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Using Gene Expression Signatures to Dissect Insulin Resistance SubtypesBrad Hayward1, Nicky Konstantopoulos1 and Ken R. Major Metabolic Defects in Type 2 Diabetes Peripheral insulin resistance in muscle and fat Peripheral insulin resistance in muscle and fat Decreased pancreatic insulin secretion Decreased pancreatic insulin secretion Increased hepatic glucose output Increased hepatic glucose output Haffner SM, et al.
Development and Progression of Type 2 Diabetes* Relative Activity Glucose Years from Diabetes Diagnosis –10–5051015202530 -10-5051015202530 *Conceptual representation. 0 UKPDS: Progressive Deterioration in Glycemic Control Over Time Intensive Conventional Time from randomization (y) 6391215 Median A1C (%) A1C Years from diagnosis -cell function (%) 100 80 60 40 20 0 UKPDS Group.
Potential Causes for Declining Insulin Secretion Glucotoxicity Lipotoxicity -cell Apoptosis Insulin Secretion Prentki M et al. Abnormalities of ? -Cell Function in Type 2 Diabetes Elevated glucagon levels Elevated glucagon levels Loss of insulin-induced suppression Loss of insulin-induced suppression Loss of glucose-induced suppression Loss of glucose-induced suppression Increased stimulatory effect of arginine Increased stimulatory effect of arginine Dunning BE et al. A recent article in the New York Times reveals that 800,000 New Yorkers – more than one adult in every eight – have diabetes.
Unfortunately, the crisis in New York City is mirrored by an unchecked epidemic of diabetes that is currently sweeping the United States. China has experienced a similar epidemic of diabetes that is blamed on the widespread adoption of a Western-style diet that is high in sugar, refined starches and processed foods. By restoring healthy blood sugar regulation and normalizing insulin production, advanced herbal formulas have been shown to aid in reversing chronic metabolic and chemical disturbances caused by long-term exposure to elevated insulin and blood glucose levels. Diabetes occurs when blood sugar (glucose) accumulates in the bloodstream instead of being burned (metabolized) in cells for energy production. Metabolic syndrome, also referred to as insulin resistance syndrome or pre-diabetes, is the number one cause of Type 2 diabetes. Metabolic syndrome doesn’t occur suddenly, but develops slowly over an extended period of time – 20 to 30 years in many cases.
While the symptoms of metabolic syndrome are varied and often appear at different times, they all arise from the disruption of normal glucose metabolism.
When food intake exceeds the body’s energy requirements, the excess glucose is converted into glycogen, another type of sugar that is stored in the liver and muscle cells as a convenient short-term energy reserve. Insulin resistance and metabolic syndrome are closely linked to obesity, and both conditions are aggravated by a lack of exercise and a diet high in refined carbohydrates.
When applied to the unaltered chemistry of an otherwise healthy body, improved diet and exercise can aid in restoring healthy metabolic balance.
How many times has someone said, “It’s my metabolism” when commenting on their weight, or talked about “starving” themselves while still putting on extra pounds? Insulin resistance often triggers a sharp craving for carbohydrates, especially late in the evening. To improve mood and generate a quick lift many people reach for a bagel, a doughnut or some other refined carbohydrate to quickly elevate their blood sugar levels. As insulin resistance becomes more entrenched additional metabolic abnormalities begin to appear. As the public was made aware of the link between carbohydrates and obesity, a number of new diets took over the weight-loss industry.
Much of the difficulty in maintaining a low-carb diet involves the body’s master gland – the hypothalamus. Since the best way to bring insulin down is to increase glucose, the hypothalamus responds to chronically elevated insulin levels by sending out signals – hunger pangs – to force the body to eat and thereby increase glucose levels. Just as metabolic syndrome works against caloric- and carb-restricting diets, the presence of metabolic abnormalities also undermines the expected benefits of many weight-loss supplements. In addition to promoting obesity and diabetes, metabolic syndrome also causes metabolic changes associated with a number of chronic degenerative diseases. Because metabolic syndrome develops over a long period of time many of the symptoms are diagnosed – and treated – as separate and unrelated conditions. In the 1980s doctors in China were alarmed by sharp increases in the incidence of diabetes, heart disease and breast cancer.
The central tenet of Chinese healing is to treat both acute symptoms and underlying causes of an illness. American Journal of Physiology - Endocrinology and Metabolism Published 1 December 2002 Vol. Insulin Resistance and Alzheimer’s DiseaseSung Min Son1, Hong Joon Shin and Inhee Mook-Jung[1] Department of Biochemistry & Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea1. Transport of insulin across the blood-brain barrier: saturability at euglycemic doses of insulin. Possible implications of Insulin Resistance and Glucose metabolism in Alzheimer’s disease pathogenesis.
Mitochondrial Abeta: a potential focal point for neuronal metabolic dysfunction in Alzheimer’s disease. Phosphorylation of amyloid precursor protein (APP) at Thr668 regulates the nuclear translocation of the APP intracellular domain and induces neurodegeneration.
Ghrelin modulates insulin sensitivity and tau phosphorylation in high glucose-induced hippocampal neurons.
Modifying IGF1 activity: an approach to treat endocrine disorders, atherosclerosis and cancer. Reduced levels of insulin-like growth factor-1 in patients with angina pectoris, positive exercise stress test, and angiographically normal epicardial coronary arteries. Reduced hippocampal insulin-degrading enzyme in late-onset Alzheimer’s disease is associated with the apolipoprotein E-epsilon4 allele. An aging pathway controls the TrkA to 75NTR receptor switch and amyloid beta-peptide generation.
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). New York health authorities admit that “diabetes is a bona fide epidemic” and, in fact, the only major disease in the city that is growing, both in the number of new cases and the number of people it kills, even as other maladies like heart disease and cancers are stable or in decline. The situation has become so dire that the Centers for Disease Control and Prevention (CDC) is now predicting that one third of all children born in the US will become diabetic in their lifetime.
To address the problem Chinese medical researchers have developed a new herbal supplement, MetaPhase®, that is designed to support recovery from insulin resistance, the primary cause of obesity and obesity-related disorders such as diabetes. Additionally, by curbing carbohydrate cravings advanced herbal formulas have been shown to support safe and natural weight loss. The result is that while cells literally starve for fuel, dangerously high levels of blood sugar course throughout the body causing widespread damage to tissues and organs. According to the Journal of the American Medical Association (JAMA), metabolic syndrome has reached epidemic proportions, currently affecting one third of all men and women in the United States, or some 80 million adults.
Vladimir Dilman in the 1960s in his book, The Metabolic Pattern of Aging, and further elucidated by antiaging pioneer, Dr.
And while people may notice the obvious early symptoms, such as obesity, exhaustion, depression and increased carbohydrate cravings, most don’t realize they have metabolic syndrome until they are diagnosed with more serious medical conditions, such as hypertension, peripheral vascular disease or diabetes. As energy demands rise and fall during the day the body converts glycogen back into glucose to stabilize blood sugar levels and maintain energy production.
Not surprisingly, many health experts believe that eating fewer carbs and increasing physical exercise will reverse metabolic syndrome and restore healthy glucose metabolism. But when the body is already in a state of chronic metabolic imbalance caused by long-term insulin resistance, such simple fixes often generate surprisingly different results. With insulin resistance even a small number of calories can result in obesity caused by an impaired ability to burn fuel and enhanced tendency to create and store new fat deposits. Unfortunately this solution is only temporary and insulin quickly clears sugar from the blood stream. Rising triglyceride levels act on muscle cells, further increasing their resistance to insulin and reducing their ability to burn fat. Human growth hormone is normally released by the pineal gland during the sleep cycle to aid in burning fat and rebuilding lean body tissues (muscles). The hypothalamus is a key component of the limbic system, responsible for regulating hunger, thirst, body temperature and sleep cycles. Given the number of abnormal signals coursing through the body, most people eventually surrender to the inexorable weight of the hypothalamus demanding food to offset the elevated insulin levels – and the quickest way to accomplish this is to ingest more carbohydrates. While it is easy to demonstrate that certain substances can enhance metabolism and speed up fat-burning in animal and laboratory settings, when unleashed upon a metabolic system already out of balance and on the verge of collapse, many products fail to deliver significant results. When insulin production is impaired, cellular energy levels plummet and biological functions are impaired. Without identifying and correctly addressing the underlying condition, insulin resistance, many patients end up saddled with a wide range of treatments that can include statin drugs (to lower cholesterol), antidepressants (for depression and mood swings), ACE-inhibitors and beta-blockers (for hypertension) and anti-thrombolytic drugs (to reduce risk of developing blood clots).
Noting that these conditions were linked with the adoption of Western eating habits, Chinese doctors turned to Western drugs to treat the disorders.
A close association between obesity and type 2 diabetes mellitus also is well established (16, 25). Schematic diagram of the effects of peripheral insulin resistance on insulin, insulin-degrading enzyme (IDE) and A? levels. Schematic representation of molecular pathways linking insulin resistance and Alzheimer disease.
IntroductionAlzheimer disease (AD) is known as a form of type III diabetes due to its similar cellular responses and pathogenesis. 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. The resulting damage to the nervous system and circulation can lead to the amputation of toes, feet and legs; even a tiny cut on the foot can lead to gangrene because it is not seen or felt.
Type 2 occurs when the body’s cells are not sufficiently receptive to insulin, or the pancreas makes too little of it, or both.
And in addition to being the leading cause of Type 2 diabetes, metabolic syndrome also contributes to increased incidence of heart attacks, stroke, cataracts and cancer. Ward Dean in his groundbreakingNeuroendocrine Theory of Aging, wide-spread acceptance of the concept by the medical community didn’t occur until 1988.
As glucose levels rise in the blood stream the pancreas responds by secreting insulin, a specialized hormone that allows cells to absorb glucose and metabolize (burn) it to produce cellular energy. When food intake exceeds the body’s capacity to store glycogen, the excess is directed into long-term energy reserves in the form of fat. To compensate for insulin resistance – and to keep blood glucose levels from spiraling out of control – the pancreas tries to restore balance by producing more insulin.
This assumption is based on a simple equation: calories equal energy, and any energy not burned as fuel will be saved as fat.
This has a profound effect on the brain, which gobbles up 25 percent of the body’s available glucose reserves to support cognitive functions. As sugar is converted into fat, energy levels plummet once again, triggering another round of intense carb cravings that can be impossible to resist. Triglycerides also affect adipose (fat) cells, making it increasingly difficult to release stored fat for energy production. Insulin exerts a direct and specific inhibitory effect on the release of growth hormone that disrupts the nightly regenerative cycle of tissue repair.
Atkins, Barry Sears and others promote a sharp reduction in carbohydrate intake to 1) reduce insulin production and 2) increase the body’s ability to burn fat. Just as the pancreas keeps an eye on blood glucose and increases insulin production to bring sugar levels down, the hypothalamus monitors insulin and attempts to restore balance when levels are too high. Additionally, excess glucose binds to proteins and cellular structures, causing damage to blood vessels, eyes, and other organs. Eventually medical experts noticed that, in addition to having side effects, the drugs were failing to address the cause of the problems. Many studies have documented that intra-abdominal visceral fat (VF) is closely associated with insulin resistance in obese nondiabetic and type 2 diabetes mellitus subjects (3, 5-11, 22, 33). Fasting plasma lipids [total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, and triglycerides], FPG, and hemoglobin (Hb) A1c were measured on the day of the insulin clamp. Transverse cross-sectional magnetic resonance image at the L4–5 vertebral level was used to evaluate visceral fat area (VF), abdominal subcutaneous fat area (SF), and abdominal deep (DSF) and superficial subcutaneous fat (SSF) areas in male (A) and female (B) subjects.
Peripheral insulin resistance and hyperinsulinemia triggers an excess release of FFA from adipocytes to liver and muscles. AD is a progressive neurodegenerative disease characterized by senile plaques, neurofibrillary tangles and neuronal loss. Peripheral insulin resistance leads to decrease insulin signaling in CNS, followed by alteration in brain metabolism. Insulin alters normal brain function and peripheral glucose metabolism, and conditions that are related to insulin dysregulation, such as obesity, diabetes mellitus, and cardiovascular disease, have potentially harmful effects on brain function. 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. Most Type 2 diabetics are typically over 40, overweight, and have the disease for seven to ten years before receiving a diagnosis.
Left unchecked, cells become even more resistant to insulin even as the pancreas secretes ever greater amounts in a desperate attempt to bring the system back under control.
When the brain is deprived of fuel mental performance is impaired, leading to fatigue, depression and more frequent headaches. Together the net effect acts like a one-way valve – fat is stored at an accelerated rate, but getting it out of storage becomes more difficult.
In this way, elevated levels of insulin in the evening contribute to accelerated premature aging and reduced cellular metabolism, leading to further increases in body fat and loss of lean body mass. Initially, many people found that they could forgo carbs for short periods of time and enjoy rapid reductions in body fat while improving blood lipid profiles. For example, diabetics are five times more likely to develop cerebrovascular, peripheral vascular and coronary artery diseases than are non-diabetics. Basal endogenous glucose production (EGP), hepatic insulin resistance index (basal EGP ? FPI), and total glucose disposal (TGD) during the first and second insulin clamp steps were similar in male and female subjects. However, several studies have demonstrated that subcutaneous fat (SF), not VF, is the best predictor of insulin resistance in obese individuals (1, 2, 21, 23). The fascia (arrows) separating the superficial subcutaneous and deep subcutaneous depots is easily visualized. Abnormal aggregates of amyloid-beta peptide (A?) are found in extracellular senile plaques and associated with neurodegeneration in AD.
Increased A? toxicity, Tau hyperphosphorylation, oxidative stress and neuroinflammation are attributed to central insulin resistance, which leads to neurodegeneration.


Many reports have demonstrated that insulin resistance increases age-related memory impairments and is a risk factor for AD. 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).
What is especially disturbing about the rapid rise of Type 2 diabetes, which accounts for an estimated 90 to 95 percent of all cases, is that it is largely a preventable disease. Gerald Reaven defined Syndrome X (later renamed Metabolic Syndrome) as a spectrum of related risk factors. This explains why it is especially important for anyone trying to lose body fat to avoid late dinners and bedtime snacks. Unfortunately, low-carb diets failed to correct the entrenched metabolic problems caused by long-term insulin resistance. If the tank is empty and the engine barely able to turn over, pouring fuel additives into the gas tank will not increase performance.
Glucose has also been shown to promote inflammation, a recognized risk factor for immune-related diseases and cancer. In addition, Increased peripheral FFA levels invoke elevations in TNF? in the periphery and in the CNS, which may result in increased accumulation of A?.
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. If not corrected the pancreas eventually becomes exhausted, resulting in diabetes and requiring daily blood monitoring and injections of insulin to manage blood sugar levels.
As a result many people discovered that low-carb diets are extremely difficult to maintain in the long-term.
Additionally, by elevating blood levels of fatty acids, especially triglycerides, metabolic syndrome contributes to atherosclerosis and related vascular diseases (Fig.
Two potential explanations that might account for these discordant reports are failure to account for 1) differences in gender and2) differences in metabolism between superficial and deep subcutaneous fat (DSF) depots (27). We discuss the potential mechanisms of these metabolic disorders in the pathogenesis of AD.
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). To the best of our knowledge, all previous studies have examined the association between insulin resistance and fat topography in men alone, in women alone, or in a combined analysis of men plus women. A sagittal localizing image was used to center transverse sections on the line through the space between L4 and L5. Glucose homeostasis is critical for energy maintenance, neurogenesis, neuronal survival, and synaptic plasticity, which are required for learning and memory.
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. Most studies involving only female subjects have reported that visceral, but not subcutaneous, fat is associated with insulin resistance (3, 8-10, 33). A second catheter was placed retrogradely in a vein on the dorsum of the hand, which was then placed in a heated box (60°C). Low insulin level in CNS, and low level of IDE may contribute to the formation of senile plaques via disability to degrade A? peptides (Jones et al., 2009), further promoting intraneuronal A? accumulation, which is the hallmark of AD.
During insulin resistance, one develops reduced sensitivity to insulin, resulting in hyperinsulinemia, and this impairment in insulin signaling mediates the pathogenesis of AD, which manifests as brain inflammation, oxidative stress, alterations in amyloid beta (A?) levels, and cell death. 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).
In contrast, most studies employing male subjects have reported that SF or both VS and SF are correlated with insulin resistance (1, 2,6, 23).Recent evidence suggests that there may be significant metabolic differences between deep and superficial subcutaneous adipose tissue depots (27, 31).
Baseline arterialized venous blood samples for determination of plasma [3-3H]glucose radioactivity and plasma glucose and insulin concentrations were drawn at 150, 160, 170, 175, and 180 min after the start of the [3-3H]glucose infusion. Human and experimental animal studies have noted that drugs that modulate insulin resistance decrease the accumulation of A? in the brain and the cognitive impairments that are associated with AD. 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.
Within the subcutaneous adipose tissue, there is a superficial fascial plane that separates the SF into a superficial layer with compact fascial septa (Camper's fascia) and a deep layer with more loosely organized fascial septa (Scarpa's fascia). Phase encoding was in the anteroposterior direction to minimize the effects of motion-induced phase artifacts that might otherwise be distributed laterally through a large portion of the abdomen. Therapeutic strategies that target the link between insulin resistance and AD might benefit the development of future AD drugs.
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 superficial fat layer is comprised of small tightly packed lobules, whereas the deeper layer is made up of larger, irregularly distributed lobules. At time 120 min, the insulin space was reprimed, and the insulin infusion rate was increased to 160 mU · min?1 · m?2 for another 120 min.
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. Signal averaging (4 signals averaged) was used to reduce the effect of motion-related artifacts.
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). Additionally, respiratory gating was used to combat motion-induced artifacts and to reduce the blurring of fat boundaries in the anterior region of the abdomen. IntroductionInsulin is a peptide hormone that comprises 51 amino acids and is synthesized in and secreted from pancreatic ?-cells in the islet of Langerhans (Huang et al., 2010). Metformin is one of only two oral anti-diabetic agents on the WHO list of essential medicines.
Images were processed using Alice software (Perceptive Systems, Boulder, CO) to determine abdominal subcutaneous and intra-abdominal VF areas. 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.
The SF was analyzed by selecting the outer and inner margins of subcutaneous adipose tissue as regions of interest from the cross-sectional images and counting the number of pixels between the outer and inner margins of subcutaneous adipose tissue. HbA1c was measured by affinity chromatography (Biochemical Methodology, Drower 4350; Isolab, Akron, OH). Defects in insulin signaling can affect many diseases, such as type 2 diabetes mellitus (T2DM), metabolic syndrome, and Alzheimer disease (AD).
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. We conclude that visceral adiposity is associated with both peripheral and hepatic insulin resistance, independent of gender, in T2DM. The abdominal SF was subdivided into superficial and DSF areas by identifying the fascial line that demarcates these two fat depots (Fig. Plasma free fatty acid (FFA) was measured by an enzymatic calorimetric quantitation (Wako Chemicals, Neuss, Germany).
Although most studies have examined the function of insulin and the dysregulation of insulin secretion or insulin receptor signaling in peripheral tissue, it has recently been reported to cause serious mental illness (Huang et al., 2010).
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. Plasma total cholesterol, HDL-cholesterol, and triglycerides were measured enzymatically (Boehringer-Mannheim, Indianapolis, IN) on a Hitachi 704 autoanalyzer.
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. The visceral (intra-abdominal) fat areas were determined using histograms specific to the visceral regions. General effects of insulin signaling Insulin increases glycogen and lipid synthesis in liver and muscle cells and simultaneously inhibits glycogenolysis and gluconeogenesis in the liver during feeding (Saltiel and Kahn, 2001). 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. All patients were in good general health without evidence of cardiac, hepatic, renal, or other chronic diseases as determined by medical history, physical examination, and screening blood tests. The histograms were summed over the range of pixel values designated as fat by fitting two normal analysis distribution curves to them. These actions are mediated by its binding to membrane-bound insulin receptors (IRs), members of the tyrosine kinase receptor family (Saltiel and Pessin, 2002). The binding of insulin to the alpha subunit of IR induces a conformational change, resulting in the autophosphorylation of tyrosine residues in the beta subunit of the receptor (Van Obberghen et al., 2001).
TZDs are ligands for the nuclear transcription factor peroxisome proliferator-activated receptor ? (PPAR?). Twenty-five subjects were taking a stable dose (for at least 6 mo) of sulfonylurea drugs, and 37 subjects were treated with diet alone. 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).
Patients who previously had received insulin, metformin, or a thiazolidinedione were excluded. 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). The protocol was approved by the Institutional Review Board of the University of Texas Health Science Center at San Antonio. After insulin binds to its receptors, GLUT4-containing vesicles fuse with the plasma membrane, and the newly inserted GLUT4 takes up glucose efficiently. In parallel, there is a PI 3-kinase independent pathway that recruits GLUT4 to the plasma membrane, led by the phosphorylation of the proto-oncogene Cbl, which is associated with the adaptor protein c-Cbl-associated protein (CAP) (Saltiel and Kahn, 2001).Insulin promotes the uptake and synthesis of fatty acids in the liver and prevents lipolysis by inhibiting the intracellular lipase that hydrolyzes triglycerides to release fatty acids.
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.
Although insulin promotes the synthesis of glycogen in the liver, further synthesis is suppressed when the liver is saturated with glycogen.
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. Insulin is believed to induce SREBP-1c proteolytic activity and, consequently, insulin-mediated lipogenesis (Sato, 2010). 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). Insulin in the brainThe brain is the primary consumer of glucose, requiring two-thirds of total circulating glucose daily (Peters, 2011); thus, the regulation of glucose homeostasis by insulin in the central nervous system (CNS) is paramount for normal brain function. 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. Insulin, by a saturable transport process that is mediated by insulin receptor, can cross the blood-brain barrier (BBB), which governs the transduction between the CNS and peripheral tissues (Neumann et al., 2008). Like GLP-1 agonists, amylin agonists can also induce nausea in patients (Schmitz et al., 2004). They have disparate functions, molecular weights, and structures from IRs in peripheral organs (Heidenreich et al., 1983), but it is unknown whether the counter effects of insulin in the CNS compared with those in peripheral tissues are attributed to such differences. 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.
Insulin increases glucose and inhibits feeding in the CNS, but decreases glucose and stimulates feeding in peripheral tissue (Florant et al., 1991). 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. Role of insulin in cognitive function In humans, brain insulin enhances learning, memory, and, in particular, verbal memory (Benedict et al., 2004).
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. These functions have been supported by evidence that insulin modulates the concentrations of neurotransmitters, which influence cognition, such as Ach (acetylcholine) (Kopf and Baratti, 1999). 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. Insulin-like growth factor (IGF)Insulin-like growth factors are polypeptides that are similar in sequence to proinsulin (Clemmons, 2007).
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-like growth factor 1, which stimulates cell growth and proliferation, especially in nerve cells, binds to IGF receptor and IRs (Jones et al., 2009). 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).
Similarly, mice in which IGF-1 has been deleted develop increased A? levels in the brain, suggesting that IGF-1 promotes the clearance of A? (Carro et al., 2002).


Likewise, the onset of insulin resistance is multifactorial and can occur in different tissues and arise from multiple causes as depicted in Fig.
Moreover, IGF-1 mediates transient site-selective increases in tau phosphorylation—hyperphosphorylated tau is a pathological hallmark of AD—in primary cortical neurons via GSK-3 pathways (Lesort and Johnson, 2000). Figure 1.Schematic diagram of the effects of peripheral insulin resistance on insulin, insulin-degrading enzyme (IDE) and A? levels. 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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