Association Between Different Indicators of Obesity and Depression in Adults in Qingdao, China: A Cross-Sectional Study

Cui, Jing; Sun, Xiufen; Li, Xiaojing; Ma, Ke; Sun, Jianping; Yasmeen, Nafeesa; Khan, Jahangir; Xin, Hualei; Xue, Shouyong; Baloch, Zulqarnain

doi:10.3389/fendo.2018.00549

Cited by 20 publications

(11 citation statements)

References 48 publications

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“…Coronary heart disease was associated with compound obesity but not central obesity alone, while stroke was associated with neither compound obesity nor central obesity alone. Studies report that the prevalence of central obesity (central obesity alone + compound obesity in our study) varies from 39.0 to 57.4% in other regions of China (6,(22)(23)(24)(25)(26)(27)(28). This variability may be primarily attributed to lifestyle difference throughout China.…”

Section: Discussionmentioning

confidence: 56%

Association Between Obesity Type and Common Vascular and Metabolic Diseases: A Cross-Sectional Study

et al. 2020

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Background: The association between different types of obesity and some chronic diseases in Dehui, Jilin province, China, is still unclear. The aim of our study was to clarify the association between different types of obesity and chronic diseases. Methods: Residents aged 40 years or older were randomly selected using a multistage stratified cluster sampling method. Data were collected by means of face-to-face interview, physical examination, and laboratory examination. Descriptive data analyses were performed, and multiple logistic regression analyses were used to explore the adjusted association between different types of obesity and common vascular and metabolic diseases. Results: The prevalence of general obesity alone, central obesity alone and compound obesity were 0.15, 54.29, and 14.36%, respectively. The prevalence of coronary heart disease, stroke, hypertension, dyslipidemia, and diabetes mellitus was highest in the compound obesity group, and lowest in the non-obesity group. Hypertension, dyslipidemia, and diabetes mellitus were associated with compound obesity and central obesity alone [compound obesity (OR = 4.703, 95% CI: 3.714-5.956 for hypertension; OR = 4.244, 95% CI: 3.357-5.365 for dyslipidemia; OR = 4.575, 95% CI: 3.194-6.552 for diabetes mellitus); central obesity alone (OR = 2.210, 95% CI: 1.901-2.570 for hypertension; OR = 2.598, 95% CI: 2.241-3.012 for dyslipidemia; OR = 2.519, 95% CI: 1.834-3.459 for diabetes mellitus)]. Coronary heart disease was associated with compound obesity (OR = 1.761, 95% CI: 1.141-2.719) but not central obesity alone (OR = 1.409, 95% CI: 0.986-2.013). Stroke was associated with neither compound obesity (OR = 1.222, 95% CI: 0.815-1.833) nor associated with central obesity alone (OR = 1.080, 95% CI: 0.786-1.485). Conclusions: Central obesity alone and compound obesity are associated with the risk of hypertension, hyperlipidemia, and diabetes mellitus. Compound obesity but not central obesity alone is associated with the risk of coronary heart disease, but further research is needed to confirm it. There are no significant relationship between stroke and central obesity alone or compound obesity.

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Section: Discussionmentioning

confidence: 56%

Association Between Obesity Type and Common Vascular and Metabolic Diseases: A Cross-Sectional Study

et al. 2020

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“…Many studies have explored the spatial patterns of obesity prevalence in various countries and regions, including the United States (Michimi and Wimberly, 2010), Canada (Hajizadeh et al 2016), the United Kingdom (Fraser et al 2012), Iran (Farhadian et al, 2013), and mainland China (Cui et al, 2018). However, in the Netherlands, similar efforts have been mostly focused on temporal variations of obesity prevalence or on factors that are associated with obesity at the individual level.…”

Section: Discussionmentioning

confidence: 99%

Geographic clustering and region-specific determinants of obesity in the Netherlands

Qiu¹,

Liu

Amiranti

et al. 2020

Geospat Health

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As a leading cause of morbidity and premature mortality, obesity has become a major global public health problem. It is therefore important to investigate the spatial variation of obesity prevalence and its associations with environmental and behavioral factors (e.g., food environment, physical activity), to optimize the targeting of scarce public health resources. In this study, the geographic clustering of obesity in the Netherlands was explored by analyzing the local spatial autocorrelation of municipal-level prevalence rates of adulthood obesity (aged ≥19 years) in 2016. The potential influential factors that may be associated with obesity prevalence were first selected from five categories of healthrelated factors through binary and Least Absolute Shrinkage and Selection Operator (LASSO) regressions. Geographically Weighted Regression (GWR) was then used to investigate the spatial variations of the associations between those selected factors and obesity prevalence. The results revealed marked geographic variations in obesity prevalence, with four clusters of high prevalence in the north, south, east, and west, and three clusters of low prevalence in the north and south of the Netherlands. Lack of sports participation, risk of anxiety, falling short of physical activity guidelines, and the number of restaurants around homes were found to be associated with obesity prevalence across municipalities. Our findings show that effective, region-specific strategies are needed to tackle the increasing obesity in the Netherlands.

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“…Prekomjerna uhranjenost i pretilost predstavljaju značajan javnozdravstveni problem (globalna prevalenca procijenjena je na 39% i 13% kod odraslih starijih od 18 godina) (1). Prate ih promjene u patofiziologiji osobe, njenom somatskom i estetskom aspektu, i nerijetko i sa snažnom neprijatnom emocionalnom komponentom koja za posljedicu ima razvoj psihijatrijskih poremećaja (2).…”

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Factors affecting development of depression in overfood and obesity people

Jandrić-Kočić¹

2020

Medicinski časopis

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Objective. Overweight and obesity are accompanied by changes in a person's pathophysiology, somatic and aesthetic aspects, and often a strong unpleasant emotional component that results in the development of psychiatric disorders, primarily depression. Objective: The study aimed to examine the existence of a statistically significant association of sociodemographic factors and body mass index (BMI) with the intensity of depression in obese and overweight people. Methods. The study was performed as a cross-sectional study at the Health Center Krupa na Uni (Republika Srpska, Bosnia and Herzegovina) in the period from 01.12.2018. to 01.10.2019. The sample consisted of 90 overweight and obese individuals, 58 (64.4%) women and 32 (35.6%) men with a mean age of 55.0 ± 6.9 years. In addition to the general questionnaire, the study used Beck's Depression Inventory (BDI). The chi-square test was used in the statistical analysis of the data. Results. Depression was found in 45 (50.0%) study participants (31.1% mild depression, 15.6% moderate depression, and 3.3% severe depression). There was a statistically significant higher intensity of depression in female subjects and body mass index ≥ 25. Conclusion. Female gender is an independent predictor of depression intensity in overweight and obese individuals. There is a strong correlation between body mass index and depression intensity. Level of education, place of residence and age are not consistent with the presence and severity of depression in overweight and obese individuals.

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Association Between Different Indicators of Obesity and Depression in Adults in Qingdao, China: A Cross-Sectional Study

Cited by 20 publications

References 48 publications

Association Between Obesity Type and Common Vascular and Metabolic Diseases: A Cross-Sectional Study

Association Between Obesity Type and Common Vascular and Metabolic Diseases: A Cross-Sectional Study

Geographic clustering and region-specific determinants of obesity in the Netherlands

Factors affecting development of depression in overfood and obesity people

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