Front. Psychiatry Frontiers in Psychiatry Front. Psychiatry 1664-0640 Frontiers Media S.A. 10.3389/fpsyt.2023.1257553 Psychiatry Original Research The relationship between COVID-19, depressive disorder, and anxiety: a bidirectional Mendelian randomization study Zihao Liang 1 † Jinyun Song 1 † Shuanglin Gu 1 Xiuzhen Chen 1 Yonglin Li 2 Hongyu Zhao 1 * 1Clinical Research Center, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China 2The First Affiliated Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China

Edited by: Gonzalo Haro, Universidad CEU Cardenal Herrera, Spain

Reviewed by: Marina Iniesta-Sepulveda, Catholic University San Antonio of Murcia, Spain; Borjanka Batinic, University of Belgrade, Serbia; Filiz Karadag, Gazi University, Türkiye

 *Correspondence: Zhao Hongyu njdie001@njucm.edu.cn

†These authors have contributed equally to this work

 19 10 2023 2023 14 1257553 13 07 2023 25 09 2023 Copyright © 2023 Zihao, Jinyun, Shuanglin, Xiuzhen, Yonglin and Hongyu. 2023 Zihao, Jinyun, Shuanglin, Xiuzhen, Yonglin and Hongyu

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 Background

Previous clinical studies have found that negative mental states such as depression and anxiety are closely related to COVID-19 infection. We used Mendelian randomization (MR) to explore the relationship between depression, anxiety, and COVID-19 infection.

 Methods

Our data were based on publicly available GWAS databases. The COVID-19 samples were obtained from the COVID-19 Host Genetics Initiative (HGI). The depression samples were obtained from the Psychiatric Genomics Consortium (PGC). The anxiety samples were derived from the Finngen database. We used inverse-variance weighting (IVW) as the primary analysis method, with weighted median, MR Egger, and multivariate MRI adjustment.

 Results

There was no causal effect of different COVID-19 infection statuses on depression and anxiety as determined by MR analysis. In addition, in the reverse MR analysis, we found a significant causal effect of anxiety on severe symptoms after COVID-19 infection. The results of the MR Egger regression, weighted median, and weighted mode methods were consistent with the IVW method. Based on sensitivity analyses, horizontal pleiotropy was unlikely to influence the final results.

 Conclusion

Our findings indicate that anxiety is a risk factor for severe symptoms following COVID-19 infection. However, the mechanism of interaction between the two needs further investigation.

 COVID-19 anxiety depression Mendelian randomization GWAS section-at-acceptance Anxiety and Stress Disorders 1. Introduction

From 2019 to 2022, COVID-19 spread worldwide, causing severe public health issues on a global scale. It is an infectious disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), with an estimated 2.75 billion persons at risk of infection (1). Despite the end of the pandemic, many patients have been found to have acute SARS-CoV-2 sequelae, also known as long COVID or post-COVID-19 syndrome (2). The World Health Organization (WHO) defines it as a condition in which individuals who have been diagnosed or may have been infected with SARS-CoV-2 in the past have persistent symptoms within 3 months of onset that persist for at least 2 months and cannot be explained by an alternative diagnosis (3).

The main symptoms of COVID-19 sequelae include shortness of breath, cognitive dysfunction, fatigue, anxiety, and depression (4). Compared to the latest WHO incidence rates for common mental health disorders, the incidence of depression in patients with COVID-19 was three times higher (15.97%) than in the general population; The prevalence of anxiety disorders is four times higher than in the general population (15.15% higher than in the general population) (5).

Depression is a prevalent mental illness that affects many individuals. In clinical practice, the most common symptoms are a depressed mood, a lack of interest, and impaired cognitive function (6). According to the most recent data, there are approximately 264 million patients worldwide (7). Major depressive disorder (MDD) can even lead to suicide and death. Depression has arisen as a risk factor for numerous illnesses. Many studies have shown that there is a significant increase in the incidence of depression in people infected with COVID-19 (8). Clinical studies have suggested that anxiety and depression are risk factors for COVID-19 infection and will lead to a longer recovery period after COVID-19 infection (9, 10). Due to the negative effects of social isolation and disruptions in health services on people's mental health and wellbeing, researchers believe that the increase in depression following COVID-19 infection is likely to be comparable to the increase following other previous pandemics, such as SARS (severe acute respiratory syndrome) and MERS (Middle East respiratory syndrome coronavirus) (4, 11).

Genetic factors significantly influence the susceptibility to and the severity of a wide range of infectious diseases and psychiatric disorders. Several recent studies have found the same genetic factor linking psychiatric disorders and infectious diseases. The presence of numerous SNP sites on the HLA gene associated with psychiatric disorders and mutations in these sites may affect the immune response to foreign antigens, which may account for the increased incidence of infections and inflammation in patients with schizophrenia and bipolar disorder and their parents (12). A large Danish genomic study identified 90 SNPs associated with mental disorders and susceptibility, most notably rs6447952 (13). Chen analyzed GWAS data from populations with psychiatric disorders and COVID-19 infections utilizing polygenic risk scores and found that genetic susceptibility to psychiatric disorders correlated with the risk of COVID-19 and severe COVID-19 (14).

Mendelian randomization (MR) is an epidemiological research technique that uses genetic variants as instrumental variables to infer the causality of a risk factor because it employs genetic variants as instrumental variables (15, 16). Mendelian randomization is independent of environmental factors and self-selected lifestyle choices (17). When the sample size is adequate, and the genetic variant is not associated with potential confounders, the quasi-random assignment of that variant outside of the exposure level ought to produce groups with nearly identical characteristics on average. MR analysis is now widely used to analyze causal relationships between diseases and risk factors, e.g., between gut microbes and disease, between two different diseases, and between metabolites and disease (18). Therefore, we used Mendelian randomization to determine whether COVID-19 as the exposure and depression/anxiety as the outcome were directly causally related.

 2. Materials and methods 2.1. Design of experiment

We hereby briefly describe the design of the bidirectional MR between COVID-19 and depression/anxiety. Using pooled data from genome-wide association studies (GWAS), we performed two MR analyses to examine bidirectional associations between various COVID-19 statuses and depression/anxiety. Reverse MR analyses used depression/anxiety as exposure and distinct COVID-19 statuses as outcomes. Figure 1 depicts the fundamental hypotheses of MR. Using three guiding principles, this study hypothesizes the following (17, 19, 20):

(1) There is a substantial association between genetic variation and exposure.

(2) Genetic mutations are unrelated to other confounding variables.

(3) Only exposure is associated with genetic variation and outcome.

Description of this bidirectional Mendelian randomization experiment.

Based on summary statistics available to the public, this research did not require ethical approval.

 2.2. Data sources

We attempted to perform MR analysis using the COVID-19 GWAS data. The COVID-19 dataset was obtained from the COVID-19 Host Genetics Initiative (HGI). GWAS provided us with the association between COVID-19 and COVID-19 genetic associations of phenotypes. The GWAS yielded three phenotypes: (1) COVID-19 patients and the general population (38,984 cases and 1,644,784 controls); (2) hospitalized COVID-19 patients and the general population (3,159 cases and 7,206 controls); and (3) patients with severe respiratory confirmed COVID-19 and the general population (5,101 cases and 1,383,241 controls) (21). Depression data were obtained from the Psychiatric Genomics Consortium (PGC), which currently contains 807,553 individuals (246,363 cases and 561,190 controls) (22, 23). The data on patients with anxiety disorders were obtained from the Finngen database (40,191 cases and 277,526 controls). To exclude the influence of ethnicity, we chose a cohort of European populations. Details and sources of the data are given in Table 1.

Specific information and sources of GWAS data.

Phenotype Trait contains ID Source
COVID-19 Very severe respiratory confirmed COVID-19 A2_ALL_eur_leave_23andme https://www.COVID19hg.org
Hospitalized COVID-19 B2_ALL_eur_leave_23andme
COVID-19 C2_ALL_eur_leave_23andme
Depression Major depression mdd2019edinburgh https://pgc.unc.edu/for-researchers
Anxiety Anxiety disorders (more control exclusions) Psychiatric endpoints from Katri Räikkönen https://r9.finngen.fi/
2.3. Screen of instrumental variable (IV) for MR analysis

In order to obtain appropriate instrumental variables from different GWAS data, we first selected genome-wide significant SNPs (p < 5 × 10−8) (24). To ensure linkage disequilibrium of instrumental variables, we chose kb = 10,000, r2 < 0.001 as a condition. Finally, in order to evaluate the tool strength, we made sure the F > 10 ones were used as instrumental variables (25, 26). We then harmonized the exposure and outcome datasets to obtain genetic instrument effects on the outcome and to remove palindromic SNPs.

 2.4. Statistical analysis

Using a random-effects inverse variance weighting (IVW) method, we estimated the bidirectional causality between COVID-19 status and depression/anxiety. The IVW method presupposes that all MR assumptions are legitimate. However, IV influenced the results through other pathways, indicating that horizontal pleiotropic effects may exist and that estimates of IVW causality may be biased. Therefore, we conducted sensitivity analyses utilizing the MR Egger and weighted median methodologies, allowing us to estimate causality accurately even in the presence of invalid SNPs.

As MR relies on the three central IV assumptions of the primary analysis (Figure 1), we hereby describe the methods used to evaluate or demonstrate the validity of these assumptions. The correlation hypothesis calculates r2, which indicates the proportion of the exposure variable's variation that can be explained by genetic variation. We calculated the f-statistics to evaluate the instrumental intensity of the relationship between IV and interest exposure risk. F represents weak instrumental vigor. MR Egger regression intercepts and their respective 95% confidence intervals (CIs) were utilized to examine the extent to which directional pleiotropy, which precludes limiting assumptions, leads to bias in arbitrary estimates. Moreover, horizontal pleiotropy was evaluated using the Mendelian randomized pleiotropy residuals and outliers (MR-PRESSO) global test, and the outlier SNPs were excluded using the MR-PRESSO outlier test. Additionally, after removing the peripheral IV, we examined whether there was a statistically significant difference between the new IV and the previous one. Using Cochran's Q statistic and funnel diagrams, we also examined the IVW and MR Egger methods for heterogeneity. Then, various sensitivity analyses (such as leave-one-out and individual SNP analyses) were conducted to determine whether individual SNPs affected primary causality. Using odds ratios (OR) and 95% confidence intervals (CIs), we estimated causality for binary outcomes. We presented causal estimates, p-values, and their standard errors for both binary and continuous outcomes. Each p-value is bilateral. All analyses were conducted utilizing the R (version 4.3.0, www.r-project.org) TwoSampleMR and Mendelian randomization packages.

 3. Results 3.1. Screening of genetic tools

We obtained 51 SNPs as instrumental variables in depression, 54 in anxiety, 29 in very severe respiratory confirmed COVID-19, 33 in COVID-19 hospitalization COVID-19 SNPs, and 15 SNPs in COVID-19 infection, which met the generally accepted genome-wide significance threshold (p < 5 × 10 −8, r2 < 0.001, kb = 10,000) for exposure. However, anxiety was adjusted to a significance threshold of p < 5 × 10 −6 because only a few SNPs were acquired. Detailed data are provided in Supplementary material 1.

 3.2. Causal effects of COVID-19 infection on anxiety and depression

As shown in Tables 2, 3 and Figure 2, the IVW results indicated no significant correlation between the genetically predicted COVID-19 infection profiles and depression and anxiety. For example, very severe respiratory confirmed COVID-19 showed no significant association with anxiety (OR, 0.99; 95% CI, 0.95–1.04; P = 0.95) and depression (OR, 0.99; 95% CI, 0.97–1.01; P = 0.35). Similarly, COVID-19 requiring hospitalization exhibited no significant relation to anxiety (OR, 0.83; 95% CI, 0.96–1.04; P = 0.83) and depression (OR, 0.99; 95% CI, 0.97–1.01; P = 0.47), while COVID-19 infection also presented with no significant association with anxiety (OR, 0.83; 95% CI, 0.96–1.04; P = 0.83) and depression (OR, 1.01; 95% CI, 0.95–1.07; P = 0.79). Further analyses using MR Egger regression, weighted median, and weighted mode methods continued to show no causal association between different COVID-19 infection statuses and depression/anxiety. Detailed information can be found in Supplementary material 2.

Association of different COVID-19 statuses with depression in MR analysis.

Exposures Outcome Method NSNP B SE PVAL
Very severe respiratory confirmed COVID-19 Depression MR Egger 25 −0.004750305 0.014698014 0.749468684
Weighted median 25 −0.003462482 0.009228979 0.707530372
Inverse variance weighted 25 −0.007485112 0.008077615 0.354108922
Simple mode 25 0.000260369 0.016283651 0.987374874
Weighted mode 25 −0.004118848 0.009492068 0.668218554
Hospitalized COVID-19 MR Egger 30 −0.014133196 0.019894181 0.48332025
Weighted median 30 −0.005353026 0.014435836 0.710775237
Inverse variance weighted 30 −0.007569588 0.010585632 0.474558809
Simple mode 30 −0.015398662 0.024174515 0.529135197
Weighted mode 30 −0.00839524 0.015219947 0.585451845
COVID-19 MR Egger 13 0.082818581 0.052502673 0.143004122
Weighted median 13 0.015392815 0.033484634 0.645733096
Inverse variance weighted 13 0.008645516 0.032104582 0.787704783
Simple mode 13 −0.006519406 0.045052035 0.887343158
Weighted mode 13 0.021868586 0.033654926 0.528071848

Association of different COVID-19 statuses with anxiety in MR analysis.

Exposures Outcome Method NSNP B SE PVAL
Very severe respiratory confirmed COVID-19 Anxiety MR Egger 11 0.10405562 0.075761299 0.202848524
Weighted median 11 0.00313751 0.032409234 0.922877973
Inverse variance weighted 11 −0.001468431 0.024205732 0.951626322
Simple mode 11 −0.025030973 0.051538129 0.637660477
Weighted mode 11 0.024661144 0.036202126 0.511206489
Hospitalized COVID-19 MR Egger 31 0.005487875 0.037303033 0.884058454
Weighted median 31 −0.019340422 0.032976124 0.557541073
Inverse variance weighted 31 −0.004411899 0.020924363 0.833004451
Simple mode 31 −0.06280267 0.060541055 0.307859095
Weighted mode 31 −0.022371918 0.032397031 0.495154045
COVID-19 MR Egger 12 0.160125115 0.152084869 0.317177605
Weighted median 12 −0.027206087 0.094539137 0.773518271
Inverse variance weighted 12 0.028543326 0.080379876 0.722510653
Simple mode 12 0.128634762 0.170710516 0.46695675
Weighted mode 12 0.106043511 0.117072928 0.384458108

MR analysis of depression and anxiety as outcomes using different infections of COVID-19 as exposure.

 3.3. Causal effects of anxiety and depression on COVID-19 infection

We further validated the relationship between different COVID-19 infection statuses and depression/anxiety using directional MR. As shown in Table 4 and Figure 3, the results differ from previous ones in that we found a possible correlation between anxiety and severe illness after COVID-19 infection. We set depression and anxiety as the exposure factors and different COVID-19 infection statuses as outcomes. Depression for very severe respiratory confirmed COVID-19 (OR, 0.82; 95% CI, 0.67–1.01; P = 0.06), depression for hospitalized COVID-19 (OR, 0.91; 95% CI, 0.81–1.04; P = 0.16), and depression for COVID-19 (OR, 1.00; 95% CI, 0.95–1.05; P = 0.86). Anxiety for very severe respiratory confirmed COVID-19 (OR, 1.12; 95% CI, 1.00–1.24; P = 0.05), anxiety for hospitalized COVID-19 (OR, 1.03; 95% CI, 0.96–1.11; P = 0.40), and anxiety for COVID-19 (OR, 1.00; 95% CI, 0.98–1.03; P = 0.74).

Association of anxiety/depression with different COVID-19 statuses in MR analysis.

Exposures Outcome Method NSNP B SE PVAL
Depression Very severe respiratory confirmed COVID-19 MR Egger 46 0.598459 0.570471 0.299878
Weighted median 46 −0.06259 0.129955 0.630088
Inverse variance weighted 46 −0.19771 0.105107 0.059962
Simple mode 46 −0.24836 0.311976 0.430156
Weighted mode 46 0.200381 0.2996 0.507021
Anxiety MR Egger 49 0.006646308 0.139774233 0.962276119
Weighted median 49 0.086616086 0.075381972 0.250543946
Inverse variance weighted 49 0.109121703 0.055551695 0.049492166
Simple mode 49 0.011127597 0.145311095 0.93927776
Weighted mode 49 0.056740439 0.110460996 0.609839692
Depression Hospitalized COVID-19 MR Egger 46 0.495696 0.333149 0.143907
Weighted median 46 −0.09427 0.082273 0.251845
Inverse variance weighted 46 −0.08681 0.061954 0.161135
Simple mode 46 −0.01787 0.178002 0.920477
Weighted mode 46 −0.05624 0.176718 0.751766
Anxiety MR Egger 49 −0.040552251 0.097312214 0.678778072
Weighted median 49 0.08425579 0.045937605 0.066633892
Inverse variance weighted 49 0.031754872 0.037417872 0.396073183
Simple mode 49 0.133054135 0.104330645 0.208336838
Weighted mode 49 0.104582907 0.078908792 0.191324383
Depression COVID-19 MR Egger 47 0.05368 0.140077 0.703362
Weighted median 47 0.008824 0.034224 0.796528
Inverse variance weighted 47 −0.0046 0.025446 0.856553
Simple mode 47 −0.0099 0.075865 0.896783
Weighted mode 47 0.006712 0.068608 0.922493
Anxiety MR Egger 51 0.012916928 0.040546914 0.751408466
Weighted median 51 0.012513017 0.020452191 0.540658208
Inverse variance weighted 51 0.004884418 0.014890149 0.742888848
Simple mode 51 0.046435184 0.042932531 0.284625993
Weighted mode 51 0.042550178 0.038202593 0.270689266

MR analysis of different infections of COVID-19 as outcomes using depression and anxiety as exposure.

 4. Discussion

In this study, we explored whether there is a causal relationship between COVID-19 infection and anxiety/depression. As described in the results, we found that there does not appear to be a highly significant causal link between COVID-19 infection and anxiety/depression. Only anxiety disorders were causally associated with severe reactions after COVID-19 infection, and anxiety disorders may be a risk factor for severe illness after COVID-19 infection.

Previous observational clinical studies have found that more than 50% of the infected patients have depression or anxiety-like symptoms after COVID-19 infection, and anxiety and depression are also the typical symptoms of COVID-19 sequelae considered by the WHO (27, 28). In addition, researchers from the United Kingdom discovered that those infected with COVID-19 who were hospitalized were 49% more likely to be diagnosed with depression, anxiety, or a mental condition than those infected but not hospitalized (29, 30). This finding is consistent with what was discovered in the Nordic countries, where patients who had been hospitalized for more than 7 days had a significantly higher risk of depression and anxiety than those who had not been hospitalized (31). According to the results of our investigation, the presence of COVID-19 infection, the severity of the COVID-19 disease, and hospitalization for COVID-19, all appeared to have no direct causal effect on the development of depression or anxiety. As a consequence of the findings of other investigations that have been published, a number of theories have been developed. One possible explanation for the depressive and anxious symptoms exhibited by patients is that these conditions are at least partially caused by the patients' social environment (32). This may include social isolation and high levels of stress. The main sources of stress during the COVID-19 pandemic were fear of infection, frustration, boredom, lack of supplies, and economic loss (33). Fear of infection and occupational stress (increased work pressure on healthcare workers during a pandemic, increased unemployment due to changes in the socio-economic environment, increased uncertainty about the future due to a pandemic and thus academic stress, etc.) were the main causes of increased stress during a pandemic (34–37). Excessive stress is one of the major causes of mental disorders such as depression and anxiety (38).

During the COVID-19 pandemic, many individuals have been required to maintain a safe distance from one another to prevent the spread of COVID-19, resulting in social isolation (39). It is believed that social isolation causes sleep disturbances (40). Insomnia and sleep disorders are recognized as major risk factors for the development of depression and anxiety (41). Regarding social isolation-induced insomnia as a mechanism leading to melancholy and anxiety, scientists believe social isolation results in hypothalamic–pituitary–adrenal (HPA) axis dysfunction (42). The disorder of the HPA axis induces hyperexcitation and sleeplessness in the human body (43). The prevalence of insomnia symptoms (36.7%) and insomnia disorders (17.4%) during the COVID-19 pandemic was approximately double the prevalence reported during non-pandemic periods, with higher rates in Brazil, Canada, the United Kingdom, and the United States, where depression and anxiety rates have also increased (44).

Intensive research on inflammation and psychiatry suggests that immune system perturbations triggered by infection may specifically promote psychopathology, increasing the psychological stress of living with a potentially fatal illness and stress-related inflammation (45). Interactions between the innate and adaptive immune system and neurotransmitters underlie mood disorders, psychosis, and anxiety disorders. Similar results have been observed in the past for similar pandemics. Some researchers believe that this is due to the virus infecting the neural tissue, resulting in the latter's inflammatory response. It has been demonstrated that coronavirus has neurophilic properties and can infect brain tissues. Additionally, COVID-19 has been detected in the cerebrospinal fluid (46–49).

Mental factors such as depression and anxiety are important risk factors for many diseases, such as cardiovascular diseases, digestive tract diseases, and susceptibility to viruses (50, 51). Our study found a genetic causal link between anxiety disorders and symptom severity following COVID-19 infection. This is consistent with the current clinical studies that have found anxiety or depression to be a risk factor for COVID-19 infection. Patients who are depressed or anxious are not only more likely to be infected with COVID-19 than the general population but also appear to have more severe symptoms after infection (52). In addition, some studies have found that patients with depression and anxiety have a longer recovery period after COVID-19 infection, which may be related to immune dysregulation caused by the HPA system (53). The detection of serum cortisol in infected patients revealed that COVID patients had higher cortisol levels and that elevated cortisol levels were positively correlated with mortality after COVID-19 infection (54, 55). Cortisol plays a key role in the development of depression and anxiety. Is it the elevated cortisol caused by depression and anxiety that makes patients more susceptible to COVID-19 infection and more severe symptoms? Whether the increased cortisol caused by COVID-19 may lead to subsequent increases in depression and anxiety requires further research.

The very interesting finding in our study is that anxiety seems to be a risk factor for developing severe illness after a COVID-19 infection. However, depression does not seem to increase the risk of developing severe illness after COVID-19 infection. Anxiety disorders are often accompanied by autonomic arousal compared to depression (56). In addition, clinical studies have found that depressed patients have lower catecholamine levels than the normal population, while anxious patients have higher catecholamine levels than the normal population (57, 58). During the inflammatory response, catecholamine concentrations are elevated, which in turn exacerbate inflammation by promoting the secretion of pro-inflammatory cytokines such as IL-6, IL-1β, and tumor necrosis factor, especially in myeloid cells (59). This inflammatory response may be exacerbated by the higher catecholamine levels in patients with anxiety disorders themselves, leading to a more severe inflammatory response after COVID-19 infection than in normally infected individuals, with a higher chance of causing a severe reaction.

However, the results regarding the causal relationship between anxiety and the risk of severe illness after COVID-19 infection do not seem to be strong. Meanwhile, a large number of clinical studies have shown that a poor psychological state prior to COVID-19 infection is a key factor in triggering severe COVID-19 disease after infection (60). Chen et al. also analyzed GWAS data from the UK Biobank. They found that depression and anxiety were more likely to result in severe and fatal COVID-19 infections (14). We therefore consider our results to be plausible.

This study also has some limitations. First, the data we selected were from a European population lacking generalizability. We also needed more specific raw data for subgroup analyses. In future studies, we will increase the sample size, expand the population to include different ethnic groups, and collect appropriate subgroup information for more in-depth analyses.

 5. Conclusion

We used a much broader population sequence than in previous studies. Our study identified anxiety disorders as a risk factor for the development of severe symptoms following COVID-19 infection. Patients with anxiety disorders are more likely to have severe symptoms after COVID-19 infection than the general population. Although this association does not appear to be strong, given that anxiety disorders are risk factors for a wide range of diseases, we should pay more attention to people with anxiety disorders during future infectious disease pandemics.

 Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

 Author contributions

LZ and ZH conceived the idea for this study. LZ, GS, and SJ obtained genetic data and performed data analysis. LZ and SJ were responsible for revising the article. CX and LY explained the results of the data analysis. ZH wrote the article. All authors approved the submitted version.

 Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by the Key Medical Science and Technology Development Project of Nanjing Health Science and Technology Development Fund (No. ZKX22039), Nature Foundation Project of Nanjing University of Chinese Medicine (No. XZR2020069), Obstetrics and Gynecology Disease Biobank, Jiangsu Biobank of Clinical Resources (BM2015004), and Jiangsu Province Major Disease Biological Resource Sample Library Open Project of Obstetrics and Gynecology Disease Subbank (Biological Sample Bank of Nanjing Gulou Hospital) (No. TC2021B021).

The authors acknowledge the PGC (Psychiatric Genomics Consortium), HGI (COVID-19 Host Genetics Initiative), and the FinnGen consortium for contributing to the data used in this study. The authors thank the genetics consortiums for making the GWAS summary data publicly available.

 Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

 Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1257553/full#supplementary-material

Instrumental variables SNPs.

MR results between COVID and anxiety/depression.

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