The Association Between Diverse Dietary Micronutrients and the Risk of Cutaneous Melanoma: Findings from the 2001-2018 NHANES Data Combined with Mendelian Randomization Analysis Volume 58- Issue 5

Sheng De Liang¹, Yu Chou Zhang², Song Huang², Ming Yang Wu¹ and Ke Ma^1*

• ¹The First Affiliated Hospital of Guangxi Medical University, Plastic and Aesthetic Surgery, China
• ²The First Affiliated Hospital of Guangxi Medical University, Intensive Care Medicine Department, China

Received: September 23, 2024; Published: October 04,2024

*Corresponding author: Ke Ma, The First Affiliated Hospital of Guangxi Medical University, Plastic and Aesthetic Surgery, Nanning, 530000, China

DOI: 10.26717/BJSTR.2024.58.009221

Abstract PDF

ABSTRACT

Background: Cutaneous melanoma poses a global public health concern. The aim of this study is to ascertain the risk factors for cutaneous melanoma and elucidate its causal relationships.

Methods: Data were derived from the National Health and Nutrition Examination Survey (NHANES) and Mendelian Randomization (MR) databases. Baseline characteristics of individuals with and without cutaneous melanoma were compared. Multivariable logistic regression analysis was employed to calculate the impact of various variables on cutaneous melanoma. MR analysis was used to assess the causal effect of micronutrient intake levels on cutaneous melanoma.

Results: A total of 38,130 patient data were included in the study (37,882 normal, 248 melanoma patients). There were no significant differences in the intake of 15 dietary micronutrients between the cutaneous melanoma group and the control group before and after propensity score matching (PSM). After adjusting for various covariates, dietary micronutrient intake was also found to have no statistically significant impact on cutaneous melanoma according to the logistic regression model. However, MR analysis revealed causal relationships between blood selenium and potassium levels and the risk of cutaneous melanoma (Selenium (IVW, P＜001, OR: 0.780 (0.694-0.877)), Potassium (IVW, P=0.012, OR: 0.399 (0.194-0.819))). There were no causal relationships between the remaining 13 micronutrients and cutaneous melanoma. Goodness-of-fit tests and sensitivity analyses were conducted to validate the reliability of the results.

Conclusion: There is no correlation between dietary micronutrients and increased risk of cutaneous melanoma, indicating that dietary selenium and potassium intake may not be responsible for the protective effect of serum or blood intake of micronutrients against melanoma risk. These findings provide a new perspective for the treatment of cutaneous melanoma.

Keywords: Dietary Micronutrients; Cutaneous Melanoma; Propensity Score Matching (PSM); National Health and Nutrition Examination Survey (NHANES); Mendelian Randomization Analysis

Abbreviations: NHANES: National Health and Nutrition Examination Survey; PSM: Propensity Score Matching; MM: Malignant Melanoma; ICIS: Immune Checkpoint Inhibitors; GWAS: Genome-Wide Association Studies; PSM: Propensity Score Matching; RM: Mendelian Randomization; LD: Linkage Disequilibrium; SNPS: Single Nucleotide Polymorphisms; MMPS: Matrix Metalloproteinases; ROS: Reactive Oxygen Species; MAPK: Mitogen-Activated Protein Kinase; ECM: Extracellular Matrix; IVW: Inverse Variance Weighted

Introduction

Melanoma represents a potentially fatal cancer, with cutaneous melanoma being the most prevalent form, consistently remaining a primary focus of clinical investigation, manifesting intricate heterogeneity in its pathogenesis and presentation. Its incidence has been reported to exhibit a sustained upward trajectory globally, with an estimated annual incidence of 325,000 cases of malignant melanoma (MM) and a related mortality rate of 57,000 cases per year. It is projected that by 2040, the incidence of MM will further increase by 50%, with the mortality rate rising to 96,000 cases annually [1]. Several risk factors have been associated with melanoma, including environmental, genetic, or immune-related changes, yet the precise etiology remains elusive. Particularly noteworthy is the prolonged exposure to sunlight or artificial ultraviolet (UV) radiation, which induces severe DNA damage in skin cells, resulting in a significant mutational burden and ultimately culminating in melanoma formation [2,3]. Certain phenotypic characteristics, such as fair skin, red hair, and freckles, are also linked to UV sensitivity and a heightened risk of melanoma [4]. Furthermore, an individual's genetic background has been demonstrated to be closely correlated with susceptibility to melanoma, with the melanoma genome classified into one of four subtypes based on the pattern of prevalent mutations in genes: mutant BRAF, mutant RAS, mutant NF1, and Triple-WT (wild-type) [5]. Additionally, research on immunosuppressive factors indicates that immune system dysregulation may provide favorable conditions for the development of melanoma.

In early-stage melanoma, when the tumor is confined to the skin, surgical excision typically results in a cure [6]; however, treatment becomes more complex upon progression to metastatic melanoma. Modern effective systemic therapies for malignant melanoma comprise two pivotal treatment modalities: immune checkpoint inhibitors (ICIs), including inhibitors of cytotoxic T-lymphocyte antigen 4, programmed cell death 1, and lymphocyte activation gene 3; and small molecule BRAF/MEK inhibitor therapy. These therapies have revolutionized the treatment approach for advanced melanoma patients, significantly enhancing clinical outcomes [7,8]. With the increased utilization of ICI therapy for melanoma and other cancers, associated immune-related adverse events become more prevalent. Some adverse events of ICI therapy, such as adrenal insufficiency, may be life-threatening but present with nonspecific symptoms, potentially easily overlooked by providers unfamiliar with these treatment modalities [9]. Hence, despite recent advances in therapeutic options, limitations persist, necessitating the development of novel therapies that can both improve outcomes and mitigate adverse reactions. Trace elements refer to elements in the human body with concentrations between 0.01% and 0.005% of body weight, such as zinc, selenium, and iron, which are crucial for normal cellular function and metabolism.

Currently, four main groups of metals are considered essential for normal biological function: sodium (Na), potassium (K), magnesium (Mg), and calcium (Ca), along with six d-block transition metals: manganese (Mn), iron (Fe), cobalt (Co), copper (Cu), zinc (Zn), and molybdenum (Mo). Essential trace elements are provided by food intake. Previous studies have suggested an association between trace element exposure and the occurrence of melanoma [10]. For instance, after adjusting for sunlight exposure and education level, individuals with higher toenail copper levels among melanoma patients exhibited an excess risk, while high iron concentration was associated with reduced disease risk, with no other differences observed in remaining elements [10,11]. When exposed to ultraviolet radiation, iron/ferrous iron levels increase in the skin [12], participating in catalyzing redox reactions. In the presence of UVA radiation, redox reactions can generate reactive oxygen species (ROS), playing a significant role in UVA-mediated skin cell damage [13]. Matrix metalloproteinases (MMPs), zinc-containing endopeptidases, contribute to changes in the extracellular matrix (ECM), possibly leading to skin wrinkles, a hallmark of premature skin aging. ECM degradation is the initial step in tumor cell invasion in photo carcinogenesis. Additionally, MMPs participate in angiogenesis, promoting cancer cell growth and migration [14]. In current research, various vitamins have been shown to play important roles in the development of skin cancer.

Vitamin C is involved in the formation of the skin barrier and collagen in the dermis, exerting physiological effects in cell signaling pathways related to skin oxidation resistance, anti-wrinkle properties, and cell growth and differentiation, all of which are relevant to the occurrence and development of various skin diseases [15]. Derivatives of vitamin D, such as 1,25 (OH)2D3 or 20 (OH)D3, can effectively reduce the proliferation of skin cancer cells by inhibiting cell growth and development, highlighting the role of vitamin D as a favorable prognostic factor [16]. Moreover, molecular and clinical pathological studies indicate a correlation between defects in vitamin D signaling and melanoma progression and disease outcomes [17]. Skin photodamage, partially mediated by oxidative pathways, provides evidence suggesting that antioxidants such as vitamin E and β-carotene in the skin may have photoprotective effects, mediated by their ability to quench singlet oxygen, scavenge free radicals, and prevent free radical formation [18]. Therefore, deficiencies or excessive intake of trace elements and various vitamins may disrupt normal cellular function, thereby affecting the proliferation and malignant transformation of melanocytes. In conclusion, epidemiological studies on trace element and multiple vitamin exposure in relation to cutaneous melanoma are limited, necessitating research with larger sample sizes and prospective designs to enhance our understanding of the effects of trace elements on melanoma.

This study will utilize data from the National Health and Nutrition Examination Survey (NHANES) from 2001 to 2018, combined with Mendelian randomization analysis methods, to systematically explore the relationship between different trace elements and the risk of melanoma. Genome-wide association studies (GWAS) on melanoma and micronutrients have facilitated large-scale meta-analyses, enabling MR analysis of two samples and significantly enhancing statistical power. By delving into extensive population data, we hope to uncover the potential mechanisms of trace elements in the pathogenesis of melanoma, providing a more scientific basis for the prevention and treatment of melanoma. This article involves trace elements including Copper, Calcium, Carotene, Folate, Iron, Magnesium, Potassium, Selenium, Zinc, Vitamin A, Vitamin B12, Vitamin B6, Vitamin C, Vitamin D, and Vitamin E.

Method

Source of Melanoma Cases

Population demographics, dietary habits, and questionnaire responses were obtained from the National Health and Nutrition Examination Survey website (https://www.cdc.gov/nchs/nhanes/index.htm). Nine two-year cycles from 2001 to 2018 were selected from the NHANES database, comprising 18 years of Demographics Data, Dietary Data, and Questionnaire Data, with a total of 70,882 participants. Excluding 32,752 patients with missing dietary data or melanoma data, a total of 38,130 patient data were included in the study (37,882 normal, 248 melanoma patients). Analysis was conducted on the dietary micronutrient intake of participants. The selection process from the NHANES database is illustrated in Figure 1A. Tumor diagnoses were obtained from the NHANES questionnaire. Participants were asked two consecutive questions: "Have you ever been told by a doctor or other health professional that you had cancer or a malignant tumor?" and "What kind of cancer?" Melanoma patients and non-tumor participants were subsequently selected for inclusion in the study. The weight for 2001-2018 (9 cycles) was calculated as 1/9*wtint2yr.Participants were asked to report their intake of trace elements from their diet. Dietary micronutrient intake was assessed using information obtained from the first recall interview, with wtint2yr selected as the study weight. Serum copper was tested in NHANES from 2001 to 2018 using laboratory methods as detailed on the NHANES website. A total of 70,882 participants underwent screening, with data recorded on dietary copper intake and serum copper levels. Covariates included demographic information. Self-reported demographic information included gender, age, race, marital status, and education level.

Statistical Analysis

All data in this study were analyzed using the "nhanes" R package in R version 4.3.2 software. Categorical variables, including all covariates, were presented as percentages and total numbers. The comparison of categorical variable groups was performed using the chi-square test or exact Fisher test. Micronutrient intake levels were continuous variables presented as mean ± standard deviation. Statistical comparisons of continuous variables were primarily conducted using independent samples t-tests or non-parametric Mann-Whitney U tests. To enhance the reliability of observational experiments and reduce bias, propensity score matching (PSM) analysis was employed with a 1:1 ratio, matching melanoma patients with non-tumor participants based on covariates. A multivariate logistic regression model was constructed using logistic regression to explore the relationship between dietary micronutrient intake levels and melanoma risk before and after PSM. Subgroup analyses were performed based on micronutrient intake levels, gender, age, education level, and marital status before and after PSM matching.

Mendelian Randomization Study and GWAS Data Source

Mendelian randomization (MR) is a prospective causal inference method that utilizes genetic variation as instrumental variables to assess the impact of exposure factors on observed data outcomes. MR can mitigate non-measurement errors or confounding factors and circumvent reverse causality through Mendelian genetic laws [19,20]. GWAS data were used for Mendelian randomization analysis. SNP information was extracted from genome-wide association studies of 15 micronutrients. Due to high statistical efficiency, the primary statistical method used was the inverse variance weighted method. Data were obtained from the IEU Open GWAS, UK Biobank, and Finn databases, and genetic variations for melanoma were acquired. All data used in this study are openly accessible and do not require additional ethical approval. Blood micronutrients served as exposure factors, with melanoma as the outcome factor. Only p-values < 5×10−8 and minor allele frequencies > 1% were considered for selection. Within 1000 kb frames, the linkage disequilibrium (LD) cutoff value was set at R2 < 0.001. Heterogeneity and pleiotropy were also assessed separately. GWAS data for 15 micronutrients were found, including Copper, Zinc, Calcium, Selenium [21], Carotene, Folate, Iron, Magnesium, Potassium, Vitamin A, Vitamin B12, Vitamin B6, Vitamin C, Vitamin D, and Vitamin E, as depicted in the Figure 1B.

For MR analysis, single nucleotide polymorphisms (SNPs) associated with exposure (P < 5 × 10–8) were selected as IVs. Inverse variance weighted (IVW), MR-Egger, weighted median, and weighted mode methods were used to evaluate the causal impact of blood micronutrients on melanoma. MR was based on three assumptions, including correlation, independence, and exclusion restrictions. Cochran's Q statistic was used to assess heterogeneity. MR-Egger regression intercept was used to check for horizontal pleiotropy. Leave-one-out sensitivity analysis was conducted to validate the robustness of the results. Causal relationships were considered only when IVW estimates were consistent with at least one direction of sensitivity analysis results and statistically significant, with no evidence of pleiotropic effects (P > 0.05). Effect sizes were expressed as odds ratios (ORs), β coefficients, or proportions, accompanied by 95% confidence intervals. All MR analyses were conducted using R software packages "Two Sample MR," "Mendelian Randomization," and "MVMR." All Mendelian randomization analyses were performed using R version 4.3.2.

Results

Participant Characteristics of Dietary Copper Intake Before PSM

The enrollment process of this study is illustrated in Figure 1. After excluding participants with missing values and those under the age of 20, a total of 38,130 participants were included in the study. Table 1 provides the baseline characteristics of this study. When comparing melanoma patients with non-tumor patients, statistically significant p-values were found for categorical variables such as race (p < 0.001), age (p < 0.001), education level (p < 0.001), marital status (p < 0.001), and gender (p = 0.032). Among participants with melanoma, 48.5% were male and 51.5% were female. In terms of racial composition, Mexican Americans accounted for 17.3%, Other Hispanics for 8.9%, Non-Hispanic whites for 41.8%, NonHispanic blacks for 22.1%, and Other races for 9.9%. Differences were observed in education level and marital status. Specific details are depicted in the Table 1. In order to reduce the impact of confounding factors and eliminate bias in observational studies, we conducted 1:1 propensity score matching (PSM). There were no statistically significant differences in covariates between melanoma patients and matched non-tumor participants. The propensity score matching and characteristics of dietary micronutrient intake after PSM are illustrated in the Figure 2.

Before and After PSM, Logistic Regression Analysis of the Relationship between Dietary Micronutrients and Melanoma

The population screening process for obtaining serum copper data from the included subjects (n= 26,401) is illustrated in Figure 1A, including 248 melanoma subjects and 37,882 non-tumor subjects. In both melanoma and non-tumor populations, age (p<0.001), dietary micronutrients vitamin B6 (p=0.036), total folate (p=0.001), calcium (p=0.003), copper (p<0.001), and selenium (p=0.004) showed statistical significance (Table 2). Given the possibility of few melanoma patients, we conducted PSM to control for the influence of covariates on the results (Figure 2). Under a 1:1 PSM, we observed no significant differences in dietary micronutrients among the melanoma population (p>0.05) (Table 2). To further explore the relationship between changes in serum copper levels and the likelihood of melanoma occurrence, we constructed multivariable logistic regression models with and without PSM, adjusting for age, gender, race, education level, and marital status. Table 3 shows that dietary micronutrient intake before and after PSM was not statistically significant and unrelated to the risk of melanoma occurrence.

Mendelian Randomization Results

A total of 198 significant SNPs were obtained for 15 micronutrients, and Mendelian randomization analysis was conducted with melanoma GWAS data (finngen_R9_C3_MELANOMA_SKIN_EXALLC). The analysis revealed a correlation between serum selenium and serum potassium and the risk of melanoma, where selenium (IVW, P<0.001, OR: 0.780 (0.694-0.877)) and potassium (IVW, P=0.012, OR: 0.399 (0.194-0.819)) (Figure 3). Based on these results, we concluded that there is a causal relationship between serum selenium, serum potassium, and melanoma in the European population. Cochran's Q and MR-Egger intercept tests confirmed the absence of heterogeneity and horizontal pleiotropy in the MR analysis. Leave-one-out analysis showed that the results remained stable when removing individual SNPs, indicating the reliability of MR estimates. In MVMR analysis, after adjusting for serum potassium, the causal relationship between serum selenium and cutaneous malignant melanoma still existed (p=0.005, OR 0.795 (0.677-0.933)), while after adjusting for serum selenium, the causal relationship between serum potassium and cutaneous malignant melanoma lost statistical significance (Figure 4). All directions and statistical significance of the IVW results in MVMR were consistent with the results of MVMR Lasso regression analysis, and MVMR Egger analysis indicated no directional pleiotropy of the instrumental variables.

Discussion

The biological characteristics of trace nutrients have long been a focal point of research. However, their role in melanoma formation remains elusive. Our study marks the first endeavor to assess the causal relationship between dietary intake of trace nutrients and melanoma using GWAS data. Within this investigation, we scrutinized data from participants in the NHANES survey spanning from 1999 to 2004 to identify risk factors associated with melanoma. Moreover, we employed MR methodology to evaluate the causal impact of trace nutrient levels in the diet on melanoma. In our MVMR analysis, even after adjusting for serum Potassium, a causal relationship between serum Selenium and cutaneous malignant melanoma persisted. Conversely, the statistical significance of the causal relationship between serum Potassium and cutaneous malignant melanoma was lost after adjusting for serum Selenium, suggesting a substantial modulatory effect of serum Selenium on the influence of serum Potassium on cutaneous malignant melanoma. Observational study findings indicate that, after adjusting for various confounding factors, the levels of trace nutrients in the diet show no statistically significant correlation with melanoma risk, whereas MR analysis confirms a negative causal relationship between Selenium and Potassium and melanoma. This represents the first large-scale study to investigate the causal relationship between trace nutrients and melanoma in real-world populations at the genetic level, with goodness-of-fit tests and sensitivity analyses validating the reliability of our results.

Selenium (Se) is an essential trace element with crucial biological functions in human health. Unlike other (semi)metals, it is incorporated into proteins via the cotranslation mechanism, serving as part of the amino acid selenocysteine (SeCys), the 21st amino acid utilized in human protein synthesis [22]. Selenium can exist in a free form in foods rich in selenoamino acids. Dietary intake of selenium comes from plant products containing selenoamino acids and methylselenocysteine (grains, fruits, Brazil nuts, broccoli, garlic, onions, and cabbage) [23]. Selenium plays a wide-ranging role in the human body, involving inflammation [24], anticancer properties [25], redox homeostasis [26], growth and development [27], and anti-aging processes [28]. Selenium exhibits antioxidant activity at optimal doses, while at supra nutritional doses, it demonstrates pro-oxidative activity. Redox-active selenium compounds can be utilized in cancer therapy, with recent focus particularly on selenium-containing nanoparticles [29]. However, selenium appears to exert a dual role in tumor development, where moderate elevation of selenium levels in the skin may accelerate the growth of early-stage tumors, while certain selenium compounds administered at very high doses can be used to treat fully malignant tumors or prevent recurrence [30]. In some studies, selenium has been found to have inhibitory effects on melanoma development. For instance, patients were categorized into four groups based on increasing selenium levels (quartiles I-IV).

Compared to patients with high selenium levels, the subgroup with low selenium levels showed significantly decreased survival rates, with HR = 8.42; p = 0.005 and HR = 5.83; p = 0.02, respectively, in univariate and multivariate models. Univariate analysis also confirmed associations between Breslow thickness, Clark classification, and melanoma prognosis age. In summary, low serum selenium levels are associated with increased 10-year mortality rates after melanoma diagnosis [31]. Biologically available selenium from selenium-rich mustard cake helps prevent hydrogen peroxide-induced cytotoxicity in melanoma cells [32]. Selenium-binding proteins are downregulated in melanoma, and their re-expression reduces melanoma cell proliferation [33]. Selenium inhibits melanoma cell proliferation in a dose-dependent manner, with growth inhibition linked to arrest in the G0/G1 phase of the cell cycle. Selenium treatment time-dependently suppresses mRNA and protein levels of CDK2/CDK4. In vivo, selenium does not inhibit tumor growth; instead, it exerts inhibitory effects on tumor metastasis in mice [34]. Conversely, in other studies, selenium has been found to promote melanoma development. Drinking water with inorganic hexavalent selenium levels close to the European standard of 10μg/L may have adverse effects on cancer incidence. Higher plasma selenium levels are closely associated with melanoma risk in unmatched and matched logistic regression models as well as nonparametric generalized additive models [35].

Melanoma incidence in the exposed cohort is 3.9 times that of the unexposed cohort [36]. Currently, various forms of selenium are utilized in melanoma treatment regimens, with particular attention given to novel nanomaterials. Intracellular ROS detection demonstrates that under the presence of Se-PEG-Cur, dual-induced ROS production from PTT and SDT reaches its peak. Therefore, Se-PEG-Cur is introduced as an absorber for laser and ultrasound in cancer therapy [37]. Surface-functionalized selenium nanoparticles with 5-fluorouracil induce caspase-dependent apoptosis in A375 cells, relying on ROS production, achieving synergistic anticancer effects [38]. PCP-SeCN induces cell apoptosis by modulating Akt activity to inhibit HDAC activity and exhibits novel inhibitory properties, leading to an increase in the sub-G₀-G₁ cell population and cleavage of caspase-3 and PARP levels. Furthermore, PCP-SeCN inhibits cell proliferation by suppressing cyclin D1 expression and elevating p21 levels, suppressing the development of melanocyte lesions in laboratory skin by up to 87%, with negligible toxic effects [39]. S, S'-1,4-phenylenebis(1,2-ethanediyl)bis(ethylselenourea) (PBISe) reduces cancer cell proliferation and increases apoptosis by targeting iNOS as an Akt3 pathway inhibitor and a cascade activator of mitogen-activated protein kinase (MAPK), inhibiting Akt3 signaling, elevating cleaved caspase-3 and PARP levels, thereby promoting melanoma cell apoptosis and inhibiting proliferation[40].Potassium is a crucial electrolyte responsible for maintaining cellular homeostasis [41,42], participating in osmotic pressure, pH regulation, transport and distribution of water molecules within cellular compartments, energy metabolism, and regulation of electron transfer reactions, as well as serving as a cofactor for numerous enzymes and their functions. Currently, research on the relationship between potassium and tumors primarily focuses on potassium ion channels.

Ion channels are transmembrane proteins that allow ions to traverse membranes, such as the plasma membrane or membranes of various cellular organelles like the nucleus, endoplasmic reticulum, Golgi apparatus, or mitochondria [43]. They play significant roles in promoting tumor occurrence and development in vivo [44-46], and among all ion channels, potassium ion channels are the most diverse and abundant, with over 70 genes in humans encoding potassium ion channels [47-49]. Aberrant expression of potassium channels in tumors has been documented in many types of cancers. In melanoma, K+ channel blockers inhibit cell cycle progression by membrane depolarization, thereby reducing intracellular calcium influx. Calcium (Ca2+) serves as a messenger in the mitotic signaling cascade of human melanoma cells, thereby exerting an inhibitory effect on melanoma by blocking potassium channels [50]. This study conducted an analysis by screening the NHANES database for the intake of trace nutrients in the population and the incidence of melanoma. Interestingly, serum trace nutrients were found to be entirely unrelated to melanoma risk, especially after accounting for factors such as age and gender. To address potential research bias due to a small number of cases and further validate the above conclusion, Mendelian randomization (MR) analysis was performed with melanoma as the outcome factor and trace nutrients as exposure factors. MR analysis demonstrated a negative causal relationship between selenium and potassium and melanoma, suggesting that dietary selenium and potassium intake may not account for the protective effect of serum or blood intake of trace elements on melanoma risk.

We are curious about the mechanisms underlying this connection. Since serum selenium is entirely derived from diet, we want to investigate whether increasing selenium intake in the diet would affect melanoma risk by influencing selenium concentration in the blood plasma. our study holds significant importance and relevance due to several advantages. Firstly, our research is based on the NHANES database of the US population. Given the representative nature of data collection in the NHANES database, the analysis results are highly representative. Additionally, there is a lack of research on the relationship between melanoma risk and trace nutrients. In this study, subgroup analysis was conducted to identify specific individuals who would benefit from this intervention. Subsequently, Mendelian randomization analysis revealed a negative causal relationship between serum selenium and melanoma in the European population. However, our study also has some limitations. Although some observational studies have found a negative correlation between selenium exposure and the risk of certain types of cancer, this cannot be considered evidence of causality, and these results should be interpreted with caution. These studies have many limitations, including issues with assessing selenium exposure and its various chemical forms, heterogeneity, confounding, and other biases. It has been reported that some cancer types show conflicting results, including inverse associations, null associations, and direct associations.

Randomized controlled trials assessing the impact of selenium supplements on cancer risk have yielded inconsistent results, although recent studies, characterized by low bias risk, have found no beneficial effects on cancer risk, specifically malignant melanoma risk, and there is almost no evidence to suggest any impact on baseline selenium status. Conversely, some trials suggest harmful effects of selenium exposure. To date, there is no convincing evidence that selenium supplements can prevent human cancer. Additionally, assessment of cancer and melanoma patients is based on subjective survey questionnaires, which may introduce bias.

Conclusion

Although Mendelian randomization analyses confirmed that serum selenium and potassium with are protective factors for melanoma, there is no evidence to suggest that selenium and potassium through the diet reduce the risk of melanoma, and we need to dig further into the underlying mechanisms. It is worth noting that the physiological mechanisms underlying the relationship between trace nutrients and melanoma pathogenesis are much more complex and extend beyond the scope of this study and its methods. Further prospective studies on trace nutrients in melanoma patients before and after treatment, compared with healthy individuals, can provide valuable mechanistic insights into the etiology and treatment of melanoma, and help identify high-risk populations.

Ethical Statement

The manuscript describes research involving animals that did not require ethical approval.

Author Contributions

Sheng de Liang: Conceptualization, data curation, formal analysis, investigation, methodology, software, validation, visualization, writing—original draft.

Yu chou Zhang: Conceptualization, data curation, formal analysis, investigation, methodology, project management, resources, software, supervision, validation, visualization, writing—review and editing.

Ming Yang Wu: Partial data organization and analysis.

Ke Ma: Oversight and leadership responsibility for the research activity planning and execution, including mentorship external to the core team.

Acknowledgments

We would like to express our gratitude to the participants and researchers involved in this study. We also thank Mi Bio Gen, the European Bioinformatics Institute, and Open GWAS for providing GWAS summary statistics data.

Data Availability

Publicly available datasets were analyzed in this study. This data of observational study can be found here: https://www.cdc.gov/nchs/nhanes/. The GWAS data are available through the MRC IEU Open GWAS database (https://gwas.mrcieu.ac.uk/), UK biobank (http://www.nealelab.is/uk-biobank), and the Finn Gen consortium round 7 (https://www.finngen.fi/en).

Funding Statement

The authors declare that the research, authorship, and publication of this article did not receive any financial support.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All statements expressed in this article are solely those of the authors and do not necessarily represent the statements of their affiliated organizations or the publisher, editors, and reviewers. Any product assessments or claims made by manufacturers in this article are not guaranteed or endorsed by the publisher.

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