PubMed · 15693574
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
Croup.. https://pubmed.ncbi.nlm.nih.gov/15693574/
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
Observational studies suggest an association between glioblastoma (GBM) and cardiovascular diseases (CVDs), but a causal relationship remains unestablished. This study aimed to investigate the causal link between multiple CVDs and GBM risk. The inverse variance weighted method indicated that all 18 CVDs had significant causal associations with GBM (P < .05). Genetically predicted CVDs were uniformly associated with a lower risk of GBM (odds ratio < 1), identifying them as potential protective factors. Sensitivity analyses confirmed the absence of significant heterogeneity or horizontal pleiotropy, and the MR-Steiger test validated the correct causal direction. This Mendelian randomization (MR) study provides evidence that a range of CVDs are causally associated with a decreased risk of developing GBM. These findings suggest shared biological pathways and offer new insights for understanding GBM etiology. We conducted a 2-sample MR analysis using publicly available genome-wide association study data. GBM was the outcome, and 18 cardiovascular-related traits (including coronary artery disease, myocardial infarction, and venous thromboembolism) were exposures. Instrumental variables were single-nucleotide polymorphisms significantly associated with exposures (P < 5 × 10-8). The primary analysis used the inverse variance weighted method, supplemented with MR-Egger, weighted median, and weighted mode methods. Sensitivity analyses, including Cochran Q test, MR-Egger intercept test, leave-one-out analysis, and MR-Steiger directionality test, were performed to ensure robustness.
There is growing recognition that the risk of many diseases in later life, such as type 2 diabetes or breast cancer, is affected by adult as well as early-life variables, including those operating prior to conception and during the prenatal period. Most of these risk factors are correlated because of common biologic and/or social pathways, while some are intrinsically ordered over time. The study of how they jointly influence later ("distal") disease outcomes is referred to as life course epidemiology. This area of research raises several issues relevant to the current debate on causal inference in epidemiology. The authors give a brief overview of the main analytical and practical problems and consider a range of modeling approaches, their differences determined by the degree with which associations present (or presumed) among the correlated explanatory variables are explicitly acknowledged. Standard multiple regression (i.e., conditional) models are compared with joint models where more than one outcome is specified. Issues arising from measurement error and missing data are addressed. Examples from two cohorts in the United Kingdom are used to illustrate alternative modeling strategies. The authors conclude that more than one analytical approach should be adopted to gain more insight into the underlying mechanisms.
Disease can be caused by different mechanisms. A possible causal model proposed by Rothman is a complete causal mechanism or a so-called "sufficient cause" consisting of a set of component causes that can be illustrated in a pie chart. However, this model does not allow finding out what sufficient causes produce the majority of cases. The authors' objective was to extend Rothman's work by quantifying the proportion of disease that can be attributed to a class of sufficient causes. The underlying idea was to consider all combinations of a given set of known risk factors and to assign each combination to a class of sufficient causes. This assignment makes it possible to evaluate a class of sufficient causes by the population attributable fraction of the corresponding combination of risk factors. The approach presented was applied to sufficient causes of myocardial infarction by use of data on participants recruited between 1994 and 1998 into the European Prospective Investigation into Cancer and Nutrition-Potsdam Study. As a result, 51.8% of cases were attributed to only four different classes of sufficient causes. In conclusion, the statistical method described in the paper may be beneficial for quantifying the importance of different sufficient causes and for improving the efficiency of public health programs.