BMC public health · 2025
Exposure-response relationship of household air pollution on body mass index among women in rural areas of Guatemala, India, Peru and Rwanda: household air pollution intervention network trial.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Ndikubwimana Adolphe, Checkley William, Chen Yunyun, Clasen Thomas, Contreras Carmen Lucía, Diaz-Artiga Anaite, Dusabimana Ephrem, de Las Fuentes Lisa, Jabbarzadeh Shirin, Johnson Michael, Kalisa Egide, Karakwende Patrick, Kirby Miles, Lovvorn Amy E, McCracken John P, Ndagijimana Florien, Ntakirutimana Theoneste, Ntivuguruzwa Jean Dieu, Peel Jennifer L, Pillarisetti Ajay, Dávila-Román Victor G, Rosa Ghislaine, Garg Sarada S, Thompson Lisa, Waller Lance A, Wang Jiantong, Clark Maggie L, Young Bonnie N, HAPIN investigators
Аннотация
BACKGROUND: Household air pollution from burning biomass materials, the main cooking fuel in low- and middle-income countries, may be linked to metabolic dysfunction. We assessed cross-sectional associations between household air pollution and body mass index (BMI), expecting to see increased BMI with higher pollution concentrations.
METHODS: We analyzed data from 414 women aged 40 to 79 years who resided in the households using biomass fuel and were enrolled in the multi-country Household Air Pollution Intervention Network (HAPIN) Trial. We explored associations of 24-h average personal exposure to fine particulate matter (PM2.5), black carbon (BC), and carbon monoxide (CO) with BMI through single pollutant linear and logistic models adjusted for potential confounders (i.e., age, socioeconomic indicators, education, dietary diversity, secondhand smoke exposure, alcohol and grain consumption).Sensitivity analyses explored air pollutants as quartiles, and other variables as potential confounders, such as physical activity, enrollment site, and dietary items. We examined effect modification of research site on the associations.
RESULTS: We observed mixed evidence of associations between household air pollution and BMI in linear regression. There was no association with BMI and PM₂.₅ (1-unit increase in log-transformed PM₂.₅ estimate 0.02 kg/m2 [95% CI: -0.51, 0.54]) or CO (1-unit increase in log-transformed CO estimate 0.42 kg/m2 [95% CI -0.31, 1.14]). However, a 1-unit increase in log-transformed BC showed an association in the opposite direction as hypothesized (BC estimate -0.59 kg/m2 [95% CI -1.17, -0.003]). Using logistic regression models, we found that only CO significantly increased the odds of overweight/obesity: a 1-unit increase in log-transformed CO led to an odds ratio of 1.66 (95% CI: 1.10, 2.51). Effect modification showed inverse association between BC exposure and BMI in Peru.
CONCLUSIONS: Evidence suggests a significant association between CO exposure and increased odds of being overweight/obese, whereas impacts of PM2.5 and BC on BMI had null or inverse effects.
METHODS: We analyzed data from 414 women aged 40 to 79 years who resided in the households using biomass fuel and were enrolled in the multi-country Household Air Pollution Intervention Network (HAPIN) Trial. We explored associations of 24-h average personal exposure to fine particulate matter (PM2.5), black carbon (BC), and carbon monoxide (CO) with BMI through single pollutant linear and logistic models adjusted for potential confounders (i.e., age, socioeconomic indicators, education, dietary diversity, secondhand smoke exposure, alcohol and grain consumption).Sensitivity analyses explored air pollutants as quartiles, and other variables as potential confounders, such as physical activity, enrollment site, and dietary items. We examined effect modification of research site on the associations.
RESULTS: We observed mixed evidence of associations between household air pollution and BMI in linear regression. There was no association with BMI and PM₂.₅ (1-unit increase in log-transformed PM₂.₅ estimate 0.02 kg/m2 [95% CI: -0.51, 0.54]) or CO (1-unit increase in log-transformed CO estimate 0.42 kg/m2 [95% CI -0.31, 1.14]). However, a 1-unit increase in log-transformed BC showed an association in the opposite direction as hypothesized (BC estimate -0.59 kg/m2 [95% CI -1.17, -0.003]). Using logistic regression models, we found that only CO significantly increased the odds of overweight/obesity: a 1-unit increase in log-transformed CO led to an odds ratio of 1.66 (95% CI: 1.10, 2.51). Effect modification showed inverse association between BC exposure and BMI in Peru.
CONCLUSIONS: Evidence suggests a significant association between CO exposure and increased odds of being overweight/obese, whereas impacts of PM2.5 and BC on BMI had null or inverse effects.