Low lipoprotein(a) concentration is associated with higher atrial fibrillation risk: A large retrospective cohort study

Background

Atrial fibrillation (AF) has been increasingly considered a leading cause of cardiovascular events worldwide [1]. In the 2010 Global Burden Study, the prevalence of AF was 27,596 out of 100,000 men and 373 out of 100,000 women worldwide [2]. Current clinical evidence suggests that the overall AF prevalence ranges from 1–2% [3, 4]. Beyond that, it is about one-fourth to two-thirds of patients have transient or paroxysmal AF, which could cause the actual prevalence to be underestimated [5]. China's AF prevalence is increasing faster than the world average; approximately 3.9 million (2%) people over the age of 60 years were affected by AF in 2008 [6], and this number is expected to increase to 9 million by 2050 [7]. In general, patients aged 65 years or over are recommended to undergo opportunistic AF screening [8].

Lipoprotein(a) [Lp(a)] is a complex compromised of apolipoprotein molecules linked through disulfide bonds. Elevated serum Lp(a) values have been confirmed as an independent arteriosclerosis and coronary heart disease (CHD) risk factor [9]. Published guidelines and consensus statements have identified serum Lp(a) over 30 mg/dL as hyperlipoproteinemia(a) and recommended screening to lower the Lp(a)-mediated risk of cardiovascular events [10]. However, the mechanisms by which Lp(a) induces AF are not clear thus far. Most research did not find an obvious connection between AF and Lp(a). In a general population cohort study containing 109,440 individuals, elevated Lp(a) and most cardiovascular diseases have a strong positive correlation, while the relationship between Lp(a) and most non-cardiovascular diseases, such as AF, was not concordant [11]. In a multivariable Mendelian randomization study, high Lp(a) was weakly correlated with AF risk (OR and 95% CI, 1.001[1.000,1.002]). Of note, large-scale clinical studies which could verify the potential relation is still lacking, especially with Asian participants.

Therefore, we conducted a retrospective study with a large sample size, purposed to investigate the potential AF-Lp(a) association in the Chinese population. Sex, CHD status, and other related factors that could influence the Lp(a)-AF relationship were also examined.

Patients And Methods

Eligibility Criteria

151,607 inpatients with data about Lp(a) levels from January 2017 to July 2021 were retrospectively collected at the Second Affiliated Hospital of Nanchang University, Jiangxi Province. Among these, 2,110 patients with unknown AF status, 12,707 cancer patients, 26,866 kidney dysfunction patients, and 16,465 patients with pregnancy, infection, and poisoning were excluded. Among the 96,089 patients who met the criteria, the patients with AF were selected as the case group, and the control group was 1:2 matched with the propensity score matching (PSM) method by the following items: sex, age, smoking, drinking, CHD, and hypertension status. Finally, the case group included 4,511 AF patients, and the control group included 9,022 non-AF patients (Figure 1).

Definition and Measurement of AF and Other Diseases

The patient's data were derived from their medical records; AF patients were identified as AF by a professional cardiologist based on the electrocardiogram. The diagnostic criteria for AF were no apparent P wave repetition and irregular RR intervals were detected on electrocardiography (ECG) [12]. We defined the first diagnosis day of AF as the onset day. A CHD diagnosis was made when satisfying at least one coronary artery or its major branch had stenosis > 50% on coronary angiography [13].

Clinical and Laboratory Analyses

General information was collected, containing age, sex, body mass index (BMI), alcohol comsumption, smoking, systolic blood pressure (SBP), and diastolic blood pressure (DBP). Measurement of Lp(a) concentration: After fasting for over 8 hours, we collected the patient’s fresh serum and used the Lp(a) Assay Kit (Latex-Enhanced Immunoturbidimetric Method, Beijing Antu Inc, China, LOT:10723C11) to measure Lp(a) levels, where 0 to 3000 mg/dL is the standard reference range for Lp(a). The experimental principle of the Lp(a) kit is as follows: Lp(a) reacts with the mouse anti-human lipoprotein(a) monoclonal antibody present on the latex particles. Then the agglomeration of latex particles increases the turbidity in the solution. The calibration curve of absorbance and concentration was established by measuring a series of calibrators. By comparison with the established calibration curve, the Lp(a) concentration of the samples can be identified.

The laboratory data of albumin, apolipoprotein (Apo(A)), apolipoprotein B (Apo(B)), blood glucose, C-reactive protein (CRP), creatine, high-density lipoprotein cholesterol (HDL-C), homocysteine (HCY), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), triglyceride (TG), and uric acid were recorded.

Statistical Analyses

Statistical analyses were performed with IBM SPSS statistical software, version 21.0 (SPSS Inc., Chicago, Illinois), and R software, version 4.1.1. The level of significance was 0.05. PASS, version 15.0, was used for the estimation of the required sample size.

The AF group and non-AF control group were 1:2 matched using the PSM method for balancing covariates. PSM is a statistical method used to ensure that study participants are comparable on clinical measures and reduce bias. PSM determines whether the variable is a responder or a confounder when creating a regression model. The propensity scores for each subject were estimated to range from 0 to 1, indicating how the subjects should be divided into treatment groups.

In the baseline analysis, the median and quantile deviation were used to describe the continuous data because almost all of the data were skewed. For categorical variables, the number and percentage of cases were used to describe the data. All patients were equally sent into four groups by Lp(a) quantiles: quantile 1 (Q1), under 8.71 mg/dL; quantile 2 (Q2), 8.71-16.54 mg/dL; quantile 3 (Q3), 16.54-32.42 mg/dL; and quantile 4 (Q4), higher than 32.42 mg/dL. Binary logistic regression models were used to evaluate the correlation, and the risk prediction equation and odds ratio with the confidence interval for each factor were calculated by SPSS. Model 1 is the unadjusted analysis model. Model 2 uses BMI and SBP for adjustment. Model 3 was further adjusted for TG, CRP, HCY, blood glucose, and statin status. Subgroup analyses were designed to evaluate the influences of age (≤ 65 years and >65 years), sex (men and women), CHD, hypertension status, and T2DM status on the relationship between Lp(a) and AF.

Results

Patient Characteristics

The baseline characteristics classified by the diagnosis of AFare summarized in Table 1. In total, 4,511 patient cases with AF and 9,022 non-AF cases were included, which were matched for age, sex, alcohol consumption status, and smoking status. The baseline median age was 65. A total of 49.6% patients in the AF group were women. The distribution of serum Lp(a) in the 13,533 participants was skewed (Supplementary Figure 1). The AF group exhibited a lower median Lp(a) value (15.95 vs. 16.90, P<0.001). Moreover, the control group exhibited higher Apo(A), Apo(B), TC, LDL-C, and creatine (Table 1). All correlation indicesbetween Lp(a) and the other clinical attributes were listed in Supplementary Table 1.

Lp(a) and AF Morbidity

Among the four groups stratified by Lp(a) quantiles, the incidence of AF was 34.2% (Q1), 34.9% (Q2), 33.2% (Q3) and 30.9% (Q4) (p for trend < 0.001). In the primary unadjusted model, Lp(a) quartiles 1-3 increased the AF risk 1.162-fold (95% CI: 1.049-1.286), 1.198-fold (1.083-1.327), and 1.111-fold (1.003-1.231), respectively (Table 2). In Model 2 adjusted for SBP and BMI, the OR with 95% CI of quantiles 1-3 exhibited 1.152 [1.040-1.277], 1.192 [1.076-1.321], and 1.119 [1.009-1.240], respectively (Table 2). In addition, all three Lp(a) quartile groups still showed a significantly increased AF risk in Model 3 after adjusting for TG, CRP, HCY, blood glucose, and statin status, plus Model 2. The ORs with 95% CIs of quantiles 1-3 exhibited 1.241 [1.117-1.378], 1.226 [1.105-1.360], and 1.120 [1.008-1.243], respectively (Table 2).

According to the stratified analysis of age (≤ 65 years and >65 years), Q1 (under 65 years group: 1.331, [1.124-1.575], P<0.001; over 65 years group: 1.188, [1.039-1.359], P<0.001) and Q2 (under 65 years group: 1.333, [1.125-1.580]; P<0.001, over 65 years group: 1.170, [1.025-1.335], P<0.001) exhibited significant negative correlations with AF in both subgroups (Figure 2A). According to the stratified analysis by sex, an inverse association was only observed to be statistically significant in women, Q1 (1.394 [1.194-1.626]; P<0.001), Q2 (1.324 [1.136-1.544]; P<0.001), and Q3 (1.191 [1.022-1.386]; P<0.001) (Figure 2B). However, significant relationships between Lp(a) and AF were not observed in patients with CHD and T2DM (Figure 2C and D). Furthermore, the status of hypertension did not affect the significance of the Lp(a)-reduced elevated AF risk (Figure S3).

Discussion

In our large-scale retrospective cohort study, a significant inverse correlation between AF risk and Lp(a) levels was found. Thus, an Lp(a) level lower than 16.54 mg/dL could be a potential risk factor for AF. Interestingly, an inverse relationship was found only in women, which could be partly explained by the race factor. Among CHD patients and T2DM patients, the negative association was eliminated, indicating their confounding effect. In addition, hypertension status and age did not seem to influence the inverse relationship in the study population.

In comparison with published studies, a prospective community-based study with 10,127 participants without AF at baseline found no significant linear trend between the prevalence of AF and Lp(a) quantiles (HR, 0.98; 95% CI, [0.82–1.17]) [14]. This study, however, did not recruit Asian participants. Compared to non-Hispanic Caucasians (12, 5-32), Hispanics (19, 8-43), and other ethnicities, Han ethnicity exhibits the lowest serum Lp(a) level (median: 11 [4-12] mg/dL) [15]. In addition, patients whose Lp(a) is over 15 mg/dL can have a significantly elevated cardiac risk among Han ethnicity, which is notably lower than the recommended screening threshold by current global consensus (30 mg/dL or 50 mg/dL) [16]. Therefore, we consider that patients from different races may have different Lp(a) risk thresholds, which could influence the evaluation result.

The mechanism driving this relationship is unclear. The potential hypothesis may include the following: First, the inverse relationship between lipoproteins and AF may be due to its cholesterol cloud function in cell membrane stabilization, which can prevent abnormal discharge of cardiomyocytes [17,18]. It was also found that cholesterol depletion disrupted the contractile function of cardiomyocytes [19]. Second, inflammation status may also be related to the relationship between the development of AF and lipid levels [20]. Studies have found that under inflammation status, blood TC, LDL-C and were reduced [20]. Our results verified this: AF group versus control: TC (4.09 vs. 4.63 mmol/L, P<0.001), LDL-C (2.35 vs. 2.70 mmol/L, P<0.001). The AF group exhibited a higher median CRP level than the control in this study (4.03 vs. 3.67 mg/L, P<0.001), which represented a higher inflammation status.

Beyond that, the counterintuitive negative correlation of elevated Lp(a) and AF risk was only found to be significant among women in this study (Figure 2B). Sex differences in plasma lipid profiles have been well studied [21]. Similarly, results from the BiomarCaRE Consortium showed that TC and other pro-atherogenic lipoproteins, such as Lp(a), are protective factors against AF, especially in women [22]. This study also showed similar results that women have significantly higher TC levels than men [4.65 (3.91, 5.46) vs. 4.29 (3.58, 5.00) mmol/L, P<0.001]. Studies had found that hormones, insulin sensitivity, and body fat distribution may attribute[陶1] to the sex differences in the AF-lipoprotein relationship [23]. However, the Women’s Health Study in Switzerland reported that cholesterol-deficient small LDL particles are driving the negative association with AF, rather than cholesterol-rich LDL, such as Lp(a) [24].

The role of CHD in the AF-Lp(a) relationship is complicated. On the one hand, CHD and ischaemic stroke are highly associated with Lp(a) [15]. On the other hand, the interaction between CHD and AF as a vicious cycle has been shown to have multiple mechanisms [25]. In this study, the inverse correlation disappeared among the patients with CHD. It is worth noting that CHD status was adjusted when including participants, and the proportions of CHD patients in AF group and control group was 26.94% and 26.93%, respectively.

Study Strength

Of note, this large-scale study is the first to focus on Lp(a) and AF relationships in the Chinese population. We designed well-constructed layered analysis models and performed subgroup analyses to support the conclusion and hypothesis. In addition, we searched for the most confounding factors between the Lp(a) concentration and AF prevalence, for instance, statin use [27], CHD status, and eGFR, and evaluated their effect to diminish the bias.

Study Limitations

However, this study still has some limitations: (1) We are limited to assessing the correlation rather than any causal association as a retrospective cohort study. (2) This study is based on clinical data from a single hospital center; thus, possible bias could exist. (3) Recent studies found that the diameters of Lp(a) particles are determined by the numbers of kringle repeats in 6q26-27 chromosomal regions in the LPA gene, which varies very greatly [26]; The particle diameter represents the particle size of lipoprotein, thus, the traditional mass index “mg/dL” cannot well evaluate the serum Lp(a) particle numbers. Considering the genetic heterogeneity among individuals simply converting the Lp(a) measurement units is not a wise choice. We hope to enhance the Lp(a) measuring methods in future studies.

Abbreviations

Abbreviations	Full names
AF	atrial fibrillation
Lp(a)	lipoprotein(a)
CHD	coronary heart disease
PSM	propensity score matching
ECG	electrocardiography
T2DM	type 2 diabetes mellitus
CAG	coronary angiography
BMI	body mass index
SBP	systolic blood pressure
DBP	diastolic blood pressure
Apo(A)	apolipoprotein A
Apo(B)	apolipoprotein B
CRP	C-reactive protein
HDL-C	high-density lipoprotein cholesterol
HCY	homocysteine
LDL-C	low-density lipoprotein cholesterol
TC	total cholesterol
TG	triglyceride
OR	odds ratio

References

Ganesan, A.N.; Chew, D.P.; Hartshorne, T.; Selvanayagam, J.B.; Aylward, P.E.; Sanders, P.; McGavigan, A.D. The impact of atrial fibrillation type on the risk of thromboembolism, mortality, and bleeding: a systematic review and meta-analysis. European heart journal 2016, 37, 1591–1602, doi:10.1093/eurheartj/ehw007.
Chugh, S.S.; Havmoeller, R.; Narayanan, K.; Singh, D.; Rienstra, M.; Benjamin, E.J.; Gillum, R.F.; Kim, Y.H.; McAnulty, J.H., Jr.; Zheng, Z.J.; et al. Worldwide epidemiology of atrial fibrillation: a Global Burden of Disease 2010 Study. Circulation 2014, 129, 837–847, doi:10.1161/circulationaha.113.005119.
Wilke, T.; Groth, A.; Mueller, S.; Pfannkuche, M.; Verheyen, F.; Linder, R.; Maywald, U.; Bauersachs, R.; Breithardt, G. Incidence and prevalence of atrial fibrillation: an analysis based on 8.3 million patients. 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 2013, 15, 486–493, doi:10.1093/europace/eus333.
Piccini, J.P.; Hammill, B.G.; Sinner, M.F.; Jensen, P.N.; Hernandez, A.F.; Heckbert, S.R.; Benjamin, E.J.; Curtis, L.H. Incidence and prevalence of atrial fibrillation and associated mortality among Medicare beneficiaries, 1993–2007. Circulation. Cardiovascular quality and outcomes 2012, 5, 85–93, doi:10.1161/circoutcomes.111.962688.
Zhang, J.; Johnsen, S.P.; Guo, Y.; Lip, G.Y.H. Epidemiology of Atrial Fibrillation: Geographic/Ecological Risk Factors, Age, Sex, Genetics. Cardiac electrophysiology clinics 2021, 13, 1–23, doi:10.1016/j.ccep.2020.10.010.
Zhou, Z.; Hu, D. An epidemiological study on the prevalence of atrial fibrillation in the Chinese population of mainland China. Journal of epidemiology 2008, 18, 209–216, doi:10.2188/jea.je2008021.
Tse, H.F.; Wang, Y.J.; Ahmed Ai-Abdullah, M.; Pizarro-Borromeo, A.B.; Chiang, C.E.; Krittayaphong, R.; Singh, B.; Vora, A.; Wang, C.X.; Zubaid, M.; et al. Stroke prevention in atrial fibrillation–an Asian stroke perspective. Heart rhythm 2013, 10, 1082–1088, doi:10.1016/j.hrthm.2013.03.017.
Camm, A.J.; Lip, G.Y.; De Caterina, R.; Savelieva, I.; Atar, D.; Hohnloser, S.H.; Hindricks, G.; Kirchhof, P. 2012 focused update of the ESC Guidelines for the management of atrial fibrillation: an update of the 2010 ESC Guidelines for the management of atrial fibrillation. Developed with the special contribution of the European Heart Rhythm Association. European heart journal 2012, 33, 2719–2747, doi:10.1093/eurheartj/ehs253.
Chung, M.K.; Refaat, M.; Shen, W.K.; Kutyifa, V.; Cha, Y.M.; Di Biase, L.; Baranchuk, A.; Lampert, R.; Natale, A.; Fisher, J.; et al. Atrial Fibrillation: JACC Council Perspectives. Journal of the American College of Cardiology 2020, 75, 1689–1713, doi:10.1016/j.jacc.2020.02.025.
Tsimikas, S.; Fazio, S.; Ferdinand, K.C.; Ginsberg, H.N.; Koschinsky, M.L.; Marcovina, S.M.; Moriarty, P.M.; Rader, D.J.; Remaley, A.T.; Reyes-Soffer, G.; et al. NHLBI Working Group Recommendations to Reduce Lipoprotein(a)-Mediated Risk of Cardiovascular Disease and Aortic Stenosis. Journal of the American College of Cardiology 2018, 71, 177–192, doi:10.1016/j.jacc.2017.11.014.
Langsted, A.; Nordestgaard, B.G.; Kamstrup, P.R. Low lipoprotein(a) levels and risk of disease in a large, contemporary, general population study. European heart journal 2021, 42, 1147–1156, doi:10.1093/eurheartj/ehaa1085.
Hindricks, G.; Potpara, T.; Dagres, N.; Arbelo, E.; Bax, J.J.; Blomström-Lundqvist, C.; Boriani, G.; Castella, M.; Dan, G.A.; Dilaveris, P.E.; et al. 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): The Task Force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) Developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC. European heart journal 2021, 42, 373–498, doi:10.1093/eurheartj/ehaa612.
Thygesen, K.; Alpert, J.S.; Jaffe, A.S.; Simoons, M.L.; Chaitman, B.R.; White, H.D.; Katus, H.A.; Lindahl, B.; Morrow, D.A.; Clemmensen, P.M.; et al. Third universal definition of myocardial infarction. Circulation 2012, 126, 2020–2035, doi:10.1161/CIR.0b013e31826e1058.
Aronis, K.N.; Zhao, D.; Hoogeveen, R.C.; Alonso, A.; Ballantyne, C.M.; Guallar, E.; Jones, S.R.; Martin, S.S.; Nazarian, S.; Steffen, B.T.; et al. Associations of Lipoprotein(a) Levels With Incident Atrial Fibrillation and Ischemic Stroke: The ARIC (Atherosclerosis Risk in Communities) Study. Journal of the American Heart Association 2017, 6, doi:10.1161/jaha.117.007372.
Nordestgaard, B.G.; Chapman, M.J.; Ray, K.; Borén, J.; Andreotti, F.; Watts, G.F.; Ginsberg, H.; Amarenco, P.; Catapano, A.; Descamps, O.S.; et al. Lipoprotein(a) as a cardiovascular risk factor: current status. European heart journal 2010, 31, 2844–2853, doi:10.1093/eurheartj/ehq386.
Liu, H.H.; Cao, Y.X.; Jin, J.L.; Zhang, H.W.; Hua, Q.; Li, Y.F.; Guo, Y.L.; Zhu, C.G.; Wu, N.Q.; Gao, Y.; et al. Association of lipoprotein(a) levels with recurrent events in patients with coronary artery disease. Heart (British Cardiac Society) 2020, 106, 1228–1235, doi:10.1136/heartjnl-2020-316586.
Dart, C. Lipid microdomains and the regulation of ion channel function. The Journal of physiology 2010, 588, 3169–3178, doi:10.1113/jphysiol.2010.191585.
Goonasekara, C.L.; Balse, E.; Hatem, S.; Steele, D.F.; Fedida, D. Cholesterol and cardiac arrhythmias. Expert review of cardiovascular therapy 2010, 8, 965–979, doi:10.1586/erc.10.79.
Hissa, B.; Oakes, P.W.; Pontes, B.; Ramírez-San Juan, G.; Gardel, M.L. Cholesterol depletion impairs contractile machinery in neonatal rat cardiomyocytes. Scientific reports 2017, 7, 43764, doi:10.1038/srep43764.
Khovidhunkit, W.; Kim, M.S.; Memon, R.A.; Shigenaga, J.K.; Moser, A.H.; Feingold, K.R.; Grunfeld, C. Effects of infection and inflammation on lipid and lipoprotein metabolism: mechanisms and consequences to the host. Journal of lipid research 2004, 45, 1169–1196, doi:10.1194/jlr.R300019-JLR200.
Peters, S.A.; Singhateh, Y.; Mackay, D.; Huxley, R.R.; Woodward, M. Total cholesterol as a risk factor for coronary heart disease and stroke in women compared with men: A systematic review and meta-analysis. Atherosclerosis 2016, 248, 123–131, doi:10.1016/j.atherosclerosis.2016.03.016.
Magnussen, C.; Niiranen, T.J.; Ojeda, F.M.; Gianfagna, F.; Blankenberg, S.; Njølstad, I.; Vartiainen, E.; Sans, S.; Pasterkamp, G.; Hughes, M.; et al. Sex Differences and Similarities in Atrial Fibrillation Epidemiology, Risk Factors, and Mortality in Community Cohorts: Results From the BiomarCaRE Consortium (Biomarker for Cardiovascular Risk Assessment in Europe). Circulation 2017, 136, 1588–1597, doi:10.1161/circulationaha.117.028981.
Palmisano, B.T.; Zhu, L.; Eckel, R.H.; Stafford, J.M. Sex differences in lipid and lipoprotein metabolism. Molecular metabolism 2018, 15, 45–55, doi:10.1016/j.molmet.2018.05.008.
Mora, S.; Akinkuolie, A.O.; Sandhu, R.K.; Conen, D.; Albert, C.M. Paradoxical association of lipoprotein measures with incident atrial fibrillation. Circulation. Arrhythmia and electrophysiology 2014, 7, 612–619, doi:10.1161/circep.113.001378.
Liang, F.; Wang, Y. Coronary heart disease and atrial fibrillation: a vicious cycle. American journal of physiology. Heart and circulatory physiology 2021, 320, H1-h12, doi:10.1152/ajpheart.00702.2020.
Clarke, R.; Peden, J.F.; Hopewell, J.C.; Kyriakou, T.; Goel, A.; Heath, S.C.; Parish, S.; Barlera, S.; Franzosi, M.G.; Rust, S.; et al. Genetic variants associated with Lp(a) lipoprotein level and coronary disease. The New England journal of medicine 2009, 361, 2518–2528, doi:10.1056/NEJMoa0902604.
Mourikis, P.; Zako, S.; Dannenberg, L.; Nia, A.M.; Heinen, Y.; Busch, L.; Richter, H.; Hohlfeld, T.; Zeus, T.; Kelm, M.; et al. Lipid lowering therapy in cardiovascular disease: From myth to molecular reality. Pharmacology & therapeutics 2020, 213, 107592, doi:10.1016/j.pharmthera.2020.107592.

Tables

Table 1 Baseline profiles classified by case group or control group

Characteristics	Control group	AF group	p-value
NO. (%)	9022(66.7)	4511(33.3)	-
Demographic data
Women, n(%)	4225(46.83)	2123(47.06)	0.798
Ages (years)	67.85[44.97,90.73]	67.85[44.95,90.75]	0.993
BMI	23.56[21.45,25.55]	23.624[21.547,25.778]	0.021
Smoker, n(%)	1300(14.40)	642(14.232)	0.781
Alcohol taking status, n(%)	1161(12.86)	583(12.924)	0.928
SBP (mmHg)	144[130,159]	138[125,152]	<0.001
DBP (mmHg)	84[76,92]	85[76,94]	0.013
Medical history
Ischemic stroke, n(%)	1647(18.25)	1129(25.028)	<0.001
Type 2 diabetes mellitus, n(%)	1908(21.14)	728(16.138)	<0.001
Coronary heart disease, n(%)	2431(26.94)	1215(26.934)	0.989
Hypertension, n(%)	5018(55.62)	2505(55.531)	0.922
Medication on admission
β-receptor antagonists, n(%)	2357(26.12)	3033(67.236)	<0.001
Statin, n(%)	4312(47.79)	2239(49.634)	0.043
Laboratory values
Lp(a) (mg/dL)	16.90[8.80,33.29]	15.95[8.57,30.70]	<0.001
LDL-C (mmol/L)	2.70[2.12,3.29]	2.35[1.84,2.93]	<0.001
ApoA (mmol/L)	1.18[0.98,1.41]	1.09[0.92,1.32]	<0.001
ApoB (mmol/L)	0.87[0.70,1.07]	0.77[0.61,0.96]	<0.001
HDL-C (mmol/L)	1.18[0.97,1.44]	1.11[0.93,1.33]	<0.001
TC (mmol/L)	4.63[3.90,5.40]	4.09[3.41,4.81]	<0.001
TG (mmol/L)	1.27[0.92,1.86]	1.09[0.81,1.55]	<0.001
HCY (μmol/L)	13.27[11.13,16.18]	13.78[11.31,17.20]	<0.001
Albumin (g/L)	39.48[36.58,42.48]	37.54[35.03,40.22]	<0.001
CRP (mg/L)	3.67[2.15,8.78]	4.03[2.21,9.96]	<0.001
Blood glucose (mmol/L)	5.58[4.90,6.81]	5.17[4.62,6.14]	<0.001
Uric acid (μmol/L)	338.03[278.77,408.09]	377.58[310.25,458.53]	<0.001
Serum creatine(μmol/L)	72.00[60.63,85.64]	78.00[65.21,91.99]	<0.001

Continuous data are presented as the median [25, 75%]. Apo(a), apolipoprotein A; Apo(B), apolipoprotein B; BMI, body mass index; CHD, coronary heart disease; CRP, C-reactive protein; DBP, diastolic blood pressure; HDL-C, high-density lipoprotein cholesterol; HCY, homocysteine; LDL-C, low-density lipoprotein cholesterol; Lp(a), lipoprotein (a); SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride.

Table 2 Odds ratios (95% confidence intervals) for the regression analysis of AF and Lp(a) quantiles

	Model1	Model2	Model3
Lipoprotein(a) quantiles
Q1 [< 8.71 mg/dL]	1.162 (1.049-1.286) **	1.152 (1.040-1.277) **	1.241(1.117-1.378) **
Q2 [8.71 - 16.54 mg/dL]	1.198 (1.083-1.327) **	1.192 (1.076-1.321) **	1.276(1.105-1.360) **
Q3 [16.54 - 32.42 mg/dL]	1.111 (1.003-1.231) *	1.119 (1.009-1.240) *	1.120(1.008-1.243) *
Q4 [> 32.42 mg/dL]	Reference	Reference	Reference

Values are expressed as ORs (95% confidence intervals). AF, atrial fibrillation; Lp(a), lipoprotein(a); BMI, body mass index; SBP, systolic blood pressure; TG, triglyceride; CRP, C-reactive protein; HCY, homocysteine. Significant interactions (P<0.05) of Lp(a) quartiles and AF are marked with *; more significant interactions (P<0.01) are marked with **.

Model 1: unadjusted model for Lp(a) quartiles and AF;

Model 2 adjusted for BMI, SBP, plus Model 1;

Model 3 adjusted for blood glucose, CRP, HCY, statin status, TG, plus Model 2.