Evidence map›Paper›PMID 39958952›Full record

ArticleLiver research (Beijing, China)2023

Predictive value of Th17 and Treg cells at baseline for HBsAg loss in chronic hepatitis B patients with low HBsAg quantification treated with pegylated interferon and nucleos(t)ide analogue.

Li-Li Wu, Xiao-Yan Li, Kai Deng, Bing-Liang Lin, Hong Deng, Dong-Ying Xie, Geng-Lin Zhang, Qi-Yi Zhao, Zhi-Shuo Mo, Yue-Hua Huang and 1 more

Erratum issuedAbstract read
In one paragraph

Article in Liver research (Beijing, China), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. [Predictive factors for HBsAg-negative seroconversion in chronic hepatitis B after antiviral therapy].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2024
    Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Li-Li WuDepartment of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
Xiao-Yan LiDepartment of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
Kai DengInstitute of Human Virology, Key Laboratory of Tropical Disease Control of Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.
Bing-Liang LinDepartment of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
Hong DengDepartment of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
Dong-Ying XieDepartment of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
Geng-Lin ZhangDepartment of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
Qi-Yi ZhaoDepartment of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
Zhi-Shuo MoDepartment of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
Yue-Hua HuangDepartment of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
Zhi-Liang GaoDepartment of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and aims: The primary goal of chronic hepatitis B (CHB) treatment is to reduce hepatitis B surface antigen (HBsAg). T helper 17 (Th17) and regulatory T (Treg) cells are essential for the development of CHB. However, how Th17 and Treg cells contribute to HBsAg loss is still unknown. Therefore, this study aimed to search for the predictive value of Th17 and Treg cells for HBsAg loss in CHB patients with low HBsAg quantification. Methods: The study included 99 hepatitis B e antigen (HBeAg)-negative CHB patients who had completed a year of nucleos(t)ide analogue (NA) monotherapy and had received both NA and pegylated interferon (PEG-IFN) treatment for less than 96 weeks (96 wk). In the cured group, 48 patients lost HBsAg within 48 wk, while 51 patients did not (uncured group). Blood samples and clinical data were collected for research. Results: During PEG-IFN and NA combination therapy, the proportion of Th17 cells in the cured group increased significantly, while the proportion of Treg cells in the uncured group increased. From 0 to 24 wk, the proportion of Th17 cells in the cured group was significantly higher than in the uncured group, while the opposite was true for Treg cells. Patients with alanine aminotransferase (ALT) ≥ 2.5 upper limit of normal (ULN) at 12 wk had a higher proportion of Th17 cells and a lower proportion of Treg cells than those with ALT <2.5 ULN at 12 wk. Additionally, the proportion of Th17 cells is inversely associated with the level of HBsAg, whereas the level of Treg cells is positively related to HBsAg quantification. The clinical cure index, including age, HBsAg quantification, and the proportions of Th17 and Treg cells, had a higher area under the curve (0.957) for predicting HBsAg loss when compared to the proportions of Th17 and Treg cells and HBsAg quantification alone. Conclusions: Combined with quantification of HBsAg, the proportions of Th17 cells and Treg cells at baseline can be used as good predictors of HBsAg loss in patients with low HBsAg quantification treated with NA and PEG-IFN.

Indexed as

Chronic hepatitis B (CHB)Clinical cureHepatitis B surface antigen (HBsAg) lossNucleos(t)ide analogue (NA)Pegylated interferon (PEG-IFN)Regulatory T (Treg) cellT helper 17 (Th17) cell

Identifiers

PMID39958952
PMCPMC11791923

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.