Different trends of Cr, Fe and Zn contents in hair between obese, overweight and normal-weight men

Multi-Elemental Analysis of Hair and Fingernails Using Energy-Dispersive X-ray Fluorescence (ED XRF) Method Supported by Inductively Coupled Plasma Mass Spectrometry (ICP MS)

This work compared the multi-element analysis of human hair and nails using inductively coupled plasma mass spectrometry (ICP MS) with an easy, fast, cheap, non-destructive method using energy-dispersive x-ray fluorescence (ED XRF). The ICP MS-based method was more sensitive (over 30 elements could be quantified) and costly (requiring more time, samples, and chemicals). The EDX-based method required laboratory and certified reference materials made of hair for instrument calibration. It was less sensitive (16 elements could be quantified: S, Si, Ca, Br, Fe, Cu, Cr, Mg, Si, K, Mn, Ni, Zn, Se, Sr, Pb), but it allowed us to replace troublesome grinding with the dissolution of keratin-based material with an alkalic agent (tetramethylammonium hydroxide, TMAH) and the formation of stable-for-days pellets. This method is simple, enables automation, and, due to the modification of wells in the autosampler of the EDX system via the immersion of home-designed inserts, it requires smaller amounts of biological material and binder (down to 70 mg instead of 500 mg required by commercially available instrument) to perform analysis. It was concluded that the EDX-based method offers complementary selectivity and sensitivity to ICP MS with the possibility of sample reuse for further analysis.

Metal Body Burden as Cardiovascular Risk Factor in Adults with Metabolic Syndrome and Overweight-Obesity Analysed with an Artificial Neural Network: The Role of Hair Mineralograms

In determining the so-called "body burden", hair has been widely accepted for assessing toxic element exposure. However, its role in assessing essential elements is controversial. This study investigates the possible relationship between hair minerals, metabolic syndrome (MetS) and cardiovascular (CV) risk in non-occupationally exposed subjects with overweight-obesity. Ninety-five voluntary participants (aged 51 ± 12) were recruited in Northern Italy. Hair samples were collected and analysed via inductively coupled plasma mass spectrometry; the total toxicity index (TI) was calculated as well. To evaluate cardiovascular risk factors in the presence or absence of MetS, the following factors were considered via the innovative artificial neural network (ANN) method Auto-CM: hair mineralograms (31 elements) and 25 variables including blood pressure, anthropometric parameters, insulin resistance and biochemical serum markers assessing inflammation. The Framingham risk score, fatty liver index (FLI), visceral adiposity index and CV risk scores were also taken into consideration. As shown by the semantic map, which was subsequently confirmed by an activation and competition system (ACS), obesity parameters are strictly associated with CV risk factors, TI and inflammation; meanwhile, the single mineral elements seem to be unimportant. Data obtained via ANN demonstrate that MetS may be at least partly mediated by altered mineral levels also in the presence of obesity and that waist circumference is a crucial point to be monitored rather than BMI alone. Furthermore, the mineral body burden is one of the important factors for CV risk.

Recent advances in the application of ionomics in metabolic diseases

Trace elements and minerals play a significant role in human health and diseases. In recent years, ionomics has been rapidly and widely applied to explore the distribution, regulation, and crosstalk of different elements in various physiological and pathological processes. On the basis of multi-elemental analytical techniques and bioinformatics methods, it is possible to elucidate the relationship between the metabolism and homeostasis of diverse elements and common diseases. The current review aims to provide an overview of recent advances in the application of ionomics in metabolic disease research. We mainly focuses on the studies about ionomic or multi-elemental profiling of different biological samples for several major types of metabolic diseases, such as diabetes mellitus, obesity, and metabolic syndrome, which reveal distinct and dynamic patterns of ion contents and their potential benefits in the detection and prognosis of these illnesses. Accumulation of copper, selenium, and environmental toxic metals as well as deficiency of zinc and magnesium appear to be the most significant risk factors for the majority of metabolic diseases, suggesting that imbalance of these elements may be involved in the pathogenesis of these diseases. Moreover, each type of metabolic diseases has shown a relatively unique distribution of ions in biofluids and hair/nails from patients, which might serve as potential indicators for the respective disease. Overall, ionomics not only improves our understanding of the association between elemental dyshomeostasis and the development of metabolic disease but also assists in the identification of new potential diagnostic and prognostic markers in translational medicine.