Category: 8. Health

  • Liver Failure and Artificial Liver Group IDB, Chinese Medical Association. Guidelines for Diagnosis and Treatment of Liver Failure (2018 Edition) [J] Chinese Journal of Hepatology. 2019;27(1):18–26. https://doi.org/10.3760/cma.j.issn.1007-3418.2019.01.006.

  • Sarin SK, Kedarisetty CK, Abbas Z, Amarapurkar D, Bihari C, Chan AC, et al. Acute-on-chronic liver failure: consensus recommendations of the Asian Pacific Association for the Study of the Liver (APASL) 2014. Hepatol Int. 2014;8(4):453–71. Epub 2015/07/24. https://doi.org/10.1007/s12072-014-9580-2. PubMed PMID: 26202751.

    Article 
    PubMed 

    Google Scholar 

  • Sarin SK, Choudhury A, Sharma MK, Maiwall R, Al Mahtab M, Rahman S, et al. Acute-on-chronic liver failure: consensus recommendations of the Asian Pacific association for the study of the liver (APASL): an update. Hepatol Int. 2019;13(4):353–90. Epub 2019/06/07. https://doi.org/10.1007/s12072-019-09946-3. PubMed PMID: 31172417; PubMed Central PMCID: PMCPMC6728300.

    Article 
    PubMed 

    Google Scholar 

  • Gao B, Xiao J, Zhang M, Zhang F, Zhang W, Yang J, et al. High-density lipoprotein cholesterol for the prediction of mortality in cirrhosis with portal vein thrombosis: a retrospective study. Lipids Health Dis. 2019;18(1):79. Epub 2019/04/01. https://doi.org/10.1186/s12944-019-1005-8. PubMed PMID: 30927926; PubMed Central PMCID: PMCPMC6441144.

    Article 
    PubMed 

    Google Scholar 

  • Trieb M, Rainer F, Stadlbauer V, Douschan P, Horvath A, Binder L, et al. HDL-related biomarkers are robust predictors of survival in patients with chronic liver failure. J Hepatol. 2020;73(1):113–20. Epub 2020/02/18. https://doi.org/10.1016/j.jhep.2020.01.026. PubMed PMID: 32061870.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Zhang Y, Chen P, Zhang Y, Nie Y, Zhu X. Low high-density lipoprotein cholesterol levels predicting poor outcomes in patients with hepatitis B virus-related acute-on-chronic liver failure. Front Med. 2022;9:1001411. Epub 2022/12/13. https://doi.org/10.3389/fmed.2022.1001411. PubMed PMID: 36507543; PubMed Central PMCID: PMCPMC9732002.

    Article 

    Google Scholar 

  • Jiang M, Liu F, Xiong WJ, Zhong L, Xu W, Xu F, et al. Combined MELD and blood lipid level in evaluating the prognosis of decompensated cirrhosis. World J Gastroenterol. 2010;16(11):1397–401. Epub 2010/03/20. https://doi.org/10.3748/wjg.v16.i11.1397. PubMed PMID: 20238407; PubMed Central PMCID: PMCPMC2842532.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Janičko M, Veselíny E, Leško D, Jarčuška P. Serum cholesterol is a significant and independent mortality predictor in liver cirrhosis patients. Ann Hepatol. 2013;12(4):581–7 Epub 2013/07/03 PubMed PMID: 23813136.

    Article 
    PubMed 

    Google Scholar 

  • He X, Liu X, Peng S, Han Z, Shen J, Cai M. Association of Low High-Density Lipoprotein Cholesterol Levels with Poor Outcomes in Hepatitis B-Associated Decompensated Cirrhosis Patients. BioMed Res Int. 2021;2021:9927330. Epub 2021/08/07. https://doi.org/10.1155/2021/9927330. PubMed PMID: 34355041; PubMed Central PMCID: PMCPMC8331308 conflict of interest.

    Article 
    PubMed 

    Google Scholar 

  • Habib A, Mihas AA, Abou-Assi SG, Williams LM, Gavis E, Pandak WM, et al. High-density lipoprotein cholesterol as an indicator of liver function and prognosis in noncholestatic cirrhotics. Clin Gastroenterol Hepatol. 2005;3(3):286–91. Epub 2005/03/15. https://doi.org/10.1016/s1542-3565(04)00622-6. PubMed PMID: 15765449.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Gurbuz B, Guldiken N, Reuken P, Fu L, Remih K, Preisinger C, et al. Biomarkers of hepatocellular synthesis in patients with decompensated cirrhosis. Hepatol Int. 2023;17(3):698–708. Epub 2023/01/19. https://doi.org/10.1007/s12072-022-10473-x. PubMed PMID: 36652164; PubMed Central PMCID: PMCPMC10224844.

    Article 
    PubMed 

    Google Scholar 

  • Mo R, Zhang Z, Zhou Y, Wang Y, Yin P, Zhang C, et al. A new prognostic model based on serum apolipoprotein AI in patients with HBV-ACLF and acutely decompensated liver cirrhosis. Lipids Health Dis. 2025;24(1):35. Epub 2025/02/04. https://doi.org/10.1186/s12944-025-02434-8. PubMed PMID: 39901194; PubMed Central PMCID: PMCPMC11789380.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Xu J, Du F, Yang N, Hou J, Fan Y, Liu X. Risk factors and prognostic model for HBV-related subacute liver failure. Annals Translational Med. 2022;10(7):406. Epub 2022/05/10. https://doi.org/10.21037/atm-22-461. PubMed PMID: 35530949; PubMed Central PMCID: PMCPMC9073775.

    Article 
    CAS 

    Google Scholar 

  • Etogo-Asse FE, Vincent RP, Hughes SA, Auzinger G, Le Roux CW, Wendon J, et al. High density lipoprotein in patients with liver failure; relation to sepsis, adrenal function and outcome of illness. Liver Int. 2012;32(1):128–36. Epub 2011/11/22. https://doi.org/10.1111/j.1478-3231.2011.02657.x. PubMed PMID: 22098564.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Bergmann CB, Beckmann N, Salyer CE, Crisologo PA, Nomellini V, Caldwell CC. Lymphocyte Immunosuppression and Dysfunction Contributing to Persistent Inflammation, Immunosuppression, and Catabolism Syndrome (PICS). Shock (Augusta, Ga). 2021;55(6):723–41. Epub 2020/10/07. https://doi.org/10.1097/shk.0000000000001675. PubMed PMID: 33021569.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Mei X, Atkinson D. Lipid-free Apolipoprotein A-I Structure: Insights into HDL Formation and Atherosclerosis Development. Archives Med Res. 2015;46(5):351–60. Epub 2015/06/07. https://doi.org/10.1016/j.arcmed.2015.05.012. PubMed PMID: 26048453; PubMed Central PMCID: PMCPMC4522339.

    Article 
    CAS 

    Google Scholar 

  • Tao X, Tao R, Wang K, Wu L. Anti-inflammatory mechanism of Apolipoprotein A-I. Front Immunol. 2024;15:1417270. Epub 2024/07/23. https://doi.org/10.3389/fimmu.2024.1417270. PubMed PMID: 39040119; PubMed Central PMCID: PMCPMC11260610.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Morita SY. Metabolism and Modification of Apolipoprotein B-Containing Lipoproteins Involved in Dyslipidemia and Atherosclerosis. Biol Pharm Bull. 2016;39(1):1–24. Epub 2016/01/05. https://doi.org/10.1248/bpb.b15-00716. PubMed PMID: 26725424.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Cao WJ, Wang TT, Gao YF, Wang YQ, Bao T, Zou GZ. Serum Lipid Metabolic Derangement is Associated with Disease Progression During Chronic HBV Infection. Clin Lab. 2019;65(12). Epub 2019/12/19. https://doi.org/10.7754/Clin.Lab.2019.190525. PubMed PMID: 31850701.

  • Ghadir MR, Riahin AA, Havaspour A, Nooranipour M, Habibinejad AA. The relationship between lipid profile and severity of liver damage in cirrhotic patients. Hepat Mon. 2010;10(4):285–8 Epub 2010/10/01. PubMed PMID: 22312394; PubMed Central PMCID: PMCPMC3271321.

    PubMed 

    Google Scholar 

  • Shalimar, Rout G, Jadaun SS, Ranjan G, Kedia S, Gunjan D, et al. Prevalence, predictors and impact of bacterial infection in acute on chronic liver failure patients.Dig Liver Dis. 2018;50(11):1225–31. Epub 2018/06/19. https://doi.org/10.1016/j.dld.2018.05.013. PubMed PMID: 29910108.

  • Yang L, Wu T, Li J, Li J. Bacterial Infections in Acute-on-Chronic Liver Failure. Seminars Liver Dis. 2018;38(2):121–33. Epub 2018/06/06. https://doi.org/10.1055/s-0038-1657751. PubMed PMID: 29871019.

    Article 

    Google Scholar 

  • Kim HY, Chang Y, Park JY, Ahn H, Cho H, Han SJ, et al. Characterization of acute-on-chronic liver failure and prediction of mortality in Asian patients with active alcoholism. J Gastroenterol Hepatol. 2016;31(2):427–33. Epub 2015/08/12. https://doi.org/10.1111/jgh.13084. PubMed PMID: 26260091.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Okada N, Sanada Y, Urahashi T, Ihara Y, Yamada N, Hirata Y, et al. Endotoxin Metabolism Reflects Hepatic Functional Reserve in End-Stage Liver Disease. Transplant Proc. 2018;50(5):1360–4. Epub 2018/05/01. https://doi.org/10.1016/j.transproceed.2018.01.052. PubMed PMID: 29705277.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Wiest R, Lawson M, Geuking M. Pathological bacterial translocation in liver cirrhosis. J Hepatol. 2014;60(1):197–209. Epub 2013/09/03. https://doi.org/10.1016/j.jhep.2013.07.044. PubMed PMID: 23993913.

    Article 
    PubMed 

    Google Scholar 

  • Sanada Y, Mizuta K, Urahashi T, Ihara Y, Wakiya T, Okada N, et al. Impact of hepatic clearance of endotoxin using endotoxin activity assay. Hepatol Int. 2012;6(4):778–82. Epub 2011/07/07. https://doi.org/10.1007/s12072-011-9289-4. PubMed PMID: 21732128.

    Article 
    PubMed 

    Google Scholar 

  • Tsai MH, Peng YS, Chen YC, Liu NJ, Ho YP, Fang JT, et al. Adrenal insufficiency in patients with cirrhosis, severe sepsis and septic shock. Hepatology (Baltimore, Md). 2006;43(4):673–81. Epub 2006/03/25. https://doi.org/10.1002/hep.21101. PubMed PMID: 16557538.

    Article 
    PubMed 

    Google Scholar 

  • González-Navajas JM. Inflammasome activation in decompensated liver cirrhosis. World J Hepatol. 2016;8(4):207–10. Epub 2016/02/09. https://doi.org/10.4254/wjh.v8.i4.207. PubMed PMID: 26855691; PubMed Central PMCID: PMCPMC4733463.

    Article 
    PubMed 

    Google Scholar 

  • Wu A, Hinds CJ, Thiemermann C. High-density lipoproteins in sepsis and septic shock: metabolism, actions, and therapeutic applications. Shock (Augusta, Ga). 2004;21(3):210–21. Epub 2004/02/11. Epub 2004/02/11. https://doi.org/10.1097/01.shk.0000111661.09279.82. PubMed PMID: 14770033.

    Article 
    PubMed 

    Google Scholar 

  • Hossain E, Ota A, Karnan S, Takahashi M, Mannan SB, Konishi H, et al. Lipopolysaccharide augments the uptake of oxidized LDL by up-regulating lectin-like oxidized LDL receptor-1 in macrophages. Mol Cell Biochem. 2015;400(1–2):29–40. Epub 2014/10/29. https://doi.org/10.1007/s11010-014-2259-0. PubMed PMID: 25348362.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Lee RP, Lin NT, Chao YF, Lin CC, Harn HJ, Chen HI. High-density lipoprotein prevents organ damage in endotoxemia. Res Nurs Health. 2007;30(3):250–60. Epub 2007/05/22. https://doi.org/10.1002/nur.20187. PubMed PMID: 17514720.

    Article 
    PubMed 

    Google Scholar 

  • Chen KL, Chou RH, Chang CC, Kuo CS, Wei JH, Huang PH, et al. The high-density lipoprotein cholesterol (HDL-C)-concentration-dependent association between anti-inflammatory capacity and sepsis: A single-center cross-sectional study. PloS one. 2024;19(4):e0296863. Epub 2024/04/11. https://doi.org/10.1371/journal.pone.0296863. PubMed PMID: 38603717; PubMed Central PMCID: PMCPMC11008828.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Guo L, Ai J, Zheng Z, Howatt DA, Daugherty A, Huang B, et al. High density lipoprotein protects against polymicrobe-induced sepsis in mice. J Biol Chem. 2013;288(25):17947–53. Epub 2013/05/10. https://doi.org/10.1074/jbc.M112.442699. PubMed PMID: 23658016; PubMed Central PMCID: PMCPMC3689940.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Jiao YL, Wu MP. Apolipoprotein A-I diminishes acute lung injury and sepsis in mice induced by lipoteichoic acid. Cytokine. 2008;43(1):83–7. Epub 2008/05/27. https://doi.org/10.1016/j.cyto.2008.04.002. PubMed PMID: 18501625.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Guo L, Morin EE, Yu M, Mei L, Fawaz MV, Wang Q, et al. Replenishing HDL with synthetic HDL has multiple protective effects against sepsis in mice. Sci Sig. 2022;15(725):eabl9322. Epub 2022/03/16. https://doi.org/10.1126/scisignal.abl9322. PubMed PMID: 35290084; PubMed Central PMCID: PMCPMC9825056.

    Article 
    CAS 

    Google Scholar 

  • Weiss E, de la Grange P, Defaye M, Lozano JJ, Aguilar F, Hegde P, et al. Characterization of Blood Immune Cells in Patients With Decompensated Cirrhosis Including ACLF. Front Immunol. 2020;11:619039. Epub 2021/02/23. https://doi.org/10.3389/fimmu.2020.619039. PubMed PMID: 33613548; PubMed Central PMCID: PMCPMC7893087.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Li J, Hu CH, Chen Y, Zhou MM, Gao ZJ, Fu MJ, et al. Characteristics of Peripheral Lymphocyte Subsets in Patients With Acute-On-Chronic Liver Failure Associated With Hepatitis B. Front Med. 2021;8:689865. Epub 2021/08/14. https://doi.org/10.3389/fmed.2021.689865. PubMed PMID: 34386507; PubMed Central PMCID: PMCPMC8353122.

    Article 

    Google Scholar 

  • Nishikawa T, Bellance N, Damm A, Bing H, Zhu Z, Handa K, et al. A switch in the source of ATP production and a loss in capacity to perform glycolysis are hallmarks of hepatocyte failure in advance liver disease. J Hepatol. 2014;60(6):1203–11. Epub 2014/03/04. https://doi.org/10.1016/j.jhep.2014.02.014. PubMed PMID: 24583248; PubMed Central PMCID: PMCPMC4028384.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Shimada K, Crother TR, Karlin J, Dagvadorj J, Chiba N, Chen S, et al. Oxidized mitochondrial DNA activates the NLRP3 inflammasome during apoptosis. Immunity. 2012;36(3):401–14. Epub 2012/02/22. https://doi.org/10.1016/j.immuni.2012.01.009. PubMed PMID: 22342844; PubMed Central PMCID: PMCPMC3312986.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Ma S, Ming Y, Wu J, Cui G. Cellular metabolism regulates the differentiation and function of T-cell subsets. Cell Mol Immunol. 2024;21(5):419–35. Epub 2024/04/03. https://doi.org/10.1038/s41423-024-01148-8. PubMed PMID: 38565887; PubMed Central PMCID: PMCPMC11061161.

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Xu DM, Li Q, Yi JX, Cai XJ, Xie L, Fang W, et al. Investigation of Lymphocyte Subsets in Peripheral Blood of Patients with Dyslipidemia. Int J Gen Med. 2021;14:5573–9. Epub 2021/09/23. https://doi.org/10.2147/ijgm.S326628. PubMed PMID: 34548808; PubMed Central PMCID: PMCPMC8449637.

    Article 
    PubMed 

    Google Scholar 

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  • Prevalence and molecular characteristics of Klebsiella pneumoniae harb

    Prevalence and molecular characteristics of Klebsiella pneumoniae harb

    Introduction

    Klebsiella pneumoniae is not only a common pathogen causing nosocomial infections but also an important cause of community-acquired infections, that colonizes human mucosal surfaces such as the nasopharynx and the gastrointestinal tract.1 In recent years, with the prevalence of multidrug-resistant and hypervirulent K. pneumoniae in the world, the incidence rate of K. pneumoniae infections has risen dramatically, such as urinary tract infection, pneumonia, liver abscess, and so on.1 Compared with the classical K. pneumoniae (cKp), hypervirulent K. pneumoniae (hvKp) possesses higher toxicity, which can cause severe infection in immunocompromised people, with high pathogenicity and mortality.2 Although many factors contribute to the high virulence of the hvKp, virulence factors, including capsule, siderophores, lipopolysaccharide, and fimbriae, play an essential role in the pathogenesis of several diseases.3–6 Numerous reports have shown that K1 and K2 serotypes are strongly associated with hvKp among 79 serotypes of K. pneumoniae.7,8 Additionally, some genes, rmpA, iutC, and ybtA, which are responsible for the production of high viscosity, iron-acquiring factors, aerobactin and yersinia actin, respectively, have been associated with the hypervirulence of K. pneumoniae.5,9 Recently, the pks (polyketide synthase) gene cluster, as a new virulence factor, has aroused great public concern.10

    The pks gene cluster is a genetic locus that was first described in some Escherichia coli strains from the B2 phylogroup by Nougayrede in 2006.11 It contains 19 genes (clbA to clbS) with 54 kb and encodes a multi-enzyme complex capable of producing a genotoxin called colibactin. Previous studies have shown that colibactin can cleave host DNA double strands, resulting in cell cycle arrest, DNA damage, and mutations.12,13 Moreover, it increases the likelihood of serious complications of bacterial infections. For instance, production of colibactin by pks+ E. coli exacerbates lymphopenia associated with septicemia and increases the morbidity and mortality of urosepsis and meningitis in immunocompromised mice.14,15 Additionally, pks-positive E. coli has been associated with mutations in colorectal cancer.13,16,17 Subsequently, the pks island has also been found in several other members of the Enterobacteriaceae family, such as Citrobacter koseri, K. pneumoniae, and Enterobacter aerogenes, but was found to be relatively infrequent.18–20 A study in Europe showed that the prevalence of the pks gene cluster was 34% in E. coli strains of phylogenic lineage B2, but only 3.5% in K. pneumoniae clinical isolates.18 While the predominance of pks genes in bloodstream-sourced K. pneumoniae is approximately 25.6% and 26.8% in Taiwan and Changsha, respectively,21,22 little is known about its epidemiology in clinical isolates from cancer patients in China.

    Given the potential role of the pks gene cluster in cancer and its association with hypervirulence, it is crucial to investigate the prevalence and molecular characteristics of pks-positive K. pneumoniae in patients with cancer. This study aimed to address this gap by examining the presence of the pks gene cluster and analyzing the clinical and molecular features of pks-positive K. pneumoniae isolates from patients with cancer in China. Understanding the distribution and characteristics of these isolates will provide valuable insights into their pathogenic potential, and inform clinical practice and epidemic surveillance.

    Materials and Methods

    Bacterial Isolates Collection

    A total of 279 non-repetitive clinical K. pneumoniae isolates were obtained from all cancer patients in China at Cancer Hospital Chinese Academy of Medical Sciences, Shenzhen Center between January 2022 and June 2024. All cases were diagnosed according to the International Classification of Diseases, 10th Revision (ICD-10) and presented with clinical evidence of infection (including clinical symptoms, laboratory indicators, and microbiological evidence). These strains were isolated from diverse specimens, including sputum, blood, urine, drainage fluid, bile, catheter, gastric juice, vaginal secretion, and wound secretion. The collection, isolation, and culture of all clinical specimens must be performed under aseptic conditions and comply with the standards of CLSI (Clinical and Laboratory Standards Institute) guidelines and WHO Laboratory Biosafety Manual. After being isolated and purified, these strains were preserved at −80 °C in a tube containing 20% glycerol for a long time. The full 10 μL loop of colonies after balancing to room temperature were spread onto the Columbia blood agar (Oxoid, Brno, Czech Republic) and incubated at 37 °C for 24 h in 5% CO2 atmosphere. At the same time, the information of these patients was also collected. This study was approved by the hospital ethics committee (Approval No: JS2024-18-1).

    Identification and Antimicrobial Susceptibility Testing

    Isolates were identified by by matrix-assisted laser desorption-ionization time-of-flight mass spectrometry (MALDI-TOF MS; bioMerieux SA, Lyons, France) according to the manufacturer’s protocol. Antimicrobial susceptibility testing was performed using automatic microbial identification and the antibiotic sensitivity analysis system, Vitek 2 Compact (bioMerieux SA, Lyons, France). The results of the antibiotic sensitivity test were determined based on the breakpoints recommended in the guidelines of the 2023 Clinical and Laboratory Standards Institute (CLSI).

    Identification of the Pks Gene Cluster in Clinical K. pneumoniae Isolates

    Genomic DNA was extracted from 279 clinical isolates using a bacterial DNA extraction kit (Tiangen Biochemical Technology, Beijing, China) and quantified using Qubit 4.0 according to the manufacturer’s instructions. PCR was used to detect pks genes (clbA, clbB, clbN, and clbQ). The primers and amplification conditions used in the present study for pks detection are listed in Table 1.11 The PCR products were visualized using 2% agarose gel electrophoresis.

    Table 1 Primers Used for Amplification of the Tested Pks Genes

    The positive of pks gene clusters were verified by blasting whole genomic coding ORFs against E. coli clb reference genes (GenBank accession: AM229678.1)11 with both identity and coverage threshold greater than 80%.

    Whole-Genome Sequencing and Analysis

    A total amount of 0.2 μg of DNA per sample was used as input material for DNA library preparations using the Rapid Plus DNA Lib Prep Kit (RK20208) (Beijing Baiao Innovation Technology, China). Subsequently, the library quality was assessed on the Agilent 5400 system (AATI) and quantified by real-time PCR (1.5 nM). The qualified libraries were pooled and sequenced on Illumina platforms (Illumina, San Diego, CA, USA). Sequencing reads were assembled using Shovill (1.1.0) (https://github.com/tseemann/shovill), and the contamination and completeness of the assembled genome were assessed using CheckM (v1.2.2).23 Whole-genome annotation was performed using the Prokka software (1.14.6).24

    SNP distance and phylogenetic tree construction were performed for pks-positive strains. Phylogenetic analysis was conducted using IQ-TREE software (version 2.3.5) and visualized with the ggtree package in R (version 4.4.2). The K159 strain was used as the reference genome, and core genomic SNPs (cgSNPs) were identified using Snippy (v4.6.0) (https://github.com/tseemann/snippy).

    Sequence types (ST) and serotypes were determined from whole-genome data using Kleborate (2.2.0)25 against pubMLST database26 and Kaptive database.27

    Virulence genes and antibiotic resistance genes were identified using the ABRicate (1.0.1)28 and AMRFinderPlus (3.11.14)29 from genome assembly, respectively.

    Statistical Analysis

    All analyses were performed with the Statistical Package for the Social Sciences version 28.0 (SPSS, Chicago, IL, USA). Significance of differences in frequencies and proportions was tested by the χ2 test or Fisher’s exact test. A P-value <0.05 was considered statistically significant.

    Results

    Clinical Characteristics of Pks-Positive K. pneumoniae

    Among 279 K. pneumoniae isolates, 35 (12.54%) pks gene cluster positive representatives were identified, which were mainly isolated from the sputum (20, 57.14%). The clinical characteristics of the patients who isolated K. pneumoniae isolates are presented in Tables S1 and S2. The average age of patients with pks-positive K. pneumoniae was 59, and most of them were male (27, 77.14%). And the diagnosis of lung cancer (15, 42.86%) was predominant in patients harbouring pks-positive isolates, followed by gastric cancer (3, 8.57%). But comparing with patients infected by pks-negative K. pneumoniae, there was no significant difference in age, specimen source, infections position, and sexes in patients harbouring pks-positive isolates (P > 0.05) (Table 2).

    Table 2 Clinical Data of Patients Infected with Pks-Positive and Pks-Negative K. pneumoniae

    Antimicrobial Susceptibility of Pks-Positive Isolates

    There was no significant difference in rates of susceptibility between the pks-positive and pks-negative K. pneumoniae isolates to most antibiotics, including β-lactam/β-lactamase inhibitors, fluoroquinolones, cephamycin, aminoglycosides, and carbapenems, except for sulfonamides (Tables S3 and S4). For example, the susceptibility rates of cefoperazone sulbactam, piperacillin tazobactam, cefuroxime, ceftazidime, ceftriaxone, cefepime, amikacin were 100%, 85.71%, 74.29%, 91.43%, 85.71%, 88.57%, and 100% in the pks-positive K. pneumoniae, and compared with the pks-negative K. pneumoniae, where the respective rates for these antibiotics were 95.90%, 88.52%, 72.95%, 84.02%, 77.05%, 84.02%, and 98.36% (Table 3). Although there was a tendency that the pks+ K. pneumoniae isolates were less resistant to carbapenem agents tested versus pks-isolates (100% vs 98.36%), the difference was insignificant. Sulfamethoxazole was the only agent to which pks-positive isolates were significantly more susceptible than pks-negative isolates (100% vs 75.82%, P<0.001) (Table 3).

    Table 3 Susceptibility of Pks-Positive and Pks-Negative K. pneumoniae to Antimicrobials

    Molecular Characteristics of Pks-Positive K. pneumoniae

    In this study, whole-genome sequencing of 35 pks+ K. pneumoniae isolates was performed, and the detailed quality assessment results are shown in Table S5. The average genome size of 35 pks+ K. pneumoniae isolates was 6.02 Mbp, and the average GC content was 57.38%. The average largest were 0.72 Mbp, and N50 scaffolds were 0.29 Mbp in length, indicating the high assembling quality. The result of genome sequencing showed that virulence associated serotype K1 (17, 48.57%) was the predominant serotype, and K2 accounted for 25.71% in pks-positive K. pneumoniae (Figure 1). Six other K serotypes (K116 (3), K113 (2), K20 (1), K25 (1), K57 (1), and K62 (1)) accounted for 25.72% of isolates.

    Figure 1 Phylogenetic tree based on SNP sites in core genes of 35 pks-positive strains.

    Among the 35 pks-positive K. pneumoniae, the multilocus sequence typing showed that the predominant sequence types were ST23 (19, 54.29%) and ST65 (8, 22.86%), while another six STs each had no more than 3 strains, ST133 (3, 8.57%), ST268 (1, 2.86%), ST348 (1, 2.86%), ST380 (1, 2.86%), ST592 (1, 2.86%), and ST792 (1, 2.86%) (Figure 1). The whole genomic phylogeny and SNP distance were inferred, and we found that there is no direct and recent transmission (cgSNP differences less than 20) among ST23 and ST65 isolates (Figure 1).

    Virulence genes were prevalent in pks-positive isolates, particularly the siderophore systems (aerobactin, enterobactin, salmochelin, and yersiniabactin) which played different roles in infection within the host. In 35 pks-positive isolates, Enterobactin synthase genes (entAB, fepC) and yersiniabactin siderophore system genes (ybtA/E/P/Q/S/T/U/X, irp1, irp2) were at least 97.14%, meanwhile the aerobactin siderophore synthesis system genes (iucA/B/C/, iutA) and salmochelin genes (iroB/C/D/N) were at least 85.71% (Table 4). Furthermore, rmpA genes, which were the positive regulator of the mucoid phenotype, and peg-344, which could encode an intracellular transporter protein, were, respectively, found in 62.86% and 54.29% of pks-positive isolates (Table 4).

    Table 4 Virulence Genes and Drug Resistance Genes of Pks-Positive K. pneumoniae

    As for antibiotic resistance genes, pks-positive isolates harbored some β-lactamase genes, including blaCTX-M, blaTEM, and blaSHV. Only four isolates proved positive for CTX-M-1 group, and two isolates proved positive for CTX-M-9 group. Additionally, the screen of SHV β-lactamase genes showed that the frequencies of SHV-11, SHV-75, SHV-26, and SHV-207 were 30 (85.71%), 3 (8.57%), 1 (2.86%), and 1 (2.86%), respectively. And only two isolates were blaTEM-1 positive. However, no pks-positive isolates proved positive for the genes that confer resistance towards carbapenems.

    Discussion

    The pks gene island, encoding the genotoxin colibactin, has garnered significant attention due to its ability to induce DNA double-strand breaks and transient G2-M cell cycle arrest in host cells.12 This genotoxic activity suggests that colibactin may contribute to various disease entities, including newborn meningitis, urinary tract infections, bloodstream infections, and potentially cancer development.15,22,30 In addition, some studies reported that the pks-positive E. coli was more highly represented in CRC patients and could promote human CRC development.17,31 Our study is the first to investigate the prevalence and molecular characteristics of K. pneumoniae harboring the pks island in Chinese cancer patients, providing valuable insights into its epidemiology and clinical significance in this specific population.

    Up to now, there have been few epidemic reports on emerging pks-positive K. pneumoniae. In Europe and Iraq, the occurrence of pks-positive K. pneumoniae was 3.5%18 and 7.14%,20 respectively. In this study, the prevalence of the pks gene cluster among K. pneumoniae isolates was 12.54%, which was higher than those reported in the literature. But in two previous studies conducted in Taiwan and Changsha, the positive rates of pks-positive K. pneumoniae isolated from blood was 16.8%32 and 26.8%,22 respectively. And some studies revealed that the prevalence of pks gene in E. coli was high, ranging from 29.2% to 72.7%.31,33,34 Therefore, we found that the epidemiological distribution of pks-positive strains exhibits regional and interspecies differences, which may be associated with environmental, host, and pathogen factors.

    Colibactin encoded by the pks gene cluster has been shown to induce host DNA damage, thus may contribute to higher mutation rates that drive the occurrence of tumors. By analyzing 3668 Dutch samples of different cancer types, a study found that the colibactin was present in a variety of tumors.35 Our findings backed up the above results, which documented pks-positive K. pneumoniae had been isolated from different types of cancer patients. Jens Puschhof et al proved that the pks gene cluster was present at a higher frequency in colorectal cancer compared to other types of cancer.35 And the presence of pks-positive K. pneumoniae has been found in 4–27% colon cancer patients.18,21,32,36 However, our findings revealed that pks-positive K. pneumoniae isolates were predominantly associated with lung cancer patients (42.86%), followed by gastric cancer, which was different from the above researches that reported higher prevalence in colorectal cancer patients. This may be due to the specific patient population and sampling bias, as only parenteral specimens were collected. However, this highlights the potential role of pks-positive K. pneumoniae in various types of cancer, not limited to colorectal cancer. Further studies are needed to elucidate the specific mechanisms by which pks-positive K. pneumoniae contributes to cancer development and progression.

    There are many similarities between pks-positive K. pneumoniae and hvKp. Firstly, previous studies have revealed that hvKp were almost exclusively of serotype K1 or K2, and ST23 and ST65 were predominant sequence types.5,7 On the other hand, the hvKp K1 strains were strongly associated with ST23, while the hvKp K2 strains belong to different STs (ST65, ST86, and others).5,8 In our study, the great majority (74.28%) of pks-positive isolates belonged to K1 or K2 serotype. And all K1 strains belong to ST23, whereas K2 strains were divided into two major clades, ST65 and ST380. To investigate whether there is transmission or possible outbreaks among single ST isolates, whole-genomic phylogeny and SNP distance were inferred, and we found that there is no direct and recent transmission (cgSNP differences less than 20) among ST23 and ST65 isolates, suggesting the patients get these infections from different sources. Two ST133 isolates, k130 and k131, showed almost no cgSNP differences (Figure 1), suggesting direct transmission among their host patients. However, the mechanism of transmission still needs further study. Secondly, another study suggested that hvKp were positive for several virulence factors, such as iucA, iroB, peg-344, rmpA, and so on.5,7 Our study found that pks-positive isolates generally carried several virulence genes. Additionally, the high prevalence of rmpA and peg-344 genes indicates that these isolates may exhibit a mucoid phenotype, which is associated with increased resistance to phagocytosis and host immune responses.5 Therefore we assumed that the emerging pks genotoxic trait is associated with the virulence genes of hvKp. We also found that the pks-positive strains in this study showed high sensitivity to most antibiotics, which is likely due to the fact that most of these isolates belong to K1 and K2 serotype to protect bacteria from phagocytosis and inhibit the host immune response. And compared with pks-negative strains, pks-positive strains showed higher sensitivity to sulfamethoxazole (P<0.05), which provided an important reference for antibiotic treatment. Although the rate of MDR in pks-positive isolates is low at present, the presence of β-lactamase genes, such as blaCTX-M, blaTEM, and blaSHV, indicates that these isolates have the potential to develop multidrug resistance. Therefore, continued surveillance of antimicrobial resistance patterns in pks-positive K. pneumoniae is essential to guide appropriate treatment strategies and prevent the emergence of multidrug-resistant strains.

    While our study provides important insights into the prevalence and molecular characteristics of pks-positive K. pneumoniae in cancer patients, several limitations should be acknowledged. The sample size was relatively small, and only parenteral specimens were included, which may limit the generalizability of our findings. Additionally, the study was conducted in a single center, and further multicenter studies with larger sample sizes are needed to confirm our results.

    Recently, it was described that the exposure to pks-positive E. coli is responsible for mutational signature in colorectal cancer, so it seems that pks-positive bacteria can induce mutation of CRC driver genes and, therefore, pks may become a marker of CRC carcinogenesis and therapy.31 Future research should focus on elucidating the specific mechanisms by which pks-positive K. pneumoniae contributes to cancer development and progression. Additionally, longitudinal studies are needed to monitor the evolution of antimicrobial resistance in these isolates and to develop targeted therapeutic strategies.

    Conclusion

    Our study highlights the potential pathogenicity of pks-positive K. pneumoniae in cancer patients in China, emphasizing the need for close clinical attention and epidemic tracking. The findings underscore the importance of continued surveillance and research to better understand the role of this genotoxic pathogen in cancer-associated infections.

    Ethics Statement

    This study was approved by the ethics committee of Cancer Hospital Chinese Academy of Medical Sciences, Shenzhen Center (Approval No. JS2024-18-1). This study was retrospective and associated with bacterial drug susceptibility and the genetic information of the specimens, hence our ethical petition for exemption from informed consent was accepted. All patients have been informed that their samples will be used for research and have signed informed consent for sample collection. The data of all patients in this study were collected anonymously and ensured the confidentiality of their information. This study was conducted in accordance with the guidelines set out in the Declaration of Helsinki.

    Acknowledgments

    We gratefully acknowledge the support and resources provided by the Microbiology Laboratory, Cancer Hospital Chinese Academy of Medical Sciences, Shenzhen Pathogen Infection Research Alliance (SPIRA) and Department of Clinical Laboratory, Shenzhen Third People’s Hospital.

    Funding

    This research was supported by Sanming Project of Medicine in Shen zhen (No.SZSM202311002) and Science and Technology Program of Shenzhen (Grant Nos. KCXFZ20230731100901003, KJZD20230923115116032, JCYJ20210324131212034).

    Disclosure

    The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

    References

    1. Wang G, Zhao G, Chao X, Xie L, Wang H. The characteristic of virulence, biofilm and antibiotic resistance of Klebsiella pneumoniae. Int J Environ Res Public Health. 2020;17(17):6278. doi:10.3390/ijerph17176278

    2. Choby JE, Howard-Anderson J, Weiss DS. Hypervirulent Klebsiella pneumoniae – clinical and molecular perspectives. J Intern Med. 2020;287(3):283–300. doi:10.1111/joim.13007

    3. Zhang QB, Zhu P, Zhang S, et al. Hypervirulent Klebsiella pneumoniae detection methods: a minireview. Arch Microbiol. 2023;205(10):326. doi:10.1007/s00203-023-03665-y

    4. Chen J, Zhang H, Liao X. Hypervirulent Klebsiella pneumoniae. Infect Drug Resist. 2023;16:5243–5249. doi:10.2147/IDR.S418523

    5. Russo TA, Marr CM. Hypervir ulent Klebsiella pneumoniae. Clin Microbiol Rev. 2019;32(3):e00001–19. doi:10.1128/CMR.00001-19

    6. Zhu J, Wang T, Chen L, Du H. Virulence factors in hypervirulent Klebsiella pneumoniae. Front Microbiol. 2021;12:642484. doi:10.3389/fmicb.2021.642484

    7. Struve C, Roe CC, Stegger M, et al. Mapping the evolution of hypervirulent Klebsiella pneumoniae. mBio. 2015;6(4):e00630. doi:10.1128/mBio.00630-15

    8. Jin M, Jia T, Liu X, et al. Clinical and genomic analysis of hypermucoviscous Klebsiella pneumoniae isolates: identification of new hypermucoviscosity associated genes. Front Cell Infect Microbiol. 2023;12:1063406. doi:10.3389/fcimb.2022.1063406

    9. Russo TA, Olson R, Fang CT, et al. Identification of biomarkers for differentiation of hypervirulent Klebsiella pneumoniae from Classical K. pneumoniae. J Clin Microbiol. 2018;56(9):e00776–18. doi:10.1128/JCM.00776-18

    10. Luo CS, Chen YS, Hu XN, et al. Genetic and functional analysis of the pks gene in clinical Klebsiella pneumoniae Isolates. Microbiol Spectr. 2023;11(4):e0017423. doi:10.1128/spectrum.00174-23

    11. Nougayrède JP, Homburg S, Taieb F, et al. Escherichia coli induces DNA double-strand breaks in eukaryotic cells. Science. 2006;313:848–851. doi:10.1126/science.1127059

    12. Bossuet-Greif N, Vignard J, Taieb F, et al. The colibactin genotoxin generates DNA interstrand cross-links in infected cells. mBio. 2018;9(2):e02393–17. doi:10.1128/mBio.02393-17

    13. Cuevas-Ramos G, Petit CR, Marcq I, Boury M, Oswald E, Nougayrède JP. Escherichia coli induces DNA damage in vivo and triggers genomic instability in mammalian cells. Proc Natl Acad Sci U S A. 2010;107(25):11537–11542. doi:10.1073/pnas.1001261107

    14. Marcq I, Martin P, Payros D, et al. The genotoxin colibactin exacerbates lymphopenia and decreases survival rate in mice infected with septicemic Escherichia coli. J Infect Dis. 2014;210(2):285–294. doi:10.1093/infdis/jiu071

    15. Bakthavatchalu V, Wert KJ, Feng Y, et al. Cytotoxic Escherichia coli strains encoding colibactin isolated from immunocompromised mice with urosepsis and meningitis. PLoS One. 2018;13(3):e0194443. doi:10.1371/journal.pone.0194443

    16. de Souza JB, de Almeida Campos LA, Palácio SB, Brelaz-de-Castro MCA, Cavalcanti IMF. Prevalence and implications of pKs-positive Escherichia coli in colorectal cancer. Life Sci. 2024;341:122462. doi:10.1016/j.lfs.2024.122462

    17. Sadeghi M, Mestivier D, Sobhani I. Contribution of pks+ Escherichia coli (E. coli) to colon carcinogenesis. Microorganisms. 2024;12(6):1111. doi:10.3390/microorganisms12061111

    18. Putze J, Hennequin C, Nougayrède JP, et al. Genetic structure and distribution of the colibactin genomic island among members of the family Enterobacteriaceae. Infect Immun. 2009;77(11):4696–4703. doi:10.1128/IAI.00522-09

    19. Morgan RN, Saleh SE, Farrag HA, Aboulwafa MM. Prevalence and pathologic effects of colibactin and cytotoxic necrotizing factor-1 (Cnf 1) in Escherichia coli: experimental and bioinformatics analyses. Gut Pathog. 2019;11:22. doi:10.1186/s13099-019-0304-y

    20. Hussein MT, Qaysi SA, Rathi MH, Moussa T. prevalence and characterization of some colibactin genes in clinical enterobacteriaceae isolates from Iraqi patients. Baghdad Science Journal. 2020. doi:10.21123/bsj.2020.17.3(Suppl.).1113

    21. Lai YC, Lin AC, Chiang MK, et al. Genotoxic Klebsiella pneumoniae in Taiwan. PLoS One. 2014;9(5):e96292. doi:10.1371/journal.pone.0096292

    22. Lan Y, Zhou M, Jian Z, Yan Q, Wang S, Liu W. Prevalence of pks gene cluster and characteristics of Klebsiella pneumoniae-induced bloodstream infections. J Clin Lab Anal. 2019;33(4):e22838. doi:10.1002/jcla.22838

    23. Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25(7):1043–1055. doi:10.1101/gr.186072.114

    24. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30(14):2068–2069. doi:10.1093/bioinformatics/btu153

    25. Lam MMC, Wick RR, Watts SC, Cerdeira LT, Wyres KL, Holt KE. A genomic surveillance framework and genotyping tool for Klebsiella pneumoniae and its related species complex. Nat Commun. 2021;12(1):4188. doi:10.1038/s41467-021-24448-3

    26. Jolley KA, Maiden MC. BIGSdb: scalable analysis of bacterial genome variation at the population level. BMC Bioinformatics. 2010;11:595. doi:10.1186/1471-2105-11-595

    27. Lam MMC, Wick RR, Judd LM, Holt KE, Wyres KL. Kaptive 2.0: updated capsule and lipopolysaccharide locus typing for the Klebsiella pneumoniae species complex. Microb Genom. 2022;8(3):000800. doi:10.1099/mgen.0.000800

    28. Chen L, Zheng D, Liu B, Yang J, Jin Q. VFDB 2016: hierarchical and refined dataset for big data analysis–10 years on. Nucleic Acids Res. 2016;44(D1):D694–D697. doi:10.1093/nar/gkv1239

    29. Feldgarden M, Brover V, Gonzalez-Escalona N, et al. AMRFinderPlus and the reference gene catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence. Sci Rep. 2021;11(1):12728. doi:10.1038/s41598-021-91456-0

    30. Lu MC, Chen YT, Chiang MK, et al. Colibactin Contributes to the Hypervirulence of pks+ K1 CC23 Klebsiella pneumoniae in Mouse Meningitis Infections. Front Cell Infect Microbiol. 2017;7:103. doi:10.3389/fcimb.2017.00103

    31. Joo JE, Chu YL, Georgeson P, et al. Intratumoral presence of the genotoxic gut bacteria pks+ E. coli, Enterotoxigenic Bacteroides fragilis, and Fusobacterium nucleatum and their association with clinicopathological and molecular features of colorectal cancer. Br J Cancer. 2024;130(5):728–740. doi:10.1038/s41416-023-02554-x

    32. Chen YT, Lai YC, Tan MC, et al. Prevalence and characteristics of pks genotoxin gene cluster-positive clinical Klebsiella pneumoniae isolates in Taiwan. Sci Rep. 2017;7:43120. doi:10.1038/srep43120

    33. Kamali Dolatabadi R, Fazeli H, Emami MH, et al. Phenotypic and genotypic characterization of clinical isolates of intracellular adherent-invasive Escherichia coli among different stages, family history, and treated colorectal cancer patients in Iran. Front Cell Infect Microbiol. 2022;12:938477. doi:10.3389/fcimb.2022.938477

    34. Yoshikawa Y, Tsunematsu Y, Matsuzaki N, et al. Characterization of colibactin-producing Escherichia coli isolated from Japanese patients with colorectal cancer. Jpn J Infect Dis. 2020;73(6):437–442. doi:10.7883/yoken.JJID.2020.066

    35. Pleguezuelos-Manzano C, Puschhof J, Rosendahl Huber A, et al. Mutational signature in colorectal cancer caused by genotoxic pks+ E. coli. Nature. 2020;580(7802):269–273. doi:10.1038/s41586-020-2080-8

    36. Lam MMC, Wyres KL, Duchêne S, et al. Population genomics of hypervirulent Klebsiella pneumoniae clonal-group 23 reveals early emergence and rapid global dissemination. Nat Commun. 2018;9(1):2703. doi:10.1038/s41467-018-05114-7

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  • Headache Common After Hemorrhagic Stroke, Often Overlooked

    Headache Common After Hemorrhagic Stroke, Often Overlooked

    MINNEAPOLIS — Roughly half of the patients experience headaches following a hemorrhagic stroke, with more than one third reporting severe pain — yet headache management remains a largely neglected aspect of post-stroke care.

    “What we found is that headaches are far more common after hemorrhagic stroke than people might think. For many, the headache lingers even for months-years.” study investigator Bradley Ong, MD, a neurology resident at the Cleveland Clinic in Cleveland, told Medscape Medical News.

    Patients often mention headaches while recovering from hemorrhagic stroke, but post-stroke headache tends to not be a focus of care, Ong added.

    The findings were presented at American Headache Society (AHS) Annual Meeting 2025.

    Knowledge Gap

    “So much of stroke research focuses on the acute event, like how to stop the bleeding, and reduce disability, but I kept thinking about what happens after,” said Ong.

    Ong added that when he looked into the literature he was “struck by how little we know about post-stroke headaches — particularly following hemorrhagic stroke.” This aspect of the patient experience has been largely overlooked, with surprisingly little research available on post-stroke headaches, particularly following hemorrhagic stroke.

    The researchers conducted a systematic review and meta-analysis, identifying 24 studies published through December 2024. The databases included MEDLINE (1964-2024), Embase (1947-2024), and Central (1996-2024).

    In all there were data on 4671 individuals who experienced a hemorrhagic stroke and were assessed for acute and chronic post-stroke headache. The majority patients were women (58.2%) living in Europe (70.8%) with a mean age of 56.9 years.

    Results showed 47% (95% CI, 39%-87%) developed a headache after a hemorrhagic stroke, with 56% of patients (95% CI, 42%-97%) developing an acute or subacute headache within 30 days post-stroke, and 39% of patients (95% CI, 30%-48%) developing a chronic headache more than 3 months post-stroke. Patients developed severe headache in over one third of cases (36.9%; 95% CI, 14.4%-67.0%), which was defined as a Numeric Rating Scale score ≥ 8.

    Among patients who developed post-stroke headache, 48% experienced it following a subarachnoid hemorrhage (SAH), whereas 38% developed it after an intracerebral hemorrhage (ICH).

    In both cases, post-stroke headache often progressed to a chronic, persistent condition, with nearly 38% continuing into the chronic phase, the researchers reported.

    “In aneurysmal SAH, most headaches had a migrainous phenotype; in contrast, most headaches in ICH had tension-type features, which ranged from moderate to severe in intensity,” the researchers noted.

    Little Clinical Guidance

    The likelihood of developing a post-stroke headache after a hemorrhagic stroke was influenced by several factors. Male sex was associated with lower odds of headache (pooled odds ratio [OR], 0.82; 95% CI, 0.68-0.99), while a prior history of headache significantly increased the risk (pooled OR, 4.83; 95% CI, 2.10-11.10).

    Although the researchers observed considerable heterogeneity among the studies reviewed, the meta-regression analysis showed no statistically significant differences related to the risk of bias, region, population source, or human development index.

    Headache does not get the same level of urgency in neurology as other symptoms such as weakness, speech problems, or seizures. “But for patients, these headaches are very real and can be debilitating. We just haven’t done enough to listen to that part of their recovery,” said Ong.

    More prospective studies are necessary to improve the understanding of headaches, which frequently receive insufficient attention in research. Ong emphasized that treating headaches in stroke patients is challenging because common over-the-counter medications like nonsteroidal anti-inflammatory drugs are often unsuitable for those who have suffered a brain bleed.

    “Long-term follow-up data would also be incredibly valuable, especially since a lot of these patients continue to struggle with headaches well after discharge,” said Ong.

    Clinicians should be more intentional in including headache treatment as a part of stroke rehabilitation, he added.

    “Right now, there’s very little guidance on how to even define post-stroke headache, and that makes it harder to study and treat. Most of the existing research also comes from a few regions in the world, so we’re missing a truly global picture. We need better, more consistent data from diverse populations to really understand how common this is and what treatments might help,” he added.

    More Data Needed

    Commenting on the research, Robert G. Kaniecki, MD, founder and director of the UPMC Headache Center in Pittsburgh, noted that the study’s size and scope were strengths. He added that the data are valuable because they specifically focus on patients who have experienced hemorrhagic stroke and subsequently develop headaches.

    “Most prior papers have addressed headaches following stroke of any kind — hemorrhagic or the more common ischemic nonhemorrhagic stroke cases,” Kaniecki told Medscape Medical News.

    The finding that acute or subacute headache affected 56% of patients was surprising — Kaniecki said he had anticipated a higher rate — while the 39% prevalence of chronic headache was also unexpected, as he had predicted it would be lower.

    One limitation in the research was that the studies were mostly published before the third edition of the International Classification of Headache Disorders (ICHD-3) were developed and post-stroke headache was defined with specific criteria, Kaniecki said.

    More data on patients with preexisting headaches are also needed, Kaniecki said, and he is interested in knowing how many patients in the study with post-stroke depression ended up developing headaches. “Post-stroke depression is common, and headache a frequent symptom reported by patients with depression,” Kaniecki said.

    Ong reported no relevant financial relationships. Kaniecki reported no relevant financial relationships.

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  • A Comparative Study on the Prevalence of Helicobacter pylori Infection

    A Comparative Study on the Prevalence of Helicobacter pylori Infection

    Introduction

    Helicobacter pylori (H. pylori) is increasingly recognized as a significant pathogen primarily associated with gastrointestinal diseases. While it colonizes the stomach lining, it triggers a systemic immune response, potentially impacting various bodily systems beyond the gastrointestinal tract. This immune response may contribute to the development of diseases in other regions, including ocular conditions.1

    Central Serous Chorioretinopathy (CSCR) is characterized by serous detachment of the neurosensory retina, primarily affecting young men (85% of cases are in males aged 25 to 45). While the acute form is clinically evident, diagnosing CSCR in older patients can be challenging due to similarities with age-related macular degeneration (AMD) and complications like choroidal neovascularization (CNV).2,3

    CSCR causes focal detachments of the neurosensory retina and/or RPE over thickened choroid. It is classified as acute or chronic, with SRD persisting beyond 3–6 months considered chronic. While most cases resolve within this timeframe, up to 50% experience recurrences, multifocal disease, or persistent SRD, leading to potential vision loss.4

    The relationship between CSCR and corticosteroids is complex. While glucocorticoids can effectively reduce macular edema from various causes, they may paradoxically exacerbate subretinal fluid accumulation in CSCR patients. This highlights the importance of careful management when considering corticosteroid treatment.5

    The pathophysiology of CSCR is not fully understood, but several risk factors have been identified, including genetics, corticosteroid use, hormonal factors, pregnancy, cardiovascular risk, stress, and obstructive sleep apnea.6

    Research has also indicated a potential link between H. pylori infection and ocular diseases, including blepharitis, glaucoma, anterior uveitis, and CSCR.7

    The initial hypothesis linking H. pylori to CSCR was proposed by Sacca, who noted a recurrence in a CSCR patient that correlated with changes in H. pylori test results. Following eradication therapy, significant improvements in retinal structure and visual acuity were observed. Various studies have since investigated this relationship, but findings have been inconsistent.8

    A 2016 meta-analysis of risk factors for CSC included three studies on the association between CSC and Helicobacter pylori, revealing a significant correlation between the infection and CSC.9

    This collection of studies highlights the multifactorial nature of both Helicobacter pylori (H. pylori) infection and CSCR, emphasizing the need for further investigation to clarify their potential relationship and implications for ocular health. The proposed association underscores the importance of considering systemic factors in the diagnosis and management of CSCR. The current study is a cross-sectional observational and interventional study design that aims to investigate this association by comparing the prevalence of H. pylori in patients with CSCR to a control group without the condition. Additionally, it will explore correlations between H. pylori infection and various clinical features of CSCR, such as recurrence rates and visual outcomes. By employing a randomized approach, this study seeks to provide more reliable data that enhances understanding of H. pylori’s potential role in the pathogenesis of CSCR and its broader systemic implications.

    Materials and Methods

    Study Design

    This study is a 3-month cross-sectional observational and interventional study, from November 2024 to January 2025, aimed at evaluating the association between H. pylori infection and CSCR. The study also assesses the effect of treatment on visual acuity, and studies the side effects of the treatment regimen.

    Study Population

    Inclusion Criteria

    Inclusion criteria are patients aged 25 to 60 years and the ability to provide informed consent. The study included a total of 80 participants divided into three groups:

    Group A

    Twenty patients diagnosed with recurrent CSCR episodes.

    Group B

    Twenty patients diagnosed with CSCR for the first time and not known to have had previous episodes.

    Control Group

    Forty age- and sex-matched subjects without CSCR.

    Exclusion criteria are history of recent ocular surgery or trauma, a known history of gastric surgery or other gastrointestinal disorders affecting H. pylori testing and the current use of corticosteroids or immunosuppressive medications.

    Recruitment

    Patients diagnosed with CSCR were recruited from Benha university hospitals and Ebsar Eye Center. Control subjects were selected from individuals visiting the two hospitals. Informed consent was obtained from all participants prior to their inclusion in the study.

    Data Collection

    Clinical data, including demographic information and medical history, were recorded. A comprehensive ophthalmologic examination was performed for all participants, including best-corrected visual acuity (BCVA) measured using Snellen’s chart and converted to LogMAR, fundus examination, fundus fluorescein angiography (FFA) and optical coherence tomography (OCT) including subretinal fluid (SRF) and choroidal neovascularization (CNV) to assess retinal structure and confirm the diagnosis of central serous chorioretinopathy (CSCR).

    Optical coherence tomography (OCT) imaging was performed using the Optovue RTVue XR Avanti OCT system (Optovue, Inc., Fremont, CA, USA). The device utilizes a spectral-domain OCT (SD-OCT) technology with a scan speed of 70,000 A-scans per second and a depth resolution of 5 µm. Standardized scanning protocols were applied to assess retinal thickness, choroidal thickness, and the presence of subretinal fluid. All scans were performed under identical lighting conditions and were reviewed for quality assurance to minimize segmentation errors and artifacts.

    Diagnosis of H. pylori was confirmed through gut biopsies obtained via UGI endoscopy, followed by histopathological examination and culture, as well as detection of H. pylori antigen in stool using a PCR test. A positive result was defined by the presence of H. pylori antigen in stool and a positive biopsy result.

    H. pylori Treatment Regimen

    Patients diagnosed with H. pylori infection from the three groups received a 10-day sequential therapy consisting of esomeprazole 40 mg twice daily and amoxicillin 1 g twice daily for the first 5 days, followed by esomeprazole 40 mg twice daily, levofloxacin 500 mg once daily, and tinidazole 500 mg twice daily for the remaining 5 days. Post-treatment visual outcomes were assessed, and any adverse effects related to the therapy were recorded.

    Sample Size

    The sample size was determined based on an expected prevalence of Helicobacter pylori infection in patients with central serous chorioretinopathy (CSCR) compared to a control population. The minimum required sample size was calculated using the G*Power software. The analysis indicated a minimum of 20 participants per group. A total of 80 participants were enrolled, distributed as follows: 20 in the recurrent CSCR group, 20 in the single-episode CSCR group, and 40 in the control group.

    Statistical Analysis

    Data were analyzed using the IBBM Statistical Package for the Social Sciences (SPSS). To compare the prevalence of H. pylori infection among the three groups (Group A, Group B, and Control), chi-square tests were employed, with a p-value of <0.05 considered statistically significant. Continuous variables, such as best-corrected visual acuity (BCVA), were analyzed using t-tests or Mann–Whitney U-tests, depending on the distribution of the data. Categorical variables, including recurrence rates, were assessed using chi-square tests or Fisher’s exact tests. Additionally, odds ratios (OR) with 95% confidence intervals (CIs) were calculated to assess the strength of the association between H. pylori infection and CSCR. The overall significance level for all tests was set at p < 0.05.

    Results

    Demographics

    The study enrolled 80 participants, including 20 patients with recurrent Central Serous Chorioretinopathy (CSCR) in Group A, 20 patients with a single episode of CSCR in Group B, and 40 control subjects.

    The mean ages were 35 ± 10 years (range: 22–48) for Group A, 38 ± 9 years (range: 26–50) for Group B, and 36 ± 11 years (range: 20–52) for the control group. The gender distribution showed a predominance of males in all groups: 17/3 in Group A, 15/5 in Group B, and 30/10 in the control group. Notably, Group A had an average of 3.5 previous episodes, while Group B had 1.0 (Table 1).

    Table 1 Demographic Data

    Clinical Features of CSCR

    Table 2 presents various clinical features of Central Serous Chorioretinopathy (CSCR) in two groups: those with recurrent episodes (Group A) and those with a single episode (Group B). Group A had significantly more poor outcomes, with a mean best-corrected visual acuity (BCVA) of 0.4 ± 0.2 logMAR compared to 0.2 ± 0.1 logMAR in Group B (p = 0.01). Additionally, Group A exhibited higher rates of retinal pigment epithelium (RPE) detachment (60% vs 30%, p = 0.02) and choroidal neovascularization (40% vs 15%, p = 0.01).

    Table 2 Clinical Features of CSCR

    Prevalence of H. pylori Infection

    Figure 1, Table 3 compares the prevalence of Helicobacter pylori (H. pylori) infection among the three groups. In Group A, 15 out of 20 patients (75%) tested positive for H. pylori, significantly higher than Group B, where 8 out of 20 patients (40%) were positive (p = 0.03, OR = 4.5). The control group showed a 30% prevalence (12 out of 40), with significant differences observed: Group A vs Control (p < 0.001, OR = 7.5) and Group B vs Control (p = 0.02, OR = 1.56). These findings suggest a strong association between H. pylori infection and recurrent CSCR, with a markedly higher odds ratio in Group A, while the association in Group B is moderate.

    Table 3 Prevalence of H. pylori Infection Among the Three Groups

    Figure 1 Prevalence of H. pylori infection among the three groups.

    The analysis highlights a significant association between H. pylori infection and more poor visual outcomes in patients with CSCR. The higher prevalence of subretinal fluid (SRF) in H. pylori-positive patients suggests that infection may exacerbate disease severity. This underscores the potential role of H. pylori in CSCR pathogenesis and the need for targeted management strategies in H. pylori-positive patients (Table 4).

    Table 4 Correlation of H. pylori Infection with CSCR Characteristics Across the Three Groups

    Figure 2 shows FFA of a case of CSCR and positive for H. pylori.

    Figure 2 FFA of a case of CSCR and positive for H. pylori.

    Figure 3 shows OCT of the same case of CSCR of Figure 2.

    Figure 3 OCT images of the same case of CSCR of Figure 2.

    Treatment Regimens for H. pylori Infection

    The treatment regimens for Helicobacter pylori (H. pylori) infection were evaluated in different groups. Post-treatment outcomes indicate significant improvements in visual acuity for Groups A and B, while the control group exhibited no significant change. Group A demonstrated a decrease in the mean Best-Corrected Visual Acuity (BCVA) from 0.4 ± 0.2 to 0.3 ± 0.1 (p < 0.001), reflecting a 50% recurrence rate and a 70% improvement in visual symptoms. Similarly, Group B showed a notable improvement, with BCVA improving from 0.3 ± 0.1 to 0.2 ± 0.1 (p = 0.03) and a 10% recurrence rate. In contrast, the control group maintained a stable BCVA (0.2 ± 0.1 both pre- and post-treatment, p = 0.21). These findings highlight the significant impact of treating H. pylori infection on visual acuity outcomes in patients with Central Serous Chorioretinopathy (CSCR), while no such benefit was observed in the control group Figure 4.

    Figure 4 Mean BCVA pre & post-treatment of H. pylori infection.

    Figure 5 shows 2 weeks post-treatment of H. pylori infection in the same case as Figure 2, OCT showing minimal subretinal fluid.

    Figure 5 2 weeks post-treatment of H. pylori Infection of the same case of Figure 2, OCT showing minimal subretinal fluid.

    Figure 6 shows 6 weeks post-treatment of H. pylori infection of the same case as Figure 2, OCT showing complete resolution of subretinal fluid.

    Figure 6 6 weeks post-treatment of H. pylori infection of the same case of Figure 2, OCT showing complete resolution of subretinal fluid.

    Figure 7 summarizes the side effects associated with H. pylori treatment among the three groups: Group A, Group B, and the Control Group. Group A reported the highest prevalence of side effects, with 30% experiencing nausea and 25% suffering from diarrhea, alongside 5% reporting other minor side effects such as abdominal discomfort. Group B showed a slightly lower incidence, with 20% reporting nausea and 15% vomiting, while other side effects occurred in 2% of patients. The Control Group demonstrated a comparable side-effect profile, with 25% reporting nausea, 10% vomiting, and 3% experiencing other minor effects. Statistical analysis revealed no large differences in side effects across the groups, highlighting the need for careful monitoring to ensure patient adherence while managing adverse effects effectively.

    Figure 7 Side effects of H. pylori treatment.

    Discussion

    This study provides valuable insights into the relationship between Helicobacter pylori (H. pylori) infection and Central Serous Chorioretinopathy (CSCR), particularly in distinguishing between recurrent and single-episode cases. The findings suggest a significant association between H. pylori infection and recurrent episodes of CSCR, with 75% of patients in Group A testing positive for the infection compared to 40% in Group B and 30% in the control group.

    This aligns with Kanda et al,10 indicating that systemic factors, including H. pylori, may contribute to ocular conditions, suggesting a multifactorial etiology for CSCR.

    Previous studies have also reported a higher prevalence of H. pylori in patients with chronic ocular conditions. For instance, a study by Kocamaz MF et al11 demonstrated that H. pylori infection was significantly associated with various retinal disorders, emphasizing the need to consider systemic infections when evaluating retinal pathologies.

    Similarly, a meta-analysis by Gravina AG et al12 highlighted a potential link between H. pylori and vascular complications, supporting the notion that H. pylori may play a role in the pathogenesis of retinal conditions.

    The clinical features observed in this study further emphasize the severity of recurrent CSCR, as evidenced by poorer mean best-corrected visual acuity (BCVA), higher recurrence rates, and increased incidence of retinal pigment epithelium (RPE) detachment and visual disturbances in Group A.

    These findings are consistent with prior studies that noted worse visual outcomes in patients with recurrent CSCR episodes, such as the research conducted by Arora et al,13 which reported that patients with multiple episodes exhibited significant visual impairment compared to those with single episodes.

    In terms of treatment, the high eradication rates of H. pylori achieved in both groups (85% in Group A and 90% in Group B) are comparable to findings from previous studies, such as the work by Wu DW et al,7 which reported similar eradication rates using triple and quadruple therapy regimens.

    Notably, post-treatment outcomes revealed significant improvements in BCVA and a reduction in recurrence rates, particularly in Group A, which echo findings from Goté et al14 that demonstrated enhanced visual outcomes following H. pylori eradication in patients with related ocular conditions.

    While this study aligns with existing literature, it also highlights the importance of further research to clarify the underlying mechanisms connecting H. pylori to CSCR and to assess the long-term benefits of H. pylori treatment in preventing recurrences. Future studies should explore larger sample sizes and consider additional systemic factors that may influence the pathogenesis of CSCR, as understanding these relationships could enhance clinical management strategies for patients affected by this retinal disorder. Study limitations also include potential patient recall bias.

    In conclusion, the findings of this study, supported by previous research, underline the relevance of systemic infections, such as H. pylori, in the context of ocular health and CSCR. This association should also be considered when managing patients with H. pylori infection.

    Conclusion

    This study establishes a significant association between Helicobacter pylori (H. pylori) infection and Central Serous Chorioretinopathy (CSCR), particularly emphasizing its prevalence in patients experiencing recurrent episodes of the condition. The results demonstrated that patients with recurrent CSCR had a notably higher incidence of H. pylori infection compared to those with a single episode and control subjects. Furthermore, the clinical characteristics of CSCR were significantly more severe in the recurrent group, highlighting the impact of H. pylori on visual acuity, recurrence rates, and associated ocular complications.

    The treatment regimens for H. pylori infection in this study achieved high eradication rates, improved visual outcomes, and reduced CSCR recurrence, emphasizing their potential role, particularly in recurrent cases.

    Overall, this study highlights the importance of considering systemic factors, such as H. pylori infection, in the diagnosis and treatment of CSCR. Given the implications for ocular health and disease management, further research is warranted to explore the underlying mechanisms linking H. pylori to CSCR and to evaluate the long-term effects of H. pylori treatment on the prevention of CSCR recurrences.

    Abbreviations

    H. pylori, Helicobacter pylori; CSCR, central serous chorioretinopathy; OCT, Optical coherence tomography; AMD, age-related macular degeneration; CNV, choroidal neovascularization; BCVA, best-corrected visual acuity; FFA, fundus fluorescein angiography; PCR, Polymerase Chain Reaction; SRF, Sub-Retinal Fluid; UGI, Upper Gastrointestinal.

    Data Sharing Statement

    The data supporting the present findings are contained within the manuscript.

    Ethics Approval and Informed Consent

    This study was conducted at Benha University Hospital in accordance with the ethical principles outlined in the Declaration of Helsinki. Informed consent was obtained from all participants, and the study protocol was approved by the Institutional Review Board/Ethics Committee. All the patients who participated in the study provided written informed consent.

    Consent to Participate

    All the patients who participated in the study gave their written consent.

    Author Contributions

    All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

    Funding

    Authors did not receive any external funding for this work.

    Disclosure

    The authors declare no competing interests in this work.

    References

    1. Bravo D, Hoare A, Soto C, Valenzuela MA, Quest AF. Helicobacter pylori in human health and disease: mechanisms for local gastric and systemic effects. World J Gastroenterol. 2018;24(28):3071–3089. doi:10.3748/wjg.v24.i28.3071

    2. Zhang X, Lim CZF, Chhablani J, Wong YM. Central serous chorioretinopathy: updates in the pathogenesis, diagnosis and therapeutic strategies. Eye Vis. 2023;10(1):33–66. doi:10.1186/s40662-023-00349-y

    3. Semeraro F, Morescalchi F, Russo A, et al. Central serous chorioretinopathy: pathogenesis and management. Clin Ophthalmol. 2019;13:2341–2352. doi:10.2147/opth.S220845

    4. Chhablani J, Cohen FB. Multimodal imaging-based central serous chorioretinopathy classification. Ophthalmol Retina. 2020;4(11):1043–1046. doi:10.1016/j.oret.2020.07.026

    5. Behar-Cohen F, Zhao M. Mineralocorticoid pathway in retinal health and diseases. Br J Pharmacol. 2022;179(13):3190–3199. doi:10.1111/bph.15770

    6. Zarnegar A, Ong J, Matsyaraja T, Arora S, Chhablani J. Pathomechanisms in central serous chorioretinopathy: a recent update. Int J Retina Vitreous. 2023;9(1):3–9. doi:10.1186/s40942-023-00443-2

    7. Wu DW, Jiang FP, Ge G, Zhang MX. Association between central serous chorioretinopathy and Helicobacter pylori infection: a systematic review and meta-analysis. Int J Ophthalmol. 2024;17(6):1120–1127. doi:10.18240/ijo.2024.06.18

    8. Jain P, Agarwal S, Agrawal A. A clinical trial to study outcome in patients of central serous chorioretinopathy by identification and management of risk factors. J Evol Med Dent Sci. 2019;8(28):2251–2255. doi:10.14260/jemds/2019/493

    9. Liu B, Deng T, Zhang J. Risk factors for central serous chorioretinopathy: a systematic review and meta-analysis. Retina. 2016;36(1):9–19. doi:10.1097/iae.0000000000000837

    10. Kanda P, Gupta A, Gottlieb C, Karanjia R, Coupland SG, Bal MS. Pathophysiology of central serous chorioretinopathy: a literature review with quality assessment. Eye. 2022;36(5):941–962. doi:10.1038/s41433-021-01808-3

    11. Kocamaz MF, Sahin G, Hosnut FO, Kocamaz NG, Ozer PA, Sengun A. OCT evaluation of retinal parameters in pediatric gastritis patients with helicobacter pylori. Beyoglu Eye J. 2021;6(4):290–297. doi:10.14744/bej.2021.42104

    12. Gravina AG, Zagari RM, De Musis C, Romano L, Loguercio C, Romano M. Helicobacter pylori and extragastric diseases: a review. World J Gastroenterol. 2018;24(29):3204–3221. doi:10.3748/wjg.v24.i29.3204

    13. Arora S, Maltsev DS, Sahoo NK, et al. Visual acuity correlates with multimodal imaging-based categories of central serous chorioretinopathy. Eye. 2022;36(3):517–523. doi:10.1038/s41433-021-01788-4

    14. Goté JT, Singh SR, Chhablani J. Comparing treatment outcomes in randomized controlled trials of central serous chorioretinopathy. Graefes Arch Clin Exp Ophthalmol. 2023;261(8):2135–2168. doi:10.1007/s00417-023-05996-4

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  • Associations between atherogenic indexes, remnant cholesterol and gest

    Associations between atherogenic indexes, remnant cholesterol and gest

    Introduction

    Gestational diabetes mellitus (GDM) refers to any form of glucose intolerance with onset or first recognition in the perinatal period.1 Various studies have revealed that GDM could contribute to adverse pregnancy outcomes for both mothers and their offspring. For mothers, GDM patients are at higher risks of subsequent GDM, cardiovascular disease, dysglycemia, and type 2 diabetes.2–4 For their offspring, GDM could bring out neonatal macrosomia, childhood obesity and metabolic syndrome.5 Moreover, the majority of pregnant women who experience GDM are young, which could affect their life for a longer time.

    The prevalence of GDM is 1%−28% worldwide, with significant variations observed across different populations.6 These differences can be attributed to several factors, including geographic and ethnic predisposition, screening strategies and diagnostic criteria, as well as the varying risk factors.7 With improvements in living conditions and strengthened nutrition during pregnancy, the prevalence of GDM is increasing year by year.8 Although there are plenty of studies have explored the risk factors of GDM on genetic, lifestyle, diet, and other factors, the pathogenesis of GDM is still unclear. Current studies emphasized placental hormone-driven insulin resistance, β-cell dysfunction, and low-grade inflammation may collectively contribute to GDM development.9

    Population GDM risk is increasing with the high prevalence of obesity. To provide sufficient energy for fetal growth, blood lipid levels rise as pregnancy progresses.10 Lipid metabolism is closely related to glycometabolism, which is regulated by insulin. Therefore, dyslipidemia has the potential to induce insulin resistance and the occurrence of GDM. Previous studies have demonstrated the relationships between lipid biomarkers and the risk of GDM. A meta-analysis based on 292 studies interpreted that blood triglyceride (TG) concentrations were significantly different between mothers with GDM and mothers without GDM.11 A few studies showed that elevated serum TG levels and/or decreased high-density lipoprotein cholesterol (HDL−C) could contribute to the development of GDM.12–16 These studies prompted that TG/HDL−C could be potential indicators of GDM, which is one of the atherogenic indexes.17

    Currently, little information has been given to the risk of GDM derived from atherogenic indices,18 which were originally calculated to evaluate the risk of atherosclerosis.19 Notably, another indicator of dyslipidemia of remnant cholesterol, which refers to the cholesterol content within triglyceride-rich lipoprotein remnants (including very-low-density lipoprotein [VLDL], intermediate-density lipoprotein [IDL], and chylomicron remnants), may relate to the occurrence GDM. The connection between remnant cholesterol and the risk of type 2 diabetes has been well-documented, but evidence regarding its role in GDM risk remains insufficient.20 While emerging studies have indicated a positive association between remnant cholesterol and GDM, none have specifically examined the predictive effect of remnant cholesterol on GDM risk.21–23 Moreover, although dyslipidemia is a potential indicator of GDM as mentioned above, GDM patients might have abnormal atherogenic indices and remnant cholesterol but normal serum lipids at the same time. Therefore, it is still necessary to apply atherogenic indices for the assessment of GDM risk. To fill this gap, this study aimed to investigate the predictive effects of atherogenic indices and remnant cholesterol on the risk of GDM.

    Materials and Methods

    Study Population

    This retrospective study was conducted at the Maternal and Child Health Hospital of Hubei Province, which is one of the largest tertiary hospitals focusing on maternal and child health care in Wuhan City, China. This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Maternal and Child Health Hospital of Hubei Province (2021IECXM005). First, the entire list of inpatients who delivered at this hospital from December 2020 to March 2022 was exported from the clinical information system. Second, patients whose outpatient examination data were complete were reviewed and selected. Complete data were defined as containing the essential independent variables of blood lipid indicators and the outcome variable of GDM diagnosis. There were a total of 6946 inpatients who underwent blood lipid examination in the 1st (0~13+6 weeks) or 2nd (14~27+6 weeks) trimester. Third, after removing missing maternal age (essential covariates, N = 26), missing family history of diabetes (essential covariates, N = 33), missing records of in vitro fertilization (IVF) (essential covariates, N = 4), missing body mass index (BMI) (essential covariates, N = 32), multiple pregnancies (have different risk of GDM compared to singleton pregnancies, N = 135), missing oral glucose tolerance test (OGTT) results (outcome measurement, N = 85), and type 1 or type 2 diabetes (pre-gestational dysglycemia confounders, N = 12), 6619 participants were included in this study.

    Data Collection

    Clinical data were exported from the Hospital’s electronic medical records from the clinical information system. General personal information was collected including maternal age, family history of diabetes (one or more clinically diagnosed diabetes patients within three generations), reproductive history, gestational weight gain, IVF, fasting plasma glucose (FPG) in the 1st trimester of pregnancy, and pre-pregnancy BMI (calculated by self-reported pre-pregnancy weight and height). Specifically, pre-pregnancy BMI was divided into four categories according to the Chinese standard of obesity: underweight (BMI < 18.5), healthy weight (18.5−23.9), overweight (24−27.9), and obese (≥28). Gestational weight gain was classified as insufficient, normal, or excessive according to the standard of recommendation for weight gain during pregnancy released by the Health Industry Standards of the People’s Republic of China.24

    Serum lipids, including TG, total cholesterol (TC), HDL−C, and low-density lipoprotein cholesterol (LDL−C) were obtained during the 1st or 2nd trimester of pregnancy. Atherogenic indices including TG/HDL−C, TC/HDL−C, and LDL−C/HDL−C, and remnant cholesterol were regarded as independent variables. All of the independent variables were classified by four interquartile ranges (IQRs).

    The dependent variable of GDM was diagnosed according to the recommendations of the International Association of the Diabetes and Pregnancy Study Groups Consensus Panel.25 A 75 g OGTT was performed at 24−28 weeks of gestation on the following criteria: fasting plasma glucose ≥5.1 mmol/L, and/or 1-hour plasma glucose ≥10.0 mmol/L, and/or 2-hour plasma glucose ≥8.5 mmol/L. GDM is diagnosed if one or more of the following glucose values is exceeded.

    Data Analysis

    The normality of continuous independent variables was examined by Shapiro–Wilk tests. Nonnormal continuous variables were classified into four categories by IQR or summarized as medium (P25, P75). The differences in the prevalence of GDM among subgroups of general personal variables and independent variables were examined by Chi-square tests for categorical variables and Wilcoxon–Mann–Whitney tests for continuous variables. Logistic regression analyses were performed to examine the associations between the independent variables and the risk of GDM. First, the risk of GDM was assessed by several unadjusted logistic regression models for the independent variables, including TC, TG, HDL−C, LDL−C, TG/HDL−C, TC/HDL−C, LDL−C/HDL−C, and remnant cholesterol. The corresponding odds ratios (ORs) and 95% confidence intervals (CIs) were obtained. Second, based on unadjusted logistic regression analysis, the associations between serum lipids and GDM were further adjusted for general personal variables including maternal age, family history of diabetes, parity history, pre-pregnancy BMI, IVF, FPG, and gestational weight gain. To explore the causal relationship between atherogenic indices, remnant cholesterol, and the risk of GDM, stratified analysis of 1st trimester and 2nd trimester were conducted in this study. The normality examinations, Chi-square tests, Wilcoxon–Mann–Whitney tests, and logistic regression analysis were performed with SAS 9.4 (SAS Institute Inc., Cary, NC, USA). Furthermore, the risk of GDM was predicted by nomogram analysis and decision curve analysis (DCA) to gauge the net benefit of identifying high-risk patients that ought to have intervention and the net reduction of unnecessary interventions. The potential nonlinear relationships between the atherogenic indices, remnant cholesterol and the risk of GDM were examined by the restricted cubic splines (RCS). We adopted an RCS with 4 knots, and the media of the atherogenic indices and remnant cholesterols were used as references to obtain the ORs. Nomogram analysis, DCA, and RCS regression analysis were performed with R 4.2.1 (The R Foundation for Statistical Computing, Vienna, Austria). A two-sided P < 0.05 was regarded as statistically significant.

    Results

    The results showed that the prevalence of GDM was 31.03% (Table 1). Nearly half of the participants were mothers aged 30−34 years (45.34%), and the median age of the participants was 31 years. The prevalence of GDM among the subgroups stratified by age was significantly different (P < 0.0001). Compared with mothers without a family history of diabetes, women with a family history of diabetes had a significantly higher prevalence of GDM (52.58% vs 29.91%, P < 0.0001). Compared with primiparous women, multiparas had a higher prevalence of GDM (34.06% vs 29.53%, P = 0.0002). The prevalence of GDM was higher among mothers who were fertilized by IVF (42.52% vs 30.57%, P < 0.0001). The higher the pre-pregnancy BMI of participants was, the greater the corresponding prevalence of GDM as well (P < 0.0001). Furthermore, the prevalence of GDM was higher among mothers with insufficient gestational weight gain than those with excessive gestational weight gain (52.00% vs 23.42%, P < 0.0001).

    Table 1 General Personal Characteristics Between Gestational Diabetes Mellitus and Control Group

    The serum atherogenic indices and remnant cholesterols were classified into four categories by IQR, and the differences in the continuity indicators were examined between the GDM group and the control group (Table 2). The prevalence of GDM among the Q4 group in terms of TG/HDL−C, TC/HDL−C, LDL-C/HDL−C, and remnant cholesterol was significantly higher than that among the Q1 group (42.25% vs 23.90%, P < 0.0001; 39.09% vs 23.65%, P < 0.0001; 38.37% vs 23.86%, P < 0.0001; 36.34% vs 24.62%, P < 0.0001; respectively). In line with the Chi-square analysis, the corresponding continuity indicators also showed significant differences between the GDM group and the control group. The results of the univariate analysis between the serum lipid indexes and GDM are provided in Table S1.

    Table 2 Atherogenic Indices and Remnant Cholesterol Between Gestational Diabetes Mellitus and Control Group

    Table 3 shows the effects of serum atherogenic indices and remnant cholesterol on the risk of GDM. Both adjusted and unadjusted logistic regression analyses presented that atherogenic indices and remnant cholesterol were significantly related to the risk of GDM. Compared to those of the lowest quartile, mothers in the highest quartile of TG/HDL−C had a 66% higher risk of GDM (adjusted OR = 1.66, 95% CI: 1.41, 1.96). Mothers in the highest quartile of TC/HDL−C, LDL-C/HDL−C, and remnant cholesterol demonstrated significantly elevated GDM risk compared to those in the lowest quartile, with adjusted ORs of 1.47 (95% CI: 1.24−1.73), 1.47 (95% CI: 1.24−1.73), and 1.39 (95% CI: 1.18−1.64), respectively. Moreover, Table S2 presented the results of multivariable analysis between serum lipid indexes and GDM stratified by stages of pregnancy. Groups in the highest quartiles of TG, TC, and LDL−C showed higher risks of GDM than did those in the lowest quartile. Furthermore, consistent with Table 3, all atherogenic indices and remnant cholesterol significantly predicted GDM risk stratified by pregnancy stage (Table S3).

    Table 3 Multivariable Analysis Between Atherogenic Indices, Remnant Cholesterol and Gestational Diabetes Mellitus

    To better visualize the predictive outcomes of TG/HDL−C, TC/HDL−C, LDL−C/HDL−C, and remnant cholesterol, the results of the monogram analyses are provided in Figure 1. The Hosmer and Lemeshow goodness of fit test results for the four groups showed that all of the prediction models were significant (P > 0.05). The AUCs for the TG/HDL−C, TC/HDL−C, LDL−C/HDL−C, and remnant cholesterol predictive models were 0.73 (95% CI: 0.71, 0.74), 0.73 (95% CI: 0.71, 0.74), 0.73 (95% CI: 0.71, 0.74), and 0.72 (95% CI: 0.71, 0.74), respectively. The result of DCA analysis showed that all indicators demonstrated high net benefit (approximately 0.22), excellent sensitivity (>97%), and good negative predictive value (>88%) at a threshold of 0.1 (Figure 2).

    Figure 1 The nomogram prediction of gestational diabetes mellitus by atherogenic indices and remnant cholesterol (A) the nomogram prediction of GDM by TG/HDL−C; (B) the nomogram prediction of GDM by TC/HDL−C; (C) the nomogram prediction of GDM by LDL−C/HDL−C; (D) the nomogram prediction of GDM by remnant cholesterol.

    Figure 2 The receiver operating characteristic curves and the decision curve analysis of the nomogram predictions (A) the receiver operating characteristic curves of the predictive outcomes of TG/HDL−C, TC/HDL−C, LDL−C/HDL−C, and remnant cholesterol; (B) the decision curve analysis of the predictive outcomes of TG/HDL−C, TC/HDL−C, LDL−C/HDL−C, and remnant cholesterol.

    The nonlinear associations between atherogenic indices, remnant cholesterol and GDM are presented in Figure 3. All of the atherogenic indices and remnant cholesterol exhibited unimodal distributions. A nonlinear relationship was detected between atherogenic indices, remnant cholesterol, and the risk of GDM (χ2= 30.91, P < 0.0001; χ2= 13.08, P = 0.0014; χ2= 6.95, P = 0.0309; χ2= 12.52, P = 0.0019; respectively). First, the risk of GDM showed an upward trend with the increasing of TG/HDL−C. But after TG/HDL−C reached 1.64, the risk of GDM started to decline. Similar nonlinear patterns were observed in the relationships between TC/HDL−C, remnant cholesterol, and GDM, with inflection points at 3.47 and 0.65, respectively. Although the LDL−C/HDL−C ratio showed a significant nonlinear relationship with GDM risk, the risk of GDM continued to rise with increasing levels of LDL−C/HDL−C. However, the rising pace decelerated once the ratio exceeded 1.54.

    Figure 3 The associations between atherogenic indices and gestational diabetes mellitus risk by RCS regression analysis (A) The associations between TG/HDL−C and GDM; (B) The associations between TC/HDL−C and GDM; (C) The associations between LDL−C /HDL−C and GDM; (D) The associations between remnant cholesterol and GDM. Adjusted for age, pre-pregnancy BMI, gestational weight gain, family history of diabetes, and parity history.

    Discussion

    The results of this study showed that atherogenic indexes and remnant cholesterol were closely related to the risk of GDM. Higher values of TG/HDL−C, TC/HDL−C, LDL−C/HDL−C, and remnant cholesterol were significantly associated with elevated risks of GDM, and the results of the nomogram analysis showed that these indicators exhibited similar predictive performance, suggesting that all of them may serve as strong predictors for GDM.

    In line with this study, previous studies have showed the positive associations between atherogenic indexes and GDM, but little of them examined the predictive ability. A cross-sectional study conducted by Khosrowbeygi et al among Iranian reported that LDL−C/HDL−C, TG/HDL−C, and TC/HDL−C levels were significantly higher in the GDM group than in the control group.18 Wang et al analyzed data from 15 hospitals in Beijing, China demonstrated that elevated TG/HDL−C and LDL−C/HDL−C in the 1st trimester of pregnancy were related to increased risks of GDM.17 Pazhohan et al reported that mothers in Iran with the highest tertile of TG/HDL−C in the 1st trimester of pregnancy contributed to a 3.9-fold of the risk of GDM compared with the lowest tertile.26 Cross-sectional studies conducted by Barat et al and Wang et al also revealed that TG/HDL−C was sensitive to GDM diagnosis,14,27,28 and a retrospective cohort study with a TG/HDL−C ratio cutoff of 3 reported that higher pre-gestational TG/HDL−C was associated with higher rates of GDM (13.1% vs 5.2%).29 Zhang et al conducted a prospective cohort study in the Korean population and suggested that a log10 (TG/HDL) below 0.36 might be beneficial for GDM control.30 Besides, Yue et al investigated serum lipids during the 2nd trimester and found that TG/HDL−C was related to the risk of GDM, but no significant difference was detected for LDL−C/HDL−C.31 Liu et al reported that Beijing mothers in the top tertile of TG/HDL−C before 12 weeks’ gestation had a significantly greater risk of GDM (OR = 2.388), but this relationship was not observed in TC/HDL−C.32 Based on the above findings, it was reconfirmed that TG/HDL−C has a positive effect on the risk of GDM, but discrepancies were noted in the relationships between TC/HDL−C and LDL−C/HDL−C and GDM. These gaps could be explained by the different study designs, populations, and gestational weeks of lipid-data collection.

    The positive associations between atherogenic indexes and GDM could be explained by insulin resistance regulated by atherogenic indexes. Case-control studies conducted by Xiang et al and Kimm et al aimed to clarify the associations between atherogenic indexes and insulin resistance and revealed that all of the atherogenic indexes were significantly correlated with insulin resistance.33,34 Specifically, previous studies have confirmed that TG/HDL−C is a reliable biomarker of insulin resistance.35,36 Moreover, increased TC or decreased HDL−C concentrations could contribute to insulin resistance, glucose intolerance, and hyperinsulinemia,37 and these factors are leading hazards for GDM.

    Consistent with our findings, elevated remnant cholesterol levels were significantly associated with an increased risk of GDM. Our study further highlights the promising predictive effect of remnant cholesterol on the risk of GDM. A high concentration of remnant cholesterol was reported to have a higher risk of GDM, even among pregnancies with low TC.21 Another prospective cohort study conducted in Korea confirmed the independent association between remnant cholesterol and GDM.22 Su et al reported a significant dose-response relationship that the risk of GDM elevated along with the increasing of remnant cholesterol.23 Although the exact mechanistic link between remnant cholesterol and GDM remains to be fully elucidated, it was hypothesized that remnant cholesterol may contribute to GDM pathogenesis through dual pathways similar to those observed in type 2 diabetes: The direct effects of remnant cholesterol-induced insulin resistance or β-cell dysfunction,38 and the indirect effects of low-grade inflammation triggered by remnant cholesterol promote insulin resistance.39,40 Further studies are warranted to validate these mechanisms.

    The present study novelly revealed intuitive changes in GDM risk with respect to atherogenic indexes and remnant cholesterol and warned the elevated risk of GDM under high values of atherogenic indexes and remnant cholesterol. However, certain limitations should be addressed. First, the causal correlations between atherogenic indexes and remnant cholesterol and GDM in this study might be undermined. Second, this study obtained clinical data from a single hospital of Chinese population; caution should be taken when generalizing this study to other populations. Third, other confounding factors that might interfere with the relationship between atherogenic indexes and remnant cholesterol and GDM, such as insulin resistance, gestational weight gain before the diagnosis of GDM, liver function indexes, lifestyle behavior factors, and socioeconomic status et al were not considered in this study because they were not routine examined in the clinical practice. Fourth, this study included only one serum lipid data point per person, and the effects of dynamic changes in lipid ratios on the risk of GDM were failed to examine. Future studies focused on the dynamic changes in lipid indicators on the risk of GDM prediction are highly promoted.

    Conclusion

    Notably, our findings highlight the promising role of atherogenic indices and remnant cholesterol as potential predictive biomarkers for GDM risk assessment, which has not been fully explored in previous studies. Elevated levels of blood TG/HDL−C, TC/HDL−C, LDL−C/HDL−C, and remnant cholesterol are linked to a significant increase in the risk of developing GDM. Therefore, it is essential to maintain atherogenic indexes and remnant cholesterols at low levels in order to reduce the risk of GDM. Specially, the turning points (TG/HDL−C = 1.64, TC/HDL−C = 3.47, LDL−C/HDL−C = 1.54, and remnant cholesterol = 0.65) identified by the nonlinear relationships could serve as potential warning thresholds for clinical interventions to optimize GDM risk assessment. These findings underscore the potential of routine lipid testing as a cost-effective strategy for the early identification and management of GDM in clinical settings.

    Abbreviations

    GDM, gestational diabetes mellitus; TG, triglyceride; TC, total cholesterol; HDL−C, high-density lipoprotein cholesterol; LDL−C, low-density lipoprotein cholesterol.

    Data Sharing Statement

    The datasets used during the current study are available from the corresponding author on reasonable request, which should be approved by the Ethics Committee of Maternal and Child Health Hospital of Hubei Province.

    Ethics Approval and Informed Consent

    This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Maternal and Child Health Hospital of Hubei Province (2021IECXM005).

    Acknowledgments

    We would like to express our gratitude to all obstetric clinical workers in Maternal and Child Health Hospital of Hubei Province for their contributions to the sample accumulation of this paper.

    Funding

    This study was funded by the Maternal and Child Health Hospital of Hubei Province Research Project [grant No. 2021SFYM007] and Hubei Provincial Natural Science Foundation of China [grant No. 2025AFD690]. All these fundings were received by Dr. Yao Cheng. The funders had no role in the design, data collection, analyses, interpretation, manuscript writing, nor in the decision to publish the results.

    Disclosure

    The authors report no conflicts of interest in this work.

    References

    1. Petry CJ. Nutrients as risk factors and treatments for gestational diabetes. Nutrients. 2023;15(22):4716. doi:10.3390/nu15224716

    2. Liang X, Zheng W, Liu C, et al. Clinical characteristics, gestational weight gain and pregnancy outcomes in women with a history of gestational diabetes mellitus. Diabetol Metab Syndr. 2021;13(1):73. doi:10.1186/s13098-021-00694-9

    3. Chen LW, Soh SE, Tint MT, et al. Combined analysis of gestational diabetes and maternal weight status from pre-pregnancy through post-delivery in future development of type 2 diabetes. Sci Rep. 2021;11(1):5021. doi:10.1038/s41598-021-82789-x

    4. Retnakaran R, Shah BR. Mediating effect of vascular risk factors underlying the link between gestational diabetes and cardiovascular disease. BMC Med. 2022;20(1):389. doi:10.1186/s12916-022-02581-0

    5. Vohr BR, Boney CM. Gestational diabetes: the forerunner for the development of maternal and childhood obesity and metabolic syndrome? J Matern Fetal Neonatal Med. 2008;21(3):149–157. doi:10.1080/14767050801929430

    6. Dewi RS, Isfandiari MA, Martini S, Yi-Li C. Prevalence and risk factors of gestational diabetes mellitus in Asia: a review. J Public Health Afr. 2023;14(2):7.

    7. Sweeting A, Hannah W, Backman H, et al. Epidemiology and management of gestational diabetes. Lancet. 2024;404(10448):175–192. doi:10.1016/S0140-6736(24)00825-0

    8. Alwash SM, Huda MM, McIntyre HD, Mamun AA. Time trends and projections in the prevalence of gestational diabetes mellitus in Queensland, Australia, 2009-2030: evidence from the Queensland perinatal data collection. Aust N Z J Obstet Gynaecol. 2023;63(6):811–820. doi:10.1111/ajo.13734

    9. Modzelewski R, Stefanowicz-Rutkowska MM, Matuszewski W, Bandurska-Stankiewicz EM. Gestational diabetes mellitus-recent literature review. J Clin Med. 2022;11(19):5736. doi:10.3390/jcm11195736

    10. Jiang HQ, Chen H, Yang J. Characteristics of blood lipids levels and relevant factor analysis during pregnancy. Zhonghua yi xue za zhi. 2016;96(9):724–726. (). doi:10.3760/cma.j.issn.0376-2491.2016.09.013

    11. Hu J, Gillies CL, Lin S, et al. Association of maternal lipid profile and gestational diabetes mellitus: a systematic review and meta-analysis of 292 studies and 97,880 women. EClinicalMedicine. 2021;34:100830. doi:10.1016/j.eclinm.2021.100830

    12. Hu M, Gu X, Niu Y, et al. Elevated serum triglyceride levels at first prenatal visit is associated with the development of gestational diabetes mellitus. Diabetes Metab Res Rev. 2022;38(2):e3491. doi:10.1002/dmrr.3491

    13. Li G, Kong L, Zhang L, et al. Early pregnancy maternal lipid profiles and the risk of gestational diabetes mellitus stratified for body mass index. Reprod Sci. 2015;22(6):712–717. doi:10.1177/1933719114557896

    14. Wang J, Li Z, Lin L. Maternal lipid profiles in women with and without gestational diabetes mellitus. Medicine. 2019;98(16):e15320. doi:10.1097/MD.0000000000015320

    15. Enquobahrie DA, Williams MA, Qiu C, Luthy DA. Early pregnancy lipid concentrations and the risk of gestational diabetes mellitus. Diabetes Res Clin Pract. 2005;70(2):134–142. doi:10.1016/j.diabres.2005.03.022

    16. Koukkou E, Watts GF, Lowy C. Serum lipid, lipoprotein and apolipoprotein changes in gestational diabetes mellitus: a cross-sectional and prospective study. J Clin Pathol. 1996;49(8):634–637. doi:10.1136/jcp.49.8.634

    17. Wang C, Zhu W, Wei Y, et al. The predictive effects of early pregnancy lipid profiles and fasting glucose on the risk of gestational diabetes mellitus stratified by body mass index. J Diabetes Res. 2016;2016:3013567. doi:10.1155/2016/3013567

    18. Khosrowbeygi A, Shiamizadeh N, Taghizadeh N. Maternal circulating levels of some metabolic syndrome biomarkers in gestational diabetes mellitus. Endocrine. 2016;51(2):245–255. doi:10.1007/s12020-015-0697-4

    19. Acay A, Ulu MS, Ahsen A, et al. Atherogenic index as a predictor of atherosclerosis in subjects with familial Mediterranean fever. Medicina. 2014;50(6):329–333. doi:10.1016/j.medici.2014.11.009

    20. Li B, Zhou X, Wang W, et al. Remnant cholesterol is independently associated with diabetes, even if the traditional lipid is at the appropriate level: a report from the REACTION study. J Diabetes. 2023;15(3):204–214. doi:10.1111/1753-0407.13362

    21. Wang W, Li N, Wang X, et al. Remnant cholesterol is associated with gestational diabetes mellitus: a cohort study. J Clin Endocrinol Metab. 2023;108(11):2924–2930. doi:10.1210/clinem/dgad262

    22. Gao Y, Hu Y, Xiang L. Remnant cholesterol, but not other cholesterol parameters, is associated with gestational diabetes mellitus in pregnant women: a prospective cohort study. J Transl Med. 2023;21(1):531. doi:10.1186/s12967-023-04322-0

    23. Su S, Zhang E, Gao S, et al. Associations of remnant cholesterol in early pregnancy with gestational diabetes mellitus risk: a prospective birth cohort study. Lipids Health Dis. 2024;23(1):243. doi:10.1186/s12944-024-02230-w

    24. National Institute for Nutrition and Health Chinese Center for Disease Control and Prevention. Standard of recommendation for weight gain during pregnancy period. Available from: https://www.chinanutri.cn/yyjkzxpt/yyjkkpzx/hdjl/202208/t20220823_260929.html. Accessed August 23, 2022.

    25. Metzger BE, Gabbe SG, Persson B, et al. International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care. 2010;33(3):676–682. doi:10.2337/dc10-0719

    26. Pazhohan A, Rezaee Moradali M, Pazhohan N. Association of first-trimester maternal lipid profiles and triglyceride-glucose index with the risk of gestational diabetes mellitus and large for gestational age newborn. J Matern Fetal Neonatal Med. 2019;32(7):1167–1175. doi:10.1080/14767058.2017.1402876

    27. Barat S, Ghanbarpour A, Bouzari Z, Batebi Z. Triglyceride to HDL cholesterol ratio and risk for gestational diabetes and birth of a large-for-gestational-age newborn. Caspian J Intern Med. 2018;9(4):368–375. doi:10.22088/cjim.9.4.368

    28. Wang D, Xu S, Chen H, Zhong L, Wang Z. The associations between triglyceride to high-density lipoprotein cholesterol ratios and the risks of gestational diabetes mellitus and large-for-gestational-age infant. Clin Endocrinol. 2015;83(4):490–497. doi:10.1111/cen.12742

    29. Arbib N, Pfeffer-Gik T, Sneh-Arbib O, Krispin E, Rosenblat O, Hadar E. The pre-gestational triglycerides and high-density lipoprotein cholesterol ratio is associated with adverse perinatal outcomes: a retrospective cohort analysis. Int J Gynaecol Obstet. 2020;148(3):375–380. doi:10.1002/ijgo.13078

    30. Zhang J, Suo Y, Wang L, et al. Association between atherogenic index of plasma and gestational diabetes mellitus: a prospective cohort study based on the Korean population. Cardiovasc Diabetol. 2024;23(1):237. doi:10.1186/s12933-024-02341-9

    31. Yue CY, Ying CM. Epidemiological analysis of maternal lipid levels during the second trimester in pregnancy and the risk of adverse pregnancy outcome adjusted by pregnancy BMI. J Clin Lab Anal. 2018;32(8):e22568. doi:10.1002/jcla.22568

    32. Liu PJ, Liu Y, Ma L, et al. The predictive ability of two triglyceride-associated indices for gestational diabetes mellitus and large for gestational age infant among Chinese pregnancies: a preliminary cohort study. Diabetes Metab Syndr Obes. 2020;13:2025–2035. doi:10.2147/DMSO.S251846

    33. Xiang SK, Hua F, Tang Y, Jiang XH, Zhuang Q, Qian FJ. Relationship between serum lipoprotein ratios and insulin resistance in polycystic ovary syndrome. Int J Endocrinol. 2012;2012:173281. doi:10.1155/2012/173281

    34. Kimm H, Lee SW, Lee HS, et al. Associations between lipid measures and metabolic syndrome, insulin resistance and adiponectin. – Usefulness of lipid ratios in Korean men and women. Circ J. 2010;74(5):931–937. doi:10.1253/circj.CJ-09-0571

    35. Quispe R, Martin SS, Jones SR. Triglycerides to high-density lipoprotein-cholesterol ratio, glycemic control and cardiovascular risk in obese patients with type 2 diabetes. Curr Opin Endocrinol Diabetes Obes. 2016;23(2):150–156. doi:10.1097/MED.0000000000000241

    36. Huhtala M, Rönnemaa T, Tertti K. Insulin resistance is associated with an unfavorable serum lipoprotein lipid profile in women with newly diagnosed gestational diabetes. Biomolecules. 2023;13(3):470. doi:10.3390/biom13030470

    37. Jeppesen J, Facchini FS, Reaven GM. Individuals with high total cholesterol/HDL cholesterol ratios are insulin resistant. J Intern Med. 1998;243(4):293–298. doi:10.1046/j.1365-2796.1998.00301.x

    38. Hao M, Head WS, Gunawardana SC, Hasty AH, Piston DW. Direct effect of cholesterol on insulin secretion: a novel mechanism for pancreatic beta-cell dysfunction. Diabetes. 2007;56(9):2328–2338. doi:10.2337/db07-0056

    39. Varbo A, Benn M, Tybjærg-Hansen A, Nordestgaard BG. Elevated remnant cholesterol causes both low-grade inflammation and ischemic heart disease, whereas elevated low-density lipoprotein cholesterol causes ischemic heart disease without inflammation. Circulation. 2013;128(12):1298–1309. doi:10.1161/CIRCULATIONAHA.113.003008

    40. Hu X, Liu Q, Guo X, et al. The role of remnant cholesterol beyond low-density lipoprotein cholesterol in diabetes mellitus. Cardiovasc Diabetol. 2022;21(1):117. doi:10.1186/s12933-022-01554-0

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  • JAK1 Inhibitor Shows Promise for Ankylosing Spondylitis

    JAK1 Inhibitor Shows Promise for Ankylosing Spondylitis

    TOPLINE:

    Ivarmacitinib, a highly selective Janus kinase 1 (JAK1) inhibitor, tamed ankylosing spondylitis with sustained efficacy through 24 weeks in a phase 2/3 trial.

    METHODOLOGY:

    • A phase 2/3 trial in China evaluated the efficacy and safety of ivarmacitinib in 504 adults with active ankylosing spondylitis who did not benefit from nonsteroidal anti-inflammatory drugs (NSAIDs).
    • In phase 2, patients were randomly assigned to receive ivarmacitinib (2 mg, 4 mg, or 8 mg) or placebo once daily for 12 weeks; 4 mg was selected as the recommended dose based on an interim analysis.
    • In phase 3, 373 patients (mean age, 33.8 years; 79.6% men) were randomly assigned to receive 4 mg ivarmacitinib (n = 187) or placebo (n = 186) once daily for 12 weeks, after which all patients received ivarmacitinib for 12 weeks.
    • The primary endpoint in both phases was the proportion of patients achieving an Assessment of Spondyloarthritis International Society (ASAS) 20 response at week 12.

    TAKEAWAY:

    • At week 12, 48.7% of patients who received 4 mg ivarmacitinib achieved an ASAS20 response compared with 29% of those who received placebo (P = .0001).
    • More patients on 4 mg ivarmacitinib vs placebo achieved an ASAS40 response (32.1% vs 18.3%; P = .0011) and an ASAS5/6 response (42.8% vs 15.6%; < .0001) at week 12, with efficacy sustained at week 24.
    • After 12 weeks of treatment, patients receiving 4 mg ivarmacitinib had greater improvements in disease symptoms, physical function, spinal mobility, and quality of life.
    • During the first 12-week period, treatment-emergent adverse events occurred in 79.7% of patients in the ivarmacitinib group and 65.6% in the placebo group but caused few treatment discontinuations.

    IN PRACTICE:

    “Ivarmacitinib 4 mg once daily provided rapid, sustained, and clinically meaningful improvements in disease activity, signs and symptoms, function, and MRI-detected inflammation in patients with active AS [ankylosing spondylitis] who had an inadequate response to NSAIDs, with a manageable safety profile,” the authors wrote.

    SOURCE:

    This study was led by Xu Liu, MD, and Liling Xu, MD, of Peking University People’s Hospital in Beijing, China. It was published online on June 12, 2025, in Arthritis & Rheumatology.

    LIMITATIONS:

    The 24-week efficacy of ivarmacitinib may not reflect long-term outcomes. The absence of an active comparator limited the comparison of ivarmacitinib with other disease-modifying antirheumatic drugs used for active ankylosing spondylitis. These findings in Chinese patients with radiographic axial spondyloarthritis may not be generalizable to other populations.

    DISCLOSURES:

    Jiangsu Hengrui Pharmaceuticals Co. Ltd. sponsored and designed the trial. Two authors reported being employees of the sponsor company while the study was conducted. 

    This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

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  • Gaze into the proteomics crystal ball

    Gaze into the proteomics crystal ball

    Discover the hottest new tools to study protein biology this August at the American Society for Biochemistry and Molecular Biology’s 15th International Symposium on Proteomics in the Life Sciences. This five-day symposium will be held August 17–21 at the Broad Institute of the Massachusetts Institute of Technology and Harvard University. Each day will be packed with scientific sessions, networking opportunities and more.

    Kathryn Lilley

    Meeting organizer Kathryn Lilley, a professor and group leader at the University of Cambridge, said she wants the symposium to give attendees a glimpse into the future of proteomics. She first attended this meeting more than a decade ago.

    “Although I was in awe of all these very decorated colleagues — leaders in their field — everybody was super welcoming,” Lilley said.

    The 2025 organizing committee includes Lilley; A. L. Burlingame, professor of pharmaceutical chemistry at the University of California, San Francisco; Steven Carr, senior director of proteomics at the Broad Institute; Ileana Cristea, director of graduate studies at Princeton University and ASBMB’s Molecular & Cellular Proteomics editor-in-chief; and Bernhard Küster, professor of proteomics and bioanalytics at the Technical University of Munich.

    Lilley said this year’s conference will continue to foster that welcoming atmosphere while showcasing speakers with diverse expertise and offering a program that examines the proteome from all angles.

    Top left: A. L. Burlingame, Top right: Bernhard Küster, Bottom left: Ileana Cristea, Bottom right: Steven Carr

    Top left, A.L. Burlingame; top right, Bernhard Küster; bottom left, Ileana Cristea; bottom right, Steven Carr.

    Plenary talks from leaders in their fields will explore innovative proteomic and post-translational modification analyses in cancer and precision medicine, pioneering approaches in drug discovery through targeted protein degradation, and fresh insights into virus-host interactions that shape immune and metabolic responses. Lilley hopes attendees will discover unexpected opportunities to collaborate.

    “These sets of speakers don’t necessarily come together in a lot of the larger meetings in our field, so I think that itself is very interesting and very empowering,” she said. “You’ll get people who have not met but will see some sort of alignment in their research programs and their methodologies.”

    In addition to plenary and session talks, organizers will select a few abstracts for short talks. Most abstract submitters will have the opportunity to present a poster.

    “Everybody takes it very seriously, and every poster will get a lot of interest and a lot of traffic,” she said.

    Looking ahead, the program will take a holistic view of the field and highlight emerging areas such as spatial proteomics, single-cell proteomics, multiomics, proteoforms and immunopeptidomics.

    “There’s going to be an element of crystal ball gazing and future-proofing proteomics as well,” she said.

    Lilley encouraged researchers new to proteomics to attend and explore how these techniques might expand and diversify their work.

    “Everybody, whatever career stage they are, needs to have their horizons expanded to be able to look for new opportunities,” she said. “This meeting is a great place to do just that.”

    The regular registration deadline is July 23. Register today!

    Cambridge, Massachusetts

    Cambridge, Massachusetts

    Plenary Lectures

    • “Motif-based approaches for analyzing phosphoproteomic mass spectrometry datasets identify signaling dependencies in cancers lacking known oncogenic drivers” | Michael MacCoss, University of Washington
    • “Shedding light on the dark viral proteome to advance our understanding of antiviral immunity” | Shira Weingarten–Gabbay, Harvard Medical School
    • “Protein degraders: New insights and twists of molecular mechanism and drug design” | Alessio Ciulli, University of Dundee
    • “Studying the mechanisms and cellular processes regulated by protein post-translational modifications” | Pedro Beltrao, Institute of Molecular Systems Biology, ETH Zurich Laboratory
    • “Phosphoproteomics as a functional molecular read-out for personalized precision oncology” | Connie Jimenez, Amsterdam University Medical Center
    • “The interface between metabolism and immunity within a virus microenvironment” | Ileana Cristea, Princeton University

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  • Engineered protein silences harmful T cells in autoimmune disease

    Engineered protein silences harmful T cells in autoimmune disease

    An engineered protein turns off the kind of immune cells most likely to damage tissue as part of Type-1 diabetes, hepatitis, multiple sclerosis, shows a new study in mice.

    In these autoimmune diseases, T cells mistakenly target the body’s own tissues instead of invading viruses or bacteria as they would during normal immune responses. Treatments focused on T cells have been elusive because blocking their action broadly weakens the immune system and creates risk for infections and cancer.

    Published online June 30 in the journal Cell, the study revealed that holding closely together two protein groups (signaling complexes) on T cells, including one found more often on T cells involved autoimmune disease, shuts down those T cells in a limited way.

    Led by researchers at NYU Langone Health, the Chinese Academy of Sciences, and Zhejiang University, the study built on biology newly discovered by the team to design an antibody that attached to both T cell signaling complexes, the T cell receptor and the LAG-3 checkpoint, held them closely together, and eliminated autoimmune tissue damage in three mouse models of disease.

    Antibodies are proteins made by the immune system that label specific markers on cells for notice by the immune system. Researchers learned decades ago to engineer antibodies to target certain molecules as treatments, and more recently, antibodies that attach to two targets.

    Our findings reveal an intricate mechanism that enables a careful treatment approach to T-cell driven autoimmune diseases, which currently lack effective immunotherapies.”


    Jun Wang, PhD., co-senior study author, assistant professor, Department of Pathology at NYU Grossman School of Medicine

    Held in place

    The study results are based on the presence on T cells of T-cell receptors (TCRs) and checkpoints. TCRs, although shaped so that bits of invading bacteria or viruses fit into them to activate the T cell, are turned on by the body’s own proteins in autoimmune diseases. Checkpoints like LAG-3 are also turned on by specific signaling partners, but when this occurs they have the opposite effect of TCRs, suppressing the T cell’s activity.

    Also important to the new study results is that TCR-triggering molecules must be presented to T cell receptors by another set of immune cells that “swallow” foreign (e.g., microbial) or bodily substances and display on their surfaces through protein groups called major histocompatibility complexes (MHC-II) just the small protein pieces that activate a given TCR.

    “We discovered that, as a T cell’s surface draws close to the MHC-II presenting its TCR trigger molecule, the T cell receptor gets particularly close to LAG-3”, said co-first author Jasper Du, a third-year medical student in Dr. Wang’s lab. “For the first time, we found that this proximity is central to the ability of LAG-3 to dial back T cell activity.”

    Mechanistically, the research team found that the proximity of LAG-3 lets it loosely stick to part of the T cell receptor called CD3ε (like two oily globs interacting). This attachment was found to pull on CD3ε enough to disrupt its interaction an enzyme called Lck, which is crucial for T cell activation. MHC-II can theoretically attach to LAG-3 and TCR at the same time, but not frequently enough to maximize LAG-3’s ability to dial down T cells, the researchers said.

    In addition, “checkpoints” like LAG-3 are used by the immune system to turn off T cells when the right signals, given off by normal cells, dock in to avert self-attack (autoimmunity). Cancer cells put off signaling molecules that dock into checkpoints and sabotage the ability of T cells to attack them. Therapies called checkpoint inhibitors counter this effect.

    LAG-3 turns off T cells, but less easily due to its spatial requirements than another checkpoint called PD-1. This feature makes LAG-3 inhibitors weaker as anti-cancer cancer treatment than PD-1-inhibiting antibody treatments that have become a mainstay, but likely better when the immune system is overactive, and targeted T cell suppression is required for maximum safe effect.

    Based on their discovery of the critical role of TCR proximity in LAG-3 function, the research team designed a molecule that enforces LAG-3/TCR proximity to achieve better LAG-3-dependent TCR inhibition and suppression of T cell responses. Their “bi-specific” antibody held LAG-3 and the T cell receptor together more strongly than MHC-II, and without depending on it.

    The current authors’ bispecific antibody, named the LAG-3/TCR Bispecific T cell Silencer or BiTS, potently suppressed T cell responses and lessened inflammatory damage to insulin-producing cells (insulitis) in BiTS-treated mice with a version of Type 1 diabetes. In autoimmune models of hepatitis, BiTS treatment reduced T cell infiltration and liver damage.

    With the diabetes and hepatitis disease models largely driven by one type of T cells (CD8+), the team also used a mouse model of multiple sclerosis known to be driven by a second major T cell type (CD4+). The team treated mice prone to develop multiple sclerosis with short-term, preventive BiTS prior to the onset of disease symptoms, and BiTS-treated mice had reduced disease by a standard measure.

    “Our study advances our understanding of LAG-3 biology and may foster more proximity-based, spatially-guided therapeutic designs like BiTS as immunotherapy for other human diseases,” said co-first author Jia You, a research scientist in Dr. Wang’s lab.

    Along with Dr. Wang, corresponding authors of the study were Jack Wei Chen of the Department of Cell Biology and Department of Cardiology at the Second Affiliated Hospital Zhejiang University School of Medicine in China; as well Jizhong Lou of the State Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences.

    Also other authors from the NYU Grossman School of Medicine were Jia Liu, Qiao Lu, Connor James, Ryan Foster, and Eric Rao in the Department of Pathology at New York University Grossman School of Medicine; Meng-ju Lin and Catherine Pei-ju Lu in the Hansjörg Wyss Department of Plastic Surgery and Department of Cell Biology; and Michael Cammer at the Microscopy Core, Division of Advanced Research Technologies, and Shohei Koide of the Perlmutter Cancer Center. Also making important contributions were Hui Chen at the State Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, and Yong Zhang from University of Chinese Academy of Sciences; Wei Hu and Jie Gao at The Second Affiliated Hospital, Zhejiang University School of Medicine; and Weiwei Yin in the Key Laboratory for Biomedical Engineering of the Ministry of Education, College of Biomedical Engineering and Instrument Science, also at Zhejiang University.

    The study was supported principally by a translational advancement award from the Judith and Stewart Colton Center for Autoimmunity at NYU Langone Health. Also funding the study were a Cancer Center Support Grant P30CA016087, NIH grant S10OD021727, the NYU melanoma SPORE and NIH R37CA273333, and an NIH/NIAMS T32 grant (AR069515-07). The biophysical analysis part of this work was also supported by multiple grants from National Science Foundations of China (32090044, T2394512, 32200549, and T2394511).

    Dr. Wang, Du and You are listed as inventors of pending patents related to the study. NYU Langone Health and its Technology Opportunities & Ventures have formed a related startup company, Remunix Inc., with Dr. Wang as founder and shareholders, to license and commercialize the patents. In addition, Dr. Wang serves as a consultant for Rootpath Genomics, Bristol Myers Squibb, LAV, Regeneron, and Hanmi. Dr. Koide has reported interests in Aethon Therapeutics and Revalia Bio not related to this study. These relationships are managed in keeping with the policies of NYU Langone Health.

    Source:

    NYU Langone Health / NYU Grossman School of Medicine

    Journal reference:

    Du, J., et al. (2025). Proximity between LAG-3 and the T cell receptor guides suppression of T cell activation and autoimmunity. Cell. doi.org/10.1016/j.cell.2025.06.004,

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  • Nutrition, Diet, and Cancer – IARC

    1 Juillet 2025

    What is the evidence currently available on the impact of anti-diabetes medication on obesity and on cancer risk? How should this evidence be interpreted and associated with evidence on the role of dietary quality and dietary diversity in cancer prevention? What are the differences and similarities between study results on diet, body weight, and cancer in adults and in children? To find out, join the next International Agency for Research on Cancer–European Society for Medical Oncology (IARC-ESMO) webinar.

    The 12th instalment in the IARC-ESMO webinar series will be broadcast live on Tuesday 22 July 2025 at 15:00 CEST. The topic of the webinar will be Nutrition, Diet, and Cancer. The event, which will last approximately 1.5 hours, will include three presentations and a question-and-answer session. Dr Inge Huybrechts, a scientist in the Nutrition and Metabolism Branch at IARC, will chair the event.

    In the introduction, Dr Huybrechts will provide a quick overview of the wide scope of research on nutrition, diet, and cancer, considering both adult and child populations and different regions of the world (i.e. high-income as well as low- and middle-income regions). Nutrition (i.e. malnutrition) and dietary factors (i.e. dietary quality, dietary diversity, nutrient adequacy, etc.) will be discussed in the context of cancer prevention and treatment.

    In the second presentation, Dr Elena J. Ladas, Sid and Helaine Lerner Professor for Global Integrative Medicine at Columbia University Irving Medical Center, USA, and Director of the International Initiative for Pediatrics and Nutrition, will present current evidence related to dietary quality, body weight, and childhood cancer.

    In the third presentation, Dr Neil M. Iyengar, Co-Director of the Breast Oncology Programme and Director of Cancer Survivorship Services at Winship Cancer Institute, USA, will present current evidence on diet, body weight, and cancer in adults.

    The IARC-ESMO webinar series aims to provide new perspectives or to present new research, to complement the large variety of educational resources that are freely accessible from the online learning platform of the IARC-ESMO Learning and Capacity-Building Initiative on Cancer Prevention. The webinar series is organized with the support of and in collaboration with the European Society for Medical Oncology (ESMO).

    Register to attend the webinar

    Read more about the webinar’s speakers and presentations

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  • Ablation Still Best Option When Patient Has AF and Obesity

    Ablation Still Best Option When Patient Has AF and Obesity

    Demonstrating that the best solution is not always a multistage approach, a new trial shows catheter ablation is superior to a combination of antiarrhythmic drugs and lifestyle changes — weight loss, more exercise, and alcohol reduction — when treating atrial fibrillation (AF) in patients who also have obesity.

    The PRAGUE-25 trial, led by Pavel Osmancik, MD, PhD, with the Cardiocenter at Charles University in Prague, found catheter ablation was roughly twice as effective in an intention-to-treat analysis at controlling AF at the 1-year mark compared with a combination of antiarrhythmic drugs and lifestyle modification (73% vs 34.6%).

    A “referral to [catheter ablation] in this population should not be delayed until the patient loses weight,” according to the researchers, who published their findings online on June 30 in the Journal of the American College of Cardiology simultaneously with a presentation at theNew York Valves 2025 Conference.

    Obesity: A Strong Predictor of AF

    AF, the most common sustained heart arrhythmia, affects about 60 million people worldwide. Obesity is one of its strongest predictors. An increase in BMI of 5 has been linked with a 19%-29% higher incidence of the rhythm disorder.

    The PRAGUE trial was a randomized, noninferiority trial conducted in five centers in the Czech Republic. Patients that were included had symptomatic AF (paroxysmal, persistent, or long-standing persistent) and a BMI of 30 to 40.

    Patients were randomly assigned 1:1 either to receive catheter ablation (n = 100) or a combination of medication and lifestyle changes (n = 103) from May 2021 to November 2023. Baseline characteristics were balanced, according to the researchers.

    After randomization, all patients had a baseline cardiopulmonary exercise test, echocardiography, quality of life analysis, blood biochemistry testing, and a baseline 7-day electrocardiographic Holter recording.

    Patients in the catheter ablation group underwent ablation within 6 weeks of randomization. Lifestyle modification was started within 4 weeks after randomization and was managed by teams of dietary specialists and physiotherapists, rather than cardiologists.

    Patients in the combination therapy group lost significantly more weight at 12 months (about 6 kg, < .001 compared to 0.35 kg in the other group), and that weight loss was maintained through the 24-month follow-up. The weight loss goal in this trial was 10%, an ambitious target in the period, especially given the physical limitations associated with AF.

    Ramesh Hariharan, MD, cardiac electrophysiologist at UTHealth Houston and Memorial Hermann Health, Houston, who was not part of the study, said much of this research was conducted before the widespread use of GLP-1 receptor agonists, and those medications may help current patients achieve greater weight loss faster.

    But even with greater weight loss, Hariharan said, the new findings reinforce the idea that no option alone is enough. Lifestyle changes and medicines need to accompany ablation, not replace it, he said, “otherwise we’re going to end up doing [ablations] more frequently.”

    What’s more, technology has improved in the last year with nonthermal pulsed field ablation, which offers “far fewer collateral damage complications” and results in a 45-minute procedure “compared to a 2- to 4-hour procedure before. It has made ablation a lot easier.”

    Gregory M. Marcus, MD, MAS, associate chief of cardiology for research at 
    UCSF Health, San Francisco, said the evidence “is already definitive that catheter ablation is superior to antiarrhythmic drugs, and there is evidence that successful lifestyle change can reduce the burden of atrial fibrillation.” But this trial is the first to show a head-to-head comparison of ablation with a combination of antiarrhythmic drugs and lifestyle changes.

    Marcus said he is not convinced the findings exclude the possibility some in this patient population may still be able to treat their AF without ablation.

    “For an obese, very sedentary person who drinks too much alcohol, those are, at least theoretically, the prime candidates for lifestyle modification as a way to effectively treat their Afib,” he said.

    One important lesson, Marcus said, is that this adds to the growing evidence that when considering the population at large with AF, “on average, catheter ablation is pretty definitively the most effective way to reduce the chance of atrial fibrillation recurrence.”

    But some of the most interesting results were in the group who underwent lifestyle modification, he said. In addition to weight loss and improved exercise capacity, they experienced a statistically significant decrease in hemoglobin A1c concentrations of 1.4 mmol/L compared with an increase of 2.5 mmol/L in patients who received catheter ablation. “Those are things that will prolong life and will also improve quality of life,” he said.

    “Whether we’re going to do an ablation or not,” Marcus added, “we should always counsel our atrial fibrillation patients about healthy lifestyle management. There are other things to life besides atrial fibrillation.”

    The study authors and Hariharan reported no relevant financial disclosures. Marcus is a consultant and was a co-founder of the startup InCarda Therapeutics, which is investigating a novel therapy for the treatment of acute AF.

    Marcia Frellick is an independent, Chicago-based healthcare journalist and a regular contributor to Medscape.

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