Page 199 - EJMO-9-2
P. 199

Eurasian Journal of
            Medicine and Oncology                                                Vitamin D and HNC: Causal association



            survival rate of 40 – 50%.  Therefore, an in-depth study on   employed as a supplementary method. This approach
                                5
            the factors associated with HNC occurrence is important   enabled the concurrent consideration of multiple
            for reducing the prevalence of this disease.       interrelated exposures and facilitated a more refined
              Vitamin D, an essential and versatile lipid-soluble   evaluation of the direct influence of specific exposures
            vitamin, can be obtained from solar exposure and dietary   on the outcome by controlling for confounding factors
                                                                                      22
            sources. 25-hydroxyvitamin D (25(OH)D), a stable   related to other exposures.  Using this approach, we
            Vitamin D metabolite, is acknowledged as a dependable   successfully  eliminated  confounding  due  to  known
            indicator for assessing Vitamin D levels in the body.    confounders, resulting in a more precise assessment of
                                                          6
            Vitamin D is critical for maintaining optimal, and its   the causality of Vitamin D for HNC risk.
            deficiency is closely linked to several diseases such as   2. Materials and methods
            cardiovascular disorders, COVID-19, bone diseases,
            diabetes, and autoimmune diseases.  In addition,   2.1. Study design
                                             7
            considerable attention has been directed toward the   Univariate and multivariate MR methods were employed
            relationship between vitamins and cancer incidence.   in this study. Vitamin D levels were treated as the exposure
            Studies have shown that Vitamin D can affect particular   variable, and HNC served as the outcome of interest.
            cellular signaling pathways involved in tumor growth,   Single-nucleotide polymorphisms (SNPs), used as IVs,
            consequently controlling the apoptosis, differentiation,   were strongly linked to Vitamin D levels. Moreover, to
            and metastasis of tumor cells.  A meta-analysis indicated   accurately assess the direct influence of Vitamin D on
                                    8,9
            that high Vitamin D levels correlate with a decrease in   increasing HNC risk, certain adjustments were made
            cancer incidence and mortality.  A randomized study   in the  MVMR analysis  to account for  the influence  of
                                       10
            indicated that increasing Vitamin D levels markedly   two important factors, that is, smoking and alcohol
            reduced cancer risk in postmenopausal women.  In   consumption. This was performed to exclude any possible
                                                      11
            particular, when examining the causality of Vitamin D for   bias attributed to these factors. MR analyses must adhere to
            HNC risk, certain studies present evidence that Vitamin   three basic assumptions (Figure 1): (1) the IVs are strongly
            D decreases HNC risk. 5,12-14  However, some studies have   linked to the exposures, (2) the IVs are not related to any
            failed to identify a noteworthy connection between the   confounders, (3) and the IVs influence the outcomes only
            two.  To overcome the challenges of confounding and   through the exposures and are not influenced by other
               15
            reverse  causality  prevalent  in  traditional  observational   pathways.
            studies and elucidate the causal association between
            Vitamin D and HNC risk, this study employed Mendelian   2.2. Data sources
            randomization (MR) to investigate this potential causality   The IEU OPEAN GWAS PROJECT database provided the
            more thoroughly.                                   data for this study. The dataset for Vitamin D levels (ebi-

              Using genetic variation as an instrumental variable   a-GCST005367) used in the genome-wide association
            (IV), MR can investigate the factors that influence   study (GWAS) was derived from the study by Jiang et al.
            disease exposure.  This approach utilizes the random   which contained a total of 79,366 samples and 2,538,249
                           16
            assignment of alleles during fertilization, similar to the   SNPs.  The HNC dataset (ieu-b-4912 was obtained
                                                                    23
            randomization employed in randomized controlled    from the UK Biobank, which involved 1106 patients and
            trials.  Because genetic variations remain stable during an   372,016 controls. Data on ever-smoking (ieu-b-4858) and
                 17
            individual’s life, independent of lifestyle, environmental   alcohol consumption (ieu-b-4834) were extracted from
            factors, and other potential  confounders, MR studies
            effectively mitigate confounding bias and reverse
            causation problems that are prevalent in traditional
            observational studies.  This approach provides reliable
                              18
            and  accurate  causal  inferences  for  understanding
            disease risk factors. In addition, other MR studies have
            explored the association of Vitamin D with conditions
            such as lung disease, cardiovascular disease, and certain
            cancers, confirming the feasibility of this approach. 19-21
            This study employed a two-sample MR approach to
            examine the causal link between Vitamin D levels and
            HNC. To ensure that our analytical results were accurate
            and robust, multivariate MR (MVMR) analysis was also    Figure 1. Core assumptions of Mendelian randomization


            Volume 9 Issue 2 (2025)                        191                              doi: 10.36922/ejmo.7099
   194   195   196   197   198   199   200   201   202   203   204