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The discovery of insulin was a Big Bang from which a vast and expanding universe of research into insulin action and resistance has issued. In the intervening century, some discoveries have matured, coalescing into solid and fertile ground for clinical application; others remain incompletely investigated and scientifically controversial. Here, we attempt to synthesize this work to guide further mechanistic investigation and to inform the development of novel therapies for type 2 diabetes T2D.

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Mechanisms of Insulin Action and Insulin Resistance

Insulin resistance is a hallmark of obesity, diabetes, and cardiovascular diseases, and leads to many of the abnormalities associated with metabolic syndrome. Our understanding of insulin resistance has improved tremendously over the years, but certain aspects of its estimation still remain elusive to researchers and clinicians. The quantitative assessment of insulin sensitivity is not routinely used during biochemical investigations for diagnostic purposes, but the emerging importance of insulin resistance has led to its wider application research studies.

Evaluation of a number of clinical states where insulin sensitivity is compromised calls for assessment of insulin resistance. Insulin resistance is increasingly being assessed in various disease conditions where it aids in examining their pathogenesis, etiology and consequences. The hyperinsulinemic euglycemic glucose clamp is the gold standard method for the determination of insulin sensitivity, but is impractical as it is labor- and time-intensive.

A number of surrogate indices have therefore been employed to simplify and improve the determination of insulin resistance. In-depth knowledge of these markers will help in better understanding and exploitation of the condition. Insulin is a key regulator of glucose homeostasis. Insulin resistance is established by genetic and environmental factors. Insulin resistance IR leads to impaired glucose tolerance, and plays an important pathophysiological role in the development of diabetes [ 1 ].

Patients with insulin resistance are likely to have impaired fasting plasma glucose levels, which in turn enhance the prevalence of more atherogenic, small dense low-density lipoprotein LDL particles. Central obesity and insulin resistance form the basis of the pathophysiology of dyslipidemia, lack of glucose tolerance, and the existence of chronic subclinical inflammation and hypertension in metabolic syndrome.

IR has been described as a condition where a greater than normal amount of insulin is required to obtain a quantitatively normal response [ 2 ].

Measuring insulin resistance has progressed from its role in the pathogenesis of diabetes, to an even more important role. The mechanism underlying IR involves a complex network of metabolism of glucose and fat, with the inflammatory cascade playing an important role. The important actions of insulin are anti-lipolysis in adipose tissue and stimulation of lipoprotein lipase [ 3 ]. Expanded adipose tissue mass associated with obesity mobilises free fatty acids FFA in circulation through the action of the cyclic-AMP dependent enzyme hormone sensitive lipase.

FFA are also released through lipolysis of Triglyceride TG -rich lipoproteins in tissues by means of lipoprotein lipase [ 4 ]. In insulin-sensitive tissue, excessive fatty acids create insulin resistance by means of the added substrate availability and by modifying down- stream signalling [ 5 ]. When insulin resistance sets in, the increased lipolysis of stored TG in adipose tissue produces more fatty acids. The increased FFA concentration inhibits the anti-lipolytic action of insulin.

The role of innate immunity and infection has also been postulated in the development of insulin resistance and can predict the development of diabetes mellitus type II [ 6 , 7 ]. Insulin resistance, metabolic syndrome and atherosclerotic events share a common inflammatory basis.

Presence of a low-grade systemic inflammation is the main mechanism that leads to impaired insulin action [ 8 ]. IR is an important clinical and biochemical determinant, not only of diabetes but also of many other clinical states. There is a need to evaluate insulin resistance, since it is an underlying mechanism and predictor of cardio-vascular diseases, diabetes, hypertension, obesity and other consequences of metabolic syndrome and impaired insulin sensitivity.

In nondiabetic individuals, the initial presentation associated with insulin resistance is hyperinsulinemia, impaired glucose tolerance, dyslipidemia [hypertriglyceridemia and decreased high-density lipoprotein HDL cholesterol] and hypertension [ 9 ]. Insulin resistance contributes significantly to the pathophysiology of type 2 diabetes and is a hallmark of obesity, dyslipidemias, hypertension, and other components of the metabolic syndrome [ 10 , 11 ].

The association between insulin resistance and subclinical or clinical cardio-vascular disease in both nondiabetic [ 12 - 14 ] and diabetic subjects [ 15 , 16 ] has been observed. Insulin resistance has been an area of interest in recent times, as it has effects on wide array of diseases. The pathophysiological conditions coupled with insulin resistance have persistently increased and include small dense LDL particles [ 17 ] , augmented postprandial lipemia [ 18 ] , enhanced renal sodium retention and high uric acid levels [ 19 ] , dysfibrinolysis [ 20 ] increased resting heart rate [ 21 ] and polycystic ovarian syndrome [ 22 ].

In clinical practice, a family history of diabetes, a history of polycystic ovarian syndrome, gestational diabetes, impaired glucose metabolism, and obesity should be seen as a herald of the possibility of insulin resistance [ 23 ]. A marker is a measurable variable found in an available biological sample or detected by tissue imaging, which can reflect the underlying disease pathophysiology, predict future events and indicate the response to treatment. Markers serve as sensitive detectors of early target organ damage [ 24 ].

Currently, validated risk-assessment tools do not satisfactorily account for the increased risk factors associated with metabolic syndrome [ 25 ]. Hence the need to identify markers of this syndrome is imperative. Estimation of insulin resistance is being studied widely in humans. It is of great importance to develop animal models that are appropriate to the investigation of the epidemiology, pathophysiological mechanisms, outcomes of therapeutic interventions, and clinical courses of patients with insulin resistance.

Insulin resistance is an established independent predictor of a range of disorders. Resistance to insulin sets in long before any disease signs start appearing. It is important to categorize and treat individuals with insulin resistance as early as possible, because hyperinsulinemia might remain undiagnosed for a long period, thereby increasing the risk of the development of other components of the syndrome, and consequent diseases.

Prompt recognition and management of this metabolic syndrome offers important preventive measures [ 23 ]. An accurate method for easily evaluating insulin sensitivity and following changes after therapeutic intervention is thus required. Among the tools to characterize IR and measure whole-body insulin action, the euglycemic hyperinsulinemic clamp technique is the direct method of estimation of IR.

As this requires insulin infusion and repeated blood sampling, there is a need for simple, accessible measures for the evaluation of insulin sensitivity. Most large scale epidemiological studies merely correlate fasting insulin levels with the concerned outcome.

IR can be assessed by various means. Most of the methods employed are difficult to apply in clinical practice. Since compensatory hyperinsulinemia is highly correlated with IR [ 31 ] , it has been observed that it may offer a better way to identify insulin-resistant patients than do measurements of glucose intolerance. On the other hand, analytic methods for insulin measurements are not standardized, thus making it hard to compare absolute values of plasma insulin concentrations from one laboratory to another [ 32 ].

There has been an urgent need for the consideration of other parameters that can be used to assess IR, along with the development of novel surrogate markers of insulin resistance, which are more applicable for large population-based epidemiological investigations. Numerous such markers have been proposed on many occasions in the literature [ 33 - 39 ]. AIn addition, the quantitative insulin sensitivity check index QUICKI derived from logarithmically-transformed fasting plasma glucose FPG and insulin levels has proven to be a first-rate index of insulin resistance in comparison with clamp-IR [ 40 ].

The efficacy and implication of surrogate assessment of insulin resistance depends on the extent to which it correlates with the direct estimate of this variable. Various methods to quantify insulin resistance have been described, and are shown below in Table 1. The hyperinsulinemic euglycemic glucose clamp technique has been described as the gold standard method for quantifying insulin sensitivity [ 41 ].

It is the reference method for quantifying insulin sensitivity in humans because it directly measures the effects of insulin in promoting glucose utilization under steady-state conditions in vivo [ 41 ]. Direct estimation of IR by means of the euglycemic clamp technique and insulin suppression test IST is experimentally demanding, complicated, and impractical when large scale epidemiological studies are involved. These methods are laborious, painstaking and expensive, are therefore rarely used in larger-scale clinical research and, as such, are irrelevant for clinical practice.

Consequently, over the years, a number of surrogate indices for insulin sensitivity or insulin resistance have been developed. The glucose clamp is difficult to apply in large scale investigations because of the chaotic procedure, which involves intra-venous infusion of insulin, taking frequent blood samples over a 3 h period, and the continuous adjustment of a glucose infusion. The oral glucose tolerance test OGTT is an easy test, and is commonly used in medical practice to detect glucose intolerance as well as type 2 diabetes [ 42 ].

It involves the administration of glucose to find out how rapidly it is cleared from the blood stream. It implicates the efficiency of the body to utilize glucose after glucose load. During OGTT, after 8 to 10 h of fasting, blood glucose levels are determined at 0, 30, 60, and min following a standard oral glucose load 75 g [ 42 , 43 ].

Since glucose tolerance and insulin sensitivity are dissimilar conceptually, OGTT provides useful information about glucose tolerance but not insulin resistance. However, OGTT is also used to estimate other surrogate indices of insulin resistance. Impaired glucose tolerance offers few aberrations during OGTT. Measurement of the fasting insulin level has long been considered the most practical approach for the measurement of insulin resistance [ 33 ].

It correlates well with insulin resistance. A considerable correlation has been found between fasting insulin levels and insulin action as measured by the clamp technique. A substantial overlap between insulin-resistant and normal subjects is a constraint, as there is a lack of standardization of the insulin assay procedure. Nevertheless, with a reliable insulin assay, insulin resistance can be detected early, before clinical disease appears [ 45 ].

As glucose levels change rapidly in the postprandial state, the use of fasting insulin for estimating IR should be done after an overnight fast, since the variable levels of glucose confound the simultaneous measure of insulin. In healthy subjects, increased fasting insulin levels with normal fasting glucose levels correspond to insulin resistance.

However, it does not cover the inappropriately low insulin secretion in the face of hyperglycemia seen in diabetic subjects or glucose-intolerant subjects. Use of fasting insulin levels for assessment of IR is limited because of a high proportion of false-positive results and by lack of standardization.

To overcome this issue, standardization of insulin assay has been recommended by the ADA Task Force, to be certified by a central laboratory [ 44 ]. A high plasma insulin value in individuals with normal glucose tolerance reflects insulin resistance, and high insulin levels presage the development of diabetes [ 45 ].

It is an index of insulin secretion derived from OGTT [ 49 ]. Insulin is measured in microunits per millilitre, whereas glucose is measured in milligrams per decilitre [ 49 ].

The insulinogenic index helps to estimate the level of insulin secretion with a more physiological route of glucose administration. While it has not been extensively validated,the insulinogenic index during the first 30 min of the OGTT has commonly been used in epidemiological studies as a surrogate measure of first-phase insulin responses to a glucose challenge.

HOMA was first developed in by Matthews et al [ 36 ]. It is a method used to quantify insulin resistance and beta-cell function from basal fasting glucose and insulin or C-peptide concentrations. Similarly, insulin resistance is reflected by the diminished suppressive effect of insulin on hepatic glucose production. The HOMA model has proved to be a robust clinical and epidemiological tool for the assessment of insulin resistance. HOMA describes this glucose-insulin homeostasis by means of a set of simple, mathematically-derived nonlinear equations.

The approximating equation for insulin resistance has been simplified, and uses a fasting blood sample. It is derived from the use of the insulin-glucose product, divided by a constant. The constant of QUICKI is an empirically-derived mathematical transformation of fasting blood glucose and plasma insulin concentrations that provides a consistent and precise index of insulin sensitivity with better positive predictive power [ 41 , 54 - 56 ].

It is simply a variation of HOMA equations, as it transforms the data by taking both the logarithm and the reciprocal of the glucose-insulin product, thus slightly skewing the distribution of fasting insulin values. QUICKI has been seen to have a significantly better linear correlation with glucose clamp determinations of insulin sensitivity than minimal-model estimates, especially in obese and diabetic subjects [ 54 ]. It employs the use of fasting values of insulin and glucose as in HOMA calculations.

It has similar drawbacks to the use of the HOMA equations, compared with the computer model. In conditions like diabetes, glucose intolerance, and hyperlipidemia associated with insulin resistance, or with various combinations of these metabolic disorders, QUICKI index values have been observed to be lower when compared to those of healthy volunteers.

Each laboratory should establish its own normal QUICKI range, since variations in insulin determinations of different laboratories is unavoidable.

The Role of Inflammation in Diabetes: Current Concepts and Future Perspectives

Diabetes is a complex metabolic disorder affecting the glucose status of the human body. Chronic hyperglycaemia related to diabetes is associated with end organ failure. The clinical relationship between diabetes and atherosclerotic cardiovascular disease is well established. This makes therapeutic approaches that simultaneously target diabetes and atherosclerotic disease an attractive area for research. The majority of people with diabetes fall into two broad pathogenetic categories, type 1 or type 2 diabetes. The role of obesity, adipose tissue, gut microbiota and pancreatic beta cell function in diabetes are under intensive scrutiny with several clinical trials to have been completed while more are in development. The emerging role of inflammation in both type 1 and type 2 diabetes T1D and T1D pathophysiology and associated metabolic disorders, has generated increasing interest in targeting inflammation to improve prevention and control of the disease.

In this Review, we discuss the pleiotropic actions of inflammation and insulin resistance in metabolic homeostasis and disease. An appreciation of the Cited by: 0 articles | PMID: | PMCID: PMC Review Free to read & use 16 publications. Claim to ORCIDOpen PDFGet citation.

Cytokines and Abnormal Glucose and Lipid Metabolism

PLoS Pathog 10 7 : e Editor: Laura J. This is an open-access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Pleiotropic actions of insulin resistance and inflammation in metabolic homeostasis.

Low-density lipoprotein LDL cholesterol plays a pivotal role in the pathogenesis of atherosclerotic cardiovascular disease CVD. Indeed, as confirmed by phase II and III clinical trials with monoclonal antibodies, targeting PCSK9 represents the newest and most promising pharmacological tool for the treatment of hypercholesterolemia and related CVD. However, clinical, genetic, and experimental evidence indicates that PCSK9 may be either a cause or an effect in the context of metabolic syndrome MetS , a condition comprising a cluster of risk factors including insulin resistance, obesity, hypertension, and atherogenic dyslipidemia. The latter is characterized by a triad of hypertriglyceridemia, low plasma concentrations of high-density lipoproteins, and qualitative changes in LDLs. PCSK9 levels seem to correlate with many of these lipid parameters as well as with the insulin sensitivity indices, although the molecular mechanisms behind this association are still unknown or not completely elucidated. Nevertheless, this area of research represents an important starting point for a better understanding of the physiological role of PCSK9, also considering the recent approval of new therapies involving anti-PCSK9. Thus, in the present review, we will discuss the current knowledge on the role of PCSK9 in the context of MetS, alteration of lipids, glucose homeostasis, and inflammation.

To maintain homeostasis under diverse metabolic conditions, it is necessary to coordinate nutrient-sensing pathways with the immune response. This coordination requires a complex relationship between cells, hormones, and cytokines in which inflammatory and metabolic pathways are convergent at multiple levels. Recruitment of macrophages to metabolically compromised tissue is a primary event in which chemokines play a crucial role. However, chemokines may also transmit cell signals that generate multiple responses, most unrelated to chemotaxis, that are involved in different biological processes. We have reviewed the evidence showing that monocyte chemoattractant protein-1 MCP-1 or CCL2 may have a systemic role in the regulation of metabolism that sometimes is not necessarily linked to the traffic of inflammatory cells to susceptible tissues.

Insulin resistance is a hallmark of obesity, diabetes, and cardiovascular diseases, and leads to many of the abnormalities associated with metabolic syndrome. Our understanding of insulin resistance has improved tremendously over the years, but certain aspects of its estimation still remain elusive to researchers and clinicians. The quantitative assessment of insulin sensitivity is not routinely used during biochemical investigations for diagnostic purposes, but the emerging importance of insulin resistance has led to its wider application research studies. Evaluation of a number of clinical states where insulin sensitivity is compromised calls for assessment of insulin resistance. Insulin resistance is increasingly being assessed in various disease conditions where it aids in examining their pathogenesis, etiology and consequences.

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Clear evidence indicates that cytokines, for instance, adipokines, hepatokines, inflammatory cytokines, myokines, and osteokines, contribute substantially to the development of abnormal glucose and lipid metabolism. Some cytokines play a positive role in metabolism action, while others have a negative metabolic role linking to the induction of metabolic dysfunction. The mechanisms involved are not fully understood, but are associated with lipid accumulation in organs and tissues, especially in the adipose and liver tissue, changes in energy metabolism, and inflammatory signals derived from various cell types, including immune cells. In this review, we describe the roles of certain cytokines in the regulation of metabolism and inter-organ signaling in regard to the pathophysiological aspects. Given the disease-related changes in circulating levels of relevant cytokines, these factors may serve as biomarkers for the early detection of metabolic disorders. Moreover, based on preclinical studies, certain cytokines that can induce improvements in glucose and lipid metabolism and immune response may emerge as novel targets of broader and more efficacious treatments and prevention of metabolic disease. Over the decades, overnutrition coupled with a sedentary lifestyle has led to a striking increase in metabolic diseases, such as type 2 diabetes T2D and non-alcoholic fatty liver disease NAFLD.

Он не заметил отражения, мелькнувшего за оконным стеклом рядом с. Крупная фигура возникла в дверях директорского кабинета. - Иису… - Слова застряли у Бринкерхоффа в глотке.  - Ты думаешь, что в ТРАНСТЕКСТ проник вирус. Мидж вздохнула: - А что еще это может. - Это может быть не вашим делом! - раздался зычный голос у них за спиной. Мидж от неожиданности стукнулась головой о стекло.

 - Я снова его запустила. Посмотрим, вернулся ли. Разумеется, на ее экране замигал значок, извещающий о возвращении Следопыта. Сьюзан положила руку на мышку и открыла сообщение, Это решит судьбу Хейла, - подумала.  - Хейл - это Северная Дакота.

Теперь предстояло принять решение. Бросить все и ехать в аэропорт. Вопрос национальной безопасности. Он тихо выругался. Тогда почему они послали не профессионального агента, а университетского преподавателя.

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    In this Review, we discuss the pleiotropic actions of inflammation and insulin resistance in metabolic homeostasis and disease. Request Full-text Paper PDF Between and , mean BMI worldwide increased by 0·4 kg/m(2) per decade (95% uncertainty interval 0··6, posterior probability of.

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