Bridging Lipids and Colon Cancer: The Mediating Influence of Inflammation

Bridging Lipids and Colon Cancer: The Mediating Influence of Inflammation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bridging Lipids and Colon Cancer: The Mediating Influence of Inflammation Zeyang Li, Muyuan Ma, Yun Jin, Yuanzhe Li, Zhuo Wang, Shujie Peng, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6407602/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Colon cancer(CC) is a common and deadly cancer. Research indicates a connection between inflammation, lipids, and colon cancer, though the precise nature of this relationship remains uncertain. Method : We performed a two-sample Mendelian randomization(MR) analysis to examine the potential mediating role of inflammation in the relationship between lipids and colon cancer. In this study, we utilized recently published genome-wide association study (GWAS) data pertaining to lipids, inflammation, and colon cancer. The GeneRISK cohort provided lipid GWAS data from 7,169 Finnish individuals of European ancestry. Inflammation GWAS data were collected from 14,824 European participants across 11 cohorts using the Olink Target Inflammation panel. Colon cancer GWAS data were sourced from the IEU GWAS catalog (UKB-B-20145). Results : In this study, we identified onemediating relationship between inflammation, lipid, and colon cancer. Specifically, Phosphatidylethanolamine(PE) was found to indirectly influence colon cancer via Axin-1 concentration (OR=0.88 [95%CI(0.78, 0.99)], P =0.04).Although no mediating role of IL-17 in the relationship between phosphatidylcholine(PC) and CC was observed, our results suggest that PC can promote the development of CC(OR=1.12 [95%CI(1.00,1.24)], P=0.040), while IL-17 can inhibit the progression of CC(OR=0.87 [95%CI(0.76, 0.99)], P=0.034). Discussion : In conclusion, our MR analysis identifies the indirect effects of and Axin1 on the PE to CC, supporting the link between genetically predicted lipid levels, inflammation, and CC. These findings furnish genetic evidence that underscores the role of lipid and inflammation mechanisms in reducing the risk of CC, thereby providing valuable insights for future mechanistic and clinical research. Mendelian randomization Lipid inflammation colon cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Colon cancer (CC) currently stands as the second deadliest and third most commonly diagnosed cancer, with a devastating impact on global health statistics ( 1 ) . The fecal occult blood test serves as a crucial first step in CC screening, aiding in the identification of potential issues and reducing mortality rates in CC patients by 15–33% (2) . Although colonoscopy has been shown to decrease CC incidence and mortality by 69% and 68% respectively (3) , its invasive nature often leads to patient discomfort and poses challenges such as the risk of infection and high costs. CT colonography is a non-invasive imaging method that utilizes computed tomography and specialized software to produce high-resolution images of the colon, offering a convenient alternative.CT colonography is less invasive than colonoscopy but cannot collect tissue samples, requiring a follow-up colonoscopy if abnormalities are detected ( 4 ) . Carcinoembryonic antigen (CEA) is a classic tumor marker specific for colorectal cancer, useful in occult CC detection. However, due to its low sensitivity, CEA testing is not ideal for widespread screening ( 5 ) .Given the limitations of current CC diagnostic methods, it is apparent that there is an urgent need for early diagnosis and prevention to reduce CC incidence and mortality ( 6 ) . Investigating how inflammation influences the effect of lipids on colon cancer development is crucial for advancing early detection and prevention strategies.Inflammation is a complex biological process characterized by various cellular and molecular interactions that may initiate and promote colon cancer progression.Exploring the link between lipids and inflammation could provide novel targets for the early diagnosis, prevention, and treatment of colon cancer. Lipids, a diverse and ubiquitous group of molecules, have garnered significant scientific interest due to their crucial roles in various biological functions, including structure, metabolism, biological activities, molecular transport, signal transduction, endocrine regulation, and inflammation ( 7 ) . Research suggests that tumor-associated lipogenesis significantly benefits cancer cells, aiding in their survival against carcinogenic and therapeutic insults ( 8 ) . Lipid metabolism is crucial for tumor growth, with numerous essential enzymes involved in cancer-related lipid processes.Inhibition of these enzymes is considered a promising approach for cancer treatment, particularly CC. However, CC enzyme markers remain incompletely studied, with unclear key enzymes ( 9 ) .Recent research examining the relationship between CC and lipids has revealed a potential link between increased liposomes and CC growth ( 10 ) . These findings suggest that lipids may play a significant role in CC development, though a definitive understanding of this relationship remains elusive. Further research is required to investigate the intricate interactions between lipids and colon cancer, with the goal of clarifying the mechanisms involved and identifying potential targets for early diagnosis and treatment. Inflammation is pivotal in cancer development and progression, as extensively demonstrated by studies ( 11 ) . Lipids, on the other hand, can have both anti-inflammatory and pro-inflammatory effects depending on their role in affecting the body's inflammatory response and mediators. Normally, lipids serve to resist inflammation ( 12 ) . However, when lipid metabolism becomes abnormal, it can exacerbate the body's inflammatory response. Lipids contribute to inflammation by both promoting the production and secretion of inflammatory mediators and being susceptible to oxidation by these mediators, creating a cycle that can lead to health issues such as cancer. Cancer development involves genetic changes and is further driven by inflammatory mediators that enhance cell proliferation and inhibit DNA repair mechanisms, potentially leading to cancer ( 13 ) . Chronic inflammation, characterized by prolonged tissue damage and DNA alterations, is exacerbated by elevated levels of reactive oxygen species (ROS) and mutagens, fostering a detrimental environment that facilitates the conversion of normal cells into cancerous ones ( 14 ) . The accumulation of genetic and epigenetic changes brought on by chronic inflammation are crucial in initiating the cellular changes that lead to cancer development ( 15 ) . Indeed, chronic inflammation has now been recognized as a hallmark of cancer ( 13 ) . Investigating its role in colorectal cancer (CC) is crucial, especially in colitis-related cancer where inflammation significantly influences disease progression ( 16 ) . Emerging evidence suggests that targeting and inhibiting persistent inflammation could potentially help prevent or delay the onset of both hereditary and sporadic CC ( 17 ) . Studies have also identified the interleukin-17 (IL-17) pathway as being closely linked to inflammatory bowel diseases ( 18 ) . Additionally,Axin-1 is crucial in molecular signaling networks, notably interacting with phosphokinases and proteins in the WNT/β-catenin pathway, which is activated early in colitis-associated carcinogenesis ( 19 , 20 ) . Clinical trials have further suggested that Axin-1 may have potential in inhibiting CC progression ( 21 ) . These findings underscore the importance of focusing on inflammation and molecular pathways for colorectal cancer prevention and management. Mendelian randomization (MR) is a technique that employs genetic variation to investigate causal links between exposures and outcomes, minimizing confounding bias and reverse causality ( 22 ) . In this study, we hypothesize that lipids and inflammatory factors may contribute to CC pathogenesis. Using a two-sample MR approach, we aim to explore the influence of lipids, as mediated by inflammation, on the incidence of CC. Methods Study design The present study employed a MR design(Figure.2). To validate potential causal inferences, MR analyses must meet three core assumptions: (I) the genetic variant must be linked to the exposure, (II) it should not be associated with any confounders, whether known or unknown, and (III) it should influence the outcome solely through the exposure variable, excluding other pathways(Figure.1). Selection of Lipid Genetic Instruments We selected genetic instruments of 179 lipids species based on a genome-wide association study (GWAS) of 7169 Finnish individuals of European ancestry within GeneRISK cohort. These lipid data encompass four major categories:glycerolipids, glycerophospholipids, sphingolipids, and sterols. Lipid GWAS summary statistics were sourced from the EBI GWAS catalog(https://www.ebi.ac.uk/gwas/home). Our analyses concentrated on genome-wide significant genetic variants (P<1×10 -5 ) for each biomarker that were independent (LD r 2 <0.001 within 10,000 kb). Genetic variants were chosen as instrumental variables (IVs) based on the following criteria: (1) SNPs demonstrated a genome-wide significant association with each lipid of interest (P value0.001, within a 10,000 kb window); (3) Alleles were consistent between exposure and outcome datasets; (4) IVs for exposure were not directly associated with the outcome (P value>1×10 -5 ); and (5) IVs with an F-statistic<10 were excluded. Selection criteria for genetic instruments related to inflammation were outlined in the studies We systematically searched 91 inflammation from 14,824 European ancestry participants generated by 11 Olink Target Inflammation panel cohorts. The full GWAS summary statistics for the 91 inflammation traits can be accessed through the EBI GWAS Catalog(https://www.ebi.ac.uk/gwas/home). Our analyses concentrated on genome-wide significant genetic variants (P<1×10 -5 ) for each biomarker that were independent (LD r 2 <0.001 within 10,000 kb). The study outcomes genetic data We acquired the colon cancer GWAS summary statistics from the IEU GWAS catalog(ukb-b-20145). The colon cancer study included 1494 cases and 461,439 controls, all of European ancestry.The complete GWAS data are accessible to the public via the IEU GWAS Catalog(https://gwas.mrcieu.ac.uk). Statistical analyses An MR study assessing the impact of lipid inhibition on CC We utilized two-sample univariable MR (UVMR) to assess the impact of lipids on CC. Prior to MR analysis, horizontal pleiotropy and heterogeneity were addressed using MR-PRESSO (23) and radial MR (24) . The primary analysis employed inverse variant weight(IVW), offering the most accurate and robust estimates when all genetic variants are valid (25, 26) . Mediation MR analysis linking lipids with CC via inflammation A two-step Mendelian randomization was performed to assess the mediating role of inflammation in the relationship between lipids and CC. Initially, we utilized UVMR to assess the impact of lipids on 91 inflammation markers(1). Second, we used UVMR to assess the impact of inflammation markers, which had statistically significant associations with lipids, on CC. Subsequently, we identified inflammation significantly associated with CC and conducted UVMR to assess the impact of each inflammation on CC, accounting for the genetic influence of lipids (2). The mediation effect of each inflammation in the relationship between lipids and CC was determined by the product of coefficients 1 and 2. All Mendelian randomization analyses were performed using the "TwoSampleMR," "MendelianRandomization," "MRPRESSO," and "RadialMR" packages within the R software environment (version 4.3.2). Statistical significance was assessed using a two-sided P-value adjusted by the Bonferroni correction for multiple comparisons. Sensitivity analysis In UVMR analysis assessing the effects of lipids on CC, we performed the MR-Egger, MR-PRESSO, weighted median, simple mode, and weighted mode methods as sensitivity analyses. The MR-Egger method assesses horizontal pleiotropy through its intercept term; a non-zero value suggests directional pleiotropy and indicates bias in the IVW estimate.And The MR-PRESSO method identifies horizontal pleiotropy by detecting outliers and recalculating estimates post-removal (27) . The weighted median method offers a reliable estimate if a minimum of 50% of the instruments are valid (23) . The simple and weighted mode methods offer a dependable estimate when horizontal pleiotropy is absent in the largest cluster (28) . In the UVMR analysis assessing the impact of lipids on inflammation and vice versa on CC, we employed MR-Egger and MR-PRESSO techniques for validation strength of the MV-IVW findings. In UVMR, we performed MR-Egger methods to validate the robustness(29). Genetic instrument strength was evaluated using F statistics, with values greater than 10 indicating strong instruments.Heterogeneity between the instruments was assessed using Cochrane’s Q statistics for IVW and the global test for MR-PRESSO. Results Effect of lipids on CC By evaluating the effects of 179 lipid species on CC, it was found that there was a relationship between 3 lipid species and the development of CC,which are Sterol ester, phosphatidylcholine (PC) and phosphatidylethanolamine (PE) (Figure.3). Genetic instruments were selected as follows: 25 independent SNPs for PC, 24 for Sterol ester, and 21 for PE, each with F statistics greater than 16(Detailed SNP can be found at Table. A1). In MR analysis, PC was linked to an increased risk of CC(odds ratio (OR)=1.12 [95% CI(1.00,1.24)], P=0.04)(Figure.5.A)(Table.1), while Sterol ester was also associated with an increased risk of CC(OR=1.14 [95%CI(1.03, 1.27)], P=0.04) (Figure.3), and PE was associated with a decreased risk of CC (OR=0.88 [95%CI(0.78,0.99)], P=0.04) (Figure.5.A)(Table.1). Detailed results can be found at list(Figure A5)(Table A4)\ Table.1 MR estimates of the effect of Lipid on inflammation and colon cancer. Outcome Method OR P Q statistic P- heterogeneity Egger intercept P- intercept PC CC IVW 1.12(1.00,1.24) 0.040 23.815 0.472 MR-Egger 1.10(0.86,1.40) 0.445 23.786 0.415 0.003 0.869 Weighted median 1.11(0.93,1.31) 0.225 Simple mode 0.92(0.68,1.24) 0.568 Weighted mode 1.09(0.86,1.39) 0.485 MR-PRESSO 0.508 PC IL-17 IVW 1.06(1.01,1.23 0.044 23.416 0.495 MR-Egger 1.17(1.03,1.32) 0.022 20.421 0.616 -0.014 0.097 Weighted median 1.09(1.00, 1.18) 0.041 Simple mode 1.06(0.91,1.24) 0.436 Weighted mode 1.10(0.98,1.22) 0.109 MR-PRESSO 0.506 IL-17 CC IVW 0.87(0.76,0.99) 0.034 31.753 0.479 MR-Egger 1.07(0.78,1.46) 0.676 29.608 0.538 -0.024 0.153 Weighted median 0.84(0.69,1.02) 0.077 Simple mode 0.67(0.45,1.00) 0.061 Weighted mode 0.78(0.55,1.11) 0.178 MR-PRESSO 0.506 PE CC IVW 0.88(0.78,0.99) 0.04 14.716 0.792 MR-Egger 1.21(0.87,1.67) 0.279 10.685 0.934 0.019 0.059 Weighted median 0.95(0.80,1.13) 0.500 Simple mode 0.71(0.51,0.99) 0.054 Weighted mode 1.00(0.81,1.25) 0.978 MR-PRESSO 0.744 PE Axin-1 IVW 0.91(0.86,0.97) 0.004 19.875 0.529 MR-Egger 0.92(0.77,1.09) 0.329 19.873 0.466 -0.0003 0.968 Weighted median 0.89(0.81,0.98) 0.013 Simple mode 0.97(0.83,1.14) 0.705 Weighted mode 0.92(0.82,1.03) 0.169 MR-PRESSO 0.506 Axin-1 CC IVW 0.79(0.63,0.99) 0.039 9.265 0.597 MR-Egger 0.93(0.50,1.73) 0.817 8.963 0.536 -0.016 0.595 Weighted median 0.84(0.62,1.14) 0.258 Simple mode 0.90(0.54,1.52) 0.712 Weighted mode 0.85(0.54,1.33) 0.494 MR-PRESSO 0.620 Odds ratio (OR), 95% confidence interval (CI), and P values were calculated for the respective method of MR analysis. The heterogeneity test in the IVW methods was performed using Cochran’s Q statistic and the global test for the MR-PRESSO method. P < 0.05 was considered significant. IVW, inverse–variance weighted; P-heterogeneity, P value for heterogeneity test; P-intercept, P value for the intercept of MR-Egger regression. Mediation of inflammation in the relationship between lipids and CC Our analysis revealed that out of 91 inflammation species, only two lipid species showed a significant association with inflammation(Detailed SNP can be found at Table A2, results at Table A5). Specifically, the rise in the levels of four inflammation species and the decrease in the levels of two inflammation species level was significantly linked to the increase in PC levels (Figure.4), while the decrease in the levels of nine inflammation species and the increase in the levels of one inflammation level correlated with the increase in PE levels (Figure.4). Other detailed results can be found at figure below(Figure A2,A3,A4). Then,we estimated the impact of inflammation species significantly linked to two lipids on CC and only found both inflammation species were significantly associated with CC(Detailed SNP can be found at Table A3, results at Table A6). A negative association was found between IL-17 and CC(OR=0.87 [95%CI(0.76, 0.99)], P=0.034)(Figure.5.B)(Table.1). Axin-1 concentration showed a negative correlation with CC(OR=0.79 [95%CI(0.63, 0.99)], P=0.039) (Figure.5.B)(Table.1). The detailed results can be seen in the figure below(Figure A5). At last, We identified an indirect influence of PE on CC via Axin-1(OR=0.79 [95%CI(0.63, 0.99)], P=0.039) (Figure.5.B), accounting for the mediated effect of 2.07%(Table A7), while we identified the direct effects of PE and CC were not significant(P=0.49)(Figure.5.B). Nevertheless, PC may not indirectly affected CC through IL-17, but we also identified the direct effects of PC and CC were not significant(P=0.58) (Figure.5.B). The associations showed no signs of heterogeneity orhorizontal pleiotropy(Figure.5.B)(Table.1). Sensitivity analysis The study indicated that MR-PRESSO P values were greater than 0.5, implying no horizontal pleiotropy in the MR analysis. Additionally, heterogeneity tests confirmed the absence of heterogeneity in these studies. Discussion In our study, we assessed genetic links between lipids, inflammation, and CC. Our findings suggest genetic variants in PC are linked to CC risk and IL-17, while those in PE are associated with Axin-1 and CC risk. Since Axin-1 and IL-17 are implicated in CC suppression, they may mediate the impact of PE and PC on CC, respectively. Association between PE, PC, and CC Numerous clinical studies have explored the complex relationship between lipids and colon cancer, with conflicting results. Research indicates that certain lipids may be associated with a higher risk of colon cancer (30) , while other studies suggest they could inhibit cancer cell proliferation. For example, Atorvastatin has demonstrated inhibitory effects on colon cancer cell growth in laboratory studies (31) , however, clinical trials have not indicated a significant impact on cancer outcomes (32) . Similarly, preclinical studies on simvastatin have demonstrated a potential inhibitory effect on colon cancer cells, yet clinical studies have not shown improvements in patient survival rates (33) . The role of lipids in various other types of cancer, such as breast, prostate, and uterine cancer, has also been investigated in numerous clinical trials, showing the inhibitory effect of lipids on cancer (34-36) . MR study have indicated no association between lipids and the risk of colon or rectal cancer (37) . PE and PC are two key types of glycerol phospholipids found in cell membranes. Preclinical studies have suggested that PE and PC may have a synergistic therapeutic effect on colon cancer by inhibiting cancer cell proliferation (10, 30, 38, 39) .The precise effect of these lipids on colon cancer in clinical settings is not well understood due to limited research. To explore the potential protective effects of PE and PC on colon cancer, our study employed strong genetic instruments for lipids as instrumental variables in a large GWAS. Our findings suggest that while PC may promote colon cancer, PE shows promise in inhibiting the development of the disease in the general population.Overall, the relationship between lipids and colon cancer is complex and warrants further research to fully understand the potential benefits and risks associated with different lipids types. By utilizing advanced genetic techniques, we hope to shed light on the intricate interplay between lipids and colon cancer to improve future treatment strategies and prevention efforts. Association between PE, PC, Axin1, and IL-17 Significant advancements have been achieved in examining the influence of lipids on inflammation. Numerous clinical trials demonstrate the catalytic effects of lipid-regulating drugs on interleukin-17(IL-17) (40, 41) . Concurrently, animal studies indicate that PC can exacerbate inflammation by stimulating IL-17 secretion via CD1d-dependent γδT cells (42) . Currently, no clinical studies have investigated the relationship between PC and IL-17. Nevertheless, Clinical data suggest that when PC levels decrease, there is no notable alteration in inflammatory markers (43) . Similarly, These experiments indicate that regulating PE can impact Wnt/β-catenin signal transduction in CC, thus exerting anti-tumor effects (44) . Despite insights from preclinical and clinical studies into the inflammatory roles of lipids, the precise relationships between PC and IL-17, as well as PE and Axin1, remain unresolved due to a lack of clinical trials. Further clinical research is needed to clarify these associations. Until then, the precise clinical implications of these lipid-inflammatory interactions remain unclear. By utilizing genetic variation in two lipids groups and conducting GWAS on inflammation, we delved deeper into the impact of blood lipids on inflammation. Our findings revealed that PC enhances IL-17 secretion, whereas PE acts as an inflammatory inhibitor by suppressing Axin1. Axin1 and IL-17 mediate the relationship between PE, PC, and CC, revealing a complex interplay Inflammation is a crucial indicator of cancer, especially in CC, significantly influencing disease progression and survival (45) . Previous studies have demonstrated a genetic association between IL-17 and CC, consistent with findings from clinical trials (46) . Inflammation may underlie the link between lipids and CC, as suggested by recent preclinical research (47) . These studies suggest that elevated IL-17 levels could potentially drive CC progression (48) . However, our study presents a contrasting observation, indicating that increased IL-17 levels may instead suppress CC progression. This finding challenges the established understanding of IL-17 role in CC. Although it remains unclear whether IL-17 mediates the promoting effect of PC on CC, our results suggest that PC promotes the development of IL-17, so IL-17 may inhibit the development of CC in one aspect. Similarly, these clinical trials have not yet investigated the link between Axin1 and CC, preclinical studies have identified a new mechanism by which Axin1 inhibits CC progression via the interferon gamma (IFN γ)/Th1 response (49) . Our study provides genetic evidence that Axin1 may mediate the inhibitory effect of PE on CC. The associations observed in this study should not be interpreted as causal relationships. Additional experiments are necessary to confirm a definitive connection between these factors and CC progression. Our findings provide new insights into the roles of IL-17 and Axin1 in colorectal cancer, establishing a basis for further exploration of their therapeutic implications. Strengths and limitations This groundbreaking study utilized MR analysis to explore the connection between lipids, inflammation, and CC among the general population. Genetic evidence suggested that PE may enhance CC via Axin1. Despite these findings, there are limitations to consider. This study elucidates the complex interplay of these factors and underscores the necessity for additional research in this crucial field. The impact of genetic variation on chronic conditions like CC is a complex interplay between long-term effects and short-term influences. While the Genetic correlation between PE and PC can provide insights into the lifetime effects on CC, it may not capture the immediate effects. MR analysis can identify potential causal links between PC and CC, guiding further research without offering exact quantification (50) . This predicted direction of causality can guide future clinical trials to explore potential therapeutic interventions. Our study examining the genetic link between PC, IL-17, and CC revealed a surprising finding - the expected effect of IL-17 in promoting CC was not observed. This situation suggests that there may be other variables in the mediating role of IL-17 in mediating PC and CC. This discrepancy indicates the necessity for additional research to explore the underlying mechanisms or identify other variables affecting this relationship. The reliance on a European-based GWAS database for MR analysis may limit the generalizability of the findings to other populations. Further experiments across diverse populations are essential to validate these findings and assess their applicability worldwide. In essence, unraveling the intricate genetic mechanisms underlying CC and its relationship to lifestyle factors like PC is a complex but crucial step in advancing our understanding and developing targeted interventions. Conclusion In conclusion, this study confirmed the link between genetically predicted lipid levels, inflammation, and CC. The promoting effect of PE on CC was mediated by the Axin1. These findings offer genetic evidence for lipid mechanisms in reducing CC risk, potentially guiding future mechanistic and clinical research. Abbreviations CC=colon cancer; CEA=Carcinoembryonic antigen; GWAS=genome-wide association study; IVW=inverse variant weight; MR=Mendelian randomization; PC=phosphatidylcholine; PE=Phosphatidylethanolamine; UVMR=univariable Mendelian randomization; OR=odds ratio Declarations Author contributions Data curation: Zeyang Li, Muyuan Ma, Yun Jin, Yuanzhe Li, Zhuo Wang, Shujie Peng and Jin zhou. Formal analysis: Zeyang Li, Yun Jin, Yuanzhe Li. Investigation: Sheng Li, Yongfeng Wang. Methodology: Muyuan Ma, Yun Jin. Writing – original draft: Zeyang Li, Muyuan Ma, Yun Jin, Yuanzhe Li. Writing – review & editing: Zeyang Li, Muyuan Ma, Yongfeng Wang. Acknowledgement We express our gratitude to the Genome-Wide Association Studies (GWAS) for making their summary data publicly accessible, and we extend our appreciation to all the investigators and participants whose contributions were integral to these studies. Funding information This research was supported by the Ningxia Natural Science Foundation-Outstanding Youth Foundation Grant numbers(2024AAC05091). Conflict of interest statement The authors declare no conflicts of interest. Data Availability Statements The lipid data underlying this article are available in GWAS catalog at (https://www.ebi.ac.uk/gwas/home) , and can be accessed with GCST90277238-GCST90277416 The inflammation data underlying this article are available in GWAS catalog at (https://www.ebi.ac.uk/gwas/home) and can be accessed with GCST90274758 to GCST90274848. Our colon cancer data is available at the IEU GWAS catalog (https://gwas.mrcieu.ac.uk/) and can be accessed with ukb-b-20145. Ethics statement The data utilized in this study were sourced from previously published research that had received approval from the relevant ethics committees; consequently, no additional ethical approval was necessary for this investigation. Clinical trial number :not applicable Our study employs Mendelian randomization, a methodological approach that does not align with the parameters of clinical trials. This study utilizes data sourced from publicly accessible databases, thereby obviating the need for ethical approval. Additionally, as there is no clinical intervention involved, registration as a clinical trial is not necessary. References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024. Shaukat A, Mongin SJ, Geisser MS, Lederle FA, Bond JH, Mandel JS, et al. Long-term mortality after screening for colorectal cancer. N Engl J Med. 2013;369(12):1106-14. Brenner H, Stock C, Hoffmeister M. Colorectal cancer screening: the time to act is now. BMC Med. 2015;13:262. Cao Q, Tian Y, Deng Z, Yang F, Chen E. Epigenetic Alteration in Colorectal Cancer: Potential Diagnostic and Prognostic Implications. Int J Mol Sci. 2024;25(6). Palmqvist R, Engarås B, Lindmark G, Hallmans G, Tavelin B, Nilsson O, et al. Prediagnostic levels of carcinoembryonic antigen and CA 242 in colorectal cancer: a matched case-control study. Dis Colon Rectum. 2003;46(11):1538-44. Rawla P, Sunkara T, Barsouk A. Epidemiology of colorectal cancer: incidence, mortality, survival, and risk factors. Prz Gastroenterol. 2019;14(2):89-103. Ibáñez C, Mouhid L, Reglero G, Ramírez de Molina A. Lipidomics Insights in Health and Nutritional Intervention Studies. J Agric Food Chem. 2017;65(36):7827-42. Rysman E, Brusselmans K, Scheys K, Timmermans L, Derua R, Munck S, et al. De novo lipogenesis protects cancer cells from free radicals and chemotherapeutics by promoting membrane lipid saturation. Cancer Res. 2010;70(20):8117-26. Che G, Wang W, Wang J, He C, Yin J, Chen Z, et al. Sulfotransferase SULT2B1 facilitates colon cancer metastasis by promoting SCD1-mediated lipid metabolism. Clin Transl Med. 2024;14(2):e1587. Xie P, Zhang H, Wu P, Chen Y, Cai Z. Three-Dimensional Mass Spectrometry Imaging Reveals Distributions of Lipids and the Drug Metabolite Associated with the Enhanced Growth of Colon Cancer Cell Spheroids Treated with Triclosan. Anal Chem. 2022;94(40):13667-75. Coussens LM, Werb Z. Inflammation and cancer. Nature. 2002;420(6917):860-7. Hyka N, Dayer JM, Modoux C, Kohno T, Edwards CK, 3rd, Roux-Lombard P, et al. Apolipoprotein A-I inhibits the production of interleukin-1beta and tumor necrosis factor-alpha by blocking contact-mediated activation of monocytes by T lymphocytes. Blood. 2001;97(8):2381-9. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell. 2000;100(1):57-70. Kawanishi S, Ohnishi S, Ma N, Hiraku Y, Murata M. Crosstalk between DNA Damage and Inflammation in the Multiple Steps of Carcinogenesis. Int J Mol Sci. 2017;18(8). Riabov V, Gudima A, Wang N, Mickley A, Orekhov A, Kzhyshkowska J. Role of tumor associated macrophages in tumor angiogenesis and lymphangiogenesis. Front Physiol. 2014;5:75. Van Der Kraak L, Gros P, Beauchemin N. Colitis-associated colon cancer: Is it in your genes? World J Gastroenterol. 2015;21(41):11688-99. Friis S, Riis AH, Erichsen R, Baron JA, Sørensen HT. Low-Dose Aspirin or Nonsteroidal Anti-inflammatory Drug Use and Colorectal Cancer Risk: A Population-Based, Case-Control Study. Ann Intern Med. 2015;163(5):347-55. Hipp AV, Bengsch B, Globig AM. Friend or Foe - Tc17 cell generation and current evidence for their importance in human disease. Discov Immunol. 2023;2(1):kyad010. Claessen MM, Schipper ME, Oldenburg B, Siersema PD, Offerhaus GJ, Vleggaar FP. WNT-pathway activation in IBD-associated colorectal carcinogenesis: potential biomarkers for colonic surveillance. Cell Oncol. 2010;32(4):303-10. Qiu L, Sun Y, Ning H, Chen G, Zhao W, Gao Y. The scaffold protein AXIN1: gene ontology, signal network, and physiological function. Cell Commun Signal. 2024;22(1):77. Innocenti F, Sibley AB, Patil SA, Etheridge AS, Jiang C, Ou FS, et al. Genomic Analysis of Germline Variation Associated with Survival of Patients with Colorectal Cancer Treated with Chemotherapy Plus Biologics in CALGB/SWOG 80405 (Alliance). Clin Cancer Res. 2021;27(1):267-75. Davies NM, Howe LJ, Brumpton B, Havdahl A, Evans DM, Davey Smith G. Within family Mendelian randomization studies. Hum Mol Genet. 2019;28(R2):R170-r9. Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(5):693-8. Bowden J, Spiller W, Del Greco MF, Sheehan N, Thompson J, Minelli C, et al. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. Int J Epidemiol. 2018;47(4):1264-78. Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, et al. Guidelines for performing Mendelian randomization investigations: update for summer 2023. Wellcome Open Res. 2019;4:186. Lin Z, Deng Y, Pan W. Combining the strengths of inverse-variance weighting and Egger regression in Mendelian randomization using a mixture of regressions model. PLoS Genet. 2021;17(11):e1009922. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512-25. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40(4):304-14. Zheng J, Baird D, Borges MC, Bowden J, Hemani G, Haycock P, et al. Recent Developments in Mendelian Randomization Studies. Curr Epidemiol Rep. 2017;4(4):330-45. de Oliveira Alves N, Dalmasso G, Nikitina D, Vaysse A, Ruez R, Ledoux L, et al. The colibactin-producing Escherichia coli alters the tumor microenvironment to immunosuppressive lipid overload facilitating colorectal cancer progression and chemoresistance. Gut Microbes. 2024;16(1):2320291. Cai S, Gao Z. Atorvastatin inhibits proliferation and promotes apoptosis of colon cancer cells via COX-2/PGE2/β-Catenin Pathway. J buon. 2021;26(4):1219-25. Limburg PJ, Mahoney MR, Ziegler KL, Sontag SJ, Schoen RE, Benya R, et al. Randomized phase II trial of sulindac, atorvastatin, and prebiotic dietary fiber for colorectal cancer chemoprevention. Cancer Prev Res (Phila). 2011;4(2):259-69. Lim SH, Kim TW, Hong YS, Han SW, Lee KH, Kang HJ, et al. A randomised, double-blind, placebo-controlled multi-centre phase III trial of XELIRI/FOLFIRI plus simvastatin for patients with metastatic colorectal cancer. Br J Cancer. 2015;113(10):1421-6. Arun BK, Gong Y, Liu D, Litton JK, Gutierrez-Barrera AM, Jack Lee J, et al. Phase I biomarker modulation study of atorvastatin in women at increased risk for breast cancer. Breast Cancer Res Treat. 2016;158(1):67-77. Ghafarzadeh M, Shakarami A, Yari F, Marzban Rad Z. The role of anti-proliferative effects of atorvastatin on uterine fibroids: findings from a clinical study. Gynecol Endocrinol. 2021;37(8):721-4. Murtola TJ, Syvälä H, Tolonen T, Helminen M, Riikonen J, Koskimäki J, et al. Atorvastatin Versus Placebo for Prostate Cancer Before Radical Prostatectomy-A Randomized, Double-blind, Placebo-controlled Clinical Trial. Eur Urol. 2018;74(6):697-701. Luo X, Tu Z, Chen H, Ding J. Blood lipids and risk of colon or rectal cancer: a Mendelian randomization study. J Cancer Res Clin Oncol. 2021;147(12):3591-9. Jennemann R, Volz M, Bestvater F, Schmidt C, Richter K, Kaden S, et al. Blockade of Glycosphingolipid Synthesis Inhibits Cell Cycle and Spheroid Growth of Colon Cancer Cells In Vitro and Experimental Colon Cancer Incidence In Vivo. Int J Mol Sci. 2021;22(19). Mirhadi E, Askarizadeh A, Farhoudi L, Mashreghi M, Behboodifar S, Alavizadeh SH, et al. The impact of phospholipids with high transition temperature to enhance Redox-Sensitive liposomal doxorubicin efficacy in colon carcinoma model. Chem Phys Lipids. 2024:105396. Ma X, Liu S, Li T, Yuan H. Intensive statin treatment ameliorate the Th17/Treg functional imbalance in patients with non-ST elevation acute coronary syndrome underwent percutaneous coronary intervention. Clin Cardiol. 2020;43(4):379-85. Maneechotesuwan K, Wongkajornsilp A, Adcock IM, Barnes PJ. Simvastatin Suppresses Airway IL-17 and Upregulates IL-10 in Patients With Stable COPD. Chest. 2015;148(5):1164-76. Li Y, Wang Y, Shi F, Zhang X, Zhang Y, Bi K, et al. Phospholipid metabolites of the gut microbiota promote hypoxia-induced intestinal injury via CD1d-dependent γδ T cells. Gut Microbes. 2022;14(1):2096994. Versleijen MW, Roelofs HM, Rombouts C, Hermans PW, Noakes PS, Calder PC, et al. Short-term infusion of a fish oil-based lipid emulsion modulates fatty acid status, but not immune function or (anti)oxidant balance: a randomized cross-over study. Eur J Clin Invest. 2012;42(3):290-302. Saini MK, Sanyal SN. Piroxicam and c-phycocyanin prevent colon carcinogenesis by inhibition of membrane fluidity and canonical Wnt/β-catenin signaling while up-regulating ligand dependent transcription factor PPARγ. Biomed Pharmacother. 2014;68(5):537-50. Ross FA, Park JH, Mansouri D, Combet E, Horgan PG, McMillan DC, et al. The role of faecal calprotectin in diagnosis and staging of colorectal neoplasia: a systematic review and meta-analysis. BMC Gastroenterol. 2022;22(1):176. Shukla GS, Krag DN, Peletskaya EN, Pero SC, Sun YJ, Carman CL, et al. Intravenous infusion of phage-displayed antibody library in human cancer patients: enrichment and cancer-specificity of tumor-homing phage-antibodies. Cancer Immunol Immunother. 2013;62(8):1397-410. Lei L, Zhang J, Decker EA, Zhang G. Roles of Lipid Peroxidation-Derived Electrophiles in Pathogenesis of Colonic Inflammation and Colon Cancer. Front Cell Dev Biol. 2021;9:665591. Jiang JK, Lin CH, Chang TA, Lo LC, Lin CP, Lu RH, et al. Decreased interleukin-17RA expression is associated with good prognosis in patients with colorectal cancer and inhibits tumor growth and vascularity in mice. Cancer Med. 2024;13(5):e7059. Sanson R, Lazzara SL, Cune D, Pitasi CL, Trentesaux C, Fraudeau M, et al. Axin1 Protects Colon Carcinogenesis by an Immune-Mediated Effect. Cell Mol Gastroenterol Hepatol. 2023;15(3):689-715. Holmes MV, Richardson TG, Ference BA, Davies NM, Davey Smith G. Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development. Nat Rev Cardiol. 2021;18(6):435-53. Additional Declarations No competing interests reported. Supplementary Files Figure.pdf Table.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6407602","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":460817971,"identity":"33d35a07-3629-4944-8f22-151cd071c1f4","order_by":0,"name":"Zeyang Li","email":"","orcid":"","institution":"Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Zeyang","middleName":"","lastName":"Li","suffix":""},{"id":460817973,"identity":"c1f9bf16-7ab6-47fe-b740-64af32d416cb","order_by":1,"name":"Muyuan Ma","email":"","orcid":"","institution":"General Hospital of Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Muyuan","middleName":"","lastName":"Ma","suffix":""},{"id":460817975,"identity":"12d42a11-abec-4986-a1df-3371180bd362","order_by":2,"name":"Yun Jin","email":"","orcid":"","institution":"Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Jin","suffix":""},{"id":460817981,"identity":"6cb59c2b-6dea-4633-b57b-65a0df7ba9c9","order_by":3,"name":"Yuanzhe Li","email":"","orcid":"","institution":"Gansu University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yuanzhe","middleName":"","lastName":"Li","suffix":""},{"id":460817982,"identity":"a935dca9-d7a4-44f4-866a-9bf1b5f10743","order_by":4,"name":"Zhuo Wang","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Zhuo","middleName":"","lastName":"Wang","suffix":""},{"id":460817983,"identity":"92791e15-5002-4bb6-8db2-7e99ce2fa75c","order_by":5,"name":"Shujie Peng","email":"","orcid":"","institution":"Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Shujie","middleName":"","lastName":"Peng","suffix":""},{"id":460817984,"identity":"1ed2108a-794e-4f75-a49f-aacbd28ada02","order_by":6,"name":"Jin zhou","email":"","orcid":"","institution":"Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"zhou","suffix":""},{"id":460817985,"identity":"93bff9d2-a54a-4c4f-8893-e32fb370ff3b","order_by":7,"name":"Yongfeng Wang","email":"","orcid":"","institution":"Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yongfeng","middleName":"","lastName":"Wang","suffix":""},{"id":460817986,"identity":"53980859-72f5-4ac6-be8a-1187bc9a2084","order_by":8,"name":"Sheng Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYDCCA2DSgodBAkRXSMjJE6GFsYGBQQKq5YyFsWEDkVrAiIGxrSIRai9uwHe8+fmDj3skZORnNx97+HWeRAJjA/PDRzfwaJE8c8ywccYzCR6DO8fSjWW3SeSxM7AZG+fg0WJwI4exmecAUItEjpm05DaJYsYGHjZporTIz8j/Ji05RyKx4QCxWhhu5LBJfmwgQgvILzNngBx2I81MmuGYhLFhMwG/AEPswYcPB2zs5WckP5P8UVMnJ8/e/PAxPi0ogJkHTBKrHAQYf5CiehSMglEwCkYMAAA6zUrT8HMAxAAAAABJRU5ErkJggg==","orcid":"","institution":"The First People's Hospital of Lanzhou City","correspondingAuthor":true,"prefix":"","firstName":"Sheng","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-04-09 03:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6407602/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6407602/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83514042,"identity":"1fc61c3a-d460-4f16-8a36-b5ee065bb41b","added_by":"auto","created_at":"2025-05-27 17:49:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3576659,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization model and three key assumptions of a Mendelian randomization analysis. MR, Mendelian randomization; SNP,single nucleotide polymorphism.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-6407602/v1/851e5b1dcf342545ec19055f.png"},{"id":83514639,"identity":"b0ae9c67-5d11-480a-bce4-babf3f115473","added_by":"auto","created_at":"2025-05-27 17:57:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2019291,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of this Mendelian randomization study. GWAS, genome-wide summary association study; UVMR, Univariable Mendelian randomization.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-6407602/v1/38ea8afbc04f77a44a342221.png"},{"id":83514640,"identity":"d6c7c4ea-6726-4bce-a454-3cdb6d282bd1","added_by":"auto","created_at":"2025-05-27 17:57:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2613508,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization result of Lipid on colon cancer. (A) Sterol ester; (B) PC(16:1_18:2), phosphatidylcholine; (C) PC(18:1_20:3), phosphatidylcholine;(D) PE, Phosphatidylethanolamine.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-6407602/v1/412d704e529cd1c14e25cda0.png"},{"id":83514047,"identity":"923ee581-1ffd-4c1f-8c4b-c37a7d553b84","added_by":"auto","created_at":"2025-05-27 17:49:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3833959,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of the distribution of results from a Mendelian randomized study of inflammation associated with lipid. (A,B,C,D,E,F) Inflammation associated with PC;(A) beta-nerve growth factor; (B) Fibroblast growth factor 23;(C) Interleukin-5;(D) Matrix metalloproteinase-10;(E) Urokinase-type plasminogen activator;(F) IL-17; (G,H,I,J,K,L,M,N,O,P) Inflammation associated with PE;(G) Eukaryotic translation initiation factor 4E-binding protein 1;(H) Adenosine Deaminase;(I) Axin-1;(J) Caspase 8;(K) CD40L receptor;(L) SIR2-like protein 2;(M) Sulfotransferase 1A1;(N) STAM binding protein;(O) TNF-related apoptosis-inducing ligand;(P) Macrophage colony-stimulating factor 1. PC,phosphatidylcholine;IL-17,Interleukin-17C;PE,Phosphatidylethanolamine. Individual inverse variance (IV) associations with inflammation risk are displayed versus individual IV associations with lipid in black dots. The 95% confidence interval of the odds ratio for each IV is shown by the vertical and horizontal lines.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-6407602/v1/a6d10f7f3c8acc7fd3a759a5.png"},{"id":83514046,"identity":"ec4fe86a-0ddd-4e5e-8984-b9f6ce168e69","added_by":"auto","created_at":"2025-05-27 17:49:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2563527,"visible":true,"origin":"","legend":"\u003cp\u003eThe potential causal evidence summarized from the MR analysis lipid, inflammation and colon cancer. (A) lipid associated with colon cancer;(B) Inflammation mediates the role of lipids in MR analysis of colon cancer. PC, phosphatidylcholine;PE, Phosphatidylethanolamine;IL-17, Interleukin-17C;MR, Mendelian randomizatio\u003c/p\u003e\n\u003cp\u003eSpecifically, we require mendelian randomization analyses to adhere to rigorous statistical analysis and significance thresholds. This includes, but is not limited to the inclusion of sample size/power calculation, appropriate statistical tests and strongly independent genetic instruments, that are robustly associated with the risk factor of interest. While we understand that these thresholds can vary, we would expect the methods section to contain detailed information regarding the choice of p value cut-offs. Additionally, considering the essential requirements of strong IV assumptions, we would expect an assessment if selected genetic variants are associated with potential confounders. While the risk for horizontal or vertical pleiotropy can be estimated using adequate statistical tests, we also require a thorough evaluation of the suitability of these SNPs based on published literature.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-6407602/v1/7af54d9481aa0fc9e6dfb5e3.png"},{"id":88397976,"identity":"db753224-a023-41fa-91c1-2f1fafac703b","added_by":"auto","created_at":"2025-08-06 06:32:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":14279893,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6407602/v1/e6e77418-1ce1-4541-a4b9-347d19dae6dc.pdf"},{"id":83514039,"identity":"7aa77e54-ec12-4935-8e82-103da12df4a0","added_by":"auto","created_at":"2025-05-27 17:49:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2346945,"visible":true,"origin":"","legend":"","description":"","filename":"Figure.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6407602/v1/50c0a950b8958fab600010b1.pdf"},{"id":83514043,"identity":"78bedd6f-e57b-458a-b531-5e265e51f92a","added_by":"auto","created_at":"2025-05-27 17:49:54","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":80188,"visible":true,"origin":"","legend":"","description":"","filename":"Table.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6407602/v1/ad3feaa12d83a803d4b55f0e.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bridging Lipids and Colon Cancer: The Mediating Influence of Inflammation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColon cancer (CC) currently stands as the second deadliest and third most commonly diagnosed cancer, with a devastating impact on global health statistics\u003csup\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/sup\u003e. The fecal occult blood test serves as a crucial first step in CC screening, aiding in the identification of potential issues and reducing mortality rates in CC patients by 15\u0026ndash;33%\u003csup\u003e(2)\u003c/sup\u003e. Although colonoscopy has been shown to decrease CC incidence and mortality by 69% and 68% respectively\u003csup\u003e(3)\u003c/sup\u003e, its invasive nature often leads to patient discomfort and poses challenges such as the risk of infection and high costs. CT colonography is a non-invasive imaging method that utilizes computed tomography and specialized software to produce high-resolution images of the colon, offering a convenient alternative.CT colonography is less invasive than colonoscopy but cannot collect tissue samples, requiring a follow-up colonoscopy if abnormalities are detected\u003csup\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/sup\u003e. Carcinoembryonic antigen (CEA) is a classic tumor marker specific for colorectal cancer, useful in occult CC detection. However, due to its low sensitivity, CEA testing is not ideal for widespread screening\u003csup\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/sup\u003e.Given the limitations of current CC diagnostic methods, it is apparent that there is an urgent need for early diagnosis and prevention to reduce CC incidence and mortality\u003csup\u003e(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/sup\u003e. Investigating how inflammation influences the effect of lipids on colon cancer development is crucial for advancing early detection and prevention strategies.Inflammation is a complex biological process characterized by various cellular and molecular interactions that may initiate and promote colon cancer progression.Exploring the link between lipids and inflammation could provide novel targets for the early diagnosis, prevention, and treatment of colon cancer.\u003c/p\u003e \u003cp\u003eLipids, a diverse and ubiquitous group of molecules, have garnered significant scientific interest due to their crucial roles in various biological functions, including structure, metabolism, biological activities, molecular transport, signal transduction, endocrine regulation, and inflammation\u003csup\u003e(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/sup\u003e. Research suggests that tumor-associated lipogenesis significantly benefits cancer cells, aiding in their survival against carcinogenic and therapeutic insults\u003csup\u003e(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/sup\u003e. Lipid metabolism is crucial for tumor growth, with numerous essential enzymes involved in cancer-related lipid processes.Inhibition of these enzymes is considered a promising approach for cancer treatment, particularly CC. However, CC enzyme markers remain incompletely studied, with unclear key enzymes\u003csup\u003e(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/sup\u003e.Recent research examining the relationship between CC and lipids has revealed a potential link between increased liposomes and CC growth\u003csup\u003e(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/sup\u003e. These findings suggest that lipids may play a significant role in CC development, though a definitive understanding of this relationship remains elusive. Further research is required to investigate the intricate interactions between lipids and colon cancer, with the goal of clarifying the mechanisms involved and identifying potential targets for early diagnosis and treatment.\u003c/p\u003e \u003cp\u003eInflammation is pivotal in cancer development and progression, as extensively demonstrated by studies\u003csup\u003e(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/sup\u003e. Lipids, on the other hand, can have both anti-inflammatory and pro-inflammatory effects depending on their role in affecting the body's inflammatory response and mediators. Normally, lipids serve to resist inflammation\u003csup\u003e(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/sup\u003e. However, when lipid metabolism becomes abnormal, it can exacerbate the body's inflammatory response. Lipids contribute to inflammation by both promoting the production and secretion of inflammatory mediators and being susceptible to oxidation by these mediators, creating a cycle that can lead to health issues such as cancer. Cancer development involves genetic changes and is further driven by inflammatory mediators that enhance cell proliferation and inhibit DNA repair mechanisms, potentially leading to cancer\u003csup\u003e(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/sup\u003e. Chronic inflammation, characterized by prolonged tissue damage and DNA alterations, is exacerbated by elevated levels of reactive oxygen species (ROS) and mutagens, fostering a detrimental environment that facilitates the conversion of normal cells into cancerous ones\u003csup\u003e(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/sup\u003e. The accumulation of genetic and epigenetic changes brought on by chronic inflammation are crucial in initiating the cellular changes that lead to cancer development\u003csup\u003e(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/sup\u003e. Indeed, chronic inflammation has now been recognized as a hallmark of cancer\u003csup\u003e(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/sup\u003e. Investigating its role in colorectal cancer (CC) is crucial, especially in colitis-related cancer where inflammation significantly influences disease progression\u003csup\u003e(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/sup\u003e. Emerging evidence suggests that targeting and inhibiting persistent inflammation could potentially help prevent or delay the onset of both hereditary and sporadic CC\u003csup\u003e(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/sup\u003e. Studies have also identified the interleukin-17 (IL-17) pathway as being closely linked to inflammatory bowel diseases\u003csup\u003e(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/sup\u003e. Additionally,Axin-1 is crucial in molecular signaling networks, notably interacting with phosphokinases and proteins in the WNT/β-catenin pathway, which is activated early in colitis-associated carcinogenesis\u003csup\u003e(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/sup\u003e. Clinical trials have further suggested that Axin-1 may have potential in inhibiting CC progression\u003csup\u003e(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/sup\u003e. These findings underscore the importance of focusing on inflammation and molecular pathways for colorectal cancer prevention and management.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is a technique that employs genetic variation to investigate causal links between exposures and outcomes, minimizing confounding bias and reverse causality\u003csup\u003e(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/sup\u003e. In this study, we hypothesize that lipids and inflammatory factors may contribute to CC pathogenesis. Using a two-sample MR approach, we aim to explore the influence of lipids, as mediated by inflammation, on the incidence of CC.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy design\u003c/h2\u003e\n\u003cp\u003eThe present study employed a MR design(Figure.2). To validate potential causal inferences, MR analyses must meet three core assumptions: (I) the genetic variant must be linked to the exposure, (II) it should not be associated with any confounders, whether known or unknown, and (III) it should influence the outcome solely through the exposure variable, excluding other pathways(Figure.1).\u003c/p\u003e\n\u003ch2\u003eSelection of Lipid Genetic Instruments\u003c/h2\u003e\n\u003cp\u003eWe selected genetic instruments of 179 lipids species based on a genome-wide association study (GWAS) of 7169 Finnish individuals of European ancestry within GeneRISK cohort. These lipid data encompass four major categories:glycerolipids, glycerophospholipids, sphingolipids, and sterols. Lipid GWAS summary statistics were sourced from the EBI GWAS catalog(https://www.ebi.ac.uk/gwas/home). Our analyses concentrated on genome-wide significant genetic variants (P\u0026lt;1\u0026times;10\u003csup\u003e-5\u003c/sup\u003e) for each biomarker that were independent (LD r\u003csup\u003e2\u003c/sup\u003e\u0026lt;0.001 within 10,000 kb). Genetic variants were chosen as instrumental variables (IVs) based on the following criteria: (1) SNPs demonstrated a genome-wide significant association with each lipid of interest (P value\u0026lt;1\u0026times;10\u003csup\u003e-5\u003c/sup\u003e); (2) SNPs underwent LD-based clumping (r\u003csup\u003e2\u003c/sup\u003e\u0026gt;0.001, within a 10,000 kb window); (3) Alleles were consistent between exposure and outcome datasets; (4) IVs for exposure were not directly associated with the outcome (P value\u0026gt;1\u0026times;10\u003csup\u003e-5\u003c/sup\u003e); and (5) IVs with an F-statistic\u0026lt;10 were excluded.\u003c/p\u003e\n\u003ch2\u003eSelection criteria for genetic instruments related to inflammation were outlined in the studies\u003c/h2\u003e\n\u003cp\u003eWe systematically searched 91 inflammation from 14,824 European ancestry participants generated by 11 Olink Target Inflammation panel cohorts. The full GWAS summary statistics for the 91 inflammation traits can be accessed through the EBI GWAS Catalog(https://www.ebi.ac.uk/gwas/home). Our analyses concentrated on genome-wide significant genetic variants (P\u0026lt;1\u0026times;10\u003csup\u003e-5\u003c/sup\u003e) for each biomarker that were independent (LD r\u003csup\u003e2\u003c/sup\u003e\u0026lt;0.001 within 10,000 kb).\u003c/p\u003e\n\u003ch2\u003eThe study outcomes genetic data\u003c/h2\u003e\n\u003cp\u003eWe acquired the colon cancer GWAS summary statistics from the IEU GWAS catalog(ukb-b-20145). The colon cancer study included 1494 cases and 461,439 controls, all of European ancestry.The complete GWAS data are accessible to the public via the IEU GWAS Catalog(https://gwas.mrcieu.ac.uk). \u003c/p\u003e\n\u003ch2\u003eStatistical analyses\u003c/h2\u003e\n\u003ch2\u003eAn MR study assessing the impact of lipid inhibition on CC\u003c/h2\u003e\n\u003cp\u003eWe utilized two-sample univariable MR (UVMR) to assess the impact of lipids on CC. Prior to MR analysis, horizontal pleiotropy and heterogeneity were addressed using MR-PRESSO\u003csup\u003e(23)\u003c/sup\u003e\u003csup\u003e \u003c/sup\u003eand radial MR\u003csup\u003e(24)\u003c/sup\u003e. The primary analysis employed inverse variant weight(IVW), offering the most accurate and robust estimates when all genetic variants are valid\u003csup\u003e(25, 26)\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003eMediation MR analysis linking lipids with CC via inflammation\u003c/h2\u003e\n\u003cp\u003eA two-step Mendelian randomization was performed to assess the mediating role of inflammation in the relationship between lipids and CC. Initially, we utilized UVMR to assess the impact of lipids on 91 inflammation markers(1). Second, we used UVMR to assess the impact of inflammation markers, which had statistically significant associations with lipids, on CC. Subsequently, we identified inflammation significantly associated with CC and conducted UVMR to assess the impact of each inflammation on CC, accounting for the genetic influence of lipids (2). The mediation effect of each inflammation in the relationship between lipids and CC was determined by the product of coefficients 1 and 2.\u003c/p\u003e\n\u003cp\u003eAll Mendelian randomization analyses were performed using the \u0026quot;TwoSampleMR,\u0026quot; \u0026quot;MendelianRandomization,\u0026quot; \u0026quot;MRPRESSO,\u0026quot; and \u0026quot;RadialMR\u0026quot; packages within the R software environment (version 4.3.2). Statistical significance was assessed using a two-sided P-value adjusted by the Bonferroni correction for multiple comparisons.\u003c/p\u003e\n\u003ch2\u003eSensitivity analysis\u003c/h2\u003e\n\u003cp\u003eIn UVMR analysis assessing the effects of lipids on CC, we performed the MR-Egger, MR-PRESSO, weighted median, simple mode, and weighted mode methods as sensitivity analyses. The MR-Egger method assesses horizontal pleiotropy through its intercept term; a non-zero value suggests directional pleiotropy and indicates bias in the IVW estimate.And The MR-PRESSO method identifies horizontal pleiotropy by detecting outliers and recalculating estimates post-removal\u003csup\u003e(27)\u003c/sup\u003e. The weighted median method offers a reliable estimate if a minimum of 50% of the instruments are valid\u003csup\u003e(23)\u003c/sup\u003e. The simple and weighted mode methods offer a dependable estimate when horizontal pleiotropy is absent in the largest cluster\u003csup\u003e(28)\u003c/sup\u003e. In the UVMR analysis assessing the impact of lipids on inflammation and vice versa on CC, we employed MR-Egger and MR-PRESSO techniques for validation strength of the MV-IVW findings. In UVMR, we performed MR-Egger methods to validate the robustness(29).\u003c/p\u003e\n\u003cp\u003eGenetic instrument strength was evaluated using F statistics, with values greater than 10 indicating strong instruments.Heterogeneity between the instruments was assessed using Cochrane\u0026rsquo;s Q statistics for IVW and the global test for MR-PRESSO.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eEffect of lipids on CC\u003c/h2\u003e\n\u003cp\u003eBy evaluating the effects of 179 lipid species on CC, it was found that there was a relationship between 3 lipid species and the development of CC,which are Sterol ester, phosphatidylcholine (PC) and phosphatidylethanolamine (PE) (Figure.3). Genetic \u0026nbsp;instruments were selected as follows: 25 independent SNPs for PC, 24 for Sterol ester, and 21 for PE, each with F statistics greater than 16(Detailed SNP can be found at Table. A1). In MR analysis, PC was linked to \u0026nbsp;an increased risk of CC(odds ratio (OR)=1.12 [95% CI(1.00,1.24)], \u0026nbsp;P=0.04)(Figure.5.A)(Table.1), while Sterol ester was also associated with an increased risk of CC(OR=1.14 [95%CI(1.03, 1.27)], P=0.04) (Figure.3), and \u0026nbsp;PE was associated with \u0026nbsp;a decreased \u0026nbsp;risk of CC (OR=0.88 [95%CI(0.78,0.99)], P=0.04) (Figure.5.A)(Table.1). Detailed results can be found at list(Figure A5)(Table A4)\\\u003c/p\u003e\n\u003cp\u003eTable.1 MR estimates of the effect of Lipid on inflammation and colon cancer.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"561\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Outcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003eQ statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eP-\u003c/p\u003e\n \u003cp\u003eheterogeneity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eEgger\u003c/p\u003e\n \u003cp\u003eintercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eP-\u003c/p\u003e\n \u003cp\u003eintercept\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.12(1.00,1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e23.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.10(0.86,1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e23.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.869\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.11(0.93,1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.92(0.68,1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eWeighted mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.09(0.86,1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eMR-PRESSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eIL-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.06(1.01,1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e23.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.17(1.03,1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e20.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.09(1.00, 1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.06(0.91,1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eWeighted mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.10(0.98,1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eMR-PRESSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eIL-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.87(0.76,0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e31.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.07(0.78,1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e29.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.84(0.69,1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.67(0.45,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eWeighted mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.78(0.55,1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eMR-PRESSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003ePE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.88(0.78,0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e14.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.21(0.87,1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e10.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.95(0.80,1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.71(0.51,0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eWeighted mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.00(0.81,1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eMR-PRESSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003ePE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eAxin-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.91(0.86,0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e19.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.92(0.77,1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e19.873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.89(0.81,0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.97(0.83,1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eWeighted mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.92(0.82,1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eMR-PRESSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eAxin-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.79(0.63,0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e9.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.93(0.50,1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e8.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.595\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.84(0.62,1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.90(0.54,1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eWeighted mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.85(0.54,1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eMR-PRESSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eOdds ratio (OR), 95% confidence interval (CI), and\u0026nbsp;P\u0026nbsp;values were calculated for the respective method of MR analysis. The heterogeneity test in the IVW methods was performed using Cochran\u0026rsquo;s Q statistic and the global test for the MR-PRESSO method.\u0026nbsp;P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e\n\u003cp\u003eIVW, inverse\u0026ndash;variance weighted; P-heterogeneity, P value for heterogeneity test; P-intercept, P value for the intercept of MR-Egger regression.\u003c/p\u003e\n\u003ch2\u003eMediation of inflammation in the relationship between lipids and CC\u003c/h2\u003e\n\u003cp\u003eOur analysis revealed that out of 91 inflammation species, only two lipid species showed a significant association with inflammation(Detailed\u0026nbsp;SNP\u0026nbsp;can be found\u0026nbsp;at Table A2, results at Table A5). Specifically, the rise\u0026nbsp;in the\u0026nbsp;levels\u0026nbsp;of\u0026nbsp;four \u0026nbsp;inflammation\u0026nbsp;species\u0026nbsp;and the\u0026nbsp;decrease in the\u0026nbsp;levels\u0026nbsp;of\u0026nbsp;two\u0026nbsp;inflammation\u0026nbsp;species\u0026nbsp;level\u0026nbsp;was significantly linked to \u0026nbsp;the\u0026nbsp;increase in PC\u0026nbsp;levels\u0026nbsp;(Figure.4), while the decrease in\u0026nbsp;the\u0026nbsp;levels of nine\u0026nbsp;inflammation\u0026nbsp;species\u0026nbsp;and\u0026nbsp;the\u0026nbsp;increase\u0026nbsp;in the\u0026nbsp;levels of one\u0026nbsp;inflammation level correlated with\u0026nbsp;the\u0026nbsp;increase in PE\u0026nbsp;levels\u0026nbsp;(Figure.4).\u0026nbsp;Other\u0026nbsp;detailed results can be found at figure below(Figure A2,A3,A4).\u003c/p\u003e\n\u003cp\u003eThen,we estimated the impact of inflammation species significantly linked to two lipids on CC and only found both inflammation species were significantly associated with CC(Detailed\u0026nbsp;SNP\u0026nbsp;can be found\u0026nbsp;at Table A3, results at Table A6).\u0026nbsp;A negative association was found between\u0026nbsp;IL-17 and CC(OR=0.87\u0026nbsp;[95%CI(0.76, 0.99)],\u0026nbsp;P=0.034)(Figure.5.B)(Table.1).\u0026nbsp;Axin-1\u0026nbsp;concentration showed a negative correlation with CC(OR=0.79 [95%CI(0.63,\u0026nbsp;0.99)], P=0.039)\u0026nbsp;(Figure.5.B)(Table.1).\u0026nbsp;The detailed results can be seen in the figure below(Figure A5).\u003c/p\u003e\n\u003cp\u003eAt last, We identified an indirect influence of PE on CC via Axin-1(OR=0.79 [95%CI(0.63, 0.99)], P=0.039) (Figure.5.B), accounting for the mediated effect of\u0026nbsp;2.07%(Table A7), while we identified\u0026nbsp;the direct effects of PE and CC were not significant(P=0.49)(Figure.5.B).\u0026nbsp;Nevertheless, PC\u0026nbsp;may not\u0026nbsp;indirectly affected CC through\u0026nbsp;IL-17, but\u0026nbsp;we also identified\u0026nbsp;the direct effects of PC and CC were not significant(P=0.58) (Figure.5.B).\u0026nbsp;The associations showed no signs of heterogeneity orhorizontal pleiotropy(Figure.5.B)(Table.1).\u003c/p\u003e\n\u003ch2\u003eSensitivity analysis\u003c/h2\u003e\n\u003cp\u003eThe study indicated that MR-PRESSO P values were greater than 0.5, implying no horizontal pleiotropy in the MR analysis. Additionally, heterogeneity tests confirmed the absence of heterogeneity in these studies.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study, we assessed genetic links\u0026nbsp;between lipids, inflammation,\u0026nbsp;and\u0026nbsp;CC. Our findings suggest genetic variants in PC are linked to CC risk\u0026nbsp;and IL-17, while those\u0026nbsp;in PE are associated\u0026nbsp;with\u0026nbsp;Axin-1 and CC risk.\u0026nbsp;Since Axin-1\u0026nbsp;and IL-17 are implicated in CC suppression, they may mediate the impact of\u0026nbsp;PE and PC on CC, respectively.\u003c/p\u003e\n\u003ch2\u003eAssociation between PE, PC, and CC\u003c/h2\u003e\n\u003cp\u003eNumerous\u0026nbsp;clinical\u0026nbsp;studies\u0026nbsp;have explored the\u0026nbsp;complex relationship\u0026nbsp;between lipids and\u0026nbsp;colon\u0026nbsp;cancer, with conflicting results. Research indicates that certain lipids may be associated with a \u0026nbsp;higher risk of colon cancer\u003csup\u003e(30)\u003c/sup\u003e, while other studies suggest \u0026nbsp;they \u0026nbsp; could inhibit cancer cell proliferation. For example,\u0026nbsp;Atorvastatin has demonstrated inhibitory \u0026nbsp;effects on colon cancer cell \u0026nbsp;growth in laboratory studies\u003csup\u003e(31)\u003c/sup\u003e,\u0026nbsp;however, clinical trials have not indicated \u0026nbsp;a significant impact on cancer outcomes\u003csup\u003e(32)\u003c/sup\u003e. Similarly, preclinical studies on simvastatin have\u0026nbsp;demonstrated a potential inhibitory effect on colon cancer cells, yet clinical studies have not shown improvements in patient survival rates\u003csup\u003e(33)\u003c/sup\u003e. The role of lipids in various\u0026nbsp;other\u0026nbsp;types\u0026nbsp;of cancer,\u0026nbsp;such as\u0026nbsp;breast,\u0026nbsp;prostate, and\u0026nbsp;uterine cancer, has \u0026nbsp;also\u0026nbsp;been \u0026nbsp; investigated in\u0026nbsp;numerous \u0026nbsp;clinical\u0026nbsp;trials, \u0026nbsp;showing the \u0026nbsp; inhibitory effect of lipids on\u0026nbsp;cancer\u003csup\u003e(34-36)\u003c/sup\u003e. MR study\u0026nbsp;have\u0026nbsp;indicated\u0026nbsp;no association between lipids \u0026nbsp;and the risk of colon or rectal cancer\u003csup\u003e(37)\u003c/sup\u003e.\u0026nbsp;PE and PC\u0026nbsp;are \u0026nbsp; two \u0026nbsp;key \u0026nbsp;types \u0026nbsp; of glycerol phospholipids found in cell membranes. Preclinical studies have suggested that PE and PC may have a synergistic therapeutic effect on colon cancer by inhibiting cancer\u0026nbsp;cell proliferation\u003csup\u003e(10, 30, 38, 39)\u003c/sup\u003e.The\u0026nbsp;precise effect of these lipids on colon cancer in clinical settings is not well understood \u0026nbsp;due to limited research.\u0026nbsp;To \u0026nbsp;explore the potential protective effects of PE and PC on colon cancer, our study employed strong genetic instruments for lipids as instrumental variables in a large GWAS. \u0026nbsp;Our \u0026nbsp; findings \u0026nbsp; suggest \u0026nbsp;that \u0026nbsp; while \u0026nbsp;PC \u0026nbsp;may promote colon cancer, PE shows promise in inhibiting the development of the disease in the \u0026nbsp;general \u0026nbsp;population.Overall, \u0026nbsp;the \u0026nbsp; relationship \u0026nbsp;between \u0026nbsp;lipids \u0026nbsp; and \u0026nbsp;colon \u0026nbsp;cancer \u0026nbsp; is complex and warrants further research to fully understand the potential benefits and risks associated with different lipids types. By utilizing advanced genetic techniques, we hope to shed light on the intricate interplay between lipids and colon cancer to improve future treatment strategies and prevention efforts.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAssociation between PE, PC, Axin1, and IL-17\u003c/h2\u003e\n\u003cp\u003eSignificant advancements have been\u0026nbsp;achieved in\u0026nbsp;examining the influence\u0026nbsp;of lipids on \u0026nbsp;inflammation. Numerous\u0026nbsp;clinical trials demonstrate\u0026nbsp;the catalytic effects\u0026nbsp;of lipid-regulating drugs on interleukin-17(IL-17)\u003csup\u003e(40, 41)\u003c/sup\u003e. \u0026nbsp; Concurrently, \u0026nbsp;animal studies indicate that\u0026nbsp;PC\u0026nbsp;can exacerbate inflammation by stimulating IL-17\u0026nbsp;secretion via CD1d-dependent \u0026gamma;\u0026delta;T cells\u003csup\u003e(42)\u003c/sup\u003e. Currently, no clinical studies have investigated the relationship between PC and IL-17.\u0026nbsp;Nevertheless,\u0026nbsp;Clinical data suggest that when PC levels decrease, there is no notable alteration in inflammatory markers\u003csup\u003e(43)\u003c/sup\u003e.\u0026nbsp;Similarly,\u0026nbsp;These experiments indicate that regulating PE can impact Wnt/\u0026beta;-catenin signal \u0026nbsp;transduction\u0026nbsp;in\u0026nbsp;CC, thus exerting\u0026nbsp;anti-tumor effects\u003csup\u003e(44)\u003c/sup\u003e. Despite insights from \u0026nbsp;preclinical and \u0026nbsp;clinical \u0026nbsp; studies \u0026nbsp;into \u0026nbsp;the \u0026nbsp; inflammatory roles \u0026nbsp;of lipids, the precise relationships between PC and \u0026nbsp; IL-17, as \u0026nbsp;well as PE and Axin1, \u0026nbsp;remain \u0026nbsp;unresolved \u0026nbsp; due \u0026nbsp;to a \u0026nbsp;lack of clinical trials. \u0026nbsp;Further clinical research is needed to clarify these associations. Until then, the precise clinical implications of these lipid-inflammatory interactions remain unclear.\u003c/p\u003e\n\u003cp\u003eBy utilizing genetic variation in two lipids groups and conducting GWAS on inflammation, we delved deeper into the impact\u0026nbsp;of blood lipids\u0026nbsp;on inflammation. Our findings\u0026nbsp;revealed\u0026nbsp;that PC enhances IL-17\u0026nbsp;secretion, whereas PE\u0026nbsp;acts as an inflammatory inhibitor by suppressing Axin1.\u003c/p\u003e\n\u003ch2\u003eAxin1 and IL-17 mediate the relationship between PE, PC, and CC, revealing a complex interplay\u003c/h2\u003e\n\u003cp\u003eInflammation is a crucial indicator of cancer, especially in CC, significantly \u0026nbsp;influencing disease progression and survival\u003csup\u003e(45)\u003c/sup\u003e. Previous\u0026nbsp;studies have demonstrated a\u0026nbsp;genetic\u0026nbsp;association\u0026nbsp;between IL-17\u0026nbsp;and\u0026nbsp;CC, \u0026nbsp; consistent \u0026nbsp;with \u0026nbsp;findings \u0026nbsp; from \u0026nbsp;clinical \u0026nbsp;trials\u003csup\u003e(46)\u003c/sup\u003e. Inflammation may underlie\u0026nbsp;the link between lipids and CC, as suggested by recent preclinical research\u003csup\u003e(47)\u003c/sup\u003e. These studies suggest that elevated IL-17\u0026nbsp;levels\u0026nbsp;could potentially\u0026nbsp;drive CC progression\u003csup\u003e(48)\u003c/sup\u003e.\u0026nbsp;However,\u0026nbsp;our\u0026nbsp;study presents a contrasting observation, indicating that increased IL-17\u0026nbsp;levels may instead suppress CC \u0026nbsp;progression. This finding\u0026nbsp;challenges the established understanding\u0026nbsp;of IL-17 role in CC. Although it remains unclear whether IL-17 mediates the\u0026nbsp;promoting\u0026nbsp;effect of PC on\u0026nbsp;CC, our\u0026nbsp;results suggest that PC\u0026nbsp;promotes the development of IL-17, so IL-17 may inhibit the development of CC in one aspect.\u0026nbsp;Similarly, these\u0026nbsp;clinical trials have not yet investigated the link between Axin1 and CC, preclinical studies have identified a new mechanism by which Axin1 inhibits CC progression via the interferon gamma (IFN \u0026gamma;)/Th1 response\u003csup\u003e(49)\u003c/sup\u003e.\u0026nbsp;Our study provides genetic\u0026nbsp;evidence that Axin1 may mediate the inhibitory effect of PE on CC.\u0026nbsp;The associations observed in this study should not be interpreted as causal relationships.\u0026nbsp;Additional experiments are \u0026nbsp;necessary to confirm a definitive connection between these factors and CC progression. Our findings provide new insights into the roles of IL-17 and Axin1 in colorectal cancer, establishing a \u0026nbsp;basis for further exploration of their therapeutic implications.\u003c/p\u003e\n\u003ch2\u003eStrengths and limitations\u003c/h2\u003e\n\u003cp\u003eThis\u0026nbsp;groundbreaking\u0026nbsp;study utilized MR analysis to explore the connection between lipids, inflammation, and CC\u0026nbsp;among\u0026nbsp;the general population.\u0026nbsp;Genetic evidence\u0026nbsp;suggested that PE may \u0026nbsp;enhance \u0026nbsp; CC \u0026nbsp;via Axin1. \u0026nbsp;Despite \u0026nbsp; these \u0026nbsp;findings, \u0026nbsp;there \u0026nbsp; are \u0026nbsp;limitations \u0026nbsp;to consider. This study elucidates the complex interplay of these factors and underscores the necessity for additional research in this crucial field.\u003c/p\u003e\n\u003cp\u003eThe impact of genetic variation on chronic conditions like CC is a complex interplay between \u0026nbsp;long-term \u0026nbsp; effects \u0026nbsp;and \u0026nbsp;short-term \u0026nbsp; influences. \u0026nbsp;While \u0026nbsp;the \u0026nbsp; Genetic \u0026nbsp;correlation between PE\u0026nbsp;and PC can provide insights into the lifetime effects\u0026nbsp;on CC, it may not capture the\u0026nbsp;immediate effects. MR analysis can identify potential causal links between PC and CC, guiding further research \u0026nbsp;without offering exact quantification\u003csup\u003e(50)\u003c/sup\u003e. This predicted direction of causality can guide future clinical trials to \u0026nbsp;explore potential therapeutic \u0026nbsp;interventions. \u0026nbsp;Our \u0026nbsp; study \u0026nbsp;examining \u0026nbsp;the genetic link between PC, IL-17, and CC revealed a surprising finding - the expected \u0026nbsp;effect \u0026nbsp; of \u0026nbsp;IL-17 in promoting \u0026nbsp;CC was not observed. This situation suggests that there may be other variables in the mediating role of IL-17 in mediating PC and CC. This discrepancy indicates the necessity for additional research to explore the underlying mechanisms or identify other variables affecting this relationship. The reliance on a European-based GWAS database for MR analysis may \u0026nbsp;limit the generalizability of the findings to other populations. Further experiments across diverse populations are essential to validate these findings and assess their applicability worldwide. In essence, unraveling the intricate genetic mechanisms underlying CC and its relationship to lifestyle factors like PC is a complex but crucial step in advancing our understanding and developing targeted interventions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study confirmed the link between genetically predicted lipid levels, inflammation, and CC. The promoting effect of PE on CC was mediated by the Axin1. These findings offer genetic evidence for lipid mechanisms in reducing CC risk, potentially guiding future mechanistic and clinical research.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCC=colon cancer;\u003c/p\u003e\n\u003cp\u003eCEA=Carcinoembryonic antigen;\u003c/p\u003e\n\u003cp\u003eGWAS=genome-wide association study;\u003c/p\u003e\n\u003cp\u003eIVW=inverse variant weight;\u003c/p\u003e\n\u003cp\u003eMR=Mendelian randomization;\u003c/p\u003e\n\u003cp\u003ePC=phosphatidylcholine;\u003c/p\u003e\n\u003cp\u003ePE=Phosphatidylethanolamine;\u003c/p\u003e\n\u003cp\u003eUVMR=univariable Mendelian randomization;\u003c/p\u003e\n\u003cp\u003eOR=odds ratio\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eData curation: Zeyang Li, Muyuan Ma, Yun Jin, Yuanzhe Li, Zhuo Wang, Shujie Peng and Jin zhou.\u003c/p\u003e\n\u003cp\u003eFormal analysis: Zeyang Li, Yun Jin, Yuanzhe Li.\u003c/p\u003e\n\u003cp\u003eInvestigation: Sheng Li, Yongfeng Wang.\u003c/p\u003e\n\u003cp\u003eMethodology: Muyuan Ma, Yun Jin.\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; original draft: Zeyang Li, Muyuan Ma, Yun Jin, Yuanzhe Li.\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; review \u0026amp; editing:\u0026nbsp; Zeyang Li, Muyuan Ma, Yongfeng Wang.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe express our gratitude to the Genome-Wide Association Studies (GWAS) for making their summary data publicly accessible, and we extend our appreciation to all the investigators and participants whose contributions were integral to these studies.\u003c/p\u003e\n\u003ch2\u003eFunding information\u003c/h2\u003e\n\u003cp\u003eThis research was supported by the Ningxia Natural Science Foundation-Outstanding Youth Foundation Grant numbers(2024AAC05091).\u003c/p\u003e\n\u003ch2\u003eConflict of interest statement\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003ch2\u003eData Availability Statements\u003c/h2\u003e\n\u003cp\u003eThe lipid data underlying this article are available in GWAS catalog at (https://www.ebi.ac.uk/gwas/home) , and can be accessed with GCST90277238-GCST90277416\u003c/p\u003e\n\u003cp\u003eThe inflammation data underlying this article are available in GWAS catalog at (https://www.ebi.ac.uk/gwas/home) and can be accessed with GCST90274758 to GCST90274848.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Our colon cancer data is available at the IEU GWAS catalog (https://gwas.mrcieu.ac.uk/) and can be accessed with ukb-b-20145.\u003c/p\u003e\n\u003ch2\u003eEthics statement\u003c/h2\u003e\n\u003cp\u003eThe data utilized in this study were sourced from previously published research that had received approval from the relevant ethics committees; consequently, no additional ethical approval was necessary for this investigation.\u003c/p\u003e\n\u003ch2\u003eClinical trial number\u0026nbsp;:not applicable\u003c/h2\u003e\n\u003cp\u003eOur study employs Mendelian randomization, a methodological approach that does not align with the parameters of clinical trials. This study utilizes data sourced from publicly accessible databases, thereby obviating the need for ethical approval. Additionally, as there is no clinical intervention involved, registration as a clinical trial is not necessary.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024.\u003c/li\u003e\n\u003cli\u003eShaukat A, Mongin SJ, Geisser MS, Lederle FA, Bond JH, Mandel JS, et al. Long-term mortality after screening for colorectal cancer. N Engl J Med. 2013;369(12):1106-14.\u003c/li\u003e\n\u003cli\u003eBrenner H, Stock C, Hoffmeister M. Colorectal cancer screening: the time to act is now. BMC Med. 2015;13:262.\u003c/li\u003e\n\u003cli\u003eCao Q, Tian Y, Deng Z, Yang F, Chen E. Epigenetic Alteration in Colorectal Cancer: Potential Diagnostic and Prognostic Implications. Int J Mol Sci. 2024;25(6).\u003c/li\u003e\n\u003cli\u003ePalmqvist R, Engar\u0026aring;s B, Lindmark G, Hallmans G, Tavelin B, Nilsson O, et al. Prediagnostic levels of carcinoembryonic antigen and CA 242 in colorectal cancer: a matched case-control study. Dis Colon Rectum. 2003;46(11):1538-44.\u003c/li\u003e\n\u003cli\u003eRawla P, Sunkara T, Barsouk A. Epidemiology of colorectal cancer: incidence, mortality, survival, and risk factors. Prz Gastroenterol. 2019;14(2):89-103.\u003c/li\u003e\n\u003cli\u003eIb\u0026aacute;\u0026ntilde;ez C, Mouhid L, Reglero G, Ram\u0026iacute;rez de Molina A. Lipidomics Insights in Health and Nutritional Intervention Studies. J Agric Food Chem. 2017;65(36):7827-42.\u003c/li\u003e\n\u003cli\u003eRysman E, Brusselmans K, Scheys K, Timmermans L, Derua R, Munck S, et al. De novo lipogenesis protects cancer cells from free radicals and chemotherapeutics by promoting membrane lipid saturation. Cancer Res. 2010;70(20):8117-26.\u003c/li\u003e\n\u003cli\u003eChe G, Wang W, Wang J, He C, Yin J, Chen Z, et al. Sulfotransferase SULT2B1 facilitates colon cancer metastasis by promoting SCD1-mediated lipid metabolism. Clin Transl Med. 2024;14(2):e1587.\u003c/li\u003e\n\u003cli\u003eXie P, Zhang H, Wu P, Chen Y, Cai Z. Three-Dimensional Mass Spectrometry Imaging Reveals Distributions of Lipids and the Drug Metabolite Associated with the Enhanced Growth of Colon Cancer Cell Spheroids Treated with Triclosan. Anal Chem. 2022;94(40):13667-75.\u003c/li\u003e\n\u003cli\u003eCoussens LM, Werb Z. Inflammation and cancer. Nature. 2002;420(6917):860-7.\u003c/li\u003e\n\u003cli\u003eHyka N, Dayer JM, Modoux C, Kohno T, Edwards CK, 3rd, Roux-Lombard P, et al. Apolipoprotein A-I inhibits the production of interleukin-1beta and tumor necrosis factor-alpha by blocking contact-mediated activation of monocytes by T lymphocytes. Blood. 2001;97(8):2381-9.\u003c/li\u003e\n\u003cli\u003eHanahan D, Weinberg RA. The hallmarks of cancer. Cell. 2000;100(1):57-70.\u003c/li\u003e\n\u003cli\u003eKawanishi S, Ohnishi S, Ma N, Hiraku Y, Murata M. Crosstalk between DNA Damage and Inflammation in the Multiple Steps of Carcinogenesis. Int J Mol Sci. 2017;18(8).\u003c/li\u003e\n\u003cli\u003eRiabov V, Gudima A, Wang N, Mickley A, Orekhov A, Kzhyshkowska J. Role of tumor associated macrophages in tumor angiogenesis and lymphangiogenesis. Front Physiol. 2014;5:75.\u003c/li\u003e\n\u003cli\u003eVan Der Kraak L, Gros P, Beauchemin N. Colitis-associated colon cancer: Is it in your genes? World J Gastroenterol. 2015;21(41):11688-99.\u003c/li\u003e\n\u003cli\u003eFriis S, Riis AH, Erichsen R, Baron JA, S\u0026oslash;rensen HT. Low-Dose Aspirin or Nonsteroidal Anti-inflammatory Drug Use and Colorectal Cancer Risk: A Population-Based, Case-Control Study. Ann Intern Med. 2015;163(5):347-55.\u003c/li\u003e\n\u003cli\u003eHipp AV, Bengsch B, Globig AM. Friend or Foe - Tc17 cell generation and current evidence for their importance in human disease. Discov Immunol. 2023;2(1):kyad010.\u003c/li\u003e\n\u003cli\u003eClaessen MM, Schipper ME, Oldenburg B, Siersema PD, Offerhaus GJ, Vleggaar FP. WNT-pathway activation in IBD-associated colorectal carcinogenesis: potential biomarkers for colonic surveillance. Cell Oncol. 2010;32(4):303-10.\u003c/li\u003e\n\u003cli\u003eQiu L, Sun Y, Ning H, Chen G, Zhao W, Gao Y. The scaffold protein AXIN1: gene ontology, signal network, and physiological function. Cell Commun Signal. 2024;22(1):77.\u003c/li\u003e\n\u003cli\u003eInnocenti F, Sibley AB, Patil SA, Etheridge AS, Jiang C, Ou FS, et al. Genomic Analysis of Germline Variation Associated with Survival of Patients with Colorectal Cancer Treated with Chemotherapy Plus Biologics in CALGB/SWOG 80405 (Alliance). Clin Cancer Res. 2021;27(1):267-75.\u003c/li\u003e\n\u003cli\u003eDavies NM, Howe LJ, Brumpton B, Havdahl A, Evans DM, Davey Smith G. Within family Mendelian randomization studies. Hum Mol Genet. 2019;28(R2):R170-r9.\u003c/li\u003e\n\u003cli\u003eVerbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(5):693-8.\u003c/li\u003e\n\u003cli\u003eBowden J, Spiller W, Del Greco MF, Sheehan N, Thompson J, Minelli C, et al. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. Int J Epidemiol. 2018;47(4):1264-78.\u003c/li\u003e\n\u003cli\u003eBurgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, et al. Guidelines for performing Mendelian randomization investigations: update for summer 2023. Wellcome Open Res. 2019;4:186.\u003c/li\u003e\n\u003cli\u003eLin Z, Deng Y, Pan W. Combining the strengths of inverse-variance weighting and Egger regression in Mendelian randomization using a mixture of regressions model. PLoS Genet. 2021;17(11):e1009922.\u003c/li\u003e\n\u003cli\u003eBowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512-25.\u003c/li\u003e\n\u003cli\u003eBowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40(4):304-14.\u003c/li\u003e\n\u003cli\u003eZheng J, Baird D, Borges MC, Bowden J, Hemani G, Haycock P, et al. Recent Developments in Mendelian Randomization Studies. Curr Epidemiol Rep. 2017;4(4):330-45.\u003c/li\u003e\n\u003cli\u003ede Oliveira Alves N, Dalmasso G, Nikitina D, Vaysse A, Ruez R, Ledoux L, et al. The colibactin-producing Escherichia coli alters the tumor microenvironment to immunosuppressive lipid overload facilitating colorectal cancer progression and chemoresistance. Gut Microbes. 2024;16(1):2320291.\u003c/li\u003e\n\u003cli\u003eCai S, Gao Z. Atorvastatin inhibits proliferation and promotes apoptosis of colon cancer cells via COX-2/PGE2/\u0026beta;-Catenin Pathway. J buon. 2021;26(4):1219-25.\u003c/li\u003e\n\u003cli\u003eLimburg PJ, Mahoney MR, Ziegler KL, Sontag SJ, Schoen RE, Benya R, et al. Randomized phase II trial of sulindac, atorvastatin, and prebiotic dietary fiber for colorectal cancer chemoprevention. Cancer Prev Res (Phila). 2011;4(2):259-69.\u003c/li\u003e\n\u003cli\u003eLim SH, Kim TW, Hong YS, Han SW, Lee KH, Kang HJ, et al. A randomised, double-blind, placebo-controlled multi-centre phase III trial of XELIRI/FOLFIRI plus simvastatin for patients with metastatic colorectal cancer. Br J Cancer. 2015;113(10):1421-6.\u003c/li\u003e\n\u003cli\u003eArun BK, Gong Y, Liu D, Litton JK, Gutierrez-Barrera AM, Jack Lee J, et al. Phase I biomarker modulation study of atorvastatin in women at increased risk for breast cancer. Breast Cancer Res Treat. 2016;158(1):67-77.\u003c/li\u003e\n\u003cli\u003eGhafarzadeh M, Shakarami A, Yari F, Marzban Rad Z. The role of anti-proliferative effects of atorvastatin on uterine fibroids: findings from a clinical study. Gynecol Endocrinol. 2021;37(8):721-4.\u003c/li\u003e\n\u003cli\u003eMurtola TJ, Syv\u0026auml;l\u0026auml; H, Tolonen T, Helminen M, Riikonen J, Koskim\u0026auml;ki J, et al. Atorvastatin Versus Placebo for Prostate Cancer Before Radical Prostatectomy-A Randomized, Double-blind, Placebo-controlled Clinical Trial. Eur Urol. 2018;74(6):697-701.\u003c/li\u003e\n\u003cli\u003eLuo X, Tu Z, Chen H, Ding J. Blood lipids and risk of colon or rectal cancer: a Mendelian randomization study. J Cancer Res Clin Oncol. 2021;147(12):3591-9.\u003c/li\u003e\n\u003cli\u003eJennemann R, Volz M, Bestvater F, Schmidt C, Richter K, Kaden S, et al. Blockade of Glycosphingolipid Synthesis Inhibits Cell Cycle and Spheroid Growth of Colon Cancer Cells In Vitro and Experimental Colon Cancer Incidence In Vivo. Int J Mol Sci. 2021;22(19).\u003c/li\u003e\n\u003cli\u003eMirhadi E, Askarizadeh A, Farhoudi L, Mashreghi M, Behboodifar S, Alavizadeh SH, et al. The impact of phospholipids with high transition temperature to enhance Redox-Sensitive liposomal doxorubicin efficacy in colon carcinoma model. Chem Phys Lipids. 2024:105396.\u003c/li\u003e\n\u003cli\u003eMa X, Liu S, Li T, Yuan H. Intensive statin treatment ameliorate the Th17/Treg functional imbalance in patients with non-ST elevation acute coronary syndrome underwent percutaneous coronary intervention. Clin Cardiol. 2020;43(4):379-85.\u003c/li\u003e\n\u003cli\u003eManeechotesuwan K, Wongkajornsilp A, Adcock IM, Barnes PJ. Simvastatin Suppresses Airway IL-17 and Upregulates IL-10 in Patients With Stable COPD. Chest. 2015;148(5):1164-76.\u003c/li\u003e\n\u003cli\u003eLi Y, Wang Y, Shi F, Zhang X, Zhang Y, Bi K, et al. Phospholipid metabolites of the gut microbiota promote hypoxia-induced intestinal injury via CD1d-dependent \u0026gamma;\u0026delta; T cells. Gut Microbes. 2022;14(1):2096994.\u003c/li\u003e\n\u003cli\u003eVersleijen MW, Roelofs HM, Rombouts C, Hermans PW, Noakes PS, Calder PC, et al. Short-term infusion of a fish oil-based lipid emulsion modulates fatty acid status, but not immune function or (anti)oxidant balance: a randomized cross-over study. Eur J Clin Invest. 2012;42(3):290-302.\u003c/li\u003e\n\u003cli\u003eSaini MK, Sanyal SN. Piroxicam and c-phycocyanin prevent colon carcinogenesis by inhibition of membrane fluidity and canonical Wnt/\u0026beta;-catenin signaling while up-regulating ligand dependent transcription factor PPAR\u0026gamma;. Biomed Pharmacother. 2014;68(5):537-50.\u003c/li\u003e\n\u003cli\u003eRoss FA, Park JH, Mansouri D, Combet E, Horgan PG, McMillan DC, et al. The role of faecal calprotectin in diagnosis and staging of colorectal neoplasia: a systematic review and meta-analysis. BMC Gastroenterol. 2022;22(1):176.\u003c/li\u003e\n\u003cli\u003eShukla GS, Krag DN, Peletskaya EN, Pero SC, Sun YJ, Carman CL, et al. Intravenous infusion of phage-displayed antibody library in human cancer patients: enrichment and cancer-specificity of tumor-homing phage-antibodies. Cancer Immunol Immunother. 2013;62(8):1397-410.\u003c/li\u003e\n\u003cli\u003eLei L, Zhang J, Decker EA, Zhang G. Roles of Lipid Peroxidation-Derived Electrophiles in Pathogenesis of Colonic Inflammation and Colon Cancer. Front Cell Dev Biol. 2021;9:665591.\u003c/li\u003e\n\u003cli\u003eJiang JK, Lin CH, Chang TA, Lo LC, Lin CP, Lu RH, et al. Decreased interleukin-17RA expression is associated with good prognosis in patients with colorectal cancer and inhibits tumor growth and vascularity in mice. Cancer Med. 2024;13(5):e7059.\u003c/li\u003e\n\u003cli\u003eSanson R, Lazzara SL, Cune D, Pitasi CL, Trentesaux C, Fraudeau M, et al. Axin1 Protects Colon Carcinogenesis by an Immune-Mediated Effect. Cell Mol Gastroenterol Hepatol. 2023;15(3):689-715.\u003c/li\u003e\n\u003cli\u003eHolmes MV, Richardson TG, Ference BA, Davies NM, Davey Smith G. Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development. Nat Rev Cardiol. 2021;18(6):435-53.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mendelian randomization, Lipid, inflammation, colon cancer","lastPublishedDoi":"10.21203/rs.3.rs-6407602/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6407602/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e : Colon cancer(CC) is a common and deadly cancer. Research indicates a connection between inflammation, lipids, and colon cancer, though the precise nature of this relationship remains uncertain.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod \u003c/strong\u003e: We performed a two-sample Mendelian randomization(MR) analysis to examine the potential mediating role of inflammation in the relationship between lipids and colon cancer. In this study, we utilized recently published genome-wide association study (GWAS) data pertaining to lipids, inflammation, and colon cancer. The GeneRISK cohort provided lipid GWAS data from 7,169 Finnish individuals of European ancestry. Inflammation GWAS data were collected from 14,824 European participants across 11 cohorts using the Olink Target Inflammation panel. Colon cancer GWAS data were sourced from the IEU GWAS catalog (UKB-B-20145).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e : In this study, we identified onemediating relationship between inflammation, lipid, and colon cancer. Specifically, Phosphatidylethanolamine(PE) was found to indirectly influence colon cancer via Axin-1 concentration (OR=0.88 [95%CI(0.78, 0.99)], P =0.04).Although no mediating role of IL-17 in the relationship between phosphatidylcholine(PC) and CC was observed, our results suggest that PC can promote the development of CC(OR=1.12 [95%CI(1.00,1.24)], P=0.040), while IL-17 can inhibit the progression of CC(OR=0.87 [95%CI(0.76, 0.99)], P=0.034).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion\u003c/strong\u003e : In conclusion, our MR analysis identifies the indirect effects of and Axin1 on the PE to CC, supporting the link between genetically predicted lipid levels, inflammation, and CC. These findings furnish genetic evidence that underscores the role of lipid and inflammation mechanisms in reducing the risk of CC, thereby providing valuable insights for future mechanistic and clinical research.\u003c/p\u003e","manuscriptTitle":"Bridging Lipids and Colon Cancer: The Mediating Influence of Inflammation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-27 17:49:49","doi":"10.21203/rs.3.rs-6407602/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"28aa7550-51b5-4571-a032-3cde487c34dd","owner":[],"postedDate":"May 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-06T06:24:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-27 17:49:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6407602","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6407602","identity":"rs-6407602","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}