Association of serum 25-hydroxyvitamin D with the incidence of 16 cancers, cancer mortality, and all-cause mortality among individuals with metabolic syndrome: a prospective cohort study

Xie, Tian; Guo, Jing; Wang, Kai; Xu, Hong-Quan; Lin, Tao; Ni, Juntao

doi:10.1007/s00394-023-03169-x

Cited by 5 publications

(2 citation statements)

References 57 publications

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“…With advancements in genetics and bioinformatics leading to increased precision in measuring genetic variation, genetic factors have become increasingly important tools in assessing disease-related associations. Selective use of single nucleotide polymorphisms relevant to the disease from GWAS databases as instrumental variables replaces exposure factors to evaluate correlations between exposures and outcomes, increasing sample sizes while reducing the in uence of confounding factors(E. Wu et al, 2023). This MR analysis shows insigni cant causal relationships between 25-OH-D levels and the likelihood of developing low back pain.…”

Section: Discussionmentioning

confidence: 97%

Vitamin D and Low Back Pain - An Mendelian Randomization Study

gao,

zhengqin,

2024

Preprint

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Backgrounds:Low back pain (LBP) is one of the leading cause of health function loss worldwide.There has been ongoing controversy regarding whether vitamin D deficiency can cause lower back pain.This study assessed the impact of 25-OH-D on LBP using Mendelian Randomization. Methods:Using GWAS databases, the exposure variable was set as 25-OH-D, and the outcome as low back pain. IVW, MR-Egger Regression, Simple Median, Weighted Median, and Weighted Mode methods were applied to assess the impact of 25-OH-D on low back pain. Results: IVW analysis revealed no causal relationship between 25-OH-D and low back pain (OR = 1.14, 95% CI: 0.95-1.38, P = 0.165). The MR-Egger intercept test (P >0.05) did not detect pleiotropic effects, indicating robust causal inference using Mendelian Randomization. Conclusion: No causal relationship between serum levels of 25-OH-D and low back pain was identified by MR analysis.

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Section: Discussionmentioning

confidence: 97%

Vitamin D and Low Back Pain - An Mendelian Randomization Study

gao,

zhengqin,

2024

Preprint

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“…Significantly, they emphasized the importance of evaluation based on specific cancer types. The association between 25(OH)D concentrations and cancer risk in individuals with metabolic syndrome was explored in ( 39 ), where the authors identified an inverse correlation between 25(OH)D concentrations and the risk of colon, lung, and kidney cancer, providing further support for the potential role of vitamin D in cancer prevention. Complementing these findings, Kuznia et al.…”

Section: Introductionmentioning

confidence: 99%

Utilizing logistic regression to compare risk factors in disease modeling with imbalanced data: a case study in vitamin D and cancer incidence

Meysami,

Kumar,

Pugh

et al. 2023

Front. Oncol.

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Imbalanced data, a common challenge encountered in statistical analyses of clinical trial datasets and disease modeling, refers to the scenario where one class significantly outnumbers the other in a binary classification problem. This imbalance can lead to biased model performance, favoring the majority class, and affecting the understanding of the relative importance of predictive variables. Despite its prevalence, the existing literature lacks comprehensive studies that elucidate methodologies to handle imbalanced data effectively. In this study, we discuss the binary logistic model and its limitations when dealing with imbalanced data, as model performance tends to be biased towards the majority class. We propose a novel approach to addressing imbalanced data and apply it to publicly available data from the VITAL trial, a large-scale clinical trial that examines the effects of vitamin D and Omega-3 fatty acid to investigate the relationship between vitamin D and cancer incidence in sub-populations based on race/ethnicity and demographic factors such as body mass index (BMI), age, and sex. Our results demonstrate a significant improvement in model performance after our undersampling method is applied to the data set with respect to cancer incidence prediction. Both epidemiological and laboratory studies have suggested that vitamin D may lower the occurrence and death rate of cancer, but inconsistent and conflicting findings have been reported due to the difficulty of conducting large-scale clinical trials. We also utilize logistic regression within each ethnic sub-population to determine the impact of demographic factors on cancer incidence, with a particular focus on the role of vitamin D. This study provides a framework for using classification models to understand relative variable importance when dealing with imbalanced data.

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