Metabolic syndrome (MetS) was developed by the National Cholesterol Education Program Adult Treatment Panel III, identifying adults with at least 3 of 5 cardiometabolic risk factors (hyperglycemia, increased central adiposity, elevated triglycerides, decreased high-density lipoprotein cholesterol, and elevated blood pressure) who are at increased risk of diabetes and cardiovascular disease. The constellation of MetS component risk factors has a shared pathophysiology and many common treatment approaches grounded in lifestyle modification. Several attempts have been made to define MetS in the pediatric population. However, in children, the construct is difficult to define and has unclear implications for clinical care. In this Clinical Report, we focus on the importance of screening for and treating the individual risk factor components of MetS. Focusing attention on children with cardiometabolic risk factor clustering is emphasized over the need to define a pediatric MetS.

Cardiovascular disease (CVD) risk factor clustering has been well recognized for decades in both children and adults, but it was not until 1988 when Gerald Reaven described a specific clustering of cardiometabolic risks as “syndrome X” that the concept that evolved into “the metabolic syndrome” (MetS) was born. Reaven’s syndrome X was an explanatory framework to understand the myriad effects of hyperinsulinemia and insulin resistance on physiology, not a diagnostic category.1 His formulation of syndrome X described mechanisms underlying insulin resistance and the effects of hyperinsulinemia on glucose and lipid metabolism, blood pressure, and coronary artery disease risk. Over time, the risk factors associated with syndrome X grew to include other factors, such as central obesity, microalbuminuria, abnormalities in fibrinolysis, and inflammation.1,2 Dissemination of the concept of syndrome X promulgated the idea of insulin resistance causing a constellation of factors that increased diabetes and CVD risk.

After publication of Reaven’s landmark article, clustering of CVD risks was variously described as insulin resistance syndrome, syndrome X, and the dysmetabolic syndrome. In 2001, the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) coined the term “metabolic syndrome” to describe the presence of any 3 of 5 particular risks: hyperglycemia, hypertriglyceridemia, central adiposity, elevated blood pressure, and low high-density lipoprotein cholesterol (HDL-C). Research on MetS has increased dramatically since 2001, with more than 1000 articles per year published on this topic since 2006. MetS has been associated with both diabetes and CVD in adults. Insulin resistance, obesity, aging, inflammation, hormonal factors, sedentary lifestyle, dietary sugar intake, and genetics all have been implicated in the pathogenesis of MetS.1,6 

Despite this vast literature, MetS remains a controversial topic in pediatrics for several reasons. First, MetS is challenging to define in pediatric populations. MetS in adults consists of a subset of at least 3 out of 5 risk factors: increased central adiposity, elevated triglycerides, decreased HDL-C, elevated blood pressure, and hyperglycemia. In adults, MetS (the presence of 3 or more of these risks) is predictive of CVD and type 2 diabetes mellitus.3,7 In children and adolescents, however, many different definitions of MetS have been proposed (Table 1), and there is no clear consensus on which to use.8,9 In addition, because the majority of MetS cases in childhood and adolescence occur in individuals with obesity, the utility of MetS as a construct above and beyond obesity itself has been questioned.8,10,12 Regardless of the definition used, there is no uniform way to treat MetS when it is diagnosed other than weight management. Instead, each risk factor must be treated individually, which leaves pediatricians wondering whether they should (and how to) define MetS in their patients. Our purpose with this Clinical Report is to provide an overview of the current state of the field in relation to MetS in pediatric populations. Given its name recognition, MetS terminology will be used in this report. However, the clinical relevance of MetS lies in its ability to be used as an organizational framework for the identification of cardiometabolic risk factor clustering. Recommendations for pediatricians regarding how to approach the concept of MetS in children and adolescents are provided.

TABLE 1

Comparison of Key Published MetS Definitions for Pediatric and Adult Populations

Pediatric DefinitionsAdult Definitions
Cook et al13 de Ferranti et al14 Zimmet et al9 (IDF Definition Ages 10–16)Alberti et al15 (IDF Definition Ages 16+)Grundy et al16 (AHA/NHLBI Consensus Statement)
Defining criterion ≥3 criteria ≥3 criteria Obesity and at least 2 of remaining 4 criteria Obesity and at least 2 of remaining 4 criteria ≥3 criteria 
Obesity WC ≥90th percentile (age and sex specific, NHANES III) WC >75th percentile WC ≥90th percentile or adult cutoff if lower WC ≥94 cm for white men and ≥80 cm for white women WC ≥102 cm (≥40 in) in men and WC ≥88 cm (≥35 in) in women 
Glucose intolerance Fasting glucose ≥110 mg/dL (≥6.1 mmol/L) Fasting glucose ≥110 mg/dL (≥6.1 mmol/L) Fasting glucose ≥100 mg/dL (>5.6 mmol/L) or known type 2 diabetes mellitus Fasting glucose ≥100 mg/dL (>5.6 mmol/L) or known type 2 diabetes mellitus Fasting glucose ≥100 mg/dL or drug treatment of elevated glucose 
Dyslipidemia (triglycerides) Triglycerides ≥110 mg/dL Triglycerides ≥100 mg/dL Triglycerides ≥150 mg/dL Triglycerides ≥150 mg/dL (1.7 mmol/L) or treatment of elevated triglycerides Triglycerides ≥150 mg/dL (1.7 mmol/L) or treatment of elevated triglycerides 
Dyslipidemia (HDL-C) HDL-C ≤40 mg/dL (1.03 mmol/L; all ages and sexes, NCEP) HDL-C ≤50 mg/dL (1.3 mmol/L) HDL-C <40 mg/dL (1.03 mmol/L) HDL-C <40 mg/dL (1.03 mmol/L) in men and <50 mg/dL (<1.29 mmol/L) in women or specific treatment of low high-density lipoprotein HDL-C <40 mg/dL (1.03 mmol/L) in men and <50 mg/dL (1.3 mmol/L) in women or on drug treatment of reduced HDL-C 
High BP BP ≥90th percentile (age, sex, and height specific) BP >90th percentile Systolic BP ≥130 mm Hg or diastolic BP ≥85 mm Hg or treatment of previously diagnosed hypertension Systolic BP ≥130 mm Hg or diastolic BP ≥85 mm Hg or treatment of previously diagnosed hypertension Systolic BP ≥130 mm Hg or diastolic BP ≥85 mm Hg or treatment of previously diagnosed hypertension 
Pediatric DefinitionsAdult Definitions
Cook et al13 de Ferranti et al14 Zimmet et al9 (IDF Definition Ages 10–16)Alberti et al15 (IDF Definition Ages 16+)Grundy et al16 (AHA/NHLBI Consensus Statement)
Defining criterion ≥3 criteria ≥3 criteria Obesity and at least 2 of remaining 4 criteria Obesity and at least 2 of remaining 4 criteria ≥3 criteria 
Obesity WC ≥90th percentile (age and sex specific, NHANES III) WC >75th percentile WC ≥90th percentile or adult cutoff if lower WC ≥94 cm for white men and ≥80 cm for white women WC ≥102 cm (≥40 in) in men and WC ≥88 cm (≥35 in) in women 
Glucose intolerance Fasting glucose ≥110 mg/dL (≥6.1 mmol/L) Fasting glucose ≥110 mg/dL (≥6.1 mmol/L) Fasting glucose ≥100 mg/dL (>5.6 mmol/L) or known type 2 diabetes mellitus Fasting glucose ≥100 mg/dL (>5.6 mmol/L) or known type 2 diabetes mellitus Fasting glucose ≥100 mg/dL or drug treatment of elevated glucose 
Dyslipidemia (triglycerides) Triglycerides ≥110 mg/dL Triglycerides ≥100 mg/dL Triglycerides ≥150 mg/dL Triglycerides ≥150 mg/dL (1.7 mmol/L) or treatment of elevated triglycerides Triglycerides ≥150 mg/dL (1.7 mmol/L) or treatment of elevated triglycerides 
Dyslipidemia (HDL-C) HDL-C ≤40 mg/dL (1.03 mmol/L; all ages and sexes, NCEP) HDL-C ≤50 mg/dL (1.3 mmol/L) HDL-C <40 mg/dL (1.03 mmol/L) HDL-C <40 mg/dL (1.03 mmol/L) in men and <50 mg/dL (<1.29 mmol/L) in women or specific treatment of low high-density lipoprotein HDL-C <40 mg/dL (1.03 mmol/L) in men and <50 mg/dL (1.3 mmol/L) in women or on drug treatment of reduced HDL-C 
High BP BP ≥90th percentile (age, sex, and height specific) BP >90th percentile Systolic BP ≥130 mm Hg or diastolic BP ≥85 mm Hg or treatment of previously diagnosed hypertension Systolic BP ≥130 mm Hg or diastolic BP ≥85 mm Hg or treatment of previously diagnosed hypertension Systolic BP ≥130 mm Hg or diastolic BP ≥85 mm Hg or treatment of previously diagnosed hypertension 

BP, blood pressure; HDL-C, high-density lipoprotein cholesterol; WC, waist circumference.

The pathophysiologic origins of MetS are in insulin resistance, a physiologic state associated with obesity. Insulin binds to receptors on multiple tissues of the body, including liver, fat, muscle, and blood vessels, with a myriad of effects (Fig 1). Insulin secreted by the pancreatic β cells travels to the liver via the portal system, where it normally acts to suppress glucose production. In the insulin-resistant state, the suppression of hepatic glucose production is impaired, resulting in abnormal glucose homeostasis. However, even in an insulin-resistant state, not all insulin effects are impaired; there is “selective” insulin resistance.17 For unknown reasons, insulin action stimulating hepatic lipogenesis is not impaired, causing the release of free fatty acids and triglycerides into the circulation. This results in dyslipidemia and ectopic adipose deposition.6 The MetS dyslipidemia pattern consists of elevated triglycerides, low HDL-C, relatively normal low-density lipoprotein cholesterol, and increased small, dense low-density lipoprotein particles,18 which are known to be atherogenic and to increase cardiovascular risk.

FIGURE 1

Proposed mechanisms for the clustering of MetS traits and the increased risk of type 2 diabetes mellitus and CVD. CRP, C-reactive protein; FFA, free fatty acids; IL-6, interleukin 6; LDL-C, low-density lipoprotein cholesterol; PAI-1, plasminogen activator inhibitor 1; TNF α, tumor necrosis factor α. (Reprinted with permission from Samson SL, Garber AJ. Metabolic syndrome. Endocrinol Metab Clin North Am. 2014;43[1]:23.)

FIGURE 1

Proposed mechanisms for the clustering of MetS traits and the increased risk of type 2 diabetes mellitus and CVD. CRP, C-reactive protein; FFA, free fatty acids; IL-6, interleukin 6; LDL-C, low-density lipoprotein cholesterol; PAI-1, plasminogen activator inhibitor 1; TNF α, tumor necrosis factor α. (Reprinted with permission from Samson SL, Garber AJ. Metabolic syndrome. Endocrinol Metab Clin North Am. 2014;43[1]:23.)

Close modal

One of the major clinical consequences of insulin resistance is adipose tissue dysfunction, or “adiposopathy.” As adipose expands, the cells hypertrophy, and these hypertrophic adipose cells are more resistant to insulin’s action to suppress lipolysis. These large adipocytes also secrete increased proinflammatory chemokine monocyte chemoattractant protein-1.19 As stated previously, insulin action stimulating fatty acid synthesis is preserved, promoting adipose tissue expansion. MetS is characterized by increased visceral as opposed to subcutaneous fat as well as ectopic fat deposited in abnormal locations, such as the liver.6 Ectopic fat distribution results in the release of adipocytokines, causing a state of low-grade inflammation, with increased inflammatory factors, such as plasminogen activator inhibitor-1, tumor necrosis factor α, interleukin 6, and acute phase reactants such as high-sensitivity C-reactive protein and fibrinogen.20 The endoplasmic reticulum acts as a nutrient sensor. Energy or nutrient excess can trigger endoplasmic reticulum stress, resulting in activation of inflammatory pathways, increased reactive oxygen species production, and mitochondrial dysfunction.21 Some emphasize the importance of the inflammatory state, with insulin resistance being a consequence of inflammation.20 Irrespective of what is the consequence or cause, insulin resistance, ectopic fat distribution, and inflammation are all key pathologic players in the components of MetS.

At least 5 health organizations have created clinical criteria for defining either the insulin resistance syndrome or MetS among adults: the World Health Organization (WHO),22 the NCEP’s ATP III,23 the American Association of Clinical Endocrinologists/American College of Endocrinology,24 the International Diabetes Federation (IDF),25 and the American Heart Association (AHA) in conjunction with the National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health.16 A detailed comparison of these definitions is beyond the scope of this report. The definitions differ significantly, with most but not all requiring a minimum number of risk factors, some excluding those with a diagnosis of type 2 diabetes mellitus, and most differing in the types, required number, and specific cut points for the criterion risk factors. As noted previously, in 2001, the NCEP first developed the “any 3 of 5” risk criteria definition. The 5 risks included in the NCEP ATP III definition are (1) hyperglycemia, (2) hypertriglyceridemia, (3) low HDL-C, (4) hypertension, and (5) increased waist circumference. In 2005, the AHA/NHLBI modified this definition by revising the glucose cut point down and adding allowance for drug treatment of dyslipidemia and impaired fasting glucose. That same year, the IDF introduced its “worldwide” definition of MetS,25 lowering the waist circumference cut points for certain racial and ethnic groups and putting greater emphasis on abdominal obesity by making it a necessary criterion for MetS diagnosis.

Although the AHA/NHLBI and IDF definitions have many similarities, there are important differences between them with respect to cut points of the various component risks (Table 1). The differences in these definitions have important implications for case identification. For example, an adult with hyperglycemia, hypertriglyceridemia, and low HDL-C but with a normal waist circumference would have MetS according the NCEP but not the IDF. In contrast, a person with hyperinsulinemia, low HDL-C, and obesity would have MetS according to the WHO criteria but not per the NCEP guidelines because hyperinsulinemia is not a risk factor used by the NCEP. These differences between definitions lead to differences in their prognostic ability and case identification.3,26 For example, in one of the earliest articles on MetS in adolescents, Goodman et al27 found a greater than twofold difference in the prevalence of WHO-defined MetS compared with NCEP-defined MetS in the Princeton School District Study.

Definitions of MetS for children and adolescents have been even more varied than the definitions used for adults. The first researchers addressing MetS in a pediatric population focused on adolescents.13,14,27,29 Even researchers that used the same database (the National Health and Nutrition Examination Survey III) had divergent prevalence estimates, ranging from 4.2%13 to 9.2%,14 a difference of greater than twofold. More than 40 different pediatric definitions of MetS have been used.30 In 2007, the IDF brought together an international group of experts and developed a consensus definition.9 In that report, the IDF recommended that pediatric MetS be based on the adult IDF definition but that it should only apply to children 10 years and older and that, among those between 10 and 16 years of age, the 90th percentile for waist circumference or adult cut point (whichever was lower) should define abdominal obesity. The IDF stated that for those 16 years and older, adult criteria should apply. Two years later, the AHA published its scientific statement on MetS in children and adolescents,8 which emphasized the need to identify pediatric cardiometabolic risks and noted that only some of these were found in the criteria used to define MetS. The AHA did not include a definition of MetS for use in pediatric populations and indeed made particular note of the limitations of adapting definitions derived for adults to pediatric populations. To date, there is no clear consensus on whether MetS should be defined in pediatric populations and, if defined, which definition to use.8,9 

The controversy over the utility of MetS in pediatrics goes beyond its definition. In adulthood, MetS predicts CVD and type 2 diabetes mellitus.2,31 Malik et al7 found that compared with those who have no MetS risk factors, the hazard ratio for coronary heart disease mortality was 2.87 for those with MetS without diabetes and 5.02 for those with MetS and diabetes. Depending on the definition used, Laaksonen et al3 found that the odds ratio (OR) for men with MetS developing diabetes in the 4-year follow-up period was 5 to 8.8. Data from the Princeton Prevalence and Follow-up Studies demonstrated that pediatric MetS predicted adult MetS with an OR of 9.4 and adult type 2 diabetes mellitus with an OR of 11.5; this study arbitrarily used 2 different MetS definitions.32 However, the utility of the syndrome in adolescents has also been questioned, given studies indicating instability of the definition when transitioning from adolescence to adulthood.10,33,35 Large proportions of children defined as having MetS during childhood do not meet the diagnostic criteria on follow-up 3 to 6 years later.10,34 In multiple observational longitudinal studies, although population-level prevalence has increased, within-person variation in presence or absence of MetS has been large, with many studies showing 50% or more of MetS-positive subjects becoming MetS-negative over time, whether that be with short-term (∼3 weeks)36 or longer-term (9 years)35 follow-up. The instability was not related to change in weight status.35 Thus, MetS is highly unstable throughout childhood. A child can meet the criteria at 1 point in time and not meet it at another point in time, and it is unclear whether this variation represents an improvement or deterioration in health status.35 

Ethnic or racial differences in rates of obesity and MetS components also exist. Hispanic and black non-Hispanic children demonstrate higher rates of obesity than white non-Hispanic children across age categories.37 However, similar to adults, black non-Hispanic youth demonstrate lower rates of dyslipidemia,38 greater insulin resistance, and higher blood pressure than white non-Hispanic and Hispanic youth.8 Hispanic children have increased dyslipidemia (elevated total cholesterol, low HDL-C, or high non–HDL-C) compared with black non-Hispanic and white non-Hispanic children.39 Because of the racial and/or ethnic differences in dyslipidemia, and despite increased prevalence of obesity and greater risk for type 2 diabetes mellitus, black non-Hispanic youth have a lower prevalence of MetS than white non-Hispanic or Hispanic youth,28 which can lead to an underestimation of cardiometabolic risk.40 

Given the absence of a consensus on the definition of MetS, the unstable nature of MetS, and the lack of clarity about the predictive value of MetS for future health in pediatric populations, pediatricians are rightly confused about MetS. The high prevalence of pediatric obesity and limited resources to address the obesity problem in pediatrics reveal the need to identify a subset of children with obesity or who are overweight and at increased risk for cardiovascular and metabolic complications beyond the physical complications of obesity. Although obesity is, in general, associated with increased mechanical stress and potential orthopedic complications, not all children with obesity manifest metabolic dysregulation as a consequence of their obesity. Identifying children with multiple metabolic derangements allows targeting of focused interventions toward children at the highest risk for cardiometabolic disease. Thus, rather than focus on defining MetS in youth, the American Academy of Pediatrics (AAP) recommends that pediatricians focus on the concept of cardiovascular risk factor clustering and associated risk factor screening. This concept is especially important because the Bogalusa Heart Study demonstrated substantially increased development and severity of atherosclerotic lesions associated with increased clustering of atherosclerotic CVD risk factors.41 Furthermore, the AAP recommends that pediatricians do not need to use cut points based on MetS definitions. The MetS construct identifies multiple component risk factors that appear to cluster together and whose pathologic origins arise from insulin resistance and adiposopathy. Much of the discrepancy in definitions derives from differences in these thresholds. Moreover, for many risk factors, the risk is a continuum. Continuous variables may be more reliable in predicting young adult risk from early adolescence and might help determine future risk.42 A number of researchers have used factor analysis of MetS components to develop a continuous risk score measure to identify children at higher risk for developing a chronic disease related to MetS into adulthood.43,44 Although such work currently is not clinically applicable, with advances in research and development of clinically applicable risk score guidelines, a continuous risk score approach may be created for use in general pediatric practice in the future. At the moment, however, risk factor screening and identification of youth with MetS risk factor abnormalities allow providers to target scarce resources to children at increased cardiometabolic risk, particularly those with multiple component abnormalities. Such screening and associated treatment (see below) is an important component of preventive pediatric care.

There are multiple determinants of the 5 risk factors currently used to define MetS in adolescents or in adults. Familial influences include shared genetic and environmental factors, which combine to make heritability of these individual MetS components strong. Twin and family studies have revealed substantial familial aggregation of MetS risk factors. Family history of atherosclerotic CVD is a well-known genetic risk for high lipid concentrations, high blood pressure, and high glucose concentration.45 Obesity, at the core of MetS, is itself highly heritable through shared genetic and environmental factors. If a parent is obese, his or her child is twice as likely to be obese, and conversely, more than half of children with obesity have at least 1 parent with obesity.46 

Several MetS risk factors have origins during the prenatal and early postnatal period. The presence of maternal gestational diabetes; low birth weight, especially with rapid catch-up growth; infant feeding practices (restrictive and pressuring); and early adiposity rebound are associated with later development of obesity and other MetS components.8,9 Throughout childhood and adolescence, socioeconomic factors and parental obesity also affect development of the 5 MetS component risk factors.8,9 

Health behavioral factors also are associated with and can predict the presence of MetS risks, particularly obesity, in youth. Specific behaviors include short duration of sleep, excessive screen time, specific dietary factors, low physical activity, and tobacco smoke exposure.47,48 Even after controlling for demographic factors, the number of hours a child spends each day in front of a screen is directly related to BMI and calories consumed per day and inversely related to minutes of physical activity.49 New AAP policies discourage screen use except for video chatting before 18 to 24 months of age and recommend that pediatricians help families develop a Family Media Use Plan specific for each child that ensures entertainment screen time does not displace healthy behavioral factors, such as adequate sleep and physical activity. (The AAP Family Media Use plan is available at www.healthychildren.org/MediaUsePlan.)50,51 Physical activity is beneficial for weight management, and it has also been negatively associated with MetS and factors that overlap with MetS, independent of weight status. Short sleep duration inversely predicts cardiometabolic risk in adolescents with obesity, even when controlling for degree of obesity and levels of physical activity.52 Some studies in adults and children have found a U-shaped relationship between sleep duration and cardiometabolic risk, with either too much or too little sleep being problematic.53,55 Although exact mechanisms remain unknown, factors related to inflammation, oxidative stress, and antioxidant status are thought to mediate the sleep duration–cardiometabolic health relationship.56 

Among the multiple dietary factors associated with obesity, lack of whole grain and fiber intake is most strongly correlated with the development of insulin resistance even after adjusting for BMI.57 Higher consumption of fruits and vegetables, which contribute dietary fiber as well as micronutrients, is known to reduce risk of atherosclerotic CVD, an end point of MetS in adulthood.58 

Comorbidities of MetS, insulin resistance, and obesity include nonalcoholic fatty liver disease (NAFLD), polycystic ovary syndrome (PCOS), obstructive sleep apnea (OSA), and mental health disorders. NAFLD represents a spectrum of damage to the liver, from steatosis to fibrosis and cirrhosis. NAFLD is defined by having liver fat >5% liver weight (not caused by alcohol consumption) and is strongly associated with insulin resistance.59 Although there is not a consensus about testing frequency among professional organizations, current AAP recommendations, published in 2007, suggest biannual screening for NAFLD by measuring aspartate aminotransferase and alanine aminotransferase among children with BMI at or greater than the 85th percentile.60 

The risk for PCOS is increased in girls with obesity. PCOS is characterized by hyperandrogenism (elevated free testosterone), menstrual irregularities and/or ovulatory dysfunction, and polycystic ovaries. Obesity and insulin resistance (with resulting hyperinsulinemia) are associated with PCOS as well as with increased free testosterone and ovarian and adrenal hyperandrogenism. The increased luteinizing hormone pulse frequency and increased luteinizing hormone–follicle-stimulating hormone ratio observed in PCOS (although not part of diagnostic criteria) result in increased androgen secretion from theca cells in the ovaries.61 

Obesity and type 2 diabetes mellitus have been associated with worse mental health, including increased risk for anxiety and depression.60,62,63 Chronic disease is a well-recognized stressor, and obesity is associated with social stigma and discrimination. Thus, obesity and diabetes screening guidelines often recommend mental health screening, as do the current AAP recommendations for children who are overweight or children with obesity.60 

OSA is a condition characterized by complete or partial obstruction of the upper airway and is associated with obesity. OSA causes sleep fragmentation, intermittent hypoxia, and increased negative airway pressure in the thoracic cavity.64 Obesity increases the risk for OSA because of enlarged soft tissues in and around the airway as well as decreased lung volumes because of increased abdominal fat.64 Interestingly, OSA is independently associated with CVD, insulin resistance, type 2 diabetes mellitus, and endothelial dysfunction and is related to hypertension. Moreover, studies have revealed that treatment of OSA improves multiple components of MetS, such as blood pressure, lipids, and glucose control.65,66 As in MetS, the comorbid conditions mentioned here share associations with insulin resistance and obesity, which potentially play a role in their pathology as well.

Given the complexity of defining MetS in adolescence, the evolving understanding of MetS, and the lack of consensus regarding definition, it is not surprising that there is no consensus as to whether or how MetS should be identified in pediatric populations, particularly adolescents. However, there is a consensus among the American Diabetes Association and AHA that obesity prevention and treatment in childhood and adolescence should be the first-line approach to alleviating cardiometabolic risk.67 Published guidelines recommend that primary care clinicians perform annual obesity screening for all children by using BMI and refer children with BMI at or greater than the 95th percentile to a comprehensive weight-management program.60,68,69 In practice, it is sometimes not possible to refer all such children to a comprehensive program. Pediatricians can develop the expertise and resources necessary to manage these patients themselves, especially when no comprehensive program exists in their catchment area.

In addition to obesity screening with BMI, children should be screened annually for elevated blood pressure in primary care by using auscultatory methods for obtaining blood pressure.69 Nonfasting non–HDL-C or fasting lipid screening should be performed in all children between the ages of 9 and 11 years.69 This approach will help to identify children with genetic forms of dyslipidemia and will also identify those with high triglycerides and low HDL-C because of metabolic problems. Although insulin resistance is the key to the etiology of MetS, the Insulin Resistance Consensus group did not recommend screening for insulin resistance with fasting insulin.70 Screening for glucose intolerance and type 2 diabetes mellitus is important because hyperglycemia is one of the MetS component risks. Risk factors for type 2 diabetes mellitus include overweight or obesity, belonging to a high-risk racial and/or ethnic group, family history of type 2 diabetes mellitus, physical signs of insulin resistance (acanthosis nigricans), PCOS, dyslipidemia, or hypertension. Methods of screening have included the oral glucose tolerance, hemoglobin A1c, fasting glucose, and random glucose tests.67,71 The authors of the expert committee obesity guidelines from 2007 recommended that children 10 years or older (or pubertal) with a BMI at or greater than the 85th percentile and 2 additional risk factors be screened with a fasting glucose test every 2 years.60 

Treatment of MetS involves both behavioral and pharmacotherapeutic interventions aimed at reducing obesity, glucose abnormalities, hypertension, and dyslipidemia. Once identified, pediatricians should treat these component risk factors by using current best practices (summarized or referenced later in this report) to reduce future risk for cardiometabolic disease.

Obesity treatment is grounded in lifestyle modification, and early treatment of obesity in childhood and adolescence is recommended as the first-line approach to reducing cardiometabolic risk.60,67,69 Obesity is a more stable trait than MetS, more likely to be present at multiple points in time, and more likely to persist into adulthood. Furthermore, treatment of obesity and treatment of MetS components share many common elements, and interventions that improve 1 condition are likely to ameliorate the other. Meta-analyses of pediatric lifestyle intervention studies have revealed that dietary modification and increased physical activity decrease weight and also improve cardiometabolic risk factors such as dyslipidemia and hypertension.68,72 Decreased obesity also results in decreases in insulin resistance and inflammatory markers.73 Good evidence suggests that moderate- to high-intensity weight-loss programs combined with behavioral counseling, negative energy balance diets, and increased physical activity, can successfully address obesity.68 Combining diet and exercise is more effective at achieving decreases in BMI than either intervention in isolation. No researchers have demonstrated evidence for recommending a specific dietary plan because appropriate restriction of calories is the main issue. Low-glycemic-load diets and low-carbohydrate diets may be more effective than low-fat diets in reducing weight and improving CVD risk, at least in the short-term.69 Specific lifestyle targets that have demonstrated efficacy in reducing BMI include substitution of sugar-sweetened beverages with water, milk, or artificially sweetened beverages74,77 and reducing television or screen time.77,79 It is important to note that achieving a normal BMI is not necessary to decrease cardiometabolic risk. Studies have revealed that weight loss and improvement in BMI by 5% to 10% can have metabolic benefits.80 

The mechanisms that explain the association between lifestyle modification and effects on MetS components are not fully understood. Dietary interventions that lower intake of simple sugars may reduce stimulus for insulin production. Reducing mitochondrial substrate by caloric restriction, particularly lipogenic substrates,6 could also be effective. In addition, increased dietary fiber intake decreases the glycemic load to the liver. Increased physical activity improves mitochondrial efficiency, which is preventive against MetS,6 and improves insulin sensitivity. As activity levels increase, inflammatory cytokines and markers of oxidative stress decrease, insulin sensitivity increases, endothelial function improves, and HDL-C concentrations increase.81 Time spent in moderate to vigorous physical activity is inversely associated with a MetS continuous risk score, and those who spent at least 88 minutes per day in moderate to vigorous physical activity were least likely to have MetS.82 

Pharmacotherapeutic options to treat obesity in children are limited.83 Currently, only orlistat has an FDA indication for weight loss in adolescents as young as 12 years of age. Orlistat, an intestinal lipase inhibitor, results in a mean 3% weight loss (on the basis of starting weight) at 6 months.84,85 Adverse effects include steatorrhea and flatulence, making it difficult to use in practice. Insurance coverage for orlistat is variable.85 Bariatric surgery in adolescents is effective86 and reserved for the most severely affected.

Treatment of MetS risk factor components is well described in several evidence-based guidelines. The authors of the NHLBI Expert Panel guidelines, published in 2011, provide evidence-based guidance for dietary and pharmacotherapeutic treatment of dyslipidemia and hypertension in children and adolescents. The type of dyslipidemia associated with MetS usually is treated with lifestyle intervention only, not with pharmacologic agents.69 Treatment of insulin resistance involves lifestyle modification only. Anecdotally, some providers are using metformin to treat children and adolescents who have insulin resistance with normal glucose concentrations. Although some studies have revealed beneficial effects of metformin on BMI and homeostatic model assessment of insulin resistance score in adolescents with insulin resistance, these trials were only 6 months in length and involved small numbers of subjects.87 Thus, metformin is not currently recommended for treatment of insulin resistance.70 No consensus exists in the pediatric diabetes community as to treatment of prediabetes in children, other than lifestyle management. Children found to have prediabetes or type 2 diabetes mellitus on screening can be referred to a pediatric endocrinologist for management and/or monitoring.88 It is also critical to screen for and address any comorbid conditions, such as PCOS or OSA, which often share the causal link of insulin resistance with MetS component risk factors.

MetS evolved from Reaven’s concept of syndrome X, a tool used to understand the many effects of insulin resistance on human physiology. In adults, a diagnosis of MetS is associated with an increased risk for CVD and diabetes. In pediatrics, there remain many unanswered questions regarding the definition of and utility of the diagnosis of MetS. Therefore,

  1. although pediatricians can use MetS as an organizing frame, the focus for clinical screening and treatment should be on cardiometabolic risk factors, many of which cluster together and are associated with obesity;

  2. pediatricians should not focus the specific levels of cardiometabolic risk factors from the multitude of MetS definitions because the risk lies on a continuum and in the context of the whole child;

  3. by following current recommendations to screen for and treat obesity, glucose abnormalities, hypertension, and dyslipidemia, pediatricians are addressing the major MetS-associated cardiometabolic risks in pediatric populations;

  4. identification of children with multiple component risks enables pediatricians to apply their most intensive intervention efforts to the children and adolescents in greatest need of risk reduction; and

  5. increasing awareness of comorbid conditions such as NAFLD, mental health disorders, PCOS, and OSA enables pediatricians to address and refer to specialists, as needed.

Continued efforts to prevent and treat obesity and its associated metabolic abnormalities among children and adolescents and vigilant attention to the early diagnosis of diabetes provide the pediatrician with the most evidence-based methods for addressing cardiometabolic risk factor clustering (MetS) in adolescence.

     
  • AAP

    American Academy of Pediatrics

  •  
  • AHA

    American Heart Association

  •  
  • ATP III

    Adult Treatment Panel III

  •  
  • CVD

    cardiovascular disease

  •  
  • HDL-C

    high-density lipoprotein cholesterol

  •  
  • IDF

    International Diabetes Federation

  •  
  • MetS

    metabolic syndrome

  •  
  • NAFLD

    nonalcoholic fatty liver disease

  •  
  • NCEP

    National Cholesterol Education Program

  •  
  • NHLBI

    National Heart, Lung, and Blood Institute

  •  
  • OR

    odds ratio

  •  
  • OSA

    obstructive sleep apnea

  •  
  • PCOS

    polycystic ovary syndrome

  •  
  • WHO

    World Health Organization

Dr Magge served as the lead author and organized the writing and revising efforts of the team, conceptualized and drafted the initial manuscript, and critically reviewed the revised manuscript; Drs Goodman and Armstrong conceptualized and drafted the initial manuscript and critically reviewed the revised manuscript; and all authors approved the final manuscript as submitted.

This document is copyrighted and is property of the American Academy of Pediatrics and its Board of Directors. All authors have filed conflict of interest statements with the American Academy of Pediatrics. Any conflicts have been resolved through a process approved by the Board of Directors. The American Academy of Pediatrics has neither solicited nor accepted any commercial involvement in the development of the content of this publication.

Clinical reports from the American Academy of Pediatrics benefit from expertise and resources of liaisons and internal (AAP) and external reviewers. However, clinical reports from the American Academy of Pediatrics may not reflect the views of the liaisons or the organizations or government agencies that they represent.

The guidance in this report does not indicate an exclusive course of treatment or serve as a standard of medical care. Variations, taking into account individual circumstances, may be appropriate.

All clinical reports from the American Academy of Pediatrics automatically expire 5 years after publication unless reaffirmed, revised, or retired at or before that time.

FUNDING: No external funding.

1
Reaven
GM
.
Banting lecture 1988. Role of insulin resistance in human disease.
Diabetes
.
1988
;
37
(
12
):
1595
1607
[PubMed]
2
Meigs
JB
.
Invited commentary: insulin resistance syndrome? Syndrome X? Multiple metabolic syndrome? A syndrome at all? Factor analysis reveals patterns in the fabric of correlated metabolic risk factors.
Am J Epidemiol
.
2000
;
152
(
10
):
908
911
3
Laaksonen
DE
,
Lakka
HM
,
Niskanen
LK
,
Kaplan
GA
,
Salonen
JT
,
Lakka
TA
.
Metabolic syndrome and development of diabetes mellitus: application and validation of recently suggested definitions of the metabolic syndrome in a prospective cohort study.
Am J Epidemiol
.
2002
;
156
(
11
):
1070
1077
[PubMed]
4
Yip
J
,
Facchini
FS
,
Reaven
GM
.
Resistance to insulin-mediated glucose disposal as a predictor of cardiovascular disease.
J Clin Endocrinol Metab
.
1998
;
83
(
8
):
2773
2776
[PubMed]
5
Grundy
SM
,
Brewer
HB
 Jr
,
Cleeman
JI
,
Smith
SC
 Jr
,
Lenfant
C
;
American Heart Association
;
National Heart, Lung, and Blood Institute
.
Definition of metabolic syndrome: Report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition.
Circulation
.
2004
;
109
(
3
):
433
438
6
Bremer
AA
,
Mietus-Snyder
M
,
Lustig
RH
.
Toward a unifying hypothesis of metabolic syndrome.
Pediatrics
.
2012
;
129
(
3
):
557
570
[PubMed]
7
Malik
S
,
Wong
ND
,
Franklin
SS
, et al
.
Impact of the metabolic syndrome on mortality from coronary heart disease, cardiovascular disease, and all causes in United States adults.
Circulation
.
2004
;
110
(
10
):
1245
1250
[PubMed]
8
Steinberger
J
,
Daniels
SR
,
Eckel
RH
, et al;
American Heart Association Atherosclerosis, Hypertension, and Obesity in the Young Committee of the Council on Cardiovascular Disease in the Young
;
Council on Cardiovascular Nursing
;
and Council on Nutrition, Physical Activity, and Metabolism
.
Progress and challenges in metabolic syndrome in children and adolescents: a scientific statement from the American Heart Association Atherosclerosis, Hypertension, and Obesity in the Young Committee of the Council on Cardiovascular Disease in the Young; Council on Cardiovascular Nursing; and Council on Nutrition, Physical Activity, and Metabolism.
Circulation
.
2009
;
119
(
4
):
628
647
[PubMed]
9
Zimmet
P
,
Alberti
KG
,
Kaufman
F
, et al;
IDF Consensus Group
.
The metabolic syndrome in children and adolescents - an IDF consensus report.
Pediatr Diabetes
.
2007
;
8
(
5
):
299
306
[PubMed]
10
Goodman
E
,
Daniels
SR
,
Meigs
JB
,
Dolan
LM
.
Instability in the diagnosis of metabolic syndrome in adolescents.
Circulation
.
2007
;
115
(
17
):
2316
2322
[PubMed]
11
Goodman
E
.
Metabolic syndrome and the mismeasure of risk.
J Adolesc Health
.
2008
;
42
(
6
):
538
540
[PubMed]
12
Goodman
E
.
Pediatric metabolic syndrome: smoke and mirrors or true magic?
J Pediatr
.
2006
;
148
(
2
):
149
151
[PubMed]
13
Cook
S
,
Weitzman
M
,
Auinger
P
,
Nguyen
M
,
Dietz
WH
.
Prevalence of a metabolic syndrome phenotype in adolescents: findings from the third National Health and Nutrition Examination Survey, 1988-1994.
Arch Pediatr Adolesc Med
.
2003
;
157
(
8
):
821
827
14
de Ferranti
SD
,
Gauvreau
K
,
Ludwig
DS
,
Neufeld
EJ
,
Newburger
JW
,
Rifai
N
.
Prevalence of the metabolic syndrome in American adolescents: findings from the third National Health and Nutrition Examination Survey.
Circulation
.
2004
;
110
(
16
):
2494
2497
15
Alberti
KG
,
Zimmet
P
,
Shaw
J
.
Metabolic syndrome–a new world-wide definition. A Consensus Statement from the International Diabetes Federation.
Diabet Med
.
2006
;
23
(
5
):
469
480
[PubMed]
16
Grundy
SM
,
Cleeman
JI
,
Daniels
SR
, et al
.
Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute scientific statement: executive summary.
Circulation
.
2005
;
112
(
17
):
2735
2752
17
Rask-Madsen
C
,
Kahn
CR
.
Tissue-specific insulin signaling, metabolic syndrome, and cardiovascular disease.
Arterioscler Thromb Vasc Biol
.
2012
;
32
(
9
):
2052
2059
[PubMed]
18
Ginsberg
HN
,
Zhang
YL
,
Hernandez-Ono
A
.
Regulation of plasma triglycerides in insulin resistance and diabetes.
Arch Med Res
.
2005
;
36
(
3
):
232
240
[PubMed]
19
Guilherme
A
,
Virbasius
JV
,
Puri
V
,
Czech
MP
.
Adipocyte dysfunctions linking obesity to insulin resistance and type 2 diabetes.
Nat Rev Mol Cell Biol
.
2008
;
9
(
5
):
367
377
[PubMed]
20
Yudkin
JS
.
Insulin resistance and the metabolic syndrome–or the pitfalls of epidemiology.
Diabetologia
.
2007
;
50
(
8
):
1576
1586
[PubMed]
21
Hotamisligil
GS
.
Endoplasmic reticulum stress and the inflammatory basis of metabolic disease.
Cell
.
2010
;
140
(
6
):
900
917
[PubMed]
22
Alberti
KG
,
Zimmet
PZ
.
Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation.
Diabet Med
.
1998
;
15
(
7
):
539
553
[PubMed]
23
Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults
.
Executive summary of the third report of the National Cholesterol Education Program (NCEP) Expert Panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III).
JAMA
.
2001
;
285
(
19
):
2486
2497
24
Einhorn
D
,
Reaven
GM
,
Cobin
RH
, et al
.
American College of Endocrinology position statement on the insulin resistance syndrome.
Endocr Pract
.
2003
;
9
(
3
):
237
252
[PubMed]
25
Alberti
KG
,
Zimmet
P
,
Shaw
J
;
IDF Epidemiology Task Force Consensus Group
.
The metabolic syndrome–a new worldwide definition.
Lancet
.
2005
;
366
(
9491
):
1059
1062
[PubMed]
26
Ford
ES
,
Giles
WH
.
A comparison of the prevalence of the metabolic syndrome using two proposed definitions.
Diabetes Care
.
2003
;
26
(
3
):
575
581
[PubMed]
27
Goodman
E
,
Daniels
SR
,
Morrison
JA
,
Huang
B
,
Dolan
LM
.
Contrasting prevalence of and demographic disparities in the World Health Organization and National Cholesterol Education Program Adult Treatment Panel III definitions of metabolic syndrome among adolescents.
J Pediatr
.
2004
;
145
(
4
):
445
451
[PubMed]
28
Duncan
GE
,
Li
SM
,
Zhou
X-H
.
Prevalence and trends of a metabolic syndrome phenotype among U.S. adolescents, 1999-2000.
Diabetes Care
.
2004
;
27
(
10
):
2438
2443
29
Cruz
ML
,
Weigensberg
MJ
,
Huang
TT
,
Ball
G
,
Shaibi
GQ
,
Goran
MI
.
The metabolic syndrome in overweight Hispanic youth and the role of insulin sensitivity.
J Clin Endocrinol Metab
.
2004
;
89
(
1
):
108
113
[PubMed]
30
Ford
ES
,
Li
C
.
Defining the metabolic syndrome in children and adolescents: will the real definition please stand up?
J Pediatr
.
2008
;
152
(
2
):
160
164
[PubMed]
31
Meigs
JB
,
D’Agostino
RB
 Sr
,
Wilson
PW
,
Cupples
LA
,
Nathan
DM
,
Singer
DE
.
Risk variable clustering in the insulin resistance syndrome. The Framingham Offspring Study.
Diabetes
.
1997
;
46
(
10
):
1594
1600
[PubMed]
32
Morrison
JA
,
Friedman
LA
,
Wang
P
,
Glueck
CJ
.
Metabolic syndrome in childhood predicts adult metabolic syndrome and type 2 diabetes mellitus 25 to 30 years later.
J Pediatr
.
2008
;
152
(
2
):
201
206
[PubMed]
33
Ventura
EE
,
Lane
CJ
,
Weigensberg
MJ
,
Toledo-Corral
CM
,
Davis
JN
,
Goran
MI
.
Persistence of the metabolic syndrome over 3 annual visits in overweight Hispanic children: association with progressive risk for type 2 diabetes.
J Pediatr
.
2009
;
155
(
4
):
535
541
[PubMed]
34
Gustafson
J
,
Easter
B
,
Keil
M
, et al
.
Instability of the diagnosis of metabolic syndrome in children.
Obesity
.
2007
;
15
(
suppl
):
A172
35
Stanley
TL
,
Chen
ML
,
Goodman
E
.
The typology of metabolic syndrome in the transition to adulthood.
J Clin Endocrinol Metab
.
2014
;
99
(
3
):
1044
1052
[PubMed]
36
Gustafson
JK
,
Yanoff
LB
,
Easter
BD
, et al
.
The stability of metabolic syndrome in children and adolescents.
J Clin Endocrinol Metab
.
2009
;
94
(
12
):
4828
4834
[PubMed]
37
Ogden
CL
,
Carroll
MD
,
Kit
BK
,
Flegal
KM
.
Prevalence of childhood and adult obesity in the United States, 2011-2012.
JAMA
.
2014
;
311
(
8
):
806
814
[PubMed]
38
Kit
BK
,
Carroll
MD
,
Lacher
DA
,
Sorlie
PD
,
DeJesus
JM
,
Ogden
C
.
Trends in serum lipids among US youths aged 6 to 19 years, 1988-2010.
JAMA
.
2012
;
308
(
6
):
591
600
[PubMed]
39
Kit
BK
,
Kuklina
E
,
Carroll
MD
,
Ostchega
Y
,
Freedman
DS
,
Ogden
CL
.
Prevalence of and trends in dyslipidemia and blood pressure among US children and adolescents, 1999-2012.
JAMA Pediatr
.
2015
;
169
(
3
):
272
279
[PubMed]
40
Yu
SS
,
Ramsey
NL
,
Castillo
DC
,
Ricks
M
,
Sumner
AE
.
Triglyceride-based screening tests fail to recognize cardiometabolic disease in African immigrant and African-American men.
Metab Syndr Relat Disord
.
2013
;
11
(
1
):
15
20
[PubMed]
41
Berenson
GS
,
Srinivasan
SR
,
Bao
W
,
Newman
WP
 III
,
Tracy
RE
,
Wattigney
WA
.
Association between multiple cardiovascular risk factors and atherosclerosis in children and young adults. The Bogalusa Heart Study.
N Engl J Med
.
1998
;
338
(
23
):
1650
1656
[PubMed]
42
Kelly
AS
,
Steinberger
J
,
Jacobs
DR
,
Hong
CP
,
Moran
A
,
Sinaiko
AR
.
Predicting cardiovascular risk in young adulthood from the metabolic syndrome, its component risk factors, and a cluster score in childhood.
Int J Pediatr Obes
.
2011
;
6
(
2–2
):
e283
e289
43
Gurka
MJ
,
Ice
CL
,
Sun
SS
,
Deboer
MD
.
A confirmatory factor analysis of the metabolic syndrome in adolescents: an examination of sex and racial/ethnic differences.
Cardiovasc Diabetol
.
2012
;
11
:
128
[PubMed]
44
Goodman
E
,
Dolan
LM
,
Morrison
JA
,
Daniels
SR
.
Factor analysis of clustered cardiovascular risks in adolescence: obesity is the predominant correlate of risk among youth.
Circulation
.
2005
;
111
(
15
):
1970
1977
[PubMed]
45
Bao
W
,
Srinivasan
SR
,
Valdez
R
,
Greenlund
KJ
,
Wattigney
WA
,
Berenson
GS
.
Longitudinal changes in cardiovascular risk from childhood to young adulthood in offspring of parents with coronary artery disease: the Bogalusa Heart Study.
JAMA
.
1997
;
278
(
21
):
1749
1754
[PubMed]
46
Whitaker
RC
,
Wright
JA
,
Pepe
MS
,
Seidel
KD
,
Dietz
WH
.
Predicting obesity in young adulthood from childhood and parental obesity.
N Engl J Med
.
1997
;
337
(
13
):
869
873
[PubMed]
47
Fadzlina
AA
,
Harun
F
,
Nurul Haniza
MY
, et al
.
Metabolic syndrome among 13 year old adolescents: prevalence and risk factors.
BMC Public Health
.
2014
;
14
(
suppl 3
):
S7
[PubMed]
48
Farber
HJ
,
Groner
J
,
Walley
S
,
Nelson
K
;
Section on Tobacco Control
.
Protecting children from tobacco, nicotine, and tobacco smoke.
Pediatrics
.
2015
;
136
(
5
). Available at: www.pediatrics.org/cgi/content/full/136/5/e1439
[PubMed]
49
Crespo
CJ
,
Smit
E
,
Troiano
RP
,
Bartlett
SJ
,
Macera
CA
,
Andersen
RE
.
Television watching, energy intake, and obesity in US children: results from the third National Health and Nutrition Examination Survey, 1988-1994.
Arch Pediatr Adolesc Med
.
2001
;
155
(
3
):
360
365
[PubMed]
50
Council on Communications and Media
.
Media and young minds.
Pediatrics
.
2016
;
138
(
5
):
e20162591
[PubMed]
51
Council on Communications and Media
.
Media use in school-aged children and adolescents.
Pediatrics
.
2016
;
138
(
5
):
e20162592
[PubMed]
52
Iglayreger
HB
,
Peterson
MD
,
Liu
D
, et al
.
Sleep duration predicts cardiometabolic risk in obese adolescents.
J Pediatr
.
2014
;
164
(
5
):
1085
1090.e1
53
Koren
D
,
Levitt Katz
LE
,
Brar
PC
,
Gallagher
PR
,
Berkowitz
RI
,
Brooks
LJ
.
Sleep architecture and glucose and insulin homeostasis in obese adolescents.
Diabetes Care
.
2011
;
34
(
11
):
2442
2447
[PubMed]
54
Yu
Y
,
Lu
BS
,
Wang
B
, et al
.
Short sleep duration and adiposity in Chinese adolescents.
Sleep
.
2007
;
30
(
12
):
1688
1697
[PubMed]
55
Ohkuma
T
,
Fujii
H
,
Iwase
M
, et al
.
Impact of sleep duration on obesity and the glycemic level in patients with type 2 diabetes: the Fukuoka Diabetes Registry.
Diabetes Care
.
2013
;
36
(
3
):
611
617
[PubMed]
56
Kanagasabai
T
,
Ardern
CI
.
Contribution of inflammation, oxidative stress, and antioxidants to the relationship between sleep duration and cardiometabolic health.
Sleep
.
2015
;
38
(
12
):
1905
1912
[PubMed]
57
Steffen
LM
,
Jacobs
DR
 Jr
,
Murtaugh
MA
, et al
.
Whole grain intake is associated with lower body mass and greater insulin sensitivity among adolescents.
Am J Epidemiol
.
2003
;
158
(
3
):
243
250
[PubMed]
58
Steffen
LM
,
Jacobs
DR
 Jr
,
Stevens
J
,
Shahar
E
,
Carithers
T
,
Folsom
AR
.
Associations of whole-grain, refined-grain, and fruit and vegetable consumption with risks of all-cause mortality and incident coronary artery disease and ischemic stroke: the Atherosclerosis Risk in Communities (ARIC) Study.
Am J Clin Nutr
.
2003
;
78
(
3
):
383
390
[PubMed]
59
Ahmed
MH
,
Barakat
S
,
Almobarak
AO
.
Nonalcoholic fatty liver disease and cardiovascular disease: has the time come for cardiologists to be hepatologists?
J Obes
.
2012
;
2012
:
1
9
60
Barlow
SE
;
Expert Committee
.
Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: summary report.
Pediatrics
.
2007
;
120
(
suppl 4
):
S164
S192
[PubMed]
61
Anderson
AD
,
Solorzano
CM
,
McCartney
CR
.
Childhood obesity and its impact on the development of adolescent PCOS.
Semin Reprod Med
.
2014
;
32
(
3
):
202
213
[PubMed]
62
Silverstein
J
,
Cheng
P
,
Ruedy
KJ
, et al;
Pediatric Diabetes Consortium
.
Depressive symptoms in youth with type 1 or type 2 diabetes: results of the pediatric diabetes consortium screening assessment of depression in diabetes study.
Diabetes Care
.
2015
;
38
(
12
):
2341
2343
[PubMed]
63
Nemiary
D
,
Shim
R
,
Mattox
G
,
Holden
K
.
The relationship between obesity and depression among adolescents.
Psychiatr Ann
.
2012
;
42
(
8
):
305
308
[PubMed]
64
Drager
LF
,
Togeiro
SM
,
Polotsky
VY
,
Lorenzi-Filho
G
.
Obstructive sleep apnea: a cardiometabolic risk in obesity and the metabolic syndrome.
J Am Coll Cardiol
.
2013
;
62
(
7
):
569
576
[PubMed]
65
Dorkova
Z
,
Petrasova
D
,
Molcanyiova
A
,
Popovnakova
M
,
Tkacova
R
.
Effects of continuous positive airway pressure on cardiovascular risk profile in patients with severe obstructive sleep apnea and metabolic syndrome.
Chest
.
2008
;
134
(
4
):
686
692
[PubMed]
66
Sharma
SK
,
Agrawal
S
,
Damodaran
D
, et al
.
CPAP for the metabolic syndrome in patients with obstructive sleep apnea [retracted in N Engl J Med. 2013;369(18):1770].
N Engl J Med
.
2011
;
365
(
24
):
2277
2286
[PubMed]
67
Steinberger
J
,
Daniels
SR
;
American Heart Association Atherosclerosis, Hypertension, and Obesity in the Young Committee (Council on Cardiovascular Disease in the Young)
;
American Heart Association Diabetes Committee (Council on Nutrition, Physical Activity, and Metabolism)
.
Obesity, insulin resistance, diabetes, and cardiovascular risk in children: an American Heart Association scientific statement from the Atherosclerosis, Hypertension, and Obesity in the Young Committee (Council on Cardiovascular Disease in the Young) and the Diabetes Committee (Council on Nutrition, Physical Activity, and Metabolism).
Circulation
.
2003
;
107
(
10
):
1448
1453
68
Whitlock
EP
,
O’Conner
EA
,
Williams
SB
,
Beil
TL
,
Lutz
KW
.
Effectiveness of Primary Care Interventions for Weight Management in Children and Adolescents: An Updated, Targeted Systematic Review for the USPSTF [Internet]
.
Rockville, MD
:
Agency for Healthcare Research and Quality (US)
;
2010
69
Expert Panel on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents
;
National Heart, Lung, and Blood Institute
.
Expert panel on integrated guidelines for cardiovascular health and risk reduction in children and adolescents: summary report.
Pediatrics
.
2011
;
128
(
suppl 5
):
S213
S256
[PubMed]
70
Levy-Marchal
C
,
Arslanian
S
,
Cutfield
W
, et al;
ESPE-LWPES-ISPAD-APPES-APEG-SLEP-JSPE
;
Insulin Resistance in Children Consensus Conference Group
.
Insulin resistance in children: consensus, perspective, and future directions.
J Clin Endocrinol Metab
.
2010
;
95
(
12
):
5189
5198
[PubMed]
71
American Diabetes Association
.
Standards of medical care in diabetes-2016: summary of revisions.
Diabetes Care
.
2016
;
39
(
suppl 1
):
S4
S5
[PubMed]
72
Ho
M
,
Garnett
SP
,
Baur
L
, et al
.
Effectiveness of lifestyle interventions in child obesity: systematic review with meta-analysis.
Pediatrics
.
2012
;
130
(
6
). Available at: www.pediatrics.org/cgi/content/full/130/6/e1647
[PubMed]
73
Meyer
AA
,
Kundt
G
,
Lenschow
U
,
Schuff-Werner
P
,
Kienast
W
.
Improvement of early vascular changes and cardiovascular risk factors in obese children after a six-month exercise program.
J Am Coll Cardiol
.
2006
;
48
(
9
):
1865
1870
[PubMed]
74
Albala
C
,
Ebbeling
CB
,
Cifuentes
M
,
Lera
L
,
Bustos
N
,
Ludwig
DS
.
Effects of replacing the habitual consumption of sugar-sweetened beverages with milk in Chilean children.
Am J Clin Nutr
.
2008
;
88
(
3
):
605
611
[PubMed]
75
Ebbeling
CB
,
Feldman
HA
,
Chomitz
VR
, et al
.
A randomized trial of sugar-sweetened beverages and adolescent body weight.
N Engl J Med
.
2012
;
367
(
15
):
1407
1416
[PubMed]
76
Ebbeling
CB
,
Feldman
HA
,
Osganian
SK
,
Chomitz
VR
,
Ellenbogen
SJ
,
Ludwig
DS
.
Effects of decreasing sugar-sweetened beverage consumption on body weight in adolescents: a randomized, controlled pilot study.
Pediatrics
.
2006
;
117
(
3
):
673
680
[PubMed]
77
French
SA
,
Sherwood
NE
,
JaKa
MM
,
Haapala
JL
,
Ebbeling
CB
,
Ludwig
DS
.
Physical changes in the home environment to reduce television viewing and sugar-sweetened beverage consumption among 5- to 12-year-old children: a randomized pilot study.
Pediatr Obes
.
2016
;
11
(
5
):
e12
e15
78
Best
JR
,
Theim
KR
,
Gredysa
DM
, et al
.
Behavioral economic predictors of overweight children’s weight loss.
J Consult Clin Psychol
.
2012
;
80
(
6
):
1086
1096
[PubMed]
79
Epstein
LH
,
Roemmich
JN
,
Robinson
JL
, et al
.
A randomized trial of the effects of reducing television viewing and computer use on body mass index in young children.
Arch Pediatr Adolesc Med
.
2008
;
162
(
3
):
239
245
[PubMed]
80
Knowler
WC
,
Barrett-Connor
E
,
Fowler
SE
, et al;
Diabetes Prevention Program Research Group
.
Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin.
N Engl J Med
.
2002
;
346
(
6
):
393
403
[PubMed]
81
Schmitz
KH
,
Jacobs
DR
 Jr
,
Hong
CP
,
Steinberger
J
,
Moran
A
,
Sinaiko
AR
.
Association of physical activity with insulin sensitivity in children.
Int J Obes Relat Metab Disord
.
2002
;
26
(
10
):
1310
1316
[PubMed]
82
Stabelini Neto
A
,
de Campos
W
,
Dos Santos
GC
,
Mazzardo Junior
O
.
Metabolic syndrome risk score and time expended in moderate to vigorous physical activity in adolescents.
BMC Pediatr
.
2014
;
14
:
42
[PubMed]
83
Yanovski
SZ
,
Yanovski
JA
.
Long-term drug treatment for obesity: a systematic and clinical review.
JAMA
.
2014
;
311
(
1
):
74
86
[PubMed]
84
McDuffie
JR
,
Calis
KA
,
Uwaifo
GI
, et al
.
Efficacy of orlistat as an adjunct to behavioral treatment in overweight African American and Caucasian adolescents with obesity-related co-morbid conditions.
J Pediatr Endocrinol Metab
.
2004
;
17
(
3
):
307
319
[PubMed]
85
McDuffie
JR
,
Calis
KA
,
Uwaifo
GI
, et al
.
Three-month tolerability of orlistat in adolescents with obesity-related comorbid conditions.
Obes Res
.
2002
;
10
(
7
):
642
650
[PubMed]
86
Inge
TH
,
Courcoulas
AP
,
Jenkins
TM
, et al;
Teen-LABS Consortium
.
Weight loss and health status 3 years after bariatric surgery in adolescents.
N Engl J Med
.
2016
;
374
(
2
):
113
123
[PubMed]
87
Park
MH
,
Kinra
S
,
Ward
KJ
,
White
B
,
Viner
RM
.
Metformin for obesity in children and adolescents: a systematic review.
Diabetes Care
.
2009
;
32
(
9
):
1743
1745
[PubMed]
88
Copeland
KC
,
Silverstein
J
,
Moore
KR
, et al;
American Academy of Pediatrics
.
Management of newly diagnosed type 2 diabetes mellitus (T2DM) in children and adolescents.
Pediatrics
.
2013
;
131
(
2
):
364
382
[PubMed]

Sheela N. Magge, MD, MSCE, FAAP

Elizabeth Goodman, MD, MBA, FAAP

Sarah C. Armstrong, MD, FAAP

Stephen Daniels, MD, PhD, FAAP, Chairperson

Mark Corkins, MD, FAAP

Sarah de Ferranti, MD, FAAP

Neville H. Golden, MD, FAAP

Jae H. Kim, MD, PhD, FAAP

Sheela N. Magge, MD, MSCE, FAAP

Sarah Jane Schwarzenberg, MD, FAAP

Carrie L. Assar, PharmD, MS – Food and Drug Administration

Jeff Critch, MD – Canadian Pediatric Society

Van Hubbard, MD, PhD, FAAP – National Institutes of Health

Kelley Scanlon, PhD – Centers for Disease Control and Prevention

Valery Soto, MS, RD, LD – US Department of Agriculture

Debra Burrowes, MHA

Irene N. Sills, MD, FAAP, Chairperson

Samuel J. Casella, MD, MSc, FAAP

Linda A. DeMeglio, MD, MPH, FAAP

Jose L. Gonzalez, MD, JD, MSEd, FAAP

Paul B. Kaplowitz, MD, FAAP, Immediate Past Chairperson

Jane L. Lynch, MD, FAAP, Chairperson Elect

Kupper A. Wintergerst, MD, FAAP

Laura Laskosz, MPH

Christopher F. Bolling, MD, FAAP, Chairperson

Sarah C. Armstrong, MD, FAAP

Natalie Digate Muth, MD, MPH, RD, FAAP

John C. Rausch, MD, MPH, FAAP

Victoria Weeks Rogers, MD, FAAP

Robert P. Schwartz, MD, FAAP

CDR Alyson Goodman, MD, MPH, FAAP – Centers for Disease Control and Prevention

Mala Thapar, MPH

Competing Interests

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.