arXiv subjects

Emanuele Di Angelantonio

Publications and source records attributed to Emanuele Di Angelantonio.

6 recordsLinked to original sources

arXiv2026

Enhancing comorbidity network inference with risk-enriched health trajectories embedding

Multimorbidity poses a growing challenge for individual health, reducing quality of life and increasing treatment burden, resulting in a multiplicative impact on healthcare system management and fragmented care trajectories. Comorbidity networks could provide crucial insight into characterising multimorbidity and disease relationships. However, existing approaches to comorbidity network construction face critical limitations: they overlook temporal information by relying on cross-sectional statistics, produce biased association estimates by ignoring confounding due to shared risk factors, and fail to distinguish between direct and indirect disease associations, thereby yielding fully connected networks. To address these limitations, we develop a methodological framework for population-level disease network inference that uses individual health trajectories to learn disease associations, capturing semantic similarity and temporal co-occurrence. Sparse network estimation is achieved via Gaussian Graphical Models with Lasso regularisation, informed by prior clinical knowledge on shared risk factors derived from a dedicated confounding evaluation step. Applied to UK Biobank data comprising 24 cardiometabolic diseases and 76 risk factors, the resulting network revealed clinically meaningful disease patterns. Topological analysis identifies key pathological hubs, reveals potential actionable targets for multimorbidity management, and identifies four distinct disease communities that align with the established cardiometabolic taxonomy. Building on this community structure, we derive community-based patient representations that capture disease progression dynamics. Clustering these representations reveals four progression phenotypes with significantly different long-term survival trajectories, highlighting the potential of the framework for risk stratification and personalised care.

stat.AP↗

arXiv2026

Prior-informed conditional Gaussian graphical models: an application to protein interaction network reconstruction

Protein-protein interaction (PPI) networks, estimated from high-throughput omics data, foster biomarker discovery and precision medicine. Gaussian graphical models (GGMs) offer a principled reconstruction framework. Yet, existing applications face two limitations: they overlook the rich existing knowledge encoded in curated biological databases, and they assume a homogeneous network structure across all individuals, neglecting the influence of covariates or confounding factors on these interactions and preventing personalised representations. Even though these limitations have been addressed separately in previous work, no current approach resolves them simultaneously. We introduce a prior-informed conditional Gaussian graphical model that integrates database-derived interaction priors with covariate-dependent network modeling in a unified, scalable framework. The key methodological innovation is a structured, weighted penalty that selectively incorporates priors into population-level network estimation, while leaving context-specific perturbations entirely data-driven, as curated databases capture canonical interactions rather than disease-specific signals. Simulation studies demonstrate consistent and robust improvements in population-level network reconstruction across diverse settings, even when prior knowledge is imperfect. Applied to UK Biobank cardiometabolic proteomics (n = 49,129, p = 366 proteins), the method recovers T2D-associated network perturbations, identifying 34 network-central candidate biomarkers, several detectable only through their connectivity, not differential expression, and revealing six biologically coherent protein communities with distinct pathway enrichments spanning metabolic, cardiovascular, and cancer-related processes. Code is available at https://github.com/AlessiaMapelli/Prior-informed-conditional-GGMs.

stat.AP↗

arXiv2026

A latent class approach to assess the effects of dynamic adherence to polytherapy in heart failure patients

Heart failure (HF) treatment relies heavily on pharmacotherapy, particularly combining multiple therapies as recommended by clinical guidelines. However, non-adherence to prescribed regimens remains a significant challenge, contributing to increased hospitalizations and poorer patient outcomes. This study introduces a novel methodological pipeline that integrates Latent Markov Models (LMM) with dynamic adherence modeling to evaluate adherence behaviors and their impact on HF rehospitalization. Using administrative healthcare data from Lombardy, Italy, we analyzed 6,818 patients hospitalized for HF between July and December 2020. Adherence was assessed monthly over a six-month observation period, and adherence profiles were linked to clinical outcomes using Cox regression. Seven latent behavioral profiles were identified, reflecting varying levels and trajectories of adherence. The findings revealed that higher adherence levels significantly reduced the risk of rehospitalization. Patients with consistently high adherence exhibited a 56% lower risk of HF rehospitalization compared to those with low adherence. Importantly, improving adherence during the observation period was associated with better survival probabilities, highlighting the potential benefits of timely interventions. Additionally, adherence behaviors were influenced by factors such as age, comorbidity burden, and hospitalization during the observation period. This study underscores the importance of dynamic and personalized strategies to monitor and enhance adherence to polytherapy. By linking adherence patterns to clinical outcomes, the proposed approach offers actionable insights for improving patient management and reducing the burden of HF on healthcare systems.

stat.AP↗

arXiv2026

mmid: Multi-Modal Integration and Downstream analyses for healthcare analytics in Python

mmid (Multi-Modal Integration and Downstream analyses for healthcare analytics) is a Python package that offers multi-modal fusion and imputation, classification, time-to-event prediction and clustering functionalities under a single interface, filling the gap of sequential data integration and downstream analyses for healthcare applications in a structured and flexible environment. mmid wraps in a unique package several algorithms for multi-modal decomposition, prediction and clustering, which can be combined smoothly with a single command and proper configuration files, thus facilitating reproducibility and transferability of studies involving heterogeneous health data sources. A showcase on personalised cardiovascular risk prediction is used to highlight the relevance of a composite pipeline enabling proper treatment and analysis of complex multi-modal data. We thus employed mmid in an example real application scenario involving cardiac magnetic resonance imaging, electrocardiogram, and polygenic risk scores data from the UK Biobank. We proved that the three modalities captured joint and individual information that was used to (1) early identify cardiovascular disease before clinical manifestations with cardiological relevance, and (2) do it better than single data sources alone. Moreover, mmid allowed to impute partially observable data modalities without considerable performance losses in downstream disease prediction, thus proving its relevance for real-world health analytics applications (which are often characterised by the presence of missing data).

stat.CO↗

arXiv2026

SNPgen: Phenotype-Supervised Genotype Representation and Synthetic Data Generation via Latent Diffusion

Polygenic risk scores and other genomic analyses require large individual-level genotype datasets, yet strict data access restrictions impede sharing. Synthetic genotype generation offers a privacy-preserving alternative, but most existing methods operate unconditionally, producing samples without phenotype alignment, or rely on unsupervised compression, creating a gap between statistical fidelity and downstream task utility. We present SNPgen, a two-stage conditional latent diffusion framework for generating phenotype-supervised synthetic genotypes. SNPgen combines GWAS-guided variant selection (1,024-2,048 trait-associated SNPs) with a variational autoencoder for genotype compression and a latent diffusion model conditioned on binary disease labels via classifier-free guidance. Evaluated on 458,724 UK Biobank individuals across four complex diseases (coronary artery disease, breast cancer, type 1 and type 2 diabetes), models trained on synthetic data matched real-data predictive performance in a train-on-synthetic, test-on-real protocol, approaching genome-wide PRS methods that use $2$-$6\times$ more variants. Privacy analysis confirmed zero identical matches, near-random membership inference (AUC $\approx 0.50$), preserved linkage disequilibrium structure, and high allele frequency correlation ($r \geq 0.95$) with source data. A controlled simulation with known causal effects verified faithful recovery of the imposed genetic association structure.

cs.LG↗

arXiv2026

Unraveling time-varying causal effects of multiple exposures: integrating Functional Data Analysis with Multivariable Mendelian Randomization

Mendelian Randomization is a widely used instrumental variable method for assessing causal effects of lifelong exposures on health outcomes. Many exposures, however, have causal effects that vary across the life course and often influence outcomes jointly with other exposures or indirectly through mediating pathways. Existing approaches to multivariable Mendelian Randomization assume constant effects over time and therefore fail to capture these dynamic relationships. We introduce Multivariable Functional Mendelian Randomization (MV-FMR), a new framework that extends functional Mendelian Randomization to simultaneously model multiple time-varying exposures. The method combines functional principal component analysis with a data-driven cross-validation strategy for basis selection and accounts for overlapping instruments and mediation effects. Through extensive simulations, we assessed MV-FMR's ability to recover time-varying causal effects under a range of data-generating scenarios and compared the performance of joint versus separate exposure effect estimation strategies. Across scenarios involving nonlinear effects, horizontal pleiotropy, mediation, and sparse data, MV-FMR consistently recovered the true causal functions and outperformed univariable approaches. To demonstrate its practical value, we applied MV-FMR to UK Biobank data to investigate the time-varying causal effects of systolic blood pressure and body mass index on coronary artery disease. MV-FMR provides a flexible and interpretable framework for disentangling complex time-dependent causal processes and offers new opportunities for identifying life-course critical periods and actionable drivers relevant to disease prevention.

stat.AP↗