Journal of Shandong University (Health Sciences) ›› 2019, Vol. 57 ›› Issue (1): 62-67.doi: 10.6040/j.issn.1671-7554.0.2018.890

Previous Articles    

Levels of soluble suppression of tumorigenicity 2 and galectin-3 as predictors of the classification and prognosis of chronic heart failure

MI Chuanxiao1, LIU Junni2, ZOU Chengwei1, ZHOU Nannan2   

  1. 1. Department of Cardiac Surgery;
    2. Department of Geriatric Cardiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan 250021, Shandong, China
  • Published:2022-09-27

Abstract: Objective To investigate the value of soluble suppression of tumorigenicity 2(sST2)and galectin-3(Gal-3)in heart failure grading and prognosis by evaluating their levels according to New York Heart Association(NYHA)classification. Methods A total of 191 patients with chronic heart failure treated during Apr. 2015 and Jan. 2016 were chosen randomly, including 115 males and 76 females, average(62.36±11.09)years. According to the NYHA criteria and follow-up results, the patients were classified into three groups: non-cardiac events group, rehospitalization group 山 东 大 学 学 报 (医 学 版)57卷1期 -米传晓,等.血清可溶性肿瘤因子2抑制剂、半乳糖凝集素-3蛋白水平在慢性心衰分级及预后中的应用 \=-and death group. Plasma sST2 and Gal-3 levels were measured at admission and every 2 months during follow-up. The incidence of cardiac events were recorded. Results The levels of sST2 and Gal-3 were positively correlated with NYHA classification(r1=0.33, P1<0.001; r2=0.21, P2=0.004), and negatively correlated with left ventricular ejection fraction(LVEF)(r1=-0.25, P1=0.001; r2=-0.24, P2=0.002). There were significant differences in sST2 and Gal-3 levels among the three groups (F1=56.76, P1<0.001; F2=31.08, P2<0.001). The level of sST2 was significantly different between the non-cardiac events group and rehospitalization group, between the non-cardiac events group and death group, and between the rehospitalization group and death group(t1=6.15, P1<0.001; t2=11.36, P2<0.001; t3=3.22, P3=0.003). The level of Gal-3 was significantly different between the non-cardiac events group and rehospitalization group, and between the non-cardiac events group and death group(t1=6.28, P1<0.001; t2=5.91, P2<0.001). Gal-3<15.67 ng/mL and sST2< 31.74 ng/mL were the predictive factors of non-cardiac events. sST2>45.031 ng/mL was a predictive factor of recent mortality. Gal-3>21.90 ng/mL and sST2>45.03ng/mL were predictors of shortened survival. Conclusion The levels of Gal-3 and sST2 are somewhat positively correlated with NYHA classification, and can indicate the cardiac function of patients with heart failure. The sST2 level is useful to predict patients' prognosis, including death, rehospitalization or non-cardiac events, while Gal-3 level is useful to predict cardiac events. The combined detection of them is valuable for the diagnosis and prognosis of heart failure.

Key words: Chronic heart failure, Soluble suppression of tumorigenicity 2, Galectin-3, Heart function classification, Prognosis

CLC Number: 

  • R541.6
[1] Mosterd A. Clinical epidemiology of heart failure[J]. Heart(British Cardiac Society), 2007, 93(9): 1137-1146.
[2] King M, Kingery J. Diagnosis and evaluation of heart failure[J]. Am Fam Physician, 2012, 85(12): 1161-1168.
[3] McMurray JJ. Clinical practice. Systolic heart failure[J]. N Engl J Med, 2010, 362(3): 228-238.
[4] Krum H, Teerlink JR. Medical therapy for chronic heart failure[J]. Lancet, 2011, 378(9792): 713-721.
[5] McMurray JJ, Adamopoulos S, Anker SD, et al. ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure 2012[J]. Turk Kardiyol Dern Ars, 2012, 40(Suppl 3): 77-137.
[6] Demissei BG, Cotter G, Prescott MF, et al. A multimarker multi-time point-based risk stratification strategy in acute heart failure: results from the RELAX-AHF trial[J]. Eur J Heart Fail, 2017, 19(8): 1001-1010.
[7] Lala RI, Lungeanu D, Darabantiu D, et al. Galectin-3 as a marker for clinical prognosis and cardiac remodeling in acute heart failure[J]. Herz, 2018, 43(2): 146-155.
[8] Tominaga S. A putative protein of a growth specific cDNA from BALB/c-3T3 cells is highly similar to the extracellular portion of mouse interleukin 1 receptor[J]. FEBS Lett, 1989, 258(2): 301-304.
[9] Schmitz J, Owyang A, Oldham E, et al. IL-33, an interleukin-1-like cytokine that signals via the IL-1 receptor-related protein ST2 and induces T helper type 2-associated cytokines[J]. Immunity, 2005, 23(5): 479-490.
[10] Weinberg EO, Shimpo M, De Keulenaer GW, et al. Expression and regulation of ST2,an interleukin-1 receptor family member, in cardiomyocytes and myocardial infarction[J]. Circulation, 2002, 106(23): 2961-2966.
[11] Januzzi JL, Peacock WF, Maisel AS, et al. Measurement of the interleukin family member ST2 in patients with acute dyspnea: results from the PRIDE(Pro-Brain Natriuretic Peptide Investigation of Dyspnea in the Emergency Department)study[J]. J Am Coll Cardiol, 2007, 50(7): 607-613.
[12] Ky B, French B, McCloskey K, et al. High-sensitivity ST2 for prediction of adverse outcomes in chronic heart failure[J]. Circ Heart Fail, 2011, 4(2): 180-187.
[13] Bayes-Genis A, de Antonio M, Galán A, et al. Combined use of high-sensitivity ST2 and NTproBNP to improve the prediction of death in heart failure[J]. Eur J Heart Fail, 2012, 14(1): 32-38.
[14] Vorovich E, French B, Ky B, et al. Biomarker predictors of cardiac hospitalization in chronic heart failure: a recurrent event analysis[J]. J Card Fail, 2014, 20(8): 569-576.
[15] Yancy CW, Jessup M, Bozkurt B, et al. 2013ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines[J]. J Am Coll Cardiol, 2013, 62(16):147-239.
[16] Liu FT, Hsu DK, Zuberi RI, et al. Expression and function of Galectin-3,a beta-galactoside-binding lectin, in human monocytes and macrophages[J]. Am J Pathol, 1995, 147(4): 1016-1028.
[17] Carrasco FJ, Aramburu O, Salamanca P, et al. Predictive value of serum Galectin-3 levels in patients with acute heart failure with preserved ejection fraction[J]. Int J Cardiol, 2013, 169(3): 177-182.
[18] Sharma UC, Pokharel S, van Brakel TJ, et al. Galectin-3 marks activated macrophages in failure-prone hypertrophied hearts and contributes to cardiac dysfunction[J]. Circulation, 2004, 110(19): 3121-3128.
[19] Lin YH, Lin LY, Wu YW, et al. The relationship between serum Galectin-3 and serum markers of cardiac extracellular matrix turnover in heart failure patients[J]. Clin Chim Acta, 2009, 409(1-2): 96-99.
[20] Lok DJ, Van Der Meer P, de la Porte PW, et al. Prognostic value of Galectin-3, a novel marker of fibrosis, in patients with chronic heart failure: data from the DEAL-HF study[J]. Clin Res Cardiol, 2010, 99(5): 323-328.
[21] Bayes-Genis A, de Antonio M, Vila J, et al. Head-to-head comparison of 2 myocardial fibrosis biomarkers for long-term heart failure risk stratification: ST2 versus galectin-3[J]. J Am Coll Cardiol, 2014, 63(2): 158-166.
[22] Ky B, French B, Levy WC, et al. Multiple biomarkers for risk prediction in chronic heart failure[J]. Circ Heart Fail, 2012, 5(2): 183-190.
[23] Lupón J, de Antonio M, Galán A, et al. Combined use of the novel high sensitivity of troponin biomarkers and ST2 for risk stratification heart failure vs conventional assessment[J]. Mayo Clin Proc, 2013, 88(3): 234-243.
[24] Wojtczak-Soska K, Sakowicz A, Pietrucha T, et al. Soluble ST2 protein in the short-term prognosis after hospitalization in chronic systolic heart failure[J]. Kardiol Pol, 2014, 72(8): 725-734.
[25] Gruson D, Ferracin B, Ahn SA, et al. Comparison of fibroblast growth factor 23, soluble ST2 and Galectin-3 for prognostication of cardiovascular death in heart failure patients[J]. Int J Cardiol, 2015,189:185-187. doi: org/10.1016/j.ijcard.2015.04.074.
[26] Piper SE, Sherwood RA, Amin-Youssef GF, et al. Serial soluble ST2 for the monitoring of pharmacologically optimised chronic stable heart failure[J]. Int J Cardiol, 2015, 178: 284-291. doi: org/10.1016/j.ijcard.2014.11.097.
[27] Miller WL, Saenger AK, Grill DE, et al. Prognostic value of serial measurements of soluble suppression of tumorigenicity 2 and galectin-3 in ambulatory patients with chronic heart failure[J]. J Card Fail, 2016, 22(4): 249-255.
[1] ZHENG Su, CHEN Shuhua, LI Hua, DENG Jie, CHEN Chunhong, WANG Xiaohui, FENG Weixing, HAN Xiaodi, ZHANG Yujia, LI Na, LI Mo, FANG Fang. Correlation between EEG variations and BASED evaluation of the efficacy of ACTH treatment in 54 cases of infantile spasms [J]. Journal of Shandong University (Health Sciences), 2022, 60(9): 91-96.
[2] WANG Lihui, GAO Min, KONG Beihua. Angiosarcoma of the uterus: a report of 2 cases and literature review [J]. Journal of Shandong University (Health Sciences), 2022, 60(9): 108-112.
[3] HE Shiqing, LI Wanwan, DONG Shuqing, MOU Jingyi, LIU Yuying, WEI Siyu, LIU Zhao, ZHANG Jiaxin. Construction of a prognostic risk model of pyroptosis-related genes in breast cancer based on database [J]. Journal of Shandong University (Health Sciences), 2022, 60(8): 34-43.
[4] ZHANG Yufeng, XU Min, XING Xiuli, PANG Shuguang, HU Keqing. Epidemiological characteristics of 689 patients with non-ST-segment elevation myocardial infarction [J]. Journal of Shandong University (Health Sciences), 2022, 60(7): 118-122.
[5] LI Linlin, WANG Kai. Prediction of hepatocellular carcinoma prognostic genes based on bioinformatics [J]. Journal of Shandong University (Health Sciences), 2022, 60(5): 50-58.
[6] CHU Yan, LIU Duanrui, ZHU Wenshuai, FAN Rong, MA Xiaoli, WANG Yunshan, JIA Yanfei. Expressions of DNA methyltransferases in gastric cancer and their clinical significance [J]. Journal of Shandong University (Health Sciences), 2021, 59(7): 1-9.
[7] CHEN Liyu, XIAO Juan, LYU Xianzhong, DUAN Baomin, HONG Fanzhen. Risk factors influencing prognosis of lower extremity deep vein thrombosis in pregnant and parturient women [J]. Journal of Shandong University (Health Sciences), 2021, 59(7): 38-42.
[8] TIAN Yaotian, WANG Bao, LI Yeqin, WANG Teng, TIAN Liwen, HAN Bo, WANG Cuiyan. Machine learning models based on interpretive CMR parameters can predict the prognosis of pediatric myocarditis [J]. Journal of Shandong University (Health Sciences), 2021, 59(7): 43-49.
[9] LI Wanwan, ZHOU Wenkai, DONG Shuqing, HE Shiqing, LIU Zhao, ZHANG Jiaxin, LIU Bin. Construct of a risk assessment model of breast cancer immune-related lncRNAs based on the database information [J]. Journal of Shandong University (Health Sciences), 2021, 59(7): 74-84.
[10] MI Qi, SHI Shuang, LI Juan, LI Peilong, DU Lutao, WANG Chuanxin. Construction of circRNA-mediated ceRNA network and prognostic assessment model for bladder cancer [J]. Journal of Shandong University (Health Sciences), 2021, 59(6): 94-102.
[11] LAN Hongtao, JIA Xu, TONG Zhoujie, ZHENG Man, HU Boang, ZHONG Ming, ZHANG Wei, WANG Zhihao. Readmission prediction of 152 non-selective adult patients with chronic heart failure [J]. Journal of Shandong University (Health Sciences), 2021, 59(4): 63-69.
[12] LI Xiangqing, YIN Xin, ZHAO Xuelian, ZHAO Peiqing. Expression and clinical significance of circulating CD56bright subset of NK cells in patients with Parkinsons disease [J]. Journal of Shandong University (Health Sciences), 2021, 59(2): 34-40.
[13] ZENG Rui, HU Xinting, YUN Xiaoya, TIAN Zheng, LI Qing, LIU Jie, ZHANG Ya, WANG Xin. Fluorescence in situ hybridization in the diagnosis of 197 cases of chronic lymphocytic leukemia [J]. Journal of Shandong University (Health Sciences), 2021, 59(11): 35-40.
[14] LI Yinglin, SONG Daoqing, XU Zhonghua. Identification of FKBP11 expression in clear cell renal cell carcinoma using bioinformatics analysis [J]. Journal of Shandong University (Health Sciences), 2020, 1(9): 45-51.
[15] SHI Shuang, LI Juan, MI Qi, WANG Yunshan, DU Lutao, WANG Chuanxin. Construction and application of a miRNAs prognostic risk assessment model of gastric cancer [J]. Journal of Shandong University (Health Sciences), 2020, 1(7): 47-52.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!