Patients who are depressed and prescribed antidepressants may report weight gain, but there has been limited research into the association between the two. However new observational research published on bmj.com aims to identify that association. Rafael Gafoor, a psychiatrist and researcher at Kings College London, and one of the authors of that...

Jennifer B. Marks
Oct 1, 2004; 22:155-156
Editorials

Anastassios G. Pittas
Dec 1, 2005; 28:2939-2941
BR Clinical Care/Education/Nutrition

Thomas M S Wolever
Apr 1, 1992; 15:562-564
Short Report

George A. Bray
Apr 1, 2014; 37:950-956
Current Concepts of Type 2 Diabetes Prevention

Tina Vilsbøll
Jun 1, 2007; 30:1608-1610
BR Emerging Treatments and Technologies

Jennifer M. Yamamoto
Jul 1, 2018; 41:1346-1361
Reconsidering Pregnancy With Diabetes

Jordi Salas-Salvadó
May 1, 2019; 42:777-788
Continuing Evolution of Nutritional Therapy for Diabetes

George A. Bray
Apr 1, 2014; 37:950-956
Current Concepts of Type 2 Diabetes Prevention

W. Timothy Garvey
May 1, 2020; 43:1085-1093
Emerging Therapies: Drugs and Regimens

OBJECTIVE

To assess functional β-cell capacity in type 2 diabetes during 2 years of remission induced by dietary weight loss.

OBJECTIVE

We examined detailed renal and cardiovascular (CV) outcomes after gastric bypass (GBP) surgery in people with obesity and type 2 diabetes mellitus (T2DM), across several renal function categories, in a nationwide cohort study.

OBJECTIVE

To examine the effect of combination therapy with canagliflozin plus liraglutide on HbA_1c, endogenous glucose production (EGP), and body weight versus each therapy alone.

The food lowers cravings for high-sugar and high-fat foods and suppresses appetite during the day.

→ Support PsyBlog for just $5 per month. Enables access to articles marked (M) and removes ads.

→ Explore PsyBlog's ebooks, all written by Dr Jeremy Dean:

• Accept Yourself: How to feel a profound sense of warmth and self-compassion
• The Anxiety Plan: 42 Strategies For Worry, Phobias, OCD and Panic
• Spark: 17 Steps That Will Boost Your Motivation For Anything
• Activate: How To Find Joy Again By Changing What You Do

does resurge work : Resurge weight reduction supplement is a distinct advantage program that would bolster your ascent to control. It will change you and make you more grounded than at any other time with improved wellbeing that can assist you with getting away from heftiness. This Resurge audit tells how the Supplement will help your lack of sleep and weight reduction cause. Improved digestion change will enable the body to acquire vitality.

Hypertension -- Diet therapy -- Recipes.

London : Scribe, 2019.

We study the misclassification error for community detection in general heterogeneous stochastic block models (SBM) with noisy or partial label information. We establish a connection between the misclassification rate and the notion of minimum energy on the local neighborhood of the SBM. We develop an optimally weighted message passing algorithm to reconstruct labels for SBM based on the minimum energy flow and the eigenvectors of a certain Markov transition matrix. The general SBM considered in this paper allows for unequal-size communities, degree heterogeneity, and different connection probabilities among blocks. We focus on how to optimally weigh the message passing to improve misclassification.

Derivatives play an important role in bandwidth selection methods (e.g., plug-ins), data analysis and bias-corrected confidence intervals. Therefore, obtaining accurate derivative information is crucial. Although many derivative estimation methods exist, the majority require a fixed design assumption. In this paper, we propose an effective and fully data-driven framework to estimate the first and second order derivative in random design. We establish the asymptotic properties of the proposed derivative estimator, and also propose a fast selection method for the tuning parameters. The performance and flexibility of the method is illustrated via an extensive simulation study.

In many areas, practitioners need to analyze large data sets that challenge conventional single-machine computing. To scale up data analysis, distributed and parallel computing approaches are increasingly needed. Here we study a fundamental and highly important problem in this area: How to do ridge regression in a distributed computing environment? Ridge regression is an extremely popular method for supervised learning, and has several optimality properties, thus it is important to study. We study one-shot methods that construct weighted combinations of ridge regression estimators computed on each machine. By analyzing the mean squared error in a high-dimensional random-effects model where each predictor has a small effect, we discover several new phenomena. Infinite-worker limit: The distributed estimator works well for very large numbers of machines, a phenomenon we call 'infinite-worker limit'. Optimal weights: The optimal weights for combining local estimators sum to more than unity, due to the downward bias of ridge. Thus, all averaging methods are suboptimal. We also propose a new Weighted ONe-shot DistributEd Ridge regression algorithm (WONDER). We test WONDER in simulation studies and using the Million Song Dataset as an example. There it can save at least 100x in computation time, while nearly preserving test accuracy.

Tree data are ubiquitous because they model a large variety of situations, e.g., the architecture of plants, the secondary structure of RNA, or the hierarchy of XML files. Nevertheless, the analysis of these non-Euclidean data is difficult per se. In this paper, we focus on the subtree kernel that is a convolution kernel for tree data introduced by Vishwanathan and Smola in the early 2000's. More precisely, we investigate the influence of the weight function from a theoretical perspective and in real data applications. We establish on a 2-classes stochastic model that the performance of the subtree kernel is improved when the weight of leaves vanishes, which motivates the definition of a new weight function, learned from the data and not fixed by the user as usually done. To this end, we define a unified framework for computing the subtree kernel from ordered or unordered trees, that is particularly suitable for tuning parameters. We show through eight real data classification problems the great efficiency of our approach, in particular for small data sets, which also states the high importance of the weight function. Finally, a visualization tool of the significant features is derived.

Abedin Haidari, Amir T. Payandeh Najafabadi, Narayanaswamy Balakrishnan.

Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 1, 150--166.

Abstract:
Consider two parallel systems, say $A$ and $B$, with respective lifetimes $T_{1}$ and $T_{2}$ wherein independent component lifetimes of each system follow exponentiated generalized gamma distribution with possibly different exponential shape and scale parameters. We show here that $T_{2}$ is smaller than $T_{1}$ with respect to the usual stochastic order (reversed hazard rate order) if the vector of logarithm (the main vector) of scale parameters of System $B$ is weakly weighted majorized by that of System $A$, and if the vector of exponential shape parameters of System $A$ is unordered mojorized by that of System $B$. By means of some examples, we show that the above results can not be extended to the hazard rate and likelihood ratio orders. However, when the scale parameters of each system divide into two homogeneous groups, we verify that the usual stochastic and reversed hazard rate orders can be extended, respectively, to the hazard rate and likelihood ratio orders. The established results complete and strengthen some of the known results in the literature.

The ability of intelligent agents to learn and remember multiple tasks sequentially is crucial to achieving artificial general intelligence. Many continual learning (CL) methods have been proposed to overcome catastrophic forgetting. Catastrophic forgetting notoriously impedes the sequential learning of neural networks as the data of previous tasks are unavailable. In this paper we focus on class incremental learning, a challenging CL scenario, in which classes of each task are disjoint and task identity is unknown during test. For this scenario, generative replay is an effective strategy which generates and replays pseudo data for previous tasks to alleviate catastrophic forgetting. However, it is not trivial to learn a generative model continually for relatively complex data. Based on recently proposed orthogonal weight modification (OWM) algorithm which can keep previously learned input-output mappings invariant approximately when learning new tasks, we propose to directly generate and replay feature. Empirical results on image and text datasets show our method can improve OWM consistently by a significant margin while conventional generative replay always results in a negative effect. Our method also beats a state-of-the-art generative replay method and is competitive with a strong baseline based on real data storage.

Min Xu, Varun Jog, Po-Ling Loh.

Source: The Annals of Statistics, Volume 48, Number 1, 183--204.

Abstract:
Community identification in a network is an important problem in fields such as social science, neuroscience and genetics. Over the past decade, stochastic block models (SBMs) have emerged as a popular statistical framework for this problem. However, SBMs have an important limitation in that they are suited only for networks with unweighted edges; in various scientific applications, disregarding the edge weights may result in a loss of valuable information. We study a weighted generalization of the SBM, in which observations are collected in the form of a weighted adjacency matrix and the weight of each edge is generated independently from an unknown probability density determined by the community membership of its endpoints. We characterize the optimal rate of misclustering error of the weighted SBM in terms of the Renyi divergence of order 1/2 between the weight distributions of within-community and between-community edges, substantially generalizing existing results for unweighted SBMs. Furthermore, we present a computationally tractable algorithm based on discretization that achieves the optimal error rate. Our method is adaptive in the sense that the algorithm, without assuming knowledge of the weight densities, performs as well as the best algorithm that knows the weight densities.

Hong Zhang, Tiejun Tong, John Landers, Zheyang Wu.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 178--201.

Abstract:
The $p$-value combination approach is an important statistical strategy for testing global hypotheses with broad applications in signal detection, meta-analysis, data integration, etc. In this paper we extend the classic Fisher’s combination method to a unified family of statistics, called TFisher, which allows a general truncation-and-weighting scheme of input $p$-values. TFisher can significantly improve statistical power over the Fisher and related truncation-only methods for detecting both rare and dense “signals.” To address wide applications, analytical calculations for TFisher’s size and power are deduced under any two continuous distributions in the null and the alternative hypotheses. The corresponding omnibus test (oTFisher) and its size calculation are also provided for data-adaptive analysis. We study the asymptotic optimal parameters of truncation and weighting based on Bahadur efficiency (BE). A new asymptotic measure, called the asymptotic power efficiency (APE), is also proposed for better reflecting the statistics’ performance in real data analysis. Interestingly, under the Gaussian mixture model in the signal detection problem, both BE and APE indicate that the soft-thresholding scheme is the best, the truncation and weighting parameters should be equal. By simulations of various signal patterns, we systematically compare the power of statistics within TFisher family as well as some rare-signal-optimal tests. We illustrate the use of TFisher in an exome-sequencing analysis for detecting novel genes of amyotrophic lateral sclerosis. Relevant computation has been implemented into an R package TFisher published on the Comprehensive R Archive Network to cater for applications.

Fan Li, Fan Li.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2389--2415.

Abstract:
Causal or unconfounded descriptive comparisons between multiple groups are common in observational studies. Motivated from a racial disparity study in health services research, we propose a unified propensity score weighting framework, the balancing weights, for estimating causal effects with multiple treatments. These weights incorporate the generalized propensity scores to balance the weighted covariate distribution of each treatment group, all weighted toward a common prespecified target population. The class of balancing weights include several existing approaches such as the inverse probability weights and trimming weights as special cases. Within this framework, we propose a set of target estimands based on linear contrasts. We further develop the generalized overlap weights, constructed as the product of the inverse probability weights and the harmonic mean of the generalized propensity scores. The generalized overlap weighting scheme corresponds to the target population with the most overlap in covariates across the multiple treatments. These weights are bounded and thus bypass the problem of extreme propensities. We show that the generalized overlap weights minimize the total asymptotic variance of the moment weighting estimators for the pairwise contrasts within the class of balancing weights. We consider two balance check criteria and propose a new sandwich variance estimator for estimating the causal effects with generalized overlap weights. We apply these methods to study the racial disparities in medical expenditure between several racial groups using the 2009 Medical Expenditure Panel Survey (MEPS) data. Simulations were carried out to compare with existing methods.

Pavel Zorin-Kranich.

Source: Bernoulli, Volume 26, Number 3, 2311--2318.

Abstract:
We prove an estimate for weighted $p$th moments of the pathwise $r$-variation of a martingale in terms of the $A_{p}$ characteristic of the weight. The novelty of the proof is that we avoid real interpolation techniques.

Giacomo Aletti, Irene Crimaldi, Andrea Ghiglietti.

Source: Bernoulli, Volume 26, Number 2, 1098--1138.

Abstract:
This work deals with a system of interacting reinforced stochastic processes , where each process $X^{j}=(X_{n,j})_{n}$ is located at a vertex $j$ of a finite weighted directed graph, and it can be interpreted as the sequence of “actions” adopted by an agent $j$ of the network. The interaction among the dynamics of these processes depends on the weighted adjacency matrix $W$ associated to the underlying graph: indeed, the probability that an agent $j$ chooses a certain action depends on its personal “inclination” $Z_{n,j}$ and on the inclinations $Z_{n,h}$, with $h eq j$, of the other agents according to the entries of $W$. The best known example of reinforced stochastic process is the Pólya urn. The present paper focuses on the weighted empirical means $N_{n,j}=sum_{k=1}^{n}q_{n,k}X_{k,j}$, since, for example, the current experience is more important than the past one in reinforced learning. Their almost sure synchronization and some central limit theorems in the sense of stable convergence are proven. The new approach with weighted means highlights the key points in proving some recent results for the personal inclinations $Z^{j}=(Z_{n,j})_{n}$ and for the empirical means $overline{X}^{j}=(sum_{k=1}^{n}X_{k,j}/n)_{n}$ given in recent papers (e.g. Aletti, Crimaldi and Ghiglietti (2019), Ann. Appl. Probab. 27 (2017) 3787–3844, Crimaldi et al. Stochastic Process. Appl. 129 (2019) 70–101). In fact, with a more sophisticated decomposition of the considered processes, we can understand how the different convergence rates of the involved stochastic processes combine. From an application point of view, we provide confidence intervals for the common limit inclination of the agents and a test statistics to make inference on the matrix $W$, based on the weighted empirical means. In particular, we answer a research question posed in Aletti, Crimaldi and Ghiglietti (2019).

Nicolas Privault, Grzegorz Serafin.

Source: Bernoulli, Volume 26, Number 1, 587--615.

Abstract:
We derive normal approximation bounds in the Kolmogorov distance for sums of discrete multiple integrals and weighted $U$-statistics made of independent Bernoulli random variables. Such bounds are applied to normal approximation for the renormalized subgraph counts in the Erdős–Rényi random graph. This approach completely solves a long-standing conjecture in the general setting of arbitrary graph counting, while recovering recent results obtained for triangles and improving other bounds in the Wasserstein distance.

CBC Kitchener-Waterloo speaks to a motherhood expert about managing work and child care during the pandemic.

Research suggests that the prevalence of obesity in children with autism spectrum disorder (ASD) is higher than in typically developing children. The US Preventive Services Task Force and the American Academy of Pediatrics (AAP) have endorsed screening children for overweight and obesity as part of the standard of care for physicians. However, the pediatric provider community has been inadequately prepared to address this issue in children with ASD. The Healthy Weight Research Network, a national research network of pediatric obesity and autism experts funded by the US Health Resources and Service Administration Maternal and Child Health Bureau, developed recommendations for managing overweight and obesity in children with ASD, which include adaptations to the AAP’s 2007 guidance. These recommendations were developed from extant scientific evidence in children with ASD, and when evidence was unavailable, consensus was established on the basis of clinical experience. It should be noted that these recommendations do not reflect official AAP policy. Many of the AAP recommendations remain appropriate for primary care practitioners to implement with their patients with ASD; however, the significant challenges experienced by this population in both dietary and physical activity domains, as well as the stress experienced by their families, require adaptations and modifications for both preventive and intervention efforts. These recommendations can assist pediatric providers in providing tailored guidance on weight management to children with ASD and their families.

There have been no previous large studies of congenital heart disease in very low birth weight infants.

This study characterized the frequency, mortality rate, and lesion distribution of serious congenital heart disease in very low birth weight infants by using a large international multicenter database. (Read the full article)

Adults born at very low birth weight (VLBW) (<1.5 kg) have higher blood pressure and higher fasting insulin levels than their peers born at term. However, they show no signs of endothelial dysfunction in childhood and in adolescence.

Adults born at a VLBW showed no endothelial dysfunction compared with term adults. They had, however, a thicker intima-media layer in relation to lumen size. More rapid growth during their first weeks of life was associated with better endothelial function. (Read the full article)

Associations between short sleep duration, obesity, and metabolic dysfunction have been proposed for children but have not been explored appropriately.

The main findings included a nonlinear trend between sleep duration and body weight and the finding that children's sleep averaged 8 hours per night regardless of body weight. Lower sleep duration values were strongly associated with increased metabolic risk. (Read the full article)

For treatment of obesity in preadolescent children, approaches that focus on parents taking sole responsibility for implementing weight-management strategies may be most effective. However, the optimal content, intensity, and duration of programs for obese children remain unclear.

Targeting parents only, a 10% relative weight loss is achievable in moderately obese preadolescent children and can be maintained 2 years from baseline, which justifies an investment in treatment as a secondary obesity-prevention strategy. (Read the full article)

Lactoferrin is a glycoprotein with anti-infective activities being part of the innate defensive network. Bovine and human lactoferrin share high homology. Bovine lactoferrin can prevent late-onset sepsis in preterm very low birth weight neonates.

In preterm very low birth weight infants, bovine lactoferrin is able to prevent not only late-onset sepsis but also systemic fungal infections. This protection is achieved independently from their colonization status. (Read the full article)

Overweight has been associated with an increased risk of asthma in children, although the published literature is contradictory. How change in overweight status during childhood affects asthma risk has not been well studied.

Among children whose weight has normalized, high BMI during the first 4 years of life does not increase the risk of asthma at school age. Current high BMI is associated with increased risk of asthma and sensitization to inhalant allergens. (Read the full article)

Human rhinovirus infections are common in children. Although historically associated with upper respiratory tract illness, rhinoviruses are increasingly recognized for their role in the exacerbation of asthma. Their role in bronchiolitis and severe lung disease in premature infants is unclear.

The authors of this study prospectively explore the role of rhinoviruses in premature infants using molecular techniques and identify these agents as the most frequent cause of hospitalization in this population. (Read the full article)

Eating disorders are characterized by preoccupation with weight and shape, which is manifested by a refusal to maintain a normal weight. An exact determination of expected body weight (EBW) is critical for diagnosis and clinical management of these disorders.

The McLaren and Moore methods present with several limitations when calculating EBW for adolescents with eating disorders. A commonly agreed upon method for EBW calculation such as the BMI percentile method is recommended for clinical and research purposes. (Read the full article)

Several studies have focused on birth weight heritability, reporting results that range between 40% and 80%. Few studies have focused on the process of weight gain and were mainly based on heterogeneous samples of infants.

The present work looks at a uniform set of healthy preterm newborn twins. The resulting high heritability estimate could suggest using the inclusion criteria to identify genes that regulate postnatal weight gain or failure. (Read the full article)

Pulmonary hypertension is associated with bronchopulmonary dysplasia in extremely low birth weight infants and contributes to morbidity and mortality.

Pulmonary hypertension affects at least 1 in 6 extremely low birth weight infants and persists to discharge in most survivors. Routine screening of these infants with echocardiography at 4 weeks of age identifies only one-third of those affected. (Read the full article)

Clinic-based weight control treatments for youth have largely been designed for preadolescent children and their families by using family-based care, a strategy that may be less appealing to adolescents as they become increasingly motivated by peer acceptance rather than parental influence.

To our knowledge, this is the first study to demonstrate the efficacy of a primary care–based, multicomponent lifestyle intervention specifically tailored for overweight adolescent females and demonstrating a sustained effect (at 12 months) extending beyond the active 5-month intervention. (Read the full article)

The demographics of trials that use antenatal consent may not be representative of the populations that they are intended to study.

This study analyzes the difference in clinical outcomes between the enrolled and eligible but not enrolled populations of a trial that required antenatal consent. (Read the full article)

Very preterm children are at risk for developmental problems and, therefore, a systematic follow-up is important. However, the relevance of early follow-up of cognitive development has been questioned because of the divergent data on the prognostic value of early measures.

Good stability of cognitive development was found between the ages of 2 and 5 years. Well-conducted assessment of cognitive development in infancy is both reliable to anticipate later development and clinically valuable to identify those children who need developmental support. (Read the full article)

Preterm infants are at increased risk of behavioral problems, which has been associated with maternal distress. Paternal psychological well-being is less studied. Parents’ concerns may affect their perceptions or attitudes and have negative effects on the behavior of the child.

Parents report more behavior problems in VLBW children at age 3 if they themselves have had symptoms of depression, parenting stress, or weak sense of coherence. Also, the paternal psychological well-being contributes to the behavioral development of a preterm child. (Read the full article)

Previous cohorts of extremely low birth weight adolescents have assessed their health status similar to that of normal birth weight controls.

Extremely low birth weight adolescents born in the 1990s assess their health similar to controls but report less risk taking. Extremely low and normal birth weight children rate their health to be poorer at 8 than at 14 years. (Read the full article)

Comprehensive lifestyle interventions for adolescent weight management, including diet, physical activity, and behavioral intervention, have been found to demonstrate modest, short-term success. However, very little is known about the long-term effectiveness of adolescent behavioral weight management trials.

This randomized controlled trial demonstrates that two 16-week group-based behavioral weight loss programs, when combined with either aerobic exercise or peer-based adventure therapy, produced sustained improvements in BMI among overweight/obese adolescents through 24 months. (Read the full article)