{
  "id": "obesity",
  "title": "Obesity",
  "lang": "en",
  "source_lang": "en",
  "summary": "Obesity is a chronic, multifactorial condition of excess body fat that raises the risk of many serious diseases and is a major global public health problem.",
  "category": "health",
  "subcategory": "diseases",
  "type": "concept",
  "aliases": [
    "Corpulence"
  ],
  "generated_at": "2026-10-08",
  "model": "pro/claude-haiku-5-5",
  "url": "https://aiwiki.page/en/obesity/",
  "translations": {
    "zh-Hans": {
      "url": "https://aiwiki.page/zh/obesity/",
      "json": "https://aiwiki.page/zh/obesity.json"
    }
  },
  "sections": [
    {
      "heading": "",
      "text": "Obesity is a chronic condition in which excess body fat accumulates to a degree that can impair health. It is studied in medicine, epidemiology and metabolism, and its causes span genes, hormones, behavior, food systems and social conditions. Its most common clinical measure is body mass index (BMI), with a value of 30 or more used as the adult cutoff. Because BMI is an imperfect proxy for body fat, diagnosis increasingly also considers waist measurements and whether organs or daily function are affected. Obesity matters because it is a major risk factor for type 2 diabetes, cardiovascular disease, several cancers and other chronic disorders. Its global prevalence has risen sharply since 1990, and it is now treated as a central problem of public health as well as an individual medical condition.\n\nThis entry first covers how obesity is defined and measured, including its historical development and the 2025 clinical-obesity framework. It then examines the scale of the problem, its causes at the genetic, neurohormonal, environmental and social levels, and the biology of adipose tissue. The health consequences are treated in two sections: cardiometabolic complications, and other physical and psychological effects, with a separate section on children and adolescents. The final sections address clinical management (lifestyle, drug and surgical treatment), prevention policy, weight stigma and the classification debate, economic costs, and open research questions."
    },
    {
      "heading": "Definition and anthropometric measures",
      "text": "Obesity is generally understood as excess adiposity, meaning fat accumulation large enough to threaten health. Body weight is a poor stand-in for this because it adds together fat, lean tissue, bone and water. Two people of the same weight can carry very different amounts of fat, and the location of that fat matters as much as the total. Clinical measures are therefore proxies. The body mass index is a surrogate marker of fatness and additional measurements, such as the waist circumference, can help the diagnosis of obesity.\n\nBody mass index\n\nThe most common proxy is the body mass index (BMI), calculated from measured weight and height: \\[\\text{BMI} = \\frac{\\text{weight (kg)}}{\\text{height (m)}^2}\\] For adults, overweight is a BMI greater than or equal to 25, and obesity is a BMI greater than or equal to 30. The U.S. Centers for Disease Control and Prevention further divides obesity into classes, with BMI 30 to 35 as class 1, 35 to 40 as class 2, and 40 and above as class 3 (The U.S. Centers for Disease Control and Prevention (CDC) further subdivides obesity based on BMI).\n\nThe measure has clear limits. WHO itself describes BMI as a rough guide because it may not correspond to the same degree of fatness in different individuals. It does not always relate to central obesity and it cannot differentiate muscle mass from bone and fat mass. Muscular athletes may therefore be classed as overweight, and people with low muscle mass and high fat may be missed. The WHO wording has also shifted over time. A 2006 fact sheet described BMI as the same for both sexes and all ages of adults, whereas the current fact sheet states that BMI categories vary by age and sex, while still listing the same adult cut-offs.\n\nWaist-based measures\n\nWaist circumference is used to estimate abdominal fat, which is more closely linked to visceral fat and cardiometabolic risk. Recent evidence has shown that excess fat stored in this area raises greater cardiovascular risk than fat stored in other areas of the body. Waist-to-height ratio, the waist circumference divided by height, is often preferred because it is a relatively constant anthropometric index of abdominal obesity across different age, sex or racial groups. One study of 81 adults proposed cut-points of 0.53 for men and 0.54 for women for whole-body obesity, and 0.59 for abdominal obesity in both sexes; that study is small, and the cut-points have not been widely validated. Waist-to-hip ratio has been recommended by some, but it has been found to be a poor predictor of fitness.\n\nThe added value of these measures is contested. A large cohort analysis found that replacing BMI with waist-based measures did not meaningfully improve risk identification for multiple long-term conditions when BMI was 30 or above. Waist measures appeared more useful in people with a BMI below 30. In children, a consensus group noted that the waist–height ratio could be more useful for the classification of abdominal obesity than waist circumference alone, although in adults the ratio changes only when waist size changes, since height is effectively fixed.\n\nReference standards for children and adolescents\n\nIn children, the BMI categories for defining overweight and obesity vary by age and sex, so adult cut-offs cannot be applied directly. For children aged 5 to 19, WHO defines overweight as BMI-for-age greater than 1 standard deviation above the WHO Growth Reference median, and obesity as more than 2 standard deviations above that median. For children under 5, the criterion is weight-for-height greater than 2 standard deviations above WHO Child Growth Standards median for overweight, and more than 3 standard deviations for obesity. Other national systems use sex- and age-specific percentiles instead; in one U.S. study, children were classed as overweight at or above the 85th percentile of BMI, using age-, race- and sex-specific percentiles. Results can differ between these systems, so the chosen reference matters when comparing prevalence figures.\n\nEthnic-specific thresholds\n\nThe same BMI does not carry the same risk everywhere. The WHO Expert Consultation of 2004 kept the 25 and 30 cut-offs but added further potential public health action points (23.0, 27.5, 32.5, and 37.5 kg/m²). The reason is that, at a given BMI, Asian adults generally have more body fat. The consultation estimated that a BMI of about 27 in Chinese and Malay adults, and about 26 in Indian adults, corresponds to the same body fat as a BMI of 30 in Europeans. The evidence on where risk begins is also uneven: the cut-off point for observed risk varies from 22 kg/m2 to 25 kg/m2 in different Asian populations; for high risk it varies from 26 kg/m2 to 31 kg/m2. Consequently, many guidelines based on BMI now adjust downward the benchmarks for overweight in Asian populations. Comparable population-specific thresholds for other ancestry groups are less well established, and the choice of cut-off remains a practical as well as a scientific question."
    },
    {
      "heading": "Historical development of the concept",
      "text": "Descriptions of heavy bodies long predate any formal way of measuring them. The Upper Paleolithic Venus figurines, of which the Venus of Willendorf is the best known, are often read as depicting generalized obesity, central adiposity, and large waist circumference, although their meaning is disputed. Some researchers have tied the figurines to periods of severe food shortage, arguing that abundant fat may have been valued as an ideal rather than seen as a health problem the figures were indeed obese, but this represented a type of beauty standard or an ideal at the time. In classical antiquity, physicians treated corpulence as a clinical matter: it was sufficiently common in ancient Greek, Roman, and Byzantine societies that physicians developed guidelines for clinical treatment. The Hippocratic tradition is commonly cited for the idea that excess fat is a disease in itself.\n\nThe quantitative tradition began in the 1830s with the Belgian statistician Adolphe Quetelet, whose interest lay elsewhere. He compared the growth of newborns, children and adults, and in his 1835 Treatise on Man established the rule that W is approximately proportional to H² in adults. His aim was to describe the \"average man\" as a social ideal, not to assess individual health. Quetelet himself never intended for the index, then called the Quetelet Index, to be used as a means of medical assessment. His samples were also narrow. All the participants were western European men, and much of the data came from military men in France and Scotland. Later eugenicists drew on his idealization of the average man, which is why the index carries a troubling origin alongside its scientific use Quetelet's idealization of the average man would be elaborated upon by Francis Galton a decade later in the development of eugenics.\n\nThe index became a medical tool only in the 20th century. In 1972 Ancel Keys and colleagues, in a study published in the Journal of Chronic Diseases, compared several height-weight indices against skinfold and underwater-weighing measures of body fat in over 7400 healthy men in five countries. They found the Quetelet ratio performed best and proposed calling it the body mass index \"if not fully satisfactory, at least as good as any other relative weight index as an indicator of relative obesity\". Its clinical reach then grew as thresholds were revised: cut-off values have gotten more stringent over the years and have led to more people being labeled obese. The weight-based risk classification that insurers used earlier had a similar trajectory. The quest for a practical index of relative body weight began shortly after actuaries reported the increased mortality of their overweight policyholders, so the search for a usable index was driven partly by mortality data from life insurance.\n\nAcross the late 20th century, obesity moved from being framed mainly as a matter of appearance or personal discipline toward a medical condition, though the change was gradual. Obesity itself cannot be considered an illness was language in the US Medicare coverage manual that remained until 2004, and in the early 1990s, obesity was not well defined in terms of either BMI or percentage body fat. Lay and clinical explanations had emphasized behavior: there was a long-held belief that obesity was just a lifestyle problem. Institutional change followed in 1998, when the National Heart, Lung and Blood Institute released the first clinical practice guidelines for obesity, and the National Institutes of Health had declared obesity a disease in 1998. The most visible step came in 2013, when the AMA recognized obesity as a \"disease state with multiple pathophysiological aspects requiring a range of interventions to advance obesity treatment and prevention\". The AMA was not the first body to take this position this decision was significant as the AMA is one of the most influential healthcare organizations in the United States, and the classification has remained contested, including over whether it reduces weight stigma."
    },
    {
      "heading": "Clinical and preclinical obesity",
      "text": "In January 2025, The Lancet Diabetes & Endocrinology published a consensus report from a 58-member international commission, chaired by Francesco Rubino, that proposed a new framework for deciding when excess adiposity counts as a disease. The commission comprises 58 experts from around the world, and among these commissioners were people with lived experience of obesity to ensure consideration of patients' perspectives.\n\nDefinition\n\nThe commission defines obesity as a condition of excess adiposity, which may or may not come with abnormal fat distribution or fat-tissue function. Its causes are multifactorial and still incompletely understood. The framework then divides people with obesity into two groups on the basis of whether fat excess has already produced measurable harm. The authors chose this split pragmatically, because health and illness are usually treated as distinct states in clinical practice, even though obesity should be biologically conceived of as a continuum.\n\nClinical and preclinical categories\n\nClinical obesity is a long-term, body-wide condition in which fat excess has already altered the workings of tissues, organs or the whole organism. Its features include signs and symptoms of ongoing dysfunction of one or more organ systems, or impairment of day-to-day activities, attributable solely to excess adiposity. The commission thus treats it as a disease, not merely a risk marker.\n\nPreclinical obesity describes excess fat without present functional harm. Such people carry a increased risk of future disease, but they are not yet ill. Preclinical obesity is not in itself a chronic disease, and people with preclinical obesity have a variable level of health risk.\n\nThe 2025 framework uses concrete tests to separate the two groups. Clinical obesity requires one or both of the following main criteria: evidence of reduced organ or tissue function due to obesity, or substantial, age-adjusted limits on daily activities such as bathing, dressing, toileting and eating. The commission sets out 18 diagnostic criteria for clinical obesity in adults and 13 in children and adolescents. Examples include raised blood pressure, pauses in breathing while asleep due to upper airway obstruction, recurrent urinary incontinence, and fatty liver. Each adult or pediatric criterion is sufficient on its own to establish the diagnosis, once other causes have been excluded.\n\nDiagnostic sequence\n\nThe first step is to confirm excess adiposity rather than rely on BMI alone. Excess fat can be shown by direct measurement by dual energy X-ray absorptiometry (DXA) or bioimpedance, by at least one anthropometric criterion in addition to BMI, or by at least two anthropometric criteria regardless of BMI. Waist circumference, waist-to-hip ratio and waist-to-height ratio are the anthropometric options, and they are interpreted with age-, sex- and ethnicity-appropriate cut-offs. A BMI of 40 or above is treated as sufficient evidence of excess fat without further testing. Once excess adiposity is confirmed, physicians should determine whether obesity is clinical or pre-clinical through formal clinical evaluation. That evaluation includes history, examination, and standard laboratory tests of metabolic, liver and renal status.\n\nImplications for management\n\nThe framework is designed to change what happens after diagnosis. People with clinical obesity should be considered as having an ongoing chronic disease and receive appropriate management and treatments. The goal is improvement (or remission, when possible) of clinical manifestations of obesity and prevent progression to end-organ damage. For preclinical obesity, the emphasis falls on prevention: evidence-based health counselling, monitoring of their health status over time, and, when applicable, appropriate intervention to reduce risk of developing clinical obesity, scaled to individual risk.\n\nThe approach also sharpens the picture of risk. In an analysis of NHANES 2017–2018 data, more than 80% of adults with confirmed obesity met the clinical criteria, and this group had a markedly higher risk of cardiovascular disease and type 2 diabetes than adults without obesity. Adults with preclinical obesity showed no excess cardiovascular risk, but their type 2 diabetes risk was markedly raised. In a separate study, a nine-month lifestyle programme reduced the share of participants classified as clinically obese from 71% to 57%. Among US youth aged 5–18 in NHANES 2017–2023, the weighted prevalence was 8.3% for preclinical and 12.5% for clinical obesity.\n\nStaging and limitations\n\nStaging systems, such as the Edmonton Obesity Staging System, grade people by comorbidity and functional limitation, and the Commission's model shares this logic. Proposals to extend staging to biomedical and psychosocial factors are still developing. Critics note several gaps. No representative data exist on the prevalence of preclinical and clinical obesity in adults, and the criteria do not yet separate visceral from subcutaneous fat. Standards for fat distribution in children are also lacking. Some paediatric clinicians have warned that the new criteria could have unintended consequences for young people, and the clinical burden implied by the framework remains uncertain."
    },
    {
      "heading": "Prevalence and global trends",
      "text": "Most population estimates of obesity are built on BMI thresholds, so they count people above a cut-off rather than measuring adiposity directly. Researchers note that this approach understates the disease burden linked to excess fat. Under the 2025 Lancet framework, which defines obesity by tissue or organ dysfunction rather than BMI alone, the estimated prevalence is markedly higher. (Prevalence figures below therefore reflect BMI-based definitions unless stated otherwise.)\n\nGlobal figures for adults and children\n\nThe WHO estimated that about one in seven people worldwide, roughly 1.1 billion, were living with obesity in 2024. Among adults aged 18 and over, about 940 million had obesity, or 16% of the adult population, and 45% were overweight, including obesity. Adult obesity has more than doubled since 1990. Pooled analyses from the NCD Risk Factor Collaboration give a similar picture: the age-standardised rate among women rose from 8.8% in 1990 to 18.5% in 2022, and among men from 4.8% to 14.0%. Small differences between compilations of the 2022 figure (roughly 880 to 890 million adults) reflect differing input data and modelling choices.\n\nChildhood obesity has risen even faster in relative terms. In 1990, about 2% of children and adolescents aged 5–19 (31 million) had obesity. By 2024, 400 million in this age group were overweight, including 170 million with obesity, and the combined overweight-and-obesity prevalence had climbed from 8% to 20%. Among children under five, about 35 million were overweight in 2024, and nearly half of them lived in Asia. The number of overweight under-fives in Africa grew by almost 12% between 2000 and 2024.\n\nRegional variation and sex differences\n\nRegional patterns differ considerably. In the WHO European Region, almost 60% of adults are overweight or obese, with a higher share among men (63%) than women (54%). In the African Region, South Africa had the highest adult obesity rate in 2022, at around three in ten adults, and child obesity roughly doubled between 2010 and 2022, from 2.92% to 5.27%. Overweight, once viewed mainly as a problem of high-income countries, is now increasing in low- and middle-income countries. Sex differences vary by age: in 2024, 21% of boys and 19% of girls aged 5–19 were overweight. Among adults, the difference between men and women is small in the WHO's overall figures. Data for the Americas and South-East Asia are not reproduced here in detail, and the regional gaps there should be read alongside national surveys.\n\nSocioeconomic patterns also differ by setting. In the European Region, adult obesity is more common among people with lower educational attainment, while in several middle-income countries the association between parental education and child overweight is reversed. Globally, obesity now outnumbers underweight in 128 countries among women and 104 among men. The global share of adults who are underweight has roughly halved since 1990.\n\nHigh-income countries: the United States\n\nIn the United States, NHANES data for August 2021 to August 2023 show that 40.3% of adults aged 20 and over had obesity, with no significant difference between men (39.2%) and women (41.3%). Prevalence peaked in middle age: 46.4% among adults aged 40–59, compared with 35.5% at ages 20–39 and 38.9% at 60 and over. Adults with a bachelor's degree or higher were less likely to have obesity than those with less education. Severe obesity, at 9.4%, has risen steadily; its age-adjusted prevalence increased from 7.7% in 2013–2014 to 9.7% in 2021–2023, while overall obesity did not change significantly over the same period. A further 31.7% of adults were classed as overweight.\n\nProjections and the changing burden\n\nThe GBD 2021 forecast projects that nearly two in three adults over 25 will be overweight or obese by 2050. A separate Bayesian analysis of NCD-RisC data projects a steep rise in adult obesity, from 16.06% in 2022 to 38.96% by 2040, although projections of this kind carry wide uncertainty. The same analysis expects the share of the global obesity burden located in the poorest countries (by GDP per capita) to rise from 6% in 1990 to 26% by 2040. As underweight declines and overweight and obesity rise, many countries are shifting from a burden dominated by undernutrition to one dominated by excess weight, often within the same population."
    },
    {
      "heading": "Genetic and epigenetic factors",
      "text": "Heritability from twin and family studies\n\nTwin and family designs have long been the main way to estimate how much of the variation in body size is inherited. An early study of about 4,000 male twin pairs found that identical twins resembled each other in BMI about twice as often as fraternal twins did, and it put BMI heritability near 0.77 at age 20 and 0.84 at a 25-year follow-up. Height, weight, and body mass index (BMI) were assessed in a sample of 1974 monozygotic and 2097 dizygotic male twin pairs. Classic twin methods estimated a high heritability for height, weight, and BMI, both at age 20 years (.80, .78, and .77, respectively) and at a 25-year follow-up (.80, .81, and .84, respectively).\n\nPooled twin estimates for BMI sit in a broad band, from about 50% to 90%, clustering around 70% to 80%. Adoption and family designs usually return lower figures. One review reports that family studies generally report parent-offspring and sibling correlations consistent with heritabilities of 20 to 80%, while adoption data are consistent with genetic factors accounting for 20 to 60% of the variation in BMI. The gap partly reflects the way shared household environments are handled. Twin designs can overstate heritability when shared environmental effects are present, and most twin studies report no substantial common environmental effect, which has the potential to inflate heritability estimates if untrue.\n\nPolygenic architecture and genome-wide association studies\n\nGenome-wide association studies (GWAS) moved the field from inferring genetic influence to locating specific variants. The first clear signals came from common variants at only two loci, FTO and MC4R, which had been reproducibly associated with BMI in humans. A large meta-analysis then identified six additional loci, TMEM18, KCTD15, GNPDA2, SH2B1, MTCH2 and NEGR1, where a 45-kb deletion polymorphism is a candidate causal variant. Each variant has a small effect. Common variants at these loci are associated with modest effects on BMI (0.2–0.4 kg/m² per allele), which translate into odds ratios of 1.1–1.3 for obesity.\n\nSubsequent work has shown that the genetic architecture is highly polygenic, with hundreds or thousands of variants each contributing little. This leaves a \"missing heritability\" problem: the variants found so far explain only a small part of the trait's heritability. A childhood study using genome-wide complex trait analysis, which estimates additive effects of common SNPs across the genome, found that 37% of the twin-estimated heritability (30/82%) can be explained by additive effects of multiple common SNPs. The remainder is thought to reflect rare variants, gene–gene and gene–environment interactions, and measurement limitations.\n\nRare monogenic forms and the leptin–melanocortin pathway\n\nRare single-gene forms of obesity show how the brain controls appetite. Monogenic obesity, caused by single-gene mutations, primarily affects the leptin-melanocortin pathway, which regulates hunger and satiety. The most common monogenic form of obesity is associated with mutations in the MC4R gene, followed by mutations in the LEPR, POMC, PCSK1, and LEP genes. In the UK, the prevalence of loss of function MC4R variants is estimated at 1 in 340.\n\nThe circuit explains these phenotypes. Leptin is released by fat cells, and POMC neurons express leptin receptors (LEPRs) on their cell surface, which can be activated by the anorexigenic, fat-derived hormone, leptin. Activated POMC neurons produce a peptide that activates the MC4R receptor in the paraventricular nucleus resulting in a satiety signal. Competing NPY/AgRP neurons produce molecules that act as potent inhibitors of MC4R signaling. Loss-of-function mutations at any step weaken this satiety signal. Loss-of-function mutations of MC4R result in increased food intake and decreased energy expenditure, as well as severe early-onset obesity in many human populations. Clinically, these disorders are distinguished by early onset, severe hyperphagia and, in some cases, specific treatments. Recombinant leptin is used for leptin-deficient patients, and the MC4R agonist setmelanotide is used for some LEPR, POMC and PCSK1 deficiencies. Estimates suggest that the number of affected people in the United States is larger than the number diagnosed. Few of these variants have been genetically diagnosed to date.\n\nCommon MC4R variants also have a modest population-level effect. A study of adults and children found that common variants near MC4R influence fat mass, weight and obesity risk at the population level. Variants in FTO and MC4R have also been linked to poorer outcomes after bariatric surgery, including diminished weight loss post-surgery, an increased likelihood of weight regain, and reduced metabolic enhancement.\n\nEvolutionary explanations\n\nEvolutionary arguments propose that genes for efficient energy storage would have been favoured under conditions of irregular food supply. The best-known version is the thrifty genotype hypothesis, proposed by the geneticist James Neel in 1962. It suggests that alleles promoting rapid fat deposition conferred survival advantages in populations that experienced cycles of feast and famine, but that the same alleles predispose to obesity in environments with abundant, energy-dense food. The hypothesis is difficult to test directly. Critics note that the rapid rise in obesity prevalence over recent decades is too fast to be explained by changes in gene frequencies, and that genetic drift and founder effects offer alternative explanations for population differences. Most researchers therefore treat it as a framework for interpreting gene–environment mismatch rather than as a settled account of natural selection on body fat.\n\nEpigenetic mechanisms and developmental programming\n\nEpigenetic mechanisms, including DNA methylation and histone modification, alter how genes are read without changing the DNA sequence. They provide a way for environmental exposures to influence metabolism. Early evidence came from animal models: in the viable yellow agouti mouse, maternal diets rich in methyl donors changed the methylation state of a regulatory element and shifted coat colour and body weight in offspring. In humans, the Dutch Hunger Winter of 1944–45 offers the best-known example. Individuals exposed to famine in utero showed altered methylation at the IGF2 locus decades later, suggesting that prenatal nutrition can leave lasting marks on gene expression. Similar links have been proposed for maternal obesity, gestational diabetes and early-life growth patterns, although many findings come from small cohorts and the causal direction is often uncertain. Whether such marks are transmitted across generations in humans remains an open question."
    },
    {
      "heading": "Environmental, economic and social drivers",
      "text": "Obesity is driven by an environment that makes energy-dense food cheap and abundant and makes physical effort optional, and it is patterned by income, education, and sleep. Genetic susceptibility sets how strongly a person responds to these conditions, but it does not explain the rapid rise in prevalence over a few decades, which points to environmental change.\n\nThe food environment is the most-studied driver. Ultra-processed foods are typically calorie-dense and low in nutrients, and they have been linked to type 2 diabetes, obesity and other chronic conditions. Their spread is attributed to high availability, low cost and aggressive marketing. A Brazilian analysis is often cited as a clear example: household food purchases showed less salt, sugar and cooking oil even as obesity rose, because people were replacing home-cooked meals with ready-made foods, drinks and snacks high in fat, salt and sugar. Experimental evidence supports a causal role for diet composition. In a small randomized trial published in 2019, participants assigned to an ultra-processed diet ate about 500 more calories per day and gained about 2 pounds more than those on the unprocessed diet over two weeks. Spatial studies show a similar pattern at the neighbourhood level. A meta-analysis found that outlets selling mostly unhealthy and ultra-processed foods were associated with higher obesity levels, while fruit and vegetable availability and supermarket access were linked to lower levels. The same authors caution that zoning regulation of food outlets alone may not be enough.\n\nPhysical activity has declined alongside food change. Occupations have shifted toward seated work, and many people now commute by car rather than on foot or by bicycle. Urban layouts that separate housing, shops and workplaces, and that offer few safe walking routes or green spaces, make routine movement less likely. These effects are difficult to isolate because diet and activity change together, and because the same urban areas often offer both more fast food and fewer recreational spaces.\n\nSocioeconomic position shapes exposure to these conditions, and its relation to obesity differs by setting. In the United States, a CDC analysis of 2011–2014 survey data found that age-adjusted obesity prevalence among adults was lower in the highest household income group (31.2%) than in the other groups (40.8% and 39.0%). Education showed a similar gradient, with college graduates at 27.8% against 40.6% for those with some college and 40.0% for those with a high school education or less. The pattern is not uniform. Among women, obesity prevalence fell steadily as income rose, while no difference appeared between the lowest and highest income groups among men, and the authors describe the association as complex and differing by sex and by race or Hispanic origin. Among older adults, obesity remained high across income levels among Black seniors. Researchers have also documented that the inverse relationship between obesity and income in the United States strengthened over three decades, with the correlation in 2015 substantially stronger than in 1990. The reversal is not universal across countries; in many low-income settings, obesity is more common among wealthier groups, and the gradient may differ between high-income and low-income countries.\n\nSleep and stress interact with the food environment. Insufficient sleep very likely increases food intake, while there is little support for the idea that it reduces energy expenditure. In an experimental setting, sleep deprivation increased daily caloric consumption by an average of 385 calories in a meta-analysis. Proposed mechanisms include hormonal change, with sleep loss affecting ghrelin, which stimulates appetite, and leptin, which signals satiety, along with a hedonic pathway in which tired people seek rewarding foods. Extra waking hours also create more time available for eating. Sleep disadvantage is itself socially patterned, and sleep deficiency among lower socioeconomic groups may carry greater obesity risk. Stress has a similar role; stress has been associated with a stronger emotional reward from palatable foods, which can contribute to weight gain.\n\nLong-run historical change underlies these proximate drivers. Industrialization moved much of the population from physically demanding agricultural and manual work to sedentary employment and, later, to motorized transport. Globalization spread the distribution networks, refined ingredients and marketing that make processed foods widely available. Researchers describe a nutritional transition in which the global diet has moved away from minimally processed foods toward ultra-processed alternatives. The timing also matters for sleep: shorter sleep became a common behaviour in industrialized countries, developing over the same decades in which obesity and diabetes prevalence rose sharply. Because these transitions affected entire populations within a few generations, they are generally used to explain why genetic variation alone cannot account for the change in prevalence."
    },
    {
      "heading": "Appetite regulation: brain and hormones",
      "text": "Hypothalamic circuits\n\nEnergy balance is coordinated largely in the hypothalamus. The arcuate nucleus of the hypothalamus (ARC) is probably the region studied in greatest detail. Within it, two neuronal populations have opposing effects: the orexigenic NPY/AgRP neurons and the anorexigenic CART/POMC neurons. The NPY/AgRP cells stimulate eating and lower energy expenditure, while the POMC cells suppress intake; the NPY/AgRP cells are inhibited by leptin and insulin. The arcuate is also responsive to a wide range of hormones and nutrients, including leptin, insulin, gonadal steroids and glucose. These neurons connect to other hypothalamic nuclei and to extrahypothalamic brain regions, which allows a coordinated behavioural response. The same circuitry governs glucose metabolism, thirst and blood pressure as well as food intake, so disruption at this level has effects beyond appetite.\n\nGut hormones: initiating and ending meals\n\nThe gut contributes a second layer of signals. Among gut hormones, ghrelin stimulates appetite and food intake, while all other known gut hormones have anorectic effects, promoting satiation (meal termination) and/or satiety (postponing the next meal). Ghrelin is the only orexigenic hormone of peripheral origin found so far. Its plasma level rises for one to two hours before a meal and falls to trough levels about 30 minutes after the meal ends, and it acts on the growth hormone secretagogue receptor on NPY/AgRP neurons in the arcuate nucleus. Its strongest effect appears to be on starting a meal rather than on how much is eaten once feeding has begun; ghrelin is considered a more potent initiator of eating than a regulator of intake once feeding has started.\n\nGlucagon-like peptide-1 (GLP-1) works in the opposite direction. It is released mainly from enteroendocrine L cells in response to meals and functions as a satiety signal. GLP-1 reaches the brain through vagal pathways and directly activates anorexigenic POMC neurons in the arcuate. Its satiating effect depends on the vagus nerve: subdiaphragmatic vagal deafferentation prevents GLP-1 from inhibiting food intake. Stretch receptors in the gut add to this, since distension of the intestine activates mechanoreceptors that contribute to stretch-induced meal termination. Ghrelin and GLP-1 also interact at the level of the vagal afferent neurons, where ghrelin prevented the GLP-1-induced calcium response, which suggests that the two signals compete for control of the meal's timing and length.\n\nLeptin, insulin and feedback loops\n\nLeptin, produced by adipose tissue, is the principal long-term signal of energy stores. It exerts negative feedback on energy intake, and it primarily acts on hypothalamic neurons to activate catabolic pathways and inhibit anabolic ones. Insulin, released by the pancreas, reports on recent energy availability through similar hypothalamic pathways. Together these signals are meant to hold body weight near a set point: rising fat stores raise leptin, which reduces intake and increases expenditure.\n\nThis feedback often fails in obesity. People with obesity usually have high circulating leptin, yet leptin loses the ability to inhibit energy intake and increase energy expenditure; this is termed leptin resistance. Several mechanisms have been proposed, including hyperleptinemia, impaired JAK2-STAT3 signalling, reduced blood-brain barrier permeability, endoplasmic reticulum stress, inflammation and decreased leptin receptor expression. The clinical consequence is that treatment with this hormone in human obesity has been unsuccessful. Resistance is also self-reinforcing, because the obese state itself worsens leptin signalling. Weight loss creates a parallel problem: compensatory responses during weight loss tend to push appetite back up, which helps explain why losses are often partly reversed.\n\nReward and dopamine pathways\n\nHomeostatic signals are not the only input to eating. Insulin and leptin also act on the midbrain dopamine reward system, and researchers have studied how these hormones modulate the energy homeostasis role of insulin and leptin, particularly in the midbrain DA reward circuit. Palatable foods, especially those rich in fat and sugar, activate mesolimbic dopamine pathways running from the ventral tegmental area to the nucleus accumbens. Cues associated with food, such as sight and smell, can trigger dopamine release before any food is consumed, which helps explain why eating is driven by anticipation and pleasure as well as by hunger.\n\nIn people with obesity, this reward system may be more strongly engaged by energy-dense food, and some imaging studies have reported altered dopamine receptor availability in the striatum, though the findings are not uniform and causal direction remains debated. Reward signals can override homeostatic ones, so a person may eat when not hungry, and the environment of constant access to such foods makes this easier. The interaction between reward circuits and the homeostatic hormones described above is an active research area, and it is one reason that appetite is treated as a system with several interacting controls rather than a single switch."
    },
    {
      "heading": "Adipose tissue and metabolic dysfunction",
      "text": "Adipose tissue was once viewed as a passive reservoir in which surplus energy was simply stored. Current physiology treats it as a signalling and immune organ. Its secretory output includes hormones, growth factors and cytokines, which are collectively called adipokines and modulate insulin signalling, glucose uptake and fatty acid oxidation White adipose tissue is the major fat-storing depot and also serves as the largest endocrine organ to secrete adipokines and cytokines systemically. Adipokines are involved in various metabolic and physiological signaling cascades and regulate insulin signaling, glucose uptake, fatty acid oxidation. The discovery of leptin, the first adipocyte-derived signal identified after the cloning of the obese gene, established this endocrine role. Leptin acts on the hypothalamus to reduce food intake and raise energy expenditure, but in most cases of obesity leptin resistance limits its biological efficacy. Adiponectin moves in the opposite direction: its secretion is often diminished in obesity. Adiponectin acts to increase insulin sensitivity, fatty acid oxidation, as well as energy expenditure and reduces the production of glucose by the liver.\n\nAdipose tissue is also an immune organ. Like the macrophage, the adipocyte is capable of linking the innate and adaptive immune system through the secretion of adipokines and cytokines. In obesity, macrophages that infiltrate the tissue become a major source of TNF-α and IL-6, and their output reaches the circulation, where it leads to a chronic low-grade inflammatory state that has been linked to the development of insulin resistance and diabetes. Dysfunctional tissue also activates intracellular inflammatory pathways such as NF-κB and the NLRP3 inflammasome Inflammation in ATs is characterized by macrophage infiltration and the activation of inflammatory pathways mediated by NF-κB, JNK, and NLRP3 inflammasomes. Although this inflammation is low-grade, it can affect distant organs, which is considered a major route to obesity-related complications Despite its low-grade nature, adipose tissue inflammation negatively impacts remote organ function, a phenomenon that is considered causative of the complications of obesity.\n\nAdipocyte hypertrophy and hyperplasia\n\nFat tissue grows in two ways. Existing adipocytes can enlarge, which is hypertrophy, or new adipocytes can arise from precursor cells, which is hyperplasia Individual WAT depots expand by enlarging existing adipocytes (adipocyte hypertrophy) or forming new adipocytes (adipocyte hyperplasia). The balance between the two matters. The adipose tissue expandability hypothesis proposes that when subcutaneous tissue cannot take up surplus energy, lipid is deposited in non-adipose organs and contributes to insulin resistance and type 2 diabetes. In one analysis, greater hypertrophy was tied to fewer adipocytes and more insulin resistance, whereas hyperplastic expansion was linked to better insulin sensitivity greater hypertrophy was linked to lower adipocyte number and insulin resistance, independent of sex and adiposity, while greater hyperplastic expansion was associated with better insulin sensitivity. Enlarged cells also cause local problems. Adipocyte hypertrophy creates areas of local AT microhypoxia at the earliest stages of expansion, and this hypoxia helps recruit macrophages into the tissue.\n\nThe pattern differs by depot and sex. In high-fat-fed male mice, the intra-abdominal epididymal depot grew by both hypertrophy and hyperplasia, while the subcutaneous inguinal depot grew almost entirely by hypertrophy Epididymal WAT, representing an intra-abdominal depot, expands in high-fat diet–fed (HFD-fed) adult male mice through both adipocyte hypertrophy and hyperplasia, while the inguinal (subcutaneous) depot in these same animals expands almost exclusively through cellular hypertrophy. In human women, women seem to be able to expand both abdominal and lower body SAT by hyperplasia to a larger extent than can men.\n\nSubcutaneous and visceral fat\n\nAbdominal fat has two principal compartments. Visceral fat lies under your belly muscles and around your organs. Compared with other depots, visceral fat shows high rates of both lipogenesis and lipolysis Visceral fat has increased metabolic activity, both lipogenesis and lipolysis, compared to other fat depots.. The classic explanation for its harm is portal drainage: free fatty acids released from visceral triglycerides reach the liver directly lipolysis of visceral adipose tissue triglycerides releases FFAs into the portal vein, which are then delivered directly to the liver. This explanation has been challenged. Visceral fat accounts for only a small share of systemic free fatty acid flux, with most coming from non-splanchnic adipose tissue Visceral fat contributes to only 15% of the total systemic free fatty acids whereas the majority of free fatty acids are contributed by nonsplanchnic adipose tissue.\n\nEmpirical findings are mixed. In the Framingham Heart Study, the odds of insulin resistance rose more steeply per standard deviation of visceral fat (OR 3.5) than of subcutaneous fat (OR 2.5) The odds ratio (OR) for insulin resistance per standard deviation increase in SAT was 2.5 (95% confidence interval (CI): 2.2–2.7; P < 0.0001), whereas the OR for insulin resistance per standard deviation increase in VAT was 3.5. Other studies have found subcutaneous fat to be the better predictor in obese individuals several studies have demonstrated that subcutaneous fat, not visceral fat, is the best predictor of insulin resistance in obese individuals. The contrast is clearest at the extremes: some women with very high BMI remain insulin sensitive Imaging studies show these individuals to have low levels of visceral and ectopic fat but a high subcutaneous fat content, whereas people with lipodystrophy who cannot store subcutaneous fat develop severe insulin resistance.\n\nEctopic fat, insulin resistance and dyslipidemia\n\nWhen storage capacity in adipose tissue is exceeded, lipid accumulates in the liver, skeletal muscle and other organs. This ectopic and visceral fat deposition is linked to greater cardiometabolic risk than subcutaneous fat. Elevated circulating free fatty acids impair insulin action. They inhibit the ability of insulin to stimulate muscle glucose uptake and to suppress hepatic glucose production. Lipid overflow into liver and muscle also promotes dyslipidemia, with excess free fatty acids feeding hepatic lipid synthesis and gluconeogenesis.\n\nIn a study of healthy people, liver fat was among the strongest predictors of low insulin sensitivity high liver and visceral fat emerged as the strongest predictors of low insulin sensitivity. Liver fat may also precede diabetes; gain of liver fat appears to precede development of T2DM in most individuals, whereas muscle insulin resistance appears to be a longer-standing and earlier abnormality. The liver may in turn affect distant tissues: one proposed mechanism involves a liver-derived hormone that reduces insulin sensitivity in muscle and liver itself We previously isolated hepatokine selenoprotein P, which is overproduced under an overnutrition state and causes IR both in the liver and skeletal muscle. Adipokine balance adds a further layer: obesity raises pro-inflammatory mediators such as leptin, resistin, chemerin and PAI-1, while lowering protective factors such as adiponectin and omentin obesity is associated with elevated levels of pro-inflammatory adipokines (e.g., leptin, resistin, chemerin, progranulin, RBP4, WISP1, FABP4, PAI-1, Follistatin-like1, MCP-1, SPARC, SPARCL1, and SAA) and reduced levels of anti-inflammatory adipokines such as adiponectin, omentin, ZAG, SFRP5, CTRP3, vaspin, and IL-10. Some individual adipokines, such as visfatin, still have contested roles in glucose metabolism the function of visfatin remains controversial, with conflicting data regarding its role in glucose metabolism and inflammation."
    },
    {
      "heading": "Cardiometabolic complications",
      "text": "Type 2 diabetes\n\nExcess adiposity affects glucose control mainly through insulin resistance, in which muscle, liver and fat tissue respond poorly to insulin. When the pancreas can no longer secrete enough insulin to compensate, Type 2 Diabetes develops. Diabetes risk rises steadily across the whole BMI range rather than starting at the obesity threshold of 30 kg/m². A higher body mass index is a strong predictor of type 2 diabetes, with a linear increase in risk across the whole spectrum of BMI. Because overweight people are far more numerous, most future cases come from the group with a BMI between 25 and 30, not from the group with obesity.\n\nWaist circumference adds predictive information beyond BMI. In the EPIC-InterAct analysis, BMI and waist circumference were each independently associated with type 2 diabetes, with waist circumference being a stronger risk factor in women than in men. Among overweight people, a large waist identified a subgroup whose 10-year cumulative incidence of type 2 diabetes was similar to that of obese people. The association is strongest where BMI is lowest. In the EPIC-Potsdam cohort, men of low or normal weight with a large waist circumference had a relative risk of 3.62, at least as high as that of overweight men with a small waist. These patterns matter for South Asian populations, in whom individuals develop metabolic complications at lower BMI values as a result of greater visceral adiposity and reduced muscle mass.\n\nRemission is possible, though not universal. Sustained weight loss, achieved through intensive dietary programmes or bariatric surgery, can return glycaemia to the non-diabetic range in a substantial share of patients, most often when the disease is recent and insulin secretion is still largely preserved. Relapse is common when weight is regained.\n\nHypertension, atherosclerosis and coronary heart disease\n\nObesity raises blood pressure through overlapping routes. Adipose-derived signals and sympathetic overactivity disturb the balance between vessel-relaxing and vessel-constricting influences. Excessive reactive oxygen species reduce nitric oxide bioavailability by impairing endothelial nitric oxide synthase function, leading to vascular inflammation and impaired vasodilation. Persistently raised pressure then promotes structural remodelling of the heart, since systemic hypertension, which often accompanies obesity, facilitates development of LV dilatation and hypertrophy.\n\nAtherosclerosis is driven by both indirect and direct effects of adiposity. Obesity clusters with insulin resistance, atherogenic dyslipidaemia and hypertension, all established risk factors. Obesity contributes indirectly to atherosclerotic CVD via increasing the incidence of other CV risk factors, and obesity contributes directly to atherosclerotic CVD via promoting atherogenesis and thrombosis. Lesions appear early: a study performed on young patients showed that atherosclerosis begins several decades before manifested coronary artery disease. Visceral fat is especially active. In visceral obesity, inflammatory adipocytokines (e.g., TNF-α, IL-6, MCP-1, leptin, and resistin) rise to higher levels. These molecules feed the inflammation that drives plaque formation, and the same cytokine signals promote a prothrombotic state.\n\nIn a large cohort, severe obesity (body mass index ≥35 versus normal weight) was associated with a hazard ratio of 2.00 for coronary heart disease, and further adjustment for traditional mediators fully explained the association of higher body mass index with CHD and stroke. Thus much of the coronary risk operates through the conventional risk factors that obesity generates.\n\nStroke and heart failure\n\nThe relationship with stroke is graded. One recent meta-analysis including two million participants suggests that there is a graded positive relationship between obesity and incident ischemic stroke. In the same cohort analysis, the hazard ratio for stroke in severe obesity was 1.75.\n\nHeart failure shows the strongest association of the cardiovascular subtypes. The link between obesity and HF was stronger than those for other CVD subtypes and was uniquely unexplained by traditional risk factors. In the Framingham data, there was an increase in the risk of heart failure of 5 percent for men and 7 percent for women for each increment of 1 in body-mass index, and elevated body-mass index was associated with an increased risk of heart failure, without evidence of a threshold. Metabolic status modifies this risk: in a pooled analysis, metabolically unhealthy individuals with obesity had a relative risk of 2.28 compared with normal-weight, metabolically healthy controls, whereas metabolically healthy obesity carried 1.52. Structural and rhythm changes contribute as well. Obesity increases SCD risk independently of HF, and for each 5-unit increase of BMI, the risk for SCD enhances by 16%.\n\nObservational studies also describe an \"obesity paradox,\" in which outcomes seem to be better in obese persons as compared with lean persons among patients who already have heart failure. Its mechanisms remain unclear, and it does not weaken the case for preventing HF in the first place.\n\nNon-alcoholic fatty liver disease\n\nFatty liver disease, now generally termed MASLD (metabolic dysfunction-associated steatotic liver disease), is closely tied to adiposity and insulin resistance. MAFLD is associated to both peripheral and hepatic insulin resistance, and especially its advanced clinical manifestation of NASH exacerbates hepatic and peripheral insulin resistance, inducing atherogenic dyslipidemia and releasing pro-inflammatory factors. The liver is therefore both a site of metabolic injury and a source of systemic signals. Several studies provide evidence for an association between MAFLD and atherosclerosis and cardio-metabolic disorders, including CVDs such as coronary heart disease and stroke.\n\nIts clinical significance lies partly in its role as a marker of wider cardiometabolic risk. MAFLD subjects have an elevated risk of cardiomyopathies such as left ventricular systolic and/or diastolic contractile dysfunction and hypertrophy, which may lead to heart failure over time, as well as arrhythmias such as atrial fibrillation. Progression from simple steatosis to inflammation and fibrosis, and eventually to cirrhosis, is the principal hepatic concern, although the cardiovascular and diabetic outcomes often matter more for overall mortality in people with obesity. Screening for liver disease is therefore part of assessing cardiometabolic risk."
    },
    {
      "heading": "Other physical and psychological consequences",
      "text": "Cancer\n\nExcess adiposity is now treated as a modifiable risk factor for at least 19 cancer types, and a 2026 review expects the related burden to grow in the coming decades. An IARC working group reached a similar conclusion after reviewing more than 1,000 cohort and other studies, and the strongest associations are with endometrial and esophageal cancers (consistent evidence that higher body fat is linked to several cancers). One review estimated that 15–20% of all cancer deaths may be attributable to obesity.\n\nSeveral biological pathways have been proposed. In peripheral fat, aromatase converts androgens into estradiol, which is the basis of the estrogen-driven mechanism in postmenopausal breast cancer (increased synthesis of estradiol from androgens due to aromatase in peripheral adipose tissue). Insulin resistance, elevated insulin-like growth factor signalling and adipokines such as leptin, whose levels rise with fat mass, have also been implicated in breast carcinogenesis. A third pathway is chronic low-grade inflammation, in which expanded adipose tissue releases pro-inflammatory mediators that favour tumour growth and immune evasion . Adipose hypoxia has been proposed as a further contributor. These mechanisms are closely tied to the inflammatory and cytokine changes described elsewhere in this article.\n\nClinical staging of obesity adds a further dimension. In a UK Biobank analysis of about 459,000 participants, clinical obesity (with organ dysfunction) was positively associated with 12 cancers, with particularly strong links to metabolically driven tumours, while the link between preclinical obesity and increased cancer risk suggests that obesity-related carcinogenesis begins before clinically detectable abnormalities. Weight loss lowers circulating cancer-related factors, and intentional weight loss is associated with lower cancer incidence and recurrence (reduces circulating levels of cancer-associated factors but that it also reduces cancer incidence and recurrence), although the amount of loss needed for substantial risk reduction is not firmly established.\n\nOsteoarthritis, back pain and mobility\n\nObesity is a well-known modifiable risk factor for osteoarthritis of weight-bearing joints, and it is present in approximately 35% of individuals with OA. Mechanical load is the most cited explanation, since higher body weight increases pressure on the joints, but inflammation also contributes, which partly explains why non-weight-bearing joints such as the hands are affected. In the spine, excess weight has been associated with a generally reduced range of motion, due to a reduced mobility at both pelvic and thoracic level. A cadaveric study found greater facet joint degeneration and a higher prevalence of disc herniations in lumbar specimens from obese donors. Obesity is also common among people with chronic low back pain, and the resulting high body weight putting increased stress on the spine, altered posture, and accelerated wear on spinal joints and discs is a commonly proposed pathway. Reduced mobility and pain in turn tend to reduce physical activity, which can sustain the cycle.\n\nRespiratory effects\n\nExcess fat alters pulmonary mechanics. BMI has a direct relationship with the degree of airways resistance and work of breathing, and is inversely correlated with thoracic lung volumes. Reduced functional residual capacity can cause early airway closure and gas trapping, which leads to ventilation–perfusion mismatch and hypoxia . In obstructive sleep apnea (OSA), fat deposited around the upper airway narrows the pharynx, and reduced lung volume increases airway collapsibility. Sleep-disordered breathing is common in obese patients, with prevalence estimates ranging from 2% to 24%. Severe cases can progress to obesity hypoventilation syndrome, a form of daytime alveolar hypoventilation, cor pulmonale, and frank respiratory failure.\n\nObesity also affects asthma. Obese asthmatics often have poor asthma control and respond poorly to therapy. The obese asthma phenotype includes chronic rhinosinusitis, gastroesophageal reflux disease, poorer asthma control, limited responsiveness to corticosteroids, and in some patients obesity appears to precede asthma onset. Improvement in asthma control after bariatric surgery has been documented in cohorts with poorly controlled disease.\n\nPregnancy and offspring outcomes\n\nMaternal obesity before and during pregnancy is associated with higher rates of gestational diabetes, hypertensive disorders such as pre-eclampsia, caesarean delivery, and large-for-gestational-age births, as well as with a higher risk of certain congenital anomalies. For the child, the associations extend beyond birth to a greater likelihood of later obesity and cardiometabolic risk, though these links are partly confounded by shared genetic and household factors.\n\nDepression, anxiety and quality of life\n\nThe relationship between obesity and depression or anxiety runs in both directions. Meta-analyses of prospective studies have found that obesity is associated with a higher subsequent risk of depression, and that depression is associated with a higher risk of later obesity, with contributions from biological factors, reduced activity, and quality of life effects. Weight stigma, mental-health treatments that promote weight gain, and disordered eating are among the mechanisms discussed. Reduced quality of life is consistently reported in people with obesity, and it is often greater with higher BMI and with physical limitations.\n\nMortality\n\nPopulation studies show that all-cause mortality is lowest in the normal-weight range and rises with increasing BMI, with the excess risk becoming substantially larger in the class II and class III obesity ranges. Much of the excess is attributable to cardiovascular-disease, type 2 diabetes and cancer, although the pattern differs by age, smoking status and the presence of illness at baseline, and some analyses suggest a flatter risk curve among older adults."
    },
    {
      "heading": "Obesity in children and adolescents",
      "text": "Growth references and identification\n\nChildren cannot be assessed with adult body mass index cut-offs, because body composition changes with age, puberty and sex. Instead, a child's BMI is compared with a reference population of the same sex and age, and the result is expressed as a growth chart position. The WHO uses two systems. For children under five, weight-for-height is compared with the 2006 WHO standards, with overweight defined at two standard deviations and obesity at three. For children and adolescents aged 5–19, BMI-for-age is used, with overweight above +1 standard deviation and obesity above +2. The 5–19 reference is a reconstruction of a 1977 US National Center for Health Statistics dataset, supplemented with data from the WHO sample for young children.\n\nIn the United States, the Centers for Disease Control and Prevention (CDC) defines obesity as a BMI at or above the 95th percentile for sex and age, and severe obesity as at least 120% of the 95th percentile or a BMI of 35 or more. The 2000 CDC charts were built on data from 1963–80, when obesity was less common, and they could not plot BMIs above 37 or show percentile curves above the 95th. The 2022 Extended charts, which use updated reference data including children with obesity, extend the range to a BMI of 60 and add the 98th, 99th, 99.9th and 99.99th percentiles. BMI is an indirect index: it tracks body fatness but does not measure fat, so a muscular child may score high and a child with low muscle mass and high fat may score in the normal range. Screening is recommended annually from age two.\n\nPrevalence and trends\n\nWHO estimates that in 2024 about 400 million children and adolescents aged 5–19 were overweight, including 170 million with obesity, and that adolescent obesity has quadrupled since 1990. Among children under five, about 35 million were overweight in 2024. The European picture is more recent: the Round 6 COSI survey of about 470,000 children aged 7–9 in 37 countries, collected after the COVID-19 pandemic, found that 25% were overweight and 11% had obesity, with boys (13%) more affected than girls (9%). Prevalence was broadly stable, but where it changed, increases outnumbered decreases, and some southern European countries had close to one in five children with obesity. In the United States, severe obesity among young people rose from about 1% in the early 1970s to 6.1% in 2017–18.\n\nThe steepest rises have been in middle-income and urban settings, where cheap energy-dense food, lower daily activity and rapid nutritional transition coincide, and where early life undernutrition may be followed by overweight. Long-run trend estimates are sensitive to survey design and to the reference standard used, so comparisons across decades and countries should be read with caution.\n\nPersistence into adulthood\n\nTracking depends strongly on age and severity. In a Dunedin-style birth cohort follow-up, obesity at age five predicted obesity around age 50 with a positive predictive value of 67%, whereas obesity at 15–17 had a value of 86%. A 1997 study of young adults found that the odds of adult obesity rose from about 1.3 for obesity in toddlers to about 17.5 for obesity at 15–17. Parental obesity roughly doubled the risk in children under ten. A simulation based on US data estimated that the relative risk of adult obesity rose from about 1.17 for overweight two-year-olds to about 3.10 for 19-year-olds with severe obesity. Rapid weight gain in infancy, early childhood and adolescence has also been linked to later obesity.\n\nDespite these associations, childhood BMI is a poor predictor of who becomes obese as an adult. In one systematic review, sensitivity was below 40% in all but one study, so most adults with obesity were not obese as children. Most adult obesity therefore arises from people whose weight rose after childhood.\n\nEarly complications\n\nChildren with obesity show early signs of cardiometabolic disease, including insulin resistance, dyslipidemia, high blood pressure, fatty liver and type 2 diabetes, which appears in adolescents with increasing frequency. WHO notes that the risks of these conditions depend partly on the age of onset and the duration of obesity. Orthopedic problems include slipped capital femoral epiphysis, Blount disease and knee pain from excess load on growing joints. Obstructive sleep apnea and idiopathic intracranial hypertension are also recognized. CDC notes that children with severe obesity face higher risks of depression and low self-esteem.\n\nPsychosocial harm is also substantial. Children with obesity are frequently teased or bullied, and weight stigma can reduce participation in physical activity and sport, which in turn can reinforce weight gain. These experiences are associated with poorer mental health and quality of life, and they can persist into adulthood.\n\nFamily, school and community\n\nPrevention is generally considered more feasible than treatment for childhood obesity. Parents of children with overweight often underestimate their child's weight status, which can delay recognition. Influences on children's diets and activity include household income and parental education; in the COSI data, daily fruit and vegetable consumption was more common among children of highly educated parents. Effective approaches usually combine family-based support with changes in school meals and physical education, and with safe community spaces for play and transport. Programs that emphasise shared responsibility, rather than blame placed on parents, are considered more likely to engage families over time."
    },
    {
      "heading": "Clinical assessment and screening",
      "text": "In primary care, an evaluation of suspected obesity usually starts with a structured history, a physical examination and a short list of routine tests. The 2025 Lancet framework puts these steps after the initial confirmation of excess body fat. Confirmation of excess body fat should be followed by further assessment to establish whether an individual with obesity has an illness. The clinician then asks about signs or symptoms of organ dysfunction and about whether the person has limitations of day-to-day activities. Anthropometry remains the entry point. The same framework recommended the use of an anthropometric index in addition to BMI, and waist circumference is the usual companion measure.\n\nBMI is a weak instrument for estimating adiposity at the individual level. In a 2026 general-population study that measured body fat by DXA, more than one-third (34%) of individuals classified as obese by BMI were actually in the overweight category, and misclassification was even more pronounced among those labeled overweight by BMI. An earlier comparison in Newfoundland found that BMI misclassified adiposity status in approximately one-third of women and men compared with DXA. Errors also run in both directions. In resistance-trained adults, BMI overestimated overweight and obese cases in males, while it underestimated them in females. Muscular and older adults are common examples where the scale can mislead. Dual-energy X-ray absorptiometry (DXA) is a criterion method for body composition estimation, and it allows separation of fat mass from lean mass and an estimate of fat distribution. Because of its cost and limited availability, DXA is mostly used in research and specialist settings rather than routine practice. Portable bioelectrical impedance devices are cheaper but less reliable; one widely used device was described as biased and imprecise compared to air displacement plethysmography and DXA.\n\nRoutine laboratory tests focus on the metabolic complications that most often accompany excess adiposity. Fasting glucose or glycated haemoglobin screens for prediabetes and a cluster of elevated blood glucose and blood fats is one of the Commission's diagnostic examples. A lipid profile and liver enzymes such as ALT are also standard, the latter as an early signal of fatty liver disease. Blood pressure is measured at the same visit. Abnormal results are not treated as separate diagnoses in isolation; they are read as evidence of harm caused by excess fat.\n\nSleep-disordered breathing is one of the conditions most closely linked to obesity and is frequently missed. The best-documented risk factor for OSA is obesity. Patients may report unrefreshing sleep, fatigue, insomnia, and snoring, although OSA can be asymptomatic. Screening starts with a sleep history and a standardized questionnaire, such as the Berlin questionnaire or the STOP-Bang tool, followed by objective testing when indicated. Full-night, attended, in-laboratory PSG is considered the reference standard diagnostic test, while portable monitors may be used as an alternative diagnostic test when PSG is unavailable. A diagnosis is generally based on an apnea-hypopnea index of at least 15 events per hour, or at least 5 with symptoms.\n\nAssessment ends with a shared decision about treatment. Under the Lancet framework, the choice of management depends on a judgement of individual risks and benefits, reached through an active conversation with the patient, and success is judged by whether symptoms and signs improve rather than by weight loss alone. Clinicians therefore need to elicit the person's priorities, prior attempts at weight loss, mental health and practical circumstances, and to set concrete goals such as reduced breathlessness, less joint pain or better glycaemic control. This process also changes who qualifies for drug treatment. In one trial population, 19.5% were classified as having no/pre‐clinical obesity using the Lancet criteria, which suggests that a sizeable share of people who would meet a BMI threshold may not need the same intensity of intervention."
    },
    {
      "heading": "Lifestyle and behavioral treatment",
      "text": "Lifestyle change is the foundation of obesity management, and most of its evidence comes from trials that combine dietary change, physical activity and behavioral support. Its effects are real but usually modest, and they tend to diminish without continued support. The sections below compare the main approaches, and what the evidence shows about their magnitude and durability. Pharmacological and surgical treatments are covered elsewhere.\n\nDietary approaches\n\nDiet is the component most consistently linked to substantial weight loss. In a meta-analysis of trials published over a 25-year period, weight lost through diet, exercise and diet plus exercise was 10.7, 2.9 and 11.0 kg respectively, and at one-year follow-up, diet plus exercise tended to be the superior program. Those studies were short, averaging 15.6 weeks, so the figures describe early response rather than long-term results.\n\nComparisons between macronutrient patterns have produced a more nuanced picture. A network meta-analysis of diets lasting at least 12 months found that all diets were associated with a significant weight loss relative to usual eating, with high-fat/low-carbohydrate diets averaging about 5.5 kg, low-fat/high-carbohydrate diets about 5.0 kg and moderate-macronutrient diets about 4.7 kg. The gaps between these groups were small. The DIETFITS trial, a randomized controlled trial of 609 adults followed for 12 months, reported that weight change over 12 months was not significantly different between a healthy low-fat diet and a healthy low-carbohydrate diet, and that neither genotype nor baseline insulin secretion predicted which diet worked better. An earlier pooled analysis of controlled trials concluded that both diets were equally effective at reducing body weight and waist circumference. Low-carbohydrate diets did, however, produce larger improvements in some cardiovascular markers in certain trials, even when weight changes were similar. Mediterranean-style patterns are generally recommended for their cardiometabolic benefits, with weight effects broadly comparable to other calorie-controlled diets. Taken together, the evidence suggests that the overall energy deficit and adherence matter more than the specific macronutrient split.\n\nPhysical activity\n\nExercise alone produces modest weight loss. A dose-response analysis of 116 randomized clinical trials involving 6880 adults with overweight or obesity found that each additional 30 minutes of aerobic exercise per week was associated with a loss of about 0.52 kg of body weight. Aerobic exercise at least 150 minutes per week was associated with clinically important reductions in waist circumference and measures of body fat. The modest weight effect has a physiological explanation: one analysis estimated that producing a 25% energy deficit through exercise alone would require roughly 120 min per day for women and approximately 90 min for men, which is impractical for most people.\n\nCombining exercise with dietary restriction is more effective than exercise by itself. One meta-analysis found that the combined intervention of exercise and diet significantly reduced body weight compared to exercise alone, and diet-only programs also outperformed exercise-only programs on weight. Exercise is nonetheless valuable for reasons beyond the scale. Studies have shown that it can improve glucose tolerance and aerobic fitness independently of changes in body weight. In a one-year trial of adults aged 65 and older, diet alone reduced body weight by about 10%, and the combination of diet and exercise reduced it by about 9%, yet a combination of weight loss and exercise provides greater improvement in physical function than either intervention alone. Some researchers have argued that the mortality benefits of higher fitness are larger and more consistent than those of intentional weight loss, and have proposed weight-neutral strategies for this reason, although this view is not universally accepted.\n\nBehavioral support and structured programs\n\nBehavioral components are generally regarded as essential to achieving meaningful results. In one two-year trial, all participants received comprehensive behavioral treatment alongside either a low-carbohydrate or low-fat diet. Earlier studies that used only a modest dose of behavioral treatment reported weight loss that was usually minimal. Structured programs typically combine several elements: a systematic review of short-term interventions found that all lifestyle interventions included were multicomponent, and that the most common components were educational resources, followed by support tools such as pedometers and food and exercise diaries. Self-monitoring of intake and activity is therefore a standard feature, as is regular contact with a dietitian or counselor, as in the trial that met with a study dietitian on a periodic basis. Delivery format has also been studied, with remote, in-person and blended programs all used in trials.\n\nTypical magnitudes and weight regain\n\nA systematic review and meta-analysis of 14 randomized trials of interventions lasting six months or less found a pooled difference of −2.59 kg (95% CI, −3.47 to −1.72) relative to controls. The authors cautioned that this effect may be further diminished when interventions are translated into real-world practice. Most of the loss tends to occur early: most people achieve their greatest weight loss in the first 3 to 6 months of a lifestyle change intervention. Longer trials show larger but still partial results. In a two-year trial, participants in both groups lost about 7% of body weight.\n\nWeight regain is the most common long-term pattern. Adults with overweight or obesity in everyday settings tend to gain weight over time, and the dietary and activity changes that produce early loss often erode without ongoing support. Exercise, in particular, has a mixed record: there is no evidence from RCTs that exercise training programmes help to prevent weight regain after weight loss, although observational registries identify an increased level of PA as the most consistent correlate of successful maintenance. Sustained physical activity may therefore help people keep weight off, even though trials have not yet confirmed a direct preventive effect."
    },
    {
      "heading": "Pharmacological treatment",
      "text": "GLP-1 receptor agonists\n\nThe main drug class for obesity is the glucagon-like peptide-1 (GLP-1) receptor agonists, which were developed for type 2 diabetes before their weight-lowering effect became the basis for obesity indications. They act on hypothalamic circuits to reduce hunger and on the gut to slow gastric emptying, so meals feel more filling and last longer. Injectable semaglutide and tirzepatide carried most of the early evidence, which was generated in randomized controlled trials with weight loss as the primary endpoint.\n\nOral agents approved in 2025 and 2026\n\nThe oral route changed the field in late 2025. In December 2025 the US regulator cleared a once-daily 25 mg semaglutide tablet for chronic weight management, the first oral agent of this class licensed for that purpose. The approval was based on results from the phase 3 OASIS 4 trial, a 64-week, randomized, double-blind, placebo-controlled study in 307 adults. Over 64 weeks, participants receiving once-daily oral semaglutide 25 mg achieved a mean weight reduction of 16.6% at week 64 if all patients remained on treatment, compared with 2.7% with placebo. Counting everyone who started, the figure was about 13.6% against 2.4% . Oral semaglutide for glycemic control had already been approved for type 2 diabetes in 2019; that product is not indicated for weight management .\n\nIn April 2026 the FDA approved orforglipron (Foundayo), a once-daily oral small-molecule GLP-1 receptor agonist. Because it is not a peptide, it can be taken any time of day without food or water restrictions. In the phase 3 ATTAIN-1 trial, participants receiving the highest dose of orforglipron achieved a mean weight reduction of approximately 12.4% from baseline at 72 weeks among those who remained on treatment, compared with 0.9% in the placebo group. Across all randomized participants, the figure was 11.1% against 2.1% for placebo .\n\nInvestigational triple agonists and amylin-based agents\n\nRetatrutide is an investigational once-weekly agent that targets GIP, GLP-1, and glucagon receptors at once. In the TRIUMPH-4 trial in adults with knee osteoarthritis, participants receiving retatrutide lost up to 28.7% of their body weight at 68 weeks. In TRIUMPH-1, the top dose produced an average loss of 28.3% among those who stayed on treatment over 80 weeks . In TRIUMPH-3, adults with severe obesity and cardiovascular disease lost up to an average of 55.8 lbs (22.6%) at 80 weeks. Lilly plans to file for approval in 2027 . A safety signal of dysesthesia has been reported in the programme, so tolerability will be a central question in regulatory review.\n\nAmylin-based compounds, which act on a hormone co-secreted with insulin, are in clinical development alone and in combination with GLP-1 agents. Their long-term safety and their effect on weight-related comorbidities remain under study.\n\nAdverse effects and contraindications\n\nGastrointestinal events dominate the safety profile. In OASIS 4, the most frequent adverse events were gastrointestinal disorders (74.0% oral semaglutide and 42.2% placebo). The oral semaglutide prescribing information lists warnings for potential thyroid C-cell tumors, pancreatitis, gallbladder disease, hypoglycemia when used with certain diabetes medications, kidney injury related to dehydration, and psychiatric adverse effects. The product is contraindicated in people with a personal or family history of medullary thyroid carcinoma or multiple endocrine neoplasia syndrome type 2. Regulators in Europe have also added non-arteritic anterior ischaemic optic neuropathy (NAION) to the labelling as a very rare adverse reaction, a point that clinicians are asked to weigh against individual risk.\n\nAccess, cost and discontinuation\n\nPrice is a major barrier. Novo Nordisk set the 25-mg pill's cost at $149 per month, according to the manufacturer. Lilly offers orforglipron through its direct-to-patient platform, with pricing starting at $25 per month with commercial coverage and $149 for self-pay. Coverage by public and private insurers varies, and reimbursement decisions shape which patients can start and continue therapy.\n\nDiscontinuation is equally important. Obesity medications are generally used for long periods, and trial extension data have suggested that much of the lost weight returns after treatment stops. Switching between agents, or between oral and injectable forms, is common in practice, and evidence on the outcomes of such switches is still limited."
    },
    {
      "heading": "Bariatric and metabolic surgery",
      "text": "Bariatric surgery changes the anatomy of the upper digestive tract to reduce food intake and, in some procedures, nutrient absorption. Its metabolic effects are often emphasised by calling it \"metabolic surgery.\" The three procedures named most often are Roux-en-Y gastric bypass (RYGB), sleeve gastrectomy (SG) and adjustable gastric banding (LAGB). RYGB creates a small upper pouch that is connected directly to a segment of jejunum, so food bypasses most of the stomach and the proximal small intestine. Its effect combines restriction with partial malabsorption, and it alters the release of gut hormones such as GLP-1 and PYY. SG removes most of the stomach along the greater curvature, leaving a narrow tube. Its weight effect relies mainly on restriction, together with a fall in ghrelin production from the removed gastric fundus. LAGB places a silicone band around the upper stomach to form a small pouch, and the band can be tightened or loosened through a subcutaneous port. It works by restriction alone and is reversible, but it typically produces smaller and slower weight loss and needs more reoperations for band slippage, erosion or port problems. Its use has fallen sharply in many health systems, and SG and RYGB now account for most procedures. A one-anastomosis (mini) gastric bypass is also performed widely in some regions.\n\nEligibility and assessment\n\nEligibility has broadened over time. The original NIH consensus framework, which many programmes still reference, centred on a BMI of 40 kg/m² or more, or 35 kg/m² or more with a significant obesity-related disease. The 2022 ASMBS/IFSO guideline is broader. It strongly recommend[s] considering metabolic and bariatric surgery in any patient with a BMI ≥35 kg/m², regardless of obesity-related comorbidities, and it also supports surgery for people with a BMI of 30–34.9 kg/m² who have comorbid metabolic disease such as type 2 diabetes or hypertension. For class I obesity, the guideline generally expects non-surgical treatment to have been tried first, except when type 2 diabetes is present. Lower thresholds apply to Asian populations, where the cut-off is 27.5 kg/m², and paediatric criteria are based on percentiles of BMI for age and sex. Diabetes is not a prerequisite for surgery at higher BMI. BMI alone does not decide candidacy. Assessment by a multidisciplinary team covering surgical, medical, nutritional and psychiatric expertise is standard. It screens for surgical risk, psychiatric disorders, eating disorders, alcohol or substance misuse and pregnancy, and it confirms the patient's ability to commit to long-term follow-up. Informed consent is an essential step.\n\nOutcomes: weight, diabetes and mortality\n\nBoth RYGB and SG produce large, sustained weight loss. A meta-analysis of randomised trials and cohorts with at least ten years of follow-up found that both procedures demonstrated sustained statistical efficacy in weight reduction and remission of metabolic comorbidities, with no statistically significant difference between them. In the short term RYGB performs better on several metabolic measures. Pooled randomised trial data show that RYGB is associated with greater short-term improvements in lipid profile and glycaemic control and higher remission rates of type 2 diabetes, but these differences narrow by long-term follow-up. Diabetes remission is one of the most consistent effects. In the Longitudinal Assessment of Bariatric Surgery study, diabetes remission occurred in 57% after RYGB and 22.5% after LAGB over seven years. In the Swedish Obese Subjects study, the two-year remission rate was 72.3% after surgery and 16.4% with usual care, and at 15 years these figures had fallen to 30.4% and 6.5%. Remission is more likely when surgery is performed early in the disease, while beta-cell function is preserved. Evidence on mortality comes mainly from observational studies. A meta-analysis of cohorts in people with type 2 diabetes reported a substantially lower mortality risk after surgery than after non-surgical treatment, but the authors noted that such comparisons are vulnerable to confounding. Findings also differ by age: an umbrella review of meta-analyses in adults over 60 reported fewer complications and deaths after SG, while RYGB produced greater weight loss.\n\nComplications and nutritional deficiencies\n\nEarly complications include bleeding, infection, staple-line or anastomotic leaks and venous thromboembolism. Late problems depend on the procedure. After RYGB, internal hernia, marginal ulceration and dumping syndrome are important. Gastro-oesophageal reflux tends to worsen after SG and often improves after RYGB. Rapid weight loss raises the risk of gallstones, and LAGB carries its own risks of band slippage and erosion. Nutritional deficiencies are a central long-term concern, particularly after RYGB, which combines restriction with malabsorption. Iron, vitamin B12, folate, calcium, vitamin D and thiamine are commonly affected, and deficiencies in vitamins A and zinc have also been described. These can cause anaemia, bone loss and neuropathy. Surgical programmes therefore require lifelong supplementation, scheduled laboratory monitoring and dietary follow-up. Mental health screening, alcohol use and weight regain are also monitored, because some patients regain weight over time. Continuity of care after the first postoperative year is a recognised weak point, and long-term follow-up is considered essential to the safe use of these procedures."
    },
    {
      "heading": "Prevention and public policy",
      "text": "Public policy on obesity works mainly on the food environment and on the conditions that shape everyday choices, rather than on individual willpower alone. Most measures target prices, labels, marketing and the availability of energy-dense products, and their effects are usually judged on public-health outcomes such as prevalence and purchasing rather than on weight in individuals.\n\nTaxes on sugary drinks\n\nSugar-sweetened beverage taxes are the most studied fiscal tool. Their rationale draws on taxation and pigouvian-tax reasoning, in which a levy is placed on a product whose health costs are not reflected in its price. The evidence on body weight is more modest than the evidence on purchasing. In Washington State, a tax on soft-drink syrup was associated with a fall of 2.2 to 4.0 percentage points in obesity relative to a synthetic control state, but the tax did not yield significant changes in diabetes, hypertension, or high cholesterol prevalence. Model-based work projects larger effects: for Germany, a 20% tax was estimated to avert 479,000 obese individuals (−4%), with the largest changes among younger men.\n\nEmpirical reviews are more cautious. A meta-analysis concluded that higher prices reduce consumption, but higher prices of SSBs may lead to modest reductions in weight in the population. Substitution is a central reason for this. Consumers who cut taxed drinks may buy other caloric beverages, which can offset, to a certain degree, the energy deficit achieved by reducing the intake of taxed beverages. In addition, estimated net reductions in energy intake may be so small that changes in weight are difficult to detect. A national time-series analysis found that only Hungary showed a significant downward trend in overweight after tax introduction. This suggests that the effect of pricing depends strongly on design and context.\n\nFront-of-package labeling, school standards and marketing limits\n\nChile's Food Labelling and Advertising Law, in force since 2016, is the most cited example of a combined package. It requires the mandatory use of front-of-package warning labels on packaged foods/beverages high in energy, sugars, saturated fats and sodium, and such foods cannot be sold nor offered in daycares/schools and cannot be promoted to children under 14yo. Label design was tested before adoption; a black-and-white stop-sign format performed better than alternatives in visibility and in its ability to change intended purchases. After the first phase, sugars and sodium decreased in packaged foods, with no significant changes for saturated fats.\n\nChild-level findings are observational and should be read cautiously. A Lancet analysis reported that children aged six to 18 months showed approximately a 2% lower chance of excess weight after Chile implemented the Food Labelling and Advertising Law (FLAL) in 2016. The study's authors argue that combining labels, marketing limits and school rules outperforms any single measure, and that nine countries have adopted front-of-package warning labels after Chile. Labeling, school food standards and marketing restrictions are often introduced together, which complicates attribution of any observed change to one component.\n\nBuilt environment\n\nBuilt-environment measures, including support for active transport, urban green space and walkability, act on physical activity and exposure to food outlets rather than on diet directly. Their effects on body weight are generally smaller and less consistent than their effects on activity levels, and they depend on local planning, transport and housing conditions, which are also linked to urbanization and to social-determinants-of-health.\n\nWHO and national guidance\n\nThe world-health-organization sets normative frameworks that national governments adapt. In December 2025 it issued its first clinical guideline on drug treatment of obesity in adults, endorsing GLP-1 therapies with qualifications. The recommendations are conditional due to limited data on their long-term efficacy and safety, maintenance and discontinuation, their current costs, inadequate health-system preparedness, and potential equity implications. The same guideline describes obesity as a chronic, relapsing disease requiring lifelong care. WHO's broader strategy rests on three pillars, creating healthier environments through robust policies, protecting individuals at high risk through screening and early intervention, and ensuring access to lifelong person-centered care.\n\nGuidance of this kind influences access and programmes mainly through national decisions. WHO recommendations do not themselves set reimbursement; they provide a basis that member states can use when they design programmes, and national regulators, insurers and health-system planners determine what is funded and who can receive it.\n\nEvaluating policies\n\nMeasuring the population effect of policy is difficult. Evaluations of SSB taxes with health outcomes are very scarce, and the lag between a change in intake and a measurable change in body weight is long. Multi-component packages make attribution harder, and natural experiments require synthetic control or interrupted time-series methods, which depend on assumptions about what would have happened without the policy. Prevalence can also be affected by secular trends, changing survey methods and the rise of new treatments, so a policy's effect is often reported as a small change in a population estimate rather than a clear reduction in weight."
    },
    {
      "heading": "Weight stigma and the disease debate",
      "text": "Whether obesity is a disease\n\nWhether excess adiposity should be classed as a disease is still contested, and the answer affects how it is diagnosed, funded and discussed. The American Medical Association adopted the disease position in 2013, even though its own science council had concluded otherwise, and it reaffirmed the decision in 2023. The WHO and the US CDC now treat obesity as a disease, and World Obesity published a supporting position statement in 2017. Opponents argue that obesity is better understood as a risk factor for type 2 diabetes, cardiovascular disease and other conditions than as an illness in its own right. Many people with excess fat have no impairment of function, and a definition built largely on BMI may overdiagnose healthy individuals. Supporters argue that the label improves access to treatment, reflects a chronic and multifactorial condition, and makes clear that weight is not simply a matter of character. A 2021 meeting of the Association for the Study of Obesity deliberately set out to examine the consequences of the label rather than to settle the question. Commercial interests have entered the debate as well: as new anti-obesity drugs gained approval, drug manufacturers have promoted the disease framing, which critics say can blur scientific argument and marketing. The clinical–preclinical distinction introduced in 2025 offers one route around the dispute, because it separates people whose excess fat causes organ dysfunction from those who carry risk without current harm.\n\nWeight bias in health care\n\nWeight bias among clinicians is well documented, though the quality of the evidence is uneven. A systematic review and meta-analysis found implicit and explicit bias toward people with obesity among physicians, nurses, dietitians and other professionals, but the authors judged the overall evidence to be of very low quality and noted that 26 different outcome measures had been used. Biased encounters have concrete consequences. Patients may delay or avoid care, be misdiagnosed, or be refused services. Clinicians have been reported to perform fewer examinations, undertreat patients and under-use effective pharmacological treatments. Patients who report weight discrimination also differ measurably: in one comparison among people with severe obesity, those who reported bias had a BMI about 1.5 kg/m² higher, and in another study, stronger bias predicted dropout from an 18-week behavioural programme. In mental health settings, experimental studies show that clinicians judge higher-weight clients to have greater pathology and poorer functioning, although with restrictive eating-disorder symptoms they rated higher-weight clients as less severe. Clinician-level remedies that have been proposed include routine screening for bias, training in weight-inclusive care and the creation of size-inclusive clinical spaces.\n\nStigma in education, employment and daily life\n\nStigma extends well beyond clinics. Weight-based teasing is among the most common forms of peer harassment in childhood and adolescence, and it can also come from teachers. Some adolescents avoid school to escape it, with consequences for their academic performance. A World Obesity overview reports that students with obesity are less likely to be admitted to university and tend to receive less financial support. In employment, discrimination has been documented at the stages of career counselling, hiring, pay, promotion and dismissal, and a systematic review of workers and job applicants found that penalties are generally larger for women.\n\nThe mental health consequences are substantial. A meta-analysis by Emmer and colleagues found a moderate-to-large inverse association between weight stigma and mental health (r = −0.35), and this association remained after adjusting for education and body weight. Studies of university students link weight stigma to symptoms of eating disorders, anxiety and depression. Internalized stigma, meaning the adoption of negative attitudes toward oneself, appears to be particularly important, and anticipated stigma alone has been associated with psychological distress.\n\nLanguage, communication and ethics\n\nTerminology is part of the debate. Many guidelines recommend person-first wording, such as \"person with obesity\" rather than \"obese person\", on the principle that a medical condition should not define a person's identity. Proponents of the disease framing, however, have argued that it can reduce blame by presenting weight as the product of genetic, biological and environmental factors. Critics counter that a message of biological determinism may leave people feeling disempowered and less likely to pursue change, so communicating risk without assigning blame is a balance rather than a fixed rule. Ethical questions also arise in the clinical encounter, including how weight-related risk is discussed, what options are presented, and how patients can take part in decisions in line with informed consent principles. The underlying tension is between accurate communication about health risks and the avoidance of moral judgement, an issue that continues to shape both medical ethics and public health practice."
    },
    {
      "heading": "Economic burden and health systems",
      "text": "Direct medical costs\n\nObesity's direct costs come from treating obesity itself and, more heavily, the type 2 diabetes, cardiovascular disease, hypertension, stroke and cancer that it promotes. In the United States, the CDC attributed about $173 billion in annual direct medical costs for adults to obesity in 2019. An adult with obesity incurred roughly $1,861 more in medical spending than an adult at a healthy weight, and about $3,097 more when obesity was severe. For children, the CDC put direct costs at $1.3 billion a year. An earlier CDC-linked study estimated that obesity accounted for as much as $147 billion in 2006 and that its share of all medical spending rose from 6.5% in 1998 to 9.1% in 2006, showing how costs grow with prevalence.\n\nCross-national estimates use similar logic but differ in scope. A World Obesity Federation and RTI analysis of 161 countries put the 2019 economic impact of overweight and obesity at about 2.19% of global GDP. Per-capita losses ranged from about $20 in Africa to about $872 in the Americas, and from about $6 in low-income countries to about $1,110 in high-income countries. The same work projected a rise to roughly 3.3% of GDP by 2060, with the steepest relative increases in lower-resourced countries. Other global figures, including a 2014 estimate of $2.0 trillion (2.8% of GDP) and a projection of about $4.32 trillion a year by 2035, rest on different assumptions, so the numbers should be compared with care.\n\nIndirect costs\n\nIndirect costs cover lost productivity through absence from work, reduced performance while at work, disability and premature death. In one Iranian cost-of-illness analysis of six chronic diseases, obesity accounted for about 31% of the total burden, and indirect costs made up about 41% of that obesity share, with only a small fraction tied to premature death. CDC data indicate that U.S. workers with obesity missed, on average, three more days a year because of injury or illness than workers at a healthy weight. Methodological choices matter: adding GDP multipliers or counting gains in life expectancy from avoided deaths raises the global estimate, to about 2.9% of GDP in the 2019 analysis. A UK study projected that each 1% reduction in the national obesity rate could yield about £245 million in productivity benefits.\n\nCost-effectiveness of prevention and treatment\n\nEconomic evaluations usually express results as an incremental cost-effectiveness ratio (ICER), the extra cost per quality-adjusted life year (QALY) gained, compared with a willingness-to-pay threshold. For bariatric surgery, models generally favour the intervention. A simulation of laparoscopic sleeve gastrectomy in patients with cirrhosis from nonalcoholic steatohepatitis gave ICERs that fell as baseline BMI rose, from about $66,000 per QALY in overweight patients to about $6,600 in severe obesity. A Hungarian model of patients with obesity and type 2 diabetes found that both sleeve gastrectomy and gastric bypass saved public-payer money while adding quality-adjusted life years. A matched cohort study in Canada found that total health-care costs were about $5,700 lower over two years after surgery.\n\nMedications depend heavily on price. A U.S. model of semaglutide 2.4 mg with lifestyle support gave an ICER of about $122,500 per QALY against lifestyle alone, which fell under a $150,000 threshold. A U.S. payer analysis of five anti-obesity drugs found that a phentermine–topiramate combination was the least costly option with nearly the same QALYs, while tirzepatide's ICER was above $355,000 per QALY. Several recent analyses have concluded that GLP-1 receptor agonists are not cost-effective at prevailing prices, and a U.S. insurance-claims cohort reported that bariatric surgery was associated with greater weight loss and roughly $11,700 lower ongoing costs than GLP-1 agonists over two years. Endoscopic sleeve gastroplasty appears favourable in class I obesity, though its evidence base is smaller.\n\nHealth-system challenges\n\nCoverage decisions are central. Effective pharmacological and surgical treatments exist, but insurance design and public programmes often restrict access, a point made by diabetes advocacy organisations. Cost-effectiveness results can change quickly when drug prices fall, so payers must reassess decisions as markets shift. Expanding treatment also strains the workforce: multidisciplinary teams, dietitians, behavioural specialists and surgeons are needed, and most health systems have limited capacity for long-term follow-up. Links to primary-health-care and universal-health-coverage are therefore important, since sustained management depends on routine services rather than episodic specialist care."
    },
    {
      "heading": "Open questions and research frontiers",
      "text": "Despite decades of research, several questions about obesity remain unresolved, and some of them now shape clinical practice directly. The first is why weight regain is so common. Lifestyle programmes and drug treatments both tend to produce weight loss that is only partly sustained. Biology contributes through the body's defence of its previous weight, including changes in appetite hormones and energy expenditure that persist after weight is lost, as described under Homeostasis and Hormones. Behaviour contributes through the difficulty of maintaining new eating and activity habits over years. Separating these two components, and identifying which people are most likely to regain, remains an active research goal.\n\nThe arrival of incretin-based drugs has made this question urgent. Trial extensions show that much of the lost weight returns once treatment stops. In the STEP 1 extension, participants who discontinued semaglutide regained about two-thirds of their lost weight within a year, whereas a pooled analysis of semaglutide and tirzepatide discontinuation projected a return to baseline weight in roughly 15 months. Real-world cohorts, however, have sometimes reported slower regain than trials, possibly because patients in trials lost more weight before stopping, and because of differences in adherence and follow-up. Long-term outcomes, safety, and access after treatment ends are therefore open questions. Researchers are also studying whether tapering doses or pairing treatment with structured support can reduce rebound, and whether new agents can preserve maintenance after withdrawal. Findings from animal models, such as a preclinical oral compound that prevented rebound in mice after semaglutide was stopped, have not yet been tested in humans. Questions about insurance coverage and the affordability of long-term use also belong to Pharmacology and Health systems research, as well as to clinical evidence.\n\nA second frontier concerns the measurement of adiposity itself. Body mass index is simple and inexpensive, but it does not distinguish fat from lean mass, does not show where fat is located, and performs differently across ages, sexes and ethnic groups. Investigators are therefore looking for measures that capture fat distribution and metabolic risk more accurately, including waist-based indices, imaging, and body-composition techniques. A central challenge is to show that a new measure improves clinical decisions, rather than merely correlating with existing ones, and to validate it in children, older adults and populations outside the groups in which BMI thresholds were first derived. Ethnic-specific cut-offs and their clinical justification are part of this same debate, as is the use of Biomarkers alongside anthropometry.\n\nA third frontier is the gut microbiome, together with epigenetic and other molecular data, in the search for precision approaches. Studies have linked the composition of gut microbial communities, including their production of short-chain fatty acids, with energy harvest, inflammation and appetite signalling, and some work has explored probiotics, prebiotics and dietary strategies that modify these communities. Much of this evidence comes from small observational studies and animal experiments, and results often vary between individuals. Bibliometric reviews have noted that interest in artificial intelligence and multi-omics is growing rapidly within obesity research, while clinical integration remains limited. The main obstacles are the lack of standardised methods, the need for long-term validation, and the cost of profiling. Whether genetic, microbial and metabolic profiles can reliably predict who will benefit from a particular diet or drug is still unknown, and the answer will depend on large, diverse and longitudinal cohorts. Related issues appear in Genetics, Gene expression and Epigenetics.\n\nFinally, population prevalence itself is changing in ways that need explanation. In the United States, national survey data from 2021 to 2023 showed an adult obesity prevalence of 40.3%, a slight decline from the peak of about 41.9% recorded in 2017–2020. Yet analyses differ on whether this marks a true turning point. Some age-adjusted reanalyses of the same survey series report a plateau, while others report a continuing rise of about 0.6 percentage points per year since 1999–2000, with severe obesity increasing throughout. The trends also differ by subgroup. Between 2017–2020 and 2021–2023, prevalence fell among young adults aged 20–39, but rose among those aged 40–59, and disparities by education and income persisted. Whether these shifts reflect changes in diet, physical activity, the spread of medications, or survey methods and sampling is an open question. Researchers are also debating how much of the decline, if any, is attributable to the wider use of Nutrition advice and drug treatments. Separating these drivers requires continued surveillance and careful modelling of Prevalence across age, sex and social groups."
    }
  ],
  "links": [
    "medicine",
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    "world-health-organization",
    "natural-selection",
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    "taxation",
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  ],
  "sources": [
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      "title": "Obesity and overweight",
      "url": "https://who.int/en/news-room/fact-sheets/detail/obesity-and-overweight?utm="
    },
    {
      "title": "Obesity and overweight",
      "url": "https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight"
    },
    {
      "title": "Obesity",
      "url": "https://en.wikipedia.org/wiki/Obesity"
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  ],
  "license": "https://creativecommons.org/licenses/by/4.0/"
}