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Association between serum cholinesterase and the prevalence of atrial fibrillation in Chinese hypertensive population: a cross-sectional study



Atrial fibrillation (AF) is a very common arrhythmia with significant incidence rate and mortality. Several studies have shown a notable correlation between non-alcoholic fatty liver disease (NAFLD) and AF. It has been observed that serum cholinesterase (SChE) levels are elevated in individuals with fatty liver. However, the relationship between the SChE index and AF is still unclear. Therefore, the purpose of this study is to explore the association between the SChE index and the prevalence of AF in patients with hypertension.


We collected cross-sectional data from January 2018 to April 2021 based on a retrospective study of cardiovascular disease. A total of 748 patients with hypertension were included, of whom 165 had AF. We used logistic regression models to test the relationship between SChE and the prevalence of AF in hypertensive patients.


In hypertensive patients, the SChE index was significantly associated with AF (OR = 0.723, P < 0.001). After adjusting for potential confounding factors, this correlation was still significant (OR = 0.778, P < 0.001). The stability of the model was verified by adjusting the variable type of SChE. The data were further stratified according to whether the patient had fatty liver. In the stratified data, the correlation between SChE and atrial fibrillation was still significant (P < 0.05).


Our study showed that SChE was significantly negatively correlated with the occurrence of AF in patients with hypertension. And this correlation was not affected by whether the patient had fatty liver.


Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia, affecting approximately 300–1000 million individuals worldwide [1]. Its incidence is positively correlated with age [2]. Epidemiological studies have demonstrated that AF prevalence ranges from 2% in the general population to 10–12% in individuals aged 80 and above [3]. With the global increase in life expectancy and the prolonged survival of individuals with chronic diseases, AF has emerged as one of the most common cardiovascular disorders in the twenty-first century, resulting in a significant rise in healthcare burden [4]. Despite significant progress in understanding AF and its corresponding treatment strategies in the past decade, early screening and diagnosis of AF remain challenging due to its asymptomatic nature and unpredictable onset time. Camm et al. proposed that one-third of all AF patients have asymptomatic AF, a finding that has been subsequently confirmed in other studies [5, 6].

The pathophysiological mechanisms of AF primarily involve electrical remodeling and structural remodeling [7]. Its occurrence and progression are influenced by various factors. These factors can increase the risk of AF to different extents, thereby promoting the initial development and onset of AF alone or in combination [8, 9]. Hypertension (HT) is the most common modifiable risk factor for AF. Studies have demonstrated that hypertensive patients have a 1.7 times higher risk of developing AF compared to individuals with normal blood pressure [10, 11]. Hypertension accounts for more than one-fifth of new cases of AF [12], and the risk of AF increases by 21% for every 20 mmHg increase in systolic blood pressure (SBP) [13, 14]. Therefore, early identification of risk factors for AF is crucial for hypertensive patients. Furthermore, emerging evidence suggests a link between non-alcoholic fatty liver disease (NAFLD) and the risk of AF [15,16,17]. However, there have been conflicting reports regarding the association between fatty liver disease and AF [18, 19]. Cholinesterase, primarily synthesized by the liver, is an important laboratory indicator for the clinical diagnosis and prediction of NAFLD. Currently, it remains unclear whether serum cholinesterase (SChE) is an independent risk factor for AF. Therefore, the objective of this study is to investigate the association between the SChE index and the risk of AF in a hypertensive population.


Study site and population

The research center and population for this cross-sectional analysis are part of a retrospective study of cardiovascular diseases conducted from September 2021 to April 2022 in the cardiovascular ward of Guang 'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China. This study involved the extraction of de-identified electronic medical records of inpatients from the cardiovascular department, covering the period from 2010 to 2022. By conducting clustering analysis on the clinical information, we were able to identify and summarize the characteristic syndromes of patients diagnosed with AF. Guang’anmen Hospital granted ethical approval for this experiment, which strictly complied with the Declaration of Helsinki (2021). We used cross-sectional data collected in the database for the period January 2018 to April 2021, including 748 patients with hypertension, 165 of whom had AF. Inclusion criteria included: (1) HT patients; (2) participants over the age of 18. Exclusion criteria included: (1) participants with acute coronary syndrome, hypertensive crisis, or other diseases that cause significant hemodynamic abnormalities; (2) participants with NYHA cardiac function rating of 3 or above; (3) participants with malignant tumors, infections or hematological diseases; and (4) participants with missing SChE data.

Data collection procedures

We collected four parts of data. 1. Demographic data: including age, sex, nationality, height, weight, smoking and drinking; 2. Diagnostic information on basic diseases such as hypertension, coronary heart disease (CHD), arrhythmia, diabetes and hyperlipidemia; 3. Cardiac ultrasound data, including Left atrial diameter (LAD), Left ventricular end-diastolic diameter (LVDD), Ejection fraction (EF), Aortic regurgitation (AR), Mitral valve regurgitation (MR), Tricuspid Regurgitation (TR), Left ventricular systolic dysfunction (LVSD), Regional wall motion abnormality (RWMA)0.4. Laboratory test data, including platelet (PLT), Lymphocyte count (LYMPH), Neutrophil count (NEUT), Fasting Blood Glucose (FBG), Uric acid (UA), Triglyceride (TG), High-density lipoprotein cholesterol (HDL-C), Low-density lipoprotein cholesterol (LDL-C), Serum creatinine (Scr), High sensitivity C-reactive protein (hs-CRP), Thyroid stimulating hormone (TSH), serum cholinesterase (SChE).


Hypertension is defined as previously having a HT history or being diagnosed based on office blood pressure (BP), which includes systolic BP ≥ 140 mmHg or diastolic BP ≥ 90 mmHg, or both [20].

Coronary heart disease is defined as previously having a CHD history or confirmed by the International Cardiology guidelines, which includes typical clinical angina manifestations, electrocardiographic changes, and coronary angiography [21].

Arrhythmia is defined as abnormal frequency and/or rhythm of heart beats, including ventricular and supraventricular arrhythmias [22].

Diabetes is defined as previously having a diabetes history or being diagnosed based on blood glucose (blood glucose at any time ≥ 11.1 mmol/L; fasting blood glucose level ≥ 7.0 mmol/L; blood glucose level in 2 h of oral glucose tolerance test ≥ 11.1 mmol/l/L) [24].

Hyperlipidemia, including high total cholesterol (TC), high total triglyceride (TG), mixed high TC and high TG, high low-density lipoprotein cholesterol (LDL-C), and lowered high-density lipoprotein cholesterol (HDL-C) [24].

Statistical analysis

This study included the clinical data of 784 patients with hypertension in the original database and interpolated the missing data. Among them, height and weight were interpolated according to the mean, and the remaining missing data were filled by multiple interpolation method of the mice function. We stratified participants into two groups based on whether they had atrial fibrillation. And tested the differences between two populations by χ2 test, and descriptive parameters were shown as mean (SD) for continuous variables and proportions for categorical variables. Initially, we explored the relationship between SChE levels and AF prevalence through a logistic regression model. Then, we verified the stability of the model by adjusting the SChE variable type.

In addition, we studied the combined effect of fatty liver and SChE on the prevalence of atrial fibrillation. The sample data were divided into two groups according to whether fatty liver was combined. In the stratified data, the correlation between SChE and the prevalence of AF was further explored. In multivariable models, we adjusted for variables indicated as potential confounders: Age, Sex, Nationality, NLR, HCRP, LDL-C, TG, TC, Scr, UA, LYMPH, Pah, RWMA, LVSD, TR, MR, AR, Left ventricular diastolic dysfunction, EF, LVDD, LAD, β-blocker use, Hyperhomocysteinemia, Hyperlipidemia, CHD and Course of HT. A two-sided P-value < 0.05 was considered statistically significant. All statistical analyses were performed using R4.2.2.


Baseline characteristics of the study subjects

A total of 784 subjects were included in the analysis. The characteristics of subjects are shown in Table 1. The mean age of the participants was 73.38 (SD: 11.69) years old, which included 390 men. In our study population, the average course of hypertension was 13.13 (SD: 11.54) years and 165 (21.05%) participants suffered from AF. Compared with the group with AF, the group without AF consisted of younger people. Moreover, there were significant differences between the two groups in the following aspects, such as β‐blocker use, LAD, EF, Left ventricular diastolic dysfunction, AR, MR, TR, LVSD, Pah, LYMPH, NLR, TG, hs-CRP and SChE.

Table 1 Characteristics of the study population

SChE and prevalence of AF in patients with hypertension

We used univariate logistic regression analysis to evaluate the association between SChE and the prevalence of AF. Meanwhile, we also showed the non-adjusted and adjusted models in Fig. 1. In the crude model, SChE showed a negative correlation with AF (OR = 0.723, 95% confidence interval CI 0.660 to 0.790, P < 0.001). In the minimally adjusted model (adjusted Age, Sex and Nationality), the result did not have obvious changes (OR = 0.739, 95% CI 0.673 to 0.811], P < 0.001). We also detected the same connection in the fully adjusted model (OR = 0.778, 95% CI 0.682 to 0.889, P < 0.001). For sensitivity analysis, we handled SChE as a categorical variable (Quartile), and found the same trend as well.

Fig. 1
figure 1

Relationship between SChE and prevalence of AF in different models. 1CI Confidence Interval, Ref reference, SChE serum cholinesterase. 2Non-ajusted model: we did not adjust other covariants. Minimally adjusted model: we adjusted Age, Sex and Nationality. Fully adjusted model: we adjusted Age, Sex, Nationality,NLR, HCRP, LDL-C, TG, TC, Scr, UA, LYMPH, Pah, RWMA, LVSD, TR, MR, AR, Left ventricular diastolic dysfunction, EF, LVDD, LAD, β-blocker use, Hyperhomocysteinemia, Hyperlipidemia, CHD, Course of HT

Association between SChE and prevalence of AF stratified by fatty liver

A similar result was observed in the hierarchical analysis, shown in Fig. 2. We also set the non-AF group as the reference group. Then, we stratified the data according to whether participants had fatty liver. SChE was considered to have an association with the prevalence of AF (OR = 0.689, 95% CI 0.489 to 0.971) in the group of participants who had fatty liver. The OR value of participants without fatty liver was 0.776 (95% CI 0.655 to 0.919). All results shown above were from an adjusted model, and the trends were mostly unchanged compared with the results before the adjustment.

Fig. 2
figure 2

Association between SChE and the prevalence of AF stratified by fatty liver. 1CI Confidence Interval, CHESChE serum cholinesterase. 2Non-ajusted model: we did not adjust other covariants. Minimally adjusted model: we adjusted Age, Sex and Nationality. Fully adjusted model: we adjusted Age, Sex, Nationality,NLR, HCRP, LDL-C, TG, TC, Scr, UA, LYMPH, Pah, RWMA, LVSD, TR, MR, AR, Left ventricular diastolic dysfunction, EF, LVDD, LAD, β-blocker use, Hyperhomocysteinemia, Hyperlipidemia, CHD, Course of HT


Hypertension is widely recognized as the predominant risk factor for AF. Studies have demonstrated that individuals with hypertension are 1.7 times more likely to develop AF compared to those with normal blood pressure [10, 11]. Moreover, patients who have experienced prolonged high blood pressure or inadequate blood pressure management are at a heightened risk of experiencing complications associated with AF, notably stroke, heart failure, and bleeding [25, 26]. Consequently, the early detection of high-risk patients with AF holds significant importance, particularly for individuals with hypertension. As we know, there is a NAFLD association with AF [27]. Liver biopsy is the gold standard for the diagnosis of NAFLD, but its disadvantages such as invasiveness, sampling error, and possible complications limit its clinical application [28]. ALT is a common way to detect NAFLD and assess the severity of liver injury, but its capability to identify NAFLD is doubted [29, 30]. Therefore, the establishment of a more sensitive biomarker to detect NAFLD is necessary. In a large cross-sectional study, SChE was shown to be better markers of fatty liver than ALT [31]. However, the correlation between the SChE index and AF risk in HT patients is still unclear. In this study, we found that SChE levels were significantly associated with AF prevalence in hypertensive patients. In the adjusted model, this relationship still existed. We further stratified the sample data according to whether the participants had fatty liver disease. In the stratified data, the correlation between SChE and AF prevalence was still significant. It showed that SChE may be an independent risk factor for AF.

Serum cholinesterase, also known as pseudo- or butyryl-cholinesterase (BChE), is synthesized by hepatocytes, and its half-life in serum is 11 days [32]. The concentration of SChE is affected by a variety of factors, such as malnutrition, systemic inflammation and liver cell damage [33, 34]. In addition, studies have shown that SChE has a predictive effect on a variety of cardiovascular diseases. There is a positive correlation between high BChE activity and identified cardiovascular risk factors. For example, Goliasch and Alcantara et al. demonstrated that BChE activity values were significantly associated with arterial hypertension [35, 36]. Furthermore, several studies demonstrated an association between BChE activity and metabolic risk factors (such as obesity, hyperlipidemia and diabetes) [37,38,39]. Interestingly, BChE activity is negatively correlated with cardiovascular mortality and seems to be a predictor of cardiovascular disease prognosis. Low levels of SChE increased the risk of death in patients with acute myocardial infarction [40], acute heart failure, stable coronary heart disease [41], ischemic stroke and other diseases [42]. All of the above conditions may lead to a higher risk of AF. And a study accidentally found a case of cholinesterase deficiency demonstrating sick sinus syndrome (SSS) and short paroxysms of AF associated with a relatively slow ventricular response [43]. Cholinesterase deficiency observed in this patient manifests as the loss of BChE activity. But most patients with silent cholinesterase activity showed no serious arrhythmia, suggesting that some mechanisms compensate for the cholinesterase deficiency [44]. In fact, BChE knockdown has been shown to increase the activity of AChE and other kinases [45]. Currently, most evidence supports that NAFLD may be linked to a slightly higher risk of developing AF [46]. As we all known, SChE is increased in patients with fatty liver [31]. Our research revealed a negative correlation between the SChE index and the risk of AF in hypertensive patients, which differed from our conventional comprehension. This phenomenon can potentially be explained by a new study, which suggested that higher liver stiffness, in particular among those without steatosis, was associated with prevalent atrial fibrillation [47]. This association could be driven by venous congestion instead of fibrogenesis. Therefore, further longitudinal studies with accurate standardized definitions of steatosis and AF are needed to determine strong evidence of the independent association between the two diseases. Based on the currently limited available references, we aim to provide an alternative explanation for this disparity. We hypothesize that the potential mechanism underlying the correlation between the SChE index and AF could be attributed to systemic inflammation. Firstly, our target audience is patients with high blood pressure. Chronic inflammation is involved in the pathogenesis of hypertension [48]. Secondly, it has been shown that the production of BChE and albumin in the liver is coupled, and these biochemical variables may be considered as negative inflammatory reactants whose serum levels are inversely correlated with the increase in the degree of clinical inflammation [49, 50]. In our study, we found that patients with combined AF had lower SChE levels and higher hs-CRP levels compared to patients with only hypertension, which confirmed this point.

For individuals diagnosed with hypertension, it is crucial to promptly identify high-risk patients who may develop atrial fibrillation. This early identification enables early screening, diagnosis, and intervention. Timely and accurate diagnosis of AF can effectively prevent complications, reduce hospitalization related to AF, and lower the mortality rate associated with AF. Although many studies have confirmed that serum biomarkers, echocardiographic markers and specific patterns on brain imaging can predict AF [51,52,53,54]. However, to our knowledge, this study is the first to try to determine that SChE is a possible predictive marker of AF in hypertension patients. We found that the SChE index was significantly associated with AF prevalence in hypertensive patients. And the measurement of SChE is easy to obtain in routine blood sampling analysis. Therefore, it represents a cheap and easy-to-obtain AF risk prediction method for hypertensive patients.

Our study has some limitations. First, this was a cross-sectional study and no statements about causality are made. Second, our study had small samples and was single center, which may cause bias. Although we adjusted for confounders in the multivariate analysis, the potential confounders were not completely eliminated. Third, hospitalized patients were typically older and had more underlying diseases. Last, diagnosis of NAFLD was made by ultrasonography rather than liver biopsy, the gold standard technique for detecting fatty liver. And the subjects' drinking history also lacked detailed records, so it was still unclear whether fatty liver was caused by alcohol. In addition, we only included HT patients in this study. Therefore, the suitable population of our findings is limited. Moreover, studies of a large and diverse population should be conducted to further verify. To our knowledge, this is the first study to investigate the association between the SChE index and AF patients with HT.


In conclusion, our study showed that SChE was significantly negatively correlated with the prevalence of AF in patients with hypertension. And this correlation was not affected by whether patients with fatty liver.

Availability of data and materials

Supplementary data are available at European Journal of Medical Research online.



Atrial fibrillation


Nonalcoholic fatty liver disease


Serum cholinesterase


Systolic blood pressure


Left atrial diameter


Left ventricular end-diastolic diameter


Ejection fraction


Aortic regurgitation


Mitral valve regurgitation


Tricuspid regurgitation


Left ventricular systolic dysfunction


Regional wall motion abnormality




Lymphocyte count


Neutrophil count


Fasting Blood Glucose


Uric acid




High-density lipoprotein cholesterol


Low-density lipoprotein cholesterol


Serum creatinine


High sensitivity C-reactive protein


Thyroid stimulating hormone




Sick sinus syndrome


  1. Chugh SS, Havmoeller R, Narayanan K, Singh D, Rienstra M, Benjamin EJ, et al. Worldwide epidemiology of atrial fibrillation: a global burden of disease 2010 study. Circulation. 2014;129(8):837–47.

    Article  PubMed  Google Scholar 

  2. Lau DH, Linz D, Sanders P. New findings in atrial fibrillation mechanisms. Card Electrophysiol Clin. 2019;11(4):563–71.

    Article  PubMed  Google Scholar 

  3. Staerk L, Sherer JA, Ko D, Benjamin EJ, Helm RH. Atrial fibrillation: epidemiology, pathophysiology, and clinical outcomes. Circ Res. 2017;120(9):1501–17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Antikainen RL, Peters R, Beckett NS, Rajkumar C, Bulpitt CJ. Atrial fibrillation and the risk of cardiovascular disease and mortality in the hypertension in the very elderly trial. J Hypertens. 2020;38(5):839–44.

    Article  CAS  PubMed  Google Scholar 

  5. Camm AJ, Corbucci G, Padeletti L. Usefulness of continuous electrocardiographic monitoring for atrial fibrillation. Am J Cardiol. 2012;110(2):270–6.

    Article  PubMed  Google Scholar 

  6. Healey JS, Connolly SJ, Gold MR, Israel CW, Van Gelder IC, Capucci A, et al. Subclinical atrial fibrillation and the risk of stroke. N Engl J Med. 2012;366(2):120–9.

    Article  CAS  PubMed  Google Scholar 

  7. Wijesurendra RS, Casadei B. Mechanisms of atrial fibrillation. Heart Br Card Soc. 2019;105(24):1860–7.

    CAS  Google Scholar 

  8. Sagris M, Antonopoulos AS, Theofilis P, Oikonomou E, Siasos G, Tsalamandris S, et al. Risk factors profile of young and older patients with myocardial infarction. Cardiovasc Res. 2022;118(10):2281–92.

    Article  CAS  PubMed  Google Scholar 

  9. Diavati S, Sagris M, Terentes-Printzios D, Vlachopoulos C. Anticoagulation treatment in venous thromboembolism: options and optimal duration. Curr Pharm Des. 2022;28(4):296–305.

    Article  CAS  PubMed  Google Scholar 

  10. Kallistratos MS, Poulimenos LE, Manolis AJ. Atrial fibrillation and arterial hypertension. Pharmacol Res. 2018;128:322–6.

    Article  CAS  PubMed  Google Scholar 

  11. Lip GYH, Coca A, Kahan T, Boriani G, Manolis AS, Olsen MH, et al. Hypertension and cardiac arrhythmias: a consensus document from the European heart rhythm association (EHRA) and ESC council on hypertension, endorsed by the heart rhythm society (HRS), Asia-Pacific heart rhythm society (APHRS) and sociedad latinoamericana de estimulación cardíaca y electrofisiología (SOLEACE). Eur Eur Pacing Arrhythm Card Electrophysiol J Work Groups Card Pacing Arrhythm Card Cell Electrophysiol Eur Soc Cardiol. 2017;19(6):891–911.

    Google Scholar 

  12. Huxley RR, Lopez FL, Folsom AR, Agarwal SK, Loehr LR, Soliman EZ, et al. Absolute and attributable risks of atrial fibrillation in relation to optimal and borderline risk factors: the atherosclerosis risk in communities (ARIC) study. Circulation. 2011;123(14):1501–8.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Emdin CA, Anderson SG, Salimi-Khorshidi G, Woodward M, MacMahon S, Dwyer T, et al. Usual blood pressure, atrial fibrillation and vascular risk: evidence from 4.3 million adults. Int J Epidemiol. 2017;46(1):162–72.

    PubMed  Google Scholar 

  14. Dzeshka MS, Shahid F, Shantsila A, Lip GYH. Hypertension and atrial fibrillation: an intimate association of epidemiology, pathophysiology, and outcomes. Am J Hypertens. 2017;30(8):733–55.

    Article  CAS  PubMed  Google Scholar 

  15. Rinella ME. Nonalcoholic fatty liver disease: a systematic review. JAMA. 2015;313(22):2263–73.

    Article  CAS  PubMed  Google Scholar 

  16. Anstee QM, Mantovani A, Tilg H, Targher G. Risk of cardiomyopathy and cardiac arrhythmias in patients with nonalcoholic fatty liver disease. Nat Rev Gastroenterol Hepatol. 2018;15(7):425–39.

    Article  PubMed  Google Scholar 

  17. Ismaiel A, Colosi HA, Rusu F, Dumitrașcu DL. Cardiac arrhythmias and electrocardiogram modifications in non-alcoholic fatty liver disease. A systematic review. J Gastrointest Liver Dis JGLD. 2019;28(4):483–93.

    Article  Google Scholar 

  18. Mantovani A, Dauriz M, Sandri D, Bonapace S, Zoppini G, Tilg H, et al. Association between non-alcoholic fatty liver disease and risk of atrial fibrillation in adult individuals: an updated meta-analysis. Liver Int Off J Int Assoc Study Liver. 2019;39(4):758–69.

    Google Scholar 

  19. Wijarnpreecha K, Boonpheng B, Thongprayoon C, Jaruvongvanich V, Ungprasert P. The association between non-alcoholic fatty liver disease and atrial fibrillation: A meta-analysis. Clin Res Hepatol Gastroenterol. 2017;41(5):525–32.

    Article  PubMed  Google Scholar 

  20. Wang GM, Li LJ, Tang WL, Wright JM. Renin inhibitors versus angiotensin converting enzyme (ACE) inhibitors for primary hypertension. Cochrane Database Syst Rev. 2020;10(10):CD012569.

    PubMed  Google Scholar 

  21. Li X, Yang S, Wang Y, Yang B, Zhang J. Effects of a transtheoretical model—based intervention and motivational interviewing on the management of depression in hospitalized patients with coronary heart disease: a randomized controlled trial. BMC Public Health. 2020;20(1):420.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Tisdale JE, Chung MK, Campbell KB, Hammadah M, Joglar JA, Leclerc J, et al. Drug-induced arrhythmias: a scientific statement from the American heart association. Circulation. 2020;142(15):e214–33.

    Article  PubMed  Google Scholar 

  23. American Diabetes Association Professional Practice Committee. Classification and diagnosis of diabetes: standards of medical care in diabetes 2022. Diabetes Care. 2022;45(1):S17-38.

    Article  Google Scholar 

  24. Dembowski E, Freedman I, Grundy SM, Stone NJ. Guidelines for the management of hyperlipidemia: how can clinicians effectively implement them? Prog Cardiovasc Dis. 2022;75:4–11.

    Article  PubMed  Google Scholar 

  25. Kirchhof P, Benussi S, Kotecha D, Ahlsson A, Atar D, Casadei B, et al. 2016 ESC guidelines for the management of atrial fibrillation developed in collaboration with EACTS. Eur Heart J. 2016;37(38):2893–962.

    Article  PubMed  Google Scholar 

  26. Hilkens NA, Algra A, Greving JP. Predicting major bleeding in ischemic stroke patients with atrial fibrillation. Stroke. 2017;48(11):3142–4.

    Article  PubMed  Google Scholar 

  27. Li X, Zhan F, Peng T, Xia Z, Li J. Association between the triglyceride-glucose index and non-alcoholic fatty liver disease in patients with atrial fibrillation. Eur J Med Res. 2023;28(1):355.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Paul S, Davis AM. Diagnosis and management of nonalcoholic fatty liver disease. JAMA. 2018;320(23):2474–5.

    Article  PubMed  Google Scholar 

  29. Nobili V, Alisi A, Valenti L, Miele L, Feldstein AE, Alkhouri N. NAFLD in children: new genes, new diagnostic modalities and new drugs. Nat Rev Gastroenterol Hepatol. 2019;16(9):517–30.

    Article  PubMed  Google Scholar 

  30. Browning JD, Szczepaniak LS, Dobbins R, Nuremberg P, Horton JD, Cohen JC, et al. Prevalence of hepatic steatosis in an urban population in the United States: impact of ethnicity. Hepatol Baltim Md. 2004;40(6):1387–95.

    Article  Google Scholar 

  31. Katoh S, Peltonen M, Wada T, Zeniya M, Sakamoto Y, Utsunomiya K, et al. Fatty liver and serum cholinesterase are independently correlated with HbA1c levels: cross-sectional analysis of 5384 people. J Int Med Res. 2014;42(2):542–53.

    Article  CAS  PubMed  Google Scholar 

  32. Montagnese C, Scalfi L, Signorini A, De Filippo E, Pasanisi F, Contaldo F. Cholinesterase and other serum liver enzymes in underweight outpatients with eating disorders. Int J Eat Disord. 2007;40(8):746–50.

    Article  PubMed  Google Scholar 

  33. Hubbard RE, O’Mahony MS, Calver BL, Woodhouse KW. Plasma esterases and inflammation in ageing and frailty. Eur J Clin Pharmacol. 2008;64(9):895–900.

    Article  CAS  PubMed  Google Scholar 

  34. Seo M, Yamada T, Tamaki S, Morita T, Furukawa Y, Iwasaki Y, et al. Prognostic significance of serum cholinesterase in patients with acute decompensated heart failure: a prospective comparative study with other nutritional indices. Am J Clin Nutr. 2019;110(2):330–9.

    Article  PubMed  Google Scholar 

  35. Goliasch G, Haschemi A, Marculescu R, Endler G, Maurer G, Wagner O, et al. Butyrylcholinesterase activity predicts long-term survival in patients with coronary artery disease. Clin Chem. 2012;58(6):1055–8.

    Article  CAS  PubMed  Google Scholar 

  36. Alcântara VM, Chautard-Freire-Maia EA, Scartezini M, Cerci MSJ, Braun-Prado K, Picheth G. Butyrylcholinesterase activity and risk factors for coronary artery disease. Scand J Clin Lab Invest. 2002;62(5):399–404.

    Article  PubMed  Google Scholar 

  37. Turecký L, Kupčová V, Urfinová M, Repiský M, Uhlíková E. Serum butyrylcholinesterase/HDL-cholesterol ratio and atherogenic index of plasma in patients with fatty liver disease. Vnitr Lek. 2021;67(E-2):4–8.

    PubMed  Google Scholar 

  38. Milano GE, Leite N, Chaves TJ, Milano GE, de Souza RLR, Alle LF. Butyrylcholinesterase activity and cardiovascular risk factors in obese adolescents submitted to an exercise program. Arq Bras Endocrinol Metabol. 2013;57(7):533–7.

    Article  PubMed  Google Scholar 

  39. Vallianou NG, Evangelopoulos AA, Bountziouka V, Bonou MS, Katsagoni C, Vogiatzakis ED, et al. Association of butyrylcholinesterase with cardiometabolic risk factors among apparently healthy adults. J Cardiovasc Med Hagerstown Md. 2014;15(5):377–83.

    Article  CAS  Google Scholar 

  40. Sun L, Qi X, Tan Q, Yang H, Qi X. Low serum-butyrylcholinesterase activity as a prognostic marker of mortality associates with poor cardiac function in acute myocardial infarction. Clin Lab. 2016;62(6):1093–9.

    CAS  PubMed  Google Scholar 

  41. Goliasch G, Kleber ME, Richter B, Plischke M, Hoke M, Haschemi A, et al. Routinely available biomarkers improve prediction of long-term mortality in stable coronary artery disease: the Vienna and Ludwigshafen coronary artery disease (VILCAD) risk score. Eur Heart J. 2012;33(18):2282–9.

    Article  CAS  PubMed  Google Scholar 

  42. Ben Assayag E, Shenhar-Tsarfaty S, Ofek K, Soreq L, Bova I, Shopin L, et al. Serum cholinesterase activities distinguish between stroke patients and controls and predict 12-month mortality. Mol Med. 2010;16(7–8):278–86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Yasuda S, Fukata M, Yokoyama T, Arita T, Odashiro K, Maruyama T, et al. Sick sinus syndrome observed in a patient with cholinesterase deficiency. Intern Med. 2019;58(6):809–12.

    Article  PubMed  Google Scholar 

  44. Manoharan I, Boopathy R, Darvesh S, Lockridge O. A medical health report on individuals with silent butyrylcholinesterase in the Vysya community of India. Clin Chim Acta Int J Clin Chem. 2007;378(1–2):128–35.

    Article  CAS  Google Scholar 

  45. Bodur E, Layer PG. Counter-regulation of cholinesterases: differential activation of PKC and ERK signaling in retinal cells through BChE knockdown. Biochimie. 2011;93(3):469–76.

    Article  CAS  PubMed  Google Scholar 

  46. Zhou BG, Ju SY, Mei YZ, Jiang X, Wang M, Zheng AJ, et al. A systematic review and meta-analysis of cohort studies on the potential association between NAFLD/MAFLD and risk of incident atrial fibrillation. Front Endocrinol. 2023;14:1160532.

    Article  Google Scholar 

  47. Van Kleef LA, Lu Z, Ikram MA, De Groot NMS, Kavousi M, De Knegt RJ. Liver stiffness not fatty liver disease is associated with atrial fibrillation: the Rotterdam study. J Hepatol. 2022;77(4):931–8.

    Article  PubMed  Google Scholar 

  48. Zhang Z, Zhao L, Zhou X, Meng X, Zhou X. Role of inflammation, immunity, and oxidative stress in hypertension: new insights and potential therapeutic targets. Front Immunol. 2022;13:1098725.

    Article  CAS  PubMed  Google Scholar 

  49. Yang Y, Yang X, Yang J. Cholinesterase level is a predictor of systemic inflammatory response syndrome and complications after cardiopulmonary bypass. Ann Palliat Med. 2021;10(11):11714–20.

    Article  PubMed  Google Scholar 

  50. Lampón N, Hermida-Cadahia EF, Riveiro A, Tutor JC. Association between butyrylcholinesterase activity and low-grade systemic inflammation. Ann Hepatol. 2012;11(3):356–63.

    Article  PubMed  Google Scholar 

  51. Szegedi I, Szapáry L, Csécsei P, Csanádi Z, Csiba L. Potential biological markers of atrial fibrillation: a chance to prevent cryptogenic stroke. BioMed Res Int. 2017;2017:8153024.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Bruun Pedersen K, Madsen C, Sandgaard NCF, Hey TM, Diederichsen ACP, Bak S, et al. Left atrial volume index and left ventricular global longitudinal strain predict new-onset atrial fibrillation in patients with transient ischemic attack. Int J Cardiovasc Imaging. 2019;35(7):1277–86.

    Article  PubMed  Google Scholar 

  53. Chen X, Luo W, Li J, Li M, Wang L, Rao Y, et al. Diagnostic accuracy of STAF, LADS, and iPAB scores for predicting paroxysmal atrial fibrillation in patients with acute cerebral infarction. Clin Cardiol. 2018;41(12):1507–12.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Antipova D, Eadie L, Macaden A, Wilson P. Diagnostic accuracy of clinical tools for assessment of acute stroke: a systematic review. BMC Emerg Med. 2019;4(19):49.

    Article  Google Scholar 

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This study is supported by the Capital Health Development Scientific Research Special Project(NO.2022-1-4153) and the Scientific and technological innovation project of China Academy of Chinese Medical Sciences (CI2021A03011).

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WX wrote the main manuscript text. WX and YW prepared Tables. YW and YH revised the manuscript. All authors reviewed the manuscript. All authors read and approved the final manuscript.

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Correspondence to Yuanhui Hu.

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Xue, W., Wei, Y. & Hu, Y. Association between serum cholinesterase and the prevalence of atrial fibrillation in Chinese hypertensive population: a cross-sectional study. Eur J Med Res 28, 500 (2023).

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