Mineral Nutrition and the Risk of Chronic Diseases: A Mendelian Randomization Study

Abstract

We applied Mendelian randomization analyses to investigate the potential causality between blood minerals (calcium, magnesium, iron, copper, and zinc) and osteoporosis (OP), gout, rheumatoid arthritis (RA), type 2 diabetes (T2D), Alzheimer's disease (AD), bipolar disorder (BD), schizophrenia , Parkinson's disease and major depressive disorder. Single nucleotide polymorphisms (SNPs) that are independent (r² < 0.01) and are strongly related to minerals (p < 5 × 10^-8) are selected as instrumental variables. Each standard deviation increase in magnesium (0.16 mmol/L) is associated with an 8.94-fold increase in the risk of RA (p = 0.044) and an 8.78-fold increase in BD (p = 0.040) but a 0.10 g/cm² increase in bone density related to OP (p = 0.014). Each per-unit increase in copper is associated with a 0.87-fold increase in the risk of AD (p = 0.050) and BD (p = 0.010). In addition, there is suggestive evidence that calcium is positively correlated (OR = 1.36, p = 0.030) and iron is negatively correlated with T2D risk (OR = 0.89, p = 0.010); both magnesium (OR = 0.26, p = 0.013) and iron (OR = 0.71, p = 0.047) are negatively correlated with gout risk. In the sensitivity analysis, causal estimation is not affected by pleiotropy. This study supports the long-standing hypothesis that magnesium supplementation can increase RA and BD risks and decrease OP risk and that copper intake can reduce AD and BD risks. This study will be helpful to address some controversial debates on the relationships between minerals and chronic diseases.

Keywords: Mendelian randomization; calcium; chronic diseases; copper; iron; magnesium; zinc.

Figure 1

The principle of the MR analysis and the assumptions that need to be met to obtain an unbiased estimate of the causal effect. Instrumental variable (IV) assumption 1: SNPs must be associated with the exposure; IV assumption 2: SNPs must be unrelated to any confounding factors that are associated with the results; IV assumption 3: SNPs must influence the outcome through exposure and not by alternative ways. SNP₁, SNP₂, and SNP_i = single nucleotide polymorphisms.

Figure 2

MR study of Mg and OP: (a) A graph that correlates the effect size of the SNP-Mg association (x-axis, SD units) and the SNP-OP association (y-axis, log OR) to the standard error bars. The slope of the line corresponds to a causal estimate using a different method. (b) The forest map, where each black dot represents a single SNP as IV, shows the logarithm of the odds ratio (OR) per standard deviation (SD) under the influence of mineral magnesium; the red dot shows the use of the IVW results for all SNPs; the horizontal line indicates the 95% confidence interval. (c) The leave-one-out method sensitivity analysis. Each black dot represents an IVW method for estimating the causal effect of the Mg element on OP and does not exclude a case where a particular SNP causes a significant change in the overall result. (d) The funnel plot shows the estimation using the inverse of the standard error of the causal estimate using each individual SNP as a tool. The vertical line shows the results of the IVW method using all SNPs.

Figure 3

MR estimates of the causality between minerals and diseases in the summary heat map. Red represents a positive correlation, blue represents a negative correlation, and the shade of color represents the significance of the degree of causality. We took the logarithm of the predicted causal effect p value, and determined the directionality of the causal relationship according to the positive and negative of the BETA value, with −log (0.05) = 1.30 as the threshold. If the absolute value of each box value is greater than the threshold, it is considered to have a significant causal relationship. The abscissa indicates the five mineral elements, Ca, Mg, Fe, Cu and Zn, and the ordinate indicates the nine diseases. NA indicates a missing value.

Figure 4

MR estimates of the positive-negative trend causality network diagram of minerals and diseases: (a) Positive correlation trend graph. (b) Negative correlation trend graph. The line thickness represents the logarithm of the p value of the causality: a thicker line represents a more significant p value. OP is present only in negative correlation networks because minerals have a negative effect on OP only.