Onset and mortality of Parkinson's disease in relation to type II diabetes.

OBJECTIVES: There is growing evidence that Parkinson's disease and diabetes are partially related diseases; however, the association between the two, and the impact of specific treatments, are still unclear. We evaluated the effect of T2D and antidiabetic treatment on age at PD onset and on all-cause mortality. RESEARCH DESIGN AND METHODS: The standardized rate of T2D was calculated for PD patients using the direct method and compared with subjects with essential tremor (ET) and the general Italian population. Age at onset and survival were also compared between patients without T2D (PD-noT2D), patients who developed T2D before PD onset (PD-preT2D) and patients who developed T2D after PD onset (PD-postT2D). RESULTS: We designed a retrospective and prospective study. The T2D standardized ratio of PD (N = 8380) and ET (N = 1032) patients was 3.8% and 6.1%, respectively, while in the Italian general population, the overall prevalence was 5.3%. In PD-preT2D patients, on antidiabetic treatment, the onset of PD was associated with a + 6.2 year delay (p < 0.001) while no difference was observed in PD-postT2D. Occurrence of T2D before PD onset negatively affected prognosis (adjusted hazard ratio = 1.64 [95% CI 1.33-2.02]; p < 0.001), while no effect on survival was found in PD-postT2D subjects (hazard ratio = 0.86, [95% CI 0.53-1.39]; p = 0.54). CONCLUSIONS: T2D, treated with any antidiabetic therapy before PD, is associated with a delay in its onset. Duration of diabetes increases mortality in PD-preT2D, but not in PD-postT2D. These findings prompt further studies on antidiabetic drugs as a potential disease-modifying therapy for PD.

Artificial neural networks to evaluate organic and inorganic contamination in agricultural soils.

The assessment of organic and inorganic contaminants in agricultural soils is a difficult challenge due to the large-scale dimensions of the areas under investigation and the great number of samples needed for analysis. On-site screening techniques, such as Field Portable X-ray Fluorescence (FPXRF) spectrometry, can be used for inorganic compounds, such as heavy metals. This method is not destructive and allows a rapid qualitative characterization, identifying hot spots from where to collect soil samples for analysis by traditional laboratory techniques. Recently, fast methods such as immuno-assays for the determination of organic compounds, such as dioxins, furans and PCBs, have been employed, but several limitations compromise their performance. The aim of the present study was to find a method able to screen contaminants in agricultural soil, using FPXRF spectrometry for metals and a statistical procedure, such as the Artificial Neural Networks technique, to estimate unknown concentrations of organic compounds based on statistical relationships between the organic and inorganic pollutants.