Biology subjects

Widschwendter, M.

Publications and source records attributed to Widschwendter, M..

6 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

Devising reliable and accurate epigenetic clocks:choosing the optimal computational solution

Illumina DNA methylation arrays are frequently used for the discovery of methylation signatures associated with aging and disease. One of the major hurdles to overcome when training trait prediction models is the high dimensionality of the data, with the number of features (CpGs) greatly exceeding the typical number of samples assessed. In addition, most large-scale DNA methylation-based studies do not include replicate measurements for a given sample, making it impossible to estimate the degree of measurement uncertainty or the reliability of the prediction models. A recent study proposed that training penalized regression models on derived principal components (PCs) rather than on the original features (CpGs) results in more reliable age predictions, as estimated from technical replication. Moreover, the same method could be applied for predicting other phenotypes more reliably. Here, we aimed at validating the proposed PC method. We found that although dimension reduction with PCA consistently led to small improvements in the reliability of age prediction models, it severely compromised their accuracy. PC-based models needed far larger training set sizes to be similarly accurate as CpG-based models, whereas reliability did not depend on the sample size of the training set data for either approach. Finally, the PC version of a novel multiclass predictor for breast, ovarian and endometrial cancer we trained using weighted ensembles of deep-learning models also had a markedly lower predictive accuracy compared to a CpG version, suggesting limited applicability of the proposed PC method for predicting phenotypes beyond age.

genomics↗

bioRxiv2023

Technical and biological sources of unreliability of Infinium type II probes of the Illumina MethylationEPIC BeadChip microarray

The Illumina Methylation array platform has facilitated countless epigenetic studies on DNA methylation (DNAme) in health and disease, yet relatively few studies have so studied its reliability, i.e., the consistency of repeated measures. Here we focus on the reliability of both type I and type II Infinium probes. We propose a method for excluding unreliable probes based on dynamic thresholds for mean intensity (MI) and unreliability, estimated by probe-level simulation of the influence of technical noise on methylation {beta}-values using the background intensities of negative control probes. We validate our method in several datasets, including Illumina MethylationEPIC BeadChip v1.0 data from paired whole blood samples taken six weeks apart. Our analysis revealed that specifically probes with low MI exhibit higher {beta}-value variability between repeated samples. MI was associated with the number of C-bases in the respective probe sequence and correlated negatively with unreliability scores. The unreliability scores were substantiated through validation in a new EPIC v1.0 (blood and cervix) and a publicly available 450k (blood) dataset, as they effectively captured the variability observed in {beta}-values between technical replicates. Finally, despite promising higher robustness, the newer version v2.0 of the MethylationEPIC BeadChip retained a substantial number of probes with poor unreliability scores. To enhance current pre-processing pipelines, we developed an R package to calculate MI and unreliability scores and provide guidance on establishing optimal dynamic score thresholds for a given data set.

genomics↗

bioRxiv2017

Correcting For Cell-Type Heterogeneity In Epigenome-Wide Association Studies: Premature Analyses And Conclusions

Recently, a study by Rahmani et al [1] claimed that a reference-free cell-type deconvolution method, called ReFACTor, leads to improved power and improved estimates of cell-type composition compared to competing reference-free and reference-based methods in the context of Epigenome-Wide Association Studies (EWAS). However, we identified many critical flaws (both conceptual and statistical in nature), which seriously question the validity of their claims. We outlined constructive criticism in a recent correspondence letter, Zheng et al [2]. The purpose of this letter is two-fold. First, to present additional analyses, which demonstrate that our original criticism is statistically sound. Second, to highlight additional serious concerns, which Rahmani et al have not yet addressed. In summary, we find that ReFACTor has not been demonstrated to outperform state-of-the-art reference-free methods such as SVA or RefFreeEWAS, nor state-of-the-art reference-based methods. Thus, the claim by Rahmani et al (a claim reiterated in their recent response letter [3]) that ReFACT or represents an advance over the state-of-the-art is not supported by an objective and rigorous statistical analysis of the data.

bioinformatics↗