Cortical structure and chronic sinusitis: analyzing through imaging data and mendelian randomization

doi:10.21203/rs.3.rs-4678837/v1

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Cortical structure and chronic sinusitis: analyzing through imaging data and mendelian randomization

https://doi.org/10.21203/rs.3.rs-4678837/v1

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Previous studies have reported abnormal brain activity in patients with chronic rhinosinusitis (CRS), but the relationship between cortical structure and CRS remains unclear. Therefore, we employed a two-sample bidirectional Mendelian randomization(MR) analysis to investigate this relationship. Data from 17,989 CRS patients and 308,457 controls were used. Magnetic Resonance Imaging (MRI) scans were conducted on 51,665 individuals to measure surface area (SA), thickness (TH), and 34 specific functional regions. For preliminary estimates, we utilized the inverse variance weighted method. Heterogeneity and pleiotropy were assessed using Mendelian randomization-Egger intercept test, MR-PRESSO, Cochran's Q test, and leave-one-out sensitivity analysis. We observed an association between reduced lateral orbitofrontal surface area (OR = 1.12, P = 0.012) and increased pericalcarine TH (OR = 0.81; 95% CI = 0.71–0.93, p = 0.002) with an increased likelihood of CRS. MRI data from all subjects were collected and analyzed using surface-based morphometry (SBM). It was found that the cortical TH of the right lateral orbitofrontal region in CRS patients(2.79 ± 0.14) showed a notable increase compared to the healthy control group(2.71 ± 0.18). Our two-sample MR and cortical TH alteration analysis provided suggestive evidence that changes in specific brain regions are involved in CRS development, supporting the notion that the brain can influence the immune system. However, the specific mechanisms by which the brain exerts direct neural control over the immune system require further investigation.

Biological sciences/Genetics

Biological sciences/Immunology

Biological sciences/Neuroscience

Health sciences/Diseases

Chronic rhinosinusitis (CRS) is a heterogeneous condition characterized by prolonged inflammation of the sinonasal area, resulting in debilitating symptoms such as nasal congestion and obstruction, facial pain, hyposmia or anosmia, fatigue, and otalgia[1]. As one of the most common chronic airway diseases, CRS affects 3–10% of the global population, with an estimated prevalence of around 8% in our country[2, 3]. First-line treatment typically includes oral and topical corticosteroids and antibiotics. However, these treatments often demonstrate limited efficacy, with up to 60% of patients experiencing disease refractory to medical management, necessitating surgical intervention[4]. Despite surgery, CRS is often not curative, and patients may require ongoing medical management or revision surgery for long-term disease control. In the United States, CRS incurs over $11 billion in annual direct medical costs and over $20 billion in indirect costs due to reduced work productivity[5–7]. Patients report that the quality of life impact from CRS is comparable to other chronic medical conditions such as end-stage renal disease, asthma, and Parkinson's disease[8].

The nasal and sinus mucosa are complex, dynamic structures designed to protect the lower airways from injury caused by inhaled factors, microbial colonization, and infection. Sensory nerves monitor the condition of the mucosal microenvironment and initiate protective mucosal responses through the axon reflex mechanism. These nerves also mediate pain and recruit systemic parasympathetic and sympathetic reflexes, which regulate the protective defenses of the epithelium, blood vessels, glands, smooth muscles, and inflammatory responses[9]. Neural dysfunction may be involved in the pathogenesis of chronic rhinosinusitis with nasal polyps (CRSwNP) and may partially explain olfactory dysfunction[10]. Wu et al. discovered that the expression of axon guidance signaling pathways and nerve growth factor pathway proteins is significantly suppressed in eosinophilic nasal polyps[11]. In cases with concurrent bronchial asthma and allergic rhinitis, increased TRPV1 expression plays a functional role in the development of CRSwNP[12]. Following a 15-minute local stimulation of TLR7, substance P is released from sensory neurons and human/mouse nasal epithelial cells, indicating that sensory neuropeptides can act as acute and localized regulators of pathogen-driven inflammatory responses, rapidly initiating innate immune defenses within the airway[13]. Compared to controls, neurotrophin-3, as a neurotrophic factor, is associated with the pathogenesis of CRSwNP[14]. Neural dysfunction is considered an endotype of chronic rhinitis, but these mechanisms may also contribute to the pathogenesis of CRS[15]. Although numerous studies have investigated the pathophysiological mechanisms of CRS, its relationship with the structure of the cerebral cortex remains unclear.

Magnetic Resonance Imaging (MRI) is an important and widely utilized tool in neuroscience and medical diagnostics[16]. As a non-invasive method, MRI enables detailed and comprehensive assessment of the brain's structural anatomy, allowing visualization of various brain regions and detection of abnormalities. However, due to the high cost and limited accessibility of MRI equipment, it is challenging to conduct randomized controlled trials to explore primary brain changes in patients with CRS. Additionally, observational studies often lack persuasive power due to issues such as reverse causality, insufficient sample sizes, confounding factors, or failure to identify specific brain functional areas. These factors challenge the reliability of interpreting the correlation between the brain and the sinus system and make it difficult to establish a clear causal relationship between the two systems. Therefore, it is necessary to employ tailored approaches to more accurately determine this causal relationship.

Mendelian randomization (MR) utilizes genetic variation as a tool for randomizing exposure in studies, aiming to deeply explore potential causal relationships. Since genetic variations are assumed to be randomly allocated at conception, MR can be seen as a naturally occurring randomized controlled trial, which minimizes potential confounders and enables more reliable causal inference[17–19]. This study employs the surface-based morphometry (SBM) method to observe cortical thickness (TH) changes in widespread brain regions of patients with CRS. Additionally, a bidirectional two-sample MR analysis is used to investigate the relationship between brain structure and changes in CRS.

Patients participating in the study were recruited from the department of otorhinolaryngology, head and neck surgery at the first affiliated hospital of Nanchang University, including a total of 67 CRS patients. The inclusion criteria for CRS were as follows: (1) patients aged 18–65 years; (2) all patients were right-handed; (3) diagnosis of CRS based on the European Position Paper on Rhinosinusitis and Nasal Polyps (EPOS). The exclusion criteria were: (1) other psychiatric or neurological disorders; (2) history of head injury, alcohol abuse, or substance abuse; (3) failure or non-compliance in MRI scanning; (4) claustrophobia. Additionally, healthy right-handed controls, matched for age and gender with the CRS patients, were recruited. These controls had no diagnosed diseases, substance abuse, neurological disorders, or significant psychiatric disorders. Written informed consent was obtained from all participants or their guardians. This study was reviewed and approved by the Medical Research Ethics Committee and the Institutional Review Board of the First Affiliated Hospital of Nanchang University and was conducted in accordance with the principles of the Helsinki Declaration.

MRI parameters

MRI was performed using a 3-Tesla MR unit (Discovery MR750; GE Healthcare, Milwaukee, WI). During the scanning, all subjects were instructed to close their eyes, keep their heads still, stay awake, and remain relaxed. Resting-state fMRI (rs-fMRI) data were acquired using an echo planar imaging sequence with the following parameters: repetition time (TR) = 2,000 ms, echo time (TE) = 30 ms, flip angle = 90°, matrix size = 64 × 64, field of view (FOV) = 220 × 220 mm, slice TH = 4 mm, and 240 time points. Additionally, high-resolution anatomical 3-D T1-weighted images were obtained with the following parameters: TR = 1,900 ms, TE = 2.26 ms, flip angle = 9°, matrix size = 240 × 256, FOV = 215 × 230 mm, slice thickness = 1.0 mm, and 176 sagittal slices. Conventional T2-weighted imaging was also conducted to exclude any visible brain structural abnormalities.

Cortical TH Analysis

T1-weighted images were preprocessed using SPM12 and CAT12 toolboxes in MATLAB. The steps included bias correction, segmentation into gray matter (GM), white matter (WM), and cerebrospinal fluid, and normalization to MNI space using DARTEL. Cortical TH was estimated using the projection-based TH method, which calculates WM distance maps and projects GM TH back to create GM TH maps. The central surface was constructed at the 50% level between WM distance and GM TH maps. Topological defects were corrected using spherical harmonics, and surfaces were reparameterized and smoothed with a Gaussian kernel (FWHM = 15 mm). The cortical TH maps were then resampled to the fsaverage template.

Mendelian randomization assumptions and study design

Data sources and genetic instruments

Participants

We obtained the GWAS data for surficial area (SA) and thickness (TH) from the ENIGMA Consortium (https://enigma.ini.usc.edu). The ENIGMA Consortium conducted a genomewide association meta-analysis study on cortical structures, which included the SA and TH of the full cortex, as well as SA and TH for thirty-four brain cortical regions with known functional specializations. The thirty-four brain regions were defined using the Desikan-Killiany cortical atlas, and established coarse partitions of the cortex. The SA and TH were measured using MRI in 51,665 individuals from 60 cohorts around the world, with approximately 94% of European descent. Both SA and TH of brain regions were weighted by the entire brain, indicating the SA and TH of specific regions across the SA and TH of the entire brain[20]. These data can be accessed at https://enigma.ini.usc.edu/research/download-enigmagwas-results/.The outcome variable data were retrieved from Finland (finn-b-J10_CHRONSITIS; https:r9.finngen.fi), A total of 326,444 samples (308,457 controls and 17,989 cases involved) were included. The cases of studies in FinnGen consortium are diagnosed based on Finnish biobanks and digital health record data from Finnish health registries according to the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10)[21].

Selection of instrumental variables

When the significance threshold of the P value was set to < 5×10^− 8, we did not identify suitable SNPs related to cortical structure. To maintain the consistency of the standard, we therefore chose the more lenient threshold of 1×10^− 5 for 2 exposure phenotypes, which was considered acceptable in previous MR analyses [22, 23]. We further excluded SNPs with chain imbalance (threshold r2 < 0.001 in a 10 Mb window). SNPs that were strongly correlated with the outcomes were also identified and removed. Data harmonization was performed by removing SNPs in incompatible alleles and those that were palindromic with intermediate allele frequencies. If no SNPs in the outcome dataset met these criteria, proxy SNPs with LD set at r2 > 0.9 were selected. We calculated the R2 and F statistic values using the following equations: R2 = 2×EAF × (1 − EAF)×β2 and F statistic = R2 × (N − 2)/(1 − R2). IVs were considered to be strongly correlated with exposure phenotypes when the F-statistic was > 10[24].

Two-sample Mendelian randomization

The MR analyses were performed using 4 different methods: the inverse variance weighted model (IVW), the weighted model, the MR-Egger method, and the weighted median. The IVW model is a weighted linear regression model that aggregates multiple IVs, with each variable weighted in inverse proportion to its variance to minimize the overall variance. The IVW model calculates the causal estimates of each SNP and combines them using a meta-analysis to obtain a total causal estimate[25].

The MR-Egger regression method was used to detect and correct for horizontal pleiotropy. The intercept of MR- Egger can be used as an indicator of unbalanced directional pleiotropy[26]. The weighted median method is a complementary method to the MR-Egger regression; it sorts MR estimates according to their inverse variances, and the weighted median estimate is the 50% weighted percentile. The weighted median can be considered an unbiased estimate of the causal effect in MR If more than 50% of the weight comes from effective SNPs. In addition, the weighted model method was used to estimate the causal effects of individual SNPs; this method provides a ratio of the estimates of the SNP effect on the exposure and the outcome to estimate the causal effect. For sensitivity analysis, we conducted a Cochran’s Q test, an MR-Egger regression analysis, and a leave-one- out analysis. Cochran’s Q test is used to explore the het- erogeneity, and a P value less than 0.05 for Cochran’s Q test was considered as statistically significant heterogeneity. A leave-one-out sensitivity analysis was used to monitor if significant associations were dominated by a single SNP[27]. Our research design framework is shown in Fig. 1.

Statistics

All our analyses utilized the "TwoSampleMR" and "MR-PRESSO" packages in R (version 4.2.1). For multiple testing scenarios, we employed False Discovery Rate (FDR) correction to avoid false positives. Therefore, P-values below the adjusted significance threshold were considered statistically significant, while P-values below 0.05 were considered nominally significant and suggestive. Statistical analysis was conducted using SPSS 22 software. The normality of different variables in two samples was confirmed using the Kolmogorov-Smirnov test, and homogeneity of variance was assessed using the Levene test. A two-sample t-test was used to compare differences in age, gender, the Patient Health Questionnaire-2 (PHQ-2), Minimental state examination (MMSE), and cortical TH. When comparing cortical TH between CRS patients and healthy controls (HC) using a two-sample t-test, age and gender were used as covariates. After correcting for multiple comparisons using the false discovery rate (FDR), a P-value < 0.05 was considered statistically significant. If no significant clusters were found after correction for multiple comparisons, uncorrected statistical thresholds were also reported as trends(p < 0.001).

Selection of the instrumental variables

Selection of the instrumental variables Using the GWAS summary dataset of cortical structures, we identified a total of 2,110 SNPs associated with cortical structure based on the threshold of P < 1×10^− 5.We retained 80 independent IVs for further MR analysis after excluding IVs that were in linkage disequilibrium (LD) (r2 > 0.01) and in proximity (within 1000 kb), and harmonizing the SNPs in the exposures and outcomes (Table S1).

Causal Associations Between cortical structure and CRS

The main findings of the forward MR analysis are shown in Table 1 and Fig. 2. The full results of the causal estimates for all brain regions on CRS are shown in Supplementary material 1; Supplementary Tables S2. Concerning the causal effect of cortical SA on CRS, IVW method showed that the SA of lateral orbitofrontal cortical regions with the weighted estimates of the entire brain were nominally associated with higher risk of CRS (OR = 1.12; 95% CI = 1.03–1.22; p = 0.012). Concerning the causal effect of cortical average TH on CRS, in the TH of pericalcarine (OR = 0.81; 95% CI = 0.71–0.93, p = 0.002) were nominally associated with lower risks of CRS (Fig. 2).

Table 1

Sensitivity analysis between cortical structure and chronic sinusitis.
	Method	Q	P-value	Intercept	P-value
SA-lateralorbitofrontal	IVW	77.75	0.02	0.006	0.46
SA-lateralorbitofrontal	MR egger	76.97	0.02	0.006	0.46
TH-pericalcarine	IVW	30.02	0.12	−0.009	0.44
TH-pericalcarine	MR egger	29.18	0.11	−0.009	0.44
MR, Mendelian randomization; IVW, inverse variance weighted; SA, surface area, TH, thickness.

In the sensitivity analysis (Supplementary material 1; Supplementary Tables S3–S4), This analysis displayed little evidence of heterogeneity was detected using the Cochran Q test. With the removal of potential outliers identified in the MR-PRESSO global test, no evidence of pleiotropic effects was detected across SNPs in the causal estimates (p > 0.05). In addition, the intercepts derived from MR-Egger analysis indicated no evidence of horizontal pleiotropy. The leave-one-out analysis did not detect any influential single SNPs that could introduce bias to the combined effect estimates.

The full results of the causal estimate for the vulnerability to CRS on all brain regions are shown in Supplementary material 2. The reverse MR analyses revealed no causal effects of CRS on cortical SA and TH with global weighted estimates.

Demographic and Clinical Characteristics

We found that there was no significant difference in age (P = 0.11) or gender (P = 0.37) between CRS and HC. There was no significant difference in PHQ-2 (P = 0.76), or MMSE scores (P = 0.94) between CRS and HC (Table 2).

Table 2

Demographic and Clinical Characteristics of the Sample
Variable	CRS	HC	p
Age, years	37.05 ± 14.63	42 ± 13.55	0.106
Male/Female	45/22	13/11	0.374
PHQ-2	1.91 ± 1.42	2.01 ± 1.45	0.762
MMSE	28.42 ± 2.79	27.98 ± 3.12	0.937
CRS, Chronic Sinusitis, HC, healthy control, MMSE, Mini-mental state examination, PHQ-2, Patient Health Questionnaire-2.

Cortical Thickness Alterations

When age and gender were used as covariates, the CRS group showed an increase in cortical TH in the left hemisphere's isthmus cingulate, precuneus, fusiform, lingual, supramarginal, bankssts, and middle temporal regions compared to the HC group. Similarly, the cortical TH in the right hemisphere's isthmus cingulate, precuneus, lateral orbitofrontal, medial orbitofrontal, posterior cingulate, and precuneus regions also increased in the CRS group (Table 3, Fig. 3). Specifically, the cortical TH of the right lateral orbitofrontal region in CRS patients showed a notable increase compared to the HC group (Fig. 4).

Table 3

Brain regions with significant changes from HC cortical thickness in patients with chronic sinusitis
Overlap of altas region		Cluster size	p	value
Left	65% isthmuscingulate	674	<0.001	3.00
	35% precuneus	674	<0.001	3.00
	89% fusiform	562	<0.001	4.00
	11% lingual	562	<0.001	4.00
	100% supramarginal	201	<0.001	6.00
	87% bankssts	104	<0.001	9.00
	13% middletemporal	104	<0.001	9.00
Right	88% isthmuscingulate	884	<0.001	2.00
	12% precuneus	884	<0.001	2.00
	89% lateralorbitofrontal	229	<0.001	5.00
	11% medialorbitofrontal	229	<0.001	5.00
	100% posteriorcingulate	143	<0.001	7.00
	100% precuneus	121	<0.001	8.00

This study aims to investigate whether changes in brain structure are associated with CRS. Our study demonstrates the causal relationship between the brain and CRS using two-sample MR. In our clinical data analysis of cortical TH, we found a difference in the average cortical TH of the orbitofrontal cortex (OFC) between the CRS group and the HC group, with the former having greater TH. Consequently, we explored the potential involvement of the OFC in the pathogenesis of chronic rhinosinusitis.

Neuro-immune dysfunction involving neuropeptides and ion channels has been proposed as a subtype of non-allergic rhinitis and may also play a role in the pathogenesis of CRS[15]. Research suggests that neuropeptides and the parasympathetic nervous system in the nasal mucosa may contribute to the formation of nasal polyps. Consequently, capsaicin, a topically applicable drug, has been considered for preventing the recurrence of nasal polyps[28]. Capsaicin acts on C fibers, and studies indicate that it may regulate nasal hyperreactivity through neural modulation, potentially preventing the recurrence of nasal polyps[29]. The brain interacts with the immune system via the neuroendocrine and autonomic nervous systems. Alterations in emotional stress or psychological state can affect immune function through these pathways, thereby potentially influencing the occurrence and progression of inflammation[30]. An acute exacerbation of CRS is defined as a worsening of symptoms, typically reverting to baseline CRS symptom levels after intervention with corticosteroids and/or antibiotics. While bacterial infection is generally believed to exacerbate nasal and sinus symptoms, the reality may involve more complex factors such as exacerbated allergic rhinitis, acute viral respiratory infections, asthma exacerbations, or other stressors, including depression[10]. In conclusion, neurological function impairment is closely associated with infections and the immune system.

So, we are curious about the association between the OFC structure and the occurrence of CRS. In a study on elevated C-reactive protein (CRP) levels and their correlation with increased cortical surface area and decreased cortical TH in youth with bipolar disorder, it was demonstrated that in ROI analyses, higher CRP was associated with higher whole-brain SA (β = 0.16; P = 0.03) and lower whole-brain (β = −0.31; P = 0.03) and OFC cortical TH (β = −0.29; P = 0.04) within the BD group, and was associated with higher OFC SA (β = 0.17; P = 0.03) within the CG[31]. Higher NGAL levels may be associated with reduced lateral orbitofrontal cortex TH, and this association might be specific to HIV-positive individuals. In our Mendelian analysis, we identified that the SA of the OFC is a risk factor for CRS[32]. Our cortical TH analysis also confirmed differences in the average cortical TH of the OFC between the CRS and HC groups. Based on these findings, we believe that structural changes in the OFC may be associated with changes in the immune status of the nasal cavity.

In recent years, the potential impact of brain structural changes on immune status has garnered increasing attention. The bidirectional signaling between the brain and the immune system plays a crucial role in emotion, motivation, and various psychological and physiological health issues[33, 34]. Notably, brain systems involved in processing rewards and promoting goal-directed behavior, such as the OFC, are of particular interest. The OFC is believed to play a critical role in assigning value to social and achievement-related rewards and assessing the likelihood of obtaining these rewards. Literature has highlighted that high reward reactivity is associated with elevated levels of inflammatory markers. For example, individuals with bipolar disorder often exhibit high levels of reward sensitivity and increased inflammatory markers. Conversely, low reward sensitivity is also linked to high levels of inflammatory markers. This phenomenon suggests that alterations in reward-related brain functions may be associated with increased inflammation, regardless of reward sensitivity levels. Data from the literature indicate a significant correlation between OFC activation during reward anticipation and higher composite inflammation scores in individuals with a high goal pursuit tendency[35]. Negative emotional states (such as depression, anger, and fatigue) and unhealthy behaviors (such as high-fat/high-sugar diets and substance use) can further increase inflammation by affecting the function of the autonomic nervous system and the hypothalamic-pituitary-adrenal (HPA) axis[34, 36, 37]. According to the literature on " Nerve Stimulation: Immunomodulation and Control of Inflammation", the role of the nervous system in regulating immune responses and inflammation is considered a key mechanism[38]. Neurostimulation (such as electroacupuncture) can regulate the function of immune cells by activating specific neural networks, thereby controlling inflammation and preventing organ damage[39, 40]. Specifically, neurostimulation can trigger the release of neurotransmitters and cytokines, which can communicate bidirectionally with the immune system[41]. For example, activation of the sympathetic nervous system can achieve localized inflammation control by releasing norepinephrine, thereby avoiding side effects on non-target tissues[42–44]. Additionally, Vagus nerve stimulation has been shown to alleviate inflammatory responses by reducing the release of pro-inflammatory cytokines[45]. These studies suggest that changes in brain structure may influence immune status by altering the function of the nervous system. This mechanism shows potential clinical application value in treating inflammatory and infectious diseases (such as sepsis). Future mechanistic studies will help develop new non-invasive neurostimulation techniques to achieve more effective control over immune and organ function.

Zhang et al. (2020) in their study published in Nature suggested that the brain can directly influence the immune system through descending neural circuits[46]. In T-dependent immune responses, activation within the paraventricular nucleus (PVN) and central nucleus of the amygdala (CeA) enhances the formation of splenic B plasma cells. The CeA and PVN consist of neurons that produce corticotropin-releasing hormone (CRH), which drives the activation of the HPA axis during stress responses, leading to the release of corticosterone from the adrenal glands. This implies that neural activity in the OFC might also regulate immune responses by affecting descending neural pathways. The OFC plays a crucial role in regulating stress responses. Stress activates the HPA axis, resulting in the release of a series of neuroendocrine mediators such as CRH, adrenocorticotropic hormone (ACTH), and cortisol. These mediators can influence systemic immune responses, including those in the nasal immune system, through the bloodstream. Acute short-term stress typically enhances immune responses, whereas chronic stress may lead to immune dysfunction. Changes in the OFC may affect the release of neurotransmitters such as acetylcholine (ACh) and norepinephrine (NE), which play significant roles in local immune regulation. For example, ChAT⁺ T cells, which synthesize acetylcholine, have been found to promote B cell plasma cell formation, thereby enhancing immune responses. Chronic stress-induced high levels of cortisol might inhibit B cell activation and antibody production, weakening nasal immune defenses. In summary, we hypothesize that changes in the OFC could lead to local or systemic inflammatory responses, which in turn could affect nasal immune cells through various mechanisms. However, evidence for direct neuronal control of the immune system by the brain remains limited. Elucidating the neural-immune circuits will require the development of a set of molecular tools that can specifically target the autonomic nervous system.

Strengths and limitations

The strengths of this study include the use of a large GWAS dataset and the exclusion of relevant confounding factors. Our research provides detailed information on structural changes in specific brain regions, examining the effects of brain structure on CRS from two dimensions of the cerebral cortex (SA and TH; with global weighting). This increases the likelihood of finding significant estimates. Our findings offer suggestive evidence that alterations in brain structure are involved in the occurrence of CRS. In our analysis of cortical TH alterations, we identified differences between CRS patients and healthy controls, but the specific neural functions and underlying mechanisms require further investigation.

It is important to note that our SNPs did not meet the conventional GWAS significance threshold (P < 5×10^⁸), which may affect the interpretability of the results. Additionally, since we used genetic data from individuals of European ancestry, it remains to be seen whether these conclusions can be generalized to other populations. Finally, our study did not analyze the correlation with clinical indicators, such as inflammatory cytokines, nasal endoscopy scores, and CT scores, which will be a focus of our future research.

Our two-sample MR and Cortical Thickness Alterations analysis revealed suggestive evidence that changes in specific brain regions are involved in the occurrence of CRS, supporting the notion that the brain can influence the immune system. However, the specific mechanisms of direct neural control of the immune system by the brain require further investigation.

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.

Funding

This work was supported by the National Natural Science Foundation of China (No. 82160211), the Jiangxi Provincial Natural Science Foundation (No. 20202ACBL206013), and the Key Research and Development Program of Jiangxi Science and Technology Department (No. 20202BBGL73018).

Author Contribution

Zhiqiang Zhang and Junhao Tu designed the research. Wei Wan, Ying Wu, Jing Ye collected and analyzed the data. Zhiqiang Zhang and Ying Wu performed the literature search. Zhiqiang Zhang and Junhao Tu drafted the manuscript. Chunping and Jing Ye supervised the study. All authors were involved in writing the manuscript, contributed to the article and approved the submitted version.

Acknowledgments

We want to acknowledge the participants and investigators of the FinnGen. We also thank the members of the ENIGMA consortium for sharing summary-level data publicity available. We also thank Jiaxin Xiong and Huiting Lv for collecting MRI image data.

Data Availability

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

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Cortical structure and chronic sinusitis: analyzing through imaging data and mendelian randomization

Status:

Version 1

Abstract

Figures

Introduce

Subjects

MRI parameters

Cortical TH Analysis

Mendelian randomization assumptions and study design

Data sources and genetic instruments

Participants

Selection of instrumental variables

Two-sample Mendelian randomization

Statistics

Results

Selection of the instrumental variables

Causal Associations Between cortical structure and CRS

Demographic and Clinical Characteristics

Cortical Thickness Alterations

Discussion

Strengths and limitations

Conclusion

Declarations

Conflict of interest

Funding

Author Contribution

Acknowledgments

Data Availability

References

Additional Declarations

Supplementary Files

Status:

Version 1