Obesity

Binns, Colin; Low, Wah Yun

doi:10.1177/1010539513479003

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…Therefore, the role of biologic differences in body fat distribution and insulin secretion needs to be elucidated in more detail to find out how to protect Asians against this excessive risk to develop diabetes while promoting public health measures that prevent further increases in obesity with all its health consequences at the same time. 36 …”

Section: Discussionmentioning

confidence: 99%

Disparity in Diabetes Risk Across Native Hawaiians and Different Asian Groups

Maskarinec

Jacobs

Morimoto

et al. 2014

Asia Pac J Public Health

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We evaluated the impact of body mass index (BMI) and lifestyle risk factors on ethnic disparity in diabetes incidence among 89,198 Asian, Native Hawaiian, and white participants of the Multiethnic Cohort who completed multiple questionnaires. After 12 years of follow-up, 11,218 new cases were identified through self-report and health plan linkages. BMI was lowest in Chinese/Koreans, Japanese, and Filipinos (22.4, 23.5, 23.9 kg/m2). Using Cox regression, the unadjusted hazard ratios were 1.9 (Chinese/Korean), 2.1 (Japanese, Mixed-Asian), 2.2 (Filipino), 2.5 (Native Hawaiian), and 2.6 (Part-Asian) as compared to whites. With BMI added, the risk for Japanese, Filipinos, Chinese/Koreans, and Mixed-Asians increased (8–42%) but declined in Part-Asians and Native Hawaiians (17–31%). When lifestyle and dietary factors were also included, the risk was attenuated in all groups (6–14%). Despite their lower BMI, Asian Americans have a higher diabetes risk than whites, but dietary and lifestyle factors do not account for the excess risk.

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Section: Discussionmentioning

confidence: 99%

Disparity in Diabetes Risk Across Native Hawaiians and Different Asian Groups

Maskarinec

Jacobs

Morimoto

et al. 2014

Asia Pac J Public Health

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“…Worse, the prevalence and associated costs of obesity are still growing [2]. By 2030, the direct US healthcare costs of obesity are predicted to be $860–960 billion [3]. Greater than 6.1 million children (2–10 years), 7.7 million adolescents (11–19 years) [4,5], and 72 million adults [6] are overweight/obese – one-third of the entire U.S. population!…”

Section: Introductionmentioning

confidence: 99%

A multifactorial obesity model developed from nationwide public health exposome data and modern computational analyses

Kilbourne

Vadapalli

Khan

et al. 2017

Obesity Research & Clinical Practice

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Summary Statement of the problem Obesity is both multifactorial and multimodal, making it difficult to identify, unravel and distinguish causative and contributing factors. The lack of a clear model of aetiology hampers the design and evaluation of interventions to prevent and reduce obesity. Methods Using modern graph-theoretical algorithms, we are able to coalesce and analyse thousands of inter-dependent variables and interpret their putative relationships to obesity. Our modelling is different from traditional approaches; we make no a priori assumptions about the population, and model instead based on the actual characteristics of a population. Paracliques, noise-resistant collections of highly-correlated variables, are differentially distilled from data taken over counties associated with low versus high obesity rates. Factor analysis is then applied and a model is developed. Results and conclusions Latent variables concentrated around social deprivation, community infrastructure and climate, and especially heat stress were connected to obesity. Infrastructure, environment and community organisation differed in counties with low versus high obesity rates. Clear connections of community infrastructure with obesity in our results lead us to conclude that community level interventions are critical. This effort suggests that it might be useful to study and plan interventions around community organisation and structure, rather than just the individual, to combat the nation’s obesity epidemic.

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The Brain–Gut Axis

Farmer

Aziz

2015

Yamada' S Textbook of Gastroenterology

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Obesity

Cited by 5 publications

References 14 publications

Disparity in Diabetes Risk Across Native Hawaiians and Different Asian Groups

Disparity in Diabetes Risk Across Native Hawaiians and Different Asian Groups

A multifactorial obesity model developed from nationwide public health exposome data and modern computational analyses

The Brain–Gut Axis

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