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Prevalence of overweight and obesity based on the body mass index; a cross-sectional study in Alkharj, Saudi Arabia

Prevalence of overweight and obesity based on the body mass index; a cross-sectional study in... Background: Obesity and overweight are accompanied with several different chronic diseases. Overweight and obesity can be measured by using body mass index (BMI) and is also used widely as an index of relative adiposity among any population. The aim of the study is to evaluate the prevalence of overweight and obesity among general population in Al-Kharj, Saudi Arabia. Methods: Cross-sectional analysis was undertaken from a representative sample (N = 1019) of the Al Kharj population. Anthropometric measurements including the waist circumference (in centimeters), height (in meters), and weight (in kilograms) of the subjects were undertaken by means of standard apparatus. SPSS 24.0 was utilized for statistical analysis of the data. Results: Majority of respondents in this study were overweight and obese (54.3%) compared with 45.7% being non-obese. A linear positive association of increasing BMI with older age groups was present in males and females. Men had larger waist circumference, weight and height measures as compared with their female counterparts. Regression analysis showed increasing age, being married and high serum cholesterol to be the significant predictors of overweight and obesity while gender, education level, job status, and having diabetes were not. Conclusions: The obesity-overweight prevalence in the Saudi population is high mainly across both genders. However, the associated factors are potentially preventable and modifiable. The regional barriers to lifestyle modifications and interventions to encourage active lifestyles, especially among adolescents to limit the occurrence of obesity and ultimately promote health and wellbeing, are warranted. Furthermore, prospective studies are needed in future to confirm the aetiological nature of such associations. Keywords: Overweight, Obesity, Body-mass-index, Al Kharj; Saudi Arabia Background million mortalities, 4% of YLL i.e. Years of Life Lost, Obesity, which broadly refers to excessive body fat, is a and at least 35.8 million worldwide DALYs i.e. Disabil- significant public health concern and its occurrence has ity-Adjusted Life Years [2]. If such secular trends continue, reached an epidemic proportion in both developing as an estimated 20% of the worldwide population will be well as developed countries [1]. Obesity tends to impact affected by obesity by 2030, while 38% will be overweight more than one third of the population around the world. [3]. It is expected that 85% of U.S. citizens will be affected Globally, obesity is estimated to cause more than 2.8 by obesity by 2030 [4]. The prevalence of obesity in Gulf Countries among children and adolescents ranges from 5% to 14% in * Correspondence: Elmetwally.ashraf@outlook.com Docent of Epidemiology, School of Health Sciences, University of Tampere, males and from 3% to 18% in young females [5]. Data Tampere, Finland are scarce from other Middle Eastern countries; how- Epidemiology & Biostatistics Department, College of Public Health & Health ever, compelling evidence is present indicating the rise Informatics, King Saud Bin Abdulaziz University for Health Sciences, P. O. BOX 3660, Riyadh, Saudi Arabia in obesity rates. For instance, recent surveys found that Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Al-Ghamdi et al. Lipids in Health and Disease (2018) 17:134 Page 2 of 8 in Kuwait, 48% of females and 36% of males were obese, To assess obesity, the World Health Organization whereas 77% of females and 74% of males were over- (WHO) has implemented the body mass index (BMI) weight. While 44% of the female population and 28% of scale, which can be obtained by dividing the total weight the male population from Saudi Arabia were found to of the body in kilograms by the square of the height be obese. However, 71% of women and 66% of men were measured in meters, as a substitute of the over-all body reported to be overweight [6]. fat measure [19]. With this, obesity can be well-defined BMI is predisposed by certain determinants that can if BMI value is ≥30Kg/m . It usually correlates with the either be environmental or genetic. Such environmental fraction of body fat in individuals having high prevalence factors include physical activity, gender, marital status, of obesity [24]. As per WHO [2], an increased risk of job and education level, diet, co-morbidities i.e. diabetes, co-morbidities exists when the BMI is between 25.0 and hypertension, cardiac and endocrinal issues, cancers etc. 29.9 while moderate-severe risk lies when it is > 30. [7]. In Saudi Arabia, 4% obesity was reported in rural Hence, the goal must be to sustain the range of BMI areas. Conversely, obesity is reported to be 10% in the between 18.5 and 24.9 kg/m . Therefore, the purpose of Western regions and 14% in the Eastern region [Jizan the study is to estimate the prevalence of overweight (12%), Riyadh (22%), and Hail (34%)] due to the con- and obesity within the general population in Al-Kharj, sumption of more fast food and a sedentary lifestyle [8]. Saudi Arabia. Regarding gender distribution, females were dispro- portionately affected by extreme obesity than males Methods [6, 9–11]. Income also predicted obesity, particularly Study design in Arab countries [12]. The role of education was A population based cross sectional study using the data clear when more obesity was found prevalent in the drawn from the general population in Saudi Arabia illiterate population as was found in Syria, Jordan and between January 2016 and June 2016 was undertaken. Lebanon [13, 14]. Married people were also more suscep- tible to being overweight and obese [9–11, 15–17]. Settings Obesity can contribute to greater risks of The research study was conducted in Al Kharj, a city lo- non-communicable diseases (NCDs) causing morbidity cated in Al Kharj Governorate in central Saudi Arabia, and mortality. It is a modifiable and preventable risk 77 km south of Riyadh, with an estimated population of factor for insulin resistance leading to impaired glucose 376,000. It is connected to the city of Dammam and tolerance [9], metabolic syndrome [18] and dyslipidemia Riyadh by rail. It possesses mixed urban (military and [19]. The relative risk after adjusting the variable of age civilian), rural, and adjacent nomadic communities. was found to be 60.9 for the development of diabetes with a BMI ≥35Kg/m [10]. Framingham study reported Sample size hypertension having the relative risks of 1.46 and 1.75 in Approximately 1019 (638 females and 381 males) were overweight and obese young adults respectively, while recruited into the study by using a multi-stage sampling the Honolulu Heart Program and Japanese survey method from different governmental and private insti- showed its predictable effect on hypertension in the tutes after obtaining verbal informed consent. The total older population [20, 21]. population of these institutions were divided into groups Obesity can also contribute to ischemic stroke and called clusters after acquiring a list of participants in Obstructive Sleep Apnea whereas 75% of adolescents each institute nominated. Samples of the respondent with asthmatic emergencies were either obese or over- were then selected using simple random sampling from weight. Gastroesophageal reflux disease, cholelithiasis, each of the group (cluster). osteoarthritis, various cancers, psychiatric illnesses, polycystic ovarian syndrome in females, infertility and Selection of participants (criteria for inclusion and impotence in males were also found to be associated exclusion) with obesity. Obese females were 20% less likely to get Saudi citizens, 18 years or older were selected, based on married, had lesser chance to complete school, had their eligibility as well as willingness to participate in the more poverty at the household level and less earnings study. Non-Saudi residents, Saudi citizens younger than in contrast to women who were not overweight [22]. 18 years of age, and participants who were not willing to Fontaine et al. concluded that a noticeable decline in sign the consent form were excluded. life expectancy was present in obese adolescents in comparison to non-obese individuals [23]. Similarly, Materials/instruments non-smoking obese females and males aged 40 years A structured questionnaire was used for collecting data. lived less than 7.1 and 5.8 years than their non-obese The participants filled a set of questionnaires on socio- counterparts. demographics including age, gender, marital status, and Al-Ghamdi et al. Lipids in Health and Disease (2018) 17:134 Page 3 of 8 level of education. Anthropometric measurements body and control) of the test was essential for which sample weight (in kilograms), height (in meters), body mass was programmed into the machine termed as Dimension index (BMI), and waist circumference (in centimeters) Xpand Plus accordingly and results were collected after were also included in this questionnaire. the test were finished. Consideration and procedures Operational definition In general practice, based on the standards for anthropo- Overweight can be defined as a BMI of 25 and more, metric measurements, the weight of an individual body and obesity as an index of 30 and more [25]. DM was was measured in socks and light clothes to the nearest defined as FPG ≥126 mg/dL or self-reported history of 0.1 kg, using a similar digital medical scale. Height of a diabetes as defined by American Diabetes Association participant was measured to the nearest of 0.1 cm using (ADA) criteria. Pre-diabetes was defined using HbA1c a stadiometer precisely noted in standing position with cutoff level of 5.7- < 6.5%, while Diabetes Mellitus no shoes on. Weight was measured through a digital (DM) was ≥6.5%, according to the American Diabetes weighing scale. Prior to the measurement, the scale was Association 2016. calibrated to the zero level and was also verified for repeatability of the readings. BMI was computed by the Data analysis dividing weight by height in meters square (kg/m ) and Data analysis was performed using SPSS 24.0 for weight categories were demarcated following the WHO Windows. Analysis involved descriptive statistics for standard as 30 kg/m2 obese. frequencies, multivariate and logistic regression Waist circumferences (WC), at the level of the hip and analysis. Categorical variables such as gender, educa- umbilicus circumference was measured at the widest girth tional level, and age groups were summarized and of the hip using a flexible non-stretchable tape. Women reported in terms of frequency distribution. A with a WC of < 80 cm, 80.0 - 87.9 cm and ≥ 88 cm and chi-square test was utilized to examine the association males with a waist circumference of < 94 cm, 94.0 - 101.9 between different categorical variables whereas the cm and ≥ 102 cm were termed as normal weight, t-test or ANOVA were used for continuous variables. overweight and obese, correspondingly. The multinomial logistic regression model was used A blood glucose monitoring system was used to to examine the relationship (adjusted odds ratios) measure the fasting blood glucose (FBG) of each between overweight, obesity and possible contributing respondent. Blood samples were obtained after a mini- factors. P-values of <0.05 were considered statistically mum of 8 h of fasting by the respondents. The results significant. obtained with the glucometer were calibrated with laboratory outcomes using the glucose oxidase method. Ethical approval Blood samples were collected from each participant by This was gained from the local Institutional Review trained nurses and phlebotomy for HbA1c, fasting lipid Board (i.e. Committee of Scientific Research and Publi- profile (total cholesterol, triglycerides, HDL- and LDL cation). Written permission and verbal consent was cholesterol). A unique ID (barcode) was assigned to sought from the respondents before commencement of these patients. Two tubes were used: one for the study. Participants were also guaranteed of the con- Hemoglobin A1c while the other for chemistry- then fidentiality as well as were notified that the participation gentle rolling was applied at roller mixer for preventing would be voluntary in the study. clotting. Any clotted samples or critical outcomes were reported, and participants were contacted immediately Results for an alternative sample. The tubes were gathered in a Table 1 illustrates the prevalence of being overweight special ice container for improved handling and care. along with obesity in males (n = 381) using BMI stand- These samples were sent to the central laboratory within ard classification stratified by four age groups. There is a 1–2 h of duration. Samples were run for the test proce- linear positive association (trend) of increasing BMI dures. The data obtained from all the samples/specimen with older age groups. For example, the Class I obese were encoded in Beckman Coulter. The heparin plasma (30–34.9 kg/m ) shows 15.4% of males in the 18– sample was used for the Chemistry analysis. The heparin 29 years; 23.3% of males in the 30–39 years; 25.5% of vacutainers were separated from the remaining. After males in the 40–49 years; and 42.1% of males in the 50– which, the vacutainers were organized along with the 67 years. As presented in Table 2 for females (n =638) and barcode numbers, and kept in the centrifuge. Samples except for the 40–49 years old, a similar linear positive were centrifuged for 5 min @ 4000 rpm for the separ- trend was noted: The Class I obese females show 12.2% in ation of plasma. Once the plasma was separated, it was the 18–29 years; 28.6% in the 30–39 years; 17.4% in the then utilized for the test procedure. Calibration (check 40–49 years; and 42.9% in the 50–67 years. Al-Ghamdi et al. Lipids in Health and Disease (2018) 17:134 Page 4 of 8 Table 1 Prevalence of Overweight and Obesity by Age Group in Males (n = 381) Age groups Non-Obese Overweight Class I Obese Class II/III Obese 2 2 2 2 (i.e. < 25 kg/m ) (i.e. 25 to 29.9 kg/m ) (i.e. 30 to 34.9 kg/m ) (i.e. ≥ 35 kg/m ) (n = 120) (n = 123) (n = 79) (n = 59) n (%) n (%) n (%) n (%) 18–29 years 74 (40.7%) 53 (29.1%) 28 (15.4%) 27 (14.8%) 30–39 years 31 (24.0) 48 (37.2%) 30 (23.3%) 20 (15.5%) 40–49 years 14 (27.5) 17 (33.3%) 13 (25.5%) 7 (13.7%) 50–67 years 1 (5.3) 5 (26.3%) 8 (42.1%) 5 (26.3%) Total 120 (31.5%) 123 (32.3%) 79 (20.7%) 59 (15.5%) Values are count (%) For the overall sample (n = 1019) in Table 3,itis We further examined diabetic status for each of the evident that being in an older age group was signifi- four age groups (Table 6). The results showed that the cantly associated with being overweight, class I or prevalence of diabetes was linearly and positively classII/IIIobese.For example, theClass I obese associated with age. The Cochran-Armitage trend test shows 13.0% in the 18–29 years; 24.9% in the 30–39 years; available from the contingency table of 4 age group- 23.0% in the 40–49 years; and 42.3% in the 50–67 years s-by- 3 diabetic status groups in SPSS 24.0 for Windows (Table 3). The Class II/III obese similarly shows a signifi- was significant (Linear-by-Linear association = 214.070, P cant association of BMI with increasing age: 7.9% in the < 0.0001). There was a significantly positive linear associ- 18–29 years; 14.6% in the 30–39 years; 27.0% in the ation with having diabetes in older age groups: 13.3% in 40–49 years; and 26.9% in the 50–67 years. Most of the the 18–29 years were diabetic; 26.7% in the 30–39 years respondents in this study are overweight and obese were diabetic; 33.3% in the 40–49 years were diabetic; and (54.3%) compared with 45.7% being the non-obese BMI finally, 26.7% in the older age group (50–67 years) were category (< 25 kg/m ). diabetic (Table 6). Table 4 demonstrates weight (in ‘kg’), height (in ‘m’) and waist circumference (in ‘cm’) for men, women, and the overall sample. Men tended to have larger WC more Multinomial logistic regression analysis than females (mean WC = 96.76 and 75.54 respectively). We conducted a multinomial logistic regression analysis Weight and height measures were also higher in males by regressing the categorical BMI outcome variable on compared with their female counterparts (Table 4). sociodemographic variables (age; gender; marital status; When diabetic status was examined according to BMI education level; and job status) and other variables categories (Table 5), we found that the proportion of which included blood cholesterol and whether a subject diabetic individuals was positively and significantly has diabetes or not (Table 7). The non-obese BMI associated with being in a higher BMI category: 11.4% category (< 25 kg/m ) was used as the reference category in the non-obese; 22.7% in the overweight; 38.6% in the to test the odds ratio (Exp B) for each of the other three Class I obese; and 27.3% in the Class II/III obese (Table 5). BMI categories: the overweight; class I obese; and class This observation is consistent with previous research that II/III obese. indicated increasing risk of diabetes with increasing body The relative risk of being overweight was associated weight status (BMI). with higher cholesterol (OR = 1.419, P = 0.001), being Table 2 Overweight and Obesity by Age Group in Females (n = 638) Age group Non-Obese Overweight Class I Obese Class II/III Obese 2 2 2 2 (< 25 kg/m ) (25–29.9 kg/m ) (30–34.9 kg/m ) (≥ 35 kg/m ) (n = 345) (n = 149) (n = 90) (n = 53) n (%) n (%) n (%) n (%) 18–29 years 336 (61.0%) 117 (21.2%) 67 (12.2%) 31 (5.6%) 30–39 years 8 (14.3) 25 (44.6%) 16 (28.6%) 7 (12.5%) 40–49 years 1 (4.3) 5 (21.7%) 4 (17.4%) 13 (56.5%) 50–67 years 0 (0.0%) 2 (28.6%) 3 (42.9%) 2 (28.6%) Total 345 (54.2%) 149 (23.4%) 90 (14.1%) 53 (8.3%) Values are count (%) Al-Ghamdi et al. Lipids in Health and Disease (2018) 17:134 Page 5 of 8 Table 3 Overweight and Obesity Prevalence by Age Group in the entire sample (n = 1019) Age group Non-Obese Overweight Class I Obese Class II/III Obese 2 2 2 2 (< 25 kg/m ) (25–29.9 kg/m ) (30–34.9 kg/m ) (≥ 35 kg/m ) (n = 465) (n = 272) (n = 169) (n = 112) n (%) n (%) n (%) n (%) 18–29 years 410 (55.9%) 170 (23.2%) 95 (13.0%) 58 (7.9%) 30–39 years 39 (21.1) 73 (39.5%) 46 (24.9%) 27 (14.6%) 40–49 years 15 (20.3) 22 (29.7%) 17 (23.0%) 20 (27.0%) 50–67 years 1 (3.8%) 7 (26.9%) 11 (42.3%) 7 (26.9%) Total 465 (45.7%) 272 (26.7%) 169 (16.6%) 112 (11.0%) Values are count (%) single/unmarried (OR = 1.367, P < 0.0001), and being a A major strength of our study was the usage of cluster civilian worker (OR = 1.607, P = 0.049). Being classi- designs for surveys as recommended by the WHO which fied as ClassIobese wasassociatedwithstatistically assures that the nominated sample specifies the entire significant risk of older age (OR = 1.045, P = 0.020), population precisely. We included common risk factors higher cholesterol (OR = 1.606, P < 0.0001), being un- and evaluated their association with a high BMI. More- married (OR = 1.542, P = 0.021), and having diabetes over, trained nurses collected the data as well as an- (OR = 1.303, P = 0.035). Whereas being classified as thropometric measurements in our study to ensure the Class II/III obese was significantly associated with reliability of the data and the anthropometric and clin- older age (OR = 1.050, P = 0.015), higher cholesterol ical measurements. Our study has used the more recent (OR = 1.575, P = 0.001), and being a civilian worker standard globally accepted WHO/NHLBI criteria for the (OR = 2.018, P = 0.042). definition of BMI cut-off values that would be applicable The adjusted R-squared (R ) for the multinomial logis- to all countries/regions including the Gulf region. tic regression model is represented by the Cox and Snell Literature shows that high BMI can be the root cause Pseudo R-Square in the SPSS output for this model, with for many of non-communicable diseases (NCDs) in the a value of 0.194. world. The majority of respondents in this study were also overweight and obese (54.3%), which has become an Discussion increasing trend around the world with increasing preva- Our study findings show that the vast number of the lence rates in the USA (36.5%) [3], Spain (29%) [16], study population was overweight and obese. Its preva- Greece (23%) [15], Lebanon (17%) [26], Kuwait (43%), lence across both genders showed a linear and positive Saudi Arabia (35%) and Qatar (33%) [12]. A study trend with older age groups with an exception of the conducted in the city of Jeddah, Saudi Arabia, found 40–49 female age group. Hence, making it evident that that 33.8% of women were found to be obese and 47% increasing age was significantly associated with over- with WC > 80 cm [27] whereas in our study the mean weight, class I or II/III obese. While evaluating the mea- WC in females was 75.54 cm. On the other hand, there sures of obesity/overweight, males were found to have remains a perception in parents that overweight children greater values of WC, height and weight in comparison are a sign of prosperity, beauty, fertility, prosperity and to their female counterparts. It was also noted that the high social status [28]. proportion of diabetic individuals also had significant Obesity was also found to be associated with certain positive association with being in a higher BMI category. factors such as diabetes in our study where its contribu- Significant predictors of high BMI also included high tion towards diabetes was also supported by other inter- serum cholesterol, being married and increased age national and national published work in India [29], USA during regression analysis while gender, diabetes, job [9, 10], China [30], Lebanon [26] and Saudi Arabia [31]. and educational status were not significant. Regarding gender differences, our study suggested that Table 4 Body weight, height, and WC measures for the study population (n = 1019) Variables Men (n = 381) Women (n = 638) Total (n = 1019) Mean (SD) Weight (kilograms) 83.83 (21.1) 63.08 (16.04) 70.84 (20.71) Height (meters) 1.71 (0.08) 1.57 (0.06) 1.62 (0.09) Waist circumference (centimeters) 96.76 (23.30) 75.54 (14.62) 83.47 (21.02) Al-Ghamdi et al. Lipids in Health and Disease (2018) 17:134 Page 6 of 8 Table 5 Prevalence of Overweight and Obesity by Diabetic Class (n = 1019) Diabetic class Non-Obese Overweight Class I Obese Class II/III Obese 2 2 2 2 (< 25 kg/m ) (25–29.9 kg/m ) (30–34.9 kg/m ) (≥ 35 kg/m ) (n = 465) (n = 272) (n = 169) (n = 112) n (%) n (%) n (%) n (%) Diabetic 5 (11.4%) 10 (22.7%) 17 (38.6%) 12 (27.3%) Pre-diabetic 65 (28.1%) 66 (28.6%) 54 (23.4%) 46 (19.9%) Non-diabetic 395 (53.2%) 196 (26.4%) 98 (13.2%) 54 (7.3%) Total 465 (45.7%) 272 (26.7%) 169 (16.6%) 112 (11.0%) Values are count (%) males tend to have more height, weight and WC than fe- females had a 21% decreased probability of being obese. males. Studies from China [1], Norway [4], USA [3] and However, a study conducted in Japan showed no rela- Greece [15] also revealed that the incidence of obesity tionship between body weight and marriage [11]. On the was more in men as compared to women. However, contrary, this relationship was not evident in Malaysians women were also found to have more obesity than men and Americans [36]. in the USA [31], Finland [32], Canada [33], Lebanon Klop et al., [18] reported a linear connection between [26] and Saudi Arabia [5]. the grade of obesity and serum cholesterol levels which A linear and positive trend was obtained between in- was in conjunction with our study. They observed higher creasing age and high BMI across both genders in our concentrations of mean total cholesterol and triglycer- study. Likewise, large population studies indicate that ides whereas low level of High Density lipoproteins BMI gradually increases during adult life reaching a peak (HDL) in obese persons when compared to normal at 50–59 years in males as well as in females, showing a weight subjects [37]. An imperative link amongst dyslip- declining BMI trend after the age of 60 years [31, 34–36]. idemia and obesity seems to be the development of insu- Consistently, in Lebanon, men and women had a linear lin resistance [18]. This mechanism along with Obesity, association between increasing age and obesity in 20– hypo HDL cholesterolemia, hypertriglyceridemia and 60 years and 20–70 years respectively [26]. In Spain, high glucose intolerance are characteristics of insulin resist- BMI figures amplified continuously from 10% as identified ance disorder which is also extensively widespread in the age cluster of 18 to 25 years to more than 50% in among the Saudi inhabitants of age 40 years and above above 55 years of age [16]. [22] in addition to Australians [38] and residents of Being married was a significant predictor of high BMI the Pacific Islands. in our study. The companionship after marriage may en- Educational level was not found as a significant pre- courage an individual to avoid obesity or even contribute dictor of obesity in our study. In contrast to Greek indi- towards it. In the same context, in Greek [15], Turkish viduals where the hazard for being obese was lesser in [17] and Spanish [16] countries, marital status was found educated females in comparison to illiterates with no significantly related to obesity. As per Kilicarslan and significant differences among males. As per the bi-ethnic colleagues [17], risk of being obese was 2.5 times greater study [11], education level was found to be a significant in individuals who were married as compared to those and vital predictor of high weight for Americans but not who were single, divorced or widowed. Similarly, in Iran, for Japanese. As in the USA, with each year of educa- threefold higher risk was found for married men and tion, the possibility of being overweight or obese was re- women [18]. In Americans, married males were 21% duced by 2–9% [9]. Likewise, in Turkey, 62% university more prone to become overweight, whereas wedded students had normal weight while 31% were obese Table 6 Prevalence of Diabetic Status by Age group (n = 1019) Age group Diabetic (n = 45) Pre-Diabetic (n = 231) Non-Diabetic (n = 743) n (%) n (%) n (%) a b 18–29 years 6 (13.3%) 117 (50.6%) 610 (82.1%) a b 30–39 years 12 (26.7%) 72 (31.2%) 102 (13.7%) a b 40–49 years 15 (33.3%) 35 (15.2%) 24 (3.2%) a b 50–67 years 12 (26.7%) 7 (3.0%) 7 (0.9%) Total 45 (100%) 231 (100%) 743 (100%) Values are count (%) Linear-by-Linear Association = 214.070, P < 0.0001 Al-Ghamdi et al. Lipids in Health and Disease (2018) 17:134 Page 7 of 8 Table 7 Multinomial logistic regression showing significant There are a few limitations in this study. Primarily, the predictors of each BMI class using odds ratio and corresponding cross-sectional design of the study limited the causal 95% confidence interval inference. The targeted age groups were consistent with Overweight the previous published literature yet both extremes Variable Odds Ratio P-value 95% CI could have been enhanced to see the risk factors in (Exp (B)) young as well as in an old age population. Additionally, Lower Upper bound various other socio-demographic characteristics, marital Intercept –– – – status, family history, diet, physical activity and con- comitant illnesses should also be considered while con- Cholesterol 1.419 .001 1.153 1.746 sidering risk factors for being overweight and obese. Single/unmarried 1.367 .000 1.236 1.570 Job status (civilian worker) 1.607 .049 1.002 2.577 Conclusion Class I obese Obesity is preventable and understanding its prevalence, Intercept –– – – associated factors across different geographic regions Age 1.045 .020 1.007 1.084 and socio-demographics is the key to our efforts in de- Cholesterol 1.606 .000 1.269 2.034 signing culturally suitable and relevant health promotion Single/unmarried 1.542 .021 1.323 1.911 activities. Obesity in adulthood can be taken as a power- ful predictor for mortality in older ages. An alarming Job status (civilian worker) 1.303 .035 1.100 1.919 global situation of increasing trends of obesity is associ- Class II/III obese ated with large decreases in life expectancy. Additionally, Intercept –– – – important risk factors like being married; increasing age, Age 1.050 .015 1.010 1.093 high cholesterol etc. were also highlighted which was in Cholesterol 1.575 .001 1.207 2.056 line with previous literature. Analysis in the study Job status (civilian worker) 2.018 .042 1.025 3.976 showed that BMI significantly increased with age and a 2 this might elevate the risk of NCDs in the next adult The reference category is the non-obese (< 25 kg/m ) generation and have a noteworthy influence on the financing and provision of future health-care services in [17], while, in Lebanon, less education was associated Saudi Arabia. Therefore, in the light of such findings it with high BMI [26]. is now particularly vital to speed up health-promotion Our study could not find significant association be- behaviors to construct effective interventions. tween job status and high BMI. Whereas, a previous Well-designed prospective studies are needed in the fu- study from the same region demonstrated an increased ture to study the etiological nature of this relationship. BMI associated with increased monthly income [34]. In Further studies are also required to make causal infer- a longitudinal study, Seiluri [22] found blue-collar ences and to examine certain barriers to physical activity workers to have 50% more chances of being insuffi- and economic, social, cultural and behavioral factors ciently active in comparison to professional and leading to high BMI in the Saudi population. white-collar workers. Contrary to these findings, obesity was found associated with unemployment in Turkey Abbreviations BMI: Body Mass Index; DALY: Disability-Adjusted Life Years; NCDs: Non- [17]. Hence, such remarkable finding gives us a message Communicable Diseases; WC: Waist Circumference; WHO: World Health to initiate preventive measures and health education to Organization; YLL: Years of Life Lost prevent the Saudi population from the vast health effects and later complications of obesity. Acknowledgements The college of medicine represented by the diabetes research unit would Finucane, Stevens, and Cowan [39] assessed a world- like to acknowledge Dr. Abdul Rahman Al Asimi; Rector of Prince Sattam Bin wide escalation in average BMI of 0.4 kg/m per decade Abdulaziz University for the financial support. while The HUNT study records [5] showed it to be 2 2 1.0 kg/m in comparison to 1.1 kg/m for America. An Funding This project was funded by Diabetes Research Unit (DRU), College of augmented level of all-purpose health education pro- Medicine, Prince Sattam Bin Abdulaziz University, Al Kharj, Saudi Arabia. vided at population level might contribute to its decrease as its consequences can be harmful for a nation. For Availability of data and materials instances, it is reported that with every 1 kg increase in The datasets analyzed/generated during the current study are not publicly available due to patient confidentiality. weight, the occurrence of diabetes escalates up to 9% [35] thus, it is extremely important to hamper this Authors’ contributions conversion at early levels to prevent our population from All authors have made equal and important contributions to the manuscript. many other obesity-related diseases. All authors read and approved the final manuscript. Al-Ghamdi et al. Lipids in Health and Disease (2018) 17:134 Page 8 of 8 Ethics approval and consent to participate 17. Kilicarslan A, Isildak M, Gulay Sain Guven GS, Oz SG, Tannover MD, Duman AE, Individual participant consent and local Institutional Review Board approval Saracbasi O, Sozen T. Demographic, socioeconomic and educational aspects was sought for this project. of obesity in an adult population. 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Prevalence of overweight and obesity based on the body mass index; a cross-sectional study in Alkharj, Saudi Arabia

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Copyright © 2018 by The Author(s).
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Life Sciences; Lipidology; Medical Biochemistry; Clinical Nutrition
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Abstract

Background: Obesity and overweight are accompanied with several different chronic diseases. Overweight and obesity can be measured by using body mass index (BMI) and is also used widely as an index of relative adiposity among any population. The aim of the study is to evaluate the prevalence of overweight and obesity among general population in Al-Kharj, Saudi Arabia. Methods: Cross-sectional analysis was undertaken from a representative sample (N = 1019) of the Al Kharj population. Anthropometric measurements including the waist circumference (in centimeters), height (in meters), and weight (in kilograms) of the subjects were undertaken by means of standard apparatus. SPSS 24.0 was utilized for statistical analysis of the data. Results: Majority of respondents in this study were overweight and obese (54.3%) compared with 45.7% being non-obese. A linear positive association of increasing BMI with older age groups was present in males and females. Men had larger waist circumference, weight and height measures as compared with their female counterparts. Regression analysis showed increasing age, being married and high serum cholesterol to be the significant predictors of overweight and obesity while gender, education level, job status, and having diabetes were not. Conclusions: The obesity-overweight prevalence in the Saudi population is high mainly across both genders. However, the associated factors are potentially preventable and modifiable. The regional barriers to lifestyle modifications and interventions to encourage active lifestyles, especially among adolescents to limit the occurrence of obesity and ultimately promote health and wellbeing, are warranted. Furthermore, prospective studies are needed in future to confirm the aetiological nature of such associations. Keywords: Overweight, Obesity, Body-mass-index, Al Kharj; Saudi Arabia Background million mortalities, 4% of YLL i.e. Years of Life Lost, Obesity, which broadly refers to excessive body fat, is a and at least 35.8 million worldwide DALYs i.e. Disabil- significant public health concern and its occurrence has ity-Adjusted Life Years [2]. If such secular trends continue, reached an epidemic proportion in both developing as an estimated 20% of the worldwide population will be well as developed countries [1]. Obesity tends to impact affected by obesity by 2030, while 38% will be overweight more than one third of the population around the world. [3]. It is expected that 85% of U.S. citizens will be affected Globally, obesity is estimated to cause more than 2.8 by obesity by 2030 [4]. The prevalence of obesity in Gulf Countries among children and adolescents ranges from 5% to 14% in * Correspondence: Elmetwally.ashraf@outlook.com Docent of Epidemiology, School of Health Sciences, University of Tampere, males and from 3% to 18% in young females [5]. Data Tampere, Finland are scarce from other Middle Eastern countries; how- Epidemiology & Biostatistics Department, College of Public Health & Health ever, compelling evidence is present indicating the rise Informatics, King Saud Bin Abdulaziz University for Health Sciences, P. O. BOX 3660, Riyadh, Saudi Arabia in obesity rates. For instance, recent surveys found that Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Al-Ghamdi et al. Lipids in Health and Disease (2018) 17:134 Page 2 of 8 in Kuwait, 48% of females and 36% of males were obese, To assess obesity, the World Health Organization whereas 77% of females and 74% of males were over- (WHO) has implemented the body mass index (BMI) weight. While 44% of the female population and 28% of scale, which can be obtained by dividing the total weight the male population from Saudi Arabia were found to of the body in kilograms by the square of the height be obese. However, 71% of women and 66% of men were measured in meters, as a substitute of the over-all body reported to be overweight [6]. fat measure [19]. With this, obesity can be well-defined BMI is predisposed by certain determinants that can if BMI value is ≥30Kg/m . It usually correlates with the either be environmental or genetic. Such environmental fraction of body fat in individuals having high prevalence factors include physical activity, gender, marital status, of obesity [24]. As per WHO [2], an increased risk of job and education level, diet, co-morbidities i.e. diabetes, co-morbidities exists when the BMI is between 25.0 and hypertension, cardiac and endocrinal issues, cancers etc. 29.9 while moderate-severe risk lies when it is > 30. [7]. In Saudi Arabia, 4% obesity was reported in rural Hence, the goal must be to sustain the range of BMI areas. Conversely, obesity is reported to be 10% in the between 18.5 and 24.9 kg/m . Therefore, the purpose of Western regions and 14% in the Eastern region [Jizan the study is to estimate the prevalence of overweight (12%), Riyadh (22%), and Hail (34%)] due to the con- and obesity within the general population in Al-Kharj, sumption of more fast food and a sedentary lifestyle [8]. Saudi Arabia. Regarding gender distribution, females were dispro- portionately affected by extreme obesity than males Methods [6, 9–11]. Income also predicted obesity, particularly Study design in Arab countries [12]. The role of education was A population based cross sectional study using the data clear when more obesity was found prevalent in the drawn from the general population in Saudi Arabia illiterate population as was found in Syria, Jordan and between January 2016 and June 2016 was undertaken. Lebanon [13, 14]. Married people were also more suscep- tible to being overweight and obese [9–11, 15–17]. Settings Obesity can contribute to greater risks of The research study was conducted in Al Kharj, a city lo- non-communicable diseases (NCDs) causing morbidity cated in Al Kharj Governorate in central Saudi Arabia, and mortality. It is a modifiable and preventable risk 77 km south of Riyadh, with an estimated population of factor for insulin resistance leading to impaired glucose 376,000. It is connected to the city of Dammam and tolerance [9], metabolic syndrome [18] and dyslipidemia Riyadh by rail. It possesses mixed urban (military and [19]. The relative risk after adjusting the variable of age civilian), rural, and adjacent nomadic communities. was found to be 60.9 for the development of diabetes with a BMI ≥35Kg/m [10]. Framingham study reported Sample size hypertension having the relative risks of 1.46 and 1.75 in Approximately 1019 (638 females and 381 males) were overweight and obese young adults respectively, while recruited into the study by using a multi-stage sampling the Honolulu Heart Program and Japanese survey method from different governmental and private insti- showed its predictable effect on hypertension in the tutes after obtaining verbal informed consent. The total older population [20, 21]. population of these institutions were divided into groups Obesity can also contribute to ischemic stroke and called clusters after acquiring a list of participants in Obstructive Sleep Apnea whereas 75% of adolescents each institute nominated. Samples of the respondent with asthmatic emergencies were either obese or over- were then selected using simple random sampling from weight. Gastroesophageal reflux disease, cholelithiasis, each of the group (cluster). osteoarthritis, various cancers, psychiatric illnesses, polycystic ovarian syndrome in females, infertility and Selection of participants (criteria for inclusion and impotence in males were also found to be associated exclusion) with obesity. Obese females were 20% less likely to get Saudi citizens, 18 years or older were selected, based on married, had lesser chance to complete school, had their eligibility as well as willingness to participate in the more poverty at the household level and less earnings study. Non-Saudi residents, Saudi citizens younger than in contrast to women who were not overweight [22]. 18 years of age, and participants who were not willing to Fontaine et al. concluded that a noticeable decline in sign the consent form were excluded. life expectancy was present in obese adolescents in comparison to non-obese individuals [23]. Similarly, Materials/instruments non-smoking obese females and males aged 40 years A structured questionnaire was used for collecting data. lived less than 7.1 and 5.8 years than their non-obese The participants filled a set of questionnaires on socio- counterparts. demographics including age, gender, marital status, and Al-Ghamdi et al. Lipids in Health and Disease (2018) 17:134 Page 3 of 8 level of education. Anthropometric measurements body and control) of the test was essential for which sample weight (in kilograms), height (in meters), body mass was programmed into the machine termed as Dimension index (BMI), and waist circumference (in centimeters) Xpand Plus accordingly and results were collected after were also included in this questionnaire. the test were finished. Consideration and procedures Operational definition In general practice, based on the standards for anthropo- Overweight can be defined as a BMI of 25 and more, metric measurements, the weight of an individual body and obesity as an index of 30 and more [25]. DM was was measured in socks and light clothes to the nearest defined as FPG ≥126 mg/dL or self-reported history of 0.1 kg, using a similar digital medical scale. Height of a diabetes as defined by American Diabetes Association participant was measured to the nearest of 0.1 cm using (ADA) criteria. Pre-diabetes was defined using HbA1c a stadiometer precisely noted in standing position with cutoff level of 5.7- < 6.5%, while Diabetes Mellitus no shoes on. Weight was measured through a digital (DM) was ≥6.5%, according to the American Diabetes weighing scale. Prior to the measurement, the scale was Association 2016. calibrated to the zero level and was also verified for repeatability of the readings. BMI was computed by the Data analysis dividing weight by height in meters square (kg/m ) and Data analysis was performed using SPSS 24.0 for weight categories were demarcated following the WHO Windows. Analysis involved descriptive statistics for standard as 30 kg/m2 obese. frequencies, multivariate and logistic regression Waist circumferences (WC), at the level of the hip and analysis. Categorical variables such as gender, educa- umbilicus circumference was measured at the widest girth tional level, and age groups were summarized and of the hip using a flexible non-stretchable tape. Women reported in terms of frequency distribution. A with a WC of < 80 cm, 80.0 - 87.9 cm and ≥ 88 cm and chi-square test was utilized to examine the association males with a waist circumference of < 94 cm, 94.0 - 101.9 between different categorical variables whereas the cm and ≥ 102 cm were termed as normal weight, t-test or ANOVA were used for continuous variables. overweight and obese, correspondingly. The multinomial logistic regression model was used A blood glucose monitoring system was used to to examine the relationship (adjusted odds ratios) measure the fasting blood glucose (FBG) of each between overweight, obesity and possible contributing respondent. Blood samples were obtained after a mini- factors. P-values of <0.05 were considered statistically mum of 8 h of fasting by the respondents. The results significant. obtained with the glucometer were calibrated with laboratory outcomes using the glucose oxidase method. Ethical approval Blood samples were collected from each participant by This was gained from the local Institutional Review trained nurses and phlebotomy for HbA1c, fasting lipid Board (i.e. Committee of Scientific Research and Publi- profile (total cholesterol, triglycerides, HDL- and LDL cation). Written permission and verbal consent was cholesterol). A unique ID (barcode) was assigned to sought from the respondents before commencement of these patients. Two tubes were used: one for the study. Participants were also guaranteed of the con- Hemoglobin A1c while the other for chemistry- then fidentiality as well as were notified that the participation gentle rolling was applied at roller mixer for preventing would be voluntary in the study. clotting. Any clotted samples or critical outcomes were reported, and participants were contacted immediately Results for an alternative sample. The tubes were gathered in a Table 1 illustrates the prevalence of being overweight special ice container for improved handling and care. along with obesity in males (n = 381) using BMI stand- These samples were sent to the central laboratory within ard classification stratified by four age groups. There is a 1–2 h of duration. Samples were run for the test proce- linear positive association (trend) of increasing BMI dures. The data obtained from all the samples/specimen with older age groups. For example, the Class I obese were encoded in Beckman Coulter. The heparin plasma (30–34.9 kg/m ) shows 15.4% of males in the 18– sample was used for the Chemistry analysis. The heparin 29 years; 23.3% of males in the 30–39 years; 25.5% of vacutainers were separated from the remaining. After males in the 40–49 years; and 42.1% of males in the 50– which, the vacutainers were organized along with the 67 years. As presented in Table 2 for females (n =638) and barcode numbers, and kept in the centrifuge. Samples except for the 40–49 years old, a similar linear positive were centrifuged for 5 min @ 4000 rpm for the separ- trend was noted: The Class I obese females show 12.2% in ation of plasma. Once the plasma was separated, it was the 18–29 years; 28.6% in the 30–39 years; 17.4% in the then utilized for the test procedure. Calibration (check 40–49 years; and 42.9% in the 50–67 years. Al-Ghamdi et al. Lipids in Health and Disease (2018) 17:134 Page 4 of 8 Table 1 Prevalence of Overweight and Obesity by Age Group in Males (n = 381) Age groups Non-Obese Overweight Class I Obese Class II/III Obese 2 2 2 2 (i.e. < 25 kg/m ) (i.e. 25 to 29.9 kg/m ) (i.e. 30 to 34.9 kg/m ) (i.e. ≥ 35 kg/m ) (n = 120) (n = 123) (n = 79) (n = 59) n (%) n (%) n (%) n (%) 18–29 years 74 (40.7%) 53 (29.1%) 28 (15.4%) 27 (14.8%) 30–39 years 31 (24.0) 48 (37.2%) 30 (23.3%) 20 (15.5%) 40–49 years 14 (27.5) 17 (33.3%) 13 (25.5%) 7 (13.7%) 50–67 years 1 (5.3) 5 (26.3%) 8 (42.1%) 5 (26.3%) Total 120 (31.5%) 123 (32.3%) 79 (20.7%) 59 (15.5%) Values are count (%) For the overall sample (n = 1019) in Table 3,itis We further examined diabetic status for each of the evident that being in an older age group was signifi- four age groups (Table 6). The results showed that the cantly associated with being overweight, class I or prevalence of diabetes was linearly and positively classII/IIIobese.For example, theClass I obese associated with age. The Cochran-Armitage trend test shows 13.0% in the 18–29 years; 24.9% in the 30–39 years; available from the contingency table of 4 age group- 23.0% in the 40–49 years; and 42.3% in the 50–67 years s-by- 3 diabetic status groups in SPSS 24.0 for Windows (Table 3). The Class II/III obese similarly shows a signifi- was significant (Linear-by-Linear association = 214.070, P cant association of BMI with increasing age: 7.9% in the < 0.0001). There was a significantly positive linear associ- 18–29 years; 14.6% in the 30–39 years; 27.0% in the ation with having diabetes in older age groups: 13.3% in 40–49 years; and 26.9% in the 50–67 years. Most of the the 18–29 years were diabetic; 26.7% in the 30–39 years respondents in this study are overweight and obese were diabetic; 33.3% in the 40–49 years were diabetic; and (54.3%) compared with 45.7% being the non-obese BMI finally, 26.7% in the older age group (50–67 years) were category (< 25 kg/m ). diabetic (Table 6). Table 4 demonstrates weight (in ‘kg’), height (in ‘m’) and waist circumference (in ‘cm’) for men, women, and the overall sample. Men tended to have larger WC more Multinomial logistic regression analysis than females (mean WC = 96.76 and 75.54 respectively). We conducted a multinomial logistic regression analysis Weight and height measures were also higher in males by regressing the categorical BMI outcome variable on compared with their female counterparts (Table 4). sociodemographic variables (age; gender; marital status; When diabetic status was examined according to BMI education level; and job status) and other variables categories (Table 5), we found that the proportion of which included blood cholesterol and whether a subject diabetic individuals was positively and significantly has diabetes or not (Table 7). The non-obese BMI associated with being in a higher BMI category: 11.4% category (< 25 kg/m ) was used as the reference category in the non-obese; 22.7% in the overweight; 38.6% in the to test the odds ratio (Exp B) for each of the other three Class I obese; and 27.3% in the Class II/III obese (Table 5). BMI categories: the overweight; class I obese; and class This observation is consistent with previous research that II/III obese. indicated increasing risk of diabetes with increasing body The relative risk of being overweight was associated weight status (BMI). with higher cholesterol (OR = 1.419, P = 0.001), being Table 2 Overweight and Obesity by Age Group in Females (n = 638) Age group Non-Obese Overweight Class I Obese Class II/III Obese 2 2 2 2 (< 25 kg/m ) (25–29.9 kg/m ) (30–34.9 kg/m ) (≥ 35 kg/m ) (n = 345) (n = 149) (n = 90) (n = 53) n (%) n (%) n (%) n (%) 18–29 years 336 (61.0%) 117 (21.2%) 67 (12.2%) 31 (5.6%) 30–39 years 8 (14.3) 25 (44.6%) 16 (28.6%) 7 (12.5%) 40–49 years 1 (4.3) 5 (21.7%) 4 (17.4%) 13 (56.5%) 50–67 years 0 (0.0%) 2 (28.6%) 3 (42.9%) 2 (28.6%) Total 345 (54.2%) 149 (23.4%) 90 (14.1%) 53 (8.3%) Values are count (%) Al-Ghamdi et al. Lipids in Health and Disease (2018) 17:134 Page 5 of 8 Table 3 Overweight and Obesity Prevalence by Age Group in the entire sample (n = 1019) Age group Non-Obese Overweight Class I Obese Class II/III Obese 2 2 2 2 (< 25 kg/m ) (25–29.9 kg/m ) (30–34.9 kg/m ) (≥ 35 kg/m ) (n = 465) (n = 272) (n = 169) (n = 112) n (%) n (%) n (%) n (%) 18–29 years 410 (55.9%) 170 (23.2%) 95 (13.0%) 58 (7.9%) 30–39 years 39 (21.1) 73 (39.5%) 46 (24.9%) 27 (14.6%) 40–49 years 15 (20.3) 22 (29.7%) 17 (23.0%) 20 (27.0%) 50–67 years 1 (3.8%) 7 (26.9%) 11 (42.3%) 7 (26.9%) Total 465 (45.7%) 272 (26.7%) 169 (16.6%) 112 (11.0%) Values are count (%) single/unmarried (OR = 1.367, P < 0.0001), and being a A major strength of our study was the usage of cluster civilian worker (OR = 1.607, P = 0.049). Being classi- designs for surveys as recommended by the WHO which fied as ClassIobese wasassociatedwithstatistically assures that the nominated sample specifies the entire significant risk of older age (OR = 1.045, P = 0.020), population precisely. We included common risk factors higher cholesterol (OR = 1.606, P < 0.0001), being un- and evaluated their association with a high BMI. More- married (OR = 1.542, P = 0.021), and having diabetes over, trained nurses collected the data as well as an- (OR = 1.303, P = 0.035). Whereas being classified as thropometric measurements in our study to ensure the Class II/III obese was significantly associated with reliability of the data and the anthropometric and clin- older age (OR = 1.050, P = 0.015), higher cholesterol ical measurements. Our study has used the more recent (OR = 1.575, P = 0.001), and being a civilian worker standard globally accepted WHO/NHLBI criteria for the (OR = 2.018, P = 0.042). definition of BMI cut-off values that would be applicable The adjusted R-squared (R ) for the multinomial logis- to all countries/regions including the Gulf region. tic regression model is represented by the Cox and Snell Literature shows that high BMI can be the root cause Pseudo R-Square in the SPSS output for this model, with for many of non-communicable diseases (NCDs) in the a value of 0.194. world. The majority of respondents in this study were also overweight and obese (54.3%), which has become an Discussion increasing trend around the world with increasing preva- Our study findings show that the vast number of the lence rates in the USA (36.5%) [3], Spain (29%) [16], study population was overweight and obese. Its preva- Greece (23%) [15], Lebanon (17%) [26], Kuwait (43%), lence across both genders showed a linear and positive Saudi Arabia (35%) and Qatar (33%) [12]. A study trend with older age groups with an exception of the conducted in the city of Jeddah, Saudi Arabia, found 40–49 female age group. Hence, making it evident that that 33.8% of women were found to be obese and 47% increasing age was significantly associated with over- with WC > 80 cm [27] whereas in our study the mean weight, class I or II/III obese. While evaluating the mea- WC in females was 75.54 cm. On the other hand, there sures of obesity/overweight, males were found to have remains a perception in parents that overweight children greater values of WC, height and weight in comparison are a sign of prosperity, beauty, fertility, prosperity and to their female counterparts. It was also noted that the high social status [28]. proportion of diabetic individuals also had significant Obesity was also found to be associated with certain positive association with being in a higher BMI category. factors such as diabetes in our study where its contribu- Significant predictors of high BMI also included high tion towards diabetes was also supported by other inter- serum cholesterol, being married and increased age national and national published work in India [29], USA during regression analysis while gender, diabetes, job [9, 10], China [30], Lebanon [26] and Saudi Arabia [31]. and educational status were not significant. Regarding gender differences, our study suggested that Table 4 Body weight, height, and WC measures for the study population (n = 1019) Variables Men (n = 381) Women (n = 638) Total (n = 1019) Mean (SD) Weight (kilograms) 83.83 (21.1) 63.08 (16.04) 70.84 (20.71) Height (meters) 1.71 (0.08) 1.57 (0.06) 1.62 (0.09) Waist circumference (centimeters) 96.76 (23.30) 75.54 (14.62) 83.47 (21.02) Al-Ghamdi et al. Lipids in Health and Disease (2018) 17:134 Page 6 of 8 Table 5 Prevalence of Overweight and Obesity by Diabetic Class (n = 1019) Diabetic class Non-Obese Overweight Class I Obese Class II/III Obese 2 2 2 2 (< 25 kg/m ) (25–29.9 kg/m ) (30–34.9 kg/m ) (≥ 35 kg/m ) (n = 465) (n = 272) (n = 169) (n = 112) n (%) n (%) n (%) n (%) Diabetic 5 (11.4%) 10 (22.7%) 17 (38.6%) 12 (27.3%) Pre-diabetic 65 (28.1%) 66 (28.6%) 54 (23.4%) 46 (19.9%) Non-diabetic 395 (53.2%) 196 (26.4%) 98 (13.2%) 54 (7.3%) Total 465 (45.7%) 272 (26.7%) 169 (16.6%) 112 (11.0%) Values are count (%) males tend to have more height, weight and WC than fe- females had a 21% decreased probability of being obese. males. Studies from China [1], Norway [4], USA [3] and However, a study conducted in Japan showed no rela- Greece [15] also revealed that the incidence of obesity tionship between body weight and marriage [11]. On the was more in men as compared to women. However, contrary, this relationship was not evident in Malaysians women were also found to have more obesity than men and Americans [36]. in the USA [31], Finland [32], Canada [33], Lebanon Klop et al., [18] reported a linear connection between [26] and Saudi Arabia [5]. the grade of obesity and serum cholesterol levels which A linear and positive trend was obtained between in- was in conjunction with our study. They observed higher creasing age and high BMI across both genders in our concentrations of mean total cholesterol and triglycer- study. Likewise, large population studies indicate that ides whereas low level of High Density lipoproteins BMI gradually increases during adult life reaching a peak (HDL) in obese persons when compared to normal at 50–59 years in males as well as in females, showing a weight subjects [37]. An imperative link amongst dyslip- declining BMI trend after the age of 60 years [31, 34–36]. idemia and obesity seems to be the development of insu- Consistently, in Lebanon, men and women had a linear lin resistance [18]. This mechanism along with Obesity, association between increasing age and obesity in 20– hypo HDL cholesterolemia, hypertriglyceridemia and 60 years and 20–70 years respectively [26]. In Spain, high glucose intolerance are characteristics of insulin resist- BMI figures amplified continuously from 10% as identified ance disorder which is also extensively widespread in the age cluster of 18 to 25 years to more than 50% in among the Saudi inhabitants of age 40 years and above above 55 years of age [16]. [22] in addition to Australians [38] and residents of Being married was a significant predictor of high BMI the Pacific Islands. in our study. The companionship after marriage may en- Educational level was not found as a significant pre- courage an individual to avoid obesity or even contribute dictor of obesity in our study. In contrast to Greek indi- towards it. In the same context, in Greek [15], Turkish viduals where the hazard for being obese was lesser in [17] and Spanish [16] countries, marital status was found educated females in comparison to illiterates with no significantly related to obesity. As per Kilicarslan and significant differences among males. As per the bi-ethnic colleagues [17], risk of being obese was 2.5 times greater study [11], education level was found to be a significant in individuals who were married as compared to those and vital predictor of high weight for Americans but not who were single, divorced or widowed. Similarly, in Iran, for Japanese. As in the USA, with each year of educa- threefold higher risk was found for married men and tion, the possibility of being overweight or obese was re- women [18]. In Americans, married males were 21% duced by 2–9% [9]. Likewise, in Turkey, 62% university more prone to become overweight, whereas wedded students had normal weight while 31% were obese Table 6 Prevalence of Diabetic Status by Age group (n = 1019) Age group Diabetic (n = 45) Pre-Diabetic (n = 231) Non-Diabetic (n = 743) n (%) n (%) n (%) a b 18–29 years 6 (13.3%) 117 (50.6%) 610 (82.1%) a b 30–39 years 12 (26.7%) 72 (31.2%) 102 (13.7%) a b 40–49 years 15 (33.3%) 35 (15.2%) 24 (3.2%) a b 50–67 years 12 (26.7%) 7 (3.0%) 7 (0.9%) Total 45 (100%) 231 (100%) 743 (100%) Values are count (%) Linear-by-Linear Association = 214.070, P < 0.0001 Al-Ghamdi et al. Lipids in Health and Disease (2018) 17:134 Page 7 of 8 Table 7 Multinomial logistic regression showing significant There are a few limitations in this study. Primarily, the predictors of each BMI class using odds ratio and corresponding cross-sectional design of the study limited the causal 95% confidence interval inference. The targeted age groups were consistent with Overweight the previous published literature yet both extremes Variable Odds Ratio P-value 95% CI could have been enhanced to see the risk factors in (Exp (B)) young as well as in an old age population. Additionally, Lower Upper bound various other socio-demographic characteristics, marital Intercept –– – – status, family history, diet, physical activity and con- comitant illnesses should also be considered while con- Cholesterol 1.419 .001 1.153 1.746 sidering risk factors for being overweight and obese. Single/unmarried 1.367 .000 1.236 1.570 Job status (civilian worker) 1.607 .049 1.002 2.577 Conclusion Class I obese Obesity is preventable and understanding its prevalence, Intercept –– – – associated factors across different geographic regions Age 1.045 .020 1.007 1.084 and socio-demographics is the key to our efforts in de- Cholesterol 1.606 .000 1.269 2.034 signing culturally suitable and relevant health promotion Single/unmarried 1.542 .021 1.323 1.911 activities. Obesity in adulthood can be taken as a power- ful predictor for mortality in older ages. An alarming Job status (civilian worker) 1.303 .035 1.100 1.919 global situation of increasing trends of obesity is associ- Class II/III obese ated with large decreases in life expectancy. Additionally, Intercept –– – – important risk factors like being married; increasing age, Age 1.050 .015 1.010 1.093 high cholesterol etc. were also highlighted which was in Cholesterol 1.575 .001 1.207 2.056 line with previous literature. Analysis in the study Job status (civilian worker) 2.018 .042 1.025 3.976 showed that BMI significantly increased with age and a 2 this might elevate the risk of NCDs in the next adult The reference category is the non-obese (< 25 kg/m ) generation and have a noteworthy influence on the financing and provision of future health-care services in [17], while, in Lebanon, less education was associated Saudi Arabia. Therefore, in the light of such findings it with high BMI [26]. is now particularly vital to speed up health-promotion Our study could not find significant association be- behaviors to construct effective interventions. tween job status and high BMI. Whereas, a previous Well-designed prospective studies are needed in the fu- study from the same region demonstrated an increased ture to study the etiological nature of this relationship. BMI associated with increased monthly income [34]. In Further studies are also required to make causal infer- a longitudinal study, Seiluri [22] found blue-collar ences and to examine certain barriers to physical activity workers to have 50% more chances of being insuffi- and economic, social, cultural and behavioral factors ciently active in comparison to professional and leading to high BMI in the Saudi population. white-collar workers. Contrary to these findings, obesity was found associated with unemployment in Turkey Abbreviations BMI: Body Mass Index; DALY: Disability-Adjusted Life Years; NCDs: Non- [17]. Hence, such remarkable finding gives us a message Communicable Diseases; WC: Waist Circumference; WHO: World Health to initiate preventive measures and health education to Organization; YLL: Years of Life Lost prevent the Saudi population from the vast health effects and later complications of obesity. Acknowledgements The college of medicine represented by the diabetes research unit would Finucane, Stevens, and Cowan [39] assessed a world- like to acknowledge Dr. Abdul Rahman Al Asimi; Rector of Prince Sattam Bin wide escalation in average BMI of 0.4 kg/m per decade Abdulaziz University for the financial support. while The HUNT study records [5] showed it to be 2 2 1.0 kg/m in comparison to 1.1 kg/m for America. An Funding This project was funded by Diabetes Research Unit (DRU), College of augmented level of all-purpose health education pro- Medicine, Prince Sattam Bin Abdulaziz University, Al Kharj, Saudi Arabia. vided at population level might contribute to its decrease as its consequences can be harmful for a nation. For Availability of data and materials instances, it is reported that with every 1 kg increase in The datasets analyzed/generated during the current study are not publicly available due to patient confidentiality. weight, the occurrence of diabetes escalates up to 9% [35] thus, it is extremely important to hamper this Authors’ contributions conversion at early levels to prevent our population from All authors have made equal and important contributions to the manuscript. many other obesity-related diseases. All authors read and approved the final manuscript. Al-Ghamdi et al. Lipids in Health and Disease (2018) 17:134 Page 8 of 8 Ethics approval and consent to participate 17. Kilicarslan A, Isildak M, Gulay Sain Guven GS, Oz SG, Tannover MD, Duman AE, Individual participant consent and local Institutional Review Board approval Saracbasi O, Sozen T. Demographic, socioeconomic and educational aspects was sought for this project. of obesity in an adult population. 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