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All recommendations have been updated and reorganized to clarify management considerations for women with pregestational or gestational diabetes in the prepregnancy period, during pregnancy, and in the intrapartum and postpartum periods. 1.All women of reproductive age with type 1 or type 2 diabetes should receive advice on reliable birth control, the importance of glycemic control prior to pregnancy, the impact of BMI on pregnancy outcomes, the need for folic acid and the need to stop potentially embryopathic drugs prior to pregnancy [Grade D, Level 4 (1)]. 4.Women with type 2 diabetes who are planning a pregnancy should switch from noninsulin antihyperglycemic agents to insulin for glycemic control [Grade D, Consensus]. 6.Women should be screened for chronic kidney disease prior to pregnancy (see Chronic Kidney Disease chapter, p. 9.Detemir [Grade C, Level 2 (24)] or glargine [Grade C, Level 3 (25)] may be used in women with pregestational diabetes as an alternative to NPH. 11.Women should receive adequate glucose during labour in order to meet their high-energy requirements [Grade D, Consensus]. 12.Women with pregestational diabetes should be carefully monitored postpartum as they have a high risk of hypoglycemia [Grade D, Consensus].
15.All women should be encouraged to breastfeed since this may reduce offspring obesity, especially in the setting of maternal obesity [Grade C, Level 3 (28)].
17.If there is a high risk of GDM based on multiple clinical factors, screening should be offered at any stage in the pregnancy [Grade D, Consensus].
21.Receive nutrition counselling from a registered dietitian during pregnancy [Grade C, Level 3 (37)] and postpartum [Grade D, Consensus]. 22.If women with GDM do not achieve glycemic targets within 2 weeks from nutritional therapy alone, insulin therapy should be initiated [Grade D, Consensus]. 23.Insulin therapy in the form of multiple injections should be used [Grade A, Level 1 (20)].
24.Rapid-acting bolus analogue insulin may be used over regular insulin for postprandial glucose control, although perinatal outcomes are similar [Grade B, Level 2 (38,39)]. 27.Women should receive adequate glucose during labour in order to meet their high-energy requirements [Grade D, Consensus].
29.Women should be screened with a 75 g OGTT between 6 weeks and 6 months postpartum to detect prediabetes and diabetes [Grade D, Consensus]. Significant determinants of diabetes are age, body-mass index (BMI), waist-hip ratio, low physical activity, and family history of diabetes.
Indians develop diabetes at a younger age and  those younger than 45 years accounts for 36% of all diabetics in India.13  Longer duration of diabetes leads to greater complications and this could threaten the national economy. CVD risk is primarily due to elevated lipids and blood pressure (much more than elevated blood sugar). The latest data (2012) shows a prevalence of diabetes in excess of 25% in most states as shown in the figure 108.
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Using Gene Expression Signatures to Dissect Insulin Resistance SubtypesBrad Hayward1, Nicky Konstantopoulos1 and Ken R. The differences depend on how advanced type 2 diabetes is whether the pancreas is still producing insulin one touch ultra glucose meter coupon or has stopped doing so. Insulin diabetes food store therapy in pregnancy complicated by diabetes: are insulin analogues a new tool?
Both forms of diabetes ultimately lead to high Here Diabetes Statistics Graph are some lifestyle changes we can make to prevent or treat Type 2 Diabetes: 1. It would daily diabetes management journal have been nice if there had been some explanation of these feet either in the assembly instructions (which were not present – see above) or in the listing.
I told them about this book they told me this is the OLD book and I should buy the new one.. Information identified as archived on the Web is for reference, research or recordkeeping purposes.
Just went “I am just posting this because I know he Acupressure Points For Diabetes Control is reddit stalking me?
This is not adequate evidence to show that vitamin D deficiency causes diabetes nor that vitamin D supplementation is a legitimate treatment for diabetes.
Type 1 diabetes is also known as insulin-dependent diabetes or juvenile diabetes because it stems from insufficient production of insulin causes the body. There are 79 million Americans with pre-diabetes and their chance of developing diabetes is about 10% per year. The contents of a Huffington Post article aren’t a good basis for making life decisions about nutrition.
In another study vitamin D deficiency increased insulin resistance decreased best eating plan for type 2 diabetes insulin production and was associated with the metabolic syndrome.53 Another study showed that a combined daily intake of 1200 mg of calcium and 800 IU of vitamin D lowered the risk of type 2 diabetes by 33% (relative risk low levels of glucose or sugar in the blood). A diabetes diet can help keep blood glucose under control and ultimately reduce the risk of diabetes complications. Fortunately doctors are vigilant about checking for gestational diabetes so that it can be identified and effectively managed.
If you have gestational diabetes the best medicine is no medicie; who knows what subtle long-term effects diabetes medications may have on an unborn child? When IgE is mistakenly activated to what it perceives to be a threat it initiates an immune response which causes bronchoconstriction. You can buy diabetes-related items from our online bookstore Gift of Hope store and apparel store. Browse Related Recipes by Topic: Cancer Prevention Diabetes Diabetes Prevention Heart type 2 diabetes prevalence cdc Health High Cholesterol Heart Disease High Blood Pressure Grilled American Dinner Main Dishes Side Dishes Vegetable Summer Alcohol Free Nut Free Low Sodium What Kind Of Snacks Can A Diabetic Eat? The study revealed that those in the lifestyle change group had significant reductions in atherosclerosis (hardening of arteries).
Women with pregestational diabetes who also have PCOS may continue metformin for ovulation induction [Grade D, Consensus]. S129) [Grade D, Level 4, for type 1 diabetes (17) ; Grade D, Consensus, for type 2 diabetes]. If the initial screening is performed before 24 weeks of gestation and is negative, rescreen between 24 and 28 weeks of gestation. Recommendations for weight gain during pregnancy should be based on pregravid BMI [Grade D, Consensus]. Use of oral agents in pregnancy is off-label and should be discussed with the patient [Grade D, Consensus]. Effectiveness of a regional prepregnancy care program in women with type 1 and type 2 diabetes: benefits beyond glycemic control. Preconception care for diabetic women for improving maternal and fetal outcomes: a systematic review and meta-analysis. Preconception care and the risk of congenital anomalies in the offspring of women with diabetes mellitus: a meta-analysis. Poor glycated hemoglobin control and adverse pregnancy outcomes in type 1 and type 2 diabetes mellitus: systematic review of observational studies. Glycemic control during early pregnancy and fetal malformations in women with type 1 diabetes mellitus.
Use of maternal GHb concentration to estimate the risk of congenital anomalies in the offspring of women with pre-pregnancy diabetes. Glycaemic control is associated with preeclampsia but not with pregnancy-induced hypertension in women with type 1 diabetes mellitus.
Strategies for reducing the frequency of preeclampsia in pregnancies with insulin-dependent diabetes mellitus. Effect of pregnancy on microvascular complications in the Diabetes Control and Complications Trial.
Maternal exposure to angiotensin converting enzyme inhibitors in the first trimester and risk of malformations in offspring: a retrospective cohort study. Central nervous system and limb anomalies in case reports of first-trimester statin exposure.
Microalbuminuria, preeclampsia, and preterm delivery in pregnancy women with type 1 diabetes: results from a nationwide Danish study. Improved pregnancy outcome in type 1 diabetic women with microalbuminuria or diabetic nephropathy: effect of intensified antihypertensive therapy? Twice daily versus four times daily insulin dose regimens for diabetes in pregnancy: randomised controlled trial. Maternal glycemic control and hypoglycemia in type 1 diabetic pregnancy: a randomized trial of insulin aspart versus human insulin in 322 pregnant women. Glycemic control and perinatal outcomes of pregnancies complicated by type 1 diabetes: influence of continuous subcutaneous insulin and lispro insulin. A comparison of lispro and regular insulin for the management of type 1 and type 2 diabetes in pregnancy. Maternal efficacy and safety outcomes in a randomized, controlled trial comparing insulin detemir with NPH insulin in 310 pregnant women with type 1 diabetes. Metformin therapy throughout pregnancy reduces the development of gestational diabetes in women with polycystic ovary syndrome. Breast-feeding and risk for childhood obesity: does maternal diabetes or obesity status matter? Fasting plasma glucose versus glucose challenge test: screening for gestational diabetes and cost effectiveness. Impact of increasing carbohydrate intolerance on maternal-fetal outcomes in 3637 women without gestational diabetes.
Postprandial versus preprandial blood glucose monitoring in women with gestational diabetes mellitus requiring insulin therapy. Recommendations for nutrition best practice in the management of gestational diabetes mellitus.
Maternal metabolic control and perinatal outcome in women with gestational diabetes treated with regular or lispro insulin: comparison with non-diabetic pregnant women. Comparison of an insulin analog, insulin aspart, and regular human insulin with no insulin in gestational diabetes mellitus. Prospective observational study to establish predictors of glyburide success in women with gestational diabetes mellitus.
Comparative placental transport of oral hypoglycemic agents in humans: a model of human placental drug transfer.
Comparison of glyburide and insulin for the management of gestational diabetes in a large managed care organization.
Effects of early breastfeeding on neonatal glucose levels of term infants born to women with gestational diabetes.
Association of breast-feeding and early childhood overweight in children from mothers with gestational diabetes mellitus.
Lactation intensity and postpartum maternal glucose tolerance and insulin resistance in women with recent GDM: the SWIFT cohort. Over the past 30 years, the prevalence of diabetes has increased to 12-18% in urban India and 3-6% in rural India with significant regional variations.1, 3, 4 5, 6 These rates in India are 50-80% higher than China (10%). The driving forces behind the epidemic are urbanization (30%) and economic development with resultant increase in GDP, sedentary lifestyle, western diet, and fast food diet on a background of genetic susceptibility.
Unfortunately, management of blood pressure and cholesterol remains dismally poor among diabetics in India (see Diabetes Control).
High prevalence of diabetes and cardiovascular risk factors associated with urbanization in India. Methods for establishing a surveillance system for cardiovascular diseases in Indian industrial populations.
Prevalence of risk factors for coronary atherosclerosis in a cross-sectional population of Andhra Pradesh. High prevalence of diabetes and impaired glucose tolerance in India: National Urban Diabetes Survey. Age- and sex-specific prevalence of diabetes and impaired glucose regulation in 11 Asian cohorts.
Peroxisome proliferator-activated receptor-gamma co-activator-1alpha (PGC-1alpha) gene polymorphisms and their relationship to Type 2 diabetes in Asian Indians.
Role of genetic polymorphism peroxisome proliferator-activated receptor-gamma2 Pro12Ala on ethnic susceptibility to diabetes in South-Asian and Caucasian subjects: Evidence for heterogeneity. Mortality rates due to diabetes in a selected urban south Indian population–the Chennai Urban Population Study [CUPS--16].
However, everyone is unique, and the extent to which the information applies specifically to you should be a key point of discussion between you and your cardiologist or health care provider. A simplified overview of the pathogenesis of type 2 diabetes, encompassing insulin resistance in muscle, adipose tissue and liver, as well as impaired insulin secretion by the ?-cells of the pancreas.
Development procedure for a GES for insulin resistance, based upon (Stegmaier et al., 2004).
Stratification of patients according to their similarities to the GES models of insulin resistance. The disease is characterised by peripheral insulin resistance, hyperglycaemia and defective insulin secretion. IGT is characterised by peripheral insulin resistance, while defects in insulin secretion coupled with increased hepatic glucose output characterise IFG (Davies et al., 2000).
Diabetes Statistics Graph it is easy to what type of food should a diabetes person eat use and the quality of the photo is nice.
The Journal has dedicated this special issue to focus in the biology and clinical and surgical aspects of chronic wounds with special Diabetes Statistics Graph emphasis in diabetic foot ulcers.
These thermal products will help keep us safe as well in that we can’t start a campfire indoors and won’t need to because these blankets as advertised will help one retain a majority of body heat!
The term insulin resistance implies the inability of insulin receptor sites on one’s cell membranes to take up insulin whether that insulin is secreted by the pancreas or taken by injection. Factors thought to contribute to insulin resistance include diet, exercise, smoking, and stress. GROSS!” We have not left any stone unturned while compiling this article on Natural Remedy Diabetes.
Then for some strange reason this guy decides to reach into my box through the tiny window with both arms stretched towards the register.
These two diabetic cookie recipes will be nutritious as well as healthy to your diabetic meal.

Where humans on different continents reproduce in their own gene pool -then with population growth and higher transportation abilities these different populations will mingle and genes will mix. I suppose she could have just owned a shitty car and had rowdy kids but at the time I really Acupressure Points For Diabetes Control thought she had kidnapped the kids and stolen the car.
Juvenile onset diabetes usually results in a failure of proper diabetes mellitus poor control growth in the puppy. If you are at risk for diabetes the Diabetes Management Center provides vitally important pre-conception and gestational diabetes education. Glycemic Index Food List Chart For Diabetes diabetes with pregnancy management diabetic recipes stuffed zucchini Pump Personality ?
So if you are like me with a family who loves baked goods but you can’t bake worth anything from scratch try masters in diabetes education and management some of these amazing recipes using Betty Crocker cake mixes.
And here is the abstract: Feta Death and Reduced Birth Rates Associated with Exposure to Lead-Contaminated Drinking Water Januvia and Pancreatic Cancer.
The Lake County Juvenile Diabetes Walk diabetes and fatigue type 2 Family is making available important information and resources that you need to keep you as strong healthy and confident as possible.
Women with microalbuminuria or overt nephropathy are at increased risk for development of hypertension and preeclampsia [Grade A, Level 1 (17,18)] and should be followed closely for these conditions [Grade D, Consensus].
The ultimate judgment regarding your care must be made by you and your healthcare provider together, in light of circumstances specific to you as a patient. Multiple clinical phenotypes such as abdominal obesity, polycystic ovary (PCOS) and Cushing’s syndromes, lipodystrophies, chronic levels of hyperinsulinemia, acromegaly (elevated growth hormone) and chronic infection are all associated with insulin resistance. Insulin stimulation of FAO hepatocytes decreased glucose production by 34 ± 1% (*, p?0.005 compared with basal cells, n=8). Defective insulin signalling in peripheral tissues including muscle, adipose tissue and the liver, adversely affects whole body glucose homeostasis. Drucker, 2003Glucagon-like peptide-1 and the islet beta-cell: Augmentation of cell proliferation and inhibition of apoptosis.
Drucker, 2005Biologic actions and therapeutic potential of the proglucagon-derived peptides.
I called Supplement Warehouse to complainthey said that the manufacturer sent over the ad picture for the new stuffbut still haven’t sent the actual new product.
Web pages that are archived on the Web are not subject to the Government of Canada Web Standards. The increasing incidence of Type 2 diabetes globally is challenging to health care systems. This was somewhat welcome since being in a sling and on Percocet hinders your ability to go out at all so I didn’t think anything of it. Exposure of FAO hepatocytes to 75µM PA for 48h decreased insulin-induced suppression of glucose production to only 6 ± 3% (*, p?0.005 compared with PA-treated, basal hepatocytes, n=8). Impaired insulin signalling, coupled with the eventual exhaustion of ?-cell insulin production, leads to T2D (Fig.
Current anti-diabetic treatmentsThe development of both insulin resistance and impaired glucose tolerance, conditions which precede the onset of T2D, are closely linked with obesity (Sharma, 2006).
Horton, 2007Progress in the treatment of type 2 diabetes: new pharmacologic approaches to improve glycemic control.
Endurance runners wanting to expand their glycogen stores have their own special variation if they wish. The canine eye has an inner third eyelid that wipes away debris and other possible eye irritants. Best Class 3 Medical Diabetes Your cholesterol it can put an effect glucose with a mix of coconut palm sugar xylitol and erythritol. Gestational Diabetes Diet regime For The Pregnant Woman The usual symptoms of gestational diabetes are extreme thirst hunger whole fruits and vegetables are far more nutritious and less calorie-dense than juices and dried fruit. The book includes a three-week meal plan and more than 50 delicious recipes- including both simple and type 2 diabetes diet guidelines adventurous offerings. Natural Diabetes Treatment Information: Treat Diabetes through Diet Naturopathy Herbs Massage Alternative Therapies Hydrotherapy and more. What we obtain too cheap we esteem too lightly; it is dearness only that gives everything its value. Foods that should be avoided include; fatty red meat organ meat highly processed food fried food fast food high cholesterol food and foods rich in saturated fat.
Superimposed on this network of interactions is the genetic variability of each individual that confers a differential susceptibility to each insult, adding another layer of complexity. The validated GES can then be used to identify novel treatments, as well as stratify patients. Excess visceral fat, and the hormones and inflammatory factors it releases, coupled with excess free fatty acid release have been implicated in the development of T2D (Mlinar et al., 2007). In combination with lifestyle measures (weight control proper nutrition and adequate exercise) medications such as insulin play an important role in managing type 1 type 2 or gestational diabetes mellitus.
Preventive Services Task Force (USPSTF) Doctors would rather address specific health problems for which there are more clear cut treatment paths and less ambiguity about the outcome. However, all of these insults can cause insulin resistance, albeit via different mechanisms. Unlike type 1 diabetes, where insulin therapy can provide effective relief, T2D requires treatment of insulin resistance, in addition to insulin secretion defects. For obese patients exhibiting these symptoms, changes to healthier eating patterns and increases in exercise can result in improvements to glucose tolerance. Find diabetic shopping list type 2 information on the role of diet and exercise in managing gestational diabetes.
You don't really need this product to use Google Voice as an international call forwarding tool. This better not be one of those your momma’s so fat jokes because reddit is like a second mother to me.
Figure 1.A simplified overview of the pathogenesis of type 2 diabetes, encompassing insulin resistance in muscle, adipose tissue and liver, as well as impaired insulin secretion by the ?-cells of the pancreas. However this approach often fails within the first year of treatment, and therefore the use of various medications is usually required (Nathan et al., 2006).
Proper control of diabetes may also lessen your risk of a heart This product should not be mixed with any other insulin.
Learn to recognize warning signs of type 2 diabetes including increased thirst and numbness in the feet. Proposed subtypes of insulin resistance and the insults which can lead to their genesis in cell models. Lifestyle changes immediately following the diagnosis of T2D can often be successful in the early treatment of the disease. The body will convert all carbohydrates to glucose so eating extra servings of rice pasta bread fruit I will however be happy to ask around for a good lawyer for DUI (or whatever) and refer you.
This alarming figure is growing rapidly, with 1.9 million people being newly diagnosed in 2010 alone (CDC, 2011). Unfortunately, a lack of diagnosis, coupled with difficulties in maintaining lifestyle changes, means that this is not a treatment option which will be effective in the long term for all patients (Nathan et al., 2006). Diabetes represents a significant health burden to the US, both in terms of the number of patients currently living with diabetes, and the huge number of patients estimated to develop diabetes in the coming years.
Klip, 2009Direct and macrophage-mediated actions of fatty acids causing insulin resistance in muscle cells.
It has been estimated that there are currently 79 million adults in the US who are pre-diabetic (as determined by fasting blood glucose or HbA1c levels). Its mechanism of action involves a reduction in hepatic gluconeogenesis, leading to a reduction in blood glucose levels (Knowler et al., 2002). Burden, 2000Impaired glucose tolerance and fasting hyperglycaemia have different characteristics. The costs associated with managing the diabetes epidemic were recently estimated at $174 billion annually, and this figure is set to increase in the coming years.
The projected increase in the prevalence of diabetes, coupled with the already significant economic costs associated with the disease, make the development of alternative effective treatments an urgent priority.2. Metformin has a number of side effects including gastrointestinal symptoms and has been linked with rare cases of lactic acidosis which can be fatal, although evidence for this has been contradicted in some studies (Salpeter et al., 2006). Metformin is one of only two oral anti-diabetic agents on the WHO list of essential medicines.
The Australian Diabetes, Obesity and Lifestyle study found that half of all subjects studied who were suffering from T2D had not been previously diagnosed (Dunstan et al., 2002).
The second oral anti-diabetic to be listed by WHO is the drug family known as the sulfonylureas, the most commonly used drug of which is glibenclamide.
Predictors of risk for the development of T2D and cardiovascular disease include body mass index (BMI), ethnic origin, blood pressure and cholesterol levels (Gavin et al., 2003). Yu, et al.2000Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling.
Current clinical guidelines for the diagnosis of diabetes however are based upon blood glucose measures. For this reason, the sulfonylureas show their best efficacy in the early stages of the disease when ?-cell function is still viable. The World Health Organisation (WHO) standard criteria for diagnosis of T2D involve fasting plasma glucose (FPG) and the response to an oral glucose tolerance test (OGTT). Side effects associated with the sulfonylureas include hypoglycaemia due to their long half life in plasma, and weight gain.The glinides are a family of drugs with a mechanism of action similar to the sulfonylureas, in that they bind to the same receptor – although at a different binding site – to induce insulin secretion from the ?-cells of the pancreas.
FPG is a measure of plasma glucose after 8 hours of fasting, while the OGTT measures plasma glucose 2 hours following an intake of 75 g glucose. The glinides have an advantage over the sulfonylureas in that they have a shorter half life in blood plasma. TZDs are ligands for the nuclear transcription factor peroxisome proliferator-activated receptor ? (PPAR?). It is through transcriptional regulation of PPAR? that this family of compounds increase the sensitivity of muscle, liver and adipose tissue to the effects of insulin (Yki-Jarvinen, 2004). Troglitazone, first approved for use in T2D patients in 1997, was withdrawn from the market in 2000 after it was linked to a number of cases of liver dysfunction and failure (Watkins, 2005). The widely used alternative rosiglitazone has in recent years been linked to increased cardiovascular disease (Nissen and Wolski, 2010). While still available in the US, rosiglitazone is currently branded with additional safety warnings and restrictions on its use, and sales in recent years have fallen significantly (GlaxoSmithKline, 2010).Exogenous insulin is a very important therapeutic agent for the treatment of diabetes, capable of increasing blood insulin levels when ?-cell function has been impaired, and can be given in increasing amounts to overcome insulin resistance. However, insulin is also associated with increases in weight gain, as well as risk of hypoglycaemia if monitoring of blood glucose levels is not rigorously performed.Glucagon-like peptide 1 agonists (GLP-1 agonists) are mimics of a protein secreted by the L-cells of the small intestine. GLP-1 agonists have also been shown to stimulate ?-cell proliferation (Drucker, 2003, 2005) and suppress glucagon release and gastric motility, while inducing weight loss. Amylin lowers blood glucose levels by inhibiting glucagon secretion following a meal, and induces satiety by acting upon the area postrema (AP) neurons within the brain stem (Potes and Lutz, 2010).
While amylin forms aggregates which make it unsuitable as a therapeutic agent, amylin agonists such as pramlintide can effectively simulate the effects of the physiological amylin. Like GLP-1 agonists, amylin agonists can also induce nausea in patients (Schmitz et al., 2004). Problems and adverse effects of current drug therapiesAs highlighted above, the currently used range of antidiabetic medicines have a number of adverse side effects, including hypoglycaemia, fluid retention and weight gain, and gastro-intestinal symptoms. As T2D generally progresses over time to a worsening in glycaemic control, the need to utilise multiple therapies together is unfortunately the reality for many patients with T2D (Nathan et al., 2006).
Difficulties in managing T2D are exacerbated by the fact that the various drugs available have a wide range of effects in individual patients, in terms of the magnitude of both efficacy and side effects. In addition to these factors, many of the current drugs used to treat T2D lose their efficacy over time (Cohen and Horton, 2007). Therefore, the focus of new treatments has to be on how to personally tailor pharmacotherapy to suit each patient’s characteristics. We believe that the reason why current therapies are not effective in all patients is that they do not address the heterogeneous nature of T2D. A number of different subtypes of insulin resistance have been described, in a number of different tissues and due to varying insults. If effective treatments for T2D are to be developed, there is a need to gain a better understanding of the different subtypes of insulin resistance.
Then, the development of new treatment regimes which specifically target the various subtypes of insulin resistance will be possible – enabling the development of a personalised medicine approach to T2D.3. Insulin resistance subtypesInsulin resistance is a major risk factor for the development of T2D (Lillioja et al., 1993).
Combating insulin resistance is therefore a key to developing effective treatments for T2D.
The etiology of T2D is multifactorial, with both genetic and environmental factors involved (Bergman and Ader, 2000). Likewise, the onset of insulin resistance is multifactorial and can occur in different tissues and arise from multiple causes as depicted in Fig.
Insults to insulin action can be both endogenous, such as inflammatory cytokines released in response to a fatty meal, and exogenous, such as the fatty acids themselves, which can lead to the development of insulin resistance. We propose that there may be multiple factors contributing to insulin resistance in an individual. We aim to identify a “signature” or “profile” for each of the causative agents of insulin resistance.
Profiling of patients could then allow the determination of which subtypes of insulin resistance each individual has. One such subtype of insulin resistance is that caused by increased saturated fatty acid levels in some obese individuals.
We hypothesise that we can use the profiles to identify a main contributing subtype to a patient’s insulin resistance.
Then we will aim to specifically target that subtype (or subtypes) in an individual for longer term and personalised management of their metabolic dysregulation.
ObesityThe most commonly associated disorder linked with the onset of insulin resistance is obesity (Cummings and Schwartz, 2003; Granberry and Fonseca, 1999).
Obesity is widespread in the western world, with the recent US National Health and Nutrition Examination Survey (NHANES) finding that 67% of Americans aged 20 and above are overweight or obese, with 34% being obese (NCHS, 2008). The WHO estimates that in 2005 there were 1.6 billion adults worldwide who were overweight, at least 400 million of who were obese.

These numbers are projected to increase to 2.3 billion overweight and at least 700 million obese adults by 2015 (WHO, 2006).
The increasing epidemic of obesity will further increase the prevalence of insulin resistance and T2D within society, making the development of effective treatments a critical challenge for the 21st century.As one of the primary risk factors for the development of T2D, obesity warrants extensive study as a target for the development of additional and alternative therapies. Increased availability of free fatty acids (FFA) in patients with obesity plays a critical role in the development of insulin resistance (DeFronzo, 2004). There are numerous factors in obesity which can lead to increases in circulating free fatty acids, including exceeding the storage capacity of adipose tissue by excess caloric intake (Langeveld and Aerts, 2009), and adipose tissue stimulation by the paracrine tumour necrosis factor alpha (TNF?) which induces triglyceride metabolism and free fatty acid release (Ruan and Lodish, 2003).
Insulin resistance in adipose tissue can also lead to excess fatty acid release, due to suppression of the antilipolytic effects of insulin (Ruan and Lodish, 2003). The direct effects of increased circulating free fatty acids on macrophages to stimulate release of pro-inflammatory cytokines such as TNF? and IL-6 has also been recently described (for a review see (Bilan et al., 2009)). The onset of insulin resistance caused by free fatty acids is therefore highly complex, and although direct action upon target tissues have been described, there are secondary actions upon other tissue types which further complicate the pathology of the disease. Given the increasing prevalence of obesity around the world, dissecting the mechanisms by which free fatty acids contribute to insulin resistance may identify new avenues for effective treatment regimes4.
Classical single target-based approachesClassical approaches for dissecting insulin resistance involve targeting signalling defects in both in vitro and in vivo models of insulin resistance. These approaches – including western blotting for proteins and PCR for genetic data have enhanced our knowledge of insulin resistance and the mechanisms by which insulin signalling is impaired. However such approaches rely upon previous knowledge to build a network of signalling connections, and implicating signalling defects in the observed in vitro or in vivo model being observed. As is becoming increasingly clear, signalling networks within cells are far more complicated than previously thought. Single insults (such as fatty acids) not only impact upon insulin signalling directly, but also on numerous signalling cascades such as inflammatory pathways, which may be either distally related to insulin signalling or not related at all. This will result in activation of many kinases, and modification of transcription of a number of genes, in the process of the cell reaching an equilibrium state.We now know that the single target or pathway approaches provide too narrow a window to appreciate the changes induced in complex disease states. Utilising insulin signalling endpoints such as hepatic glucose output or muscle glucose transport can provide a more global overview of the cellular state compared with the phosphorylation of a single kinase amongst a signalling network.
The discovery of new therapies targeted against endpoints allow us to bypass the upstream complexity that hinders the target-based approaches. Current mass spectroscopy techniques allow for the study of nearly the entire lipid or protein fraction of a sample, allowing characterisation of disease states in an unprecedented way. The requirement to investigate and treat many diseases with multifactorial natures has necessitated the need for more powerful technologies to give researchers a “global” view of disease states. The search for effective early diagnostic tools, insight into the development of disease states, and the development of new therapies are increasingly relying on one or more of these new platform approaches.In the context of obesity, lipidomic approaches are proving to be very useful in identifying characteristic changes in tissue-specific lipid profiles of patients with T2D (Meikle and Christopher, 2011), which has been made possible by advances in mass spectroscopy techniques.
Genomics-based approachesDeveloped in the mid 90’s for the analysis of the expression of multiple genes in parallel (Schena et al., 1995), microarray technology can now be used to assess the expression of tens of thousands of genes in a sample simultaneously.
This provides a powerful tool to assess whole cell transcriptional events for any given cell or tissue in any biological state. Microarray technology has a range of applications including identifying disease-causing genes, identifying targets for new therapies and prediction of drug responsiveness (Jayapal and Melendez, 2006).Two major applications for microarray technology involve examining gene sets for pathway analysis, and examining differentially expressed genes between two or more experimental conditions (Kauffmann and Huber, 2010). Gene set enrichment analysis (GSEA) involves taking a gene list, ranked according to the difference in expression between the phenotypes or treatments being investigated. The goal of GSEA is to determine whether members of specific gene sets (grouped on functional similarity), are ranked together towards the top or bottom of the list. This pathway analysis approach to dissecting disease is complimented well by proteomic approaches which can similarly be used for pathway analysis.The second of the two applications involves performing microarray analysis on gene sets from multiple experimental conditions, and can be used to identify differentially expressed genes in differing disease states. This ‘shotgun’ style approach to genome analysis can yield previously unknown information about the regulation of disease states at the transcriptional level, which can have important implications for understanding the pathophysiology of disease. The set of differentially expressed genes can also be used for a diagnostic approach to the disease. Applying Bayesian Linear statistical modelling to gene sets allows for selection of a relatively small gene set which can characterise the particular biological state of the cell or tissue being investigated (Smyth, 2004). This process statistically evaluates which set of genes have the greatest differential expression between the conditions tested, and identifies a ‘fingerprint’ indicative of the biological state of the cell or tissue involved, known as a gene expression signature (GES).
Previously, GESs have been applied to the field of cancer research, for applications such as classifying tumour types and predicting tumour response to chemotherapy. By classifying tumours into distinct types, and with knowledge of how each type will respond to particular therapies, clinicians are therefore able to treat patients more effectively by personalising treatment regimes (Lee et al., 2007). Personalised medicine approaches such as this are becoming increasingly important tools in fighting diseases and the use of GES are likewise increasing in disease research.5. Gene expression signatures as a diagnostic toolFirst described in 2000, GESs were developed in the field of cancer research.
The differences in patient response to therapies led researchers to believe that groups of cancers that were not able to be histologically characterised were actually a heterogeneous group of tumours. Seeking a non-biased method for classification, gene expression data was investigated to search for patterns which could differentiate classes of B-cell lymphomas with differing patient survival rates (Alizadeh et al., 2000).
The main outcome of the study was the finding of two subgroups, classified on the basis of differential gene expression of hundreds of genes, with differing survival outcomes for patients. This early study was instrumental in highlighting the use of gene expression data as a disease classification tool.
The power of the GES approach is that entire genome datasets are narrowed down to the smallest number of genes capable of robustly characterising differences between biological samples. Using complex statistical analysis of large datasets, the prediction power of these small subsets of genes has been shown to be equivalent to the whole dataset.
Once developed, the GES tool allows for rapid, reliable characterisation of various cellular states, which has a number of important applications.Accurate classification of disease states plays a vital role in diagnosis and treatment. The use of GESs for the discovery and development of new therapies is perhaps the most promising application of this technology.
The use of GESs to develop new therapies is especially powerful when a specific endpoint is known, but intermediate signalling steps or the molecular targets have not been identified.
Provided a model for the disease of interest has been developed, high throughput screening of small molecule libraries can be performed by assessing the effects of those agents on the mRNA levels of the genes identified as the GES.
The GES approach has been used in a number of cancer models to identify new therapies, which have increased efficacy over current treatments.
For acute myelogenous leukemias, the identification of inducers of terminal differentiation has opened up new therapeutic avenues previously unavailable (Stegmaier et al., 2004). What makes GESs unique is that the GES genes are not limited to genes known to be involved in the particular physiological process being investigated.
A GES is the minimal set of genes that best defines the difference between two biological samples – be that a disease state or the physiological response to a particular drug or chemical. While it is possible that a GES gene plays a role in the specific model being investigated, it is also possible it does not, and thus any conclusions based upon the identity of genes in the GES must be confirmed with subsequent studies.6.
Application to dissecting insulin resistance subtypesWe propose that GESs can be applied to dissect and study insulin resistance subtypes. The GES methodology described here can be undertaken in either animal tissues or cell culture models. Due to the high reproducibility required when extracting the data from relevant platform technology (for example, microarray), we have found that working in cell culture systems is the most robust and consistent approach.
Once the GES is developed from a cell culture model, the biological relevance of an in vitro-derived GES requires validation in human tissue.
Validation of the GES in human cohorts tests for a correlation between the homeostasis model assessment (HOMA) measures of insulin resistance, based upon plasma glucose and insulin levels (Matthews et al., 1985), and similarity to the GES profile for each subject. If it can be shown that those patients whose expression profiles were most similar to the GES showed a greater degree of insulin resistance as indicated by the HOMA score, the GES is considered to be valid in human tissue. The modelling of insulin resistance subtypes in the GES models involves the use of a specific insult to induce insulin resistance which are known to cause insulin resistance in individuals.
Such insults include saturated fatty acids (PA) or mediators of chronic inflammation (TNF?).The development of a GES in cell culture requires modelling three distinct cellular states relating to insulin sensitivity.
This is achieved by treatment of the target cells with the insulin resistance insult such as TNF? or PA. The third state represents a ‘recovered from disease’ state, which is achieved by treating insulin resistant cells with a cocktail of antidiabetic agents to restore insulin action.
The definition of these three states is deliberate and critical to the integrity of the GES. In order to determine which genes are being affected due to insulin resistance and not non-specific changes induced by the insult per se, the changes induced by the ‘recovered from disease’ state was then assessed. Only those genes whose expression levels were significantly changed in the ‘diseased’ state, and then changed again in the reverse direction in the ‘recovered from disease’ state are used for the development of the GES. Characterising insulin resistance in vitroIn order to effectively model insulin resistance in vitro, an endpoint measure of insulin action is required.
Cell-based models offer a number of assays which can be used to determine insulin signalling in both sensitive and insulin resistant states.
In vitro models of insulin resistance can be developed in each of the main insulin sensitive tissues; muscle, adipose and liver. In liver cells, regulation of gluconeogenesis by insulin is one of the key endpoints of insulin action. These assays work by measuring the relevant endpoint (glucose uptake or gluconeogenesis) in the presence and absence of an insulin resistance insult to characterise insulin resistance.
As the in vitro cell culture model must be manipulated from healthy to diseased and then restored, a robust and large dynamic range is needed in the bioassay used to measure the insulin resistance endpoint parameter.Reversal of insulin resistance involves assessing a wide range of known insulin sensitisers in the model of choice.
A combination therapy which is able to fully reverse insulin resistance is selected, based upon its ability to not only reverse insulin resistance, but also avoid negatively impacting upon cellular viability. Combination therapy is required, as this will ensure that the GES is characteristic of an insulin resistant state which has been reversed by a multi-target approach. There is a greater chance that in drug development the GES will identify novel therapies, rather than the individual therapies used in its creation – as may happen with a single treatment GES. Personalised treatment for patientsThe GES holds promise for personalised treatments for patients by allowing the stratification of patients based on subgroups of insulin resistance.
Once patients are sub grouped, treatments can be personalised to their individual diagnosis, leading to improved health outcomes. The subgrouping of patients according to the GES involves measuring the expression levels of the GES genes in the patient. Regardless of which tissue or cell type the GES is derived from, a non-invasive, easy to obtain sample is needed to facilitate screening of as many individuals as possible. Lymphocyte gene expression profiles have been shown to correlate well with gene expression profiles of insulin responsive tissues including liver and adipose tissue (Iida et al., 2006). We propose that by measuring the expression levels of the GES genes in a patients white blood cells we can subtype patients according to one or more GES. The GES which best correlates with the gene expression pattern of a patient’s white blood cells will therefore indicate a specific avenue of treatment for that patient (see Fig. Development of “targeted” therapiesThe GES can be used to aid in the development of new therapies for T2D, by allowing for high throughput screening for new drugs with insulin sensitising and antidiabetic properties. Screening involves treating cells with chemical libraries, which can include previously known and marketed drugs. After screening the GES genes in the treated cells, the key analysis is comparing the GES genes in the treated cells with the GES profile of the specific model being used. Those chemicals which mimic the GES profile of successful reversal of insulin resistance are identified as the most promising candidate drugs.
We propose that new therapies identified via this approach may show increased efficacy in treating patients subtyped by the same gene expression signature. Using 3T3-L1 adipocytes as the cell-based model, we identified 3325 genes whose expression was altered by the induction of insulin resistance by TNF?. Of those genes, only 1022 showed altered expression by the reversal of insulin resistance with the insulin sensitisers aspirin and troglitazone. From those 1022 genes, a set of 5 genes were selected whose combined expression profile gave the highest predictive power to differentiate the insulin resistant state, and the re-sensitised state.As described above, GESs can be used for screening of patients with T2D. We evaluated this by assessing whether the in vitro-derived GES for TNF? could characterise insulin resistant subtypes in a human cohort. The TNF? GES of 5 genes was detected in the human profile dataset, and GES score assigned – comprising the sum of the absolute values of the standardised expression units of each of the 5 genes. This correlation is consistent with the use of GES technology to characterise an insulin resistant subtype in this population.In vitro screening of compound libraries has also been used in this model, assessing the ability of a given compound to affect the genes identified in the GES (Fig.
We conducted a series of studies to assess what role (if any) the GES genes might play in the development of insulin resistance.
Our investigation of the GES gene STEAP4 was mirrored by the results of data published at that time which showed that STEAP4 protects against inflammation and metabolic dysfunction (Wellen et al., 2007). The cell model has been established in FAO liver cells, with insulin resistance achieved after incubating the cells with 75µM PA for 48h. This insulin resistant phenotype has been reversed by treating PA treated cells with 0.25mM metformin and 2mM sodium salicylate (NAS) in the final 24 hours of PA incubation (Fig.
We anticipate that the PA derived GES will identify an insulin resistant subpopulation from the cohorts we test it in. A key comparison with the different GES models will be the identity of the subgroups identified, and the degree of overlap (if any) observed in the groups. Drug screening, as well as investigation of the GES genes will also be performed for the PA derived GES. ConclusionThe use of ‘omics’ style approaches to disease states such as T2D are becoming increasingly accepted as one way research should investigate these diseases in the 21st century.
The success of GES technology in the cancer field as both a diagnostic tool and a drug discovery tool is becoming increasingly apparent, and we have shown this technology is equally applicable to the study of T2D. As disease research is progressing towards the development of personalised medicine as the ‘holy grail’ for treatment regimes, we foresee a future where personalised medicine is seen as the gold standard for patient care.
We believe GES technology will provide a platform for the development of novel, personalised treatments for patients with T2D.8.

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