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. 2009 Sep;14(5):261-6.
doi: 10.1007/s12199-009-0092-y. Epub 2009 Jun 26.

Metallomics study using hair mineral analysis and multiple logistic regression analysis: relationship between cancer and minerals

Hiroshi Yasuda  1 , Kazuya Yoshida, Mitsuru Segawa, Ryoichi Tokuda, Toyoharu Tsutsui, Yuichi Yasuda, Shunichi Magara
Affiliations

Metallomics study using hair mineral analysis and multiple logistic regression analysis: relationship between cancer and minerals

Hiroshi Yasuda et al. Environ Health Prev Med. 2009 Sep.

Abstract

Objectives: The objective of this metallomics study is to investigate comprehensively some relationships between cancer risk and minerals, including essential and toxic metals.

Methods: Twenty-four minerals including essential and toxic metals in scalp hair samples from 124 solid-cancer patients and 86 control subjects were measured with inductively coupled plasma mass spectrometry (ICP-MS), and the association of cancer with minerals was statistically analyzed with multiple logistic regression analysis.

Results: Multiple logistic regression analysis demonstrated that several minerals are significantly correlated to cancer, positively or inversely. The most cancer-correlated mineral was iodine (I) with the highest correlation coefficient of r = 0.301, followed by arsenic (As; r = 0.267), zinc (Zn; r = 0.261), and sodium (Na; r = 0.190), with p < 0.01 for each case. In contrast, selenium (Se) was inversely correlated to cancer (r = -0.161, p < 0.05), followed by vanadium (V) (r = -0.128). Multiple linear regression value was highly significantly correlated with probability of cancer (R (2) = 0.437, p < 0.0001), and the area under the receiver-operating characteristic (ROC) curve was calculated to be 0.918. In addition, using contingency table analysis and the chi-square test, the precision of discrimination for cancer was estimated to be 0.871 (chi-square = 99.1, p < 0.0001).

Conclusions: These findings suggest that some minerals such as arsenic, selenium, and probably iodine, zinc, sodium, and vanadium contribute to regulation of cancer and also that metallomics study using multiple logistic regression analysis is a useful tool for estimating cancer risk.

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Figures

Fig. 1
Fig. 1
Positive or inverse relationship between cancer and minerals in multiple logistic regression analysis (a iodine, b arsenic, c zinc, d selenium). The relationship between minerals and probability of cancer was obtained by logistic multiple regression analysis of data for 24 minerals from 124 cancer (open square) and 86 control (dots) subjects. The abscissa and ordinate express the logarithmic values of hair mineral (a iodine, b arsenic, c zinc, d selenium) level and probability of cancer, respectively. The blue and red marks show male and female subjects, respectively
Fig. 2
Fig. 2
Significantly high relationship between cancer and multiple linear regression value in multiple logistic regression analysis. The relationship between multiple linear regression value and probability of cancer was obtained by multiple logistic regression analysis of data for 24 minerals from 124 cancer (open square) and 86 control (dots) subjects. The abscissa and ordinate express the calculated multiple linear regression value of the tested subjects and the probability of cancer, respectively. The blue and red marks show male and female, respectively
See this image and copyright information in PMC

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