Obesity in adults: a 2022 adapted clinical practice guideline for Ireland

This Clinical Practice Guideline (CPG) for the management of obesity in adults in Ireland, adapted from the Canadian CPG, defines obesity as a complex chronic disease characterised by excess or dysfunctional adiposity that impairs health. The guideline reflects substantial advances in the understanding of the determinants, pathophysiology, assessment, and treatment of obesity. It shifts the focus of obesity management toward improving patient-centred health outcomes, functional outcomes, and social and economic participation, rather than weight loss alone. It gives recommendations for care that are underpinned by evidence-based principles of chronic disease management; validate patients' lived experiences; move beyond simplistic approaches of "eat less, move more" and address the root drivers of obesity. People living with obesity face substantial bias and stigma, which contribute to increased morbidity and mortality independent of body weight. Education is needed for all healthcare professionals in Ireland to address the gap in skills, increase knowledge of evidence-based practice, and eliminate bias and stigma in healthcare settings. We call for people living with obesity in Ireland to have access to evidence-informed care, including medical, medical nutrition therapy, physical activity and physical rehabilitation interventions, psychological interventions, pharmacotherapy, and bariatric surgery. This can be best achieved by resourcing and fully implementing the Model of Care for the Management of Adult Overweight and Obesity. To address health inequalities, we also call for the inclusion of obesity in the Structured Chronic Disease Management Programme and for pharmacotherapy reimbursement, to ensure equal access to treatment based on health-need rather than ability to pay

Metabolic adaptation, transitions and resilience in overweight individuals

Overweight and obesity are refractory conditions of human physiology. By using personalized computer models of human metabolism understanding is obtained about the dynamics of the ‘obese system’ and whether the effects of changes in nutrition and gut microbiota on metabolic health can be predicted

Transcriptomic signature of fasting in adipose tissue

This SuperSeries is composed of the SubSeries listed below. Overall design: Refer to individual Serie

Transcriptomic signature of fasting in murine adipose tissue

Little is known about the impact of fasting on gene regulation in human adipose tissue. Accordingly, the objective of this study was to investigate the effects of fasting on adipose tissue gene expression in humans. To that end, subcutaneous adipose tissue biopsies were collected from volunteers 2h and 26h after consumption of a standardized meal. For comparison, epididymal adipose tissue was collected from C57Bl/6J mice after a 16h fast and in the ab-libitum fed state. Transcriptome analysis was carried out using Affymetrix microarrays. We found that, 1) fasting downregulated numerous metabolic pathways in human adipose tissue, including triglyceride and fatty acid synthesis, glycolysis and glycogen synthesis, TCA cycle, oxidative phosphorylation, mitochondrial translation, and insulin signaling; 2) fasting downregulated genes involved in proteasomal degradation in human adipose tissue; 3) fasting had much less pronounced effects on the adipose tissue transcriptome in humans than mi ce; 4) although major overlap in fasting-induced gene regulation was observed between human and mouse adipose tissue, many genes were differentially regulated in the two species, including genes involved in insulin signaling (PRKAG2, PFKFB3), PPAR signaling (PPARG, ACSL1, HMGCS2, SLC22A5, ACOT1), glycogen metabolism (PCK1, PYGB), and lipid droplets (PLIN1, PNPLA2, CIDEA, CIDEC). In conclusion, although numerous genes and pathways are regulated similarly by fasting in human and mouse adipose tissue, many genes show very distinct responses to fasting in humans and mice. Our data provide a useful resource to study adipose tissue function during fasting. Overall design: Microarray analysis was performed on gonadal adipose tissue in the fed or after a 16 hours fast: Three to four month old C57BL/6 mice were fasted for 16 hours or fed ad libitum. Gonadal white adipose tissue was collected to find changes in gene expression upon fasting in the white adipose tissue

Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge

Plasma glucose and insulin responses following an oral glucose challenge are representative of glucose tolerance and insulin resistance, key indicators of type 2 diabetes mellitus pathophysiology. A large heterogeneity in individuals' challenge test responses has been shown to underlie the effectiveness of lifestyle intervention. Currently, this heterogeneity is overlooked due to a lack of methods to quantify the interconnected dynamics in the glucose and insulin time-courses. Here, a physiology-based mathematical model of the human glucose-insulin system is personalized to elucidate the heterogeneity in individuals' responses using a large population of overweight/obese individuals (n = 738) from the DIOGenes study. The personalized models are derived from population level models through a systematic parameter selection pipeline that may be generalized to other biological systems. The resulting personalized models showed a 4-5 fold decrease in discrepancy between measurements and model simulation compared to population level. The estimated model parameters capture relevant features of individuals' metabolic health such as gastric emptying, endogenous insulin secretion and insulin dependent glucose disposal into tissues, with the latter also showing a significant association with the Insulinogenic index and the Matsuda insulin sensitivity index, respectively

E-DES-PROT: A novel computational model to describe the effects of amino acids and protein on postprandial glucose and insulin dynamics in humans

Current computational models of whole-body glucose homeostasis describe physiological processes by which insulin regulates circulating glucose concentrations. While these models perform well in response to oral glucose challenges, interaction with other nutrients that impact postprandial glucose metabolism, such as amino acids (AAs), is not considered. Here, we developed a computational model of the human glucose-insulin system, which incorporates the effects of AAs on insulin secretion and hepatic glucose production. This model was applied to postprandial glucose and insulin time-series data following different AA challenges (with and without co-ingestion of glucose), dried milk protein ingredients, and dairy products. Our findings demonstrate that this model allows accurate description of postprandial glucose and insulin dynamics and provides insight into the physiological processes underlying meal responses. This model may facilitate the development of computational models that describe glucose homeostasis following the intake of multiple macronutrients, while capturing relevant features of an individual's metabolic health

A computational model of postprandial adipose tissue lipid metabolism derived using human arteriovenous stable isotope tracer data

\u3cp\u3eGiven the association of disturbances in non-esterified fatty acid (NEFA) metabolism with the development of Type 2 Diabetes and Non-Alcoholic Fatty Liver Disease, computational models of glucose-insulin dynamics have been extended to account for the interplay with NEFA. In this study, we use arteriovenous measurement across the subcutaneous adipose tissue during a mixed meal challenge test to evaluate the performance and underlying assumptions of three existing models of adipose tissue metabolism and construct a new, refined model of adipose tissue metabolism. Our model introduces new terms, explicitly accounting for the conversion of glucose to glyceraldehye-3-phosphate, the postprandial influx of glycerol into the adipose tissue, and several physiologically relevant delays in insulin signalling in order to better describe the measured adipose tissues fluxes. We then applied our refined model to human adipose tissue flux data collected before and after a diet intervention as part of the Yoyo study, to quantify the effects of caloric restriction on postprandial adipose tissue metabolism. Significant increases were observed in the model parameters describing the rate of uptake and release of both glycerol and NEFA. Additionally, decreases in the model's delay in insulin signalling parameters indicates there is an improvement in adipose tissue insulin sensitivity following caloric restriction.\u3c/p\u3

Quantifying the contribution of triglycerides to metabolic resilience through the mixed meal model

Despite the pivotal role played by elevated circulating triglyceride levels in the pathophysiology of cardio-metabolic diseases many of the indices used to quantify metabolic health focus on deviations in glucose and insulin alone. We present the Mixed Meal Model, a computational model describing the systemic interplay between triglycerides, free fatty acids, glucose, and insulin. We show that the Mixed Meal Model can capture deviations in the post-meal excursions of plasma glucose, insulin, and triglyceride that are indicative of features of metabolic resilience; quantifying insulin resistance and liver fat; validated by comparison to gold-standard measures. We also demonstrate that the Mixed Meal Model is generalizable, applying it to meals with diverse macro-nutrient compositions. In this way, by coupling triglycerides to the glucose-insulin system the Mixed Meal Model provides a more holistic assessment of metabolic resilience from meal response data, quantifying pre-clinical metabolic deteriorations that drive disease development in overweight and obesity