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Alcohol and alcohol-related diseases have become a major cause of death in Western countries. The most sensitive and specific of the commonly used biomarkers of alcohol intake are carbohydrate-deficient transferrin (CDT), and the combination of gamma-glutamyltransferase (GGT) and CDT. Other widely used laboratory markers are GGT, mean corpuscular volume of erythrocytes and the ratio of aspartate aminotransferase to alanine aminotransferase. Blood ethanol levels reveal recent alcohol use. However, more specific and sensitive biomarkers to improve the detection of excessive alcohol use at an early stage are needed. New biomarkers, not yet used in routine clinical work, include phosphatidylethanol, fatty acid ethyl esters, ethyl glucuronide, sialic acid, and acetaldehyde adducts.

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BackgroundA key challenge in metabonomics is to uncover quantitative associations between multidimensional spectroscopic data and biochemical measures used for disease risk assessment and diagnostics. Here we focus on clinically relevant estimation of lipoprotein lipids by 1H NMR spectroscopy of serum.ResultsA Bayesian methodology, with a biochemical motivation, is presented for a real 1H NMR metabonomics data set of 75 serum samples. Lipoprotein lipid concentrations were independently obtained for these samples via ultracentrifugation and specific biochemical assays. The Bayesian models were constructed by Markov chain Monte Carlo (MCMC) and they showed remarkably good quantitative performance, the predictive R-values being 0.985 for the very low density lipoprotein triglycerides (VLDL-TG), 0.787 for the intermediate, 0.943 for the low, and 0.933 for the high density lipoprotein cholesterol (IDL-C, LDL-C and HDL-C, respectively). The modelling produced a kernel-based reformulation of the data, the parameters of which coincided with the well-known biochemical characteristics of the 1H NMR spectra; particularly for VLDL-TG and HDL-C the Bayesian methodology was able to clearly identify the most characteristic resonances within the heavily overlapping information in the spectra. For IDL-C and LDL-C the resulting model kernels were more complex than those for VLDL-TG and HDL-C, probably reflecting the severe overlap of the IDL and LDL resonances in the 1H NMR spectra.ConclusionThe systematic use of Bayesian MCMC analysis is computationally demanding. Nevertheless, the combination of high-quality quantification and the biochemical rationale of the resulting models is expected to be useful in the field of metabonomics.

In all, 896 Finnish farmers (305 men and 587 women), representing 77% of those reporting hand or forearm dermatosis in a questionnaire survey in 1979, were asked again about their dermatosis and current work in 1991. More than 50% of the study population had left farming since 1979. In 1991, 26% of men and 21% of women had a current dermatosis on the hands or forearms, and altogether, 44% of men and 39% of women reported a hand dermatosis within the past 12 months. Significant determinants of persistent hand dermatosis, in a logistic regression model, were continuation of farm work, history of skin atopy, symptoms of metal allergy, and age under 45 years. Handling cattle, e.g., milking, was considered an exacerbating factor of the dermatosis by 37% of those who had milked sometimes in their lives. In this group, 75% of hand dermatoses in those who had finished milking work had healed. The results indicate that giving up or changing work improves the prognosis of hand dermatosis in farming.