Longer sleep is associated with lower BMI and favorable metabolic profiles in UK adults: Findings from the National Diet and Nutrition Survey

Ever more evidence associates short sleep with increased risk of metabolic diseases such as obesity, which may be related to a predisposition to non-homeostatic eating. Few studies have concurrently determined associations between sleep duration and objective measures of metabolic health as well as sleep duration and diet, however. We therefore analyzed associations between sleep duration, diet and metabolic health markers in UK adults, assessing associations between sleep duration and 1) adiposity, 2) selected metabolic health markers and 3) diet, using National Diet and Nutrition Survey data. Adults (n = 1,615, age 19–65 years, 57.1% female) completed questions about sleep duration and 3 to 4 days of food diaries. Blood pressure and waist circumference were recorded. Fasting blood lipids, glucose, glycated haemoglobin (HbA1c), thyroid hormones, and high-sensitivity C-reactive protein (CRP) were measured in a subset of participants. We used regression analyses to explore associations between sleep duration and outcomes. After adjustment for age, ethnicity, sex, smoking, and socioeconomic status, sleep duration was negatively associated with body mass index (-0.46 kg/m2 per hour, 95% CI -0.69 to -0.24 kg/m2, p < 0.001) and waist circumference (-0.9 cm per hour, 95% CI -1.5 to -0.3cm, p = 0.004), and positively associated with high-density lipoprotein cholesterol (0.03 mmol/L per hour, 95% CI 0.00 to 0.05, p = 0.03). Sleep duration tended to be positively associated with free thyroxine levels and negatively associated with HbA1c and CRP (p = 0.09 to 0.10). Contrary to our hypothesis, sleep duration was not associated with any dietary measures (p ≥ 0.14). Together, our findings show that short-sleeping UK adults are more likely to have obesity, a disease with many comorbidities

Overview of data-synthesis in systematic reviews of studies on outcome prediction models

Background: Many prognostic models have been developed. Different types of models, i.e. prognostic factor and outcome prediction studies, serve different purposes, which should be reflected in how the results are summarized in reviews. Therefore we set out to investigate how authors of reviews synthesize and report the results of primary outcome prediction studies. Methods: Outcome prediction reviews published in MEDLINE between October 2005 and March 2011 were eligible and 127 Systematic reviews with the aim to summarize outcome prediction studies written in English were identified for inclusion. Characteristics of the reviews and the primary studies that were included were independently assessed by 2 review authors, using standardized forms. Results: After consensus meetings a total of 50 systematic reviews that met the inclusion criteria were included. The type of primary studies included (prognostic factor or outcome prediction) was unclear in two-thirds of the reviews. A minority of the reviews reported univariable or multivariable point estimates and measures of dispersion from the primary studies. Moreover, the variables considered for outcome prediction model development were often not reported, or were unclear. In most reviews there was no information about model performance. Quantitative analysis was performed in 10 reviews, and 49 reviews assessed the primary studies qualitatively. In both analyses types a range of different methods was used to present the results of the outcome prediction studies. Conclusions: Different methods are applied to synthesize primary study results but quantitative analysis is rarely performed. The description of its objectives and of the primary studies is suboptimal and performance parameters of the outcome prediction models are rarely mentioned. The poor reporting and the wide variety of data synthesis strategies are prone to influence the conclusions of outcome prediction reviews. Therefore, there is much room for improvement in reviews of outcome prediction studies. (aut.ref.

The disruption of proteostasis in neurodegenerative diseases

Cells count on surveillance systems to monitor and protect the cellular proteome which, besides being highly heterogeneous, is constantly being challenged by intrinsic and environmental factors. In this context, the proteostasis network (PN) is essential to achieve a stable and functional proteome. Disruption of the PN is associated with aging and can lead to and/or potentiate the occurrence of many neurodegenerative diseases (ND). This not only emphasizes the importance of the PN in health span and aging but also how its modulation can be a potential target for intervention and treatment of human diseases.info:eu-repo/semantics/publishedVersio

Temporal trends in hospitalisation for stroke recurrence following incident hospitalisation for stroke in Scotland

&lt;p&gt;Background: There are few studies that have investigated temporal trends in risk of recurrent stroke. The aim of this study was to examine temporal trends in hospitalisation for stroke recurrence following incident hospitalisation for stroke in Scotland during 1986 to 2001.&lt;/p&gt; &lt;p&gt;Methods: Unadjusted survival analysis of time to first event, hospitalisation for recurrent stroke or death, was undertaken using the cumulative incidence method which takes into account competing risks. Regression on cumulative incidence functions was used to model the temporal trends of first recurrent stroke with adjustment for age, sex, socioeconomic status and comorbidity. Complete five year follow-up was obtained for all patients. Restricted cubic splines were used to determine the best fitting relationship between the survival events and study year.&lt;/p&gt; &lt;p&gt;Results: There were 128,511 incident hospitalisations for stroke in Scotland between 1986 and 2001, 57,351 (45%) in men. A total of 13,835 (10.8%) patients had a recurrent hospitalisation for stroke within five years of their incident hospitalisation. Another 74,220 (57.8%) patients died within five years of their incident hospitalisation without first having a recurrent hospitalisation for stroke. Comparing incident stroke hospitalisations in 2001 with 1986, the adjusted risk of recurrent stroke hospitalisation decreased by 27%, HR = 0.73 95% CI (0.67 to 0.78), and the adjusted risk of death being the first event decreased by 28%, HR = 0.72 (0.70 to 0.75).&lt;/p&gt; &lt;p&gt;Conclusions: Over the 15-year period approximately 1 in 10 patients with an incident hospitalisation for stroke in Scotland went on to have a hospitalisation for recurrent stroke within five years. Approximately 6 in 10 patients died within five years without first having a recurrent stroke hospitalisation. Using hospitalisation and death data from an entire country over a 20-year period we have been able to demonstrate not only an improvement in survival following an incident stroke, but also a reduction in the risk of a recurrent event.&lt;/p&gt

Ratios of involved nodes in early breast cancer

INTRODUCTION: The number of lymph nodes found to be involved in an axillary dissection is among the most powerful prognostic factors in breast cancer, but it is confounded by the number of lymph nodes that have been examined. We investigate an idea that has surfaced recently in the literature (since 1999), namely that the proportion of node-positive lymph nodes (or a function thereof) is a much better predictor of survival than the number of excised and node-positive lymph nodes, alone or together. METHODS: The data were abstracted from 83,686 cases registered in the Surveillance, Epidemiology, and End Results (SEER) program of women diagnosed with nonmetastatic T1–T2 primary breast carcinoma between 1988 and 1997, in whom axillary node dissection was performed. The end-point was death from breast cancer. Cox models based on different expressions of nodal involvement were compared using the Nagelkerke R(2 )index (R(2)(N)). Ratios were modeled as percentage and as log odds of involved nodes. Log odds were estimated in a way that avoids singularities (zero values) by using the empirical logistic transform. RESULTS: In node-negative cases both the number of nodes excised and the log odds were significant, with hazard ratios of 0.991 (95% confidence interval 0.986–0.997) and 1.150 (1.058–1.249), respectively, but without improving R(2)(N). In node-positive cases the hazard ratios were 1.003–1.088 for the number of involved nodes, 0.966–1.005 for the number of excised nodes, 1.015–1.017 for the percentage, and 1.344–1.381 for the log odds. R(2)(N )improved from 0.067 (no nodal covariate) to 0.102 (models based on counts only) and to 0.108 (models based on ratios). DISCUSSION: Ratios are simple optimal predictors, in that they provide at least the same prognostic value as the more traditional staging based on counting of involved nodes, without replacing them with a needlessly complicated alternative. They can be viewed as a per patient standardization in which the number of involved nodes is standardized to the number of nodes excised. In an extension to the study, ratios were validated in a comparison with categorized staging measures using blinded data from the San Jose–Monterey cancer registry. A ratio based prognostic index was also derived. It improved the Nottingham Prognostic Index without compromising on simplicity

A genetic programming approach to development of clinical prediction models: A case study in symptomatic cardiovascular disease

BACKGROUND:Genetic programming (GP) is an evolutionary computing methodology capable of identifying complex, non-linear patterns in large data sets. Despite the potential advantages of GP over more typical, frequentist statistical approach methods, its applications to survival analyses are rare, at best. The aim of this study was to determine the utility of GP for the automatic development of clinical prediction models. METHODS:We compared GP against the commonly used Cox regression technique in terms of the development and performance of a cardiovascular risk score using data from the SMART study, a prospective cohort study of patients with symptomatic cardiovascular disease. The composite endpoint was cardiovascular death, non-fatal stroke, and myocardial infarction. A total of 3,873 patients aged 19-82 years were enrolled in the study 1996-2006. The cohort was split 70:30 into derivation and validation sets. The derivation set was used for development of both GP and Cox regression models. These models were then used to predict the discrete hazards at t = 1, 3, and 5 years. The predictive ability of both models was evaluated in terms of their risk discrimination and calibration using the validation set. RESULTS:The discrimination of both models was comparable. At time points t = 1, 3, and 5 years the C-index was 0.59, 0.69, 0.64 and 0.66, 0.70, 0.70 for the GP and Cox regression models respectively. At the same time points, the calibration of both models, which was assessed using calibration plots and the generalization of the Hosmer-Lemeshow test statistic, was also comparable, but with the Cox model being better calibrated to the validation data. CONCLUSION:Using empirical data, we demonstrated that a prediction model developed automatically by GP has predictive ability comparable to that of manually tuned Cox regression. The GP model was more complex, but it was developed in a fully automated way and comprised fewer covariates. Furthermore, it did not require the expertise normally needed for its derivation, thereby alleviating the knowledge elicitation bottleneck. Overall, GP demonstrated considerable potential as a method for the automated development of clinical prediction models for diagnostic and prognostic purposes

Are social norms associated with smoking in French university students? A survey report on smoking correlates

<p>Abstract</p> <p>Background</p> <p>Knowledge of the correlates of smoking is a first step to successful prevention interventions. The social norms theory hypothesises that students' smoking behaviour is linked to their perception of norms for use of tobacco. This study was designed to test the theory that smoking is associated with perceived norms, controlling for other correlates of smoking.</p> <p>Methods</p> <p>In a pencil-and-paper questionnaire, 721 second-year students in sociology, medicine, foreign language or nursing studies estimated the number of cigarettes usually smoked in a month. 31 additional covariates were included as potential predictors of tobacco use. Multiple imputation was used to deal with missing values among covariates. The strength of the association of each variable with tobacco use was quantified by the inclusion frequencies of the variable in 1000 bootstrap sample backward selections. Being a smoker and the number of cigarettes smoked by smokers were modelled separately.</p> <p>Results</p> <p>We retain 8 variables to predict the risk of smoking and 6 to predict the quantities smoked by smokers. The risk of being a smoker is increased by cannabis use, binge drinking, being unsupportive of smoke-free universities, perceived friends' approval of regular smoking, positive perceptions about tobacco, a high perceived prevalence of smoking among friends, reporting not being disturbed by people smoking in the university, and being female. The quantity of cigarettes smoked by smokers is greater for smokers reporting never being disturbed by smoke in the university, unsupportive of smoke-free universities, perceiving that their friends approve of regular smoking, having more negative beliefs about the tobacco industry, being sociology students and being among the older students.</p> <p>Conclusion</p> <p>Other substance use, injunctive norms (friends' approval) and descriptive norms (friends' smoking prevalence) are associated with tobacco use.</p> <p>University-based prevention campaigns should take multiple substance use into account and focus on the norms most likely to have an impact on student smoking.</p