Genetically predicted selenium is negatively associated with serum TC, LDL-C and positively associated with HbA1C levels

Higher Levels of Blood Selenium are Associated with Higher Levels of Serum Lipid Profile in US Adults with CKD: Results from NHANES 2013-2018

The association between selenium (Se) and lipid profile has been controversial in different populations, and the aim of the study was to investigate the relationship between Se and lipid profile in patients with chronic kidney disease (CKD). A total of 861 US adult patients with CKD (male: female = 404:457) from the National Health and Nutrition Examination Survey database were enrolled in this cross-sectional study. We used smoothing spline plots and multivariate binary logistic regression analyses to elucidate the relationships between blood Se and lipid profile. Multivariate adjusted smoothing spline plots showed that higher levels of blood Se were associated with higher levels of serum remnant cholesterol (RC), total cholesterol (TC), triglycerides (TG), and low-density lipoprotein cholesterol (LDL-C) levels. Threshold and saturation effects were also observed between serum RC, TC, TG, LDL-C, and blood Se. In multivariate binary logistic regression analyses, the fully adjusted model showed that as blood Se increases by every 1 µg/L, the OR of high RC, high TG and high LDL-C in patients was 1.012 (95% CI: 1.001, 1.023 P = 0.046), 1.011 (95% CI: 1.001, 1.021 P = 0.043) and 1.009 (95% CI: 1.003, 1.016 P = 0.012), respectively. Furthermore, stratified analyses showed that the associations between blood Se and high RC/high TG were significantly stronger in patients aged < 65 years. Higher levels of blood Se were associated with increased serum lipid profile levels and increased risk of high RC, high TC, high LDL-C, and low HDL-C dyslipidemia in adult patients with CKD in the US. However, the real associations between blood Se and lipid profiles in this population should be verified in future prospective and randomized trials.

Selenium, Stroke, and Infection: A Threefold Relationship; Where Do We Stand and Where Do We Go?

Stroke is currently the second most common cause of death worldwide and a major cause of serious long-term morbidity. Selenium is a trace element with pleotropic effects on human health. Selenium deficiency has been associated with a prothrombotic state and poor immune response, particularly during infection. Our aim was to synthesize current evidence on the tripartite interrelationship between selenium levels, stroke, and infection. Although evidence is contradictory, most studies support the association between lower serum selenium levels and stroke risk and outcomes. Conversely, limited evidence on the role of selenium supplementation in stroke indicates a potentially beneficial effect of selenium. Notably, the relationship between stroke risk and selenium levels is bimodal rather than linear, with higher levels of serum selenium linked to disturbances of glucose metabolism and high blood pressure, morbidities which are, in turn, substrates for stroke. Another such substrate is an infection, albeit forming a bidirectional relationship with both stroke and the consequences of impaired selenium metabolism. Perturbed selenium homeostasis leads to impaired immune fitness and antioxidant capacity, which both favor infection and inflammation; specific pathogens may also contend with the host for transcriptional control of the selenoproteome, adding a feed-forward loop to this described process. Broader consequences of infection such as endothelial dysfunction, hypercoagulation, and emergent cardiac dysfunction both provide stroke substrates and further feed-forward feedback to the consequences of deficient selenium metabolism. In this review, we provide a synthesis and interpretation of these outlined complex interrelationships that link selenium, stroke, and infection and attempt to decipher their potential impact on human health and disease. Selenium and the unique properties of its proteome could provide both biomarkers and treatment options in patients with stroke, infection, or both.

Genetically predicted body selenium concentration and eGFR: A Mendelian randomization study

Introduction

Selenium is a trace mineral that is commonly included in micronutrient supplements. The effect of selenium on kidney function remains unclear. A genetically predicted micronutrient and its association with estimated glomerular filtration rate (eGFR) can be used to assess the causal estimates by Mendelian randomization (MR).

Methods

In this MR study, we instrumented 11 genetic variants associated with blood or total selenium levels from a previous genome-wide association study (GWAS). The association between genetically predicted selenium concentration and eGFR was first assessed by summary-level MR in the chronic kidney disease(CKDGen) GWAS meta-analysis summary statistics, including 567,460 European samples. Inverse-variance weighted and pleiotropy-robust MR analyses were performed, in addition to multivariable MR adjusted for the effects of type 2 diabetes mellitus. Replication analysis was performed with individual-level UK Biobank data, including 337,318 White individuals of British ancestry.

Results

Summary-level MR analysis indicated that a genetically predicted 1 SD increase in selenium concentration was significantly associated with lower eGFR (-1.05 [-1.28, -0.82] %). The results were similarly reproduced by pleiotropy-robust MR analysis, including MR-Egger and weighted-median methods, and consistent even in the multivariable MR adjusted for diabetes. In the UK Biobank data, genetically predicted higher selenium concentration was also significantly associated with lower eGFR (- 0.36 [-0.52, -0.20] %), and the results were similar when body mass index, waist circumference, hypertension, and diabetes mellitus covariates were adjusted (-0.33 [-0.50, -0.17] %).

Conclusion

This MR study supports the hypothesis that higher genetically predicted body selenium is causally associated with lower eGFR.

Gamma-glutamyl-leucine levels are causally associated with elevated cardio-metabolic risks

Objective

Gamma-glutamyl dipeptides are bioactive peptides involved in inflammation, oxidative stress, and glucose regulation. Gamma-glutamyl-leucine (Gamma-Glu-Leu) has been extensively reported to be associated with the risk of cardio-metabolic diseases, such as obesity, metabolic syndrome, and type 2 diabetes. However, the causality remains to be uncovered. The aim of this study was to explore the causal-effect relationships between Gamma-Glu-Leu and metabolic risk.

Materials and methods

In this study, 1,289 subjects were included from a cross-sectional survey on metabolic syndrome (MetS) in eastern China. Serum Gamma-Glu-Leu levels were measured by untargeted metabolomics. Using linear regressions, a two-stage genome-wide association study (GWAS) for Gamma-Glu-Leu was conducted to seek its instrumental single nucleotide polymorphisms (SNPs). One-sample Mendelian randomization (MR) analyses were performed to evaluate the causality between Gamma-Glu-Leu and the metabolic risk.

Results

Four SNPs are associated with serum Gamma-Glu-Leu levels, including rs12476238, rs56146133, rs2479714, and rs12229654. Out of them, rs12476238 exhibits the strongest association (Beta = -0.38, S.E. = 0.07 in discovery stage, Beta = -0.29, S.E. = 0.14 in validation stage, combined P-value = 1.04 × 10^-8). Each of the four SNPs has a nominal association with at least one metabolic risk factor. Both rs12229654 and rs56146133 are associated with body mass index, waist circumference (WC), the ratio of WC to hip circumference, blood pressure, and triglyceride (5 × 10^-5 < P < 0.05). rs56146133 also has nominal associations with fasting insulin, glucose, and insulin resistance index (5 × 10^-5 < P < 0.05). Using the four SNPs serving as the instrumental SNPs of Gamma-Glu-Leu, the MR analyses revealed that higher Gamma-Glu-Leu levels are causally associated with elevated risks of multiple cardio-metabolic factors except for high-density lipoprotein cholesterol and low-density lipoprotein cholesterol (P > 0.05).

Conclusion

Four SNPs (rs12476238, rs56146133, rs2479714, and rs12229654) may regulate the levels of serum Gamma-Glu-Leu. Higher Gamma-Glu-Leu levels are causally linked to cardio-metabolic risks. Future prospective studies on Gamma-Glu-Leu are required to explain its role in metabolic disorders.

Associations between whole blood trace elements concentrations and HbA1c levels in patients with type 2 diabetes

Previous researches have been conducted to study the associations of trace elements on Type 2 diabetes (T2D) risk. The present study focuses on the evaluation of potential associations between trace elements and Hemoglobin A1c (HbA1c) in patients with T2D, via the determination of their levels in human whole blood. 100 diabetes without complications, 75 prediabetes and 40 apparently healthy subjects were studied. The levels of eleven trace elements including lithium (Li), vanadium (V), chromium (Cr), manganese (Mn), iron (Fe), cobalt (Co), copper (Cu), zinc (Zn), selenium (Se), strontium (Sr) and molybdenum (Mo) were measured using inductively coupled plasma mass spectrometry (ICP-MS). The levels of fasting glucose, HbA1c, Hemoglobin, lipid, liver function, kidney function, thyroid function and demographic data were obtained from the Laboratory Information System. Nonparametric correlation (Spearman) was used to analyze the relationship between trace elements and HbA1c. The contents of V, Cr, Mn, Fe, Co, Cu, Zn and Mo in diabetes increased comparing with the healthy subject while Li decreased. But the levels of Li, V, Cr, Mn, Co, Se and Mo negatively correlated with HbA1c in the diabetes subjects (r value: - 0.2189, - 0.2421, - 0.3260, - 0.2744, - 0.2812, - 0.2456, - 0.2240; 95% confidence interval - 0.4032 to - 0.0176, - 0.4235 to - 0.0420, - 0.4955 to - 0.1326, - 0.4515 to - 0.0765, - 0.4573 to - 0.0838, - 0.4266 to - 0.0458, - 0.4076 to - 0.0229; p < 0.05, p < 0.05, p < 0.001, p < 0.01, p < 0.01, p < 0.05, p < 0.05). Accordingly, the contents of V, Cr, Mn and Se showed lower in HbA1c ≥ 7.0% group in contrast to HbA1c < 7.0% group. No correlation of HbA1c (or FBG) and trace elements was found in the healthy subjects. Trace element levels and metabolic abnormalities of blood glucose may be mutually affected. The extra supplement of trace elements needs to be cautious.

Gene-environment interaction analysis of redox-related metals and genetic variants with plasma metabolic patterns in a general population from Spain: The Hortega Study

Background

Limited studies have evaluated the joint influence of redox-related metals and genetic variation on metabolic pathways. We analyzed the association of 11 metals with metabolic patterns, and the interacting role of candidate genetic variants, in 1145 participants from the Hortega Study, a population-based sample from Spain.

Methods

Urine antimony (Sb), arsenic, barium (Ba), cadmium (Cd), chromium (Cr), cobalt (Co), molybdenum (Mo) and vanadium (V), and plasma copper (Cu), selenium (Se) and zinc (Zn) were measured by ICP-MS and AAS, respectively. We summarized 54 plasma metabolites, measured with targeted NMR, by estimating metabolic principal components (mPC). Redox-related SNPs (N = 291) were measured by oligo-ligation assay.

Results

In our study, the association with metabolic principal component (mPC) 1 (reflecting non-essential and essential amino acids, including branched chain, and bacterial co-metabolism versus fatty acids and VLDL subclasses) was positive for Se and Zn, but inverse for Cu, arsenobetaine-corrected arsenic (As) and Sb. The association with mPC2 (reflecting essential amino acids, including aromatic, and bacterial co-metabolism) was inverse for Se, Zn and Cd. The association with mPC3 (reflecting LDL subclasses) was positive for Cu, Se and Zn, but inverse for Co. The association for mPC4 (reflecting HDL subclasses) was positive for Sb, but inverse for plasma Zn. These associations were mainly driven by Cu and Sb for mPC1; Se, Zn and Cd for mPC2; Co, Se and Zn for mPC3; and Zn for mPC4. The most SNP-metal interacting genes were NOX1, GSR, GCLC, AGT and REN. Co and Zn showed the highest number of interactions with genetic variants associated to enriched endocrine, cardiovascular and neurological pathways.

Conclusions

Exposures to Co, Cu, Se, Zn, As, Cd and Sb were associated with several metabolic patterns involved in chronic disease. Carriers of redox-related variants may have differential susceptibility to metabolic alterations associated to excessive exposure to metals.

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In a population-based sample, cobalt, copper, selenium, zinc, arsenic, cadmium and antimony exposures were related to some metabolic patterns.

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Carriers of redox-related variants displayed differential susceptibility to metabolic alterations associated to excessive metal exposures.

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Cobalt and zinc showed a number of statistical interactions with variants from genes sharing biological pathways with a role in chronic diseases.

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The metabolic impact of metals combined with variation in redox-related genes might be large in the population, given metals widespread exposure.

A Bidirectional Mendelian Randomization Study of Selenium Levels and Ischemic Stroke

Background: Previous observational studies have shown that circulating selenium levels are inversely associated with ischemic stroke (IS). Our aims were to evaluate the causal links between selenium levels and IS, and its subtypes by Mendelian randomization (MR) analysis.

Methods: We used the two-sample Mendelian randomization (MR) method to determine whether the circulating selenium levels are causally associated with the risk of stroke. We extracted the genetic variants (SNPs) associated with blood and toenail selenium levels from a large genome-wide association study (GWAS) meta-analysis. Inverse variance-weighted (IVW) method was used as the determinant of the causal effects of exposures on outcomes.

Results: A total of 4 SNPs (rs921943, rs6859667, rs6586282, and rs1789953) significantly associated with selenium levels were obtained. The results indicated no causal effects of selenium levels on ischemic stroke by MR analysis (OR = 0.968, 95% CI 0.914–1.026, p = 0.269). Meanwhile, there was no evidence of a causal link between circulating selenium levels and subtypes of IS.

Conclusion: The MR study indicated no evidence to support the causal links between genetically predicted selenium levels and IS. Our results also did not support the use of selenium supplementation for IS prevention at the genetic level.