These findings demonstrate epidemiologic evidence for an independent association between arterial stiffness and microalbuminuria, indices of subclinical target organ damage in nonhypertensive, nondiabetic individuals, which suggests the possibility of a similar pathophysiologic mechanism involved in these two indices of subclinical target organ damage.

The aim of our study was to assess the relationship between serum gamma-glutamyl transferase (GGT) level within the normal range and incident hypertension according to drinking and obesity status in nonhypertensive individuals. We followed up 4783 normotensive adults (mean age = 44 years) who had serum GGT levels within the normal range at baseline for 3 years. Subjects were divided into four GGT quartile groups according to their serum GGT level at baseline. The overall incidence of hypertension was 8.1%, and the incidence increased with increasing GGT quartile (3.8%, 6.9%, 9.0%, and 12.4% in the lowest, second, third, and highest GGT quartiles, respectively; P < .001). In the logistic regression analysis adjusted for age, sex, body mass index, lifestyle factors, glucose, uric acid, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglyceride, high-sensitivity C-reactive protein, and baseline systolic blood pressure, the odds ratio (ORs) for incident hypertension increased with increasing GGT quartile (P for trend = .030). In the above model, the highest quartile group showed increased ORs compared with those in the lowest quartile group (ORs [95% confidence interval], 2.638 [1.259-5.528]). Subgroup analyses revealed a significant association between GGT quartile and the incidence of hypertension in the drinker and non-overweight groups. Our results indicate that elevated serum GGT levels within the normal range are associated with a higher risk of incident hypertension in Korean adults, particularly, in drinkers and non-overweight individuals, suggesting possible different pathophysiologic mechanisms in the incidence of alcohol- and obesity-related hypertension.

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Objective Recent studies have suggested that deep-learning models can satisfactorily assist in fracture diagnosis. We aimed to evaluate the performance of two of such models in wrist fracture detection. MethodsWe collected image data of patients who visited with wrist trauma at the emergency department. A dataset extracted from January 2018 to May 2020 was split into training (90%) and test (10%) datasets, and two types of convolutional neural networks (i.e., DenseNet-161 and ResNet-152) were trained to detect wrist fractures. Gradient-weighted class activation mapping was used to highlight the regions of radiograph scans that contributed to the decision of the model. Performance of the convolutional neural network models was evaluated using the area under the receiver operating characteristic curve. ResultsFor model training, we used 4,551 radiographs from 798 patients and 4,443 radiographs from 1,481 patients with and without fractures, respectively. The remaining 10% (300 radiographs from 100 patients with fractures and 690 radiographs from 230 patients without fractures) was used as a test dataset. The sensitivity, specificity, positive predictive value, negative