Set points, settling points and some alternative models: theoretical options to understand how genes and environments combine to regulate body adiposity

The close correspondence between energy intake and expenditure over prolonged time periods, coupled with an apparent protection of the level of body adiposity in the face of perturbations of energy balance, has led to the idea that body fatness is regulated via mechanisms that control intake and energy expenditure. Two models have dominated the discussion of how this regulation might take place. The set point model is rooted in physiology, genetics and molecular biology, and suggests that there is an active feedback mechanism linking adipose tissue (stored energy) to intake and expenditure via a set point, presumably encoded in the brain. This model is consistent with many of the biological aspects of energy balance, but struggles to explain the many significant environmental and social influences on obesity, food intake and physical activity. More importantly, the set point model does not effectively explain the ‘obesity epidemic' - the large increase in body weight and adiposity of a large proportion of individuals in many countries since the 1980s. An alternative model, called the settling point model, is based on the idea that there is passive feedback between the size of the body stores and aspects of expenditure. This model accommodates many of the social and environmental characteristics of energy balance, but struggles to explain some of the biological and genetic aspects. The shortcomings of these two models reflect their failure to address the gene-by-environment interactions that dominate the regulation of body weight. We discuss two additional models - the general intake model and the dual intervention point model - that address this issue and might offer better ways to understand how body fatness is controlled

Arabidopsis thaliana encodes a bacterial-type heterodimeric isopropylmalate isomerase involved in both Leu biosynthesis and the Met chain elongation pathway of glucosinolate formation

The last steps of the Leu biosynthetic pathway and the Met chain elongation cycle for glucosinolate formation share identical reaction types suggesting a close evolutionary relationship of these pathways. Both pathways involve the condensation of acetyl-CoA and a 2-oxo acid, isomerization of the resulting 2-malate derivative to form a 3-malate derivative, the oxidation-decarboxylation of the 3-malate derivative to give an elongated 2-oxo acid, and transamination to generate the corresponding amino acid. We have now analyzed the genes encoding the isomerization reaction, the second step of this sequence, in Arabidopsis thaliana. One gene encodes the large subunit and three encode small subunits of this enzyme, referred to as isopropylmalate isomerase (IPMI) with respect to the Leu pathway. Metabolic profiling of large subunit mutants revealed accumulation of intermediates of both Leu biosynthesis and Met chain elongation, and an altered composition of aliphatic glucosinolates demonstrating the function of this gene in both pathways. In contrast, the small subunits appear to be specialized to either Leu biosynthesis or Met chain elongation. Green fluorescent protein tagging experiments confirms the import of one of the IPMI small subunits into the chloroplast, the localization of the Met chain elongation pathway in these organelles. These results suggest the presence of different heterodimeric IPMIs in Arabidopsis chloroplasts with distinct substrate specificities for Leu or glucosinolate metabolism determined by the nature of the different small subunit

Formal specification of multi-agent systems by using EUSMs

The behavior of e-commerce agents can be defined at different levels of abstraction. A formalism allowing to define them in terms of their economic activities, Utility State Machines, has been proposed. Due to its high level of abstraction, this formalism focuses on describing the economic goals rather on how they are achieved. Though this approach is suitable to specify the objectives of e-commerce agents, as well as to construct formal analysis methodologies, this framework is not suitable to define the strategic behavior of agents. In this paper we develop a new formalism to explicitly define the strategic behavior of agents in a modular way. In particular, we reinterpret the role of utility functions, already used in USMs in a more restrictive manner, so that they define strategic preferences and activities of agents. We apply the formalism to define the agents in a benchmark e-commerce agent environment, the Supply Chain Management Game. Since the strategic behavior of agents is located in a specific part of the formalism, different strategies can be easily considered, which enhances the reusability of the proposed specification

The combined effect of smoking tobacco and drinking alcohol on cause-specific mortality: a 30 year cohort study

<p><b>Background:</b> Smoking and consuming alcohol are both related to increased mortality risk. Their combined effects on cause-specific mortality were investigated in a prospective cohort study.</p> <p><b>Methods:</b> Participants were 5771 men aged 35-64, recruited during 1970-73 from various workplaces in Scotland. Data were obtained from a questionnaire and a screening examination. Causes of death were all cause, coronary heart disease (CHD), stroke, alcohol-related, respiratory and smoking-related cancer. Participants were divided into nine groups according to their smoking status (never, ex or current) and reported weekly drinking (none, 1-14 units and 15 or more). Cox proportional hazards models were used to obtain relative rates of mortality, adjusted for age and other risk factors.</p> <p><b>Results:</b> In 30 years of follow-up, 3083 men (53.4%) died. Compared with never smokers who did not drink, men who both smoked and drank 15+ units/week had the highest all-cause mortality (relative rate = 2.71 (95% confidence interval 2.31-3.19)). Relative rates for CHD mortality were high for current smokers, with a possible protective effect of some alcohol consumption in never smokers. Stroke mortality increased with both smoking and alcohol consumption. Smoking affected respiratory mortality with little effect of alcohol. Adjusting for a wide range of confounders attenuated the relative rates but the effects of alcohol and smoking still remained. Premature mortality was particularly high in smokers who drank 15 or more units, with a quarter of the men not surviving to age 65. 30% of men with manual occupations both smoked and drank 15+ units/week compared with only 13% with non-manual ones.</p> <p><b>Conclusions:</b> Smoking and drinking 15+ units/week was the riskiest behaviour for all causes of death.</p&gt

Interventions to Promote Healthy Eating, Physical Activity and Smoking in Low-Income Groups: a Systematic Review with Meta-Analysis of Behavior Change Techniques and Delivery/Context

Purpose Healthy eating, physical activity and smoking interventions for low-income groups may have small, positive effects. Identifying effective intervention components could guide intervention development. This study investigated which content and delivery components of interventions were associated with increased healthy behavior in randomised controlled trials (RCTs) for low-income adults. Method Data from a review showing intervention effects in 35 RCTs containing 45 interventions with 17,000 participants were analysed to assess associations with behavior change techniques (BCTs) and delivery/context components from the template for intervention description and replication (TIDieR) checklist. The associations of 46 BCTs and 14 delivery/context components with behavior change (measures of healthy eating, physical activity and smoking cessation) were examined using random effects subgroup meta-analyses. Synergistic effects of components were examined using classification and regression trees (meta-CART) analyses based on both fixed and random effects assumptions. Results For healthy eating, self-monitoring, delivery through personal contact, and targeting multiple behaviors were associated with increased effectiveness. Providing feedback, information about emotional consequences, or using prompts and cues were associated with reduced effectiveness. In synergistic analyses, interventions were most effective without feedback, or with selfmonitoring excluding feedback. More effective physical activity interventions included behavioral practice/rehearsal or instruction, focussed solely on physical activity or took place in home/community settings. Information about antecedents was associated with reduced effectiveness. In synergistic analyses, interventions were most effective in home/community settings with instruction. No associations were identified for smoking. Conclusion This study identified BCTs and delivery/context components, individually and synergistically, linked to increased and reduced effectiveness of healthy eating and physical activity interventions. The identified components should be subject to further experimental study to help inform the development effective behavior change interventions for low-income groups to reduce health inequalities