The association of vitamin D and fibrotic disease: a Mendelian randomization Study

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The association of vitamin D and fibrotic disease: a Mendelian randomization Study

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Previous observational studies have suggested a potential link between vitamin D deficiency and an increased risk of fibrotic disease. However, the results may be influenced by various factors, including reverse causation. To investigate the causal relationship between serum vitamin D levels and the risk of fibrotic disease, we performed a Mendelian randomization (MR) analysis. We conducted a comprehensive analysis using genome-wide association study summary statistics for 25-hydroxyvitamin D and five fibrotic diseases: systemic sclerosis, idiopathic pulmonary fibrosis, liver fibrosis and cirrhosis, skin scarring and fibrosis, and primary sclerosing cholangitis. We screened SNPs significantly associated with serum 25-hydroxyvitamin D levels as preliminary instrumental variables excluding SNPs associated with potential confounding factors. We utilized MR-PRESSO to identify potential horizontal pleiotropy effects. After removing outliers, we conducted MR analysis on the remaining SNPs. Furthermore, a series of sensitivity analyses were conducted to ensure the robustness and reliability of the results. This study reveals that there is no established causal relationship between genetically predicted vitamin D concentration and the risk of fibrotic diseases. Consequently, the role of vitamin D as a potential intervention and monitoring tool for fibrotic diseases may not have practical clinical significance.

Biological sciences/Genetics

Biological sciences/Immunology

Health sciences/Diseases

Health sciences/Endocrinology

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Fibrosis can impact nearly every organ system, and its common characteristic is the abnormal activation of myofibroblasts, which secrete large amounts of extracellular matrix (ECM), including collagen and fibronectin ¹. The excessive accumulation of extracellular matrix (ECM) often leads to structural and functional impairments in organs, and in some cases, it can pose a significant and life-threatening risk. The risk factors linked to the advancement of fibrotic diseases mainly include genetic predisposition, persistent infections, exposure to toxins or smoke, chronic immune inflammation, obesity, and diabetes, among others ². Epidemiological surveys indicate that the average annual incidence rate of diseases associated with fibrosis is approximately 4,968 cases per 100,000 person-years ³. Furthermore, in developed countries, organ fibrosis and its associated failures are responsible for up to 50% of all reported deaths ⁴. Overall, the prevalence of fibrosis and its associated societal impact are increasing and are increasingly recognized as a significant health challenge in the current era.

Vitamin D is a steroid hormone that serves various physiological functions. Traditionally, it has been acknowledged for its role in maintaining calcium balance and promoting bone health. Vitamin D deficiency is linked to an imbalance in phosphor calcium, which can lead to decreased bone density and the onset of osteoporosis ⁵. Recent research has revealed that vitamin D is involved in a wide range of cellular processes, including cell proliferation, differentiation, wound healing, and modulation of the immune system and inflammation ^6,7. These findings emphasize the diverse roles of vitamin D in maintaining overall cellular health. Additionally, emerging evidence suggests that vitamin D has anti-fibrotic properties. Numerous observational studies have consistently shown a negative correlation between serum vitamin D levels and the risk of fibrotic diseases. A retrospective study involving 3,970 participants identified a significant association between low levels of 25-hydroxyvitamin D and non-alcoholic hepatitis (OR = 0.73, 95% CI 0.55–0.98, P = 0.038), as well as an increased risk of liver fibrosis (OR = 0.65, 95% CI 0.44–0.96, P = 0.033) ⁸. Conversely, Luo et al. ⁹ reported no statistically significant association between low levels of vitamin D and the risk of fibrosis in patients with metabolic-associated fatty liver disease (MAFLD). In a meta-analysis ¹⁰, a similar pattern was observed. Hence, the causal relationship between serum vitamin D levels and the risk of fibrotic disease remains largely uncertain. It is important to note that observational studies have limitations because they are susceptible to confounding factors and reverse causal associations ¹¹. Consequently, the reliability of the existing evidence is low. Therefore, a new approach is needed to investigate the potential link between vitamin D and fibrotic disease.

Mendelian randomization (MR) is a research design method that relies on genetics. It uses genetic instrumental variables (IVs) to investigate the causal relationship between exposure factors and outcomes. The fundamental principle of Mendelian randomization (MR) is to use the random allocation of genotypes to infer the impact of biological factors on disease. In contrast to traditional cross-sectional observational studies, MR analysis can effectively reduce the impact of confounding factors and reverse causality, resulting in more reliable results ^12,13. Therefore, in this study, we conducted an MR analysis of vitamin D with five fibrotic diseases, using IVs to investigate their causal relationship. This approach aims to enhance our understanding of the potential clinical implications of vitamin D levels in fibrotic diseases, considering the accessibility of the data.

Study design

25-Hydroxyvitamin D is the primary form of vitamin D found in the body ¹⁴. As a result, we have selected serum 25-hydroxyvitamin D as the main exposure factor for our study. The MR analysis in this study is based on three assumptions ¹²: The genetic instrument variables are strongly associated with the exposure factors (association hypothesis).The genetic instrument variables are not associated with any potential confounders (independence hypothesis).The genetic instrument variables can only affect the outcome through the exposure, and there is no horizontal pleiotropy (exclusivity hypothesis).The flow of the MR analysis in this study is depicted in Fig. 1.

Data source

For this study, we sourced information on serum 25-hydroxyvitamin D levels from the UK Biobank GWAS study ¹⁵, which had a sample size of 496,946. Specifically, we extracted the corresponding GWAS summary statistics (GCST90000618) from the NHGRI-EBI Catalog of human genome-wide association studies (https://www.ebi.ac.uk/gwas/). The summary data for fibrotic diseases are obtained from FinnGen¹⁶, which is available at https://www.finngen.fi/en/access_results. The specific codes and sample sizes for various fibrotic diseases are as follows:1.Systemic sclerosis: Table code "M13_SYSTSLCE", includes 366,152 samples (619 cases; 365,533 controls). 2.Idiopathic pulmonary fibrosis: Table code "IPF", includes 375,082 samples (2,018 cases; 373,064 controls). 3. Liver fibrosis and cirrhosis: Table code "K11_FIBROCHIRLIV", includes 368,291 samples (1,841 cases; 366,450controls). 4.Skin scar and fibrotic: Table code "L12_SCARCONDITIONS", includes 354,115 samples (1,027 cases; 353,088 controls). 5. Primary sclerosing cholangitis: Table code "K11_CHOLANGI", includes 332,618 samples (1,715 cases; 330,903 controls). The details of these summary statistics can be obtained from Table 1. The GWAS data sources for serum 25-hydroxyvitamin D and fibrotic disease are restricted to individuals of European origin to minimize bias arising from population stratification.

Table 1

The information about the datasets used in our study.
Traits	Year	Ancestor	Sample size	ncase	ncontrol	Study accession
Serum 25-Hydroxyvitamin D levels	2020	European	496946	—	—	GCST90000618
Systemic sclerosis	2021	European	366152	619	365533	R9_M13_SYSTSLCE
Idiopathic pulmonary fibrosis	2021	European	375082	2018	373064	R9_IPF
Fibrosis and cirrhosis of liver	2021	European	368291	1841	366450	R9_K11_FIBROCHIRLIV
Scar conditions and fibrosis of skin	2021	European	354115	1027	353088	R9_L12_SCARCONDITIONS
Primary sclerosing Cholangitis	2021	European	332618	1715	330903	R9_K11_CHOLANGI

Ethical approval and consent to participate

The data used in this paper are publicly available, ethically approved, and the subjects have given their informed consent.

Genetic instrumental variables

To meet the first hypothesis of the MR analysis, we conducted a screening of SNPs significantly associated with 25-hydroxyvitamin D levels as preliminary instrumental variables (IVs) using the GWAS data. The criteria for selecting these IVs were a significance level of P < 5×10^− 8, LD-r² of 0.001, clumping distance of 10,000 kb, and a minimum allele frequency > 0.01. After selecting the preliminary IVs, we proceeded to assess the concordance of SNP effects for both the exposure (25-hydroxyvitamin D levels) and the outcome. This step allowed us to exclude palindromic SNPs with intermediate allele frequencies or incompatible alleles from further analysis. To avoid potential confounding effects of genetic variation, we conducted a search in the PhenoScanner database ¹⁷ (https://www.phenoscanner.medschl.cam.ac.uk) and excluded SNPs (p < 1×10^− 5) that were associated with confounding factors. These factors included risk factors for idiopathic pulmonary fibrosis ¹⁸ (such as smoking, dust exposure, and gastroesophageal reflux disease), risk factors for liver fibrosis and cirrhosis ¹⁹ (such as alcohol consumption, viral hepatitis, metabolic syndrome, obesity, and type 2 diabetes), and risk factors for primary sclerosing cholangitis ²⁰ (such as inflammatory bowel disease). By excluding these SNPs, we aimed to minimize the influence of confounding factors on our analysis and enhance the validity of the causal inference in the subsequent Mendelian randomization study. In Mendelian randomization (MR) analysis, the coefficient of determination (R²) can be used to measure the proportion of variability in the exposure variable explained by instrumental variables (IVs) ²¹. The R² values of each IV are summed to obtain the overall R². The F statistic is used to assess the strength of the IVs in relation to the exposure variable. A higher F statistic indicates a smaller bias of the instrumental variables towards the exposure variable. If the F statistic is less than 10, it is considered a weak instrument and should be excluded. The average F statistic of each IV represents the overall F statistic²². Therefore, we used the widely accepted and reliable formula F = R² × (n − 2) / (1 - R²) (where n represents the sample size of GWAS) to calculate the F statistic ²³. Additionally, we calculated R² using the formula R² = 2 × β² × (1 - EAF) × EAF (where β represents the estimate of the genetic effect of each SNP on iron status and EAF represents the effect allele frequency) ²¹.

Statistics analysis

To ensure the reliability of the research findings, we conducted a series of rigorous statistical analysis methods. Firstly, we utilized MR-PRESSO to conduct a global test for detecting the presence of horizontal pleiotropy. For each SNP, the MR-PRESSO outlier test calculated a p-value to assess its significance in terms of pleiotropy. The SNPs were sorted in ascending order and then progressively removed one by one. After removing each SNP, a global test was conducted on the remaining SNPs. This recursive process was repeated until the p-value of the global test became insignificant (P > 0.05) ²⁴.

Subsequently, we performed MR analysis on the remaining SNPs after removing outliers. The main MR analysis in this study was the inverse-variance weighted (IVW) method under the random effects model. IVW ²⁵ is a method used in MR analysis to meta-analyze the effects of multiple SNPs at multiple loci. The prerequisite for using IVW is that all SNPs are valid instrumental variables and are completely independent of each other. IVW is considered the most effective method for Mendelian randomization estimation, but it is susceptible to bias from pleiotropy. Additionally, we conducted additional analyses using the weighted median and MR-Egger methods. The weighted median ²⁶ is the median of the distribution obtained by sorting the individual SNP effect sizes according to their weights. The weighted median method provides robust estimates when over 50% of the information is derived from valid instrumental variables. MR-Egger ²⁷ does not assume that the regression line passes through the origin and allows for the inclusion of instrumental variables with directional pleiotropy. The three main principles for choosing MR methods are as follows ²⁸: 1. In the absence of heterogeneity and pleiotropy, the estimation results from IVW are preferred. 2. When there is heterogeneity but no pleiotropy, the results from the weighted median method are prioritized. Alternatively, the random effects model of IVW can also be used. 3. In the presence of pleiotropy, the results obtained from the MR-Egger method take precedence.

Finally, we conducted sensitivity analyses. The MR-Egger Intercept method was used to test for horizontal pleiotropy, which refers to the situation where the exposure SNP influences the outcome through mechanisms other than the exposure itself ²⁷. Cochran's Q test was utilized to evaluate heterogeneity among the SNPs in both IVW and MR-Egger analyses ²⁹. Leave-one-out analysis was performed to assess whether individual SNPs had a disproportionate influence on the overall MR estimates ³⁰. All statistical analyses were performed using the "TwoSampleMR" package (version 0.5.7) in R software version 4.3.1.

Selection of instrumental variables

In this analysis, we initially selected 111 SNPs as potential instrumental variables (p < 5×10^− 8). We then excluded the following palindromic SNPs: rs57601828, rs7955128. Additionally, we retrieved SNPs from the PhenoScanner database associated with risk factors for idiopathic pulmonary fibrosis (smoking: rs7652808, rs13108245, rs1128535), liver fibrosis and cirrhosis (alcohol consumption: rs1260326, rs7652808, rs1128535, rs1558902, rs3114045, viral hepatitis: rs1321247, obesity: rs4420638, metabolic syndrome: rs964184, rs1532085, type 2 diabetes: rs8107974, rs62007299), and primary sclerosing cholangitis (inflammatory bowel disease: rs1260326, rs1128535, rs11264361) with a p-value threshold of < 1×10 − 5. As a result, 106 SNPs were used for risk analysis of idiopathic pulmonary fibrosis, 98 SNPs for risk analysis of liver fibrosis and cirrhosis, 106 SNPs for risk analysis of primary sclerosing cholangitis, and 109 SNPs for risk analysis of systemic sclerosis, skin scarring, and fibrosis (excluding only palindromic SNPs). All F statistics for the SNPs used as instrumental variables for 25(OH)D were greater than 10, thus avoiding the bias associated with weak instrumental variables (Supplementary material S1-S5).

Causal effect in the main analysis and sensitivity analyses

The MR analysis results for this study are shown in Figs. 2 and 3.

Prior to conducting MR analysis, we first performed MR-PRESSO analysis. The global test results indicated no significant horizontal pleiotropy for systemic sclerosis (P = 0.939), primary sclerosing cholangitis (P = 0.722), and skin fibrosis and scarring (P = 0.057). No outliers were detected. However, there was evidence of horizontal pleiotropy for idiopathic pulmonary fibrosis (P = 0.022) and liver fibrosis/cirrhosis (P = 0.020). Following the method, the global test p-values for idiopathic pulmonary fibrosis (rs4420638) and liver fibrosis/cirrhosis (rs11207969) became nonsignificant after removing 1 outlier each (P > 0.05). The remaining SNPs were utilized for the subsequent MR analysis. (Refer to supplementary material S1-S5 for all SNPs used in the analysis.)

The inverse variance-weighted (IVW) analysis revealed no significant causal relationship between serum 25-hydroxyvitamin D levels and the risk of several fibrosis-related diseases: systemic sclerosis (OR = 0.671, 95% CI 0.390–1.156, P = 0.151), idiopathic pulmonary fibrosis (OR = 0.855, 95% CI 0.629–1.162, P = 0.316), liver fibrosis and liver cirrhosis (OR = 0.958, 95% CI 0.686–1.337, P = 0.799), skin scar and fibrosis (OR = 0.746, 95% CI 0.505–1.101, P = 0.140), and primary sclerosing cholangitis (OR = 1.066, 95% CI 0.760–1.495, P = 0.710) (see Fig. 2, Supplementary material M1-M5). The combined odds ratio (OR) was 0.883 (95% CI 0.751–1.038, P = 0.131) (see Fig. 3). Results from the MR-Egger regression also indicated no causal relationship between serum 25(OH)D and any of the five diseases (p > 0.05). Using the weighted median method, we identified a causal relationship between serum 25(OH)D and systemic sclerosis (OR = 0.416, 95% CI 0.177–0.974, P = 0.043). However, we did not find a significant relationship with the other four diseases (p > 0.05) (see Fig. 2, Supplementary material M1-M5).

Additionally, we conducted sensitivity analyses. The Cochran's Q heterogeneity test results for both the IVW and MR Egger methods showed no evidence of heterogeneity among all the instrumental variables (p > 0.05, see Fig. 2). The symmetry of the funnel plots also indicates no heterogeneity (Supplementary material H1-H5). The MR-Egger regression analysis revealed no significant horizontal pleiotropy for any of the instrumental variables (p > 0.05, see Fig. 2). The leave-one-out analysis did not reveal any outlier SNPs, indicating that the MR analysis results were stable (Supplementary material L1-L5).

According to the principles of MR method selection outlined above, our study does not show heterogeneity or pleiotropy. Therefore, we prioritize the results from the IVW method, which indicate that there is no causal relationship between serum 25-hydroxyvitamin D levels and the risk of several fibrosis-related diseases.

In this study, we assessed the relationship between serum 25-hydroxyvitamin D levels and the risk of five fibrotic diseases (liver fibrosis and cirrhosis, primary sclerosing cholangitis, skin scar and fibrosis, idiopathic pulmonary fibrosis, and systemic sclerosis) using GWAS data from the UK Biobank and Finnish Genomes. MR analysis showed no significant causal relationship between decreased serum 25-hydroxyvitamin D levels and an elevated risk of fibrotic diseases. Our findings align with several previous epidemiological studies. For instance, initial researchers have reported no correlation between vitamin D levels and liver fibrosis, and vitamin D cannot be utilized as a predictor of the risk for fibrosis ³¹. In a meta-analysis involving six cross-sectional biopsy-based studies with a total of 937 subjects, Saberi et al. ³² reported that there was no significant correlation between serum vitamin 25 (OH) D levels and the risk of non-alcoholic fatty liver disease-related fibrosis (95% CI -4.17 to 2.46, P = 0.08). A recent study based on NHANES III found that late-stage fibrosis, as determined by non-invasive scoring in metabolic-associated fatty liver disease (MAFLD) was not associated with low levels of vitamin D ³³. Furthermore, in the study by Gao et al. ³⁴, no significant difference in serum vitamin D levels was reported between patients with idiopathic pulmonary fibrosis (IPF) and the healthy control group. It is worth mentioning that in another two-sample Mendelian randomization analysis, a different database was used for the analysis, and it also reported no causal relationship between vitamin D and idiopathic pulmonary fibrosis ³⁵

Indeed, the current literature on the association between vitamin D and fibrotic diseases is inconclusive. Several clinical cohort studies have demonstrated a negative correlation between vitamin D and fibrosis in patients with chronic liver diseases ^36–40. In a meta-analysis of the relationship between vitamin D and chronic hepatitis C (CHC) and advanced liver fibrosis (ALF) ⁴¹, 14 studies were included, of which 7 focusing on ALF (1,083 cases). The results indicated that CHC patients with vitamin D deficiency were more likely to develop ALF after antiviral treatment. Although early studies ⁴² indicated a negative correlation between serum vitamin D levels and fibrosis staging in primary sclerosing cholangitis (PSC), the results showed that patients with advanced PSC (stages 3 and 4) had lower levels of serum vitamin D compared to those in stages 1 and 2. However, these studies are limited, and it is still unclear whether vitamin D is a cause, an exacerbating factor, or a consequence of fibrosis. Similarly, studies involving skin scarring, systemic sclerosis (SSc) and idiopathic pulmonary fibrosis (IPF) have also commonly reported vitamin D deficiency ^43–54.

The inconsistent results in these studies may have several reasons. Firstly, most of the current studies are primarily ordinary cross-sectional observational analyses, with few large-sample, long-term follow-up, prospective randomized controlled clinical studies. The studies only examine the connection between vitamin D and fibrotic diseases at a single time point, failing to account for the fluctuation in disease progression over time, which could act as a confounding factor. Furthermore, the majority of studies were conducted at a single center, which means their findings may be applicable only to specific populations and geographical regions. Secondly, most studies did not assess potential confounding factors that could influence vitamin D levels, such as diet, circadian rhythm, seasonality, renal function, insulin resistance, gender, racial differences, genetic polymorphisms related to vitamin D synthesis and metabolism, and other variables related to vitamin D metabolism, such as parathyroid hormone. The measurement of serum vitamin D levels should aim to minimize the potential impact of confounding factors. Although some studies have attempted to account for certain confounding factors, they often rely on lifestyle adjustments rather than specific and objective data. Furthermore, because of variations in study designs and measurement methods for serum vitamin D concentrations, as well as diverse assessment indicators for fibrosis that do not always adhere to the gold standard biopsy, it is important to interpret the findings of these studies cautiously.

Multiple potential biological mechanisms can explain the negative correlation between vitamin D levels and the risk of fibrotic diseases. Studies have shown that vitamin D can increase the expression of antimicrobial peptides in different immune cells. These peptides work by directly destroying the cell membrane of various harmful microorganisms, such as bacteria, enveloped viruses, protozoa, and fungi ^55,56. This suggests that sufficient vitamin D may decrease susceptibility to fibrotic diseases. Vitamin D can also suppress the expression of several matrix metalloproteinases, such as MMP-7, MMP-9, and MMP-10, in monocytes and macrophages, in order to protect tissues from damage ⁵⁷. Furthermore, vitamin D can not only weaken the antigen-presenting ability of macrophages and dendritic cells ⁵⁸ but also directly interfere with the activation of B cells and pathogenic T cells, which is critical for activating the immune defense ⁵⁹.

The TGF-β1/Smad signaling pathway plays a critical role in tissue fibrosis. Multiple studies have shown that vitamin D regulates the activity of the TGF-β1/Smad signaling pathway in fibroblast cells by binding to the VDR receptor ⁶⁰. It decreases the serum levels of various proinflammatory cytokines ⁶¹, inhibits the expression of pro-fibrotic genes such as Col-1, α-SMA, and TIMP-1, increases the expression of anti-fibrotic factors BMP7 and MMP8, reduces the deposition of extracellular matrix components such as fibronectin and collagen, and attenuates the transition of epithelial cells to mesenchymal cells, demonstrating its anti-fibrotic effects ⁶². However, it is important to note that while the anti-fibrotic effects of vitamin D have been demonstrated in some functional experiments, in vitro models are often simplistic and do not fully represent the complexity of the human body. Furthermore, there are concerns about using individual animal models to replicate human diseases. Therefore, additional mechanistic research is needed to better understand the relationship between vitamin D and the risk of fibrotic diseases.

This study has several distinctive features. Firstly, our analysis is the first to investigate the causal relationship between vitamin D and fibrotic diseases using GWAS data. We included five different fibrotic diseases for analysis, making the study more comprehensive. Secondly, we were very rigorous in our selection of instrumental variables (IVs) and chose highly correlated and independently inherited non-dependent SNPs as IVs to estimate the causal effect of vitamin D on the risk of fibrotic diseases. We used an F-statistic greater than 10 to assess the strength of the association between IVs and vitamin D. The GWAS datasets for vitamin D and fibrotic diseases were obtained from two different databases, reducing the potential bias from sample overlap. Finally, we employed various MR methods based on different model assumptions and carried out a series of sensitivity analyses to address the limitations of observational studies, which are vulnerable to confounding factors. As a result, our study findings are robust.

However, this study also has some limitations. Firstly, the GWAS biobank included in the analysis is derived from European populations, which reduces the reliability of generalizing the study results to other populations. Secondly, the study includes a relatively small number of cases for fibrotic diseases, which may lead to false negatives. Therefore, more data with larger sample sizes are needed for validation. Thirdly, due to the use of aggregated-level data, it was not possible to conduct gender-specific and age-stratified effects analysis. Lastly, like any Mendelian randomization study, this study cannot completely rule out the potential influence of pleiotropy, although we did not find evidence of horizontal pleiotropy.

In summary, this MR study demonstrated no significant causal relationship between genetically predicted serum 25(OH)D levels and the risk of various fibrotic diseases. Our study findings indicate that supplementing serum vitamin D levels as an intervention and monitoring strategy for fibrotic diseases may not have significant clinical implications. Previous research on the relationship between vitamin D and the risk of fibrotic diseases may have been affected by confounding factors. The association between vitamin D and fibrotic diseases remains a topic of debate, and additional research is necessary to thoroughly investigate it.

Data availability statement

The datasets analysed during the current study are available in a public, open access repository. Data URLs: GWAS summary statistics for serum 25-hydroxyvitamin D levels could be download form GWAS Catalog (Study accession: GCST90000618, https://www.ebi.ac.uk/gwas/); GWAS summary statistics for fibrotic disease could be available form https://www.finngen.fi/en/access_results. Te comprehensive details for GWAS database used in this research was presented in Supplementary materials S1-S5.

Data availability statement

Acknowledgments

We thank the NHGRI-EBI GWAS Catalog, UK Biobank consortium and FinnGen consortium for providing summary statistics data for the analyses.

Author contributions

QX, MW and XZ designed the study. QX and YL performed the data collection and analyses. ML and XH interpreted the data. QX and XZ drafted the manuscript. QX and MW commented on and improved the manuscript. SS reviewed the manuscript and supervised all research work. All authors read and approved the final version of the manuscript.

Funding

This study was supported by 2023 Nanchong City Science and Technology Plan Project (23JCYJPT0020).

Conflicts of Interest

The authors declare that they have No competing financial interests exist.

Consent for publication

Not applicable.

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The association of vitamin D and fibrotic disease: a Mendelian randomization Study

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Abstract

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Introduction

Materials and methods

Study design

Data source

Genetic instrumental variables

Statistics analysis

Results

Selection of instrumental variables

Causal effect in the main analysis and sensitivity analyses

Discussion

Conclusion

Data availability statement

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References

Additional Declarations

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