The widening BMI distribution in the United States

Kivimäki, Mika; Stenholm, Sari; Kawachi, Ichiro

doi:10.3945/ajcn.115.107904

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…Other theories have been proposed and discussed to explain increasing dispersion at the individual level (Jenkins and Campbell 2015;Frohlich and Potvin 2008;Kivimäki et al 2015), and many suggest this may be the result of the interaction between the individuals' genetic susceptibility and environment factors (Jenkins and Campbell 2014;Razak et al 2015). These new findings put forward several unanswered questions.…”

Section: Discussionmentioning

confidence: 99%

Population-level trends in the distribution of body mass index in Canada, 2000–2014

Lebel

Subramanian

Hamel

et al. 2018

Can J Public Health

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Section: Discussionmentioning

confidence: 99%

Population-level trends in the distribution of body mass index in Canada, 2000–2014

Lebel

Subramanian

Hamel

et al. 2018

Can J Public Health

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“…Exposure to an obesogenic environment, the consumption of an abundance of unhealthy foods, limited physical activity, family history, and low education are associated with a higher mean BMI, which deserves increased attention. [43] This study has several limitations. First, the case definition of asthma was based on a diagnosis of wheezing reported by a physician in the past year.…”

Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Secular trends of asthma mortality in China and the United States from 1990 to 2019

Li,

Guo,

Niu

et al. 2023

Chinese Medical Journal

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Background: Asthma imposes a large healthcare burden in China and the United States (US). However, the trends of asthma mortality and the relative risk factors have not been comparatively analyzed between the countries. The aim of this study was to compare the mortality and risk factors between China and the US. Methods: The deaths, and mortality rates of asthma in China and the US during 1990–2019 were obtained from the Global Burden of Disease Study 2019. The age–period–cohort model was used to estimate these mortality rates based on a log-linear scale with additive age, period, and cohort effects. The population attributable fractions of risk factors for asthma were estimated. Results: In 1990–2019, the asthma mortality rate was higher in China than in the US. The crude and age-standardized asthma mortality rates trended downward in both China and the US from 1990 to 2019. The decline in mortality was more obvious in China. Mortality gap between the two countries was narrowing. A sex difference in asthma mortality was observed with higher mortality in males in China and females in the US. The age effects showed that mortality increased with age in adults older than 20 years, particularly in the elderly. Downward trends were generally observed in the period and cohort rate ratios in both countries, with China experiencing a more obvious decrease. Smoking and high body mass index (BMI) were the leading risk factors for asthma mortality in China and the US, respectively. Mortality attributable to occupational asthmagens and smoking decreased the most in China and the US, respectively. Conclusions: In 1990–2019, the asthma mortality rate was higher in China than in the US; however, the mortality gap has narrowed. Mortality increased with age in adults. The improvements in asthma death risk with period and birth cohort were more obvious in China than in the US. Smoking, high BMI, and aging are major health problems associated with asthma control. The role of occupational asthmagens in asthma mortality underscores the importance of management and prevention of occupational asthma.

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Segregation of Familial Risk of Obesity in NHANES Cohort Supports a Major Role for Large Genetic Effects in the Current Obesity Epidemic

Jenkins

Batterham

Campbell

2019

Preprint

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17The continuing increase in many countries in adult body mass index (BMI kg/m 2 ) and its dispersion 18 is contributed to by interaction between genetic susceptibilities and an increasingly obesogenic 19 environment (OE). The determinants of OE-susceptibility are unresolved, due to uncertainty around 20 relevant genetic and environmental architecture. We aimed to test the multi-modal distributional 21 predictions of a Mendelian genetic architecture based on collectively common, but individually rare, 22 large-effect variants and their ability to account for current trends in a large population-based sample. 23We studied publicly available adult BMI data (n = 9102) from 3 cycles of NHANES (1999, 2005, 24 2013). A first degree family history of diabetes served as a binary marker (FH 0 /FH 1 ) of genetic 25 obesity susceptibility. We tested for multi-modal BMI distributions non-parametrically using kernel-26 smoothing and conditional quantile regression (CQR), obtained parametric fits to a Mendelian model 27 in FH 1 , and estimated FH x OE interactions in CQR models and ANCOVA models incorporating 28 secular time. Non-parametric distributional analyses were consistent with multi-modality and fits to a 29Mendelian model in FH 1 reliably identified 3 modes. Mode separation accounted for ~40% of BMI 30 variance in FH 1 providing a lower bound for the contribution of large effects. CQR identified strong 31 FH x OE interactions and FH 1 accounted for ~60% of the secular trends in BMI and its SD in 32 ANCOVA models. Multimodality in the FH effect is inconsistent with a predominantly polygenic, 33 small effect architecture. and we conclude that large genetic effects interacting with OE provide a 34 better quantitative explanation for current trends in BMI. 35 18 388

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The widening BMI distribution in the United States

Cited by 3 publications

References 4 publications

Population-level trends in the distribution of body mass index in Canada, 2000–2014

Population-level trends in the distribution of body mass index in Canada, 2000–2014

Secular trends of asthma mortality in China and the United States from 1990 to 2019

Segregation of Familial Risk of Obesity in NHANES Cohort Supports a Major Role for Large Genetic Effects in the Current Obesity Epidemic

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