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Adult obesity complications: challenges and clinical impact

Saleem ansari.

Clinical Biochemistry, King’s College Hospital, Denmark Hill, London, England SE5 9RS, UK

Hasan Haboubi

Department of Gastroenterology, Guy’s & St Thomas’ Foundation trust, London, England, UK

Nadim Haboubi

Consultant Physician, University of South Wales, Pontypridd, Rhondda Cynon Taff, UK

The complications associated with adult obesity are overwhelming national healthcare systems. No country has yet implemented a successful population-level strategy to reverse the rising trends of obesity. This article presents epidemiological data on the complications of adult obesity and discusses some of the challenges associated with managing this disease at a population and individual level.

Introduction

Adult obesity [body mass index (BMI) >30 kg/m 2 ] was estimated to affect 10.8% of men (266 million) and 14.9% of women (375 million) worldwide in 2014. This has more than doubled when compared with worldwide figures in 1975 where 3.2% of men and 6.4% of women were obese. If this trend persists, by 2025, 18% of men and 21% of women will be obese. 1 Since 2006, the rise in adult obesity has remained stable in many developed countries except for morbid obesity (BMI > 40 kg/m 2 ), which continues to rise 2 ; in developing countries obesity prevalence is rising towards levels seen in the Western world. 3 Indeed, the World Health Organisation (WHO) has set governments across the world the challenge of preventing further rises in obesity by 2025 to meet the overarching aim of preventing premature death from the four most common non-communicable diseases – cardiovascular disease (CVD), diabetes, cancer and chronic respiratory disease. 4

The current review presents epidemiological data pertaining to the complications of adult obesity and some of the challenges associated with managing this disease at a population and individual level.

Obesity, mortality and BMI

Obesity, as defined by BMI ( Table 1 ), is associated with an increased risk of all-cause mortality, with CVD and malignancy being the most common causes of death. 5 – 8 A meta-analysis of 239 prospective studies involving 10.6 million individuals from Asia, Australia, New Zealand, Europe and North America found that all-cause mortality was lowest between a BMI of 20–25 kg/m 2 but increased significantly just below this range and throughout the overweight/obese categories, 8 which suggests a J-shaped relationship between BMI and mortality. Ethnic differences for BMI ranges defining overweight and obesity exist, especially between Caucasian and Asian populations, reflecting the higher risk of cardiometabolic complications at a lower BMI in the latter population ( Table 1 ). 9 Although BMI is the simplest and most common anthropometric method for diagnosing obesity, waist circumference (WC) or waist-to-hip ratio (WHR) may better predict cardiometabolic disease because they are better measures of abdominal obesity. 10 , 11 Combining BMI and WC or WHR will capture total body fat distribution better than BMI alone and may help identify individuals with metabolic syndrome ( Table 2 ) at an earlier stage. Given that individuals frequently know their waist size, this may be a more practical measure to self-report compared with height and weight, which can often be misreported. 12

Adult BMI classification. 13

ClassificationBMI (kg/m )
CaucasianSouth Asian and Chinese
Healthy or ‘normal’ weight18.5–24.918.5–23
Overweight or pre-obesity25–29.923–27.5
Obesity I30–34.9⩾27.5
Obesity II35–39.9
Obesity III⩾40

BMI, body mass index.

Risk factors used in the clinical diagnosis of the metabolic syndrome. 15

MeasureCut-off values
Elevated WC Caucasian: >80 cm in females; >94 cm in males.
Reduced HDL cholesterol<1.3 mmol/l in females; <1 mmol/l in males or on drug therapy to increase HDL
Elevated triglycerdies>1.7 mmol/l or on drug therapy to reduce triglycerdies
Elevated blood pressureSystolic ⩾130 and/or diastolic ⩾85 mm Hg or on anti-hypertensive therapy
Elevated fasting plasma glucose>5.6 mmol/l or on drug therapy for hyperglycaemia

Three risk factors from Table 2 are required for a diagnosis of the metabolic syndrome.

HDL, high-density lipoprotein; IDF, International Diabetes Federation; WC, waist circumference.

Mechanisms by which obesity causes complications

The excess adiposity that characterises obesity can cause complications through anatomical and metabolic effects.

Anatomical effects

Increased adipose tissue can place strain at various body sites leading to obstructive sleep apnoea (OSA), obesity hypoventilation syndrome (OHS) and osteoarthritis, especially of weight bearing joints. 16 – 18 Also, increased intra-abdominal pressure is associated with oesophageal disorders such as gastro-oesophageal reflux disease (GORD) and Barrett’s oesphagus. 19

Subcutaneous adipose tissue is a ‘metabolic sink’ that stores excess calories as triglycerdies through adipocyte hyperplasia and hypertrophy, which protects lean visceral organs such as the heart, kidney, liver and pancreas. However, if subcutaneous adipose tissue capacity is exceeded, hypertrophied adipocytes rupture, triggering inflammation, and triglycerdies are deposited within visceral adipose tissue 20 ; indeed obesity is associated with diastolic heart failure, chronic kidney disease (CKD), non-alcoholic fatty liver disease (NAFLD) and type 2 diabetes mellitus (T2DM). 21

Metabolic effects

Visceral adipose tissue is a potent source of proinflammatory cytokines [tumour necrosis factor alpha (TNF-α), interleukin (IL)-1 and IL-6], which are implicated in cardiometabolic diseases, malignancy and infectious diseases among patients with obesity. 20 Lipid-induced cellular insults (lipotoxicity) due to elevated free fatty acids and lipid intermediates such as ceramides are also implicated in cardiometabolic disorders (e.g. insulin resistance, NAFLD, CVD) that are associated with the metabolic syndrome. 22 Chronic inflammation and endothelial dysfunction are also key mediators linking obesity with CVD. 23

Type 2 diabetes mellitus

Diabetes mellitus affected 8.5% of the adult European population in 2013, which equates to 56.3 million people. 24 Latest figures suggest that 4.7 million people in the United Kingdom (UK) are affected by diabetes (6% of the UK population), of which 90% have T2DM. UK diabetes prevalence is expected to reach 5 million by 2025. 25 ‘Diabesity’ describes the concurrent obesity and T2DM epidemic over the past few decades because the risk of T2DM increases with BMI. A recent population study involving 2.8 million UK adults between 2000 and 2018 showed that a BMI of 30–35 kg/m 2 was associated with a five times increased risk of T2DM, which increased to a 12 times higher risk in those with a BMI of 40–45 km/m 2 . 26 One mechanism linking obesity to T2DM is related to an increase in liver and pancreatic visceral fat, 27 which is better measured by WC or WHR than BMI. Excess hepatic triglycerdies are transported in very low-density lipoproteins to all tissues, including the beta-cells of the pancreas, and over many years this results in progressive pancreatic beta-cell dedifferentiation with a subsequent relatively sudden onset of clinical diabetes. 27 Data from the Counterpoint, Counterbalance and DIRECT studies have demonstrated that remission of T2DM and improvements in liver and pancreatic fat using magnetic resonance imaging were achieved with a very low-calorie diet (600–853 kcal/day) for 8 weeks to achieve weight loss of 15 kg. 28 – 30 These studies demonstrate that remission of T2DM depended primarily on weight loss through reductions in liver and pancreatic visceral fat. 27

Cardiovascular disease

Approximately 17.9 million people die from CVD annually, which accounts for 31% of all deaths worldwide. Ischaemic heart disease and stroke are the two most common causes of mortality worldwide. 31

Coronary heart disease

A case-control study involving 27,000 participants from 52 countries demonstrated that WHR was the strongest predictor of myocardial infarction (MI), independent of age, gender, ethnicity, smoking status or CVD risk factors (hypertension, diabetes, dyslipidaemia). The relationship between BMI and MI was weaker and less consistent across ethnic groups. 32 The EPIC-Norfolk prospective cohort study involving 24,508 UK men and women followed over 9.1 years also found that WHR was more consistently and strongly predictive of coronary heart disease (CHD) after adjusting for BMI, smoking, hypertension and hypercholesterolaemia. 33 Clearly, CHD is strongly associated with obesity but indices of abdominal obesity are better predictors than BMI. 34 The distribution of fat independently mediates the risk between obesity and CHD and this is likely to be due to ectopic visceral fat promoting chronic inflammation, which participates in all stages of atherosclerosis, including acute thrombosis. 35 Indeed, abdominal obesity is the hallmark of the metabolic syndrome ( Table 2 ) which increases cardiometabolic risk. 15

Obesity is associated with an increased risk of stroke but this relationship is stronger and more consistent for ischaemic stroke. A meta-analysis of 25 studies involving 2,247,961 participants from Western and Eastern countries showed that obese individuals (BMI > 30 kg/m 2 ) had a 64% increased risk of ischaemic stroke [relative risk (RR) 1.64, 95% confidence interval (CI) 1.36–1.99] and 24% increased risk of haemorrhagic stroke, which was not significant (RR 1.24, 95% CI 0.99–1.54). 36 The association between obesity and ischaemic stroke is mediated by conventional modifiable CVD risk factors and independent mechanisms related to proinflammatory cytokines, reduced levels of adiponectin and a prothrombotic state (hyperfibrinogenaemia, hyperviscosity), which contribute to endothelial cell dysfunction and atherosclerosis. 37 , 38 The relationship between obesity and haemorrhagic stroke is less consistent. 39

Gastrointestinal complications

There are several gastrointestinal and hepatobiliary complications of obesity ( Table 3 ), many of which are common and present sooner than cardiometabolic disorders. 19 Therefore screening for obesity in patients with gastrointestinal and hepatobiliary disease should be common practice for early weight loss intervention.

Quantified risk ratios and physiological mechanism of selected gastrointestinal diseases associated with obesity. Taken and adapted from Camilleri et al. 19

Gastrointestinal diseaseObesity as a risk factor Physiological mechanism by which obesity is associated with gastrointestinal disease
Risk expressed as OR or RR95% CI
GORDOR, 1.941.46–2.57↑ intra-abdominal pressure, ↓ Oesophageal pressure.
↑ Oestrogen
Erosive oesophagitisOR, 1.871.51–2.31Abdominal adiposity
Barrett’s oesophagusOR, 4.01.4–11.1Abdominal adiposity,
↓ Adiponectin, ↑Leptin
Oesophageal adenocarcinomaMen: OR, 2.4
Women: OR, 2.1
RR, 4.8
1.9–3.2
1.4–3.2
3.0–7.7
Abdominal adiposity, ↓ Adiponectin,
↑ Leptin, Insulin-like growth factor –1 and –2
GastritisOR, 2.231.59–3.11↓ Adiponectin
Gastric cancerOR, 1.55
RR (Cardia), 1.8
1.31–1.84
1.3–2.5
Proinflammatory, adipokines, Insulin-like growth factor –1
NAFLDRR, 4.62.5–11.0Abdominal obesity, ↑ serum free fatty acids, ↑ hepatic triglycerides, hepatic de novo lipogenesis
Liver cirrhosisRR, 4.11.4–11.4Non-alcoholic fatty liver disease, non-alcoholic steatohepatitis, proinflammatory
Hepatocellular carcinomaRR, 1.81.6–2.1Non-alcoholic fatty liver disease, non-alcoholic steatohepatitis, proinflammatory
Gallstone disease (gallstones, cholecystitis)Men: RR 2.51
Women: RR, 2.32
2.16–2.91
1.17–4.57
Abdominal obesity, ↑ Insulin, ↑ leptin, ↑ lipids, insulin resistance, dysmotility
Gallbladder cancerRR, 1.31.2–1.4↑ risk of gallstones, chronic inflammation
Acute pancreatitisRR, 2.201.82–2.66Hyperlipidaemia, chronic inflammation
Pancreatic cancerMen: RR, 1.10
Women: RR, 1.13
RR, 1.5
1.04–1.22
1.05–1.18
1.2–1.8
Insulin-like growth factor binding protein 1
DiarrhoeaOR, 2.71.10–6.8↑ Bile acids, accelerated colonic transit
Diverticular diseaseRR, 1.781.08–2.94Chronic inflammation, alteration in gut microbiota
Colonic polypsOR, 1.441.23–1.70Chronic inflammation
Colorectal cancerMen: RR, 1.95
Women: RR, 1.15
RR, 1.3
1.59–2.39
1.06–1.24
1.3–1.4
Chronic inflammation, ↑ adipokines, bile acids, insulin resistance, gut microbiota

CI, confidence interval; GORD, gastro-oesophageal reflux disease; NAFLD, non-alcoholic fatty liver disease; OR, odds ratio; RR, relative risk.

Non-alcoholic fatty liver disease

NAFLD has an estimated prevalence of 25.2% worldwide and 23.7% in Europe, 40 but the true incidence is difficult to characterise due to different diagnostic criteria between studies. The prevalence of NAFLD has increased over the past four decades alongside the increase in obesity. 40 A meta-analysis of 20 studies (12,065 cases, 33,693 controls), 17 from Asian countries and 3 from Western countries, demonstrated that the odds of NAFLD increased by 3–10% per 1 cm increase in WC and 13–38% per 1-unit increase in BMI. 41 Although both BMI and WC were independently associated with NAFLD, markers of abdominal obesity were stronger predictors and remained associated with NAFLD after adjusting for BMI. This may explain why some patients with a normal BMI can develop NAFLD, which is more commonly seen in rural areas of some Asian countries (25–30%) compared with the United States (US) and Europe (10–20%). 41 Therefore, both BMI and WC or WHR should be used to assess NAFLD risk. NAFLD is considered the hepatic manifestation of the metabolic syndrome, 42 whereas longitudinal studies suggest that NAFLD precedes the metabolic syndrome and T2DM. 43 NAFLD increases the risk of T2DM, hypertension, dyslipidaemia and CKD, and it is no surprise that CVD is the leading cause of mortality among this patient group. 10 , 11 Up to one-third of NAFLD patients are at risk of developing non-alcoholic steatohepatitis (NASH), 44 which can progress to liver cirrhosis, hepatocellular carcinoma (HCC), decompensated liver cirrhosis and death. 45 Therefore, individuals with NAFLD require early weight loss intervention to prevent both cardiovascular- and liver-related morbidity and mortality.

Biliary complications

Obesity increases the risk of gallbladder disease. A systematic review of 17 prospective studies involving 1,921,103 participants found a RR of 1.63 for a 5-unit increase in BMI and a RR of 1.46 for a 10 cm increase in WC. 46 There was an almost two-fold increased risk of gallbladder disease from the lower to the upper limit of the normal BMI range (18.5–24.9 kg/m 2 ), which suggests that even moderate increases in adiposity increase risk. 46 Hormone changes and gallbladder dysmotility are suggested mechanisms to explain the association between obesity and gallbladder disease ( Table 3 ). 19

Oesphageal complications

Obesity is also associated with oesophageal disorders ( Table 3 ). The prevalence of GORD increases with obesity and meta-analyses report a positive association between BMI and GORD. 47 , 48 Central obesity is an independent predictor of the consequences of GORD (oesphagitis, Barrett’s oesphagus, adenocarcinoma). 49

Respiratory

Obstructive sleep apnoea.

Obesity is the most common risk factor for the development of OSA. Observational data from 2.8 million UK adults found that class I and III obesity were associated with a 5-times and 22-times increased risk of OSA, respectively, 26 which suggests that the risk of OSA increases considerably at a higher BMI. Untreated OSA can cause excessive daytime somnolence, negatively affect work performance, increase the risk of CVD and threaten vehicle licence if driving is affected. 50 , 51 Proposed mechanisms linking obesity to OSA include adipokines, upper airway adiposity and increased neck circumference causing pharyngeal collapse. 50

Obesity hypoventilation syndrome

OHS is defined as a combination of obesity (BMI > 30 kg/m 2 ), daytime hypercapnia (pCO 2  > 6 kpa) and sleep disordered breathing that are not due to other conditions associated with alveolar hypoventilation. 17 OHS has an estimated prevalence of 8.5% in patients with OSA and 19–31% among obese patients. 55 , 56 The pathophysiology of OHS may be related to leptin resistance causing central hypoventilation, impaired compensatory response to hypercapnia and impaired respiratory mechanics due to obesity. 57 The morbidity and mortality of OHS is greater than OSA. The chronic daytime hypoxia and hypercapnia increase the risk of pulmonary hypertension, right-sided heart failure and cor pulmonale. 17 Weight loss is recommended for both OSA and OHS, but adherence to lifestyle interventions can be difficult for this cohort because their exercise capacity is limited due to daytime somnolence, fatigue and chronic hypoxia, whereas poor sleep is associated with increased appetite. 16 Pharmacological therapy has not been proven to be effective in OSA and OHS. 16 Bariatric surgery is an effective treatment for OSA and parameters of sleep quality, 58 but data on OHS is limited due to the associated pulmonary and cardiac complications and therefore weight loss in this group of patients with chronic cardiorespiratory disease can be challenging. 17 Presently, no randomised control data exist to support bariatric surgery as an intervention to treat OHS. 59

Obesity increases the risk of asthma in children and adults. Over the past 40 years, there have been parallel increases in childhood obesity and asthma, with asthma prevalence doubling between 1980 and 1994. 52 A meta-analysis of seven prospective epidemiological studies involving 333,102 adult participants found that the prevalence of asthma was 38% in overweight individuals and 92% in obese individuals. 53 Two distinct asthma phenotypes have been described in obese patients; the early-onset allergic form and the late-onset non-allergic form, 54 and weight loss has been associated with improvements in lung function and asthma symptoms among obese patients. 16 The mechanism by which obesity increases asthma risk is unclear but may be related to mechanical, inflammatory and hormonal factors. 52

After smoking, obesity is the second biggest preventable cause of cancer in the UK and maintaining a normal weight could prevent 22,800 annual UK cases. 60 In 2001, the International Agency for Research on Cancer concluded that obesity accounted for 10% of post-menopausal breast cancers and 11% of colon cancers. For kidney, lower oesophageal adenocarcinoma and endometrial cancer, the risks attributed to BMI alone were 25%, 37% and 39%, respectively. 61

A population-based prospective cohort study using data from 5.24 million UK adults concluded that BMI was associated with 17 cancers. 62 Each 5 kg/m 2 increase in BMI was approximately linearly associated with cancer of the uterus, gallbladder, kidney, cervix, thyroid and leukaemia. There was a non-linear but positive association between BMI and liver, colon, ovarian and post-menopausal breast cancer. 62 The authors concluded that the heterogeneity in the effects of BMI on cancer risk suggests that there may be different mechanisms based malignancy type and patient sub-group. 62 Frequently cited mechanisms linking obesity to malignancy include systemic alterations in endogenous hormone metabolism (e.g. insulin, insulin-like growth factor, sex steroids) and chronic inflammation mediated by adipokines. 61

Obesity also impacts cancer prognosis. A meta-analysis of 82 studies involving 213,075 breast cancer patients showed that obesity (BMI > 30 kg/m 2 ) was associated with increased cancer-related mortality. 63 Similarly, the Nurses’ Health Study, which included 5204 patients with non-metastatic breast cancer, showed that weight gain after diagnosis was associated with increased risk of recurrence and breast-cancer specific mortality. 64 Weight loss by diet and physical activity has been shown to reduce the risk of postmenopausal breast cancer; however, evidence for other cancers is less robust. 65

Obesity and cognition

Cardiovascular risk factors such as T2DM, dyslipidaemia and hypertension are well-established complications of obesity that increase the risk of dementia and Alzheimer’s disease. 21 An independent relationship between mid-life obesity and dementia also exists. A meta-analysis of 39 prospective cohort studies analysing data from 1.3 million adults across the US, Europe and Asia found that a high BMI (overweight or obese range) was associated with an increased risk of dementia when BMI was measured 20 years prior to dementia diagnosis, but this relationship was reversed when BMI was measured closer to dementia diagnosis (<10 years). 66 The latter finding could be interpreted as obesity being protective; however, it is likely to be explained by reverse causation and the former finding can be explained by the fact that clinical dementia is preceded by a long (20–30 years) preclinical phase where weight loss is common. 67 , 68

Genitourinary

Obesity is an important preventable risk factor for the development CKD because it is associated with major CKD risk factors: diabetes mellitus and hypertension. 69 A large cohort study accruing over 8 million person-years found that a BMI > 25 was an independent predictor for end-stage renal disease. When compared with normal-weight controls (BMI 18.5–24.9 kg/m 2 ) the RR of end-stage renal disease for overweight individuals was 1.87 (95% CI; 1.64–2.14) and 7.07 (95% CI; 5.37–9.31) for those with class III obesity after adjusting for other CKD risk factors. 69 One proposed independent mechanism linking obesity to CKD is hyperfiltration due to the increased metabolic demands of excess body weight. 70

Between 1986 and 2000, there was a 10-fold increase in obesity-related glomerulopathy, which is characterised by proteinuria, glomerulomegaly, progressive glomerulosclerosis and renal function decline. 71 Short-term improvement is achieved with renin-angiotensin-aldosterone blockade, whereas weight loss through low-calorie dieting or bariatric surgery is associated with improvements in proteinuria and kidney function. 72 A prospective randomised control trial observed that 3 months of endurance and endurance-strength exercise among obese women (BMI 35 kg/m 2 ) was associated with an 10 ml/min/1.73 m 2 improvements in estimated glomerular filtration rate. 73

Obesity can increase the risk of kidney stones, 74 and roux-en-y gastric bypass, an operation used to treat obesity, can also increase the risk of hyperoxaluric kidney stones due to increased enteral oxalate absorption. 75 General and central obesity are both associated with urinary incontinence in men and women, overactive bladder syndrome in women and benign prostatic hyperplasia in men. 76 , 77

Musculoskeletal

Obesity is a well-recognised risk factor for the development and progression of osteoarthritis in weight-bearing joints, especially the knee. 18 There is a 36% increased risk of knee osteoarthritis with every 2 unit increase in BMI and patients with obesity suffer more severe joint degeneration. 78 Both obesity and osteoarthritis can reduce mobility, which can increase the risk of weight gain. In patients with osteoarthritis, weight loss of 10% has been associated with an improvement in joint symptoms, physical function and health related quality of life. 18

Osteoarthritis. Obesity is also associated with osteoarthritis in non-weight bearing joints such as the hands, which is linked to increased levels of adipokines. 79 Similarly, inflammatory markers observed in obesity are also associated with pre-clinical rheumatoid arthritis. 80 Prospective cohort data from the Nurses’ Health Study accruing more than 4,500,000 person-years of follow up showed that excess body weight (BMI > 24.9 kg/m 2 ) was associated with a 40–70% increased risk of rheumatoid arthritis in women, with the highest risk observed in overweight or obese women aged 18 years old. 80 Therefore, interventions that combat childhood obesity may reduce the incidence of adult rheumatoid arthritis.

Obesity has been independently associated with gout. A longitudinal community-based cohort study involving 15,533 men and women demonstrated that the relative risk of gout was almost doubled in those with a BMI > 30 kg/m 2 , and that obesity was associated with earlier onset of the disease. 81 Both gout and obesity are associated with elevated levels of serum uric acid and weight loss has been associated with reduced incidence of hyperuricaemia and gout attacks. 82

Psychosocial

Individuals with obesity are often stigmatised in education, health and employment settings. This results in obesity discrimination, 83 which has increased by 66% over the past decade with prevalence rates comparable with those of race-based discrimination. 84 Discrimination can result in low self-esteem and poor body image, which can negatively impact engagement in physical activity. 85 Obesity is also associated with psychiatric comorbidity. A cross-sectional US epidemiological survey showed that obesity (BMI > 30 kg/m 2 ) was associated with an approximately 25% increased odds of mood and anxiety disorders. 86 Similarly, another US epidemiological study involving 41,654 respondents in the National Epidemiologic Survey on Alcohol and Related Conditions showed that obesity was associated with an increased odds of alcohol use and mood, anxiety, and personality disorders, with odds ratio ranging from 1.28 to 2.08. 87 Increased BMI is also associated with an increased risk of suicidal ideation in women but not in men. 88 , 89

Obesity has a complex aetiology that requires a multifaceted strategy for prevention and treatment at a population and individual level. 90 The social ecological model can provide a framework to help identify the personal and environmental determinants of obesity which can facilitate the development of interventions. 91 Indeed, primary and secondary prevention of obesity requires input and collaboration from multiple bodies, such as the government, policy makers, legislative powers and healthcare system. Figure 1 provides an overview of selected interventions, superimposed onto a modified social ecological model, that have been implemented in different countries. No country has yet implemented a successful population-level strategy to reverse the rising trends of obesity. 1

An external file that holds a picture, illustration, etc.
Object name is 10.1177_2042018820934955-fig1.jpg

Examples of selected interventions used by different countries to prevent and treat obesity displayed on a modified social ecological model.

UK, United Kingdom; US, United States.

The environment is obesogenic. Healthful messages from policy makers are often undermined by advertisements that promote large portions of highly palatable energy-dense processed foods and sugar-sweetened beverages, 92 which are key drivers of obesity. 93 The availability of fast-food outlets around schools may be associated with an increased risk of unhealthy eating patterns and childhood obesity, especially in deprived areas. 94 , 95 This could be curtailed by governments granting local authorities’ the power to restrict take-away outlets, especially close to schools. Furthermore, fast-foods are more readily available to both children and adults at any time of day through ordering via mobile phone applications; however, the implications of this on eating behaviour and childhood obesity remain to be elucidated. Policy makers should strongly consider implementing legislation regarding the age at which take-away foods can be purchased, and responsibility must be shared by local providers of fast-foods to enforce this legislation. 96 , 97 Clearly, labelling the calorie content of takeaway foods may also help consumers opt for more sensible food choices. 93

Obesity impacts the poorest in society. A UK study of 119,669 individuals aged 37–73 found a strong association between higher BMI and lower socioeconomic status, especially in women. 98 Similarly, a US study reported that overweight women are more likely to work in lower paying-jobs than non-overweight women and all men. 99 This health inequality is further compounded by the fact that fast-food availability is greater in areas of higher deprivation. 100 Taxation of unhealthy foods may be one strategy to limit the availability of fast-foods. Indeed, a tax on sugary-sweetened drinks in Mexico led to an average reduction of 7.6% in purchases of these beverages, 101 whilst a 21% reduction in consumption was observed amongst low-income neighbourhoods in California. 102 In the UK, the soft drinks levy has raised money from taxation to invest into physical activity and healthy eating in UK schools, 103 but whether any of these changes will prevent obesity remains to be seen.

Individuals with obesity face a pervasive form of social stigma due to their weight that subjects them to discrimination in employment, education and healthcare. In the workplace, there is a lack of legislation that protects the vast majority of individuals with obesity who experience discrimination. The UK Equality Act (2010) does not specifically prohibit discrimination against obesity 104 and in 2014, the European Court of Justice ruled that being severely overweight could be considered a disability yet obesity per se is not specified as a disabling condition in European Union (EU) employment law. 105 However, some US states have recently introduced legislation that protects against height and weight discrimination, 106 and legislation is a key step to tackling the stigma associated with obesity.

Recognising obesity as a disease rather than a lifestyle choice will address the fallacy that obesity is the fault of the individual due to laziness or gluttony and replace it with scientific knowledge that body weight is maintained within a relatively narrow individualised range by a precise subconscious homeostatic mechanism. 105 , 107 Changing this narrative is fundamental so that patients with obesity receive appropriate treatment because there is evidence that patients with obesity are not receiving appropriate referral to specialist services. Worldwide, 0.1–2% of eligible obese patients undergo bariatric or metabolic surgery. 108 In the UK, access to specialist weight management centres is variable in some areas and absent in others. Only 1% of patients who fulfil the National Institute of Clinical Excellence (NICE) eligibility for bariatric surgery are able to access this service in the UK. 109 Greater awareness of the efficacy and cost-effectiveness of surgical interventions for obesity and morbid obesity as well as pathways to access this service should be easily available for local clinicians so that their patients can receive appropriate treatment. 110 , 111

In 2012, the US Preventative Services Task Force recommended that all adults be screened for obesity and those with a BMI > 30 kg/m 2 should be offered referral for an intensive multicomponent behavioural intervention. 112 Screening may be one way to increase referral to specialist weight management centres and there is good evidence that treating patients with obesity early in their disease course, especially those with T2DM, can prevent or delay complications. 93

Patients with obesity can be challenging to manage because the causes and complications of the disease are patient specific and this requires bespoke management at a specialist multidisciplinary weight management centre. Behavioural interventions are fundamental to lifelong weight management, and unique strategies are required for weight loss, maintenance of weight loss and avoiding weight regain, all of which require motivation and commitment from patients. 93 This can be challenging because patients with obesity often have psychological, psychiatric and medical comorbidities that can negatively impact long-term adherence to behavioural interventions. 93 Data from two large randomised control trials of lifestyle interventions, the Diabetes Prevention Programme and the Look AHEAD trial, 113 , 114 suggest that frequency of patient contact, individualising patient care and face-to-face interventions were important predictors of weight loss. In a separate study, patients who attended group sessions every other week for 1 year after weight loss maintained 13 kg of their initial 13.2 kg weight loss, 115 which suggests that regular group sessions may prevent weight regain. However, implementing behavioural interventions can be difficult due to a lack of resources and time. Remotely delivered behavioural programmes via telephone or the internet are alternative approaches that may be more easily accessible and affordable. Patients who received 20 weight loss intervention phone calls over 6 months lost an average of 4.9 kg; those who received 10 calls lost 3.2 kg and those who were self-directed lost 2.3 kg. 116 In another study, patients who received 24 weekly bespoke weight loss sessions via email in addition to internet resources lost 4.4 kg after 1 year when compared with a group receiving internet resources only who lost 2.0 kg. 117 Despite their popularity, little is known about the effectiveness of smart-phone applications for weight management and therefore more research is needed.

Obesity is a multisystem disease that increases the risk of the most common non-communicable chronic diseases of the 21st century. 21 , 57 The population is developing obesity at a younger age and it is likely that these individuals will suffer morbidity for longer. 2 , 118 This will be challenging for clinicians because the symptom and disease burden from multi-organ impairment can become irreversible without timely intervention. Early identification of individuals with obesity through simple anthropometric measurements should be a priority for prompt interventions to prevent morbidity and the associated healthcare and economic costs. 119

Tackling obesity requires a whole systems approach. Governments and policy makers, rather than individuals, have the ability to change the food environment through regulation, taxation and restricting the availability of high-calorie processed foods to adults and children. Patients with obesity who face weight-based discrimination deserve policies and legislation that aim to prevent weight-based inequality. This will help change the current narrative that patients with obesity are to blame for their disease, which fuels a pervasive form of social stigma. Replacing this fallacy with scientific knowledge can prevent discrimination and facilitate referral to specialist weight management centres where a multidisciplinary team can provide bespoke patient care.

Author contribution(s): Saleem Ansari: Conceptualization; Writing-original draft; Writing-review & editing.

Nadim Haboubi: Conceptualization; Formal analysis; Supervision; Visualization; Writing-review & editing.

Conflict of interest statement: The authors declare that there is no conflict of interest.

Funding: The authors received no financial support for the research, authorship, and/or publication of this article.

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Object name is 10.1177_2042018820934955-img1.jpg

Contributor Information

Saleem Ansari, Clinical Biochemistry, King’s College Hospital, Denmark Hill, London, England SE5 9RS, UK.

Hasan Haboubi, Department of Gastroenterology, Guy’s & St Thomas’ Foundation trust, London, England, UK.

Nadim Haboubi, Consultant Physician, University of South Wales, Pontypridd, Rhondda Cynon Taff, UK.

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  • Published: 27 February 2019

Obesity: global epidemiology and pathogenesis

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Nature Reviews Endocrinology volume  15 ,  pages 288–298 ( 2019 ) Cite this article

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  • Health policy
  • Pathogenesis

The prevalence of obesity has increased worldwide in the past ~50 years, reaching pandemic levels. Obesity represents a major health challenge because it substantially increases the risk of diseases such as type 2 diabetes mellitus, fatty liver disease, hypertension, myocardial infarction, stroke, dementia, osteoarthritis, obstructive sleep apnoea and several cancers, thereby contributing to a decline in both quality of life and life expectancy. Obesity is also associated with unemployment, social disadvantages and reduced socio-economic productivity, thus increasingly creating an economic burden. Thus far, obesity prevention and treatment strategies — both at the individual and population level — have not been successful in the long term. Lifestyle and behavioural interventions aimed at reducing calorie intake and increasing energy expenditure have limited effectiveness because complex and persistent hormonal, metabolic and neurochemical adaptations defend against weight loss and promote weight regain. Reducing the obesity burden requires approaches that combine individual interventions with changes in the environment and society. Therefore, a better understanding of the remarkable regional differences in obesity prevalence and trends might help to identify societal causes of obesity and provide guidance on which are the most promising intervention strategies.

Obesity prevalence has increased in pandemic dimensions over the past 50 years.

Obesity is a disease that can cause premature disability and death by increasing the risk of cardiometabolic diseases, osteoarthritis, dementia, depression and some types of cancers.

Obesity prevention and treatments frequently fail in the long term (for example, behavioural interventions aiming at reducing energy intake and increasing energy expenditure) or are not available or suitable (bariatric surgery) for the majority of people affected.

Although obesity prevalence increased in every single country in the world, regional differences exist in both obesity prevalence and trends; understanding the drivers of these regional differences might help to provide guidance for the most promising intervention strategies.

Changes in the global food system together with increased sedentary behaviour seem to be the main drivers of the obesity pandemic.

The major challenge is to translate our knowledge of the main causes of increased obesity prevalence into effective actions; such actions might include policy changes that facilitate individual choices for foods that have reduced fat, sugar and salt content.

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World Health Organization. Noncommunicable diseases progress monitor, 2017. WHO https://www.who.int/nmh/publications/ncd-progress-monitor-2017/en/ (2017).

Fontaine, K. R., Redden, D. T., Wang, C., Westfall, A. O. & Allison, D. B. Years of life lost due to obesity. JAMA 289 , 187–193 (2003).

PubMed   Google Scholar  

Berrington de Gonzalez, A. et al. Body-mass index and mortality among 1.46 million white adults. N. Engl. J. Med. 363 , 2211–2219 (2010).

CAS   PubMed   Google Scholar  

Prospective Studies Collaboration. Body-mass index and cause-specific mortality in 900000 adults: collaborative analyses of 57 prospective studies. Lancet 373 , 1083–1096 (2009).

PubMed Central   Google Scholar  

Woolf, A. D. & Pfleger, B. Burden of major musculoskeletal conditions. Bull. World Health Organ. 81 , 646–656 (2003).

PubMed   PubMed Central   Google Scholar  

Bray, G. A. et al. Obesity: a chronic relapsing progressive disease process. A position statement of the World Obesity Federation. Obes. Rev. 18 , 715–723 (2017).

World Health Organization. Obesity and overweight. WHO https://www.who.int/mediacentre/factsheets/fs311/en/ (2016).

World Health Organization. Political declaration of the high-level meeting of the general assembly on the prevention and control of non-communicable diseases. WHO https://www.who.int/nmh/events/un_ncd_summit2011/political_declaration_en.pdf (2012).

Franco, M. et al. Population-wide weight loss and regain in relation to diabetes burden and cardiovascular mortality in Cuba 1980-2010: repeated cross sectional surveys and ecological comparison of secular trends. BMJ 346 , f1515 (2013).

Swinburn, B. A. et al. The global obesity pandemic: shaped by global drivers and local environments. Lancet 378 , 804–814 (2011).

Yanovski, J. A. Obesity: Trends in underweight and obesity — scale of the problem. Nat. Rev. Endocrinol. 14 , 5–6 (2018).

Heymsfield, S. B. & Wadden, T. A. Mechanisms, pathophysiology, and management of obesity. N. Engl. J. Med. 376 , 254–266 (2017).

Murray, S., Tulloch, A., Gold, M. S. & Avena, N. M. Hormonal and neural mechanisms of food reward, eating behaviour and obesity. Nat. Rev. Endocrinol. 10 , 540–552 (2014).

Farooqi, I. S. Defining the neural basis of appetite and obesity: from genes to behaviour. Clin. Med. 14 , 286–289 (2014).

Google Scholar  

Anand, B. K. & Brobeck, J. R. Hypothalamic control of food intake in rats and cats. Yale J. Biol. Med. 24 , 123–140 (1951).

CAS   PubMed   PubMed Central   Google Scholar  

Zhang, Y. et al. Positional cloning of the mouse obese gene and its human homologue. Nature 372 , 425–432 (1994).

Coleman, D. L. & Hummel, K. P. Effects of parabiosis of normal with genetically diabetic mice. Am. J. Physiol. 217 , 1298–1304 (1969).

Farooqi, I. S. & O’Rahilly, S. 20 years of leptin: human disorders of leptin action. J. Endocrinol. 223 , T63–T70 (2014).

Börjeson, M. The aetiology of obesity in children. A study of 101 twin pairs. Acta Paediatr. Scand. 65 , 279–287 (1976).

Stunkard, A. J., Harris, J. R., Pedersen, N. L. & McClearn, G. E. The body-mass index of twins who have been reared apart. N. Engl. J. Med. 322 , 1483–1487 (1990).

Montague, C. T. et al. Congenital leptin deficiency is associated with severe early-onset obesity in humans. Nature 387 , 903–908 (1997).

Farooqi, I. S. et al. Effects of recombinant leptin therapy in a child with congenital leptin deficiency. N. Engl. J. Med. 341 , 879–884 (1999).

Clément, K. et al. A mutation in the human leptin receptor gene causes obesity and pituitary dysfunction. Nature 392 , 398–401 (1998).

Farooqi, I. S. et al. Dominant and recessive inheritance of morbid obesity associated with melanocortin 4 receptor deficiency. J. Clin. Invest. 106 , 271–279 (2000).

Krude, H. et al. Severe early-onset obesity, adrenal insufficiency and red hair pigmentation caused by POMC mutations in humans. Nat. Genet. 19 , 155–157 (1998).

Hebebrand, J., Volckmar, A. L., Knoll, N. & Hinney, A. Chipping away the ‘missing heritability’: GIANT steps forward in the molecular elucidation of obesity - but still lots to go. Obes. Facts 3 , 294–303 (2010).

Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42 , 937–948 (2010).

Sharma, A. M. & Padwal, R. Obesity is a sign - over-eating is a symptom: an aetiological framework for the assessment and management of obesity. Obes. Rev. 11 , 362–370 (2010).

Berthoud, H. R., Münzberg, H. & Morrison, C. D. Blaming the brain for obesity: integration of hedonic and homeostatic mechanisms. Gastroenterology 152 , 1728–1738 (2017).

Government Office for Science. Foresight. Tackling obesities: future choices – project report. GOV.UK https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/287937/07-1184x-tackling-obesities-future-choices-report.pdf (2007).

World Health Organization. International Statistical Classification of Diseases and Related Health Problems 10th revision. WHO http://apps.who.int/classifications/icd10/browse/2010/en (2010).

Hebebrand, J. et al. A proposal of the European Association for the Study of Obesity to improve the ICD-11 diagnostic criteria for obesity based on the three dimensions. Obes. Facts 10 , 284–307 (2017).

Ramos Salas, X. et al. Addressing weight bias and discrimination: moving beyond raising awareness to creating change. Obes. Rev. 18 , 1323–1335 (2017).

Sharma, A. M. et al. Conceptualizing obesity as a chronic disease: an interview with Dr. Arya Sharma. Adapt. Phys. Activ Q. 35 , 285–292 (2018).

Hebebrand, J. et al. “Eating addiction”, rather than “food addiction”, better captures addictive-like eating behavior. Neurosci. Biobehav. Rev. 47 , 295–306 (2014).

Phelan, S. M. et al. Impact of weight bias and stigma on quality of care and outcomes for patients with obesity. Obes. Rev. 16 , 319–326 (2015).

Kushner, R. F. et al. Obesity coverage on medical licensing examinations in the United States. What is being tested? Teach Learn. Med. 29 , 123–128 (2017).

NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. Lancet 390 , 2627–2642 (2017).

NCD Risk Factor Collaboration (NCD-RisC). 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 , 1377–1396 (2016).

Organisation for Economic Co-operation and Development. Obesity update 2017. OECD https://www.oecd.org/els/health-systems/Obesity-Update-2017.pdf (2017).

Geserick, M. et al. BMI acceleration in early childhood and risk of sustained obesity. N. Engl. J. Med. 379 , 1303–1312 (2018).

Ezzati, M. & Riboli, E. Behavioral and dietary risk factors for noncommunicable diseases. N. Engl. J. Med. 369 , 954–964 (2013).

Kleinert, S. & Horton, R. Rethinking and reframing obesity. Lancet 385 , 2326–2328 (2015).

Roberto, C. A. et al. Patchy progress on obesity prevention: emerging examples, entrenched barriers, and new thinking. Lancet 385 , 2400–2409 (2015).

Lundborg, P., Nystedt, P. & Lindgren, B. Getting ready for the marriage market? The association between divorce risks and investments in attractive body mass among married Europeans. J. Biosoc. Sci. 39 , 531–544 (2007).

McCabe, M. P. et al. Socio-cultural agents and their impact on body image and body change strategies among adolescents in Fiji, Tonga, Tongans in New Zealand and Australia. Obes. Rev. 12 , 61–67 (2011).

Hayashi, F., Takimoto, H., Yoshita, K. & Yoshiike, N. Perceived body size and desire for thinness of young Japanese women: a population-based survey. Br. J. Nutr. 96 , 1154–1162 (2006).

Hardin, J., McLennan, A. K. & Brewis, A. Body size, body norms and some unintended consequences of obesity intervention in the Pacific islands. Ann. Hum. Biol. 45 , 285–294 (2018).

Monteiro, C. A., Conde, W. L. & Popkin, B. M. Income-specific trends in obesity in Brazil: 1975–2003. Am. J. Public Health 97 , 1808–1812 (2007).

Mariapun, J., Ng, C. W. & Hairi, N. N. The gradual shift of overweight, obesity, and abdominal obesity towards the poor in a multi-ethnic developing country: findings from the Malaysian National Health and Morbidity Surveys. J. Epidemiol. 28 , 279–286 (2018).

Gebrie, A., Alebel, A., Zegeye, A., Tesfaye, B. & Ferede, A. Prevalence and associated factors of overweight/ obesity among children and adolescents in Ethiopia: a systematic review and meta-analysis. BMC Obes. 5 , 19 (2018).

Rokholm, B., Baker, J. L. & Sørensen, T. I. The levelling off of the obesity epidemic since the year 1999 — a review of evidence and perspectives. Obes. Rev. 11 , 835–846 (2010).

Hauner, H. et al. Overweight, obesity and high waist circumference: regional differences in prevalence in primary medical care. Dtsch. Arztebl. Int. 105 , 827–833 (2008).

Myers, C. A. et al. Regional disparities in obesity prevalence in the United States: a spatial regime analysis. Obesity 23 , 481–487 (2015).

Wilkinson, R. G. & Pickett, K. The Spirit Level: Why More Equal Societies Almost Always Do Better 89–102 (Bloomsbury Press London, 2009).

Sarget, M. Why inequality is fatal. Nature 458 , 1109–1110 (2009).

Plachta-Danielzik, S. et al. Determinants of the prevalence and incidence of overweight in children and adolescents. Public Health Nutr. 13 , 1870–1881 (2010).

Bell, A. C., Ge, K. & Popkin, B. M. The road to obesity or the path to prevention: motorized transportation and obesity in China. Obes. Res. 10 , 277–283 (2002).

Ludwig, J. et al. Neighborhoods, obesity, and diabetes — a randomized social experiment. N. Engl. J. Med. 365 , 1509–1519 (2011).

Beyerlein, A., Kusian, D., Ziegler, A. G., Schaffrath-Rosario, A. & von Kries, R. Classification tree analyses reveal limited potential for early targeted prevention against childhood overweight. Obesity 22 , 512–517 (2014).

Reilly, J. J. et al. Early life risk factors for obesity in childhood: cohort study. BMJ 330 , 1357 (2005).

Kopelman, P. G. Obesity as a medical problem. Nature 404 , 635–643 (2000).

CAS   Google Scholar  

Bouchard, C. et al. The response to long-term overfeeding in identical twins. N. Engl. J. Med. 322 , 1477–1482 (1990).

Sadeghirad, B., Duhaney, T., Motaghipisheh, S., Campbell, N. R. & Johnston, B. C. Influence of unhealthy food and beverage marketing on children’s dietary intake and preference: a systematic review and meta-analysis of randomized trials. Obes. Rev. 17 , 945–959 (2016).

Gilbert-Diamond, D. et al. Television food advertisement exposure and FTO rs9939609 genotype in relation to excess consumption in children. Int. J. Obes. 41 , 23–29 (2017).

Frayling, T. M. et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316 , 889–894 (2007).

Loos, R. J. F. & Yeo, G. S. H. The bigger picture of FTO-the first GWAS-identified obesity gene. Nat. Rev. Endocrinol. 10 , 51–61 (2014).

Wardle, J. et al. Obesity associated genetic variation in FTO is associated with diminished satiety. J. Clin. Endocrinol. Metab. 93 , 3640–3643 (2008).

Tanofsky-Kraff, M. et al. The FTO gene rs9939609 obesity-risk allele and loss of control over eating. Am. J. Clin. Nutr. 90 , 1483–1488 (2009).

Hess, M. E. et al. The fat mass and obesity associated gene (Fto) regulates activity of the dopaminergic midbrain circuitry. Nat. Neurosci. 16 , 1042–1048 (2013).

Fredriksson, R. et al. The obesity gene, FTO, is of ancient origin, up-regulated during food deprivation and expressed in neurons of feeding-related nuclei of the brain. Endocrinology 149 , 2062–2071 (2008).

Cohen, D. A. Neurophysiological pathways to obesity: below awareness and beyond individual control. Diabetes 57 , 1768–1773 (2008).

Richard, D. Cognitive and autonomic determinants of energy homeostasis in obesity. Nat. Rev. Endocrinol. 11 , 489–501 (2015).

Clemmensen, C. et al. Gut-brain cross-talk in metabolic control. Cell 168 , 758–774 (2017).

Timper, K. & Brüning, J. C. Hypothalamic circuits regulating appetite and energy homeostasis: pathways to obesity. Dis. Model. Mech. 10 , 679–689 (2017).

Kim, K. S., Seeley, R. J. & Sandoval, D. A. Signalling from the periphery to the brain that regulates energy homeostasis. Nat. Rev. Neurosci. 19 , 185–196 (2018).

Cutler, D. M., Glaeser, E. L. & Shapiro, J. M. Why have Americans become more obese? J. Econ. Perspect. 17 , 93–118 (2003).

Löffler, A. et al. Effects of psychological eating behaviour domains on the association between socio-economic status and BMI. Public Health Nutr. 20 , 2706–2712 (2017).

Chan, R. S. & Woo, J. Prevention of overweight and obesity: how effective is the current public health approach. Int. J. Environ. Res. Public Health 7 , 765–783 (2010).

Hsueh, W. C. et al. Analysis of type 2 diabetes and obesity genetic variants in Mexican Pima Indians: marked allelic differentiation among Amerindians at HLA. Ann. Hum. Genet. 82 , 287–299 (2018).

Schulz, L. O. et al. Effects of traditional and western environments on prevalence of type 2 diabetes in Pima Indians in Mexico and the US. Diabetes Care 29 , 1866–1871 (2006).

Rotimi, C. N. et al. Distribution of anthropometric variables and the prevalence of obesity in populations of west African origin: the International Collaborative Study on Hypertension in Blacks (ICSHIB). Obes. Res. 3 , 95–105 (1995).

Durazo-Arvizu, R. A. et al. Rapid increases in obesity in Jamaica, compared to Nigeria and the United States. BMC Public Health 8 , 133 (2008).

Hu, F. B., Li, T. Y., Colditz, G. A., Willett, W. C. & Manson, J. E. Television watching and other sedentary behaviors in relation to risk of obesity and type 2 diabetes mellitus in women. JAMA 289 , 1785–1791 (2003).

Rissanen, A. M., Heliövaara, M., Knekt, P., Reunanen, A. & Aromaa, A. Determinants of weight gain and overweight in adult Finns. Eur. J. Clin. Nutr. 45 , 419–430 (1991).

Zimmet, P. Z., Arblaster, M. & Thoma, K. The effect of westernization on native populations. Studies on a Micronesian community with a high diabetes prevalence. Aust. NZ J. Med. 8 , 141–146 (1978).

Ulijaszek, S. J. Increasing body size among adult Cook Islanders between 1966 and 1996. Ann. Hum. Biol. 28 , 363–373 (2001).

Snowdon, W. & Thow, A. M. Trade policy and obesity prevention: challenges and innovation in the Pacific Islands. Obes. Rev. 14 , 150–158 (2013).

McLennan, A. K. & Ulijaszek, S. J. Obesity emergence in the Pacific islands: why understanding colonial history and social change is important. Public Health Nutr. 18 , 1499–1505 (2015).

Becker, A. E., Gilman, S. E. & Burwell, R. A. Changes in prevalence of overweight and in body image among Fijian women between 1989 and 1998. Obes. Res. 13 , 110–117 (2005).

Swinburn, B., Sacks, G. & Ravussin, E. Increased food energy supply is more than sufficient to explain the US epidemic of obesity. Am. J. Clin. Nutr. 90 , 1453–1456 (2009).

Swinburn, B. A. et al. Estimating the changes in energy flux that characterize the rise in obesity prevalence. Am. J. Clin. Nutr. 89 , 1723–1728 (2009).

US Department of Agriculture. Food availability (per capita) data system. USDA https://www.ers.usda.gov/data-products/food-availability-per-capita-data-system/ (updated 29 Oct 2018).

Carden, T. J. & Carr, T. P. Food availability of glucose and fat, but not fructose, increased in the U.S. between 1970 and 2009: analysis of the USDA food availability data system. Nutr. J. 12 , 130 (2013).

Hall, K. D., Guo, J., Dore, M. & Chow, C. C. The progressive increase of food waste in America and its environmental impact. PLOS ONE 4 , e7940 (2009).

Scarborough, P. et al. Increased energy intake entirely accounts for increase in body weight in women but not in men in the UK between 1986 and 2000. Br. J. Nutr. 105 , 1399–1404 (2011).

McGinnis, J. M. & Nestle, M. The Surgeon General’s report on nutrition and health: policy implications and implementation strategies. Am. J. Clin. Nutr. 49 , 23–28 (1989).

Krebs-Smith, S. M., Reedy, J. & Bosire, C. Healthfulness of the U.S. food supply: little improvement despite decades of dietary guidance. Am. J. Prev. Med. 38 , 472–477 (2010).

Malik, V. S., Popkin, B. M., Bray, G. A., Després, J. P. & Hu, F. B. Sugar-sweetened beverages, obesity, type 2 diabetes mellitus, and cardiovascular disease risk. Circulation 121 , 1356–1364 (2010).

Schulze, M. B. et al. Sugar-sweetened beverages, weight gain, and incidence of type 2 diabetes in young and middle-aged women. JAMA 292 , 927–934 (2004).

Mozaffarian, D., Hao, T., Rimm, E. B., Willett, W. C. & Hu, F. B. Changes in diet and lifestyle and long-term weight gain in women and men. N. Engl. J. Med. 364 , 2392–2404 (2011).

Malik, V. S. & Hu, F. B. Sugar-sweetened beverages and health: where does the evidence stand? Am. J. Clin. Nutr. 94 , 1161–1162 (2011).

Qi, Q. et al. Sugar-sweetened beverages and genetic risk of obesity. N. Engl. J. Med. 367 , 1387–1396 (2012).

Heiker, J. T. et al. Identification of genetic loci associated with different responses to high-fat diet-induced obesity in C57BL/6N and C57BL/6J substrains. Physiol. Genomics 46 , 377–384 (2014).

Wahlqvist, M. L. et al. Early-life influences on obesity: from preconception to adolescence. Ann. NY Acad. Sci. 1347 , 1–28 (2015).

Rohde, K. et al. Genetics and epigenetics in obesity. Metabolism . https://doi.org/10.1016/j.metabol.2018.10.007 (2018).

Article   PubMed   Google Scholar  

Panzeri, I. & Pospisilik, J. A. Epigenetic control of variation and stochasticity in metabolic disease. Mol. Metab. 14 , 26–38 (2018).

Ruiz-Hernandez, A. et al. Environmental chemicals and DNA methylation in adults: a systematic review of the epidemiologic evidence. Clin. Epigenet. 7 , 55 (2015).

Quarta, C., Schneider, R. & Tschöp, M. H. Epigenetic ON/OFF switches for obesity. Cell 164 , 341–342 (2016).

Dalgaard, K. et al. Trim28 haploinsufficiency triggers bi-stable epigenetic obesity. Cell 164 , 353–364 (2015).

Michaelides, M. et al. Striatal Rgs4 regulates feeding and susceptibility to diet-induced obesity. Mol. Psychiatry . https://doi.org/10.1038/s41380-018-0120-7 (2018).

Article   PubMed   PubMed Central   Google Scholar  

Weihrauch-Blüher, S. et al. Current guidelines for obesity prevention in childhood and adolescence. Obes. Facts 11 , 263–276 (2018).

Nakamura, R. et al. Evaluating the 2014 sugar-sweetened beverage tax in Chile: An observational study in urban areas. PLOS Med. 15 , e1002596 (2018).

Colchero, M. A., Molina, M. & Guerrero-López, C. M. After Mexico implemented a tax, purchases of sugar-sweetened beverages decreased and water increased: difference by place of residence, household composition, and income level. J. Nutr. 147 , 1552–1557 (2017).

Brownell, K. D. & Warner, K. E. The perils of ignoring history: Big Tobacco played dirty and millions died. How similar is Big Food? Milbank Q. 87 , 259–294 (2009).

Mialon, M., Swinburn, B., Allender, S. & Sacks, G. ‘Maximising shareholder value’: a detailed insight into the corporate political activity of the Australian food industry. Aust. NZ J. Public Health 41 , 165–171 (2017).

Peeters, A. Obesity and the future of food policies that promote healthy diets. Nat. Rev. Endocrinol. 14 , 430–437 (2018).

Hawkes, C., Jewell, J. & Allen, K. A food policy package for healthy diets and the prevention of obesity and diet-related non-communicable diseases: the NOURISHING framework. Obes. Rev. 14 (Suppl. 2), 159–168 (2013).

World Health Organisation. Global database on the Implementation of Nutrition Action (GINA). WHO https://www.who.int/nutrition/gina/en/ (2012).

Popkin, B., Monteiro, C. & Swinburn, B. Overview: Bellagio Conference on program and policy options for preventing obesity in the low- and middle-income countries. Obes. Rev. 14 (Suppl. 2), 1–8 (2013).

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