Andrea Ganna

Abstract Polygenic risk scores (PRS) for breast cancer have potential to improve risk prediction, but there is limited information on their utility in various clinical situations. Here we show that among 122,978 women in the FinnGen study with 8401 breast cancer cases, the PRS modifies the breast cancer risk of two high-impact frameshift risk variants. Similarly, we show that after the breast cancer diagnosis, individuals with elevated PRS have an elevated risk of developing contralateral breast cancer, and that the PRS can considerably improve risk assessment among their female first-degree relatives. In more detail, women with the c.1592delT variant in PALB2 (242-fold enrichment in Finland, 336 carriers) and an average PRS (10–90 th percentile) have a lifetime risk of breast cancer at 55% (95% CI 49–61%), which increases to 84% (71–97%) with a high PRS ( > 90 th percentile), and decreases to 49% (30–68%) with a low PRS ( < 10 th percentile). Similarly, for c.1100delC in CHEK2 (3.7–fold enrichment; 1648 carriers), the respective lifetime risks are 29% (27–32%), 59% (52–66%), and 9% (5–14%). The PRS also refines the risk assessment of women with first-degree relatives diagnosed with breast cancer, particularly among women with positive family history of early-onset breast cancer. Here we demonstrate the opportunities for a comprehensive way of assessing genetic risk in the general population, in breast cancer patients, and in unaffected family members.

Abstract Plasma protein levels are influenced by both genetic and non-genetic factors and can serve as early disease biomarkers. When a protein is correlated with, but not causally linked to, disease, its genetic determinants can add unwanted variability to protein–disease associations. In such cases, removing the genetic component may improve their predictive performances. Here, we tested this hypothesis by genetically adjusting 94 highly heritable proteins spanning diverse biological pathways and evaluating their associations with the onset of 37 diseases in 39,871 UK Biobank participants. Genetically adjusted proteins showed stronger associations in 88% of 1,312 significant protein–disease pairs, equivalent to a 30% median reduction in required sample size for comparable power. Of 96 protein–disease pairs with significant differences, all but one showed larger effects for adjusted proteins. Most proteins also showed consistently stronger associations with environmental and lifestyle factors once genetic effects were removed. Finally, we constructed multi-protein scores from genetically adjusted proteins and demonstrated that they significantly improve prediction for 7 diseases compared to unadjusted proteins. These findings demonstrate that removing genetic effects from plasma proteins is an effective strategy to increase power for biomarker discovery and clinical trial design, consistent with the largely non-causal role of most plasma proteins in disease risk.

The European Health Data Space (EHDS) regulation aims to facilitate cross-border sharing of health data across Europe. However, practical challenges related to data access, interoperability, quality, and interpretive competence remain, particularly when working with health systems across countries. This study aimed to evaluate and report the user journey of researchers accessing and utilizing health data across four European countries for secondary research purposes prior to implementation of EHDS. We conducted a narrative reflection of individual and collective experiences on key aspects of the user journey-discovery, access, use, and finalization. Data were gathered from various structured and unstructured sources, including an online log, prospective questionnaires, regular meetings, and interviews. Researchers faced challenges at different steps of the user journey, which included lack of data quality in national metadata catalogues (discovery stage). Differences in national regulations led to inconsistent timelines for gaining access to data (access stage), with approval times ranging from a few months to over a year. At the use stage, researchers experienced challenges in harmonizing health data due to variations in coding practices and data quality. Issues related to computational capacity caused further delays. Substantial challenges must be addressed for EHDS to succeed. Establishing knowledge hubs, fostering collaborations, and streamlining access processes are essential. Close collaboration with experts will likely be essential for an effective user journey. This analysis underscores the importance of collaboration, analytical reproducibility, and clear documentation to ensure the success and timely delivery of cross-border projects.

Abstract Diabetic kidney disease (DKD) is a health burden that lacks specific and early diagnostic biomarkers. For their discovery, we sequenced urinary extracellular vesicle miRNAs in a type 1 diabetes cohort of males with and without DKD. The results were replicated by sequencing or qPCR in two independent cohorts and six published datasets, including type 1 and 2 diabetes, and both sexes. We also validated stable reference gene candidate miRNAs. Chronic kidney disease, hypertensive nephropathy, IgA nephropathy, polycystic kidney disease, kidney stones, prostate cancer and non‐diabetic cohorts served as additional controls. MiRNAs changed due to urine collection type or centrifugation before storage were excluded. We analyzed differentially expressed miRNAs and their correlations with clinical measurements, receiver operating characteristic curves and target mRNAs, proteins and pathways, incorporating single‐cell data and circulating proteins of type 1 and 2 diabetes cohorts. By studying the uEV miRNAs ( N = 490 individuals total) and plasma proteins ( N = 4335), we pinpointed 6 stable miRNAs, 11 differentially expressed miRNAs, 9 target proteins and 16 DKD‐associated pathways. Differentially expressed miRNAs overlapped between diabetes subtypes and sexes, with strongest evidence for miR‐192‐5p, miR‐146a‐5p, miR‐486‐5p and miR‐574‐5p. The miRNAs alone or combined with clinical measurements classified individuals with the fastest kidney function decline (sensitivity 0.75–1.00, specificity 0.83–1.00) even in the normoalbuminuria group. The differentially expressed miRNAs did not cluster the control cohorts except for the chronic kidney disease cohort, which showed some clustering based on proteinuria status. Altogether, the miRNAs showed potential to identify early kidney function decline and may target key kidney cells, mRNAs, proteins and pathogenic mechanisms in DKD.