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• W4378078369 abstract "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." @default.
• W4378078369 created "2023-05-25" @default.
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• W4378078369 date "2023-05-23" @default.
• W4378078369 modified "2023-09-29" @default.
• W4378078369 title "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" @default.
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• W4378078369 doi "https://doi.org/10.3390/metabo13060679" @default.
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• W4378078369 hasPublicationYear "2023" @default.
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