants in the RetiAGE first quartile, those in the RetiAGE fourth quartile had a 67% higher risk of 10-year all-cause mortality (HR = 1.67 [1.42–1.95]), a 142% higher risk of CVD mortality (HR = 2.42 [1.69–3.48]) and a 60% higher risk of cancer mortality (HR = 1.60 [1.31–1.96]), independent of CA and established ageing phenotypic biomarkers. Likewise, compared with the first quartile group, the risk of CVD and cancer events in the fourth quartile group increased by 39% (HR = 1.39 [1.14–1.69]) and 18% (HR = 1.18 [1.10–1.26]), respectively. The best discrimination ability for RetiAGE alone was found for CVD mortality (c-index = 0.70, sensitivity = 0.76, specificity = 0.55). Furthermore, adding RetiAGE increased the discrimination ability of the model beyond CA and phenotypic biomarkers (increment in c-index between 1 and 2%). Conclusions the DL-derived RetiAGE provides a novel, alternative approach to measure ageing. [ABSTRACT FROM AUTHOR]