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Addressing the Perfect Storm: Biomarkers in Obesity and Pathophysiology of Cardiometabolic Risk.

The prevalence of overweight and obesity has increased dramatically in recent decades around the world in both children and adults, reaching epidemic proportions. In 2015, 108 million children and 604 million adults globally were defined as obese (1). The rise in childhood obesity is especially concerning, highlighting an urgent need to counteract risk for future generations. Parallel to the rise in obesity, the burdens of obesity-associated diseases have sharply increased, in particular metabolic conditions such as type 2 diabetes (1). Obesity is a well-established risk factor for coronary heart disease (CHD), [7] hypertension, stroke, ventricular dysfunction, congestive heart failure, cardiac arrhythmias, and type 2 diabetes (2), but also certain types of cancer, including colorectal cancer, renal cell carcinoma, postmenopausal breast cancer, esophageal adenocarcinoma, pancreatic cancer, endometrial cancer, and liver cancer (3). Support for a causal link between obesity and cardiometabolic diseases was provided by a recent Mendelian randomization analysis within the UK Biobank (4). On the basis of data from 119859 individuals, significant positive associations were seen between genetically linked higher body mass index (BMI) and risk of hypertension, CHD, and type 2 diabetes (4). However, despite these clear links, the pathophysiological basis for these associations is not fully understood. In addition, BMI--which is currently used to classify states of obesity--is only a crude measure of body composition and may not sufficiently capture risk associated with body fat distribution (5). Visceral adipose tissue is metabolically more active than subcutaneous adipose tissue. Waist circumference--as an indicator of abdominal body fat distribution--is more closely related to cardiometabolic diseases, thus highlighting the need to define obesity based on the anatomical location of fat rather than solely on its volume (5).

The identification and measurement of biomarkers in human circulation related to obesity and cardiometabolic risk have been of growing scientific interest for several reasons. Such biomarkers can provide compelling new insights into pathophysiologic pathways; potentially improve the clinical and public health identification of persons at risk for disease; facilitate monitoring of disease progression and prognosis; represent targets for interventions through means of diet, lifestyle, or drug treatment; and allow more personalized treatment decisions for individual patients (5). Biomarkers might also quantify metabolically active adipose tissue beyond anthropometric parameters as an alternative or complementary approach to define an "obesity phenotype" that is relevant for cardiometabolic diseases (e.g., beyond BMI and waist circumference). Thus, biomarkers could prove useful at each stage of a health- disease continuum from monitoring health to disease management (Fig. 1). However, while an explosion of biomarker research has occurred, the precise roles and relevance of many obesity biomarkers remains uncertain.

Despite extensive efforts and policy strategies, not a single nation globally has experienced a reduction in obesity in the last 20 years, indicating that the "perfect storm" of obesity-promoting environmental, biological, psychological, social, and economic factors has not been addressed (5). A better understanding of the pathophysiology of obesity and related biomarkers is crucial to avail development of new and more effective strategies for disease prevention. To assess the current evidence on obesity biomarkers, we review and summarize the role of selected obesity-related biomarkers--both established and novel--with a special focus on recent evidence for detrimental associations with cardiometabolic diseases. We have mostly focused on circulating biomarkers evaluated in epidemiological research such as (a) biomarkers of glucose-insulin homeostasis, (b) adipose-tissue biomarkers, (c) inflammatory biomarkers, and (d) omics-based biomarkers.


Obesity-associated increases in visceral, subcutaneous, and liver fat, and in insulin-induced muscle microvascular recruitment were shown to contribute independently to insulin resistance. Moreover, decreased intrahepatic lipid content and insulin-induced muscle microvascular recruitment seem also to be main contributors of improved insulin resistance accompanying weight loss (6). Currently, metabolic risk subgroups are often defined based on traditional measures such as insulin secretion or insulin resistance (7). Insulin resistance refers locally to a decrease in a target cell's metabolic response to insulin, and systemically to an impaired effect of endogenous or exogenous insulin to lower circulating blood glucose levels. Insulin resistance and hyperinsulinemia are suggested as key pathways through which obesity increases risk of type 2 diabetes, hypertension, dyslipidemia, and cardiovascular events (8). Interestingly, there is accumulating evidence that these pathways may also be relevant for some cancers, particularly colorectal cancer (9). Different viewpoints exist on whether hyperinsulinemia is cause or consequence of insulin resistance (10). The traditional view is that overnutrition may cause insulin resistance in peripheral tissues, which raises blood glucose concentrations and stimulates pancreatic insulin secretion. However, more recent observations suggest that obesity may also induce hyperinsulinemia first, which may then induce hepatic gluconeogenesis and cause insulin resistance (10).


Although there is a strong interest in the effects of insulin resistance and hyperinsulinemia on disease risk, differentiating these effects based on biomarkers measured in blood on a systemic level is challenging. Increased circulating concentrations of insulin or its precursor proinsulin were suggested to mediate the association between obesity, type 2 diabetes, and cardiovascular diseases (CVDs) and cancer (11). In contrast to insulin, proinsulin was suggested to act as a more direct agent of vascular damage and to better reflect impaired i-cell function. Several metaanalyses of observational, epidemiological studies conducted in healthy individuals in the last decades pinpointed proinsulin levels to be more strongly associated with cardiovascular risk than insulin levels (12). A more recently conducted metaanalysis of 22 studies reported that each 50 pmol/L increase in fasting insulin level was associated with a 1.3-fold higher risk of hypertension and a 1.2-fold higher risk of CHD, whereas no association was observed with risk of stroke (13).


Insulin-like growth factor-1 (IGF-1) is homologous to proinsulin and involved in regulation of cell proliferation, apoptosis, migration, and differentiation. As such, IGF-1 has been proposed to be among the mediators for the association of obesity with cancer risk, and multiple studies have been conducted in this field. However, epidemiological studies have not always been consistent, and the evidence remains somewhat inconclusive for a role of circulating IGF-1 on cancer risk, although IGF-1 may play a role on a tissue level (14, 15). Interestingly, experimental studies suggested that IGF-1 regulates vascular smooth muscle cells via oxidation, inflammation, cell senescence, and epigenetic modifications, which increases the probability of atherosclerosis (16). A metaanalysis of 12 studies with 14938 participants suggested a nonlinear trend of the association between prediagnostic circulating IGF-1 concentrations and CVD such that both very low and very high circulating IGF-1 concentrations have been associated with a higher risk of CVD (17).


During insulin synthesis, proinsulin is cleaved into insulin and C-peptide (connecting peptide), and both are secreted in equimolar amounts. The initial assumption that C-peptide is an inert substance has been replaced, as it was shown that C-peptide may have effects on various cell membranes, including endothelial, renal, and nerve cells (18). High levels of insulin and C-peptide complement each other and are suggested to promote atherogenesis independent of diabetes and to contribute to an increased risk for cardiovascular diseases and associated mortality (19, 20). C-peptide levels have been shown to outperform established metabolic biomarkers (e.g., serum insulin, the homeostatic model assessment of insulin resistance, and the quantitative insulin sensitivity check index) in predicting all-cause and cardiovascular-related mortality among adults without diabetes (21, 22). Increased C-peptide levels have also been found to be associated with risk of cancers, particularly colorectal cancer (9, 14). In a metaanalysis of 9 nested case--control studies with 3109 cases and 4285 controls, circulating high vs low C-peptide concentrations were associated with a 2-fold higher risk for colon cancer, whereas no such association could be seen for rectal cancer (23). Further exploration of the molecular mechanisms whereby chronic hyperinsulinemia modulates pathways that may lead to insulin resistance, such as adipose whitening and inflammation, has been suggested as an important direction in research (10).


Research over recent decades changed the view on adipose tissue as sole energy storage depot and uncovered its role as an immune-metabolic organ (24). Energy imbalance leads to storage of excess energy in adipocytes, which exhibit both hypertrophy and hyperplasia. The processes of adipose hypertrophy and hyperplasia are associated with intracellular abnormalities of adipocyte function, particularly endoplasmic reticulum and mitochondrial stress. In addition, the adipocytes but also other adipose tissue cells, particularly macrophages, secrete several cytokines and hormones that may have autocrine, paracrine, and endocrine (systemic) effects (25). Resulting systemic consequences include insulin resistance and subclinical inflammation, which largely predispose to obesity complications (Fig. 2). Adipokines signal to other tissues to coordinate energy homeostasis, but may also induce pleiotropic actions that are still under investigation (24). Imbalanced production of adipokines was suggested to contribute to the pathogenesis of obesity-associated metabolic and cardiovascular complications (26). Numerous adipokines upregulated in the obese state have been identified in the past 2 decades. Some have been described to exert antiinflammatory and cardioprotective effects (i.e., adiponectin, omentin, and apelin), whereas others have been suggested to have proinflammatory properties and to impair cardiovascular function (i.e., leptin, resistin, and fatty-acid-binding protein-4) (27).


Adiponectin is the most abundant circulating peptide hormone, accounting for approximately 0.01% of total plasma protein (28). In animal models, adiponectin was shown to exert insulin sensitizing, antiinflammatory, antiatherogenic, and cardioprotective properties (29). However, the systemic effects of adiponectin in humans are not well understood. In serum, adiponectin is circulating in the form of 3 distinct oligomers, namely, a trimeric, a hexameric, and a high-molecular-weight form (composedof 12-18 adiponectin molecules) (30). It was largely suggested that the high-molecular-weight adiponectin form exerts higher biological activities and is more strongly positively correlated with insulin sensitivity than trimeric or hexameric adiponectin forms (31). However, there is some evidence that low-molecular-weight adiponectin may be more relevant for the antiinflammatory actions of adiponectin (32). Paradoxically, although adiponectin is exclusively secreted by adipocytes, its serum concentration is reduced in obesity and obesity-linked diseases (33). Experimental studies have suggested that adiponectin exerts beneficial effects on the cardiovascular system by directly acting on the component cells in the heart and blood vessels (29). Several observational studies found inverse associations between plasma adiponectin levels and risk of CHD, although in some of them, the associations were no longer significant after adjustment for other metabolic factors, particularly HDL cholesterol concentrations (34). However, other studies did not confirm these associations, and results from a recent metaanalysis of 16 prospective cohort studies, comprising 23 919 patients and 6870 CHD or stroke outcome events did not suggest that adiponectin is related to incident CHD (35). In addition, some studies found higher adiponectin levels to be associated with higher risk of poor prognosis among high-risk individuals, such as elderly persons and patients with existing CHD or chronic kidney disease (36). A recent Mendelian randomization study did not find evidence for a causal relationship between adiponectin levels and risk of CHD (37). Studies have also suggested that adiponectin may directly or indirectly (via the insulin resistance pathway) lower cancer risk, particularly for colorectal cancer; however, Mendelian randomization studies did not confirm these associations (38, 39). Interestingly, adiponectin seems to be in a close interplay with HDL cholesterol (15), and both biomarkers have been revealed as important mediators of the association between abdominal obesity and colon cancer in a large prospective cohort study (11). Overall, the evidence for the role of circulating adiponectin for the development of cardiometabolic diseases and cancer is still conflicting, and more research is needed to clarify the systemic functions of adiponectin in humans.


Omentin (also known as "intestinal lectin," intelectin-1, or intestinal lactoferrin receptor) is a 34-kDa secretory protein that was first identified by its ability to bind galactofuranose units in the carbohydrate chains of bacterial cell walls and to play a role in innate immunity against bacteria (40, 41). More recently omentin was reported to be preferentially produced by visceral adipose tissue, but also in smaller amounts in the subcutaneous adipose tissue, and in other tissues such as the intestines (42, 43). Omentin is predominantly expressed by visceral adipose stromal-vascular cells, but also by Paneth cells and endothelial cells (42, 43). It is highly abundant in human plasma (42, 43). Similarly to adiponectin, lower plasma omentin-1 levels have been associated with obesity and insulin resistance (44, 45). Omentin is also positively related to adiponectin, HDL cholesterol levels and negatively related to triglyceride and leptin levels (46). Omentin was shown to induce vasodilatation of blood vessels and to modulate inflammation-induced angiogenesis potentially via the nuclear factor B signaling pathway (47). Thus, low omentin was suggested as a link between obesity, inflammation, and cardiovascular diseases and a protective influence on cardiometabolic disease was postulated (47). Surprisingly, epidemiological studies provided provocative findings pointing to a higher CVD risk with increasing omentin concentrations (48). Similar findings have been also reported for colorectal cancer in a prospective cohort study (49). Interestingly, the positive association between omentin and colorectal cancer risk seemed to be present only in nonobese individuals (BMI < 30 kg/[m.sup.2]) for whom each doubling of omentin concentrations was associated with a 2-fold higher risk for colorectal cancer, whereas among obese participants (BMI [greater than or equal to] 30 kg/[m.sup.2]) no association was revealed. After myocardial ischemia, a decrease in omentin expression in the heart and a corresponding increase in plasma levels was reported (50). Further roles of omentin in obesity and cardiovascular diseases are still to be unraveled.


Apelin is a recently discovered vasoactive peptide that has been demonstrated to be the endogenous ligand of the APJ receptor. It was named "apelin" after the APJ endogenous ligand (51). Apelin and APJ receptor were found in the central nervous system and in different peripheral tissues. In the cardiovascular system, the peptide is present both in the heart and in the endothelium and smooth muscles cells of the vascular wall (52). In addition, apelin is also produced and secreted by adipocytes and thus considered as an adipokine. This has opened a new field of investigation establishing a link between apelin and metabolic disorders (53) (52). In metaanalyses of observational studies, low circulating apelin concentrations have been associated with a higher risk of hypertension, especially in Caucasian populations (54). Similarly, in CHD patients apelin concentrations were reported to be lower than healthy controls, suggesting a potential role as an athero-protective factor (55). Reported physiologic actions of apelin range from acting as endocrine adipokine (56) to being an important regulator of immune responses (57). Overall, apelin has been described as a beneficial adipokine, and there has been speculation on whether it could be a promising therapeutic target in metabolic disorders and CVDs (58, 59).


Since its discovery in 1994, leptin has become probably the most well-investigated obesity-related biomarker (60). Leptin was described as a 16-kDa protein primarily secreted by adipocytes and is now well known to play a pivotal role in regulating food intake, energy expenditure, and neuroendocrine function (61). Mutation of the leptin gene results in increased food intake, high insulin, and severe obesity in noninsulin dependent diabetes mellitus. Although rare in humans, leptin deficiency leads to severe obesity because of increased food intake, reduced energy expenditure, and development of hyperinsulinemia (62). By contrast the administration of leptin in leptin-deficient humans or animals induces the reduction of excessive eating and obesity (62). However, in wild-type animals and in most human subjects, leptin levels increase with increasing body fat, pointing to leptin resistance in obese individuals (61). Leptin resistance caused by a high-fat diet results from defects in access to sites of action in the hypothalamus. This significantly decreases the ability of peripheral leptin to activate hypothalamic signaling (63). Persons who have recently lost weight have relative leptin deficiency that may drive them to regain weight (64). One of the leptin receptor isoforms circulates in a soluble form that can bind leptin; thus, leptin circulates as both a free and a soluble receptor bound form (65). The soluble leptin receptor is the main binding protein for leptin in human circulation and modulates leptin bioavailability (66). As recently reviewed, leptin therapy has not been successful in lowering body weight in common forms of obesity, but it has proven effective in people with rare single gene mutations of the leptin gene (67). Consequently, treatment of obese people with leptin was given less attention, and the focus of obesity research shifted toward the prevention and reversal of the state of leptin resistance (67). New promising approaches aim to restore or sensitize the impaired function of the leptin receptor by pharmacological means (67). Leptin has been correlated with established vascular risk factors and the association between leptin and cardiovascular diseases has been evaluated in several studies. A metaanalysis of 8 observational studies reported that each one unit rise in leptin concentrations was associated with a 4% higher risk of CVD (68). However, subsequent metaanalyses refined to account for sociodemographic and metabolic factors have not supported an independent association (69, 70). Associations between the leptin receptor (LEPR) [8] gene polymorphisms and the risk of CVDs have been evaluated in several studies. A recent metaanalysis summarizing data from 7 observational studies involving 44133 participants reported a borderline significant association between the LEPR gene polymorphisms (rs1137101, rs1137100, rs6700896, and rs8179183) and a 1.10-fold higher risk of CVD (71). Leptin and the soluble leptin receptor may also be relevant for obesity-related cancers (72). In epidemiological studies, soluble leptin receptor was suggested to be one of the main mediators of the association between abdominal adiposity and weight gain, and colon cancer (11, 73). However, further evidence from Mendelian randomization studies is needed to draw firm conclusions on its etiological role.


Resistin is a 12.5-kDa, cysteine-rich peptide that induces low-grade inflammation by stimulating monocytes in humans (74). Resistin was shown to abrogate insulin function (resulting in resistance to insulin). While in rodents resistin has been described as an adipocyte-secreted hormone linked to obesity and insulin resistance, in humans it is predominantly expressed and secreted by macrophages and potentially reflects immune activities (75). It has been suggested that in humans resistin is probably of greater relevance in relation to the immune stress response than in the regulation of glucose homeostasis (76). Induction of resistin via inflammatory cytokine cascade was largely suggested to contribute to insulin resistance in obesity and other inflammatory states (77). Resistin also appears to mediate the pathogenesis of atherosclerosis by promoting endothelial dysfunction, vascular smooth muscle cell proliferation, arterial inflammation, and the formation of foam cells (78). Epidemiological studies have revealed increased resistin levels in relation to the development of insulin resistance, diabetes, and CVDs (79). A metaanalysis based on 22 observational studies including 2070 subjects showed that increased serum resistin level are significantly associated with severity of CVDs (80). Moreover, increased resistin levels in metabolically disadvantaged individuals, i.e., high-risk patients with diabetes and coronary artery disease, have been associated with all-cause mortality. Metaanalyses reported that each SD increment in resistin levels was associated with 21% higher risk of all-cause mortality and 5% higher risk of all-cause and cardiovascular disease mortality, respectively (81). The causal relationship between resistin and all-cause mortality in patients with type 2 diabetes was recently evaluated in an analysis of 1479 participants (403 events/12454 person-years) from 3 cohorts. A significant nonlinear association was observed between genetically raised resistin levels and all-cause mortality such that each genetic equivalent SD increase in log-resistin levels was associated with a 2-fold higher risk of all-cause mortality (82).


Adipocyte fatty-acid-binding protein (FABP4) is a member of the cytoplasmatic fatty-acid-binding protein multigene family that is primarily expressed in adipocytes and macrophages (83). Initially described as a lipid buffer, FABP4 has recently been shown to exert multiple functions on a local and systemic level including modulation of inflammatory processes (84). In mouse models, FABP4 has been described to exert effects on hypertriglyceridemia, hyperinsulinemia, and insulin resistance in the presence of genetically induced or diet-induced obesity (85). Inhibition or knockout of FABP4 prevents the development of atherosclerotic lesions of severe hypercholesteremia and accelerated atherosclerosis (86). Circulating FABP4 levels have been reported in relation to adiposity measures and components of metabolic syndrome (87). Higher FABP4 concentrations have been associated with degree of body fat accumulation (88) and worsened glycemic status (89). An association between higher FABP4 concentrations and a higher risk of CVDs was also suggested (90). Interestingly, a study by Tuncman et al. found that a genetic variant at the FABP4 locus that is associated with reduced FABP-4 activity was related to a lower risk of hypertriglyceridemia, type 2 diabetes, and CVD in men and women, which would support a causal role of FABP4 (91). However, a level of uncertainty remains, as other observational studies did not reveal significant associations of FABP4 concentrations with cardiometabolic diseases beyond obesity (90, 92).


So far, the contribution of immune factors to inflammatory and metabolic alterations in adipose tissue is not well understood. On one side, chronic low-grade inflammation has been indicated to induce hepatic insulin resistance (93), while on the other side metabolic factors have been implicated in immunity and pathogen defense including the coordination of innate and adaptive immune responses (94). In a recent metaanalysis of 60 population-based studies, the correlations between circulating concentrations of adipokines (leptin and adiponectin) and inflammatory biomarkers [C-reactive protein (CRP), interleukin (IL)-6, and tumor necrosis factor a] have been supported (95).


The notion that low-grade inflammation in obesity plays a role in the pathogenesis of cardiovascular diseases has gained much research interest over the last decade. In this regard, a number of studies in the late 1990s evaluated CRP as a plausible inflammatory risk marker (96). Data from the Emerging Risk Factors Collaboration Project published in 2010 based on individual-participant metaanalysis of 160309 people without a history of vascular disease from 54 long-term prospective studies suggested that CRP concentration has continuous associations with risk of CHD, ischemic stroke, vascular mortality, and death from several cancers and lung disease independent of obesity and metabolic biomarkers (97). However, overall a moderate association was revealed, and the value of CRP in improving risk prediction was questioned by some reports (98). The associations with ischemic vascular disease seemed to be largely dependent on conventional risk factors and other markers of inflammation (97). Also, Mendelian randomization studies have not supported a causal role of CRP on cardiovascular risk (99) or cancer (100). Inflammation remains an important trigger in CVD development, and there remains a high interest in research on proinflammatory cytokines, although some of them are difficult to measure in the circulation (such as tumor necrosis factor a) due to their short half-life.


Adipose tissue contains a variety of immune cells, which vary in abundance and phenotype with obesity, and recent research has highlighted the close interplay between obesity and immune responses (101, 102). Beyond adipokines, a wide complex network of cytokines and growth factors is also known to contribute to a disrupted balance between proinflammatory and antiinflammatory effects (94, 103). As compared with adipokines such as leptin or adiponectin, which are almost exclusively secreted by adipocytes in adipose tissue, most classical cytokines are predominantly secreted by nonfat cells within nonadipose tissues. For example, within adipose tissue, the release of IL-8, monocyte chemoattractant protein-1, vascular endothelial growth factor, transforming growth factor-beta, IL-6, tumor necrosis factor-[alpha], IL-1beta, and IL-10 by adipocytes was shown to be <12% of that by the nonfat cells present in human adipose tissue (104). In a recent metaanalysis of 29 population-based prospective studies, several different proinflammatory cytokines were significantly associated with CHD risk independent of obesity and conventional risk factors and in an approximately log-linear manner (105). Thus, reported relative risks for cardiovascular events per 1-SD higher levels were 1.25 (95% CI, 1.19-1.32) for IL-6; 1.13 (95% CI, 1.05-1.20) for IL-18; 1.07 (95% CI, 0.97-1.19) for matrix metalloproteinase-9; 1.07 (95% CI, 0.95-1.21) for soluble CD40 ligand; and 1.17 (95% CI, 1.09-1.25) for tumor necrosis factor a (105).

Any such data should be interpreted in the context of available assay procedures. So far, the ELISAs and bioassays have been most commonly used for cytokine-level measurements in clinical practice. Multiplexing technology allows measurement of many of these soluble proteins simultaneously and can be a cost-effective and efficient alternative with use of a minimal sample volume. However, the methodological utility of cytokine detection for many of the promising analytes remains unresolved.


Most biological pathways interact in a complex manner, which complicates the establishment of a net value for a particular biomarker. The process of identification and clinical application of novel omics-based biomarkers starts with well-designed epidemiological studies, sample collection, and measurements using novel omics platforms--genomics, transcriptomics, metabolomics, proteomics, lipidomics. In this context, bioinformatics approaches based on novel high-dimensional and high-throughput "-omics" are particularly promising to identify disease-risk signatures, but their application is still in an early stage. Issues related to biological and clinical validation are critical for evaluating any potentially promising set of biomarkers (Fig. 3). Examples of novel obesity-associated biomarkers provided by the systems biology field include findings on metabolites and microRNAs.


Metabolites are small molecules of diverse biochemical properties that can be measured in body fluids such as blood, serum, and urine. Following advances in biotechnological platform development, metabolite analyses enabled the discovery of previously undetected biological mechanisms in obesity-related metabolic pathways (106). Genome-wide association studies with metabolic traits uncovered many genetic variants that influence human metabolism. Genetically influenced metabotypes were suggested to contribute to our metabolic individuality, our capacity to respond to environmental challenges, and our susceptibility to specific diseases (107).

In a recent study of 2577 individuals within the Korean Association REsource (KARE) cohort, metabolites based on the FTO (FTO, [alpha]-ketoglutarate dependent dioxygenase) genotype that were significantly associated with obesity and type 2 diabetes have been identified (108). Among a total of 134 serum metabolites quantified with the use of a targeted metabolomics approach, 7 metabolites relevant to the phosphatidylcholine metabolic pathway were identified to be significantly altered in obesity on the basis of the FTO risk allele (adjusted P <0.05). In another study, metabolomics analyses were used to identify potential metabolites that are associated with different metabolic characteristics. Twelve major metabolites, including long- and medium-chain fatty acids, have been shown to distinguish overweight subjects with low visceral fat area and high visceral fat area, suggesting that chronic lipid surplus from visceral fat is likely associated with substantial increases in plasma fatty acids closely related to atherogenic traits (109). In a twin study among 531 monozygotic and 837 dizygotic twins, a wide range of unfavorable alterations in the serum metabolome was associated with abdominal obesity, insulin resistance, and low-grade inflammation (110). Abdominal fat area, together with the homeostatic model assessment of insulin resistance and CRP correlated significantly with an atherogenic lipoprotein profile, higher levels of branched-chain and aromatic amino acids, higher levels of glycoprotein, and a more saturated fatty acid profile. In contrast, a higher proportion of gynoid to total fat associated with a favorable metabolite profile. Waist circumference was associated with several metabolites, most strongly with phenylalanine (r = 0.40), glycoprotein (r = 0.37), serum triglycerides (r = 0.36), branched-chain amino acids (r = 0.30 to 0.40), HDL particle diameter (r = -0.33), and HDL cholesterol (r = -0.30). These associations could be partly explained by shared genes but also other mechanisms independent of genetic liability (110). A recent Mendelian randomization study provided evidence that lower BMI may be causally related to favorable lipoprotein subclass profiles and to lower concentration of insulin inflammatory markers and branched-chain amino acids (111). Thus, among different metabolite patterns, alterations in circulating levels of branched-chain amino acids have been found to be most commonly associated with obesity and metabolic dysfunction (112). Whether branched-chain amino acids--leucine, isoleucine, and valine--play a role in CVD was recently evaluated with use of a case-cohort design within the Prevencion con Dieta Mediterranea (PREDIMED) study including 226 incident cardiovascular disease cases and 744 noncases. The study suggested that higher leucine and isoleucine concentrations are associated with a 2-fold higher cardiovascular disease risk (113). Further analyses within the PREDIMED study also reported associations between acylcarnitines, glutamine/glutamate, ceramides, and lipid profiles with CVD (114, 115).


MicroRNAs represent a class of noncoding RNAs that regulate the expression of genes by inducing cleavage of mRNAs or inhibition ofprotein translation. MicroRNAs are suggested to play an important role in various biological processes, including obesity-related, chronic, low-grade inflammation and insulin resistance (116). Measured in circulation, microRNAs could be useful as prediagnostic or early diagnostic indicators for cardiovascular disease or cancer (117). So far, several studies have reported alterations in blood plasma concentrations of microRNAs in obese compared to nonobese persons; however, these studies were based on small numbers and varied in design and conclusion (117), (118). For example, a cross-sectional study of 32 men reported on 36 circulating microRNAs associated with anthropometric indices of obesity. The study further suggested 5 microRNAs (miR-142-3p, miR-140-5p, miR-15a, miR-520c3c, and miR-423-5p) that seem to be differentially expressed in morbidly obese patients (118). In another analysis of 13 patients with type 2 diabetes, 20 obese patients without type 2 diabetes, 16 obese patients with type 2 diabetes, and 20 healthy controls, among 739 miRNAs measured in serum pools of each group, 3 serum microRNAs, miR-138, miR-15b and miR-376a, were found to distinguish obese patients from normal healthy controls, diabetic patients, and obese diabetic patients (116). New lines of evidence from animal research suggest that the exosomal microRNAs produced by adipose tissue affect gene expression in distant organs such as the liver and indicate that exosomal microRNAs could act as adipokines (119). These findings open exciting new horizons for future research and the work to be done bridging animal and human data. Ultimately, novel omics-based biomarkers bring the promise for providing a more precise risk profiling and therefore could strengthen the precision medicine approach.


Biomarkers provide an innovative approach to understanding the spectrum of cardiometabolic diseases with potential applications in risk prediction, screening, and diagnosis and prognosis. However, they are prone to several methodological limitations such as high variability, ranging from the individual (biological variability) to the laboratory (methodological variability). It is important to consider reliability, validity, sensitivity, specificity, ascertainment bias, and interpretation of data when using biomarkers. Biomarker assessment in large population-based studies is particularly challenging because these biomarkers may reflect several pathophysiologic processes. It is therefore important to account for any potential confounders and to identify potential mediators, but these may not always be available in such studies. This is particularly relevant with regard to emerging biomarkers that are not well explored in population research. In addition to large prospective cohorts, studies utilizing Mendelian randomization analysis--which employ SNPs encoded in biomarker genes as instrumental variables--are particularly important to evaluate association vs causation. Novel omics platforms provide a rich set of biomarkers; however, reliability studies and standardized protocols for sample collection, storage and use, and biological variability in large populations are required before markers of cardiometabolic disease can be properly validated and used in the clinical setting. Additional challenges include lack of standardization of assay methods and clinical trial designs that allow evaluation of the effectiveness of the biomarkers (120).


Despite extensive clinical and policy efforts, obesity prevalence continues to increase in every country around the world. At the same time, considerable new evidence on novel biomarkers has emerged to advance our understanding of the pathophysiology of obesity and associated diseases. The question remains whether and how this new knowledge can be used for counteracting the spread of obesity-related diseases. Initial promise from different studies on the potential role of obesity-associated biomarkers, such as CRP, adiponectin, and leptin, in evaluating disease risk was often not sustained in the long term. Further, it has often not been possible to provide evidence for causality of biomarker--disease associations, e.g., using Mendelian randomization analyses. Nevertheless, in the era of "personalized medicine" there is continuous interest in novel biomarkers specific to obesity and cardiometabolic diseases. A number of promising biomarkers have appeared, including adipokines (such as apelin), cytokines, metabolites, and microRNAs. However, the data are conflicting as to which biomarker is more suitable for the reduction, diagnosis, or prognosis of cardiometabolic diseases. As experience has shown, any novel biomarker needs to go through a long process with evaluation of temporal associations, replication of findings, and clinical utility testing before any conclusion for its potential relevance in clinical practice can be reached. Further, it appears to be more likely that instead of using single biomarkers, combinations of biomarkers or signatures that come out of high-throughput analyses with use of systems biology, may bring new opportunities for an improved disease prediction. The predictive value of biomarkers could be additive and beyond that of conventional risk factors in assessing cardiometabolic risk. Finally, studies aiming at determining therapeutic implications of potential biomarkers beyond the prediction of established metabolic risk factors are also warranted. Intensified development in this field brings promise that biomarkers can prove useful in establishing "precision medicine" as a new concept aimed at personalized prevention.

Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article.

Authors' Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest:

Employment or Leadership: None declared.

Consultant or Advisory Role: D. Mozaffarian, Domada Health, Elysium Health, Bunge.

Stock Ownership: None declared.

Honoraria: D. Mozaffarian, Pollock Communications, Nutrition Impact.

Research Funding: D. Mozaffarian, NIH, Gates Foundation.

Expert Testimony: None declared.

Patents: D. Mozaffarian, US 8889739 B2.


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Krasimira Aleksandrova, [1] Dariush Mozaffarian, [2] and Tobias Pischon [3,4,5,6] *

[1] Nutrition, Immunity and Metabolism Start-up Lab, Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; [2] Friedman School of Nutrition Science & Policy, Tufts University, Boston, MA; [3] Molecular Epidemiology Research Group, Max Delbruck Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany; [4] Charite--Universitatsmedizin Berlin, Berlin, Germany; [5] MDC/BIH Biobank, Max Delbruck Center for Molecular Medicine in the Helmholtz Association and Berlin Institute of Health (BIH), Berlin, Germany; [6] German Center for Cardiovascular Research (DZHK), partner site Berlin, Berlin, Germany.

* Address correspondence to this author at: Molecular Epidemiology Research Group, Max Delbruck Center for Molecular Medicine in the Helmholtz Association, Robert-Rossle- Strasse 10, 13125 Berlin, Germany. Fax +49 30 9406-4576; e-mail:

Received September 25, 2017; accepted October 25, 2017.

Previously published online at DOI: 10.1373/clinchem.2017.275172

[7] Nonstandard abbreviations: CHD, coronary heart disease; BMI, body mass index; IGF, insulin-like growth factor; CRP, C-reactive protein; CVD, cardiovascular disease; IL, interleukin; PREDIMED, Prevencion con Dieta Mediterranea.

[8] Human Genes: LEPR, leptin receptor; FABP4, fatty-acid-binding protein 4.

Caption: Fig. 1. Potential uses and essential features of biomarkers in preclinical and clinical settings.

Caption: Fig. 2. B omarkers in obesity and associated cardiometabolic effects. Obesity activates inflammatory responses in fat and liver, with associated increases in the production of adipokines and cytokines. Immune cells including macrophages are recruited and activated, acting as mediators in the initiation of insulin resistance. Portal flow of adipokines contributes to hepatic inflammation and insulin resistance. Proinflammatory mediators are produced in the adipose tissue and liver and associated immune cells. This environment predisposes to a systemic inflammatory state that promotes insulin resistance and endothelial dysfunction.

Caption: Fig. 3. Omics-based biomarkers in cardiometabolic research.
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Author:Aleksandrova, Krasimira; Mozaffarian, Dariush; Pischon, Tobias
Publication:Clinical Chemistry
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Date:Jan 1, 2018
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