2 resultados para Topic modeling
Resumo:
Aim: The aim of the study was to investigate the influence of dietary intake of commercial hydrolyzed collagen (Gelatine Royal ®) on bone remodeling in pre-pubertal children. Methods: A randomized double-blind study was carried out in 60 children (9.42 ± 1.31 years) divided into three groups according to the amount of partially hydrolyzed collagen taken daily for 4 months: placebo (G-I, n = 18), collagen (G-II, n = 20) and collagen + calcium (G-III, n = 22) groups. Analyses of the following biochemical markers were carried out: total and bone alkaline phosphatase (tALP and bALP), osteocalcin, tartrate-resistant acid phosphatase (TRAP), type I collagen carboxy terminal telopeptide, lipids, calcium, 25-hydroxyvitamin D, insulin-like growth factor 1 (IGF-1), thyroid-stimulating hormone, free thyroxin and intact parathormone. Results: There was a significantly greater increase in serum IGF-1 in G-III than in G II (p < 0.01) or G-I (p < 0.05) during the study period, and a significantly greater increase in plasma tALP in G-III than in G-I (p < 0.05). Serum bALP behavior significantly (p < 0.05) differed between G-II (increase) and G-I (decrease). Plasma TRAP behavior significantly differed between G-II and G-I (p < 0.01) and between G-III and G-II (p < 0.05). Conclusion: Daily dietary intake of hydrolyzed collagen seems to have a potential role in enhancing bone remodeling at key stages of growth and development.
Resumo:
In epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted two-part regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model.