No genetic causal association between systemic lupus erythematosus and COVID-19.

Objective: Emerging evidence suggests an increased prevalence of coronavirus disease 2019 (COVID-19) in patients with systemic lupus erythematosus (SLE), the prototype of autoimmune disease, compared to the general population. However, the conclusions were inconsistent, and the causal relationship between COVID-19 and SLE remains unknown. Methods: In this study, we aimed to evaluate the bidirectional causal relationship between COVID-19 and SLE using bidirectional Mendelian randomization (MR) analysis, including MR-Egger, weighted median, weighted mode, and the inverse variance weighting (IVW) method. Results: The results of IVW showed a negative effect of SLE on severe COVID-19 (OR = 0.962, p = 0.040) and COVID-19 infection (OR = 0.988, p = 0.025), which disappeared after Bonferroni correction. No causal effect of SLE on hospitalized COVID-19 was observed (OR = 0.983, p = 0.148). In the reverse analysis, no causal effects of severe COVID-19 infection (OR = 1.045, p = 0.664), hospitalized COVID-19 (OR = 0.872, p = 0.109), and COVID-19 infection (OR = 0.943, p = 0.811) on SLE were found. Conclusion: The findings of our bidirectional causal inference analysis did not support a genetically predicted causal relationship between SLE and COVID-19; thus, their association observed in previous observational studies may have been caused by confounding factors.

The causal links between gut microbiota and COVID-19: A Mendelian randomization study.

Several studies have shown a possible correlation between gut microbiota and COVID-19. However, the cause-and-effect relationship between the two has not been investigated. We conducted a two-sample Mendelian randomization study (MR) study using publicly available GWAS data. Inverse variance weighted (IVW) analysis was the main MR analysis technique and was supplemented with other sensitivity analyses. Forty-two bacterial genera were associated with COVID-19 susceptibility, hospitalization, and severity in the IVW method. Among these gut microbiota, five gut microbiota (genus unknowngenus [id.1000005472], family unknownfamily [id.1000005471], genus Tyzzerella3, order MollicutesRF9.id.11579, and phylum Actinobacteria) were significantly associated with COVID-19 hospitalization and severity. Three gut microbiota (class Negativicutes, order Selenomonadales, and class Actinobacteria) were significantly associated with COVID-19 hospitalization and susceptibility, while two microbiota (class Negativicutes and order Selenomonadales) were significantly associated with COVID-19 hospitalization and severity, and susceptibility. Sensitivity analysis did not detect any heterogeneity and horizontal pleiotropy. Our findings demonstrated that several microorganisms were causally linked to COVID-19, and improved our understanding of the relationship between gut microbiota and COVID-19 pathology.

Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarction.

Background: Increasing evidence suggests that people with Coronavirus Disease 2019 (COVID-19) have a much higher prevalence of Acute Myocardial Infarction (AMI) than the general population. However, the underlying mechanism is not yet comprehended. Therefore, our study aims to explore the potential secret behind this complication. Materials and methods: The gene expression profiles of COVID-19 and AMI were acquired from the Gene Expression Omnibus (GEO) database. After identifying the differentially expressed genes (DEGs) shared by COVID-19 and AMI, we conducted a series of bioinformatics analytics to enhance our understanding of this issue. Results: Overall, 61 common DEGs were filtered out, based on which we established a powerful diagnostic predictor through 20 mainstream machine-learning algorithms, by utilizing which we could estimate if there is any risk in a specific COVID-19 patient to develop AMI. Moreover, we explored their shared implications of immunology. Most remarkably, through the Bayesian network, we inferred the causal relationships of the essential biological processes through which the underlying mechanism of co-pathogenesis between COVID-19 and AMI was identified. Conclusion: For the first time, the approach of causal relationship inferring was applied to analyzing shared pathomechanism between two relevant diseases, COVID-19 and AMI. Our findings showcase a novel mechanistic insight into COVID-19 and AMI, which may benefit future preventive, personalized, and precision medicine.Graphical abstract.

Oil prices and the green bond market: Evidence from time-varying and quantile-varying aspects

This paper investigates the link between crude oil prices (COP) and green bonds through a rolling-window Granger-causality test. The positive, negative, and uncorrelated impacts of COP on the green bond index (GBI) are captured with the same sample. The positive effects show that the prosperity of the green bond market is promoted by the high COP, demonstrating that green bonds can avoid shocks from COP. Nevertheless, due to the high profits of the green energy industry and the excess supply on the oil market, the negative impact between COP and GBI is also found. These results are not completely consistent with the price correlation model between oil and green bonds. Furthermore, the positive impact of the GBI on COP shows that green bonds cannot moderate the oil crisis due to COVID-19, instability in the international political environment, and the immaturity of green bonds market. In addition, depending on the quantile Granger-causality test, only high COP affects the GBI, and this asymmetric feature is attributed to increasing production costs and environmental protection pressure. Understanding the nexus between COP and the GBI is of practical significance for bond issuers, regulators, and investors. © 2022 Borsa Ä°stanbul Anonim Åžirketi

No Causal Effects Detected in COVID-19 and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Two Sample Mendelian Randomization Study.

New clinical observational studies suggest that Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a sequela of COVID-19 infection, but whether there is an exact causal relationship between COVID-19 and ME/CFS remains to be verified. To investigate whether infection with COVID-19 actually causes ME/CFS, this paper obtained pooled data from the Genome Wide Association Study (GWAS) and analyzed the relationship between COVID susceptibility, hospitalization and severity of COVID and ME/CFS, respectively, using two-sample Mendelian randomization (TSMR). TSMR analysis was performed by inverse variance weighting (IVW), weighted median method, MR-Egger regression and weighted mode and simple mode methods, respectively, and then the causal relationship between COVID-19 and ME/CFS was further evaluated by odds ratio (OR). Eventually, we found that COVID-19 severity, hospitalization and susceptibility were all not significantly correlated with ME/CFS (OR:1.000,1.000,1.000; 95% CI:0.999-1.000, 0.999-1.001, 0.998-1.002; p = 0.333, 0.862, 0.998, respectively). We found the results to be reliable after sensitivity analysis. These results suggested that SARS-CoV-2 infection may not significantly contribute to the elevated risk of developing CFS, and therefore ME/CFS may not be a sequela of COVID-19, but may simply present with symptoms similar to those of CFS after COVID-19 infection, and thus should be judged and differentiated by physicians when diagnosing and treating the disease in clinical practice.

Oil Prices and the Green Bond Market: Evidence from Time-Varying and Quantile-Varying Aspects

This paper investigates the link between crude oil prices (COP) and green bonds through a rolling-window Granger-causality test. The positive, negative, and uncorrelated impacts of COP on the green bond index (GBI) are captured with the same sample. The positive effects show that the prosperity of the green bond market is promoted by the high COP, demonstrating that green bonds can avoid shocks from COP. Nevertheless, due to the high profits of the green energy industry and the excess supply on the oil market, the negative impact between COP and GBI is also found. These results are not completely consistent with the price correlation model between oil and green bonds. Furthermore, the positive impact of the GBI on COP shows that green bonds cannot moderate the oil crisis due to COVID-19, instability in the international political environment, and the immaturity of green bonds market. In addition, depending on the quantile Granger-causality test, only high COP affects the GBI, and this asymmetric feature is attributed to increasing production costs and environmental protection pressure. Understanding the nexus between COP and the GBI is of practical significance for bond issuers, regulators, and investors.

Is a consumer loan a catalyst for confidence?

In the context of the global economic downturn, the approach guided by consumer loans (CL) to boost consumer confidence is a feasible way to promote the internal circulation of the Chinese economy. Therefore, we use a time-varying rolling-window approach to identify how CL affects the consumer confidence index (CCI). We find that CL can be seen as vital support for promoting confidence because it can ease liquidity constraints and improve consumption levels. The empirical outcome is supported by the Rational Expectations Perpetual Income Hypothesis (RE-PIH), emphasizing that increasing CL can boost consumer confidence. Conversely, CCI has both positive and negative effects on CL. The positive effects suggest that consumers’ optimistic confidence leads them to increase borrowing, which in turn creates a heavier debt burden. This statement cannot be supported by the negative effect due to consumers’ blind self-confidence will cause cognitive bias, which is not conducive to the loan market development. Against the backdrop of increased global uncertainty due to the COVID-19 pandemic and the government’s continuous adjustment of loan policies, consumers can effectively optimise their consumption decision-making through borrowing. The policymaker can maintain loan stability by effectively promoting consumer confidence and raising the consumption level of the whole society. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Is a consumer loan a catalyst for confidence?

In the context of the global economic downturn, the approach guided by consumer loans (CL) to boost consumer confidence is a feasible way to promote the internal circulation of the Chinese economy. Therefore, we use a time-varying rolling-window approach to identify how CL affects the consumer confidence index (CCI). We find that CL can be seen as vital support for promoting confidence because it can ease liquidity constraints and improve consumption levels. The empirical outcome is supported by the Rational Expectations Perpetual Income Hypothesis (RE-PIH), emphasizing that increasing CL can boost consumer confidence. Conversely, CCI has both positive and negative effects on CL. The positive effects suggest that consumers' optimistic confidence leads them to increase borrowing, which in turn creates a heavier debt burden. This statement cannot be supported by the negative effect due to consumers' blind self-confidence will cause cognitive bias, which is not conducive to the loan market development. Against the backdrop of increased global uncertainty due to the COVID-19 pandemic and the government's continuous adjustment of loan policies, consumers can effectively optimise their consumption decision-making through borrowing. The policymaker can maintain loan stability by effectively promoting consumer confidence and raising the consumption level of the whole society.

Foreign Direct Investments and Economic Growth in the Post-Covid-19 Period: A Causality Analysis for Ukraine

Given the openness of most national economies, ensuring sustainable economic growth, trends of linearization, transnationalization, there has been a significant increase in foreign direct investment. Foreign direct investments (FDI) is a key component of economic growth and development, as the essence of economic growth is the rapid and efficient transfer of "best practices". In addition to direct capital investing, FDI can be a source of the valuable transfer of technology and exchange, know-how, and foster international connections of companies that have an impact on economy. In addition, it should be noted that FDI have potentially desirable features that affect the quality of growth with significant social consequences. This can decrease negative shocks caused by financial instability. FDI generate income directed to support other economies. The volumes of foreign direct investments are growing under the influence of globalization, the intensification of existing ties creating problems and threats to secure the national economic development, and in today's sustainable and inclusive (integral) growth. The evolution of the world economy has strongly influenced the dynamics of foreign direct investments and foreign capital flows, as well as economies of host countries. Current trends of the impact of foreign direct investments on economic growth have not bypassed economy of Ukraine, which is in a transitional stage of its development and is a host country. Therefore, the importance of attracting safe foreign direct investments is extremely ripe for the national economy and its economic growth at the current stage.

Increased Online Aggression During COVID-19 Lockdowns: Two-Stage Study of Deep Text Mining and Difference-in-Differences Analysis.

BACKGROUND: The COVID-19 pandemic caused a critical public health crisis worldwide, and policymakers are using lockdowns to control the virus. However, there has been a noticeable increase in aggressive social behaviors that threaten social stability. Lockdown measures might negatively affect mental health and lead to an increase in aggressive emotions. Discovering the relationship between lockdown and increased aggression is crucial for formulating appropriate policies that address these adverse societal effects. We applied natural language processing (NLP) technology to internet data, so as to investigate the social and emotional impacts of lockdowns. OBJECTIVE: This research aimed to understand the relationship between lockdown and increased aggression using NLP technology to analyze the following 3 kinds of aggressive emotions: anger, offensive language, and hate speech, in spatiotemporal ranges of tweets in the United States. METHODS: We conducted a longitudinal internet study of 11,455 Twitter users by analyzing aggressive emotions in 1,281,362 tweets they posted from 2019 to 2020. We selected 3 common aggressive emotions (anger, offensive language, and hate speech) on the internet as the subject of analysis. To detect the emotions in the tweets, we trained a Bidirectional Encoder Representations from Transformers (BERT) model to analyze the percentage of aggressive tweets in every state and every week. Then, we used the difference-in-differences estimation to measure the impact of lockdown status on increasing aggressive tweets. Since most other independent factors that might affect the results, such as seasonal and regional factors, have been ruled out by time and state fixed effects, a significant result in this difference-in-differences analysis can not only indicate a concrete positive correlation but also point to a causal relationship. RESULTS: In the first 6 months of lockdown in 2020, aggression levels in all users increased compared to the same period in 2019. Notably, users under lockdown demonstrated greater levels of aggression than those not under lockdown. Our difference-in-differences estimation discovered a statistically significant positive correlation between lockdown and increased aggression (anger: P=.002, offensive language: P<.001, hate speech: P=.005). It can be inferred from such results that there exist causal relations. CONCLUSIONS: Understanding the relationship between lockdown and aggression can help policymakers address the personal and societal impacts of lockdown. Applying NLP technology and using big data on social media can provide crucial and timely information for this effort.