Underweight, overweight, and obesity in childhood and adolescence are associated with adverse health consequences throughout the life-course. Our aim was to estimate worldwide trends in mean body-mass index (BMI) and a comprehensive set of BMI categories that cover underweight to obesity in children and adolescents, and to compare trends with those of adults. We pooled 2416 population-based studies with measurements of height and weight on 128·9 million participants aged 5 years and older, including 31·5 million aged 5-19 years. We used a Bayesian hierarchical model to estimate trends from 1975 to 2016 in 200 countries for mean BMI and for prevalence of BMI in the following categories for children and adolescents aged 5-19 years: more than 2 SD below the median of the WHO growth reference for children and adolescents (referred to as moderate and severe underweight hereafter), 2 SD to more than 1 SD below the median (mild underweight), 1 SD below the median to 1 SD above the median (healthy weight), more than 1 SD to 2 SD above the median (overweight but not obese), and more than 2 SD above the median (obesity). Regional change in age-standardised mean BMI in girls from 1975 to 2016 ranged from virtually no change (-0·01 kg/m2 per decade; 95% credible interval -0·42 to 0·39, posterior probability [PP] of the observed decrease being a true decrease=0·5098) in eastern Europe to an increase of 1·00 kg/m2 per decade (0·69-1·35, PP>0·9999) in central Latin America and an increase of 0·95 kg/m2 per decade (0·64-1·25, PP>0·9999) in Polynesia and Micronesia. The range for boys was from a non-significant increase of 0·09 kg/m2 per decade (-0·33 to 0·49, PP=0·6926) in eastern Europe to an increase of 0·77 kg/m2 per decade (0·50-1·06, PP>0·9999) in Polynesia and Micronesia. Trends in mean BMI have recently flattened in northwestern Europe and the high-income English-speaking and Asia-Pacific regions for both sexes, southwestern Europe for boys, and central and Andean Latin America for girls. By contrast, the rise in BMI has accelerated in east and south Asia for both sexes, and southeast Asia for boys. Global age-standardised prevalence of obesity increased from 0·7% (0·4-1·2) in 1975 to 5·6% (4·8-6·5) in 2016 in girls, and from 0·9% (0·5-1·3) in 1975 to 7·8% (6·7-9·1) in 2016 in boys; the prevalence of moderate and severe underweight decreased from 9·2% (6·0-12·9) in 1975 to 8·4% (6·8-10·1) in 2016 in girls and from 14·8% (10·4-19·5) in 1975 to 12·4% (10·3-14·5) in 2016 in boys. Prevalence of moderate and severe underweight was highest in India, at 22·7% (16·7-29·6) among girls and 30·7% (23·5-38·0) among boys. Prevalence of obesity was more than 30% in girls in Nauru, the Cook Islands, and Palau; and boys in the Cook Islands, Nauru, Palau, Niue, and American Samoa in 2016. Prevalence of obesity was about 20% or more in several countries in Polynesia and Micronesia, the Middle East and north Africa, the Caribbean, and the USA. In 2016, 75 (44-117) million girls and 117 (70-178) million boys worldwide were moderately or severely underweight. In the same year, 50 (24-89) million girls and 74 (39-125) million boys worldwide were obese. The rising trends in children's and adolescents' BMI have plateaued in many high-income countries, albeit at high levels, but have accelerated in parts of Asia, with trends no longer correlated with those of adults. Wellcome Trust, AstraZeneca Young Health Programme.

Body-mass index (BMI) has increased steadily in most countries in parallel with a rise in the proportion of the population who live in cities^1,2. This has led to a widely reported view that urbanization is one of the most important drivers of the global rise in obesity^3–6. Here we use 2,009 population-based studies, with measurements of height and weight in more than 112 million adults, to report national, regional and global trends in mean BMI segregated by place of residence (a rural or urban area) from 1985 to 2017. We show that, contrary to the dominant paradigm, more than 55% of the global rise in mean BMI from 1985 to 2017—and more than 80% in some low- and middle-income regions—was due to increases in BMI in rural areas. This large contribution stems from the fact that, with the exception of women in sub-Saharan Africa, BMI is increasing at the same rate or faster in rural areas than in cities in low- and middle-income regions. These trends have in turn resulted in a closing—and in some countries reversal—of the gap in BMI between urban and rural areas in low- and middle-income countries, especially for women. In high-income and industrialized countries, we noted a persistently higher rural BMI, especially for women. There is an urgent need for an integrated approach to rural nutrition that enhances financial and physical access to healthy foods, to avoid replacing the rural undernutrition disadvantage in poor countries with a more general malnutrition disadvantage that entails excessive consumption of low-quality calories.

Obesity is a global health problem mainly attributed to lifestyle changes such as diet, low physical activity or socioeconomics factors. However, several evidences consistently showed that genetics contributes significantly to the weight-gain susceptibility. A systematic literature search of most relevant original, review and meta-analysis, restricted to English was conducted in PubMed, Web of Science and Google scholar up to May 2017 concerning the contribution of genetics and environmental factors to obesity. Several evidences suggest that obesogenic environments contribute to the development of an obese phenotype. However, not every individual from the same population, despite sharing the same obesogenic environment, develop obesity. After more than 10 years of investigation on the genetics of obesity, the variants found associated with obesity represent only 3% of the estimated BMI-heritability, which is around 47–80%. Moreover, genetic factors per se were unable to explain the rapid spread of obesity prevalence. The integration of multi-omics data enables scientists having a better picture and to elucidate unknown pathways contributing to obesity. New studies based on case–control or gene candidate approach will be important to identify new variants associated with obesity susceptibility and consequently unveiling its genetic architecture. This will lead to an improvement of our understanding about underlying mechanisms involved in development and origin of the actual obesity epidemic. The integration of several omics will also provide insights about the interplay between genes and environments contributing to the obese phenotype.

Screen-viewing time has been associated with adverse health outcomes. Data on the predictors of youth screen-viewing time is predominately from older children in North America. Parental and home media environment factors that are associated with screen-viewing time could be targeted in interventions. Examine if parental screen-viewing time and electronic media (access to game equipment, TVs, PCs, and laptops) environment factors were associated with Portuguese children's screen-viewing time and if associations differed by child age (1 hour using combined other media on weekdays and weekend days. Parental TV-viewing time was associated with Portuguese children's TV-viewing time. The numbers of TVs in the household and electronic games equipment access were also associated with TV- and combined other media-viewing/usage time.

Aim: To identify risk factors for overweight and obesity in Portuguese children. Methods: A cross‐sectional study of children 7 to 9.5 y old was performed between October 2002 and June 2003. A total of 2274 girls and 2237 boys were observed. Weight and height were measured, and parents filled out a questionnaire about family characteristics. Overweight and obesity, using age‐ and sex‐specific body mass index (BMI) cut‐off points as defined by the International Obesity Taskforce, were used. Results: After adjustment for age and sex, the odds ratio for childhood obesity increased by television viewing (reference 0–2 h; 2–3 h: 1.51; 3–4 h: 1.72; ≥4 h: 1.63), paternal obesity (reference < 18.5 kg/m²; 25.0–29.9: 3.06; ≥30.0: 7.09), maternal obesity (reference < 18.5 kg/m²; 25.0–29.9: 9.06; ≥30.0: 18.13) and birthweight (reference < 2500 g; 2500–3000 g: 1.55; 3000–3500 g: 1.87; 3500–4000 g:2.13; 5 ≥ 4000 g: 2.74), and decreased by sleeping duration (reference 8 h/d; 9–10 h/d: 0.44; ≥ll h/d: 0.39), paternal education (reference primary school; secondary: 0.91; university: 0.42), maternal education (reference primary school; secondary: 1.13; university: 0.56), being a single child (reference yes; no: 0.56) and family size (reference one child; two children: 0.59; three children: 0.44; more than four children: 0.37). Conclusion: Our data support the perspective that education about energy intake and energy expenditure should be used much earlier in those families with high‐risk children, namely those with high parental BMI or high birthweight. Protective factors were parental education and family size.