1043 The Relationship Between Nighttime Eating And Body Mass Index

Introduction

Late night eating has been associated with higher odds of being overweight or obese. This study aims to evaluate the relationship between late night eating and body mass index in a nationally representative sample.

Methods

Actigraphy was used to estimate the average bedtime, waketime, duration and midpoint of sleep in the National Health and Nutrition Examination Survey 2003-04 and 2005-06 cohorts. Given the circular nature of clock time, the average was calculated to be the point that minimized the sum of squares of differences between time points. Dietary data was collected through two detailed interviews of the participants. Nighttime calories were defined as the average amount of calories consumed between the average bedtime and the average midpoint of time-in-bed, based on the data recorded during the dietary interviews.

Results

Higher average nighttime caloric consumption (in units of 100 kcal) was associated with higher BMI [B(95% CI): 0.062 (0.003, 0.121)]; this remained significant after adjustment for age, gender, and race [B(95% CI): 0.084 (0.026, 0.142)]. Higher nighttime caloric consumption (as a percentage of total average daily calories consumption) was associated with higher BMI [B(95% CI): 1.522 (0.312, 2.733)]. This remained significant after adjustment for age, gender, and race [B(95% CI): 1.718 (0.505, 2.931)].

Conclusion

Higher nighttime caloric consumption, both in average amount (in units of 100 kcal) and as a percentage of average daily calories consumption, was associated with higher BMI. Additional study is needed to further elucidate the relationship between nighttime eating habits and body mass index.

Support

NHLBI T32HL110952

Dysregulation of objectively assessed 24-hour motor activity patterns as a potential marker for bipolar I disorder: results of a community-based family study

There has been a growing number of studies that have employed actigraphy to investigate differences in motor activity in mood disorders. In general, these studies have shown that people with bipolar disorders (BPDs) tend to exhibit greater variability and less daytime motor activity than controls. The goal of this study was to examine whether patterns of motor activity differ in euthymic individuals across the full range of mood disorder subtypes (Bipolar I (BPI), Bipolar II (BPII) and major depression (MDD)) compared with unaffected controls in a community-based family study of mood spectrum disorders. Minute-to-minute activity counts derived from actigraphy were collected over a 2-week period for each participant. Prospective assessments of the level, timing and day-to-day variability of physical activity measures were compared across diagnostic groups after controlling for a comprehensive list of potential confounding factors. After adjusting for the effects of age, sex, body mass index (BMI) and medication use, the BPI group had lower median activity intensity levels across the second half of the day and greater variability in the afternoon compared with controls. Those with a history of BPII had increased variability during the night time compared with controls, indicating poorer sleep quality. No differences were found in the average intensity, variability or timing of activity in comparisons between other mood disorder subgroups and controls. Findings confirm evidence from previous studies that BPI may be a manifestation of a rhythm disturbance that is most prominent during the second half of the day. The present study is the largest study to date that included the full range of mood disorder subgroups in a nonclinical sample that increases the generalizability of our findings to the general community. The manifestations of activity patterns outside of acute episodes add to the accumulating evidence that dysregulation of patterns of activity may constitute a potential biomarker for BPD.

Corrected confidence bands for functional data using principal components

Functional principal components (FPC) analysis is widely used to decompose and express functional observations. Curve estimates implicitly condition on basis functions and other quantities derived from FPC decompositions; however these objects are unknown in practice. In this article, we propose a method for obtaining correct curve estimates by accounting for uncertainty in FPC decompositions. Additionally, pointwise and simultaneous confidence intervals that account for both model- and decomposition-based variability are constructed. Standard mixed model representations of functional expansions are used to construct curve estimates and variances conditional on a specific decomposition. Iterated expectation and variance formulas combine model-based conditional estimates across the distribution of decompositions. A bootstrap procedure is implemented to understand the uncertainty in principal component decomposition quantities. Our method compares favorably to competing approaches in simulation studies that include both densely and sparsely observed functions. We apply our method to sparse observations of CD4 cell counts and to dense white-matter tract profiles. Code for the analyses and simulations is publicly available, and our method is implemented in the R package refund on CRAN.

Predicting breakdown of the blood-brain barrier in multiple sclerosis without contrast agents

BACKGROUND AND PURPOSE

Disruption of the BBB in MS is associated with the development of new lesions and clinical relapses and signifies the presence of active inflammation. It is most commonly detected as enhancement on MR imaging performed with contrast agents that are costly and occasionally toxic. We investigated whether the BBB status in white matter lesions may be indirectly ascertained via examination of features on T1- and T2-weighted images obtained before the injection of a contrast agent.

MATERIALS AND METHODS

We considered 93 brain MR imaging studies on 16 patients that included T1-, T2-, and T2-weighted FLAIR images and predicted voxel wise enhancement after intravenous injection of a gadolinium chelate. We then used these voxel-level predictions to determine the presence or absence of abnormal enhancement anywhere in the brain.

RESULTS

On a voxel-by-voxel basis, enhancement can be predicted by using contrast-free measures with an AUC of 0.83 (95% CI, 0.80-0.87). At the whole-brain level, enhancement can be predicted with an AUC of 0.72 (95% CI, 0.62-0.82).

CONCLUSIONS

In many cases, breakdown of the BBB in acute MS lesions may be inferred without the need to inject an MR imaging contrast agent. The inference relies on intrinsic properties of tissue damage in acute lesions. Although contrast studies are more accurate, they may sometimes be unnecessary.

Semiparametric Regression in Capture-Recapture Modeling

Capture-recapture models were developed to estimate survival using data arising from marking and monitoring wild animals over time. Variation in survival may be explained by incorporating relevant covariates. We propose nonparametric and semiparametric regression methods for estimating survival in capture-recapture models. A fully Bayesian approach using Markov chain Monte Carlo simulations was employed to estimate the model parameters. The work is illustrated by a study of Snow petrels, in which survival probabilities are expressed as nonlinear functions of a climate covariate, using data from a 40-year study on marked individuals, nesting at Petrels Island, Terre Adélie.

Prevalence estimates for paratuberculosis adjusted for test variability using Bayesian analysis

The ELISA tests that are available to detect an infection with Mycobacterium avium subsp. paratuberculosis (MAP) have a limited validity expressed as the sensitivity (Se) and specificity (Sp). In many studies, the Se and Sp of the tests are treated as constants and this will result in an underestimation of the variability of the true prevalence (TP). Bayesian inference provided a natural framework for using information on the test variability (i.e., the uncertainty) in the estimates of test Se and Sp when estimating the TP. Data from two prevalence studies for MAP using an ELISA in several regions in two locations were available for the analyses. In location 1, all cattle of at least 3 years of age were sampled in approximately 90 randomly sampled herds in each of the four regions of the country. In location 2, in 30 randomly sampled herds in each of three regions, approximately 30 randomly selected cows were sampled. Information about the unknown test Se and Sp and MAP prevalence was incorporated into a Bayesian model by joint prior probability distributions. Posterior estimates were obtained by combining the actual likelihood with the prior distributions using Bayes' formula. The corrected cow-level TP (proportion of infected cows in a herd) was low, 5.8 and 3.6% in locations 1 and 2, respectively. Certain regions within a location differed significantly in herd-level TP (proportion of infected herds). The herd-level TP was 54.3% in location 1 (95% credible interval (CI) 46.1, 63.3%) and 32.9% in location 2 (95% CI: 14.4, 73.3%). The variation in the herd-level TP estimate for location 2 was more than three times as large as the variation in location 1 mainly because of the relatively small number of investigated herds in location 2. In future prevalence studies for MAP, sample size calculations should be based on a very low cow-level prevalence. Approximately 50 and 90% of the herds in the current study had an estimated cow-level TP below 4 and 10%, respectively.