Next Article in Journal
Effects of Heat Treatment on the Microstructure and Hardness of A356 (AlSi7Mg0.3) Manufactured by Vertical Centrifugal Casting
Next Article in Special Issue
Perivascular Adipose Tissue Inflammation: The Anti-Inflammatory Role of Ghrelin in Atherosclerosis Progression
Previous Article in Journal
Evaluation of NOx Reduction Effect and Impact on Asphalt Pavement of Surface Treatment Technology including TiO2 and Asphalt Rejuvenator
Previous Article in Special Issue
Assessment of Eating Habits and Perceived Benefits of Physical Activity and Body Attractiveness among Adolescents from Northeastern Romania
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Causative Mechanisms of Childhood and Adolescent Obesity Leading to Adult Cardiometabolic Disease: A Literature Review

Mihai Octavian Negrea
Bogdan Neamtu
Ioana Dobrotă
Ciprian Radu Sofariu
Roxana Mihaela Crisan
Bacila Ionut Ciprian
Carmen Daniela Domnariu
1 and
Minodora Teodoru
Faculty of Medicine, Lucian Blaga University, 550024 Sibiu, Romania
Research and Telemedicine Center for Neurological Diseases in Children, Pediatric Clinical Hospital Sibiu, 550166 Sibiu, Romania
Department of Electrical Engineering and Computer Science, Faculty of Engineering, Lucian Blaga University, 550012 Sibiu, Romania
Author to whom correspondence should be addressed.
Appl. Sci. 2021, 11(23), 11565;
Submission received: 1 October 2021 / Revised: 20 November 2021 / Accepted: 1 December 2021 / Published: 6 December 2021
(This article belongs to the Special Issue Trends and Prospects in Pathophysiology of Diet-Related Diseases)



Featured Application

A brief summary of the current knowledge on the mechanisms of childhood obesity and its repercussions on adult health.


The past few decades have shown a worrisome increase in the prevalence of obesity and its related illnesses. This increasing burden has a noteworthy impact on overall worldwide mortality and morbidity, with significant economic implications as well. The same trend is apparent regarding pediatric obesity. This is a particularly concerning aspect when considering the well-established link between cardiovascular disease and obesity, and the fact that childhood obesity frequently leads to adult obesity. Moreover, most obese adults have a history of excess weight starting in childhood. In addition, given the cumulative character of both time and severity of exposure to obesity as a risk factor for associated diseases, the repercussions of obesity prevalence and related morbidity could be exponential in time. The purpose of this review is to outline key aspects regarding the current knowledge on childhood and adolescent obesity as a cardiometabolic risk factor, as well as the most common etiological pathways involved in the development of weight excess and associated cardiovascular and metabolic diseases.

1. Introduction

The past few decades have shown a worrisome increase in the prevalence of obesity and its’ related illnesses [1]. This increasing burden has a noteworthy impact on overall worldwide mortality and morbidity, with significant economic implications as well [2,3]. The same trend is apparent regarding pediatric obesity [4]. This is a particularly concerning aspect when considering the well-established link between cardiovascular disease and obesity [5,6] and the fact that childhood obesity frequently leads to adult obesity [7]. Moreover, most obese adults have a history of excess weight starting in childhood [8]. In addition, given the cumulative character of both time and severity of exposure to obesity as a risk factor for associated diseases, the repercussions of obesity prevalence and related morbidity could be exponential in time [9].
When considering these aspects, it becomes apparent that early intervention for preventing obesity and its related diseases may be the optimal approach. Accurate knowledge of the underlying mechanisms that lead from health to obesity and from obesity to associated disease can prove crucial when determining a strategy for action.
The purpose of this review is to outline key aspects regarding the current knowledge on childhood and adolescent obesity as a cardiometabolic risk factor, as well as the most common etiological pathways involved in the development of weight excess and associated cardiovascular and metabolic diseases.
Our approach starts by stating the currently accepted definitions for childhood and adolescent obesity and the existing limitations in this regard (Section 2, Defining obesity). A section on the prevalence of childhood obesity follows, in order to highlight the severity of this growing global burden (Section 3, Epidemiology).
Section 4 (The anatomy of obesity) covers the particularities of fat disposition in the body, starting from a macroscopic view and focusing progressively towards the sectional aspects, to peri-organic fat depots, and finally to the microscopic and metabolic characteristics of the constitutive cells of adipose tissues. The mechanisms that lead from weight excess to pathology are discussed within each level of this approach, firstly regarding the general distribution of surface fat describing the mechanisms behind sexual dimorphism in the android versus gynoid fat disposition, as well as their link to obesity-related disease by correlating to a major culprit of cardiometabolic risk, which is central obesity. The physiopathological pathways explaining the connection between central obesity and cardiometabolic risk are discussed in the perspective of the possible role of increased lipolytic activity of visceral adipose tissue altering hepatic and general metabolism. Further on, the particular role of excess fat localized in the proximity of specific organs is presented. Excess perihepatic fat and the flawed intracellular deposition of triglycerides in hepatocytes linked to non-alcoholic fatty liver disease are presented. Epicardial, perivascular, and perirenal excess adipose tissue disposition and their detrimental effect on hemodynamics and metabolism are discussed subsequently. Section 4.4, Central obesity and metabolically healthy obesity, summarizes the importance of central obesity related to cardiometabolic risk and raises the issue of apparently metabolically healthy obesity. The final subsection increases the order of magnification in studying the mechanisms behind obesity-related disease and aims to describe the link between particular histological aspects and cardiometabolic diseases. The main topic regards the differences between hypercellular and hypertrophic adipose tissue in respect to their development in childhood versus adulthood, as well as the different cardiometabolic prognosis implied by each entity.
The information in Section 4 emphasizes the need for more refined methods to assess obesity by considering adipose tissue disposition.
Section 5 (Obesity assessment) summarizes current efforts regarding the development of techniques and parameters which better describe weight excess in correlation to the risk of obesity-related diseases. Imaging diagnosis is important to study the obesity distribution characteristics which seem to be relevant to the mechanisms linking obesity to cardiovascular function impairment and cardiometabolic diseases.
Section 6 (Determinant factors of obesity) starts with a short presentation of the physiological aspects of appetite regulation. The following subsections describe the factors that interfere with this schematic representation and can be incriminated in the shift between physiological and pathological. The rationale of this section follows the interplay between genetic causality and environmental factors while focusing on pediatric populations from conception to puberty and adolescence.
Section 7 (Childhood obesity as an adult risk factor) provides a short review of the observational evidence showing childhood obesity leading to adult disease. Section 8 (Mechanisms of obesity-related cardiometabolic disease) aims to describe the mechanisms behind these associations. These two sections mostly refer to cardiovascular and metabolic diseases and the mechanisms incriminated in their development in obese patients, i.e., arterial hypertension, ventricular hypertrophy, heart failure, atherosclerotic vascular diseases (ischemic heart disease, cerebrovascular disease, and peripheral artery disease), type 2 diabetes, and dyslipidemia.
Section 9 (Obesity biomarkers and risk assessment) aims to provide a short review of the known and novel markers associated with obesity and its related diseases, with a focus on pediatric populations, as derived mostly from observational studies.
Section 10 concludes this review, highlighting its main goal, to provide relevant data regarding the physiopathology of obesity-associated disease originating in childhood.

2. Defining Obesity

Conceptually, the World Health Organization defines obesity as “abnormal or excessive fat accumulation that presents a risk to health” [10]. Quantification of excess body fat makes use of the Body Mass Index (BMI) in adults. The BMI is defined by the following formula: BMI = BW H 2 , where BW represents body weight (kilograms) and H represents height (meters) [1].
Quantifying obesity in children can prove to be problematic, due to several reasons. Childhood and adolescence, the latter being defined by WHO as “the phase of life between childhood and adulthood, from ages 10 to 19” [11], are marked by a series of significant physiological somatic changes in relatively short periods of time; while in adults, establishing normal values using statistical analysis of anthropometric parameters can yield satisfactory guidelines, pediatric populations tend to be characterized by a greater inhomogeneity in relation to several confounding factors such as age, sex, pubertal stage, and even ethnicity [12,13]. Alternative techniques that can be implemented to quantify weight excess in children are outlined in Section 5, each with its own advantages and caveats.
The currently accepted method to determine a child’s weight status makes use of weight charts endorsed by the CDC and WHO that take into account the influence of age and gender. For children under the age of 2, the use of BMI is not recommended. In this age bracket, assessing body weight is accomplished using gender-specific weight-for-height charts. A value greater than two standard deviations above the median for this parameter defines overweight, while a value higher than three standard deviations above the median defines obesity. Gender-specific weight-for-height charts can also be used up to the age of 5. From ages 2 and up, gender-specific BMI for age charts are advocated for determining weight status. The CDC recommends the 85th and 95th percentiles as cutoff points for overweight and obesity, respectively. For children above the age of 5, the World Health Organization defines overweight by a BMI-for-age greater than one standard deviation above the WHO Growth Reference median, and obesity by a BMI-for-age greater than two standard deviations above the WHO Growth Reference median [14,15].
Several authors have proposed a further stratification of obesity with cutoff points at the 95th percentile of BMI-for age (grade 1 obesity), 120% of the 95th percentile (grade 2 obesity), and 140% of the 95th percentile (grade 3 obesity), in order to better define the degree of obesity-associated risk in pediatric populations [16,17].

3. Epidemiology

The prevalence of childhood obesity is on the rise globally, particularly in urban areas [1]. In 2019, an estimated 38.2 million children under the age of 5 were overweight or obese. The prevalence of overweight and obesity among children and adolescents between 5 and 19 years of age has seen an alarming increase, from 4% in 1975 to approximately 18% in 2016. Both genders were similarly affected by this increase [1,18]. A brief overview of the prevalence of obesity in relation to geographic location is presented in Table 1.

4. The Anatomy of Obesity

The distribution of body fat plays an important role in determining the deleterious effect of adiposity on the organism. In this regard, certain anatomical particularities are of significance, as presented in the following subsections.

4.1. Surface Disposition of Somatic Adipose Tissue

One of the first aspects that becomes apparent when observing an individual with excess adipose tissue is the superficial distribution of body fat, with a particular predisposition to certain anatomical areas. The simplest form of categorizing superficial fat distribution is to discern between android and gynecoid obesity patterns. Developmentally, the difference between the two becomes apparent under the influence of sex hormones, typically during adolescence. Android obesity is characteristic for males and implies the distribution of fat around the central areas of the body, particularly the abdomen, whilst in gynoid adipose distribution, usually seen in overweight women, the hips and thighs are the most prominently interested areas [25]. Sexual dimorphism of body fat distribution can be explained in part by the particular sensitivity of adipose depots to the influence of sex hormones. The femoral–gluteal region, for example, is more prone to the inhibitory influence of testosterone on lipoprotein activity [26] as well as the estradiol-dependent increase in the expression of lipolytic a2-adrenergic receptors [27]. In addition, sex hormones also act upon adipocyte maturation. Testosterone suppresses adipocyte formation [28] while estrogen promotes the proliferation of preadipocytes while progestins initiate their differentiation [29].
Although visually objectifiable due to the distribution of somatic fat, android obesity is typically associated with a higher accumulation of visceral adipose tissue and has been shown to be responsible for a greater increase in cardiovascular increase when compared to gynoid obesity [30]. The relevance of discerning between these two types of deposits is outlined in the following section.

4.2. Depth of Adipose Tissue

Adipose tissue presents different cardiovascular risk profiles in accordance with the anatomical depth of the surplus. This has led to the distinction between somatic and visceral fat. The latter is responsible for a more significant correlation with an unfavorable metabolic profile and increased cardiovascular risk [31]. One proposed mechanism postulates that an increased lipolytic activity of visceral adipose tissue in comparison to its somatic counterpart leads to an increase in circulating free fatty acids. The anatomical proximity of the portal vein would in consequence lead to an increased hepatic intake of free fatty acids, independently of their systemic concentration. This could lead to decreased local insulin sensitivity, which would determine an increase in insulin production, which in turn would cause a decrease in systemic insulin sensitivity. Furthermore, there appears to be a significant relationship between visceral body fat and increased inflammatory status, a condition which is found frequently in conjunction with increased cardiovascular risk. Visceral adipose tissue also appears to play a part in increasing leptin resistance. The role of leptin in the neuro-hormonal regulation of appetite is outlined in Section 6.1.2. Further mechanisms involved in the noxious effects of visceral adipose tissue are the increase in sympathetic tonus, oxidative stress, and vascular calcification, all of which influence the development of cardiovascular disease. Visceral adiposity appears to have detrimental effects on cardiovascular risk even in the absence of classically defined overweight and obesity, as shown by the fact that patients with normal weight with an increased visceral adipose mass have a higher risk of cardiovascular disease and type 2 diabetes when compared to those with a predominantly somatic disposition of adipose tissue [32,33,34,35,36,37]. The noxious effects of surplus adipose tissue manifest both in a systemic manner as well as locally [38]. Therefore, in addition to the general assertion regarding the increased risk revolving around the predominance of visceral fat in obese individuals, it has become apparent that a certain predilection of fatty disposition involving specific areas or viscera can increase the risk for particular diseases. One example regards the mechanical effect of predominantly intra-abdominal adipose excess. Pressure generated by abundant intraabdominal fatty tissue can create a predisposition towards developing a series of gastro-intestinal diseases such as gastro-esophageal reflux or hiatal and abdominal hernia. The same conditions can promote chronic venous insufficiency due to venous system compression [39].
In addition to the pure mechanical local effects of adipose surplus, local functional effects can also be incriminated in the mechanisms leading to a series of pathologies, as presented in the following section.

4.3. Local Effects of Adipose Surplus

The adverse functional effects of excess adipose tissue are related to the secretion of proinflammatory and prothrombotic adipokines [40,41], local hypoxia [42], fibrosis [43], and mitochondrial function alteration [44]. Several localizations present a series of particularities worth noting, in lieu of their prominent noxious local effects that also have a systemic resonance.
The effects of perihepatic adipose surplus have already been previously described. Excess adiposity is, however, also associated with the intracellular accumulation of fatty deposits, in the form of triglycerides within hepatic cells, with injurious effects upon their function. The resulting pathological entity is defined as non-alcoholic fatty liver disease, which is frequently associated with metabolic syndrome and weight excess [45,46].
Increased intracellular triglyceride deposits can also be found within striated muscular cells, which may play a role in increasing insulin resistance and may predispose to developing dyslipidemia [47].
A further example of relevant local effects of adipose surplus is with regard to epicardial adipose tissue. In physiological conditions, the epicardial adipose tissue has an important role in the energetic balance of the heart. Through the uptake of excess free fatty acids, it offers a metabolic support for the myocardium during ischemia. In addition, this tissue is responsible for the thermal insulation of the heart, isolating it, and maintaining the ideal temperature for the optimal functioning of the enzymatic apparatus within the cardiomyocytes. Furthermore, the connective tissue surrounding the epicardium has an important structural role and is maintained by the synthesis of adiponectin and adrenomedullin. Excess adipose tissue surrounding the heart, however, leads to the decrease in adiponectin, an increase in inflammatory markers, myocardial fibrosis and hypertrophy, and cardiomyocyte apoptosis. These mechanisms could help explain the association between increased epicardial adiposity and ischemic heart disease, heart failure, hypertension, left ventricular hypertrophy, dyslipidemia, and insulin resistance [48,49,50,51,52,53]. Local macrophage accumulation and angiogenesis within epicardial adipose tissue also seem to play an important part in the inflammatory-mediated mechanisms that link increased epicardial fat to worse outcomes in patients with coronary artery disease [54].
Perivascular adipose tissue is defined by a series of particular characteristics involved in the mechanisms behind the obesity-associated cardiovascular risk. The histological distinction between brown and white adipose tissue is of relevance concerning this matter. While brown fatty tissue has a significant thermogenic role, particularly important in newborns, white adipose tissue serves mostly as a deposit for energetic surplus in the form of lipids, releasing them into the circulation and thus making them available to tissues in need, when such a need arises. White adipose tissue is mostly responsible for the mechanisms involved in metabolic obesity-related disease. In certain conditions, white adipose tissue has shown the capacity of transforming into brown adipose tissue (mostly due to exposure to very low temperatures).
Differently localized vessels in the organism present different proportions between white and brown adipose tissue. This variability may be in conjecture with the different predominant functions of perivascular tissue according to localization. Large central vessels, for example, such as the aorta and its main ramifications, are mostly surrounded by brown adipose tissue, thus playing a key part in maintaining central temperature within normal ranges. Peripherally increased perivascular adipose tissue, on the other hand, has been associated with increased insulin resistance [55].
Excessive perirenal adipose tissue can lead to the increase in intrarenal pressure, with potential involvement in the development of microalbuminuria. Perirenal adipose tissue is implicated in a series of processes related to cardiometabolic risks such as maintaining renal vascular tonus and inflammatory marker secretion [47].

4.4. Central Obesity and Metabolically Healthy Obesity

The aforementioned principles can be used to delineate two major types of adipose tissue disposition which start to develop during childhood or adolescence. Central obesity, with the adipose tissue predominantly concentrated around the viscera, is considered to have an increased unfavorable effect on cardiovascular risk [56,57]. A recent meta-analysis of prospective studies evaluating the link between abdominal obesity and cardiovascular risk has shown a strong association between the parameters describing central obesity (waist circumference, waist:hip ratio, and waist:height ratio) and cardiovascular diseases (including ischemic heart disease, cerebrovascular disease) [58]. The deleterious effect of visceral adipose surplus appears to manifest itself even in otherwise normal-weight individuals (according to BMI values) which show evidence of excess abdominal adiposity (measured by waist circumference for example) [59].
Central obesity has been shown to be a strong risk factor for cardiometabolic disease, not only in adults but in children and adolescents as well [60,61,62,63,64,65,66]. Moreover, it seems to exhibit a stronger correlation with cardiovascular risk in children when compared to BMI-defined obesity [67,68]. In addition, the development of central obesity during childhood appears to persist into adolescence and adulthood as well [69,70,71].
Conversely, due to the apparent predominant impact of visceral fat on cardiovascular risk, the existence of a so-called “metabolically healthy obese” pattern has been postulated, where most of the weight excess is accounted for by somatic adipose tissue, with relatively reduced visceral fat excess [72]. A large observational study involving 3.5 million individuals, however, has shown an increased risk of incident cardiovascular events in this population compared to individuals with normal weight [73]. With this observation, “safe obesity” might in fact represent a form of precursor state that can be found before the manifestation of the metabolic disbalances traditionally associated with obesity.

4.5. Histological Aspects

The adipocyte is the fundamental cellular unit of adipose tissue. Beyond the role of a mere energetic depository, adipocytes act as a part of a system similar to a standalone endocrine organ by secreting a large variety of peptides and metabolites involved in weight regulation. In addition to the metabolic functions exerted by means of the enzyme pathways involved in beta-oxidation and free fatty acid metabolism, many of the adipokines secreted by these cells have a proinflammatory and procoagulant influence. Other peptides are implicated in insulin resistance and hunger regulation, with a significant effect on body weight and cardiovascular obesity-associated risk. Many of the substances secreted by adipocytes have a still unknown role and are a key interest for medical research [74].
Adipose tissue is highly cellular in nature. The adipocytes that conglomerate in order to form this tissue can respond to external stimuli that determine the increase in adipose mass, either by increasing their individual dimensions or by increasing the physical number of cells. The increase in adipocyte size defines adipocyte hypertrophy. This type of response is typically found in android obesity, with a high proportion of visceral fat. Hypercellular obesity, on the other hand, has a more variable character, frequently identified in individuals that become overweight since childhood. It is however almost always present in severely obese patients, regardless of age. Hypertrophic obesity generally develops during adulthood and has a strong connection with cardiovascular risk. This type of obesity usually responds well to body weight interventions, which are generally inefficient in hypercellular adiposity. This particular resistance to treatment is one of the main aspects that drives the imperative need for assertive preventive action during childhood [75]. Figure 1 and Figure 2 were taken with permission from the pathology laboratory of the Pediatric Clinical Hospital Sibiu, Lucian Blaga University of Sibiu, and display the different histological appearances of hypertrophic and hypercellular adipose tissue.
A further aspect to be considered upon examining the cellular foundation of adiposity is regarding the maturation of adipocytes, as they differentiate from preadipocytes. Characterizing the stimuli responsible for the initiation of this process and identifying the factors that lead to preadipocyte recruitment, differentiation, hypertrophy, and/or apoptosis could play a key role in defining the mechanisms behind weight excess. Research in this field could provide potential targets for weight reduction by means of controlling the process of adipocyte proliferation, thus avoiding the development of hypertrophic, metabolically dysfunctional adipocytes responsible for increasing cardiovascular risk, insulin resistance, and the risk of recurrence after weight loss [76].

5. Obesity Assessment

Utilizing the BMI in general practice has the advantage of accessibility and ease of use, in addition to providing a good overall image regarding obesity-associated risk of morbidity. The relationship between BMI values and cardiovascular risk has been validated in numerous studies in the form of a U-shaped curve, implying the existence of an optimal interval for this parameter, bordered on one side by the morbidity correlated with undernutrition and that pertaining weight excess on the other [77,78]. The BMI, however, offers only a gross approximation of one’s body weight in relation to height, with no regard for body composition, ignoring the different densities of specific tissues, such as muscle mass and bone tissue, which are under great influence of the specific developmental phases during childhood and adolescence. This applies both in regard to percentual contribution to body weight, and the variable intrinsic structure and density of non-fat tissue throughout specific growth phases (i.e., bone mineralization in accordance with growth stage for example). In addition, growth stage charts only take into consideration the relationship between BMI and gender and age, with no regard concerning the wide range of acceptable values for height in children of a certain age, in and of itself an important marker for the growth stage of a child and, implicitly, the corresponding physiologically variable body composition [79]. A further aspect in which body composition plays a major role is in regard to race. An African child will, for example, for the same BMI value, have a higher percentage of muscle mass than a Caucasian one, whereas an Asian child will have a higher value of body fat percentage [80]. In addition, the BMI offers no insight regarding the distribution of body fat within the body, and the implications presented in Section 4.
To this avail, significant effort has been made in researching methods that better assess obesity in children, with the desire of identifying parameters that better correlate to obesity-associated risk, as presented in the following sub-sections, adapted from [81].

5.1. Inferential Methods

These methods rely, similar to the BMI, on inferring the weight status based on measurements requiring relatively simple instruments.

5.1.1. Anthropometric Parameters

Although the BMI is the most widely utilized anthropometric parameter in general practice, several others can be taken into consideration for estimating obesity-associated risk. Abdominal circumference, for example, has shown a strong correlation with hypertension and intraabdominal adiposity and is used to define metabolic syndrome [82]. The waist-to-height [83] and waist-to-hip ratios [84] also correlate with cardiovascular risk, providing insight on the disposition of both somatic adipose tissue as well as the proportion between somatic and perivisceral fat. Central obesity in children, defined in the current literature as WHtR ≥ 0.5, was correlated with poorer dietary habits when compared to their peers without central obesity [60]. Further refined parameters, such as the ABSI (A Body Shape Index) as defined by the formula ABSI = WC BMI 2 3 × H 1 2 ,   where WC is waist circumference (centimeters), BMI is body mass index, and H is the height (meters), and the Hip Index, defined by the formula HI = HC · ( h 166 ) 0.310 · ( BW 73 ) 0.482 ,   where h is HC is hip circumference and h is height in cm, have shown great potential in determining adipose distribution and in inferring obesity-associated risk [85,86].
Neck circumference correlates with the risk of developing certain respiratory diseases, some of which (obstructive sleep apnea, for example) are frequently associated with cardiovascular disease [87,88,89].

5.1.2. Skinfold Thickness Measurement

The measurement of skinfold thickness in certain key areas of the body (bicipital, tricipital, subscapular, suprailiac, and thigh area) can be implemented in adults to assess adipose tissue disposition. The inhomogeneity of pediatric populations, however, poses significant difficulty in the applicability of standardized equations for determining adipose tissue distribution based on these measurements. The method is also particularly cumbersome, and involves a steep learning curve. The relatively minimal material requirements of the technique are, however, an important advantage, as is the potential of providing relevant results if appropriate protocols are developed for pediatric patients [90,91,92,93,94,95,96,97,98,99].

5.2. Methods of Determining Body Composition

The utility of these methods relies on their ability to determine body fat percentage, without, however, offering information regarding the particular distribution of adipose tissue. The most common techniques are outlined in Table 2.

5.3. Imaging

Using imaging techniques for the assessment of weight status allows for the differentiation between visceral and somatic adipose tissue. As such, the methods described for determining body composition and imaging are complementary. Table 3 outlines commonly used means of image acquisition for the evaluation of obesity. All of these techniques can be used to obtain images for the gross quantification of visceral to somatic adipose tissue proportion, as well as measurements of the local fat depots described in Section 4.3.
Sectional imaging (CT or MRI), using a single section (generally at the level of L4–L5), with the help of dedicated software, the ratio between abdominal wall adipose tissue surface and visceral adipose tissue can be calculated in order to evaluate the proportion between visceral and somatic fat [129,130,131,132,133,134,135,136,137,138,139,140,141]. MRI can also identify cardiac remodeling found in obese patients [142].
The following images (Figure 3, Figure 4, Figure 5, Figure 6, Figure 7, Figure 8, Figure 9, Figure 10 and Figure 11) taken from obese children provide examples of the aforementioned imaging techniques.
The importance of a holistic approach to obesity assessment methods is two-fold.
Firstly, these methods can provide quantifiable insights concerning the processes involved in the development of high cardiometabolic risk-associated obesity. The “limited expandability” theory suggests that the ill effects of visceral adiposity are mediated by the limited capacity of somatic adipose tissue to deposit lipid excess in the circumstance of sustained positive energy balance [143,144,145]. The precise capabilities of SAT in this regard may be genetically preconditioned, as shown by studies examining the differences between obese adolescents with different VAT/SAT ratios, where an increased VAT/SAT correlated with the downregulation of lipogenic and adipogenic genes and decreased SIRT1 expression [146]. Nevertheless, it seems plausible that once the storage capacity of SAT is reached, surpluses secondary to lipid metabolism may be diverted towards visceral adipose tissue and non-adipose tissues [147]. The interplay between the specific characteristics of each fat depot (as described in Section 4.3) and the altered diversion of lipid metabolites towards key structures and organs involved in insulin sensitivity may play a key role in explaining the lipotoxicity of VAT [148]. Sustained positive energy balance may affect not only the overall capacity to store lipids of adipose tissue, in the sense of achieving a maximum tolerance, but also the capability of coping with the need to manage ever-increasing energy depots leading to altered adipokine and inflammatory marker secretion and consequent dysregulation of appetite and metabolism [149]. CT and MRI can provide an objectifiable assessment of the resulting aspects, including in pediatric populations, by measurement of specific fat depots (VAT, SAT) and ectopic fat depots such as the hepatic fat fraction, pancreatic fat fraction, and intramyocellular fat [138]. This allows us to create the connection between the descriptive processes involved in lipotoxic fat accumulation and the parameter-based interpretation necessary to real-world evaluation.
Secondly, the different methods of assessing obesity can provide a varied translation into pathology and clinical rationale. Due to the limitations of each technique, providing different truncated views of the association between obesity and cardiometabolic risk can aid in forming a more detailed general picture. More precisely, parameters obtained from different techniques correlate to each other in accordance with the underlying physiopathological pathways leading to disease. This applies to children and adolescents as well as adults. For example, overweight and obesity in children as evaluated by DEXA correlates with higher cardiac measures [150,151]. Increased VAT, as measured by anthropometric methods or sectional imaging, has been linked to unfavorable lipid profiles [138]. The interconnection between adipose tissue localization and imaging-derived cardiovascular parameters, certain serological biomarkers and functional assessment techniques strengthens the coherence of the various data points obtained from different methods. A pertinent example in this regard is related to levels of adipocyte fatty acid-binding protein (FABP4) in children and its connection to total body fat, abdominal fat, body fat distribution, aerobic fitness, blood pressure, cardiac dimensions, and the increase in body fat in time, as presented by Dencker et al. [151]. FABP4 is an adipokine involved in weight control, metabolism, and atherosclerosis. This study illustrates how the use of several different techniques, including anthropometric parameters (BMI), clinical measurements/indicators (blood pressure, tanner stage), body composition measurement methods (dual-energy X-ray absorptiometry), imaging diagnosis (Echocardiography), functional assessment (indirect calorimetry during stress testing), and circulating biomarkers (FABP4) provide cohesive results.
It can be viewed that the common denominator of the techniques presented in the section on obesity assessment, and the fundament behind their cohesion is linked to the physiopathological mechanisms involved in weight excess and associated cardiometabolic disease, of which they all provide different interpretations.

6. Determinant Factors of Obesity

Alteration of the balance between energy intake and expenditure is the main culprit typically incriminated in the etiology of obesity. When caloric intake exceeds consumption, the excess is stored in the form of lipids in adipose tissue. Chronic exposure to this disbalance leads to an increase in adipose mass [1]. There is evidence, however, that the etiology of excess body weight extends beyond this simplified approach, as is further described. A brief overview of the physiology of appetite regulation is also provided in order to better visualize at which points certain mutations for example hamper these processes.

6.1. Neurohormonal Regulation of Appetite

The sensation of hunger is the product of a complex interaction between the central nervous system, with a key role attributable to a series of hypothalamic nuclei, and a large number of hormones, many of which are secreted by the gastrointestinal tract [152].

6.1.1. Hypothalamic Centers

The hypothalamus acts as the most important relay between the rest of the central nervous system and incoming orexigenic/anorexigenic stimuli. Integrating the information processed within the hypothalamic nuclei leads to the generation of the sensation of hunger or satiety. The most important nuclei are the arcuate nucleus of the hypothalamus (ARC), the paraventricular nucleus of the hypothalamus (PVH), the ventromedial nucleus of the hypothalamus (VMH), and the lateral hypothalamic areas (LHA). A schematic representation of these areas, the types of neurons found within, and the effect of regulatory peptides involved in modulating appetite, as well as the main neural connections between these centers and other relevant neural structures, is outlined in Figure 12, and described in [153] as well.

6.1.2. Adipokines

The most significant adipokine implicated in hunger modulation is leptin. The discovery of this peptide has had a substantial impact on the understanding of appetite regulation. Leptin is synthesized within white fat cells. Leptin receptors are exhibited by neurons within the hypothalamic centers responsible for appetite control. By activating POMC neurons and inhibiting NPY/AgRP neurons, leptin inhibits the sensation of hunger and leads to the feeling of satiety, thus reducing food intake and lipid accumulation. Furthermore, leptin also plays a role in modulating energy use and carbohydrate metabolism, reducing weight gain. Several genetic mutations altering the effect of leptin, the function of its receptor, as well as the POMC-aMSH pathway have been described [154,155]. They are further detailed in Section 6.2.2.
Despite the beneficial effects of leptin, it seems that circulating leptin levels correlate with BMI. This phenomenon can be explained by the decrease in leptin receptor sensitivity, which leads to an increase in peripheral leptin secretion [156,157]. This mechanism is similar in principle with the hyperinsulinism found in individuals with decreased peripheral insulin sensitivity, a condition which most commonly precedes the development of type 2 diabetes [158].

6.1.3. Gastrointestinal Tract Peptides

The peptides that are secreted by the gastrointestinal tract that increase appetite are glucagon-like-peptide 1, neuropeptide y, cholecystokinin, and amylin [158,159,160,161]. Ghrelin has an opposing action, thus stimulating hunger, and is secreted in the area of the stomach fundus [162].

6.1.4. Other Factors

In addition to the mechanisms described, several further factors are involved in appetite regulation. This refers, on the one hand, to several important circulating mediators such as endocannabinoids which, by interacting with specific receptors, will determine an increase in appetite and nutrient absorption and promote lipogenesis [163]. On the other hand, this refers to certain secondary neural circuits which may have an important influence in generating the sensation of hunger. One such example is the involvement of olfactory stimuli in generating hunger. The link between smell and metabolism could be mediated by insulin, an increase in which determines the attenuation of olfactory input, thus reducing appetite [164].
This brief description of the physiology of neuro-hormonal appetite regulation can aid in better illustrating where some of the most common genetic defects intervene in the normal process of food intake. More than 500 genetic loci have been associated with obesity-related traits in a genome-wide association study performed on nearly 700,000 individuals [147,165]. Notable examples include fat mass and obesity-associated (FTO) genes, which are highly expressed in the arcuate nucleus. FTO genotype correlates with weight status in children [166], dietary habits [167,168,169], and may even play a role in the distribution of somatic and visceral fat and associated cardiometabolic risk [138]. Further examples include OLFM4 and HOXB5. These genes impact the development of the gastrointestinal tract and may therefore influence gut-regulated appetite signaling [170,171]. The PCSK1 gene encodes PCI (prohormone convertase 1), is involved in the synthesis of aMSH from POMC, and defects of the gene can cause early onset obesity [172,173]. It has also been found to be weakly expressed in Prader–Willi syndrome [172]. Genes implicated in the development of other syndromic forms of early onset obesity are also of relevance in understanding both the normal pathways of weight regulation as well as the various components of multifactorial non-syndromic obesity. ALMS1 mutations associated with Alström syndrome, for example, have shown a link between adipose increase and insulin resistance [174,175]. In Bardet-Biedl syndrome, genetic defects lead to a ciliopathy that may be involved in leptin signaling. This could explain the severe leptin resistance in these patients. The main mechanism leading to obesity in Bardet–Biedl patients is related to the dysregulation of food-seeking activity [176].
An interesting aspect linking genotype to phenotypical expression regards the different relationships between certain anthropometric measurements and specific gene loci. In particular, loci associated with WHRadjBMI seem to be mainly comprised of genes influencing adipose tissue biology [177], while BMI-associated loci show a stronger connection to genes related to appetite regulation, predominantly expressed in the brain regions with functions attributable to this purpose [178].

6.2. Obesity as a Symptom

Understanding diseases that often present with obesity can aid in the understanding of the mechanisms involved in generating this ailment. A brief description of the most common of such diseases can be structured as follows.

6.2.1. Genetic Syndromes

Several genetic syndromes intervene in obesogenic mechanisms and lead to weight excess [179].
Prader–Willi syndrome (PWS) is one such example, where an anomaly involving the partial disappearance of oxytocin neurons [180] within the hypothalamus leads to the impossibility of achieving satiety. The resulting hyperphagia leads to severe obesity, which in turn leads to limitation of physical activity and corresponding energy expenditure, thus intensifying weight gain and leading to loss of muscle mass. Obesity in Prader–Willi syndrome commonly has a central predisposition (abdomen, hips, thighs) in both genders and is usually the main causative factor behind morbidity and mortality associated with this disease. The current literature reports higher levels for orexigenic hormone (ghrelin) in PWS children than in other obese children. In PWS, children starting with nutritional phase 3 (8 years of age) ghrelin levels increase before the meal but also after the meal, pointing to a hypothalamic pathway dysfunction related to appetite regulation [181].
Bardet–Biedl syndrome is associated with a prevalence of obesity of 72–86%. Children with Bardet–Biedl syndrome are typically born with a normal weight, a third of which will however develop weight excess by the age of 1. Individuals with Bardet–Biedl syndrome are prone to developing diabetes mellitus, hypertension, and metabolic syndrome [182,183].
Carpenter syndrome is a rare genetic disease that frequently associates obesity involving the proximal regions of the limbs, the face, neck, and thorax [184]. It is an acrocephalopolysyndactyly type II associating craniosynostosis, learning disability, cardiac defects, obesity, and polysyndactyly. A mutation in the gene RAB23 gene leads to subsequent RAS dysfunction and impaired intracellular vesicular transport. The encoded GTPase is a negative regulator for hedgehog (HH) family signaling; however, it is not yet clear how these pathways can be linked with obesity. It is known that HH signaling inhibits adipose tissue hypertrophy and hyperplasia [185].
Cohen syndrome (CS) is a recessive autosomal disease defined by the presence of multiple congenital malformations and intellectual disability. In this disease, obesity predominantly affects the torso and has a characteristic disposition, characterized as “truncal obesity”. CS was described in the Finnish population and is due to mutations in a vacuolar protein sorting 13 homolog B(VPS13B) gene. This protein is necessary to the Golgi network and endosomal transport [186,187].
Alström syndrome is a recessive autosomal disease involving the mutation of ALMS1, defined by the presence of obesity, type 2 diabetes, and neurosensory degeneration [188]. Recent data presents ALMS as a ciliopathy due to mutation in the ALMS1 gene (located in the centromeres) along with another ciliopathy, Bardet–Biedl syndrome, which is polygenic. However, in ALMS patients, obesity is more severe and ensues in the first years of life (until the age of 5). ALMS gene has its location in the centrosomes and is involved in the microtubules’ functions. It has important roles in both visual and auditory analyzers, lungs, heart, liver, and kidneys’ function and, most importantly, in metabolic regulation. It seems that not only the subcutaneous adipose tissues’ function is severely deregulated, but also the skeletal and hepatic ones with subsequent insulin resistance. These patients have large dysfunctional adipocytes [189].

6.2.2. Monogenic Causes

The discovery of several diseases that involve a single gene has aided in better understanding both the normal function of weight regulation, as well as some of the genetic factors involved in weight excess. Most of the mutations affect elements within the leptin/melanocortin-hypothalamic axis involved in appetite regulation. Mutations in the genes that code for leptin or the leptin receptor are pertinent examples [190].
Congenital leptin deficiency is a recessive autosomal disease caused by a series of possible mutations of its coding gene. It is characterized by the presence of severe obesity, hyperphagia, and a series of metabolic, neuroendocrine, and immune dysfunctions. It typically responds well to the parenteral supplementation of leptin, with significant weight reduction after treatment and a decrease in voluntary food ingestion. Mutations involving the hypothalamic receptor for leptin have a similar clinical presentation but do not respond to treatment involving leptin [191].
Alteration of POMC production (including mutations of the enzymes responsible for the cleavage of POMC and production of aMSH) or loss of function of MC4R receptors fit into this category as well. Due to the pigmentation effects of MSH, patients with POMC deficit or abnormal MC4R function will frequently associate obesity with red hair and pale complexion. Some studies have shown a prevalence of up to 5% of these types of mutations in morbidly obese children [192,193,194].
Another example concerns the activity of PPAR-gamma, a transcription factor involved in adipocyte differentiation. Patients with mutations affecting the receptor for PPAR-gamma will invariably be severely obese [195].

6.2.3. Endocrine Disorders

Hormonal imbalance, due to the implications on energy metabolism, will most often tip weight balance in one direction or the other, by affecting either the basal metabolism or the general capacity of managing energy expenditure. Examples of endocrine diseases that often associate obesity include growth hormone deficit or resistance, hypothyroidism, Cushing syndrome, and polycystic ovary syndrome [196,197,198].

6.2.4. Iatrogenic Obesity

Obesity caused by therapeutic intervention is a common side effect of certain medications. The most frequently incriminated agents are antipsychotics, antiepileptics, sedatives, antidepressants, anxiolytics, mood stabilizers, antimigraine drugs, some oral antidiabetics, insulin, corticosteroids, thyroid hormone replacements, oral contraceptives, diuretics, and even some antibiotics [199]. The latter may be involved in inducing obesity by acting upon the intestinal microbiota. Additionally, it may be plausible that probiotic treatment could aid weight loss in certain individuals [200].

6.3. Genetic Predisposition

Along with the discovery of genetic diseases with implications on weight excess, several further aspects bolster the importance of genetic predisposition in obesity.
The fact that most obese pediatric patients come from families where one or both parents have excess weight is an expression of the complex interaction between genetic and environmental factors [201]. In addition to the hereditary influence of obesity, the often-encountered familial character of obesity is related to the exposure to risk factors associated with the environment created by cohabitation with family members. Sedentarism, inefficient time management, and unhealthy dietary habits are all responsible for altering the lifestyle of the youngest members of a family and are influenced by a large variety of socio-economic and cultural factors [202]. There are, however, several arguments that underline the importance of genetic determinism in obesity.
One such argument is in relation to studies on twins. Type 2 diabetes and obesity are more frequently simultaneously encountered in monozygotic twins than in dizygotic twins. Furthermore, some studies have even shown a correlation between the percentages of adipose tissue in monozygotic twins raised in different environments. The simultaneous character of obesity in monozygotic twins seems to take little regard to family environment. In addition, monozygotic twins tend to have similar mechanisms of adapting their weight to the environmental factors they are exposed to [203].
A further argument is brought forth by studies on adopted children. Frequently, adopted children exhibit a weight pattern more similar to their biological parents rather than the adoptive ones [204]. The importance of epigenetic mechanisms has also been outlined by studies that have shown a strong correlation between the presence of adiposity in males and their descendants [205].
Finally, another hypothesis worth taking into consideration is the theory of “thrifty” genes. The fundament behind it states that across evolution, a genetic arsenal tailored towards creating energy storages, in a time when sources of nutrition were scarce, was a survival advantage. The same genetic configuration has become a major disadvantage in modern times. This theory could explain the puzzling differences in obesity between certain races, as well as the variable influence of the same environmental factors on different ethnicities of people. A relevant example of where this theory could provide an explanation regards the development of weight excess in migrating populations from areas where the prevalence of obesity is low, to locations where it is higher. The prevalence of obesity frequently becomes greater in the migratory population when compared to natives [206].

6.4. Vulnerable Periods

From the point of conception onwards, human organisms are under the continual influence of external factors. Certain timeframes are particularly important, however, with regard to the susceptibility towards obesogenic factors.

6.4.1. Pregnancy

One theory regarding the genome-environment interaction postulates that most pathologies are the result of a genetic preconditioning of disease which becomes phenotypically apparent under the influence of environmental factors [207]. From this perspective, pregnancy represents the earliest period in which an individual is exposed to potentially disease-inducing environmental conditions. Obesity is one such disease. The hypothesis that in utero exposure is the fundamental event leading to the genesis of adult disease has been proposed by Dr. DJ Barker in his studies [208,209]. The fetal origin of disease hypothesis is based on the concept of phenotypic plasticity, which embodies the concept that living organisms can have different phenotypical expressions of the same genetic code under the influence of different environmental exposures. In this respect, fetal exposure to inadequate nutrition may have a role in “programming” the individual towards developing a significant array of cardiovascular diseases and risk factors, including obesity [210,211].
When viewed from the traditional standpoint of the etiology of obesity as the result of an altered balance between energy intake and expenditure, it is the mother’s behavior that establishes the quantity and quality of nutrients the fetus receives. Increased refined sugars intake and inadequate dietary polyunsaturated fatty acid ratio (i.e., high omega-6 fatty acid and low omega-3 fatty acid intake), for example, are associated with an increase in developing excess weight in childhood [212,213]. With regard to energy expenditure, few mothers follow the recommendations for physical activity during pregnancy. Some studies have shown that less than 15% of interviewed pregnant women participated in moderate physical activity at least 3 times a week for 20 min or more [214,215].
All the aforementioned factors influence one of the most important predictive parameters of childhood obesity: mother’s weight status during pregnancy. The mother’s weight at the beginning of pregnancy can be regarded as a partial representation of the genetic material available to the child, as well as an insight into the family environment to which the mother had been exposed and in which the child will be integrated. Both the BMI before pregnancy, as well as weight gain during pregnancy can influence the child’s weight status after birth [216,217,218]. During the first two trimesters, weight gain is predominantly attributable to an increase in the mother’s adipose tissue, and not fetal mass. It is plausible that excessive weight gain of the mother during this period could lead to an increase in child adiposity after birth, due to an increase in available nutrients in utero [216,219].
One of the most pertinent arguments sustaining the link between mother and child in respect of weight status derives from studies on obese mothers that have given birth to children both before and after gastric bypass surgery. Children born after such an intervention and, implicitly, after a significant correction of excess weight in mothers, have shown an improved weight status as compared to children born before the mother’s surgery [220,221,222].
Malnourishment is, however, also a risk factor for childhood obesity. Although malnourished mothers more frequently give birth to children who are small for gestational age [221], they are at risk for developing an excessive body weight during childhood [209]. A possible explanation for this phenomenon could be connected to the aforementioned intrauterine “programming” which leads to childhood and adult obesity along with a wide range of metabolic disorders and cardiovascular diseases [223].
Similarly, intrauterine injury caused by tobacco smoke, alcohol ingestion, or other toxins can lead to the birth of small for gestational age children, with the same predisposition of developing obesity during childhood [224,225,226]. Iatrogenic exposure to certain medications, such as hormonal treatments or antibiotics, can have a similar effect [227,228,229,230].
Of the most relevant metabolic disorders during pregnancy, gestational diabetes is a dysmetabolic entity with significant repercussions on metabolic health well beyond pregnancy, both for the mother and the child. Gestational diabetes is yet another potential exposure that can create a predisposition towards early obesity [231,232].
The end of pregnancy is the point in which environmental factors no longer pass through the mother as a filter. Some studies have shown that even the way in which this process occurs can influence childhood weight status. A prospective study on more than 20,000 subjects has found a correlation between cesarian birth and the risk of developing obesity. This association was even stronger in mothers that did not have a clear indication for cesarean birth [233].

6.4.2. New-Born Period and Infancy

In the first 6 months of life, exclusive breastfeeding is the nutrition of choice for infants, preferably on demand, according to the AAP, ESPGHAN, and WHO [234,235,236]. Deviating from this ideal has been linked to an increase in the risk of developing obesity [237,238,239].

6.4.3. Early Childhood, Preschool, and School-Age Periods

Initiation of solid foods is essentially synchronous with the onset of exposure to the day-to-day dietary habits within a child’s environment. The conditions created for the child, initially by the family or legal guardians, followed by kindergarten or other means of social integration and school, as well as the general cultural background of the society they are brought into (i.e., development status of the place of birth, urban versus rural area, ethnic, social, and cultural background, family education level, etc.) to which the addition of the influence of mass media is not negligible—all of these factors have significant implications on establishing whether or not the child will be exposed to the unhealthy influences which lead to obesity.
Furthermore, this developmental time frame contains one of the most vulnerable periods in respect to weight balance, around the age of 4 to 7 years, due to BMI rebound. In this interval, the BMI will reach a nadir value from which, under physiological conditions, it will continue to rise during childhood, extending into adulthood as well. Early BMI rebound is a risk factor for obesity and reveals a period of significant vulnerability to unhealthy behavior concerning energy balance, with potentially long-term implications [240].

6.4.4. Puberty and Adolescence

In a similar fashion to pregnancy and the BMI rebound time span, adolescence defines an individual period of vulnerability to obesogenic factors [240]. In addition to the aforementioned external influences on lifestyle, which continue to stay relevant during adolescence, this period is marked by a series of distinct particularities relevant to the matter.
Puberty is responsible for a wide range of homeostatic and somatic alterations, with significant psychological and behavioral implications. The beginning of sexual maturation leads to diverging modifications in body composition and adipose disposition between boys and girls. In girls, there is a typical increase in adipose tissue percentage and the disposition in specific areas due to sexualizing hormones. In contrast, boys usually present an increase in muscle mass and a reduction in adipose tissue percentage [241,242,243]. Partly due to these significant changes and the adaptive necessary coping mechanisms, adolescence is regarded as a very demanding growth stage [244].
The influence of peers also becomes more potent during adolescence, as well as potentially harmful behaviors promoted in society. Transitioning from the childhood environment to adult independence, characterized by the need for social acceptance, exposes the vulnerability to the aforementioned influences. Marginalization of overweight teens leads to lowering of self-esteem and burdens social interaction, potentially leading to anxiety and depression. The same stigma around teenage obesity can lead to a situation where unhealthy eating and sedentary behavior lead to weight excess, which leads to social isolation and further lack of activity due to fear of judgment.

6.5. Energy Balance

6.5.1. Caloric Intake

One way to describe the characteristics of caloric intake is to view it from the perspective of quantity, quality, and rhythm of nutrition.
The aspect of quantity of nutrition refers to the ideal absolute value of calorie intake corresponding to each age group. There is no general consensus in this regard, however, the optimal strategy most probably takes into account the level of activity for each individual and establishes optimal caloric intake accordingly. Several guidelines provide a detailed description in this respect [245,246].
The quality of nutrition refers to achieving an optimal proportion of macro and micronutrients through dietary intake. Foods with an excessive amount of lipids and carbohydrates accompanied by a reduced proportion of protein, vitamins, minerals, and micronutrients are particularly harmful to maintaining a normal weight. The WHO recommends reducing the intake of free sugars to a value under 10% of total caloric intake, regardless of age. This refers mostly to mono and disaccharides added to food and drinks, as well as naturally occurring sugars in honey, syrups, and fruit juices. An additional reduction to under 5% of caloric intake can be warranted in certain conditions. Elevated free sugars intake frequently implies an increased overall caloric intake, which leads to weight gain. A dietary reduction in free sugars with the purpose of reducing total caloric intake leads to significant weight loss, regardless of the initial values. In studies where complex carbohydrates have been exchanged with other nutrients while maintaining the same overall caloric intake, weight reduction was not achieved [247,248]. It is therefore most probable that the weight-reducing efficiency of free sugar intake reduction is due to the high caloric density of such foods. In consequence, a relatively modest reduction in free sugars can imply a significantly lower overall daily energy intake [249]. To this avail, sweetened beverages and “fast-food” should be avoided in children before reaching school age [250,251]. On the opposing side, the consumption of fruit and vegetables provides an optimal nutrient composition, along with preferentially searching for foods with a low glycemic index to provide sources for carbohydrates, such as whole-grain foods [245].
When regarding lipid intake, lipid-rich foods such as high-fat dairy should generally be limited and an optimal ratio between saturated and unsaturated fats should be sought after. Saturated fats have a detrimental effect on weight status and cardiovascular risk. They are generally found in animal-origin foods. For this reason, it may be preferable to achieve optimal lipid intake using vegetal oils, a good source of essential fatty acids, and vitamin E. A further aspect worth considering relates to the intake of trans fats, which can increase the risk of weight excess and cardiovascular disease. Partially hydrogenated oils are the main source of trans fats. Protein intake should be achieved mostly through animal source foods with low lipid content and vegetal foods with high protein content such as beans, peas, soy, nuts, and seeds. Limitation of sodium intake should also be taken into account when considering the optimal diet [245].
When regarding the rhythm of energy intake, dividing daily calorie consumption into three main meals and 1–2 snacks is the recommended strategy starting from age two. Replacing main meals or omitting one of them with subsequent compensation, as well as frequent dining out have all been incriminated in increasing the risk for obesity [252]. Eating at irregular intervals (particularly during the night), and binge eating are typical examples of a maladjusted eating rhythm that can lead to weight excess [198,250,253]. A possible explanation concerning the deleterious effects of nocturnal calory intake refers to the inversed disposition of circadian energy expenditure versus intake. Most of the calories consumed during the day should be spent during daily activity in order to avoid energy storage in the form of adipose tissue. Calorie intake during the night will tip the balance towards the creation of energy reserves in the absence of physical activity. Although this pattern is more common in adults, it may be prudent to avoid late meals in children, in particular those at risk of becoming obese [254,255,256].
Finally, one aspect worth mentioning is the general recommendation of avoiding recompense through food. This conduct usually implies an alteration of all three of the described elements of a healthy diet. It can increase the total caloric intake above the recommended quantity, it alters the optimal nutrient proportion, as these types of rewards are usually high in rapidly absorbed free sugars, and it alters the adequate rhythm of food intake, as they are usually offered between meals or planned snacks [250].

6.5.2. Energy Expenditure

Adequate energy expenditure is a key element in maintaining a normal weight. The proportion of active and sedentary intervals is essential in this respect, regardless of age. In this respect, unrestrained non-academic screen time is an important risk factor for obesity and should be avoided completely until the age of 2 [257,258,259]. Between the age of 2 and 4, non-academic screen time should be reduced to a minimum. The habits acquired in this age interval may resonate even into adult life. From the age of 5 and onwards, children can be encouraged to participate in team sports and to achieve a minimum of 60 min of moderate to intense physical activity every day.
Rest is just as important in achieving a balanced development. In adults, sleep deprivation has been linked to obesity by reducing circulating leptin and increasing ghrelin synthesis, thus increasing appetite and inducing insulin resistance [260,261]. The association of obesity with lack of sleep applies to children as well [262,263,264,265,266,267,268,269,270]. Cortisol levels and growth hormone imbalances associated with insufficient sleep are contributory to generating weight excess [271].
In essence, energy expenditure can also be described similarly to caloric intake, by taking into consideration its quantity, quality, and rhythm. This refers to the quantity of time spent engaged in physical activity or rest, the quality of both physical activity (preferably moderate to high intensity) and rest, where prioritizing sleep over screen time may be beneficial, as well as the rhythm of physical activity through establishing a healthy circadian rhythm.

6.6. Psychological Aspects

Psychological stress has been linked to obesity in children. One of the proposed mechanisms that leads to this connection could include inflammation and the interference in the hypothalamic-pituitary-adrenal axis with a subsequent increase in cortisol levels and increased appetite. Several major stress-generating events such as abuse or divorce have been linked to excess weight [272].
The mental status of caretakers is also an important determinant factor in forming the basis of a healthy lifestyle for children. Maternal depression can have a significant impact on this matter. Post-partum depression, for example, is associated with adverse postnatal feeding practices, including early cessation of breastfeeding. In older children, parental depression is associated with a lack of physical activity and an increase in screen time, both of which could translate into similar behavior in children [273].

6.7. Social Background

As children grow and become more and more conscious of their surrounding environment, they also become more susceptible to certain elements that define the background of society and day-to-day life which can influence all behavioral aspects, including those linked to dietary habits, physical activity, and weight control.
One relevant example relates to the increased availability of nutritionally inadequate foods, both financially, as well as concerning the ubiquity of such foods in fast-food restaurants and vending machines in public spaces including schools, most often in large quantities and with a significant detrimental potential regarding caloric intake and weight status [274]. Even in facilities used to fulfill daily nutritional necessities such as supermarkets, the proportion of foods rich in free sugars, lipids, and sodium has become worrisome [275]. Marketing techniques that promote such foods are also to be incriminated in the obesogenic tendencies of today’s society. Commercials often depict unhealthy foods as palatable, financially accessible, and easy to prepare, appealing both to adults, also exposing the children they are caring for to the same products, and directly to children, sometimes even by associating a favorite cartoon character with unhealthy, sugar-laden products [276,277]. A further example refers to promoting impulse-buying by placing products with a high caloric content in front of cash registers or in waiting zones [278]. All of these methods are rooted in carefully studied manipulative techniques meant to increase the consumption of products that are scientifically proven to be inherently harmful. The filter through which the available information reaches the mind of a child can also be altered due to the social and economic status of their caretakers, the cultural and ethnic aspects that shape their perception of the world, as well as the tolerance towards noxious behaviors of the micro-environment they pertain to [198].

7. Childhood Obesity as an Adult Risk Factor

In adults, there is a well-documented link between obesity and a wide array of cardiovascular diseases, including ischemic heart disease, hypertension, cerebrovascular disease, atrial fibrillation, ventricular arrhythmia, and sudden cardiac death [38,279]. Furthermore, obesity promotes the development of a series of afflictions which are themselves individual risk factors for cardiovascular diseases, such as type 2 diabetes, dyslipidemia, and obstructive sleep apnea. In this view, obesity appears to be more than a standalone ailment and can be better described as a complex dysmetabolic and mechanically dysfunctional condition [280,281,282].
Considerable efforts have been made to study whether the link between adipose excess and cardiometabolic disease finds its origin during childhood. Table 4 summarizes some of the available observational evidence showing childhood obesity leading to adult disease.

8. Mechanisms of Obesity-Related Cardiometabolic Disease

The physio-pathological pathways leading from obesity to cardiovascular disease involve both direct and indirect mechanisms with both local and systemic action [279]. From a hemodynamic standpoint, the adaptive changes of the cardiovascular system are due to the structural and functional alterations imposed by the increase in circulating volume and metabolic strain attributable to excess adipose tissue. This generates a hyperdynamic cardiovascular system constrained to adapt the cardiac output by increasing stroke volume and heart rate. Peripheral vascular resistance increases due to sympathetic hyperreactivity and the systemic proinflammatory status associated with obesity. The most direct consequence of these modifications is an increase in blood pressure, which partly explains the causation behind the higher prevalence of arterial hypertension in obese patients [289]. The left ventricle is under the direct effect of the strain induced by the aforementioned processes and adapts by altering its geometry and remodeling its structure in an attempt to manage the increased load. Beyond a certain point, the adaptive mechanisms of the cardiac muscle become dysfunctional, leading to progressive dilation and cardiac hypertrophy, eventually impeding the function of the cardiac pump [290,291,292]. The initiation of cardiac remodeling is a relatively early process, as demonstrated by studies identifying its presence in obese children [293]. A recent study conducted by Esanu et al. found signs of LV remodeling in up to one third of obese children investigated. The most common pattern was that of concentric left ventricular hypertrophy [294].
The hemodynamic changes in obesity have an effect on the other chambers of the heart as well. The left atrium is also affected in obesity [295]. A possible mechanism is related to the increase in the filling pressures of the left ventricle, which can lead to the progressive distension of the left atrium. This process might aid in explaining why obese patients have an increased prevalence of atrial fibrillation [291]. The increases in pressure on the right heart can be objectified by increased pulmonary artery systolic pressure, a frequent finding among obese patients [296].
The characteristic hemodynamic reshaping in obesity may develop long before the clinical debut of cardiovascular disease. Already in obese children, a circulatory hyperdynamic status can become apparent as demonstrated by an increase in central aortic pressure when compared to children with a normal weight, regardless of the presence of clinically manifest hypertension, dyslipidemia, or sedentarism [297]. The repercussions of this status also affect the morphology and function of the myocardium in obese children. Morphologically, obese children frequently exhibit larger cardiac cavities, thicker ventricular walls, and an increased total cardiac mass. Functionally, even in the absence of a significant modification in left ventricular ejection fraction when compared to children with normal body weight, obese children show important differences in the parameters measured by tissue Doppler and speckle-tracking echocardiography, as well as a comparative decrease in diastolic function [298].
In addition, despite the rare occurrence of clinical hypertension in pediatric populations, when this pathology is identified, it is most frequently identified in obese children [299,300,301]. The relationship between these two entities is further strengthened by the fact that assertive intervention upon weight excess frequently leads to substantial reductions in systolic arterial pressure [6]. Similar to obesity, if arterial hypertension is developed during childhood, it frequently continues to affect individuals into adulthood [82,302,303]. These children often have a characteristic pattern of subclinical hypertension-related organ damage [304]. Excess weight in children is also associated with decreased diastolic function, microalbuminuria, and increased intima-media index, as well as an increase in vascular rigidity [305,306,307,308].
Amongst the ill effects of obesity on the vascular system, atherosclerosis is one of the key mechanisms involved. This process begins during early childhood, as demonstrated in postmortem studies by the presence of lipidic striae on coronary arteries even in the first decade of life [309,310]. The initial lesions are progressive in nature and lead to the formation of atherosclerotic plaques, sometimes even reaching the form of advanced lesions such as fibrotic plaques as early as adolescence [309]. The inceptive subclinical vascular deterioration can in time lead to the illnesses typically linked to atherosclerosis: coronary heart disease, peripheral artery disease, and cerebrovascular disease [311,312]. Obesity accelerates the process of atherosclerosis [313] and its presence in childhood increases the risk of developing atherosclerosis-related diseases in adulthood [314,315]. The risk can, however, be reduced in obese patients who manage to achieve adequate weight loss, which acts as an incentive for the development of efficient and timely preventive programs [316,317].
From a metabolic standpoint, there are several modifications relevant to the interrelation between obesity and cardiovascular risk. The increase in insulin resistance in children shows similar pathways with adults and is a fundamental phase in the pathogenesis of type 2 diabetes and is more frequent amongst obese individuals, even more so the sooner the onset of weight excess [318,319].
Dyslipidemia is yet another metabolic disturbance frequently associated with obesity. Obese patients frequently manifest a typical pattern of hypertriglyceridemia, hyper-LDL-cholesterolemia, and hipo-HDL-cholesterolemia. Obese children exhibit a similar pattern, however, hyper-LDL-cholesterolemia is not as frequent in this population. In children, hypertriglyceridemia generally responds well to the reduction in artificially sweetened foods and beverages [319,320].

9. Obesity Biomarkers and Risk Assessment

The use of biomarkers can mitigate risk assessment in obesity and provide useful information regarding the mechanisms that generate weight excess and how the latter can negatively influence health. A brief overview of such biomarkers follows.

9.1. Genetic and Epigenetic Biomarkers

Messenger RNA (mRNA) levels corresponding to the genes coding the receptor for leptin, insulin, and CPT1A (Carnitine Palmitoyl transferase 1 A) have shown increased levels in obese children compared to children with a normal weight, while the mRNA levels of SLC27A2 (very long-chain acyl-CoA synthetase) had lower values in overweight individuals [321].
MicroRNA(miRNAs) are molecules of noncoding RNA nucleotides that regulate the genes’ expression post-transcription. The literature is scarce regarding these biomarkers in obese children compared to current reports in adults. Nevertheless, in a recent systematic review, Oses M et al. identified six mi-ARN overexpressed in obese children and associated with other adiposity biomarkers: (1) miR-34a, miR-122 (obese children with insulin resistance or nonalcoholic fatty liver disease), (2) miR- 140-5p,142–3143 (obese children). All of them had a significant correlation with BMI values; however, miR-122 seems to play a crucial role in cholesterol and fatty acids regulators in the liver [322].
The expression of specific genes in epiploic adipose cells that code for microtubule-associated protein tau (MAPT), destrin (actin depolymerizing factor–ADF or DSTN), spectrin β non-erythrocytic 1 (SPTBN1), Rho/Rac Guanine Nucleotide Exchange Factor 2 (ARHGEF2), and spindle and kinetochore-associated protein 1 (SKA1) have been linked to childhood obesity [323].

9.2. Inflammatory Markers

The most common inflammatory markers associated with obesity include tumor necrosis factor alpha (TNFα), interleukin-6 (IL-6), and c-reactive protein (CRP). In obese children, elevated CRP levels correlate with insulin resistance and intima-media thickness. CRP, IL-6, and TNFα are frequently elevated in obese children with risk factors for atherosclerosis. IL-6 has been associated with hyperinsulinism, insulin resistance, BMI, and abdominal circumference values. These correlations serve to quantify the known link between inflammation and obesity within the spectrum of cardiovascular risk [81,324].
The neutrophil to lymphocyte ratio (NLR) can serve as a marker for cardiovascular risk. NLR has shown a particularly strong relationship to the evolution of coronary heart disease, including response to treatment. The implicated mechanisms that provide a rationale to using the NLR refer on the one hand to the contribution of neutrophils in nonspecific inflammatory response, with a value that correlates with oxidative stress in the organism even before reaching cut-off values for neutrophilia. Lymphocyte number, on the other hand, provides a good image of the overall immune responsiveness of the body, and their reduction can indicate a hindrance of the capacities of the immune system [325,326,327,328,329].
The NLRs value correlates with vascular parameters in children and is indicative of the inflammatory processes involved in the initial phases of atherosclerosis [325,326].
A further argument that strengthens the parallel nature of the evolution of weight excess and inflammatory state is that dietary measures implemented to reduce adipose surplus may have a beneficial effect on relieving the proinflammatory status associated with obesity as well [330].

9.3. Serological Markers

Given the key implications of leptin in the mechanisms governing appetite and weight regulation, it becomes evident that measuring its circulating levels could aid in tracking the evolution of weight excess. Leptin levels may also aid in evaluating an individual’s responsiveness to weight reduction programs [331].
In addition to leptin, adipocytes secrete a wide array of biologically active factors, or adipokines. FABP4 falls into this category. FABP4 has been linked to weight control, metabolism, and formation of atherosclerosis, and has demonstrated increased circulating levels in patients with obesity, cardiovascular diseases, or metabolic syndrome [130,332]. Adiponectin is an adipokine with antiatherogenic, anti-inflammatory, insulin-sensitizing, and cardioprotective effects. Its serum concentration can provide details regarding the atherosclerotic process in children, as its decrease correlates with premature thickening of carotid walls in pediatric subjects, as well as regarding insulin sensitivity assessment, due to its relation to circulating insulin levels and the HOMA-IR [333].
HOMA-IR, defined as: ( Serum   glucose   ( mmol L ) ) · ( Serum   insulin   ( µ UI mL ) ) 22.5 can, in and of itself, provide information on the initially subclinical stages of diabetes, particularly in obese patients [334].
The atherogenic index of plasma (AIP), defined by the following formula:
AIP = lg Serum   Triglycerides   ( mmol / L ) Serum   HDL   cholesterol   ( mmol / L ) , is a good measure of the balance between the harmful effects of hypertriglyceridemia and the cardio-protective properties of HDL-cholesterol. AIP has shown a potentially stronger correlation to cardiovascular risk when compared to its individual components, and may aid in the quantification of treatment response [335,336,337].
Certain markers for hepatic injury also foretell the ill effects of obesity on metabolism. An increase in ALT (alanine aminotransferase) levels has been linked to insulin resistance and altered glucose tolerance as well as to increased levels of circulating free fatty acids and triglycerides [338]. The AST/ALT (aspartate aminotransferase/ALT) ratio has been proposed as a potential marker for screening adolescents with increased cardiometabolic risk. Gama-glutamyl peptidase (GGT) may also provide useful information regarding hepatic involvement in obesity [339].
Other potential biomarkers for obesity include isoleucine, glyceric acid, serin, 2,3,4-trihydroxybutyric acid, and phenylalanine [301].

10. Conclusions

Given the points presented in this review, one can conclude that obesity is much more than just a simple disproportion between weight and height. A thorough understanding of the epidemiology and the mechanisms involved in the genesis of this illness is necessary in order to identify potential key points in which preventive or therapeutic action can be implemented. The collection of information summarized in this review may hopefully be of aid in providing a structured approach to the current knowledge on this subject.

Author Contributions

Conceptualization, M.O.N. and B.N.; methodology, M.O.N. and B.N.; validation, C.D.D.; formal analysis, M.O.N. and B.N.; investigation M.O.N., B.N., I.D., C.R.S., R.M.C., B.I.C., C.D.D. and M.T.; resources, M.O.N., B.N., I.D., C.R.S., R.M.C., B.I.C., C.D.D. and M.T.; data curation, M.O.N.; writing—original draft preparation, M.O.N., B.N. and I.D.; writing—review and editing, M.O.N., B.N., I.D., C.R.S., R.M.C., B.I.C., C.D.D. and M.T.; visualization, M.O.N., B.N., C.R.S. and M.T.; supervision, C.D.D.; project administration, M.O.N. and B.N.; and funding acquisition, M.O.N., B.N., I.D., C.R.S., R.M.C., B.I.C., C.D.D. and M.T. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee) of the Pediatric Clinical Hospital Sibiu (protocol code 6731/05.10.2021).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the paraclinical investigations displayed.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to data protection legislation.


This work is part of the Ph.D. thesis of candidate Mihai Octavian Negrea under the supervision of Domnariu Carmen at the Lucian Blaga University. We would like to acknowledge the work of Irina Ioana Negrea for her contribution regarding the design of Figure 12. All rights for this figure have been transferred to the authors.

Conflicts of Interest

The authors declare no conflict of interest.


  1. World Health Org. Obesity and Overweight. 9 June 2021. Available online: (accessed on 11 November 2021).
  2. Kelly, T.; Yang, W.; Chen, C.S.; Reynolds, K.; He, J. Global burden of obesity in 2005 and projections to 2030. Int. J. Obes. 2008, 32, 1431–1437. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Finkelstein, E.A.; Trogdon, J.G.; Cohen, J.W.; Dietz, W. Annual medical spending attributable to obesity: Payer-and service-specific estimates. Health Aff. 2009, 28, w822–w831. [Google Scholar] [CrossRef] [Green Version]
  4. Di Cesare, M.; Sorić, M.; Bovet, P.; Miranda, J.J.; Bhutta, Z.; Stevens, G.A.; Laxmaiah, A.; Kengne, A.P.; Bentham, J. The epidemiological burden of obesity in childhood: A worldwide epidemic requiring urgent action. BMC Med. 2019, 17, 212. [Google Scholar] [CrossRef] [Green Version]
  5. Ortega, F.B.; Blair, S.N.; Lavie, C.J. Obesity and cardiovascular disease. Circ. Res. 2016, 118, 1752–1770. [Google Scholar] [CrossRef] [Green Version]
  6. Falkner, B. Monitoring and management of hypertension with obesity in adolescents. Integr. Blood Press Control 2017, 10, 33–39. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Kelsey, M.M.; Zaepfel, A.; Bjornstad, P.; Nadeau, K.J. Age-related consequences of childhood obesity. Gerontology 2014, 60, 222–228. [Google Scholar] [CrossRef]
  8. Simmonds, M.; Llewellyn, A.; Owen, C.G.; Woolacott, N. Predicting adult obesity from childhood obesity: A systematic review and meta-analysis. Obes. Rev. 2016, 17, 95–107. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Abdullah, A.; Wolfe, R.; Stoelwinder, J.U.; de Courten, M.; Stevenson, C.; Walls, H.L.; Peeters, A. The number of years lived with obesity and the risk of all-cause and cause-specific mortality. Int. J. Epidemiol. 2011, 40, 985–996. [Google Scholar] [CrossRef] [Green Version]
  10. World Health Org. Obesity Overview. Available online: (accessed on 29 September 2021).
  11. World Health Org. Adolescent Health. Available online: (accessed on 15 November 2021).
  12. Eneli, I.; Dele Davis, H. Epidemiology of childhood obesity. In Obesity in Childhood & Adolescence; Dele Davis, H., Ed.; Praeger Publishers: Westport, CT, USA, 2008; Volume 1, pp. 3–19. [Google Scholar]
  13. Ortega, F.B.; Labayen, I.; Ruiz, J.R.; Kurvinen, E.; Loit, H.M.; Harro, J.; Veidebaum, T.; Sjöström, M. Improvements in fitness reduce the risk of becoming overweight across puberty. Med. Sci. Sports Exerc. 2011, 43, 1891–1897. [Google Scholar] [CrossRef]
  14. CDC. CDC Growth Charts. Available online: (accessed on 29 September 2021).
  15. Noncommunicable Diseases: Childhood Overweight and Obesity. World Health Org. 19 October 2020. Available online: (accessed on 29 September 2021).
  16. Kelly, A.S.; Barlow, S.E.; Rao, G.; Inge, T.H.; Hayman, L.L.; Steinberger, J.; Urbina, E.M.; Ewing, L.J.; Daniels, S.R.; American Heart Association Atherosclerosis, Hypertension, and Obesity in the Young Committee of the Council on Cardiovascular Disease in the Young, Council on Nutrition, Physical Activity and Metabolism, and Council on Clinical Cardiology. Severe obesity in children and adolescents: Identification, associated health risks, and treatment approaches: A scientific statement from the American Heart Association. Circulation 2013, 128, 1689–1712. [Google Scholar] [CrossRef]
  17. Skinner, A.C.; Skelton, J.A. Prevalence and trends in obesity and severe obesity among children in the United States, 1999–2012. JAMA Pediatr. 2014, 168, 561–566. [Google Scholar] [CrossRef] [Green Version]
  18. Wang, Y.; Lim, H. The global childhood obesity epidemic and the association between socio-economic status and childhood obesity. Int. Rev. Psychiatry 2012, 24, 176–188. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  19. Ogden, C.L.; Carroll, M.D.; Kit, B.K.; Flegal, K.M. Prevalence of childhood and adult obesity in the United States, 2011–2012. JAMA 2014, 311, 806–814. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  20. Rivera, J.Á.; De Cossío, T.G.; Pedraza, L.S.; Aburto, T.C.; Sánchez, T.G.; Martorell, R. Childhood and adolescent overweight and obesity in Latin America: A systematic review. Lancet Diabetes Endocrinol. 2014, 2, 321–332. [Google Scholar] [CrossRef]
  21. Mazidi, M.; Banach, M.; Kengne, A.P. Prevalence of childhood and adolescent overweight and obesity in Asian countries: A systematic review and meta-analysis. Arch. Med. Sci. 2018, 14, 1185–1203. [Google Scholar] [CrossRef]
  22. Klingberg, S.; Draper, C.E.; Micklesfield, L.K.; Benjamin-Neelon, S.E.; van Sluijs, E.M.F. Childhood Obesity Prevention in Africa: A Systematic Review of Intervention Effectiveness and Implementation. Int. J. Environ. Res. Public Health 2019, 16, 1212. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Australian Institute of Health and Welfare. A Picture of Overweight and Obesity in Australia. Cat. no.PHE 216. 2017; 60p. Available online: (accessed on 29 September 2021).
  24. Garrido-Miguel, M.; Cavero-Redondo, I.; Álvarez-Bueno, C.; Rodríguez-Artalejo, F.; Moreno, L.A.; Ruiz, J.R.; Aherens, W.; Martinez-Vizcaíno, V. Prevalence and Trends of Overweight and Obesity in European Children from 1999 to 2016: A Systematic Review and Meta-analysis. JAMA Pediatr. 2019, 173, e192430. [Google Scholar] [CrossRef]
  25. Janjic, D. “Obésité de type androïde et obésité de type gynoïde” [Android-type obesity and gynecoid-type obesity]. Praxis 1996, 85, 1578–1583. [Google Scholar] [PubMed]
  26. Ramirez, M.E.; McMurry, M.P.; Wiebke, G.A.; Felten, K.J.; Ren, K.; Meikle, A.W.; Iverius, P.H. Evidence for sex steroid inhibition of lipoprotein lipase in men: Comparison of abdominal and femoral adipose tissue. Metabolism 1997, 46, 179–185. [Google Scholar] [CrossRef]
  27. Pedersen, S.B.; Kristensen, K.; Hermann, P.A.; Katzenellenbogen, J.A.; Richelsen, B. Estrogen controls lipolysis by up-regulating alpha2A-adrenergic receptors directly in human adipose tissue through the estrogen receptor alpha. Implications for the female fat distribution. J. Clin. Endocrinol. Metab. 2004, 89, 1869–1878. [Google Scholar] [CrossRef] [Green Version]
  28. Singh, R.; Artaza, J.N.; Taylor, W.E.; Braga, M.; Yuan, X.; Gonzalez-Cadavid, N.F.; Bhasin, S. Testosterone inhibits adipogenic differentiation in 3T3-L1 cells: Nuclear translocation of androgen receptor complex with beta-catenin and T-cell factor 4 may bypass canonical Wnt signaling to down-regulate adipogenic transcription factors. Endocrinology 2006, 147, 141–154. [Google Scholar] [CrossRef] [Green Version]
  29. Lacasa, D.; Le Liepvre, X.; Ferre, P.; Dugail, I. Progesterone stimulates adipocyte determination and differentiation 1/sterol regulatory element-binding protein 1c gene expression. potential mechanism for the lipogenic effect of progesterone in adipose tissue. J. Biol. Chem. 2001, 276, 11512–11516. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Guglielmi, V.; Sbraccia, P. Obesity phenotypes: Depot-differences in adipose tissue and their clinical implications. Eat Weight Disord. 2018, 23, 3–14. [Google Scholar] [CrossRef] [PubMed]
  31. Ashwell, M. Obesity risk: Importance of the waist–to–height ratio. Nurs. Stand. 2009, 23, 49–54. [Google Scholar] [CrossRef]
  32. Anderson, P.J.; Chan, J.C.; Chan, Y.L.; Tomlinson, B.; Young, R.P.; Lee, Z.S.; Lee, K.K.C.; Metreweli, C.; Cockram, C.S.; Critchley, J.A.J.H. Visceral fat and cardiovascular risk factors in Chinese NIDDM patients. Diabetes Care 1997, 20, 1854–1858. [Google Scholar] [CrossRef]
  33. Després, J.P. Body fat distribution and risk of cardiovascular disease an update. Circulation 2012, 126, 1301–1313. [Google Scholar] [CrossRef] [Green Version]
  34. Sironi, A.M.; Petz, R.; De Marchi, D.; Buzzigoli, E.; Ciociaro, D.; Positano, V.; Lombardi, M.; Ferrannini, E.; Gastaldelli, A. Impact of increased visceral and cardiac fat on cardiometabolic risk and disease. Diabet. Med. 2012, 29, 622–627. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. St St-Pierre, J.; Lemieux, I.; Vohl, M.C.; Perron, P.; Tremblay, G.; Després, J.P.; Gaudet, D. Contribution of abdominal obesity and hypertriglyceridemia to impaired fasting glucose and coronary artery disease. Am. J. Cardiol. 2007, 99, 369–373. [Google Scholar] [CrossRef]
  36. Lavie, C.J.; Milani, R.V.; Ventura, H.O. Obesity and cardiovascular disease: Risk factor, paradox, and impact of weight loss. J. Am. Coll. Cardiol. 2009, 53, 1925–1932. [Google Scholar] [CrossRef] [Green Version]
  37. Klöting, N.; Blüher, M. Adipocyte dysfunction, inflammation and metabolic syndrome. Rev. Endocr. Metab. Disord. 2014, 15, 277–287. [Google Scholar] [CrossRef]
  38. Koliaki, C.; Liatis, S.; Kokkinos, A. Obesity and cardiovascular disease: Revisiting an old relationship. Metabolism 2019, 92, 98–107. [Google Scholar] [CrossRef] [PubMed]
  39. Sugerman, H.J. Effects of increased intra-abdominal pressure in severe obesity. Surg. Clin. N. Am. 2001, 81, 1063–1075. [Google Scholar] [CrossRef]
  40. Apovian, C.M.; Bigornia, S.; Mott, M.; Meyers, M.R.; Ulloor, J.; Gagua, M.; McDonnell, M.; Hess, D.; Joseph, L.; Gokce, N. Adipose macrophage infiltration is associated with insulin resistance and vascular endothelial dysfunction in obese subjects. Arterioscler. Thromb. Vasc. Biol. 2008, 28, 1654–1659. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  41. Skurk, T.; Alberti-Huber, C.; Herder, C.; Hauner, H. Relationship between adipocyte size and adipokine expression and secretion. J. Clin. Endocrinol. Metab. 2007, 92, 1023–1033. [Google Scholar] [CrossRef] [PubMed]
  42. Jiang, C.; Qu, A.; Matsubara, T.; Chanturiya, T.; Jou, W.; Gavrilova, O.; Shah, Y.M.; Gonzalez, F.J. Disruption of hypoxia-inducible factor 1 in adipocytes improves insulin sensitivity and decreases adiposity in high-fat diet-fed mice. Diabetes 2011, 60, 2484–2495. [Google Scholar] [CrossRef] [Green Version]
  43. Henegar, C.; Tordjman, J.; Achard, V.; Lacasa, D.; Cremer, I.; Guerre-Millo, M.; Poitou, C.; Basdevant, A.; Stich, V.; Viguerie, N.; et al. Adipose tissue transcriptomic signature highlights the pathological relevance of extracellular matrix in human obesity. Genome Biol. 2008, 9, R14. [Google Scholar] [CrossRef] [PubMed]
  44. Heinonen, S.; Buzkova, J.; Muniandy, M.; Kaksonen, R.; Ollikainen, M.; Ismail, K.; Hakkarainen, A.; Lundbom, J.; Lundbom, N.; Vuolteenaho, K.; et al. Impaired mitochondrial biogenesis in adipose tissue in acquired obesity. Diabetes 2015, 64, 3135–3145. [Google Scholar] [CrossRef] [Green Version]
  45. Capeau, J. Insulin resistance and steatosis in humans. Diabetes Metab. 2008, 34, 649–657. [Google Scholar] [CrossRef]
  46. Kotronen, A.; Yki-Jarvinen, H. Fatty liver: A novel component of the metabolic syndrome. Arterioscler. Thromb. Vasc. Biol. 2008, 28, 27–38. [Google Scholar] [CrossRef]
  47. Gruzdeva, O.; Borodkina, D.; Uchasova, E.; Dyleva, Y.; Barbarash, O. Localization of fat depots and cardiovascular risk. Lipids Health Dis. 2018, 17, 218. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Yamaguchi, Y.; Cavallero, S.; Patterson, M.; Shen, H.; Xu, J.; Kumar, S.R.; Sucov, H.M. Adipogenesis and epicardial adipose tissue: A novel fate of the epicardium induced by mesenchymal transformation and PPARγ activation. Proc. Natl. Acad. Sci. USA 2015, 112, 2070–2075. [Google Scholar] [CrossRef] [Green Version]
  49. Manzella, D.; Barbieri, M.; Rizzo, M.R.; Ragno, E.; Passariello, N.; Gambardella, A.; Marfella, R.; Giugliano, D.; Paolisso, G. Role of free fatty acids on cardiac autonomic nervous system in noninsulin-dependent diabetic patients: Effects of metabolic control. J. Clin. Endocrinol. Metab. 2001, 86, 2769–2774. [Google Scholar] [CrossRef] [PubMed]
  50. Mahabadi, A.A.; Massaro, J.M.; Rosito, G.A.; Levy, D.; Murabito, J.M.; Wolf, P.A.; O’Donnell, C.J.; Fox, C.S.; Hoffmann, U. Association of pericardial fat, intrathoracic fat, and visceral abdominal fat with cardiovascular disease burden: The Framingham heart study. Eur. Heart J. 2009, 30, 850–856. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  51. Corradi, D.; Maestri, R.; Callegari, S.; Pastori, P.; Goldoni, M.; Luong, T.V.; Bordi, C. The ventricular epicardial fat is related to the myocardial mass in normal, ischemic and hypertrophic hearts. Cardiovasc. Pathol. 2004, 13, 313–316. [Google Scholar] [CrossRef] [PubMed]
  52. Eroglu, S.; Sade, L.E.; Yildirir, A.; Bal, U.; Ozbicer, S.; Ozgul, A.S.; Bozbas, H.; Aydinalp, A.; Muderrisoglu, H. Epicardial adipose tissue thickness by echocardiography is a marker for the presence and severity of coronary artery disease. Nutr. Metab. Cardiovasc. Dis. 2009, 19, 211–217. [Google Scholar] [CrossRef]
  53. Wang, C.P.; Hsu, H.L.; Hung, W.C.; Yu, T.H.; Chen, Y.H.; Chiu, C.A.; Lu, L.F.; Chung, F.M.; Shin, S.J.; Lee, Y.J. Increased epicardial adipose tissue (EAT) volume in type 2 diabetes mellitus and association with metabolic syndrome and severity of coronary atherosclerosis. Clin. Endocrinol. 2009, 70, 876–882. [Google Scholar] [CrossRef] [PubMed]
  54. Butcovan, D.; Mocanu, V.; Timofte, D.V.; Costan, V.V.; Danila, R.; Veselin, A.P.; Ciuntu, B.M.; Haliga, R.E.; Sascau, R.A.; Ghiga, G.; et al. Macrophage Accumulation and Angiogenesis in Epicardial Adipose Tissue in Cardiac Patients with or without Chronic Heart Failure. Appl. Sci. 2020, 10, 5871. [Google Scholar] [CrossRef]
  55. Rittig, K.; Staib, K.; Machann, J.; Böttcher, M.; Peter, A.; Schick, F.; Claussen, C.; Stefan, N.; Fritsche, A.; Häring, H.U.; et al. Perivascular fatty tissue at the brachial artery is linked to insulin resistance but not to local endothelial dysfunction. Diabetologia 2008, 51, 2093–2099. [Google Scholar] [CrossRef]
  56. Ashwell, M.; Gunn, P.; Gibson, S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: Systematic review and meta-analysis. Obes. Rev. 2012, 13, 275–286. [Google Scholar] [CrossRef]
  57. Lee, C.M.; Huxley, R.R.; Wildman, R.P.; Woodward, M. Indices of abdominal obesity are better discriminators of cardiovascular risk factors than BMI: A meta-analysis. J. Clin. Epidemiol. 2008, 61, 646–653. [Google Scholar] [CrossRef]
  58. Xue, R.; Li, Q.; Geng, Y.; Wang, H.; Wang, F.; Zhang, S. Abdominal obesity and risk of CVD: A dose-response meta-analysis of thirty-one prospective studies. Br. J. Nutr. 2021, 126, 1420–1430. [Google Scholar] [CrossRef]
  59. Bosomworth, N.J. Normal-weight central obesity: Unique hazard of the toxic waist. Can. Fam. Physician 2019, 65, 399–408. [Google Scholar] [PubMed]
  60. Grigorakis, D.A.; Georgoulis, M.; Psarra, G.; Tambalis, K.D.; Panagiotakos, D.B.; Sidossis, L.S. Prevalence and lifestyle determinants of central obesity in children. Eur. J. Nutr. 2016, 55, 1923–1931. [Google Scholar] [CrossRef]
  61. Canoy, D.; Boekholdt, S.M.; Wareham, N.; Luben, R.; Welch, A.; Bingham, S.; Buchan, I.; Day, N.; Khaw, K.T. Body fat distribution and risk of coronary heart disease in men and women in the European Prospective Investigation Into Cancer and Nutrition in Norfolk cohort: A population-based prospective study. Circulation 2007, 116, 2933–2943. [Google Scholar] [CrossRef] [Green Version]
  62. Despres, J.P.; Lemieux, I.; Bergeron, J.; Pibarot, P.; Mathieu, P.; Larose, E.; Rodes-Cabau, J.; Bertrand, O.F.; Poirier, P. Abdominal obesity and the metabolic syndrome: Contribution to global cardiometabolic risk. Arterioscler. Thromb. Vasc. Biol. 2008, 28, 1039–1049. [Google Scholar] [CrossRef] [PubMed]
  63. Krekoukia, M.; Nassis, G.P.; Psarra, G.; Skenderi, K.; Chrousos, G.P.; Sidossis, L.S. Elevated total and central adiposity and low physical activity are associated with insulin resistance in children. Metab. Clin. Exp. 2007, 56, 206–213. [Google Scholar] [CrossRef] [PubMed]
  64. Olza, J.; Aguilera, C.M.; Gil-Campos, M.; Leis, R.; Bueno, G.; Valle, M.; Canete, R.; Tojo, R.; Moreno, L.A.; Gil, A. Waist-to-height ratio, inflammation and CVD risk in obese children. Public Health Nutr. 2014, 17, 2378–2385. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Manios, Y.; Moschonis, G.; Kourlaba, G.; Bouloubasi, Z.; Grammatikaki, E.; Spyridaki, A.; Hatzis, C.; Kafatos, A.; Fragiadakis, G.A. Prevalence and independent predictors of insulin resistance in children from Crete, Greece: The children study. Diabet. Med. 2008, 25, 65–72. [Google Scholar] [CrossRef]
  66. Kollias, A.; Psilopatis, I.; Karagiaouri, E.; Glaraki, M.; Grammatikos, E.; Grammatikos, E.E.; Garoufi, A.; Stergiou, G.S. Adiposity, blood pressure, and carotid intima-media thickness in greek adolescents. Obesity 2013, 21, 1013–1017. [Google Scholar] [CrossRef] [PubMed]
  67. Ochiai, H.; Shirasawa, T.; Nishimura, R.; Yoshimoto, T.; Minoura, A.; Oikawa, K.; Miki, A.; Hoshino, H.; Kokaze, A. Changes in overweight/obesity and central obesity status from preadolescence to adolescence: A longitudinal study among schoolchildren in Japan. BMC Public Health 2020, 20, 241. [Google Scholar] [CrossRef] [PubMed]
  68. Hassapidou, M.; Tzotzas, T.; Makri, E.; Pagkalos, I.; Kaklamanos, I.; Kapantais, E.; Abrahamian, A.; Polymeris, A.; Tziomalos, K. Prevalence and geographic variation of abdominal obesity in 7- and 9-year-old children in Greece; World Health Organization childhood obesity surveillance initiative 2010. BMC Public Health 2017, 17, 126. [Google Scholar] [CrossRef] [Green Version]
  69. Leitao, R.; Rodrigues, L.P.; Neves, L.; Carvalho, G.S. Changes in adiposity status from childhood to adolescence: A 6-year longitudinal study in Portuguese boys and girls. Ann. Hum. Biol. 2011, 38, 520–528. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  70. Chrzanowska, M.; Suder, A.; Kruszelnicki, P. Tracking and risk of abdominal obesity in the adolescence period in children aged 7–15. The Cracow longitudinal growth study. Am. J. Hum. Biol. 2012, 24, 62–67. [Google Scholar] [CrossRef] [PubMed]
  71. Hou, Y.P.; Li, Z.X.; Yang, L.; Zhao, M.; Xi, B. Effect of abdominal obesity in childhood on abdominal obesity in adulthood. Zhonghua Liu Xing Bing Xue Za Zhi 2020, 41, 385–388. (In Chinese) [Google Scholar] [CrossRef]
  72. Vishvanath, L.; Gupta, R.K. Contribution of adipogenesis to healthy adipose tissue expansion in obesity. J. Clin. Investig. 2019, 129(10), 4022–4031. [Google Scholar] [CrossRef] [PubMed]
  73. Caleyachetty, R.; Thomas, G.N.; Toulis, K.A.; Mohammed, N.; Gokhale, K.M.; Balachandran, K.; Nirantharakumar, K. Metabolically Healthy Obese and Incident Cardiovascular Disease Events Among 3. 5 Million Men and Women. J. Am. Coll. Cardiol. 2017, 70, 1429–1437. [Google Scholar] [CrossRef] [PubMed]
  74. Hamdy, O. The role of adipose tissue as an endocrine gland. Curr. Diab. Rep. 2005, 5, 317–319. [Google Scholar] [CrossRef]
  75. Aronne, L.J. Classification of obesity and assessment of obesity-related health risks. Obes. Res. 2002, 10, 105S–115S. [Google Scholar] [CrossRef] [PubMed]
  76. Bays, H.; Blonde, L.; Rosenson, R. Adiposopathy: How do diet, exercise and weight loss drug therapies improve metabolic disease in overweight patients? Expert Rev. Cardiovasc. Ther. 2006, 4, 871–895. [Google Scholar] [CrossRef]
  77. World Health Organization. Obesity: Preventing and Managing the Global Epidemic; World Health Organization: Geneva, Switzerland, 2000. [Google Scholar]
  78. Holley, T.J.; Collins, C.E.; Morgan, P.J.; Callister, R.; Hutchesson, M.J. Weight expectations, motivations for weight change and perceived factors influencing weight management in young Australian women: A crosssectional study. Public Health Nutr. 2016, 19, 275–286. [Google Scholar] [CrossRef] [Green Version]
  79. Soheilipour, F.; Hatami, M.; Salehiniya, H.; Alaei, M. Indicators of Obesity and Cardio-Metabolic Risks: Important Consideration in Adults and Children. Curr. Diabetes Rev. 2021. e-pub ahead of print. [Google Scholar] [CrossRef] [PubMed]
  80. Region WWP. _The Asia-Pacific Perspective: Redefining Obesity and Its Treatment_; International Association for the Study of Obesity, International Obesity Task Force; WHO Western Pacific Region: Geneva, Switzerland, 2000.
  81. Horan, M.; Gibney, E.; Molloy, E.; McAuliffe, F. Methodologies to assess paediatric adiposity. Ir. J. Med. Sci. 2015, 184, 53–68. [Google Scholar] [CrossRef] [PubMed]
  82. Zhao, Y.; Wang, L.; Xue, B.; Wang, Y. Associations between general and central obesity and hypertension among children: The Childhood Obesity Study in China Mega-Cities. Sci. Rep. 2017, 7, 16895. [Google Scholar] [CrossRef] [PubMed]
  83. Ma, C.; Lu, Q.; Wang, R.; Yin, F. Using height-corrected definition of metabolic syndrome in children and adolescents. J. Pediatr. Endocrinol. Metab. 2019, 32, 429–438. [Google Scholar] [CrossRef] [PubMed]
  84. Emdin, C.A.; Khera, A.V.; Natarajan, P.; Klarin, D.; Zekavat, S.M.; Hsiao, A.J.; Kathiresan, S. Genetic association of waist-to-hip ratio with cardiometabolic traits, type 2 diabetes, and coronary heart disease. JAMA 2017, 317, 626–634. [Google Scholar] [CrossRef]
  85. Krakauer, N.Y.; Krakauer, J.C. An Anthropometric Risk Index Based on Combining Height, Weight, Waist, and Hip Measurements. J. Obes. 2016, 2016, 8094275. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  86. Mameli, C.; Krakauer, N.Y.; Krakauer, J.C.; Bosetti, A.; Ferrari, C.M.; Moiana, N.; Schneider, L.; Borsani, B.; Genoni, T.; Zuccotti, G. The association between a body shape index and cardiovascular risk in overweight and obese children and adolescents. PLoS ONE 2018, 13, e0190426. [Google Scholar] [CrossRef] [Green Version]
  87. Arias Téllez, M.J.; Martinez-Tellez, B.; Soto, J.; Sánchez-Delgado, G. Validez del perímetro del cuello como marcador de adiposidad en niños, adolescentes y adultos: Una revisión sistemática [Validity of neck circumference as a marker of adiposity in children and adolescents, and in adults: A systematic review]. Nutr. Hosp. 2018, 35, 707–721. [Google Scholar] [CrossRef] [PubMed]
  88. Akın, O.; Arslan, M.; Haymana, C.; Karabulut, E.; Hacihamdioglu, B.; Yavuz, S.T. Association of neck circumference and pulmonary function in children. Ann. Allergy Asthma Immunol. 2017, 119, 27–30. [Google Scholar] [CrossRef] [PubMed]
  89. Floras, J.S. Sleep apnea and cardiovascular disease. Circ. Res. 2018, 122, 1741–1764. [Google Scholar] [CrossRef] [PubMed]
  90. Oppliger, R.A.; Clark, R.R.; Kuta, J.M. Efficacy of skinfold training clinics: A comparison between clinic trained and experienced testers. Res. Q. Exerc. Sport 1992, 63, 438–443. [Google Scholar] [CrossRef] [PubMed]
  91. Durnin, J.; Womersley, J. Body fat assessed from total body density and its estimation from skinfold thickness: Measurements on 481 men and women aged from 16 to 72 years. Br. J. Nutr. 1974, 32, 77–97. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  92. Jackson, A.S.; Pollock, M.L.; Ward, A. Generalized equations for predicting body density of women. Med. Sci. Sports Exerc. 1979, 12, 175–181. [Google Scholar] [CrossRef] [Green Version]
  93. Jackson, A.S.; Pollock, M.L. Generalized equations for predicting body density of men. Br. J. Nutr. 1978, 40, 497–504. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  94. Peterson, M.J.; Czerwinski, S.A.; Siervogel, R.M. Development and validation of skinfold-thickness prediction equations with a 4-compartment model. Am. J. Clin. Nutr. 2003, 77, 1186–1191. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  95. Reilly, J.; Wilson, J.; Durnin, J. Determination of body composition from skinfold thickness: A validation study. Arch. Dis. Child 1995, 73, 305–310. [Google Scholar] [CrossRef] [Green Version]
  96. Brook, C. Determination of body composition of children from skinfold measurements. Arch. Dis. Child 1971, 46, 182–184. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  97. Deurenberg, P.; Pieters, J.J.; Hautvast, J.G. The assessment of the body fat percentage by skinfold thickness measurements in childhood and young adolescence. Br. J. Nutr. 1990, 63, 293–303. [Google Scholar] [CrossRef] [Green Version]
  98. Durnin, J.; Rahaman, M. The assessment of the amount of fat in the human body from measurements of skinfold thickness. Br. J. Nutr. 1967, 21, 681–689. [Google Scholar] [CrossRef] [PubMed]
  99. Slaughter, M.H.; Lohman, T.; Boileau, R.; Horswill, C.; Stillman, R.; Van Loan, M.; Bemben, D. Skinfold equations for estimation of body fatness in children and youth. Hum. Biol. 1988, 60, 709–723. [Google Scholar] [PubMed]
  100. Toombs, R.J.; Ducher, G.; Shepherd, J.A.; Souza, M.J. The impact of recent technological advances on the trueness and precision of DXA to assess body composition. Obesity 2012, 20, 30–39. [Google Scholar] [CrossRef]
  101. Wells, J.C.; Haroun, D.; Williams, J.E.; Wilson, C.; Darch, T.; Viner, R.M.; Eaton, S.; Fewtrell, M.S. Evaluation of DXA against the four-component model of body composition in obese children and adolescents aged 5–21 years. Int. J. Obes. 2010, 34, 649–655. [Google Scholar] [CrossRef] [Green Version]
  102. Ward, L.C.; Poston, L.; Godfrey, K.M.; Koletzko, B. Assessing early growth and adiposity: Report from an EarlyNutrition Academy Workshop. Ann. Nutr. Metab. 2013, 63, 120–130. [Google Scholar] [CrossRef] [Green Version]
  103. Harrington, T.; Thomas, E.; Modi, N.; Frost, G.; Coutts, G.; Bell, J. Fast and reproducible method for the direct quantitation of adipose tissue in newborn infants. Lipids 2002, 37, 95–100. [Google Scholar] [CrossRef]
  104. Lukaski, H.C.; Johnson, P.E.; Bolonchuk, W.; Lykken, G. Assessment of fat-free mass using bioelectrical impedance measurements of the human body. Am. J. Clin. Nutr. 1985, 41, 810–817. [Google Scholar] [CrossRef] [PubMed]
  105. Kyle, U.G.; Bosaeus, I.; De Lorenzo, A.D.; Deurenberg, P.; Elia, M.; Go’mez, J.M.; Heitmann, B.L.; Kent-Smith, L.; Melchior, J.-C.; Pirlich, M. Bioelectrical impedance analysis—Part I: Review of principles and methods. Clin. Nutr. 2004, 23, 1226–1243. [Google Scholar] [CrossRef] [PubMed]
  106. Gallagher, M.; Walker, K.; O’Dea, K. The influence of a breakfast meal on the assessment of body composition using bioelectrical impedance. Eur. J. Clin. Nutr. 1998, 52, 94–97. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  107. De Beer, M.; Timmers, T.; Weijs, P.J.; Gemke, R.J. Validation of total body water analysis by bioelectrical impedance analysis with deuterium dilution in (pre) school children. e-SPEN: Eur. e-J. Clin. Nutr. Metab. 2011, 6, e223–e226. [Google Scholar] [CrossRef]
  108. Shafer, K.J.; Siders, W.A.; Johnson, L.K.; Lukaski, H.C. Validity of segmental multiple-frequency bioelectrical impedance analysis to estimate body composition of adults across a range of body mass indexes. Nutrition 2009, 25, 25–32. [Google Scholar] [CrossRef]
  109. Lukaski, H.C. Methods for the assessment of human body composition: Traditional and new. Am. J. Clin. Nutr. 1987, 46, 537–556. [Google Scholar] [CrossRef] [Green Version]
  110. Claros, G.; Hull, H.R.; Fields, D.A. Comparison of air displacement plethysmography to hydrostatic weighing for estimating total body density in children. BMC Pediatr. 2005, 5, 37. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  111. Demerath, E.; Guo, S.; Chumlea, W.; Towne, B.; Roche, A.; Siervogel, R. Comparison of percent body fat estimates using air displacement plethysmography and hydrodensitometry in adults and children. Int. J. Obes. Relat. Metab. Disord. 2002, 26, 389–397. [Google Scholar] [CrossRef] [Green Version]
  112. Holmes, J.C.; Gibson, A.L.; Cremades, J.G.; Mier, C.M. Bodydensity measurement in children: The BOD POD versus Hydrodensitometry. Int. J. Sport Nutr. Exerc. Metab. 2011, 21, 240–247. [Google Scholar] [CrossRef] [PubMed]
  113. Caprio, S.; Hyman, L.D.; McCarthy, S.; Lange, R.; Bronson, M.; Tamborlane, W.V. Fat distribution and cardiovascular risk factors in obese adolescent girls: Importance of the intraabdominal fat depot. Am. J. Clin. Nutr. 1996, 64, 12–17. [Google Scholar] [CrossRef] [Green Version]
  114. Fields, D.A.; Goran, M.I.; McCrory, M.A. Body-composition assessment via air-displacement plethysmography in adults and children: A review. Am. J. Clin. Nutr. 2002, 75, 453–467. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  115. Hawkes, C.P.; Hourihane, J.O.B.; Kenny, L.C.; Irvine, A.D.; Kiely, M.; Murray, D.M. Gender-and gestational age-specific body fat percentage at birth. Pediatrics 2011, 128, e645–e651. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  116. Fields, D.A.; Allison, D.B. Air-displacement plethysmography pediatric option in 2–6 years old using the four-compartment model as a criterion method. Obesity 2012, 20, 1732–1737. [Google Scholar] [CrossRef] [PubMed]
  117. Gately, P.; Radley, D.; Cooke, C.; Carroll, S.; Oldroyd, B.; Truscott, J.; Coward, W.; Wright, A. Comparison of body composition methods in overweight and obese children. J. Appl. Physiol. 2003, 95, 2039–2046. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  118. Wells, J.C.; Williams, J.E.; Chomtho, S.; Darch, T.; Grijalva-Eternod, C.; Kennedy, K.; Haroun, D.; Wilson, C.; Cole, T.J.; Fewtrell, M.S. Body-composition reference data for simple and reference techniques and a 4-component model: A new UK reference child. Am. J. Clin. Nutr. 2012, 96, 1316–1326. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  119. Moon, J.R.; Tobkin, S.E.; Costa, P.B.; Smalls, M.; Mieding, W.K.; O’Kroy, J.A.; Zoeller, R.F.; Stout, J.R. Validity of the BOD POD for assessing body composition in athletic high school boys. J. Strength Cond. Res. 2008, 22, 263–268. [Google Scholar] [CrossRef]
  120. Wells, J.C.; Haroun, D.; Williams, J.E.; Darch, T.; Eaton, S.; Viner, R.; Fewtrell, M. Evaluation of lean tissue density for use in air displacement plethysmography in obese children and adolescents. Eur. J. Clin. Nutr. 2011, 65, 1094–1101. [Google Scholar] [CrossRef] [Green Version]
  121. Fields, D.A.; Goran, M.I. Body composition techniques and the four-compartment model in children. J. Appl. Physiol. 2000, 89, 613–620. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  122. Bila, W.C.; Freitas, A.E.; Galdino, A.S.; Ferriolli, E.; Pfrimer, K.; Lamounier, J.A. Deuterium oxide dilution and body composition in overweight and obese schoolchildren aged 6–9 years. J. Pediatr. 2016, 92, 46–52. [Google Scholar] [CrossRef] [Green Version]
  123. Koletzko, B.; Demmelmai, H.; Hartl, W.; Kindermann, A.; Koletzko, S.; Sauerwald, T.; Szitanyi, P. The use of stable isotope techniques for nutritional and metabolic research in paediatrics. Early Hum. Dev. 1998, 53 (Suppl. S1), S77–S97. [Google Scholar] [CrossRef]
  124. De Lucia Rolfe, E.; Modi, N.; Uthaya, S.; Hughes, I.A.; Dunger, D.B.; Acerini, C.; Stolk, R.P.; Ong, K.K. Ultrasound estimates of visceral and subcutaneous-abdominal adipose tissues in infancy. J. Obes. 2013, 2013, 951954. [Google Scholar] [CrossRef] [PubMed]
  125. Wagner, D.R. Ultrasound as a tool to assess body fat. J. Obes. 2013, 2013, 280713. [Google Scholar] [CrossRef] [PubMed]
  126. Liem, E.; Rolfe, E.D.L.; L’abee, C.; Sauer, P.; Ong, K.; Stolk, R. Measuring abdominal adiposity in 6 to 7-year-old children. Eur. J. Clin. Nutr. 2009, 63, 835–841. [Google Scholar] [CrossRef]
  127. Koot, B.; Westerhout, R.; Bohte, A.; Vinke, S.; Pels Rijcken, T.; Nederveen, A.; Caan, M.; Baan-Slootweg, O.; Merkus, M.; Stoker, J. Ultrasonography is not more reliable than anthropometry for assessing visceral fat in obese children. Pediatr. Obes 2013, 9, 443–447. [Google Scholar] [CrossRef] [PubMed]
  128. Mook-Kanamori, D.O.; Holzhauer, S.; Hollestein, L.M.; Durmus, B.; Manniesing, R.; Koek, M.; Boehm, G.; Van der Beek, E.M.; Hofman, A.; Witteman, J.C. Abdominal fat in children measured by ultrasound and computed tomography. Ultrasound Med. Biol. 2009, 35, 1938–1946. [Google Scholar] [CrossRef] [PubMed]
  129. Zemel, B.S. Quantitative computed tomography and computed tomography in children. Curr. Osteoporos Rep. 2011, 9, 284–290. [Google Scholar] [CrossRef] [PubMed]
  130. Huang, T.T.K.; Johnson, M.S.; Figueroa-Colon, R.; Dwyer, J.H.; Goran, M.I. Growth of visceral fat, subcutaneous abdominal fat, and total body fat in children. Obes. Res. 2001, 9, 283–289. [Google Scholar] [CrossRef] [PubMed]
  131. Shen, W.; Punyanitya, M.; Wang, Z.; Gallagher, D.; St-Onge, M.-P.; Albu, J.; Heymsfield, S.B.; Heshka, S. Visceral adipose tissue: Relations between single-slice areas and total volume. Am. J. Clin. Nutr. 2004, 80, 271–278. [Google Scholar] [CrossRef] [Green Version]
  132. Shen, W.; Punyanitya, M.; Wang, Z.; Gallagher, D.; Onge, M.-P.S.; Albu, J.; Heymsfield, S.B.; Heshka, S. Total body skeletal muscle and adipose tissue volumes: Estimation from a single abdominal crosssectional image. J. Appl. Physiol. 2004, 97, 2333–2338. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  133. Shen, W.; Liu, H.; Punyanitya, M.; Chen, J.; Heymsfield, S.B. Pediatric obesity phenotyping by magnetic resonance methods. Curr. Opin. Clin. Nutr. Metab. Care 2005, 8, 595. [Google Scholar]
  134. Shen, W.; Chen, J.; Gantz, M.; Velasquez, G.; Punyanitya, M.; Heymsfield, S.B. A single MRI slice does not accurately predict visceral and subcutaneous adipose tissue changes during weight loss. Obesity 2012, 20, 2458–2463. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  135. Uthaya, S.; Bell, J.; Modi, N. Adipose tissue magnetic resonance imaging in the newborn. Horm. Res. Paediatr. 2004, 62 (Suppl. S3), 143–148. [Google Scholar] [CrossRef]
  136. Gale, C.; Jeffries, S.; Logan, K.M.; Chappell, K.E.; Uthaya, S.N.; Modi, N. Avoiding sedation in research MRI and spectroscopy in infants: Our approach, success rate and prevalence of incidental findings. Arch. Dis. Child Fetal Neonatal Ed. 2013, 98, F267–F268. [Google Scholar] [CrossRef] [PubMed]
  137. Dumoulin, C.L.; Rohling, K.W.; Piel, J.E.; Rossi, C.J.; Giaquinto, R.O.; Watkins, R.D.; Vigneron, D.B.; Barkovich, A.J.; Newton, N. Magnetic resonance imaging compatible neonate incubator. Concepts Magn. Reson. 2002, 15, 117–128. [Google Scholar] [CrossRef]
  138. Samara, A.; Ventura, E.; Alfadda, A.; Goran, M. Use of MRI and CT for fat imaging in children and youth: What have we learned about obesity, fat distribution and metabolic disease risk? Obes. Rev. 2012, 13, 723–732. [Google Scholar] [CrossRef]
  139. Takatalo, J.; Karppinen, J.; Taimela, S.; Niinimäki, J.; Laitinen, J.; Sequeiros, R.B.; Samartzis, D.; Korpelainen, R.; Näyhä, S.; Remes, J.; et al. Association of abdominal obesity with lumbar disc degeneration—a magnetic resonance imaging study. PLoS ONE 2013, 8, e56244. [Google Scholar] [CrossRef] [Green Version]
  140. Brown, R.E.; Kuk, J.L.; Lee, S. Measurement site influences abdominal subcutaneous and visceral adipose tissue in obese adolescents before and after exercise. Pediatric. Obes. 2015, 10, 98–104. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  141. Eloi, J.C.; Epifanio, M.; de Gonçalves, M.M.; Pellicioli, A.; Vieira, P.F.; Dias, H.B.; Bruscato, N.; Soder, R.B.; Santana, J.C.; Mouzaki, M.; et al. Quantification of Abdominal Fat in Obese and Healthy Adolescents Using 3 Tesla Magnetic Resonance Imaging and Free Software for Image Analysis. PLoS ONE 2017, 12, e0167625. [Google Scholar] [CrossRef]
  142. Binkley, C.M.; Jing, L.; Suever, J.D.; Umasankar, N.; Wehner, G.J.; Hamlet, S.M.; Powell, D.; Radulescu, A.; Epstein, F.H.; Fornwalt, B.K. Children with obesity have cardiac remodeling and dysfunction: A cine DENSE magnetic resonance imaging study. J. Cardiovasc. Magn. Reson. 2015, 17, Q57. [Google Scholar] [CrossRef] [Green Version]
  143. Orsso, C.E.; Colin-Ramirez, E.; Field, C.J.; Madsen, K.L.; Prado, C.M.; Haqq, A.M. Adipose Tissue Development and Expansion from the Womb to Adolescence: An Overview. Nutrients 2020, 12, 2735. [Google Scholar] [CrossRef]
  144. Haylett, W.L.; Ferris, W.F. Adipocyte–progenitor cell communication that influences adipogenesis. Cell Mol. Life Sci. 2020, 77, 115–128. [Google Scholar] [CrossRef] [PubMed]
  145. Sun, K.; Kusminski, C.M.; Scherer, P.E. Adipose tissue remodeling and obesity. J. Clin. Investig. 2011, 121, 2094–2101. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  146. Gillum, M.P.; Kotas, M.E.; Erion, D.M.; Kursawe, R.; Chatterjee, P.; Nead, K.T.; Muise, E.S.; Hsiao, J.J.; Frederick, D.W.; Yonemitsu, S.; et al. SirT1 regulates adipose tissue infammation. Diabetes 2011, 60, 3235–3245. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  147. Caprio, S.; Santoro, N.; Weiss, R. Childhood obesity and the associated rise in cardiometabolic complications. Nat. Metab. 2020, 2, 223–232. [Google Scholar] [CrossRef]
  148. Petersen, M.C.; Shulman, G.I. Mechanisms of insulin action and insulin resistance. Physiol. Rev. 2018, 98, 2133–2223. [Google Scholar] [CrossRef] [Green Version]
  149. Sethi, J.K.; Vidal-Puig, A.J. Thematic review series: Adipocyte biology. Adipose tissue function and plasticity orchestrate nutritional adaptation. J. Lipid Res. 2007, 48, 1253–1262. [Google Scholar] [CrossRef] [Green Version]
  150. Toemen, L.; Santos, S.; Roest, A.A.; Jelic, G.; van der Lugt, A.; Felix, J.F.; Helbing, W.A.; Gaillard, R.; Jaddoe, V.W.V. Body Fat Distribution, Overweight, and Cardiac Structures in School-Age Children: A Population-Based Cardiac Magnetic Resonance Imaging Study. J. Am. Heart Assoc. 2020, 9, e014933. [Google Scholar] [CrossRef]
  151. Dencker, M.; Danielson, A.; Karlsson, M.K.; Wollmer, P.; Andersen, L.B.; Thorsson, O. Total body fat, abdominal fat, body fat distribution and surrogate markers for health related to adipocyte fatty acid-binding protein (FABP4) in children. J. Pediatr. Endocrinol. Metab. JPEM 2017, 30, 375–382. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  152. Farias, G.; Netto, B.; Bettini, S.C.; Dâmaso, A.R.; de Freitas, A. Neuroendocrine regulation of energy balance: Implications on the development and surgical treatment of obesity. Nutr. Health 2017, 23, 131–146. [Google Scholar] [CrossRef] [PubMed]
  153. Ueno, H.; Nakazato, M. Mechanistic relationship between the vagal afferent pathway, central nervous system and peripheral organs in appetite regulation. J. Diabetes Investig. 2016, 7, 812–818. [Google Scholar] [CrossRef]
  154. Farooqi, S.I. Genetic, molecular and physiological mechanisms involved in human obesity: Society for Endocrinology Medal Lecture 2012. Clin. Endocrinol. 2015, 82, 23–28. [Google Scholar] [CrossRef]
  155. Loos, R.J.F.; Yeo, G.S.H. The genetics of obesity: From discovery to biology. Nat. Rev. Genet. 2021, 22, 1–14. [Google Scholar] [CrossRef]
  156. Lieb, W.; Sullivan, L.M.; Harris, T.B.; Roubenoff, R.; Benjamin, E.J.; Levy, D.; Fox, C.S.; Wang, T.J.; Wilson, P.W.; Kannel, W.B.; et al. Plasma leptin levels and incidence of heart failure, cardiovascular disease, and total mortality in elderly individuals. Diabetes Care 2009, 32, 612–616. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  157. Klok, M.D.; Jakobsdottir, S.; Drent, M.L. The role of leptin and ghrelin in the regulation of food intake and body weight in humans: A review. Obes. Re. 2007, 8, 21–34. [Google Scholar] [CrossRef]
  158. Thomas, D.D.; Corkey, B.E.; Istfan, N.W.; Apovian, C.M. Hyperinsulinemia: An Early Indicator of Metabolic Dysfunction. J. Endocr. Soc. 2019, 3, 1727–1747. [Google Scholar] [CrossRef]
  159. Shah, M.; Vella, A. Effects of GLP-1 on appetite and weight. Rev. Endocr. Metab. Disord. 2014, 15, 181–187. [Google Scholar] [CrossRef]
  160. Kokot, F.; Ficek, R. Effects of neuropeptide Y on appetite. Min. Electrol. Metab. 1999, 25, 303–305. [Google Scholar] [CrossRef] [PubMed]
  161. Suzuki, K.; Channa, N.J.; Bloom, S.R. The Gut Hormones in Appetite Regulation. J. Obes. 2011, 2011, 528401. [Google Scholar] [CrossRef] [Green Version]
  162. Cummings, D.E.; Shannon, M.H. Roles for Ghrelin in the Regulation of Appetite and Body Weight. Arch. Surg. 2003, 138, 389–396. [Google Scholar] [CrossRef] [Green Version]
  163. Kirkham, T.C. Endocannabinoids in the regulation of appetite and body weight. Behav. Pharmacol. 2005, 16, 297–313. [Google Scholar] [CrossRef] [PubMed]
  164. Ketterer, C.; Heni, M.; Thamer, C.; Herzberg-Schäfer, S.A.; Häring, H.U.; Fritsche, A. Acute, short-term hyperinsulinemia increases olfactory threshold in healthy subjects. Int. J. Obes. 2011, 35, 1135–1138. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  165. Pulit, S.L.; Stoneman, C.; Morris, A.P.; Wood, A.R.; Glastonbury, C.A.; Tyrrell, J.; Yengo, L.; Ferreira, T.; Marouli, E.; Ji, Y.; et al. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum. Mol. Genet. 2019, 28, 166–174. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  166. Lobstein, T.; Baur, L.; Uauy, R. Obesity in children and young people: A crisis in public health. Obes. Rev. 2004, 5 (Suppl. S1), 4–104. [Google Scholar] [CrossRef]
  167. Gluckman, P.; Nishtar, S.; Armstrong, T. Ending childhood obesity: A multidimensional challenge. Lancet 2015, 385, 1048–1050. [Google Scholar] [CrossRef]
  168. Cecil, J.E.; Tavendale, R.; Watt, P.; Hetherington, M.M.; Palmer, C.N. An obesity-associated FTO gene variant and increased energy intake in children. N. Engl. J. Med. 2008, 359, 2558–2566. [Google Scholar] [CrossRef]
  169. Qi, Q.; Chu, A.Y.; Kang, J.H.; Huang, J.; Rose, L.M.; Jensen, M.K.; Liang, L.; Curhan, G.C.; Pasquale, L.R.; Wiggs, J.L.; et al. Fried food consumption, genetic risk, and body mass index: Gene-diet interaction analysis in three US cohort studies. BMJ. 2014, 348, g1610. [Google Scholar] [CrossRef] [Green Version]
  170. Felix, J.F.; Bradfield, J.P.; Monnereau, C.; van der Valk, R.J.; Stergiakouli, E.; Chesi, A.; Gaillard, R.; Feenstra, B.; Thiering, E.; Kreiner-Møller, E.; et al. Genome-wide association analysis identifes three new susceptibility loci for childhood body mass index. Hum. Mol. Genet. 2016, 25, 389–403. [Google Scholar] [CrossRef] [Green Version]
  171. Bradfeld, J.P.; Taal, H.R.; Timpson, N.J.; Scherag, A.; Lecoeur, C.; Warrington, N.M.; Hypponen, E.; Holst, C.; Valcarcel, B.; Thiering, E.; et al. A genome-wide association meta-analysis identifes new childhood obesity loci. Nat. Genet. 2012, 44, 526–531. [Google Scholar] [CrossRef]
  172. Burnett, L.C.; LeDuc, C.A.; Sulsona, C.R.; Paull, D.; Rausch, R.; Eddiry, S.; Carli, J.F.; Morabito, M.V.; Skowronski, A.A.; Hubner, G.; et al. Deficiency in prohormone convertase PC1 impairs prohormone processing in Prader-Willi syndrome. J. Clin. Investig. 2017, 127, 293–305. [Google Scholar] [CrossRef] [PubMed]
  173. Jackson, R.S.; Creemers, J.W.; Ohagi, S.; Raffin-Sanson, M.L.; Sanders, L.; Montague, C.T.; Hutton, J.C.; O’Rahilly, S. Obesity and impaired prohormone processing associated with mutations in the human prohormone convertase 1 gene. Nat. Genet. 1997, 16, 303–306. [Google Scholar] [CrossRef]
  174. Paisey, R.B.; Steeds, R.; Barrett, T.; Williams, D.; Geberhiwot, T.; Gunay-Aygun, M. Alström Syndrome. In GeneReviews; Adam, M.P., Ardinger, H.H., Pagon, R.A., Wallace, S.E., Eds.; University of Washington: Seattle, WA, USA, 1993. [Google Scholar]
  175. Han, J.C.; Reyes-Capo, D.P.; Liu, C.Y.; Reynolds, J.C.; Turkbey, E.; Turkbey, I.B.; Bryant, J.; Marshall, J.D.; Naggert, J.K.; Gahl, W.A.; et al. Comprehensive endocrine-metabolic evaluation of patients with Alström syndrome compared with BMI-matched controls. J. Clin. Endocrinol. Metab. 2018, 103, 2707–2719. [Google Scholar] [CrossRef]
  176. Sherafat-Kazemzadeh, R.; Ivey, L.; Kahn, S.R.; Sapp, J.C.; Hicks, M.D.; Kim, R.C.; Krause, A.J.; Shomaker, L.B.; Biesecker, L.G.; Han, J.C.; et al. Hyperphagia among patients with Bardet-Biedl syndrome. Pediatr. Obes. 2013, 8, e64–e67. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  177. Shungin, D.; Winkler, T.W.; Croteau-Chonka, D.C.; Ferreira, T.; Locke, A.E.; Mägi, R.; Strawbridge, R.J.; Pers, T.H.; Fischer, K.; Justice, A.E.; et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature 2015, 518, 187–196. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  178. Locke, A.E.; Kahali, B.; Berndt, S.I.; Justice, A.E.; Pers, T.H.; Day, F.R.; Powell, C.; Vedantam, S.; Buchkovich, M.L.; Yang, J.; et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 2015, 518, 197–206. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  179. Seth, A.; Sharma, R. Childhood obesity. Indian J. Pediatr. 2013, 80, 309–317. [Google Scholar] [CrossRef]
  180. Swaab, D.F.; Purba, J.S.; Hofman, M.A. Alterations in the hypothalamic paraventricular nucleus and its oxytocin neurons (putative satiety cells) in Prader-Willi syndrome: A study of five cases. J. Clin. Endocrinol. Metab. 1995, 80, 573–579. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  181. Cassidy, S.B.; Schwartz, S.; Miller, J.L.; Driscoll, D.J. Prader-Willi syndrome. Genet. Med. 2012, 14, 10–26. [Google Scholar] [CrossRef] [Green Version]
  182. Forsythe, E.; Beales, P.L. Bardet-Biedl syndrome. Eur. J. Hum. Genet. 2013, 21, 8–13. [Google Scholar] [CrossRef]
  183. Mykytyn, K.; Nishimura, D.; Searby, C.; Shastri, M.; Yen, H.J.; Beck, J.S.; Braun, T.; Streb, L.M.; Cornier, A.S.; Cox, G.F.; et al. Identification of the gene (BBS1) most commonly involved in Bardet-Biedl syndrome, a complex human obesity syndrome. Nat. Genet. 2002, 31, 435–438. [Google Scholar] [CrossRef]
  184. Tarhan, E.; Oǧuz, H.; Şafak, M.A.; Samim, E. The Carpenter syndrome phenotype. Int. J. Pediatr. Otorhinolaryngol. 2004, 68, 353–357. [Google Scholar] [CrossRef]
  185. Lodhia, J.; Rego-Garcia, I.; Koipapi, S.; Sadiq, A.; Msuya, D.; Spaendonk, R.V.; Hamel, B.; Dekker, M. Carpenter syndrome in a patient from Tanzania. Am. J. Med. Genet. Part A 2021, 185A, 986–989. [Google Scholar] [CrossRef] [PubMed]
  186. Langmann, A.; Lindner, S. Cohen syndrome. Spektrum Augenheilkd 1995, 9, 218–220. [Google Scholar] [CrossRef]
  187. Rodrigues, J.M.; Fernandes, H.D.; Caruthers, C.; Braddock, S.R.; Knutsen, A.P. Cohen Syndrome: Review of the Literature. Cureus 2018, 10, 1–8. [Google Scholar] [CrossRef] [Green Version]
  188. Kaya, A.; Orbak, Z.; Ca̧yir, A.; Döneray, H.; Taşdemir, Ş.; Ozanẗurk, A.; Bingöl, F. Combined occurrence of Alström syndrome and bronchiectasis. Pediatrics 2014, 133, e780–e783. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  189. Kang, S. Adipose Tissue Malfunction Drives Metabolic Dysfunction in Alström Syndrome. Diabetes 2021, 70, 323–325. [Google Scholar] [CrossRef]
  190. Huvenne, H.; Dubern, B.; Clément, K.; Poitou, C. Rare Genetic Forms of Obesity: Clinical Approach and Current Treatments in 2016. Obes. Facts 2016, 9, 158–173. [Google Scholar] [CrossRef] [PubMed]
  191. Gibson, W.T.; Farooqi, I.S.; Moreau, M.; DePaoli, A.M.; Lawrence, E.; O’Rahilly, S.; Trussell, R.A. Congenital leptin deficiency due to homozygosity for the Delta133G mutation: Report of another case and evaluation of response to four years of leptin therapy. J. Clin. Endocrinol. Metab. 2004, 89, 4821–4826. [Google Scholar] [CrossRef] [Green Version]
  192. Cummings, D.E.; Schwartz, M.W. Melanocortins and body weight: A tale of two receptors. Nat. Genet. 2000, 26, 8–9. [Google Scholar] [CrossRef] [PubMed]
  193. Vaisse, C.; Clement, K.; Durand, E.; Hercberg, S.; Guy-Grand, B.; Froguel, P. Melanocortin-4 receptor mutations are a frequent and heterogeneous cause of morbid obesity. J. Clin. Investig. 2000, 106, 253–262. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  194. Wardlaw, S.L. Clinical review 127: Obesity as a neuroendocrine disease: Lessons to be learned from proopiomelanocortin and melanocortin receptor mutations in mice and men. J. Clin. Endocrinol. Metab. 2001, 86, 1442–1446. [Google Scholar] [CrossRef] [PubMed]
  195. Celi, F.S.; Shuldiner, A.R. The role of peroxisome proliferator-activated receptor gamma in diabetes and obesity. Curr. Diab. Rep. 2002, 2, 179–185. [Google Scholar] [CrossRef] [PubMed]
  196. Karam, J.; McFarlane, S. Secondary causes of obesity. Therapy 2007, 4, 641–650. [Google Scholar] [CrossRef]
  197. Stipancić, G. Secondary causes of obesity in children and adolescents. Cent. Eur. J. Paediatr. 2018, 14, 1–11. [Google Scholar] [CrossRef]
  198. Gurnani, M.; Birken, C.; Hamilton, J. Childhood Obesity. Pediatr. Clin. N. Am. 2015, 62, 821–840. [Google Scholar] [CrossRef]
  199. Del Fiol, F.S.; Balcão, V.M.; Barberato-Fillho, S.; Lopes, L.C.; Bergamaschi, C.C. Obesity: A New Adverse Effect of Antibiotics? Front. Pharmacol. 2018, 9, 1408. [Google Scholar] [CrossRef]
  200. Robert, M.; Kliegman, M.D.; Geme, J.S. Nelson Textbook of Pediatrics, 21st ed.; Elsevier: Amsterdam, The Netherlands, 2020. [Google Scholar]
  201. Ranjani, H.; Pradeepa, R.; Mehreen, T.S.; Anjana, R.M.; Anand, K.; Garg, R.; Mohan, V. Determinants, consequences and prevention of childhood overweight and obesity: An Indian context. Indian J. Endocrinol. Metab. 2014, 18 (Suppl. S1), S17–S25. [Google Scholar] [CrossRef]
  202. Reilly, J.J.; Armstrong, J.; Dorosty, A.R.; Emmett, P.M.; Ness, A.; Rogers, I.; Steer, C.; Sherriff, A.; Avon Longitudinal Study of Parents and Children Study Team. Early life risk factors for obesity in childhood: Cohort study. BMJ 2005, 330, 1357. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  203. Bouchard, C.; Tremblay, A.; Després, J.P.; Nadeau, A.; Lupien, P.J.; Thériault, G.; Dussault, J.; Moorjani, S.; Pinault, S.; Fournier, G. The response to long-term overfeeding in identical twins. N. Engl. J. Med. 1990, 322, 1477–1482. [Google Scholar] [CrossRef]
  204. Sørensen, T.I.; Holst, C.; Stunkard, A.J.; Skovgaard, L.T. Correlations of body mass index of adult adoptees and their biological and adoptive relatives. Int. J. Obes. 1992, 16, 227–236. [Google Scholar]
  205. Freeman, E.; Fletcher, R.; Collins, C.E.; Morgan, P.J.; Burrows, T.; Callister, R. Preventing and treating childhood obesity: Time to target fathers. Int. J. Obes. 2012, 36, 12–15. [Google Scholar] [CrossRef] [Green Version]
  206. Neel, J.V. The “thrifty genotype” in 1998. Nutr. Rev. 1999, 57 Pt 2, S2–S9. [Google Scholar] [CrossRef]
  207. Hunter, D.J. Gene-environment interactions in human diseases. Nat. Rev. Genet. 2005, 6, 287–298. [Google Scholar] [CrossRef] [PubMed]
  208. Barker, D.J. Fetal origins of coronary heart disease. BMJ 1995, 311, 171–174. [Google Scholar] [CrossRef]
  209. Barker, D.J.; Eriksson, J.G.; Forsen, T.; Osmond, C. Fetal origins of adult disease: Strength of effects and biological basis. Int. J. Epidemiol. 2002, 31, 1235–1239. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  210. Dietz, W.H. Overweight in childhood and adolescence. N. Engl. J. Med. 2004, 350, 855–857. [Google Scholar] [CrossRef] [PubMed]
  211. Labayen, I.; Ruiz, J.R.; Vicente-Rodríguez, G.; Turck, D.; Rodríguez, G.; Meirhaeghe, A.; Molnár, D.; Sjöström, M.; Castillo, M.J.; Gottrand, F.; et al. Healthy Lifestyle in Europe by Nutrition in Adolescence (HELENA) Study Group. Early life programming of abdominal adiposity in adolescents: The HELENA Study. Diabetes Care 2009, 32, 2120–2122. [Google Scholar] [CrossRef] [Green Version]
  212. Vidakovic, A.J.; Gishti, O.; Voortman, T.; Felix, J.F.; Williams, M.A.; Hofman, A.; Demmelmair, H.; Koletzko, B.; Tiemeier, H.; Jaddoe, V.W.; et al. Maternal plasma PUFA concentrations during pregnancy and childhood adiposity: The Generation R Study. Am. J. Clin. Nutr. 2016, 103, 1017–1025. [Google Scholar] [CrossRef] [Green Version]
  213. Hakola, L.; Takkinen, H.M.; Niinistö, S.; Ahonen, S.; Erlund, I.; Rautanen, J.; Veijola, R.; Ilonen, J.; Toppari, J.; Knip, M.; et al. Maternal fatty acid intake during pregnancy and the development of childhood overweight: A birth cohort study. Pediatr. Obes. 2016, 12, S26–S37. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  214. Gjestland, K.; Bo, K.; Owe, K.M.; Eberhard-Gran, M. Do pregnant women follow exercise guidelines? Prevalence data among 3482 women, and prediction of low-back pain, pelvic girdle pain and depression. Br. J. Sports Med. 2013, 47, 515–520. [Google Scholar] [CrossRef] [Green Version]
  215. Evenson, K.R.; Savitz, D.A.; Huston, S.L. Leisuretime physical activity among pregnant women in the US. Paediatr. Perinat. Epidemiol. 2004, 18, 400–407. [Google Scholar] [CrossRef] [PubMed]
  216. Starling, A.P.; Brinton, J.T.; Glueck, D.H.; Shapiro, A.L.; Harrod, C.S.; Lynch, A.M.; Siega-Riz, A.M.; Dabelea, D. Associations of maternal BMI and gestational weight gain with neonatal adiposity in the Healthy Start Study. Am. J. Clin. Nutr. 2015, 101, 302–309. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  217. Lin, X.; Aris, I.M.; Tint, M.T.; Soh, S.E.; Godfrey, K.M.; Yeo, G.S.H.; Kwek, K.; Chan, J.K.Y.; Gluckman, P.D.; Chong, Y.S.; et al. Ethnic differences in effects of maternal pre-pregnancy and pregnancy adiposity on offspring size and adiposity. J. Clin. Endocrinol. Metab. 2015, 100, 3641–3650. [Google Scholar] [CrossRef] [Green Version]
  218. Castillo, H.; Santos, I.S.; Matijasevich, A. Relationship between maternal pre- pregnancy body mass index, gestational weight gain and childhood fatness at 6–7 years by air displacement plethysmography. Matern. Child Nutr. 2015, 11, 606–617. [Google Scholar] [CrossRef] [Green Version]
  219. Hivert, M.F.; Rifas-Shiman, S.L.; Gillman, M.W.; Oken, E. Greater early and mid- pregnancy gestational weight gains are associated with excess adiposity in mid-childhood. Obesity 2016, 24, 1546–1553. [Google Scholar] [CrossRef] [Green Version]
  220. Kral, J.G.; Biron, S.; Simard, S.; Hould, F.S.; Lebel, S.; Marceau, S.; Marceau, P. Large maternal weight loss from obesity surgery prevents transmission of obesity to children who were followed for 2 to 18 years. Pediatrics 2006, 118, e1644–e1649. [Google Scholar] [CrossRef]
  221. Smith, J.; Cianflone, K.; Biron, S.; Hould, F.S.; Lebel, S.; Marceau, S.; Lescelleur, O.; Biertho, L.; Simard, S.; Kral, J.G.; et al. Effects of maternal surgical weight loss in mothers on intergenerational transmission of obesity. J. Clin. Endocrinol. Metab. 2009, 94, 4275–4283. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  222. Branum, A.M.; Parker, J.D.; Keim, S.A.; Schempf, A.H. Prepregnancy body mass index and gestational weight gain in relation to child body mass index among siblings. Am. J. Epidemiol. 2011, 174, 1159–1165. [Google Scholar] [CrossRef] [Green Version]
  223. Larqué, E.; Labayen, I.; Flodmark, C.E.; Lissau, I.; Czernin, S.; Moreno, L.A.; Pietrobelli, A.; Widhalm, K. From conception to infancy—early risk factors for childhood obesity. Nat. Rev. Endocrinol. 2019, 15, 456–478. [Google Scholar] [CrossRef] [PubMed]
  224. Oken, E.; Levitan, E.B.; Gillman, M.W. Maternal smoking during pregnancy and child overweight: Systematic review and meta- analysis. Int. J. Obes. 2008, 32, 201–210. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  225. Zhang, C.R.; Kurniawan, N.D.; Yamada, L.; Fleming, W.; Kaminen-Ahola, N.; Ahola, A.; Galloway, G.; Chong, S. Early gestational ethanol exposure in mice: Effects on brain structure, energy metabolism and adiposity in adult offspring. Alcohol 2018, 75, 1–10. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  226. Seo, M.Y.; Kim, S.H.; Park, M.J. Air pollution and childhood obesity. Clin. Exp. Pediatr. 2020, 63, 382–388. [Google Scholar] [CrossRef] [Green Version]
  227. Lupattelli, A.; Spigset, O.; Twigg, M.J.; Zagorodnikova, K.; Mårdby, A.C.; Moretti, M.E.; Drozd, M.; Panchaud, A.; Hämeen-Anttila, K.; Rieutord, A.; et al. Medication use in pregnancy: A cross- sectional, multinational web- based study. BMJ Open 2014, 4, e004365. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  228. Vidal, A.C.; Murphy, S.K.; Murtha, A.P.; Schildkraut, J.M.; Soubry, A.; Huang, Z.; Neelon, S.E.; Fuemmeler, B.; Iversen, E.; Wang, F.; et al. Associations between antibiotic exposure during pregnancy, birth weight and aberrant methylation at imprinted genes among offspring. Int. J. Obes. 2013, 37, 907–913. [Google Scholar] [CrossRef] [Green Version]
  229. Jepsen, P.; Skriver, M.V.; Floyd, A.; Lipworth, L.; Schønheyder, H.C.; Sørensen, H.T. A population- based study of maternal use of amoxicillin and pregnancy outcome in Denmark. Br. J. Clin. Pharmacol. 2003, 55, 216–221. [Google Scholar] [CrossRef] [Green Version]
  230. Mor, A.; Antonsen, S.; Kahlert, J.; Holsteen, V.; Jørgensen, S.; Holm-Pedersen, J.; Sørensen, H.T.; Pedersen, O.; Ehrenstein, V. Prenatal exposure to systemic antibacterials and overweight and obesity in Danish schoolchildren: A prevalence study. Int. J. Obes. 2015, 39, 1450–1455. [Google Scholar] [CrossRef]
  231. Logan, K.M.; Gale, C.; Hyde, M.J.; Santhakumaran, S.; Modi, N. Diabetes in pregnancy and infant adiposity: Systematic review and meta- analysis. Arch. Dis. Child Fetal Neonatal Ed. 2017, 102, F65–F72. [Google Scholar] [CrossRef]
  232. Lawlor, D.A.; Lichtenstein, P.; Langstrom, N. Association of maternal diabetes mellitus in pregnancy with offspring adiposity into early adulthood: Sibling study in a prospective cohort of 280,866 men from 248,293 families. Circulation 2011, 123, 258–265. [Google Scholar] [CrossRef] [PubMed]
  233. Yuan, C.; Gaskins, A.J.; Blaine, A.I.; Zhang, C.; Gillman, M.W.; Missmer, S.A.; Field, A.E.; Chavarro, J.E. Association Between Cesarean Birth and Risk of Obesity in Offspring in Childhood, Adolescence, and Early Adulthood. JAMA Pediatr. 2016, 170, e162385. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  234. Section on Breastfeeding. Breastfeeding and the use of human milk. Pediatrics 2012, 129, e827–e841. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  235. ESPGHAN Committee on Nutrition; Agostoni, C.; Braegger, C.; Decsi, T.; Kolacek, S.; Koletzko, B.; Michaelsen, K.F.; Mihatsch, W.; Moreno, L.A.; Puntis, J.; et al. Breast-feeding: A commentary by the ESPGHAN Committee on Nutrition. J. Pediatr. Gastroenterol. Nutr. 2009, 49, 112–125. [Google Scholar] [CrossRef] [Green Version]
  236. World Health Org. Breastfeeding. Available online: (accessed on 29 September 2021).
  237. Kries, V.R.; Koletzko, B.; Sauerwald, T.; Mutius, V.E.; Barnert, D.; Grunert, V.; von Voss, H. Breast feeding and obesity: Cross sectional study. BMJ 1999, 319, 147–150. [Google Scholar] [CrossRef] [Green Version]
  238. Gillman, M.W.; Rifas-Shiman, S.L.; Camargo, C.A., Jr.; Berkey, C.S.; Frazier, A.L.; Rockett, H.R.; Field, A.E.; Colditz, G.A. Risk of overweight among adolescents who were breastfed as infants. JAMA 2001, 285, 2461–2467. [Google Scholar] [CrossRef] [Green Version]
  239. World Health Organization. Protecting, Promoting and Supporting Breast-Feeding in Facilities Providing Maternity and Newborn Services; World Health Organization: Geneva, Switzerland, 2017. [Google Scholar]
  240. Daniels, S.R.; Arnett, D.K.; Eckel, R.H.; Gidding, S.S.; Hayman, L.L.; Kumanyika, S.; Robinson, T.N.; Scott, B.J.; St Jeor, S.; Williams, C.L. Overweight in children and adolescents: Pathophysiology, consequences, prevention, and treatment. Circulation 2005, 111, 1999–2012. [Google Scholar] [CrossRef] [Green Version]
  241. Mueller, W.H. The changes with age of the anatomical distribution of fat. Soc. Sci. Med. 1982, 16, 191–196. [Google Scholar] [CrossRef]
  242. Morrison, J.A.; Sprecher, D.L.; Barton, B.A.; Waclawiw, M.A.; Daniels, S.R. Overweight, fat patterning, and cardiovascular disease risk factors in black and white girls: The National Heart, Lung, and Blood Institute Growth and Health Study. J. Pediatr. 1999, 135, 458–464. [Google Scholar] [CrossRef]
  243. Morrison, J.A.; Barton, B.A.; Biro, F.M.; Daniels, S.R.; Sprecher, D.L. Overweight, fat patterning, and cardiovascular disease risk factors in black and white boys. J. Pediatr. 1999, 135, 451–457. [Google Scholar] [CrossRef]
  244. Juliot, L. Modernité et désarrois de l’adolescence [Modernity and turmoil of adolescence]. Soins Psychiatr. 2020, 41, 39–43. [Google Scholar] [CrossRef]
  245. U.S. Department of Agriculture; U.S. Department of Health and Human Services. Dietary Guidelines for Americans, 2020-2025. 9th Edition. December 2020. Available online: (accessed on 11 November 2021).
  246. Ross, A.C.; Caballero, B.H.; Cousins, R.J.; Tucker, K.L.; Ziegler, T.R. Modern Nutrition in Health and Disease, 11th ed.; Wolters Kluwer Health Adis (ESP): Baltimore, MD, USA, 2012; 1616p. [Google Scholar]
  247. Te Morenga, L.; Mallard, S.; Mann, J. Dietary sugars and body weight: Systematic review and meta-analyses of randomised controlled trials and cohort studies. BMJ Clin. Res. Ed. 2012, 346, e7492. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  248. Fattore, E.; Botta, F.; Agostoni, C.; Bosetti, C. Effects of free sugars on blood pressure and lipids: A systematic review and meta-analysis of nutritional isoenergetic intervention trials. Am. J. Clin. Nutr. 2017, 105, 42–56. [Google Scholar] [CrossRef] [Green Version]
  249. Prinz, P. The role of dietary sugars in health: Molecular composition or just calories? Eur. J. Clin. Nutr. 2019, 73, 1216–1223. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  250. Cuda, S.E.; Censani, M. Pediatric Obesity Algorithm: A Practical Approach to Obesity Diagnosis and Management. Front. Pediatr. 2018, 6, 431. [Google Scholar] [CrossRef]
  251. World Health Organization. Guideline: Sugars Intake for Adults and Children; World Health Organization: Geneva, Switzerland, 2015. [Google Scholar]
  252. Aggarwal, T.; Bhatia, R.C.; Singh, D.; Sobti, P.C. Prevalence of obesity and overweight in affluent adolescents from Ludhiana, Punjab. Indian Pediatr. 2008, 45, 500–502. [Google Scholar] [PubMed]
  253. Pérez-Elvira, R.; Oltra-Cucarella, J.; Carrobles, J.A.; Moltó, J.; Flórez, M.; Parra, S.; Agudo, M.; Saez, C.; Guarino, S.; Costea, R.M.; et al. Enhancing the Effects of Neurofeedback Training: The Motivational Value of the Reinforcers. Brain Sci. 2021, 11, 457. [Google Scholar] [CrossRef] [PubMed]
  254. Zarrinpar, A.; Chaix, A.; Panda, S. Daily eating patterns and their impact on health and disease. Trends Endocrinol. Metab. 2016, 27, 69–83. [Google Scholar] [CrossRef] [Green Version]
  255. Eng, S.; Wagstaff, D.A.; Kranz, S. Eating late in the evening is associated with childhood obesity in some age groups but not in all children: The relationship between time of consumption and body weight status in U.S. children. Int. J. Behav. Nutr. Phys. Act. 2009, 6, 1479–5868. [Google Scholar] [CrossRef] [Green Version]
  256. Miller, A.L.; Lumeng, J.C.; LeBourgeois, M.K. Sleep patterns and obesity in childhood. Curr. Opin. Endocrinol. Diabetes Obes. 2015, 22, 41–47. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  257. Sreevatsava, M.; Narayan, K.M.; Cunningham, S.A. Evidence for interventions to prevent and control obesity among children and adolescents: Its applicability to India. Indian J. Pediatr. 2013, 80 (Suppl. S1), S115–S122. [Google Scholar] [CrossRef]
  258. Dehghan, M.; Danesh, N.A.; Merchant, A.T. Childhood obesity, prevalence and prevention. Nutr. J. 2005, 4, 24. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  259. Raj, M.; Krishna Kumar, R. Obesity in children and adolescents. Indian J. Med. Res 2010, 132, 598–607. [Google Scholar]
  260. Gangwisch, J.E.; Malaspina, D.; Boden-Albala, B.; Heymsfield, S.B. Inadequate sleep as a risk factor for obesity: Analyses of the NHANES I. Sleep 2005, 28, 1289–1296. [Google Scholar] [CrossRef] [Green Version]
  261. Spiegel, K.; Tasali, E.; Penev, P.; Van Cauter, E. Brief communication: Sleep curtailment in healthy young men is associated with decreased leptin levels, elevated ghrelin levels, and increased hunger and appetite. Ann. Intern. Med. 2004, 141, 846–850. [Google Scholar] [CrossRef] [PubMed]
  262. Monasta, L.; Batty, G.D.; Cattaneo, A.; Lutje, V.; Ronfani, L.; Van Lenthe, F.J.; Brug, J. Early-life determinants of overweight and obesity: A review of systematic reviews. Obes. Rev. 2010, 11, 695–708. [Google Scholar] [CrossRef] [PubMed]
  263. Collings, P.J.; Ball, H.L.; Santorelli, G.; West, J.; Barber, S.E.; McEachan, R.R.; Wright, J. Sleep duration and adiposity in early childhood: Evidence for bidirectional associations from the born in Bradford Study. Sleep 2017, 40, zsw054. [Google Scholar] [CrossRef] [PubMed]
  264. Baird, J.; Hill, C.M.; Harvey, N.C.; Crozier, S.; Robinson, S.M.; Godfrey, K.M.; Cooper, C.; Inskip, H.; SWS Study Group. Duration of sleep at 3 years of age is associated with fat and fat-free mass at 4 years of age: The Southampton Women’s Survey. J. Sleep Res. 2016, 25, 412–418. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  265. Cespedes, E.M.; Hu, F.B.; Redline, S.; Rosner, B.; Gillman, M.W.; Rifas-Shiman, S.L.; Taveras, E.M. Chronic insufficient sleep and diet quality: Contributors to childhood obesity. Obesity 2016, 24, 184–190. [Google Scholar] [CrossRef]
  266. Taveras, E.M.; Gillman, M.W.; Pena, M.M.; Redline, S.; Rifas-Shiman, S.L. Chronic sleep curtailment and adiposity. Pediatrics 2014, 133, 1013–1022. [Google Scholar] [CrossRef] [Green Version]
  267. Bornhorst, C.; Hense, S.; Ahrens, W.; Hebestreit, A.; Reisch, L.; Barba, G.; von Kries, R.; Bayer, O.; IDEFICS Consortium. From sleep duration to childhood obesity—What are the pathways? Eur. J. Pediatr. 2012, 171, 1029–1038. [Google Scholar] [CrossRef]
  268. Diethelm, K.; Bolzenius, K.; Cheng, G.; Remer, T.; Buyken, A.E. Longitudinal associations between reported sleep duration in early childhood and the development of body mass index, fat mass index and fat free mass index until age 7. Int. J. Pediatr. Obes. 2011, 6, e114–e123. [Google Scholar] [CrossRef] [PubMed]
  269. Neamțu, B.M.; Visa, G.; Maniu, I.; Ognean, M.L.; Pérez-Elvira, R.; Dragomir, A.; Agudo, M.; Șofariu, C.R.; Gheonea, M.; Pitic, A.; et al. A Decision-Tree Approach to Assist in Forecasting the Outcomes of the Neonatal Brain Injury. Int. J. Environ. Res. Public Health 2021, 18, 4807. [Google Scholar] [CrossRef]
  270. Paruthi, S.; Brooks, L.J.; D’Ambrosio, C.; Hall, W.A.; Kotagal, S.; Lloyd, R.M.; Malow, B.A.; Maski, K.; Nichols, C.; Quan, S.F.; et al. Recommended amount of sleep for pediatric populations: A consensus statement of the American Academy of Sleep Medicine. J. Clin. Sleep Med. 2016, 12, 785–786. [Google Scholar] [CrossRef]
  271. Chen, X.; Beydoun, M.A.; Wang, Y. Is sleep duration associated with childhood obesity? A systematic review and meta-analysis. Obesity 2008, 16, 265–274. [Google Scholar] [CrossRef]
  272. Campbell, M.K. Biological, environmental, and social influences on childhood obesity. Pediatr. Res. 2016, 79, 205–211. [Google Scholar] [CrossRef] [Green Version]
  273. Lampard, A.M.; Franckle, R.L.; Davison, K.K. Maternal depression and childhood obesity: A systematic review. Prev. Med. 2014, 59, 60–67. [Google Scholar] [CrossRef] [Green Version]
  274. Rolls, B.J. The Supersizing of America: Portion size and the obesity epidemic. Nutr. Today 2003, 38, 42–53. [Google Scholar] [CrossRef] [PubMed]
  275. Wright, S.M.; Aronne, L.J. Causes of obesity. Abdom. Imaging 2012, 37, 730–732. [Google Scholar] [CrossRef]
  276. James, W.P. The challenge of childhood obesity. Int. J. Pediatr. Obes 2006, 1, 7–10. [Google Scholar] [CrossRef] [PubMed]
  277. Pombo-Rodrigues, S.; Hashem, K.M.; Tan, M.; Davies, Z.; He, F.J.; MacGregor, G.A. Nutrition Profile of Products with Cartoon Animations on the Packaging: A UK Cross-Sectional Survey of Foods and Drinks. Nutrients 2020, 12, 707. [Google Scholar] [CrossRef] [Green Version]
  278. Cohen, D.; Babey, S. Candy at the Cash Register—A Risk Factor for Obesity and Chronic Disease. N. Engl. J. Med. 2012, 15, 1381–1383. [Google Scholar] [CrossRef]
  279. Abel, E.D.; Litwin, S.E.; Sweeney, G. Cardiac remodeling in obesity. Physiol. Rev. 2008, 88, 389–419. [Google Scholar] [CrossRef]
  280. Poirier, P.; Bray, G.A.; Giles, T.D.; Hong, Y.; Stern, J.S.; Pi-Sunyer, F.X.; Eckel, R.H.; American Heart Association, & Obesity Committee of the Council on Nutrition, Physical Activity, and Metabolism. Obesity and cardiovascular disease: Pathophysiology, evaluation, and effect of weight loss: An update of the 1997 American Heart Association Scientific Statement on Obesity and Heart Disease from the Obesity Committee of the Council on Nutrition, Physical Activity, and Metabolism. Circulation 2006, 113, 898–918. [Google Scholar] [CrossRef] [Green Version]
  281. Mathew, B.; Francis, L.; Kaylar, A.; Cone, J. Obesity: Effects on Cardiovascular Disease and its Diagnosis. J. Am. Board Fam. Med. 2008, 21, 562–568. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  282. Schwartz, S.M. Obesity in Children [Internet]. 20 February 2019. Available online: (accessed on 29 September 2019).
  283. Llewellyn, A.; Simmonds, M.; Owen, C.G.; Woolacott, N. Childhood obesity as a predictor of morbidity in adulthood: A systematic review and meta-analysis. Obes. Rev. 2016, 17, 56–67. [Google Scholar] [CrossRef] [PubMed]
  284. Juonala, M.; Magnussen, C.G.; Berenson, G.S.; Venn, A.; Burns, T.L.; Sabin, M.A.; Srinivasan, S.R.; Daniels, S.R.; Davis, P.H.; Chen, W.; et al. Childhood adiposity, adult adiposity, and cardiovascular risk factors. N. Engl. J. Med. 2011, 365, 1876–1885. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  285. Owen, C.G.; Whincup, P.H.; Orfei, L.; Chou, Q.A.; Rudnicka, A.R.; Wathern, A.K.; Kaye, S.J.; Eriksson, J.G.; Osmond, C.; Cook, D.G. Is body mass index before middle age related to coronary heart disease risk in later life? Evidence from observational studies. Int. J. Obes. 2009, 33, 866–877. [Google Scholar] [CrossRef] [Green Version]
  286. Kindblom, J.M.; Bygdell, M.; Sondén, A.; Célind, J.; Rosengren, A.; Ohlsson, C. BMI change during puberty and the risk of heart failure. J. Intern. Med. 2018, 283, 558–567. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  287. Heiskanen, J.S.; Hernesniemi, J.A.; Ruohonen, S.; Hutri-Kähönen, N.; Kähönen, M.; Jokinen, E.; Tossavainen, P.; Kallio, M.; Laitinen, T.; Lehtimäki, T. Influence of early-life body mass index and systolic blood pressure on left ventricle in adulthood—the Cardiovascular Risk in Young Finns Study. Ann. Med. 2021, 53, 160–168. [Google Scholar] [CrossRef]
  288. Adelborg, K.; Ängquist, L.; Ording, A.; Gjærde, L.K.; Bjerregaard, L.G.; Sørensen, H.T.; Sørensen, T.; Baker, J.L. Levels of and Changes in Childhood Body Mass Index in Relation to Risk of Atrial Fibrillation and Atrial Flutter in Adulthood. Am. J. Epidemiol. 2019, 188, 684–693. [Google Scholar] [CrossRef] [PubMed]
  289. Messerli, F.H.; Reisin, E.; Ventura, H.O.; Reisin, E.; Dreslinski, G.R.; Dunn, F.G.; MacPhee, A.A.; Frohlich, E.D. Borderline hypertension and obesity: Two prehypertensive states with elevated cardiac output. Circulation 1982, 66, 55–60. [Google Scholar] [CrossRef] [Green Version]
  290. Alpert, M.A. Obesity cardiomyopathy: Pathophysiology and evolution of the clinical syndrome. Am. J. Med. Sci. 2001, 321, 225–236. [Google Scholar] [CrossRef]
  291. Chakko, S.; Allison, M.D.; Mayor, M.; Kessler, K.M.; Materson, B.J.; Myerburg, R.J. Abnormal left ventricular diastolic filling in eccentric left ventricular hypertrophy of obesity. Am. J. Cardiol. 1991, 68, 95–98. [Google Scholar] [CrossRef]
  292. Lavie, C.J.; Milani, R.V.; Ventura, H.O.; Cardenas, G.A.; Mehra, M.R.; Messerli, F.H. Disparate effects of left ventricular geometry and obesity on mortality in patients with preserved left ventricular ejection fraction. Am. J. Cardiol. 2007, 100, 1460–1464. [Google Scholar] [CrossRef]
  293. Tadic, M.; Cuspidi, C. Childhood obesity and cardiac remodeling: From cardiac structure to myocardial mechanics. J. Cardiovasc. Med. 2015, 16, 538–546. [Google Scholar] [CrossRef]
  294. Esanu, V.; Palii, I.; Mocanu, V.; Vudu, L.; Esanu, V. Left ventricular remodeling patterns in children with metabolic syndrome. One Health Risk Manag. 2020, 1, 41–49. [Google Scholar] [CrossRef]
  295. Lavie, C.J.; Amodeo, C.; Ventura, H.O.; Messerli, F.H. Left atrial abnormalities indicating diastolic ventricular dysfunction in cardiopathy of obesity. Chest 1987, 92, 1042–1046. [Google Scholar] [CrossRef]
  296. De Scheerder, I.; Cuvelier, C.; Verhaaren, R.; De Buyzere, M.; De Backer, G.; Clement, D. Restrictive cardiomyopathy caused by adipositas cordis. Eur. Heart J. 1987, 8, 661–663. [Google Scholar] [CrossRef] [PubMed]
  297. Castro, J.M.; García-Espinosa, V.; Curcio, S.; Arana, M.; Chiesa, P.; Giachetto, G.; Zócalo, Y.; Bia, D. Childhood obesity associates haemodynamic and vascular changes that result in increased central aortic pressure with augmented incident and reflected wave components, without changes in peripheral amplification. Int. J. Vasc. Med. 2016, 2016, 3129304. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  298. Mangner, N.; Scheuermann, K.; Winzer, E.; Wagner, I.; Hoellriegel, R.; Sandri, M.; Zimmer, M.; Mende, M.; Linke, A.; Kiess, W.; et al. Childhood obesity: Impact on cardiac geometry and function. JACC Cardiovasc. Imaging 2014, 7, 1198–1205. [Google Scholar] [CrossRef] [Green Version]
  299. Genovesi, S.; Antolini, L.; Giussani, M.; Pieruzzi, F.; Galbiati, S.; Valsecchi, M.G.; Brambilla, P.; Stella, A. Usefulness of waist circumference for the identification of childhood hypertension. J. Hypertens. 2008, 26, 1563–1570. [Google Scholar] [CrossRef]
  300. Faulkner, B. Recent clinical and translational advances in pediatric hypertension. Hypertension 2015, 65, 926–931. [Google Scholar] [CrossRef] [Green Version]
  301. Zeng, M.; Liang, Y.; Li, H.; Wang, M.; Wang, B.; Chen, X.; Zhou, N.; Cao, D.; Wu, J. Plasma metabolic fingerprinting of childhood obesity by GC/MS in conjunction with multivariate statistical analysis. J. Pharm. Biomed. Anal. 2010, 52, 265–272. [Google Scholar] [CrossRef]
  302. Urbina, E.M.; Khoury, P.R.; Bazzano, L.; Burns, T.L.; Daniels, S.; Dwyer, T.; Hu, T.; Jacobs, D.R., Jr.; Juonala, M.; Prineas, R.; et al. Relation of Blood Pressure in Childhood to Self-Reported Hypertension in Adulthood. Hypertension 2019, 73, 1224–1230. [Google Scholar] [CrossRef]
  303. Chen, X.; Wang, Y. Tracking of blood pressure from childhood to adulthood: A systematic review and meta-regression analysis. Circulation 2008, 117, 3171–3180. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  304. Marcon, D.; Tagetti, A.; Fava, C. Subclinical Organ Damage in Children and Adolescents with Hypertension: Current Guidelines and Beyond. High Blood Press Cardiovasc. Prev. 2019, 26, 361–373. [Google Scholar] [CrossRef] [PubMed]
  305. Wunsch, R.; de Sousa, G.; Toschke, A.M.; Reinehr, T. Intima-media thickness in obese children before and after weight loss. Pediatrics 2006, 118, 2334–2340. [Google Scholar] [CrossRef]
  306. Weberruß, H.; Böhm, B.; Pirzer, R.; Böhm, B.; Pozza, R.D.; Netz, H.; Oberhoffer, R. Intima-media thickness and arterial function in obese and non-obese children. BMC Obes. 2016, 3, 2. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  307. Nunez, F.; Martinez-Costa, C.; Sanchez-Zahonero, J.; Morata, J.; Chorro, F.J.; Brines, J. Carotid artery stiffness as an early marker of vascular lesions in children and adolescents with cardiovascular risk factors. Rev. Esp. Cardiol. 2010, 63, 1253–1260. [Google Scholar] [CrossRef]
  308. Lurbe, E.; Agabiti-Rosei, E.; Cruickshank, J.K.; Dominiczak, A.; Erdine, S.; Hirth, A.; Invitti, C.; Litwin, M.; Mancia, G.; Pall, D.; et al. 2016 European Society of Hypertension guidelines for the management of high blood pressure in children and adolescents. J. Hypertens. 2016, 34, 1887–1920. [Google Scholar] [CrossRef] [Green Version]
  309. Berenson, G.S.; Srinivasan, S.R.; Bao, W.; Newman, W.P., 3rd; Tracy, R.E.; Wattigney, W.A. Association between multiple cardiovascular risk factors and atherosclerosis in children and young adults: The Bogalusa Heart Study. N. Engl. J. Med. 1998, 338, 1650–1656. [Google Scholar] [CrossRef] [PubMed]
  310. Milei, J.; Lavezzi, A.M.; Ottaviani, G.; Grana, D.R.; Stella, I.; Matturri, L. Perinatal and infant early atherosclerotic coronary lesions. Can. J. Cardiol. 2008, 24, 137–141. [Google Scholar] [CrossRef] [Green Version]
  311. Celermajer, D.S.; Ayer, J.G.J. Childhood risk factors for adult cardiovascular disease and primary prevention in childhood. Heart 2006, 92, 1701–1706. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  312. Napoli, C.; Lerman, L.O.; de Nigris, F.; Gossl, M.; Balestrieri, M.L.; Lerman, A. Rethinking primary prevention of atherosclerosis-related diseases. Circulation 2006, 114, 2517–2527. [Google Scholar] [CrossRef] [Green Version]
  313. Lovren, F.; Teoh, H.; Verma, S. Obesity and atherosclerosis: Mechanistic insights. Can. J. Cardiol. 2015, 31, 177–183. [Google Scholar] [CrossRef] [PubMed]
  314. Baker, J.L.; Olsen, L.W.; Sorensen, T.I. Childhood body-mass index and the risk of coronary heart disease in adulthood. N. Engl. J. Med. 2007, 357, 2329–2337. [Google Scholar] [CrossRef]
  315. Daniels, S.R. Diet and primordial prevention of cardiovascular disease in children and adolescents. Circulation 2007, 116, 973–974. [Google Scholar] [CrossRef] [Green Version]
  316. Piepoli, M.F.; Hoes, A.W.; Agewall, S.; Albus, C.; Brotons, C.; Catapano, A.L.; Cooney, M.T.; Corrà, U.; Cosyns, B.; Deaton, C.; et al. 2016 European Guidelines on cardiovascular disease prevention in clinical practice: The Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts) Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Eur. Heart J. 2016, 37, 2315–2381. [Google Scholar] [CrossRef]
  317. Genovesi, S.; Giussani, M.; Orlando, A.; Battaglino, M.G.; Nava, E.; Parati, G. Prevention of cardiovascular diseases in children and adolescents. High Blood Press Cardiovasc. Prev. 2019, 26, 191–197. [Google Scholar] [CrossRef]
  318. Tirosh, A.; Afek, A.; Shai, I.; Dubnov-Raz, G.; Ayalon, N.; Gordon, B.; Derazne, E.; Tzur, D.; Shamis, A.; Vinker, S.; et al. Adolescent BMI trajectory and risk of diabetes versus coronary disease. N. Engl. J. Med. 2011, 364, 1315–1325. [Google Scholar] [CrossRef] [Green Version]
  319. National Heart, Lung, and Blood Institute; US Department of Health and Human Service; National Institutes of Health. Expert Panel on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents, Summary Report. Pediatrics 2011, 128 (Suppl. S5), S213. [Google Scholar] [CrossRef] [Green Version]
  320. Cook, S.; Kavey, R.E. Dyslipidemia and pediatric obesity. Pediatr. Clin. N. Am. 2011, 58, 1363–1373. [Google Scholar] [CrossRef] [Green Version]
  321. Sánchez, J.; Priego, T.; Picó, C.; Ahrens, W.; De Henauw, S.; Fraterman, A.; Mårild, S.; Molnár, D.; Moreno, L.A.; Peplies, J.; et al. Blood cells as a source of transcriptional biomarkers of childhood obesity and its related metabolic alterations: Results of the IDEFICS study. J. Clin. Endocrinol. Metab. 2012, 97, 648–652. [Google Scholar] [CrossRef] [Green Version]
  322. MicroRNAs-Oses, M.; Margareto Sanchez, J.; Portillo, M.P.; Aguilera, C.M.; Labayen, I. Circulating miRNAs as Biomarkers of Obesity and Obesity-Associated Comorbidities in Children and Adolescents: A Systematic Review. Nutrients 2019, 11, 2890. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  323. Zhu, Z.L.; Yang, Q.M.; Li, C.; Chen, J.; Xiang, M.; Chen, M.M.; Yan, M.; Zhu, Z.G. Identification of biomarkers for childhood obesity based on expressional correlation and functional similarity. Mol. Med. Rep. 2018, 17, 109–116. [Google Scholar] [CrossRef] [Green Version]
  324. Stolzman, S.; Bement, M.H. Inflammatory Markers in Pediatric Obesity: Health and Physical Activity Implications. Infant Child. Adolesc. Nutr. 2012, 4, 297–302. [Google Scholar] [CrossRef] [Green Version]
  325. Prats-Puig, A.; Gispert-Saüch, M.; Díaz-Roldán, F.; Carreras-Badosa, G.; Osiniri, I.; Planella-Colomer, M.; Mayol, L.; de Zegher, F.; Ibánez, L.; Bassols, J.; et al. Neutrophil-to-lymphocyte ratio: An inflammation marker related to cardiovascular risk in children. Thromb. Haemost. 2015, 114, 727–734. [Google Scholar] [CrossRef]
  326. Aydin, M.; Yilmaz, A.; Donma, M.M.; Tulubas, F.; Demirkol, M.; Erdogan, M.; Gurel, A. Neutrophil/lymphocyte ratio in obese adolescents. North Clin. Istanb. 2015, 2, 87–91. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  327. Buyukkaya, E.; Karakas, M.F.; Karakas, E.; Akcay, A.B.; Tanboga, I.H.; Kurt, M.; Sen, N. Correlation of neutrophil to lymphocyte ratio with the presence and severity of metabolic syndrome. Clin. Appl. Thromb. Hemost. 2014, 20, 159–163. [Google Scholar] [CrossRef]
  328. Bhat, T.; Teli, S.; Rijal, J.; Bhat, H.; Raza, M.; Khoueiry, G.; Meghani, M.; Akhtar, M.; Costantino, T. Neutrophil to lymphocyte ratio and cardiovascular diseases: A review. Expert Rev. Cardiovasc. Ther. 2013, 11, 55–59. [Google Scholar] [CrossRef]
  329. Furuncuoğlu, Y.; Tulgar, S.; Dogan, A.N.; Cakar, S.; Tulgar, Y.K.; Cakiroglu, B. How obesity affects the neutrophil/lymphocyte and platelet/lymphocyte ratio, systemic immune-inflammatory index and platelet indices: A retrospective study. Eur. Rev. Med. Pharmacol. Sci. 2016, 20, 1300–1306. [Google Scholar] [PubMed]
  330. Sacheck, J. Pediatric obesity: An inflammatory condition? J. Parenter. Enter Nutr. 2008, 32, 633–637. [Google Scholar] [CrossRef]
  331. Venner, A.A.; Lyon, M.E.; Doyle-Baker, P.K. Leptin: A potential biomarker for childhood obesity? Clin. Biochem. 2006, 39, 1047–1056. [Google Scholar] [CrossRef]
  332. Kralisch, S.; Fasshauer, M. Adipocyte fatty acid binding protein: A novel adipokine involved in the pathogenesis of metabolic and vascular disease? Diabetologia 2013, 56, 10–21. [Google Scholar] [CrossRef] [Green Version]
  333. Cambuli, V.M.; Musiu, M.C.; Incani, M.; Paderi, M.; Serpe, R.; Marras, V.; Cossu, E.; Cavallo, M.G.; Mariotti, S.; Loche, S.; et al. Assessment of adiponectin and leptin as biomarkers of positive metabolic outcomes after lifestyle intervention in overweight and obese children. J. Clin. Endocrinol. Metab. 2008, 93, 3051–3057. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  334. Matthews, D.R.; Hosker, J.P.; Rudenski, A.S.; Naylor, B.A.; Treacher, D.F.; Turner, R.C. Homeostasis model assessment: Insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985, 28, 412–419. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  335. Niroumand, S.; Khajedaluee, M.; Khadem-Rezaiyan, M.; Abrishami, M.; Juya, M.; Khodaee, G.; Dadgarmoghaddam, M. Atherogenic Index of Plasma (AIP): A marker of cardiovascular disease. Med. J. Islam. Repub. Iran. 2015, 29, 240. [Google Scholar]
  336. Zhu, X.; Yu, L.; Zhou, H.; Ma, Q.; Zhou, X.; Lei, T.; Hu, J.; Xu, W.; Yi, N.; Lei, S. Atherogenic index of plasma is a novel and better biomarker associated with obesity: A population-based cross-sectional study in China. Lipids Health Dis. 2018, 17, 37. [Google Scholar] [CrossRef] [Green Version]
  337. Dobiášová, M. Atherogenic index of plasma [log(triglycerides/HDL-cholesterol)]: Theoretical and practical implications. Clin. Chem. 2004, 50, 1113–1115. [Google Scholar] [CrossRef]
  338. Burgert, T.S.; Taksali, S.E.; Dziura, J.; Goodman, T.R.; Yeckel, C.W.; Papademetris, X.; Constable, R.T.; Weiss, R.; Tamborlane, W.V.; Savoye, M.; et al. Alanine aminotransferase levels and fatty liver in childhood obesity: Associations with insulin resistance, adiponectin, and visceral fat. J. Clin. Endocrinol. Metab. 2006, 91, 4287–4294. [Google Scholar] [CrossRef] [PubMed]
  339. Labayen, I.; Ruiz, J.R.; Ortega, F.B.; Davis, C.L.; Rodríguez, G.; González-Gross, M.; Breidenassel, C.; Dallongeville, J.; Marcos, A.; Widhalm, K.; et al. Liver enzymes and clustering cardiometabolic risk factors in European adolescents: The HELENA study. Pediatr. Obes. 2015, 10, 361–370. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Adipose tissue stained with hematoxylin and eosin (H&E); (a) original magnification ×10, adipose hyperplasia tissue with thickened fibrous septae and increased vascular network. (b) original magnification ×40, adipose tissue demonstrating enlarged (hypertrophic) adipocytes. Prepared by the authors. Courtesy of the Pediatric Clinical Hospital Sibiu, Lucian Blaga University of Sibiu.
Figure 1. Adipose tissue stained with hematoxylin and eosin (H&E); (a) original magnification ×10, adipose hyperplasia tissue with thickened fibrous septae and increased vascular network. (b) original magnification ×40, adipose tissue demonstrating enlarged (hypertrophic) adipocytes. Prepared by the authors. Courtesy of the Pediatric Clinical Hospital Sibiu, Lucian Blaga University of Sibiu.
Applsci 11 11565 g001
Figure 2. Ultrasound image showing the thickness of the inter-spleno-renal adipose tissue corresponding to the inferior renal pole = 6.94 mm. Prepared by the authors. Courtesy of the Pediatric Clinical Hospital Sibiu, Lucian Blaga University of Sibiu.
Figure 2. Ultrasound image showing the thickness of the inter-spleno-renal adipose tissue corresponding to the inferior renal pole = 6.94 mm. Prepared by the authors. Courtesy of the Pediatric Clinical Hospital Sibiu, Lucian Blaga University of Sibiu.
Applsci 11 11565 g002
Figure 3. Ultrasound image showing subcutaneous abdominal wall adipose tissue thickness = 45.12 mm, approximately 2 cm below the umbilicus. Prepared by the authors. Courtesy of the Pediatric Clinical Hospital Sibiu, Lucian Blaga University of Sibiu.
Figure 3. Ultrasound image showing subcutaneous abdominal wall adipose tissue thickness = 45.12 mm, approximately 2 cm below the umbilicus. Prepared by the authors. Courtesy of the Pediatric Clinical Hospital Sibiu, Lucian Blaga University of Sibiu.
Applsci 11 11565 g003
Figure 4. MRI T2 HASTE, T5 transversal section showing the measurement of abdominal wall subcutaneous adipose tissue thickness (46.85 mm). Prepared by the authors. Courtesy of the Pediatric Clinical Hospital Sibiu, Lucian Blaga University of Sibiu.
Figure 4. MRI T2 HASTE, T5 transversal section showing the measurement of abdominal wall subcutaneous adipose tissue thickness (46.85 mm). Prepared by the authors. Courtesy of the Pediatric Clinical Hospital Sibiu, Lucian Blaga University of Sibiu.
Applsci 11 11565 g004
Figure 5. MRI T2 HASTE, sagittal section tangent to T5 showing the measurement of abdominal wall subcutaneous adipose tissue thickness (49.85 mm). Prepared by the authors. Courtesy of the Pediatric Clinical Hospital Sibiu, Lucian Blaga University of Sibiu.
Figure 5. MRI T2 HASTE, sagittal section tangent to T5 showing the measurement of abdominal wall subcutaneous adipose tissue thickness (49.85 mm). Prepared by the authors. Courtesy of the Pediatric Clinical Hospital Sibiu, Lucian Blaga University of Sibiu.
Applsci 11 11565 g005
Figure 6. 2D Echocardiography, parasternal long-axis view showing concentric left ventricle hypertrophy Prepared by the authors. Courtesy of the Pediatric Clinical Hospital Sibiu, Lucian Blaga University of Sibiu.
Figure 6. 2D Echocardiography, parasternal long-axis view showing concentric left ventricle hypertrophy Prepared by the authors. Courtesy of the Pediatric Clinical Hospital Sibiu, Lucian Blaga University of Sibiu.
Applsci 11 11565 g006
Figure 7. M-Mode Echocardiography of the same patient showing cardiac chamber and wall measurements. Prepared by the authors. Courtesy of the Pediatric Clinical Hospital Sibiu, Lucian Blaga University of Sibiu.
Figure 7. M-Mode Echocardiography of the same patient showing cardiac chamber and wall measurements. Prepared by the authors. Courtesy of the Pediatric Clinical Hospital Sibiu, Lucian Blaga University of Sibiu.
Applsci 11 11565 g007
Figure 8. PW Doppler Echocardiography of the same patient showing grade I diastolic dysfunction (impaired relaxation). Prepared by the authors. Courtesy of the Pediatric Clinical Hospital, Lucian Blaga University of Sibiu.
Figure 8. PW Doppler Echocardiography of the same patient showing grade I diastolic dysfunction (impaired relaxation). Prepared by the authors. Courtesy of the Pediatric Clinical Hospital, Lucian Blaga University of Sibiu.
Applsci 11 11565 g008
Figure 9. Tissue Doppler Echocardiography, four chamber view, tissue Doppler, estimation of LV filling pressures by measuring E/E’. Prepared by the authors. Courtesy of the Pediatric Clinical Hospital Sibiu, Lucian Blaga University of Sibiu.
Figure 9. Tissue Doppler Echocardiography, four chamber view, tissue Doppler, estimation of LV filling pressures by measuring E/E’. Prepared by the authors. Courtesy of the Pediatric Clinical Hospital Sibiu, Lucian Blaga University of Sibiu.
Applsci 11 11565 g009
Figure 10. Cardiac MRI, BTFE sequence, cine four chamber view, 8 mm, telediastolic measurement of interventricular septum exhibiting hypertrophy (14.9 mm), epicardial fat thickness of 8 mm lateral of the right ventricle. Prepared by the authors. Courtesy of the Pediatric Clinical Hospital Sibiu, Lucian Blaga University of Sibiu.
Figure 10. Cardiac MRI, BTFE sequence, cine four chamber view, 8 mm, telediastolic measurement of interventricular septum exhibiting hypertrophy (14.9 mm), epicardial fat thickness of 8 mm lateral of the right ventricle. Prepared by the authors. Courtesy of the Pediatric Clinical Hospital Sibiu, Lucian Blaga University of Sibiu.
Applsci 11 11565 g010
Figure 11. Cardiac MRI Philips Ingenia 3Tardiac T2-STIR sequence, short-axis view, 8 mm, showing hypointensity lateral of right ventricle signifying adipose tissue. Prepared by the authors. Courtesy of the Pediatric Clinical Hospital Sibiu, Lucian Blaga University of Sibiu.
Figure 11. Cardiac MRI Philips Ingenia 3Tardiac T2-STIR sequence, short-axis view, 8 mm, showing hypointensity lateral of right ventricle signifying adipose tissue. Prepared by the authors. Courtesy of the Pediatric Clinical Hospital Sibiu, Lucian Blaga University of Sibiu.
Applsci 11 11565 g011
Figure 12. Neurohormonal regulation of appetite [153]. Pro-opiomelanocortin (POMC) neurons in the arcuate nucleus (ARC) suppress appetite by means of secreting a-melanocyte-stimulating hormone (a-MSH) which acts upon melanocortin 4 receptors (MC4R) exhibited by neurons in the paraventricular nucleus (PVH), ventromedial nucleus (VMH), lateral hypothalamic area (LHA), dorsomedial nucleus, and several other non-hypothalamic brain regions. Neuropeptide Y (NPY)/agouti-related peptide (AgRP) neurons have an opposite effect and stimulate hunger by acting upon the NPY receptor and by antagonizing the action of a-MSH on MC4R. In addition, AgRP also acts upon the Kir 7.1 potassium channel and induces the hyperpolarization of MC4R-expressing neurons. By projecting both stimulatory and inhibitory appetite signals in the same areas, the neurons within ARC serve as key modulators in this respect, depending on which particular neuron population is activated predominantly. Furthermore, NPY/AgRP neurons inhibit POMC neurons by interacting with the GABA receptor, thus prioritizing the generation of hunger sensation over suppressing it. Leptin secreted by adipocytes acts upon the arcuate nucleus by passing through the blood–brain barrier, while peptides produced by the gastrointestinal tract use the vagus nerve to interact with the hypothalamus. The effects of these peptides on appetite are further detailed in text (Section 6.1.2 and Section 6.1.3). Red lines represent inhibitory pathways, while green ones represent stimulatory ones. Hormones and peptides with blue text have an overall effect of stimulating appetite, while those with orange text have an inhibitory effect in this respect. Genetic mutations presenting with obesity in childhood include congenital leptin deficiency, the hypothalamic receptor for leptin mutations, alteration of POMC-aMSH pathway, and loss of function mutations of MC4R receptors [154,155]. Further detailed in Section 6.2.2.
Figure 12. Neurohormonal regulation of appetite [153]. Pro-opiomelanocortin (POMC) neurons in the arcuate nucleus (ARC) suppress appetite by means of secreting a-melanocyte-stimulating hormone (a-MSH) which acts upon melanocortin 4 receptors (MC4R) exhibited by neurons in the paraventricular nucleus (PVH), ventromedial nucleus (VMH), lateral hypothalamic area (LHA), dorsomedial nucleus, and several other non-hypothalamic brain regions. Neuropeptide Y (NPY)/agouti-related peptide (AgRP) neurons have an opposite effect and stimulate hunger by acting upon the NPY receptor and by antagonizing the action of a-MSH on MC4R. In addition, AgRP also acts upon the Kir 7.1 potassium channel and induces the hyperpolarization of MC4R-expressing neurons. By projecting both stimulatory and inhibitory appetite signals in the same areas, the neurons within ARC serve as key modulators in this respect, depending on which particular neuron population is activated predominantly. Furthermore, NPY/AgRP neurons inhibit POMC neurons by interacting with the GABA receptor, thus prioritizing the generation of hunger sensation over suppressing it. Leptin secreted by adipocytes acts upon the arcuate nucleus by passing through the blood–brain barrier, while peptides produced by the gastrointestinal tract use the vagus nerve to interact with the hypothalamus. The effects of these peptides on appetite are further detailed in text (Section 6.1.2 and Section 6.1.3). Red lines represent inhibitory pathways, while green ones represent stimulatory ones. Hormones and peptides with blue text have an overall effect of stimulating appetite, while those with orange text have an inhibitory effect in this respect. Genetic mutations presenting with obesity in childhood include congenital leptin deficiency, the hypothalamic receptor for leptin mutations, alteration of POMC-aMSH pathway, and loss of function mutations of MC4R receptors [154,155]. Further detailed in Section 6.2.2.
Applsci 11 11565 g012
Table 1. Childhood obesity prevalence in relation to geographical location.
Table 1. Childhood obesity prevalence in relation to geographical location.
LocationYearType of Weight ExcessAge Group (Years)PrevalenceReferences
United States of America2012Overweight or obesity2–522.8%[19]
Latin America2008–2013Overweight0–57.1%[20]
Overweight or obesity6–1118.9–36.9%
Africa2017Overweight and obesity0–58–16%[22]
Europe2011–2016Overweight and obesity2–1321.3%[24]
Obesity 5.7%
Table 2. Methods of determining body composition.
Table 2. Methods of determining body composition.
MethodFunctioning PrincipleAdvantagesDisadvantagesRef
Dual-energy X-Ray absorptiometryVariable X-Ray absorption of different tissuesProven accuracy in animal studiesUse of algorithms not tailored to pediatric populations
Unsatisfactory reproducibility
X-Ray exposure
Bioelectrical impedance analysisVariable electrical impedance of different tissues, in accordance with different water contentNon-invasiveError susceptibility due to the approximation of the water content of each tissue
Use of algorithms not tailored to pediatric populations
Cumbersome protocol
Imprecise results for extreme values of the determined parameter
Hydrostatic weighingVariable density of different tissues, determined by comparison with the density of waterNon-invasiveError susceptibility due to the approximation of the density of different tissues which can be particularly variable in pediatric patientsProblematic adherence to measuring protocol of pediatric patients[109,110,111,112]
Air displacement plethysmographyDetermining body density by measuring different parameters obtained during a series of thermodynamic processes.Non-invasive
Very good adherence to measurement protocol
Can be used even in newborns and infants
High cost
Error susceptibility due to the approximation of the density of different tissues
Error susceptibility due to approximations regarding the thermodynamic processes involved
Stable isotope dilution techniquesCalculating total body water based on the ingestion of stable isotopes with uniform distribution within the body and the variable water content of different tissuesNon-invasive
Relatively low cost
No adverse effects documented yet
Error susceptibility due to the approximation of the water content of different tissues[122,123]
Table 3. Imaging modalities for evaluating obesity.
Table 3. Imaging modalities for evaluating obesity.
MethodFunctioning PrincipleAdvantagesDisadvantagesRef
UltrasoundReflection of ultrasound waves at the interface between tissues of different densities
Measurement of subcutaneous adipose tissue thickness and approximation of visceral adipose burden based on the thickness of preperitoneal fat
Readily accessible
Lack of standardized measurement protocol
Insufficient data on pediatric patients
Computerized TomographyVarying absorption of X-rays in different tissuesSectional imaging and 3D reconstructionHigh accuracyContraindicated in pediatric patients for adiposity evaluation due to high X-Ray exposure[129,130]
Magnetic Resonance ImagingSectional imaging technique based on the behavior of protons under the influence of a variable high-intensity electromagnetic fieldHigh accuracy
High cost[131,132,133,134,135,136,137,138,139,140,141]
Table 4. Observational evidence showing childhood obesity leading to adult disease.
Table 4. Observational evidence showing childhood obesity leading to adult disease.
ReferenceObesity ParameterInvestigated AssociationSubject Ages (Childhood)Subject Ages (Adulthood)ParameterResult (95%CI)
Llewellyn et al. * [283]BMIDiabetes type 26 and under19–73OR/StdBMI1.23 (1.10–1.37)
7 to 111.78 (1.51–2.10)
12 to 181.70 (1.30–2.22)
Coronary heart disease7 to 111.14 (1.08–1.21)
12 to 181.30 (1.16–1.47)
Hypertension7 to 111.67 (0.89–3.13)
12 to 181.29 (1.19–1.40)
Juonala et al. *[284]BMIDiabetes type 23 to 1923–46RR (O)2.4 (1.6–3.6)
Hypertension1.8 (1.5–2.1)
High-risk LDL cholesterol1.4 (1.2–1.8)
High-risk HDL cholesterol1.4 (1.2–1.6)
High risk triglycerides1.6 (1.3–1.9)
Owen et al. * [285]BMICoronary heart disease7 to 1825–77OR/StdBMI1.09 (1–1.20)
Kindblom et al. [286]BMIHeart failure8 vs. 20Mean FUP = 37.7yrs after age 20HR(Nw/O)3.14 (2.25–4.38)
HR(O/O)2.85 (1.83–4.45)
Heiskanen et al. [287]BMIEccentric LV hypertrophy6 to 1834–49OR(O/Ob)2.04 (1.35–3.07)
Adelborg et al. [288]BMI (>90th percentile of study population)Atrial fibrillation/flutter7Boys>25HR1.35 (1.25–1.45)
Girls1.26 (1.14–1.38)
10Boys1.42 (1.32–1.53)
Girls1.32 (1.20–1.45)
13Boys1.46 (1.36–1.56)
Girls1.38 (1.27–1.51)
Studies marked with an asterisk * are meta-analyses. The following notations refer to the characteristics of the study subgroups: Nw = normal weight, Ov = overweight, Ob = obese, O = Overweight or obese. When more than one BMI measurement was carried out during the study, the first value represents the group category on initial determination, while the second refers to the later measurement. Childhood age refers to the age at which the obesity parameter was determined. Entries containing “vs” describe the ages of BMI measurement when more than one determination of this parameter was carried out during the study. Adulthood ages represent the ages at which the investigated associations were explored. OR/StdBMI = odds ratio/increase of 1 standard deviation of BMI, RR = relative risk, and HR = hazard ratio.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Negrea, M.O.; Neamtu, B.; Dobrotă, I.; Sofariu, C.R.; Crisan, R.M.; Ciprian, B.I.; Domnariu, C.D.; Teodoru, M. Causative Mechanisms of Childhood and Adolescent Obesity Leading to Adult Cardiometabolic Disease: A Literature Review. Appl. Sci. 2021, 11, 11565.

AMA Style

Negrea MO, Neamtu B, Dobrotă I, Sofariu CR, Crisan RM, Ciprian BI, Domnariu CD, Teodoru M. Causative Mechanisms of Childhood and Adolescent Obesity Leading to Adult Cardiometabolic Disease: A Literature Review. Applied Sciences. 2021; 11(23):11565.

Chicago/Turabian Style

Negrea, Mihai Octavian, Bogdan Neamtu, Ioana Dobrotă, Ciprian Radu Sofariu, Roxana Mihaela Crisan, Bacila Ionut Ciprian, Carmen Daniela Domnariu, and Minodora Teodoru. 2021. "Causative Mechanisms of Childhood and Adolescent Obesity Leading to Adult Cardiometabolic Disease: A Literature Review" Applied Sciences 11, no. 23: 11565.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop