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Background: Obesity is a well-established risk factor for many cancers, but its association with breast cancer remains inconsistent. Obesity is a highly complex and polygenic condition, driven by multiple biological pathways, some of which have been linked to beneficial health effects, defining a metabolically healthy obese phenotype. We hypothesise that this heterogeneity may partly underlie the variable association with breast cancer, where different biological pathways may play divergent roles in disease risk.

Methods: We applied an approach in two parts to explore the heterogeneous effects of high body mass index (BMI) on breast cancer risk. First, we applied Mendelian randomisation (MR)-based clustering on genetic data (FinnGen, GIANT, BCAC) to identify clusters of BMI-increasing SNPs with differential effects on breast cancer, and the cluster-specific MR results were replicated in the UK Biobank. Second, we integrated proteomic data to estimate the cluster-specific effects of BMI on protein levels and the effect of these proteins on breast cancer risk, using two-sample MR. Mediation analyses were then carried out using the product-of-coefficient method.

Results: We identified three clusters of SNPs associated with increased BMI while having differential effects on breast cancer risk: one risk-increasing (inverse variance weighted MR estimate: 1.27 [1.10, 1.44]), one medium protective (-0.75 -0.82,-0.68]), and one highly protective (-1.92 [-2.21,-1.62]), comprising 24, 73, and 7 SNPs respectively, with similar magnitude of effects in the UK Biobank. In the two-sample MR, each cluster was associated with many proteins (70, 369 and 44 respectively, FDR q-value <0.05), and the mediation analysis identified that three proteins significantly mediate cluster-specific effects on breast cancer risk. Among these proteins, MET is known to be involved in breast cancer, while little is known about the roles of CPM and CST6.

Conclusions: These findings suggest that the effect of obesity on breast cancer is mediated through multiple distinct biological pathways. The identified proteins provide a first insight into the mechanisms underlying these pathways. With further investigation, these results could provide a basis for developing personalised treatment strategies.

Obesity is a major risk factor for type 2 diabetes, a disease affecting approximately 10% of the global population. Obesity is a heterogeneous condition, with different components exerting distinct, and sometimes opposing, effects on type 2 diabetes risk. Here, we aim to identify molecular mechanisms through which distinct components of obesity influence risk for type 2 diabetes by integrating multi-omics data. We identify SNPs associated with body mass index in males and females and cluster them based on their Mendelian randomisation estimates on type 2 diabetes risk. This analysis reveals four SNP clusters with distinct effects on type 2 diabetes ranging from strongly harmful to protective. We perform cluster-specific two-sample Mendelian randomisation analyses across over 3000 molecular traits to delineate metabolic, lipid, endocrine and glycaemic pathways mediating the harmful and protective effects of distinct obesity components on type 2 diabetes risk.

BACKGROUND: Oral contraceptive (OC) use is widespread globally. Despite their significant benefits, concerns persist about a potential rise in breast cancer risk linked to their use. Genetic predisposition also influences breast cancer risk; however, its interaction with OC use remains inconclusive. This study aims to explore the association between OC use and breast cancer risk in women with varying genetic predispositions to breast cancer, as measured by polygenic risk scores (PRS).

METHOD: A total of 257,185 white female participants from the UK Biobank were included. Time-varying Cox regression was used to estimate hazard ratios (HRs) with 95% confidence intervals (CIs) to examine the association between OC and invasive breast cancer events, stratified by PRS. Age was used as the primary time scale, and analyses were adjusted for year of birth, Townsend Deprivation Index, body mass index, smoking status, age at menarche, menopausal status, family history of breast cancer, parity, hormone replacement therapy use, history of hysterectomy, as well as genetic principal components.

RESULTS: Current use of OC was associated with an increased risk of breast cancer, with a HR of 1.21 (95% CI: 1.03–1.41). In contrast, previous use showed no association (HR = 0.99, 95% CI: 0.92–1.05). Genetic risk, as measured by the PRS, was strongly associated with breast cancer risk (P < 0.001). Individuals in the highest PRS decile had approximately three times higher risk compared to those in the mid deciles. Importantly, for the association between current OC use and breast cancer risk, a statistically significant trend was observed across both PRS deciles (P = 0.04) and tertiles (P = 0.05), with decreasing HRs as genetic risk increased. Specifically, the HR for current OC use was 1.43 (95% CI: 1.02–2.01) in the lowest PRS tertile, 1.14 (95% CI: 0.89–1.45) in the middle tertile, and 0.96 (95% CI: 0.80–1.14) in the highest tertile.

CONCLUSION: Both OC use and a high PRS increase the risk of breast cancer. There is a trend toward a decreased relative risk associated with OC use among those with higher genetic predisposition. Therefore, there is no evidence to suggest that women with a high genetic risk for breast cancer are more adversely affected by OC use.

IMPORTANCE: Hormonal contraceptives are widely used but how breast cancer risk differs by hormonal content remains unclear.

OBJECTIVE: To estimate the difference in breast cancer risk associated with different hormonal contraceptive formulations.

DESIGN, SETTING, AND PARTICIPANTS: This Swedish nationwide, population-based cohort study was conducted using linked national registers. All adolescent girls and women aged 13 to 49 years residing in Sweden as of January 1, 2006, with no history of breast cancer, ovarian cancer, cervical cancer, uterine cancer, bilateral oophorectomy, or infertility treatment were included and followed up from 2006 to 2019. Individuals were censored on meeting an exclusion criterion, reaching age 50 years, or study end, whichever occurred first. Data were analyzed from November 2023 to August 2025.

EXPOSURE: Ever use and duration of use of hormonal contraceptives, categorized by hormone formulations and route of administration.

MAIN OUTCOMES AND MEASURES: Time-dependent Cox regression was used to estimate hazard ratios (HRs) with 95% CIs for incident cases of in situ and invasive breast cancer.

RESULTS: Among 2 095 130 adolescent girls and women (median [IQR] age at diagnosis, 45 [41-48] years) who were followed up for 21 020 846 person-years, 16 385 breast cancer cases occurred. Ever use of any hormonal contraceptive was associated with increased breast cancer risk (HR, 1.24; 95% CI, 1.20-1.28), corresponding to 1 additional case per 7752 (95% CI, 5350-14 070) users, with both combined (HR, 1.12; 95% CI, 1.07-1.17) and progestin-only formulations (HR, 1.21; 95% CI, 1.17-1.25) being associated. Higher risk was associated with oral desogestrel-only formulations (HR, 1.18; 95% CI, 1.13-1.23) and oral desogestrel-combined formulations (HR, 1.19; 95% CI, 1.08-1.31), as well as implants containing etonogestrel, desogestrel's active metabolite (HR, 1.22; 95% CI, 1.11-1.35), compared to levonorgestrel-containing combined pills (HR, 1.09; 95% CI, 1.03-1.15) and levonorgestrel, 52 mg, intrauterine system (HR, 1.13; 95% CI, 1.09-1.18). No statistically significant increased risk was observed for medroxyprogesterone acetate injection, etonogestrel vaginal ring, or combined oral drospirenone, despite having many users.

CONCLUSIONS AND RELEVANCE: Findings of this cohort study highlight that breast cancer risk varies substantially by progestin content in hormonal contraceptives, providing valuable insights to support more informed contraceptive prescription.

Advances in high-throughput technologies enable large-scale studies on genomics and molecular phenotypes. However, the trade-off between quality and quantity reduces assay sensitivity, and several measurements in large-scale proteomics and metabolomics analytes fall below the limit of detection (LOD). If not properly addressed, this may introduce bias in effect estimates. To address this, we conducted a simulation study to evaluate the performance of linear, Tobit, Cox, and logistic modeling in the presence of below-LOD measurements in genome-wide association studies. We identified the optimal strategy as a two-step Linear-Tobit scheme, including rapid screening with linear regression followed by refinement with Tobit regression to retrieve accurate effect estimates. This higher accuracy helps mitigate a 1.3-fold and 2.7-fold inflation in causal estimates in a Mendelian randomization (MR) study, which would otherwise be present with 50% and 90% values below LOD. Validation through case studies on estradiol and testosterone levels in the UK Biobank confirmed the simulation results across subgroups with varying proportions of below-LOD measurements. The Linear-Tobit scheme offers optimal detection power and efficiency, with a focus on its applicability to biobank-scale datasets and accuracy in effect estimates to mitigate bias in downstream applications such as MR and polygenic risk scores.

Osteoporotic fractures, largely resulting from reduced estrogen levels after menopause and subsequent bone loss, are a leading cause of disability among older women. Although oral contraceptive pills (OCPs) contain estrogen, their long-term impact on bone health and osteoporosis risk remain uncertain. Here, we assessed the effect of OCP use on bone mineral density (BMD) and osteoporosis using data from 257,185 women from the UK Biobank, born 1936-1970. Time-dependent Cox regression was used to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CI) for osteoporosis, while multivariable linear regression was used to assess the effect of OCP use on BMD, measured as T-scores in standard deviation units based on quantitative ultrasound of the calcaneus. By the end of follow-up in 2020, 7.6% of the participants had received an osteoporosis diagnosis. The rate of osteoporosis was lower among ever OCP users (HR = 0.86; 95% CI 0.83-0.89; P = 2.8 × 10^-17). OCP use was also associated with a higher BMD T-score (0.052; 0.038-0.067; P = 2.1 × 10^-12) with an increasing effect with longer use. Use of OCPs for 0-1 years had no significant effect on BMD (P = 0.081). However, longer durations were associated with increased BMD T-scores compared to never users: 2-5 years (0.046; 0.027-0.065, P = 2.2 × 10^-6), 6-10 years (0.062; 0.043-0.080; P = 3.5 × 10^-12), 11-15 years (0.062; 0.042-0.081; P = 3.2 × 10^-12) and 16 + years (0.064; 0.044-0.083; P = 1.2 × 10^-10). We found prior OCP use to be associated with higher BMD and a reduced risk of osteoporosis, potentially offering long-term benefits and suggesting that OCP use could reduce osteoporotic complications in older women.

The T2T-CHM13 reference genome, released in March 2022, fills in the 8% of the human genome that was not resolved in GRCh38 and reconstructs large parts of the known genome. The more accurate and complete reference genome is expected to improve the quality of read mapping and variant calling. Even though whole-genome sequencing (WGS)-based approaches have become the gold standard in medical genetics, the extent of the benefits of the improved reference genome remains unclear. In this study, we aim to evaluate alignment and variant call performance with T2T-CHM13 as a reference using a cross-sectional Swedish cohort (SweGen) comprising 1000 individuals with short-read Illumina WGS data available. Remapping and variant calling is performed using the nf-core/sarek pipeline. T2T-CHM13 improves a wide range of mapping- and variant calling-related metrics, including a higher fraction of properly paired reads, lower mismatch rate, and more uniform coverage of coding regions. Moreover, the fraction of ambiguous alignments is higher, reflecting segmental duplications that were incorrectly collapsed in GRCh37 and GRCh38. In comparison to GRCh38, we identify 10 million additional variants in the cohort, including 5.5 million singletons, and observe an increased sensitivity for rare variants. SnpEff assigns impact ratings of moderate or high to 13% more variants in T2T-CHM13 than GRCh38. In summary, we conclude that T2T-CHM13 improves alignment metrics with higher alignment quality, better variant calling performance, and confidence, including for rare and deleterious variants. The T2T-CHM13 genome reference thus facilitates enhanced discovery of new disease-causing variation, benefiting, for example, rare-disease diagnostics.

The global burden of disease attributable to obesity continues to rise. The disease incidence is substantially higher in elderly populations, but how obesity affects disease risk across a lifetime is largely unknown. To quantify the long-term temporal impact of obesity, access to large-scale longitudinal cohorts spanning many decades would typically be required. However, these longitudinal studies are rare and may be heavily biased by the presence of unaccountable confounding. Here, we develop a method-time-resolved Mendelian randomization-to estimate the cumulative effect of body mass index on disease risk at different ages. Using the UK Biobank, we find strong age-varying patterns for type 2 diabetes mellitus, coronary artery disease, and atrial fibrillation, as well as for osteoarthritis. We demonstrate that some of the most notable temporal characteristics are sex specific, while other features are shared between sexes. Analyses suggest that these features can be manifestations of primary prevention strategies.

Introduction and aim: The risk of asthma and its phenotypes may be modified by gene-environmental interactions. The previous studies on the interactions between genetic variations in the toll like 4 (TLR4), the main receptor for bacterial endotoxin, and asthma were contradictory as they were underpowered and did not consider different asthma phenotypes. The main aim of this study was to identify interactions between two single nucleotide polymorphisms (SNPs) within the TLR4 gene, Asp299Gly and Thr399Ile, and residential area (urban or rural) in females and males with asthma and different asthma phenotypes.

Method: This study was performed on 38,332 asthmatics and 322,852 non-asthma (both British Caucasians) subjects from the UK Biobank database. Asthma was also divided into phenotypes, such as asthma with/without allergy and early/late onset asthma. The residential area was based on the population area density and classified as urban or rural living. Multivariate regression models adjusted for age, body mass index, and smoking status were used to analyze interactions between the SNPs, residential area in asthma, and asthma phenotypes. The association between asthma and residential area or the SNPs was also determined.

Result: There were no significant associations between the SNPs and asthma risk (for Asp299Gly: OR (95% CI): 1.00 (0.97–1.02), for Thr399Ile: 0.99 (0.96–1.02) or between the SNPs and asthma phenotypes in either sex or combined cohorts. The effects of the SNPs were not modified by residential area population density in either sex with asthma or across asthma phenotypes. Asthma and its phenotypes were not associated with the SNPs or residential area.

Conclusions: Our study found no statistically significant association between TLR4 polymorphisms and asthma, regardless of sex or residential area. Further studies are needed to clarify the functional impact of TLR4 variation in asthma pathophysiology.