Sven Bergmann

While sex as a biological variable has gained increasing attention in recent years, its effect in shaping retinal vascular phenotypes and their genetic architecture remains poorly characterized. Given known sex differences in vascular biology and disease risk, elucidating these effects may provide insights into sex-specific mechanisms of systemic vascular disease. Here, we investigated sex differences in retinal vascular phenotypes in the UK Biobank (N = 68k), which are emerging as non-invasive biomarkers for ocular, cardiovascular, and neurodegenerative diseases. We used both interaction and sex-stratified analyses in disease association and genome-wide association studies, including the X chromosome. Our analyses identified sex-differential associations between retinal traits and cardiometabolic risk factors as well as disease outcomes. Several phenotypes showed prognostic relevance for mortality and cardiovascular endpoints in Cox models, predominantly in one sex, illustrating the limitations of sex-agnostic analyses. Genetic analyses indicated modest differences in genetic architectures, with females showing a greater number of associated variants and gene-level signals, and evidence for sex-specific trait–gene associations. These findings suggest sex-differential genetic and environmental contributions to microvascular morphology, possibly reflecting divergent biological pathways and risk profiles. Our study underscores the necessity of integrating sex-aware approaches in genetic and prognostic research to advance precision medicine and improve tailored risk stratification.

Retinal fundus images offer a non-invasive window into systemic aging. Here, we fine-tune a foundation model (RETFound) to predict chronological age from color fundus images in 71,343 participants from the UK Biobank, achieving a mean absolute error of 2.85 years. The resulting retinal age gap, i.e. the difference between predicted and chronological age, is associated with cardiometabolic traits, inflammation, cognitive performance, all-cause mortality, dementia, cancer, and incident cardiovascular disease. Genome-wide analyses identify genes related to longevity, metabolism, neurodegeneration, and age-related eye diseases. Sex-stratified models reveal consistent performance but divergent biological signatures: males have stronger links to metabolic syndrome, while in females, both model attention and genetics point to a greater involvement of retinal vasculature. Additional analyses indicate that retinal aging patterns in females vary across the menopausal transition, with postmenopausal females exhibiting higher retinal age gap values and clinical associations that more closely resemble those observed in males. Our study positions the retinal age gap as a biologically relevant and sex-specific phenotype associated with multiple aging-related diseases and outcomes beyond conventional risk factors, including chronological age.

Deep learning models based on Vision Transformers (ViTs) have shown strong performance in retinal fundus imaging, but their interpretability remains poorly understood. In particular, attention-based attribution methods are widely used to explain ViT predictions, despite limited evaluation of their faithfulness and biological relevance in medical imaging. Here, we systematically benchmark four attention-based interpretability methods for RETFound, a retinal ViT-based foundation model, that we previously fine-tuned to predict 17 retinal vascular phenotypes from UK Biobank fundus images ¹ . We compare raw attention, attention rollout, gradient-weighted attention rollout, and Chefer’s hybrid relevance-based method using both qualitative visualisation and quantitative evaluation frameworks. To assess attribution faithfulness, we perform perturbation-based deletion and insertion experiments, quantifying changes in model predictions as highly attended image regions are progressively removed or restored. To evaluate biological specificity, we run structure-aware analyses combining attribution maps with vessel segmentation and artery-vein labels through the Relative ratio of Attention Intensity (RAI) metric. Across models, attribution maps differed substantially depending on the selected interpretability method, highlighting the need for rigorous quantitative evaluation. Among the evaluated approaches, gradient-weighted attention rollout consistently achieved the strongest perturbation performance and produced attribution maps most closely aligned with the anatomical definition of the predicted retinal traits. Furthermore, vessel-type specific models systematically concentrate attention on the corresponding vascular structures despite being trained using only a single scalar value per image as supervision. These findings demonstrate that attention-based attribution methods capture biologically meaningful vascular representations, while also revealing method-dependent variability in attribution behaviour. This work provides a quantitative framework for evaluating interpretability methods in medical imaging with annotated segmentation and contributes toward more transparent and biologically grounded medical AI systems.

Genetic predisposition and alcohol consumption are risk factors for increased blood pressure (BP), but their interactions influencing BP remain understudied. We conducted population-specific and cross-population meta-analyses of genome-wide gene-alcohol (GxAlc) interactions affecting BP in >1.1M individuals from multiple populations. We identified 46 GxAlc interaction loci for BP, including 21 from one-degree-of-freedom interaction tests (PGxAlc<5x10-8; or <0.05/Meff, Meff independent BP associations at P<10-5), and 25 from two-degree-of-freedom tests of main and interaction effects (PGxAlc<0.05/M2df, M2df independent 2df-associations at P2df<5x10-8), including 7 novel and 39 known BP loci. The 12q24 locus highlights the genetic effect of BRAP-rs11066001 on BP, being ~6 times larger in current drinkers than in non-drinkers. Gene prioritization with 46 GxAlc loci identified 15 genes with ≥3 lines of evidence (location, literature, druggability, functional/regulatory annotation, or pathway analyses). Several loci showed sex- and population-specific effects and revealed biological pathways of alcohol’s influence on BP, suggesting mechanisms underlying alcohol-induced hypertension.

Background: Vascular properties of the retina are indicative of both ocular and systemic cardio- and cerebrovascular health. However, the specific relationships between retinal and non-retinal vascular phenotypes have not been systematically investigated in large samples. This study aims to compare cross-organ phenotypic and genetic relationships between vascular characteristics across different body sites. Methods: We compared vascular image-derived phenotypes from the brain, carotid artery, aorta, and retina, using UK Biobank sample sizes ranging from 18,808 to 68,740 participants. We examined phenotypic and genetic correlations, as well as common associated genes and pathways. Results: Here we show that white matter hyperintensities are positively correlated with carotid intima-media thickness ( r = 0.03), lumen diameter ( r = 0.14), and aortic cross-sectional areas ( r = 0.09), but negatively correlated with aortic distensibilities ( r ≤ −0.05). Arterial retinal vascular density shows negative correlations with white matter hyperintensities ( r = −0.04), intima-media thickness ( r = −0.04), lumen diameter ( r = −0.06), and aortic areas ( r = −0.05), while positively correlating with aortic distensibilities ( r = 0.04). Significant correlations also persist after correcting for hypertension. Conclusions: Our findings shed light on the complex interplay between vascular morphology across different organs, revealing both shared and distinct genetic underpinnings. Retinal vascular features reflect broader systemic vascular morphology and offer an accessible window into cardio- and cerebrovascular health.

Background and aims: Tobacco smoking is a major risk factor for cardiovascular and lung diseases. A better understanding of its neurobiological underpinnings will benefit the prevention of smoking‐related illnesses and mortality. Previous studies link smoking to increased iron concentration in the striatum, a central component of the brain's reward system, and to reduced cognitive performance. This study aimed to investigate whether smoking and striatal iron share common biological pathways and to assess potential causal relationships between the two. Methods: Using data from the UK Biobank, we investigated phenotypic and genetic correlations, and causal relationships between smoking initiation and magnetic resonance imaging (MRI)‐derived markers of iron content (T2* and quantitative susceptibility mapping) in the bilateral putamen, caudate and accumbens nuclei. Results: We found positive correlations between smoking and striatal iron (β ∈ [0.03, 0.40], P < 0.001), particularly when comparing current smokers with never smokers. Striatal iron was positively associated with pack‐years (β ∈ [0.11, 0.13], P < 0.001) and inversely related to years since smoking cessation (β ∈ [0.06, 0.10], P < 0.001), suggesting iron levels may decrease after quitting. Genetic analysis confirmed phenotypic correlations, with shared genetic associations ( P < 2.73 × 10 ⁻⁶ , or 0.01 for candidate genes) in genes related to dopaminergic, glutamatergic and synaptic systems ( DRD2 , PPP1R1B , NCAM1 , DLX5 , GGACT , NAT16 , PLEKHM1 ). Causality analysis revealed a relationship from smoking to striatal iron via genes involved in synaptogenesis and plasticity ( BAI3 , SEMA6D , TENM2 ), with evidence of reverse causality from iron to smoking through inflammatory and immune system‐related genes ( ING5 , NLRP7 ). Conclusions: There appear to be links between smoking and striatal iron with complex causal mechanisms involving synaptic transmission and inflammatory circuits. Striatal iron content could serve as a biomarker for smoking‐related neurobiological changes and a potential target for interventions aimed at mitigating cognitive decline related to striatal iron accumulation.

Sex as a biological variable (SABV) remains underemphasized in biomedical research, despite its well-established impact on disease prevalence, progression, and genetic architecture. Here, we investigated sex differences in retinal vascular phenotypes, which are emerging as non-invasive biomarkers for ocular, cardiovascular, and neurodegenerative diseases. We used both interaction and sex-stratified analyses in disease association and genome-wide association studies (GWAS), including the X chromosome. Our analyses revealed sex-specific differences in associations between retinal traits, cardiometabolic risk factors, and disease outcomes. Several phenotypes showed prognostic value for mortality and cardiovascular endpoints, predominantly in one sex, illustrating the limitations of sex-agnostic analyses. Genetic analyses revealed differences in genetic architectures, with females exhibiting higher heritability and a greater number of associated loci, as well as sex-specific trait-gene associations. Notably, we report the first genetic association on the X chromosome for retinal vascular phenotypes. These findings suggest sex-differential genetic and environmental contributions to microvascular morphology, possibly reflecting divergent biological pathways and risk profiles. Our study underscores the necessity of integrating SABV and sex-aware approaches in genetic and prognostic research to advance precision medicine and improve tailored risk stratification.

Cardiorespiratory fitness (CRF) is a strong predictor of cardiovascular events and all-cause mortality, often outperforming traditional risk factors. However, its clinical assessment remains limited due to the need for specialized equipment, personnel, and time demands. Because CRF is closely tied to vascular health, surrogate measures that capture vascular features may provide a practical alternative for its estimation. Retinal Color Fundus Images (CFIs) provide a non-invasive window into systemic vascular health and have already proven useful in predicting cardiovascular risk factors and diseases. However, CFIs have yet to be explored for their potential to predict CRF. In this study, we introduce RetFit, a novel CRF estimator derived from CFIs by leveraging state-of-the-art vision transformers. We evaluated RetFit’s clinical relevance by analyzing its associations with cardiovascular risk factors and disease outcomes, and exploring its genetic architecture, benchmarking it against a submaximal-exercise-test CRF (SETCRF) estimate. RetFit was prognostic of both cardiovascular events (hazard ratios as low as 0.668, 95%CI 0.617–0.723, pr =0.45), their significant genetic associations were disjoint. Interpretability analyses suggest a role for retinal vasculature in RetFit’s predictions, with attention maps emphasizing vascular regions and segmentation analyses showing arterial bifurcation count as the strongest associated feature (β=0.287, 95% CI 0.263–0.311, p<0.001). These findings highlight the potential of retinal imaging as a scalable, cost-effective, and accessible alternative for CRF estimation, supporting its use in large-scale screening and risk stratification in both clinical and public health contexts.

Although genome-wide association studies have provided valuable insights into the genetic basis of complex traits and diseases, translating these findings to causal genes and their downstream mechanisms remains challenging. We performed trans expression quantitative trait locus (trans-eQTL) meta-analysis in 3734 lymphoblastoid cell line samples, identifying four robust loci that replicated in an independent multi-ethnic dataset of 682 individuals. The trans-eQTL signal at the ubiquitin specific peptidase 18 (USP18) locus colocalised with a GWAS signal for systemic lupus erythematosus (SLE). USP18 is a known negative regulator of interferon signalling and the SLE risk allele increased the expression of 50 interferon-inducible genes, suggesting that the risk allele impairs USP18’s ability to effectively limit the interferon response. Intriguingly, the USP18 trans-eQTL signal would not have been discovered in a meta-analysis of up to 43,301 whole blood samples, reaffirming the importance of capturing context-specific genetic effects for GWAS interpretation.

Retinal fundus images offer a non-invasive window into systemic aging. Here, we fine-tuned a foundation model (RETFound) to predict chronological age from color fundus images in 71,343 participants from the UK Biobank, achieving a mean absolute error of 2.85 years. The resulting retinal age gap, i.e. the difference between predicted and chronological age, was associated with cardiometabolic traits, inflammation, cognitive performance, all-cause mortality, dementia, cancer, and incident cardiovascular disease. Genome-wide analyses identified genes related to longevity, metabolism, neurodegeneration, and age-related eye diseases. Sex-stratified models revealed consistent performance but divergent biological signatures: males had younger - appearing retinas and stronger links to metabolic syndrome, while in females, both model attention and genetic associations pointed to a greater involvement of retinal vasculature. Our study positions retinal aging as a biologically meaningful and sex-sensitive biomarker that can support more personalized approaches to risk assessment and aging-related healthcare.