BackgroundEmerging evidence suggests an association between remnant cholesterol (RC) and vascular damage and hypertension. However, this association has not been explored in a large-scale population in China, and a temporal relationship between RC and hypertension also needs to be investigated.MethodsWe conducted a retrospective cross-sectional study in 2,199,366 individuals and a longitudinal study in 24,252 individuals with repeated measurements of lipid profile and blood pressure in at least a 3-year follow-up. The logistic model was used to explore the association between lipid components and hypertension in the cross-sectional analysis. The Cox model was used to analyze the association between high RC (HRC) at baseline and the subsequent incidence of hypertension or the association between hypertension at baseline and incidence of HRC. The cross-lagged panel model was applied to analyze the temporal relationship between RC and hypertension.ResultsRC level as a continuous variable had the highest correlation with hypertension among lipid profiles, including RC, low-density lipoprotein cholesterol, total cholesterol, non-high-density lipoprotein cholesterol, and triglycerides, with an odds ratio of 1.59 (95% confidence interval: 1.58–1.59). In the longitudinal cohort, HRC at baseline was associated with incident hypertension. We further explored the temporal relationship between RC and hypertension using the cross-lagged analysis, and the results showed that RC increase preceded the development of hypertension, rather than vice versa.ConclusionsRC had an unexpected high correlation with the prevalence and incidence of hypertension. Moreover, RC increase might precede the development of hypertension, suggesting the potential role of RC in the development of hypertension.
Medicine recommendation systems target to recommend a set of medicines given a set of symptoms which play a crucial role in assisting doctors in their daily clinics. Existing approaches are either rule-based or supervised. However, the former heavily relies on expert labeling which is time-consuming and costly to collect, and the latter suffers from the data sparse problem. To automate medicine recommendation on sparse data, we propose MedRec which introduces two graphs in modeling: 1) a knowledge graph connecting diseases, medicines, symptoms and examinations; 2) an attribute graph connecting medicines via shared attributes and molecular structures. These two graphs enhance the connectivity between symptoms and medicines which thus alleviate the data sparse problem. By learning the interrelationship between diseases, medicines, symptoms and examinations and the inner relationship within medicine, we can acquire unified embedding representations of symptoms and medicines which can be used in medicine recommendation. The experimental results show that the proposed model outperforms state-of-the-art methods. In addition, we find that these two tasks: learning graph representation and medical recommendation can benefit each other.
Background: Although there are many studies showing potential benefit in aortic stenosis (AS) patients taking angiotensin-converting enzyme inhibitors (ACEI), but these studies are subject to significant selection and other biases, making the results challenging to interpret. Furthermore, the evidence on the use of ACEI in AS patients has not been reviewed systematically; we thus conducted this protocol assess the clinical effectiveness and safety of ACEI for patients with AS. Methods: The following search terms will be used in PUBMED, Scopus, EMBASE, and Cochrane Library databases on May, 2021, as the search algorithm: (angiotensin-converting enzyme inhibitors) OR (ACEI) AND (aortic stenosis) OR (AS). Two searchers will independently draft and carry out the search strategy, and the third member will further complete it. The studies on cohort study focusing on assessing the efficacy of ACEI on AS patients will be included in our meta-analysis. At least one of the following outcomes should have been measured: left ventricular mass, exercise tolerance, B-type natriuretic peptide, adverse event, functional outcomes, and aortic valve area. All outcomes are pooled on random-effect model. A P value of <.05 is considered to be statistically significant. Results: The results of this research will be delivered in a peer-reviewed journal. Conclusion: Depending on the previous studies, we assumed that ACEI could possibly improve the clinical symptoms and outcomes of symptomatic AS. Systematic review registration number: 10.17605/OSF.IO/G9KPT.
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