The high-fat diet (HFD) has complex health implications, shaped by the composition of macronutrients and food sources. However, existing dietary assessment tools lack the precision required for effective risk stratification.
Objectives
This study developed novel HFD scores to accurately characterize dietary patterns and examined their associations with total and cardiovascular disease (CVD) mortality, while also identifying relevant protein biomarkers.
Methods
Data from the UK Biobank and NHANES were utilized to develop a novel HFD scoring system incorporating both macronutrient ratios and quality indicators. Mortality associations were evaluated using Cox proportional hazards models, Kaplan-Meier analysis, and restricted cubic splines (RCS). Proteomic analysis was conducted to identify plasma proteins associated with mortality.
Results
The unhealthy HFD score showed a linear correlation with increased total and CVD mortality. In contrast, the healthy HFD score exhibited a U-shaped relationship with CVD mortality. On a 30-point HFD scale, individuals with low total mortality risk in the UK and US should maintain scores below 15, while those with low CVD risk in the UK should aim for scores between 10 and 15. An online HFD risk calculator was developed for practical application. Proteomic analysis revealed 48 proteins linked to total mortality and 153 proteins associated with CVD mortality, with significant enrichment in immune regulation and cardiovascular pathways. Machine learning models identified key predictors, such as EDA2R and NTproBNP, which demonstrated mediation effects of 12.92% and 13.86%, respectively.
Conclusion
These findings establish a precision nutrition framework that links HFD patterns, proteomic biomarkers, and mortality outcomes, offering actionable insights for clinical and public health intervention.
{"title":"High-fat diet score reveals total and cardiovascular mortality and identifies protein biomarkers in large cohorts","authors":"Xiaomin Li, Xiangju Kong, Shanpeng Liu, Chenyu Hou, Shali Cui, Xuan Zhu, Yue Guan, Songliu Hu, Changhao Sun, Yucun Niu","doi":"10.1016/j.jare.2026.01.016","DOIUrl":"https://doi.org/10.1016/j.jare.2026.01.016","url":null,"abstract":"<h3>Introduction</h3>The high-fat diet (HFD) has complex health implications, shaped by the composition of macronutrients and food sources. However, existing dietary assessment tools lack the precision required for effective risk stratification.<h3>Objectives</h3>This study developed novel HFD scores to accurately characterize dietary patterns and examined their associations with total and cardiovascular disease (CVD) mortality, while also identifying relevant protein biomarkers.<h3>Methods</h3>Data from the UK Biobank and NHANES were utilized to develop a novel HFD scoring system incorporating both macronutrient ratios and quality indicators. Mortality associations were evaluated using Cox proportional hazards models, Kaplan-Meier analysis, and restricted cubic splines (RCS). Proteomic analysis was conducted to identify plasma proteins associated with mortality.<h3>Results</h3>The unhealthy HFD score showed a linear correlation with increased total and CVD mortality. In contrast, the healthy HFD score exhibited a U-shaped relationship with CVD mortality. On a 30-point HFD scale, individuals with low total mortality risk in the UK and US should maintain scores below 15, while those with low CVD risk in the UK should aim for scores between 10 and 15. An online HFD risk calculator was developed for practical application. Proteomic analysis revealed 48 proteins linked to total mortality and 153 proteins associated with CVD mortality, with significant enrichment in immune regulation and cardiovascular pathways. Machine learning models identified key predictors, such as EDA2R and NTproBNP, which demonstrated mediation effects of 12.92% and 13.86%, respectively.<h3>Conclusion</h3>These findings establish a precision nutrition framework that links HFD patterns, proteomic biomarkers, and mortality outcomes, offering actionable insights for clinical and public health intervention.","PeriodicalId":14952,"journal":{"name":"Journal of Advanced Research","volume":"23 1","pages":""},"PeriodicalIF":10.7,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145907653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-03DOI: 10.1016/j.jare.2026.01.008
Guoxing Tang, Wenjin Xing, Lin Zhu, Yuting Lu, Kaishan Jiang, Shiji Wu, Ziyong Sun, Hongyan Hou, Liming Cheng, Fan He, Feng Wang
Introduction
Sepsis remains one of the leading causes of death worldwide. Monocytes play a pivotal role in sepsis due to their dual role in both pro-inflammation and immunosuppression. However, the phenotypic markers and developmental characteristics of immunosuppressive monocytic myeloid-derived suppressor cells (M-MDSCs) in sepsis remain largely unknown.
Objectives
This study aimed to investigate the functional heterogeneity of M-MDSCs in sepsis.
Methods
The frequency of M-MDSCs was assessed for the prognosis of sepsis. Single-cell RNA sequencing was conducted on purified HLA-DRhighCD14+ and HLA-DRlowCD14+ monocytes, respectively, from patients with sepsis to study their heterogeneity.
Results
We find that the frequency of M-MDSCs, as defined by classical markers, has limited value in the prognosis of sepsis due to their broad heterogeneity. Based on scRNA-seq analysis, M-MDSCs in sepsis are established as HLA-DRlowCD14+ monocytes, which display high expression of RETN and low expression of HLA-DPB1. We further segregate M-MDSCs into five subsets: IL1R2_M-MDSC, THBS1_M-MDSC, S100A_M-MDSC, IFN-sti_M-MDSC, and PPBP_M-MDSC, with the first three subsets accounting for the majority of the population. Pro-inflammatory S100A_M-MDSC dominates early-stage population and is characterized by high expression of S100A. Middle-stage IL1R2_M-MDSC exhibits cytokine production, while THBS1_M-MDSC is associated with TGF-β signaling, revealing concurrent pro-inflammatory and immunosuppressive functions. In the late stage, both THBS1_M-MDSC and IL1R2_M-MDSC display immunosuppressive functions. Furthermore, our data suggest a metabolic shift from oxidative phosphorylation to fatty acid and amino acid biosynthesis as pro-inflammatory monocytes transition into immunosuppressive M-MDSCs. VSIG4, a novel functional marker of immunosuppressive M-MDSCs, is specifically expressed in THBS1_M-MDSC. Subsequently, our preliminary results suggest that the combined detection of surface VSIG4 and IL1R2 on HLA-DRlowCD14+ monocytes shows potential for predicting sepsis outcomes.
Conclusion
This study illuminates the characteristics of M-MDSCs in sepsis, providing a new direction for disease prognosis.
{"title":"Illustrating the functional heterogeneity of M-MDSCs to predict sepsis outcomes","authors":"Guoxing Tang, Wenjin Xing, Lin Zhu, Yuting Lu, Kaishan Jiang, Shiji Wu, Ziyong Sun, Hongyan Hou, Liming Cheng, Fan He, Feng Wang","doi":"10.1016/j.jare.2026.01.008","DOIUrl":"https://doi.org/10.1016/j.jare.2026.01.008","url":null,"abstract":"<h3>Introduction</h3>Sepsis remains one of the leading causes of death worldwide. Monocytes play a pivotal role in sepsis due to their dual role in both pro-inflammation and immunosuppression. However, the phenotypic markers and developmental characteristics of immunosuppressive monocytic myeloid-derived suppressor cells (M-MDSCs) in sepsis remain largely unknown.<h3>Objectives</h3>This study aimed to investigate the functional heterogeneity of M-MDSCs in sepsis.<h3>Methods</h3>The frequency of M-MDSCs was assessed for the prognosis of sepsis. Single-cell RNA sequencing was conducted on purified HLA-DR<sup>high</sup>CD14<sup>+</sup> and HLA-DR<sup>low</sup>CD14<sup>+</sup> monocytes, respectively, from patients with sepsis to study their heterogeneity.<h3>Results</h3>We find that the frequency of M-MDSCs, as defined by classical markers, has limited value in the prognosis of sepsis due to their broad heterogeneity. Based on scRNA-seq analysis, M-MDSCs in sepsis are established as HLA-DR<sup>low</sup>CD14<sup>+</sup> monocytes, which display high expression of RETN and low expression of HLA-DPB1. We further segregate M-MDSCs into five subsets: IL1R2_M-MDSC, THBS1_M-MDSC, S100A_M-MDSC, IFN-sti_M-MDSC, and PPBP_M-MDSC, with the first three subsets accounting for the majority of the population. Pro-inflammatory S100A_M-MDSC dominates early-stage population and is characterized by high expression of S100A. Middle-stage IL1R2_M-MDSC exhibits cytokine production, while THBS1_M-MDSC is associated with TGF-β signaling, revealing concurrent pro-inflammatory and immunosuppressive functions. In the late stage, both THBS1_M-MDSC and IL1R2_M-MDSC display immunosuppressive functions. Furthermore, our data suggest a metabolic shift from oxidative phosphorylation to fatty acid and amino acid biosynthesis as pro-inflammatory monocytes transition into immunosuppressive M-MDSCs. VSIG4, a novel functional marker of immunosuppressive M-MDSCs, is specifically expressed in THBS1_M-MDSC. Subsequently, our preliminary results suggest that the combined detection of surface VSIG4 and IL1R2 on HLA-DR<sup>low</sup>CD14<sup>+</sup> monocytes shows potential for predicting sepsis outcomes.<h3>Conclusion</h3>This study illuminates the characteristics of M-MDSCs in sepsis, providing a new direction for disease prognosis.","PeriodicalId":14952,"journal":{"name":"Journal of Advanced Research","volume":"17 1","pages":""},"PeriodicalIF":10.7,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145895085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-03DOI: 10.1016/j.jare.2026.01.007
Guiling Cheng, Yan Liu, Yangkun Xing, Zhewei Shi, Mohamed Ali Farag, Songheng Jin, Bo Xia
{"title":"Lactylation at the metabolic-epigenetic interface in cardiovascular diseases: context-dependent mechanisms and translational roadmap","authors":"Guiling Cheng, Yan Liu, Yangkun Xing, Zhewei Shi, Mohamed Ali Farag, Songheng Jin, Bo Xia","doi":"10.1016/j.jare.2026.01.007","DOIUrl":"https://doi.org/10.1016/j.jare.2026.01.007","url":null,"abstract":"","PeriodicalId":14952,"journal":{"name":"Journal of Advanced Research","volume":"165 1","pages":""},"PeriodicalIF":10.7,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145895088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Structural optimization and functional evaluation of cytisine for redox homeostasis regulation in lung cancer cells","authors":"Zhicui Qin, Zilu Xin, Xueli Zhang, Xin Liu, Yang Yang, Feng Feng, Zongwei Xia, Xiuling Yu","doi":"10.1016/j.jare.2026.01.006","DOIUrl":"https://doi.org/10.1016/j.jare.2026.01.006","url":null,"abstract":"","PeriodicalId":14952,"journal":{"name":"Journal of Advanced Research","volume":"28 1","pages":""},"PeriodicalIF":10.7,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145895089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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