Original Article


Obesity Prevalence, Associated Factors and Complications among Adult Population Attending Family Medicine Clinic in Hatta Hospital, United Arab Emirates

Authors: Ayesha Rashid , Uzma Farheen , Zahid Nabi Qureshi , Muhammad Ejaz
DOI: https://doi.org/10.37184/lnjpc.2707-3521.6.38
Year: 2024
Volume: 6
Received: Jan 01, 2024
Revised: Mar 11, 2024
Accepted: Mar 12, 2024
Corresponding Auhtor: Ayesha Rashid (ayrashid11@gmail.com)
All articles are published under the Creative Commons Attribution License



Obesity Prevalence, Associated Factors and Complications among Adult Population Attending Family Medicine Clinic in Hatta Hospital, United Arab Emirates

Abstract

Background: The United Arab Emirates (UAE) has experienced rapid economic growth in the last few decades, resulting in swift urbanization and an influx of foreign labor. This has facilitated an advanced, yet sedentary lifestyle characterized by increased reliance on technology, unhealthy dietary habits, and a reduction in physical activity levels. These factors significantly contributed to the rise in the prevalence of obesity.

Objective: To determine the prevalence of obesity, it’s associated factors, and complications among adults presenting to the primary

care clinic at Hatta Hospital, located in the Hatta area UAE.

Materials and Methods: This cross-sectional study enrolled adult Emirati patients aged between 18-55 year. The study duration was from January 2022 to August 2022 Body Mass Index (BMI) was categorized based on the World Health Organization’s threshold. Patients’ socio-demographic and clinical data was obtained and recorded.

Results: A total of 600 Emirati patients were included in this study, with a median age of 33 (IQR=25-45) years. Gender distribution was kept almost equal with 50.5% male and 49.5% female participants. The median BMI among the participants was 28.2 (IQR=24.3- 32.5) kg/m2. The prevalence of underweight, normal weight, overweight, and obesity was found as 4.3%, 23.5%, 33.7% and 38.5% respectively. After adjusting the co-variants in the multivariable model, lower education levels were found to be significantly associated with higher odds of obesity. Conversely, being single and adhering to a healthy lifestyle was associated with lower odds of obesity in the multivariable model.

Conclusion: The present study identified that 38.5% of the adult population attending Hatta Hospital family medicine clinics were obese and 33.7% were overweight. Conclusively, 72.2% of the adult population of the Hatta area have a BMI above normal, indicating a very high prevalence of obesity and overweight cases. Furthermore, a significant association of obesity was found with lower education levels, being married, having unhealthy dietary habits, and insufficient physical activity.

Keywords: Body mass index (BMI), obesity, adult population, outpatient clinics.

INTRODUCTION

The World Health Organization (WHO) defines overweight and obesity as abnormal and excessive amounts of fat accumulation in the body, resulting in significant health risks [1]. It is directly connected to an imbalance between energy intake and expenditure, which is further influenced by a variety of modifiable and non-modifiable risk factors [2]. This inequality in caloric balance results in excessive storage of energy and fat in the body, which ultimately leads to an oversized physique.

The global prevalence of obesity is rising at an alarming

rate and the current data shows that 39 million children,

340 million adolescents, and 650 million adults are obese worldwide. The prevalence is estimated to rise and according to WHO, it is predicted that 167 million more adults and children will face significant health risks because of being overweight or obese by the year 2025 [3]. Based on this fact, the American Medical Association 

identified obesity as an illness in 2013 to call doctors’ attention to the problem [4].

Obesity is considered a leading cause of morbidity and mortality due to its strong association with certain neurological, gastrointestinal, respiratory, and cardiovascular conditions [5]. Noncommunicable diseases (NCDs) like type 2 diabetes, ischemic heart disease (IHD), cerebrovascular accident (CVA), and many psychological disorders are significantly influenced by obesity [5]. The rising trend of overweight and obesity has hazardous effects on global health, leading to increased death and disability-adjusted life years (DALYs) [6, 7]. Furthermore, being overweight or obese is associated with a dysfunctional immune system, which not only makes a person more vulnerable to contracting infections but can also lead to the development of certain cancers [8]. Research estimated that elevated BMI is linked to 4.7 million deaths and 147.7 million DALYs from NCDs in 2017. By 2025, that number is expected to rise to 5.5 million deaths and 176.9 million DALYs [9].

Obesity is a complex medical disorder that negatively influences health by various mechanisms [10]. Few of these include biological [11], psychological [12],

socioeconomic [13], and environmental components [14], though these represent only a minor portion of its characteristics. In addition to factors such as sleep deprivation, obesity may also be influenced by psychological factors, stress, imbalance of the gut microbiota (dysbiosis), genetical influence, and certain medical disorders like thyroid dysfunction, Cushing syndrome, and polycystic ovarian disease [15, 16]. Body mass index (BMI), which is determined by dividing weight in kilograms by height in meters square (kg/m2), is a widely used and approved metric for estimating the prevalence of obesity. Even though BMI is not considered as a reliable indicator of obesity, it remains a convenient tool for epidemiological studies and health screening [17].

The Gulf region specifically the UAE has undergone a rapid socioeconomic advancement, attributed to the discovery of oil reserves. This has been followed by accelerated urbanization and the influx of foreign labor. Therefore, a lavish, technologically advanced but sedentary lifestyle has emerged, which led to reduced physical activity across the workplace,   domestic, and leisure settings. Furthermore, there is prevalent consumption of processed, prepackaged foods with low nutritional value and high sugar, trans-fat, and calorie content [18, 19].

Hatta, situated as an exclave of Dubai, is approximately 134 kilometers to the east. It has a population of 14,985 as per the Dubai Statistics Center Population Bulletin 2021. Hatta Hospital is a secondary care multidisciplinary facility, located at the center of Hatta city. It primarily serves patients from Hatta and nearby areas of the UAE and Oman, providing healthcare to a population of around 50,000 residents. There are several previous research studies on the prevalence of obesity in the UAE, mostly focusing on cities like Dubai, Abu Dhabi, and Ras Al Khaimah, revealing an alarming trend of obesity. However, there is a notable lack of research specifically addressing the prevalence of obesity in the Hatta area of UAE.

This study aims to address this gap, and the results will provide valuable insight into obesity prevalence in the Hatta area and serve as a basis for implementing early intervention and developing strategies to deal with this growing problem in the UAE.

MATERIALS AND METHODS

This cross-sectional study was performed in Hatta Hospital from January 2022 to August 2022, after acquiring ethical approval from the Dubai Scientific Research Ethics Committee (DSCRE) (IRB# DSREC-09/2021_09). According to The Surveys system calculator, the sample size should be 593 with a confidence level of 95% and a confidence interval of 4. This study included 600 Emirati patients in the age range of 18-55 years, with no significant medical conditions or diseases that could develop obesity. The ratio of male and female participants was kept equal. Patients with any major disease such as cancer, liver diseases, kidney diseases, endocrinology issues, and those who have undergone bariatric surgery were not included.

BMI was calculated by dividing the patient’s body weight in kg by the square of their height in meters (kg/m2). BMI was categorized according to WHO as underweight (<18.5 kg/m2), normal weight (18.5-24.9 kg/m2), overweight (25-29.9 kg/m2) and obese (≥30 kg/m2) [20]. Body weight was measured using an electronic weighing machine in light clothes and without shoes. Height was measured using a stadiometer without shoes. Non- communicable diseases were determined based on the patient’s previous medical record or evidence of medication to manage the disease.

The demographic data of the participants including age, gender, education, occupation, marital status, physical activity, type of diet intake (healthy/unhealthy), and smoking status were documented. In this study, an unhealthy diet is classified as the consumption of junk foods including packaged or canned foods, foods with high trans-fat, sugar, or calories, and/or soft drinks based on the patient’s self-reporting. The patients who reported consuming junk foods at least three times per week or more were classified as having an unhealthy diet.

SPSS was used for statistical analysis of collected data. Categorical variables were expressed as frequency and percentage. Numerical variables were presented as median with inter-quartile range (IQR) as they were non-normally distributed which was confirmed through applying the normality assumption test, and the Shapiro- wilk test. Patients’ features were compared among different categories of BMI using a chi-square test. Ordinal logistic regression was applied to determine the association of patients’ features with obesity. Variables with p-values <0.25 in univariates were put up in a final regression model. A P-value less than or equal to

0.05 was considered statistically significant in the final regression model.

RESULTS

Summary of Socio-Demographic Profile

A total of 600 Emirati patients were included in the study. Median age was 33 (IQR=25-45) years with a range of 18-55 years. Gender distribution was almost equal with 50.5% males and 49.5% female participation. Table 1 presents the socio-demographic characteristics of the study participants categorized based on male and female gender.

Table 1: Summary statistics for patients’ demographic features.

Variables

Total n(%)

Male n(%)

Female n(%)

Age groups

18-30 years

275(45.8)

142(46.9)

133(44.8)

31-49 years

233(38.8)

108(35.6)

125(42.1)

50-55 years

92(15.3)

53(17.5)

39(13.1)

Variables

Total n(%)

Male n(%)

Female n(%)

Education

Illiterate

42(7)

2(0.7)

40(13.5)

Middle school

11(1.8)

1(0.3)

10(3.4)

High school

220(36.7)

118(38.9)

102(34.3)

College

295(49.2)

173(57.1)

122(41.1)

University

32(5.3)

9(3)

23(7.7)

Occupation

Employed

332(55.3)

9(3)

167(56.2)

Unemployed

176(29.3)

255(84.2)

77(25.9)

Student

92(15.3)

39(12.9)

53(17.8)

Marital status

Single

171(28.5)

87(28.7)

84(28.3)

Married/divorced/widow

429(71.5)

216(71.3)

213(71.7)

Type of Diet Intake

Healthy

376(62.7)

187(61.7)

189(63.6)

Unhealthy

224(37.3)

116(38.3)

108(36.4)

Smoking Status

Smoker

103(17.2)

103(34)

0(0)

Never smoked

497(82.8)

200(66)

297(100)

Physical activity

None

302(50.3)

119(39.3)

183(61.6)

<30 minutes per day

161(26.8)

104(34.3)

57(19.2)

≥30 minutes per day

137(22.8)

80(26.4)

57(19.2)

Obesity Prevalence and Comparison of Participants’ Features, among Different Body Mass Index Categories

Median BMI levels among patients were 28.2 (IQR=24.3- 32.5) kg/m2. BMI range was 14.1-56.9 Kg/m2. The 

percentage of underweight, normal, overweight, and obese participants was found to be 4.3%, 23.5%, 33.7%, and 38.5% respectively. Table 2 displays a comparison of participants’ characteristics across different BMI categories. This study found that the prevalence of obesity was significantly lower among younger participants of the age group 18-30 years. Gender, education level, occupation, marital status, type of diet, and physical activity were found significantly different among obese and non-obese groups as shown in Table 2.

Univariate and Multivariable Association of Participants’ Features with Obesity Prevalence

The univariate analysis reveals that younger patients are at a lower risk of obesity. The odds of obesity were significantly higher with the patients who have lower education levels compared to those who have university levels of education. Obesity risk was higher among both unemployed and employed individuals in contrast to the students. Furthermore, being single and consuming a healthy diet were related to a reduced risk of obesity whereas smokers and individuals who do not engage in physical activity regularly or allocate <30 minutes/ day for physical activity had higher obesity odds. The multivariable regression model when adjusted with other covariates suggested that lower education levels are significantly associated with higher odds of obesity. In contrast, being unmarried and adhering to a healthy diet were identified as factors significantly associated with lower odds of obesity (Table 3).

Table 2: Comparison of participant’s characteristics across various body mass index (BMI) classes.

 

Variables

 

Groups

Body Mass Index

 

p-value

Underweight n(%)

Normal n(%)

Overweight n(%)

Obese n(%)

 

Age

18-30 years

25(9.1)

86(31.3)

83(30.2)

81(29.5)

 

*<0.001

31-49 years

1(0.4)

41(17.6)

77(33)

114(48.9)

50 years and above

0(0)

14(15.2)

42(45.7)

36(39.1)

Gender

Male

3(1)

75(24.8)

124(40.9)

101(33.3)

0.941

Female

23(7.7)

66(22.2)

78(26.3)

130(43.8)

 

 

Education

Illiterate

0(0)

2(4.8)

14(33.3)

26(61.9)

 

 

*<0.001

Middle school

0(0)

0(0)

1(9.1)

10(90.9)

High school

6(2.7)

47(21.4)

72(32.7)

95(43.2)

College

12(4.1)

82(27.8)

106(35.9)

95(32.2)

University

8(25)

10(31.3)

9(28.1)

5(15.6)

 

Occupation

Unemployed

7(4)

29(16.5)

45(25.6)

95(54)

 

*<0.001

Employed

7(2.1)

77(23.2)

132(39.8)

116(34.9)

Students

12(13)

35(38)

25(27.2)

20(21.7)

Marital status

Single

16(9.4)

65(38)

47(27.5)

43(25.1)

*<0.001

Married

10(2.3)

76(17.7)

155(36.1)

188(43.8)

Diet

Healthy

19(5.1)

110(29.3)

138(36.7)

109(29)

*<0.001

unhealthy

7(3.1)

31(13.8)

64(28.6)

122(54.5)

Smoking status

Never

25(5)

113(22.7)

164(33)

195(39.2)

0.949

Smoker

1(1)

28(27.2)

38(36.9)

36(35)

 

Physical activity

None

18(6)

54(17.9)

88(29.1)

142(47)

 

*<0.001

<30 minutes per day

4(2.5)

34(21.1)

69(42.9)

54(33.5)

≥30 minutes per day

4(2.9)

53(38.7)

45(32.8)

35(25.5)

 

*Significant at p<0.05.

Table 3: Univariate and multivariable association of patients’ features with obesity.

Variables

Groups

OR

(95% CI)

p-value

aOR

(95% CI)

p-value

 

 

Age

18-30 years

0.4

(0.3-0.7)

<0.001

0.8

(0.5-1.4)

0.503

31-49 years

1.2

(0.8-1.9)

0.363

1.5

(0.9-2.4)

0.126

50 years and above

Reference category

-

-

 

Gender

Male

0.9

(0.7-1.2)

0.464

-

-

Female

Reference category

-

-

 

 

 

 

Education

Illiterate

13.7

(5.5-33.8)

*<0.001

5.4

(1.9-15.3)

*0.002

Middle school

77.2

(8.7-682)

 

*<0.001

77.2

(3.9-

383.4)

 

*0.002

High school

5.6

(2.8-11.2)

*<0.001

5.6

(1.4-6.1)

*0.005

College

3.6

(1.8-7.1)

*<0.001

2.7

(1.3-5.5)

*0.002

University

Reference category

Reference category

 

 

Occupation

Unemployed

4.9

(3.1-8)

*<0.001

1.5

(0.8-2.9)

0.189

Employed

2.7

(1.8-4.2)

*<0.001

1.2

(0.7-2)

0.571

Students

Reference category

Reference category

 

Marital status

Single

0.3

(0.2-0.5)

*<0.001

0.6

(0.3-0.9)

*0.011

Married

Reference category

Reference category

 

Diet

Healthy

0.4

(0.3-0.5)

*<0.001

0.4

(0.3-0.5)

*<0.001

Unhealthy

Reference category

Reference category

Smoking status

Never

1.1

(0.7-1.6)

0.721

-

-

Smoker

Reference category

-

-

 

Physical activity per day

None

2.3

(1.6-3.4)

<0.001

1.3(0.9-2)

0.200

<30 minutes

1.7

(1.1-2.6)

0.012

1.4(0.9-

2.2)

0.124

≥30 minutes

Reference category

Reference category

 

CI: Confidence interval, OR= Odds ratio, aOR= Adjusted Odds ratio,

*Significant at p<0.05.

Burden of Non-Communicable Diseases among Study Subjects

Fig. (1) displays the prevalence of non-communicable diseases among patients with BMI <25 Kg/m2 and ≥25 Kg/m2. Frequency of dyslipidemia (p<0.001), type 2

 

AVI2VzFuxSxsAAAAAElFTkSuQmCC

Fig. (1): Complications of obesity among patients with BMI

<25 Kg/m2 and ≥25 Kg/m2.

DLD: Dyslipidemia, T2DM: Type 2 diabetes, HTN: Hypertension: IFG:

Impaired fasting glucose, CKD: Chronic kidney disease

diabetes (p<0.001), hypertension (p<0.001), and cardiac problems (p<0.001) were significantly higher among patients with BMI ≥25 Kg/m2. Other complications including impaired fasting glucose, hyperuricemia, asthma, and hypothyroid were not significantly different among patients with a BMI of <25 Kg/m2 and ≥25 Kg/m2.

DISCUSSION

The present study found that 33.7% of participants were overweight and 38.5% were obese. In other words, 72.2% of the adult population attending Hatta Hospital is either obese or overweight. A similar study conducted in UAE and including a larger sample of expatriates reported the percentage of overweight and obese as 43% and 32.3% respectively [21]. On the other hand, in 2019, data collected from a community-based study in the UAE reported a lower obesity prevalence of 17.8%, using a BMI threshold >30 kg/m2 [22]. Several previous studies conducted among Gulf countries reported a higher obesity prevalence. According to WHO, overweight and obesity prevalence in Saudi Arabia is 68.2% and 33.7% respectively [23]. Data from Kuwait shows the prevalence of obesity was 42.1% [24]. Studies revealed that Oman’s predicament of overweight and obesity is comparable to that of the Eastern Mediterranean Region as a whole, where 49% of adults are classified as overweight or obese [25]. Another similar study conducted by Bahrain primary care clinics reported the obesity prevalence as 42% [26].

The elevated prevalence of obesity in this study may be due to the conduction timing, as the data was collected shortly after the release of lockdown measures for the COVID-19 pandemic, when a significant number of people spent time in quarantine leading to increased BMI levels. Pellegrini et al., [27] also reported a noticeable rise in weight gain among individuals who were in quarantine lasting longer than a month. The World Obesity Atlas 2023 report also validates this fact, highlighting that in many countries during the lockdown period from 2020 to 2022, there was an elevated risk of weight gain due to restricted movements outside the home. This exacerbated unhealthy dietary and sedentary behaviors associated with weight gain and significantly reduced access to care [28].

The present study demonstrated higher obesity prevalence among the female participants than males (43.8% versus 33.3%), although this difference is not statistically significant as shown in Table 2. Similar previous surveys conducted in the UAE reported that females were more likely to be obese than males [21, 29, 30]. Sulaiman et al., [21] carried out a larger study on 3064 subjects and analyzed that based on BMI, females tend to be obese than males (OR =1.24, 95% CI 1.01– 1.53). Another similar larger survey from UAE reported that female gender was significantly associated with higher odds of obesity (aOR=1.62, 95% CI: 1.10-2.39) [22]. The pattern of higher obesity rate among females 

than males was also seen in previous studies from Gulf countries [29, 30].

The present study did not demonstrate a significant association between age and obesity in multivariable regression analysis. Although, the univariate analysis found younger people are at lower risk of obesity. Again, the larger surveys performed in the UAE reported an association between increasing age and a high risk of obesity [21].

Our study demonstrated a reduced risk of obesity among individuals with higher education levels. This could be attributed to the fact that highly educated participants usually have greater access to knowledge regarding healthy lifestyles and available healthcare services. Consequently, they are more aware of the factors that can contribute to obesity and associated health risks. This observation is evident in our study which found a higher risk of obesity among those with lower education levels compared to individuals who have university-level education. A meta-analysis reported an association of higher obesity risk with lower education levels among women but this association was weak in males [31]. Similarly, a study conducted in America reported that among women there was an inverse relationship between obesity and educational status and also for males living in highly developed cities, higher education was found a protective factor against obesity [32]. In contrast to our findings, Mamdouh et al., [22] did not find a significant association between education and obesity. Other comparable studies from Egypt and Ethiopia were also unable to find an association between education and obesity [33, 34].

The present study revealed a significant link between occupation and obesity. Similarly, prior research has consistently shown a direct relation between occupation with obesity. For instance, Mamdouh et al., [22] analyzed that after adjusting for age and sex, individuals in clerical and unskilled professions face a high risk of obesity. However, contrasting findings emerged from a larger- scale study that did not identify a significant association between occupation and obesity [24]. Similarly, Sulaiman et al., [21] were unable to establish occupation as a contributing factor for obesity.

Occupational physical   activity   is   recognized   as a significant factor in estimating the daily energy expenditure, while the availability of type of food in the workplace can impact daily caloric intake. Consequently, analyzing occupation as a precise factor of obesity in such surveys may not provide an accurate insight into its role in obesity and overweight. There is a need for in-depth research to uncover the complex relationship between obesity and workplace variables, including working hours, occupation demand, physical activity, and available food options.

This study estimated that unmarried individuals exhibit a lower risk of obesity compared to married, divorced, or widowed individuals. Similarly, Sulaiman et al., [21] reported a considerable association of marital status with obesity, where married individuals (aOR=2.40, 95% CI: 1.87-3.08) and divorced/widowed individuals

(aOR=1.40, 95% CI: 0.64-3.11) had significantly increased obesity risk compared to those who were single. However, in contrast to our study, another similar study reported that being single, divorced, or separated had higher odds of abdominal and general obesity [35]. One possible explanation for contrasting findings among different studies could be due to differences in population anthropometry. The explanation for higher odds of obesity in our region may be that single individuals may prioritize their body weight and fitness more, as they want to attract an appealing partner to marry. However, once married, their focus may shift towards earning income and fulfilling other responsibilities, potentially leading to a change in lifestyle behavior that contributes to obesity.

Obesity was indeed found to be significantly associated with diet and physical activity in the current study. The individuals who managed to adhere to a healthy lifestyle were found to have lower obesity risk compared to those who did not engage in regular exercise. These findings align with other similar studies [21, 22], and emphasize the well-established association between unhealthy lifestyles and obesity.

Interestingly, this study did not find any significant link between smoking and obesity even though smoking is routinely claimed to be one of the drivers of obesity [26, 30]. The lack of association observed in this study could be attributed to several factors, such as limited sample size or the predominance of non-smokers, particularly among females, which may influence the detection of this association.

The present study found that NCDs including hypertension, type 2 diabetes mellitus (T2DM), dyslipidemia, and cardiac problems were significantly more prevalent among the overweight or obese participants, consistent with the prior research [7, 9, 17]. However, other NCDs such as chronic kidney disease (CKD), asthma, hyperuricemia, prediabetes, and impaired fasting glucose did not exhibit significant differences between the participants with BMI <25 Kg/ m2   and ≥25 Kg/m2. Diseases such as asthma, CKD, and hyperuricemia might impact the lean participants for several reasons. Nevertheless, it is surprising to find that the prevalence of prediabetes did not differ significantly among lean and obese participants. This lack of significant association could be due to the small sample size.

STRENGTH AND LIMITATIONS

The present study shed light on the prevalence of overweight and obesity among the patients visiting the primary care clinic at Hatta Hospital in the Hatta area. Understanding the existing load of obesity will be beneficial in establishing public health strategies 

to address this issue at the governmental level and recognizing the role of physicians in lowering the elevated BMI burden. However, there are some limitations to consider, as this study solely analyzed the data from a single center. Additionally, the sample size was small, body anthropometry measures other than BMI, such as waist and neck circumference and hip-to-waist ratio were not assessed. Furthermore, NCDs were not classified based on updated laboratory investigations. These limitations restrict to generalize of the findings of the current study.

CONCLUSION

The preset study discovered a higher obesity prevalence among the patients attending the family medicine clinic at Hatta Hospital, with associations observed between obesity and lower education levels, marital status, unhealthy diet, and insufficient physical activity.

ETHICAL APPROVAL

Ethical approval was obtained from the Dubai Scientific Research Ethics Committee (DSREC), Dubai Health Authority, Dubai, UAE (REF letter No.: DSREC-09/2021_09). All procedures performed in studies involving human participants were following the ethical standards of the institutional and/ or national research committee and the Helsinki Declaration.

CONSENT FOR PUBLICATION

Written informed consent was taken from the participants.

AVAILABILITY OF DATA

The data set may be acquired from the corresponding

author upon a reasonable request.

FUNDING

None.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

ACKNOWLEDGEMENTS

Declared none.

AUTHORS’ CONTRIBUTION

AR conceptualized the study. AR & UF performed literature search and designed the study protocol. AR, ZNQ and ME collected data and entered in spreadsheets. AR and UF analyzed the data and prepared initial manuscript draft. AR critically revised the initial draft. All authors read and approved the manuscript.

REFERENCES

1. Safaei M, Sundararajan EA, Driss M, Boulila W, Shapi’i A. A systematic literature review on obesity: Understanding the causes & consequences of obesity and reviewing various machine learning approaches used to predict obesity. Comput Biol Med 2021; 136:

DOI: https://doi.org/10.1016/j.compbiomed.2021.104754

2. Jehan S, Zizi F, Pandi-Perumal SR, McFarlane SI, Jean-Louis G, Myers AK. Energy imbalance: obesity, associated comorbidities, prevention, management and public health implications. Adv Obes Weight Manag Control 2020; 10(5): 146-61.

3. NCD Risk Factor Collaboration (NCD-RisC). Trends in adult body- mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19.2 million Lancet 2016; 387: 1377-96.

DOI: https://doi.org/10.1016/s0140-6736(16)30054-x

4. Addo PNO, Nyarko KM, Sackey SO, Akweongo P, Sarfo Prevalence of obesity and overweight and associated factors among financial institution workers in Accra Metropolis, Ghana: a cross sectional study. BMC Res Notes 2015; 8: 599.

DOI: https://doi.org/10.1186/s13104-015-1590-1

5. Kivimäki M, Strandberg T, Pentti J, Nyberg ST, Frank P, Jokela M, et al. Body-mass index and risk of obesity-related complex multimorbidity: an observational multicohort Lancet Diabetes Endocrinol 2022; 10(4): 253-63.

DOI: https://doi.org/10.1016/s2213-8587(22)00033-x

6. Li Y, Schoufour J, Wang DD, Dhana K, Pan A, Liu X, et al. Healthy lifestyle and life expectancy free of cancer, cardiovascular disease, and type 2 diabetes: prospective cohort BMJ 2020; 368: 16669.

DOI: https://doi.org/10.1136/bmj.l6669

7. The GBD 2015 Obesity Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med 2017; 377: 13-27.

DOI: https://doi.org/10.1056/NEJMoa1614362

8. Francisco V, Pino J, Campos-Cabaleiro V, Ruiz-Fernández C, Mera A, Gonzalez-Gay MA, et al. Obesity, fat mass and immune system: role for Front Physiol 2018; 9: 640.

DOI: https://doi.org/10.3389/fphys.2018.00640

9. Dai H, Alsalhe TA, Chalghaf N, Riccò M, Bragazzi NL, Wu J. The global burden of disease attributable to high body mass index in 195 countries and territories, 1990-2017: An analysis of the Global Burden of Disease PLoS Med 2020; 17(7): e1003198.

DOI: https://doi.org/10.1371/journal.pmed.1003198

10. Lin X, Xu Y, Xu J, Pan X, Song X, Shan L, et al. Global burden of noncommunicablediseaseattributabletohighbodymassindexin195 countries and territories, 1990-2017. Endocrine 2020; 69(2): 310-20.

DOI: https://doi.org/10.1007/s12020-020-02352-y

11. Wen X, Zhang B, Wu B, Xiao H, Li Z, Li R, et Signaling pathways in obesity: mechanisms and therapeutic interventions. Signal Transduct Target Ther 2022; 7(1): 298.

DOI: https://doi.org/10.1038/s41392-022-01149-x

12. Gebreab SZ, Vandeleur CL, Rudaz D, Strippoli M-PF, Gholam- Rezaee M, Castelao E, et Psychosocial stress over the lifespan, psychological factors, and cardiometabolic risk in the community. Psychosomatic Med 2018; 80(7): 628-39.

DOI: https://doi.org/10.1097/psy.0000000000000621

13. Fan JX, Wen M, Li Associations between obesity and neighborhood socioeconomic status: variations by gender and family income status. SSM Popul Health 2019; 10: 100529.

DOI: https://doi.org/10.1016/j.ssmph.2019.100529

14. Flores-Dorantes MT, Díaz-López YE, Gutiérrez-Aguilar Environment and gene association with obesity and their impact on neurodegenerative and neurodevelopmental diseases. Front Neurosci 2020; 14: 863.

DOI: https://doi.org/10.3389/fnins.2020.00863

15. Ghanemi A, Yoshioka M, St-Amand Broken energy homeostasis and obesity pathogenesis: the surrounding concepts. J Clin Med 2018; 7(11): 453.

DOI: https://doi.org/10.3390/jcm7110453

16. Fan Y, Pedersen Gut microbiota in human metabolic health and disease. Nat Rev Microbiol 2021; 19(1): 55-71.

DOI: https://doi.org/10.1038/s41579-020-0433-9

17. Lee DH, Keum N, Hu FB, Orav EJ, Rimm EB, Willett WC, et al. Comparison of the association of predicted fat mass, body mass index, and other obesity indicators with type 2 diabetes risk: two large prospective studies in US men and Eur J Epidemiol 2018; 33(11): 1113-23.

DOI: https://doi.org/10.1007/s10654-018-0433-5

18. Samarkandy MM, Mohamed BA, Al-Hamdan Nutritional assessment and obesity in Down syndrome children and their siblings in Saudi Arabia. Saudi Med J 2012; 33(11): 1216-21.

19. Radwan H, Ballout RA, Hasan H, Lessan N, Karavetian M, Rizk The epidemiology and economic burden of obesity and related cardiometabolic disorders in the United Arab Emirates: a systematic review and qualitative synthesis. J Obes 2018; 2018: 2185942.

DOI: https://doi.org/10.1155/2018/2185942

20. Weir CB, Jan A. BMI Classification Percentile and Cut off Points. (Updated 2023 Jun 26). In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024

21. Sulaiman N, Elbadawi S, Hussein A, Abusnana S, Madani A, Mairghani M, et Prevalence of overweight and obesity in United Arab Emirates Expatriates: the UAE national diabetes and lifestyle study. Diabetol Metab Syndr 2017; 9: 88.

DOI: https://doi.org/10.1186/s13098-017-0287-0

22. Mamdouh H, Hussain HY, Ibrahim GM, Alawadi F, Hassanein M, Zarooni AA, et Prevalence and associated risk factors of overweight and obesity among adult population in Dubai: a population-based cross-sectional survey in Dubai, the United Arab Emirates. BMJ Open 2023; 13(1): e062053.

DOI: https://doi.org/10.1136/bmjopen-2022-062053

23. Althumiri NA, Basyouni MH, AlMousa N, AlJuwaysim MF, Almubark RA, BinDhim NF, et al. Obesity in Saudi Arabia in 2020: prevalence, distribution, and its current association with various health Healthcare (Basel) 2021; 9(3): 311.

DOI: https://doi.org/10.3390/healthcare9030311

24. Oguoma VM, Coffee NT, Alsharrah S, Abu-Farha M, Al-Refaei FH, Al-Mulla F, et al. Prevalence of overweight and obesity, and associations with socio-demographic factors in BMC Public Health 2021; 21(1): 667.

DOI: https://doi.org/10.1186/s12889-021-10692-1

25. World Health Organization. Improving nutrition in Oman. [Last accessed on March 6, 2023]. Available from: https://www.emro. int/nutrition/news/improving-nutrition-in-oman.html

26. Ali AAM, Alalwan NF, Ali MAM, Alsayyad Obesity and depression, an analytical study among adults attending primary care clinics in Bahrain. Int J Comm Med Public Health Gujrat 2020; 7: 4713-9.

DOI: https://doi.org/10.18203/2394-6040.ijcmph20205139

27. Pellegrini M, Ponzo V, Rosato R, Scumaci E, Goitre I, Benso A, et Changes in weight and nutritional habits in adults with obesity during the “lockdown” period caused by the COVID-19 virus emergency. Nutrients 2020; 12(7): 2016.

DOI: https://doi.org/10.3390/nu12072016

28. World Obesity Available from: https://s3-eu-west-1. amazonaws.com/wof-files/World_Obesity_Atlas_2023_Report.pdf

29. Ng SW, Zaghloul S, Ali HI, Harrison G, Popkin The prevalence and trends of overweight, obesity and nutrition-related non- communicable diseases in the Arabian Gulf States. Obes Rev 2011; 12(1): 1-13.

DOI: https://doi.org/10.1111/j.1467-789x.2010.00750.x

30. Alhyas L, McKay A, Balasanthiran A, Majeed A. Prevalences of overweight, obesity, hyperglycaemia, hypertension and dyslipidemia in the Gulf: systematic review. JRSM Short Rep 2011; 2(7): 55.

DOI: https://doi.org/10.1258/shorts.2011.011019

31. Witkam R, Gwinnutt JM, Humphreys J, Gandrup J, Cooper R, Verstappen Do associations between education and obesity varydependingonthemeasureofobesityused?Asystematicliterature review and meta-analysis. SSM Popul Health 2021; 15: 100884.

DOI: https://doi.org/10.1016/j.ssmph.2021.100884

32. Mazariegos M, Auchincloss AH, Braverman-Bronstein A, Kroker- Lobos MF, Ramírez-Zea M, Hessel P, et Educational inequalities in obesity: a multilevel analysis of survey data from cities in Latin America. Public Health Nutr 2021; 25(7): 1790-8.

DOI: https://doi.org/10.1017%2FS1368980021002457

33. Dagne S, Gelaw YA, Abebe Z, Wassie MM. Factors associated with overweight and obesity among adults in northeast Ethiopia: a cross-sectional Diabetes Metab Syndr Obes 2019; 12: 391-9.

DOI: https://doi.org/10.2147/dmso.s179699

34. Saleh MS, Shaban EE, Amer Factors attributing to obesity among working adults in Egypt. J Biosci App Res 2018; 4(4): 425-31.

DOI: https://doi.org/10.21608/jbaar.2018.152674

35. Hosseini Z, Veenstra G, Khan NA, Conklin Associations between social connections, their interactions, and obesity differ by gender: a population-based, cross-sectional analysis of the Canadian longitudinal study on aging. PLoS One 2020; 15(7): e0235977.

DOI: https://doi.org/10.1371/journal.pone.0235977