Association of relative fat mass with prevalence of erectile dysfunction in US men: an analysis of NHANES 2001-2004
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Separate orexigenic hippocampal ensembles shape dietary choice by enhancing contextual memory and motivation
The hippocampus (HPC) has emerged as a critical player in the control of food intake, beyond its well-known role in memory. While previous studies have primarily associated the HPC with food intake inhibition, recent research suggests a role in appetitive processes. Here we identified spatially distinct neuronal populations within the dorsal HPC (dHPC) that respond to either fats or sugars, potent natural reinforcers that contribute to obesity development. Using activity-dependent genetic capture of nutrient-responsive dHPC neurons, we demonstrate a causal role of both populations in promoting nutrient-specific intake through different mechanisms. Sugar-responsive neurons encoded spatial memory for sugar location, whereas fat-responsive neurons selectively enhanced the preference and motivation for fat intake. Importantly, stimulation of either nutrient-responsive dHPC neurons increased food intake, while ablation differentially impacted obesogenic diet consumption and prevented diet-induced weight gain. Collectively, these findings uncover previously unknown orexigenic circuits underlying macronutrient-specific consumption and provide a foundation for developing potential obesity treatments.
Estimated human intake of endogenous and exogenous hormones from beef in the United States
Endogenous and exogenous hormones may be present in beef. Human consumption of hormones has been linked to adverse health effects.
Predictive equation derived from 6,497 doubly labelled water measurements enables the detection of erroneous self-reported energy intake
Nutritional epidemiology aims to link dietary exposures to chronic disease, but the instruments for evaluating dietary intake are inaccurate. One way to identify unreliable data and the sources of errors is to compare estimated intakes with the total energy expenditure (TEE). In this study, we used the International Atomic Energy Agency Doubly Labeled Water Database to derive a predictive equation for TEE using 6,497 measures of TEE in individuals aged 4 to 96 years. The resultant regression equation predicts expected TEE from easily acquired variables, such as body weight, age and sex, with 95% predictive limits that can be used to screen for misreporting by participants in dietary studies. We applied the equation to two large datasets (National Diet and Nutrition Survey and National Health and Nutrition Examination Survey) and found that the level of misreporting was >50%. The macronutrient composition from dietary reports in these studies was systematically biased as the level of misreporting increased, leading to potentially spurious associations between diet components and body mass index.
Bayesian p-curve mixture models as a tool to dissociate effect size and effect prevalence
Much research in the behavioral sciences aims to characterize the “typical” person. A statistically significant group-averaged effect size is often interpreted as evidence that the typical person shows an effect, but that is only true under certain distributional assumptions for which explicit evidence is rarely presented. Mean effect size varies with both within-participant effect size and population prevalence (proportion of population showing effect). Few studies consider how prevalence affects mean effect size estimates and existing estimators of prevalence are, conversely, confounded by uncertainty about effect size. We introduce a widely applicable Bayesian method, the p-curve mixture model, that jointly estimates prevalence and effect size by probabilistically clustering participant-level data based on their likelihood under a null distribution. Our approach, for which we provide a software tool, outperforms existing prevalence estimation methods when effect size is uncertain and is sensitive to differences in prevalence or effect size across groups or conditions.
Role of pancreatic lipase inhibition in obesity treatment: mechanisms and challenges towards current insights and future directions
The worldwide health emergency of obesity is closely connected to how dietary fats are metabolized, whereas the process is significantly influenced by pancreatic lipase (PL), an enzyme critical for lipid hydrolysis into fatty acids. This narrative review employs a methodological approach utilizing literature searches of PubMed data up to March 2024. The search term criteria encompasses keywords related to the role, mechanism, challenges, and current and future treatments of pancreatic lipase in obesity with an overall references is 106. This paper offers a comprehensive explanation of the role of PL, underlining its significance in the digestive process and lipid imbalances that contribute to obesity and by extension, its impact on obesity development and progression. Additionally, it delves into the dual functionality of the pancreas, emphasizing its impact on metabolism and energy utilization which, when dysregulated, promotes obesity. A focal point of this review is the investigation into the efficacy, challenges, and adverse effects of current pancreatic lipase inhibitors, with orlistat being highlighted as a primary current drug delivery. By discussing advanced obesity treatments, including the exploration of novel anti-obesity medications that target specific biological pathways, this review underscores the complexity of obesity treatment and the necessity for a multifaceted approach. In conclusion, this paper emphasizing the importance of understanding the role of enzymes like pancreatic lipase mechanistic and adopting a multidisciplinary approach to treatment and side effects of current obesity drugs and explore new emerging therapeutic strategies for more effective obesity management.
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