European Journal of Epidemiology最新文献

Faster nicotine metabolism, defined as the nicotine metabolite ratio (NMR), is known to associate with heavier smoking and challenges in smoking cessation. However, the broader health implications of genetically determined nicotine metabolism are not well characterized. We performed a hypothesis-free phenome-wide association study (PheWAS) of over 21,000 outcome variables from UK Biobank (UKB) to explore how the NMR (measured as the 3-hydroxycotinine-to-cotinine ratio) associates with the phenome. As the exposure variable, we used a genetic score for faster nicotine metabolism based on 10 putative causal genetic variants, explaining 33.8 % of the variance in the NMR. We analysed ever and never smokers separately to assess whether a causal pathway through nicotine metabolism is plausible. A total of 57 outcome variables reached phenome-wide significance at a false discovery rate of 5 %. We observed expected associations with several phenotypes related to smoking and nicotine, but could not replicate prior findings on cessation. Importantly, we found novel associations between genetically determined faster nicotine metabolism and adverse health outcomes, including unfavourable liver enzyme and lipid values, as well as increased caffeine consumption. These associations did not appear to differ between ever and never smokers, suggesting the corresponding pathways may not involve nicotine metabolism. No favourable health outcomes were linked to genetically determined faster nicotine metabolism. Our findings support a possibility that a future smoking cessation therapy converting fast metabolizers of nicotine to slower ones could work without adverse side effects and potentially even provide other health-related benefits.

Approximately 30%-50% of dementia cases are attributable to modifiable risk factors, but the impact of risk reduction strategies on dementia incidence at a population level is uncertain. Reliable estimates of intervention effects require accounting for changes in life expectancy when intervening on risk factors, and model realistic reduction scenarios that consider co-occurrence of risk factors. Using the microsimulation model MISCAN-Dementia, we assessed the effect of interventions on mid-life hypertension and late-life smoking on dementia and mortality risk. We modeled risk factor reductions, from small reductions to complete elimination, and evaluated effects on dementia incidence and prevalence, number of cases, and life years with and without dementia. All risk factor reductions resulted in lower dementia incidence and prevalence, fewer dementia cases, and more dementia-free life years. Eliminating smoking resulted in 1.4% fewer dementia cases for women and 2.5% for men over their lifetime. Eliminating hypertension reduced dementia cases by 1.1% for women and 3.3% for men. The number of dementia cases and life years with dementia decreased until around age 90, after which a slight increase was observed due to prolonged life expectancy with the reductions. Reducing smoking and hypertension will result in additional life years without dementia and a modest reduction in overall dementia cases, with some additional dementia cases in the oldest old. These findings emphasize the potential of dementia risk reduction strategies and the importance of considering concurrent changes in mortality when evaluating risk factor reductions.

The West-China Hospital Alliance Longitudinal Epidemiology Wellness (WHALE) Study establishes a robust, multidimensional database to provide comprehensive insights into health-to-disease transitions, advancing proactive healthcare and enhancing understanding of the interplay among genetic, behavioral, and environmental factors in disease. The WHALE Study includes a database and a cohort. The WHALE Database, established in 2010, integrates health check-up data from 11 hospitals, covering sociodemographic, lifestyle, medical history, and clinical data. The WHALE Health Trajectory Cohort, launched in November 2024, recruits adults with at least three health check-ups, featuring biennial active follow-ups and passive linkage with regional healthcare databases. As of January 2025, the WHALE Database includes over 3.4 million health records from 1,526,686 participants, with a mean age of 40.3 years and a balanced gender distribution. Notably, 23.88% of participants had at least three health check-ups, and 3.31% had more than ten, highlighting a significant proportion with repeated measurements. The study provides key insights into health trajectories by examining the associations of biomarker data and their trajectory patterns with aging, pre-disease conditions, and disease diagnoses. The strengths of the WHALE Study include its large sample size, longitudinal design, diverse representation, comprehensive data, and robust quality control. Limitations include potential selection bias, data variability across centers, and reliance on self-reported data for some variables.

Previous studies on the association of potentially inappropriate medication (PIM) use with hospitalization risk and all-cause mortality among older adults were prone to confounding and biases. Using data from 217,111 participants of the population-based United Kingdom Biobank, aged 60-69 years, including 95,187 participants with primary care data linkage, the main analysis was a prospective new user design with 1:1 propensity-score stratified by indication matching of new PIM users and new appropriate medication (AM) users (assessed with the EURO-FORTA list). Results were compared to previous approaches with a prevalent user design and a new user design without propensity score matching. 43,307 (19.9%) participants used at least one PIM at baseline. Among 11,812 propensity score matched individuals with new PIM or new AM prescription within 2 years after baseline, new PIM use was associated with non-significantly 20% increased 1-month hospitalization (hazard ratio (HR) [95% confidence interval (95% CI)]: 1.20 [0.76-1.90]) and 23% increased 1-year mortality (1.23 [0.80-1.89]). Null-results were obtained with the prevalent user design (HRs [95% CIs]: 1-month hospitalization: 1.04 [0.83-1.31]; 1-year mortality: 1.01 [0.82-1.23]) and slightly stronger associations in new user design without propensity score matching stratified by indication (1-month hospitalization (1.24 [0.95-1.61]); 1-year mortality (HR [95% CI] 1.57 [1.24-2.00]). This first study with an appropriate methodology showed that previous pharmacoepidemiologic studies on the risk of PIM for hospitalization and mortality have either under- or overestimated the risk. Effect sizes of about 20% appear biologically plausible and larger studies are needed to detect such weak associations with statistical significance.