Objective To explore the causal association between radiation exposure and risk of head and neck cancer using Mendelian randomization (MR) method. Methods Genome-wide association studies of radiation exposure and head and neck cancer in the public database IEU OpenGWAS were identified, and single nucleotide polymorphisms (SNPs) were screened as instrumental variables. Two-sample MR analyses were performed using random-effect inverse variance weighted (IVW), fixed-effect IVW, weighted median, and MR-Egger methods to assess the causal association between radiation exposure and risk of head and neck cancer. Outliers were tested using the MR-PRESSO method, and heterogeneity was assessed using the Cochran Q test. MR-Egger regression intercept was utilized to detect gene-level pleiotropy, and a leave-one-out sensitivity analysis was conducted to evaluate the robustness of the study results. Results96 valid SNPs were included as instrumental variables. The analysis results of random-effect IVW method, fixed-effect IVW method, and weighted median method all showed that radiation exposure was associated with an increased risk of head and neck cancer [odds ratio and 95% confidence interval: 1.139 (1.065, 1.218), 1.139 (1.068, 1.215), and 1.141 (1.039, 1.253); P<0.05]. Heterogeneity testing did not reveal significant heterogeneity, MR-Egger regression analysis did not find gene level pleiotropy, and the leave-one-out method did not find a single SNP significantly affecting the overall estimation results. Conclusion Radiation exposure increases the risk of head and neck cancer, but this conclusion still needs further validation in more high-quality, large sample studies.
Objective To systematically analyze the characteristics of the disease burden of hypertensive heart disease (HHD) in the elderly (≥60 years) globally and in China from 1990 to 2021, and to predict its future trends from 2022 to 2040, with the aim of providing data support for optimizing comprehensive prevention and control strategies for HHD. MethodsBased on the Global Burden of Disease (GBD) 2021 database, the number of prevalent cases and disability-adjusted life years (DALYs) of HHD in the elderly were extracted for the world, China, and five regions categorized by sociodemographic index (SDI). Joinpoint regression was used to analyze the temporal trends of age-standardized prevalence rate and age-standardized DALYs rate of HHD in the elderly. A three-factor decomposition method was applied to evaluate the relative contributions of aging, population growth, and epidemiological changes to the variations in the elderly HHD burden. Additionally, a Bayesian age-period-cohort model was used to predict the elderly HHD burden from 2022 to 2040. ResultsIn 2021, the number of prevalent elderly HHD cases reached 10 283 000 globally and 3 412 400 in China, representing increases of 179.20% and 159.20% respectively, compared with 1990. The DALYs of elderly HHD were 18 812 700 person-years globally and 4 731 400 person-years in China, rising by 76.08% and 29.45% respectively from 1990. Meanwhile, the growth rates of the number of prevalent cases and DALYs of elderly HHD varied across different SDI regions. From 1990 to 2021, the age-standardized prevalence rate of elderly HHD in China, as well as the age-standardized DALYs rate of elderly HHD both globally and in China, showed significant downward trends (all average annual percentage changes<0, all P<0.001). In 2021, the 70-74 years age group accounted for the highest proportion of prevalent cases and DALYs of elderly HHD, both globally and in China. Decomposition analysis revealed that population growth was the dominant factor driving the increase in the elderly HHD burden across all regions. The prediction model results indicated that the number of prevalent cases and DALYs of elderly HHD would continue to rise globally and in China from 2022 to 2040, with the growth rate of the elderly HHD burden in China between 2021 and 2040 expected to exceed the global average. ConclusionOver the past 32 years, although the age-standardized disease rates of elderly HHD have mainly shown a downward trend globally and in China, the absolute number of the disease burden has increased substantially. The projection model indicates a continued upward trajectory, with the growth rate in China higher than the global average. Therefore, there is an urgent need to implement precise prevention and control strategies to effectively mitigate the disease burden of elderly HHD.
Clarifying the burden of disease is of great significance for determining the focus of healthcare and optimizing the allocation of medical resources. However, differences in research methods and assumptions often affect the comparability of different research results, thus leading to difficulties in healthcare decision-making. Disability-adjusted life year (DALY) is the most commonly used indicator to measure the burden of disease, but the reporting quality of disease burden studies using the DALY metric is uneven. To standardize the reporting of such studies, international scholars developed and recently published the STROBOD statement in Population Health Metrics. Its checklist includes seven parts: title, abstract, introduction, methods, results, discussion, and open science, involving a total of 28 items. To assist domestic scholars in better understanding and applying this reporting standard, this article interprets each item with published examples, aiming to improve the overall quality of disease burden research and provide high-quality evidence for public health decision-making.
Clinical prediction models typically utilize a combination of multiple variables to predict individual health outcomes. However, multiple prediction models for the same outcome often exist, making it challenging to determine the suitable model for guiding clinical practice. In recent years, an increasing number of studies have evaluated and summarized prediction models using the systematic review/meta-analysis method. However, they often report poorly on critical information. To enhance the reporting quality of systematic reviews/meta-analyses of prediction models, foreign scholars published the TRIPOD-SRMA reporting guideline in BMJ in March 2023. As the number of such systematic reviews/meta-analyses is increasing rapidly domestically, this paper interprets the reporting guideline with a published example. This study aims to assist domestic scholars in better understanding and applying this reporting guideline, ultimately improving the overall quality of relevant research.