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Abstract
OBJECTIVE
We examined longitudinal associations of air pollution exposure, including fine particulate
matter (PM
2.5), nitrogen dioxide (NO
2), and ozone (O
3), with weight, BMI, waist circumference, fat mass, lean mass, and proportion fat
mass in midlife women.
RESEARCH DESIGN AND METHODS
The study population included 1,654 White, Black, Chinese, and Japanese women from
the Study of Women’s Health Across the Nation, with the baseline median age of 49.6
years, followed from 2000 to 2008. Annual air pollution exposures were assigned by
linking residential addresses with hybrid estimates of air pollutant concentrations
at 1-km
2 resolution. Body size was measured, and body composition was measured using DXA at
approximately annual visits. Linear mixed effects models were used to examine the
associations between air pollution and body size and composition measures and whether
these associations differed by physical activity.
RESULTS
After adjusting for potential confounders, an interquartile range increase in PM
2.5 concentration (4.5 μg/m
3) was associated with 4.53% (95% CI 3.85%, 5.22%) higher fat mass, 1.10% (95% CI 0.95%,
1.25%) higher proportion fat mass, and 0.39% (95% CI −0.77%, −0.01%) lower lean mass.
Similar associations were also observed for NO
2 and O
3. Weaker associations of PM
2.5 and NO
2 with body composition were observed in participants who engaged in more physical
activity.
CONCLUSIONS
Our analyses provide evidence that exposure to PM
2.5, NO
2, and O
3, is adversely associated with body composition, including higher fat mass, higher
proportional fat mass, and lower lean mass, highlighting their potential contribution
to obesity.
Summary Background 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. Methods 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). Findings 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. Interpretation 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. Funding Wellcome Trust, AstraZeneca Young Health Programme.
In the United States, obesity among adults and overweight among children and adolescents have increased markedly since 1980. Among adults, obesity is defined as a body mass index of 30 or greater. Among children and adolescents, overweight is defined as a body mass index for age at or above the 95th percentile of a specified reference population. In 2003-2004, 32.9% of adults 20-74 years old were obese and more than 17% of teenagers (age, 12-19 y) were overweight. Obesity varies by age and sex, and by race-ethnic group among adult women. A higher body weight is associated with an increased incidence of a number of conditions, including diabetes mellitus, cardiovascular disease, and nonalcoholic fatty liver disease, and with an increased risk of disability. Obesity is associated with a modestly increased risk of all-cause mortality. However, the net effect of overweight and obesity on morbidity and mortality is difficult to quantify. It is likely that a gene-environment interaction, in which genetically susceptible individuals respond to an environment with increased availability of palatable energy-dense foods and reduced opportunities for energy expenditure, contributes to the current high prevalence of obesity. Evidence suggests that even without reaching an ideal weight, a moderate amount of weight loss can be beneficial in terms of reducing levels of some risk factors, such as blood pressure. Many studies of dietary and behavioral treatments, however, have shown that maintenance of weight loss is difficult. The social and economic costs of obesity and of attempts to prevent or to treat obesity are high.
Various approaches have been proposed to model PM 2.5 in the recent decade, with satellite-derived aerosol optical depth, land-use variables, chemical transport model predictions, and several meteorological variables as major predictor variables. Our study used an ensemble model that integrated multiple machine learning algorithms and predictor variables to estimate daily PM 2.5 at a resolution of 1 km×1 km across the contiguous United States. We used a generalized additive model that accounted for geographic difference to combine PM 2.5 estimates from neural network, random forest, and gradient boosting. The three machine learning algorithms were based on multiple predictor variables, including satellite data, meteorological variables, land-use variables, elevation, chemical transport model predictions, several reanalysis datasets, and others. The model training results from 2000 to 2015 indicated good model performance with a 10-fold cross-validated R 2 of 0.86 for daily PM 2.5 predictions. For annual PM 2.5 estimates, the cross-validated R 2 was 0.89. Our model demonstrated good performance up to 60 μg/m 3 . Using trained PM 2.5 model and predictor variables, we predicted daily PM 2.5 from 2000 to 2015 at every 1 km×1 km grid cell in the contiguous United States. We also used localized land-use variables within 1 km×1 km grids to downscale PM 2.5 predictions to 100 m × 100 m grid cells. To characterize uncertainty, we used meteorological variables, land-use variables, and elevation to model the monthly standard deviation of the difference between daily monitored and predicted PM 2.5 for every 1 km×1 km grid cell. This PM 2.5 prediction dataset, including the downscaled and uncertainty predictions, allows epidemiologists to accurately estimate the adverse health effect of PM 2.5 . Compared with model performance of individual base learners, an ensemble model would achieve a better overall estimation. It is worth exploring other ensemble model formats to synthesize estimations from different models or from different groups to improve overall performance.
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History
Date
received
: 17
May
2022
Date
accepted
: 18
August
2022
Funding
Funded by: National Institute on Aging, DOI 10.13039/100000049;
Award ID: U01AG012495
Award ID: U01AG012505
Award ID: U01AG012531
Award ID: U01AG012535
Award ID: U01AG012539
Award ID: U01AG012546
Award ID: U01AG012553
Award ID: U01AG012554
Award ID: U19AG063720
Funded by: National Institute of Nursing Research, DOI 10.13039/100000056;
Award ID: U01NR004061
Funded by: Center for Disease Control and Prevention (CDC)/National Institute for Occupational
Safety and Health (NIOSH);
Award ID: T42-OH008455
Funded by: National Institute of Environmental Health Sciences (NIEHS);
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