The Causal Relationships between Lipids related Traits and Atrial Fibrillation/Flutter: Two-sample Mendelian Randomization Analysis

The Causal Relationships between Lipids related Traits and Atrial Fibrillation/Flutter: Two-sample Mendelian Randomization Analysis | 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 Article The Causal Relationships between Lipids related Traits and Atrial Fibrillation/Flutter: Two-sample Mendelian Randomization Analysis Shuo Zhang, Linghua Fan, Long Zhang, Fangfang Fan, Jia Jia, Yan Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6624447/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: Atrial fibrillation (AF) stands as the most prevalent form of sustained arrhythmia, but the relationship between lipid is unclear. Objective: The objective of this research is to examine the causal relationships between 249 blood lipid-related traits and the likelihood of developing AF and atrial flutter by utilizing two-sample Mendelian randomization (MR) method. Methods: Data on the single nucleotide polymorphisms (SNPs) related to lipid-related traits were obtained from a proof-of-concept cross-platform Genome-wide association study GWAS datasets including participants ranging from 110,051 to 115,082 from the UK Biobank study, and data for AF were from the meta-analysis of GWASs and FinnGen study. The univariable and multivariable MR analysis were conducted to explore whether genetic evidence of individual lipid-related traits was significantly associated with AF risks. Results: We identified thirty lipid traits that exhibit a significant correlation with AF, in addition to 17 traits linked to AF and atrial flutter. Very-low-density lipoprotein (VLDL) and particle diameters were associated with AF (OR: 0.93, 95%CI 0.88-0.98, p=0.0055). A high percentage of triglycerides in VLDL is a protective factor for AF, whereas an elevated percentage of cholesterol, cholesteryl esters, free cholesterol, and phospholipids are risk factors for AF. Additionally, higher concentrations of intermediate-density lipoprotein (IDL) particles and elevated phospholipid levels in IDL were identified as risk factors for AF (OR: 1.06, 95%CI 1.0-1.13, p=0.0497 for particles; OR: 1.06, 95%CI 1.0-1,12, p=0.0404 for phospholipids). Notably, a higher percentage of monounsaturated fatty acids was found to be protective against AF (OR: 0.94, 95%CI 0.88-0.99, p=0.0258). Conclusion: our study identified certain lipid-relates traits are associated with AF/atrial flutter. Future research should focus on the underlying biological mechanisms and the role of lipid modulation to further inform AF prevention strategies. Health sciences/Diseases Health sciences/Diseases/Endocrine system and metabolic diseases blood lipids very low-density lipoprotein atrial fibrillation atrial flutter Mendelian randomization causal effect Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Atrial fibrillation (AF) stands as the most prevalent form of sustained arrhythmia, representing a significant public health concern due to its association with complications such as stroke, heart failure, and increased rates of mortality. According to the 2021 Global Burden of Disease (GBD) study, AF and atrial flutter impact approximately 52.55 million individuals globally, reflecting a remarkable 137% rise since 1990. Furthermore, the mortality rates linked to these conditions have experienced a twofold increase during the same timeframe ( 1 ). Traditional risk factors for AF encompass various demographic, anthropometric, and cardiovascular elements ( 2 ). In a study involving 15,400 participants with AF from 47 different countries, it was observed that most patients lacking the conventionally recognized AF risk factors demonstrate a positive prognosis over one year; nonetheless, the likelihood of rehospitalization due to AF remains high ( 3 ). Consequently, researchers have identified four overarching categories of nontraditional risk factors for AF: unhealthy lifestyle (such as sleep patterns, night shift work, and dietary habits), biomarkers (including gut microbiota, hyperuricemia, and homocysteine levels), adverse health conditions or diseases (like depression, epilepsy, clonal hematopoiesis of undetermined significance, infections, and asthma), and environmental influences (such as noise pollution and other environmental aspects) ( 4 ). Notably, elevated lipid levels can lead to an inflammatory response in some patients, which may increase the risk of AF ( 5 ). Healthy dietary patterns, such as the Mediterranean diet, may reduce epicardial adipose tissue and subsequently decrease the incidence of AF( 6 ). The potential mechanisms in it may be related to the change in lipid profiles ( 4 ). Previous research has extensively investigated the associations between lipid profiles and AF; however, the causal relationships remain largely uncharacterized (Table 1 ). Observational research has yielded inconsistent findings, with the absence of adjustments for variables such as obesity, geographical and ethnic disparities, along with other risk factors associated with coronary artery diseases (CAD), potentially elucidating these discrepancies( 7 – 9 ). Two meta-analyses encompassing large cohort studies have indicated a negative correlation between serum total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) levels and the risk of AF. Conversely, no significant relationship was established between triglyceride (TG) levels and the incidence of AF( 10 , 11 ). It is important to note that these investigations were limited by small sample sizes and the presence of potential confounding variables. Findings from MR studies present contradictions to earlier studies, suggesting an absence of a causal relationship between HDL-C, LDL-C, TG, and AF, with the exception of lipoprotein (a)( 12 – 14 ). Nonetheless, the majority of MR studies tend to concentrate on a narrow range of lipid profiles, which may not accurately represent the genuine risk factors for AF. Table 1 Summary of current findings on different research types of lipids and AF; TC : total cholesterol; HDL-C: high-density lipoprotein cholesterol; LDL-C : low-density lipoprotein cholesterol; TG: triglycerides; apo : apolipoprotein; lp(a) : lipoprotein (a). Research Type Author, year (ref) Population Sample size Follow-up time (years) N cases Finding(s) in relation to incidence or prevalence of AF Cohort study Alvaro Alonso, 2014 ( 7 ) Multi-Ethnic 7142 9.6 480 Higher HDL-C (HR 0.64, 95% CI 0.48 to 0.87) Higher TG (HR 1.60, 95% CI 1.25 to 2.05) TC or LDL-C not found to be risk factors Xintao Li,2018 ( 8 ) East Asian 88785 7.12 328 Higher TC (HR 0.60, 95%CI: 0.43–0.84) Higher LDL-C (HR 0.60, 95%CI: 0.43–0.83) Higher TC/HDL-C(HR 0.88, 95% CI: 0.79–0.98) Higher LDL-C/HDL-C (HR: 0.77, 95% CI: 0.66–0.91) HDL-C or TG not found to be risk factors Samia Mora,2014 ( 9 ) Caucasian (women) 23738 16.4 795 Higher LDL-C(HR 0.72,95%CI 0.56–0.92) Higher total number of LDL particles (HR 0.77, 95%CI 0.60–0.99) Higher VLDL particles (HR 0.78,95%CI 0.61–0.99) Higher cholesterol-poor small LDL (HR 0.78,95%CI 0.61-1.00) larger cholesterol-rich LDL particles and all HDL not found to be risk factors Meta analysis Bo Guan, 2020 ( 10 ) Multi-Ethnic 28,449 to 22,886,663 1 to 18.7 168 to 225,529 Higher TC (RR 0.81, 95% CI 0.72–0.92) Higher LDL-C (RR 0.79, 95% CI 0.70–0.88) Higher HDL-C (RR 0.86, 95% CI 0.76–0.97) TG not found to be risk factors Yisong Yao,2020 ( 11 ) Multi-Ethnic 3549 to 3660385 3.44 to 34.3 311 to 27581 Higher TC (RR 0.95,95% CI 0.93–0.96) Higher LDL-C (RR 0.95 95% CI 0.92–0.97) Higher HDL-C (RR 0.97,95% CI 0.96–0.99) TG not found to be risk factor Mendelian randomization Shengyi Yang,2021 ( 12 ) European ancestry 290198 to 318,674 NA 60,620 LDL-C,HDL-C,TG, apo A1,apo B not found to be risk factor Yuhang Tao,2024 ( 13 ) European and East Asian ancestry 2279 to 1320016 NA 837 to 60620 Higher lp(a) (OR 1.04,95% CI 1.03–1.06) only in European LDL-C,TG not found to be risk factors Pedrum,2022 ( 14 ) European ancestry 374,517 NA 17325 to 60620 Higher lp(a) (OR 1.04; 95% CI 1.03–1.05) Two-sample MR employs genetic variants as instrumental variables (IVs) to evaluate the causal relationships between exposures and outcomes. This methodology provides a robust framework for investigating the causal links between genetic risk factors and phenotypic outcomes, effectively reducing the potential for environmental confounding ( 15 ). The strength of this approach lies in its capacity to leverage genome-wide association studies (GWAS) with substantial sample sizes, which facilitate the identification of a multitude of genetic variants associated with lipids and lipid-related characteristics, thus serving as significant IVs for causal inference ( 16 ). In the present investigation, we conducted two-sample MR analyses to assess the causal influences of 249 traits linked to blood lipids on AF. The lipid and lipid-related traits considered in our analysis included cholesterol, free cholesterol (FC), cholesteryl esters (CE), phospholipids, TG and their subclasses, fatty acids, amino acids, ketone bodies, among others. Our results provide novel insights into the relationship between endogenous lipid metabolism and AF, thereby enriching the understanding of the genetic risk factors associated with this condition. Materials and Methods Study design We conducted a two-sample MR analysis to investigate the potential causal associations between 249 blood lipid measurements and lipid-related traits and the occurrence of AF/atrial flutter. The framework of the study is depicted in Fig. 1 . Genome-wide association study data sources In total, 249 blood lipid profiles and lipid-related phenotypes served as exposures (see Supplementary Table S1 ), sourced from a proof-of-concept cross-platform GWAS datasets. All 249 metabolic traits were measured by targeted high-throughput nuclear magnetic resonance metabolomics. The sample sizes for the 249 lipid traits in the GWAS ranged from 110,051 to 115,082 participants from the UK Biobank study ( 17 ). In addition, 173 of them were published in 2022, while the remaining were published in 2020. The main classifications of lipids and lipid-related phenotypes included: 1) cholesterol (major classes and subclasses), 2) CE (subclasses), 3) FC (subclasses), 4) phospholipids (major classes and subclasses), 5) TG (major classes and subclasses), 6) ketone bodies, 7) amino acids, 8) apolipoproteins, 9) miscellaneous, 10) fatty acids, 11) glycolysis-related components, 12) particle size, and 13) particle concentrations (both major classes and subclasses). The “major classes” mainly indicated HDL, LDL, very-low-density lipoprotein (VLDL) and TC; The “subclass” mainly indicated: IDL (intermediated density lipoprotein), large HDL, large LDL, large VLDL, medium HDL, medium LDL, medium VLDL, small HDL, small LDL, small VLDL, very small VLDL, very large HDL, very large VLDL, chylomicrons (CM) and extremely large VLDL. The data pertaining to the outcomes of AF were derived from a classic GWAS study encompassing more than 1,000,000 participants, comprising 60,620 individuals diagnosed with AF and 970,216 controls. This investigation successfully identified 142 independent risk variants distributed across 111 loci and highlighted 151 functional candidate genes that are likely implicated in the pathogenesis of AF( 18 ). Furthermore, the outcome data concerning both AF and atrial flutter originated from the FinnGen study, which included 22,068 cases alongside 116,926 controls. All datasets utilized in this research received approval from the appropriate ethical review board. Genetic instruments selection In our study, we chose single nucleotide polymorphisms (SNPs) guided by three fundamental criteria essential for MR genetic instrumental variables: these SNPs must achieve genome-wide significance (with a P value threshold set at 0.001 over a window size of 10,000 kb), and demonstrate resilience against weak instrument bias (with an F-statistic exceeding 10). Subsequently, we employed LDtrait to eliminate SNPs associated with potential confounders affecting the outcomes. Our investigation pinpointed blood pressure, blood glucose levels, body mass index, chronic nephropathy, CAD, and C-reactive protein as confounding variables (LDlink, accessed on February 19, 2025) ( 19 ). Following the selection of instrumental variables, we proceeded to harmonize the allele effects and adjust the β values in the outcome dataset to ensure alignment with the exposure dataset( 20 ). Statistical analyses In alignment with the principles of MR and its applications in cardiovascular diseases ( 21 ), we performed two-sample MR analyses employing five distinct methodologies: inverse-variance weighted (IVW), Simple mode, MR-Egger, weighted median, and weighted mode, with IVW serving as the primary analytical technique. All MR analyses were executed utilizing the TwoSampleMR R package ( 22 ). The intercept obtained from MR-Egger regression is instrumental in identifying pleiotropy within MR estimates ( 23 ). Cochran’s Q statistic (Q) is applied to evaluate heterogeneity in the context of IVW ( 24 ). Should the P-value derived from the Cochran’s Q test fall below 0.05, we will adopt IVW with a multiplicative random-effects model to validate our findings; conversely, a fixed-effects model will be implemented ( 25 ). The MR-Egger intercept test was employed to ascertain the presence of pleiotropy, whereby a P-value less than 0.05, suggesting the existence of pleiotropy, necessitated the exclusion of the results. Additionally, a leave-one-out (LOO) sensitivity analysis was conducted to identify any potentially dominant SNPs. Results The Mendelian randomization estimates of blood lipids and lipid-related phenotypes on AF The comprehensive findings of MR analyses and the evaluation of pleiotropy concerning 249 blood lipids and lipid-associated characteristics in relation to atrial AF are presented in Supplementary Table S2 . Utilizing the IVW approach, we discovered 31 lipid-related characteristics that exhibited a causal relationship with AF. This group comprised six subclasses of cholesterol, six subclasses of CE, five subclasses of FC, three subclasses of phospholipids, six subclasses of TG, one type of fatty acid, three subclasses pertaining to particle size and concentration, as well as one trait associated with total lipids (refer to Supplementary Table S3 and Fig. 2 ). The majority of these phenotypic traits displayed p-values below 0.05 when assessed through Cochran’s Q test, and the findings derived from the multiplicative random-effects model aligned with prior research. Notably, the MR-Egger regression analysis for one specific lipid-related trait, namely the TG to total lipids ratio in CM and very large VLDL, yielded a p-value of less than 0.05. This outcome suggested the existence of pleiotropy within the MR analyses, prompting us to exclude this specific result from consideration. The minimum F-statistic values for each SNP varied between 26 and 31, suggesting a minimal risk of weak instrumental variable bias affecting the overall outcomes (see Supplementary Table S4). Figure 2 reports the MR estimated of 30 lipid associated traits on AF risks. Our findings were categorized into four key areas. Firstly, the diameter of VLDL particles was negatively correlated with the development of AF. Specifically, smaller VLDL particle diameters corresponded to greater AF risk (OR: 0.93, 95% CI: 0.88–0.98, p = 0.0055). This finding aligns with the increased AF risk associated with higher levels of very small VLDL particles (OR: 1.05, 95% CI: 1.01–1.09, p = 0.0265).Further MR analysis showed that increased levels of cholesterol (OR: 1.06, 95% CI:1.01–1.11, p = 0.0125), CE (OR: 1.07, 95% CI:1.01–1.13, p = 0.03), and FC (OR: 1.05, 95% CI:1.00-1.09, p = 0.0473) in very small VLDL particles were risk factors for the development of AF. Secondly, we discovered that IDL-related lipid profiles were associated with AF. Specifically, higher concentrations of IDL particles (OR: 1.06, 95% CI: 1.00-1.13, p = 0.0497) and increased levels of phospholipids in IDL (OR: 1.06, 95% CI: 1.00-1.12, p = 0.0404) were identified as risk factors for AF development. Thirdly, different proportions of lipids were differentially associated with the development of AF in VLDL, with a higher proportion of TG being more protective against AF; elevated proportions of cholesterol, CE, FC, and phospholipids were risk factors for AF, and the largest ORs in CM and extremely large VLDL. Finally, a higher proportion of monounsaturated fatty acids(MUFAs) compared to total fatty acids was found to be protective against AF (OR: 0.94, 95% CI: 0.88–0.99, p = 0.0258). Supplementary Figures S1 and S2 illustrate the scatter plots and forest plots, respectively. The funnel plots displayed a symmetrical pattern, as shown in Supplementary Figure S3. Furthermore, the application of the LOO analysis revealed that no single SNP significantly influenced the relationship between lipid traits and the risk of AF, as depicted in Supplementary Figure S4. The Mendelian randomization estimates of blood lipids and lipid-related phenotypes on AF/ atrial flutter To explore the association between lipid profiles and AF/ atrial flutter, we selected the FinnGen study, which includes atrial flutter as an endpoint to validate the relationship between lipid traits and AF/atrial flutter. We found that among the thirty AF-related lipid traits, twenty-one were associated with AF/atrial flutter. Four lipid traits showed pleiotropy and were subsequently excluded from the outcomes. Ultimately, Seventeen lipid related traits were identified as having a causal effect on both AF/atrial flutter (Fig. 3 , Supplement Table 6). Consistent with results above, a higher percentage of TG in VLDL protected against AF/atrial flutter, while a higher percentage of cholesterol, FC, and CE in VLDL increased the risk of AF/atrial flutter. Very small VLDL and IDL are equally risk factors for AF/atrial flutter. Differently, the percentage of MUFAs had no significant correlation in the modified outcome. (Supplement Table S6). All minimal F values for the instrumental variables exceeded 28–30, indicating robustness (Supplement Table 7). Supplementary Figures S5 and S6 present scatterplots and forest plots, respectively. The funnel plot, depicted in Supplementary Figure S7, exhibits symmetry, while the LOO plots, illustrated in Supplementary Figure S8, did not reveal any SNPs with significant abnormalities. Multivariable MR Analysis of lipid-related traits and atrial AF We constructed various models based on lipid subgroups to better investigate the relationship between lipid-related traits and AF using multivariate MR analysis. The detailed results are shown in Supplementary Table 5. We found that in multivariate analysis, cholesterol(subclass), CE (subclass), and IDL-related subclass were in the presence of no significant correlation with AF. The association between the ratio of FC to total lipids in chylomicrons and extremely large VLDL and AF was found to be statistically significant (OR: 1.15, 95%CI: 1.03–1.28, p = 0.0145). Furthermore, the ratio of phospholipids to total lipids within medium VLDL also exhibited a significant correlation with AF, (OR:1.09, 95%CI: 1.03–1.15, p = 0.0019). Conversely, the ratio of TG to total lipids in very large VLDL was inversely related to AF (OR: 0.70, 95% CI: 0.50–0.99,p = 0.0442). In the subgroup analysis concerning VLDL, the mean diameter of VLDL particles was significantly associated with AF (OR: 0.89, 95%CI: 0.82–0.97, p = 0.007). In a model assessing various lipid characteristics, the total lipids within chylomicrons and extremely large VLDL were notably linked to AF (OR: 2.38, 95% CI:1.07–5.29, p = 0.0336). Additionally, the proportion of MUFAs to total fatty acids was found to have a significant association with AF (OR: 0.40, 95% CI: 0.19–0.84, p = 0.0157). Discussion In our study, we used a two-sample MR approach to explore the causal relationships between 249 blood lipids and lipid-related traits associated with AF. Thirty of these lipids were causally associated with AF, and seventeen also had a causal relationship with AF/atrial flutter. Our results indicate that very small VLDL and IDL particles, along with their lipid composition, are risk factors for developing AF. The proportion of TG in VLDL serves as a protective factor, while cholesterol, FC, CE, and phospholipids are risk factors for AF. Finally monounsaturated fatty acid proportion also serves as a protective factor for AF. This study lays the groundwork for exploring the metabolic risk factors of AF through genetic mechanisms, aiding future hypothesis-driven analyses. The association between serum cholesterol levels and CAD has been acknowledged since 1964. Concurrently, atherosclerotic disease has been recognized as an independent risk factor for the onset of AF ( 26 ). Consequently, one might anticipate that heightened TC and LDL-C levels would elevate the risk of developing AF. However, current research presents findings that challenge this assumption. Numerous well-designed cohort studies and meta-analyses have revealed an unexpected inverse correlation between conventional lipid profiles and the prevalence of AF (Table 1 ). The investigation into the mechanisms that underlie the relationship between lipoproteins and AF remains limited (see Fig. 4 ). Mora et al. ( 9 ) propose that the observed inverse association may stem from cholesterol’s role in stabilizing myocardial cell membranes. Furthermore, a reduction in cholesterol levels can negatively affect cardiomyocyte contractility by impairing calcium handling, adrenergic signaling, and the integrity of myofibrillar structure (27). Additionally, the relationship between cholesterol concentrations and AF development may also be linked to inflammatory processes. Research indicates that during states of inflammation, levels of TC, LDL-C, and HDL-C decline, while TG increase ( 28 ). Thus, lower cholesterol levels may indicate underlying inflammatory mechanisms within the host that contribute to the occurrence of AF. Moreover, lipoproteins play a critical role in modulating the progression of sepsis by binding to bacterial endotoxins, thereby mitigating the adverse effects associated with inflammatory responses ( 29 ). It is essential to note that lipids are naturally occurring substances that perform a variety of biological functions, including the formation of plasma membranes, acting as signaling molecules, and serving as an energy source. Each lipid subtype possesses distinct molecular structures and inherent properties. Conventional lipid profiles could not become precisely enough to reflect the true pathogenic components of lipids. Thus, we utilized more refined GWAS data on lipid-related traits to examine their relationship with AF, revealing several lipid fractions that are genuinely linked to the condition. It is especially important to note that the causal associations between lipid traits and AF primarily involve VLDL, including its particle concentrations and composition. VLDL acts as a precursor to IDL, which subsequently leads to the formation of LDL. The lipid composition of VLDL is primarily comprised of TG accounting for 50–70% of the particle mass, with cholesterol esters constituting 10–25%, and fatty acids making up less than 10% ( 30 ). VLDL is synthesized in the liver and subsequently released into the bloodstream, performing the essential function of transporting triglycerides to adipose tissue for storage or for utilization by other tissues. Following a meal, VLDL particles demonstrate a propensity to bind effectively and be internalized by cells expressing the VLDL receptor, such as cardiomyocytes ( 31 ). Upon their secretion from the liver into the bloodstream, VLDLs undergo alterations in size and composition as they engage with various enzymes, including phospholipid transfer protein (PLTP), cholesteryl ester transfer protein (CETP), and lipoprotein lipase (LPL). Hepatic PLTP plays a crucial role in modulating the secretion of VLDL from the liver. Within circulation, PLTP enhances the transfer of phospholipids from VLDLs to HDLs. Furthermore, CETP facilitates the exchange of CE from HDLs to TG-rich lipoproteins. The utilization of VLDL is a significant energy source for cardiac tissue, with approximately 70% of the heart's energy being derived from fatty acid oxidation under normal physiological conditions( 32 ). Excessive lipid accumulation in cardiomyocytes can lead to structural damage, cell death, mitochondrial dysfunction, and increased fibrosis, which may reduce cardiac function and subsequently increase the risk of AF( 33 ). Various lipid ingredients within VLDL may play different role in the onset of AF. Research has shown that post-menopausal women carrying the CETP B2B2/AA genotype exhibit an elevated risk of developing AF( 34 ). In the cohort of females diagnosed with AF, those with the B2B2 genotype displayed significantly reduced levels of HDL-C and elevated TG levels when compared to B2B2 female control participants. Furthermore, the study revealed a correlation between all B2 alleles of the TaqIB polymorphism and the A alleles of the − 629 promoter polymorphism. In light of our findings, it may be inferred that mutations in the CETP B2B2/AA genotype could facilitate the transfer of a greater amount of CE from HDL to VLDL, thereby heightening the likelihood of AF development. Our study also revealed that elevated levels of CE, cholesterol, and FC in VLDL are risk factors for AF. And the underlying mechanism may be that excessive cholesterol in VLDL may lead to the deposition of lipids in cardiac myocytes, which may in turn damage cardiac myocytes and lead to an elevated risk of AF. Research indicates that lipoproteins influence the development of AF not solely based on their lipid composition, but also in relation to the particle size of VLDL particles. Specifically, smaller lipoprotein particles are associated with an inverse relationship to AF, whereas larger, cholesterol-rich LDL particles, as well as total HDL-C, lipoprotein (a), and TG, do not exhibit a significant correlation with the onset of AF ( 9 ). Furthermore, a limited study focusing on female patients undergoing catheter ablation revealed that those diagnosed with AF possessed smaller lipoprotein particles, alongside heightened levels of oxidation, glycation, and TG content when compared to a control group in sinus rhythm ( 35 ). These changes led to increased foam cell formation due to faster phagocytosis by macrophages and a decrease in the antioxidant capacity of HDL ( 35 ). Our study found that smaller particles of VLDL and the cholesterol, CE, and phospholipids therein are risk factors for AF, proving the veracity of the clinical findings at a different level. Meanwhile, other researchers have identified a significant relationship between VLDL levels in patients with metabolic syndrome (MetS) and AF. In vivo studies indicate that the intravenous administration of VLDL derived from individuals diagnosed with metabolic syndrome into murine models over a six-week period results in enhanced lipid deposition and apoptosis within the atrial tissue, yielding a significantly more pronounced dilation of the left atrium compared to VLDL sourced from healthy controls ( 36 ). Additional studies emphasize the heterogeneity present within VLDL particles. Notably, VLDL extracted from MetS patients exhibits a more negative charge and demonstrates considerably greater cytotoxicity towards human vascular endothelial cells in contrast to VLDL obtained from non-MetS individuals( 37 ). Research conducted by Lee et al. ( 38 ) illustrated that postprandial negatively charged VLDL is independently correlated with atrial enlargement; specifically, every 1% increase in negatively charged VLDL corresponds to a 0.23 cm increment in the diameter of the left atrium. Moreover, Nakajima et al.( 31 ) revealed that postprandial VLDL possesses a heightened affinity for the VLDL receptor, resulting in superior cellular internalization compared to non-postprandial VLDL, which contributes to cytotoxic effects in atrial tissues and ultimately leads to significant atrial remodeling. The presence of VLDL in MetS may induce atrial cardiomyopathy and heighten susceptibility to AF through mechanisms that include direct cytotoxicity, alterations in action potentials, disrupted calcium homeostasis, reduced conduction velocities, modifications in gap junctions, and variations in sarcomeric proteins ( 39 )(Fig. 4 ). Furthermore, the synthesis of TG-rich chylomicrons in the intestine, along with the release of VLDL from the liver, may be modulated by circulating TG-rich lipoproteins and remnants and further influence the autonomic nervous system ( 40 ). Consequently, it can be inferred that VLDL may affect autonomic innervation or functionality within the heart, thereby elevating the risk of arrhythmias, especially among individuals with MetS. In light of these observations, postprandial modified VLDL has been proposed as a potential therapeutic target for atrial remodeling in MetS patients ( 41 ). This underscores the importance of recognizing that an exclusive focus on the quantity of lipoproteins, without considering the alterations in lipid profiles under physiological conditions, may restrict our comprehension of the mechanisms that contribute to the paradoxical inverse relationship observed between lipoproteins and AF. VLDL levels and composition are influenced by several factors, such as diet, gut flora, and medications. Dietary habits have a strong influence on the composition of VLDL, especially postprandial VLDL. Animal and human studies have shown that a high sucrose diet can change VLDL particle size and triglyceride levels. This alteration can lead to metabolic syndromes, including hepatic steatosis and insulin resistance( 42 – 44 ). In contrast, 8 weeks of a polyphenol-rich diet or 8 weeks of recipes containing fruits, avocados, whole grains, and trout modulated the lipid composition of postprandial VLDL and lowered levels of insulin resistance ( 45 , 46 ).Regarding fish consumption, only high levels of eicosapentaenoic acid (EPA) significantly reduced VLDL particles and VLDL-TGs, while the other two n-3 fatty acids, docosahexaenoic acid (DHA) and alpha-linolenic acid (ALA), did not show a similar effect ( 47 ). A cohort study involving 40 healthy individuals found that fasting for 7–14 days significantly improved their lipid profiles, particularly by increasing the concentration of large postprandial VLDL particles ( 48 ). While diet primarily influences lipid composition, gut flora also plays a significant role in lipid regulation. A prospective population-based cohort study found that 18 of the 32 microbial families in the gut microbiota were significantly associated with VLDL particles of different sizes ( 49 ). Another study indicated that low microbiota diversity was linked to obesity, abdominal obesity, and low HDL-C levels in healthy individuals ( 50 ). Together, these findings suggest that both synthetic and probiotic supplements aimed at correcting gut microbiota imbalances may potentially improve serum VLDL-C levels and have been tentatively demonstrated ( 51 ). Numerous oral hypoglycemic agents have been recognized for their favorable effects on VLDL metabolism. Pioglitazone, which acts as a PPAR-γ activator, enhances LPL activity, thereby promoting the clearance of VLDL ( 52 ). Additionally, glucagon-like peptide-1 (GLP-1) receptor agonists have been demonstrated to lower TG concentrations in hepatic tissues and diminish the rate of VLDL secretion ( 53 ). While mainstream lipid-lowering medications do not specifically target VLDL, they can still contribute to reductions in VLDL levels. For instance, 3-hydroxy-3-methyl glutaryl coenzyme A (HMG-CoA) reductase inhibitors, commonly known as statins, have been shown to lower approximately one-third of VLDL-TG and reduce levels of apolipoprotein C-III (apoC III) by over 40% ( 54 ). Moreover, agonists of peroxisome proliferator-activated receptor-α (PPAR-α), commonly referred to as fibrates, which are primarily utilized for the treatment of hypertriglyceridemia, have also been found to decrease VLDL-apo C III levels ( 54 ). Similar to selective estrogen receptor modulators, the first selective PPAR-α modulator, LY-518674, specifically alters tissue and gene expression responses by targeting the receptor-cofactor binding profile of the PPARα ligand. In phase II/III clinical trials, the SPPARMα agonist has been shown to decrease TG and apoC III levels by roughly 50%( 55 ). Proprotein convertase subtilisin-kexin type 9 (PCSK9) inhibitors function by inhibiting LDL receptor degradation and promoting the uptake of LDL in the liver. They also elevate the levels of VLDL receptors while concurrently lowering VLDL concentrations. Furthermore, PCSK9 inhibitors have been observed to preferentially alter the size and apolipoprotein composition of VLDL particles ( 56 ). Based on our findings, we hypothesize that a healthy diet, supplementation with intestinal probiotics, or medications to modify the body’s lipid metabolic profile may further reduce the incidence and recurrence of AF. Therefore, future studies should focus on both genetic and clinical levels to explore dietary and intestinal flora influences, as well as pharmacologic modulation of specific VLDL subcomponents. This research could help determine whether managing relevant lipid fractions can help control AF. Lastly, our results indicate that a higher percentage of MUFAs, rather than polyunsaturated fatty acids, protects against AF. MUFAs include ω-7 fatty acids like isoleic acid and neuraminic acid, and ω-9 fatty acids such as palmitoleic acid and oleic acid. Olive oil, palm oil, rapeseed oil, tea seed oil, and nuts are currently regarded as major sources of MUFAs intake. Previous studies have shown that diets rich in MUFAs, such as olive oil in the mediterranean diet, are associated with longevity in humans ( 57 , 58 )and promote longevity in rodents ( 59 ). The mechanism may involve MUFAs increasing the number of lipid droplets in fat storage tissues and boosting peroxisome counts. Both are essential for MUFA-induced longevity and predicting remaining lifespan. MUFAs can also modify the ratio of membrane lipids and further decrease lipid oxidation ( 60 ). Assy et al.( 61 ) reported that oleic acid suppresses the activation of the nuclear factor-кB transcription factor, which in turn diminishes inflammatory responses and mitigates endothelial injury. Nevertheless, the impact of MUFAs on cardiovascular health remains a topic of active debate in contemporary research. Merkel et al.( 62 )investigated the influence of diets rich in MUFAs on atherosclerosis by utilizing two distinct mouse models. Their findings indicated that such dietary fats enhance susceptibility to atherosclerosis by elevating levels of VLDL-C through an apo E-independent mechanism. Additionally, an analysis of the National Health and Nutrition Examination Survey (NHANES) database revealed that increased consumption of either monounsaturated or polyunsaturated fatty acids correlates with a reduction in the 10-year risk of cardiovascular events, particularly among non-diabetic individuals with low LDL levels ( 63 ). Furthermore, a separate study explored the association between fatty acid intake and the onset of AF, concluding that substituting saturated fatty acids with monounsaturated, total, or n-6 polyunsaturated fatty acids does not elevate the risk of developing AF. Our investigation employed MR to propose a potential causal link between MUAFs and AF; however, additional research involving larger clinical trials is imperative to elucidate this association. This study presents several specific limitations. First, our study included only 249 blood lipid-related traits and two types of AF associated GWAS datasets. Future research should include more metabolites and various types of AF. This will enhance our comprehensive understanding of AF risk factors and their underlying mechanisms. Secondly, Due to the complex interactions among different lipid components, relying solely on a p-value of for IVW less than 0.05 may lead to false-positive results. However, in this study, we further constructed different multivariate models according to lipid subgroups, and the outcomes still found that some of the lipid traits were significantly correlated with AF, which compensated for the possibility of false-positive results of the present study to a certain extent. Thirdly, reliance on existing genomic datasets may introduce inherent biases, such as unavoidable horizontal pleiotropy between exposures, potential confounders that cannot be controlled for between exposures and outcomes, variations in population characteristics that could affect the generalizability of our findings. Moreover, MR studies serve as a valuable tool in determining if the identified correlations suggest a causal link supported by genetic data. To elucidate the precise causal relationship between particular lipid species and AF, further laboratory investigations and clinical trials are frequently required to uncover the underlying biological mechanisms. Lastly, we should consider that the impact of dynamic lipid changes not addressed in our study, such as postprandial VLDL and VLDL changes in patients with metabolic syndrome. All in all, the complexity of lipid metabolism and its multifactorial nature may constrain the precision of our conclusions regarding specific lipid traits and their effects on atrial fibrillation. Conclusion In conclusion, our research identified thirty lipid traits related to AF, highlighting their potential as vital biomarkers for cardiovascular health. Larger clinical studies are necessary to confirm the link between our findings and AF, and to investigate whether lipid interventions can lower the risk of AF occurrence or recurrence. Abbreviations atrial fibrillation AF coronary artery disease CAD metabolic syndrome MetS Mendelian randomization MR instrumental variables IVs genome-wide association study GWAS inverse-variance weighted IVW Cochran’s Q statistic Q leave-one-out LOO odds ratio OR total cholesterol TC phospholipid transfer protein PLTP cholesteryl ester transfer protein CETP alpha-linolenic acid ALA triglyceride TG cholesteryl esters CE chylomicrons CM free cholesterol FC lipoprotein (a) lp(a) glucagon-like peptide-1 GLP-1 proprotein convertase subtilisin-kexin type 9 PCSK9 Global Burden of Disease GBD National Health and Nutrition Examination Survey NHANES Declarations Data availability statement The summary data for 249 lipid-related traits GWAS ID and the outcome GWAS for AF and atrial flutter were obtained from the website( IEU OpenGWAS project) Acknowledgments We are grateful for the Noncommunicable Chronic Diseases-National Science and Technology Major Project (grant 2023ZD0503400/2023ZD0503402), National Key Research and Development Program of China (2021YFC2500600/2021YFC2500601), Projects of National Natural Science Foundation of China (grant 82370442), and National High Level Hospital Clinical Research Funding (Interdepartmental Research Project of Peking University First Hospital, No. 2023IR35) support of our research. Declarations of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Cheng, S. et al. Global burden of atrial fibrillation/atrial flutter and its attributable risk factors from 1990 to 2021. Europace: European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology 26. (2024). Elliott, A. D., Middeldorp, M. E., Van Gelder, I. C., Albert, C. M. & Sanders, P. Epidemiology and modifiable risk factors for atrial fibrillation. Nat. Rev. Cardiol. 20 , 404–417 (2023). Kloosterman, M. et al. Characteristics and outcomes of atrial fibrillation in patients without traditional risk factors: an RE-LY AF registry analysis. Europace: European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology 22: 870–877. (2020). Lu, Y. et al. Non-traditional risk factors for atrial fibrillation: epidemiology, mechanisms, and strategies. Eur. Heart J. 46 , 784–804 (2025). Lüscher, T. F. Frontiers in lipid research: lipoprotein(a), apolipoprotein C-III and E, and PCSK9 and inflammation. Eur. Heart J. 40 , 2741–2744 (2019). Barrio-Lopez, M. T. et al. Mediterranean diet and epicardial adipose tissue in patients with atrial fibrillation treated with ablation: a substudy of the 'PREDIMAR' trial. Eur. J. Prev. Cardiol. 31 , 348–355 (2024). Alonso, A. et al. Blood lipids and the incidence of atrial fibrillation: the Multi-Ethnic Study of Atherosclerosis and the Framingham Heart Study. J. Am. Heart Association . 3 , e001211 (2014). Li, X. et al. Lipid profile and incidence of atrial fibrillation: A prospective cohort study in China. Clin. Cardiol. 41 , 314–320 (2018). Mora, S., Akinkuolie, A. O., Sandhu, R. K., Conen, D. & Albert, C. M. Paradoxical association of lipoprotein measures with incident atrial fibrillation. Circ. Arrhythm. Electrophys. 7 , 612–619 (2014). Guan, B. et al. Blood lipid profiles and risk of atrial fibrillation: A systematic review and meta-analysis of cohort studies. J. Clin. Lipidol. 14 , 133–142e133 (2020). Yao, Y., Liu, F., Wang, Y. & Liu, Z. Lipid levels and risk of new-onset atrial fibrillation: A systematic review and dose-response meta-analysis. Clin. Cardiol. 43 , 935–943 (2020). Yang, S., Pudasaini, R., Zhi, H. & Wang, L. The Relationship between Blood Lipids and Risk of Atrial Fibrillation: Univariable and Multivariable Mendelian Randomization Analysis. Nutrients 14. (2021). Tao, Y. et al. Associations of lipids and lipid-modifying drug target genes with atrial fibrillation risk based on genomic data. Lipids Health Dis. 23 , 175 (2024). Mohammadi-Shemirani, P. et al. Elevated Lipoprotein(a) and Risk of Atrial Fibrillation: An Observational and Mendelian Randomization Study. J. Am. Coll. Cardiol. 79 , 1579–1590 (2022). Sekula, P., Del Greco, M. F., Pattaro, C. & Köttgen, A. Mendelian Randomization as an Approach to Assess Causality Using Observational Data. J. Am. Soc. Nephrology: JASN . 27 , 3253–3265 (2016). Kastenmüller, G., Raffler, J., Gieger, C. & Suhre, K. Genetics of human metabolism: an update. Hum. Mol. Genet. 24 , R93–r101 (2015). Richardson, T. G. et al. Characterising metabolomic signatures of lipid-modifying therapies through drug target mendelian randomisation. PLoS Biol. 20 , e3001547 (2022). Nielsen, J. B. et al. Biobank-driven genomic discovery yields new insight into atrial fibrillation biology. Nature genetics 50: 1234–1239. (2018). Lin, S. H., Brown, D. W. & Machiela, M. J. LDtrait: An Online Tool for Identifying Published Phenotype Associations in Linkage Disequilibrium. Cancer Res. 80 , 3443–3446 (2020). Hartwig, F. P., Davies, N. M., Hemani, G. & Davey Smith, G. Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique. Int. J. Epidemiol. 45 , 1717–1726 (2016). Larsson, S. C., Butterworth, A. S. & Burgess, S. Mendelian randomization for cardiovascular diseases: principles and applications. Eur. Heart J. 44 , 4913–4924 (2023). Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7. (2018). Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44 , 512–525 (2015). Egger, M., Davey Smith, G., Schneider, M. & Minder, C. Bias in meta-analysis detected by a simple, graphical test. BMJ (Clinical Res. ed) . 315 , 629–634 (1997). Bowden, J. et al. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat. Med. 36 , 1783–1802 (2017). Chyou, J. Y., Hunter, T. D., Mollenkopf, S. A., Turakhia, M. P. & Reynolds, M. R. Individual and Combined Risk Factors for Incident Atrial Fibrillation and Incident Stroke: An Analysis of 3 Million At-Risk US Patients. J. Am. Heart Association 4 . (2015). Hissa, B. et al. Cholesterol depletion impairs contractile machinery in neonatal rat cardiomyocytes. Sci. Rep. 7 , 43764 (2017). Khovidhunkit, W. et al. Effects of infection and inflammation on lipid and lipoprotein metabolism: mechanisms and consequences to the host. J. Lipid Res. 45 , 1169–1196 (2004). Berbée, J. F., Havekes, L. M. & Rensen, P. C. Apolipoproteins modulate the inflammatory response to lipopolysaccharide. J. Endotoxin Res. 11 , 97–103 (2005). Huang, J. K. & Lee, H. C. Emerging Evidence of Pathological Roles of Very-Low-Density Lipoprotein (VLDL). International journal of molecular sciences 23. (2022). Nakajima, K., Tokita, Y., Tanaka, A. & Takahashi, S. The VLDL receptor plays a key role in the metabolism of postprandial remnant lipoproteins. Clin. Chim. Acta . 495 , 382–393 (2019). Niu, Y. G. & Evans, R. D. Very-low-density lipoprotein: complex particles in cardiac energy metabolism. J. lipids . 2011 , 189876 (2011). Schulze, P. C., Drosatos, K. & Goldberg, I. J. Lipid Use and Misuse by the Heart. Circul. Res. 118 , 1736–1751 (2016). Galati, F. et al. CETP TaqIB Polymorphism, Serum Lipid Levels And Risk Of Atrial Fibrillation: A Case-Control Study. J. Atr. fibrillation . 6 , 964 (2014). Kim, S. M. et al. Female patients with atrial fibrillation have increased oxidized and glycated lipoprotein properties and lower apolipoprotein A-I expression in HDL. Int. J. Mol. Med. 27 , 841–849 (2011). Lee, H. C. et al. VLDL from Metabolic Syndrome Individuals Enhanced Lipid Accumulation in Atria with Association of Susceptibility to Atrial Fibrillation. International journal of molecular sciences 17. (2016). Chen, C. H. et al. Effects of electronegative VLDL on endothelium damage in metabolic syndrome. Diabetes care . 35 , 648–653 (2012). Lee, H. C. et al. The role of postprandial very-low-density lipoprotein in the development of atrial remodeling in metabolic syndrome. Lipids Health Dis. 19 , 210 (2020). Lee, H. C. & Lin, Y. H. The Pathogenic Role of Very Low Density Lipoprotein on Atrial Remodeling in the Metabolic Syndrome. International journal of molecular sciences 21. (2020). Luchoomun, J., Zhou, Z., Bakillah, A., Jamil, H. & Hussain, M. M. Assembly and secretion of VLDL in nondifferentiated Caco-2 cells stably transfected with human recombinant ApoB48 cDNA. Arterioscler. Thromb. Vasc. Biol. 17 , 2955–2963 (1997). Adam, R. C. et al. Angiopoietin-like protein 3 governs LDL-cholesterol levels through endothelial lipase-dependent VLDL clearance. J. Lipid Res. 61 , 1271–1286 (2020). Dornas, W. C., de Lima, W. G., Pedrosa, M. L. & Silva, M. E. Health implications of high-fructose intake and current research. Adv. Nutr. (Bethesda Md) . 6 , 729–737 (2015). Plazas Guerrero, C. G. et al. and U. Osuna Martínez. 2021. Evaluation of sucrose-enriched diet consumption in the development of risk factors associated to type 2 diabetes, atherosclerosis and non-alcoholic fatty liver disease in a murine model. Int. J. Environ. Health Res. 31 : 651–669 . Sabaka, P. et al. Postprandial changes of lipoprotein profile: effect of abdominal obesity. Lipids Health Dis. 12 , 179 (2013). Della Pepa, G. et al. Effects of a diet naturally rich in polyphenols on lipid composition of postprandial lipoproteins in high cardiometabolic risk individuals: an ancillary analysis of a randomized controlled trial. Eur. J. Clin. Nutr. 74 , 183–192 (2020). Muñoz-Perez, D. M. et al. and O. A. Rangel-Zuñiga. Alternative Foods in Cardio-Healthy Dietary Models That Improve Postprandial Lipemia and Insulinemia in Obese People. Nutrients 13. (2021). Amigó, N. et al. Habitual Fish Consumption, n-3 Fatty Acids, and Nuclear Magnetic Resonance Lipoprotein Subfractions in Women. J. Am. Heart Association . 9 , e014963 (2020). Grundler, F. et al. Long-term fasting improves lipoprotein-associated atherogenic risk in humans. Eur. J. Nutr. 60 , 4031–4044 (2021). Vojinovic, D. et al. and C. M. van Duijn. Relationship between gut microbiota and circulating metabolites in population-based cohorts. Nature communications 10: 5813. (2019). Kashtanova, D. A. et al. Subfractional Spectrum of Serum Lipoproteins and Gut Microbiota Composition in Healthy Individuals. Microorganisms 9. (2021). Shakeri, H. et al. Consumption of synbiotic bread decreases triacylglycerol and VLDL levels while increasing HDL levels in serum from patients with type-2 diabetes. Lipids 49 , 695–701 (2014). Rodriguez, V., Newman, J. D. & Schwartzbard, A. Z. Towards more specific treatment for diabetic dyslipidemia. Curr. Opin. Lipidol. 29 , 307–312 (2018). Patel, V. J., Joharapurkar, A. A., Shah, G. B. & Jain, M. R. Effect of GLP-1 based therapies on diabetic dyslipidemia. Curr. Diabetes. Rev. 10 , 238–250 (2014). Ooi, E. M., Ng, T. W., Watts, G. F., Chan, D. C. & Barrett, P. H. Effect of fenofibrate and atorvastatin on VLDL apoE metabolism in men with the metabolic syndrome. J. Lipid Res. 53 , 2443–2449 (2012). Fruchart, J. C. et al. The selective peroxisome proliferator-activated receptor alpha modulator (SPPARMα) paradigm: conceptual framework and therapeutic potential: A consensus statement from the International Atherosclerosis Society (IAS) and the Residual Risk Reduction Initiative (R3i) Foundation. Cardiovascular diabetology 18: 71. (2019). Hollstein, T. et al. Treatment with PCSK9 inhibitors reduces atherogenic VLDL remnants in a real-world study. Vascul. Pharmacol. 116 , 8–15 (2019). Mendez, M. A. & Newman, A. B. Can a Mediterranean Diet Pattern Slow Aging? J. Gerontol. A . 73 , 315–317 (2018). Tosti, V., Bertozzi, B. & Fontana, L. Health Benefits of the Mediterranean Diet: Metabolic and Molecular Mechanisms. J. Gerontol. A . 73 , 318–326 (2018). Ramirez-Tortosa, C. L. et al. Longevity and Cause of Death in Male Wistar Rats Fed Lifelong Diets Based on Virgin Olive Oil, Sunflower Oil, or Fish Oil. J. Gerontol. A . 75 , 442–451 (2020). Papsdorf, K. et al. Lipid droplets and peroxisomes are co-regulated to drive lifespan extension in response to mono-unsaturated fatty acids. Nat. Cell Biol. 25 , 672–684 (2023). Assy, N., Nassar, F., Nasser, G. & Grosovski, M. Olive oil consumption and non-alcoholic fatty liver disease. World J. Gastroenterol. 15 , 1809–1815 (2009). Merkel, M., Velez-Carrasco, W., Hudgins, L. C. & Breslow, J. L. Compared with saturated fatty acids, dietary monounsaturated fatty acids and carbohydrates increase atherosclerosis and VLDL cholesterol levels in LDL receptor-deficient, but not apolipoprotein E-deficient, mice. Proc. Natl. Acad. Sci. U.S.A. 98 , 13294–13299 (2001). Yang, Z. et al. Dietary Saturated, Monounsaturated, or Polyunsaturated Fatty Acids and Estimated 10-Year Risk of a First Hard Cardiovascular Event. Am. J. Med. 136 , 796–803e792 (2023). Additional Declarations No competing interests reported. Supplementary Files supplementfigure.pdf supplementtable.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. 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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-6624447","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":454046095,"identity":"bebf3b2f-cf3e-423a-bfbf-974fa6749025","order_by":0,"name":"Shuo Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYDACCRBRISHHz97Y+PAD8VrO2BhL9hxuNpYgWgtjW1rihhvpbQI8xOiQn91j+PAH22HGhpsP24D67eR0GwhoMbhzxthAgucwM+PsxLYHBQzJxmYHCGmRyDGTMJA4zMYsndhuIMFwIHEbIS3yM4BaEgwO87BJHmyT4CFGC8MNoJYDCWkSPBKMRGoxuJFWbNhwwAbon0RgIBsQ4Rf5GckbH/78J1G///jxhw8/VNjJEdSCbilpykfBKBgFo2AU4AAAWgFBLpj2OWYAAAAASUVORK5CYII=","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":true,"prefix":"","firstName":"Shuo","middleName":"","lastName":"Zhang","suffix":""},{"id":454046096,"identity":"8b62d7be-bc06-4f74-ab6b-d8a83c9bc845","order_by":1,"name":"Linghua Fan","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Linghua","middleName":"","lastName":"Fan","suffix":""},{"id":454046097,"identity":"4cb8addb-e73f-408d-84df-cbe1e4a9d538","order_by":2,"name":"Long Zhang","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"Zhang","suffix":""},{"id":454046098,"identity":"d9560dde-d42d-4ebf-9065-b4bccefb7bef","order_by":3,"name":"Fangfang Fan","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fangfang","middleName":"","lastName":"Fan","suffix":""},{"id":454046099,"identity":"0b3136ac-90d4-4ea7-9f07-c1bc1e9f030f","order_by":4,"name":"Jia Jia","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Jia","suffix":""},{"id":454046100,"identity":"98c7f59e-1cf4-451b-897d-45bfe1c3f1d0","order_by":5,"name":"Yan Zhang","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Zhang","suffix":""},{"id":454046101,"identity":"5cdebfe6-b4d0-469e-917d-80f0f53647ff","order_by":6,"name":"Jianping Li","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jianping","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-05-09 02:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6624447/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6624447/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82891700,"identity":"bea6a2ad-03b9-46dd-b1f4-9a4e1227ed0d","added_by":"auto","created_at":"2025-05-16 12:14:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":244280,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the study\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6624447/v1/30779ff4a59fabb9a6c5247c.png"},{"id":82891702,"identity":"68352a5c-effb-459d-a61e-67ddca7eb4f1","added_by":"auto","created_at":"2025-05-16 12:14:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":119826,"visible":true,"origin":"","legend":"\u003cp\u003eCausal effects of 30 lipid associated traits on AF. The p value of inverse-variance weighted (IVW) method less than 0.05 was considered as significant. The error bars represent 95% confidence intervals (CI). AF: atrial fibrillation, CM: chylomicrons; VLDL: very low-density lipoprotein; IDL: intermediate-density lipoprotein; %: lipids to total lipids ratio; OR: odds ratio.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6624447/v1/68de920e343f23977ba66edc.png"},{"id":82892987,"identity":"e2bdfd00-0e31-4019-b7ca-ce3dc69b8fe8","added_by":"auto","created_at":"2025-05-16 12:22:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":79264,"visible":true,"origin":"","legend":"\u003cp\u003eCausal effects of lipid traits on AF and Flutter. The p value of inverse-variance weighted (IVW) method less than 0.05 was considered as significant. The error bars represent 95% confidence intervals (CI). AF: atrial fibrillation, CM: chylomicrons; VLDL: very low-density lipoprotein; IDL: intermediate-density lipoprotein; %: to total lipids ratio OR: odds ratio.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6624447/v1/a6a26bec86bc35bddddfa48e.png"},{"id":82892988,"identity":"83a8764c-478d-45c4-bb3f-79237d18857e","added_by":"auto","created_at":"2025-05-16 12:22:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":443748,"visible":true,"origin":"","legend":"\u003cp\u003eMechanism of the relationship between lipids and AF\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6624447/v1/510814cbbca5900ceb10a195.png"},{"id":83322248,"identity":"cad7052c-bad6-4a95-904f-f06cfbe61a60","added_by":"auto","created_at":"2025-05-23 04:31:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1768618,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6624447/v1/7ec3bd8b-7439-45ff-af05-5dbd0a8f4ca5.pdf"},{"id":82893820,"identity":"ac1a1653-853d-45ef-a3e9-6eb049bf2885","added_by":"auto","created_at":"2025-05-16 12:30:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12494940,"visible":true,"origin":"","legend":"","description":"","filename":"supplementfigure.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6624447/v1/050ad12e7a859fed5ecbad4e.pdf"},{"id":82893816,"identity":"802b0598-6451-4766-9bee-95db17bd3366","added_by":"auto","created_at":"2025-05-16 12:30:04","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":204335,"visible":true,"origin":"","legend":"","description":"","filename":"supplementtable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6624447/v1/294ee71caddf59ac64efeedb.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Causal Relationships between Lipids related Traits and Atrial Fibrillation/Flutter: Two-sample Mendelian Randomization Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAtrial fibrillation (AF) stands as the most prevalent form of sustained arrhythmia, representing a significant public health concern due to its association with complications such as stroke, heart failure, and increased rates of mortality. According to the 2021 Global Burden of Disease (GBD) study, AF and atrial flutter impact approximately 52.55\u0026nbsp;million individuals globally, reflecting a remarkable 137% rise since 1990. Furthermore, the mortality rates linked to these conditions have experienced a twofold increase during the same timeframe (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Traditional risk factors for AF encompass various demographic, anthropometric, and cardiovascular elements (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In a study involving 15,400 participants with AF from 47 different countries, it was observed that most patients lacking the conventionally recognized AF risk factors demonstrate a positive prognosis over one year; nonetheless, the likelihood of rehospitalization due to AF remains high (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Consequently, researchers have identified four overarching categories of nontraditional risk factors for AF: unhealthy lifestyle (such as sleep patterns, night shift work, and dietary habits), biomarkers (including gut microbiota, hyperuricemia, and homocysteine levels), adverse health conditions or diseases (like depression, epilepsy, clonal hematopoiesis of undetermined significance, infections, and asthma), and environmental influences (such as noise pollution and other environmental aspects) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNotably, elevated lipid levels can lead to an inflammatory response in some patients, which may increase the risk of AF (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Healthy dietary patterns, such as the Mediterranean diet, may reduce epicardial adipose tissue and subsequently decrease the incidence of AF(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The potential mechanisms in it may be related to the change in lipid profiles (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Previous research has extensively investigated the associations between lipid profiles and AF; however, the causal relationships remain largely uncharacterized (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Observational research has yielded inconsistent findings, with the absence of adjustments for variables such as obesity, geographical and ethnic disparities, along with other risk factors associated with coronary artery diseases (CAD), potentially elucidating these discrepancies(\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Two meta-analyses encompassing large cohort studies have indicated a negative correlation between serum total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) levels and the risk of AF. Conversely, no significant relationship was established between triglyceride (TG) levels and the incidence of AF(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). It is important to note that these investigations were limited by small sample sizes and the presence of potential confounding variables. Findings from MR studies present contradictions to earlier studies, suggesting an absence of a causal relationship between HDL-C, LDL-C, TG, and AF, with the exception of lipoprotein (a)(\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Nonetheless, the majority of MR studies tend to concentrate on a narrow range of lipid profiles, which may not accurately represent the genuine risk factors for AF.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of current findings on different research types of lipids and AF; TC : total cholesterol; HDL-C: high-density lipoprotein cholesterol; LDL-C : low-density lipoprotein cholesterol; TG: triglycerides; apo : apolipoprotein; lp(a) : lipoprotein (a).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResearch Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAuthor, year (ref)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFollow-up time (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFinding(s) in relation to\u003c/p\u003e \u003cp\u003eincidence or prevalence of AF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCohort study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlvaro Alonso,\u003c/p\u003e \u003cp\u003e2014\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMulti-Ethnic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigher HDL-C (HR 0.64, 95% CI 0.48 to 0.87)\u003c/p\u003e \u003cp\u003eHigher TG (HR 1.60, 95% CI 1.25 to 2.05)\u003c/p\u003e \u003cp\u003eTC or LDL-C not found to be risk factors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXintao Li,2018\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEast Asian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigher TC (HR 0.60, 95%CI: 0.43\u0026ndash;0.84)\u003c/p\u003e \u003cp\u003eHigher LDL-C (HR 0.60, 95%CI: 0.43\u0026ndash;0.83)\u003c/p\u003e \u003cp\u003eHigher TC/HDL-C(HR 0.88, 95% CI: 0.79\u0026ndash;0.98)\u003c/p\u003e \u003cp\u003eHigher LDL-C/HDL-C (HR: 0.77, 95% CI: 0.66\u0026ndash;0.91)\u003c/p\u003e \u003cp\u003eHDL-C or TG not found to be risk factors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSamia Mora,2014\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaucasian\u003c/p\u003e \u003cp\u003e(women)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigher LDL-C(HR 0.72,95%CI 0.56\u0026ndash;0.92)\u003c/p\u003e \u003cp\u003eHigher total number of LDL particles (HR 0.77, 95%CI 0.60\u0026ndash;0.99)\u003c/p\u003e \u003cp\u003eHigher VLDL particles (HR 0.78,95%CI 0.61\u0026ndash;0.99)\u003c/p\u003e \u003cp\u003eHigher cholesterol-poor small LDL (HR 0.78,95%CI 0.61-1.00)\u003c/p\u003e \u003cp\u003elarger cholesterol-rich LDL particles and all HDL not found to be risk factors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMeta analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBo Guan, 2020\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMulti-Ethnic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28,449 to 22,886,663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 to 18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e168 to 225,529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigher TC (RR 0.81, 95% CI 0.72\u0026ndash;0.92)\u003c/p\u003e \u003cp\u003eHigher LDL-C (RR 0.79, 95% CI 0.70\u0026ndash;0.88)\u003c/p\u003e \u003cp\u003eHigher HDL-C (RR 0.86, 95% CI 0.76\u0026ndash;0.97)\u003c/p\u003e \u003cp\u003eTG not found to be risk factors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYisong Yao,2020\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMulti-Ethnic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3549 to 3660385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.44 to 34.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e311 to 27581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigher TC (RR 0.95,95% CI 0.93\u0026ndash;0.96)\u003c/p\u003e \u003cp\u003eHigher LDL-C (RR 0.95 95% CI 0.92\u0026ndash;0.97)\u003c/p\u003e \u003cp\u003eHigher HDL-C (RR 0.97,95% CI 0.96\u0026ndash;0.99)\u003c/p\u003e \u003cp\u003eTG not found to be risk factor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMendelian randomization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShengyi Yang,2021\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropean ancestry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e290198 to 318,674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60,620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLDL-C,HDL-C,TG, apo A1,apo B not found to be risk factor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYuhang Tao,2024\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropean and East Asian\u003c/p\u003e \u003cp\u003eancestry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2279 to 1320016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e837 to 60620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigher lp(a) (OR 1.04,95% CI 1.03\u0026ndash;1.06) only in European\u003c/p\u003e \u003cp\u003eLDL-C,TG not found to be risk factors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePedrum,2022\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropean ancestry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e374,517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17325 to 60620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigher lp(a) (OR 1.04; 95% CI 1.03\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTwo-sample MR employs genetic variants as instrumental variables (IVs) to evaluate the causal relationships between exposures and outcomes. This methodology provides a robust framework for investigating the causal links between genetic risk factors and phenotypic outcomes, effectively reducing the potential for environmental confounding (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The strength of this approach lies in its capacity to leverage genome-wide association studies (GWAS) with substantial sample sizes, which facilitate the identification of a multitude of genetic variants associated with lipids and lipid-related characteristics, thus serving as significant IVs for causal inference (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In the present investigation, we conducted two-sample MR analyses to assess the causal influences of 249 traits linked to blood lipids on AF. The lipid and lipid-related traits considered in our analysis included cholesterol, free cholesterol (FC), cholesteryl esters (CE), phospholipids, TG and their subclasses, fatty acids, amino acids, ketone bodies, among others. Our results provide novel insights into the relationship between endogenous lipid metabolism and AF, thereby enriching the understanding of the genetic risk factors associated with this condition.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eWe conducted a two-sample MR analysis to investigate the potential causal associations between 249 blood lipid measurements and lipid-related traits and the occurrence of AF/atrial flutter. The framework of the study is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGenome-wide association study data sources\u003c/h3\u003e\n\u003cp\u003eIn total, 249 blood lipid profiles and lipid-related phenotypes served as exposures (see Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), sourced from a proof-of-concept cross-platform GWAS datasets. All 249 metabolic traits were measured by targeted high-throughput nuclear magnetic resonance metabolomics. The sample sizes for the 249 lipid traits in the GWAS ranged from 110,051 to 115,082 participants from the UK Biobank study (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). In addition, 173 of them were published in 2022, while the remaining were published in 2020. The main classifications of lipids and lipid-related phenotypes included: 1) cholesterol (major classes and subclasses), 2) CE (subclasses), 3) FC (subclasses), 4) phospholipids (major classes and subclasses), 5) TG (major classes and subclasses), 6) ketone bodies, 7) amino acids, 8) apolipoproteins, 9) miscellaneous, 10) fatty acids, 11) glycolysis-related components, 12) particle size, and 13) particle concentrations (both major classes and subclasses). The \u0026ldquo;major classes\u0026rdquo; mainly indicated HDL, LDL, very-low-density lipoprotein (VLDL) and TC; The \u0026ldquo;subclass\u0026rdquo; mainly indicated: IDL (intermediated density lipoprotein), large HDL, large LDL, large VLDL, medium HDL, medium LDL, medium VLDL, small HDL, small LDL, small VLDL, very small VLDL, very large HDL, very large VLDL, chylomicrons (CM) and extremely large VLDL. The data pertaining to the outcomes of AF were derived from a classic GWAS study encompassing more than 1,000,000 participants, comprising 60,620 individuals diagnosed with AF and 970,216 controls. This investigation successfully identified 142 independent risk variants distributed across 111 loci and highlighted 151 functional candidate genes that are likely implicated in the pathogenesis of AF(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Furthermore, the outcome data concerning both AF and atrial flutter originated from the FinnGen study, which included 22,068 cases alongside 116,926 controls. All datasets utilized in this research received approval from the appropriate ethical review board.\u003c/p\u003e\n\u003ch3\u003eGenetic instruments selection\u003c/h3\u003e\n\u003cp\u003eIn our study, we chose single nucleotide polymorphisms (SNPs) guided by three fundamental criteria essential for MR genetic instrumental variables: these SNPs must achieve genome-wide significance (with a P value threshold set at \u0026lt;\u0026thinsp;5\u0026times;10^-8), exhibit a lack of linkage equilibrium (defined by an r\u0026sup2; threshold\u0026thinsp;\u0026gt;\u0026thinsp;0.001 over a window size of 10,000 kb), and demonstrate resilience against weak instrument bias (with an F-statistic exceeding 10). Subsequently, we employed LDtrait to eliminate SNPs associated with potential confounders affecting the outcomes. Our investigation pinpointed blood pressure, blood glucose levels, body mass index, chronic nephropathy, CAD, and C-reactive protein as confounding variables (LDlink, accessed on February 19, 2025) (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Following the selection of instrumental variables, we proceeded to harmonize the allele effects and adjust the β values in the outcome dataset to ensure alignment with the exposure dataset(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eIn alignment with the principles of MR and its applications in cardiovascular diseases (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), we performed two-sample MR analyses employing five distinct methodologies: inverse-variance weighted (IVW), Simple mode, MR-Egger, weighted median, and weighted mode, with IVW serving as the primary analytical technique. All MR analyses were executed utilizing the TwoSampleMR R package (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The intercept obtained from MR-Egger regression is instrumental in identifying pleiotropy within MR estimates (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Cochran\u0026rsquo;s Q statistic (Q) is applied to evaluate heterogeneity in the context of IVW (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Should the P-value derived from the Cochran\u0026rsquo;s Q test fall below 0.05, we will adopt IVW with a multiplicative random-effects model to validate our findings; conversely, a fixed-effects model will be implemented (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The MR-Egger intercept test was employed to ascertain the presence of pleiotropy, whereby a P-value less than 0.05, suggesting the existence of pleiotropy, necessitated the exclusion of the results. Additionally, a leave-one-out (LOO) sensitivity analysis was conducted to identify any potentially dominant SNPs.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eThe Mendelian randomization estimates of blood lipids and lipid-related phenotypes on AF\u003c/h2\u003e \u003cp\u003eThe comprehensive findings of MR analyses and the evaluation of pleiotropy concerning 249 blood lipids and lipid-associated characteristics in relation to atrial AF are presented in Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. Utilizing the IVW approach, we discovered 31 lipid-related characteristics that exhibited a causal relationship with AF. This group comprised six subclasses of cholesterol, six subclasses of CE, five subclasses of FC, three subclasses of phospholipids, six subclasses of TG, one type of fatty acid, three subclasses pertaining to particle size and concentration, as well as one trait associated with total lipids (refer to Supplementary Table S3 and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The majority of these phenotypic traits displayed p-values below 0.05 when assessed through Cochran\u0026rsquo;s Q test, and the findings derived from the multiplicative random-effects model aligned with prior research. Notably, the MR-Egger regression analysis for one specific lipid-related trait, namely the TG to total lipids ratio in CM and very large VLDL, yielded a p-value of less than 0.05. This outcome suggested the existence of pleiotropy within the MR analyses, prompting us to exclude this specific result from consideration. The minimum F-statistic values for each SNP varied between 26 and 31, suggesting a minimal risk of weak instrumental variable bias affecting the overall outcomes (see Supplementary Table S4).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports the MR estimated of 30 lipid associated traits on AF risks. Our findings were categorized into four key areas. Firstly, the diameter of VLDL particles was negatively correlated with the development of AF. Specifically, smaller VLDL particle diameters corresponded to greater AF risk (OR: 0.93, 95% CI: 0.88\u0026ndash;0.98, p\u0026thinsp;=\u0026thinsp;0.0055). This finding aligns with the increased AF risk associated with higher levels of very small VLDL particles (OR: 1.05, 95% CI: 1.01\u0026ndash;1.09, p\u0026thinsp;=\u0026thinsp;0.0265).Further MR analysis showed that increased levels of cholesterol (OR: 1.06, 95% CI:1.01\u0026ndash;1.11, p\u0026thinsp;=\u0026thinsp;0.0125), CE (OR: 1.07, 95% CI:1.01\u0026ndash;1.13, p\u0026thinsp;=\u0026thinsp;0.03), and FC (OR: 1.05, 95% CI:1.00-1.09, p\u0026thinsp;=\u0026thinsp;0.0473) in very small VLDL particles were risk factors for the development of AF. Secondly, we discovered that IDL-related lipid profiles were associated with AF. Specifically, higher concentrations of IDL particles (OR: 1.06, 95% CI: 1.00-1.13, p\u0026thinsp;=\u0026thinsp;0.0497) and increased levels of phospholipids in IDL (OR: 1.06, 95% CI: 1.00-1.12, p\u0026thinsp;=\u0026thinsp;0.0404) were identified as risk factors for AF development. Thirdly, different proportions of lipids were differentially associated with the development of AF in VLDL, with a higher proportion of TG being more protective against AF; elevated proportions of cholesterol, CE, FC, and phospholipids were risk factors for AF, and the largest ORs in CM and extremely large VLDL. Finally, a higher proportion of monounsaturated fatty acids(MUFAs) compared to total fatty acids was found to be protective against AF (OR: 0.94, 95% CI: 0.88\u0026ndash;0.99, p\u0026thinsp;=\u0026thinsp;0.0258). Supplementary Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2 illustrate the scatter plots and forest plots, respectively. The funnel plots displayed a symmetrical pattern, as shown in Supplementary Figure S3. Furthermore, the application of the LOO analysis revealed that no single SNP significantly influenced the relationship between lipid traits and the risk of AF, as depicted in Supplementary Figure S4.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThe Mendelian randomization estimates of blood lipids and lipid-related phenotypes on AF/ atrial flutter\u003c/h3\u003e\n\u003cp\u003eTo explore the association between lipid profiles and AF/ atrial flutter, we selected the FinnGen study, which includes atrial flutter as an endpoint to validate the relationship between lipid traits and AF/atrial flutter. We found that among the thirty AF-related lipid traits, twenty-one were associated with AF/atrial flutter. Four lipid traits showed pleiotropy and were subsequently excluded from the outcomes. Ultimately, Seventeen lipid related traits were identified as having a causal effect on both AF/atrial flutter (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplement Table\u0026nbsp;6). Consistent with results above, a higher percentage of TG in VLDL protected against AF/atrial flutter, while a higher percentage of cholesterol, FC, and CE in VLDL increased the risk of AF/atrial flutter. Very small VLDL and IDL are equally risk factors for AF/atrial flutter. Differently, the percentage of MUFAs had no significant correlation in the modified outcome. (Supplement Table S6). All minimal F values for the instrumental variables exceeded 28\u0026ndash;30, indicating robustness (Supplement Table\u0026nbsp;7). Supplementary Figures S5 and S6 present scatterplots and forest plots, respectively. The funnel plot, depicted in Supplementary Figure S7, exhibits symmetry, while the LOO plots, illustrated in Supplementary Figure S8, did not reveal any SNPs with significant abnormalities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eMultivariable MR Analysis of lipid-related traits and atrial AF\u003c/h3\u003e\n\u003cp\u003eWe constructed various models based on lipid subgroups to better investigate the relationship between lipid-related traits and AF using multivariate MR analysis. The detailed results are shown in Supplementary Table\u0026nbsp;5. We found that in multivariate analysis, cholesterol(subclass), CE (subclass), and IDL-related subclass were in the presence of no significant correlation with AF. The association between the ratio of FC to total lipids in chylomicrons and extremely large VLDL and AF was found to be statistically significant (OR: 1.15, 95%CI: 1.03\u0026ndash;1.28, p\u0026thinsp;=\u0026thinsp;0.0145). Furthermore, the ratio of phospholipids to total lipids within medium VLDL also exhibited a significant correlation with AF, (OR:1.09, 95%CI: 1.03\u0026ndash;1.15, p\u0026thinsp;=\u0026thinsp;0.0019). Conversely, the ratio of TG to total lipids in very large VLDL was inversely related to AF (OR: 0.70, 95% CI: 0.50\u0026ndash;0.99,p\u0026thinsp;=\u0026thinsp;0.0442). In the subgroup analysis concerning VLDL, the mean diameter of VLDL particles was significantly associated with AF (OR: 0.89, 95%CI: 0.82\u0026ndash;0.97, p\u0026thinsp;=\u0026thinsp;0.007). In a model assessing various lipid characteristics, the total lipids within chylomicrons and extremely large VLDL were notably linked to AF (OR: 2.38, 95% CI:1.07\u0026ndash;5.29, p\u0026thinsp;=\u0026thinsp;0.0336). Additionally, the proportion of MUFAs to total fatty acids was found to have a significant association with AF (OR: 0.40, 95% CI: 0.19\u0026ndash;0.84, p\u0026thinsp;=\u0026thinsp;0.0157).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study, we used a two-sample MR approach to explore the causal relationships between 249 blood lipids and lipid-related traits associated with AF. Thirty of these lipids were causally associated with AF, and seventeen also had a causal relationship with AF/atrial flutter. Our results indicate that very small VLDL and IDL particles, along with their lipid composition, are risk factors for developing AF. The proportion of TG in VLDL serves as a protective factor, while cholesterol, FC, CE, and phospholipids are risk factors for AF. Finally monounsaturated fatty acid proportion also serves as a protective factor for AF. This study lays the groundwork for exploring the metabolic risk factors of AF through genetic mechanisms, aiding future hypothesis-driven analyses.\u003c/p\u003e \u003cp\u003eThe association between serum cholesterol levels and CAD has been acknowledged since 1964. Concurrently, atherosclerotic disease has been recognized as an independent risk factor for the onset of AF (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Consequently, one might anticipate that heightened TC and LDL-C levels would elevate the risk of developing AF. However, current research presents findings that challenge this assumption. Numerous well-designed cohort studies and meta-analyses have revealed an unexpected inverse correlation between conventional lipid profiles and the prevalence of AF (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The investigation into the mechanisms that underlie the relationship between lipoproteins and AF remains limited (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Mora et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) propose that the observed inverse association may stem from cholesterol\u0026rsquo;s role in stabilizing myocardial cell membranes. Furthermore, a reduction in cholesterol levels can negatively affect cardiomyocyte contractility by impairing calcium handling, adrenergic signaling, and the integrity of myofibrillar structure (27). Additionally, the relationship between cholesterol concentrations and AF development may also be linked to inflammatory processes. Research indicates that during states of inflammation, levels of TC, LDL-C, and HDL-C decline, while TG increase (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Thus, lower cholesterol levels may indicate underlying inflammatory mechanisms within the host that contribute to the occurrence of AF. Moreover, lipoproteins play a critical role in modulating the progression of sepsis by binding to bacterial endotoxins, thereby mitigating the adverse effects associated with inflammatory responses (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). It is essential to note that lipids are naturally occurring substances that perform a variety of biological functions, including the formation of plasma membranes, acting as signaling molecules, and serving as an energy source. Each lipid subtype possesses distinct molecular structures and inherent properties. Conventional lipid profiles could not become precisely enough to reflect the true pathogenic components of lipids. Thus, we utilized more refined GWAS data on lipid-related traits to examine their relationship with AF, revealing several lipid fractions that are genuinely linked to the condition.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIt is especially important to note that the causal associations between lipid traits and AF primarily involve VLDL, including its particle concentrations and composition. VLDL acts as a precursor to IDL, which subsequently leads to the formation of LDL. The lipid composition of VLDL is primarily comprised of TG accounting for 50\u0026ndash;70% of the particle mass, with cholesterol esters constituting 10\u0026ndash;25%, and fatty acids making up less than 10% (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). VLDL is synthesized in the liver and subsequently released into the bloodstream, performing the essential function of transporting triglycerides to adipose tissue for storage or for utilization by other tissues. Following a meal, VLDL particles demonstrate a propensity to bind effectively and be internalized by cells expressing the VLDL receptor, such as cardiomyocytes (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Upon their secretion from the liver into the bloodstream, VLDLs undergo alterations in size and composition as they engage with various enzymes, including phospholipid transfer protein (PLTP), cholesteryl ester transfer protein (CETP), and lipoprotein lipase (LPL). Hepatic PLTP plays a crucial role in modulating the secretion of VLDL from the liver. Within circulation, PLTP enhances the transfer of phospholipids from VLDLs to HDLs. Furthermore, CETP facilitates the exchange of CE from HDLs to TG-rich lipoproteins. The utilization of VLDL is a significant energy source for cardiac tissue, with approximately 70% of the heart's energy being derived from fatty acid oxidation under normal physiological conditions(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Excessive lipid accumulation in cardiomyocytes can lead to structural damage, cell death, mitochondrial dysfunction, and increased fibrosis, which may reduce cardiac function and subsequently increase the risk of AF(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Various lipid ingredients within VLDL may play different role in the onset of AF. Research has shown that post-menopausal women carrying the CETP B2B2/AA genotype exhibit an elevated risk of developing AF(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). In the cohort of females diagnosed with AF, those with the B2B2 genotype displayed significantly reduced levels of HDL-C and elevated TG levels when compared to B2B2 female control participants. Furthermore, the study revealed a correlation between all B2 alleles of the TaqIB polymorphism and the A alleles of the \u0026minus;\u0026thinsp;629 promoter polymorphism. In light of our findings, it may be inferred that mutations in the CETP B2B2/AA genotype could facilitate the transfer of a greater amount of CE from HDL to VLDL, thereby heightening the likelihood of AF development. Our study also revealed that elevated levels of CE, cholesterol, and FC in VLDL are risk factors for AF. And the underlying mechanism may be that excessive cholesterol in VLDL may lead to the deposition of lipids in cardiac myocytes, which may in turn damage cardiac myocytes and lead to an elevated risk of AF.\u003c/p\u003e \u003cp\u003eResearch indicates that lipoproteins influence the development of AF not solely based on their lipid composition, but also in relation to the particle size of VLDL particles. Specifically, smaller lipoprotein particles are associated with an inverse relationship to AF, whereas larger, cholesterol-rich LDL particles, as well as total HDL-C, lipoprotein (a), and TG, do not exhibit a significant correlation with the onset of AF (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Furthermore, a limited study focusing on female patients undergoing catheter ablation revealed that those diagnosed with AF possessed smaller lipoprotein particles, alongside heightened levels of oxidation, glycation, and TG content when compared to a control group in sinus rhythm (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). These changes led to increased foam cell formation due to faster phagocytosis by macrophages and a decrease in the antioxidant capacity of HDL (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Our study found that smaller particles of VLDL and the cholesterol, CE, and phospholipids therein are risk factors for AF, proving the veracity of the clinical findings at a different level.\u003c/p\u003e \u003cp\u003eMeanwhile, other researchers have identified a significant relationship between VLDL levels in patients with metabolic syndrome (MetS) and AF. In vivo studies indicate that the intravenous administration of VLDL derived from individuals diagnosed with metabolic syndrome into murine models over a six-week period results in enhanced lipid deposition and apoptosis within the atrial tissue, yielding a significantly more pronounced dilation of the left atrium compared to VLDL sourced from healthy controls (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Additional studies emphasize the heterogeneity present within VLDL particles. Notably, VLDL extracted from MetS patients exhibits a more negative charge and demonstrates considerably greater cytotoxicity towards human vascular endothelial cells in contrast to VLDL obtained from non-MetS individuals(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Research conducted by Lee et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) illustrated that postprandial negatively charged VLDL is independently correlated with atrial enlargement; specifically, every 1% increase in negatively charged VLDL corresponds to a 0.23 cm increment in the diameter of the left atrium. Moreover, Nakajima et al.(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) revealed that postprandial VLDL possesses a heightened affinity for the VLDL receptor, resulting in superior cellular internalization compared to non-postprandial VLDL, which contributes to cytotoxic effects in atrial tissues and ultimately leads to significant atrial remodeling. The presence of VLDL in MetS may induce atrial cardiomyopathy and heighten susceptibility to AF through mechanisms that include direct cytotoxicity, alterations in action potentials, disrupted calcium homeostasis, reduced conduction velocities, modifications in gap junctions, and variations in sarcomeric proteins (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e)(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Furthermore, the synthesis of TG-rich chylomicrons in the intestine, along with the release of VLDL from the liver, may be modulated by circulating TG-rich lipoproteins and remnants and further influence the autonomic nervous system (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Consequently, it can be inferred that VLDL may affect autonomic innervation or functionality within the heart, thereby elevating the risk of arrhythmias, especially among individuals with MetS. In light of these observations, postprandial modified VLDL has been proposed as a potential therapeutic target for atrial remodeling in MetS patients (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). This underscores the importance of recognizing that an exclusive focus on the quantity of lipoproteins, without considering the alterations in lipid profiles under physiological conditions, may restrict our comprehension of the mechanisms that contribute to the paradoxical inverse relationship observed between lipoproteins and AF.\u003c/p\u003e \u003cp\u003eVLDL levels and composition are influenced by several factors, such as diet, gut flora, and medications. Dietary habits have a strong influence on the composition of VLDL, especially postprandial VLDL. Animal and human studies have shown that a high sucrose diet can change VLDL particle size and triglyceride levels. This alteration can lead to metabolic syndromes, including hepatic steatosis and insulin resistance(\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). In contrast, 8 weeks of a polyphenol-rich diet or 8 weeks of recipes containing fruits, avocados, whole grains, and trout modulated the lipid composition of postprandial VLDL and lowered levels of insulin resistance (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e).Regarding fish consumption, only high levels of eicosapentaenoic acid (EPA) significantly reduced VLDL particles and VLDL-TGs, while the other two n-3 fatty acids, docosahexaenoic acid (DHA) and alpha-linolenic acid (ALA), did not show a similar effect (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). A cohort study involving 40 healthy individuals found that fasting for 7\u0026ndash;14 days significantly improved their lipid profiles, particularly by increasing the concentration of large postprandial VLDL particles (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). While diet primarily influences lipid composition, gut flora also plays a significant role in lipid regulation. A prospective population-based cohort study found that 18 of the 32 microbial families in the gut microbiota were significantly associated with VLDL particles of different sizes (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Another study indicated that low microbiota diversity was linked to obesity, abdominal obesity, and low HDL-C levels in healthy individuals (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Together, these findings suggest that both synthetic and probiotic supplements aimed at correcting gut microbiota imbalances may potentially improve serum VLDL-C levels and have been tentatively demonstrated (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Numerous oral hypoglycemic agents have been recognized for their favorable effects on VLDL metabolism. Pioglitazone, which acts as a PPAR-γ activator, enhances LPL activity, thereby promoting the clearance of VLDL (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Additionally, glucagon-like peptide-1 (GLP-1) receptor agonists have been demonstrated to lower TG concentrations in hepatic tissues and diminish the rate of VLDL secretion (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). While mainstream lipid-lowering medications do not specifically target VLDL, they can still contribute to reductions in VLDL levels. For instance, 3-hydroxy-3-methyl glutaryl coenzyme A (HMG-CoA) reductase inhibitors, commonly known as statins, have been shown to lower approximately one-third of VLDL-TG and reduce levels of apolipoprotein C-III (apoC III) by over 40% (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Moreover, agonists of peroxisome proliferator-activated receptor-α (PPAR-α), commonly referred to as fibrates, which are primarily utilized for the treatment of hypertriglyceridemia, have also been found to decrease VLDL-apo C III levels (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Similar to selective estrogen receptor modulators, the first selective PPAR-α modulator, LY-518674, specifically alters tissue and gene expression responses by targeting the receptor-cofactor binding profile of the PPARα ligand. In phase II/III clinical trials, the SPPARMα agonist has been shown to decrease TG and apoC III levels by roughly 50%(\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Proprotein convertase subtilisin-kexin type 9 (PCSK9) inhibitors function by inhibiting LDL receptor degradation and promoting the uptake of LDL in the liver. They also elevate the levels of VLDL receptors while concurrently lowering VLDL concentrations. Furthermore, PCSK9 inhibitors have been observed to preferentially alter the size and apolipoprotein composition of VLDL particles (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). Based on our findings, we hypothesize that a healthy diet, supplementation with intestinal probiotics, or medications to modify the body\u0026rsquo;s lipid metabolic profile may further reduce the incidence and recurrence of AF. Therefore, future studies should focus on both genetic and clinical levels to explore dietary and intestinal flora influences, as well as pharmacologic modulation of specific VLDL subcomponents. This research could help determine whether managing relevant lipid fractions can help control AF.\u003c/p\u003e \u003cp\u003eLastly, our results indicate that a higher percentage of MUFAs, rather than polyunsaturated fatty acids, protects against AF. MUFAs include ω-7 fatty acids like isoleic acid and neuraminic acid, and ω-9 fatty acids such as palmitoleic acid and oleic acid. Olive oil, palm oil, rapeseed oil, tea seed oil, and nuts are currently regarded as major sources of MUFAs intake. Previous studies have shown that diets rich in MUFAs, such as olive oil in the mediterranean diet, are associated with longevity in humans (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e)and promote longevity in rodents (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). The mechanism may involve MUFAs increasing the number of lipid droplets in fat storage tissues and boosting peroxisome counts. Both are essential for MUFA-induced longevity and predicting remaining lifespan. MUFAs can also modify the ratio of membrane lipids and further decrease lipid oxidation (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). Assy et al.(\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e) reported that oleic acid suppresses the activation of the nuclear factor-кB transcription factor, which in turn diminishes inflammatory responses and mitigates endothelial injury. Nevertheless, the impact of MUFAs on cardiovascular health remains a topic of active debate in contemporary research. Merkel et al.(\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e)investigated the influence of diets rich in MUFAs on atherosclerosis by utilizing two distinct mouse models. Their findings indicated that such dietary fats enhance susceptibility to atherosclerosis by elevating levels of VLDL-C through an apo E-independent mechanism. Additionally, an analysis of the National Health and Nutrition Examination Survey (NHANES) database revealed that increased consumption of either monounsaturated or polyunsaturated fatty acids correlates with a reduction in the 10-year risk of cardiovascular events, particularly among non-diabetic individuals with low LDL levels (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). Furthermore, a separate study explored the association between fatty acid intake and the onset of AF, concluding that substituting saturated fatty acids with monounsaturated, total, or n-6 polyunsaturated fatty acids does not elevate the risk of developing AF. Our investigation employed MR to propose a potential causal link between MUAFs and AF; however, additional research involving larger clinical trials is imperative to elucidate this association.\u003c/p\u003e \u003cp\u003eThis study presents several specific limitations. First, our study included only 249 blood lipid-related traits and two types of AF associated GWAS datasets. Future research should include more metabolites and various types of AF. This will enhance our comprehensive understanding of AF risk factors and their underlying mechanisms. Secondly, Due to the complex interactions among different lipid components, relying solely on a p-value of for IVW less than 0.05 may lead to false-positive results. However, in this study, we further constructed different multivariate models according to lipid subgroups, and the outcomes still found that some of the lipid traits were significantly correlated with AF, which compensated for the possibility of false-positive results of the present study to a certain extent. Thirdly, reliance on existing genomic datasets may introduce inherent biases, such as unavoidable horizontal pleiotropy between exposures, potential confounders that cannot be controlled for between exposures and outcomes, variations in population characteristics that could affect the generalizability of our findings. Moreover, MR studies serve as a valuable tool in determining if the identified correlations suggest a causal link supported by genetic data. To elucidate the precise causal relationship between particular lipid species and AF, further laboratory investigations and clinical trials are frequently required to uncover the underlying biological mechanisms. Lastly, we should consider that the impact of dynamic lipid changes not addressed in our study, such as postprandial VLDL and VLDL changes in patients with metabolic syndrome. All in all, the complexity of lipid metabolism and its multifactorial nature may constrain the precision of our conclusions regarding specific lipid traits and their effects on atrial fibrillation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our research identified thirty lipid traits related to AF, highlighting their potential as vital biomarkers for cardiovascular health. Larger clinical studies are necessary to confirm the link between our findings and AF, and to investigate whether lipid interventions can lower the risk of AF occurrence or recurrence.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eatrial fibrillation\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAF\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecoronary artery disease\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emetabolic syndrome\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMetS\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMendelian randomization\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003einstrumental variables\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIVs\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003egenome-wide association study\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGWAS\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003einverse-variance weighted\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCochran\u0026rsquo;s Q statistic\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQ\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eleave-one-out\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLOO\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eodds ratio\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003etotal cholesterol\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ephospholipid transfer protein\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePLTP\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003echolesteryl ester transfer protein\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCETP\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ealpha-linolenic acid\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eALA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003etriglyceride\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003echolesteryl esters\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCE\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003echylomicrons\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCM\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003efree cholesterol\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFC\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003elipoprotein (a)\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elp(a)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eglucagon-like peptide-1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGLP-1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eproprotein convertase subtilisin-kexin type 9\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePCSK9\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGlobal Burden of Disease\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGBD\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNational Health and Nutrition Examination Survey\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNHANES\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe summary data for 249 lipid-related traits GWAS ID and the outcome GWAS for AF and atrial flutter were obtained from the website( IEU OpenGWAS project)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful for the Noncommunicable Chronic Diseases-National Science and Technology Major Project (grant 2023ZD0503400/2023ZD0503402), National Key Research and Development Program of China (2021YFC2500600/2021YFC2500601), Projects of National Natural Science Foundation of China (grant 82370442), and National High Level Hospital Clinical Research Funding (Interdepartmental Research Project of Peking University First Hospital, No. 2023IR35) support of our research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCheng, S. et al. Global burden of atrial fibrillation/atrial flutter and its attributable risk factors from 1990 to 2021. \u003cem\u003eEuropace: European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology\u003c/em\u003e 26. (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElliott, A. D., Middeldorp, M. E., Van Gelder, I. C., Albert, C. M. \u0026amp; Sanders, P. Epidemiology and modifiable risk factors for atrial fibrillation. \u003cem\u003eNat. Rev. Cardiol.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, 404\u0026ndash;417 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKloosterman, M. et al. Characteristics and outcomes of atrial fibrillation in patients without traditional risk factors: an RE-LY AF registry analysis. \u003cem\u003eEuropace: European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology\u003c/em\u003e 22: 870\u0026ndash;877. (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu, Y. et al. Non-traditional risk factors for atrial fibrillation: epidemiology, mechanisms, and strategies. \u003cem\u003eEur. Heart J.\u003c/em\u003e \u003cb\u003e46\u003c/b\u003e, 784\u0026ndash;804 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026uuml;scher, T. F. Frontiers in lipid research: lipoprotein(a), apolipoprotein C-III and E, and PCSK9 and inflammation. \u003cem\u003eEur. Heart J.\u003c/em\u003e \u003cb\u003e40\u003c/b\u003e, 2741\u0026ndash;2744 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarrio-Lopez, M. T. et al. Mediterranean diet and epicardial adipose tissue in patients with atrial fibrillation treated with ablation: a substudy of the 'PREDIMAR' trial. \u003cem\u003eEur. J. Prev. Cardiol.\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e, 348\u0026ndash;355 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlonso, A. et al. Blood lipids and the incidence of atrial fibrillation: the Multi-Ethnic Study of Atherosclerosis and the Framingham Heart Study. \u003cem\u003eJ. Am. Heart Association\u003c/em\u003e. \u003cb\u003e3\u003c/b\u003e, e001211 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, X. et al. Lipid profile and incidence of atrial fibrillation: A prospective cohort study in China. \u003cem\u003eClin. Cardiol.\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e, 314\u0026ndash;320 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMora, S., Akinkuolie, A. O., Sandhu, R. K., Conen, D. \u0026amp; Albert, C. M. Paradoxical association of lipoprotein measures with incident atrial fibrillation. \u003cem\u003eCirc. Arrhythm. Electrophys.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 612\u0026ndash;619 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuan, B. et al. Blood lipid profiles and risk of atrial fibrillation: A systematic review and meta-analysis of cohort studies. \u003cem\u003eJ. Clin. Lipidol.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 133\u0026ndash;142e133 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYao, Y., Liu, F., Wang, Y. \u0026amp; Liu, Z. Lipid levels and risk of new-onset atrial fibrillation: A systematic review and dose-response meta-analysis. \u003cem\u003eClin. Cardiol.\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e, 935\u0026ndash;943 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, S., Pudasaini, R., Zhi, H. \u0026amp; Wang, L. The Relationship between Blood Lipids and Risk of Atrial Fibrillation: Univariable and Multivariable Mendelian Randomization Analysis. \u003cem\u003eNutrients\u003c/em\u003e 14. (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTao, Y. et al. Associations of lipids and lipid-modifying drug target genes with atrial fibrillation risk based on genomic data. \u003cem\u003eLipids Health Dis.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e, 175 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohammadi-Shemirani, P. et al. Elevated Lipoprotein(a) and Risk of Atrial Fibrillation: An Observational and Mendelian Randomization Study. \u003cem\u003eJ. Am. Coll. Cardiol.\u003c/em\u003e \u003cb\u003e79\u003c/b\u003e, 1579\u0026ndash;1590 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSekula, P., Del Greco, M. F., Pattaro, C. \u0026amp; K\u0026ouml;ttgen, A. Mendelian Randomization as an Approach to Assess Causality Using Observational Data. \u003cem\u003eJ. Am. Soc. Nephrology: JASN\u003c/em\u003e. \u003cb\u003e27\u003c/b\u003e, 3253\u0026ndash;3265 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKastenm\u0026uuml;ller, G., Raffler, J., Gieger, C. \u0026amp; Suhre, K. Genetics of human metabolism: an update. \u003cem\u003eHum. Mol. Genet.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, R93\u0026ndash;r101 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichardson, T. G. et al. Characterising metabolomic signatures of lipid-modifying therapies through drug target mendelian randomisation. \u003cem\u003ePLoS Biol.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, e3001547 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNielsen, J. B. et al. Biobank-driven genomic discovery yields new insight into atrial fibrillation biology. \u003cem\u003eNature genetics\u003c/em\u003e 50: 1234\u0026ndash;1239. (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin, S. H., Brown, D. W. \u0026amp; Machiela, M. J. LDtrait: An Online Tool for Identifying Published Phenotype Associations in Linkage Disequilibrium. \u003cem\u003eCancer Res.\u003c/em\u003e \u003cb\u003e80\u003c/b\u003e, 3443\u0026ndash;3446 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHartwig, F. P., Davies, N. M., Hemani, G. \u0026amp; Davey Smith, G. Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique. \u003cem\u003eInt. J. Epidemiol.\u003c/em\u003e \u003cb\u003e45\u003c/b\u003e, 1717\u0026ndash;1726 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLarsson, S. C., Butterworth, A. S. \u0026amp; Burgess, S. Mendelian randomization for cardiovascular diseases: principles and applications. \u003cem\u003eEur. Heart J.\u003c/em\u003e \u003cb\u003e44\u003c/b\u003e, 4913\u0026ndash;4924 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. \u003cem\u003eeLife\u003c/em\u003e 7. (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden, J., Davey Smith, G. \u0026amp; Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. \u003cem\u003eInt. J. Epidemiol.\u003c/em\u003e \u003cb\u003e44\u003c/b\u003e, 512\u0026ndash;525 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEgger, M., Davey Smith, G., Schneider, M. \u0026amp; Minder, C. Bias in meta-analysis detected by a simple, graphical test. \u003cem\u003eBMJ (Clinical Res. ed)\u003c/em\u003e. \u003cb\u003e315\u003c/b\u003e, 629\u0026ndash;634 (1997).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden, J. et al. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. \u003cem\u003eStat. Med.\u003c/em\u003e \u003cb\u003e36\u003c/b\u003e, 1783\u0026ndash;1802 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChyou, J. Y., Hunter, T. D., Mollenkopf, S. A., Turakhia, M. P. \u0026amp; Reynolds, M. R. Individual and Combined Risk Factors for Incident Atrial Fibrillation and Incident Stroke: An Analysis of 3 Million At-Risk US Patients. \u003cem\u003eJ. Am. Heart Association\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e. (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHissa, B. et al. Cholesterol depletion impairs contractile machinery in neonatal rat cardiomyocytes. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 43764 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhovidhunkit, W. et al. Effects of infection and inflammation on lipid and lipoprotein metabolism: mechanisms and consequences to the host. \u003cem\u003eJ. Lipid Res.\u003c/em\u003e \u003cb\u003e45\u003c/b\u003e, 1169\u0026ndash;1196 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerb\u0026eacute;e, J. F., Havekes, L. M. \u0026amp; Rensen, P. C. Apolipoproteins modulate the inflammatory response to lipopolysaccharide. \u003cem\u003eJ. Endotoxin Res.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 97\u0026ndash;103 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang, J. K. \u0026amp; Lee, H. C. Emerging Evidence of Pathological Roles of Very-Low-Density Lipoprotein (VLDL). \u003cem\u003eInternational journal of molecular sciences\u003c/em\u003e 23. (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNakajima, K., Tokita, Y., Tanaka, A. \u0026amp; Takahashi, S. The VLDL receptor plays a key role in the metabolism of postprandial remnant lipoproteins. \u003cem\u003eClin. Chim. Acta\u003c/em\u003e. \u003cb\u003e495\u003c/b\u003e, 382\u0026ndash;393 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiu, Y. G. \u0026amp; Evans, R. D. Very-low-density lipoprotein: complex particles in cardiac energy metabolism. \u003cem\u003eJ. lipids\u003c/em\u003e. \u003cb\u003e2011\u003c/b\u003e, 189876 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchulze, P. C., Drosatos, K. \u0026amp; Goldberg, I. J. Lipid Use and Misuse by the Heart. \u003cem\u003eCircul. Res.\u003c/em\u003e \u003cb\u003e118\u003c/b\u003e, 1736\u0026ndash;1751 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalati, F. et al. CETP TaqIB Polymorphism, Serum Lipid Levels And Risk Of Atrial Fibrillation: A Case-Control Study. \u003cem\u003eJ. Atr. fibrillation\u003c/em\u003e. \u003cb\u003e6\u003c/b\u003e, 964 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, S. M. et al. Female patients with atrial fibrillation have increased oxidized and glycated lipoprotein properties and lower apolipoprotein A-I expression in HDL. \u003cem\u003eInt. J. Mol. Med.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, 841\u0026ndash;849 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, H. C. et al. VLDL from Metabolic Syndrome Individuals Enhanced Lipid Accumulation in Atria with Association of Susceptibility to Atrial Fibrillation. \u003cem\u003eInternational journal of molecular sciences\u003c/em\u003e 17. (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, C. H. et al. Effects of electronegative VLDL on endothelium damage in metabolic syndrome. \u003cem\u003eDiabetes care\u003c/em\u003e. \u003cb\u003e35\u003c/b\u003e, 648\u0026ndash;653 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, H. C. et al. The role of postprandial very-low-density lipoprotein in the development of atrial remodeling in metabolic syndrome. \u003cem\u003eLipids Health Dis.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, 210 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, H. C. \u0026amp; Lin, Y. H. The Pathogenic Role of Very Low Density Lipoprotein on Atrial Remodeling in the Metabolic Syndrome. \u003cem\u003eInternational journal of molecular sciences\u003c/em\u003e 21. (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuchoomun, J., Zhou, Z., Bakillah, A., Jamil, H. \u0026amp; Hussain, M. M. Assembly and secretion of VLDL in nondifferentiated Caco-2 cells stably transfected with human recombinant ApoB48 cDNA. \u003cem\u003eArterioscler. Thromb. Vasc. Biol.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 2955\u0026ndash;2963 (1997).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdam, R. C. et al. Angiopoietin-like protein 3 governs LDL-cholesterol levels through endothelial lipase-dependent VLDL clearance. \u003cem\u003eJ. Lipid Res.\u003c/em\u003e \u003cb\u003e61\u003c/b\u003e, 1271\u0026ndash;1286 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDornas, W. C., de Lima, W. G., Pedrosa, M. L. \u0026amp; Silva, M. E. Health implications of high-fructose intake and current research. \u003cem\u003eAdv. Nutr. (Bethesda Md)\u003c/em\u003e. \u003cb\u003e6\u003c/b\u003e, 729\u0026ndash;737 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePlazas Guerrero, C. G. et al. and U. Osuna Mart\u0026iacute;nez. 2021. Evaluation of sucrose-enriched diet consumption in the development of risk factors associated to type 2 diabetes, atherosclerosis and non-alcoholic fatty liver disease in a murine model. \u003cem\u003eInt. J. Environ. Health Res.\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e: 651\u0026ndash;669 .\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSabaka, P. et al. Postprandial changes of lipoprotein profile: effect of abdominal obesity. \u003cem\u003eLipids Health Dis.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 179 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDella Pepa, G. et al. Effects of a diet naturally rich in polyphenols on lipid composition of postprandial lipoproteins in high cardiometabolic risk individuals: an ancillary analysis of a randomized controlled trial. \u003cem\u003eEur. J. Clin. Nutr.\u003c/em\u003e \u003cb\u003e74\u003c/b\u003e, 183\u0026ndash;192 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMu\u0026ntilde;oz-Perez, D. M. et al. and O. A. Rangel-Zu\u0026ntilde;iga. Alternative Foods in Cardio-Healthy Dietary Models That Improve Postprandial Lipemia and Insulinemia in Obese People. \u003cem\u003eNutrients\u003c/em\u003e 13. (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmig\u0026oacute;, N. et al. Habitual Fish Consumption, n-3 Fatty Acids, and Nuclear Magnetic Resonance Lipoprotein Subfractions in Women. \u003cem\u003eJ. Am. Heart Association\u003c/em\u003e. \u003cb\u003e9\u003c/b\u003e, e014963 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrundler, F. et al. Long-term fasting improves lipoprotein-associated atherogenic risk in humans. \u003cem\u003eEur. J. Nutr.\u003c/em\u003e \u003cb\u003e60\u003c/b\u003e, 4031\u0026ndash;4044 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVojinovic, D. et al. and C. M. van Duijn. Relationship between gut microbiota and circulating metabolites in population-based cohorts. \u003cem\u003eNature communications\u003c/em\u003e 10: 5813. (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKashtanova, D. A. et al. Subfractional Spectrum of Serum Lipoproteins and Gut Microbiota Composition in Healthy Individuals. \u003cem\u003eMicroorganisms\u003c/em\u003e 9. (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShakeri, H. et al. Consumption of synbiotic bread decreases triacylglycerol and VLDL levels while increasing HDL levels in serum from patients with type-2 diabetes. \u003cem\u003eLipids\u003c/em\u003e \u003cb\u003e49\u003c/b\u003e, 695\u0026ndash;701 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodriguez, V., Newman, J. D. \u0026amp; Schwartzbard, A. Z. Towards more specific treatment for diabetic dyslipidemia. \u003cem\u003eCurr. Opin. Lipidol.\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e, 307\u0026ndash;312 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel, V. J., Joharapurkar, A. A., Shah, G. B. \u0026amp; Jain, M. R. Effect of GLP-1 based therapies on diabetic dyslipidemia. \u003cem\u003eCurr. Diabetes. Rev.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 238\u0026ndash;250 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOoi, E. M., Ng, T. W., Watts, G. F., Chan, D. C. \u0026amp; Barrett, P. H. Effect of fenofibrate and atorvastatin on VLDL apoE metabolism in men with the metabolic syndrome. \u003cem\u003eJ. Lipid Res.\u003c/em\u003e \u003cb\u003e53\u003c/b\u003e, 2443\u0026ndash;2449 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFruchart, J. C. et al. The selective peroxisome proliferator-activated receptor alpha modulator (SPPARMα) paradigm: conceptual framework and therapeutic potential: A consensus statement from the International Atherosclerosis Society (IAS) and the Residual Risk Reduction Initiative (R3i) Foundation. \u003cem\u003eCardiovascular diabetology\u003c/em\u003e 18: 71. (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHollstein, T. et al. Treatment with PCSK9 inhibitors reduces atherogenic VLDL remnants in a real-world study. \u003cem\u003eVascul. Pharmacol.\u003c/em\u003e \u003cb\u003e116\u003c/b\u003e, 8\u0026ndash;15 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMendez, M. A. \u0026amp; Newman, A. B. Can a Mediterranean Diet Pattern Slow Aging? \u003cem\u003eJ. Gerontol. A\u003c/em\u003e. \u003cb\u003e73\u003c/b\u003e, 315\u0026ndash;317 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTosti, V., Bertozzi, B. \u0026amp; Fontana, L. Health Benefits of the Mediterranean Diet: Metabolic and Molecular Mechanisms. \u003cem\u003eJ. Gerontol. A\u003c/em\u003e. \u003cb\u003e73\u003c/b\u003e, 318\u0026ndash;326 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamirez-Tortosa, C. L. et al. Longevity and Cause of Death in Male Wistar Rats Fed Lifelong Diets Based on Virgin Olive Oil, Sunflower Oil, or Fish Oil. \u003cem\u003eJ. Gerontol. A\u003c/em\u003e. \u003cb\u003e75\u003c/b\u003e, 442\u0026ndash;451 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePapsdorf, K. et al. Lipid droplets and peroxisomes are co-regulated to drive lifespan extension in response to mono-unsaturated fatty acids. \u003cem\u003eNat. Cell Biol.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 672\u0026ndash;684 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAssy, N., Nassar, F., Nasser, G. \u0026amp; Grosovski, M. Olive oil consumption and non-alcoholic fatty liver disease. \u003cem\u003eWorld J. Gastroenterol.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 1809\u0026ndash;1815 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMerkel, M., Velez-Carrasco, W., Hudgins, L. C. \u0026amp; Breslow, J. L. Compared with saturated fatty acids, dietary monounsaturated fatty acids and carbohydrates increase atherosclerosis and VLDL cholesterol levels in LDL receptor-deficient, but not apolipoprotein E-deficient, mice. \u003cem\u003eProc. Natl. Acad. Sci. U.S.A.\u003c/em\u003e \u003cb\u003e98\u003c/b\u003e, 13294\u0026ndash;13299 (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, Z. et al. Dietary Saturated, Monounsaturated, or Polyunsaturated Fatty Acids and Estimated 10-Year Risk of a First Hard Cardiovascular Event. \u003cem\u003eAm. J. Med.\u003c/em\u003e \u003cb\u003e136\u003c/b\u003e, 796\u0026ndash;803e792 (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"blood lipids, very low-density lipoprotein, atrial fibrillation, atrial flutter, Mendelian randomization, causal effect","lastPublishedDoi":"10.21203/rs.3.rs-6624447/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6624447/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Atrial fibrillation (AF) stands as the most prevalent form of sustained arrhythmia, but the relationship between lipid is unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e The objective of this research is to examine the causal relationships between 249 blood lipid-related traits and the likelihood of developing AF and atrial flutter by utilizing two-sample Mendelian randomization (MR) method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eData on the single nucleotide polymorphisms (SNPs) related to lipid-related traits were obtained from a proof-of-concept cross-platform Genome-wide association study GWAS datasets including participants ranging from 110,051 to 115,082 from the UK Biobank study, and data for AF were from the meta-analysis of GWASs and FinnGen study. The univariable and multivariable MR analysis were conducted to explore whether genetic evidence of individual lipid-related traits was significantly associated with AF risks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eWe identified thirty lipid traits that exhibit a significant correlation with AF, in addition to 17 traits linked to AF and atrial flutter. Very-low-density lipoprotein (VLDL) and particle diameters were associated with AF (OR: 0.93, 95%CI 0.88-0.98, p=0.0055). A high percentage of triglycerides in VLDL is a protective factor for AF, whereas an elevated percentage of cholesterol, cholesteryl esters, free cholesterol, and phospholipids are risk factors for AF. Additionally, higher concentrations of intermediate-density lipoprotein (IDL) particles and elevated phospholipid levels in IDL were identified as risk factors for AF (OR: 1.06, 95%CI 1.0-1.13, p=0.0497 for particles; OR: 1.06, 95%CI 1.0-1,12, p=0.0404 for phospholipids). Notably, a higher percentage of monounsaturated fatty acids was found to be protective against AF (OR: 0.94, 95%CI 0.88-0.99, p=0.0258).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e our study identified certain lipid-relates traits are associated with AF/atrial flutter. Future research should focus on the underlying biological mechanisms and the role of lipid modulation to further inform AF prevention strategies.\u003c/p\u003e","manuscriptTitle":"The Causal Relationships between Lipids related Traits and Atrial Fibrillation/Flutter: Two-sample Mendelian Randomization Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 12:13:59","doi":"10.21203/rs.3.rs-6624447/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":"5c17d9f3-ce87-403c-b0ad-f39ac5ac6094","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48283473,"name":"Health sciences/Diseases"},{"id":48283474,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases"}],"tags":[],"updatedAt":"2025-05-23T04:23:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-16 12:13:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6624447","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6624447","identity":"rs-6624447","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}