Search

Background: Eating frequency (EF) may influence obesity-related disease risk by attenuating postprandial fluctuations in hormones involved in metabolism, appetite regulation, and inflammation.Materials/methods: This randomized crossover intervention trial tested the effects of EF on fasting plasma insulin-like growth factor-I (IGF-1) and leptin. Fifteen subjects (4 males, 11 females) completed two eucaloric intervention phases lasting 21 days each: low EF (“low-EF”; 3 eating occasions/day) and high EF (“high-EF”; 8 eating occasions/day). Subjects were free-living and consumed their own meals using individualized structured meal plans with instruction from study staff. Subjects completed fasting blood draws and anthropometry on the first and last day of each study phase. The generalized estimated equations modification of linear regression tested the intervention effect on fasting serum IGF-1 and leptin.Results: Mean (± SD) age was 28.5 ± 8.70 years, and mean (± SD) Body Mass Index was 23.3 (3.4) kg/m2. We found lower mean serum IGF-1 following the high-EF condition compared to the low-EF condition (P< 0.001). There was no association between EF and plasma leptin (P= 0.83).Conclusion: These results suggest that increased EF may lower serum IGF-1, which is a hormonal biomarker linked to increased risk of breast, prostate, and colorectal cancer. [ABSTRACT FROM PUBLISHER]

Pediatric cardiac surgery may lead to poor outcomes such as acute kidney injury (AKI) and prolonged hospital length of stay (LOS). Plasma and urine biomarkers may help with early identification and prediction of these adverse clinical outcomes. In a recent multi-center study, 311 children undergoing cardiac surgery were enrolled to evaluate multiple biomarkers for diagnosis and prognosis of AKI and other clinical outcomes. LOS is often analyzed as count data, thus Poisson regression and negative binomial (NB) regression are common choices for developing predictive models. With many correlated prognostic factors and biomarkers, variable selection is an important step. The present paper proposes new variable selection methods for Poisson and NB regression. We evaluated regularized regression through penalized likelihood function. We first extend the elastic net (Enet) Poisson to two penalized Poisson regression: Mnet, a combination of minimax concave and ridge penalties; and Snet, a combination of smoothly clipped absolute deviation (SCAD) and ridge penalties. Furthermore, we extend the above methods to the penalized NB regression. For the Enet, Mnet, and Snet penalties (EMSnet), we develop a unified algorithm to estimate the parameters and conduct variable selection simultaneously. Simulation studies show that the proposed methods have advantages with highly correlated predictors, against some of the competing methods. Applying the proposed methods to the aforementioned data, it is discovered that early postoperative urine biomarkers including NGAL, IL18, and KIM-1 independently predict LOS, after adjusting for risk and biomarker variables. [ABSTRACT FROM AUTHOR]

In many biomedical studies, covariates of interest may be measured with errors. However, frequently in a regression analysis, the quantiles of the exposure variable are often used as the covariates in the regression analysis. Because of measurement errors in the continuous exposure variable, there could be misclassification in the quantiles for the exposure variable. Misclassification in the quantiles could lead to bias estimation in the association between the exposure variable and the outcome variable. Adjustment for misclassification will be challenging when the gold standard variables are not available. In this paper, we develop two regression calibration estimators to reduce bias in effect estimation. The first estimator is normal likelihood-based. The second estimator is linearization-based, and it provides a simple and practical correction. Finite sample performance is examined via a simulation study. We apply the methods to a four-arm randomized clinical trial that tested exercise and weight loss interventions in women aged 50-75 years. [ABSTRACT FROM AUTHOR]

Objectives To compare the effects of 12 months of vitamin D3 supplementation with that placebo on lean mass, bone mineral density ( BMD), and muscle strength in overweight or obese postmenopausal women completing a structured weight-loss program. Design Double-blind, placebo-controlled randomized clinical trial. Setting Fred Hutchinson Cancer Research Center, Seattle, Washington. Participants Postmenopausal women aged 50 to 75 with a body mass index ( BMI) of 25 kg/m2 or greater and a serum 25-hydroxyvitamin D (25(OH)D) concentration between 10.0 and 32.0 ng/mL (insufficient) (N = 218). Intervention Oral vitamin D3 2,000 IU/d or placebo in combination with a lifestyle-based weight loss intervention consisting of a reduction of 500 kcal to 1,000 kcal per day and 225 min/wk of moderate- to vigorous-intensity aerobic exercise. Measurements Serum 25(OH)D, body composition, and muscle strength were measured before randomization (baseline) and at 12 months. Mean changes of the groups were compared (intention to treat) using generalized estimating equations. Results Change in 25(OH)D was significantly different between the vitamin D and placebo groups at 12 months (13.6 ng/mL vs −1.3 ng/mL, P < .001), but no differences in change in lean mass (−0.8 kg vs −1.1 kg, P = .53) or BMD of the spine (−0.01 g/cm2 vs 0.0 g/cm2, P = .82) or right femoral neck (both −0.01 g/cm2, P = .49) were detected between the groups. Leg strength decreased in the vitamin D group but not in the placebo group (−2.6 pounds vs 1.8 pounds, P = .03). In women randomized to vitamin D, achieving repletion (25(OH)D ≥ 32 ng/mL) did not alter results. Conclusion Vitamin D3 supplementation during weight-loss decreased leg strength but did not alter changes in lean mass or BMD in postmenopausal women with vitamin D insufficiency. [ABSTRACT FROM AUTHOR]

Background: Consumption of small, frequent meals is suggested as an effective approach to control appetite and food intake and might be a strategy for weight loss or healthy weight maintenance. Despite much speculation on the topic, scientific evidence is limited to support such a relation in the absence of changes to diet composition.Objective: We examined the effects of high compared with low eating frequency (EF) on self-reported appetite as a secondary outcome in a controlled trial.Methods: We conducted a randomized, crossover intervention trial in 12 participants (4 men, 8 women) who completed 2 isocaloric 3-wk intervention phases of low EF (3 eating occasions/d) compared with high EF (8 eating occasions/d). On the last morning of each study phase, participants completed a 4-h appetite testing session. During the appetite testing session, participants completing the low EF phase consumed a meal at 0800. Participants completing the high EF intervention consumed the same meal spread evenly over 2 eating occasions at 0800 and 1030. Standardized ratings of hunger, desire to eat, fullness, thirst, and nausea were completed every 30 min with the use of paper-and-pencil semianchored 100-mm visual analog scales. A composite appetite score was calculated as the mean of hunger, desire to eat, and the inverse of fullness (calculated as 100-fullness rating). Linear regression analysis compared ratings between low EF and high EF conditions.Results: The mean composite appetite score was higher in the high EF condition for the total testing period (baseline through 1200) (P < 0.05) and for the time period from baseline through 1030 (P < 0.001).Conclusion: The results from this study in 12 healthy adults do not support the popularized notion that small, frequent meals help to decrease overall appetite. This trial was registered at clinicaltrials.gov as NCT02548026. [ABSTRACT FROM AUTHOR]

Secondary trait genetic association provides insight into the genetic architecture of disease etiology but requires caution in estimation. Ignoring case-control sampling may introduce bias into secondary trait association. In this paper, we compare the efficiency and robustness of various inverse probability weighted (IPW) estimators and maximum likelihood (ML) estimators. ML methods have been proposed but require correct modeling of both the secondary and the primary trait associations for valid inference. We show that ML methods using a misspecified primary trait model can severely inflate the type I error. IPW estimators are typically less efficient than ML estimators but are robust against model misspecification. When the secondary trait is available for the entire cohort, the IPW estimator with selection probabilities estimated nonparametrically and the augmented IPW estimator improve efficiency over the simple IPW estimator. We conclude that in large genetic association studies with complex sampling schemes, IPW-based estimators offer flexibility and robustness, and therefore are a viable option for analysis. [ABSTRACT FROM PUBLISHER]

Background: Ambulatory care sensitive hospitalizations (ACSHs) are commonly used as measures of access to and quality of care. They are defined as hospitalizations for certain acute and chronic conditions; yet, they are most commonly used in analyses comparing different groups without adjustment for individual-level comorbidity. We present an exploration of their roles in predicting ACSHs for acute and chronic conditions. Methods: Using 1998–99 US Medicare claims for 1 06 930 SEER-Medicare control subjects and 1999 Area Resource File data, we modelled occurrence of acute and chronic ACSHs with logistic regression, examining effects of different predictors on model discriminatory power. Results: Flags for the presence of a few comorbid conditions—congestive heart failure, chronic obstructive pulmonary disease, diabetes, hypertension and, for acute ACSHs, dementia—contributed virtually all of the discriminative ability for predicting ACSHs. C-statistics were up to 0.96 for models predicting chronic ACSHs and up to 0.87 for predicting acute ACSHs. C-statistics for models lacking comorbidity flags were lower, at best 0.73, for both acute and chronic ACSHs. Conclusion: Comorbidity is far more important in predicting ACSH risk than any other factor, both for acute and chronic ACSHs. Imputations about quality and access should not be made from analyses that do not control for presence of important comorbid conditions. Acute and chronic ACSHs differ enough that they should be modelled separately. Unaggregated models restricted to persons with the relevant diagnoses are most appropriate for chronic ACSHs. [ABSTRACT FROM PUBLISHER]

Objectives: This study aimed to identify demographic, psychological, health-related, and geographic predictors of adherence to home-based and supervised components of a yoga intervention in breast cancer survivors. Methods: Participants were the 32 post-treatment breast cancer survivors who were randomized to the Viniyoga intervention arm of a controlled trial. Participants were asked to practice yoga 5 times per week for 6 months, including at least one weekly facility-based session. Adherence was monitored using sign-in sheets and logs. Height and weight were measured; other potential predictors of adherence were obtained from baseline questionnaires. Results: Participants attended 19.6±13.0 yoga classes and performed 55.8±32.8 home-based yoga sessions. Participants adhered to 58% of the overall yoga practice goal (75% of the goal for yoga classes and 54% of the goal for home based-sessions). Higher class attendance and home practice were predicted by greater self-efficacy for yoga ( p=0.004 and 0.06, respectively). Additionally, employment outside the home was associated with greater class attendance ( p=0.004), while higher waist circumference was marginally associated with lower adherence to home-based yoga ( p=0.05). Conclusions: High levels of facility- and home-based yoga practice were achieved. Breast cancer survivors who have lower self-efficacy for yoga or who have a higher waist circumference may benefit from additional support or intervention tailoring. Adherence may also be improved by ensuring that class times are convenient to both working and nonworking women. [ABSTRACT FROM AUTHOR]

Abstract: Lifestyle-based interventions, which typically promote various behavior modification strategies, can serve as a setting for evaluating specific behaviors and strategies thought to promote or hinder weight loss. The aim of our study was to test the associations of self-monitoring (ie, self-weighing and food journal completion) and eating-related (ie, dietary intake, diet-related weight-control strategies, and meal patterns) behaviors with weight loss in a sample of postmenopausal overweight-to-obese women enrolled in a 12-month dietary weight loss intervention. Changes in body weight and adoption of self-monitoring and eating-related behaviors were assessed in 123 participants. Generalized linear models tested associations of these behaviors with 12-month weight change after adjusting for potential confounders. Mean percent weight loss was 10.7%. In the final model, completing more food journals was associated with a greater percent weight loss (interquartile range 3.7% greater weight loss; P<0.0001), whereas skipping meals (4.3% lower weight loss; P<0.05) and eating out for lunch (at least once a week, 2.5% lower weight loss; P<0.01) were associated with a lower amount of weight loss. These findings suggest that a greater focus on dietary self-monitoring, home-prepared meals, and consuming meals at regular intervals may improve 12-month weight loss among postmenopausal women enrolled in a dietary weight loss intervention. [Copyright &y& Elsevier]

Abstract: Snacking may play a role in weight control. The associations of timing and frequency of snacking with observed weight change and nutrient intake were assessed in an ancillary study to a 12-month randomized controlled trial in Seattle, WA. Overweight-to-obese postmenopausal women (n=123) enrolled in the two dietary weight-loss arms from 2007 to 2008 with complete data at 12 months were included in these analyses. Generalized linear models were used to test the associations between snacking and weight loss (percent) and nutrient intake at the 12-month time point. Participants were, on average, 58 years old and mainly non-Hispanic white (84%). Ninety-seven percent reported one or more snacks per day. Weight loss (percent) was significantly lower among mid-morning (10:30 am to 11:29 am) snackers (7.0%, 95% confidence interval: 4.3 to 9.7) compared to non–mid-morning snackers (11.4%, 95% confidence interval: 10.2 to 12.6; P=0.005). A higher proportion of mid-morning snackers reported more than one snack per day (95.7%), compared to afternoon (82.8%) and evening (80.6%) snackers, although differences were not statistically significant (P>0.005). Women who reported two or more snacks per day vs one or no snacks per day had higher fiber intake (P=0.027). Afternoon snackers had higher fruit and vegetable intake compared to non–afternoon-snackers (P=0.035). These results suggest that snack meals can be a source for additional fruits, vegetables, and fiber-rich foods; however, snacking patterns might also reflect unhealthy eating habits and impede weight-loss progress. Future dietary weight-loss interventions should evaluate the effects of timing, frequency, and quality of snacks on weight loss. [Copyright &y& Elsevier]