Potential value of identifying type 2 diabetes subgroups for guiding intensive treatment: a comparison of novel data-driven clustering with risk-driven subgroups

OBJECTIVETo estimate the impact on lifetime health and economic outcomes of different methods of stratifying individuals with type 2 diabetes, followed by guideline-based treatment intensification targeting BMI and LDL in addition to HbA1c.RESEARCH DESIGN AND METHODSWe divided 2,935 newly diagnosed individuals from the Hoorn Diabetes Care System (DCS) cohort into five Risk Assessment and Progression of Diabetes (RHAPSODY) data-driven clustering subgroups (based on age, BMI, HbA1c, C-peptide, and HDL) and four risk-driven subgroups by using fixed cutoffs for HbA1c and risk of cardiovascular disease based on guidelines. The UK Prospective Diabetes Study Outcomes Model 2 estimated discounted expected lifetime complication costs and quality-adjusted life-years (QALYs) for each subgroup and across all individuals. Gains from treatment intensification were compared with care as usual as observed in DCS. A sensitivity analysis was conducted based on Ahlqvist subgroups.RESULTSUnder care as usual, prognosis in the RHAPSODY data-driven subgroups ranged from 7.9 to 12.6 QALYs. Prognosis in the risk-driven subgroups ranged from 6.8 to 12.0 QALYs. Compared with homogenous type 2 diabetes, treatment for individuals in the high-risk subgroups could cost 22.0% and 25.3% more and still be cost effective for data-driven and risk-driven subgroups, respectively. Targeting BMI and LDL in addition to HbA1c might deliver up to 10-fold increases in QALYs gained.CONCLUSIONSRisk-driven subgroups better discriminated prognosis. Both stratification methods supported stratified treatment intensification, with the risk-driven subgroups being somewhat better in identifying individuals with the most potential to benefit from intensive treatment. Irrespective of stratification approach, better cholesterol and weight control showed substantial potential for health gains.Molecular Epidemiolog

Artificial neural network for ecological-economic zoning as a tool for spatial planning

O objetivo deste trabalho foi analisar informações socioambientais por meio de rede neural artificial-mapa auto-organizável (RNA-SOM), para fornecer subsídio ao zoneamento ecológico econômico (ZEE) como instrumento para diminuir a subjetividade do processo. A área de estudo compreende 16 municípios do Nordeste Paraense, expressivos no desenvolvimento agropecuário do estado. O tratamento dos dados envolveu três etapas: preparação dos dados em ambiente de sistema de informação geográfica (SIG); processamento matemático (RNA-SOM) dos dados; e visualização e interpretação dos resultados dos processamentos, o que permitiu o ordenamento territorial do Nordeste Paraense. Os resultados compreenderam 13 classes, reagrupadas de acordo com critérios de similaridade de comportamento em quatro categorias, que representam os principais eixos de sustentabilidade propostos para o Estado do Pará, a partir do ZEE existente. A metodologia proposta permite individualizar zonas na região que o ZEE não havia definido, principalmente em razão da maior possibilidade de conjugar e integrar um grande número de variáveis físicas, sociais e econômicas por meio do SOM.The objective of this work was to analyze social and environmental information through an artificial neural network-self-organizing map (ANN-SOM), in order to provide subsidy to ecologicaleconomic zoning (EEZ) as a tool to reduce the subjectivity of the process. The study area comprises 16 municipalities in the northeast of the state of Pará, Brazil, representative of the agricultural development in the state. Data processing involved three steps: preparation of the data in a geographic information system (GIS) environment; mathematical processing (ANN-SOM) of the data; and visualization and interpretation of the processing results, allowing the spatial planning of northeastern Pará. The results comprised 13 classes, regrouped according to behavioral similarity criteria into four categories, which represent the main areas of sustainability proposed for the state of Pará, according to existing EEZ. The proposed methodology allows individualizing areas in the region that EEZ had not defined, mainly due to the greater possibility of combining and integrating a large number of physical, social, and economic variables through the SOM

Selected heavy metals and selenium in the blood of black sea turtle (Chelonia mydas agasiizzi) from Sonora, Mexico

The concentration of heavy metals (Zn, Cd, Ni, Cu, Mn) and selenium (Se) was analyzed in blood collected from 12 black turtles (Chelonia mydas agasiizzi) captured in Canal del Infiernillo, Punta Chueca, Mexico. The most abundant metals were Zn (63.58 ?g g-1) and Se (7.66 ?g g-1), and Cd was the lower (0.99 ?g g-1). The sequential concentrations of trace metals were Zn>Se>Cu>Mn>Ni>Cd. In conclusion, this information is important as a baseline when using blood as tissue analysis of heavy metals; however, these levels could represent recent exposure in foraging grounds of black turtles in the Sea of Cortez. � Springer Science+Business Media New York 2013