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. 2021 Nov 16;16(11):e0260158. doi: 10.1371/journal.pone.0260158

Obesity and the risk of developing chronic diseases in middle-aged and older adults: Findings from an Australian longitudinal population survey, 2009–2017

Syed Afroz Keramat 1,2,3,*, Khorshed Alam 2,3, Rezwanul Hasan Rana 2,4, Rupok Chowdhury 1, Fariha Farjana 1, Rubayyat Hashmi 2,3, Jeff Gow 2,5, Stuart J H Biddle 3
Editor: David Meyre6
PMCID: PMC8594821  PMID: 34784404

Abstract

Background

Overweight and obesity impose a significant health burden in Australia, predominantly the middle-aged and older adults. Studies of the association between obesity and chronic diseases are primarily based on cross-sectional data, which is insufficient to deduce a temporal relationship. Using nationally representative panel data, this study aims to investigate whether obesity is a significant risk factor for type 2 diabetes, heart diseases, asthma, arthritis, and depression in Australian 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. This study fitted longitudinal random-effect logistic regression models to estimate the between-person differences in the association between obesity and chronic diseases.

Results

The findings indicated that obesity was associated with a higher prevalence of chronic diseases among Australian middle-aged and older adults. Obese adults (Body Mass Index [BMI] ≥ 30) were at 12.76, 2.05, 1.97, 2.25, and 1.96, times of higher risks of having type 2 diabetes (OR: 12.76, CI 95%: 8.88–18.36), heart disease (OR: 2.05, CI 95%: 1.54–2.74), asthma (OR: 1.97, CI 95%: 1.49–2.62), arthritis (OR: 2.25, 95% CI: 1.90–2.68) and depression (OR: 1.96, CI 95%: 1.56–2.48), respectively, compared with healthy weight counterparts. However, the study did not find any evidence of a statistically significant association between obesity and cancer. Besides, gender stratified regression results showed that obesity is associated with a higher likelihood of asthma (OR: 2.64, 95% CI: 1.84–3.80) among female adults, but not in the case of male adults.

Conclusion

Excessive weight is strongly associated with a higher incidence of chronic disease in Australian middle-aged and older adults. This finding has clear public health implications. Health promotion programs and strategies would be helpful to meet the challenge of excessive weight gain and thus contribute to the prevention of chronic diseases.

Introduction

According to the World Health Organisation (WHO), 1.9 billion adults in the world were either overweight or obese in 2016, and the prevalence of obesity has increased threefold since 1975 [1]. It is also estimated that at least 7% of deaths from all causes globally in 2015 were related to overweight or obesity [2]. In 2017–2018, 67% (12.5 million) of Australian adults were overweight or obese, increasing from 63.4% in 2014–2015. In Australia, the prevalence of severe obesity (BMI≥ 35kg/m2) has almost doubled between 1995 and 2014–15 [3]. A recent study also confirmed that over one in four Australian adults (26%) were obese in 2019 [4]. Overweight and obesity impose a considerable burden (both direct and indirect) in Australia. Overweight and obesity contributed 8.4% of the risk factor of the burden of diseases in Australia in 2015 [5]. 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]. Although there is a clear link between obesity and chronic health conditions, the severity of the burden of risk might vary based on an individual’s socioeconomic and demographic conditions as well as lifestyle characteristics. For policy-making purposes, it is crucial to understand whether obesity causes an increase in specific types of chronic disease among the poor, the elderly, and physically inactive compared to the affluent, younger and/or physically active population. 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.g. 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.g., the link between obesity and chronic diseases) and to better identify the influence of one factor (e.g., obesity) over the other (e.g., chronic diseases). 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. To the best of the authors’ knowledge, no previous research has focused on the obesity and chronic disease nexus from the Australian perspective, especially for middle-aged and older adults using longitudinal data.

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. The HILDA survey was initiated in 2001 by collecting detailed information on 13,000 individuals within 7,000 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 2009, 2013 and 2017, respectively. The reason behind considering these waves was that these three waves substantially capture the respondents’ health and lifestyle-related characteristics. Fig 1 demonstrates the procedure of obtaining the final analytic sample. The analytical sample is restricted to adults aged 45 years and over. The inclusion criteria for the subsample analyses were no missing information on participants’ Body Mass Index (BMI) and chronic diseases. This study also excludes pregnant women’s data to avoid potential biases. The final analytic sample consisting of 20,538 person-year observations from 9,822 unique participants was achieved by applying inclusion and exclusion criteria.

Fig 1. Flow chart of sample selection and missing data.

Fig 1

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Outcome variable

The outcome variable of the study is self-reported chronic disease. The HILDA survey collects information on an individual’s chronic disease status by asking questions, ‘are you diagnosed with a serious illness?’ This study considered six types of chronic diseases, including type 2 diabetes, heart disease, asthma, cancer, arthritis and depression, as the outcome variables of interest. Responses on the outcome variables were taken in binary form (0 = no, 1 = yes).

Exposure variable

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. The authors categorised BMI as underweight (<18.50), normal/healthy weight (18.50–24.99), overweight (25.00–29.99), and obese (≥ 30.00) following WHO guidelines [1]. 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.

Other covariates

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. For instance, age is categorised as middle-aged (45 to 59 years) and older adults (≥ 60 years). Other socio-demographic confounders include gender (male and female), civil status (partnered, unpartnered), education (year 12 or below, professional qualifications, and university qualifications), household yearly disposable income (expressed in quintiles), labour force status (employed, unemployed, and not in the labour force), Indigenous status (non-Indigenous, and Aboriginal/Torres Strait Islander [ATSI]), location (major city, regional city and remote areas).

Table 1. Description of other covariates.

Covariates Categories
Age Middle-aged (45 to 59 years), and older adults (≥ 60 years).
Gender Male and female.
Civil status Partnered (married, and never married but living with someone in a relationship), and unpartnered (separated but not divorced, divorced, widowed, and never married and not living with someone in a relationship).
Education Year 12 and below (year 12, and Year 11 and below), professional qualifications (advance diploma or diploma, and certificate III or IV), and university qualifications (postgraduate—masters or doctorate, graduate diploma or certificate, bachelor or honours).
Household yearly disposable income quintile Quintiles (quintile 1 [lowest] to quintile 5 [highest]).
Labour force status Employed, unemployed, and not in the labour force.
Indigenous status Non-Indigenous, and Aboriginal/Torres Strait Islander (ATSI) or both.
Location Major city, regional city (inner and outer regional) and remote areas (remote and very remote).
Smoking status never smoked, a former smoker and current smoker.
Alcohol consumption Never drank, ex-drinker, only rarely to 4 days/week, and 4+ days/week.
Physical activity (≥ 30 minutes) Not at all to <1/week, 1–3 times/week, and ≥ 4 times/week.
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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.

Estimation strategy

The authors prepared an unbalanced longitudinal data set consisting of 20,538 person-year observations by linking de-identified records of 9,822 unique adults. This study considered three distinct waves (waves 9, 13, and 17) of the HILDA survey covering the period from 2009 to 2017. Due to the longitudinal nature of the data, repeated observations on the same individual were used for subsample analyses. This study reports baseline, final wave, and pooled prevalence of obesity, six chronic diseases, socio-demographic and behavioural characteristics in the form of frequency(n) and percentages (%) with 95% confidence intervals (CI). The relationships between the exposure and other covariates with chronic diseases were first identified through bivariate analysis (test results not reported here). Statistically significant (P-value <0.05) variables in the bivariate analyses were then considered for the final regression model.

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. To ease the interpretation, this study reports regression results in the form of adjusted Odds Ratios (AOR) along with the 95% confidence interval. This study sets p-value <0.05 for the statistical significance of a variable. 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).

Ethics approval

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.gov.au) and ADA (ada@anu.edu.au) before the data applications’ approval. Therefore, datasets analysed and/or generated during the current study are subject to the signed confidentiality deed.

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. Among the study participants, 47% were male, and 53% were female, a higher proportion (53.26%) were middle-aged, nearly two-thirds (65.53%) were unpartnered, over one-fifth (21.86%) had university qualifications, over half were employed (53.22%), primarily non-Indigenous and lived in major cities (61.96%) at the baseline. The results also show that nearly 48% of participants never smoke, 59% consume alcohol from rarely to four days per week, and 35% performed physical activities that last at least 30 minutes over three times per week (baseline wave).

Table 2. Distribution of the analytic sample: Baseline, final and pooled across all waves (persons = 9,822; observations = 20,538).

Characteristics Baseline wave (2009) Final wave (2017) Pooled in all waves (2009, 2013 & 2017)
n % n % n %
Outcome variables    
Type 2 diabetes
No 4,946 91.56 7,082 90.82 18,688 90.99
Yes 456 8.44 716 9.18 1,850 9.01
Heart disease
No 4,981 92.21 7,120 91.31 18,824 91.65
Yes 421 7.79 678 8.69 1,714 8.35
Asthma
No 4,876 90.26 6,986 89.59 18,492 90.04
Yes 526 9.74 812 10.41 2,046 9.96
Cancer
No 5,096 94.34 7,339 94.11 19,372 94.32
Yes 306 5.66 459 5.89 1,166 5.68
Arthritis
No 3,747 69.36 5,406 69.33 14,246 69.36
Yes 1,655 30.64 2,392 30.67 6,292 30.64
Depression
No 4,835 89.5 6,633 85.06 17,869 87.00
Yes 567 10.5 1,165 14.94 2,669 13.00
Exposure and covariates
BMI
Underweight 83 1.54 95 1.22 289 1.41
Healthy weight 1,815 33.6 2,428 31.14 6,574 32.01
Overweight 2,133 39.49 2,942 37.73 7,902 38.48
Obesity 1,371 25.38 2,333 29.92 5,773 28.11
Age
Middle-aged (45–59 years) 2,877 53.26 3,713 47.61 10,304 50.17
Older adults (≥ 60 years) 2,525 46.74 4,085 52.39 10,234 49.83
Gender
Male 2,546 47.13 3,676 47.14 9,684 47.15
Female 2,856 52.87 4,122 52.86 10,854 52.85
Civil Status
Partnered 1,862 34.47 2,770 35.52 7,140 34.76
Unpartnered 3,540 65.53 5,028 64.48 13,398 65.24
Education
Year 12 and below 2,511 46.48 2,999 38.46 8,624 41.99
Professional qualifications 1,710 31.65 2,780 35.65 6,974 33.96
University qualifications 1,181 21.86 2,019 25.89 4,940 24.05
Household yearly disposable income quintile
Quintile 1 (lowest) 1,081 20.01 1,561 20.02 4,109 20.01
Quintile 2 1,081 20.01 1,559 19.99 4,107 20.00
Quintile 3 1,081 20.01 1,559 19.99 4,107 20.00
Quintile 4 1,079 19.97 1,561 20.02 4,109 20.01
Quintile 5 (highest) 1,080 19.99 1,558 19.98 4,106 19.99
Labour force status
Employed 2,875 53.22 4,006 51.37 10,665 51.93
Unemployed 75 1.39 122 1.56 326 1.59
Not in the labour force 2,452 45.39 3,670 47.06 9,547 46.48
Indigenous status
Non-Indigenous 5,317 98.43 7,653 98.14 20,181 98.26
Aboriginal or Torres Strait Islander 85 1.57 145 1.86 357 1.74
Location
Major city 3,347 61.96 4,885 62.64 12,865 62.64
Regional 1,968 36.43 2,792 35.8 7,352 35.80
Remote 87 1.61 121 1.55 321 1.56
Smoking status
Never smoked 2,597 48.07 3,878 49.73 10,034 48.86
Former smoker 2,004 37.10 2,855 36.61 7,609 37.04
Current smoker 801 14.83 1,065 13.66 2,895 14.10
Alcohol consumption
Never drank 562 10.4 785 10.07 2,101 10.23
Ex-drinker 379 7.02 759 9.73 1,788 8.71
Only rarely to 4 days/week 3,203 59.29 4,650 59.63 12,210 59.45
4+ days/week 1,258 23.29 1,604 20.57 4,439 21.61
Physical activity (≥ 30 minutes)
Not at all to <1/week 1,502 27.80 2,473 31.71 6,121 29.80
1–3 times/week 2,009 37.19 2,832 36.32 7,493 36.49
≥4 times/week 1,891 35.01 2,493 31.97 6,924 33.71
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Of 9,822 participants (20,538 observations), approximately 38.48% were overweight, and 28.11% were obese. The pooled prevalence of chronic conditions, such as type 2 diabetes, heart diseases, asthma, cancer, arthritis, and depression in study participants was approximately 9.01%, 8.35%, 9.96%, 5.68%, 30.64%, and 13.0%, respectively (pooled in all waves).

Fig 2 displays the overall prevalence of various chronic diseases among Australia’s middle-aged and older adults at three different periods: 2009, 2013 and 2017. Fig 2 manifests that the prevalence of chronic conditions and obesity among the study population had increased from 2009 to 2017. Among all of them, depression increased sharply from 10% to 15% approximately. 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. The prevalence of obesity was almost 25% in 2009, which increased to nearly 30% in less than ten years.

Fig 2. Prevalence of chronic conditions among middle-aged and older adults.

Fig 2

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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 (16.18%), asthma (12.99%) and arthritis (37.52%), was highest in obese people. However, underweight middle-aged and older adults are more vulnerable to heart diseases (11.76%), cancer (7.96%) and depression (19.72%). For obese people, the percentage is also noticeable, i.e. 10.27%, 5.68% and 17.11% for heart diseases, cancer and depression, respectively.

Fig 3. Prevalence of chronic conditions among middle-aged and older adults by weight status.

Fig 3

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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 (16.77% vs 8.44%), arthritis (44.55% vs 29.06%), and depression (19.53% vs 14.20%) are substantially higher among females than males. However, cancer (6.64% vs 4.88%), heart diseases (13.13% vs 7.89%) and type 2 diabetes (17.72% vs 14.90%) were more prevalent among males than females.

Fig 4. Gender differences in the prevalence of the chronic conditions among obese middle-aged and older adults.

Fig 4

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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: 12.76, 95% CI: 8.88–18.36), heart diseases (OR: 2.05, 95% CI: 1.54–2.74), asthma (OR: 1.97, 95% CI: 1.49–2.62), and arthritis (OR: 2.25, 95% CI: 1.90–2.68) compared with their healthy-weight counterparts. It is also observed that obese people were at 1.96 times higher risk of suffering from depression (OR: 1.96, 95% CI: 1.56–2.48) than peers with a healthy weight.

Table 3. Adjusted random-effect regression results for the between-person differences in chronic conditions due to obesity; 9,822 persons, 20,538 observations.

Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Type 2 diabetes Heart disease Asthma Cancer Arthritis Depression
aOR (95% CI) aOR (95% CI) aOR (95% CI) aOR (95% CI) aOR (95% CI) aOR (95% CI)
BMI
Underweight 0.33 (0.07–1.67), 0.18 2.98 (1.44–6.17), 0.01 0.49 (0.19–1.23), 0.13 1.36 (0.70–2.67), 0.37 1.07 (0.66–1.74), 0.80 1.46 (0.78–2.71), 0.24
Healthy weight (ref)
Overweight 3.81 (2.71–5.36), <0.001 1.41 (1.09–1.82), 0.01 1.21 (0.94–1.56), 0.14 0.82 (0.66–1.01), 0.07 1.42 (1.22–1.64), <0.001 1.25 (1.01–1.54), 0.04
Obesity 12.76 (8.88–18.36), <0.001 2.05 (1.54–2.74), <0.001 1.97 (1.49–2.62), <0.001 0.89 (0.69–1.13), 0.33 2.25 (1.90–2.68), <0.001 1.96 (1.56–2.48), <0.001
Socio-demographic characteristics
Age
Middle-aged (45–59 years) (ref)
Older adults (≥ 60 years) 4.36 (3.23–5.89), <0.001 4.83 (3.60–6.48), <0.001 0.92 (0.72–1.19), 0.54 2.35 (1.87–2.96), <0.001 3.63 (3.12–4.21), <0.001 0.39 (0.31–0.48), <0.001
Gender
Male (ref)
Female 0.29 (0.22–0.04), <0.001 0.28 (0.21–0.36), <0.001 2.45 (1.89–3.19), <0.001 0.53 (0.43–0.65), <0.001 2.91 (2.49–3.41), <0.001 2.10 (1.7–2.6), <0.001
Education
Year 12 or below (ref)
Professional qualifications 0.89 (0.65–1.23), 0.47 0.88 (0.66–1.17), 0.39 1.07 (0.81–1.42), 0.62 1.25 (0.99–1.57), 0.06 0.80 (0.68–0.95), 0.01 1.19 (0.95–1.50), 0.13
University qualifications 0.62 (0.42–0.94),0.02 0.92 (0.65–1.30), 0.63 1.08 (0.77–1.51), 0.69 1.05 (0.79–1.39), 0.72 0.61 (0.50–0.75), <0.001 1.02 (0.77–1.35), 0.89
Civil Status
Partnered (ref)
Unpartnered 0.68 (0.51–0.89), 0.01 0.69 (0.54–0.88), 0.01 0.82 (0.64–1.04), 0.10 0.92 (0.75–1.13), 0.45 0.80 (0.69–0.93), 0.01 0.46 (0.38–0.56), <0.001
Household yearly disposable income quintile
Quintile 1 1.57 (1.04–2.38), 0.03 1.23 (0.86–1.75), 0.26 1.59 (1.12–2.27), 0.01 1.11 (0.81–1.51), 0.53 1.43 (1.16–1.77), 0.01 1.70 (1.27–2.29), <0.001
Quintile 2 1.18 (0.79–1.76), 0.42 1.17 (0.83–1.66), 0.36 1.31 (0.94–1.83), 0.12 1.24 (0.92–1.67), 0.15 1.25 (1.03–1.52), 0.03 1.63 (1.23–2.15), 0.01
Quintile 3 1.28 (0.87–1.89), 0.21 0.95 (0.67–1.35), 0.77 1.08 (0.78–1.49), 0.65 0.92 (0.68–1.25), 0.60 1.07 (0.89–1.29), 0.48 1.42 (1.09–1.86), 0.01
Quintile 4 1.11 (0.76–1.64),0.59 1.04 (0.74–1.47), 0.80 1.16 (0.85–1.58), 0.34 1.13 (0.85–1.51), 0.40 1.08 (0.90–1.29), 0.42 1.18 (0.91–1.53), 0.22
Quintile 5 (ref)
Labour force status
Employed (ref)
Unemployed 2.13 (0.88–5.13), 0.09 1.52 (0.65–3.57), 0.34 0.85 (0.40–1.80), 0.67 0.66 (0.26–1.66), 0.37 1.16 (0.74–1.83), 0.52 4.03 (2.41–6.74), <0.001
Not in the labor force 3.40 (2.48–4.66), <0.001 5.72 (4.18–7.85), <0.001 1.90 (1.45–2.49), <0.001 2.44 (1.91–3.11), <0.001 3.14 (2.68–3.68), <0.001 4.28 (3.41–5.37), <0.001
Indigenous status
Non-indigenous (ref)
Aboriginal/Torres Strait Islander 8.27 (3.37–20.34), <0.001 2.44 (1.08–5.52),0.03 1.87 (0.82–4.30), 0.14 1.01 (0.47–2.12), 0.99 0.94 (0.55–1.60), 0.81 1.95 (1.00–3.81), 0.05
Location
Major city (ref)
Regional 1.04 (0.78–1.37), 0.81 1.02 (0.80–1.30), 0.90 1.20 (0.94–1.54), 0.14 1.03 (0.84–1.26), 0.80 1.23 (1.07–1.43), 0.01 1.07 (0.88–1.31), 0.48
Remote 0.45 (0.14–1.45), 0.18 1.31 (0.54–3.16), 0.55 0.72 (0.26–2.03), 0.54 1.28 (0.62–2.63), 0.51 0.48 (0.27–0.85), 0.01 0.44 (0.19–1.01), 0.05
Behavioural Characteristics
Smoking status
Never smoked (ref)
ex-smoker 1.50 (1.12–2.01), 0.01 1.56 (1.21–2.02), 0.01 1.57 (1.21–2.04), 0.01 1.09 (0.88–1.35), 0.41 1.21 (1.04–1.41), 0.01 1.42 (1.14–1.76), 0.01
Current smoker 0.98 (0.64–1.49), 0.92 1.05 (0.72–1.52), 0.80 2.13 (1.50–30), <0.001 0.95 (0.69–1.29), 0.72 1.07 (0.86–1.32), 0.55 2.56 (1.95–3.36), <0.001
Alcohol consumption
Never drink (ref)
Ex-drinker 0.65 (0.40–1.05), 0.08 1.19 (0.78–1.82), 0.42 0.84 (0.54–1.31), 0.44 1.37 (0.92–2.03), 0.12 1.20 (0.91–1.58), 0.19 2.04 (1.41–2.94), <0.001
Only rarely to 3 days/week 0.44 (0.29–0.65), <0.001 0.71 (0.50–1.02), 0.07 0.72 (0.50–1.04), 0.08 1.18 (0.85–1.64), 0.32 1.17 (0.94–1.46), 0.17 1.12 (0.83–1.52), 0.47
3+ days/week 0.16 (0.10–0.27), <0.001 0.51 (0.33–0.77), 0.01 0.74 (0.48–1.13), 0.17 1.12 (0.78–1.62), 0.54 1.19 (0.92–1.54), 0.18 1.14 (0.8–1.63), 0.46
Physical activity
Not at all to <1/week (ref)
1–3 times/week 0.73 (0.56–0.94), 0.02 0.59 (0.47–0.74), <0.001 0.93 (0.74–1.16), 0.52 0.72 (0.59–0.89), 0.01 0.78 (0.68–0.89), <0.001 0.52 (0.43–0.62), <0.001
≥ 4 times/week 0.60 (0.46–0.80), 0.01 0.55 (0.43–0.70), <0.001 0.67 (0.52–0.86), 0.01 0.71 (0.57–0.88), 0.01 0.58 (0.50–0.68), <0.001 0.34 (0.27–0.41), <0.001
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Abbreviations: aOR, Adjusted Odds Ratio; ref, reference. Values in bold are statistically significant. All models (Models 1 to 6) were adjusted for age, gender, civil status, education, household yearly disposable income, labour force status, indigenous status, location, smoking status, alcohol consumption, and physical activity. Values in bold are statistically significant.

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 17.61 (OR: 17.61, 95% CI: 10.49–29.54), and 9.55 (OR: 9.55, 95% CI: 5.69–16.03) times higher among obese female and male adults, respectively, compared to their healthy-weight counterparts. Besides, the results showed that obesity is associated with a higher incidence of asthma (OR: 2.64, 95% CI: 1.84–3.80) among female adults, but not statistically significant in the case of male adults (Table 4).

Table 4. Adjusted random-effect regression results for the between-person differences in chronic conditions due to obesity stratified by gender.

Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Type 2 diabetes Heart disease Asthma Cancer Arthritis Depression
aOR (95% CI) aOR (95% CI) aOR (95% CI) aOR (95% CI) aOR (95% CI) aOR (95% CI)
Gender: Male
BMI Categories
Underweight 0.46 (0.03–2.61), 0.27 2.11 (0.57–7.78), 0.26 0.19 (0.02–1.57), 0.12 0.53 (0.13–2.18), 0.38 1.22 (0.49–3.09), 0.66 2.15 (0.64–7.19), 0.21
Healthy weight (ref)            
Overweight 3.01 (1.88–4.81), <0.001 1.22 (0.85–1.75), 0.27 0.79 (0.53–1.16), 0.23 0.89 (0.66–1.20), 0.45 1.34 (1.07–1.69), 0.01 1.05 (0.75–1.47), 0.78
Obesity (≥30) 9.55 (5.69–16.03), <0.001 2.19 (1.44–3.33), <0.001 1.17 (0.75–1.84), 0.45 0.92 (0.64–1.31), 0.63 2.24 (1.71–2.93), <0.001 1.96 (1.34–2.87), 0.01
Gender: Female
BMI Categories
Underweight 0.29 (0.04–4.75), 0.51 3.43 (1.42–8.25), 0.01 0.70 (0.24–2.01), 0.50 1.92 (0.90–4.09), 0.09 0.99 (0.56–1.77), 0.99 1.33 (0.64–2.76), 0.44
Healthy weight (ref)            
Overweight 5.02 (3.04–8.28), <0.001 1.60 (1.11–2.31), 0.01 1.58 (1.14–2.20), 0.01 0.76 (0.56–1.04), 0.09 1.46 (1.20–1.78), <0.001 1.43 (1.10–1.87), 0.01
Obesity (≥30) 17.61 (10.49–29.54), <0.001 1.83 (1.22–2.73), 0.01 2.64 (1.84–3.80), <0.001 0.89 (0.64–1.24), 0.50 2.25 (1.80–2.81), <0.001 1.96 (1.46–2.62), <0.001
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Abbreviations: aOR, Adjusted Odds Ratio; ref, reference. All models (Models 1 to 6) were adjusted for age, gender, civil status, education, household yearly disposable income, labour force status, indigenous status, location, smoking status, alcohol consumption, and physical activity. Values in bold are statistically significant.

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. 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]. The possible reason could be that obesity affects lung function by superfluous tissues constricting the thoracic cage, increasing the chest wall’s insinuation with fat tissue and pulmonary blood volume [29]. 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. Other studies estimating the association indicated that obesity is a major risk factor of osteoarthritis for Australian adults [31], and there is evidence that a 5-unit in BMI increases the risk of osteoarthritis (knee) by 35% [32]. 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. [16] identified eating disorders, interpersonal effectiveness and experience of stigma as other key factors influencing the relationship between co-morbid obesity and depression.

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.g. 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.g. 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. Participants engaged in physical activities more than three times a week had a 40% less probability of suffering from chronic depression than those that did not undertake physical activities. 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. The study’s findings suggest that physical activities, community-level gym facilities, and the availability of nutritionists to curb excessive weight are necessary. 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.

Conclusion

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.

Acknowledgments

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: 10.26193/OFRKRH, ADA Dataverse, V2”.

Abbreviations

BMI

Body Mass Index

HILDA

Household, Income and Labour Dynamics in Australia Survey

AOR

Adjusted Odds Ratios

WHO

World Health Organization

Data Availability

The data used for the study were collected by the Melbourne Institute of Applied Economic and Social Research. There are some restrictions on accessing this data and it is not available to the public. Those interested in accessing this data should contact the Melbourne Institute of Applied Economic and Social Research, The University of Melbourne, VIC 3010, Australia (Tel: +61 3 8344 2073 or Fax: +61 3 9347 6739).

Funding Statement

The authors received no specific funding for this work.

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Obesity and chronic disease burden in Australia: Findings from a longitudinal population survey, 2009-2017

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This is an important topic. Obesity and non-communicable diseases are both correlated and with a pathophysiological explanation and well-established theoretical framework behind their link. However, there isn’t enough high-quality temporal evidence from Australia, and this study utilizes longitudinal data from an Australian questionnaire to further provide a broader perspective.

My major concern in this article is that it opts to establish associations for a very wide age range between BMI and NCD. Clinically and public health-wise, the interpretation of findings critically depends by age and it is certainly not something that a simple adjustment to age solves. This holds for all study outcomes, and especially to diabetes and cancer. Therefore, stratification of all data to age range is critical here. This is specifically relevant to cancer where there is no cancer-specific data. In this case working with more homogenous groups age-wise is important for mitigating cancer case-mix. The fact that overweight was nearly protective is this cohort OR=0.82 (upper CI, 1.02) is a good evidence for that. A recent Lancet DE paper demonstrated the complexity of looking on the entire cancer types together given critical sex-related differences (32027851). With this respect reverse causality is a serious source of bias that may not have received the attention it deserves in the discussion and analytically. (for example, consider applying a wash out period [one wave]).

In general, presentation of critical data is missing. For example the number of cases diagnosed in each wave, including their reported BMI. Not really clear how people who switched categories were handled. More is needed to be added to the Methods to clarify these.

Other comments:

• Abstract

• Conclusion, line 25: "could be helpful" instead of "would be helpful".

• Introduction:

• Missing reference to the World Health Organization data on obesity (line 4)

• Line 18: should say "it is strongly associated with a higher incidence of type 2 diabetes and depression".

• Page 4, line 1: "…compared to men" – should explain why normal men? Is there literature about men suffering from less obesity-related diseases? This is what's inferred from this sentence (and is not true according to the results of this study).

• Page 4, line 4: perhaps replace "dynamic" with "temporal".

• Theoretical framework

• Page 5, figure 1: should say "insulin resistance" instead of "resistant".

• Page 5, line 1: abdominal obesity is correlated with asthma, it does not lead to asthma.

• Exposure variable: the exposure variable is BMI, however some of the study subjects are 15-19 years old. Adult BMI values are only relevant from ages 20 and above. Ages 2-19 have a different way of calculating their BMI percentile. Study should account for these differences and explain it in this section.

• Outcome variable: it is unclear if patients from the first wave (wave 9) are excluded if they suffer from the disease (exclusion criteria seems to be pregnant women only). If not, obesity is found to be prevalent with the disease, but one cannot conclude that it is a risk factor.

• Results

• figure 3: it is unclear if this figure illustrates the prevalence of chronic diseases among obese people, or the occurrence.

• Table 3: In general, most of the data is somewhat distracting and I suggest you move it to the supplements. it is also unclear if these odds ratio are adjusted to certain confounders. If so, to which ones? If not – why not? It is shown in table 2 that gender, for example, is a confounder in regard to obesity in this study sample.

• Another table of different categorical BMIs in the first wave (wave 9) with their diseases in the last wave (wave 17) could be added (accounting for those who were already sick in wave 9).

• Discussion

• Page 14, line 6 – "cancer" is mentioned as a major risk factor for chronic diseases, even though it is not shown in this study.

• Could there be an explanation to the fact that this study is supposed to be representative of the general Australian population, but 58% are overweight/obese and the introduction says 67% are? That's almost a 10% difference. A volunteer selection bias is suspected.

• The subsample analyses should be further explained, as it is described as a study limitation, but the method of analysis was not mentioned in the article. It is unclear why causality could not be drawn from the study findings.

Reviewer #2: I commend the authors for their approach to studying obesity and the risk of different chronic diseases in the Australian population. The HILDA dataset used seems like a great choice for this analysis. However, I found that the paper was lacking in a lot of details about how the analyses was conducted. Substantial revisions are required for full transparency of the methods. Specifically, I had trouble understanding if the data was cross-sectional or longitudinal. The authors repeatedly stated that the data was longitudinal, but often the presentation of the data made it appear cross-sectional. This needs to be sorted out. The longitudinal nature of the data is the novel part of this project. 

I also question the selection of participants for analyses. The authors chose to include all participants aged 15 and over in their study. However, this is problematic given that the chronic diseases being investigated are very different in younger individuals than in older adults. This can be observed in the main analyses which shows astoundingly high odds ratios for age with most of the conditions. This finding makes sense, however it skews the interpretation of the results. The results are an average across all the age groups adjusted for, and I'm not convinced that the associations of the other variables including BMI category with the outcomes should be pooled across age groups. The analyses should either be stratified by age, or it should be limited to older adults only. The authors should also consider conducting sex-stratified analyses because the risk factors for the outcomes can also substantially differ between males and females. 

For transparency, I have not reviewed the discussion, but am happy to do so after the analyses has appropriately been revised. I feel that I am unable to provide meaningful comments on the discussion when I am very uncertain about the validity of the results.

Page 3, Line 3 - rate of obesity or prevalence? Rate implies a unit of measurement over time. 

Page 3, Line 4 - Why not just write "It is also estimated that at least 7% of deaths from all causes globally"?

Page 3, Line 5 - Not sure predominance is the word you're looking for - prevalence? 

Page 3, Line 9 - In general this first paragraph could be more concise. Overweight and obesity are prevalent in Australia and globally, with the prevalence currently increasing. This is well known information and can therefore be briefly mentioned. Instead, I would expand upon the point you're making in line 11 - burden of what diseases? Which other risk factors? 

Page 3, Line 12 - I don't think there's much of a debate anymore about the role of obesity in many of the non-communicable diseases you've listed?  

Page 3, Line 17 - Citation? Is the evidence actually robust that obesity increases the risk of heart disease-related morbidity and mortality? Isn't this what people refer to as the obesity paradox (though please note, the obesity paradox is likely not true, but instead is at least partially explained by collider stratification bias)? Given the amount of discussion around the obesity paradox in the literature, I would be cautious with this point. 

Page 3, Line 27 - This paragraph would overall benefit from a better synthesis of evidence. It's choppy and the point you're trying to make gets a little lost

Page 3, LIne 31 - I'm confused about this paragraph - you don't mention anything in your abstract about how SES and demographics, age, life-style behaviours etc. in your abstract. 

Page 4, Line 4 - Are you considering variables like SES and demographics as confounders which should be controlled for or as stratification factors which the first part of this paragraph indicates? 

Page 4, Line 7 - Are you speaking about just the Australian context or internationally? There are quite a lot of nationally representative longitudinal survey out there. 

Page 4, Line 11 - I think the justification could be improved upon. What gaps are are you actually filling with a longitudinal study design? Longitudinal study designs themselves don't fix certain methodological challenges. Rather, how you use longitudinal data can resolve methodological challenges. Your objective should reflect what is actually novel about your project. 

Page 4, Line 18 - This whole theoretical framework belongs in the actual background after making it far more concise. This paper does not specifically look at the mediation of obesity through metabolic conditions to the outcomes of interest, therefore it doesn't make sense to have the theoretical framework be front and center. I'm also not aware of inflammation being considered a metabolic syndrome criteria. Inflammation is certainly an important link between obesity (specifically abdominal obesity which you haven't mentioned) and disease, but it's not usually thought of under the heading of metabolism to my knowledge. 

Page 6, Line 13 - as written, the question seems to be an open text question asking about any illness rather individual questions about specific illnesses. Is that the case? 

Page 6, Line 14 - I'm not sure it's fair to say that classifying individuals based on the WHO categories means you can assess how and in what context pre-obese and obese participants are suspectible to different chronic diseases. Please check if this wording is correct. 

Page 6, Line 24 - Please define "defacto". This is not a term I am familiar with. 

Page 6, Line 24 - What are equivalised household incomes? 

Page 6, Line 26 - Indigenous should be capitalized. Can you please also verify that there are not any rules governing the use of Aboriginal data in Australia. I'm unfamiliar with Australian guidelines, but I know that for many Canadian studies, we are not allowed to specifically report on Indigeuous People without additional permissions. Given the similar histories of colonization in Canada and Australia, please confirm that there aren't any issues related to this when using HILDA data.  

Page 6, Line 29 - Are there citations available for how you chose to categorize your behavioural risk factor variables? 

Page 7, Line 7 - What do you mean by repeated observations on the same individual were used for subsample analyses?

Page 7, LIne 11 - Not every variable in your model is a confounder, are these covariates? Confounder has a specific meaning statistically. 

Page 7, Line 11 - What does "long-term association" mean? Though there aren't any hard and fast rules about model building, generally it's recommended that a p value of <0.25 for the Wald statistic is considered as a threshold for inclusion. There may be variables that while not significant based on the univariate statistics, may still be important confounders. Using p<0.25 helps ensure these are included. What techniques did you use for model building and deciding which variables were actually relevant? I would recommend following the guidance of  Hosmer DW, Lemeshow S. Applied Logistic Regression, 2nd ed. John Wiley & Sons, Inc: New York, New York; 2000.

Page 7, Line 15 - For GEE models, the inferences are only valid when data is missing completely at random which is rarely true for observational studies. If data is missing at random, additional considerations such as using the inverse-probability-weighted method are required. Please comment about how you handled missing data (based on your methods, there could be missing data for some covariates as they were not an inclusion/exclusion criteria). Also please go back to Page 5, Line 26 and provide a flow of participants. The 41,169 person-year observations from 20,145 participants were reduced from how many after applying the inclusion and exclusion criteria? 

Page 8 - Consider how many decimal places are actually required. Generally one is sufficient and makes for a far cleaner table. How did you calculate the confidence intervals? Also, this is showing the person-years not the number of people based on the denominator? That is not a good way of presenting data. You can show the baseline characteristics of people at one time point. 

Page 9, Line 4 - I'm having trouble understanding these results. This is just prevalence data? Are the people the same age at all three time points or are people supposed to be aging eight years as time goes on? If it's a series of cross-sectional data measures, that should be made clear. If it's the same individuals at three time points, I worry there may be something wrong. Teh prevalence of things like heart disease or ever being diagnosed with cancer go up with age to a far greater extent than is shown here. 

Page 10, Line 3 - by weight category, not just in obese people. 

Page 11, Line 4 - I'm still confused if this is meant to be cross-sectional or longitudinal. 

Page 12 - Your age categories are problematic. You have hugely inflated odds ratios in the older age groups because you're comparing them to people so young that heart disease etc. are hardly ever diagnosed in. This has major implications for your whole model - your odds ratios for the over variables are averaged over all the age groups. Given that the youngest two age groups are so different from the oldest two, it's hard to understand what the rest of your models actually mean. Either limit your analyses by age or conduct age-stratified analyses. Also, how did you have data for something like civil status and education for the younger individuals? Even a variable like household income has a different meaning in younger versus older adults.

Reviewer #3: This study by Keramat et al. explores the longitudinal association between obesity and 6 chronic diseases in Australian adults. The study utilizes nine years of longitudinal data obtained through a national survey. Overall, the study is well designed, method is appropriate, and the results are clear and well organized. The manuscript is presented in a logical sequence and is easy to understand. The study fills an important gap in literature and the use of nationally representative longitudinal data further contributes to the generalizability of these results to the larger Australian population. Some comments/suggestions are as follows:

1. In the methods section, the explanation for why participants were selected from only 3 waves of the survey is unclear. While the authors mention that “the particular reason behind considering these waves was that these three waves substantially capture the health and lifestyle-related characteristics of the respondents,” this explanation is vague and unclear. If the authors are specifically alluding to the number of survey respondents being the greatest across the three selected waves, then that should be specifically mentioned. Alternatively, if there is another reason or selection criteria that the authors used, then that should be specifically described in this section to make it more clear.

2. In the results section (page 10), line 3 states “Figure 3 illustrates the occurrence of chronic diseases among obese people.” Since the figure depicts the prevalence of disease across all weight statuses and not only the obese group, it may be more accurate to modify this sentence so that it says “Figure 3 illustrates the occurrence of chronic diseases by weight status.” This would also be consistent with Figure heading which states the same.

3. In the results section on page 10, line 12 states “The prevalence of these three chronic diseases were significantly higher in obese females compared with obese male.” The use of the word significant would usually imply a statistically important difference; however, in this case it does not seem like the gender distribution findings presented in table 2 were statistically tested. The authors may consider performing a statistical test (e.g. Chi-square test) to check if the prevalence of the those chronic diseases is in fact significantly higher in obese females compared to obese males.

4. In the footnotes for Table 3, the authors should consider including an indication of all the covariates that were adjusted for in the statistical model. While the authors provide a discussion of covariates in the methods section, including additional footnotes with table 3 in the results section which precisely outline the adjustment factors will be helpful for the readers.

5. Based on the description of covariates in the methods section, it seems like while sociodemographic and behavioral factors were adjusted for as covariates, the authors may not have considered adjusting for the presence of other chronic conditions when investigating the association between obesity and any one particular chronic disease (i.e. while the chronic diseases have only been considered as outcome variables in this case, they can also be potentially considered as covariates). In this case, it would be interesting to see if the association between obesity and each of the chronic diseases remains the same after adjustment for other chronic diseases that are also potential risk factors. For example, it would be of interesting to know whether the association between say obesity and cancer changes when adjusted for other chronic diseases like asthma or diabetes which are also known risk factors for cancer. Adjusting for the other chronic diseases within this context can also allow the authors to potentially explore mediation effects.

6. The sample size of the study should also be indicated in the abstract. While the authors mention using data from three waves of the survey, there is no indication of study size in the abstract.

7. The manuscript requires minor grammatical and syntax revisions. Just to give an example, the following line from page 14 (line 4) contains an error and is unclear: “the multivariate regression results reveal that with longitudinal data signified obesity as a major risk factor for chronic diseases…” It is recommended that the manuscript be revised for all such errors.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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PLoS One. 2021 Nov 16;16(11):e0260158. doi: 10.1371/journal.pone.0260158.r002

Author response to Decision Letter 0

19 Oct 2021

Please refer to the respose to the reviewers files in word document. Thank you.

Decision Letter 1

David Meyre

4 Nov 2021

Obesity and the risk of developing chronic diseases in middle-aged and older adults: Findings from an Australian longitudinal population survey, 2009-2017

PONE-D-21-14337R1

Dear Dr. Keramat,

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Acceptance letter

David Meyre

8 Nov 2021

PONE-D-21-14337R1

Obesity and the risk of developing chronic diseases in middle-aged and older adults: Findings from an Australian longitudinal population survey, 2009-2017

Dear Dr. Keramat:

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