About this Journal Submit a Manuscript Table of Contents
Mediators of Inflammation
Volume 2012 (2012), Article ID 456456, 14 pages
http://dx.doi.org/10.1155/2012/456456
Review Article

Does Inflammation Determine Whether Obesity Is Metabolically Healthy or Unhealthy? The Aging Perspective

1Tubingen Aging and Tumor Immunology (TATI) Group, University of Tubingen (ZMF), Waldhornlestrasse 22, 72072 Tubingen, Germany
2Faculty of Agriculture, Abdul Wali Khan University Mardan, Khyber Pakhtunkhwa (KPK), Mardan 25000, Pakistan
3Department of Psychological Medicine, National University Health System, Gerontological Research Programme, National University of Singapore, Singapore 119228
4Singapore Immunology Network (SIgN), Biopolis, Agency for Science, Technology and Research (A*STAR), Singapore 138648

Received 1 June 2012; Revised 30 August 2012; Accepted 5 September 2012

Academic Editor: Peter Plomgaard

Copyright © 2012 Iftikhar Alam et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Obesity is a major health issue in developed as well as developing countries. While obesity is associated with relatively good health status in some individuals, it may become a health issue for others. Obesity in the context of inflammation has been studied extensively. However, whether obesity in its various forms has the same adverse effects is a matter of debate and requires further research. During its natural history, metabolically healthy obesity (MHO) converts into metabolically unhealthy obesity (MUHO). What causes this transition to occur and what is the role of obesity-related mediators of inflammation during this transition is discussed in this paper.

1. Background

Obesity and its associated comorbidities have become major health problems in the world [1]. The association between obesity and the development of major complications in acute pancreatitis [2], fatty liver diseases [3], vascular inflammation and coronary heart disease [4, 5], chronic obstructive pulmonary disease [6], risk of cerebral ischemia and brain injury [7], atherosclerotic vascular disease and myocardial infarction [8], and cancers [911] are strongly linked to chronic inflammation (Figure 1). In particular, insulin resistance, a direct or indirect result of obesity, is characterized by a chronic state of subclinical inflammation [12] and inactivation of a number of inflammatory mediators [1315]. Elevated serum concentrations of C-reactive protein [16], interleukin IL-6, IL-8, and tumor necrosis factor (TNF)-α are observed in obese individuals with elevated insulin resistance [17].

Figure 1: The relationship between obesity and comorbidities. The consequences of obesity are depicted and include the immunological component (T cells and macrophages) that drives inflammation together with adipocytes. The associated pathologies mentioned have been associated with increased levels of inflammatory markers that are also increased in obesity. Although it is not clear which one is the consequence and which one is the cause both are associated to higher clinical vulnerability. MIP-1α: macrophage inflammatory protein-1 alpha; MCP-1: monocyte chemotactic protein-1; IP-10: interferon gamma-induced protein 10; SAA: serum amyloid A; Th1: T helper 1 cytokine; COPD: chronic obstructive pulmonary disease.

Interestingly, however, a life course perspective on obesity recognizes that obese individuals are not a homogeneous group, but highly heterogeneous, given individual differences in terms of health status and functional ability among obese individuals. A substantially large number of the adult obese population is reported to remain relatively “metabolically healthy” obese (MHO) [18]. Unlike “metabolically unhealthy” obese (MUHO) individuals, MHO individuals demonstrate an absence of impaired glucose tolerance, dyslipidemia, hyperuricemia, and hypertension [19]. Whether these represent valid subtypes of obesity, and whether obesity is an independent predictor of metabolic outcomes or mediated by inflammatory status is still unclear. Research so far has not elucidated the factor(s) responsible for causing obesity to become metabolically unhealthy. These are important questions since a lifetime burden of obesity and its consequences, and a clear understanding of the metabolic transition process carries a host of clinical and public health implications.

2. The Disease Burden of Obesity

Obesity is a condition of energy imbalance between intake and expenditure. When an organism encounters food excess, it conserves the nutrients either as glycogen for short-term storage or as lipids for longer storage duration, which may result in a consequent state of obesity. The mechanisms underlying these biological changes are beyond the scope of this paper and the readers are directed to some excellent work elsewhere [20, 21].

Human obesity is one of the oldest reported health disorders and can be traced as early as 25,000 years; nevertheless, it still remains relatively a rare condition for most of the human history [22]. However, due to unparalleled socio-demographic trends over the recent past, there has been a drastic increase in the prevalence of obesity in most regions of the world and particularly in developed countries [2325]. It was previously estimated that at least 1.1 billion people across the globe are overweight and 312 million of them are obese [26]. World Health Organization (WHO) has estimated that by the end of 2030, there will be approximately 370 million people suffering from obesity and its associated comorbidities [27]. In fact, the prevalence of adult obesity, as defined by a body mass index (BMI) in excess of 30 kg/m2, has been escalated to such a degree that a normal BMI (18.5–24.9 kg/m2) is no longer the norm, as only a small minority of the population will fall into this category. It is now apparent that most countries of the world, even those which have just recently struggled with undernutrition (underweight), and some that continue to do so [28], are experiencing increases in obesity prevalence. The face of the fact is that the epidemic of obesity has now spread throughout most of the civilized world rendering it more appropriate to term it the “obesity pandemic.” Although a number of potential factors have been suggested to explain the obesity pandemic, including increased sedentary lifestyle—particularly a decrease in occupational physical activity [29], increased dietary caloric intake and alterations in diet composition [24], smoking [30], and others [31], the exact causes of the obesity pandemic are still in doubts and continue to be debated.

3. Obesity-Induced Inflammation

Obesity in humans is associated with low-level inflammation [3237]. In this respect, obesity may be viewed as a form of chronic inflammation [38]. The inflammatory response triggered by obesity involves a number of well-known components of the typical classical inflammatory response to pathogens. These components include (i) systemic increases in circulating inflammatory cytokines and adipokines and acute phase proteins, (ii) recruitment of leukocytes to inflamed tissues, (iii) activation of tissue leukocytes, and (iv) generation of reparative tissue responses (Figure 1). However, the nature of obesity-induced inflammation is unique in comparison to other inflammatory paradigms of infections, autoimmune diseases, and others of the type. Qualitatively, for example, in chronic obesity, a low-grade activation of the innate immune system is produced that affects steady-state measures of metabolic homeostasis over time. Hence obesity-associated inflammation is characterized by a low-level but chronic inflammatory state [39]. On the other hand, in response to pathogens or acute injury, inflammation seems to be robust but resolves very quickly once the threat is removed.

A great number of metabolic disorders are caused by obesity. Among them, insulin resistance (Figure 1) has been shown to be the most important, associated with a chronic state of subclinical inflammation, and characterized by increased serum concentrations of C-reactive protein [16]. In addition, interleukin (IL)-6, IL-8, monocyte chemotactic protein (MCP)-1, and tumor necrosis factor (TNF- ) have also been found to be increased with elevated insulin resistance [17, 40, 41] along with factors such as serum amyloid A, resistin, leptin, and adiponectin [42, 43]. These may result in a large number of associated complications that have been studied and reviewed extensively [211].

There are 24 different adipokines, which have been reported in relation to body fat and obesity [44]. The levels of these adipokines are usually elevated in obese humans, and circulating concentrations of each of these factors increase with the degree of obesity. Except for IL-1, the majority are secreted by adipocytes themselves [43, 45, 46]. Serum amyloid-A is an adipokine secreted by adipocytes, that can act directly on macrophages to increase its production of inflammatory cytokines such as TNF- , IL-1, and IL-6, and resistin [43, 47]. Most of these adipokines are inflammatory proteins (such as IL-8, PAI-1, MCP-1, IL-6, IL-1Ra, TNFα, sTNF RII, and IL-18), while a few adipokines such as CRP, haptoglobin, and amyloid A are actually acute phase proteins primarily released by the liver in response to mild inflammatory response associated with human obesity. How and why these adipokines are elevated in serum in obesity is, however, still very much unclear. Furthermore, obesity is also associated with macrophage accumulation in adipose tissue [48], and there is evidence that certain factors secreted by adipocytes can act in an endocrine-like manner to activate monocytes as well (Figure 1). For example, resistin can induce cytokine production in macrophages, and its production is also regulated by the inflammatory cytokines TNF-α, IL-1, and IL-6, creating a potential positive feedback loop [49, 50]. Leptin has the capability to exert an endocrine effect to increase cytokine production in blood monocytes [51]. Therefore, a continuous cycle of cross-talk between adipocytes and monocytes [52] may stimulate and perpetuate the proinflammatory status associated with obesity.

4. Inflammatory Signaling and Mediators of Inflammation in Obesity

Once it was discovered that there is an increased expression of the proinflammatory cytokine (TNF ) in adipose tissue in obese mice, much research was focused and turned on the key roles of inflammatory mediators in obesity [40]. It is now well established that changes in inflammatory signaling by adipocytes and infiltration of adipose tissue by immune cells are the key features of obesity-induced insulin resistance and a number of associated metabolic diseases [16, 48, 53].

It has been shown in obese mice that both adipocytes and macrophages residing in adipose tissue secrete a number of cytokines including TNF , IL-6, IL-1, and migration inhibitory factors [53]. Increased expressions of inflammatory mediators (primarily cytokines but also adipokines) have also been observed in visceral fat of obese human individuals [53, 54]. Some of these cytokines have been shown to cause a disruption in insulin signaling. This disruption is attributed to a multitude of mechanisms, including induction of the suppressors of cytokine signaling family of proteins, which have been shown to inhibit insulin receptor kinase activity [55, 56]. Several other in vitro and in vivo studies suggest that TNF-α, IL-1, and IL-6 can each directly impair insulin sensitivity by interfering with insulin-stimulated glucose uptake in peripheral tissues [5759].

Cytokines have also been reported to activate inflammatory signaling via c-Jun N-terminal kinase (JNK) and inhibitor of kappa kinase (IKK) pathways in both immune and neighboring nonimmune cells [60, 61]. This results in increased inflammation and direct inhibition of insulin action, as well as possible alterations in other metabolic targets, that in combination contribute to overall metabolic deterioration. The negative impact of inflammatory pathway activation on carbohydrate metabolism has been studied in mice [62]. Although the nature of detailed mechanisms underlying these pathways is still in doubts, there have been some reports of the possible causes of this impact on carbohydrate metabolism that are attributed to deletion of either JNK [61] or inhibition of IKK [13, 60, 63] or even neutralization of TNF-α [40] or IL-1 [64]. These mechanisms collectively may lead to a situation of decreased inflammatory signaling and improved insulin responsiveness and glucose tolerance. Immune and neighboring nonimmune cells have been shown to be important in the observed improvements in glucose homeostasis [13, 15, 60, 65]. Of particular note, manipulation of levels of these inflammatory mediators can impact insulin resistance and other metabolic parameters whether it is adipose cell accumulation in muscle or lipid accumulation in liver [16]. This may be an indication that alterations in immune signaling are triggered by excess adiposity, which may be essential mediators of the metabolic dysfunction observed in obesity.

Macrophage infiltration has also been observed in skeletal muscle, suggesting that local inflammatory signaling could also directly influence muscle insulin resistance [66]. It is important to note that in many experimental systems, muscle effects appear to emerge secondary to alterations in other organs, including adipose tissue and liver [67]. However, muscle-specific expression of the anti-inflammatory cytokine IL-10 has been shown to greatly improve muscle insulin sensitivity, consequently yielding reduced inflammation in this tissue despite normal development of obesity when fed a high-fat diet [68]. In addition, increased inflammatory signaling in the brain has also been observed in response to overnutrition, or in the context of obesity, resulting in improper regulation of energy uptake and energy expenditure by peripheral tissues [69]. Thus, it appears that increased inflammation is a systemic feature associated with surplus energy intake. Therefore interventional or therapeutic solutions for reducing inflammatory signaling induced by metabolic stress would, therefore, be necessarily expected to improve on systemic energy homeostasis at multiple tissue and organ or system levels.

5. Metabolically Healthy and Unhealthy Phenotypes of Obesity

5.1. Metabolically Healthy Obesity

Not all obese individuals exhibit increased risk of inflammation and not all normal-weight individuals are metabolically healthy or free from CVD [70]. Two distinct subtypes of obesity have been proposed, referred to in different ways by various authors. One type is “metabolically healthy” and the other is “metabolically unhealthy” obese (MHO and MUHO, resp.). It is interesting to note that approximately 20–30% of the adult obese population remains at the level of relatively “metabolically healthy” obesity (MHO) [18, 19, 70] as compared to those with “metabolically unhealthy” obesity (Figure 2). Individuals in the first subtype have also been termed “metabolically normal obese” [71], “metabolically healthy but obese” [70], “obese metabolically normal” [72] or described as having metabolically benign obesity [73] or uncomplicated obesity [74]. MHO individuals exhibit increased levels of body mass index (BMI) and body fat (BF) but no other metabolic complications [72]. Significantly, both subtypes associate with different inflammatory profiles. MUHO exhibit increased levels of inflammation compared to other normal-weight individuals [75], while MHO exhibit reduced levels of inflammation compared to other obese individuals [76]. MHO individuals may display an absence of impaired glucose tolerance, dyslipidemia, hyperuricemia, and hypertension [19]. In addition, their metabolic and CVD risk profiles are relatively mild [77], with high levels of insulin sensitivity [71], absence of hypertension [78], normal lipid, inflammation, and hormonal profiles [73], and importantly a favorable immune profile [79]. It has been suggested that the unique metabolically healthy subgroup of obese individuals appear to be protected or more resistant to the development of comorbidities associated with obesity [7072, 80].

Figure 2: Differences between metabolically healthy and unhealthy obesity and similarities with aging. The hallmarks of metabolically healthy obesity are depicted and associate poorly with comorbidities while metabolically unhealthy obesity shares similarities with the aging process and are both associated with increased prevalence to pathologies and chronic conditions. BMI: body mass index; CVD: cardiovascular diseases.

Despite the knowledge of the MHO phenotype for substantially longer time now, there still currently exist no established criteria for the definition of MHO individuals [80]. Some have used arbitrary cut-points of insulin sensitivity [19], cardiometabolic risk factor clustering [73], or the complete absence of any metabolic aberration [74] to delineate MHO from MUHO individuals. Furthermore, no convincing answers on how the mechanism is in place which provides protection against a number of diseases in MHO individuals. Several hypotheses have been proposed in an attempt to explain the role of the adipose tissue in the metabolic dysfunction associated with obesity in order to better understand the difference between the two distinct groups (Figure 2). Thus, the altered pattern of adipokine secretion by the obese adipose tissue [81, 82], the inflammatory state associated with obesity [83], or the inability of the adipose tissue to expand its mass in response to increased energy intake [84] have all been signaled as possible culprits. However, all these different hypotheses are not mutually exclusive. As a plausible mechanism for the normal cardiometabolic profile, it has been reported that for the same BMI, MHO subjects tend to have a lower waist circumference [19], and specifically less VAT accumulation [71] in contrast to MUHO individuals, therefore, closely resembling the metabolically benign gynoid obesity phenotype. Some recent studies have shown that increased visceral/abdominal fat (characteristics of MUHO) is positively associated with metabolic disease [85, 86], independent of overall adiposity [87, 88]. Similarly, high thigh intermuscular fat is associated with poorer glucose tolerance. On the other hand, subcutaneous thigh fat (characteristic of MHO) is associated with more favorable levels of glucose and lipids [89, 90]. Another explanation for the more favorable metabolic profile of some obese people may be related to inflammatory status [91].

Another critical unanswered question is why and how MHO individuals may differ from MUHO regarding the inflammatory mediators in obesity. Inflammatory markers, such as IL-6, TNF-α and other cytokines and adipokines such as resistin and adiponectin are associated with metabolic alterations [92, 93]. These adipocytokines are closely linked to abdominal obesity, particularly to visceral adipose tissues, while some evidence suggests that thigh subcutaneous fat is related to more favorable inflammatory profiles [9496]. Overall, the unique protective mechanism in these individuals is attributable in part to a reduced inflammatory profile [77], and uncoupling of inflammatory signal transduction from obesity-driven inflammatory response [97, 98].

Theoretically, MHO and MUHO may represent distinct subtypes of obesity that were predetermined genetically to confer differing metabolic and cardiovascular risks. Another theoretical possibility is that MHO and MUHO represent transitions phases from nonobesity in the development and natural history of obesity, with MHO individual ultimately turning into MUHO. It is tempting to think over the possibility of the other way round, that is, the possibility of converting MUHO into MHO. Whether MHO is sustained or not for substantially a longer period may depend upon a number of factors, including the levels of cytokines and/or adipokines. A major challenge is to understand both the initiating signals and downstream mechanisms involved in the establishment of inflammation that occurs during these transitions from relatively healthy form of obesity into unhealthy one. Further studies are needed to investigate the differences in concentrations of these inflammatory factors and to establish whether it is an imbalance in the concentrations of these cytokines that causes this transition or whether the transition to MUHO occurs first due to various other factors and then brings about changes in the levels of these cytokines (which came first, the chicken or the egg?). The answer to this question may have paramount clinical and public health implications. Once the temporal relationship is established, the course of obesity can be stopped or delayed at some stage during its natural history before it becomes “metabolically unhealthy.”

One possible way to investigate the mode of this transition is to study obesity over enough time to observe the impact of its related chronic inflammation. Thus, after the 5th decade may be the best time to study such transitions, for a number of reasons. First, this is the age where an individual starts entering into old age, particularly in the developing countries [99]. Second, most of the changes in hormonal output and functions [100], life-style behaviors [101], diet and nutrition, body composition [99, 102, 103], and immunological alterations [104, 105], and overall physical, physiological, and immunological parameters [106, 107] are likely to emerge significantly differently in the elderly with an overall impact on obesity and the inflammatory mediators. Third, obesity is more prevalent in older age. Finally, part of the comorbidity associated with obesity is common to aging (Figure 2) and may suggest similar mechanisms. In relation to obesity, the transition between MHO and MUHO is likely to occur from adulthood-to-early old age and hence it may be wise to focus on obesity and its related issues at this age.

In the last sections of this paper, an overview of metabolically healthy and unhealthy obesity in the context of aging is presented followed by a description of obesity- and age-associated inflammation.

5.2. Metabolically Healthy Obesity in Old Age

A clear distinction between MHO and MUHO is even of great clinical implication in the aged subjects as ageing populations face the challenges of both rising numbers of the elderly and increasing obesity prevalence. In addition, elderly subjects may present with differences in the etiology, pathogenesis and prevalence of obesity and its comorbidities and related inflammatory patterns and implications.

It is important to note that the prevalence of overweight and obesity is increasing among older age groups in developed countries [108]. The prevalence of obesity increases from 15 to 20% at puberty to 40% in 60–69 years old individuals [109, 110], which is also a rational for studying obesity in this age group in particular. The problem of obesity in the elderly thus has great relevance and requires more research and clinical attention.

Research studies so far have not considered in greater depth the phenotypic heterogeneity of obesity such as the metabolically healthy and unhealthy subtypes, particularly in the aged populations. In virtually all studies, measures of adiposity were investigated alongside metabolic risk factors and individual components of the metabolic syndrome as independent risk factors predicting health outcomes. The health risks of overweight-obese individuals without metabolic risk factors and those with metabolic risk factors have been investigated, to the best of our knowledge, in one study so far [111]. This longitudinal study assessed risk for diabetes or cardiovascular disease (CVD) stratified by body mass index (BMI) and the presence or absence of metabolic syndrome (MetS) or insulin resistance (IR). It found that metabolic risk factor clustering or IR appeared to confer much of the risk for diabetes or CVD commonly associated with elevated BMI. Of note, among people older than 70 years, there is decline in obesity prevalence probably due to selective mortality of people in middle ages, such that relatively fewer obese people survive into older ages. Aging is also associated with emergence of frailty and sarcopenia which are partly defined by weight loss, especially fat-free muscle. Thus, very old individuals may convert from obesity defined by increased BMI to obesity due to reduced muscle/fat ratio but with a normal BMI. In connection to that, the normal-weight obese (NWO) syndrome in the elderly subjects of normal body weight and BMI has been identified (Figure 2). Interestingly, the fat mass in these subjects was 30% of their total body weight and these subjects had a likelihood of increased risk of developing obesity-related diseases [112]. The data suggests that in the elderly obesity should be redefined as the amount of excess fat storage associated with elevated health risk. The relationship between overweight/obesity and total mortality in the elderly is controversial [113]. The expected increase on mortality associated with increasing BMI is not observed in many older population studies, and a number of studies, in fact, observed a U-shaped curve describing the relationship between BMI and mortality in old age [114]. Higher BMI values were associated with a smaller relative mortality risk in elderly persons compared with young and middle-aged populations [115]. These data suggest that use of BMI alone as the sole indices of excess adiposity in studies of older populations is a contributing factor for discrepant findings. On the other hand, studies using indices of amount and distribution of fat tend to consistently show a greater association with specific and total mortality in the elderly than BMI alone. Larger waist circumference or waist-to-hip ratio (relatively stronger indicators for adiposity) has been shown significantly associated with mortality in older subjects, whilst BMI alone was not. Thus, central adiposity and relative loss of fat-free mass may be more important than BMI in determining the health risk associated with obesity in older ages. The interpretation of the relationship between adiposity and mortality in the elderly is complicated by the difficulty in accounting for selection and confounding factors (such as survival bias, smoking, and physical inactivity), but is probably also inherently complex.

Numerous studies have shown that overweight/obesity is associated with a host of known nonfatal health outcomes, mostly in the aged populations [116122]. Hypothetically, MUHO might be responsible for much of the association with metabolic syndrome, diabetes, cardiovascular disease, and other obesity-related metabolic complications including depression and dementia; on the other hand, MHO might still be associated with similar risk of other related complications such as physical limitations and disability, osteoarthritis, and obstructive sleep apnea. As with total mortality, a J-shape relation between BMI and disability in older persons has been reported [118] with observed greater disability for both low and high values of BMI in both sexes. The relationships with various morbidity outcomes are particularly stronger with indices of central adiposity than BMI, and some indeed showed associations independently of BMI. Taking another case in point, although some studies showed a significant negative relationship between high BMI and depressive symptoms (Jolly Fat hypothesis) [123, 124] other studies showed that increased waist circumference was either not associated or positively associated with depressive symptoms [125, 126].

6. Aging and Inflammation

In the previous sections, obesity and its different phenotypic forms were reviewed with emphasis of possible differences due to natural aging. The consequences related to MHO and MUHO are likely to be greatly affected by aging as a variety of disorders and infections are unique to the elderly, which may additionally exacerbate the effects of obesity. Thus, aging seems to present with a great variety of patterns and unique sets of obesity and age-related infections and diseases. It is, therefore, very tempting and urging to study obesity, inflammation, and the related comorbidities in the context of aging.

In general, inflammation is a very tightly regulated process that ensures recruitment of competent and experienced cells, clearance of pathogen with minimal tissue damage and sequels. The duration, intensity and variety of its components will determine the impact of inflammation on health including the cardiovascular, bone/joint, respiratory and immune systems. A lack of balance in this equilibrium may lead to significant consequences locally (tissue site) or in a systemic manner. Inflammation is the most necessary step for recovery and most cells produce cytokines/chemokines/adipokines and soluble mediators of inflammation. Not only immune cells but also endothelial cells, fibroblasts, and keratinocytes among other cell types are able to produce these mediators and are partners and players of the immune responses, fully involved in the inflammatory process. One example is the atherosclerotic plaques that develop over years. There is no doubt in the scientific community regarding the involvement of monocytes/macrophages in the formation of these plaques. The increase in oxidized low-density-lipoproteins (oxLDL) and its associated secretion of proinflammatory molecules from the endothelium is responsible for the initial recruitment of immune cells (Figure 3). The molecular events leading to LDL oxidation and subsequent effects are extensively reviewed by Leonarduzzi et al. [127]. Briefly, the monocytes differentiate into macrophages, which possess a high propensity to uptake oxLDL. The uncontrolled accumulation of the oxLDL initiates the formation of lipid droplets within the monocytes/macrophages, ultimately leading to the formation of foam cells. These foam cells in turn release proinflammatory mediators that sustain this process. The accumulation of foam cells leads to the formation of a physical barrier in the vessels characterizing atherosclerosis. This pathology is a process that requires years or decades to be observed clinically. For this reason, many consequences of uncontrolled inflammation are seen later in life, that is, in the elderly population. Recent animal models (zebra fish) allow to now study and modulate the effect of oxLDL in a shorter period of time [128], which should enable to improve our understanding of relationship between inflammation and obesity-related diseases.

Figure 3: Conversion of naïve LDL to oxidized LDL and formation of foam cells from monocytes: implication of aging and obesity in sustaining/worsening inflammation. A typical example of how chronic condition can participate in the development of diseases. In this case, atherosclerotic plaque formation is increased by parallel proinflammatory signals derived from obesity and/or inflammaging. LDL: low-density lipoprotein; ROS: reactive oxygen species; CMV: cytomegalovirus; RANTES: regulated and normal T cell expressed and secreted.

Cardiovascular diseases (CVD), including heart and vascular disease and atherosclerosis, are highly prevalent, particularly in the old age. Inflammation induced by obesity accelerates atherosclerosis. Adipose tissue and adipocytes produce leptin and large numbers of a variety of other hormones, peptides, and other molecules that affect cardiovascular function. The production of these substances is supposed to be through distinct endocrine, autocrine and paracrine mechanisms and believed to lead to cytokine-mediated inflammatory changes in the liver and systemic inflammation and atherosclerosis [129]. CVD and the related heart diseases are recognized as chronic inflammatory conditions of the blood vessels that result from the excessive transendothelial passage (transcytosis) of cholesterol rich atherogenic lipoproteins (VLDL, IDL, and LDL) from the plasma into the intima. Once there, these lipoproteins are likely to be retained in the subendothelial spaces leading to infiltration of macrophages and T cells into these spaces and ultimately interact not only with each other but also with the cells of the arterial wall [130132], forming foam cells (FC) in the vicinity [133], which is considered as the first step of plaque formation (Figure 3). FC are generated from altered and/or oxidized LDL, termed as modified LDL. The rate and intensity of FC formation depends upon the relative size and shape of LDL [134] as LDL comprises a group of very heterogeneous particles, which comprise multiple distinct subclasses that differ in size, density, physicochemical composition, metabolic and oxidative behavior, as well as atherogenicity [135]. Increasing evidence suggests that size and density of LDL have a direct influence on cardiovascular risk [136]. There is also evidence suggestive of the coexistence of proatherogenic LDL subclasses and elevated inflammation [137]. For instance, elevated levels of IL-18 are associated with reduced LDL size, independently of other inflammatory and metabolic risk factors [138]. There is a close relationship between atherogenic small, dense LDL and inflammation [139] and a reduction in the concentrations of these LDL has been shown to lead to a strong reduction in CVD-associated morbidity and mortality rate [140142].

Based on the observations of greater than expected reduction in CVD events in the JUPITER study (Justification for the Use of Statins in Primary Prevention: An Intervention Trial Evaluating Rosuvastatin) [143], it has been suggested that other factors besides LDL-cholesterol may also be involved in the reduction of CVD event. Therefore the modulation of these factors, particularly of inflammation and atherogenic lipoproteins, represents a main target of CVD prevention [144]. In addition, there is overwhelming evidence supporting the pathogenetic role of fibrinogen and CRP in atherosclerosis [145] and their predictive capability for future cerebro- and cardiovascular events in patients with subclinical and clinical atherosclerosis [146148].

Obesity and type 2 diabetes (T2D) are probably inter-twined. Unfortunately, however, no direct study has ever provided the evidence that obesity can cause diabetes. Interestingly, reports published by Solinas et al. [15] indicate that inflammation could be the key factor causing obesity-induced type 2 diabetes. The group has indicated that obesity without inflammation is not prominently associated with insulin resistance and/or diabetes. In addition, gestational diabetes (GDM) is very common and high prevalence rates have been reported from both developed and developing countries [149152]. Women with previous gestational diabetes (pGDM) are characterized by chronic subclinical inflammation which is associated with insulin resistance and abnormality in glucose metabolism. Approximately 30% of these women have metabolic syndrome and many of them will develop T2DM within 5 years of diagnosis. The conversion rates from GDM to T2DM range from 2.6% to 70% over a period of 6 weeks to 28 years postpartum [153].

As described in the previous section, obesity is not restricted to younger populations. However, the intensity of obesity-related inflammation may not be the same over age. Also, obesity-related or unrelated inflammation may not be similarly handled in young versus elderly individuals. This raises the importance of understanding the unbalance in inflammation during aging. Two types of unwanted inflammation must be considered in aging (i) acute inflammation resulting from bad management of the inflammatory process, which in turn results from immune responses, and (ii) chronic inflammation resulting from persistent condition(s) in the elderly including age-specific infections such as cytomegalovirus CMV. This later is often referred to as low-grade inflammation or even “inflammaging” as coined by Franceschi et al. [154], who identified a variety of these proinflammatory mediators in aging [155], namely, RANTES, MIP-1α, IL-8, MCP-1, IL-6, and TNF-α amongst others. Individually these mediators are involved in the development of many subclinical and clinically assessable events such as atherosclerosis, dementia, and diabetes (Figure 3). Not only is the presence of these markers in higher quantity linked to comorbidities but also to mortality [155]. The unbalance between IL-6/TNF-α levels and IGF-I levels in the elderly directly accounts for the loss of fat-free mass suggesting that obesity and sarcopenia are highly inflammation dependent [156]. An important set of markers is IL-6, TNF- , and CRP. This triad is involved in much comorbidity and has shown significant correlations with mortality [157]. The increased levels of such inflammatory mediators are not observed in all elderly individuals. Many mechanisms influence how inflammation is resolved and many events will induce such inflammation. Genetics lifestyle and immunological history are probably the most important players in the inter-individual differences [158]. Immunological history refers to the collection of events that lead to an immune response. This response may result in clearance of infection (acute response) or may simply resolve primary infection without clearing the pathogen. This later, chronic/persistent infection is a major issue for the immune system as it requires life-long immunosurveillance and dedicated resources. Pathogens requiring life-long control can be viruses (HIV, Cytomegalovirus), bacteria (H. pylori, M. tuberculosis), or parasites (P. falciparum, T. gondii). HIV has been shown to deeply impact on body composition such as increased visceral adipose tissue even with therapies. Such changes were also associated to increased susceptibility to atherosclerosis and associated complications suggesting that inflammation may be a corner stone in these processes [159].

Helicobacter pylori is now easily eradicated following its identification, however, there is evidence showing that even after resolution of infection there is no reduction in the incidence of gastric cancers suggesting that the inflammation initiated has long-lasting effects suggesting that immunological history in this case have irreversible effects [160]. While the infection with HIV, parasites, and even H. pylori may be reduced greatly in the next decades and already low in developed countries, a majority of individuals encounter CMV during their life. The seroconversion ranges between 0.5 and 1.5% per year [161] but may be very high in younger populations, especially in developing countries. Thus, such a persistent infection may impact on individuals’ health for decades. One of the most important studies on the relationship between CMV seropositivity and mortality was performed recently in a US-based platform [162]. Individuals aged 25 and above from the NHANES III (started in 1988) were followed-up for mortality and adjustment for multiple confounders revealed that CMV seropositivity was statistically significantly associated with all-cause mortality. Individuals with high CRP levels showed a 30% higher risk for all-cause and cardiovascular diseases-mortality compared to those with low CRP levels. That study however did not investigate the putative synergy of obesity and CMV seropositivity on all-cause mortality.

7. Conclusion

Obesity in humans is a unique form of low-grade inflammation. In chronic obesity, a low-grade activation of the innate immune system is provoked affecting metabolic homeostasis steadily. In contrast to the inflammatory response to pathogens or acute injury which is robust but resolves very quickly once the threat is removed, inflammation in obesity seems to sustain for relatively longer period of time. Inflammatory state of obesity is characterized by insulin resistance, increased serum concentrations of C-reactive protein, IL-6, IL-8, TNF-α, and so forth. Obesity-induced insulin resistance also causes changes in inflammatory signaling by adipocytes and infiltration of adipose tissue by immune cells. Alterations in immune signaling are triggered by excess adiposity, which may be essential mediators of the metabolic dysfunction observed in obesity. However, not all obese individuals exhibit increased risk of inflammation and not all normal-weight individuals are metabolically healthy. A critical unanswered question is why and how the difference exists between metabolically healthy and unhealthy obese individuals regarding the obesity-associated inflammatory mediators. The answers to this question becomes even more important in the context of elderly, who present with diverse but more complex dimensions of body weight, patterns of body fat distribution, and obesity. This is why now, for example, there is great disagreement among the researchers whether to use the conventional BMI as an indicator of health outcomes or not as BMI is considered not sensitive enough to discriminate between lean body mass and body fat, particularly in elderly. Unfortunately, much of the previous work on the elderly focused on obesity assessed by BMI without considering the overall metabolic profile, which often yield controversial conclusions and misleading interpretations about obesity and its related comorbidities. Nevertheless, the association between obesity, morbidity, functional disability, and even mortality has been well established. It is noteworthy that the intensity of obesity-related inflammation may not be the same over age and unlike young, elderly may be victims of chronic inflammation beside acute inflammation, which results from persistent condition(s) in the elderly over the course of life-span including specific infections, CMV, for example. This state of chronic inflammation is often referred to as low-grade inflammation or even “inflammaging,” characterized by a variety of proinflammatory mediators with aging, namely, RANTES, MIP-1α, IL-8, MCP-1, IL-6, and TNF-α amongst others. As depicted in the paper, this resembles to some extent to the obesity-related inflammation. Individually, these mediators are involved in the development of many subclinical and clinically assessable events such as atherosclerosis, dementia, and diabetes in old age, which linked to a number of other comorbidities and the overall mortality. While the prevalence of most of current infections is decreasing, elderly individuals are more susceptible to infections, respond poorly to vaccination. The role of inflammation in immune erosion is to be considered seriously, especially when other chronic conditions such as obesity are present.

References

  1. P. Zimmet, K. G. M. M. Alberti, and J. Shaw, “Global and societal implications of the diabetes epidemic,” Nature, vol. 414, no. 6865, pp. 782–787, 2001. View at Publisher · View at Google Scholar · View at Scopus
  2. A. C. Evans, G. I. Papachristou, and D. C. Whitcomb, “Obesity and the risk of severe acute pancreatitis,” Minerva Gastroenterologica e Dietologica, vol. 56, no. 2, pp. 169–179, 2010. View at Google Scholar · View at Scopus
  3. H. Tilg, “The role of cytokines in non-alcoholic fatty liver disease,” Digestive Diseases, vol. 28, no. 1, pp. 179–185, 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. C. X. Andersson, B. Gustafson, A. Hammarstedt, S. Hedjazifar, and U. Smith, “Inflamed adipose tissue, insulin resistance and vascular injury,” Diabetes/Metabolism Research and Reviews, vol. 24, no. 8, pp. 595–603, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. F. Gomes, D. F. Telo, H. P. Souza, J. C. Nicolau, A. Halpern, and C. V. Serrano, “Obesity and coronary artery disease: role of vascular inflammation,” Arquivos Brasileiros de Cardiologia, vol. 94, no. 2, pp. 255–279, 2010. View at Google Scholar · View at Scopus
  6. R. Tkacova, “Systemic inflammation in chronic obstructive pulmonary disease: may adipose tissue play a role? Review of the literature and future perspectives,” Mediators of Inflammation, vol. 2010, Article ID 585989, 11 pages, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Denes, P. Thornton, N. J. Rothwell, and S. M. Allan, “Inflammation and brain injury: acute cerebral ischaemia, peripheral and central inflammation,” Brain, Behavior, and Immunity, vol. 24, no. 5, pp. 708–723, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. M. K. Öhman, A. P. Wright, K. J. Wickenheiser, W. Luo, and D. T. Eitzman, “Visceral adipose tissue and atherosclerosis,” Current Vascular Pharmacology, vol. 7, no. 2, pp. 169–179, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. S. D. Hursting, L. M. Lashinger, K. W. Wheatley et al., “Reducing the weight of cancer: mechanistic targets for breaking the obesity-carcinogenesis link,” Best Practice and Research, vol. 22, no. 4, pp. 659–669, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. D. L. Roberts, C. Dive, and A. G. Renehan, “Biological mechanisms linking obesity and cancer risk: new perspectives,” Annual Review of Medicine, vol. 61, pp. 301–316, 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. K. Y. Wolin, K. Carson, and G. A. Colditz, “Obesity and cancer,” Oncologist, vol. 15, no. 6, pp. 556–565, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. J. P. Bastard, M. Maachi, C. Lagathu et al., “Recent advances in the relationship between obesity, inflammation, and insulin resistance,” European Cytokine Network, vol. 17, no. 1, pp. 4–12, 2006. View at Google Scholar · View at Scopus
  13. M. C. Arkan, A. L. Hevener, F. R. Greten et al., “IKK-β links inflammation to obesity-induced insulin resistance,” Nature Medicine, vol. 11, no. 2, pp. 191–198, 2005. View at Publisher · View at Google Scholar · View at Scopus
  14. S. P. Weisberg, D. Hunter, R. Huber et al., “CCR2 modulates inflammatory and metabolic effects of high-fat feeding,” Journal of Clinical Investigation, vol. 116, no. 1, pp. 115–124, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. G. Solinas, C. Vilcu, J. G. Neels et al., “JNK1 in hematopoietically derived cells contributes to diet-induced inflammation and insulin resistance without affecting obesity,” Cell Metabolism, vol. 6, no. 5, pp. 386–397, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. S. E. Shoelson, J. Lee, and A. B. Goldfine, “Inflammation and insulin resistance,” Journal of Clinical Investigation, vol. 116, no. 7, pp. 1793–1801, 2006. View at Publisher · View at Google Scholar · View at Scopus
  17. S. E. Kahn, B. Zinman, S. M. Haffner et al., “Obesity is a major determinant of the association of C-reactive protein levels and the metabolic syndrome in type 2 diabetes,” Diabetes, vol. 55, no. 8, pp. 2357–2364, 2006. View at Publisher · View at Google Scholar · View at Scopus
  18. N. B. Ruderman, S. H. Schneider, and P. Berchtold, “The “metabolically-obese” normal-weight individual,” American Journal of Clinical Nutrition, vol. 34, no. 8, pp. 1617–1621, 1981. View at Google Scholar · View at Scopus
  19. R. P. Wildman, P. Muntner, K. Reynolds et al., “The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering: prevalence and correlates of 2 phenotypes among the US population (NHANES 1999–2004),” Archives of Internal Medicine, vol. 168, no. 15, pp. 1617–1624, 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. S. B. Racette, S. S. Deusinger, and R. H. Deusinger, “Obesity: overview of prevalence, etiology, and treatment,” Physical Therapy, vol. 83, no. 3, pp. 276–288, 2003. View at Google Scholar · View at Scopus
  21. W. D. McArdle, F. I. Katch, and V. L. Katch, Exercise Physiology: Energy, Nutrition, and Human, Lippincott Williams and Wilkins, Philadelphia, Pa, USA, 2001.
  22. L. A. Helmchen and R. M. Henderson, “Changes in the distribution of body mass index of white US men, 1890–2000,” Annals of Human Biology, vol. 31, no. 2, pp. 174–181, 2004. View at Publisher · View at Google Scholar · View at Scopus
  23. P. T. Katzmarzyk, “The Canadian obesity epidemic, 1985–1998,” Canadian Medical Association Journal, vol. 166, no. 8, pp. 1039–1040, 2002. View at Google Scholar · View at Scopus
  24. K. Silventoinen, S. Sans, H. Tolonen et al., “Trends in obesity and energy supply in the WHO MONICA Project,” International Journal of Obesity, vol. 28, no. 5, pp. 710–718, 2004. View at Publisher · View at Google Scholar · View at Scopus
  25. K. M. Flegal, M. D. Carroll, R. J. Kuczmarski, and C. L. Johnson, “Overweight and obesity in the United States: prevalence and trends, 1960–1994,” International Journal of Obesity, vol. 22, no. 1, pp. 39–47, 1998. View at Google Scholar · View at Scopus
  26. P. T. James, N. Rigby, and R. Leach, “The obesity epidemic, metabolic syndrome and future prevention strategies,” European Journal of Cardiovascular Prevention and Rehabilitation, vol. 11, no. 1, pp. 3–8, 2004. View at Publisher · View at Google Scholar · View at Scopus
  27. S. Wild, G. Roglic, A. Green, R. Sicree, and H. King, “Global prevalence of diabetes: estimates for the year 2000 and projections for 2030,” Diabetes Care, vol. 27, no. 5, pp. 1047–1053, 2004. View at Publisher · View at Google Scholar · View at Scopus
  28. A. M. Prentice, “The emerging epidemic of obesity in developing countries,” International Journal of Epidemiology, vol. 35, no. 1, pp. 93–99, 2006. View at Publisher · View at Google Scholar · View at Scopus
  29. J. O. Hill and E. L. Melanson, “Overview of the determinants of overweight and obesity: current evidence and research issues,” Medicine and Science in Sports and Exercise, vol. 31, no. 11, pp. S515–S521, 1999. View at Google Scholar · View at Scopus
  30. K. M. Flegal, R. P. Troiano, E. R. Pamuk, R. J. Kuczmarski, and S. M. Campbell, “The influence of smoking cessation on the prevalence of overweight in the United States,” New England Journal of Medicine, vol. 333, no. 18, pp. 1165–1170, 1995. View at Publisher · View at Google Scholar · View at Scopus
  31. P. F. Baillie-Hamilton, “Chemical toxins: a hypothesis to explain the global obesity epidemic,” Journal of Alternative and Complementary Medicine, vol. 8, no. 2, pp. 185–192, 2002. View at Google Scholar · View at Scopus
  32. K. Clement and D. Langin, “Regulation of inflammation-related genes in human adipose tissue,” Journal of Internal Medicine, vol. 262, no. 4, pp. 422–430, 2007. View at Publisher · View at Google Scholar · View at Scopus
  33. A. W. Ferrante Jr., “Obesity-induced inflammation: a metabolic dialogue in the language of inflammation,” Journal of Internal Medicine, vol. 262, no. 4, pp. 408–414, 2007. View at Publisher · View at Google Scholar · View at Scopus
  34. S. Engeli and A. M. Sharma, “Role of adipose tissue for cardiovascular-renal regulation in health and disease,” Hormone and Metabolic Research, vol. 32, no. 11-12, pp. 485–499, 2000. View at Google Scholar · View at Scopus
  35. K. M. Pou, J. M. Massaro, U. Hoffmann et al., “Visceral and subcutaneous adipose tissue volumes are cross-sectionally related to markers of inflammation and oxidative stress. The Framingham Heart Study,” Circulation, vol. 116, no. 11, pp. 1234–1241, 2007. View at Publisher · View at Google Scholar · View at Scopus
  36. P. Trayhurn and I. S. Wood, “Adipokines: inflammation and the pleiotropic role of white adipose tissue,” British Journal of Nutrition, vol. 92, no. 3, pp. 347–355, 2004. View at Publisher · View at Google Scholar · View at Scopus
  37. K. E. Wellen and G. S. Hotamisligil, “Inflammation, stress, and diabetes,” Journal of Clinical Investigation, vol. 115, no. 5, pp. 1111–1119, 2005. View at Publisher · View at Google Scholar · View at Scopus
  38. U. N. Das, “Is obesity an inflammatory condition?” Nutrition, vol. 17, no. 11-12, pp. 953–966, 2001. View at Publisher · View at Google Scholar · View at Scopus
  39. G. S. Hotamisligil, “Inflammation and metabolic disorders,” Nature, vol. 444, no. 7121, pp. 860–867, 2006. View at Publisher · View at Google Scholar · View at Scopus
  40. G. S. Hotamisligil, N. S. Shargill, and B. M. Spiegelman, “Adipose expression of tumor necrosis factor-α: direct role in obesity-linked insulin resistance,” Science, vol. 259, no. 5091, pp. 87–91, 1993. View at Google Scholar · View at Scopus
  41. C. S. Kim, H. S. Park, T. Kawada et al., “Circulating levels of MCP-1 and IL-8 are elevated in human obese subjects and associated with obesity-related parameters,” International Journal of Obesity, vol. 30, no. 9, pp. 1347–1355, 2006. View at Publisher · View at Google Scholar · View at Scopus
  42. P. Arner, “Insulin resistance in type 2 diabetes—role of the adipokines,” Current Molecular Medicine, vol. 5, no. 3, pp. 333–339, 2005. View at Publisher · View at Google Scholar · View at Scopus
  43. H. Tilg and A. R. Moschen, “Adipocytokines: mediators linking adipose tissue, inflammation and immunity,” Nature Reviews Immunology, vol. 6, no. 10, pp. 772–783, 2006. View at Publisher · View at Google Scholar · View at Scopus
  44. J. N. Fain, “Release of inflammatory mediators by human adipose tissue is enhanced in obesity and primarily by the nonfat cells: a review,” Mediators of Inflammation, vol. 2010, Article ID 513948, 20 pages, 2010. View at Publisher · View at Google Scholar · View at Scopus
  45. P. A. Kern, M. Saghizadeh, J. M. Ong, R. J. Bosch, R. Deem, and R. B. Simsolo, “The expression of tumor necrosis factor in human adipose tissue. Regulation by obesity, weight loss, and relationship to lipoprotein lipase,” Journal of Clinical Investigation, vol. 95, no. 5, pp. 2111–2119, 1995. View at Google Scholar · View at Scopus
  46. M. Good, F. M. Newell, L. M. Haupt, J. P. Whitehead, L. J. Hutley, and J. B. Prins, “TNF and TNF receptor expression and insulin sensitivity in human omental and subcutaneous adipose tissue—influence of BMI and adipose distribution,” Diabetes and Vascular Disease Research, vol. 3, no. 1, pp. 26–33, 2006. View at Publisher · View at Google Scholar · View at Scopus
  47. R. Z. Yang, M. J. Lee, H. Hu et al., “Acute-phase serum amyloid A: an inflammatory adipokine and potential link between obesity and its metabolic complications,” PLoS Medicine, vol. 3, no. 6, article e287, 2006. View at Publisher · View at Google Scholar · View at Scopus
  48. S. P. Weisberg, D. McCann, M. Desai, M. Rosenbaum, R. L. Leibel, and A. W. Ferrante Jr., “Obesity is associated with macrophage accumulation in adipose tissue,” Journal of Clinical Investigation, vol. 112, no. 12, pp. 1796–1808, 2003. View at Publisher · View at Google Scholar · View at Scopus
  49. M. Lehrke, M. P. Reilly, S. C. Millington, N. Iqbal, D. J. Rader, and M. A. Lazar, “An inflammatory cascade leading to hyperresistinemia in humans,” PLoS Medicine, vol. 1, article 45, 2004. View at Publisher · View at Google Scholar · View at Scopus
  50. N. Silswal, A. K. Singh, B. Aruna, S. Mukhopadhyay, S. Ghosh, and N. Z. Ehtesham, “Human resistin stimulates the pro-inflammatory cytokines TNF-α and IL-12 in macrophages by NF-κB-dependent pathway,” Biochemical and Biophysical Research Communications, vol. 334, no. 4, pp. 1092–1101, 2005. View at Publisher · View at Google Scholar · View at Scopus
  51. V. Sánchez-Margalet, C. Martín-Romero, J. Santos-Alvarez, R. Goberna, S. Najib, and C. Gonzalez-Yanes, “Role of leptin as an immunomodulator of blood mononuclear cells: mechanisms of action,” Clinical and Experimental Immunology, vol. 133, no. 1, pp. 11–19, 2003. View at Publisher · View at Google Scholar · View at Scopus
  52. T. Suganami, J. Nishida, and Y. Ogawa, “A paracrine loop between adipocytes and macrophages aggravates inflammatory changes: role of free fatty acids and tumor necrosis factor α,” Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 25, no. 10, pp. 2062–2068, 2005. View at Publisher · View at Google Scholar · View at Scopus
  53. J. M. Olefsky and C. K. Glass, “Macrophages, inflammation, and insulin resistance,” Annual Review of Physiology, vol. 72, pp. 219–246, 2009. View at Publisher · View at Google Scholar · View at Scopus
  54. G. S. Hotamisligil, P. Arner, J. F. Caro, R. L. Atkinson, and B. M. Spiegelman, “Increased adipose tissue expression of tumor necrosis factor-α in human obesity and insulin resistance,” Journal of Clinical Investigation, vol. 95, no. 5, pp. 2409–2415, 1995. View at Google Scholar · View at Scopus
  55. J. K. Howard and J. S. Flier, “Attenuation of leptin and insulin signaling by SOCS proteins,” Trends in Endocrinology and Metabolism, vol. 17, no. 9, pp. 365–371, 2006. View at Publisher · View at Google Scholar · View at Scopus
  56. P. Lebrun and E. Van Obberghen, “SOCS proteins causing trouble in insulin action,” Acta Physiologica, vol. 192, no. 1, pp. 29–36, 2008. View at Publisher · View at Google Scholar · View at Scopus
  57. G. S. Hotamisligil, P. Peraldi, A. Budavari, R. Ellis, M. F. White, and B. M. Spiegelman, “IRS-1-mediated inhibition of insulin receptor tyrosine kinase activity in TNF-α- and obesity-induced insulin resistance,” Science, vol. 271, no. 5249, pp. 665–668, 1996. View at Google Scholar · View at Scopus
  58. J. J. Senn, P. J. Klover, I. A. Nowak et al., “Suppressor of cytokine signaling-3 (SOCS-3), a potential mediator of interleukin-6-dependent insulin resistance in hepatocytes,” Journal of Biological Chemistry, vol. 278, no. 16, pp. 13740–13746, 2003. View at Publisher · View at Google Scholar · View at Scopus
  59. J. He, I. Usui, K. Ishizuka et al., “Interleukin-1α inhibits insulin signaling with phosphorylating insulin receptor substrate-1 on serine residues in 3T3-L1 adipocytes,” Molecular Endocrinology, vol. 20, no. 1, pp. 114–124, 2006. View at Publisher · View at Google Scholar · View at Scopus
  60. D. Cai, M. Yuan, D. F. Frantz et al., “Local and systemic insulin resistance resulting from hepatic activation of IKK-β and NF-κB,” Nature Medicine, vol. 11, no. 2, pp. 183–190, 2005. View at Publisher · View at Google Scholar · View at Scopus
  61. J. Hirosumi, G. Tuncman, L. Chang et al., “A central, role for JNK in obesity and insulin resistance,” Nature, vol. 420, no. 6913, pp. 333–336, 2002. View at Publisher · View at Google Scholar · View at Scopus
  62. C. M. Larsen, M. Faulenbach, A. Vaag et al., “Interleukin-1-receptor antagonist in type 2 diabetes mellitus,” New England Journal of Medicine, vol. 356, no. 15, pp. 1517–1526, 2007. View at Publisher · View at Google Scholar · View at Scopus
  63. M. Yuan, N. Konstantopoulos, J. Lee et al., “Reversal of obesity- and diet-induced insulin resistance with salicylates or targeted disruption of Ikkβ,” Science, vol. 293, no. 5535, pp. 1673–1677, 2001. View at Publisher · View at Google Scholar · View at Scopus
  64. N. S. Sauter, F. T. Schulthess, R. Galasso, L. W. Castellani, and K. Maedler, “The antiinflammatory cytokine interleukin-1 receptor antagonist protects from high-fat diet-induced hyperglycemia,” Endocrinology, vol. 149, no. 5, pp. 2208–2218, 2008. View at Publisher · View at Google Scholar · View at Scopus
  65. S. N. Vallerie, M. Furuhashi, R. Fucho, and G. S. Hotamisligil, “A predominant role for parenchymal c-Jun amino terminal kinase (JNK) in the regulation of systemic insulin sensitivity,” PLoS ONE, vol. 3, no. 9, Article ID e3151, 2008. View at Publisher · View at Google Scholar · View at Scopus
  66. V. Varma, A. Yao-Borengasser, N. Rasouli et al., “Muscle inflammatory response and insulin resistance: synergistic interaction between macrophages and fatty acids leads to impaired insulin action,” American Journal of Physiology, vol. 296, no. 6, pp. E1300–E1310, 2009. View at Publisher · View at Google Scholar · View at Scopus
  67. F. Mauvais-Jarvis, R. N. Kulkarni, and C. R. Kahn, “Knockout models are useful tools to dissect the models of insulin resistance pathophysiology and genetics of insulin resistance,” Clinical Endocrinology, vol. 57, no. 1, pp. 1–9, 2002. View at Publisher · View at Google Scholar · View at Scopus
  68. E. G. Hong, J. K. Hwi, Y. R. Cho et al., “Interleukin-10 prevents diet-induced insulin resistance by attenuating macrophage and cytokine response in skeletal muscle,” Diabetes, vol. 58, no. 11, pp. 2525–2535, 2009. View at Publisher · View at Google Scholar · View at Scopus
  69. X. Zhang, G. Zhang, H. Zhang, M. Karin, H. Bai, and D. Cai, “Hypothalamic IKKβ/NF-κB and ER Stress Link Overnutrition to Energy Imbalance and Obesity,” Cell, vol. 135, no. 1, pp. 61–73, 2008. View at Publisher · View at Google Scholar · View at Scopus
  70. A. D. Karelis, M. Brochu, and R. Rabasa-Lhoret, “Can we identify metabolically healthy but obese individuals (MHO)?” Diabetes and Metabolism, vol. 30, no. 6, pp. 569–572, 2004. View at Google Scholar · View at Scopus
  71. M. Brochu, A. Tchernof, I. J. Dionne et al., “What are the physical characteristics associated with a normal metabolic profile despite a high level of obesity in postmenopausal women?” Journal of Clinical Endocrinology and Metabolism, vol. 86, no. 3, pp. 1020–1025, 2001. View at Publisher · View at Google Scholar · View at Scopus
  72. E. A. H. Sims, “Are there persons who are obese, but metabolically healthy?” Metabolism, vol. 50, no. 12, pp. 1499–1504, 2001. View at Publisher · View at Google Scholar · View at Scopus
  73. N. Stefan, K. Kantartzis, J. Machann et al., “Identification and characterization of metabolically benign obesity in humans,” Archives of Internal Medicine, vol. 168, no. 15, pp. 1609–1616, 2008. View at Publisher · View at Google Scholar · View at Scopus
  74. G. Iacobellis, M. C. Ribaudo, A. Zappaterreno, C. V. Iannucci, and F. Leonetti, “Prevalence of uncomplicated obesity in an Italian obese population,” Obesity Research, vol. 13, no. 6, pp. 1116–1122, 2005. View at Google Scholar · View at Scopus
  75. Y. J. Hyun, S. J. Koh, J. S. Chae et al., “Atherogenecity of LDL and unfavorable adipokine profile in metabolically obese, normal-weight woman,” Obesity, vol. 16, no. 4, pp. 784–789, 2008. View at Publisher · View at Google Scholar · View at Scopus
  76. M. J. Shin, Y. J. Hyun, O. Y. Kim, J. Y. Kim, Y. Jang, and J. H. Lee, “Weight loss effect on inflammation and LDL oxidation in metabolically healthy but obese (MHO) individuals: low inflammation and LDL oxidation in MHO women,” International Journal of Obesity, vol. 30, no. 10, pp. 1529–1534, 2006. View at Publisher · View at Google Scholar · View at Scopus
  77. E. Succurro, M. A. Marini, S. Frontoni et al., “Insulin secretion in metabolically obese, but normal weight, and in metabolically healthy but obese individuals,” Obesity, vol. 16, no. 8, pp. 1881–1886, 2008. View at Publisher · View at Google Scholar · View at Scopus
  78. C. A. Aguilar-Salinas, E. García, L. Robles et al., “High adiponectin concentrations are associated with the metabolically healthy obese phenotype,” Journal of Clinical Endocrinology and Metabolism, vol. 93, no. 10, pp. 4075–4079, 2008. View at Publisher · View at Google Scholar · View at Scopus
  79. L. A. Lynch, J. M. O'Connell, A. K. Kwasnik, T. J. Cawood, C. O'Farrelly, and D. B. O'Shea, “Are natural killer cells protecting the metabolically healthy obese patient?” Obesity, vol. 17, no. 3, pp. 601–605, 2009. View at Publisher · View at Google Scholar · View at Scopus
  80. A. D. Karelis, “Metabolically healthy but obese individuals,” The Lancet, vol. 372, no. 9646, pp. 1281–1283, 2008. View at Publisher · View at Google Scholar · View at Scopus
  81. O. Ukkola and M. Santaniemi, “Protein tyrosine phosphatase 1B: a new target for the treatment of obesity and associated co-morbidities,” Journal of Internal Medicine, vol. 251, no. 6, pp. 467–475, 2002. View at Publisher · View at Google Scholar · View at Scopus
  82. P. A. Tataranni and E. Ortega, “A burning question: does an adipokine-induced activation of the immune system mediate the effect of overnutrition on type 2 diabetes?” Diabetes, vol. 54, no. 4, pp. 917–927, 2005. View at Publisher · View at Google Scholar · View at Scopus
  83. D. R. Cottam, S. G. Mattar, E. Barinas-Mitchell et al., “The chronic inflammatory hypothesis for the morbidity associated with morbid obesity: implications and effect of weight loss,” Obesity Surgery, vol. 14, no. 5, pp. 589–600, 2004. View at Publisher · View at Google Scholar · View at Scopus
  84. S. Virtue and A. Vidal-Puig, “Adipose tissue expandability, lipotoxicity and the metabolic syndrome—an allostatic perspective,” Biochimica et Biophysica Acta, vol. 1801, no. 3, pp. 338–349, 2010. View at Publisher · View at Google Scholar · View at Scopus
  85. E. J. Boyko, W. Y. Fujimoto, D. L. Leonetti, and L. Newell-Morris, “Visceral adiposity and risk of type 2 diabetes: a prospective study among Japanese Americans,” Diabetes Care, vol. 23, no. 4, pp. 465–471, 2000. View at Google Scholar · View at Scopus
  86. B. H. Goodpaster, S. Krishnaswami, T. B. Harris et al., “Obesity, regional body fat distribution, and the metabolic syndrome in older men and women,” Archives of Internal Medicine, vol. 165, no. 7, pp. 777–783, 2005. View at Publisher · View at Google Scholar · View at Scopus
  87. S. Zhu, Z. Wang, S. Heshka, M. Heo, M. S. Faith, and S. B. Heymsfield, “Waist circumference and obesity-associated risk factors among whites in the third National Health and Nutrition Examination Survey: clinical action thresholds,” American Journal of Clinical Nutrition, vol. 76, no. 4, pp. 743–749, 2002. View at Google Scholar · View at Scopus
  88. C. S. Fox, J. M. Massaro, U. Hoffmann et al., “Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the framingham heart study,” Circulation, vol. 116, no. 1, pp. 39–48, 2007. View at Publisher · View at Google Scholar · View at Scopus
  89. M. B. Snijder, J. M. Dekker, M. Visser et al., “Trunk fat and leg fat have independent and opposite associations with fasting and postload glucose levels. The Hoorn Study,” Diabetes Care, vol. 27, no. 2, pp. 372–377, 2004. View at Publisher · View at Google Scholar · View at Scopus
  90. M. B. Snijder, M. Visser, J. M. Dekker et al., “Low subcutaneous thigh fat is a risk factor for unfavourable glucose and lipid levels, independently of high abdominal fat. The Health ABC Study,” Diabetologia, vol. 48, no. 2, pp. 301–308, 2005. View at Publisher · View at Google Scholar · View at Scopus
  91. S. Stenholm, A. Koster, D. E. Alley et al., “Adipocytokines and the metabolic syndrome among older persons with and without obesity. The InCHIANTI study,” Clinical Endocrinology, vol. 73, no. 1, pp. 55–65, 2010. View at Publisher · View at Google Scholar · View at Scopus
  92. T. You, B. J. Nicklas, J. Ding et al., “The metabolic syndrome is associated with circulating adipokines in older adults across a wide range of adiposity,” Journals of Gerontology Series A, vol. 63, no. 4, pp. 414–419, 2008. View at Google Scholar · View at Scopus
  93. J. Hung, B. M. McQuillan, P. L. Thompson, and J. P. Beilby, “Circulating adiponectin levels associate with inflammatory markers, insulin resistance and metabolic syndrome independent of obesity,” International Journal of Obesity, vol. 32, no. 5, pp. 772–779, 2008. View at Publisher · View at Google Scholar · View at Scopus
  94. L. E. Beasley, A. Koster, A. B. Newman et al., “Inflammation and race and gender differences in computerized tomography-measured adipose depots,” Obesity, vol. 17, no. 5, pp. 1062–1069, 2009. View at Publisher · View at Google Scholar · View at Scopus
  95. E. E. Kershaw and J. S. Flier, “Adipose tissue as an endocrine organ,” Journal of Clinical Endocrinology and Metabolism, vol. 89, no. 6, pp. 2548–2556, 2004. View at Publisher · View at Google Scholar · View at Scopus
  96. B. L. Wajchenberg, “Subcutaneous and visceral adipose tissue: their relation to the metabolic syndrome,” Endocrine Reviews, vol. 21, no. 6, pp. 697–738, 2000. View at Publisher · View at Google Scholar · View at Scopus
  97. F. Wang, H. Liu, W. P. Blanton, A. Belkina, N. K. Lebrasseur, and G. V. Denis, “Brd2 disruption in mice causes severe obesity without Type 2 diabetes,” Biochemical Journal, vol. 425, no. 1, pp. 71–83, 2010. View at Publisher · View at Google Scholar · View at Scopus
  98. A. C. Belkina and G. V. Denis, “Obesity genes and insulin resistance,” Current Opinion in Endocrinology, Diabetes and Obesity, vol. 17, no. 5, pp. 472–477, 2010. View at Publisher · View at Google Scholar · View at Scopus
  99. I. Alam, A. Larbi, G. Pawelec, and P. I. Paracha, “Relationship between anthropometric variables and nutrient intake in apparently healthy male elderly individuals: a study from Pakistan,” Nutrition Journal, vol. 10, pp. 111–120, 2011. View at Google Scholar
  100. H. S. Chahal and W. M. Drake, “The endocrine system and ageing,” Journal of Pathology, vol. 211, no. 2, pp. 173–180, 2007. View at Publisher · View at Google Scholar · View at Scopus
  101. D. Minors, G. Atkinson, N. Bent, P. Rabbitt, and J. Waterhouse, “The effects of age upon some aspects of lifestyle and implications for studies on circadian rhythmicity,” Age and Ageing, vol. 27, no. 1, pp. 67–72, 1998. View at Publisher · View at Google Scholar · View at Scopus
  102. I. Alam, A. Larbi, and G. Pawelec Paracha PI, “A comparison of anthropometrics, biochemical variables and nutrient intake between young and elderly men,” The Journal of Aging Research & Clinical Practice, vol. 1, no. 2, pp. 116–124, 2012. View at Google Scholar
  103. I. Alam and F. Bangash, “Oral health and nutritional status of the free-living elderly in Peshawar, Pakistan,” Saudi Medical Journal, vol. 31, no. 6, pp. 713–715, 2010. View at Google Scholar · View at Scopus
  104. I. Alam, A. Larbi, and G. Pawelec, “Aging affects the number of T and B cells in a group of elderly in developing countries—a pilot study from Pakistan,” Age. In press.
  105. I. Alam, A. Larbi, and G. Pawelec, “Nutritional status influences peripheral immune cell phenotypes in healthy men in rural Pakistan,” Immunity & Ageing, vol. 9, 16 pages, 2012. View at Google Scholar
  106. G. Sharma and J. Goodwin, “Effect of aging on respiratory system physiology and immunology,” Clinical Interventions in Aging, vol. 1, no. 3, pp. 253–260, 2006. View at Publisher · View at Google Scholar · View at Scopus
  107. A. A. Mangoni and S. H. D. Jackson, “Age-related changes in pharmacokinetics and pharmacodynamics: basic principles and practical applications,” British Journal of Clinical Pharmacology, vol. 57, no. 1, pp. 6–14, 2004. View at Publisher · View at Google Scholar · View at Scopus
  108. D. E. Arterburn, P. K. Crane, and S. D. Sullivan, “The coming epidemic of obesity in elderly Americans,” Journal of the American Geriatrics Society, vol. 52, no. 11, pp. 1907–1912, 2004. View at Publisher · View at Google Scholar · View at Scopus
  109. K. M. Flegal, M. D. Carroll, C. L. Ogden, and C. L. Johnson, “Prevalence and trends in obesity among US adults, 1999-2000,” Journal of the American Medical Association, vol. 288, no. 14, pp. 1723–1727, 2002. View at Google Scholar · View at Scopus
  110. J. C. Seidell, “Prevalence and time trends of obesity in Europe,” Journal of Endocrinological Investigation, vol. 25, no. 10, pp. 816–822, 2002. View at Google Scholar · View at Scopus
  111. J. B. Meigs, I. Lipinska, S. Kathiresan et al., “Visceral and subcutaneous adipose tissue volumes are cross-sectionally related to markers of inflammation and oxidative stress. The Framingham Heart Study,” Circulation, vol. 116, no. 11, pp. 1234–1241, 2007. View at Publisher · View at Google Scholar · View at Scopus
  112. A. De Lorenzo, V. Del Gobbo, M. G. Premrov, M. Bigioni, F. Galvano, and L. Di Renzo, “Normal-weight obese syndrome: early inflammation?” American Journal of Clinical Nutrition, vol. 85, no. 1, pp. 40–45, 2007. View at Google Scholar · View at Scopus
  113. M. Zamboni, G. Mazzali, E. Zoico et al., “Health consequences of obesity in the elderly: a review of four unresolved questions,” International Journal of Obesity, vol. 29, no. 9, pp. 1011–1029, 2005. View at Publisher · View at Google Scholar · View at Scopus
  114. R. Andres, D. Elahi, and J. D. Tobin, “Impact of age on weight goals,” Annals of Internal Medicine, vol. 103, no. 6, pp. 1030–1033, 1985. View at Google Scholar · View at Scopus
  115. A. Heiat, V. Vaccarino, and H. M. Krumholz, “An evidence-based assessment of federal guidelines for overweight and obesity as they apply to elderly persons,” Archives of Internal Medicine, vol. 161, no. 9, pp. 1194–1203, 2001. View at Google Scholar · View at Scopus
  116. J. C. Seidell, R. Andres, J. D. Sorkin, and D. C. Muller, “The sagittal waist diameter and mortality in men. The Baltimore longitudinal study on aging,” International Journal of Obesity, vol. 18, no. 1, pp. 61–67, 1994. View at Google Scholar · View at Scopus
  117. J. Haarbo, C. Hassager, B. J. Riis, and C. Christiansen, “Relation of body fat distribution to serum lipids and lipoproteins in elderly women,” Atherosclerosis, vol. 80, no. 1, pp. 57–62, 1989. View at Google Scholar · View at Scopus
  118. A. N. Galanos, C. F. Pieper, J. C. Cornoni-Huntley, C. W. Bales, and G. G. Fillenbaum, “Nutrition and function: is there a relationship between body mass index and the functional capabilities of community-dwelling elderly?” Journal of the American Geriatrics Society, vol. 42, no. 4, pp. 368–373, 1994. View at Google Scholar · View at Scopus
  119. M. C. Hochberg, M. Lethbridge-Cejku, W. W. Scott, R. Reichle, C. C. Plato, and J. D. Tobin, “The association of body weight, body fatness and body fat distribution with osteoarthritis of the knee: data from the baltimore longitudinal study of aging,” Journal of Rheumatology, vol. 22, no. 3, pp. 488–493, 1995. View at Google Scholar · View at Scopus
  120. D. Carmelli, G. E. Swan, and D. L. Bliwise, “Relationship of 30-year changes in obesity to sleep-disordered breathing in the Western Collaborative Group Study,” Obesity Research, vol. 8, no. 9, pp. 632–637, 2000. View at Google Scholar · View at Scopus
  121. O. P. Almeida, J. Calver, K. Jamrozik, G. J. Hankey, and L. Flicker, “Obesity and metabolic syndrome increase the risk of incident depression in older men: the health in men study,” American Journal of Geriatric Psychiatry, vol. 17, no. 10, pp. 889–898, 2009. View at Publisher · View at Google Scholar · View at Scopus
  122. D. Gustafson, “Adiposity indices and dementia,” Lancet Neurology, vol. 5, no. 8, pp. 713–720, 2006. View at Publisher · View at Google Scholar · View at Scopus
  123. A. H. Crisp and B. McGuiness, “Jolly fat: relation between obesity and psychoneurosis in general population,” British Medical Journal, vol. 1, no. 6000, pp. 7–9, 1976. View at Google Scholar · View at Scopus
  124. S. Kuriyama, Y. Koizumi, K. Matsuda-Ohmori et al., “Obesity and depressive symptoms in elderly Japanese. The Tsurugaya Project,” Journal of Psychosomatic Research, vol. 60, no. 3, pp. 229–235, 2006. View at Publisher · View at Google Scholar · View at Scopus
  125. R. C. M. Ho, M. Niti, E. H. Kua, and T. P. Ng, “Body mass index, waist circumference, waist-hip ratio and depressive symptoms in Chinese elderly: a population-based study,” International Journal of Geriatric Psychiatry, vol. 23, no. 4, pp. 401–408, 2008. View at Publisher · View at Google Scholar · View at Scopus
  126. N. Vogelzangs, S. B. Kritchevsky, A. T. F. Beekman et al., “Obesity and onset of significant depressive symptoms: results from a prospective community-based cohort study of older men and women,” Journal of Clinical Psychiatry, vol. 71, no. 4, pp. 391–399, 2010. View at Publisher · View at Google Scholar · View at Scopus
  127. G. Leonarduzzi, P. Gamba, S. Gargiulo, F. Biasi, and G. Poli, “Inflammation-related gene expression by lipid oxidation-derived products in the progression of atherosclerosis,” Free Radical Biology and Medicine, vol. 52, no. 1, pp. 19–34, 2012. View at Google Scholar
  128. L. Fang, S. R. Green, J. S. Baek et al., “In vivo visualization and attenuation of oxidized lipid accumulation in hypercholesterolemic zebrafish,” The Journal of Clinical Investigation, vol. 121, no. 12, pp. 4861–4869, 2011. View at Google Scholar
  129. H. Mangge, G. Almer, M. Truschnig-Wilders, A. Schmidt, R. Gasser, and D. Fuchs, “Inflammation, adiponectin, obesity and cardiovascular risk,” Current Medicinal Chemistry, vol. 17, no. 36, pp. 4511–4520, 2010. View at Publisher · View at Google Scholar · View at Scopus
  130. I. Tabas, K. J. Williams, and J. Borén, “Subendothelial lipoprotein retention as the initiating process in atherosclerosis: update and therapeutic implications,” Circulation, vol. 116, no. 16, pp. 1832–1844, 2007. View at Publisher · View at Google Scholar · View at Scopus
  131. K. J. Williams and I. Tabas, “Lipoprotein retention- and clues for atheroma regression,” Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 25, no. 8, pp. 1536–1540, 2005. View at Publisher · View at Google Scholar · View at Scopus
  132. K. J. Williams and I. Tabas, “The response-to-retention hypothesis of atherogenesis reinforced,” Current Opinion in Lipidology, vol. 9, no. 5, pp. 471–474, 1998. View at Publisher · View at Google Scholar · View at Scopus
  133. P. Shashkin, B. Dragulev, and K. Ley, “Macrophage differentiation to foam cells,” Current Pharmaceutical Design, vol. 11, no. 23, pp. 3061–3072, 2005. View at Publisher · View at Google Scholar · View at Scopus
  134. A. J. Lusis, “Atherosclerosis,” Nature, vol. 407, no. 6801, pp. 233–241, 2000. View at Publisher · View at Google Scholar · View at Scopus
  135. M. Rizzo, K. Berneis, E. Corrado, and S. Novo, “The significance of low-density-lipoproteins size in vascular diseases,” International Angiology, vol. 25, no. 1, pp. 4–9, 2006. View at Google Scholar · View at Scopus
  136. D. P. Mikhailidis, M. Elisaf, and M. Rizzo, “European panel on low density lipoprotein (LDL) subclasses’: a statement on the pathophysiology, atherogenicity and clinical significance of LDL subclasses: executive summary,” Current Vascular Pharmacology, vol. 9, pp. 531–532, 2011. View at Google Scholar
  137. M. Rizzo, E. Corrado, G. Coppola et al., “The predictive role of C-reactive protein in patients with hypertension and subclinical atherosclerosis,” Internal Medicine Journal, vol. 39, no. 8, pp. 539–545, 2009. View at Publisher · View at Google Scholar · View at Scopus
  138. K. Berneis, M. Rizzo, J. Evans, G. B. Rini, G. A. Spinas, and J. H. Goedecke, “Interleukin-18 levels are associated with low-density lipoproteins size,” European Journal of Clinical Investigation, vol. 40, no. 1, pp. 54–55, 2010. View at Publisher · View at Google Scholar · View at Scopus
  139. M. S. V. Elkind, “Inflammatory mechanisms of stroke,” Stroke, vol. 41, no. 10, supplement, pp. S3–S8, 2010. View at Publisher · View at Google Scholar · View at Scopus
  140. H. R. Superko, K. K. Berneis, P. T. Williams, M. Rizzo, and P. D. Wood, “Gemfibrozil reduces small low-density lipoprotein more in normolipemic subjects classified as low-density lipoprotein pattern B compared with pattern A,” American Journal of Cardiology, vol. 96, no. 9, pp. 1266–1272, 2005. View at Publisher · View at Google Scholar · View at Scopus
  141. M. Rizzo, J. Kotur-Stevuljevic, K. Berneis et al., “Atherogenic dyslipidemia and oxidative stress: a new look,” Translational Research, vol. 153, no. 5, pp. 217–223, 2009. View at Publisher · View at Google Scholar · View at Scopus
  142. K. Berneis, M. Rizzo, C. Stettler et al., “Comparative effects of rosiglitazone and pioglitazone on fasting and postprandial low-density lipoprotein size and subclasses in patients with Type 2 diabetes,” Expert Opinion on Pharmacotherapy, vol. 9, no. 3, pp. 343–349, 2008. View at Publisher · View at Google Scholar · View at Scopus
  143. P. M. Ridker, E. Danielson, F. A. H. Fonseca et al., “Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein,” New England Journal of Medicine, vol. 359, no. 21, pp. 2195–2207, 2008. View at Publisher · View at Google Scholar · View at Scopus
  144. M. Rizzo and D. P. Mikhailidis, “There is more to predicting vascular disease than just established risk factors,” Current Pharmaceutical Design, vol. 17, pp. 3608–3610, 2011. View at Google Scholar
  145. P. Libby, P. M. Ridker, and A. Maseri, “Inflammation and atherosclerosis,” Circulation, vol. 105, no. 9, pp. 1135–1143, 2002. View at Publisher · View at Google Scholar · View at Scopus
  146. E. Corrado, M. Rizzo, G. Coppola, I. Muratori, M. Carella, and S. Novo, “Endothelial dysfunction and carotid lesions are strong predictors of clinical events in patients with early stages of atherosclerosis: a 24-month follow-up study,” Coronary Artery Disease, vol. 19, no. 3, pp. 139–144, 2008. View at Publisher · View at Google Scholar · View at Scopus
  147. E. Corrado, M. Rizzo, I. Muratori, G. Coppola, and S. Novo, “Association of elevated fibrinogen and C-reactive protein levels with carotid lesions in patients with newly diagnosed hypertension or type II diabetes,” Archives of Medical Research, vol. 37, no. 8, pp. 1004–1009, 2006. View at Publisher · View at Google Scholar · View at Scopus
  148. M. Rizzo, V. Pernice, A. Frasheri et al., “Small, dense low-density lipoproteins (LDL) are predictors of cardio - and cerebro-vascular events in subjects with the metabolic syndrome,” Clinical Endocrinology, vol. 70, no. 6, pp. 870–875, 2009. View at Publisher · View at Google Scholar · View at Scopus
  149. N. Vrachnis, A. Augoulea, Z. Iliodromiti, I. Lambrinoudaki, S. Sifakis, and G. Creatsas, “Previous gestational diabetes mellitus and markers of cardiovascular risk,” International Journal of Endocrinology, vol. 2012, Article ID 458610, 6 pages, 2012. View at Publisher · View at Google Scholar
  150. J. B. Echouffo-Tcheugui and S. Dagogo-Jack, “Preventing diabetes mellitus in developing countries,” Nature Reviews Endocrinology, vol. 8, pp. 557–562, 2012. View at Google Scholar
  151. K. K. Nielsen, M. de Courten, and A. Kapur, “The urgent need for universally applicable simple screening procedures and diagnostic criteria for gestational diabetes mellitus - lessons from projects funded by the World Diabetes Foundation,” Global Health Action, vol. 5, Article ID 17277, 2012. View at Google Scholar
  152. G. E. Rice, S. E. Illanes, and M. D. Mitchell, “Gestational diabetes mellitus: a positive predictor of type 2 diabetes?” International Journal of Endocrinology, vol. 2012, Article ID 721653, 10 pages, 2012. View at Publisher · View at Google Scholar
  153. N. Vrachnis, P. Belitsos, S. Sifakis et al., “Role of adipokines and other inflammatory mediators in gestational diabetes mellitus and previous gestational diabetes mellitus,” International Journal of Endocrinology, vol. 2012, Article ID 549748, 12 pages, 2012. View at Publisher · View at Google Scholar
  154. C. Franceschi, M. Bonafè, S. Valensin et al., “Inflamm-aging. An evolutionary perspective on immunosenescence,” Annals of the New York Academy of Sciences, vol. 908, pp. 244–254, 2000. View at Google Scholar · View at Scopus
  155. M. De Martinis, C. Franceschi, D. Monti, and L. Ginaldi, “Inflamm-ageing and lifelong antigenic load as major determinants of ageing rate and longevity,” FEBS Letters, vol. 579, no. 10, pp. 2035–2039, 2005. View at Publisher · View at Google Scholar · View at Scopus
  156. M. Barbieri, L. Ferrucci, E. Ragno et al., “Chronic inflammation and the effect of IGF-I on muscle strength and power in older persons,” American Journal of Physiology, vol. 284, no. 3, pp. E481–E487, 2003. View at Google Scholar · View at Scopus
  157. H. Bruunsgaard, S. Ladelund, A. N. Pedersen, M. Schroll, T. Jørgensen, and B. K. Pedersen, “Predicting death from tumour necrosis factor-alpha and interleukin-6 in 80-year-old people,” Clinical and Experimental Immunology, vol. 132, no. 1, pp. 24–31, 2003. View at Publisher · View at Google Scholar · View at Scopus
  158. D. Lio, L. Scola, A. Crivello et al., “Inflammation, genetics, and longevity: further studies on the protective effects in men of IL-10-1082 promoter SNP and its interaction with TNF-α-308 promoter SNP,” Journal of Medical Genetics, vol. 40, no. 4, pp. 296–299, 2003. View at Google Scholar · View at Scopus
  159. J. Falutz, “HIV infection, body composition changes and related metabolic complications: contributing factors and evolving management strategies,” Current Opinion in Clinical Nutrition and Metabolic Care, vol. 14, no. 3, pp. 255–260, 2011. View at Publisher · View at Google Scholar · View at Scopus
  160. R. M. Peek Jr. and M. J. Blaser, “Helicobacter pylori and gastrointestinal tract adenocarcinomas,” Nature Reviews Cancer, vol. 2, no. 1, pp. 28–37, 2002. View at Google Scholar · View at Scopus
  161. M. Hecker, D. Qiu, K. Marquardt, G. Bein, and H. Hackstein, “Continuous cytomegalovirus seroconversion in a large group of healthy blood donors,” Vox Sanguinis, vol. 86, no. 1, pp. 41–44, 2004. View at Publisher · View at Google Scholar · View at Scopus
  162. A. M. Simanek, J. B. Dowd, G. Pawelec, D. Melzer, A. Dutta, and A. E. Aiello, “Seropositivity to cytomegalovirus, inflammation, all-cause and cardiovascular disease-related mortality in the United States,” PLoS ONE, vol. 6, no. 2, Article ID e16103, 2011. View at Publisher · View at Google Scholar · View at Scopus