The causal relationship between 5 serum lipid parameters and diabetic nephropathy: a Mendelian randomization study.

Background: Serum lipids were found to be correlated with chronic kidney disease and cardiovascular disease. Here, we aimed to research the potential causal associations between five serum lipid parameters and the risk of diabetic nephropathy using several Mendelian Randomization methods. Methods: Genetic data was obtained from the UK Biobank datasets. Causal effects were estimated using multiple MR methods. Heterogeneity and pleiotropy tests were performed. Results: MR analysis revealed that HDL-C and TG exhibited causal associations with diabetic nephropathy (P<0.05). Similar trends were not observed for other lipid parameters. Conclusions: Our research has suggested links between HDL-C, TG and diabetic nephropathy. The findings could contribute to further elucidation of the disease etiology. Strengths and limitations of this study: This article only uses Mendel randomization method to analyze the relationship between blood lipids and diabetes nephropathy, which is more convincing when combined with population data.

Using deep learning method to identify left ventricular hypertrophy on echocardiography.

BACKGROUND: Left ventricular hypertrophy (LVH) is an independent prognostic factor for cardiovascular events and it can be detected by echocardiography in the early stage. In this study, we aim to develop a semi-automatic diagnostic network based on deep learning algorithms to detect LVH. METHODS: We retrospectively collected 1610 transthoracic echocardiograms, included 724 patients [189 hypertensive heart disease (HHD), 218 hypertrophic cardiomyopathy (HCM), and 58 cardiac amyloidosis (CA), along with 259 controls]. The diagnosis of LVH was defined by two experienced clinicians. For the deep learning architecture, we introduced ResNet and U-net++ to complete classification and segmentation tasks respectively. The models were trained and validated independently. Then, we connected the best-performing models to form the final framework and tested its capabilities. RESULTS: In terms of individual networks, the view classification model produced AUC = 1.0. The AUC of the LVH detection model was 0.98 (95% CI 0.94-0.99), with corresponding sensitivity and specificity of 94.0% (95% CI 85.3-98.7%) and 91.6% (95% CI 84.6-96.1%) respectively. For etiology identification, the independent model yielded good results with AUC = 0.90 (95% CI 0.82-0.95) for HCM, AUC = 0.94 (95% CI 0.88-0.98) for CA, and AUC = 0.88 (95% CI 0.80-0.93) for HHD. Finally, our final integrated framework automatically classified four conditions (Normal, HCM, CA, and HHD), which achieved an average of AUC 0.91, with an average sensitivity and specificity of 83.7% and 90.0%. CONCLUSION: Deep learning architecture has the ability to detect LVH and even distinguish the latent etiology of LVH.