International journal of epidemiology最新文献

Background: Statins lower low-density lipoprotein cholesterol (LDL-C) and reduce the risk of coronary artery disease (CAD). However, they also increase the risk of type 2 diabetes (T2D).

Methods: We consider genetic variants in the region of the HMGCR gene, which encodes the target of statins, and their associations with downstream consequences of statins. We use various statistical methods to identify causal pathways influencing CAD and T2D, and investigate whether these are the same or different for the two diseases.

Results: Colocalization analyses indicate that LDL-C and body mass index (BMI) have distinct genetic predictors in this gene region, suggesting that they do not lie on the same causal pathway. Multivariable Mendelian randomization analyses restricted to variants in the HMGCR gene region revealed LDL-C and BMI as causal risk factors for CAD, and BMI as a causal risk factor for T2D, but not LDL-C. A Bayesian model averaging method prioritized BMI as the most likely causal risk factor for T2D, and LDL-C as the second most likely causal risk factor for CAD (behind ubiquinone). Colocalization analyses provided consistent evidence of LDL-C colocalizing with CAD, and BMI colocalizing with T2D; evidence was inconsistent for colocalization of LDL-C with T2D, and BMI with CAD.

Conclusions: Our analyses suggest cardiovascular and metabolic consequences of statin usage are on different causal pathways, and hence could be influenced separately by targeted interventions. More broadly, our analysis workflow offers potential insights to identify pathway-specific causal risk factors that could provide possible repositioning or refinement opportunities for existing drug targets.

Introduction: In air-pollution epidemiology, measured or modelled surrogate exposure estimates, prone to measurement error (ME), are used to investigate the health effects of exposure to pollution of outdoor origin, potentially leading to biased effect estimates. We predicted the annual personal exposure from outdoor sources by using personal measurements, compared it with concentrations from surrogate metrics, and quantified the ME magnitude, type, and determinants.

Methods: We used measurements from four panel studies in London, UK, and predicted personal exposures to fine particulate matter (PM2.5), nitrogen dioxide (NO2), ozone (O3), and black carbon (BC). We compared those with surrogate exposures, including measurements from fixed-site monitors, modelled ambient concentrations, or hybrid methods accounting for people's mobility. We estimated the exposure ME magnitude, correlations, and variance ratios between surrogate measures and personal exposure, and the percentages of classical/Berkson-type errors. Individual- and area-level characteristics, such as age, sex, socio-economic status, and time spent outdoors, were assessed as potential error determinants.

Results: Predicted annual personal exposures to PM2.5, NO2, O3, and BC from outdoor sources were overestimated by surrogate metrics, with mean differences of up to 10.1, 40.0, 61.7, and 2.6 μg/m3, respectively. The variance ratios and Pearson correlation coefficients between surrogate and predicted personal exposures ranged from 0.03 to 165.02 and -0.24 to 0.25. Time-activity adjustment reduced errors substantially. Berkson-type errors dominated the ME for PM2.5 and BC (43%-81% and 26%-98%, respectively), whilst classical errors characterized gases (>94% for both NO2 and O3). Time spent outdoors, house type, and deprivation were associated with exposure error.

Conclusion: The use of surrogate exposures to investigate the health effects of long-term exposure to air pollution from outdoor sources may bias the epidemiological estimates due to ME. Information about the error structures and their determinants can be used for correction and the identification of the true exposure-response functions.

Background: With rising longevity, multimorbidity is an increasingly important challenge for healthcare systems. We describe trends in the prevalence and incidence of multimorbidity across socioeconomic groups in Catalonia.

Methods: We use a random sample of 1 551 126 individuals (22% of the Catalan population, for whom we have the complete primary care health records) and follow them from 2010 until 2021. We document the age- and sex-specific prevalence and incidence of multimorbidity stratifying by income groups and birth cohorts. Logistic regression models are used to estimate the association between multimorbidity and mortality.

Results: Between 2010 and 2021, the prevalence of multimorbidity, higher among women, has increased for both sexes and all cohorts in our analysis. Importantly, each cohort attains the same ages, with higher multimorbidity prevalence than their predecessors had 10 years ago. Older generations are mostly affected by degenerative diseases, while younger age groups are more affected by mental health problems. Incidence tends to be higher among the older cohorts across all adult ages. We observe a strong socioeconomic gradient, with lower-income individuals experiencing worse multimorbidity prevalence and incidence. Such a gradient is persistent and becomes more pronounced at the end of the study period. Across all age groups, individuals experiencing multimorbidity have a higher risk of dying than those who do not.

Conclusion: The documented increases in multimorbidity alongside its socioeconomic gradients suggest that preventive strategies are urgently needed to defer or prevent its onset and slow its progression-especially among younger generations.

Background: The association between wildfire smoke (WFS) exposure and pregnancy loss has been understudied. Here, we examined the association between prenatal wildfire-specific particulate matter ≤2.5 µm (PM2.5) exposure and pregnancy loss in Colorado, USA.

Methods: We retrieved all birth records from the 17 'Front Range' counties (just east of the Rocky Mountains) of Colorado from 2007 to 2018 (n = 614 321). We considered two PM2.5 exposures-wildfire-specific PM2.5 from a novel machine learning model and non-wildfire PM2.5 constructed using the Community Multiscale Air Quality model. We fitted quasi-Poisson distributed lag models to estimate the associations between the two weekly-resolved PM2.5 exposures during pregnancy and live birth-identified conceptions (LBICs) in each county. That is, we used the predicted change in the LBICs to directly infer the change in the number of pregnancy losses due to the exposure.

Results: Average weekly non-wildfire PM2.5 was 6.2 µg/m3 (SD 2.3). In weeks with non-zero WFS (27% of all county-weeks), the average wildfire-specific PM2.5 was 0.92 µg/m3 (SD: 1.55). Wildfire-specific PM2.5 appeared important in gestational weeks 6-13-a 1-µg/m3 higher exposure sustained in these gestational weeks was associated with 20 [95% confidence interval (CI): 4-34] losses/year. In contrast, the cumulative association with non-wildfire PM2.5 was stronger-a 1-µg/m3 higher exposure sustained in every week of pregnancy was associated with 84 (95% CI: 46-129) losses/year.

Conclusion: Our findings suggest that both wildfire-specific and non-wildfire PM2.5 exposures were associated with more pregnancy loss and add to the growing literature on the harmful effects of wildfires and, more broadly, air pollution.

Background: Childhood exposure to air pollution has long-term effects on adult bronchitic symptoms, but the age windows of susceptibility are understudied.

Methods: We included 1444 participants from the Southern California Children's Health Study, who were recruited at ages ∼9-10 years in 1992-1993 or 1995-1996 or ages ∼5-7 years in 2002-2003, followed until high-school graduation, and re-contacted again in adulthood (mean age = 33 years) to collect self-reported bronchitic symptoms. Yearly average nitrogen dioxide (NO2), 8-h maximum ground-level ozone (O3), and particulate matter of ≤10 µm in diameter (PM10) were estimated by using inverse-distance squared spatial interpolation to participants' residential history from conception to age 16 years. Log Poisson Distributed Lag Models were fitted to identify susceptible windows of childhood exposure to air pollution on adult bronchitic symptoms adjusted for childhood and adult confounders. We explored sex-specific susceptible windows.

Results: We identified ages 1-2 years as a susceptible window in which NO2 exposure was associated with a higher risk of adult bronchitic symptoms, with the largest associations observed at age 1 year (risk ratio per 10 ppb = 1.12; 95% confidence interval: 1.01, 1.25). We observed both positive (ages 12-15 years) and inverse (ages 8-11 years) associations with O3 exposure. Suggestive evidence of increased risk at ages 3-4 years was observed for PM10. There was no evidence of sex differences.

Conclusion: Early childhood might be a particularly susceptible window of exposure to NO2 (ages 1-2 years) and possibly for PM10 (ages 3-4 years) for increased risk of adult bronchitic symptoms, while early adolescence (ages 12-15 years) might be a susceptible window for O3 exposure.