Back

Effects of hypertension, diabetes and coronary heart disease on COVID-19 diseases severity: a systematic review and meta-analysis

Chen, Y.; Gong, X.; Wang, L.; Guo, J.

2020-03-30 endocrinology
10.1101/2020.03.25.20043133 medRxiv
Show abstract

BackgroundCOVID-19 patients with chronic diseases such as hypertension, diabetes and coronary heart diseases is more likely to worsen, but with mixed results for COVID-19 severity. This meta-analysis is to analyze the correlation between hypertension, diabetes, coronary heart disease and COVID-19 disease severity. MethodsAvailable data from PubMed, Web of Science, China National Knowledge Infrastructure Database, WanFang Database and VIP Database, were analyzed using a fixed effects model meta-analysis to derive overall odds ratios (OR) with 95% CIs. Funnel plots and Beggs were used to assess publication bias. FindingsOf 182 articles found following our initial search, we assessed 34 full-text articles, of which 9 articles with 1936 COVID-19 patients met all selection criteria for our meta-analysis. No significant heterogeneity between studies. There were significant correlations between COVID-19 severity and hypertension [OR=2.3 [95% CI (1.76, 3.00), P<0.01], diabetes [OR=2.67, 95% CI (1.91, 3.74), P<0.01], coronary heart disease [OR=2.85 [95% CI (1.68, 4.84), P<0.01]. Most of the studies in the funnel plot are on the upper part and few on the base part, and are roughly symmetrical left and right. Beggs test: hypertension (Z=-0.1, P=1.0), diabetes (Z=0.73, P=0.466), coronary heart disease (Z=0.38, P=0.707), all found no publication bias. InterpretationHypertension, diabetes, and coronary heart disease can affect the severity of COVID-19. It may be related to the imbalance of angiotensin-converting enzyme 2 (ACE2) and the cytokine storm induced by Glucolipid metabolic disorders (GLMD). FundingNational Natural Science Foundation of China (No. 81830113, 81530102); Major basic and applied basic research projects of Guangdong Province of China (No. 2019B030302005); National key R & D plan "Research on modernization of traditional Chinese medicine" (No. 2018YFC1704200) and Natural Science Foundation of Guangdong Province (No. 2018A030313391)

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.