Citations

Abstract Polygenic risk scores (PRSs), which quantify inherited susceptibility to complex traits and diseases, have emerged as valuable tools for risk stratification and precision medicine. Despite their promise, PRS developed on European cohorts often demonstrate substantially reduced predictive accuracy in non-European populations, due to differences in genetic architecture. The disproportionate representation of European ancestry cohorts in genome-wide association studies (GWAS) leads to inequitable deployment of PRS technologies across diverse populations. Here, we introduce PRANA (Polygenic Risk Adaptation via Neural-network Architecture), a deep learning framework that adapts an existing PRS developed on one population to other ancestries. Unlike methods that require large-scale GWAS in the target population, PRANA leverages pre-trained PRS models derived from European cohorts and adapts them using modestly sized cohorts from the target population. We evaluated PRANA on seven complex traits in South Asian, East Asian and Ashkenazi Jewish populations, as well as in selected smaller East Asian subpopulations where the scarcity of training data poses a particular challenge. PRANA mostly improved predictive performance of the baseline PRS models by 5%-20% in terms of effect size (β) and Nagelkerke’s R², and, in most cases, outperformed existing cross-ancestry multi-PRS approaches. These results highlight PRANA as a scalable and practical strategy to reduce disparities in genomic risk prediction and advance the equitable application of PRS in diverse populations.

ABSTRACT Introduction Chronic gastroduodenal disorders remain challenging to manage, and new diagnostic approaches are needed to better delineate underlying causes and guide therapeutic decisions. Body Surface Gastric Mapping (BSGM) technologies combine high‐resolution gastric myoelectrical activity measurements with symptom and psychological profiling to provide mechanistic insights into gastric motor and sensory dysfunction. An International Working Group convened to derive the first consensus classification of BSGM phenotypes (the “Auckland Classification”). Methods A Technical Group conducted a systematic literature and clinical database review to identify objective test biomarkers and candidate disease mechanisms. Evidence was synthesized across 50 studies (primarily in gastroparesis, chronic nausea and vomiting, and functional dyspepsia), and BSGM phenotypes were mapped to existing treatment guidelines. Subsequently, iterative review and development of consensus was performed by a Consensus Group composed of international clinical experts familiar with BSGM. Eleven statements underlying the classification were then derived and circulated as a final survey to establish agreement. Results Six BSGM phenotypes were endorsed: three defined by myoelectrical abnormalities (Dysrhythmic, High Frequency, and Low Meal Response) and three by characteristic symptom associations (Sensorimotor, Continuous, and Delayed Onset Symptoms). Published studies plausibly linked these phenotypes to mechanisms including interstitial cell of Cajal depletion, vagal impairment, hypomotility, visceral hypersensitivity, gut‐brain dysregulation, and small bowel dysfunction. Phenotypes were also mapped to existing mechanism‐based treatment guidelines. Ten out of the eleven statements had > 80% agreement. Conclusions The Auckland Classification, derived by international consensus, presents a structured framework for BSGM‐defined patient phenotypes. Evidence and mechanism‐based treatment options are suggested for each phenotype to provide a foundation for research, further validation, and a pathway for integrating BSGM into clinical care.

Image-derived artificial intelligence (AI) risk models have shown promise in short-term risk assessment for improving breast cancer (BC) screening. No image-derived long-term AI risk model for primary prevention has been developed and externally validated. Individuals aged 31 to 94 years were recruited between 2010 and 2020 in two population-based case cohorts (Olmsted County, Minnesota, US, and KARMA, Sweden) and one hospital-based case-control study (EMBED, Atlanta, US). Median follow-up in the case cohorts was 10 years, with BCs diagnosed through June 2022. Additional validation was performed in EMBED with 3-year follow-up. An AI risk model was developed in Sweden, and we report independent validation in Olmsted/KARMA/EMBED. Absolute 10-year risks were estimated at study entry. Time-dependent discriminatory performance [AUC( t )] and expected-to-observed events (E/O) were estimated. Comparisons were performed with clinical risk tools and the image-based Mirai tool. Across Olmsted/KARMA, 8696 individuals (mean age, 54.4±10.6) and 1633 individuals with incident BC (mean age, 57.0±10.6) were included. Average 10-year risks were 3.83 and 3.14%, and E/O ratios were 0.99 and 0.99. Ten-year AUC( t ) for invasive BCs were 0.72 in Olmsted and 0.72 in KARMA. Similar discriminatory results were observed in EMBED. Our AI risk tool performed significantly better than Mirai in Olmsted/KARMA/EMBED. In KARMA, in the top 10% of high-risk individuals, the AI risk tool predicted 33% of BCs compared with 23%/20%/24% predicted by Tyrer-Cuzick-v8/BCSC-v3/Mirai (all P < 0.01). The 10-year image-derived AI risk model showed significantly higher performance than clinical risk models and Mirai in diverse populations, supporting its clinical potential for primary prevention.

BACKGROUND: Bicuspid aortic valve (BAV) is a frequent congenital heart defect with a high heritability. Despite this, only a limited number of genes have been associated with the disease, and the molecular mechanisms remain unexplained in most cases. This study aimed to further understand the genetic architecture of BAV. METHODS: A genome-wide association study meta-analysis including 9631 cases among 65 677 participants was performed. Genes were prioritized using transcriptomic analyses based on RNA sequencing in relevant tissues, including human fetal and adult aortic valves. The impact of the knockdown or knockout of 4 candidate genes on cardiac development was verified in zebrafish. A polygenic risk score was developed, its association with BAV was evaluated in an independent cohort, and its association with a wide range of phenotypes (n=976) was evaluated in UK Biobank (n=355 618 individuals). RESULTS: Thirty-six genomic loci were identified, including 32 that were not described previously. Among the prioritized genes, KANK2 and ERBB4 were identified as potentially causal through transcriptomic analyses, colocalization, and Mendelian randomization based on gene expression in human aortic valves (n=484), whereas PRDM6 and STRN were prioritized using similar analyses from aortic (n=326) and left ventricular tissues (n=326), respectively. Targeting 4 candidate genes ( WNT4 , LEF1 , STRN , and KANK2 ) in zebrafish led to disruption in cardiac development. A polygenic risk score was associated with an odds ratio of 2.07 (95% CI, 1.90–2.25; P =5.43×10 -62 ) per SD for BAV and significantly associated with thoracic aortic aneurysm and atrial fibrillation in UK Biobank. CONCLUSIONS: This study supports a significant polygenic contribution to BAV, where the combination of multiple common variants in genes involved in heart morphogenesis disrupts aortic valve development.

Abstract Background Inflammatory bowel disease (IBD) is associated with increased risk of major adverse cardiovascular events (MACE), likely driven by systemic inflammation. Treating inflammation may reduce this risk. We aimed to investigate MACE across different therapies in patients with IBD. Methods We used Swedish nationwide registers (2007–2023) to describe MACE rates (ischemic heart disease, stroke, heart failure, and cardiovascular mortality) in patients with IBD receiving different therapies. For contextualization, we compared MACE rates during therapy exposure periods in patients with IBD with matched (by age, sex, and residence) general population comparators without IBD. Comparisons included exposure periods naïve to immunomodulators/advanced therapies and periods of different treatments (immunomodulators, TNF-α inhibitors (TNFi), vedolizumab, ustekinumab, and Janus kinase inhibitors). Each patient could contribute with multiple periods, with a new period starting upon switch of therapy. Incidence rates (IRs) and adjusted hazard ratios (aHRs) were estimated by using Cox regression (Model 1: conditioned on matching variables; Model 2: additional adjustment for Charlson Comorbidity Index). Among patients with IBD, we performed 1:1 propensity score (PS)-matched analysis, with exposure periods of TNFi or vedolizumab as the reference, to compare MACE rates between advanced therapies. In these comparisons, we adjusted for demographics, disease characteristics, healthcare utilization, prior treatment, and cardiovascular risk factors. Results We included 106,894 patients with IBD with 99,661 exposure periods naïve to immunomodulators/advanced therapy, 60,573 exposure periods of immunomodulators, and 71,551 of advanced therapies. Distribution of age, sex and disease duration differed substantially across therapy exposures (data not shown). Compared with the general population, MACE rates in patients with IBD were similar across therapies (IRs=3.0-13.0/1,000 person-years; aHRs=1.02-1.49) (Table 1), and significantly increased only for exposure periods naïve to immunomodulators/advanced therapy, immunomodulators, or ustekinumab. In PS-matched comparisons, no significant differences were found between periods of TNFi, vedolizumab and other advanced therapies (Table 2). Conclusion Therapy exposure periods in patients with IBD showed only slightly increased MACE rates compared to corresponding periods in the general population, and significantly so only for periods naïve to immunomodulators/advanced therapy and exposure to immunomodulators or ustekinumab. In PS-matched analyses of patients with IBD, no significant differences in MACE rates were observed when comparing exposure periods of different advanced therapies. Conflict of interest: Eriksson, Carl: Served as speaker/advisory board member for Janssen Cilag AB and Tillotts Pharma. Forss, Anders: Served as speaker/advisory board member for Janssen Cilag AB and Tillotts Pharma. Singh, Siddharth: Dr. Singh has research grants from Pfizer. Halfvarson, Jonas: Grant support: Swedish Foundation for Strategic Research (RB13-0160 to J.H.), the Swedish Research Council (2020-02021 to J.H.), the Örebro University Hospital research foundation (OLL-890291 to J.H.), NordForsk (90569 to J.H.) and Vinnova ( 2019-01185 to JH and 2024-01155 co-applicant), IHI, EU, INTERCEPT (Grant agreement number 101194780, co-applicant), miGut-Health, HORIZON-HLTH-2022, EU (Grant Agreement 101095470, Co-applicant), 3TR, IMI 2, EU, (Grant agreement number 831434, Co-applicant), Janssen, MSD, and Takeda. Consulting and/or advisory board fees from: AbbVie, Alfasigma, Aqilion, Bristol Myers Squibb, Celgene, Celltrion, Eli Lilly, Ferring, Galapagos, Gilead Sciences, Hospira, Index Pharmaceuticals, Janssen, Johnson &amp Johnson, MEDA, Medivir, Medtronic, Merck, Merck Sharp &amp Dohme, Novartis, Pfizer, Prometheus Laboratories Inc., Sandoz, Shire, STADA, Takeda, Thermo Fisher Scientific, Tillotts Pharma, Vifor Pharma, UCB and speaker’s fees from: AbbVie, Alfasigma, Bristol Myers Squibb, Celgene, Eli Lilly, Ferring, Galapagos, Gilead, Hospira, Janssen, Johnson &amp Johnson, Merck Sharp &amp Dohme, Novartis, Pfizer, Shire, Takeda, Thermo Fisher Scientific, Tillotts Pharma and research grant support from Janssen, Merck Sharp &amp Dohme and Takeda. Sun, Jiangwei: No conflicts Söderling, Jonas: Dr. Söderling has worked on projects at Karolinska Institutet and SWIBREG partly financed by grants from Ferring and Janssen. Everhov, Åsa H: Dr Everhov has worked on projects at Karolinska Institutet and SWIBREG, partly financed by grants from Ferring and Janssen. Bröms, Gabriella: Dr. Bröms has served as a speaker for Takeda and Janssen and worked on projects at Karolinska Institutet, partly financed by Janssen, UCB and Pfizer. James, Stefan: Dr. James reports institutional research grants from Novo Nordisk, Astra Zeneca, Amgen, Jansen, Alnylam and proctor fees from Medtronic. Ludvigsson, Jonas: Dr. Ludvigsson has coordinated an unrelated study for the Swedish IBD Quality Register (SWIBREG). That study received funding from Jansen Corporation. Ludvigsson has also received financial support from MSD/Merck for unrelated research on inflammatory bowel disease, and for developing a paper reviewing national healthcare registers in China. Ludvigsson has an ongoing research collaboration with Takeda about celiac disease and is discussing additional collaboration on chronic liver disease with this company. Olen, Ola: Karolinska Institutet has received research grants from Pfizer, Janssen, AbbVie, Takeda, Ferring, Bristol Myers Squibb, and Alfasigma for projects led by Olén. Ola Olén has also received fees for lectures from Pfizer, Janssen, Bristol Myers Squibb, and Takeda.

Background: Breast cancer polygenic risk scores (PRS) and traditional risk models (e.g., the Gail model [Gail]) are known to contribute largely independent information, but it is unclear how the overlap varies by ancestry, age, disease type (invasive breast cancer, DCIS), and risk threshold. Methods: In a retrospective case–control study, we evaluated risk prediction performance in 180,398 women (161,849 of European ancestry; 18,549 of Asian ancestry). Odds ratios (ORs) from logistic regression models and the area under the receiver operating characteristic curve (AUC) were estimated. Results: PRS for invasive disease showed a stronger association in younger (<50 years) women (OR = 2.51, AUC = 0.622) than in women ≥ 50 years (OR = 2.06, AUC = 0.653) of European ancestry. PRS performance in Asians was lower (OR range = 1.62–1.64, AUC = 0.551–0.600). Gail performance was modest across groups and poor in younger Asian women (OR = 0.94–0.99, AUC = 0.523–0.533). Age interactions were observed for both PRS (p < 0.001) and Gail (p < 0.001) in Europeans, whereas in Asians, age interaction was observed only for Gail (invasive: p < 0.001; DCIS: p = 0.002). PRS identified more high-risk individuals than Gail in Asian populations, especially ≥50 years, while Gail identified more in Europeans. Overlap between PRS, Gail, and family history was limited at higher thresholds. Calibration analysis, comparing empirical and model-based ROC curves, showed divergence for both PRS and Gail (p < 0.001), which indicates miscalibration. In Europeans, family history and prior biopsies drove Gail discrimination. In younger Asians, age at first live birth was influential. Conclusions: PRS adds value to risk stratification beyond traditional tools, especially in younger women and Asian ancestry populations.

AbstractBackgroundGlial fibrillary acidic protein (GFAP) is a marker of cerebral astrogliosis and occasionally elevated in patients with dementia. GFAP in cerebrospinal fluid (CSF), is routinely requested in referrals to neurochemistry laboratories; however, its ability to differentiate dementias and diagnostic capability is unclear. Our aim was to elucidate this, using two large datasets.MethodFirst, GFAP data measured since 2015 was retrieved from the database of the Clinical Neurochemistry Laboratory at the Sahlgrenska University hospital. We then cross‐referenced with the Swedish dementia registry (SveDem). Here, information on ten different diagnoses such as early onset AD (EAD [<65 years]), late onset AD (LAD [≥65 years]), Parkinson disease with dementia (PDD), vascular dementia (VaD) and frontotemporal dementia (FTD), each with specific diagnostic criteria, were retrieved.The GFAP data was log10‐transformed, followed by an analysis of covariance (ANCOVA) and a subsequent post‐hoc Tukey’s test, with GFAP as dependent variable, diagnosis as independent variable and sex and age as covariates.ResultIn total, 1912 individuals (mean [SD] age, 71.9 [8.2] years; 52% male), were included. Lower log10‐transformed GFAP concentrations were seen in PDD (mean [SD], 2.68, [0.28] pg/mL), than in EAD, LAD, VaD and FTD; here, mean concentrations of 2.76 (0.24), 2.89 (0.23), 2.89 (0.32) and 2.76 (0.25) pg/mL were observed, respectively.In the post hoc analysis, GFAP differentiated VaD from EAD (p<0.001). PDD concentrations were significantly different from VaD (p<0.001) and LAD (p<0.001). Further, it also differentiated FTD from VaD (p=0.006) and LAD (p=0.001).ConclusionCSF GFAP could on a group level help differentiate VaD from EAD, FTD and PDD. Also, it could differentiate PDD from LAD. These results bear potential clinical relevance, where clinicians in some uncertain cases could use this marker as a differential tool.

Abstract Introduction Incorporation of mammographic density to breast cancer risk models could improve risk stratification to tailor screening and prevention strategies according to risk. Robust evaluation of the value of adding mammographic density to models with comprehensive information on questionnaire-based risk factors and polygenic risk score is needed to determine its effectiveness in improving risk stratification of such models. Methods We used the Individualized Coherent Absolute Risk Estimator (iCARE) tool for risk model building and validation to incorporate density to a previously validated literature-based model with questionnaire-based risk factors and a 313-variant polygenic risk score (PRS). The model was evaluated for calibration and discrimination in three prospective cohorts of European-ancestry women (1,468 cases, 19,104 controls): US-based Nurses’ Health Study (NHS I and II) and Mayo Mammography Health Study (MMHS); and Sweden-based Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) study. Analyses were done separately for women younger (NHS II, KARMA) and older than 50 years (NHS I, MMHS, KARMA). Improvements in terms of risk stratification and reclassification proportions were assessed among European-ancestry women aged 50–70 years in US and Sweden. Results For women younger and older than 50 years, the model with questionnaire-based risk factors, PRS and density was generally well calibrated across risk with some evidence of miscalibration at the extremes of the risk distribution. Incorporation of density led to modest improvements risk discrimination beyond the model with questionnaire-based risk factors and PRS: the area under the curve (AUC) among younger women was 67.0% (95% CI: 63.5–70.6%) vs. 65.6% (95% CI: 61.9–69.3%) for models with and without density; and 66.1% (95% CI 64.4–67.8%) vs. 65.5% (95% CI: 63.8–67.2%) among older women. The model with density identified 18.4% of US women 50–70 years old ≥ 3% 5-year predicted risk (threshold used for recommending risk-reducing medication in the US), with 42.4% of future cases expected to occur in this group. At this threshold, 7.9% of US women were reclassified by adding density to the model, resulting in the identification of 2.8% of additional future cases. The model with density identified 10.3% of Swedish women ≥ 3% 5-year predicted risk, with 29.4% of future cases expected to occur in this group. At this threshold, 5.3% of women were reclassified with the addition of density, leading to the identification of an additional 4.4% of future cases. Conclusion Integrating density with questionnaire-based risk factors and PRS could potentially identify more women of European-ancestry with elevated risk of breast cancer in the United States and Sweden. Further investigations of the integrated model in non-European ancestry populations are needed prior to considering clinical applications.

ImportanceYoung women with breast cancer who have germline pathogenic variants in BRCA1 or BRCA2 face unique challenges regarding fertility. Previous studies demonstrating the feasibility and safety of pregnancy in breast cancer survivors included limited data regarding BRCA carriers.ObjectiveTo investigate cumulative incidence of pregnancy and disease-free survival in young women who are BRCA carriers.Design, Setting, and ParticipantsInternational, multicenter, hospital-based, retrospective cohort study conducted at 78 participating centers worldwide. The study included female participants diagnosed with invasive breast cancer at age 40 years or younger between January 2000 and December 2020 carrying germline pathogenic variants in BRCA1 and/or BRCA2. Last delivery was October 7, 2022; last follow-up was February 20, 2023.ExposurePregnancy after breast cancer.Main Outcomes and MeasuresPrimary end points were cumulative incidence of pregnancy after breast cancer and disease-free survival. Secondary end points were breast cancer–specific survival, overall survival, pregnancy, and fetal and obstetric outcomes.ResultsOf 4732 BRCA carriers included, 659 had at least 1 pregnancy after breast cancer and 4073 did not. Median age at diagnosis in the overall cohort was 35 years (IQR, 31-38 years). Cumulative incidence of pregnancy at 10 years was 22% (95% CI, 21%-24%), with a median time from breast cancer diagnosis to conception of 3.5 years (IQR, 2.2-5.3 years). Among the 659 patients who had a pregnancy, 45 (6.9%) and 63 (9.7%) had an induced abortion or a miscarriage, respectively. Of the 517 patients (79.7%) with a completed pregnancy, 406 (91.0%) delivered at term (≥37 weeks) and 54 (10.4%) had twins. Among the 470 infants born with known information on pregnancy complications, 4 (0.9%) had documented congenital anomalies. Median follow-up was 7.8 years (IQR, 4.5-12.6 years). No significant difference in disease-free survival was observed between patients with or without a pregnancy after breast cancer (adjusted hazard ratio, 0.99; 95% CI, 0.81-1.20). Patients who had a pregnancy had significantly better breast cancer–specific survival and overall survival.Conclusions and RelevanceIn this global study, 1 in 5 young BRCA carriers conceived within 10 years after breast cancer diagnosis. Pregnancy following breast cancer in BRCA carriers was not associated with decreased disease-free survival.Trial RegistrationClinicalTrials.gov Identifier: NCT03673306

Background Polygenic risk score (PRS), calculated based on genome-wide association studies (GWASs), can improve breast cancer (BC) risk assessment. To date, most BC GWASs have been performed in individuals of European (EUR) ancestry, and the generalisation of EUR-based PRS to other populations is a major challenge. In this study, we examined the performance of EUR-based BC PRS models in Ashkenazi Jewish (AJ) women. Methods We generated PRSs based on data on EUR women from the Breast Cancer Association Consortium (BCAC). We tested the performance of the PRSs in a cohort of 2161 AJ women from Israel (1437 cases and 724 controls) from BCAC (BCAC cohort from Israel (BCAC-IL)). In addition, we tested the performance of these EUR-based BC PRSs, as well as the established 313-SNP EUR BC PRS, in an independent cohort of 181 AJ women from Hadassah Medical Center (HMC) in Israel. Results In the BCAC-IL cohort, the highest OR per 1 SD was 1.56 (±0.09). The OR for AJ women at the top 10% of the PRS distribution compared with the middle quintile was 2.10 (±0.24). In the HMC cohort, the OR per 1 SD of the EUR-based PRS that performed best in the BCAC-IL cohort was 1.58±0.27. The OR per 1 SD of the commonly used 313-SNP BC PRS was 1.64 (±0.28). Conclusions Extant EUR GWAS data can be used for generating PRSs that identify AJ women with markedly elevated risk of BC and therefore hold promise for improving BC risk assessment in AJ women.