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Background: This study aimed to investigate the associations between serum lipoprotein subclasses and the long-term risk of gastrointestinal (GI) cancers to enhance our understanding of the etiology of GI cancers.
Methods: This prospective cohort study included 249,450 participants from the UK Biobank. Cox proportional hazard models were used to assess the association between 17 serum lipoprotein subclasses with the risk of GI cancers. Restricted cubic spline (RCS) analysis was employed to assess the corresponding dose-response relationships. Additionally, Mendelian randomization (MR) analysis was used to evaluate the causal relationships between the lipoproteins and the risk of GI cancers.
Results: A total of 4,787 cases of GI cancers were recorded over a median follow-up period of 12.92 years. Our results revealed that the majority of the high-density lipoprotein (HDL) subclasses, such as very large-, large-, and medium-HDL-particles, were positively associated, while several low-density lipoprotein (LDL) subclasses were negatively associated with the risk of overall GI cancer. Additionally, RCS analysis revealed a linear dose-response relationship between elevated levels of most lipoprotein particles and the risk of overall GI cancer development. Additionally, subgroup analysis indicated a significant sex-dependent interaction between lipoprotein particles and the risk of GI cancers. However, MR analysis revealed a different causal relationships between lipoprotein and GI cancers at the genetic level.
Conclusion: In this large-scale metabolomics study, we identified several associations between lipoprotein subclasses and the long-term risk of GI cancers. However, further research is needed to fully elucidate their roles in the mechanisms of cancer development.
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http://dx.doi.org/10.3389/fnut.2025.1501263 | DOI Listing |
Diabetes Obes Metab
September 2025
School of Medicine, University of Auckland, Auckland, New Zealand.
Aim: To investigate the associations of intra-pancreatic fat deposition (IPFD) with low-density lipoprotein (LDL) subfractions and hepatic lipase.
Materials And Methods: IPFD was quantified using a single 3.0 Tesla magnetic resonance imaging scanner.
Sci Rep
August 2025
Graduate School of Medicine, Center for Genomic Medicine, Kyoto University, Kyoto, 606-8507, Japan.
Metabolomics is a powerful molecular phenotyping technology which can be used in population studies to identify metabolites underlying disease conditions. To identify plasma biomarkers potentially predicting chronic diseases we applied H nuclear magnetic resonance (NMR) metabolomics using a 600 MHz spectrometer fitted with an In Vitro Diagnostics Research (IVDr) platform to test associations between 18 known metabolites and 111 lipoprotein constituents that could be quantified and passed our quality control procedure and 944 phenotypes determined in 302 healthy participants of the Japanese Nagahama Study. We identified 907 statistically significant associations (p < 4.
View Article and Find Full Text PDFClin Chim Acta
August 2025
Department of Pathophysiology, Institute of Cardiovascular Disease, Key Lab for Arteriosclerology of Hunan Province, International Joint Laboratory for Arteriosclerotic Disease Research of Hunan Province, Hengyang Medical School, University of South China, Hengyang, China. Electronic address: huangj
High-density lipoprotein (HDL) plays a key role in reverse cholesterol transport (RCT), traditionally associated with cardiovascular protection. However, while low HDL-cholesterol (HDL-C) levels correlate with increased cardiovascular risk, therapeutic interventions raising HDL-C (e.g.
View Article and Find Full Text PDFJ Sports Sci
August 2025
Department of Chemistry, University of Bergen, Bergen, Norway.
It is not known whether aerobic capacity (fitness) or overweight/obesity (fatness) moderate the association between physical activity (PA) and lipoproteins/lipids in children. The aim of this study was to investigate moderation of the cross-sectional and longitudinal associations between PA and the lipoprotein subclass profile by fitness and fatness in children. Eight hundred and sixty-two (cross-sectional analysis) or 787 (longitudinal analysis) children (age 10.
View Article and Find Full Text PDFHeart
August 2025
National Clinical Research Center for Kidney Disease, State Key Laboratory of Multi-organ Injury Prevention and Treatment, Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangzhou, China
Objective: To examine the association between nuclear magnetic resonance (NMR)-based metabolomics and aortic stenosis (AS) risk, and determine whether metabolomic profiling can enhance AS prediction beyond conventional clinical risk factors.
Methods: We included 168 metabolites in our study. The primary outcome of interest was incident AS.