Higher Circulating Testosterone Linked to Higher CAD Risk in Men: Mendelian Randomization and Survival Analyses
Publication: The Journal of Clinical Endocrinology & Metabolism
24 October 2025
Emily J Morbey, Felix R Day, Adam S Butterworth, Nicholas J Wareham, John R B Perry, Ken K Ong
Abstract:
Testosterone supplementation is increasingly widespread and has well-established beneficial effects on sexual function and metabolic health. However, there remains uncertainty regarding associated cardiovascular risks.
Human genetics studies demonstrated that Mendelian randomisation approaches recapitulate the beneficial effects of testosterone therapy; here we apply this to cardiovascular disease.
Researchers performed a Mendelian randomisation study to assess the causal effect of higher circulating testosterone on coronary artery disease (CAD). We also tested the phenotypic association between measured circulating testosterone and CAD in the cohort of men aged 40 to 69.
SMMILe enables accurate spatial quantification in digital pathology using multiple-instance learning
Publication: Nature Cancer
19 November 2025
Zeyu Gao, Anyu Mao, Yuxing Dong, Hannah Clayton, Jialun Wu, Jiashuai Liu, ChunBao Wang, Kai He, Tieliang Gong, Chen Li & Mireia Crispin-Ortuzar
Abstract:
Spatial quantification is a critical step in most computational pathology tasks, from guiding pathologists to areas of clinical interest to discovering tissue phenotypes behind novel biomarkers. To circumvent the need for manual annotations, modern computational pathology methods have favored multiple-instance learning approaches that can accurately predict whole-slide image labels, albeit at the expense of losing their spatial awareness. Here we prove mathematically that a model using instance-level aggregation could achieve superior spatial quantification without compromising on whole-slide image prediction performance. We then introduce a superpatch-based measurable multiple-instance learning method, SMMILe, and evaluate it across 6 cancer types, 3 highly diverse classification tasks and 8 datasets involving 3,850 whole-slide images. We benchmark SMMILe against nine existing methods using two different encoders—an ImageNet pretrained and a pathology-specific foundation model—and show that in all cases SMMILe matches or exceeds state-of-the-art whole-slide image classification performance while simultaneously achieving outstanding spatial quantification.
Common Diseases in Clinical Cohorts – Not Always What They Seem
Publication: New England Journal of Medicine
23 October 2025
Abstract
Background: Misdiagnosis or underdiagnosis of rare diseases in patients with diagnoses of common diseases can lead to delayed or inappropriate treatments, thereby complicating the management of both rare and common conditions. Despite advances in molecular diagnostic techniques, the effect of rare diseases on the diagnosis of common diseases in research and clinical trials has not been comprehensively investigated.
Methods: We used exome- and genome-sequencing data from participants in the U.K. Biobank, a research study, and five clinical trials involving patients who had received a primary diagnosis of multiple sclerosis, inflammatory bowel disease, or atopic dermatitis to assess the incidence of monogenic rare diseases that often manifest with clinical symptoms overlapping with those of these common diseases.
Results: We identified 153 U.K. Biobank participants who carried a rare variant that contributes to a molecular diagnosis of a monogenic disorder – 53 of 1850 (2.86%) with a diagnosis of multiple sclerosis, 75 of 6681 (1.12%) with a diagnosis of inflammatory bowel disease, and 25 of 998 (2.50%) with a diagnosis of atopic dermatitis. We replicated the findings regarding such rare disease-causing variants in two independent cohorts – one including patients with a diagnosis of multiple sclerosis, and the other patients with a diagnosis of inflammatory bowel disease – who had undergone genome sequencing for research and for clinical trials, respectively. By combining genome and transcriptome analyses, we showed that molecular diagnosis can potentially elucidate mechanisms of inadequate response to therapeutic intervention.
Conclusions: Our study shows the value of systematic genome sequencing in understanding the phenotypic heterogeneity of common diseases and identifying failure to diagnose rare diseases and highlights the benefits of deep molecular phenotyping in clinical trials and patient care.
Obesity due to MC4R deficiency is associated with reduced cholesterol, triglycerides and cardiovascular disease risk
Publication: Nature Medicine
16 October 2025
Stefanie Zorn, Rebecca Bounds, Alice Williamson, Katherine Lawler, Ruth Hanssen, Julia Keogh, Elana Henning, Miriam Smith, Barbara A. Fielding, A. Margot Umpleby, Summaira Yasmeen, Maria Marti-Solano, Claudia Langenberg, Martin Wabitsch, Tinh-Hai Collet & I. Sadaf Farooqi
Abstract
Obesity causes dyslipidemia and is a major risk factor for cardiovascular disease. However, the mechanisms coupling weight gain and lipid metabolism are poorly understood. Brain melanocortin 4 receptors (MC4Rs) regulate body weight and lipid metabolism in mice, but the relevance of these findings to humans is unclear. Here we investigated lipid levels in men and women with obesity due to MC4R deficiency. Among 7,719 people from the Genetics of Obesity Study cohort, we identified 316 probands and 144 adult family members with loss-of-function (LoF) MC4R mutations. Adults with MC4R deficiency had lower levels of total and low-density lipoprotein (LDL)-cholesterol and triglycerides than 336,728 controls from the UK Biobank, after adjusting for adiposity. Carriers of LoF MC4R variants within the UK Biobank had lower lipid levels and a lower risk of cardiovascular disease, after accounting for body weight, compared to noncarriers. After a high-fat meal, the postprandial rise in triglyceride-rich lipoproteins and metabolomic markers of fatty acid oxidation were reduced in people with MC4R deficiency compared to controls, changes that favor triglyceride storage in adipose tissue. We concluded that central MC4Rs regulate lipid metabolism and cardiovascular disease risk in humans, highlighting potential therapeutic approaches for cardiovascular risk reduction.
Polygenic and developmental profiles of autism differ by age at diagnosis
Publication: Nature
01 October 2025
Xinhe Zhang, Jakob Grove, Yuanjun Gu, Cornelia K. Buus, Lea K. Nielsen, Sharon A. S. Neufeld, Mahmoud Koko, Daniel S. Malawsky, Emma M. Wade, Ellen Verhoef, Anna Gui, Laura Hegemann, APEX Consortium, iPSYCH Autism Consortium, PGC-PTSD Consortium, Daniel H. Geschwind, Naomi R. Wray, Alexandra Havdahl, Angelica Ronald, Beate St Pourcain, Elise B. Robinson, Thomas Bourgeron, Simon Baron-Cohen, Anders D. Børglum, Hilary C. Martin & Varun Warrier
Abstract
Although autism has historically been conceptualized as a condition that emerges in early childhood, many autistic people are diagnosed later in life. It is unknown whether earlier- and later-diagnosed autism have different developmental trajectories and genetic profiles. Using longitudinal data from four independent birth cohorts, we demonstrate that two different socioemotional and behavioural trajectories are associated with age at diagnosis. In independent cohorts of autistic individuals, common genetic variants account for approximately 11% of the variance in age at autism diagnosis, similar to the contribution of individual sociodemographic and clinical factors, which typically explain less than 15% of this variance. We further demonstrate that the polygenic architecture of autism can be broken down into two modestly genetically correlated (rg = 0.38, s.e. = 0.07) autism polygenic factors. One of these factors is associated with earlier autism diagnosis and lower social and communication abilities in early childhood, but is only moderately genetically correlated with attention deficit–hyperactivity disorder (ADHD) and mental-health conditions. Conversely, the second factor is associated with later autism diagnosis and increased socioemotional and behavioural difficulties in adolescence, and has moderate to high positive genetic correlations with ADHD and mental-health conditions. These findings indicate that earlier- and later-diagnosed autism have different developmental trajectories and genetic profiles. Our findings have important implications for how we conceptualize autism and provide a model to explain some of the diversity found in autism.
Integrated omics reveals disease-associated radial glia-like cells with epigenetically dysregulated interferon response in multiple sclerosis
Publication: Neuron
10 October 2025
Summary
Progressive multiple sclerosis (PMS) involves a persistent, maladaptive inflammatory process with numerous cellular drivers. We generated induced neural stem cells (iNSCs) from patient fibroblasts through a direct reprogramming protocol that preserved their epigenome,which revealed a PMS-specific hypome-thylation of lipid metabolism and interferon(IFN) signaling genes.S ingle-cell multi-omics uncovered a novel, disease-associated radial glia-like cell (DARG) subpopulation in PMS cell lines exhibiting senes-cence and potent IFN responsiveness driven by specific transcription factors. Functionally, PMS iNSCs induced paracrine senescence and inflammation onto control cells, which was inhibited upon senolytic treatment. We identified inPMS brains a distinct population of senescent, IFN-responsive DARGs that developmentally aligned with the trajectories of iNSCs in vitro and spatially associated with inflammatory glia in chronically active lesions. DARGs may sustain smoldering inflammation, unveiling a previously unrecognized cellular axis that could un-derpin mechanisms in neurodegeneration. This discovery offers novel insights into disease mechanisms and highlights potential therapeutic targets.
Clinical potential of whole-genome data linked to mortality statistics in patients with breast cancer in the UK: a retrospective analysis
Publication: The Lancet Oncology
07 October 2025
Daniella Black, Helen Ruth Davies, Gene Ching Chiek Koh, Lucia Chmelova, Marko Cubric, Georgia Chalivelaki Chan, Andrea Degasperi, Jan Czarnecki, Ping Jing Toong, Yasin Memari, James Whitworth, Salome Jingchen Zhao, Yogesh Kumar, Shadi Basyuni, Giuseppe Rinaldi, Scott Shooter, Vladyslav Dembrovskyi, Rosie Davies, Maria Chatzou Dunford, Ellen Copson, Carlo Palmieri, Åke Borg, John Ambrose, Catey Bunce, Alona Sosinsky, Prabhu Arumugam, Matthew Arthur Brown, Johan Staaf, Nicholas Turner,
Background
Breast cancer is the most frequently diagnosed cancer in women. Survival is generally considered favourable, yet some patients remain at risk of early death. We aimed to assess whether comprehensive whole-genome sequencing (WGS) linked to mortality data could add prognostic value to existing clinical measures and identify patients who might respond to targeted therapeutics.
Methods
In this integrative, retrospective analysis, 2445 breast cancer tumours were analysed (any stage and molecular subtype) collected from 2403 patients recruited through 13 National Health Service Genomic Medicine Centres or hospitals in England affiliated to the 100 000 Genomes Project (100kGP) between 2012 and 2018. 2208 (90%) cases were linked with clinical data; mortality data were obtained for 1188 patients. Following high-depth WGS of tumour and matched normal DNA, comprehensive WGS profiling was performed, seeking driver mutations, mutational signatures, and compound algorithmic scores for homologous recombination repair deficiency (HRD), mismatch repair deficiency, and tumour mutational burden. Data from 1803 additional patients with breast cancer from three independent cohorts were used to validate various findings. To evaluate the prognostic value of WGS features, univariable and multivariable Cox regression on data from patients was performed with stage I–III, ER-positive, HER2-negative breast cancer with a cancer-specific mortality endpoint (around 5-year follow-up).
Findings
Among 2445 tumours in the 100kGP breast cancer cohort, genomic characteristics with immediate personalised medicine potential in 656 (26·8%) was observed, including features reporting HRD (298 [12·2%] total cases and 76 [6·3%] ER-positive, HER2-negative cases), highly individualised driver events, mutations underpinning resistance to endocrine therapy, and mutational signatures indicating therapeutic vulnerabilities. 373 (15·2%) cases had WGS features with potential for translational research, including compromised base excision repair and non-homologous end-joining dependency. Structural variation burden (hazard ratio 3·9 [95 CI% 2·4–6·2]; p<0·0001), high levels of APOBEC signatures (2·5 [1·6–4·1]; p<0·0001), and TP53 drivers (3·9 [2·4–6·2]; p<0·0001) were independently prognostic of customary clinical measures (age at diagnosis, stage, and grade) in patients with ER-positive, HER2-negative breast cancer. A prognosticator was developed for ER-positive, HER2-negative breast cancer capable of identifying patients who require either increased intervention or therapy de-escalation, validating the framework in the independent Swedish Cancerome Analysis Network-Breast (SCAN-B) dataset.
Interpretation
Breast cancer genomes are rich in predictive and prognostic value. A two-step model is proposed for effective clinical application. First, the identification of candidates for targeted therapies or clinical trials using highly individualised genomic markers. Second, for patients without such features, the implementation of enhanced prognostication using genomic features alongside existing clinical decision-making factors.
Polygenic risk score for breast cancer risk prediction in Asian BRCA1 and BRCA2 pathogenic variants carriers
Publication: npj Breast Cancer
30 September 2025
Mei-Chee Tai, Joe Dennis, Sue K. Park, Sung-Won Kim, Jong Won Lee, Nur Tiara Hassan, Ava Kwong, Mikael Hartman, Sook-Yee Yoon, Joanne Ngeow, Yin-Ling Woo, Boyoung Park, Zhi-Lei Wong, Goska Leslie, Manjeet K. Bolla, Daniel R. Barnes, Michael T. Parsons, Penny Soucy, Jacques Simard, Nur Aishah Mohd Taib, Cheng-Har Yip, Douglas F. Easton, Georgia Chenevix-Trench, Antonis C. Antoniou, Soo-Hwang Teo & Weang-Kee Ho
Abstract
Polygenic risk scores (PRS) have been shown to be predictive of breast cancer (BC) risk in European BRCA1 and BRCA2 pathogenic variant (PV) carriers, but their utility in Asian populations has not been evaluated. In this study, we evaluated the association of two breast cancer PRS developed for the East Asian general population and three versions of a PRS developed for the European general population in 604 BRCA1 (390 affected by breast cancer) and 785 BRCA2 (552 affected by breast cancer) PV female carriers of Asian ancestry. Only the Asian-based PRS, constructed using approximately 1 million single-nucleotide variations (SNVs), showed a significant association with breast cancer risk (Hazard Ratio per standard deviation (95% Confidence Interval) is 1.47 (1.10–1.95) for BRCA1 and 1.43 (1.04–1.95) for BRCA2). Incorporating this PRS into risk prediction models may improve cancer risk assessment among PV carriers of Asian ancestry.
Identifying people with potentially undiagnosed dementia with Lewy bodies using natural language processing
Publication: NJP Aging
18 July 2025
Abstract
Natural language processing (NLP) can expand the utility of clinical records data in dementia research. We deployed NLP algorithms to detect core features of dementia with Lewy bodies (DLB) and applied those to a large database of patients diagnosed with dementia in Alzheimer’s disease (AD) or DLB. Of 14,329 patients identified, 4.3% had a diagnosis of DLB and 95.7% of dementia in AD. All core features were significantly commoner in DLB than in dementia in AD, although 18.7% of patients with dementia in AD had two or more DLB core features. In conclusion, NLP applications can identify core features of DLB in routinely collected data. Nearly one in five patients with dementia in AD have two or more DLB core features and potentially qualify for a diagnosis of probable DLB. NLP may be helpful to identify patients who may fulfil criteria for DLB but have not yet been diagnosed.
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