ABSTRACT
Introduction: Asthma and stroke share many risk factors. Previous meta-analysis has indicated that asthma is associated with an increased risk of stroke. However, this study were limited by the small number of articles included and the lack of subgroup analyses of different stroke types and different populations. This meta-analysis aimed to synthesize evidence systematically to investigate the impact of asthma on stroke. Methods: We searched Medline (via PubMed), Web of Science and EMBASE databases and manually identified eligible studies (inception dates to December 25, 2021) that analyzed the association between asthma and stroke. We conducted quality assessment to evaluate the risk of bias of studies and sensitivity analyses to test the robustness of results. Results: We included 8 cohort studies and 10 cross-sectional studies comprised 3,011,016 participants. We found patients with asthma had a higher risk of stroke than patients without asthma [relative risk (RR): 1.34, 95% confidence interval (CI): 1.21-1.47]. Moreover, asthma significantly increased the risk of ischemic stroke (RR: 1.25, 95% CI: 1.06-1.47) without increasing the risk of hemorrhagic stroke (RR: 1.08, 95% CI: 0.87-1.34). Asthma increased the risk of stroke in both men (RR: 1.20, 95% CI: 1.10-1.32) and women (RR: 1.29, 95% CI: 1.12-1.48) with no significant difference between the sexes. We also found that patients with inactive asthma, child-onset asthma, or no smoking history did not have an increased risk of stroke. Conclusions: These results supported the finding that asthma could significantly increase the risk of stroke, but this impact was not consistent in different populations. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=290745, identifier: CRD42021290745.
ABSTRACT
Background: Asthma and cardiovascular disease (CVD) share many risk factors. Previous meta-analyses indicated that asthma is associated with an increased risk of CVD and all-cause mortality, but these studies were limited by unstandardized search strategies and the number of articles included. Objective: We sought to systematically synthesize evidence investigating the impact of asthma on all-cause mortality and CVD morbidity and mortality. Methods: We searched in PubMed and EMBASE for observational cohort studies (inception dates to November 10, 2021) that had both asthma groups and control groups. We also manually searched the reference lists of correlative articles to include other eligible studies. Data for associations between asthma and all-cause mortality and CVD morbidity and mortality were needed. Results: We summarized the findings from 30 cohort studies comprising 4,157,823 participants. Asthma patients had increased CVD morbidity [relative risk (RR) = 1.28, 95% confidence interval (CI) = 1.16-1.40] and increased CVD mortality (RR = 1.25, 95% CI = 1.14-1.38). Asthma patients also had increased risk of all-cause mortality (RR = 1.38, 95% CI = 1.07-1.77). In subgroup analyses, female asthma patients had a higher risk of CVD morbidity and all-cause mortality than male asthma patients, and late-onset asthma patients had a higher risk of CVD morbidity than early-onset asthma patients. Conclusion: Asthma patients have increased risk of all-cause mortality and CVD morbidity and mortality. This information reminds clinicians to be aware of the risk of CVD and all-cause mortality in asthma patients. Systematic Review Registration: http://www.crd.york.ac.uk/PROSPERO/, PROSPERO, identifier: CRD 42021290082.
ABSTRACT
Species identification through DNA barcoding or metabarcoding has become a key approach for biodiversity evaluation and ecological studies. However, the rapid accumulation of barcoding data has created some difficulties: for instance, global enquiries to a large reference library can take a very long time. We here devise a two-step searching strategy to speed identification procedures of such queries. This firstly uses a Hidden Markov Model (HMM) algorithm to narrow the searching scope to genus level and then determines the corresponding species using minimum genetic distance. Moreover, using a fuzzy membership function, our approach also estimates the credibility of assignment results for each query. To perform this task, we developed a new software pipeline, FuzzyID2, using Python and C++. Performance of the new method was assessed using eight empirical data sets ranging from 70 to 234,535 barcodes. Five data sets (four animal, one plant) deployed the conventional barcode approach, one used metabarcodes, and two were eDNA-based. The results showed mean accuracies of generic and species identification of 98.60% (with a minimum of 95.00% and a maximum of 100.00%) and 94.17% (with a range of 84.40%-100.00%), respectively. Tests with simulated NGS sequences based on realistic eDNA and metabarcode data demonstrated that FuzzyID2 achieved a significantly higher identification success rate than the commonly used Blast method, and the TIPP method tends to find many fewer species than either FuzztID2 or Blast. Furthermore, data sets with tens of thousands of barcodes need only a few seconds for each query assignment using FuzzyID2. Our approach provides an efficient and accurate species identification protocol for biodiversity-related projects with large DNA sequence data sets.
