bioRxiv Science⌕ Search

bioRxiv · 10.1101/041582

Metabolic profiling of alcohol consumption in 9778 young adults

Abstract

BackgroundHigh alcohol consumption is a major cause of morbidity, yet alcohol is associated with both favourable and adverse effects on cardiometabolic risk markers. We aimed to characterize the associations of usual alcohol consumption with a comprehensive systemic metabolite profile in young adults.\n\nMethodsCross-sectional associations of alcohol intake with 86 metabolic measures were assessed for 9778 individuals from three population-based cohorts from Finland (age 24-45 years, 52% women). Metabolic changes associated with change in alcohol intake during 6-year follow-up were further examined for 1466 individuals. Alcohol intake was assessed by questionnaires. Circulating lipids, fatty acids and metabolites were quantified by high-throughput nuclear magnetic resonance metabolomics and biochemical assays.\n\nResultsIncreased alcohol intake was associated with cardiometabolic risk markers across multiple metabolic pathways, including higher lipid concentrations in HDL subclasses and smaller LDL particle size, increased proportions of monounsaturated fatty acids and decreased proportion of omega-6 fatty acids, lower concentrations of glutamine and citrate (P<0.001 for 56 metabolic measures). Many metabolic biomarkers displayed U-shaped associations with alcohol consumption. Results were coherent for men and women, consistent across the three cohorts, and similar if adjusting for body mass index, smoking and physical activity. The metabolic changes accompanying change in alcohol intake during follow-up resembled the cross-sectional association pattern (R2=0.83, slope=0.72{+/-}0.04).\n\nConclusionsAlcohol consumption is associated with a complex metabolic signature, including aberrations in multiple biomarkers for elevated cardiometabolic risk. The metabolic signature tracks with long-term changes in alcohol consumption. These results elucidate the double-edged effects of alcohol on cardiovascular risk.\n\nKey messagesO_LIAlcohol intake in young adults associates with multiple novel metabolic biomarkers for the risk of cardiovascular disease and type 2 diabetes. The metabolic aberrations are mainly adversely related to cardiometabolic risk\nC_LIO_LIProminent metabolic associations with alcohol consumption include monounsaturated fatty acids, omega-6 fatty acids, glutamine, citrate and lipoprotein particle size. Many of these cardiometabolic biomarkers were as strongly associated with alcohol intake as HDL cholesterol\nC_LIO_LIThe strongest novel biomarkers of alcohol consumption followed linear association shapes, whereas many lipid measures displayed U-shaped associations\nC_LIO_LIThe detailed metabolic signature of alcohol consumption tracked with long-term changes in alcohol use, suggesting that the observed metabolic changes are at least partly due to alcohol consumption\nC_LIO_LIThe results provide improved understanding of the diverse molecular processes related to alcohol intake. Novel metabolic biomarkers reflecting both alcohol intake and cardiovascular risk could serve as intermediates that may help to bridge the complex relation between alcohol and cardiometabolic risk\nC_LI

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Peter Würtz, Sarah Cook, Qin Wang, Mika Tiainen, Tuulia Tynkkynen, Antti J Kangas, Pasi Soininen, Jaana Laitinen, Jorma Viikari, Mika Kähönen, Terho Lehtimäki, Markus Perola, Stefan Blankenberg, Tanja Zeller, Satu Männistö, Veikko Salomaa, Marjo-Riitta Järvelin, Olli T Raitakari, Mika Ala-Korpela, David A Leon. 2016-02-29. Metabolic profiling of alcohol consumption in 9778 young adults. https://doi.org/10.1101/041582

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Personalized Immunotherapy via Multiscale Tumor-Immune Modeling and Optimal Control

Cancer remains a global health challenge requiring sophisticated understanding of tumor-immune dynamics for effective treatment design. Mathematical oncology has emerged as a rapidly evolving interdisciplinary field that uses mathematical models to enhance our understanding of cancer dynamics, including tumor growth, metastasis, and treatment response. This paper presents a comprehensive multiscale framework integrating patient-specific data, machine learning, and optimal control for personalized immunotherapy design. We develop a hybrid model that combines deterministic dynamics with stochastic elements and time delays, capturing the inherent variability and temporal lags in biological processes. The model incorporates biologically realistic Holling Type-II functional responses and is validated against longitudinal clinical data from 100+ cancer patients and patient-derived organoid experiments. Using deep neural networks with Bayesian regularization, we learn patient-specific parameter distributions from clinical biomarkers and predict treatment responses with high accuracy. Our optimal control framework, incorporating clinical constraints and toxicity limits, generates personalized treatment protocols that stabilize otherwise unstable dynamics. The framework establishes a new paradigm for precision immuno-oncology, bridging mathematical theory, computational methods, and clinical practice. Author summaryCancer remains one of the leading causes of death worldwide, and the immune system plays a crucial role in controlling tumor growth. However, the complex interactions between tumor cells and immune cells make it difficult to predict how individual patients will respond to immunotherapy. In this work, we develop a mathematical framework that integrates patient-specific data, machine learning, and optimal control to design personalized immunotherapy strategies. Our model captures the realistic dynamics of tumor-immune interactions by incorporating biologically relevant features such as time delays (representing immune response lags) and stochastic effects (representing biological variability). Using deep learning, we estimate patient-specific parameters from clinical biomarkers, enabling personalized predictions of treatment outcomes. We validate our framework against data from over 100 cancer patients and patient-derived organoid experiments, demonstrating excellent agreement. Our optimal control approach generates personalized treatment protocols that stabilize otherwise unstable tumor dynamics, achieving 78% tumor reduction compared to 52% for standard-of-care protocols. These findings suggest that therapies targeting immunological thresholds may be as important as those directly killing tumor cells, providing a new perspective for immunotherapy design. This framework bridges mathematical theory, computational methods, and clinical practice, offering a pathway toward truly personalized cancer treatment.

Systems Biology↗

Reversible Opto-Chemical activation of KRASG12V signaling with near single-cell precision

KRAS mutations drive some of the most lethal carcinomas, and genomic and inducible systems have established many of the cellular and tissue-level consequences. However, these approaches operate at the level of oncogene expression, allowing for cellular adaptation that masks the individual role of KRAS oncoprotein signaling. Here, we developed a reversible Opto-Chemical system to activate KRAS signaling by chemically translocating a cytosolic mutant KRASG12V G-domain to the plasma membrane upon light or small-molecule input. In MDCK cells, the G-domain plasma membrane recruitment activated downstream signaling and reduced collective migration. In mouse small Intestinal Organoids, G-domain recruitment promoted increased crypt size and number under Epidermal Growth Factor-deprived conditions. We further showed that the increased number of crypts depended on continuous KRASG12V signaling. Finally, under the same deprived conditions, localized activation in just one budding crypt promoted crypt formation compared to controls. This system decouples oncoprotein activity from oncogene expression, allowing to investigate the KRAS signaling contribution to early epithelial transformation.

Systems Biology↗

Mammalian aging involves genome-wide splicing degeneration leading to functional decline

Alternative splicing exhibits significant changes during development and aging, affecting the composition and variance in the transcriptome. However, it is unclear whether and how age-associated splicing dysregulation leads to functional consequences. Here, an integrative analysis of transcriptome data across mouse and human tissues revealed that aging is characterized by systematic deterioration of the fidelity of RNA splicing, here termed splicing degeneration, a measure of functional alteration of reading frame and domain configuration of protein products. Genes with higher aging-associated splicing degeneration were more conserved and enriched for processes such as RNA metabolism and antigen presentation. By assessing alternative splicing events associated with functional deterioration, we quantified the degree of splicing degeneration. Its level increased with age but was alleviated following calorie restriction or rapamycin treatment, indicating that it can serve as a new molecular hallmark of aging. Mechanistically, through a comprehensive meta-data analysis, we discovered that splicing degeneration is associated with age-associated changes in specific splicing factors, which in turn showed a strong association with age-related transcriptome changes. Overall, our study demonstrates the intricate relationship between aging and genome-wide splicing degeneration, revealing a promising target for aging interventions acting to reverse splicing degeneration.

Systems Biology↗