Exploration of the core gene signatures and mechanisms between NAFLD and sarcopenia through transcriptomic level

Xu, Ziying; Yu, Zihui; Li, Shang; Tian, Ziyan; Yuan, Jing; You, Fangtian

doi:10.3389/fendo.2023.1140804

Cited by 3 publications

(1 citation statement)

References 49 publications

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“…In recent years, the rapid advancement of high-throughput sequencing [11] and single-cell sequencing technologies [12] in the life sciences has signi cantly enhanced our understanding of the molecular mechanisms of diseases, cellular types, and subpopulation dynamics, thereby providing a scienti c foundation for the development of precision medicine and therapeutic strategies [13]. Within the realm of NAFLD, these technologies have made notable progress, primarily focusing on the pathogenesis and biomarkers of NAFLD [14][15][16]. However, bioinformatic and single-cell sequencing studies concerning the progression of liver brosis in NASH patients remain an area ripe for further exploration.…”

Section: Introductionmentioning

confidence: 99%

Single cell sequencing and multiple machine learning identified CD2 and ITGAV as novel biomarkers for NASH-related fibrosis

Yan,

Li,

et al. 2024

Preprint

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Background Non-Alcoholic Steatohepatitis (NASH) is a prevalent form of liver inflammation that can progress to fibrosis and even hepatocellular carcinoma. The purpose of this research is to explore the biomarkers for NASH-related fibrosis based on single cell sequencing and machine learning. Methods We retrieved three datasets from the GEO database (GSE228232, GSE162694, GSE130970). Within GSE228232, we conducted cell annotation, pseudotime analysis, cell communication, and high-dimensional Weighted Gene Co-expression Network Analysis (hdWGCNA). In GSE162694, differential analysis, immune cell infiltration, and enrichment analyses were performed to discern the gene differences between the NASH and NASH-F groups. Ultimately, multiple machine learning algorithms were employed to validate the biological markers of NASH-F. Results In the analysis of the GSE162694 dataset, immune infiltration studies revealed significant differences in various types of T cells between the NASH and NASH-F groups. Pseudotime analysis indicated a strong association between NASH-F and T cells with high expression of Cd8a/b, Cxcr6, and Pdcd-1. Through single-cell sequencing and transcriptome analysis, we have isolated a set of 15 genes conserved between mouse models and human cases of NASH. This conserved gene set includes BCL11B, CD2, CD3E, CD5, GLS, GZMK, ICOS, ITGAV, LEF1, NEURL3, NR4A3, PFKP, RGS1, THEMIS, and THY1. Subsequent machine learning models corroborated CD2 and ITGAV as biomarkers for NASH-F. Conclusion Leveraging single-cell sequencing and multiple machine learning, our study delves into the pathogenesis of T cells in NASH-associated fibrosis and identifies CD2 and ITGAV as biomarkers of NASH-F.

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Section: Introductionmentioning

confidence: 99%

Single cell sequencing and multiple machine learning identified CD2 and ITGAV as novel biomarkers for NASH-related fibrosis

Yan,

Li,

et al. 2024

Preprint

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The relationship between sarcopenia and metabolic dysfunction-associated fatty liver disease among the young and middle-aged populations

Feng,

Zhao,

Wang

et al. 2024

BMC Gastroenterol

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Background Metabolic dysfunction-associated fatty liver disease (MAFLD) has been proposed as a new term for diagnosing fatty liver disease, which is considered to be a multi-systemic disease with multiple extrahepatic manifestations, including sarcopenia. The link between sarcopenia and MAFLD remains uncertain, especially among young and middle-aged adults. Thus, we examined the relationship between MAFLD and sarcopenia in young and middle-aged individuals in this study. Methods A total of 2214 individuals with laboratory tests, dual-energy X-ray absorptiometry and ultrasound transient elastography from NHANES 2017–2018 were selected for this study. MAFLD was diagnosed as fatty liver disease with any one of the situations: overweight/obesity, diabetes mellitus, presence of metabolic dysregulation. Sarcopenia was defined by appendicular lean mass adjusted for body mass index (BMI). Multivariable logistic regression and restricted cubic spline (RCS) model were applied to explore the relationship between MAFLD and sarcopenia, and the mediation analyses were also conducted. Moreover, subgroup analyses stratified by BMI and lifestyles were done. Results The prevalence of MAFLD was 47.85%, and nearly 8.05% of participants had sarcopenia. The prevalence of sarcopenia was higher in participants with MAFLD (12.75%; 95% CI 10.18–15.31%) than in the non-MAFLD (3.73%; 95% CI 2.16–5.31%). MAFLD was significantly positively associated with sarcopenia after adjustments [OR = 2.87 (95% CI: 1.62–5.09)]. Moreover, significant positive associations were observed between liver fibrosis and sarcopenia prevalence in MAFLD patients (OR = 2.16; 95% CI 1.13–4.15). The RCS curve revealed that MAFLD was linearly associated with sarcopenia. The relationship between the MAFLD and sarcopenia were mediated by C-reactive protein (mediation proportion: 15.9%) and high-density lipoprotein cholesterol (mediation proportion: 18.9%). Subgroup analyses confirmed the association between MAFLD and sarcopenia differed in different lifestyle groups. Conclusions Both MAFLD prevalence and severity was significantly associated with sarcopenia. Thus, clinicians should advise comorbidity screening and lifestyle changes to young and middle-aged patients.

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Metabolic-associated fatty liver disease and sarcopenia: A double whammy

Viswanath,

Fouda,

Fernandez

et al. 2024

World J Hepatol

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The prevalence of metabolic-associated fatty liver disease (MAFLD) has increased substantially in recent years because of the global obesity pandemic. MAFLD, now recognized as the number one cause of chronic liver disease in the world, not only increases liver-related morbidity and mortality among sufferers but also worsens the complications associated with other comorbid conditions such as cardiovascular disease, type 2 diabetes mellitus, obstructive sleep apnoea, lipid disorders and sarcopenia. Understanding the interplay between MAFLD and these comorbidities is important to design optimal therapeutic strategies. Sarcopenia can be either part of the disease process that results in MAFLD (e.g. , obesity or adiposity) or a consequence of MAFLD, especially in the advanced stages such as fibrosis and cirrhosis. Sarcopenia can also worsen MAFLD by reducing exercise capacity and by the production of various muscle-related chemical factors. Therefore, it is crucial to thoroughly understand how we deal with these diseases, especially when they coexist. We explore the pathobiological interlinks between MAFLD and sarcopenia in this comprehensive clinical update review article and propose evidence-based therapeutic strategies to enhance patient care.

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Exploration of the core gene signatures and mechanisms between NAFLD and sarcopenia through transcriptomic level

Cited by 3 publications

References 49 publications

Single cell sequencing and multiple machine learning identified CD2 and ITGAV as novel biomarkers for NASH-related fibrosis

Single cell sequencing and multiple machine learning identified CD2 and ITGAV as novel biomarkers for NASH-related fibrosis

The relationship between sarcopenia and metabolic dysfunction-associated fatty liver disease among the young and middle-aged populations

Metabolic-associated fatty liver disease and sarcopenia: A double whammy

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