Sang‐Hak Lee

The pathogenic role of T cells in hypertension has been documented well in recent animal studies. However, the existence of T-cell-driven inflammation in human hypertension has not been confirmed. Therefore, we undertook immunologic characterization of T cells from patients with hypertension and measured circulating levels of C-X-C chemokine receptor type 3 chemokines, which are well-known tissue-homing chemokines for T cells. We analyzed immunologic markers on T cells from patients with hypertension by multicolor flow cytometry. We then measured circulating levels of the C-X-C chemokine receptor type 3 chemokines, monokine induced by γ interferon (IFN), IFN γ-induced protein 10, and IFN-inducible T-cell α chemoattractant, in patients with hypertension and in age- and sex-matched control subjects by the cytometric bead array method. In addition, we examined histological features of IFN-inducible T-cell α chemoattractant expression from renal biopsy specimens of patients with hypertensive nephrosclerosis and control subjects. The total T-cell population from patients with hypertension showed an increased fraction of immunosenescent, proinflammatory, cytotoxic CD8(+) T cells. Circulating levels of C-X-C chemokine receptor type 3 chemokines were significantly higher in patients with hypertension than in control subjects. Furthermore, immunohistochemical staining revealed increased expression of the T-cell chemokine, IFN-inducible T-cell α chemoattractant, in the proximal and distal tubules of patients with hypertensive nephrosclerosis. Immunosenescent CD8(+) T cells and C-X-C chemokine receptor type 3 chemokines are increased in human hypertension, suggesting a role for T-cell-driven inflammation in hypertension. A more detailed characterization of CD8(+) T cells may offer new opportunities for the prevention and treatment of human hypertension.

eases of the circulatory system was 187 men per 100,000 population and 145 women per 100,000 population in 1983 and 111 men per 100,000 population and 125 women per 100,000 in 2016, indicating little change over the years. However, the age-adjusted mortality rate, which excludes the influence of aging of the population during this period, decreased to about one-fifth of the initial rate (Fig. Death from CAD has consistently increased since 1983, when the cause of death statistics were first measured, reaching 31 men per 100,000 and 26 women per 100,000 in 2016. However, age-adjusted mortality of CAD reached a peak in the early and mid-2000s and began to decline since then (Fig. Deaths from cerebrovascular disease declined since 2000s, reaching 44 men per 100,000 and 47 women per 100,000 in 2016. Age-adjusted mortality for cerebrovascular disease has declined very quickly (Fig. Among various cerebrovascular diseases, there were more deaths from cerebral hemorrhage (non-traumatic intracerebral hemorrhage and subarachnoid hemorrhage) until 2002, but deaths from cerebral infarction (ischemic stroke) have become more common since then. This is speculated to be due to the marked decline in the incidence and improved treatment outcomes of cerebral hemorrhage as a result 500 400

BACKGROUND: Coronary artery calcium (CAC) score is a clinically validated marker of cardiovascular disease risk. We developed and validated a novel cardiovascular risk stratification system based on deep-learning-predicted CAC from retinal photographs. METHODS: We used 216 152 retinal photographs from five datasets from South Korea, Singapore, and the UK to train and validate the algorithms. First, using one dataset from a South Korean health-screening centre, we trained a deep-learning algorithm to predict the probability of the presence of CAC (ie, deep-learning retinal CAC score, RetiCAC). We stratified RetiCAC scores into tertiles and used Cox proportional hazards models to evaluate the ability of RetiCAC to predict cardiovascular events based on external test sets from South Korea, Singapore, and the UK Biobank. We evaluated the incremental values of RetiCAC when added to the Pooled Cohort Equation (PCE) for participants in the UK Biobank. FINDINGS: RetiCAC outperformed all single clinical parameter models in predicting the presence of CAC (area under the receiver operating characteristic curve of 0·742, 95% CI 0·732-0·753). Among the 527 participants in the South Korean clinical cohort, 33 (6·3%) had cardiovascular events during the 5-year follow-up. When compared with the current CAC risk stratification (0, >0-100, and >100), the three-strata RetiCAC showed comparable prognostic performance with a concordance index of 0·71. In the Singapore population-based cohort (n=8551), 310 (3·6%) participants had fatal cardiovascular events over 10 years, and the three-strata RetiCAC was significantly associated with increased risk of fatal cardiovascular events (hazard ratio [HR] trend 1·33, 95% CI 1·04-1·71). In the UK Biobank (n=47 679), 337 (0·7%) participants had fatal cardiovascular events over 10 years. When added to the PCE, the three-strata RetiCAC improved cardiovascular risk stratification in the intermediate-risk group (HR trend 1·28, 95% CI 1·07-1·54) and borderline-risk group (1·62, 1·04-2·54), and the continuous net reclassification index was 0·261 (95% CI 0·124-0·364). INTERPRETATION: A deep learning and retinal photograph-derived CAC score is comparable to CT scan-measured CAC in predicting cardiovascular events, and improves on current risk stratification approaches for cardiovascular disease events. These data suggest retinal photograph-based deep learning has the potential to be used as an alternative measure of CAC, especially in low-resource settings. FUNDING: Yonsei University College of Medicine; Ministry of Health and Welfare, Korea Institute for Advancement of Technology, South Korea; Agency for Science, Technology, and Research; and National Medical Research Council, Singapore.

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