Mendelian randomization analysis does not reveal a causal influence between lipoprotein(A) and immune-mediated inflammatory diseases

doi:10.21203/rs.3.rs-4878166/v1

Download PDF

Article

Mendelian randomization analysis does not reveal a causal influence between lipoprotein(A) and immune-mediated inflammatory diseases

https://doi.org/10.21203/rs.3.rs-4878166/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Observational studies have reported an association between lipoprotein(a) (Lp(a)) and immune-mediated inflammatory diseases (IMIDs). Here, we aimed to explore causal relationship between Lp(a) and IMIDs by Mendelian randomization (MR) analysis. We performed a two-sample mendelian randomization analyses based on genome-wide association study (GWAS) summary statistics of Lp(a) and nine IMIDs, specifically celiac disease (CeD), Crohn’s disease (CD), ulcerative colitis (UC), inflammatory bowel disease (IBD), multiple sclerosis (MS), psoriasis (Pso), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), type 1 diabetes (T1D), and summary-level data for lipid traits. We performed bidirectional and multivariable MR (MVMR) to examine the causal relationship of Lp(a) with IMIDs analysis and its independence after controlling other lipid traits, namely high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and triglycerides (TG). The results indicated no causal relationship between Lp(a) and the risk of IMIDs in univariable and multivariable MR analysis (with all IVW P values > 0.05). In multivariable MR, genetically predicted HDL (OR_MVMR 0.80(0.68–0.95); P = 0.011) and TG (OR_MVMR 0.80(0.66–0.98); P = 0.033) linked to higher risk of type 1 diabetes, and genetically predicted LDL linked to higher risk of psoriasis (OR_MVMR 0.80(0.64–0.99); P = 0.045). This MR study found no evidence suggesting a causal link between lipoprotein(a) and IMIDs, which is contrary to the results of many observational studies. The identification of potential mechanisms underlying the observed associations in observational studies necessitates further investigation.

Lipoprotein(a)

immune-mediated inflammatory diseases

Mendelian randomization analysis

lipid traits

multivariable MR analysis

Lipoprotein(a) is synthesized by the covalent linkage of apolipoprotein A to apolipoprotein B via disulfide bonds, and its concentration demonstrates an inverse association with the size of apolipoprotein A. Moreover, this concentration is predominantly influenced by genetic factors at a level of 90% [1]. The significance of Lp(a) as a risk factor for cardiovascular diseases [2, 3], degenerative aortic stenosis [4], atrial fibrillation [5] and heart failure [6] has gained recognition. Despite the genetic determination of Lp(a) levels, several studies have demonstrated that chronic inflammation disrupts Lp(a) expression and elevates plasma levels of Lp(a)[7, 8]. On the contrary, when it comes to other lipoproteins, Lp(a) has a tendency to undergo oxidative alterations and produce oxidized phospholipids (OxPLs) that are associated with inflammation and atherosclerosis, thereby augmenting the inflammatory activity within the arterial wall [9, 10]. Collectively, the available evidence suggests a reciprocal association between Lp(a) and inflammation.

Immune-mediated inflammatory diseases encompass a diverse array of pathological conditions, such as celiac disease, Crohn’s disease and inflammatory bowel disease. Multiple observational studies have consistently indicated increased concentrations of Lp(a) in individuals diagnosed with different inflammatory disorders, such as rheumatoid arthritis, systemic lupus erythematosus, and acquired immunodeficiency syndrome [11–19]. However, the majority of clinical investigations are limited in scope and rely on an observational design. While epidemiological studies indicate potential links between Lp(a) and autoimmune disorders, the underlying causality of these connections remains uncertain. The presence of unmeasured covariates and reverse causation in these studies introduces bias, making it challenging to establish a definitive causal relationship. Nonetheless, exploring the potential causal linkage between Lp(a) and autoimmune disorders could offer valuable insights into specific biological pathways and contribute to the development of preventive strategies.

Mendelian randomization is a methodologically robust approach to establish causal associations between exposures and outcomes in epidemiological studies. As these variants are randomly assigned during conception, MR has the potential to mitigate bias arising from environmental confounders when conducted appropriately [20, 21]. In this study, we employed bidirectional and multivariable MR approaches to investigate the causal association between immune-mediated inflammatory diseases and lipoprotein(a), employing summary statistics obtained from GWAS conducted on European populations for both characteristics. Our study seeks to offer fresh perspectives and empirical data regarding the correlation between lipoprotein(a) and IMIDs.

2.1 Study design

To ascertain the causal directionality between lipoprotein(a) and immune-mediated inflammatory diseases, we conducted univariable and multivariable MR analyses using GWAS datasets[22]. Firstly, we conducted univariable Mendelian randomization (UVMR) analyses to establish a bidirectional causal link between Lp(a) and IMIDs. Additionally, multivariable Mendelian randomization analyses [23] were performed on lipid traits (Lp(a), HDL-C, LDL-C, TG) for autoimmune diseases to assess the independent association of Lp(a) with autoimmune diseases. The genetic association between UC and CD has been documented in previous studies [24]. We also conducted MVMR analyses using ulcerative colitis and Crohn's disease as exposures, accounting for the pleiotropy. The overall design of this study is depicted in Fig. 1 through a comprehensive flow chart. The assumptions outlined below were applied to all MR analyses and are collectively described for both the UVMR and MVMR approaches. We adopted three fundamental hypotheses of classical MR analysis, as follows: 1. Instrumental variables (IVs) exhibit a direct relationship with exposure; 2. Confounding variables do not affect the independence of IVs; 3. IVs solely influence the outcomes through exposure [21, 25]. We didn’t pre-register any study protocol or details.

2.2 Data sources

To perform our MR analyses, we used summary-level data from the publicly available GWAS for each trait [26–33]. Genetic IVs for HDL-C, LDL-C and TG were obtained through genome-wide association studies conducted in the UK Biobank [34]. GWAS data for exposure and outcomes were obtained from different databases to ensure minimal overlap. All study participants were of European descent, avoiding racial differences. The original publications provide comprehensive information regarding recruitment procedures and diagnostic criteria. The detail information of used GWAS datasets was listed in Table 1.

Table 1

Data sources used to identify genetic variants in this study.
Phenotypes	Study	Consortium	Phenotypic code	Cases/controls	Sample size	Ancestry
Celiac disease	Trynka et al [26]	NA	ieu-a-1058	12041/12228	24269	European
Crohn's disease	Liu et al [27]	IIBDGC	ieu-a-12	17897/33977	51874	European
Inflammatory bowel disease	Liu et al [27]	IIBDGC	ieu-a-294	31665/33977	65642	European
Multiple sclerosis	Patsopoulos NA et al [28]	IMSGC	ieu-b-18	47429/68374	115803	European
Psoriasis	Stuart PE et al [29]	NA	ebi-a-GCST90019016	15967/28194	44161	European
Rheumatoid arthritis	Okada Y et al [30]	NA	ieu-a-832	14361/43923	58284	European
Systemic lupus erythematosus	Bentham J et al [31]	NA	ebi-a-GCST003156	5201/9066	14297	European
Type 1 diabetes	Chiou J et al [32]	NA	ebi-a-GCST90014023	18942/501638	520580	European
Ulcerative colitis	Liu et al [27]	IIBDGC	ieu-a-970	13768/33977	47745	European
lipoprotein(a)	Barton AR et al [33]	NA	ebi-a-GCST90025993	/	348806	European
LDL-Cholesterol	Richardson TG et al [34]	UK Biobank	ieu-b-110	/	440546	European
HDL-Cholesterol	Richardson TG et al [34]	UK Biobank	ieu-b-109	/	403943	European
Triglycerides	Richardson TG et al [34]	UK Biobank	ieu-b-111	/	441016	European
IIBDGC: International Inflammatory Bowel Disease Genetic Consortium; IMSGC: Internationnal Multiple Sclerosis Genetics Corsotium

2.3 Instrumental variable selection

Initially, we identified single nucleotide polymorphism (SNPs) associated with each trait using a threshold of p = 5×10 − 8 based on the comprehensive summary-level GWAS statistics. Subsequently, to ensure the independence among the SNPs, a strict linkage disequilibrium (LD) threshold of r2 = 0.001 was applied when clustering IVs within 10 Mb. We ensured the effect estimates were standardized for exposure and outcome, while excluding any alleles that could potentially cause incompatibility or palindromic SNPs. For consistency, we used only SNPs that were tested for the trait as IVs and did not use proxies to replace SNPs that were missing in the outcome data. Additionally, we eliminated any SNPs linked to the confounding variable affecting the result using the PhenoScanner. For Lp(a) as the exposure and IMIDs as the outcomes, we regard the C-reactive protein levels (CRP) as the confounding variable. For IMIDs as the exposures and Lp(a) as the outcome, we regard the fat content, blood lipid and the coronary disease as the confounding variables. We employed F statistics (beta2/se2) [35] to evaluate the robustness of genetically determined instrumental variables (IVs), with a threshold of F > 10 in accordance with the first assumption of Mendelian randomization and to avoid bias towards weak IVs [36, 37].

2.4 Mendelian randomization analysis

We utilized the statistical software R and employed the TwoSampleMR and MR-PRESSO packages for conducting all analyses [38]. Multiple MR methods, including inverse variance weighting (IVW) [39], weighted median (WM) [40], MR-Egger [41], and MR-pleiotropy residual sum and outlier (MR-PRESSO) [41] were utilized for deducing causal connections between lipoprotein(a) and IMIDs. The IVW method was primarily employed for fundamental causal estimates, which would provide the most precise results when all selected SNPs were valid IVs. The IVW method calculates a weighted average of Wald ratio estimates. Under the assumption of Instrument Strength Independent of Direct Effect (InSIDE), the MR-Egger regression executes a weighted linear regression and yields a consistent causal estimate, even though the genetic IVs are all invalid[41]. However, it exhibits low precision and is susceptible to outlying genetic variants. Additionally, We utilized the weighted median technique, which computes the midpoint of the weighted approximations and ensures consistent effects even in scenarios where 50% of instrumental variables exhibit pleiotropy. The Weighted Median regression method, which does not demand the InSIDE hypothesis, calculates a weighted median of the Wald ratio estimates and is robust to horizontal pleiotropic bias [40]. It is confirmed that the Weighted Median method has some advantages over the MR-Egger regression, as it provides lower type I error and higher causal estimate power. MR-PRESSO identifies and eliminates outliers in IVW linear regression to offer refined MR estimations. To evaluate the potential for horizontal pleiotropy of the SNPs, we used MR Egger regression. The heterogeneity of selected SNPs was assessed using the Cochrane's Q test (P < 0.05). In instances where significant heterogeneity was observed, we employed the random effects IVW test to obtain more cautious and reliable estimates [41]. In addition, we performed a sensitivity analysis using the "leave-one-out" method to detect any SNPs that may have a significant impact. In this method, every SNP was methodically eliminated and its impact on the correlation was evaluated [42]. The mr_funnel_plot function was utilized to generate funnel plots for visualizing the heterogeneity of IVs.

3.1 Instrumental variables

For lipoprotein(a) as the exposure, IVs were chosen as SNPs linked to lipoprotein(a) (4 SNPs for CeD, 8 SNPs for CD, 7 SNPs for IBD, 52 SNPs for MS, 65 SNPs for Pso, 46 SNPs for RA, 57 SNPs for SLE, 77 SNPs for T1D, 8 SNPs for UC). For lipoprotein(a) as the outcome, IVs were chosen as SNPs linked to IMIDs (10 SNPs for CeD, 44 SNPs for CD, 40 SNPs for IBD,12 SNPs for MS, 6 SNPs for Pso,15 SNPs for RA, 9 SNPs for SLE, 21 SNPs for T1D, 29 SNPs for UC). The absence of weak instrument bias was indicated by all F-statistics > 10. The comprehensive details regarding the instrumental variables can be found in Table S1-S3.

3.2 Causal estimates of genetic susceptibility to lipoprotein(a) and IMIDs risk

The findings from the MR analysis exploring the causal association between lipoprotein(a) and nine IMIDs traits are depicted in Fig. 2. Lipoprotein(a) exhibited no causal association with CeD (OR = 0.797, 95% CI: 0.498 to 1.274), CD (OR = 1.231, 95% CI: 0.519 to 2.920), IBD (OR = 1.026, 95% CI: 0.804 to 1.309), MS (OR = 1.007, 95% CI: 0.822 to 1.232), Pso (OR = 1.059, 95% CI: 0.910 to 1.231), RA (OR = 0.936, 95% CI: 0.782 to 1.120), SLE (OR = 1.039, 95% CI: 0.801 to 1.349), T1D (OR = 1.065, 95% CI: 0.931 to 1.218), and UC (OR = 1.016, 95% CI: 0.707 to 1.461). This discovery aligns with the outcomes derived from alternative MR techniques, including MR Egger and weighted median.

There was noticeable heterogeneity observed in our instrumental variables for Lp(a) in relation to CD (Q P.val = 7.65E-11), SLE (Q P.val = 3.66E-5), MS (Q P.val = 0.0001), Pso (Q P.val = 0.0043) and T1D (Q P.val = 0.0051) as outcome (Table S4). We observed that all MR-Egger regression intercepts were not significantly different from zero, indicating no indication of horizontal pleiotropy between the Lp(a) instrumental variables and IMIDs (intercept p > 0.05), except for T1D where a marginal deviation was found (intercept p = 0.021) (Table S4). However, the MR-PRESSO analysis revealed significant horizontal pleiotropy in certain analysis. Nevertheless, the causal estimates of Lp(a) with MS, Pso, SLE, and T1D remained consistent even after conducting outlier-corrected analyses (Table S5).

In addition, the sensitivity analysis plots indicated that no individual SNP was expected to have a substantial impact on the causal relationship, thus affirming the reliability of our findings (Figure S1-4). Taken collectively, these findings provide compelling evidence supporting the absence of a causal association between Lp(a) and IMIDs.

3.3 Causal estimates of genetic susceptibility to IMIDs and lipoprotein(a) levels

Furthermore, conducting reverse studies investigating the association between exposure to the risk of 9 IBIDs and the outcome of Lp(a) levels, we found no significant association between CeD (OR = 0.998, 95% CI: 0.995 to 1.002), CD (OR = 1.000, 95% CI: 0.991 to 1.008), IBD (OR = 0.995, 95% CI: 0.987 to 1.003), MS (OR = 0.992, 95% CI: 0.983 to 1.001), Pso (OR = 1.003, 95% CI: 0.989 to 1.018), RA (OR = 1.003, 95% CI: 0.998 to 1.009), SLE (OR = 0.998, 95% CI: 0.993 to 1.004), T1D (OR = 0.998, 95% CI: 0.994 to 1.002), UC (OR = 1.004, 95% CI: 0.994 to 1.013) and Lp(a) in the IVW analysis results (Fig. 3). The outcomes obtained from each of the three MR techniques exhibited concurrence. There was noticeable diversity observed in our instrumental variables for CD (Q P.val = 1.60E-6), and UC (Q P.val = 0.0024 (Table S6). The presence of imbalanced horizontal pleiotropy was not indicated by the MR-Egger intercept, as it exhibited a central tendency around zero in all MR analyses (Table S6). Although in certain analyses, MR-PRESSO revealed the presence of substantial horizontal pleiotropy, the causal estimates of Lp(a) with UC and CD remained robust after outlier-corrected analyses (Table S7). Additionally, the sensitivity analysis plots, which employed a leave-one-out approach, indicated that the individual impact of each SNP on the causal association was not significant. This finding further strengthens our conclusions (Figure S5-8).

3.4 Multivariable MR

To account for potential pleiotropic pathways arising from the relationship between different lipid traits, we employed a multivariable Mendelian randomization model incorporating Lp(a), HDL-C, LDL-C, and TG as joint exposures for each IMIDs outcome. Following adjustment for HDL-C, LDL-C, and TG, genetically elevated Lp(a) showed no causal association with the onset of IMIDs, consistent with the findings of univariable MR analysis. However after adjusting for other lipid traits, genetically predicted HDL-C (OR_MVMR = 0.80, 95% CI: 0.68–0.95; P = 0.011) and TG (OR_MVMR = 0.80, 95% CI: 0.66–0.98; P = 0.033) were negatively associated with type 1 diabetes. The association between genetically predicted LDL and Pso remained marginally significant even after adjusting for multiple lipid traits. (OR_MVMR = 0.80, 95% CI: 0.64–0.99; P = 0.045) (Table S8).

Incorporating a distinct panel of 75 and 42 SNPs that have demonstrated strong and separate associations with CD and UC, correspondingly, multivariable Mendelian randomization analysis provided compelling evidence suggesting no causal relationship between the genetic liability to these autoimmune traits and lipoprotein(a) levels (CD (OR = 1.00, 95% CI: 0.99–1.01, P = 0.997), UC (OR = 1.00, 95% CI: 0.99–1.01, P = 0.679)).

The correlation between levels of lipoprotein(a) and inflammatory conditions has garnered increasing attention. However, to our knowledge, this study represents the first systematic exploration of potential causal relationships between lipoprotein(a) levels and IMIDs using MR methods. Our results suggest that there is insufficient evidence to establish a causal relationship between genetic susceptibility to lipoprotein(a) and the risk of IMIDs. This implies that the reported associations in epidemiological studies may be influenced by unaccounted factors or shared genetic characteristics. To differentiate between a genuine adverse outcome and the lack of validity in the MR studies, we conducted various sensitivity analyses to ensure adherence to the three MR assumptions. Taking into account the consistency of our MR findings across these diverse methodologies, we possess confidence regarding the veracity of our MR analyses.

Although most studies have suggested that Lp(a) is associated with inflammatory levels, there is still no complete agreement. Several investigations have indicated that Lp(a) itself may promote inflammation in diverse cellular populations, encompassing endothelial cells, monocytes and macrophages, through its association with oxidized phospholipids, mediating the increased cardiovascular disease (CVD) risk[43–45]. Numerous observational studies have consistently reported elevated levels of Lp(a) in various inflammatory conditions, including rheumatoid arthritis[46, 13] and systemic lupus erythematosus[47, 12]. However, Holm et al performed a cross-sectional observational investigation and found no statistically significant disparities in serum Lp(a) levels among individuals with coronary artery disease (CAD), regardless of the presence or absence of inflammatory rheumatic disease[48]. The relationship between Lp(a) and inflammation is reciprocal, as the levels of Lp(a) not only possess inflammatory characteristics but are also influenced by inflammatory stimuli or anti-inflammatory interventions. Nevertheless, the research conducted by Ueland et al. discovered that the temporary blocking of IL-6 with tocilizumab in patients diagnosed with NSTEMI did not demonstrate any noteworthy impact on Lp(a) levels, thus failing to provide evidence supporting the hypothesis proposing an inflammatory-driven connection between IL-6 and Lp(a)[49].

IMIDs encompass a wide range of disorders influenced by immunological and genetic pathways, resulting in disruptions to the body's cellular homeostasis, and deviant activation of the immune system and inflammatory pathways. However, whether a causal relationship exists between lipoprotein(a) and autoimmune diseases remains undetermined. Our findings suggest that there is no evidence from Mendelian randomization studies supporting a causal relationship between lipoprotein(a) levels and the risk of immune-mediated inflammatory diseases. However, we have identified some discrepancies between these reported estimates and our own results. We believe that these differences may be attributed to variations in the analytical methods employed. Observational research is susceptible to clinical confounding factors, which can impact both exposure and outcome variables, thereby weakening the ability of observational studies to accurately establish causality. Consequently, even if an observational study reports a strong correlation, it cannot definitively prove the existence of a direct causal association. In contrast, Mendelian randomization overcomes this limitation by utilizing genetic instrumental variables to mitigate the influence of confounding factors and provide a relatively accurate assessment of causality.

We would like to emphasize several strengths of our study and acknowledge certain limitations. Firstly, this study represents the inaugural investigation aimed at evaluating the bidirectional causal association between lipoprotein(a) and immune-mediated inflammatory diseases using a two sample Mendelian randomization approach. This methodology offers enhanced resilience against confounding factors, reverse causation, and non-differential exposures compared to observational studies. Secondly, we meticulously selected a diverse range of relatively prevalent autoimmune diseases along with their associated genome-wide association study data comprising large sample sizes. Lastly, we performed a sensitivity analysis to maintain the coherence of causal estimation and verify the reliability of our results. Our study also has several limitations. First and foremost, it is crucial to acknowledge that this research was carried out specifically within a European demographic. Consequently, prudence must be exercised when extrapolating our discoveries to alternative populations. Additionally, it is worth considering the possibility of other autoimmune diseases associated with lipoprotein(a) that were not encompassed within the scope of our analysis. Additionally, some of our Mendelian randomization analyses had inadequate statistical power for detecting minor impacts owing to the restricted variability explained by the single nucleotide polymorphism instruments or the relatively small sample sizes in the GWAS for outcome traits. In this regard, the exclusion of ambiguous or palindromic SNPs may have potentially compromised the statistical power of our MR study. The implementation of larger genome-wide association studies on autoimmune traits will markedly augment the statistical power of subsequent MR studies intended to detect and establish associations between these traits and potential risk factors or comorbidities.

Our results do not support the presence of a bilateral causal link between lipoprotein(a) levels and the susceptibility to immune-mediated inflammatory diseases in Europeans. This suggests that the observed associations could be attributed to shared genetic factors or confounding environmental influences, rather than a direct cause-effect relationship.

Author contribution

LJ, BP and TY conceived the study, participated in its design and coordination, and critically revised the manuscript. HY searched the databases. QX and XY reviewed the GWAS datasets and finished the data collection. ZQ finished the

data analysis. HY drafted the manuscript. LJ, QX and TY had full access to all the data collection, analysis, and interpretation.

All authors read and approved the final manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (82100279), the Taishan Scholars Program of Shandong Province (Li J), and the Chinese Cardiovascular Association-Access fund (2021-CCA-ACCESS-142). The study funders/sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Availability of data and materials

All data generated used in the current study are available in this published article and supplementary file associated with it.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Marcovina SM, Koschinsky ML. Lipoprotein(a) as a risk factor for coronary artery disease. Am J Cardiol. 1998;82(12a):57U-66U; discussion 86U. doi:10.1016/s0002-9149(98)00954-0.
Kotani K, Serban MC, Penson P, Lippi G, Banach M. Evidence-based assessment of lipoprotein(a) as a risk biomarker for cardiovascular diseases - Some answers and still many questions. Crit Rev Clin Lab Sci. 2016;53(6):370-8. doi:10.1080/10408363.2016.1188055.
Erqou S, Kaptoge S, Perry PL, Di Angelantonio E, Thompson A, White IR et al. Lipoprotein(a) concentration and the risk of coronary heart disease, stroke, and nonvascular mortality. Jama. 2009;302(4):412-23. doi:10.1001/jama.2009.1063.
Guddeti RR, Patil S, Ahmed A, Sharma A, Aboeata A, Lavie CJ et al. Lipoprotein(a) and calcific aortic valve stenosis: A systematic review. Prog Cardiovasc Dis. 2020;63(4):496-502. doi:10.1016/j.pcad.2020.06.002.
Mohammadi-Shemirani P, Chong M, Narula S, Perrot N, Conen D, Roberts JD et al. Elevated Lipoprotein(a) and Risk of Atrial Fibrillation: An Observational and Mendelian Randomization Study. J Am Coll Cardiol. 2022;79(16):1579-90. doi:10.1016/j.jacc.2022.02.018.
Kamstrup PR, Nordestgaard BG. Elevated Lipoprotein(a) Levels, LPA Risk Genotypes, and Increased Risk of Heart Failure in the General Population. JACC Heart Fail. 2016;4(1):78-87. doi:10.1016/j.jchf.2015.08.006.
Nordestgaard BG, Langsted A. Lipoprotein (a) as a cause of cardiovascular disease: insights from epidemiology, genetics, and biology. J Lipid Res. 2016;57(11):1953-75. doi:10.1194/jlr.R071233.
Tomic Naglic D, Manojlovic M, Pejakovic S, Stepanovic K, Prodanovic Simeunovic J. Lipoprotein(a): Role in atherosclerosis and new treatment options. Biomol Biomed. 2023;23(4):575-83. doi:10.17305/bb.2023.8992.
Tsimikas S, Witztum JL. Oxidized phospholipids in cardiovascular disease. Nat Rev Cardiol. 2023. doi:10.1038/s41569-023-00937-4.
Reyes-Soffer G, Westerterp M. Beyond Lipoprotein(a) plasma measurements: Lipoprotein(a) and inflammation. Pharmacol Res. 2021;169:105689. doi:10.1016/j.phrs.2021.105689.
Govindan KP, Basha S, Ramesh V, Kumar CN, Swathi S. A comparative study on serum lipoprotein (a) and lipid profile between rheumatoid arthritis patients and normal subjects. J Pharm Bioallied Sci. 2015;7(Suppl 1):S22-5. doi:10.4103/0975-7406.155767.
Borba EF, Santos RD, Bonfa E, Vinagre CG, Pileggi FJ, Cossermelli W et al. Lipoprotein(a) levels in systemic lupus erythematosus. J Rheumatol. 1994;21(2):220-3.
Dursunoğlu D, Evrengül H, Polat B, Tanriverdi H, Cobankara V, Kaftan A et al. Lp(a) lipoprotein and lipids in patients with rheumatoid arthritis: serum levels and relationship to inflammation. Rheumatol Int. 2005;25(4):241-5. doi:10.1007/s00296-004-0438-0.
García-Gómez C, Martín-Martínez MA, Castañeda S, Sanchez-Alonso F, Uriarte-Ecenarro M, González-Juanatey C et al. Lipoprotein(a) concentrations in rheumatoid arthritis on biologic therapy: Results from the CARdiovascular in rheuMAtology study project. J Clin Lipidol. 2017;11(3):749-56.e3. doi:10.1016/j.jacl.2017.02.018.
Koutroubakis IE, Malliaraki N, Vardas E, Ganotakis E, Margioris AN, Manousos ON et al. Increased levels of lipoprotein (a) in Crohn's disease: a relation to thrombosis? Eur J Gastroenterol Hepatol. 2001;13(12):1415-9. doi:10.1097/00042737-200112000-00004.
Missala I, Kassner U, Steinhagen-Thiessen E. A Systematic Literature Review of the Association of Lipoprotein(a) and Autoimmune Diseases and Atherosclerosis. Int J Rheumatol. 2012;2012:480784. doi:10.1155/2012/480784.
Kronenberg F, König P, Neyer U, Auinger M, Pribasnig A, Lang U et al. Multicenter study of lipoprotein(a) and apolipoprotein(a) phenotypes in patients with end-stage renal disease treated by hemodialysis or continuous ambulatory peritoneal dialysis. J Am Soc Nephrol. 1995;6(1):110-20. doi:10.1681/asn.v61110.
Thillet J, Doucet C, Issad B, Allouache M, Chapman JM, Jacobs C. Elevated Lp(a) levels in patients with end-stage renal disease. Am J Kidney Dis. vol 4. United States1994. p. 620-1.
Van den Hof M, Klein Haneveld MJ, Blokhuis C, Scherpbier HJ, Jansen HPG, Kootstra NA et al. Elevated Lipoprotein(a) in Perinatally HIV-Infected Children Compared With Healthy Ethnicity-Matched Controls. Open Forum Infect Dis. 2019;6(9):ofz301. doi:10.1093/ofid/ofz301.
Emdin CA, Khera AV, Kathiresan S. Mendelian Randomization. Jama. 2017;318(19):1925-6. doi:10.1001/jama.2017.17219.
Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. Bmj. 2018;362:k601. doi:10.1136/bmj.k601.
Hemani G, Tilling K, Davey Smith G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet. 2017;13(11):e1007081. doi:10.1371/journal.pgen.1007081.
Burgess S, Thompson SG. Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am J Epidemiol. 2015;181(4):251-60. doi:10.1093/aje/kwu283.
McGovern DP, Gardet A, Törkvist L, Goyette P, Essers J, Taylor KD et al. Genome-wide association identifies multiple ulcerative colitis susceptibility loci. Nat Genet. 2010;42(4):332-7. doi:10.1038/ng.549.
Sanderson E, Glymour MM, Holmes MV, Kang H, Morrison J, Munafò MR et al. Mendelian randomization. Nat Rev Methods Primers. 2022;2. doi:10.1038/s43586-021-00092-5.
Trynka G, Hunt KA, Bockett NA, Romanos J, Mistry V, Szperl A et al. Dense genotyping identifies and localizes multiple common and rare variant association signals in celiac disease. Nat Genet. 2011;43(12):1193-201. doi:10.1038/ng.998.
Liu JZ, van Sommeren S, Huang H, Ng SC, Alberts R, Takahashi A et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat Genet. 2015;47(9):979-86. doi:10.1038/ng.3359.
Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science. 2019;365(6460). doi:10.1126/science.aav7188.
Stuart PE, Tsoi LC, Nair RP, Ghosh M, Kabra M, Shaiq PA et al. Transethnic analysis of psoriasis susceptibility in South Asians and Europeans enhances fine-mapping in the MHC and genomewide. HGG Adv. 2022;3(1). doi:10.1016/j.xhgg.2021.100069.
Okada Y, Wu D, Trynka G, Raj T, Terao C, Ikari K et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature. 2014;506(7488):376-81. doi:10.1038/nature12873.
Bentham J, Morris DL, Graham DSC, Pinder CL, Tombleson P, Behrens TW et al. Genetic association analyses implicate aberrant regulation of innate and adaptive immunity genes in the pathogenesis of systemic lupus erythematosus. Nat Genet. 2015;47(12):1457-64. doi:10.1038/ng.3434.
Chiou J, Geusz RJ, Okino ML, Han JY, Miller M, Melton R et al. Interpreting type 1 diabetes risk with genetics and single-cell epigenomics. Nature. 2021;594(7863):398-402. doi:10.1038/s41586-021-03552-w.
Barton AR, Sherman MA, Mukamel RE, Loh PR. Whole-exome imputation within UK Biobank powers rare coding variant association and fine-mapping analyses. Nat Genet. 2021;53(8):1260-9. doi:10.1038/s41588-021-00892-1.
Richardson TG, Sanderson E, Palmer TM, Ala-Korpela M, Ference BA, Davey Smith G et al. Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: A multivariable Mendelian randomisation analysis. PLoS Med. 2020;17(3):e1003062. doi:10.1371/journal.pmed.1003062.
Bowden J, Del Greco MF, Minelli C, Zhao Q, Lawlor DA, Sheehan NA et al. Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption. Int J Epidemiol. 2019;48(3):728-42. doi:10.1093/ije/dyy258.
Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23(R1):R89-98. doi:10.1093/hmg/ddu328.
Pierce BL, Burgess S. Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators. Am J Epidemiol. 2013;178(7):1177-84. doi:10.1093/aje/kwt084.
Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018;7. doi:10.7554/eLife.34408.
Slob EAW, Burgess S. A comparison of robust Mendelian randomization methods using summary data. Genet Epidemiol. 2020;44(4):313-29. doi:10.1002/gepi.22295.
Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40(4):304-14. doi:10.1002/gepi.21965.
Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512-25. doi:10.1093/ije/dyv080.
Burgess S, Bowden J, Fall T, Ingelsson E, Thompson SG. Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants. Epidemiology. 2017;28(1):30-42. doi:10.1097/ede.0000000000000559.
Hoogeveen RC, Ballantyne CM. Residual Cardiovascular Risk at Low LDL: Remnants, Lipoprotein(a), and Inflammation. Clin Chem. 2021;67(1):143-53. doi:10.1093/clinchem/hvaa252.
Swerdlow DI, Rider DA, Yavari A, Wikström Lindholm M, Campion GV, Nissen SE. Treatment and prevention of lipoprotein(a)-mediated cardiovascular disease: the emerging potential of RNA interference therapeutics. Cardiovasc Res. 2022;118(5):1218-31. doi:10.1093/cvr/cvab100.
Cybulska B, Kłosiewicz-Latoszek L, Penson PE, Banach M. What do we know about the role of lipoprotein(a) in atherogenesis 57 years after its discovery? Prog Cardiovasc Dis. 2020;63(3):219-27. doi:10.1016/j.pcad.2020.04.004.
García-Gómez C, Nolla JM, Valverde J, Gómez-Gerique JA, Castro MJ, Pintó X. Conventional lipid profile and lipoprotein(a) concentrations in treated patients with rheumatoid arthritis. J Rheumatol. 2009;36(7):1365-70. doi:10.3899/jrheum.080928.
Sari RA, Polat MF, Taysi S, Bakan E, Capoğlu I. Serum lipoprotein(a) level and its clinical significance in patients with systemic lupus erythematosus. Clin Rheumatol. 2002;21(6):520-4. doi:10.1007/s100670200127.
Holm S, Oma I, Hagve TA, Saatvedt K, Brosstad F, Mikkelsen K et al. Levels of Lipoprotein (a) in patients with coronary artery disease with and without inflammatory rheumatic disease: a cross-sectional study. BMJ Open. 2019;9(5):e030651. doi:10.1136/bmjopen-2019-030651.
Ueland T, Kleveland O, Michelsen AE, Wiseth R, Damås JK, Holven KB et al. Serum lipoprotein(a) is not modified by interleukin-6 receptor antagonism or associated with inflammation in non-ST-elevation myocardial infarction. Int J Cardiol. 2019;274:348-50. doi:10.1016/j.ijcard.2018.06.093.

No competing interests reported.

Download PDF

Editor invited by journal
20 Aug, 2024
Submission checks completed at journal
19 Aug, 2024
First submitted to journal
08 Aug, 2024

You are reading this latest preprint version

Mendelian randomization analysis does not reveal a causal influence between lipoprotein(A) and immune-mediated inflammatory diseases

Status:

Version 1

Abstract

Figures

1 Introduction

2 Methods

2.1 Study design

2.2 Data sources

2.3 Instrumental variable selection

2.4 Mendelian randomization analysis

3 Results

3.1 Instrumental variables

3.2 Causal estimates of genetic susceptibility to lipoprotein(a) and IMIDs risk

3.3 Causal estimates of genetic susceptibility to IMIDs and lipoprotein(a) levels

3.4 Multivariable MR

4 Discussion

5 Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1