Invited commentary: dietary fiber, estradiol, and cholesterol

Associations of dietary fiber intake with long-term predicted cardiovascular disease risk and C-reactive protein levels (from the National Health and Nutrition Examination Survey Data [2005-2010])

Dietary fiber intake might reduce cardiovascular risk factor levels and, in turn, might lower the long-term risk of cardiovascular disease (CVD). A total of 11,113 subjects, aged 20 to 79 years with no history of CVD, from the 2005 to 2010 National Health and Nutrition Examination Survey were included in the present study to examine associations of dietary fiber intake with predicted lifetime CVD risk and C-reactive protein levels. Dietary fiber intake showed a significant gradient association with the likelihood of having a low or an intermediate predicted lifetime CVD risk among young and middle-age adults. In fully adjusted multinomial logistic models, dietary fiber intake was related to a low lifetime CVD risk with an odds ratio of 2.71 (95% confidence interval 2.05 to 3.59) in the young adults and 2.13 (95% confidence interval 1.42 to 3.20) in the middle-age adults and was related to an intermediate lifetime risk of 2.65 (95% confidence interval 1.79 to 3.92) in the young and 1.98 (95% confidence interval 1.32 to 2.98) in the middle-age adults compared with a high lifetime risk. A significant inverse linear association was seen between dietary fiber intake and log-transformed C-reactive protein levels with a regression coefficient ± standard error of -0.18 ± 0.04 in the highest quartile of fiber intake compared with the lowest fiber intake. In conclusion, these data suggest that dietary fiber intake is independently associated with the predicted lifetime CVD risk, especially in young and middle-age adults. A greater amount of dietary fiber intake might be associated with lower C-reactive protein levels.

Linear spline multilevel models for summarising childhood growth trajectories: A guide to their application using examples from five birth cohorts

Childhood growth is of interest in medical research concerned with determinants and consequences of variation from healthy growth and development. Linear spline multilevel modelling is a useful approach for deriving individual summary measures of growth, which overcomes several data issues (co-linearity of repeat measures, the requirement for all individuals to be measured at the same ages and bias due to missing data). Here, we outline the application of this methodology to model individual trajectories of length/height and weight, drawing on examples from five cohorts from different generations and different geographical regions with varying levels of economic development. We describe the unique features of the data within each cohort that have implications for the application of linear spline multilevel models, for example, differences in the density and inter-individual variation in measurement occasions, and multiple sources of measurement with varying measurement error. After providing example Stata syntax and a suggested workflow for the implementation of linear spline multilevel models, we conclude with a discussion of the advantages and disadvantages of the linear spline approach compared with other growth modelling methods such as fractional polynomials, more complex spline functions and other non-linear models.