Introduction
Worldwide epidemic obesity is a major public health issue in Australia and the prevalence of obese adults rises from 19% to 27.5% between 1995 and 2014-2015(Australian Institute of Health and Welfare, 2017). Obesity increases the incidence of many chronic diseases, such as insulin resistance, type 2 diabetes, and cardiovascular diseases, which lead to further morbidity and mortality (Ma, Gao & Liu, 2016). The burden of co-morbidities is not only for individuals but also impacts on the Australian economy. PwC Australia estimated in 2015 that there will be $87.7 billion in additional costs due to obesity over a 10-year period (2015-2016 to 2024-2025) if no further action is carried out to slow down the increasing rate of obesity. Therefore, a novel and preventive intervention to normalize metabolism in obese people and reduce incidences of obesity-related diseases is of great importance for individuals and also society.
1 Obesity
Obesity is defined as ectopic fat accumulation in the body that may impair health(Chan & Woo, 2010). Body mass index (BMI in kg/m2) is a simple index to classify underweight, overweight and obesity in adults and the cut-off point for obesity in western countries is 30 or more based on the association between BMI and mortality (World Health Organisation, 2000). Although BMI is the most common way to distinguish obesity and it can be assumed that individuals with a BMI of 30 or greater have excessive fat accumulations in the body, the relationship between BMI and body fat content is varied in individuals owing to different body build and proportion (Chan & Woo, 2010). Waist circumference estimates the fat distribution in intra-abdominal sites, and it has strong evidences to show that waist circumference coupled with BMI predicts obesity-related health risks and metabolic syndrome better than does BMI alone (Janssen, Katzmarzyk & Ross, 2004).
1.1 Nongenetic factors leading to obesity
Obesity is a heterogeneous medical problem with multiple causes, including genetic, environment and self-behaviours. Although genetics plays a significant role in obesity, the increasing prevalence of obesity in a short period of time is better to be explained by the environmental and behavioural factors (Hariri & Thibault, 2010; Kopelman, 2000). Unlimited and excessive calorie intake, low physical activity, sedentary lifestyle and the obesogenic environment result in disequilibrium between energy intake and energy expenditure, which is the major contributor to obesity epidemics.
1.1.1 Energy-dense diet enriched with fat
A shift in the dietary structure called nutrition transition has been occurred worldwide, changing to higher energy-dense diets with enriched saturated fat, greater animal product consumption and less fruit and vegetable (Popkin, 2001). Australia also has gone through nutrition transition with higher-fat and lower-fibre western diet as a result of migration and urbanization (Naughton et al., 2015). There is strong evidence that macronutrients (fat, protein and carbohydrate) have different effects on satiety, like fat has a weak satiating ability compared to protein, and subjects usually overeat in high-fat experimental situations (Kopelman, 2000). With the increased availability and accessibility of fat in food supply and unchanged food weight, it seems likely that the energy of food intake can be easily excessive, which may contribute to weight gain and elevate the likelihood to get overweight and obesity.
1.1.2 Low physical activity and sedentary lifestyle
There are indirect evidence argue that the requirement of energy expenditure has been reduced in many aspects of daily life, like fewer jobs requiring physical activity owing to labour-saving technology in the house and working area, and also reductions in walking and cycling with increasing number of vehicles(Fox & Hillsdon, 2007). In 2014-2015, Australian Institute of Health and Welfare reported that 30% of Australian adults aged 18-64 had insufficient physical activity. Physical activity is the most variable component of energy expenditure (Kopelman, 2000). Moreover, sedentary lifestyle is detrimental to health, increasing the incidence of health risks, independent of physical activity levels (van der Ploeg et al., 2012). Sedentary behaviour and not enough physical activity decline energy expenditure and retain energy in the body, contributing to energy imbalance, weight gain and then overweight and obesity in a long term.
1.1.3 Obesogenic environment
The obesogenic environment is defined as the sum of all influences from life rising obesity in individuals and populations, including physical, economic, political and sociocultural factors (Lake & Townshend, 2006). The built environment with labour-saved technologies, screen-based activities, and office-type settings decreases physical activity time while increases sitting time during daily life contributing to the incidence of overweight and obesity (Lake & Townshend, 2006). Additionally, the media influence to unhealthy food and drinks, increasing portion size of discretionary foods and prevalence of convenience under hectic lifestyles affect individuals’ energy balance with the trend toward energy imbalance (Australian Institute of Health and Welfare, 2017).
1.2 Benign obesity
Recent studies have reported a so-called ‘benign obesity’ phenomenon, or metabolically healthy obesity in humans, in which this population has better metabolic capacities and fewer features of the metabolic syndrome than equally obese, metabolically unhealthy people (Samocha-Bonet et al., 2012). Around 20% of obese people have been reported with normal insulin sensitivity and metabolic profile based on preliminary experiments with unclear mechanism (Rasouli et al., 2007). This points out that the development of metabolic syndrome is not the necessary result of obesity.
1.3 Ectopic fat accumulation
Ectopic fat accumulation in visceral organs, rather than stored in adipose tissue is highly associated with the incidence of metabolic syndromes, like glucose intolerance and insulin resistance (Kahn & Flier, 2000; Rasouli et al., 2007). Excessive lipids are deposited into visceral organs that are non-adipose tissues and normally contains small amount of fat, such as liver and skeletal muscle, developing ectopic fat deposition and contributing to cell dysfunction which is positively correlated with insulin resistance (Rasouli et al., 2007; (Samocha-Bonet et al., 2012). A current finding reported that those obese people with metabolical health are associated with less fat accumulation in visceral organs, compared to equally obese people, which indicates that visceral fat deposition may have more detrimental effects on obesity-related metabolic disease than subcutaneous fat (Neeland et al., 2012).
1.4 Metabolic syndromes
Obesity with excessive fat accumulated in intra-abdominal sites escalates the likelihood of developing metabolic disorders, including glucose intolerance, hyperinsulinemia, insulin resistance and type 2 diabetes (Han & Lean, 2016). Clinically, it results in significantly higher blood glucose concentration and prevalence of glucose intolerance in obese people compared to healthy people (Rahman Al-Nuaim, 1997). In a long-term, obesity strongly contributes to the prediabetic state with impaired fasting glucose and insulin resistance, which may develop diabetes (Dominiczak Marek, 2003).
1.4.1 Insulin resistance
Insulin resistance is defined by a high plasma insulin concentration that could not normalise blood glucose level (Han & Lean, 2016). In the presence of insulin resistance in obese people, insulin-sensitive tissues fail response to insulin and thus the defect of insulin signalling impairs insulin-stimulated glucose disposal and metabolism in adipocytes and skeletal muscle and also impairs suppression of hepatic glucose output resulting in hyperglycaemia, hyperinsulinemia and hyperlipidaemia in fasting condition (Kahn & Flier, 2000).
As fat is another main substrate competed with glucose, accumulation of excess fatty acids and metabolites such as diacylglycerol and ceramides are also the major factors contributing to the development of insulin resistance (Barnett, 2012). With unlimited consumption of high-fat diet, the imbalance between the supply of lipids and utilization in the cell leads to excessive adipose lipid storage in obesity resulting in increased free fatty acid (FFA) flux to insulin-responsive tissues like liver and skeletal muscle, and thus accumulating intracellular triacylglycerol and other lipid moieties, which further develop insulin resistance and other adverse effects (Barnett, 2012; Kahn & Flier, 2000). The association between excessive lipid content and insulin resistance in liver and muscle is well recognised.
There are multiple mechanisms behind insulin signalling defects in insulin-sensitive tissues in obesity. Studies revealed that the expression and activity of protein tyrosine phosphatases were increased in muscle and liver of obese human and animal models, which is a negative modulator of insulin sensitivity by dephosphorylating downstream signalling molecules such as insulin receptors and insulin-receptor substrate and thus terminating insulin signalling(González-RodrÃguez et al., 2018; Saltiel & Kahn, 2001). This may be one of the mechanisms for the signalling defects in obesity. Another indicated mechanism contributing to insulin resistance is the reduced expression of downstream signalling molecules like GLUT4 in adipocytes and the impaired translocation, docking or fusion of GLUT4-containing vesicles to defect glucose uptake in skeletal muscle with normal expression of GLUT4 (Kahn & Flier, 2000). As shown in Figure 1., the increased build-up of bioactive lipid metabolites such as diacylglycerol (DAG), ceramides or intermediates of fatty acids oxidation pathways can interfere downstream insulin action cascade resulting in impaired activation of protein kinases or inhibiting phosphorylation of Akt, which suppress insulin sensitivity and adversely affect the expression of GLUT4 in the plasma membrane to uptake glucose efficiently (Kahn & Flier, 2000; Turner et al., 2013a; Ye, 2013).
Liver is a cardinal tissue to regulate glucose homeostasis under the control of hormones, especially insulin action (Roden, 2008). Liver also has the capacity to metabolise fatty acids or package them in lipoproteins for peripheral demands (Turner et al., 2013a). It has been reported that hepatic fat content is closely correlated with hepatic insulin sensitivity, which means excess lipid accumulated in liver impairs insulin action to suppress hepatic glucose output via affecting protein phosphorylation in the downstream insulin signalling cascade, referred as hepatic insulin resistance (Kahn & Flier, 2000; Roden, 2008; Seppälä-Lindroos et al., 2002). With significant evidence highlighting the effects of excessive FFAs and bioactive lipid intermediates, it is supported the intricate association between lipid accumulation and insulin action in insulin-sensitive tissues, i.e. fat accumulation is deleterious for insulin action and leads to insulin resistance in a long term.
Figure 1. proposed mechanisms for the build-up of bioactive lipid species and how they interfere with insulin action in muscle to produce insulin resistance. Adapted from (Turner et al., 2013a).
1.4.2 Type 2 diabetes
The manifestation of glucose intolerance and insulin resistance is a predisposing factor to the development of type 2 diabetes (T2D), which is characterised by elevated hyperinsulinemia and pancreatic ß-cell dysfunction (Chatterjee, Khunti & Davies, 2017). The epidemiology of T2D is not only resulted from genetic backgrounds, but also from the increasing tide of high-fat diets, obesity, physical inactivity and sedentary lifestyles (Chatterjee, Khunti & Davies, 2017). T2D is an intricate and progressive disease with co-morbidity, including cardiovascular disease, hypertension, stroke, and blindness, which escalates health burden and accounts for a considerable proportion of costs to the health economies (Fonseca, 2008). Therefore, it is critical to identify patients at higher risks and detect prediabetes to prevent progression to T2D by normalising glucose metabolism and managing obesity with diet and exercise interventions (Chatterjee, Khunti & Davies, 2017; Fonseca, 2008).
2 Role of insulin in metabolism
Insulin is an anabolic hormone produced and secreted by ß-pancreatic islet cells in response to nutrient influx after a meal (Saltiel & Kahn, 2001). Insulin has many stimulatory and inhibitory actions in various tissues via complex intracellular pathways (Turner et al., 2013a). The significant roles of insulin are to increase nutrient uptake and storage in peripheral metabolic tissues and suppress hepatic glucose output, which is critical to maintain fuel homeostasis in the body (Dimitriadis et al., 2011). After a meal consumption, ß-pancreatic islet cells detect the increased dietary nutrients (carbohydrate, fat and proteins), especially glucose which is the primary insulin secretagogue in the bloodstream, and react directly to release insulin into the blood stream targeting insulin-sensitive tissues to normalise blood nutrient levels (Newsholme et al., 2014). The mainly physiological effects of insulin are on skeletal muscle, adipose tissues and muscle by binding to transmembrane insulin receptors on the cell membrane, phosphorylating tyrosine kinase and activating insulin receptor substrates to further stimulate downstream phosphorylation cascade of insulin signalling pathways (Figure 1) (Lizcano & Alessi, 2002).
In skeletal muscle, the phosphorylation of Akt promotes translocation of GLUT4 from intracellular pools to the cell membrane surface resulting in increased glucose uptake from bloodstream. Additionally, insulin increases glucose utilisation by activating enzymes involved in glycolysis such as hexokinase and 6-phosphorfuctokinase and also increases glycogen synthesis in the myocytes, which both significantly results to maintain glucose homeostasis in the circulation (Dimitriadis et al., 2011).
In adipose tissue, insulin stimulates lipogenesis and suppresses lipolysis to decrease the release of fatty acids into the circulation, contributing to regulate fatty acid metabolism and normalise plasma fatty acid levels (Rask-Madsen & Kahn, 2012). A large number of experiments point out competition of fuel metabolism, which is also called the glucose/fatty acid cycle, so that there is a reciprocal relationship between glucose and fatty acid utilization and oxidation (Dimitriadis et al., 2011; Keung et al., 2013; Turner et al., 2013a). Increase in fatty acid oxidation impairs glucose uptake, utilization and oxidation in myocytes. Thus to elevate glucose utilization, it is significant for insulin to suppress lipolysis and mobilization of fatty acids from adipocytes to myocytes through bloodstream to decrease fatty acid oxidation (Keung et al., 2013). As skeletal muscles account for around 75% of whole-body insulin-stimulated glucose uptake, impaired fatty acid oxidation in skeletal muscle largely contributes to glucose intolerance and insulin resistance (DeFronzo et al., 1981).
In liver, insulin blocks gluconeogenesis and glycogenolysis, while increases glycogen synthesis and lipoprotein uptake resulting in decreased hepatic glucose output (Leavens & Birnbaum, 2011). Moreover, insulin signalling promotes rate of hepatic de novo lipogenesis while suppresses fatty acid oxidation by switching the liver from oxidation to synthesis of fatty acids from excess dietary nutrients (Turner et al., 2013a). Therefore, hepatic insulin signalling is essential to maintain nutrient homeostasis through its regulation of glucose and lipid metabolism (Leavens & Birnbaum, 2011).
3 Intervention by using animal models
As mentioned above, the association between glucose and lipid content and insulin resistance is well established in obese humans. Because complications resulted from obesity usually require decades, it is common for studies to use animal models to study pathophysiological effects of obesity and investigate the mechanisms behind metabolic disorders (Wang & Liao, 2012). Independent of genetic background in animal models, most studies use C57BL6 mice to investigate the glucose metabolism and insulin action with high-fat diets as C57BL6 mice is a good model mimicking human pathophysiological effects in obesity in weeks (Wang & Liao, 2012).
3.1 Using Hi-F diet induced obese mice
In a recent review, male C57BL6 mice has been identified as the gold standard for a diet-induced obese model as other strains like A/J mouse or the C57BL/6J have resistance to high-fat diet (Reuter, 2007). Additionally, the oestrous cycle of the female animal repeated every 4-6 days may affect the amount of food intake during the study period which indicates that male animal may be better than female in dietary study (Hariri & Thibault, 2010). It is reported that high-fat diets with more than 30% of energy from fat can easily develop obesity in human and animal models (Hariri & Thibault, 2010). Epidemiological studies fed C57BL6 mice with continuous high-fat diets and observed that these mice become obese and were associated with glucose intolerance, hyperglycaemia, hyperinsulinemia, and insulin resistance (Agardh & Ahren, 2012; Turner et al., 2013b). The obesity phenotypes of models of high-fat diet-induced obesity are the typical features of obese human and thus, the obese animal models have been developed to investigate the pathophysiological effects of obesity, relationships of obesity to complications, specific mechanisms behind these diseases and also to test the effective interventions to prevent or reverse obesity and complications (Reuter, 2007).
3.2 Reversal of high-fat diets back to chow
Animal studies have shown that switching from a high-fat diet to a low-fat diet without caloric restriction normalized glucose tolerance, improved insulin secretion and sensitivity (Agardh & Ahren, 2012). These phenotypes were not just found in the pattern with 18-month high-fat feeding and 4-week low-fat diets but also found in actual reversal with 8 weeks of high-fat feeding to low-fat feeding for 1 weeks in C57BL6 mice (Agardh & Ahren, 2012; Hariri & Thibault, 2010; Kowalski et al., 2016). However, the improvements of switching back to low-fat diets are associated with reduction of food intake, body weight and fat mass, which may be factors to affect the positive effects.
3.3 High-carbohydrate diet
A study from Solon-biet et al in 2014 reported that mice given a high carbohydrate/low fat diet did not have impaired glucose tolerance even though fat mass was higher than mice fed with standard chow diet and similar to obese, glucose intolerant mice fed a high fat diet (Solon-biet et al., 2014). This means that there is not a straight forward association between obesity and glucose intolerance, which seemed to mimic the metabolically healthy obesity observed in humans. However, a study that compared the effects of high-saturated fat and high-digestible starch diets on metabolic control and insulin resistance in male rats was reported that there was no significant differences of weight gain and glycaemic control parameters between rats with these two diets (Ble-Castillo et al., 2012). As at present there is a dearth of papers that have evidence to report the impacts of high-carbohydrate diets maintaining glucose metabolism in obese people, it is significant to produce mice model with high-carbohydrate diets to investigate mechanism of metabolically healthy obesity.
3.4 Significance of actual reversal from a high-fat to high-starch diet
Although caloric intake limitation has been considered as a traditional and rational approach to lose weight and decrease incidence of metabolic disorders validated by preclinical settings, these successes are only applied to a small proportion of populations with poor compliance due to the obesogenic environment (Ma, Gao & Liu, 2016). Therefore, effective dietary interventions that are practical and easy to adapt for a long term and could improve metabolic abilities without necessary weight loss are urgently needed.
Starch is the most common source of carbohydrate in human diets (Chen et al., 2013). Based on the metabolic results of high-carbohydrate diets reported by the previous studies, we are trying to switch the diets of C57BL6 male mice from a 12-week pattern of high-fat diets to high-starch diets for 1 week, which could help to investigate whether glucose metabolism is normalized in high-fat diet-induced obese mice. This model system could further explore the underlying mechanisms behind differences in metabolism with equal obesity but markedly different glucose tolerance and insulin sensitivity and may find out the positive factors in molecular levels. Thus, new treatment approaches could be provided to improve glucose metabolism in obese people who find it hard to lose weight or maintain weight loss.
4 Hypothesis and aim
The current aim of the study is to determine if glucose tolerance and glucose metabolism can be improved by switching from a high-fat diet to a high-starch diet while maintaining fat mass in mice. We hypothesise that switching from a high-fat diet to a high-starch diet will improve glucose tolerance without decreasing fat mass in C57BL6 mice.
In the study, 30 male C57BL6 mice will be randomly separated into three groups and fed with one of the three diets for 12 weeks: Chow, high-fat or high-starch diets. After the initial feeding period, high-fat group will be switched to a high-starch diet and high-starch group will be switched to a high-fat diet for 1 week. Chow group will be continued on chow diet for the entire experiment. Glucose tolerance, body composition, food intake and amount of fat mass in different tissues will be compared between different diet groups and also before and after diet switching.
Reference
Agardh, CD & Ahren, B 2012, ‘Switching from high-fat to low-fat diet normalizes glucose metabolism and improves glucose-stimulated insulin secretion and insulin sensitivity but not body weight in C57BL/6J mice’, Pancreas, vol. 41, no. 2, pp. 253-7.
Australian Institute of Health and Welfare 2017, A picture of overweight and obesity in Australia 2017, viewed 4 Sept,
Barnett, A 2012, Type 2 Diabetes (Oxford Diabetes Library), Oxford, UK.
Ble-Castillo, JL, Aparicio-Trapala, MA, Juarez-Rojop, IE, Torres-Lopez, JE, Mendez, JD, Aguilar-Mariscal, H, Olvera-Hernandez, V, Palma-Cordova, LC & Diaz-Zagoya, JC 2012, ‘Differential effects of high-carbohydrate and high-fat diet composition on metabolic control and insulin resistance in normal rats’, Int J Environ Res Public Health, vol. 9, no. 5, pp. 1663-76.
Chan, RS & Woo, J 2010, ‘Prevention of overweight and obesity: how effective is the current public health approach’, Int J Environ Res Public Health, vol. 7, no. 3, pp. 765-83.
Chatterjee, S, Khunti, K & Davies, MJ 2017, ‘Type 2 diabetes’, The Lancet, vol. 389, no. 10085, pp. 2239-2251.
Chen, Y-Y, Lai, M-H, Hung, H-Y & Liu, J-F 2013, ‘Sweet Potato [<i>Ipomoea batatas</i> (L.) Lam. “Tainong 57”] Starch Improves Insulin Sensitivity in High-Fructose Diet-Fed Rats by Ameliorating Adipocytokine Levels, Pro-Inflammatory Status, and Insulin Signaling’, Journal of Nutritional Science and Vitaminology, vol. 59, no. 4, pp. 272-280.
Defronzo, RA, Jacot, E, Jequier, E, Maeder, E, Wahren, J & Felber, JP 1981, ‘The Effect of Insulin on the Disposal of Intravenous Glucose: Results from Indirect Calorimetry and Hepatic and Femoral Venous Catheterization’, Diabetes, vol. 30, no. 12, pp. 1000.
Dimitriadis, G, Mitrou, P, Lambadiari, V, Maratou, E & Raptis, SA 2011, ‘Insulin effects in muscle and adipose tissue’, Diabetes Research and Clinical Practice, vol. 93, pp. S52-S59.
Dominiczak Marek, H 2003, ‘Obesity, Glucose Intolerance and Diabetes and Their Links to Cardiovascular Disease. Implications for Laboratory Medicine’, Clinical Chemistry and Laboratory Medicine.
Fonseca, VA 2008, ‘Identification and Treatment of Prediabetes to Prevent Progression to Type 2 Diabetes’, Clinical Cornerstone, vol. 9, no. 2, pp. 51-61.
Fox, KR & Hillsdon, M 2007, ‘Physical activity and obesity’, Obesity Reviews, vol. 8, no. s1, pp. 115-121.
González-RodrÃguez, Ã, Valdecantos, MP, Rada, P, Addante, A, Barahona, I, Rey, E, Pardo, V, Ruiz, L, Laiglesia, LM, Moreno-Aliaga, MJ, GarcÃa-Monzón, C, Sánchez, A & Valverde, ÃM 2018, ‘Dual role of protein tyrosine phosphatase 1B in the progression and reversion of non-alcoholic steatohepatitis’, Molecular Metabolism, vol. 7, pp. 132-146.
Han, TS & Lean, MEJ 2016, ‘A clinical perspective of obesity, metabolic syndrome and cardiovascular disease’, JRSM Cardiovascular Disease, vol. 5, pp. 2048004016633371.
Hariri, N & Thibault, L 2010, ‘High-fat diet-induced obesity in animal models’, Nutr Res Rev, vol. 23, no. 2, pp. 270-99.
Janssen, I, Katzmarzyk, PT & Ross, R 2004, ‘Waist circumference and not body mass index explains obesity-related health risk’, The American Journal of Clinical Nutrition, vol. 79, no. 3, pp. 379-384.
Kahn, BB & Flier, JS 2000, ‘Obesity and insulin resistance’, Journal of Clinical Investigation, vol. 106, no. 4, pp. 473-481.
Keung, W, Ussher, JR, Jaswal, JS, Raubenheimer, M, Lam, VHM, Wagg, CS & Lopaschuk, GD 2013, ‘Inhibition of Carnitine Palmitoyltransferase-1 Activity Alleviates Insulin Resistance in Diet-Induced Obese Mice’, Diabetes, vol. 62, no. 3, pp. 711.
Kopelman, PG 2000, ‘Obesity as a medical problem’, Nature, vol. 404, pp. 635.
Kowalski, GM, Hamley, S, Selathurai, A, Kloehn, J, De Souza, DP, O’callaghan, S, Nijagal, B, Tull, DL, Mcconville, MJ & Bruce, CR 2016, ‘Reversing diet-induced metabolic dysregulation by diet switching leads to altered hepatic de novo lipogenesis and glycerolipid synthesis’, Sci Rep, vol. 6, pp. 27541.
Lake, A & Townshend, T 2006, ‘Obesogenic environments: exploring the built and food environments’, Journal of the Royal Society for the Promotion of Health, vol. 126, no. 6, pp. 262-267.
Leavens, KF & Birnbaum, MJ 2011, ‘Insulin signaling to hepatic lipid metabolism in health and disease’, Crit Rev Biochem Mol Biol, vol. 46, no. 3, pp. 200-15.
Lizcano, JM & Alessi, DR 2002, ‘The insulin signalling pathway’, Current Biology, vol. 12, no. 7, pp. R236-R238.
Ma, Y, Gao, M & Liu, D 2016, ‘Alternating Diet as a Preventive and Therapeutic Intervention for High Fat Diet-induced Metabolic Disorder’, Scientific Reports, vol. 6, no. 1.
Naughton, SS, Mathai, ML, Hryciw, DH & Mcainch, AJ 2015, ‘Australia’s nutrition transition 1961–2009: a focus on fats’, British Journal of Nutrition, vol. 114, no. 3, pp. 337-346.
Neeland, IJ, Turer, AT, Ayers, MCR, Powell-Wiley, TM, Vega, GL, Farzaneh-Far, R, Grundy, SM, Khera, A, Mcguire, DK & De Lemos, JA 2012, ‘Dysfunctional Adiposity and the Risk of Prediabetes and Type 2 Diabetes in Obese Adults’, JAMA : the journal of the American Medical Association, vol. 308, no. 11, pp. 1150-1159.
Newsholme, P, Cruzat, V, Arfuso, F & Keane, K 2014, ‘Nutrient regulation of insulin secretion and action’, J Endocrinol, vol. 221, no. 3, pp. R105-20.
Popkin, BM 2001, ‘The Nutrition Transition and Obesity in the Developing World’, The Journal of Nutrition, vol. 131, no. 3, pp. 871S-873S.
Pwc Australia 2015, Weighing the cost of obesity: a case for action, viewed 6 Sept, https://www.pwc.com.au/pdf/weighing-the-cost-of-obesity-final.pdf
Rahman Al-Nuaim, A 1997, ‘Effect of overweight and obesity on glucose intolerance and dyslipidemia in Saudi Arabia, epidemiological study’, Diabetes Research and Clinical Practice, vol. 36, no. 3, pp. 181-191.
Rask-Madsen, C & Kahn, CR 2012, ‘Tissue-specific insulin signaling, metabolic syndrome, and cardiovascular disease’, Arterioscler Thromb Vasc Biol, vol. 32, no. 9, pp. 2052-9.
Rasouli, N, Molavi, B, Elbein, SC & Kern, PA 2007, ‘Ectopic fat accumulation and metabolic syndrome’, Diabetes, Obesity and Metabolism, vol. 9, no. 1, pp. 1-10.
Reuter, TY 2007, ‘Diet-induced models for obesity and type 2 diabetes’, Drug Discovery Today: Disease Models, vol. 4, no. 1, pp. 3-8.
Roden, M 2008, ‘Hepatic glucose production and insulin resistance’, Wiener Medizinische Wochenschrift, vol. 158, no. 19, pp. 558-561.
Saltiel, AR & Kahn, CR 2001, ‘Insulin signalling and the regulation of glucose and lipid metabolism’, Nature, vol. 414, pp. 799.
Samocha-Bonet, D, Chisholm, DJ, Tonks, K, Campbell, LV & Greenfield, JR 2012, ‘Insulin-sensitive obesity in humans – a ‘favorable fat’ phenotype?’, Trends in Endocrinology & Metabolism, vol. 23, no. 3, pp. 116-124.
SeppäLä-Lindroos, A, Vehkavaara, S, HäKkinen, A-M, Goto, T, Westerbacka, J, SovijäRvi, A, Halavaara, J & Yki-JäRvinen, H 2002, ‘Fat Accumulation in the Liver Is Associated with Defects in Insulin Suppression of Glucose Production and Serum Free Fatty Acids Independent of Obesity in Normal Men’, The Journal of Clinical Endocrinology & Metabolism, vol. 87, no. 7, pp. 3023-3028.
Turner, N, Cooney, GJ, Kraegen, EW & Bruce, CR 2013a, ‘Fatty acid metabolism, energy expenditure and insulin resistance in muscle’, Journal of Endocrinology, vol. 220, no. 2, pp. T61-T79.
Turner, N, Kowalski, GM, Leslie, SJ, Risis, S, Yang, C, Lee-Young, RS, Babb, JR, Meikle, PJ, Lancaster, GI, Henstridge, DC, White, PJ, Kraegen, EW, Marette, A, Cooney, GJ, Febbraio, MA & Bruce, CR 2013b, ‘Distinct patterns of tissue-specific lipid accumulation during the induction of insulin resistance in mice by high-fat feeding’, Diabetologia, vol. 56, no. 7, pp. 1638-1648.
Van Der Ploeg, HP, Chey, T, Korda, RJ, Banks, E & Bauman, A 2012, ‘Sitting time and all-cause mortality risk in 222 497 australian adults’, Archives of Internal Medicine, vol. 172, no. 6, pp. 494-500.
Wang, CY & Liao, JK 2012, ‘A mouse model of diet-induced obesity and insulin resistance’, Methods Mol Biol, vol. 821, pp. 421-33.
World Health Organisation 2000, ‘WHO Technical Report Series 894 Obesity: Preventing and Managing The Global Epidemic’.
Ye, J 2013, ‘Mechanisms of insulin resistance in obesity’, Frontiers of medicine, vol. 7, no. 1, pp. 14-24.