Objective: To determine the extent to which weight gain and eating behaviours in infancy predict later adiposity. Design: Population-based, prospective, longitudinal birth cohort study. Weights collected in infancy were used to calculate Z-scores for weight gain to age 1 year conditional on birth weight (CWG). To avoid multiple significance tests, variables from the parent questionnaire completed at age 1 year describing eating avidity were combined using general linear modelling to create an infancy avidity score. Anthropometry, skinfold thicknesses and bioelectrical impedance data collected at age 7-8 years were combined using factor analysis, to create an adiposity index. Setting: Gateshead, UK. Subjects: Members of the Gateshead Millennium Study cohort with data at both time points (n 561). Results: CWG in infancy significantly predicted adiposity at age 7 years, but related more strongly to length and lean mass. High adiposity (. 90th internal percentile) at age 7 years was significantly associated with high CWG (relative risk 2?76; 95 % CI 1?5, 5?1) in infancy, but less so with raised (. 74th internal percentile) eating avidity in infancy (relative risk 1?87; 95 % CI 0?9, 3?7). However, the majority of children with high weight gain (77?6 %) or avidity (85?5 %) in infancy did not go on to have high adiposity at age 7 years. Conclusions: Rapid weight gain in infancy and the eating behaviours which relate to it do predict later adiposity, but are more strongly predictive of later stature and lean mass. Keywords Obesity Weight velocity Feeding behaviour InfancyA number of studies have found significant associations between rapid infancy weight gain and later overweight (1) , leading to the suggestion that prevention (2,3) and even treatment of childhood obesity (4,5) should begin as early as the first year of life. However, weight gain in infancy reflects growth in bone and muscle as well as fat and some infants will be showing rapid gain in height or lean mass rather than adiposity (6) . Thus while on average rapid weight gain may predict later adiposity, what is not clear is how well it would prospectively identify individual children at risk. There is also recent research that suggests there are distinctive childhood eating behaviours related to overweight which may reflect an inherent tendency to overeat (7) , so eating behaviour in infancy could predispose to, or protect against, later obesity (7,8) .Apart from studies examining how the type and style of milk feeding relates to later obesity (9-11) , we currently know little about eating behaviour in infancy and even less about how it tracks on to later adiposity or eating style. We hypothesised that eating avidity, a global term to denote enthusiasm and hunger for food, might be a useful predictor of gain in fat. The Gateshead Millennium Study was set up in order to examine infant growth and weight gain and how this relates to eating behaviour, prospectively measured from birth (12) . These children have now been followed into childhood where me...
Abstract. The quantification of measurement uncertainty of atmospheric parameters is a key factor in assessing the uncertainty of global change estimates given by numerical prediction models. One of the critical contributions to the uncertainty budget is related to the collocation mismatch in space and time among observations made at different locations. This is particularly important for vertical atmospheric profiles obtained by radiosondes or lidar.In this paper we propose a statistical modelling approach capable of explaining the relationship between collocation uncertainty and a set of environmental factors, height and distance between imperfectly collocated trajectories. The new statistical approach is based on the heteroskedastic functional regression (HFR) model which extends the standard functional regression approach and allows a natural definition of uncertainty profiles. Along this line, a five-fold decomposition of the total collocation uncertainty is proposed, giving both a profile budget and an integrated column budget.HFR is a data-driven approach valid for any atmospheric parameter, which can be assumed smooth. It is illustrated here by means of the collocation uncertainty analysis of relative humidity from two stations involved in the GCOS reference upper-air network (GRUAN). In this case, 85 % of the total collocation uncertainty is ascribed to reducible environmental error, 11 % to irreducible environmental error, 3.4 % to adjustable bias, 0.1 % to sampling error and 0.2 % to measurement error.
ObjectivesTo explore the usefulness of Bioelectrical Impedance Analysis (BIA) for general use by identifying best-evidenced formulae to calculate lean and fat mass, comparing these to historical gold standard data and comparing these results with machine-generated output. In addition, we explored how to best to adjust lean and fat estimates for height and how these overlapped with body mass index (BMI).DesignCross-sectional observational study within population representative cohort study.SettingUrban community, North East EnglandParticipantsSample of 506 mothers of children aged 7–8 years, mean age 36.3 years.MethodsParticipants were measured at a home visit using a portable height measure and leg-to-leg BIA machine (Tanita TBF-300MA).MeasuresHeight, weight, bioelectrical impedance (BIA).Outcome measuresLean and fat mass calculated using best-evidenced published formulae as well as machine-calculated lean and fat mass data.ResultsEstimates of lean mass were similar to historical results using gold standard methods. When compared with the machine-generated values, there were wide limits of agreement for fat mass and a large relative bias for lean that varied with size. Lean and fat residuals adjusted for height differed little from indices of lean (or fat)/height2. Of 112 women with BMI >30 kg/m2, 100 (91%) also had high fat, but of the 16 with low BMI (<19 kg/m2) only 5 (31%) also had low fat.ConclusionsLean and fat mass calculated from BIA using published formulae produces plausible values and demonstrate good concordance between high BMI and high fat, but these differ substantially from the machine-generated values. Bioelectrical impedance can supply a robust and useful field measure of body composition, so long as the machine-generated output is not used.
Atmospheric thermodynamic data are gathered by high technology remote instruments such as radiosondes, giving rise to profiles that are usually modelled as functions depending only on height. The radiosonde balloons, however, drift away in the atmosphere resulting in not necessarily vertical but threedimensional (3D) trajectories. To model this kind of functional data, we introduce a "point based" formulation of an heteroskedastic functional regression model that includes a trivariate smooth function and results to be an extension of a previously introduced unidimensional model. Functional coefficients of both the conditional mean and variance are estimated by reformulating the model as a standard generalized additive model and subsequently as a mixed model. This reformulation leads to a double mixed model whose parameters are fitted by using an iterative algorithm that allows to adjust for heteroskedasticity. The proposed modelling approach is applied to describe collocation mismatch when we deal with couples of balloons launched at two
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