European Journal of Epidemiology最新文献

The Rotterdam Study is a population-based cohort study, started in 1990 in the district of Ommoord in the city of Rotterdam, the Netherlands, with the aim to describe the prevalence and incidence, unravel the etiology, and identify targets for prediction, prevention or intervention of multifactorial diseases in mid-life and elderly. The study currently includes 17,931 participants (overall response rate 65%), aged 40 years and over, who are examined in-person every 3 to 5 years in a dedicated research facility, and who are followed-up continuously through automated linkage with health care providers, both regionally and nationally. Research within the Rotterdam Study is carried out along two axes. First, research lines are oriented around diseases and clinical conditions, which are reflective of medical specializations. Second, cross-cutting research lines transverse these clinical demarcations allowing for inter- and multidisciplinary research. These research lines generally reflect subdomains within epidemiology. This paper describes recent methodological updates and main findings from each of these research lines. Also, future perspective for coming years highlighted.

Health data integrity, as an emergent concept, stands to reshape the lifecycle of data-driven healthcare and research, ensuring a shared commitment to ethical practices and improved patient care.

In this study, we aimed to provide novel evidence on the impact of changing lifestyle habits on cancer risk. In the EPIC cohort, 295,865 middle-aged participants returned a lifestyle questionnaire at baseline and during follow-up. At both timepoints, we calculated a healthy lifestyle index (HLI) score based on cigarette smoking, alcohol consumption, body mass index and physical activity. HLI ranged from 0 (most unfavourable) to 16 (most favourable). We estimated the association between HLI change and risk of lifestyle-related cancers-including cancer of the breast, lung, colorectum, stomach, liver, cervix, oesophagus, bladder, and others-using Cox regression models. We reported hazard ratios (HR) with 95% confidence intervals (CI). Median time between the two questionnaires was 5.7 years, median age at follow-up questionnaire was 59 years. After the follow-up questionnaire, we observed 14,933 lifestyle-related cancers over a median follow-up of 7.8 years. Each unit increase in the HLI score was associated with 4% lower risk of lifestyle-related cancers (HR 0.96; 95%CI 0.95-0.97). Among participants in the top HLI third at baseline (HLI > 11), those in the bottom third at follow-up (HLI ≤ 9) had 21% higher risk of lifestyle-related cancers (HR 1.21; 95%CI 1.07-1.37) than those remaining in the top third. Among participants in the bottom HLI third at baseline, those in the top third at follow-up had 25% lower risk of lifestyle-related cancers (HR 0.75; 95%CI 0.65-0.86) than those remaining in the bottom third. These results indicate that lifestyle changes in middle age may have a significant impact on cancer risk.

The China Surgery and Anaesthesia Cohort (CSAC) study was launched in July 2020 and is an ongoing prospective cohort study recruiting patients aged 40-65 years who underwent elective surgeries with general anaesthesia across four medical centres in China. The general objective of the CSAC study is to improve our understanding of the complex interaction between environmental and genetic components as well as to determine their effects on a wide range of interested surgery/anaesthesia-related outcomes. To achieve this goal, we collected enriched phenotypic data, e.g., sociodemographic characteristics, lifestyle factors, perioperative neuropsychological changes, anaesthesia- and surgery-related complications, and medical conditions, at recruitment, as well as through both active (at 1, 3, 7 days and 1, 3, 6, 12 months after surgery) and passive (for more than 1 year after surgery) follow-up assessments. We also obtained omics data from blood samples. In addition, COVID-19-related information was collected from all participants since January 2023, immediately after COVID-19 restrictions were eased in China. As of July 18, 2023, 12,766 participants (mean age = 52.40 years, 57.93% were female) completed baseline data collection (response rate = 94.68%), among which approximately 70% donated blood and hair samples. The follow-up rates within 12 months after surgery were > 92%. Our initial analyses have demonstrated the incidence of and risk factors for chronic postsurgical pain (CPSP) and postoperative cognitive dysfunction (POCD) among middle-aged Chinese individuals, which may prompt further mechanistic exploration and facilitate the development of effective interventions for preventing those conditions. Additional studies, such as genome-wide association analyses for identifying the genetic determinants of CPSP and POCD, are ongoing, and their findings will be released in the future.

In 1953, Morton Levin introduced a simple approach to estimating population attributable fractions (PAF) depending only on risk factor prevalence and relative risk. This formula and its extensions are still in widespread use today, particularly to estimate PAF in populations where individual data is unavailable. Unfortunately, Levin's approach is known to be asymptotically biased for the PAF when the risk factor-disease relationship is confounded even if relative risks that are correctly adjusted for confounding are used in the estimator. Here we describe a simple re-expression of Miettinen's estimand that depends on the causal relative risk, the unadjusted relative risk and the population risk factor prevalence. While this re-expression is not new, it has been underappreciated in the literature, and the associated estimator may be useful in estimating PAF in populations when individual data is unavailable provided estimated adjusted and unadjusted relative risks can be transported to the population of interest. Using the re-expressed estimand, we develop novel analytic formulae for the relative and absolute asymptotic bias in Levin's formula, solidifying earlier work by Darrow and Steenland that used simulations to investigate this bias. We extend all results to settings with non-binary valued risk factors and continuous exposures and discuss the utility of these results in estimating PAF in practice.

Dietary factors have been extensively investigated as possible risk factors for liver cancer, but the evidence is inconclusive. Our study systematically assessed the association between 142 foods and nutrients and liver cancer risk in a Chinese population using a diet-wide association study. Based on data from 59,844 men in the Shanghai Men's Health Study (SMHS), we assessed the diet intake by dietary questionnaires. Cox regression was used to quantify the association between each food and nutrient and liver cancer risk. A false discovery rate (FDR) of 0.05 was used to select the foods and nutrients for validation. In the cohort, 431 liver cancer cases were identified during 712,373 person-years of follow-up. Retinol (HR per 1 SD increment = 1.09, 95% CI: 1.03-1.14) was associated with a higher risk of liver cancer, whereas onions (HR per 1 SD increment = 0.67, 95% CI: 0.54-0.84) and manganese (HR per 1 SD increment = 0.85, 95% CI: 0.78-0.94) were inversely associated with liver cancer risk. In the replication analysis, estimates for these foods and nutrients were similar in magnitude and direction. Our findings confirm that retinol, onions and manganese were associated with liver cancer risk, which provides reliable evidence between diet and liver cancer development.

The characterization of the socioeconomic profile of COVID-19 mortality is limited. Likewise, the mapping of potential indirect adverse outcomes of the pandemic, such as suicide and alcohol abuse, along socioeconomic lines is still meagre. The main aim of this paper is to (i) depict SES-differences in COVID-19 mortality, and (ii) to assess the impact of the COVID-19 pandemic on suicide and alcohol mortality across socioeconomic groups. We used Swedish monthly data spanning the period January 2016-December 2021. We chose education as indicator of socioeconomic status (SES). The following causes of deaths were included in the analysis: COVID-19, all-cause mortality excluding COVID-19, suicide and a composite index of alcohol-specific deaths. SARIMA-modelling was used to assess the impact of the pandemic on suicide and alcohol-specific mortality. Two alternative measures of the pandemic were used: (1) a dummy that was coded 1 during the pandemic (March 2020 and onwards), and 0 otherwise, and (2) the Oxford COVID-19 Government Response Tracker's Stringency Index. There was a marked SES-gradient in COVID-19 mortality in the working-age population (25-64) which was larger than for other causes of death. A SES-gradient was also found in the old-age population, but this gradient did not differ from the gradient for other causes of death. The outcome from the SARIMA time-series analyses suggested that the pandemic did not have any impact on suicide or alcohol-specific mortality in any of the educational and gender groups.

The mixed evidence of the association between high levels of cardiovascular risk factors (CVRF) and the risk for cognitive impairment may be due to confounding of age across studies. We pooled and harmonized individual-level data (30,967 persons, age range 42-96 years) from five prospective cohorts to investigate by 1 year age increments to investigate whether or not there is change in slope describing the association of CVRF to a cognitive outcome (Digit Symbol Substitution Test; DSST). The CVRF included: systolic and diastolic blood pressure, total cholesterol, fasting glucose and body mass index. Linear and quadratic piecewise regression models were fit to the trajectory patterns of these slopes (betas). The pattern of yearly slope changes showed higher CVRF were associated with lower DSST, but associations attenuated toward zero as age increased for all but DBP where 1 year slopes for DBP changed direction from negative to positive from mid- to late-age. Age is not only a driver of cognitive decline-age also modifies the direction and strength of the association of cognitive function to CVRF and cohort age may be one reason why the evidence for CVRF-CD association is mixed.

Identifying factors that are causes of disease progression, especially in neurodegenerative diseases, is of considerable interest. Disease progression can be described as a trajectory of outcome over time—for example, a linear trajectory having both an intercept (severity at time zero) and a slope (rate of change). A technique for identifying causal relationships between one exposure and one outcome in observational data whilst avoiding bias due to confounding is two sample Mendelian Randomisation (2SMR). We consider a multivariate approach to 2SMR using a multilevel model for disease progression to estimate the causal effect an exposure has on the intercept and slope. We carry out a simulation study comparing a naïve univariate 2SMR approach to a multivariate 2SMR approach with one exposure that effects both the intercept and slope of an outcome that changes linearly with time since diagnosis. The simulation study results, across six different scenarios, for both approaches were similar with no evidence against a non-zero bias and appropriate coverage of the 95% confidence intervals (for intercept 93.4–96.2% and the slope 94.5–96.0%). The multivariate approach gives a better joint coverage of both the intercept and slope effects. We also apply our method to two Parkinson’s cohorts to examine the effect body mass index has on disease progression. There was no strong evidence that BMI affects disease progression, however the confidence intervals for both intercept and slope were wide.

Abstract

Fixed-effect meta-analysis has been used to summarize genetic effects on a phenotype across multiple Genome-Wide Association Studies (GWAS) assuming a common underlying genetic effect. Genetic effects may vary with age (or other characteristics), and not allowing for this in a GWAS might lead to bias. Meta-regression models between study heterogeneity and allows effect modification of the genetic effects to be explored. The aim of this study was to explore the use of meta-analysis and meta-regression for estimating age-varying genetic effects on phenotypes. With simulations we compared the performance of meta-regression to fixed-effect and random -effects meta-analyses in estimating (i) main genetic effects and (ii) age-varying genetic effects (SNP by age interactions) from multiple GWAS studies under a range of scenarios. We applied meta-regression on publicly available summary data to estimate the main and age-varying genetic effects of the FTO SNP rs9939609 on Body Mass Index (BMI). Fixed-effect and random-effects meta-analyses accurately estimated genetic effects when these did not change with age. Meta-regression accurately estimated both main genetic effects and age-varying genetic effects. When the number of studies or the age-diversity between studies was low, meta-regression had limited power. In the applied example, each additional minor allele (A) of rs9939609 was inversely associated with BMI at ages 0 to 3, and positively associated at ages 5.5 to 13. Our findings challenge the assumption that genetic effects are consistent across all ages and provide a method for exploring this. GWAS consortia should be encouraged to use meta-regression to explore age-varying genetic effects.