Role of Lipolysis and Lipogenesis in the Development of Diet-Induced Obesity

Obesity is an increasingly common public health problem with approximately one-half of the American adult population overweight and one-quarter considered obese. This alarming trend has led researchers to determine potential causative factors of excess weight gain in humans. However, it is difficult to discern whether perturbations that result in obesity are the cause or simply the result of the obese state. Diet-induced obesity is one of the animal models that allow researchers to address temporal issues. Our laboratory utilizes a diet-induced obesity model in which Sprague-Dawley rats are placed on a purified moderately high fat diet and ultimately diverge into two distinct populations based on body weight gain. Approximately 50% of the rats gain more body weight and fat and are considered obesity-prone (OP), whereas the other half (obesity-resistant—OR) are similar in body composition to rats fed a low fat diet. Interestingly, rates of body weight gain and food consumption are greater for OP rats than OR rats during early phases of the dietary challenge, but not during later phases. Moreover, weight gain is associated with excess fat accretion in OP rats. These data led us to examine the potential causes of increased fat weight gain during the early phases. The major site of lipid storage is the adipose tissue. Two major processes occurring in adipocytes are lipolysis (lipid mobilization) and lipogenesis (lipid formation), which are controlled by different metabolic hormones. Potential differences in these processes or hormone sensitivity may predispose OP rats to develop obesity or protect OR rats from the obese state. In experiment 1, in vivo lipolysis was measured in outbred OP and OR rats prior to exposure to an obesity-inducing diet. In vitro lipolysis was assessed in various adipocytes from inbred OP and OR rats in experiment 2. Early effects of moderately high fat feeding on insulin-stimulated glucose uptake and metabolism and body composition were examined in another set of experiments. Results demonstrated that in vivo lipolytic responses were not a causative factor in excess body weight and fat accretion in OP rats. Next, in vitro responses to various lipolytic agents were reduced in visceral adipocytes of inbred OP rats, which were already fatter than inbred OR rats. In the last set of experiments, MHF-feeding reduced insulin-stimulated glucose uptake and metabolism in adipocytes vs. LF feeding. Epididymal fat cells of OP rats synthesized more fatty acids from glucose than those of OR rats after short-term exposures to the same MHF diet. It may be speculated that altered lipolysis is not a causative factor for excess adiposity in OP rats. Moreover, increased insulin responsiveness (via lipid synthesis) may promote excess fat accretion in OP rats. As obesity develops, adipocytes of OP rats may become less responsive to lipolytic agents, which may exacerbate visceral fatness

GluNet: A Deep Learning Framework For Accurate Glucose Forecasting

For people with Type 1 diabetes (T1D), forecasting of \red{blood glucose (BG)} can be used to effectively avoid hyperglycemia, hypoglycemia and associated complications. The latest continuous glucose monitoring (CGM) technology allows people to observe glucose in real-time. However, an accurate glucose forecast remains a challenge. In this work, we introduce GluNet, a framework that leverages on a personalized deep neural network to predict the probabilistic distribution of short-term (30-60 minutes) future CGM measurements for subjects with T1D based on their historical data including glucose measurements, meal information, insulin doses, and other factors. It adopts the latest deep learning techniques consisting of four components: data pre-processing, label transform/recover, multi-layers of dilated convolution neural network (CNN), and post-processing. The method is evaluated in−silico for both adult and adolescent subjects. The results show significant improvements over existing methods in the literature through a comprehensive comparison in terms of root mean square error (RMSE) (8.88 ± 0.77 mg/dL) with short time lag (0.83 ± 0.40 minutes) for prediction horizons (PH) = 30 mins (minutes), and RMSE (19.90 ± 3.17 mg/dL) with time lag (16.43 ± 4.07 mins) for PH = 60 mins for virtual adult subjects. In addition, GluNet is also tested on two clinical data sets. Results show that it achieves an RMSE (19.28 ± 2.76 mg/dL) with time lag (8.03 ± 4.07 mins) for PH = 30 mins and an RMSE (31.83 ± 3.49 mg/dL) with time lag (17.78 ± 8.00 mins) for PH = 60 mins. These are the best reported results for glucose forecasting when compared with other methods including the neural network for predicting glucose (NNPG), the support vector regression (SVR), the latent variable with exogenous input (LVX), and the auto regression with exogenous input (ARX) algorithm

In silico assessment of biomedical products: the conundrum of rare but not so rare events in two case studies

In silico clinical trials, defined as “The use of individualized computer simulation in the development or regulatory evaluation of a medicinal product, medical device, or medical intervention,” have been proposed as a possible strategy to reduce the regulatory costs of innovation and the time to market for biomedical products. We review some of the the literature on this topic, focusing in particular on those applications where the current practice is recognized as inadequate, as for example, the detection of unexpected severe adverse events too rare to be detected in a clinical trial, but still likely enough to be of concern. We then describe with more details two case studies, two successful applications of in silico clinical trial approaches, one relative to the University of Virginia/Padova simulator that the Food and Drug Administration has accepted as possible replacement for animal testing in the preclinical assessment of artificial pancreas technologies, and the second, an investigation of the probability of cardiac lead fracture, where a Bayesian network was used to combine in vivo and in silico observations, suggesting a whole new strategy of in silico-augmented clinical trials, to be used to increase the numerosity where recruitment is impossible, or to explore patients’ phenotypes that are unlikely to appear in the trial cohort, but are still frequent enough to be of concern

Tissue-specific insulin sensitivity in humans - with special reference to the liver

The number of individuals affected by Type 2 diabetes mellitus (T2DM) is increasing rapidly nearly everywhere in the world. Insulin resistance, which means reduced impact of insulin in its target tissues, especially in the liver, skeletal muscle and adipose tissue, is the defining hallmark of T2DM. Insulin resistance in these tissues manifests as impaired ability to take up glucose and fatty acids from blood after a meal, increased production of glucose and triglycerides into the blood circulation by the liver and increased release of free fatty acids into the blood by adipose tissue. This thesis work focuses on studying insulin resistance using a cross-sectional cohort of a wide range of individuals of different ages and body mass index (BMI). P.Pro12Ala polymorphism of the PPARG gene reduces diabetes risk, and the aim of this study was to determine whether this variant affects liver insulin sensitivity. Further goals were to study associations between insulin sensitivity in different tissues and to develop inexpensive and fast models to identify muscle and whole-body insulin resistance. Moreover, possible benefits of resistance training to liver and adipose tissue insulin sensitivity among elderly women were evaluated. It was found that overweight and obese carriers of the p.Pro12Ala polymorphism of the PPARG gene display higher insulin-stimulated liver glucose uptake when compared to carriers of the common p.Pro12Pro genotype. It was discovered that insulin resistance is more likely to be present simultaneously in skeletal muscle and adipose tissue than in the liver, and that insulin sensitivity is affected by obesity, sex and age. The developed regression models based on serum metabolomics for identifying whole-body and skeletal muscle insulin resistance correlated with insulin sensitivity better than the currently used fasting surrogate markers for insulin resistance. Moreover, it was revealed resistance training does not affect adipose tissue glucose uptake but instead improves insulin suppression of endogenous glucose production in elderly women and may thus prevent glucose levels rising too high after a meal. In conclusion, this thesis work shows that genetic mutations can alter tissue insulin sensitivity, and insulin resistance tends to be simultaneously present in several tissues. The newly developed models for identifying insulin resistance may improve the possibility of finding persons at risk of diabetes and ultimately cardiovascular disease. In addition, resistance training is an effective tool for improving diabetes and cardiovascular disease risk factors.Insuliinin vaikutus ihmisen kudoksissa – erityisesti maksassa Tyypin 2 diabetes lisääntyy nopeasti lähes kaikkialla maailmassa. Tyypin 2 diabetekselle on tunnusomaista erityisesti lihaksessa, maksassa sekä rasvakudoksessa esiintyvä insuliiniresistenssi. Insuliiniresistenssi esiintyy näissä kudoksissa heikentyneenä kykynä ottaa sokeria ja rasvahappoja verenkierrosta aterian jälkeen. Lisäksi maksassa se esiintyy lisääntyneenä glukoosin ja triglyseridien tuottamisena ja rasvakudoksessa lisääntyneenä rasvahappojen erityksenä verenkiertoon. Tässä väitöskirjatyössä tutkittiin insuliiniresistenssin esiintymistä eri kudoksissa positroniemissiotomografiaa hyödyntäen kohortissa, jonka ikä- ja painoindeksijakauma on laaja. Työssä selvitettiin vaikuttaako diabetekselta suojaava PPARG-geenin p.Pro12Ala-alleeli maksan insuliiniherkkyyteen sekä kehitettiin yksinkertaisia malleja, joita voidaan hyödyntää koko kehon ja luurankolihaksen insuliiniresistenssin tunnistamisessa. Lisäksi tutkittiin voidaanko lihasvoimaharjoittelun avulla parantaa maksan ja rasvakudoksen insuliiniherkkyyttä iäkkäillä naisilla. Insuliiniherkkyyden geneettistä taustaa selvitettäessä havaittiin maksan glukoosinkäyttökyvyn olevan suurempi ylipainoisilla ja lihavilla PPARG-geenin p.Pro12Ala-alleelin kantajilla verrattuna p.Pro12Pro-genotyyppiä kantaviin. Työssä havaittiin lisäksi, että insuliiniresistenssi esiintyy useammin yhtäaikaisesti luurankolihaksessa ja rasvakudoksessa kuin maksassa sekä todettiin lihavuuden, sukupuolen ja iän olevan insuliiniherkkyyteen vaikuttavia tekijöitä. Työssä koko kehon ja luurankolihaksen insuliiniherkkyyden mittaamiseksi luodut metabolomiikkaan perustuvat mallit ovat tarkempia, kuin tällä hetkellä suurissa kliinisissä tutkimuksissa käytössä olevat insuliiniherkkyyttä mittaavat epäsuorat indeksit. Lihasvoimaharjoittelua käsittelevässä työssä havaittiin, että harjoittelu ei vaikuta rasvakudoksen insuliiniherkkyyteen, mutta parantaa insuliinin kykyä vähentää maksan glukoosintuotantoa iäkkäillä naisilla, mikä puolestaan voi ehkäistä verensokerin kohoamista. Tässä työssä osoitettiin, että p.Pro12Ala polymorfia parantaa maksan insuliiniherkkyyttä ja insuliiniresistenssi esiintyy usein useissa kudoksissa yhtäaikaisesti. Työssä kehitettiin lupaavia malleja insuliiniresistenssin tunnistamiseen, jotka saattavat auttaa löytämään henkilöitä, joilla on kohonnut riski tyypin 2 diabeteksen ja lopulta sydän- ja verenkiertosairauksien kehittymiseen. Lisäksi työssä näytettiin, kuinka tyypin 2 diabeteksen ja sydän- ja verisuonitautien riskitekijöitä voidaan vähentää voimaharjoittelun avulla

Metabolically healthy obesity: Facts and fantasies

Although obesity is typically associated with metabolic dysfunction and cardiometabolic diseases, some people with obesity are protected from many of the adverse metabolic effects of excess body fat and are considered metabolically healthy. However, there is no universally accepted definition of metabolically healthy obesity (MHO). Most studies define MHO as having either 0, 1, or 2 metabolic syndrome components, whereas many others define MHO using the homeostasis model assessment of insulin resistance (HOMA-IR). Therefore, numerous people reported as having MHO are not metabolically healthy, but simply have fewer metabolic abnormalities than those with metabolically unhealthy obesity (MUO). Nonetheless, a small subset of people with obesity have a normal HOMA-IR and no metabolic syndrome components. The mechanism(s) responsible for the divergent effects of obesity on metabolic health is not clear, but studies conducted in rodent models suggest that differences in adipose tissue biology in response to weight gain can cause or prevent systemic metabolic dysfunction. In this article, we review the definition, stability over time, and clinical outcomes of MHO, and discuss the potential factors that could explain differences in metabolic health in people with MHO and MUO - specifically, modifiable lifestyle factors and adipose tissue biology. Better understanding of the factors that distinguish people with MHO and MUO can produce new insights into mechanism(s) responsible for obesity-related metabolic dysfunction and disease

Regularised Model Identification Improves Accuracy of Multisensor Systems for Noninvasive Continuous Glucose Monitoring in Diabetes Management

Continuous glucose monitoring (CGM) by suitable portable sensors plays a central role in the treatment of diabetes, a disease currently affecting more than 350 million people worldwide. Noninvasive CGM (NI-CGM), in particular, is appealing for reasons related to patient comfort (no needles are used) but challenging. NI-CGM prototypes exploiting multisensor approaches have been recently proposed to deal with physiological and environmental disturbances. In these prototypes, signals measured noninvasively (e.g., skin impedance, temperature, optical skin properties, etc.) are combined through a static multivariate linear model for estimating glucose levels. In this work, by exploiting a dataset of 45 experimental sessions acquired in diabetic subjects, we show that regularisation-based techniques for the identification of the model, such as the least absolute shrinkage and selection operator (better known as LASSO), Ridge regression, and Elastic-Net regression, improve the accuracy of glucose estimates with respect to techniques, such as partial least squares regression, previously used in the literature. More specifically, the Elastic-Net model (i.e., the model identified using a combination of ${l}_{1}$ and ${l}_{2}$ norms) has the best results, according to the metrics widely accepted in the diabetes community. This model represents an important incremental step toward the development of NI-CGM devices effectively usable by patients