1.1. Background and definitions

1.1.1. Scientific definitions of obesity

Obesity is the abnormal or excessive accumulation of body fat that results from energy imbalance, i.e. energy intake exceeding energy expenditure, and presents a risk to health. Obesity is both a condition and an important risk factor for other noncommunicable diseases, including diabetes, cardiovascular disease, and many types of cancer.

Overweight and obesity in humans are often classified by the body mass index (BMI), which is obtained by dividing the body weight (in kilograms) by the square of the height (in metres).

In adults, overweight is defined as BMI ≥ 25 kg⁄m² and obesity as BMI ≥ 30 kg⁄m² (WHO, 2000). Obesity can be further classified, by level of severity and the corresponding different medical approaches for treatment, into class I (30–34.9 kg⁄m²), class II (35–39.9 kg⁄m²), and class III (≥ 40 kg⁄m²) obesity (Table 1.1). In children younger than 5 years, overweight is defined as a weight-for-height more than 2 standard deviations (SD) above the WHO Child Growth Standards median, and obesity is often defined as a weight-for-height more than 3 SD above the WHO Child Growth Standards median. In children and adolescents from age 5 years to younger than 19 years, overweight and obesity are defined as a BMI-for-age more than 1 SD and more than 2 SD, respectively, above the WHO Growth Reference median (WHO, 2016, 2017a, b).

Table 1.1

Overweight and obesity cut-off values.

1.2. Prevalence and trends

1.2.1. Prevalence and trends in adults

Worldwide, in 2014 more than 640 million adults (14% of adults) were obese, 6 times the number in 1975; of those, more than 18% lived in high-income English-speaking countries, and 13.9% lived in the Middle East and North Africa (NCD Risk Factor Collaboration, 2016).

From 1975 to 2014, the average weight of the population increased in all world regions; during those four decades, the global prevalence of underweight decreased and the global prevalence of obesity increased, so that the number of obese adults surpassed the number of underweight adults (NCD Risk Factor Collaboration, 2016). The global average BMI in the adult population (≥ 18 years) in 1975 was 21.7 kg/m² in men and 22.1 kg/m² in women, and by 2014 these averages had increased to 24.2 kg/m² in men and 24.4 kg/m² in women, according to the latest available estimates (NCD Risk Factor Collaboration, 2016). This means that between 1975 and 2014, the average weight of a man with a height of 170 cm increased by about 7 kg, and that of a woman with a height of 160 cm increased by about 6 kg. This general increase in the average BMI affected both high-income countries and low- and middle-income countries.

The overall increase in the average BMI corresponded to a general increase in the prevalence of obesity. In 2014, the global prevalence of obesity in the adult population was 10.8% in men and 14.9% in women, ranging from less than 1% (Burundi) to almost 50% (Cook Islands and French Polynesia) in men, and from less than 3% (Timor-Leste and Japan) to more than 58% (American Samoa) in women (NCD Risk Factor Collaboration, 2016; Fig. 1.1). In 2014, the prevalence of obesity was lowest in low-income countries, whereas lower-middle-, upper-middle-, and high-income countries were characterized by a high level of heterogeneity; in countries in any of these three income categories, the highest prevalence of obesity was more than 40% in men and about 55% in women. In general, the prevalence of obesity was higher in women than in men, but trends over the four decades suggested a greater increase in the prevalence of obesity in men. However, it should be noted that despite the worldwide increase in the prevalence of obesity and the fact that the number of obese adults is now higher that the number of underweight adults, the proportion of the population that is underweight is still very high (> 20%) in some countries (India and Bangladesh) (NCD Risk Factor Collaboration, 2016).

Fig. 1.1

Prevalence of obesity (BMI ≥ 30 kg/m²) by sex and country in 2014.

In light of the obesity epidemic, there is an increasing interest in understanding weight-loss strategies in the population. A study of university students in 22 countries in different regions of the world suggested that the prevalence of trying to lose weight increases with levels of BMI, and is higher in women than in men at any given BMI level (Wardle et al., 2006). Similarly, figures from a survey of European Union citizens aged 15 years and older showed that 34% of people reported changing their eating and drinking behaviour to lose weight, a percentage that increased to 48% among those who perceived their weight as being too high (European Commission, 2006).

With earlier diagnosis and improved cancer treatments, many cancer survivors have similar BMI levels to those of the general population; in addition, some cancer treatments may induce weight gain. Data on BMI distribution by cancer diagnosis are not routinely reported. It was found that 47.3% of patients with breast cancer had a BMI greater than 25 kg/m² (Nichols et al., 2009), and in a cohort of colorectal cancer survivors, 29.1% were obese (Rohan et al., 2015). Gross et al. (2015) reported that breast cancer survivors gained weight at a higher rate than their cancer-free peers. The implications of high body mass for treatment dosing, subsequent morbidity, and recurrence are not fully understood (see Section 2.4).

1.2.3. Prevalence by age and sex

Several studies in the USA and Europe, using cross-sectional data, have shown that BMI increases with age up to the sixth decade of life and then starts to decrease (Flegal et al., 2002; Vasan et al., 2005; Ogden et al., 2006). However, results from cohort studies suggest a more modest decline in BMI at older ages (Grinker et al., 1995). This difference in findings may be due to higher mortality rates at younger ages in the obese population, leading to a lower prevalence of obesity in the surviving older population. Ageing is also associated with a change in body composition, with a decline in fat-free mass, which may cause a decrease in weight, and therefore in the measured obesity (Villareal et al., 2005). [The validity of assessing obesity by BMI is limited in elderly people (see Section 1.4.2).]

The prevalence of obesity and its association with age vary widely across countries, showing both similarities and differences regardless of the socioeconomic level of the country (IHME, 2014). For example, low prevalences of obesity are observed at all ages in China, Ethiopia, India, and Japan (Fig. 1.2), whereas in Colombia, France, the Islamic Republic of Iran, New Zealand, the Russian Federation, and the USA the prevalence increases with age, followed by a decrease later in life.

Fig. 1.2

Prevalence of obese population by age and sex in selected countries.

The difference between the prevalence of obesity in men and in women is only weakly associated with the level of socioeconomic development of the country. The prevalence of obesity is markedly higher in women than in men in Colombia, the Islamic Republic of Iran, and the Russian Federation, whereas a smaller difference or no difference is observed, for example, in China, Ethiopia, France, India, Japan, New Zealand, and the USA.

1.2.5. Prevalence by social class and education

The association between socioeconomic status and prevalence of overweight and obesity changes according to the level of economic development of the country (Dinsa et al., 2012). Lower socioeconomic status is protective against obesity in low-income countries, whereas it is a risk factor for obesity in middle- and high-income countries, mostly in women (Monteiro et al., 2004a, b; Dinsa et al., 2012). Similar patterns have been observed in children. Studies in high-income countries have shown an inverse association between socioeconomic status and child obesity (Lamerz et al., 2005; Shrewsbury & Wardle, 2008; Due et al., 2009). For example, data from the National Child Measurement Programme in England in the 2012–2013 school year showed a positive association [increasing deprivation leads to increasing prevalence] between level of deprivation and prevalence of obesity in children aged 4–5 years and 10–11 years (Fig. 1.3). Using the deciles of the Index of Multiple Deprivation 2010 (Department for Communities and Local Government, 2011), children in the most deprived decile had a prevalence of obesity twice that observed in the least deprived decile, with a prevalence of about 10% in children aged 4–5 years and about 20% in children aged 10–11 years (National Obesity Observatory, 2012; Health and Social Care Information Centre, 2013). In contrast, in low-income countries the association is inverted, and children in affluent groups are more likely to be obese (Dinsa et al., 2012; Lissner et al., 2016).

Fig. 1.3

Prevalence of obesity by age and decile of Index of Multiple Deprivation in England.

As is the case for the socioeconomic level, BMI has been shown to be positively associated with education in lower-middle-income countries in women, and for men also in middle-income countries, whereas in high-income countries an inverse association was observed between level of education and BMI in women and no association was seen in men (Di Cesare et al., 2013; Fig. 1.4; see also Section 1.3.7).

Fig. 1.4

Mean BMI by education level in adults aged 25–64 years in selected countries.

1.3. Risk factors

1.3.4. Endocrine disruptors

An endocrine-disrupting chemical (EDC) is an exogenous chemical, or mixture of chemicals, that interferes with any aspect of the regulation of hormone action (Zoeller et al., 2012). EDCs act directly by binding or interfering with receptors, or indirectly by disrupting hormone levels or by altering hormonal transport mechanisms (Heindel et al., 2015). Because endocrine systems exhibit tissue-, cell-, and receptor-specific actions during the life-cycle, EDCs can produce a complex mosaic of effects across the life span (Zoeller et al., 2012).

Some EDCs, referred to as “obesogens” (Janesick & Blumberg, 2016), improperly interfere with lipid homeostasis, and promote adipogenesis by perturbing various endocrine axes (Burgio et al., 2015). The developmental age at which EDC exposures occur is a critical consideration in understanding their effects. Exposure to obesogenic EDCs during fetal life and early childhood appears to have an impact on obesity during childhood and adulthood (Grün & Blumberg, 2009; Heindel et al., 2015).

The EDCs with obesogenic properties that are most prevalently used are presented in Table 1.2 (adapted from Heindel et al., 2015), and the available data are summarized below. [The overall limitations of the data from humans lie in the cross-sectional nature of the majority of the studies and in the use of a single measure of exposure. The inconsistences in results in the small number of birth cohort studies is likely to be related to the small sample size, differences in population characteristics, and differences in levels of exposure and timing of exposure.]

Table 1.2

Endocrine-disrupting chemicals with obesogenic properties.

(a) Bisphenol A

Studies in animals have shown that exposure to bisphenol A can disrupt many metabolic pathways and could alter the hypothalamic energy balance circuitry, resulting in increased susceptibility to developing diet-induced obesity and metabolic impairment (Heindel et al., 2015). Some studies reported an association between urinary concentrations of bisphenol A and increased obesity in children (OR, 2.53; 95% CI, 1.72–3.74, between the highest and lowest quartile of bisphenol A concentration; Trasande et al., 2012) and in adults (OR, 1.50; 95% CI, 1.15–1.97, between the highest and lowest quartile) (Wang et al., 2012), whereas other studies reported no association (Lakind et al., 2014). Also, exposure to bisphenol A during fetal life increased adipokine levels in childhood (Volberg et al., 2013; Ashley-Martin et al., 2014).

(b) Phthalates

In animal models, prenatal exposure to phthalates led to increased body weight, increased number and size of adipocytes, and activation of peroxisome proliferator-activated receptor gamma in male offspring (Heindel et al., 2015). Also, cross-sectional studies suggest an association between exposure to phthalates and weight gain (Kim & Park, 2014). However, data from human birth cohorts are inconsistent (Ashley-Martin et al., 2014; Maresca et al., 2015).

(c) Tributyltin

In animals, prenatal exposure to tributyltin causes lipid accumulation in adipose tissue, with effects persisting into adulthood and into future generations (Heindel et al., 2015). A recent study showed a positive association between placenta tributyltin levels and weight gain in male infants at age 3 months (Rantakokko et al., 2014).

(d) Polychlorinated biphenyls and other persistent organic pollutants

Data from the National Health and Nutrition Examination Survey 1999–2002, in adults and children, showed an association of detectable levels of persistent organic pollutants with waist circumference and BMI (Elobeid et al., 2010). Cohort studies in children have not confirmed the association with weight gain (Cupul-Uicab et al., 2013; Tang-Péronard et al., 2015). However, in a birth cohort in the Faroe Islands, higher prenatal exposure to polychlorinated biphenyls was associated with increased BMI at age 7 years in daughters of overweight mothers (Tang-Péronard et al., 2014). In addition, higher levels of persistent organic pollutants have been observed in visceral fat of obese subjects with larger metabolic dysfunction compared with those without metabolic dysfunction (Pestana et al., 2014).

(e) Polycyclic aromatic hydrocarbons

Studies in animals have shown that the combination of exposure to diesel exhaust in utero and a high-fat diet as an adult increases susceptibility to diet-induced obesity and neuroinflammation in females but not in males (Bolton et al., 2012). In a birth cohort of women who were exposed to air pollutants during pregnancy, higher exposure levels were associated with higher obesity in their children at ages 5 years and 7 years (Rundle et al., 2012).

(f) Other compounds

Very limited data in humans are available for perfluorinated chemicals and flame retardants, and the results are inconclusive (Erkin-Cakmak et al., 2015; Heindel et al., 2015).

1.4. Assessment of anthropometric measures and body composition

The assessment of body composition is essential in obesity research, and several approaches have been proposed. The simpler and less expensive approach is to use weight and height – the most commonly used measures of nutritional status in epidemiological studies – and waist circumferences as a measure of abdominal obesity. More technologically advanced methods include bioelectrical impedance analysis (BIA), dual-energy X-ray absorptiometry (DXA), computed tomography (CT), and magnetic resonance imaging (MRI). These methods provide more precise and accurate estimates of body composition and fat distribution, but their use is limited by the lack of practicality and the cost; they are mostly used to validate simpler methods or in small clinical and epidemiological studies (Willett & Hu, 2013).

1.4.2. Indexes of adiposity

(a) BMI

The most commonly used index of obesity is the BMI (see Section 1.1). The cut-off value for underweight (18.5 kg/m²) is based largely on health-related problems associated with malnutrition in developing countries (IARC, 2002).

BMI is easy to assess and has high precision and accuracy. It has been shown to correlate strongly with both absolute body fat and body fat percentage in different age, sex, and racial groups when compared with more sophisticated measures of fat distribution (DXA) (Gallagher et al., 1996; Blew et al., 2002; Evans et al., 2006). BMI is a useful indicator of body fatness, but it is an imperfect measure of obesity, because it does not differentiate between lean mass and fat mass, the relative proportions of which vary between individuals and with age, sex, and race/ethnicity (Garn et al., 1986; Prentice & Jebb, 2001).

(i) Age and sex differences

For biological reasons, women have higher body fat percentages than men for the same BMI (Gallagher et al., 1996). For children, BMI centiles and Z-scores [measures of standard deviation] should be used to determine BMI status for ages up to 18 years, because these indicators are age- and sex-specific in children and adolescents (Cole, 2002; WHO 2016, 2017a, b). Among elderly people, the validity of BMI as a measure of body fatness appears to be reduced because of changes in body composition, such as loss of lean mass (sarcopenia) and increase in abdominal fat mass associated with ageing (Gallagher et al., 1996: Hu, 2008). This lower validity of BMI in elderly people may explain why the relationship of BMI with mortality is less pronounced in elderly people than in younger adults; in elderly people, waist circumference is a better marker of adiposity, in particular of abdominal fatness (Seidell & Visscher, 2000, Janssen et al., 2005).

(ii) Ethnic differences

The interpretation of BMI in epidemiological studies is further complicated by the ethnic variation in body composition (Hu, 2008). For the same BMI, Blacks have a lower adiposity and body fat percentage than Whites, on average, whereas Asians have a higher body fat percentage than Whites (Wagner & Heyward, 2000; Rush et al., 2009; Liu et al., 2011). On the basis of these observations, lower cut-off values have been proposed for overweight (23 kg/m² to 27.4 kg/m²) and obesity (≥ 27.5 kg/m²) in Asian populations (WHO Expert Consultation, 2004). However, the available data do not allow a clear cut-off value to be established for all Asians, given the heterogeneity in the observed risks related to BMI in different Asian populations (WHO Expert Consultation, 2004).

(b) Waist circumference and hip circumference

There is increasing recognition that body fat distribution contributes to obesity-related disease risk, independently of overall adiposity (Eckel et al., 2005). Distribution of body fat has been used to delineate two body shapes: gynecoid (or pear shape), with fat accumulation in the lower part of the body, such as the hips and the thighs), and android (or apple shape), with fat accumulation in the upper part of the body, such as in the abdomen. Consistent evidence has linked android obesity with more metabolic alterations than gynecoid obesity (Hu, 2008).

Waist circumference is widely used as an indirect measure of abdominal and central obesity in epidemiological studies. Waist circumference is measured at the natural waist (midway between the lowest rib margin and the iliac crest) at the level of the umbilicus (Hu, 2008). Because a “natural waist” may be difficult to locate for obese subjects, the umbilicus site is preferred, although it may introduce substantial variations in defining the measurement site for very obese patients. Therefore, waist circumference is a less standardized metric than BMI. Correlation of waist circumference with predicted total abdominal fat and abdominal visceral fat using CT scan was 0.87–0.93 and 0.84–0.93, respectively, in men and women, and waist circumference performed as well as DXA (Clasey et al., 1999). However, in non-obese women, DXA appears to be superior to waist circumference (Kamel et al., 1999). Despite the fact that waist circumference has greater accuracy than BMI for measuring abdominal fat, it still does not differentiate the subcutaneous fat from the visceral fat.

Hip circumference is typically measured at the maximal circumference over the buttocks (Hu, 2008). Hip circumference is more difficult to interpret than waist circumference, because it can reflect more accumulation of subcutaneous fat, greater gluteal muscle mass, or larger bone structure (pelvic width) (Willett, 1998).

Waist-to-hip ratio is used as an indirect measure of abdominal and central obesity, but there is some evidence that waist circumference may be superior to waist-to-hip ratio as a surrogate marker of central obesity, in particular among elderly people, for whom waist-to-hip ratio may be an indicator of visceral obesity combined with muscle loss (Clasey et al., 1999; Snijder et al., 2006).

Waist circumference-to-height ratio has been used as an alternative to waist-to-hip ratio as a measure of central obesity, but it does not seem to be a better predictor of total mortality than waist circumference or waist-to-hip ratio (Hu, 2008).

Other more complex measures, such as sagittal abdominal diameter (i.e. the horizontal distance between the anterior and the posterior of the abdomen), waist-to-thigh ratio, or conicity index (waist circumference (m)/{0.109 × square root of weight (kg)/height (m)}) have been proposed; however, the difference with other measures is small (IARC, 2002).

Waist circumference has been recommended as a measure of central obesity over waist-to-hip ratio (NHLBI Obesity Education Initiative, 2000). Recommended cut-off values for waist circumference were 102 cm for men and 88 cm for women (Table 1.3), and for waist-to-hip ratio were 0.95 for men and 0.88 for women (Hu, 2008). However, as for BMI, these cut-off values are arbitrary, given that metabolic risk appears to increase linearly. In addition, the association of central obesity with chronic disease varies across different ages, sexes, and ethnicities. Recently, the International Diabetes Federation proposed a new definition of metabolic syndrome diagnosis that includes central obesity measured by waist circumference (Zimmet et al., 2005; IDF, 2006). Table 1.4 presents the recommended waist circumference cut-off values for Caucasian and Asian populations.

Table 1.3

Recommendations of body mass index and waist circumference cut-off values made for overweight or obesity.

Table 1.4

International Diabetes Federation criteria for waist circumference cut-off by ethnicity.

In large cohort studies, waist circumference and hip circumference are self-reported. Although obese participants tend to underestimate waist circumference, validation studies have shown high correlations between self-reported and technician-measured waist circumferences (r = 0.95 for men and 0.89 for women), whereas for waist-to-hip ratio the correlation was slightly lower (r = 0.69 for men and 0.70 for women) (Hu, 2008). [It is important to note that the validation studies on self-reported height, weight, waist circumference, and hip circumference have been done in educated populations in the USA and that, depending on the cultural and social context, self-reported anthropometric measures may have less validity.]

1.4.3. Other measures of adiposity or body composition

(a) Skinfold thickness

Skinfold thickness measurements are used as an indirect assessment of body fat distribution using a special caliper (skinfold caliper) to measure the thickness of a double layer of skin and the fat beneath it at predetermined sites, such as the triceps, the biceps, the subscapular region, the abdomen, and the thigh. The measurements are repeated most commonly at four body sites that are high in fat mass (usually the chest, arm, abdomen, and thigh), and in some cases at seven body sites. Skinfold measurement requires great expertise, is prone to inter-observer variations, and is less reproducible than other anthropometric methods. Skinfold thickness measurements and the use of standardized equations have been validated as a good measure of body fat percentage in adults and in children, but this is highly dependent on the validity of the prediction equation, which is population-specific (Hu, 2008; Horan et al., 2015). However, because skinfold thickness measurements are measures of subcutaneous fat, they are unable to quantify intra-abdominal or visceral fat (Steinberger et al., 2005).

(b) Bioelectrical impedance (BIA)

BIA estimates body composition by measuring the impedance or resistance to a small electrical current (typically 800 µA; 50 kHz) passing across the body tissues. A simple version is foot-to-foot BIA assessment, where an individual steps onto scales with electrode foot plates; this is particularly advantageous for children. The BIA method is based on the principle that resistance to an applied alternating electrical current is a function of tissue composition; because fat is a non-conductor of electricity, the greater the lean mass or water content of a person, the faster the current will pass through, and the greater the fatty tissue content, the greater the resistance to the current (Hu, 2008). BIA measures total body water content and enables the calculation of lean mass and fat mass (Kyle et al., 2004). Numerous prediction equations have been used to determine fat-free mass and body fat percentage. Models typically involved height, age, sex, race, weight, and other anthropometric measures. As with other prediction equations for body fat, those for BIA tend to be population-specific. [The limitations of this method include the hydration levels of the individual, the composition of the latest meal, whether a workout took place before the measurement, and the time of the day when the measurement took place.]

Comparison of estimates of body fat percentage has shown a good correlation between BIA and DXA in subjects within a normal range of body fat (r = 0.88 for the whole population; 0.78 for men and 0.85 for women). However, BIA tended to overestimate body fat percentage in lean subjects and to underestimate it in overweight and obese subjects (by 3–4%) (Sun et al., 2005).

Because BIA equipment is portable, it can be used in large surveys or epidemiological studies (Kyle et al., 2003).

(c) Ultrasound

Ultrasound, or ultrasonography, involves exposure of the body to high-frequency sound waves which reflect off the structures and tissues of the body and are detected by transducers. This method is non-invasive, portable, and quick, and ultrasound is a readily available technique in the clinical setting. It can also distinguish between visceral and subcutaneous fat (Wagner, 2013). However, interpretation of ultrasound images requires technical skill and practice. Ultrasound measures subcutaneous and pre-peritoneal fat, which give a good approximation of visceral fat in adults. Among children, results are conflicting (Horan et al., 2015).

(d) Dual-energy X-ray absorptiometry (DXA)

DXA involves the use of X-ray beams with different photon energies to determine body composition. High-density material (i.e. bone) attenuates the X-ray beam the most, whereas lower-density material allows more photons to pass through. DXA enables the determination of three components of the whole body (fat-free mass, fat mass, and bone mineral density) as well as of specific regions, such as the arms, legs, and trunk (Hu, 2008). The procedure is relatively simple, and the measurements are highly reproducible and accurate for lean mass and fat mass. DXA has the inconvenience of involving ionizing radiation, but the exposure is very low.

DXA measures of body composition have shown high reproducibility over several months (Cordero-MacIntyre et al., 2002) and high validity (Lohman & Chen, 2005). DXA estimates of trunk and abdominal fat mass were strongly correlated with CT scan estimates of total abdominal fat (correlations, 0.94–0.97) and abdominal visceral fat (correlations, 0.86–0.90) (Clasey et al., 1999; Glickman et al., 2004).

Although the DXA instrument is expensive and immobile, it has been used in several epidemiological studies either as a references method or in an association study with metabolic disorders or chronic outcomes. DXA has been used as a reference method to determine the body fat percentage corresponding to BMI cut-off values stratified by age, sex, and race/ethnicity in a large sample from the United States population (n = 12 906) (Heo et al., 2012). The oldest age group had the highest cut-off values of body fat percentage. Non-Hispanic Blacks had lower cut-off values, whereas Mexicans had the highest. Cut-off values of body fat percentage were higher in women than in men.

DXA is becoming a frequent measure of human body composition in small clinical and epidemiological studies to obtain a reliable estimate of total body fat mass and fat distribution, alone or together with ultrasound.

(e) Computed tomography (CT) and magnetic resonance imaging (MRI)

CT and MRI are considered to be the most accurate methods for assessing body composition and ascertaining fat distribution at the levels of tissues and organs. Both CT and MRI provide high-resolution cross-sectional scans of selected tissues or organs and can be used to measure the volume and distribution of subcutaneous versus visceral fat, muscle mass, and organ composition. Unlike CT, MRI does not expose subjects to ionizing radiation and can be used in children and pregnant women (Hu, 2008). The measurements are highly reproducible and accurate (Ross & Janssen, 2005). These methods are expensive and not readily accessible and are used mostly for calibrating or validating simpler and less expensive methods for measuring body fat distribution. CT and MRI have been used in small clinical and epidemiological studies to measure total adipose tissue, subcutaneous adipose tissue, visceral adipose tissue, and hepatic and intramuscular triglyceride content. Significant correlations have been found between visceral adipose tissue (but not subcutaneous adipose tissue), insulin resistance, and metabolic syndrome (Lebovitz & Banerji, 2005).

Recent epidemiological studies have implemented CT or MRI measurements on large subsamples to investigate biomarkers of fat distribution (Shah et al., 2016) or to validate simpler anthropometric indices (Neamat-Allah et al., 2014). Measured BMI, waist circumference, and hip circumference correlated well with MRI measures of total body volume, total adipose tissue, and subcutaneous adipose tissue in a cohort of German men (n = 598) and women (n = 594) aged 51–81 years (see Table 1.5).

Table 1.5

Prediction of body compartments by anthropometric indices in multiple linear regression analysis (partial correlation coefficient adjusted for age and height) in a cohort of German men (n = 598) and women (n = 594) aged (more...)

References

• Adams J, White M (2015). Characterisation of UK diets according to degree of food processing and associations with socio-demographics and obesity: cross-sectional analysis of UK National Diet and Nutrition Survey (2008-12). Int J Behav Nutr Phys Act. 12(1):160. 10.1186/s12966-015-0317-y [PMC free article: PMC4683717] [PubMed: 26684833] [CrossRef]
• Ainsworth BE, Haskell WL, Herrmann SD, Meckes N, Bassett DR Jr, Tudor-Locke C, et al. (2011). 2011 Compendium of Physical Activities: a second update of codes and MET values. Med Sci Sports Exerc. 43(8):1575–81. 10.1249/MSS.0b013e31821ece12 [PubMed: 21681120] [CrossRef]
• Almandil NB, Liu Y, Murray ML, Besag FM, Aitchison KJ, Wong IC (2013). Weight gain and other metabolic adverse effects associated with atypical antipsychotic treatment of children and adolescents: a systematic review and meta-analysis. Paediatr Drugs. 15(2):139–50. 10.1007/s40272-013-0016-6 [PubMed: 23519708] [CrossRef]
• Anderson AS, Caswell S, MacAskill S, Steele RJC, Wells M (2010). LiveWell, a feasibility study of a personalized lifestyle programme for colorectal cancer survivors. Support Care Cancer. 18(4):409–15. 10.1007/s00520-009-0677-4 [PubMed: 19554354] [CrossRef]
• Anderson AS, Caswell S, Wells M, Steele RJC (2013). Obesity and lifestyle advice in colorectal cancer survivors – how well are clinicians prepared? Colorectal Dis. 15(8):949–57. 10.1111/codi.12203 [PubMed: 23480570] [CrossRef]
• Ashley-Martin J, Dodds L, Arbuckle TE, Ettinger AS, Shapiro GD, Fisher M, et al. (2014). A birth cohort study to investigate the association between prenatal phthalate and bisphenol A exposures and fetal markers of metabolic dysfunction. Environ Health. 13(84):84. 10.1186/1476-069X-13-84 [PMC free article: PMC4271497] [PubMed: 25336252] [CrossRef]
• Aslibekyan S, Demerath EW, Mendelson M, Zhi D, Guan W, Liang L, et al. (2015). Epigenome-wide study identifies novel methylation loci associated with body mass index and waist circumference. Obesity (Silver Spring). 23(7):1493–501. 10.1002/oby.21111 [PMC free article: PMC4482015] [PubMed: 26110892] [CrossRef]
• Bagci Bosi AT, Eriksen KG, Sobko T, Wijnhoven TM, Breda J (2016). Breastfeeding practices and policies in WHO European Region Member States. Public Health Nutr. 19(4):753–64. 10.1017/S1368980015001767 [PMC free article: PMC4754616] [PubMed: 26096540] [CrossRef]
• Bahadoran Z, Mirmiran P, Azizi F (2016). Fast food pattern and cardiometabolic disorders: a review of current studies. Health Promot Perspect. 5(4):231–40. 10.15171/hpp.2015.028 [PMC free article: PMC4772793] [PubMed: 26933642] [CrossRef]
• Ball K, Crawford D (2005). Socioeconomic status and weight change in adults: a review. Soc Sci Med. 60(9):1987–2010. 10.1016/j.socscimed.2004.08.056 [PubMed: 15743649] [CrossRef]
• Barker DJ (1990). The fetal and infant origins of adult disease. BMJ. 301(6761):1111. 10.1136/bmj.301.6761.1111 [PMC free article: PMC1664286] [PubMed: 2252919] [CrossRef]
• Bayon V, Leger D, Gomez-Merino D, Vecchierini MF, Chennaoui M (2014). Sleep debt and obesity. Ann Med. 46(5):264–72. 10.3109/07853890.2014.931103 [PubMed: 25012962] [CrossRef]
• Benedict C, Axelsson T, Söderberg S, Larsson A, Ingelsson E, Lind L, et al. (2014). Fat mass and obesity-associated gene (FTO) is linked to higher plasma levels of the hunger hormone ghrelin and lower serum levels of the satiety hormone leptin in older adults. Diabetes. 63(11):3955–9. 10.2337/db14-0470 [PubMed: 24898142] [CrossRef]
• Black JL, Macinko J (2008). Neighborhoods and obesity. Nutr Rev. 66(1):2–20. 10.1111/j.1753-4887.2007.00001.x [PubMed: 18254880] [CrossRef]
• Black RE, Victora CG, Walker SP, Bhutta ZA, Christian P, de Onis M, et al.; Maternal and Child Nutrition Study Group (2013). Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet. 382(9890):427–51. 10.1016/S0140-6736(13)60937-X [PubMed: 23746772] [CrossRef]
• Blakely T, Hales S, Kieft C, Wilson N, Woodward A (2005). The global distribution of risk factors by poverty level. Bull World Health Organ. 83(2):118–26. [PMC free article: PMC2623808] [PubMed: 15744404]
• Blew RM, Sardinha LB, Milliken LA, Teixeira PJ, Going SB, Ferreira DL, et al. (2002). Assessing the validity of body mass index standards in early postmenopausal women. Obes Res. 10(8):799–808. 10.1038/oby.2002.108 [PubMed: 12181389] [CrossRef]
• Blundell JE, Finlayson G (2004). Is susceptibility to weight gain characterized by homeostatic or hedonic risk factors for overconsumption? Physiol Behav. 82(1):21–5. 10.1016/j.physbeh.2004.04.021 [PubMed: 15234585] [CrossRef]
• Blundell JE, Finlayson G, Gibbons C, Caudwell P, Hopkins M (2015b). The biology of appetite control: do resting metabolic rate and fat-free mass drive energy intake? Physiol Behav. 152(Pt B):473–8. 10.1016/j.physbeh.2015.05.031 [PubMed: 26037633] [CrossRef]
• Blundell JE, Gibbons C, Caudwell P, Finlayson G, Hopkins M (2015a). Appetite control and energy balance: impact of exercise. Obes Rev. 16(Suppl 1):67–76. 10.1111/obr.12257 [PubMed: 25614205] [CrossRef]
• Bobrow KL, Quigley MA, Green J, Reeves GK, Beral V; Million Women Study Collaborators (2013). Persistent effects of women’s parity and breastfeeding patterns on their body mass index: results from the Million Women Study. Int J Obes (Lond). 37(5):712–7. 10.1038/ijo.2012.76 [PMC free article: PMC3647235] [PubMed: 22777544] [CrossRef]
• Bolton JL, Smith SH, Huff NC, Gilmour MI, Foster WM, Auten RL, et al. (2012). Prenatal air pollution exposure induces neuroinflammation and predisposes offspring to weight gain in adulthood in a sex-specific manner. FASEB J. 26(11):4743–54. 10.1096/fj.12-210989 [PubMed: 22815382] [CrossRef]
• Boyland EJ, Nolan S, Kelly B, Tudur-Smith C, Jones A, Halford JC, et al. (2016). Advertising as a cue to consume: a systematic review and meta-analysis of the effects of acute exposure to unhealthy food and nonalcoholic beverage advertising on intake in children and adults. Am J Clin Nutr. 103(2):519–33. 10.3945/ajcn.115.120022 [PubMed: 26791177] [CrossRef]
• Bray GA, Popkin BM (1998). Dietary fat intake does affect obesity! Am J Clin Nutr. 68(6):1157–73. [PubMed: 9846842]
• Brisbois TD, Farmer AP, McCargar LJ (2012). Early markers of adult obesity: a review. Obes Rev. 13(4):347–67. 10.1111/j.1467-789X.2011.00965.x [PMC free article: PMC3531624] [PubMed: 22171945] [CrossRef]
• Brown PJ (1991). Culture and the evolution of obesity. Hum Nat. 2(1):31–57. 10.1007/BF02692180 [PubMed: 24222189] [CrossRef]
• Buckland G, Bach A, Serra-Majem L (2008). Obesity and the Mediterranean diet: a systematic review of observational and intervention studies. Obes Rev. 9(6):582–93. 10.1111/j.1467-789X.2008.00503.x [PubMed: 18547378] [CrossRef]
• Burgio E, Lopomo A, Migliore L (2015). Obesity and diabetes: from genetics to epigenetics. Mol Biol Rep. 42(4):799–818. 10.1007/s11033-014-3751-z [PubMed: 25253098] [CrossRef]
• Caballero B (2007). The global epidemic of obesity: an overview. Epidemiol Rev. 29(1):1–5. 10.1093/epirev/mxm012 [PubMed: 17569676] [CrossRef]
• Cairns G, Angus K, Hastings G, Caraher M (2013). Systematic reviews of the evidence on the nature, extent and effects of food marketing to children. A retrospective summary. Appetite. 62:209–15. 10.1016/j.appet.2012.04.017 [PubMed: 22561190] [CrossRef]
• Capers PL, Fobian AD, Kaiser KA, Borah R, Allison DB (2015). A systematic review and meta-analysis of randomized controlled trials of the impact of sleep duration on adiposity and components of energy balance. Obes Rev. 16(9):771–82. 10.1111/obr.12296 [PMC free article: PMC4532553] [PubMed: 26098388] [CrossRef]
• Cappuccio FP, Taggart FM, Kandala NB, Currie A, Peile E, Stranges S, et al. (2008). Meta-analysis of short sleep duration and obesity in children and adults. Sleep. 31(5):619–26. [PMC free article: PMC2398753] [PubMed: 18517032]
• Caprio S, Daniels SR, Drewnowski A, Kaufman FR, Palinkas LA, Rosenbloom AL, et al. (2008). Influence of race, ethnicity, and culture on childhood obesity: implications for prevention and treatment: a consensus statement of Shaping America’s Health and the Obesity Society. Diabetes Care. 31(11):2211–21. 10.2337/dc08-9024 [PMC free article: PMC2571048] [PubMed: 18955718] [CrossRef]
• Caspersen CJ, Powell KE, Christenson GM (1985). Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research. Public Health Rep. 100(2):126–31. [PMC free article: PMC1424733] [PubMed: 3920711]
• CDC (2016). NHANES III standardized anthropometric procedures, videos. Atlanta (GA), USA: Centers for Disease Control and Prevention. Available from: https://wwwn​.cdc.gov​/nchs/nhanes/nhanes3​/anthropometricvideos.aspx.
• Chakravartty S, Tassinari D, Salerno A, Giorgakis E, Rubino F (2015). What is the mechanism behind weight loss maintenance with gastric bypass? Curr Obes Rep. 4(2):262–8. 10.1007/s13679-015-0158-7 [PubMed: 26627220] [CrossRef]
• Chamorro-García R, Blumberg B (2014). Transgenerational effects of obesogens and the obesity epidemic. Curr Opin Pharmacol. 19:153–8. 10.1016/j.coph.2014.10.010 [PMC free article: PMC4297575] [PubMed: 25460228] [CrossRef]
• Chaput J-P, Tremblay A (2012). Insufficient sleep as a contributor to weight gain: an update. Curr Obes Rep. 1(4):245–56. 10.1007/s13679-012-0026-7 [CrossRef]
• Chen X, Beydoun MA, Wang Y (2008). Is sleep duration associated with childhood obesity? A systematic review and meta-analysis. Obesity (Silver Spring). 16(2):265–74. 10.1038/oby.2007.63 [PubMed: 18239632] [CrossRef]
• Clasey JL, Bouchard C, Teates CD, Riblett JE, Thorner MO, Hartman ML, et al. (1999). The use of anthropometric and dual-energy X-ray absorptiometry (DXA) measures to estimate total abdominal and abdominal visceral fat in men and women. Obes Res. 7(3):256–64. 10.1002/j.1550-8528.1999.tb00404.x [PubMed: 10348496] [CrossRef]
• Cobb LK, Appel LJ, Franco M, Jones-Smith JC, Nur A, Anderson CA (2015). The relationship of the local food environment with obesity: a systematic review of methods, study quality, and results. Obesity (Silver Spring). 23(7):1331–44. 10.1002/oby.21118 [PMC free article: PMC4482774] [PubMed: 26096983] [CrossRef]
• Cole TJ (2000). Secular trends in growth. Proc Nutr Soc. 59(2):317–24. 10.1017/S0029665100000355 [PubMed: 10946801] [CrossRef]
• Cole TJ (2002). A chart to link child centiles of body mass index, weight and height. Eur J Clin Nutr. 56(12):1194–9. 10.1038/sj.ejcn.1601473 [PubMed: 12494304] [CrossRef]
• Cordero-MacIntyre ZR, Peters W, Libanati CR, España RC, Abila SO, Howell WH, et al. (2002). Reproducibility of DXA in obese women. J Clin Densitom. 5(1):35–44. 10.1385/JCD:5:1:035 [PubMed: 11940727] [CrossRef]
• Cupul-Uicab LA, Klebanoff MA, Brock JW, Longnecker MP (2013). Prenatal exposure to persistent organochlorines and childhood obesity in the US Collaborative Perinatal Project. Environ Health Perspect. 121(9):1103–9. [PMC free article: PMC3764072] [PubMed: 23799652]
• Cutfield WS, Hofman PL, Mitchell M, Morison IM (2007). Could epigenetics play a role in the developmental origins of health and disease? Pediatr Res. 61(5 Pt 2):68R–75R. 10.1203/pdr.0b013e318045764c [PubMed: 17413843] [CrossRef]
• De Bourdeaudhuij I, Van Dyck D, Salvo D, Davey R, Reis RS, Schofield G, et al. (2015). International study of perceived neighbourhood environmental attributes and body mass index: IPEN Adult study in 12 countries. Int J Behav Nutr Phys Act. 12(1):62. 10.1186/s12966-015-0228-y [PMC free article: PMC4440250] [PubMed: 25982214] [CrossRef]
• Department for Communities and Local Government (2011). The English Indices of Deprivation 2010. Available from: https://www​.gov.uk/government​/uploads/system​/uploads/attachment_data​/file/6871/1871208.pdf.
• de Jonge L, DeLany JP, Nguyen T, Howard J, Hadley EC, Redman LM, et al. (2007). Validation study of energy expenditure and intake during calorie restriction using doubly labeled water and changes in body composition. Am J Clin Nutr. 85(1):73–9. [PMC free article: PMC2662402] [PubMed: 17209180]
• Dearth-Wesley T, Popkin BM, Ng SW (2014). Estimated and forecasted trends in domain specific time-use and energy expenditure among adults in Russia. Int J Behav Nutr Phys Act. 11(1):11. 10.1186/1479-5868-11-11 [PMC free article: PMC3931326] [PubMed: 24475868] [CrossRef]
• Deglaire A, Méjean C, Castetbon K, Kesse-Guyot E, Hercberg S, Schlich P (2015). Associations between weight status and liking scores for sweet, salt and fat according to the gender in adults (the Nutrinet-Santé study). Eur J Clin Nutr. 69(1):40–6. 10.1038/ejcn.2014.139 [PubMed: 25074389] [CrossRef]
• Di Cesare M, Khang YH, Asaria P, Blakely T, Cowan MJ, Farzadfar F, et al.; Lancet NCD Action Group (2013). Inequalities in non-communicable diseases and effective responses. Lancet. 381(9866):585–97. 10.1016/S0140-6736(12)61851-0 [PubMed: 23410608] [CrossRef]
• Di Pietro L, Dziura J, Blair SN (2004). Estimated change in physical activity level (PAL) and prediction of 5-year weight change in men: the Aerobics Center Longitudinal Study. Int J Obes Relat Metab Disord. 28(12):1541–7. 10.1038/sj.ijo.0802821 [PubMed: 15543159] [CrossRef]
• Dick KJ, Nelson CP, Tsaprouni L, Sandling JK, Aïssi D, Wahl S, et al. (2014). DNA methylation and body-mass index: a genome-wide analysis. Lancet. 383(9933):1990–8. 10.1016/S0140-6736(13)62674-4 [PubMed: 24630777] [CrossRef]
• Ding D, Gebel K (2012). Built environment, physical activity, and obesity: what have we learned from reviewing the literature? Health Place. 18(1):100–5. 10.1016/j.healthplace.2011.08.021 [PubMed: 21983062] [CrossRef]
• Dinsa GD, Goryakin Y, Fumagalli E, Suhrcke M (2012). Obesity and socioeconomic status in developing countries: a systematic review. Obes Rev. 13(11):1067–79. 10.1111/j.1467-789X.2012.01017.x [PMC free article: PMC3798095] [PubMed: 22764734] [CrossRef]
• Dior UP, Lawrence GM, Sitlani C, Enquobahrie D, Manor O, Siscovick DS, et al. (2014). Parental smoking during pregnancy and offspring cardio-metabolic risk factors at ages 17 and 32. Atherosclerosis. 235(2):430–7. 10.1016/j.atherosclerosis.2014.05.937 [PMC free article: PMC4123626] [PubMed: 24937467] [CrossRef]
• Dombrowski SU, Knittle K, Avenell A, Araújo-Soares V, Sniehotta FF (2014). Long term maintenance of weight loss with non-surgical interventions in obese adults: systematic review and meta-analyses of randomised controlled trials. BMJ. 348:g2646. 10.1136/bmj.g2646 [PMC free article: PMC4020585] [PubMed: 25134100] [CrossRef]
• Dominguez-Salas P, Moore SE, Baker MS, Bergen AW, Cox SE, Dyer RA, et al. (2014). Maternal nutrition at conception modulates DNA methylation of human metastable epialleles. Nat Commun. 5:3746. 10.1038/ncomms4746 [PMC free article: PMC4015319] [PubMed: 24781383] [CrossRef]
• Donnelly JE, Blair SN, Jakicic JM, Manore MM, Rankin JW, Smith BK; American College of Sports Medicine (2009). American College of Sports Medicine Position Stand. Appropriate physical activity intervention strategies for weight loss and prevention of weight regain for adults. Med Sci Sports Exerc. 41(2):459–71. 10.1249/MSS.0b013e3181949333 [PubMed: 19127177] [CrossRef]
• Due P, Damsgaard MT, Rasmussen M, Holstein BE, Wardle J, Merlo J, et al.; HBSC obesity writing group (2009). Socioeconomic position, macroeconomic environment and overweight among adolescents in 35 countries. Int J Obes. 33(10):1084–93. 10.1038/ijo.2009.128 [PMC free article: PMC3421462] [PubMed: 19621018] [CrossRef]
• Dunton GF, Kaplan J, Wolch J, Jerrett M, Reynolds KD (2009). Physical environmental correlates of childhood obesity: a systematic review. Obes Rev. 10(4):393–402. 10.1111/j.1467-789X.2009.00572.x [PMC free article: PMC3833101] [PubMed: 19389058] [CrossRef]
• Eckel RH, Grundy SM, Zimmet PZ (2005). The metabolic syndrome. Lancet. 365(9468):1415–28. 10.1016/S0140-6736(05)66378-7 [PubMed: 15836891] [CrossRef]
• Egger G, Swinburn B (1997). An “ecological” approach to the obesity pandemic. BMJ. 315(7106):477–80. 10.1136/bmj.315.7106.477 [PMC free article: PMC2127317] [PubMed: 9284671] [CrossRef]
• Eissing L (2013). Metabolism: FTO-associated obesity risk is linked to brain food responses via modulation of ghrelin levels. Nat Rev Endocrinol. 9(10):564. 10.1038/nrendo.2013.155 [PubMed: 23917583] [CrossRef]
• Ello-Martin JA, Ledikwe JH, Rolls BJ (2005). The influence of food portion size and energy density on energy intake: implications for weight management. Am J Clin Nutr. 82(1 Suppl):236S–41S. [PubMed: 16002828]
• Elobeid MA, Padilla MA, Brock DW, Ruden DM, Allison DB (2010). Endocrine disruptors and obesity: an examination of selected persistent organic pollutants in the NHANES 1999–2002 data. Int J Environ Res Public Health. 7(7):2988–3005. 10.3390/ijerph7072988 [PMC free article: PMC2922741] [PubMed: 20717554] [CrossRef]
• Erkin-Cakmak A, Harley KG, Chevrier J, Bradman A, Kogut K, Huen K, et al. (2015). In utero and childhood polybrominated diphenyl ether exposures and body mass at age 7 years: the CHAMACOS study. Environ Health Perspect. 123(6):636–42. [PMC free article: PMC4455588] [PubMed: 25738596]
• European Commission (2006). Health and food. Special Eurobarometer 246/Wave 64.3 – TNS Opinion & Social. Available from: http://ec​.europa.eu/public_opinion​/archives/ebs/ebs_246_en​.pdf.
• Evans EM, Rowe DA, Racette SB, Ross KM, McAuley E (2006). Is the current BMI obesity classification appropriate for black and white postmenopausal women? Int J Obes (Lond). 30(5):837–43. 10.1038/sj.ijo.0803208 [PubMed: 16418761] [CrossRef]
• Ezzati M, Martin H, Skjold S, Vander Hoorn S, Murray CJ (2006). Trends in national and state-level obesity in the USA after correction for self-report bias: analysis of health surveys. J R Soc Med. 99(5):250–7. 10.1258/jrsm.99.5.250 [PMC free article: PMC1457748] [PubMed: 16672759] [CrossRef]
• Fardet A, Boirie Y (2014). Associations between food and beverage groups and major diet-related chronic diseases: an exhaustive review of pooled/meta-analyses and systematic reviews. Nutr Rev. 72(12):741–62. 10.1111/nure.12153 [PubMed: 25406801] [CrossRef]
• Farpour-Lambert NJ, Baker JL, Hassapidou M, Holm JC, Nowicka P, O’Malley G, et al. (2015). Childhood obesity is a chronic disease demanding specific health care – a position statement from the Childhood Obesity Task Force (COTF) of the European Association for the Study of Obesity (EASO). Obes Facts. 8(5):342–9. 10.1159/000441483 [PMC free article: PMC5644867] [PubMed: 26469067] [CrossRef]
• Feng J, Glass TA, Curriero FC, Stewart WF, Schwartz BS (2010). The built environment and obesity: a systematic review of the epidemiologic evidence. Health Place. 16(2):175–90. 10.1016/j.healthplace.2009.09.008 [PubMed: 19880341] [CrossRef]
• Flegal KM, Carroll MD, Ogden CL, Johnson CL (2002). Prevalence and trends in obesity among US adults, 1999–2000. JAMA. 288(14):1723–7. 10.1001/jama.288.14.1723 [PubMed: 12365955] [CrossRef]
• Fogelholm M, Anderssen S, Gunnarsdottir I, Lahti-Koski M (2012). Dietary macronutrients and food consumption as determinants of long-term weight change in adult populations: a systematic literature review. Food Nutr Res. 56(0). 10.3402/fnr.v56i0.19103 [PMC free article: PMC3418611] [PubMed: 22893781] [CrossRef]
• Fogelholm M, Kukkonen-Harjula K (2000). Does physical activity prevent weight gain – a systematic review. Obes Rev. 1(2):95–111. 10.1046/j.1467-789x.2000.00016.x [PubMed: 12119991] [CrossRef]
• Forshee RA, Anderson PA, Storey ML (2008). Sugar-sweetened beverages and body mass index in children and adolescents: a meta-analysis. Am J Clin Nutr. 87(6):1662–71. [PubMed: 18541554]
• Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM, et al. (2007). A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science. 316(5826):889–94. 10.1126/science.1141634 [PMC free article: PMC2646098] [PubMed: 17434869] [CrossRef]
• Freedman DS, Khan LK, Serdula MK, Dietz WH, Srinivasan SR, Berenson GS (2005). The relation of childhood BMI to adult adiposity: the Bogalusa Heart Study. Pediatrics. 115(1):22–7. [PubMed: 15629977]
• Gallagher D, Visser M, Sepúlveda D, Pierson RN, Harris T, Heymsfield SB (1996). How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups? Am J Epidemiol. 143(3):228–39. 10.1093/oxfordjournals.aje.a008733 [PubMed: 8561156] [CrossRef]
• García-Fernández E, Rico-Cabanas L, Rosgaard N, Estruch R, Bach-Faig A (2014). Mediterranean diet and cardiodiabesity: a review. Nutrients. 6(9):3474–500. 10.3390/nu6093474 [PMC free article: PMC4179172] [PubMed: 25192027] [CrossRef]
• Garn SM, Leonard WR, Hawthorne VM (1986). Three limitations of the body mass index. Am J Clin Nutr. 44(6):996–7. [PubMed: 3788846]
• Gibson RS (2005). Principles of nutritional assessment. 2nd edition. Oxford, UK: Oxford University Press.
• Gibson S (2008). Sugar-sweetened soft drinks and obesity: a systematic review of the evidence from observational studies and interventions. Nutr Res Rev. 21(2):134–47. 10.1017/S0954422408110976 [PubMed: 19087367] [CrossRef]
• Glickman SG, Marn CS, Supiano MA, Dengel DR (2004). Validity and reliability of dual-energy X-ray absorptiometry for the assessment of abdominal adiposity. J Appl Physiol (1985). 97(2):509–14. 10.1152/japplphysiol.01234.2003 [PubMed: 15075304] [CrossRef]
• Gordon-Larsen P, Hou N, Sidney S, Sternfeld B, Lewis CE, Jacobs DR Jr, et al. (2009). Fifteen-year longitudinal trends in walking patterns and their impact on weight change. Am J Clin Nutr. 89(1):19–26. 10.3945/ajcn.2008.26147 [PMC free article: PMC2715291] [PubMed: 19056560] [CrossRef]
• Gotsis E, Anagnostis P, Mariolis A, Vlachou A, Katsiki N, Karagiannis A (2015). Health benefits of the Mediterranean Diet: an update of research over the last 5 years. Angiology. 66(4):304–18. 10.1177/0003319714532169 [PubMed: 24778424] [CrossRef]
• Graham C, Mullen A, Whelan K (2015). Obesity and the gastrointestinal microbiota: a review of associations and mechanisms. Nutr Rev. 73(6):376–85. 10.1093/nutrit/nuv004 [PubMed: 26011912] [CrossRef]
• Grinker JA, Tucker K, Vokonas PS, Rush D (1995). Body habitus changes among adult males from the normative aging study: relations to aging, smoking history and alcohol intake. Obes Res. 3(5):435–46. 10.1002/j.1550-8528.1995.tb00173.x [PubMed: 8521163] [CrossRef]
• Gross AL, May BJ, Axilbund JE, Armstrong DK, Roden RB, Visvanathan K (2015). Weight change in breast cancer survivors compared to cancer-free women: a prospective study in women at familial risk of breast cancer. Cancer Epidemiol Biomarkers Prev. 24(8):1262–9. 10.1158/1055-9965.EPI-15-0212 [PMC free article: PMC4531263] [PubMed: 26178712] [CrossRef]
• Grün F, Blumberg B (2009). Endocrine disrupters as obesogens. Mol Cell Endocrinol. 304(1–2):19–29. 10.1016/j.mce.2009.02.018 [PMC free article: PMC2713042] [PubMed: 19433244] [CrossRef]
• Guo SS, Chumlea WC (1999). Tracking of body mass index in children in relation to overweight in adulthood. Am J Clin Nutr. 70:145S–8S. [PubMed: 10419418]
• Han TS, Hart CL, Haig C, Logue J, Upton MN, Watt GC, et al. (2015). Contribution of maternal and paternal adiposity and smoking to adult offspring adiposity and CVD risk. BMJ Open. 5(11):e007682. 10.1136/bmjopen-2015-007682 [PMC free article: PMC4636631] [PubMed: 26525718] [CrossRef]
• Han TS, Morrison CE, Lean ME (1999). Age and health indications assessed by silhouette photographs. Eur J Clin Nutr. 53(8):606–11. 10.1038/sj.ejcn.1600816 [PubMed: 10477246] [CrossRef]
• Hansen AY, Umstattd Meyer MR, Lenardson JD, Hartley D (2015). Built environments and active living in rural and remote areas: a review of the literature. Curr Obes Rep. 4(4):484–93. 10.1007/s13679-015-0180-9 [PubMed: 26364307] [CrossRef]
• Harris K, Kassis A, Major G, Chou CJ (2012). Is the gut microbiota a new factor contributing to obesity and its metabolic disorders? J Obes. 2012:879151. 10.1155/2012/879151 [PMC free article: PMC3270440] [PubMed: 22315672] [CrossRef]
• Haus EL, Smolensky MH (2013). Shift work and cancer risk: potential mechanistic roles of circadian disruption, light at night, and sleep deprivation. Sleep Med Rev. 17(4):273–84. 10.1016/j.smrv.2012.08.003 [PubMed: 23137527] [CrossRef]
• Health and Social Care Information Centre (2013). National Child Measurement Programme: England, 2012-13 school year. Available from: http://content​.digital​.nhs.uk/catalogue/PUB13115​/nati-chil-meas-prog-eng-2012-2013-rep.pdf.
• Heard-Costa NL, Zillikens MC, Monda KL, Johansson A, Harris TB, Fu M, et al. (2009). NRXN3 is a novel locus for waist circumference: a genome-wide association study from the CHARGE Consortium. PLoS Genet. 5(6):e1000539. 10.1371/journal.pgen.1000539 [PMC free article: PMC2695005] [PubMed: 19557197] [CrossRef]
• Heindel JJ, Newbold R, Schug TT (2015). Endocrine disruptors and obesity. Nat Rev Endocrinol. 11(11):653–61. 10.1038/nrendo.2015.163 [PubMed: 26391979] [CrossRef]
• Heo M, Faith MS, Pietrobelli A, Heymsfield SB (2012). Percentage of body fat cutoffs by sex, age, and race-ethnicity in the US adult population from NHANES 1999–2004. Am J Clin Nutr. 95(3):594–602. 10.3945/ajcn.111.025171 [PubMed: 22301924] [CrossRef]
• Hill JO, Peters JC, Peters JC (1998). Environmental contributions to the obesity epidemic. Science. 280(5368):1371–4. 10.1126/science.280.5368.1371 [PubMed: 9603719] [CrossRef]
• Hill JO, Wyatt HR, Peters JC (2012). Energy balance and obesity. Circulation. 126(1):126–32. 10.1161/CIRCULATIONAHA.111.087213 [PMC free article: PMC3401553] [PubMed: 22753534] [CrossRef]
• Himes JH (2009). Challenges of accurately measuring and using BMI and other indicators of obesity in children. Pediatrics. 124(Suppl 1):S3–22. 10.1542/peds.2008-3586D [PubMed: 19720665] [CrossRef]
• Hooper L, Abdelhamid A, Bunn D, Brown T, Summerbell CD, Skeaff CM (2015). Effects of total fat intake on body weight. Cochrane Database Syst Rev. 8:CD011834. 10.1002/14651858.CD011834 [PMC free article: PMC10403157] [PubMed: 26250104] [CrossRef]
• Horan M, Gibney E, Molloy E, McAuliffe F (2015). Methodologies to assess paediatric adiposity. Ir J Med Sci. 184(1):53–68. 10.1007/s11845-014-1124-1 [PubMed: 24791970] [CrossRef]
• Horta BL, Loret de Mola C, Victora CG (2015). Long-term consequences of breastfeeding on cholesterol, obesity, systolic blood pressure and type 2 diabetes: a systematic review and meta-analysis. Acta Paediatr. 104(467):30–7. 10.1111/apa.13133 [PubMed: 26192560] [CrossRef]
• Hu FB (2008). Measurements of adiposity and body composition. In: Hu FB, editor. Obesity epidemiology. Oxford, UK: Oxford University Press. 10.1093/acprof:oso/9780195312911.003.0005 [CrossRef]
• Hu FB, Li TY, Colditz GA, Willett WC, Manson JE (2003). Television watching and other sedentary behaviors in relation to risk of obesity and type 2 diabetes mellitus in women. JAMA. 289(14):1785–91. 10.1001/jama.289.14.1785 [PubMed: 12684356] [CrossRef]
• Hu FB, Malik VS (2010). Sugar-sweetened beverages and risk of obesity and type 2 diabetes: epidemiologic evidence. Physiol Behav. 100(1):47–54. 10.1016/j.physbeh.2010.01.036 [PMC free article: PMC2862460] [PubMed: 20138901] [CrossRef]
• Hu T, Mills KT, Yao L, Demanelis K, Eloustaz M, Yancy WS Jr, et al. (2012). Effects of low-carbohydrate diets versus low-fat diets on metabolic risk factors: a meta-analysis of randomized controlled clinical trials. Am J Epidemiol. 176(Suppl 7):S44–54. 10.1093/aje/kws264 [PMC free article: PMC3530364] [PubMed: 23035144] [CrossRef]
• IARC (2002). Weight control and physical activity. Lyon, France: IARC Press (IARC Handbooks of Cancer Prevention, Vol. 6). Available from: http://publications​.iarc.fr/376.
• IDF (2006). The IDF consensus worldwide definition of the metabolic syndrome. Brussels, Belgium: International Diabetes Federation. Available from: https://www​.idf.org/e-library​/consensus-statements​/60-idfconsensus-worldwide-definitionof-the-metabolic-syndrome.
• IHME (2014). Overweight and Obesity Viz. Seattle (WA), USA: Institute for Health Metrics and Evaluation. Available from: http://www​.healthdata​.org/data-visualization​/overweight-and-obesity-viz.
• Institute of Medicine (2015). Examining a developmental approach to childhood obesity: the fetal and early childhood years. Workshop summary. Washington (DC), USA: National Academies Press. Available from: https://www​.nap.edu/catalog/21782. [PubMed: 26512393]
• Janesick AS, Blumberg B (2016). Obesogens: an emerging threat to public health. Am J Obstet Gynecol. 214(5):559–65. 10.1016/j.ajog.2016.01.182 [PMC free article: PMC4851574] [PubMed: 26829510] [CrossRef]
• Janssen I, Katzmarzyk PT, Ross R (2005). Body mass index is inversely related to mortality in older people after adjustment for waist circumference. J Am Geriatr Soc. 53(12):2112–8. 10.1111/j.1532-5415.2005.00505.x [PubMed: 16398895] [CrossRef]
• Jarlenski MP, Bennett WL, Bleich SN, Barry CL, Stuart EA (2014). Effects of breastfeeding on postpartum weight loss among U.S. women. Prev Med. 69:146–50. 10.1016/j.ypmed.2014.09.018 [PMC free article: PMC4312189] [PubMed: 25284261] [CrossRef]
• Jaworowska A, Blackham T, Davies IG, Stevenson L (2013). Nutritional challenges and health implications of takeaway and fast food. Nutr Rev. 71(5):310–8. 10.1111/nure.12031 [PubMed: 23590707] [CrossRef]
• Jiang P, Turek FW (2017). Timing of meals: when is as critical as what and how much. Am J Physiol Endocrinol Metab. 312(5):E369–80. 10.1152/ajpendo.00295.2016 [PMC free article: PMC6105931] [PubMed: 28143856] [CrossRef]
• Kaiser KA, Shikany JM, Keating KD, Allison DB (2013). Will reducing sugar-sweetened beverage consumption reduce obesity? Evidence supporting conjecture is strong, but evidence when testing effect is weak. Obes Rev. 14(8):620–33. 10.1111/obr.12048 [PMC free article: PMC3929296] [PubMed: 23742715] [CrossRef]
• Kamel EG, McNeill G, Han TS, Smith FW, Avenell A, Davidson L, et al. (1999). Measurement of abdominal fat by magnetic resonance imaging, dual-energy X-ray absorptiometry and anthropometry in non-obese men and women. Int J Obes Relat Metab Disord. 23(7):686–92. 10.1038/sj.ijo.0800904 [PubMed: 10454101] [CrossRef]
• Kelley GA, Kelley KS, Pate RR (2015). Exercise and BMI in overweight and obese children and adolescents: a systematic review and trial sequential meta-analysis. Biomed Res Int. 2015:704539. 10.1155/2015/704539 [PMC free article: PMC4633529] [PubMed: 26579538] [CrossRef]
• Kim SH, Park MJ (2014). Phthalate exposure and childhood obesity. Ann Pediatr Endocrinol Metab. 19(2):69–75. 10.6065/apem.2014.19.2.69 [PMC free article: PMC4114051] [PubMed: 25077088] [CrossRef]
• Kim TW, Jeong JH, Hong SC (2015). The impact of sleep and circadian disturbance on hormones and metabolism. Int J Endocrinol. 2015:591729. 10.1155/2015/591729 [PMC free article: PMC4377487] [PubMed: 25861266] [CrossRef]
• Krieger N, Williams DR, Moss NE (1997). Measuring social class in US public health research: concepts, methodologies, and guidelines. Annu Rev Public Health. 18(1):341–78. 10.1146/annurev.publhealth.18.1.341 [PubMed: 9143723] [CrossRef]
• Krul AJ, Daanen HA, Choi H (2011). Self-reported and measured weight, height and body mass index (BMI) in Italy, the Netherlands and North America. Eur J Public Health. 21(4):414–9. 10.1093/eurpub/ckp228 [PubMed: 20089678] [CrossRef]
• Kumar BN, Meyer HE, Wandel M, Dalen I, Holmboe-Ottesen G (2006). Ethnic differences in obesity among immigrants from developing countries, in Oslo, Norway. Int J Obes (Lond). 30(4):684–90. 10.1038/sj.ijo.0803051 [PubMed: 16130029] [CrossRef]
• Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Gómez JM, et al.; Composition of the ESPEN Working Group (2004). Bioelectrical impedance analysis – part I: review of principles and methods. Clin Nutr. 23(5):1226–43. 10.1016/j.clnu.2004.06.004 [PubMed: 15380917] [CrossRef]
• Kyle UG, Pirlich M, Schuetz T, Luebke HJ, Lochs H, Pichard C (2003). Prevalence of malnutrition in 1760 patients at hospital admission: a controlled population study of body composition. Clin Nutr. 22(5):473–81. 10.1016/S0261-5614(03)00049-9 [PubMed: 14512035] [CrossRef]
• Lakind JS, Goodman M, Mattison DR (2014). Bisphenol A and indicators of obesity, glucose metabolism/type 2 diabetes and cardiovascular disease: a systematic review of epidemiologic research. Crit Rev Toxicol. 44(2):121–50. 10.3109/10408444.2013.860075 [PubMed: 24392816] [CrossRef]
• Lamerz A, Kuepper-Nybelen J, Wehle C, Bruning N, Trost-Brinkhues G, Brenner H, et al. (2005). Social class, parental education, and obesity prevalence in a study of six-year-old children in Germany. Int J Obes (Lond). 29(4):373–80. 10.1038/sj.ijo.0802914 [PubMed: 15768043] [CrossRef]
• Langley-Evans SC (2015). Nutrition in early life and the programming of adult disease: a review. J Hum Nutr Diet. 28(Suppl 1):1–14. 10.1111/jhn.12212 [PubMed: 24479490] [CrossRef]
• Lawrence W, Barker M (2009). A review of factors affecting the food choices of disadvantaged women. Proc Nutr Soc. 68(2):189–94. 10.1017/S0029665109001013 [PubMed: 19208268] [CrossRef]
• Le Chatelier E, Nielsen T, Qin J, Prifti E, Hildebrand F, Falony G, et al.; MetaHIT consortium (2013). Richness of human gut microbiome correlates with metabolic markers. Nature. 500(7464):541–6. 10.1038/nature12506 [PubMed: 23985870] [CrossRef]
• Lean MEJ, Malkova D (2015). Altered gut and adipose tissue hormones in overweight and obese individuals: cause or consequence? Int J Obes (Lond). 1:1–10. [PMC free article: PMC4827002] [PubMed: 26499438]
• Lebovitz HE, Banerji MA (2005). Point: visceral adiposity is causally related to insulin resistance. Diabetes Care. 28(9):2322–5. 10.2337/diacare.28.9.2322 [PubMed: 16123512] [CrossRef]
• Lee IM, Djoussé L, Sesso HD, Wang L, Buring JE (2010). Physical activity and weight gain prevention. JAMA. 303(12):1173–9. 10.1001/jama.2010.312 [PMC free article: PMC2846540] [PubMed: 20332403] [CrossRef]
• Leslie WS, Hankey CR, Lean ME (2007). Weight gain as an adverse effect of some commonly prescribed drugs: a systematic review. QJM. 100(7):395–404. 10.1093/qjmed/hcm044 [PubMed: 17566010] [CrossRef]
• Liao Y, Liao J, Durand CP, Dunton GF (2014). Which type of sedentary behaviour intervention is more effective at reducing body mass index in children? A meta-analytic review. Obes Rev. 15(3):159–68. 10.1111/obr.12112 [PMC free article: PMC4131845] [PubMed: 24588966] [CrossRef]
• Link BG, Phelan JC (2001). Conceptualizing stigma. Annu Rev Sociol. 27(1):363–85. 10.1146/annurev.soc.27.1.363 [CrossRef]
• Lipsky LM, Iannotti RJ (2012). Associations of television viewing with eating behaviors in the 2009 Health Behaviour in School-aged Children Study. Arch Pediatr Adolesc Med. 166(5):465–72. 10.1001/archpediatrics.2011.1407 [PMC free article: PMC4733642] [PubMed: 22566548] [CrossRef]
• Lissner L, Wijnhoven TMA, Mehlig K, Sjöberg A, Kunesova M, Yngve A, et al. (2016). Socioeconomic inequalities in childhood overweight: heterogeneity across five countries in the WHO European Childhood Obesity Surveillance Initiative (COSI-2008). Int J Obes (Lond). 40(5):796–802. 10.1038/ijo.2016.12 [PMC free article: PMC4856730] [PubMed: 27136760] [CrossRef]
• Liu A, Byrne NM, Kagawa M, Ma G, Poh BK, Ismail MN, et al. (2011). Ethnic differences in the relationship between body mass index and percentage body fat among Asian children from different backgrounds. Br J Nutr. 106(9):1390–7. 10.1017/S0007114511001681 [PubMed: 21736824] [CrossRef]
• Liu KY, Chow JM, Sherry C (2013). Early life obesity and diabetes: origins in pregnancy. Open J Endocr Metab Dis. 3:1–12. 10.1007/s11154-013-9238-8 [CrossRef]
• Livingstone MB, Black AE (2003). Markers of the validity of reported energy intake. J Nutr. 133(Suppl 3):895S–920S. [PubMed: 12612176]
• Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al.; LifeLines Cohort Study; ADIPOGen Consortium; AGEN-BMI Working Group; CARDIOGRAMplusC4D Consortium; CKDGen Consortium; GLGC; ICBP; MAGIC Investigators; MuTHER Consortium; MIGen Consortium; PAGE Consortium; ReproGen Consortium; GENIE Consortium; International Endogene Consortium (2015). Genetic studies of body mass index yield new insights for obesity biology. Nature. 518(7538):197–206. 10.1038/nature14177 [PMC free article: PMC4382211] [PubMed: 25673413] [CrossRef]
• Lohman TG, Chen Z (2005). Dual-energy x-ray absorptiometry. In: Heymsfield SB, Lohman TG, Wang Z, Going SB, editors. Human body composition. 2nd edition. Champaign (IL), USA; Leeds, UK: Human Kinetics Press; pp. 63–77.
• Lopez R, Hynes HP (2003). Sprawl in the 1990s: measurement, distribution, and trends. Urban Aff Rev Thousand Oaks Calif. 38(3):325–55. 10.1177/1078087402238805 [PMC free article: PMC1570399] [PubMed: 16990927] [CrossRef]
• Louzada ML, Baraldi LG, Steele EM, Martins AP, Canella DS, Moubarac JC, et al. (2015). Consumption of ultra-processed foods and obesity in Brazilian adolescents and adults. Prev Med. 81:9–15. 10.1016/j.ypmed.2015.07.018 [PubMed: 26231112] [CrossRef]
• Ludwig J, Sanbonmatsu L, Gennetian L, Adam E, Duncan GJ, Katz LF, et al. (2011). Neighborhoods, obesity, and diabetes – a randomized social experiment. N Engl J Med. 365(16):1509–19. 10.1056/NEJMsa1103216 [PMC free article: PMC3410541] [PubMed: 22010917] [CrossRef]
• Lynch BM, Healy GN, Dunstan DW, Owen N (2010). Sedentary versus inactive: distinctions for disease prevention. Nat Rev Cardiol. 7(11):68c1, author reply, 68-c2. 10.1038/nrcardio.2010.68-c1 [PubMed: 21080592] [CrossRef]
• Mackenbach JD, Rutter H, Compernolle S, Glonti K, Oppert JM, Charreire H, et al. (2014). Obesogenic environments: a systematic review of the association between the physical environment and adult weight status, the SPOTLIGHT project. BMC Public Health. 14(1):233. 10.1186/1471-2458-14-233 [PMC free article: PMC4015813] [PubMed: 24602291] [CrossRef]
• Magee L, Hale L (2012). Longitudinal associations between sleep duration and subsequent weight gain: a systematic review. Sleep Med Rev. 16(3):231–41. 10.1016/j.smrv.2011.05.005 [PMC free article: PMC3202683] [PubMed: 21784678] [CrossRef]
• Malik VS, Pan A, Willett WC, Hu FB (2013). Sugar-sweetened beverages and weight gain in children and adults: a systematic review and meta-analysis. Am J Clin Nutr. 98(4):1084–102. 10.3945/ajcn.113.058362 [PMC free article: PMC3778861] [PubMed: 23966427] [CrossRef]
• Marchesi JR, Adams DH, Fava F, Hermes GD, Hirschfield GM, Hold G, et al. (2016). The gut microbiota and host health: a new clinical frontier. Gut. 65(2):330–9. 10.1136/gutjnl-2015-309990 [PMC free article: PMC4752653] [PubMed: 26338727] [CrossRef]
• Maresca MM, Hoepner LA, Hassoun A, Oberfield SE, Mooney SJ, Calafat AM, et al. (2015). Prenatal exposure to phthalates and childhood body size in an urban cohort. Environ Health Perspect. 124(4). 10.1289/ehp.1408750 [PMC free article: PMC4829975] [PubMed: 26069025] [CrossRef]
• Marshall NS, Glozier N, Grunstein RR (2008). Is sleep duration related to obesity? A critical review of the epidemiological evidence. Sleep Med Rev. 12(4):289–98. 10.1016/j.smrv.2008.03.001 [PubMed: 18485764] [CrossRef]
• Martínez Steele E, Baraldi LG, Louzada ML, Moubarac JC, Mozaffarian D, Monteiro CA (2016). Ultra-processed foods and added sugars in the US diet: evidence from a nationally representative cross-sectional study. BMJ Open. 6(3):e009892. 10.1136/bmjopen-2015-009892 [PMC free article: PMC4785287] [PubMed: 26962035] [CrossRef]
• McCrory MA (2014). Meal skipping and variables related to energy balance in adults: a brief review, with emphasis on the breakfast meal. Physiol Behav. 134:51–4. 10.1016/j.physbeh.2014.05.005 [PubMed: 24825781] [CrossRef]
• McCrory MA, Fuss PJ, Hays NP, Vinken AG, Greenberg AS, Roberts SB (1999). Overeating in America: association between restaurant food consumption and body fatness in healthy adult men and women ages 19 to 80. Obes Res. 7(6):564–71. 10.1002/j.1550-8528.1999.tb00715.x [PubMed: 10574515] [CrossRef]
• McFadden E, Jones ME, Schoemaker MJ, Ashworth A, Swerdlow AJ (2014). The relationship between obesity and exposure to light at night: cross-sectional analyses of over 100,000 women in the Breakthrough Generations Study. Am J Epidemiol. 180(3):245–50. 10.1093/aje/kwu117 [PubMed: 24875371] [CrossRef]
• McKinnon L, Giskes K, Turrell G (2014). The contribution of three components of nutrition knowledge to socio-economic differences in food purchasing choices. Public Health Nutr. 17(8):1814–24. 10.1017/S1368980013002036 [PMC free article: PMC10282305] [PubMed: 23920283] [CrossRef]
• McPheeters ML, Warren Z, Sathe N, Bruzek JL, Krishnaswami S, Jerome RN, et al. (2011). A systematic review of medical treatments for children with autism spectrum disorders. Pediatrics. 127(5):e1312–21. 10.1542/peds.2011-0427 [PubMed: 21464191] [CrossRef]
• Miller CT, Fraser SF, Levinger I, Straznicky NE, Dixon JB, Reynolds J, et al. (2013). The effects of exercise training in addition to energy restriction on functional capacities and body composition in obese adults during weight loss: a systematic review. PLoS One. 8(11):e81692. 10.1371/journal.pone.0081692 [PMC free article: PMC3884087] [PubMed: 24409219] [CrossRef]
• Monasta L, Batty GD, Cattaneo A, Lutje V, Ronfani L, Van Lenthe FJ, et al. (2010). Early-life determinants of overweight and obesity: a review of systematic reviews. Obes Rev. 11(10):695–708. 10.1111/j.1467-789X.2010.00735.x [PubMed: 20331509] [CrossRef]
• Monteiro CA, Conde WL, Lu B, Popkin BM (2004a). Obesity and inequities in health in the developing world. Int J Obes Relat Metab Disord. 28(9):1181–6. 10.1038/sj.ijo.0802716 [PubMed: 15211362] [CrossRef]
• Monteiro CA, Moura EC, Conde WL, Popkin BM (2004b). Socioeconomic status and obesity in adult populations of developing countries: a review. Bull World Health Organ. 82(12):940–6. [PMC free article: PMC2623095] [PubMed: 15654409]
• Moore CJ, Cunningham SA (2012). Social position, psychological stress, and obesity: a systematic review. J Acad Nutr Diet. 112(4):518–26. 10.1016/j.jand.2011.12.001 [PubMed: 22709702] [CrossRef]
• Mozaffarian D, Afshin A, Benowitz NL, Bittner V, Daniels SR, Franch HA, et al.; American Heart Association Council on Epidemiology and Prevention, Council on Nutrition, Physical Activity and Metabolism, Council on Clinical Cardiology, Council on Cardiovascular Disease in the Young, Council on the Kidney in Cardiovascular Disease, Council on Peripheral Vascular Disease, and the Advocacy Coordinating Committee (2012). Population approaches to improve diet, physical activity, and smoking habits: a scientific statement from the American Heart Association. Circulation. 126(12):1514–63. 10.1161/CIR.0b013e318260a20b [PMC free article: PMC3881293] [PubMed: 22907934] [CrossRef]
• Mozaffarian D, Hao T, Rimm EB, Willett WC, Hu FB (2011). Changes in diet and lifestyle and long-term weight gain in women and men. N Engl J Med. 364(25):2392–404. 10.1056/NEJMoa1014296 [PMC free article: PMC3151731] [PubMed: 21696306] [CrossRef]
• Murray CJ, Kulkarni S, Ezzati M (2005). Eight Americas: new perspectives on U.S. health disparities. Am J Prev Med. 29(5 Suppl 1):4–10. 10.1016/j.amepre.2005.07.031 [PubMed: 16389119] [CrossRef]
• Must A, Willett WC, Dietz WH (1993). Remote recall of childhood height, weight, and body build by elderly subjects. Am J Epidemiol. 138(1):56–64. [PubMed: 8333427]
• Nathan PC, Jovcevska V, Ness KK, Mammone D’Agostino N, Staneland P, Urbach SL, et al. (2006). The prevalence of overweight and obesity in pediatric survivors of cancer. J Pediatr. 149(4):518–25. 10.1016/j.jpeds.2006.06.039 [PubMed: 17011325] [CrossRef]
• National Obesity Observatory (2011). Obesity and ethnicity. Available from: http://webarchive​.nationalarchives​.gov.uk​/20160805121933/http://www​.noo.org.uk/uploads​/doc/vid_9851_Obesity_ethnicity​.pdf.
• National Obesity Observatory (2012). NOO data factsheet: child obesity and socioeconomic status. Available from: http://webarchive​.nationalarchives​.gov.uk​/20160805121933/http://www​.noo.org.uk/uploads​/doc/vid_16967​_ChildSocioeconSep2012.pdf.
• NCD Risk Factor Collaboration (2016). Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19.2 million participants. Lancet. 387(10026):1377–96. 10.1016/S0140-6736(16)30054-X [PMC free article: PMC7615134] [PubMed: 27115820] [CrossRef]
• Ndahimana D, Kim EK (2017). Measurement methods for physical activity and energy expenditure: a review. Clin Nutr Res. 6(2):68–80. 10.7762/cnr.2017.6.2.68 [PMC free article: PMC5426207] [PubMed: 28503503] [CrossRef]
• Neamat-Allah J, Wald D, Hüsing A, Teucher B, Wendt A, Delorme S, et al. (2014). Validation of anthropometric indices of adiposity against whole-body magnetic resonance imaging – a study within the German European Prospective Investigation into Cancer and Nutrition (EPIC) cohorts. PLoS One. 9(3):e91586. 10.1371/journal.pone.0091586 [PMC free article: PMC3953447] [PubMed: 24626110] [CrossRef]
• Neville CE, McKinley MC, Holmes VA, Spence D, Woodside JV (2014). The relationship between breastfeeding and postpartum weight change – a systematic review and critical evaluation. Int J Obes (Lond). 38(4):577–90. 10.1038/ijo.2013.132 [PubMed: 23892523] [CrossRef]
• Newby PK, Muller D, Hallfrisch J, Qiao N, Andres R, Tucker KL (2003). Dietary patterns and changes in body mass index and waist circumference in adults. Am J Clin Nutr. 77(6):1417–25. [PubMed: 12791618]
• Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. (2014). Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 384(9945):766–81. 10.1016/S0140-6736(14)60460-8 [PMC free article: PMC4624264] [PubMed: 24880830] [CrossRef]
• Ng SW, Popkin BM, Popkin BM (2012). Time use and physical activity: a shift away from movement across the globe. Obes Rev. 13(8):659–80. 10.1111/j.1467-789X.2011.00982.x [PMC free article: PMC3401184] [PubMed: 22694051] [CrossRef]
• NHLBI Obesity Education Initiative (2000). The practical guide: identification, evaluation, and treatment of overweight and obesity in adults. Bethesda (MD), USA: National Heart, Lung, and Blood Institute, National Institutes of Health. Available from: https://www​.nhlbi.nih​.gov/files/docs/guidelines/prctgd_c.pdf.
• Nichols HB, Trentham-Dietz A, Egan KM, Titus-Ernstoff L, Holmes MD, Bersch AJ, et al. (2009). Body mass index before and after breast cancer diagnosis: associations with all-cause, breast cancer, and cardiovascular disease mortality. Cancer Epidemiol Biomarkers Prev. 18(5):1403–9. 10.1158/1055-9965.EPI-08-1094 [PMC free article: PMC2715918] [PubMed: 19366908] [CrossRef]
• Obesity Society (2014). Americans’ view on obesity is changing: fewer adults see it as a personal problem of bad choices. Science Daily, 5 November 2014. Available from: https://www​.sciencedaily​.com/releases/2014/11/141105112514​.htm.
• Ogden CL, Carroll MD, Curtin LR, McDowell MA, Tabak CJ, Flegal KM (2006). Prevalence of overweight and obesity in the United States, 1999–2004. JAMA. 295(13):1549–55. 10.1001/jama.295.13.1549 [PubMed: 16595758] [CrossRef]
• Ogden CL, Carroll MD, Kit BK, Flegal KM (2014). Prevalence of childhood and adult obesity in the United States, 2011–2012. JAMA. 311(8):806–14. 10.1001/jama.2014.732 [PMC free article: PMC4770258] [PubMed: 24570244] [CrossRef]
• Okorodudu DO, Jumean MF, Montori VM, Romero-Corral A, Somers VK, Erwin PJ, et al. (2010). Diagnostic performance of body mass index to identify obesity as defined by body adiposity: a systematic review and meta-analysis. Int J Obes. 34(5):791–9. 10.1038/ijo.2010.5 [PubMed: 20125098] [CrossRef]
• Opalinski A (2013). Cultural perspectives of childhood obesity in Haitan and Hispanic children. J Nurse Pract. 9(3):149–54. 10.1016/j.nurpra.2012.10.009 [CrossRef]
• Owen N, Sparling PB, Healy GN, Dunstan DW, Matthews CE (2010). Sedentary behavior: emerging evidence for a new health risk. Mayo Clin Proc. 85(12):1138–41. 10.4065/mcp.2010.0444 [PMC free article: PMC2996155] [PubMed: 21123641] [CrossRef]
• Paeratakul S, Ferdinand DP, Champagne CM, Ryan DH, Bray GA (2003). Fast-food consumption among US adults and children: dietary and nutrient intake profile. J Am Diet Assoc. 103(10):1332–8. 10.1016/S0002-8223(03)01086-1 [PubMed: 14520253] [CrossRef]
• Palmer JR, Kipping-Ruane K, Wise LA, Yu J, Rosenberg L (2015). Lactation in relation to long-term maternal weight gain in African-American women. Am J Epidemiol. 181(12):932–9. 10.1093/aje/kwv027 [PMC free article: PMC4462338] [PubMed: 25944887] [CrossRef]
• Pampel FC, Denney JT, Krueger PM (2012). Obesity, SES, and economic development: a test of the reversal hypothesis. Soc Sci Med. 74(7):1073–81. 10.1016/j.socscimed.2011.12.028 [PMC free article: PMC3298576] [PubMed: 22341204] [CrossRef]
• Papas MA, Alberg AJ, Ewing R, Helzlsouer KJ, Gary TL, Klassen AC (2007). The built environment and obesity. Epidemiol Rev. 29(1):129–43. 10.1093/epirev/mxm009 [PubMed: 17533172] [CrossRef]
• Parizkova J, Chin MK, Chia M, Yang J (2007). An international perspective on obesity, health and physical activity: current trends and challenges in China and Asia. J Exerc Sci Fit. 5(1):7–23.
• Parsons TJ, Power C, Logan S, Summerbell CD (1999). Childhood predictors of adult obesity: a systematic review. Int J Obes Relat Metab Disord. 23(Suppl 8):S1–107. [PubMed: 10641588]
• Patel SR, Hu FB (2008). Short sleep duration and weight gain: a systematic review. Obesity (Silver Spring). 16(3):643–53. 10.1038/oby.2007.118 [PMC free article: PMC2723045] [PubMed: 18239586] [CrossRef]
• Peña MM, Dixon B, Taveras EM (2012). Are you talking to ME? The importance of ethnicity and culture in childhood obesity prevention and management. Child Obes. 8(1):23–7. [PMC free article: PMC3647541] [PubMed: 22799474]
• Pereira MA, Kartashov AI, Ebbeling CB, Van Horn L, Slattery ML, Jacobs DR Jr, et al. (2005). Fast-food habits, weight gain, and insulin resistance (the CARDIA study): 15-year prospective analysis. Lancet. 365(9453):36–42. 10.1016/S0140-6736(04)17663-0 [PubMed: 15639678] [CrossRef]
• Pestana D, Faria G, Sá C, Fernandes VC, Teixeira D, Norberto S, et al. (2014). Persistent organic pollutant levels in human visceral and subcutaneous adipose tissue in obese individuals – depot differences and dysmetabolism implications. Environ Res. 133:170–7. 10.1016/j.envres.2014.05.026 [PubMed: 24949816] [CrossRef]
• Popkin BM (1993). Nutritional patterns and transitions. Popul Dev Rev. 19(1):138–57. 10.2307/2938388 [CrossRef]
• Popkin BM, Richards MK, Montiero CA (1996). Stunting is associated with overweight in children of four nations that are undergoing the nutrition transition. J Nutr. 126(12):3009–16. [PubMed: 9001368]
• Poppitt SD (2015). Beverage consumption: are alcoholic and sugary drinks tipping the balance towards overweight and obesity? Nutrients. 7(8):6700–18. 10.3390/nu7085304 [PMC free article: PMC4555143] [PubMed: 26270675] [CrossRef]
• Poskitt EME, Breda J (2012). Complementary feeding and non communicable diseases: current knowledge and future research needs. Nutr Metab Cardiovasc Dis. 22(10):819–22. 10.1016/j.numecd.2012.08.005 [PubMed: 22917600] [CrossRef]
• Power C, Atherton K, Thomas C (2010). Maternal smoking in pregnancy, adult adiposity and other risk factors for cardiovascular disease. Atherosclerosis. 211(2):643–8. 10.1016/j.atherosclerosis.2010.03.015 [PubMed: 20400081] [CrossRef]
• Prentice AM, Jebb SA (2001). Beyond body mass index. Obes Rev. 2(3):141–7. 10.1046/j.1467-789x.2001.00031.x [PubMed: 12120099] [CrossRef]
• Puhl R, Brownell KD (2001). Bias, discrimination, and obesity. Obes Res. 9(12):788–805. 10.1038/oby.2001.108 [PubMed: 11743063] [CrossRef]
• Rahat-Rozenbloom S, Fernandes J, Gloor GB, Wolever TM (2014). Evidence for greater production of colonic short-chain fatty acids in overweight than lean humans. Int J Obes. 38(12):1525–31. 10.1038/ijo.2014.46 [PMC free article: PMC3970979] [PubMed: 24642959] [CrossRef]
• Rantakokko P, Main KM, Wohlfart-Veje C, Kiviranta H, Airaksinen R, Vartiainen T, et al. (2014). Association of placenta organotin concentrations with growth and ponderal index in 110 newborn boys from Finland during the first 18 months of life: a cohort study. Environ Health. 13(1):45. 10.1186/1476-069X-13-45 [PMC free article: PMC4061538] [PubMed: 24899383] [CrossRef]
• Reddon H, Guéant JL, Meyre D (2016). The importance of gene-environment interactions in human obesity. Clin Sci (Lond). 130(18):1571–97. 10.1042/CS20160221 [PMC free article: PMC6381863] [PubMed: 27503943] [CrossRef]
• Rohan EA, Townsend JS, Fairley TL, Stewart SL (2015). Health behaviors and quality of life among colorectal cancer survivors. J Natl Compr Canc Netw. 13(3):297–302. [PMC free article: PMC4597894] [PubMed: 25736006]
• Romieu I, Escamilla-Núñez MC, Sánchez-Zamorano LM, Lopez-Ridaura R, Torres-Mejía G, Yunes EM, et al. (2012). The association between body shape silhouette and dietary pattern among Mexican women. Public Health Nutr. 15(1):116–25. 10.1017/S1368980011001182 [PubMed: 21875454] [CrossRef]
• Ross R, Janssen I (2005). Computed tomography and magnetic resonance imaging. In: Heymsfield SB, Lohman TG, Wang Z, Going SB, editors. Human body composition. 2nd edition. Champaign (IL), USA; Leeds, UK: Human Kinetics Press; pp. 89–108.
• Rummel-Kluge C, Komossa K, Schwarz S, Hunger H, Schmid F, Lobos CA, et al. (2010). Head-to-head comparisons of metabolic side effects of second generation antipsychotics in the treatment of schizophrenia: a systematic review and meta-analysis. Schizophr Res. 123(2–3):225–33. 10.1016/j.schres.2010.07.012 [PMC free article: PMC2957510] [PubMed: 20692814] [CrossRef]
• Rundle A, Hoepner L, Hassoun A, Oberfield S, Freyer G, Holmes D, et al. (2012). Association of childhood obesity with maternal exposure to ambient air polycyclic aromatic hydrocarbons during pregnancy. Am J Epidemiol. 175(11):1163–72. 10.1093/aje/kwr455 [PMC free article: PMC3491973] [PubMed: 22505764] [CrossRef]
• Rush EC, Freitas I, Plank LD (2009). Body size, body composition and fat distribution: comparative analysis of European, Maori, Pacific Island and Asian Indian adults. Br J Nutr. 102(4):632–41. 10.1017/S0007114508207221 [PubMed: 19203416] [CrossRef]
• Scientific Advisory Committee on Nutrition (2011). The influence of maternal, fetal and child nutrition on the development of chronic disease later in life. London, UK: TSO. Available from: https://www​.gov.uk/government​/publications​/sacn-early-life-nutrition-report.
• Scientific Advisory Committee on Nutrition (2015). Carbohydrates and health. London, UK: TSO. Available from: https://www​.gov.uk/government​/uploads/system​/uploads/attachment_data​/file/445503​/SACN_Carbohydrates_and_Health.pdf.
• Sallis JF, Floyd MF, Rodríguez DA, Saelens BE (2012). Role of built environments in physical activity, obesity, and cardiovascular disease. Circulation. 125(5):729–37. 10.1161/CIRCULATIONAHA.110.969022 [PMC free article: PMC3315587] [PubMed: 22311885] [CrossRef]
• Sanmiguel C, Gupta A, Mayer EA (2015). Gut microbiome and obesity: a plausible explanation for obesity. Curr Obes Rep. 4(2):250–61. 10.1007/s13679-015-0152-0 [PMC free article: PMC4443745] [PubMed: 26029487] [CrossRef]
• Saris WH, Blair SN, van Baak MA, Eaton SB, Davies PS, Di Pietro L, et al. (2003). How much physical activity is enough to prevent unhealthy weight gain? Outcome of the IASO 1st Stock Conference and consensus statement. Obes Rev. 4(2):101–14. 10.1046/j.1467-789X.2003.00101.x [PubMed: 12760445] [CrossRef]
• Schellong K, Schulz S, Harder T, Plagemann A (2012). Birth weight and long-term overweight risk: systematic review and a meta-analysis including 643,902 persons from 66 studies and 26 countries globally. PLoS One. 7(10):e47776. 10.1371/journal.pone.0047776 [PMC free article: PMC3474767] [PubMed: 23082214] [CrossRef]
• Schulze MB, Fung TT, Manson JE, Willett WC, Hu FB (2006). Dietary patterns and changes in body weight in women. Obesity (Silver Spring). 14(8):1444–53. 10.1038/oby.2006.164 [PubMed: 16988088] [CrossRef]
• Schutz Y, Byrne NM, Dulloo A, Hills AP (2014). Energy gap in the aetiology of body weight gain and obesity: a challenging concept with a complex evaluation and pitfalls. Obes Facts. 7(1):15–25. 10.1159/000357846 [PMC free article: PMC5644816] [PubMed: 24457473] [CrossRef]
• Schwingshackl L, Dias S, Strasser B, Hoffmann G (2013). Impact of different training modalities on anthropometric and metabolic characteristics in overweight/obese subjects: a systematic review and network meta-analysis. PLoS One. 8(12):e82853. 10.1371/journal.pone.0082853 [PMC free article: PMC3866267] [PubMed: 24358230] [CrossRef]
• Seidell JC, Visscher TL (2000). Body weight and weight change and their health implications for the elderly. Eur J Clin Nutr. 54(Suppl 3):S33–9. 10.1038/sj.ejcn.1601023 [PubMed: 11041073] [CrossRef]
• Shah RV, Allison MA, Lima JA, Abbasi SA, Eisman A, Lai C, et al. (2016). Abdominal fat radiodensity, quantity and cardiometabolic risk: the Multi-Ethnic Study of Atherosclerosis. Nutr Metab Cardiovasc Dis. 26(2):114–22. [PMC free article: PMC4775418] [PubMed: 26817938]
• Shaw K, Gennat H, O’Rourke P, Del Mar C (2006). Exercise for overweight or obesity. Cochrane Database Syst Rev. (4):CD003817. [PMC free article: PMC9017288] [PubMed: 17054187]
• Sherry B, Jefferds ME, Grummer-Strawn LM (2007). Accuracy of adolescent self-report of height and weight in assessing overweight status: a literature review. Arch Pediatr Adolesc Med. 161(12):1154–61. 10.1001/archpedi.161.12.1154 [PubMed: 18056560] [CrossRef]
• Shrewsbury V, Wardle J (2008). Socioeconomic status and adiposity in childhood: a systematic review of cross-sectional studies 1990–2005. Obesity (Silver Spring). 16(2):275–84. 10.1038/oby.2007.35 [PubMed: 18239633] [CrossRef]
• Sikorski C, Luppa M, Kaiser M, Glaesmer H, Schomerus G, König HH, et al. (2011). The stigma of obesity in the general public and its implications for public health – a systematic review. BMC Public Health. 11(1):661. 10.1186/1471-2458-11-661 [PMC free article: PMC3175190] [PubMed: 21859493] [CrossRef]
• Simkin-Silverman LR, Wing RR, Boraz MA, Kuller LH (2003). Lifestyle intervention can prevent weight gain during menopause: results from a 5-year randomized clinical trial. Ann Behav Med. 26(3):212–20. 10.1207/S15324796ABM2603_06 [PubMed: 14644697] [CrossRef]
• Simmonds M, Burch J, Llewellyn A, Griffiths C, Yang H, Owen C, et al. (2015). The use of measures of obesity in childhood for predicting obesity and the development of obesity-related diseases in adulthood: a systematic review and meta-analysis. Health Technol Assess. 19(43):1–336. 10.3310/hta19430 [PMC free article: PMC4781104] [PubMed: 26108433] [CrossRef]
• Singh AS, Mulder C, Twisk JW, van Mechelen W, Chinapaw MJ (2008). Tracking of childhood overweight into adulthood: a systematic review of the literature. Obes Rev. 9(5):474–88. 10.1111/j.1467-789X.2008.00475.x [PubMed: 18331423] [CrossRef]
• Singh M (2014). Mood, food, and obesity. Front Psychol. 5:925. 10.3389/fpsyg.2014.00925 [PMC free article: PMC4150387] [PubMed: 25225489] [CrossRef]
• Snijder MB, van Dam RM, Visser M, Seidell JC (2006). What aspects of body fat are particularly hazardous and how do we measure them? Int J Epidemiol. 35(1):83–92. 10.1093/ije/dyi253 [PubMed: 16339600] [CrossRef]
• Sørensen TI, Stunkard AJ, Teasdale TW, Higgins MW (1983). The accuracy of reports of weight: children’s recall of their parents’ weights 15 years earlier. Int J Obes. 7(2):115–22. [PubMed: 6862758]
• Spahlholz J, Baer N, König HH, Riedel-Heller SG, Luck-Sikorski C (2016). Obesity and discrimination – a systematic review and meta-analysis of observational studies. Obes Rev. 17(1):43–55. 10.1111/obr.12343 [PubMed: 26596238] [CrossRef]
• Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, et al.; MAGIC; Procardis Consortium (2010). Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. 42(11):937–48. 10.1038/ng.686 [PMC free article: PMC3014648] [PubMed: 20935630] [CrossRef]
• Steinberger J, Jacobs DR, Raatz S, Moran A, Hong CP, Sinaiko AR (2005). Comparison of body fatness measurements by BMI and skinfolds vs dual energy X-ray absorptiometry and their relation to cardiovascular risk factors in adolescents. Int J Obes (Lond). 29(11):1346–52. 10.1038/sj.ijo.0803026 [PubMed: 16044176] [CrossRef]
• Sun G, French CR, Martin GR, Younghusband B, Green RC, Xie YG, et al. (2005). Comparison of multifrequency bioelectrical impedance analysis with dual-energy X-ray absorptiometry for assessment of percentage body fat in a large, healthy population. Am J Clin Nutr. 81(1):74–8. [PubMed: 15640463]
• Swinburn B, Sacks G, Ravussin E (2009). Increased food energy supply is more than sufficient to explain the US epidemic of obesity. Am J Clin Nutr. 90(6):1453–6. 10.3945/ajcn.2009.28595 [PubMed: 19828708] [CrossRef]
• Szewczyk-Golec K, Woźniak A, Reiter RJ (2015). Inter-relationships of the chronobiotic, melatonin, with leptin and adiponectin: implications for obesity. J Pineal Res. 59(3):277–91. 10.1111/jpi.12257 [PubMed: 26103557] [CrossRef]
• Tang-Péronard JL, Heitmann BL, Andersen HR, Steuerwald U, Grandjean P, Weihe P, et al. (2014). Association between prenatal polychlorinated biphenyl exposure and obesity development at ages 5 and 7 y: a prospective cohort study of 656 children from the Faroe Islands. Am J Clin Nutr. 99(1):5–13. 10.3945/ajcn.113.066720 [PMC free article: PMC3862459] [PubMed: 24153349] [CrossRef]
• Tang-Péronard JL, Jensen TK, Andersen HR, Ried-Larsen M, Grøntved A, Andersen LB, et al. (2015). Associations between exposure to persistent organic pollutants in childhood and overweight up to 12 years later in a low exposed Danish population. Obes Facts. 8(4):282–92. 10.1159/000438834 [PMC free article: PMC5644795] [PubMed: 26228100] [CrossRef]
• Te Morenga L, Mallard S, Mann J (2012). Dietary sugars and body weight: systematic review and meta-analyses of randomised controlled trials and cohort studies. BMJ. 346:e7492. 10.1136/bmj.e7492 [PubMed: 23321486] [CrossRef]
• Thomée S, Lissner L, Hagberg M, Grimby-Ekman A (2015). Leisure time computer use and overweight development in young adults – a prospective study. BMC Public Health. 15(1):839. 10.1186/s12889-015-2131-5 [PMC free article: PMC4556216] [PubMed: 26329006] [CrossRef]
• Thompson MA, Gray JJ (1995). Development and validation of a new body-image assessment scale. J Pers Assess. 64(2):258–69. 10.1207/s15327752jpa6402_6 [PubMed: 7722852] [CrossRef]
• Thorogood A, Mottillo S, Shimony A, Filion KB, Joseph L, Genest J, et al. (2011). Isolated aerobic exercise and weight loss: a systematic review and meta-analysis of randomized controlled trials. Am J Med. 124(8):747–55. 10.1016/j.amjmed.2011.02.037 [PubMed: 21787904] [CrossRef]
• Tobi EW, Goeman JJ, Monajemi R, Gu H, Putter H, Zhang Y, et al. (2014). DNA methylation signatures link prenatal famine exposure to growth and metabolism. Nat Commun. 5:5592. 10.1038/ncomms6592 [PMC free article: PMC4246417] [PubMed: 25424739] [CrossRef]
• Tobias DK, Chen M, Manson JE, Ludwig DS, Willett W, Hu FB (2015). Effect of low-fat diet interventions versus other diet interventions on long-term weight change in adults: a systematic review and meta-analysis. Lancet Diabetes Endocrinol. 3(12):968–79. 10.1016/S2213-8587(15)00367-8 [PMC free article: PMC4667723] [PubMed: 26527511] [CrossRef]
• Tompkins CL, Seablom M, Brock DW (2015). Parental perception of child’s body weight: a systematic review. J Child Fam Stud. 24(5):1384–91. 10.1007/s10826-014-9945-0 [CrossRef]
• Torloni MR, Betrán AP, Merialdi M (2012). Demography of obesity. In: Gillman MW, Poston L, editors. Maternal obesity. Cambridge, UK: Cambridge University Press; pp. 1–7.
• Trasande L, Attina TM, Blustein J (2012). Association between urinary bisphenol A concentration and obesity prevalence in children and adolescents. JAMA. 308(11):1113–21. 10.1001/2012.jama.11461 [PubMed: 22990270] [CrossRef]
• Troy LM, Hunter DJ, Manson JE, Colditz GA, Stampfer MJ, Willett WC (1995). The validity of recalled weight among younger women. Int J Obes Relat Metab Disord. 19(8):570–2. [PubMed: 7489028]
• Troy LM, Michels KB, Hunter DJ, Spiegelman D, Manson JE, Colditz GA, et al. (1996). Self-reported birthweight and history of having been breastfed among younger women: an assessment of validity. Int J Epidemiol. 25(1):122–7. 10.1093/ije/25.1.122 [PubMed: 8666479] [CrossRef]
• Trumbo PR, Rivers CR (2014). Systematic review of the evidence for an association between sugar-sweetened beverage consumption and risk of obesity. Nutr Rev. 72(9):566–74. 10.1111/nure.12128 [PubMed: 25091794] [CrossRef]
• Ulijaszek SJ, Kerr DA (1999). Anthropometric measurement error and the assessment of nutritional status. Br J Nutr. 82(3):165–77. 10.1017/S0007114599001348 [PubMed: 10655963] [CrossRef]
• UNICEF, WHO, and World Bank Group (2015). Levels and trends in child malnutrition: UNICEF – WHO – World Bank Group joint child malnutrition estimates: key findings of the 2015 edition. New York, USA: United Nations Children’s Fund; Geneva, Switzerland: World Health Organization; Washington (DC), USA: The World Bank. Available from: http://www​.who.int/nutgrowthdb​/jme_brochure2015.pdf?ua=1.
• Van Cauwenberghe J, Delvaux I, Michels N, Den Hond E, Schoeters G, Nelen V, et al. (2014). Validity of parentally reported versus measured weight, length and waist in 7- to 9-year-old children for use in follow-up studies. Eur J Pediatr. 173(7):921–8. 10.1007/s00431-014-2274-x [PubMed: 24497184] [CrossRef]
• van der Heijden AA, Hu FB, Rimm EB, van Dam RM (2007). A prospective study of breakfast consumption and weight gain among U.S. men. Obesity (Silver Spring). 15(10):2463–9. 10.1038/oby.2007.292 [PubMed: 17925472] [CrossRef]
• Vandevijvere S, Chow CC, Hall KD, Umali E, Swinburn BA (2015). Increased food energy supply as a major driver of the obesity epidemic: a global analysis. Bull World Health Organ. 93(7):446–56. 10.2471/BLT.14.150565 [PMC free article: PMC4490816] [PubMed: 26170502] [CrossRef]
• Vasan RS, Pencina MJ, Cobain M, Freiberg MS, D’Agostino RB (2005). Estimated risks for developing obesity in the Framingham Heart Study. Ann Intern Med. 143(7):473–80. 10.7326/0003-4819-143-7-200510040-00005 [PubMed: 16204159] [CrossRef]
• Villareal DT, Apovian CM, Kushner RF, Klein S; American Society for Nutrition; NAASO, The Obesity Society (2005). Obesity in older adults: technical review and position statement of the American Society for Nutrition and NAASO, The Obesity Society. Am J Clin Nutr. 82(5):923–34. [PubMed: 16280421]
• Volberg V, Harley K, Calafat AM, Davé V, McFadden J, Eskenazi B, et al. (2013). Maternal bisphenol A exposure during pregnancy and its association with adipokines in Mexican-American children. Environ Mol Mutagen. 54(8):621–8. 10.1002/em.21803 [PMC free article: PMC4422176] [PubMed: 23908009] [CrossRef]
• Wagner DR (2013). Ultrasound as a tool to assess body fat. J Obes. 2013:1-9 10.1155/2013/280713 [PMC free article: PMC3770049] [PubMed: 24062944] [CrossRef]
• Wagner DR, Heyward VH (2000). Measures of body composition in blacks and whites: a comparative review. Am J Clin Nutr. 71(6):1392–402. [PubMed: 10837277]
• Wang L, Mamudu HM, Alamian A, Anderson JL, Brooks B (2014). Independent and joint effects of prenatal maternal smoking and maternal exposure to second-hand smoke on the development of adolescent obesity: a longitudinal study. J Paediatr Child Health. 50(11):908–15. 10.1111/jpc.12667 [PubMed: 24920104] [CrossRef]
• Wang T, Li M, Chen B, Xu M, Xu Y, Huang Y, et al. (2012). Urinary bisphenol A (BPA) concentration associates with obesity and insulin resistance. J Clin Endocrinol Metab. 97(2):E223–7. 10.1210/jc.2011-1989 [PubMed: 22090277] [CrossRef]
• Wannamethee SG, Shaper AG, Lennon L (2005). Reasons for intentional weight loss, unintentional weight loss, and mortality in older men. Arch Intern Med. 165(9):1035–40. 10.1001/archinte.165.9.1035 [PubMed: 15883243] [CrossRef]
• Wardle J, Haase AM, Steptoe A (2006). Body image and weight control in young adults: international comparisons in university students from 22 countries. Int J Obes. 30(4):644–51. Available from: http://www​.nature.com​/ijo/journal/v30/n4/pdf/0803050a.pdf. 10.1038/sj.ijo.0803050 [PubMed: 16151414] [CrossRef]
• WCRF/AICR (2007). Food, nutrition, physical activity, and the prevention of cancer: a global perspective. Washington (DC), USA: American Institute for Cancer Research. Available from: www​.aicr.org/assets/docs​/pdf/reports/Second_Expert_Report​.pdf.
• Weng SF, Redsell SA, Swift JA, Yang M, Glazebrook CP (2012). Systematic review and meta-analyses of risk factors for childhood overweight identifiable during infancy. Arch Dis Child. 97(12):1019–26. 10.1136/archdischild-2012-302263 [PMC free article: PMC3512440] [PubMed: 23109090] [CrossRef]
• Westerterp KR, Speakman JR (2008). Physical activity energy expenditure has not declined since the 1980s and matches energy expenditures of wild mammals. Int J Obes. 32(8):1256–63. 10.1038/ijo.2008.74 [PubMed: 18504442] [CrossRef]
• WHO (2000). Obesity: preventing and managing the global epidemic: report of a WHO Consultation. Geneva, Switzerland: World Health Organization (WHO Technical Report Series, No. 894). Available from: http://www​.who.int/nutrition​/publications​/obesity/WHO_TRS_894/en/. [PubMed: 11234459]
• WHO (2003). Diet, nutrition and the prevention of chronic diseases. Report of a joint WHO/FAO Expert Consultation. Geneva, Switzerland: World Health Organization (WHO Technical Report Series, No. 916). Available from: http://www​.who.int/dietphysicalactivity​/publications​/trs916/en/. [PubMed: 12768890]
• WHO (2008). Closing the gap in a generation: health equity through action on the social determinants of health. Final report of the Commission on Social Determinants of Health. Geneva, Switzerland: World Health Organization. Available from: http://www​.who.int/social_determinants​/thecommission​/finalreport/en/.
• WHO (2011). Global database on body mass index: an interactive surveillance tool for monitoring nutrition transition. Available from: www​.who.int/bmi/index.jsp.
• WHO (2014). Obesity and inequities: guidance for addressing inequities in overweight and obesity. Available from: http://www​.euro.who.int​/en/publications/abstracts​/obesity-and-inequities​.-guidance-for-addressing-inequities-in-overweight-and-obesity-2014.
• WHO (2015). Information note about intake of sugars recommended in the WHO guideline for adults and children. Available from: http://www​.who.int/nutrition​/publications​/guidelines/sugar_intake​_information_note_en.pdf.
• WHO (2016). Obesity and overweight: fact sheet. Available from: http://www​.who.int/mediacentre​/factsheets/fs311/en/.
• WHO (2017a). WHO child growth standards. Geneva, Switzerland: World Health Organization. Available from: http://www​.who.int/childgrowth​/standards/Technical_report​.pdf.
• WHO (2017b). WHO growth reference data for 5–19 years. Geneva, Switzerland: World Health Organization. Available from: http://www​.who.int/nutrition​/publications​/growthref_who_bulletin/en/.
• WHO Expert Consultation (2004). Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 363(9403):157–63.10.1016/S0140-6736(03)15268-3 [PubMed: 14726171] [CrossRef]
• WHO Regional Office for Europe (2016). Good maternal nutrition: the best start in life. Copenhagen, Denmark: World Health Organization Regional Office for Europe. Available from: http://www​.euro.who.int​/en/health-topics​/disease-prevention/nutrition​/publications​/2016/good-maternal-nutrition​.-the-best-start-in-life-2016.
• Wijnhoven TM, van Raaij JM, Spinelli A, Starc G, Hassapidou M, Spiroski I, et al. (2014). WHO European Childhood Obesity Surveillance Initiative: body mass index and level of overweight among 6-9-year-old children from school year 2007/2008 to school year 2009/2010. BMC Public Health. 14(1):806. 10.1186/1471-2458-14-806 [PMC free article: PMC4289284] [PubMed: 25099430] [CrossRef]
• Willett W (1998). Nutritional epidemiology. 2nd edition. Oxford, UK: Oxford University Press. 10.1093/acprof:oso/9780195122978.001.0001 [CrossRef]
• Willett W, Hu F (2013). Anthropometric measures and body composition. In: Willett W, editor. Nutritional epidemiology. 3rd edition. New York (NY), USA: Oxford University Press; pp. 213–40.
• Willett WC (2002). Dietary fat plays a major role in obesity: no. Obes Rev. 3(2):59–68. 10.1046/j.1467-789X.2002.00060.x [PubMed: 12120421] [CrossRef]
• Williams SM, Goulding A (2009). Patterns of growth associated with the timing of adiposity rebound. Obesity (Silver Spring). 17(2):335–41. 10.1038/oby.2008.547 [PubMed: 19057527] [CrossRef]
• Williamson DF, Madans J, Anda RF, Kleinman JC, Kahn HS, Byers T (1993). Recreational physical activity and ten-year weight change in a US national cohort. Int J Obes Relat Metab Disord. 17(5):279–86. [PubMed: 8389337]
• Wormser D, Kaptoge S, Di Angelantonio E, Wood AM, Pennells L, Thompson A, et al.; Emerging Risk Factors Collaboration (2011). Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies. Lancet. 377(9771):1085–95. 10.1016/S0140-6736(11)60105-0 [PMC free article: PMC3145074] [PubMed: 21397319] [CrossRef]
• Zimmet P, Magliano D, Matsuzawa Y, Alberti G, Shaw J (2005). The metabolic syndrome: a global public health problem and a new definition. J Atheroscler Thromb. 12(6):295–300. 10.5551/jat.12.295 [PubMed: 16394610] [CrossRef]
• Zoeller RT, Brown TR, Doan LL, Gore AC, Skakkebaek NE, Soto AM, et al. (2012). Endocrine-disrupting chemicals and public health protection: a statement of principles from The Endocrine Society. Endocrinology. 153(9):4097–110. 10.1210/en.2012-1422 [PMC free article: PMC3423612] [PubMed: 22733974] [CrossRef]