诺和诺德基金会

Investment in artificial intelligence (AI) has grown rapidly, yet its returns to scientific research remain poorly understood. We study how AI reshapes the production of science using a comprehensive dataset of research proposals submitted to a large international funding agency, including both funded and unfunded projects. Combining keyword extraction with large language model classification, we identify the presence, type, and functional role of AI within each proposal and link these measures to detailed budget allocations, team structure, and subsequent publication outcomes. We find that, in the short run, AI adoption is associated with modest improvements in scientific outcomes concentrated in the upper tail. Instead, its primary effects arise in the organization of research: AI-enabled projects reallocate resources toward human capital, involve larger teams, and undertake a broader set of tasks. These patterns are consistent with a reorganization of the scientific production process rather than immediate efficiency gains, in line with theories of general-purpose technologies. Task-level analyses further show that activities expanded in AI-enabled projects, particularly ideation and experimentation, are increasingly compatible with large language model capabilities, suggesting potential for future productivity gains as these technologies mature.

We consider the problem of indirect comparison, where a treatment arm of interest is absent by design in the target randomized control trial (RCT) but available in a source RCT. The identifiability of the target population average treatment effect often relies on conditional transportability assumptions. However, it is a common concern whether all relevant effect modifiers are measured and controlled for. We highlight a new proximal identification result in the presence of shifted, unobserved effect modifiers based on proxies: an adjustment proxy in both RCTs and an additional reweighting proxy in the source RCT. We propose an estimator which is doubly-robust against misspecifications of the so-called bridge functions and asymptotically normal under mild consistency of the nuisance models. An alternative estimator is presented to accommodate missing outcomes in the source RCT, which we then apply to conduct a proximal indirect comparison analysis using two weight management trials.

Smoking and obesity are associated with risk of hidradenitis suppurativa, and both are considered important environmental risk factors. However, a causal relationship remains unproven. To primarily investigate the relationship between body mass index (BMI, calculated as weight in kilograms divided by height in meters squared) and smoking and HS, and secondarily to investigate potential relationships between 3 inflammatory diseases (psoriasis, inflammatory bowel disease [IBD], and systemic sclerosis [SSc]) and HS. A mendelian randomization (MR) study conducted in 2024 on 5 exposure phenotypes (BMI, smoking, psoriasis, IBD, and SSc) on the outcome of phenotype HS was conducted. The MR analyses used large genetic White European cohorts from genome-wide association studies (GWAS) of each of the 6 phenotypes. Initial analyses were conducted May, 2024, and were updated in May, 2025. The 5 exposure phenotypes using predetermined genome-wide significant single-nucleotide variants as proxies for each particular exposure. The GWAS on HS included 4814 case patients and more than 1.2 million controls from Denmark, Iceland, Finland, the UK, and the US. The BMI GWAS involved 700 000 individuals from the UK Biobank and GIANT consortium. Smoking data were obtained from 1.23 million participants in an international consortium. The psoriasis GWAS analyzed 39 498 case patients and 286 769 controls from White European populations and a DNA genetic testing company. The IBD GWAS meta-analysis included 38 155 case patients and 48 485 controls from the International Inflammatory Bowel Disease (IBD) Genetics Consortium. The SSc GWAS included 9095 case patients and 17 584 controls from White European populations. Genetic correlations ( r g ) were found between HS and all exposure phenotypes except SSc (BMI: r g = 0.36, P < .001; smoking: r g = 0.33, P < .001; IBD: r g = 0.25, P < .001; psoriasis: r g = 0.34, P < .001; SSc: r g = 0.33, P = .22). MR analyses supported an effect of BMI on HS (β = 0.87; odds ratio [OR] per BMI unit, 1.20; 95% CI, 1.17-1.23; P < .001) without signs of pleiotropy (slope: β = 0.91, P < .001, P for intercept = .76). Smoking showed a significant causal estimate (β = 0.59, P < .001), but results became inconclusive in subsequent sensitivity analyses. Among IBD, psoriasis, and SSc, results supported a causal effect of IBD on HS (β = 0.18, OR = 1.20; 95% CI, 1.15-1.24; P < .001), without signs of pleiotropy. These findings indicate causal effects of IBD and increased BMI on the risk of HS. This information may help physicians inform patients about disease risk contributed by modifiable lifestyle behaviors, which can be beneficial for planning lifestyle interventions.

Respiratory pathogens cause substantial global morbidity, mortality, and pandemic risk. While parenteral vaccines can effectively reduce severe disease for some respiratory infections, they are often less effective at preventing infection and onward transmission. Vaccines delivered directly to the airways have the potential to induce protective mucosal immunity at the site of pathogen entry. However, progress in mucosal vaccine development has been constrained by limited understanding of airway immune mechanisms, the technical challenges of sampling respiratory tissues, and the lack of standardised, validated immunological assays capable of defining mucosal correlates of protection. To address these challenges, the Novo Nordisk Foundation and Wellcome Trust convened a workshop on airway mucosal sampling and immunological assays, bringing together researchers, clinicians, funders, and representatives from major research consortia. Participants reviewed approaches to sampling the upper and lower respiratory tract, assay technologies, and regulatory considerations for integrating mucosal endpoints into vaccine development. Discussions highlighted major barriers including variability in sampling techniques, low and inconsistent cell yields, limited assay standardisation, and insufficient cross-study comparability. The workshop produced a set of recommendations in four priority areas (i.e., Collaboration, Communication, Consistency and Conduct of research) to share knowledge, accelerate standardisation and validation, and generate evidence supporting mucosal vaccine development. Together, these advances will help strengthen the scientific and regulatory foundations needed to realize the full potential of airway-delivered vaccines for the prevention of respiratory diseases.