Breast cancer is highly prevalent yet a more complete understanding of the interplay between genes and probable environmental risk factors, such as night work, remains lagging. Using a discordant twin pair design, we examined the association between night shift work and breast cancer risk, controlling for familial confounding. Shift work pattern was prospectively assessed by mailed questionnaires among 5,781 female twins from the Older Finnish Twin Cohort. Over the study period (1990–2018), 407 incident breast cancer cases were recorded using the Finnish Cancer Registry. Cox proportional hazards models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) adjusting for potential confounders. Within-pair co-twin analyses were employed in 57 pairs to account for potential familial confounding. Compared to women who worked days only, women with shift work that included night shifts had a 1.58-fold higher risk of breast cancer (HR = 1.58; 95%CI, 1.16–2.15, highest among the youngest women i.e. born 1950–1957, HR = 2.08; 95%CI, 1.32–3.28), whereas 2-shift workers not including night shifts, did not (HR = 0.84; 95%CI, 0.59–1.21). Women with longer sleep (average sleep duration > 8 h/night) appeared at greatest risk of breast cancer if they worked night shifts (HR = 2.91; 95%CI, 1.55–5.46; P_intx=0.32). Results did not vary by chronotype (P_intx=0.74). Co-twin analyses, though with limited power, suggested that night work may be associated with breast cancer risk independent of early environmental and genetic factors. These results confirm a previously described association between night shift work and breast cancer risk. Genetic influences only partially explain these associations.

Consider a parallel group trial for the comparison of an experimental treatment to a control, where the second‐stage sample size may depend on the blinded primary endpoint data as well as on additional blinded data from a secondary endpoint. For the setting of normally distributed endpoints, we demonstrate that this may lead to an inflation of the type I error rate if the null hypothesis holds for the primary but not the secondary endpoint.We derive upper bounds for the inflation of the type I error rate, both for trials that employ random allocation and for those that use block randomization. We illustrate the worst‐case sample size reassessment rule in a case study. For both randomization strategies, the maximum type I error rate increases with the effect size in the secondary endpoint and the correlation between endpoints. The maximum inflation increases with smaller block sizes if information on the block size is used in the reassessment rule. Based on our findings, we do not question the well‐established use of blinded sample size reassessment methods with nuisance parameter estimates computed from the blinded interim data of the primary endpoint. However, we demonstrate that the type I error rate control of these methods relies on the application of specific, binding, pre‐planned and fully algorithmic sample size reassessment rules and does not extend to general or unplanned sample size adjustments based on blinded data. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Shift Work Sleep Disorder (SWSD) is a significant and highly prevalent condition affecting up to 48% of individuals with irregular work schedules. The diagnostic criteria for SWSD include persistent insomnia or sleepiness in relation to shift work, not attributable to other disorders or external factors. To explore risk factors of SWSD, we conducted a cross-sectional analysis among 10,787 night shift workers in the UK Biobank. To determine correlates of SWSD using multivariable-adjusted logistic regression models, a preselection of potential risk factors was made on the basis of previous literature. Self-identifying as ‘Asian or Asian British’ or ‘Black or Black British’ (compared to being ‘White’), male sex, and high scores on sociability, warmth and diligence were associated with lower odds for SWSD. We did not find significant associations of chronotype, frequency of alcohol intake, smoking, and time employed in current job with SWSD. These findings underscore the need for targeted interventions and workplace policies to mitigate the adverse effects of SWSD. Future research should aim to explore the mechanisms behind these associations and develop strategies to enhance shift work tolerance among night shift workers.

The Circadian Imbalance Index (CII) integrates chronotype, sleep duration, neuroticism, caffeine intake, and vitamin D. In a genome wide association study (GWAS) of CII in 312,935 European ancestry UK Biobank participants, we identified 27 loci mapping to 72 genes, including circadian regulators CALCA, DHCR7, KDM5A, HAL, and CRX. Gene-overlap analyses demonstrated shared architecture with CII components, while EPHB1, SERPING1, C12orf74, PLEKHG7, and EEA1 were uniquely associated with CII. A CII polygenic score (CII-PRS) showed phenome-wide associations with type 2 diabetes (T2D), major depressive disorder, and obesity. Genetic correlations linked CII with insomnia, mood symptoms, body mass index (BMI), T2D, coronary artery disease (CAD), and myocardial infarction (MI). Mendelian randomization suggested causal effects of CII on T2D, mood swings, and MI, and reverse effects of CAD, mood, and MI on CII. This work shows that circadian imbalance is a polygenic trait connecting sleep-related biology to metabolic, cardiovascular and mood health outcomes.

Chronic kidney disease (CKD) affects roughly 10% of the world’s population, and growing evidence suggests that disruption of the circadian system—now recognized as an important regulator of numerous physiological pathways, may contribute to impaired kidney function and increased CKD risk. This narrative review aims to synthesize the existing literature on the association between determinants of circadian rhythm (shift work, artificial light at night [ALAN], melatonin) as well as sleep health and CKD risk. Three databases (PubMed, Web of Science, and Embase) were searched for original articles published from 1966 through 28 February 2026. A total of 34 studies examined the association between circadian factors and CKD risk: 7 on shift work, 2 on ALAN, 4 on melatonin, 4 on chronotype, 2 on social jetlag, 2 on insomnia, and 18 on sleep duration. Taken together, these studies offer consistent evidence that shift work, evening chronotype, prolonged social jetlag, and both short and long sleep duration are associated with elevated CKD risk. Given the potential implications for early detection, risk stratification, and targeted behavioral interventions, future research should employ standardized approaches and improved measures using, for example, actigraphy, to clarify causal pathways and strengthen the evidence base.