Biology subjects

Hagen, M.

Publications and source records attributed to Hagen, M..

4 recordsLinked to original sources

bioRxiv2025

Longitudinal multi-omic evaluation of biomarkers of health and ageing over smoking cessation intervention

Smoking is one of the single most important preventable risk factors for cancer and other adverse health outcomes 1,2. Smoking cessation represents a key public health intervention with the potential to reduce its negative health outcomes 2-4. While epidemiological, cross-sectional, and individual longitudinal omic or biomarker studies have evaluated the impact of smoking cessation, no study to date has systematically profiled molecular and clinical changes in several organ systems or tissues longitudinally over the course of smoking cessation that could allow for more detailed assessment of response biomarkers and the identification of interindividual differences in the recovery of physiological functions. Here, we report the first human longitudinal multi-omic study of smoking cessation, evaluating 2,501 unique single or composite features from 1,094 longitudinal samples. Our comprehensive analysis, leveraging over half a million longitudinal data points, revealed a profound effect of smoking cessation on epigenetic biomarkers and microbiome features across multiple organ systems within 6 months of smoking cessation, alongside shifts in the immune and blood oxygenation system. Moreover, our multi-omic analysis provided unprecedented granularity that allows for identification of new cross-ome associations for mechanistic discovery. We anticipate that data and an interactive app from the Tyrol Lifestyle Atlas (eutops.github.io/lifestyle-atlas), comprising the current study and a parallel study arm evaluating the impact of diet on biomarkers of health and disease, will provide the basis for future discovery, biomarker benchmarking in their responsiveness to health-promoting interventions, and study of individualised response group, representing a major advance for personalised health monitoring using biomarkers.

systems biology↗

bioRxiv2025

Systemic multi-omic remodelling underlies health benefits of intermittent fasting

While intermittent fasting (IF) promotes longevity in animal models, its systemic effects in humans remain poorly understood. Here, we present a six-month longitudinal IF intervention in 114 women (BMI 25-35) with deep clinical, molecular, and microbiome profiling across >3,400 biospecimens from six tissues. Analyses spanning >2,200 multi-omic features and 11,000 microbial function predictions demonstrate coordinated clinical benefits, including improvements in body composition and cardiorespiratory fitness, and reveal coordinated molecular responses across tissues. Iron metabolism emerged as a central axis: transferrin increased while ferritin, haemoglobin, and erythrocytes decreased, changes that opposed ageing trajectories yet remained within physiological limits. Epithelial DNA methylation biomarkers (cervical, buccal) of cancer risk reduced, while blood clocks were largely unresponsive, underscoring tissue-specificity of the epigenome. Immune profiling uncovered dynamic, partially reversible shifts. Notably, we derived a new immunophenotyping-based ImmuneAge score that increased during fasting and tracked with inflammatory function, while the pro-inflammatory cytokine IL-17A declined selectively in postmenopausal women. Oral microbiota showed rapid restructuring, whereas gut microbiota shifted more subtly toward enhanced metabolic capacity. Together, these data provide unprecedented insight into the systemic and tissue-specific responses to IF in humans and identify iron homeostasis and immune remodelling as candidate mechanisms. Our findings are available through the Lifestyle Atlas (https://eutops.github.io/lifestyle-atlas). HighlightsO_LIIntermittent fasting remodels iron metabolism, opposing age-associated trajectories C_LIO_LIEpithelial but not blood methylation biomarkers respond to fasting C_LIO_LIFasting transiently elevates ImmuneAge and TNF-producing cytotoxic T cells C_LIO_LIOral microbiota restructure rapidly, while gut microbiota show subtler functional shifts C_LIO_LIIntegrative networks link iron, immunity, adiposity, and epithelial barrier function C_LI

systems biology↗

bioRxiv2025

Multi-modal atlas of lifestyle interventions reveals malleability of ageing-linked molecular features

Extending human healthspan requires understanding how lifestyle interventions impact molecular systems across tissues and time. Here, we present the TirolGESUND Lifestyle Atlas (ClinicalTrials.gov: NCT05678426), a longitudinal, multi-modal resource profiling 156 healthy women (aged 30-60 years) undergoing 6-month intermittent fasting (n=114) or smoking cessation (n=42) interventions. Participants were sampled up to four times across seven tissues and fluids, generating >3,450 biospecimens with harmonised DNA methylation, metabolomics, microbiome, and immune profiling, alongside skin histology, barrier measurements, and rich clinical metadata. We demonstrate the utility of this dataset through: (i) multi-omics-wide association studies linking traits to molecular features; (ii) integrative factor modelling revealing coordinated cross-tissue signatures; (iii) epigenetic-biomarker cross-omic associations, and (iv) CpG-level variance decomposition mapping stable, individual-specific, tissue-restricted, and intervention-responsive methylation patterns. We further show that ageing-linked features are selectively malleable: highly compliant intermittent fasting participants exhibited attenuated or even age-opposing molecular trajectories within six months. The atlas enables unprecedented within-cohort comparisons across omic layers and tissues, supporting discovery of context-dependent biomarkers, cross-system coordination, and intervention responsiveness. Data are available via an interactive portal, with sensitive data under controlled access (https://eutops.github.io/lifestyle-atlas/). This resource provides a foundation for exploring biomarker association and multi-tissue epigenetics, enabling hypothesis generation and benchmarking for systems biology and human healthspan research. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=186 HEIGHT=200 SRC="FIGDIR/small/673115v2_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@fec2d9org.highwire.dtl.DTLVardef@1aa5d13org.highwire.dtl.DTLVardef@1c4a6f2org.highwire.dtl.DTLVardef@1ac62fb_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIMulti-tissue multi-modal profiling of two clinically-relevant lifestyle interventions in 156 women aged 30-60 C_LIO_LIIntermittent fasting modifies ageing-linked molecular features in an age-opposing direction C_LIO_LICross-tissue epigenetic biomarker mapping links immunity, metabolism, and microbiome C_LIO_LITissue-specific epigenetic plasticity maps reveal candidate meQTLs, C_LIO_LIData sharing and interactive portal enable broad reuse for hypothesis generation and exploration C_LI

systems biology↗

bioRxiv2023

Interpretable Machine Learning Decodes Soil Microbiome's Response to Drought Stress

BackgroundExtreme weather events induced by climate change, particularly droughts, have detrimental consequences for crop yields and food security. Concurrently, these conditions provoke substantial changes in the soil metagenome and affect plant health. Early recognition of soil affected by drought enables farmers to implement appropriate agricultural management practices. In this context, interpretable Machine Learning holds immense potential for drought stress classification in the soil metagenome based on marker taxa. ResultsThis study demonstrates that the metagenomic approach of Differential Abundance Analysis methods and Machine Learning-based Shapley Additive Explanation values provide similar information. They exhibit their potential as complementary approaches for identifying marker taxa and investigating their enrichment or depletion under drought stress in grass lineages. Additionally, the Random Forest Classifier trained on a diverse range of relative abundance data from the soil metagenome of various plant species achieves a high accuracy of 92.3 % at the genus rank for drought stress prediction. It demonstrates its generalization capacity for the lineages tested. ConclusionsIn the detection of drought stress in the soil metagenome, this study emphasizes the potential of an optimized and generalized location-based ML classifier. By identifying marker taxa, this approach holds promising implications for microbe-assisted plant breeding programs and contributes to the development of sustainable agriculture practices. These findings are crucial for preserving global food security in the face of climate change.

bioinformatics↗