RESUMO
Resumo Fundamento: O índice de massa corporal (IMC) é o índice mais usado para categorizar uma pessoa como obesa ou não-obesa, e está sujeito a limitações importantes. Objetivo: Avaliar o efeito direto do IMC nos desfechos cardiovasculares em participantes sem obesidade central. Métodos: Esta análise incluiu 14.983 homens e mulheres com idades entre 45-75 anos do Estudo de Risco de Aterosclerose em Comunidades (ARIC). O IMC foi medido como obesidade geral e a circunferência da cintura (CC), a relação cintura-quadril (RCQ) e circunferência do quadril como obesidade central. A estimativa de máxima verossimilhança direcionada (TMLE, no acrônimo em inglês) foi usada para estimar os efeitos totais (TEs) e os efeitos diretos controlados (CDEs). A proporção de ET que seria eliminada se todos os participantes fossem não obesos em relação à obesidade central foi calculada usando o índice de proporção eliminada (PE). P<0,05 foi considerado estatisticamente significativo. As análises foram realizadas no pacote TMLE R. Resultados: O risco de desfechos cardiovasculares atribuídos ao IMC foi significativamente revertido com a eliminação da obesidade na RCQ (p <0,001). A proporção eliminada dos efeitos do IMC foi mais tangível para participantes não obesos em relação à CC (PE = 127%; IC95% (126,128)) e RCQ (PE = 97%; IC95% (96,98)) para doença arterial coronariana (DAC), e RCQ (PE = 92%; IC95% (91,94)) para acidente vascular cerebral, respectivamente. Com relação ao sexo, a proporção eliminada dos efeitos do IMC foi mais tangível para participantes não obesos em relação a RCQ (PE = 428%; IC95% (408.439)) para DAC em homens e CC (PE = 99%; IC95% (89,111)) para acidente vascular cerebral em mulheres, respectivamente. Conclusão: Esses resultados indicam diferentes efeitos potenciais da eliminação da obesidade central na associação entre IMC e desfechos cardiovasculares em homens e mulheres. (Arq Bras Cardiol. 2021; 116(5):879-886)
Background: Body mass index (BMI) is the most commonly used index to categorize a person as obese or non-obese, which is subject to important limitations. Objective: To evaluate the direct effect of BMI on cardiovascular outcomes among participants without central obesity. Methods: This analysis included 14,983 males and females aged 45-75 years from the Atherosclerosis Risk in Communities Study (ARIC). BMI was measured as general obesity, and waist circumference (WC), waist-to-hip ratio (WHR) and hip circumference as central obesity. Targeted maximum likelihood estimation (TMLE) was used to estimate the total effects (TEs) and the controlled direct effects (CDEs). The proportion of TE that would be eliminated if all participants were non-obese regarding central obesity was computed using the proportion eliminated (PE) index. P <0.05 was considered statistically significant. Analyses were performed in the TMLE R package. Results: The risk of cardiovascular outcomes attributed to BMI was significantly reversed by eliminating WHR obesity (p<0.001). The proportion eliminated of BMI effects was more tangible for non-obese participants regarding WC (PE=127%; 95%CI (126,128)) and WHR (PE=97%; 95%CI (96,98)) for coronary heart disease (CHD), and WHR (PE=92%; 95%CI (91,94)) for stroke, respectively. With respect to sex, the proportion eliminated of BMI effects was more tangible for non-obese participants regarding WHR (PE=428%; 95%CI (408,439)) for CHD in males, and WC (PE=99%; 95%CI (89,111)) for stroke in females, respectively. Conclusion: These results indicate different potential effects of eliminating central obesity on the association between BMI and cardiovascular outcomes for males and females. (Arq Bras Cardiol. 2021; 116(5):879-886)
Assuntos
Humanos , Masculino , Feminino , Idoso , Obesidade Abdominal/complicações , Índice de Massa Corporal , Funções Verossimilhança , Fatores de Risco , Relação Cintura-Quadril , Circunferência da Cintura , Pessoa de Meia-Idade , Obesidade/complicaçõesRESUMO
Methods for inferring average causal effects have traditionally relied on two key assumptions: (i) the intervention received by one unit cannot causally influence the outcome of another; and (ii) units can be organized into nonoverlapping groups such that outcomes of units in separate groups are independent. In this article, we develop new statistical methods for causal inference based on a single realization of a network of connected units for which neither assumption (i) nor (ii) holds. The proposed approach allows both for arbitrary forms of interference, whereby the outcome of a unit may depend on interventions received by other units with whom a network path through connected units exists; and long range dependence, whereby outcomes for any two units likewise connected by a path in the network may be dependent. Under network versions of consistency and no unobserved confounding, inference is made tractable by an assumption that the networks outcome, treatment and covariate vectors are a single realization of a certain chain graph model. This assumption allows inferences about various network causal effects via the auto-g-computation algorithm, a network generalization of Robins' well-known g-computation algorithm previously described for causal inference under assumptions (i) and (ii). Supplementary materials for this article are available online.
RESUMO
Chlorophyll-a (Chl-a) has been widely used in the assessment and monitoring of aquatic environments. Local and regional factors can influence Chl-a concentrations; moreover, the connection between terrestrial and aquatic ecosystems is a major paradigm within aquatic ecology. Here, we investigate the spatial distribution of Chl-a concentrations in a tropical savannah floodplain in Central Brazil using a broad spatial data set (a 900-km north-south transect; 30 lakes). We determine the relative importance of local environmental variables (limnological and morphometric) and regional (land use) and spatial distances (spatial eigenvector) on Chl-a concentrations using partial linear regression. We evaluate the direct and indirect effects of local and regional variables on Chl-a with a path analysis. Our results indicate spatially autocorrelated patterns wherein lakes in closer proximity showed more similar levels of Chl-a than more distant lakes. Local environmental factors explained most variance in Chl-a (R 2adj = 0.28; P = 0.02); more specifically, both lake area and total nitrogen significantly (P < 0.05) explained Chl-a concentrations (direct effects). Meanwhile, regional factors neither directly nor indirectly predicted Chl-a. Thus, internal processes, such as the resuspension of sediment (which is frequent in tropical floodplains), rather than external influences, were the main factors that explained Chl-a concentrations in this study. The importance of local variables in structuring Chl-a concentration may be used to guide the conservation of the aquatic ecosystems in these tropical floodplain lakes.