Weight and chronic disease risks -
Besides, there is evidence that obesity is strongly associated with a higher acquisition of disability [ 6 ]. Further, obese Australias are more likely to report poor general health and mental health [ 7 ].
Moreover, obesity has a substantial negative impact on diverse labour market outcomes, such as high absenteeism [ 8 ], increased presenteeism [ 9 ], job dissatisfaction [ 10 ], and a higher rate of job discrimination [ 11 ].
There is increasing empirical evidence that obesity triggers the likelihood of different non-communicable diseases NCDs , such as type 2 diabetes, high blood pressure, cardiovascular disease CVD , cancer, asthma, sleep apnea, and poor mental health [ 12 ].
An excessive gain of body weight from early childhood to adulthood is consistently associated with the risk of heart disease [ 13 ]. Obesity is also significantly related to the risk of heart disease-related morbidity and mortality [ 14 ].
Further, it is strongly associated with the incidence of type 2 diabetes [ 15 ] and depression [ 16 ]. Furthermore, the likelihood of different patterns of arthritis, such as osteoarthritis, rheumatoid arthritis, and psoriatic arthritis, is often associated with increased body weight [ 17 ].
The burden of these chronic diseases includes low quality of life, productivity loss, and increased healthcare costs [ 18 , 19 ].
While the prevalence of obesity and chronic diseases is high across Australia, people from lower socioeconomic backgrounds are often disproportionately affected [ 20 ]. Previous studies estimating the obesity and chronic disease nexus in Australia often focused on a single disease using cross-sectional survey data, which is insufficient to deduce a temporal relationship.
Besides, there is a lack of emphasis on the critical confounding factors e. socioeconomic and demographic that might explain the severity of the risks of obesity for a specific cohort of people, but not others.
There is also a lack of literature that has employed nationally representative longitudinal survey data to examine the association between obesity and chronic disease burden. Longitudinal designs are essential for the understanding of the dynamics of the relationship and interdependence e.
Therefore, this study aims to fill these gaps in the literature by employing the longitudinal study design. The main objective of this study is to estimate the between-person differences in the relationship between obesity and chronic diseases in Australian adults.
The study utilised nationally representative data from the Household, Income and Labor Dynamics in Australia HILDA survey. The HILDA survey was initiated in by collecting detailed information on 13, individuals within 7, households using a multistage sampling approach.
Since then, the survey has gathered information on a wide range of topics: wealth, retirement, fertility, health, education, skills and abilities from members of households aged 15 years or over through a self-completed questionnaire SCQ and face-to-face interviews by trained interviewers.
The description of the HILDA survey design is shown elsewhere [ 21 ]. Participants of this longitudinal study were selected from three waves waves 9, 13 and 17 of the HILDA survey, and data were collected during the years , and , respectively.
Fig 1 demonstrates the procedure of obtaining the final analytic sample. The analytical sample is restricted to adults aged 45 years and over. The final analytic sample consisting of 20, person-year observations from 9, unique participants was achieved by applying inclusion and exclusion criteria.
The outcome variable of the study is self-reported chronic disease. This study checks if obesity is a significant risk factor for chronic diseases among Australian middle-aged and older adults.
The current study measures obesity through BMI. HILDA survey collects data on BMI using self-reported weight and height following the formula of weight in kilograms divided by height in metres square.
This classification allows an assessment of how and in what context underweight, overweight and obese participants are susceptible to different chronic diseases compared with their healthy weight counterparts.
This study considered potential confounders following previous studies [ 22 , 23 ]. One significant advantage of the HILDA survey is that it provides a considerable amount of data on the demographic characteristics of respondents, such as age, gender, income level, education, area of residence and other behavioural factors.
Table 1 shows the set of the confounders with their nature and categories considered for the present study. Besides, three behavioural factors: smoking status, alcohol consumption and physical activity, served as the confounders.
Smoking status was categorised as never smoked, ex-smoker, and current smoker. The variable alcohol consumption was classified as never drank, ex-drinker, only rarely to four days and more than four days per week.
Physical activity-related information was collected by questioning how often the respondent participates in physical activity each week for at least 30 minutes.
This study categorised physical activity as: not at all to less than one, 1 to 3 times, and more than three times per week. The authors prepared an unbalanced longitudinal data set consisting of 20, person-year observations by linking de-identified records of 9, unique adults.
This study considered three distinct waves waves 9, 13, and 17 of the HILDA survey covering the period from to Due to the longitudinal nature of the data, repeated observations on the same individual were used for subsample analyses. The relationships between the exposure and other covariates with chronic diseases were first identified through bivariate analysis test results not reported here.
This study employed the longitudinal random-effects logistic regression model to capture between-person variation as the study data were derived from a longitudinal dataset repeated measures. The outcome variables type 2 diabetes, heart disease, asthma, cancer, arthritis and depression are binary whether they have a particular chronic condition or not.
Therefore, this study utilised the logistic link. A variable will be considered statistically significant if the p-value for the variable is less than the significance level in the regression models.
All statistical analyses were performed using Stata, version 16 StataCorp LLC. This study did not require ethical approval as the analysis used only de-identified existing unit record data from the HILDA survey.
However, the authors completed and signed the Confidentiality Deed Poll and sent it to NCLD ncldresearch dss. au and ADA ada anu. Table 2 displays the characteristics of the study participants in terms of their chronic diseases, socio-demographic, and behavioural characteristics at the baseline, final, and pooled in all waves.
Of 9, participants 20, observations , approximately The pooled prevalence of chronic conditions, such as type 2 diabetes, heart diseases, asthma, cancer, arthritis, and depression in study participants was approximately 9.
Fig 2 manifests that the prevalence of chronic conditions and obesity among the study population had increased from to Incidence of type 2 diabetes, asthma, and arthritis marginally increased over the period, and the prevalence of heart diseases and cancer also increased over time.
Fig 3 illustrates the percentage of chronic diseases among middle-aged and older adults based on their weight status. Prevalence of chronic conditions, such as type 2 diabetes However, underweight middle-aged and older adults are more vulnerable to heart diseases For obese people, the percentage is also noticeable, i.
Fig 4 shows the prevalence of co-morbid conditions in middle-aged and older adults stratified by gender pooled in all waves.
It is observed that the prevalence of asthma However, cancer 6. Table 3 exhibits the results obtained from the adjusted random-effect logistic regression model to investigate between-person differences in the relationship between obesity and six types of chronic diseases.
The results show that the risk of having a chronic disease was more pronounced among obese adults compared with their healthy-weight counterparts. Obese people were at higher risks of suffering from type 2 diabetes OR: It is also observed that obese people were at 1.
Gender differences in the relationship between obesity and six types of chronic conditions among middle-aged and older Australian adults were reported in Table 4.
The results showed that the odds of having chronic conditions, such as type 2 diabetes, heart diseases, arthritis and depression, were higher among obese adults compared to healthy weight counterparts irrespective of gender.
However, the magnitudes vary with gender. For example, the risk of having type 2 diabetes were Besides, the results showed that obesity is associated with a higher incidence of asthma OR: 2.
The current study is one of the first pieces of evidence that examined the between-person differences in the association between obesity and common chronic diseases among middle-aged and older Australian adults by utilising three waves spanning nine years of a nationally representative longitudinal survey.
After controlling for socio-demographic and behavioural covariates, the longitudinal random-effect logistic regression results reveal that obesity is a major risk factor for chronic diseases type 2 diabetes, heart disease, asthma, arthritis, and depression.
This study identified obesity as a significant risk factor for type 2 diabetes. This notion fits well with previous findings [ 15 , 24 ], wherein the authors concluded that overeating and obesity were strongly associated with type 2 diabetes.
The present analysis has also revealed a significant positive relationship between obesity and the risk of heart disease. Identical results are available in numerous past studies showing that increasing BMI increases the risk of heart failure in both men and women [ 25 ]. Excess weight is a high-risk factor for ischemic stroke and hemorrhagic stroke [ 26 ].
A recent study demonstrated that the increased risk of heart disease might be due to a higher incidence of hypertension, adverse hemodynamic effects, maladaptive modifications in cardiovascular structure and function and increased atrial fibrillation among obese people [ 27 ].
The finding of a positive association between obesity and asthma is consistent with the existing literature [ 28 ]. Besides, obesity also causes changes in lung volume and respiratory muscle function [ 30 ], leading to asthmatic problems.
Another novel finding of the present study is that obesity is a statistically significant risk factor for arthritis in Australian adults. The possible reason might be obesity causes increased pressure on the knee joints during daily activities, which causes proliferation of periarticular bone, leading to decreased joint space [ 33 ].
The present study findings reveal that obese adults are more likely to develop depression irrespective of socioeconomic and demographic status.
Many studies have come to identical conclusions [ 16 , 34 , 35 ]. There are several reasons for this association. Obese and overweight people generally have low health status and higher co-morbidities severe chronic diseases which might cause depression [ 34 ]. Apart from this, a model developed by Markowitz et al.
illustrated that lack of mobility, lower quality of life and physical functionalities, social stigma and dissatisfaction with body size caused by overweight and obesity, contributes to a higher level of depression [ 36 ]. The systematic literature review of Preiss et al.
Interestingly, this study observed no significant association between obesity and cancer among adults in Australia. The findings are contradictory to some of the existing literature. In an earlier review, Calle et al.
commented that obesity increases the risk of selected types of cancer [ 37 ]. Renehan et al. conducted a meta-analysis on BMI and cancer incidence, and they found that obesity is a significant risk factor for developing cancer, and the association was consistent in several continents of the world [ 38 ].
Besides, several other studies concluded that obesity-related biological mechanisms e. hormones, calorie constraints, growth factors, inflammatory progressions influence the development of malignant cells in the body [ 39 , 40 ]. Therefore, the findings of the lack of association in our study should be interpreted with caution.
It should be noted that the HILDA survey does not specify which type of cancer the respondents have developed. Hence, one possibility is that the most common type of cancers e. Share sensitive information only on official, secure websites. Obesity means weighing more than what is healthy for a given height.
Obesity is a serious, chronic disease. It can lead to other health problems, including diabetes, heart disease, and some cancers. People with obesity have a higher chance of developing these health problems:. Three things can be used to determine if a person's weight gives them a higher chance of developing obesity-related diseases:.
Experts often rely on BMI to determine if a person is overweight. The BMI estimates your level of body fat based on your height and weight. Starting at These ranges of BMI are used to describe levels of risk:.
There are many websites with calculators that give your BMI when you enter your weight and height. For individuals, BMI is a screening tool, but it does not diagnose body fatness or health.
Your health care provider can evaluate your health status and risks. If you have questions about your BMI, talk with your provider. Other methods to measure body fatness include skinfold thickness measurements with calipers , underwater weighing, bioelectrical impedance, dual-energy x-ray absorptiometry DXA , and isotope dilution.
However, these methods are not always readily available. Women with a waist size greater than 35 inches 89 centimeters and men with a waist size greater than 40 inches centimeters have an increased risk for heart disease and type 2 diabetes. People with "apple-shaped" bodies waist is bigger than the hips also have an increased risk for these conditions.
Having a risk factor for a disease doesn't mean that you will get the disease. But it does increase the chance that you will. Some risk factors, like age, race, or family history can't be changed. The more risk factors you have, the more likely it is that you will develop the disease or health problem.
Given that obesity plays such a prominent role in cancer — even in largely preventable varieties like colon cancer — more investigation into the factors that link obesity with cancer holds the promise of further reducing cancer rates. And its link to obesity is undeniable.
The good news is diabetes is largely controllable if obesity can be controlled. However, the obesity epidemic increasingly drives the rates of type 2 diabetes, the most common form of the disease. About 26 million people it the U.
have diabetes, up from about 17 million a decade ago. As obesity rates increase in children, it puts them at greater risk of developing type 2 diabetes as adults. Obese children and adolescents are more likely to have pre-diabetes, a condition in which blood glucose levels put the person at high risk for developing diabetes.
Left uncontrolled, diabetes can lead to severe kidney damage and failure, limb amputations associated with poor circulation, and death. Treating diabetes and all of its fallout effects puts a tremendous extra financial burden on employers, insurers, care providers and individuals. A healthy diet and daily exercise remain pillars of preventing and managing both obesity and type 2 diabetes.
But given the rising rates of each, we clearly need more avenues. Attacking obesity through science — learning how it functions, the hormones that influence it, what roles genetics play, how appetite can more accurately be controlled and why recidivism rates following weight loss are more than percent — offers a twofold promise of combating both problems.
More people die from heart disease in the U. And the obesity epidemic threatens to increase that rate. But the link goes deeper.
BMC Public Skin rejuvenation benefits volume 14Weigth number: Cite chrnoic article. Metrics details. Riskz and obesity prevalence has risen dramatically in recent Cholesterol maintenance tips. Skin rejuvenation benefits it is known that overweight and obesity is associated with snd wide range of chronic diseases, the cumulative burden of chronic disease in the population associated with overweight and obesity is not well quantified. The aims of this paper were to examine the associations between BMI and chronic disease prevalence; to calculate Population Attributable Fractions PAFs associated with overweight and obesity; and to estimate the impact of a one unit reduction in BMI on the population prevalence of chronic disease. Risk and obesity may increase your risk for Skin rejuvenation benefits health problems—especially if you carry extra fat around cchronic waist. Reaching and staying at a healthy Cisease can help prevent these problems, stop Endurance nutrition from getting Weiggt, or even make them go away. Type 2 diabetes is a disease that occurs when your blood glucosealso called blood sugar, is too high. Nearly 9 in 10 people with type 2 diabetes have overweight or obesity. High blood pressurealso called hypertension, is a condition in which blood flows through your blood vessels with a force greater than normal. Having a large body size may increase blood pressure because your heart needs to pump harder to supply blood to all your cells.
Weight and chronic disease risks -
Clinical Guidelines on the Identification, Evaluation, And Treatment of Overweight And Obesity in Adults [PDF Health Risks of Overweight and Obesity Causes, risk factors, screening, prevention and more—National Heart, Lung and Blood Institute.
Adult Obesity Maps Self-reported US adult obesity prevalence by race, ethnicity, and location. Managing Overweight and Obesity in Adults: Systematic Evidence Review from the Obesity Expert Panel. Skip directly to site content Skip directly to search.
Español Other Languages. Health Effects of Overweight and Obesity. Español Spanish Print. Minus Related Pages. Want to learn more? References 1 NHLBI. Top of Page. Connect with Nutrition, Physical Activity, and Obesity. Last Reviewed: September 24, Source: Division of Nutrition, Physical Activity, and Obesity , National Center for Chronic Disease Prevention and Health Promotion.
Facebook Twitter LinkedIn Syndicate. home Healthy Weight, Nutrition, and Physical Activity. To receive email updates about this topic, enter your email address.
This study considered three distinct waves waves 9, 13, and 17 of the HILDA survey covering the period from to Due to the longitudinal nature of the data, repeated observations on the same individual were used for subsample analyses.
The relationships between the exposure and other covariates with chronic diseases were first identified through bivariate analysis test results not reported here. This study employed the longitudinal random-effects logistic regression model to capture between-person variation as the study data were derived from a longitudinal dataset repeated measures.
The outcome variables type 2 diabetes, heart disease, asthma, cancer, arthritis and depression are binary whether they have a particular chronic condition or not. Therefore, this study utilised the logistic link. A variable will be considered statistically significant if the p-value for the variable is less than the significance level in the regression models.
All statistical analyses were performed using Stata, version 16 StataCorp LLC. This study did not require ethical approval as the analysis used only de-identified existing unit record data from the HILDA survey. However, the authors completed and signed the Confidentiality Deed Poll and sent it to NCLD ncldresearch dss.
au and ADA ada anu. Table 2 displays the characteristics of the study participants in terms of their chronic diseases, socio-demographic, and behavioural characteristics at the baseline, final, and pooled in all waves. Of 9, participants 20, observations , approximately The pooled prevalence of chronic conditions, such as type 2 diabetes, heart diseases, asthma, cancer, arthritis, and depression in study participants was approximately 9.
Fig 2 manifests that the prevalence of chronic conditions and obesity among the study population had increased from to Incidence of type 2 diabetes, asthma, and arthritis marginally increased over the period, and the prevalence of heart diseases and cancer also increased over time.
Fig 3 illustrates the percentage of chronic diseases among middle-aged and older adults based on their weight status. Prevalence of chronic conditions, such as type 2 diabetes However, underweight middle-aged and older adults are more vulnerable to heart diseases For obese people, the percentage is also noticeable, i.
Fig 4 shows the prevalence of co-morbid conditions in middle-aged and older adults stratified by gender pooled in all waves. It is observed that the prevalence of asthma However, cancer 6.
Table 3 exhibits the results obtained from the adjusted random-effect logistic regression model to investigate between-person differences in the relationship between obesity and six types of chronic diseases.
The results show that the risk of having a chronic disease was more pronounced among obese adults compared with their healthy-weight counterparts. Obese people were at higher risks of suffering from type 2 diabetes OR: It is also observed that obese people were at 1.
Gender differences in the relationship between obesity and six types of chronic conditions among middle-aged and older Australian adults were reported in Table 4. The results showed that the odds of having chronic conditions, such as type 2 diabetes, heart diseases, arthritis and depression, were higher among obese adults compared to healthy weight counterparts irrespective of gender.
However, the magnitudes vary with gender. For example, the risk of having type 2 diabetes were Besides, the results showed that obesity is associated with a higher incidence of asthma OR: 2. The current study is one of the first pieces of evidence that examined the between-person differences in the association between obesity and common chronic diseases among middle-aged and older Australian adults by utilising three waves spanning nine years of a nationally representative longitudinal survey.
After controlling for socio-demographic and behavioural covariates, the longitudinal random-effect logistic regression results reveal that obesity is a major risk factor for chronic diseases type 2 diabetes, heart disease, asthma, arthritis, and depression.
This study identified obesity as a significant risk factor for type 2 diabetes. This notion fits well with previous findings [ 15 , 24 ], wherein the authors concluded that overeating and obesity were strongly associated with type 2 diabetes.
The present analysis has also revealed a significant positive relationship between obesity and the risk of heart disease. Identical results are available in numerous past studies showing that increasing BMI increases the risk of heart failure in both men and women [ 25 ].
Excess weight is a high-risk factor for ischemic stroke and hemorrhagic stroke [ 26 ]. A recent study demonstrated that the increased risk of heart disease might be due to a higher incidence of hypertension, adverse hemodynamic effects, maladaptive modifications in cardiovascular structure and function and increased atrial fibrillation among obese people [ 27 ].
The finding of a positive association between obesity and asthma is consistent with the existing literature [ 28 ]. Besides, obesity also causes changes in lung volume and respiratory muscle function [ 30 ], leading to asthmatic problems.
Another novel finding of the present study is that obesity is a statistically significant risk factor for arthritis in Australian adults. The possible reason might be obesity causes increased pressure on the knee joints during daily activities, which causes proliferation of periarticular bone, leading to decreased joint space [ 33 ].
The present study findings reveal that obese adults are more likely to develop depression irrespective of socioeconomic and demographic status.
Many studies have come to identical conclusions [ 16 , 34 , 35 ]. There are several reasons for this association. Obese and overweight people generally have low health status and higher co-morbidities severe chronic diseases which might cause depression [ 34 ]. Apart from this, a model developed by Markowitz et al.
illustrated that lack of mobility, lower quality of life and physical functionalities, social stigma and dissatisfaction with body size caused by overweight and obesity, contributes to a higher level of depression [ 36 ].
The systematic literature review of Preiss et al. Interestingly, this study observed no significant association between obesity and cancer among adults in Australia. The findings are contradictory to some of the existing literature. In an earlier review, Calle et al.
commented that obesity increases the risk of selected types of cancer [ 37 ]. Renehan et al. conducted a meta-analysis on BMI and cancer incidence, and they found that obesity is a significant risk factor for developing cancer, and the association was consistent in several continents of the world [ 38 ].
Besides, several other studies concluded that obesity-related biological mechanisms e. hormones, calorie constraints, growth factors, inflammatory progressions influence the development of malignant cells in the body [ 39 , 40 ].
Therefore, the findings of the lack of association in our study should be interpreted with caution. It should be noted that the HILDA survey does not specify which type of cancer the respondents have developed.
Hence, one possibility is that the most common type of cancers e. skin, prostate, colorectal, melanoma and lung associated with Australian adults are insignificantly impacted by obesity and overweight.
Future research should focus on addressing this issue. Finally, similar to the common knowledge in the public health literature, the results indicate that increased physical activities reduce the risk of chronic diseases irrespective of obesity and socio-demographic status.
Noticeably, the most considerable positive impact of physical activities was on the level of depression. An extensive literature related to Australian adults validates this study finding [ 41 , 42 ].
Therefore, the present study suggests the promotion of physical activities to prevent chronic diseases in Australian adults. This study calls for future research that will explore the potential of lifestyle interventions and dietary modification to curb excessive weight gain. Managing obesity has the potential to reduce the prevalence of and mortality from these chronic diseases [ 43 ], and improve health-related quality of life [ 44 ].
A previous study has claimed that the prevalence of diabetes, high cholesterol, high blood pressure, and CVD among Australian adults could be reduced significantly by reducing body weight [ 12 ].
Policymakers and health practitioners might use these findings to devise appropriate strategies and targeted health programs for overweight and obese Australians to reduce their probable burden of chronic diseases.
This study explores the longitudinal association between obesity and chronic diseases in Australian adults. The longitudinal random-effect logistic regression results showed significant associations between excess body fat obesity and chronic diseases.
Association between obesity and chronic diseases using longitudinal data is relatively uncommon. This study is one of the few studies that considered six different types of chronic conditions covering nine years of data.
The study found that the prevalence and incidence of chronic conditions, such as type 2 diabetes, heart diseases, asthma, arthritis and depression, are higher among obese adults than their healthy-weight counterparts. More specifically, people with obesity are at higher risk of having type 2 diabetes compared to their healthy counterparts than any other chronic disease in Australia.
The present study has several strengths. Firstly, this study identified which chronic diseases have the strongest association with obesity in Australian adults. Secondly, this study considered a wide range of chronic diseases while checking their relationship with obesity.
Thirdly, unlike previous studies, this study employed longitudinal data from the HILDA survey, which is broadly representative of the national population.
Fourthly, this study has identified that obesity increase the incidence of chronic diseases differently among men and women. This study has some drawbacks in estimating the relationships between obesity and chronic diseases. Firstly, this study used self-reported data on BMI, chronic diseases, and lifestyle characteristics.
Secondly, this study formed an unbalanced panel data for the subsample analyses. Therefore, causality cannot be drawn from the present study findings. Thirdly, this study did not consider genetic or familial aggregation factors, which are common causes of some chronic diseases, such as type 2 diabetes.
Fourthly, the HILDA survey questionnaire does not specify the exact type of cancer or arthritis the participants have developed. The authors would like to thank the Melbourne Institute of Applied Economic and Social Research for providing the HILDA data set. This paper uses unit record data from the Household, Income and Labour Dynamics in Australia Survey HILDA conducted by the Australian Government Department of Social Services DSS.
The findings and views reported in this paper, however, are those of the authors and should not be attributed to the Australian Government, DSS, or any of DSS contractors or partners.
DOI: Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Article Authors Metrics Comments Media Coverage Peer Review Reader Comments Figures.
Abstract Background Overweight and obesity impose a significant health burden in Australia, predominantly the middle-aged and older adults. Methods Longitudinal data comprising three waves waves 9, 13 and 17 of the Household, Income and Labour Dynamics in Australia HILDA survey were used in this study.
Results The findings indicated that obesity was associated with a higher prevalence of chronic diseases among Australian middle-aged and older adults. Conclusion Excessive weight is strongly associated with a higher incidence of chronic disease in Australian middle-aged and older adults.
Funding: The authors received no specific funding for this work. Introduction According to the World Health Organisation WHO , 1. Materials and methods Data source and sample selection The study utilised nationally representative data from the Household, Income and Labor Dynamics in Australia HILDA survey.
Download: PPT. Outcome variable The outcome variable of the study is self-reported chronic disease. Exposure variable This study checks if obesity is a significant risk factor for chronic diseases among Australian middle-aged and older adults.
Other covariates This study considered potential confounders following previous studies [ 22 , 23 ]. Estimation strategy The authors prepared an unbalanced longitudinal data set consisting of 20, person-year observations by linking de-identified records of 9, unique adults. Ethics approval This study did not require ethical approval as the analysis used only de-identified existing unit record data from the HILDA survey.
Results Table 2 displays the characteristics of the study participants in terms of their chronic diseases, socio-demographic, and behavioural characteristics at the baseline, final, and pooled in all waves. Table 2. Fig 2. Prevalence of chronic conditions among middle-aged and older adults.
Fig 3. Prevalence of chronic conditions among middle-aged and older adults by weight status. Fig 4. Gender differences in the prevalence of the chronic conditions among obese middle-aged and older adults.
Table 3. Adjusted random-effect regression results for the between-person differences in chronic conditions due to obesity; 9, persons, 20, observations. Table 4. Adjusted random-effect regression results for the between-person differences in chronic conditions due to obesity stratified by gender.
Discussion The current study is one of the first pieces of evidence that examined the between-person differences in the association between obesity and common chronic diseases among middle-aged and older Australian adults by utilising three waves spanning nine years of a nationally representative longitudinal survey.
Conclusion This study explores the longitudinal association between obesity and chronic diseases in Australian adults. Acknowledgments The authors would like to thank the Melbourne Institute of Applied Economic and Social Research for providing the HILDA data set.
References 1. World Health Organization. Obesity and overweight. GBD Obesity Collaborators. Health Effects of Overweight and Obesity in Countries over 25 Years. N Engl J Med. Australian Institute of Health and Welfare.
Impact of overweight and obesity as a risk factor for chronic conditions: Australian burden of disease Study. Australian Burden of Disease Study series no. BOD Canberra: AIHW; Keramat SA, Alam K, Al-Hanawi MK, Gow J, Biddle SJH, Hashmi R.
Trends in the prevalence of adult overweight and obesity in Australia, and its association with geographic remoteness. Sci Rep.
Australian Burden of Disease Study: impact and causes of illness and death in Australia Australian Burden of Disease series no. Keramat SA, Alam K, Sathi NJ, Gow J, Biddle SJH, Al-Hanawi MK. Self-reported disability and its association with obesity and physical activity in Australian adults: Results from a longitudinal study.
SSM—Popul Heal. Keramat SA, Alam K, Ahinkorah BO, Islam MS, Islam MI, Hossain MZ, et al. Obesity, Disability and Self-Perceived Health Outcomes in Australian Adults: A Longitudinal Analysis Using 14 Annual Waves of the HILDA Cohort. Clin Outcomes Res. Keramat SA, Alam K, Gow J, Biddle SJH.
Gender differences in the longitudinal association between obesity, and disability with workplace absenteeism in the Australian working population.
PLoS ONE 15 5 e,. A longitudinal exploration of the relationship between obesity, and long term health condition with presenteeism in Australian workplaces, — PLoS ONE 15 8 e,.
Obesity, Long-Term Health Problems, and Workplace Satisfaction: A Longitudinal Study of Australian Workers.
Fat accumulates in our Chronlc when the energy diseaxe we consume from diseaase and drink diseease Weight and chronic disease risks than Protein for athletic injury rehabilitation energy we use in activities and at rest. Consuming even slightly more energy than you use, over chgonic periods of time, can cause you to become overweight or obese. The latest National Health Survey shows that men are more likely to be overweight or obese than women Men and women living in regional and remote areas of Australia are more likely to be overweight or obese than men and women living in major cities. The number of Australian children who are overweight or obese has also continued to increase since In —18,
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