Burden of diseases attributable to excess body weight in the Middle East and North Africa region, 1990–2019

High body mass index (BMI), or excess body weight (EBW), represents a significant risk factor for a range of diseases, including cardiovascular diseases and cancers. The study sought to determine the burden of diseases attributable to EBW in the Middle East and North Africa (MENA) region from 1990 and 2019. The analysis also included an exploration of this burden by age, sex, underlying cause, and sociodemographic index (SDI). We utilized publicly available data from the Global Burden of Disease (GBD) study 2019 to identify the deaths and disability-adjusted life-years (DALYs) of diseases associated with EBW in MENA, spanning the period from 1990 to 2019. The GBD estimated the mean BMI and the prevalence of EBW using hierarchical mixed-effects regression, followed by spatiotemporal Gaussian process regression to determine the most accurate BMI distribution through comparison with actual data. In 2019, there were an estimated 538.4 thousand deaths (95% UI 369.9–712.3) and 17.9 million DALYs (12.9–23.1) attributable to EBW in the region. The DALYs attributable to EBW were higher in men (9.3 million [6.5–12.4]) than in women (8.5 million [6.4–10.8]). The age-standardized death and DALY rates for the diseases associated with EBW increased by 5.1% (− 9.0–25.9) and 8.3% (− 6.5–28.8), respectively, during the study period which was not significant. Egypt had the highest age-standardized mortality rate due to EBW (217.7 [140.0, 307.8]), while Yemen (88.6 [45.9, 143.5]) had the lowest. In 2019, the highest number of DALYs occurred among individuals aged 60 to 64 years old. Furthermore, we found a positive association between a nation's SDI and the age-standardized DALY rate linked to EBW. Cardiovascular disease emerged as the leading contributor to the EBW burden in MENA. The disease burden attributable to EBW showed a non-significant increase in MENA from 1990 to 2019.


Case definition and data sources
BMI was used to determine whether adults were overweight (BMI 25-30.0kg/m 2 ) or obese (BMI > 30 kg/m 2 ).The burden of diseases linked to EBW was determined using the GBD 2019 methodology 19 .The prevalence of high BMI was estimated in the GBD 2019, for both children and adults, in 204 countries and territories from 1990 to 2019.The exact methodology used to estimate mean BMI has been reported previously 19,20 .To identify studies that estimated the prevalence of EBW and mean BMI at the national or subnational level, the IHME conducted a comprehensive search of the online Medline database in 2015.This comprehensive literature review was updated using the same process for articles published in 2016 (January 1 to December 31).In addition, a search of the Global Health Data Exchange (GHDx) database was conducted to identify individual-level information from large international or national surveys 19,21 .The search strategy and data sources obtained for each country and territory have been previously published 19,21 .
The IHME extracted information from the literature on sample size, mean BMI, prevalence of overweight, prevalence of obesity, uncertainty measures for overweight and obesity, and locations and years for the most accurate age and sex categories available.In addition, IHME also collected details about each data source such as primary sampling unit, strata, and survey weights, which were used to formulate individual-level microdata and provide more accurate estimates of uncertainty.Three study-level variables were created to provide information on whether: (1) the sample was representative of the population; (2) the study was conducted primarily in urban areas, rural areas, or both; and (3) height and weight data were self-reported or measured.For countries with multiple data sources, all were incorporated into the analyses 19 .

Estimations of BMI and the prevalence of overweight and obesity
To calculate the avarage BMI, a stratified hierarchical mixed-effects regression was applied to examine the connection between BMI, overweight and obesity in data sources that provided information on all three variables.For this purpose, sex-specific MR-BRT models were used to analyze both overweight and obesity.These models www.nature.com/scientificreports/focused on the logit disparity between measured and self-reported data.The coefficients derived from this regression were then used in a spatiotemporal Gaussian process regression (ST-GPR) to model the prevalence of individuals with overweight and obesity in each country by age, sex and year.Multiple distributions were evaluated against the actual data to determine the BMI distribution that most closely resembled it.The final form of the beta distribution was determined using the mean BMI and the prevalence of being classified as overweight or obese in each country, by age, sex and year.For a more comprehensive understanding of the modeling process please refer to the GBD capstone paper 19 .
The prevalence of overweight and obesity was calculated via ST-GPR models 19,22 .To enhance estimates in countries with limited data, three country-level covariates were used: per capita energy intake with a 10-year lag, country latitude, and the urban population proportion.The selection of these three variables was informed by extensive prior research 20,22 , as they demonstrated the most favorable fit and coefficients that aligned with anticipated trends 19,21 .
In GBD 2019, Meta-Regression with Bayesian priors, Regularization and Trimming (MR-BRT) was used to adjust for self-report bias.Sex-specific MR-BRT models were conducted, with a fixed effect for super-region on the logit difference between overweight and obesity, depending on whether they were measured or self-reported 19 .

Data on the estimated relative risk
The evidence substantiating the relationships high BMI had with the different diseases was evaluated using the CRA approach.In brief, the CRA contains six main steps, which include: identification of the risk-outcome pairs to utilise in the analysis; estimating the relative risk as a function of exposure; estimating exposure levels and distributions; determining the counterfactual level of exposure; computing the fraction of the population that can be attributed and the burden that can be attributed; and estimating the mediation of various risk factors 19 .Additional information on this estimation process has been published previously 19 .The evidence indicated that the following conditions are associated with high BMI: cardiovascular diseases, diabetes and kidney diseases, neoplasms, gallbladder and biliary diseases, asthma, cataracts, osteoarthritis, low back pain, gout, as well as Alzheimer's disease and other dementias 19 .

Estimation of the proportion of diseases associated with EBW
The population-attributable fraction (PAF) was employed to gauge the disease burden linked to EBW by country, age group, sex, and year.The theoretical minimum risk exposure level (TMREL), measured using the estimated relative risk (RR), was used in GBD 2019 to estimate the level of exposure to each risk factor that would minimize the chances of suffering any EBW-related burden.The following formula was used to calculate the PAF: n is the lowest level of exposure observed, m is the highest level of exposure recorded, RR(x) is the relative risk at an exposure level of x and P(x) is the fraction of risk exposure 19 .

Compilation of results
In GBD 2019, the number of deaths and DALYs linked to EBW were estimated for each nation, age group, sex, year and attributable disease by multiplying the appropriate numbers with the corresponding PAFs.Detailed information regarding the methods utilized for estimating the number of deaths and the disease-attributable DALYs has been reported previously 19 .All estimations were presented as numerical counts, proportions (PAFs), and age-standardized rates per 100,000.Furthermore, they were accompanied by 95% uncertainty intervals (UIs) which encompassed the 25th and 975th values of the 1000 ordered draws 19 .The association between socioeconomic development and the diseases attributable to EBW was also examined.The GBD project measures socio-economic development using SDI, which is a composite measure comprised of three components: the fertility rate for women under 25 years old, the average years at school for individuals over 15 years old, and the lag-distributed income per capita.SDI ranges from the least developed (0) to the most developed 1 .

Ethical approval
The present study was approved by the ethics committee of Shahid Beheshti University of Medical Sciences (IR.SBMU.PHNS.REC.1401.101).All methods were performed in accordance with the national guidelines and regulations.This study is based on publicly available data and solely reflects the opinion of its authors and not that of the Institute for Health Metrics and Evaluation.

Age and sex patterns
In 2019, the number of deaths linked to EBW in MENA peaked in the 60-64 age group for males and 65-69 age group for females.The mortality rate due to EBW increased with age and the highest values were found in the 95 + age group (Fig. 2A).Furthermore, in 2019 the number of DALYs linked with EBW in the region were highest in the 60-64 age group for both sexes.The DALY rate increased with rose age but decreased in the 80-84 age group.There were no significant sex in the DALY rates (Fig. 2B).

Attributable diseases
In 2019, cardiovascular diseases were the largest cause of deaths, with the highest death rates due to EBW observed in the 65-69 and 95 + categories (Fig. 3A).The death rates attributable to diabetes and kidney diseases were second in all age groups except for the 95 + age group (Fig. 3A).The DALY rate associated with cardiovascular diseases exhibited an increased up to the 75-79 age group, followed by a decreased in the 80-84 age group, before increasing over the subsequent age groups.In addition, the DALY rate attributable to diabetes and kidney diseases increased up to those aged 60-64 years old, reached a plateau in the 65-79 age groups, then declined in the 80-84 age group, before increasing over the remaining age groups (Fig. 3B).

Burden of diseases attributable to EBW by the Socio-demographic Index (SDI)
There was a generally positive relasionship between a country's SDI and their corresponding age-standardized DALY rates associated with EBW.Countries like Afghanistan, Iraq, and Egypt had higher-than-expected burdens, while Yemen, Iran, Tunisia, Lebanon, Algeria, and Libya had lower-than-expected burdens.In addition, the agestandardized DALY rates increased from 1990 to 2019 in most of the MENA countries (Fig. 4).

Discussion
This study found that about 17% of deaths and 11% of all DALYs in MENA were attributable to EBW.Egypt, the United Arab Emirates, and Afghanistan had the highest attributable burden in 2019, whereas Iran, Yemen and Turkey had the lowest.Moreover, the EBW-attributable burden increased with advancing age and had a positive association with socioeconomic development.Cardiovascular diseases accounted for the largest number of deaths and DALYs in 2019, followed by diabetes and kidney diseases.
A previous study using GBD 2017 data showed decreases in the age-standardized death (4.7%) and DALY (2.2%) rates attributable to EBW in MENA over the period from 1990 to 2017 1 .In contrast, in the Eastern Mediterranean Region, the death and DALY rates attributable to obesity increased by 11.0% and 13.9%, respectively, between 1990 and 2015 13 .Our findings of a 5.1% increase in the age-standardized death rate and an 8.3% increase in the DALY rate were slightly lower than the increases observed in the Eastern Mediterranean Region.This minor differences between these two studies may be due to the different time periods measured, differences in the geographical location (e.g., Eastern Mediterranean Region vs. MENA), the reporting of age-standardized vs. all age rates, or the use of different data sources and definitions for EBW.The MENA region includes 21 countries, as mentioned in the Methods section, and the classification is based on the GBD study.In contrast, the Eastern Mediterranean Region includes 22 countries (i.e.Afghanistan, Bahrain, Djibouti, Egypt, Iran, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Pakistan, Palestine, Qatar, Saudi Arabia, Somalia, Sudan, Syrian Arab Republic, Tunisia, United Arab Emirates, and Yemen) and is the classification system used by the World Health Organization.Nevertheless, there is a clear increasing trend in the age-standardized prevalence of EBW in the Eastern Mediterranean Region and globally, which suggests the potential for further increases in the EBW-attributable burden 23 .Furthermore, the age-standardized mortality and DALY rates of diseases due to EBW were higher in 2019 than they were in 2017, as reported by Dai and colleagues (133.6 vs. 109.5 per 100,000 for age-standardized death rates and 3777.2 vs. 3256.0for age-standardized DALY rates) 1 .The results of our research underscore the importance of implementing preventive measures against EBW at various stages to mitigate the prospective disease burden lined to it.
Egypt registered the largest age-standardized mortality rate (217.7) and DALY rate (5929.6)per 100,000 in 2019, followed by Qatar, the United Arab Emirates and Afghanistan.Likewise, the GBD 2017 data indicated that Egypt had the highest age-standardized mortality (187.3 per 100,000) and DALY rates (5322.5 per 100,000) associated with high BMI 1 .Moreover, in Egypt, the prevalence of EBW among adolescents (aged 12-15 years) increased by 1.44 times between 2006 and 2011 24 .The consistent relationships between high BMI and poor health outcomes in Egypt are concerning.Previous studies have indicated that, despite Egypt's status as a highincome country, the prevalence of obesity is high not only among the educated and wealthy, but also among the less educated and less wealthy 25 .Furthermore, many children in Egypt tend to be overnourished.Health survey www.nature.com/scientificreports/data from 2000 showed that 47% of children received more than 100% of their Recommended Daily Allowance (RDA), compared with only 14% in 1995 26 .The increasingly sedentary lifestyles, coupled with the modernization and globalization of the food supply, have contributed to the rising obesity problem.Unfortunately, efforts to prevent and manage obesity are not currently on the Egyptian government's agenda.This was revealed in qualitative data obtained from 25 interviews involving 22 organizations relevant to the obesity epidemic in Egypt 27 .Consequently, there is a need for more research and attention to generate awareness and solutions to the obesity problem in Egypt.Qatar, facing a situation akin to that of to Egypt, has recognised the significance of obesity prevention by prioritising it in the Qatari National Health Strategy (2018-22) 28 .Furthermore, in 2019, Yemen and Iran had among the lowest EBW-attributable burdens in MENA.Encouragingly, Iran has implemented the recommendations from the World Health Organization on Ending Childhood Obesity (WHO-ECHO), known as IRAN-ECHO, to prevent and control EBW in the Iranian population 29,30 .In addition, the results showed that eight countries in the Eastern Mediterranean Region, namely Afghanistan, Kuwait, Oman, the occupied Palestinian Territory, Qatar, Sudan, Syria, and Yemen, have implemented nutritional surveillance systems as part of the WHO Recommended Policies and Interventions on Healthy Diets.This initiative aims to improve nutrition in order to reduce the prevalence of EBW 31 .
Previous studies have consistently shown that the burden of diseases attributed to EBW was slightly higher in women compared to men, both at the global and regional levels, although these differences were relatively modest 1 .Moreover, research has highlighted the fact that in Middle Eastern countries and the broader Eastern Mediterranean Region, females exhibited a greater prevalence of EBW 13,32 .Building upon these established findings, the present study also found that the age-standardized mortality and DALY rates were higher among females, with these disparities being particularly notable among older adults.Furthermore, it is noteworthy that the 60-64 age group had the largest number of DALYs which underscores the importance of addressing this issue among those nearing retirement.In addition, the highest age-standardized rates due to EBW were identified in individuals aged 95 years and older, further reinforcing the importance of preventive measures and interventions targeting this group.These findings were also in accordance with research by Dai et al. using GBD 2017 data 1 .In summary, it is advisable to implement preventive measures during adolescence and among young adults to reduce the burden attributable to EBW later in life.
The current study showed that cardiovascular diseases had the highest death and DALY numbers and rates, which is consistent with the findings reported in 2017 1 .Diabetes and kidney disease accounted for the second highest number of deaths and death rates.It's important to note that EBW serves as a well-established risk factors for all of these conditions, with particularly strong associations with cardiovascular diseases and diabetes.Unfortunately, lipid accumulation and fatty streaks develop in young adults 33 , with obesity accelerating atherosclerotic changes through mechanisms such as insulin resistance and inflammation 34 .The presence of obesity can lead to the exacerbation of metabolic cardiovascular risk factors, such as elevated blood pressure, dyslipidemia and hyperglycemia, all of which play a significant role in the development and progression of various diseases in affected individuals.For obesity-related kidney diseases, mechanisms include activation of the renin-angiotensin-aldosterone system, systemic inflammation, endothelial dysfunction, adipokine release, insulin resistance, and hypertension 35 .Importantly, these modifiable risk factors can be effectively prevented and managed through improved diet and lifestyle, as shown in clinical trials [36][37][38] , underscoring the potential of these strategies to mitigate the disease burden associated with excess body weight.
Several preventive strategies for obesity have been developed, primarily in high-income countries like Australia 39 , the USA 40 , and the UK 41 , which focus on improved nutrition, physical activity, optimal sleep and stress reduction.In the MENA region, prevention strategies have been developed based on recommendations provided by the World Obesity Federation 42 .In addition to understanding the problem of obesity and its underlying causes, MENA prevention strategies are generally in line with those of high-income countries.However, countries in the MENA region face several different obstacles compared to those in high-income countries.These include a severely limited number of health professionals, inconsistencies in training and approaches to obesity prevention, a lack of formal sponsored guidelines, poor public transport systems, and insufficient funding for obesity research.The frameworks developed by the WHO guide obesity prevention strategies in the MENA region, encompassing both individual and societal level changes.These changes include limiting energy intake from total fats, increasing consumption of fruit, vegetables, legumes, whole grains, and nuts, restricting sugar intake, and promoting regular physical activity 42 .Public health prevention at the national and regional level is also being considered, including the use of mandatory food labeling, marketing restrictions, taxation of certain foods and beverages, and responsible marketing practices, especially when targeting children.The prevention of excess body weight requires multi-factorial strategies, but individual behavior change is also required.

Strengths and limitations
This study has several strengths, including the use of advanced analyses to study the burden of diseases linked to EBW, as well as its focus on the MENA region over a 30-year period.However, it also has several limitations.One of these limitations pertains to the absence of high-quality data in several low-and middle-income countries in the MENA region, a circumstance that may introduce the potential for either underestimating or overestimating the burden associated with EBW.Moreover, the GBD study employed a different modeling system to estimate the burden in countries with no data available, meaning that the findings are based on estimations rather than real data.Secondly, certain diseases attributable to EBW, such as reproductive disorders (e.g.infertility) 43 , dental disorders (e.g.periodontitis) 44 and psychiatric disorders (e.g.depression) 45 , were not reported in the present study.Thirdly, the study's definition of overweight and obesity solely relied on BMI, a practical but imprecise measure, whereas more sophisticated methods like computed tomography, magnetic resonance imaging or dual-energy x-ray absorptiometry, which can offer greater accuracy, were not incorporated in the present

BFigure 1 .
Figure 1.Age-standardized death (A) and DALY (B) rates (per 100,000 population) of diseases attributable to excess body weight in the Middle East and North Africa region, by sex and country.DALY = disability adjusted life years.(Generated from data available from http:// ghdx.healt hdata.org/ gbd-resul ts-tool).

Figure 2 .
Figure 2. Numbers of deaths and death rate (A) and number of DALYs and the DALY rate (B) of diseases attributable to excess body weight in the Middle East and North Africa region, by age and sex in 2019.The right axis corresponds to the line chart and the left axis corresponds to the bar chart.DALY = disability adjusted life years.(Generated from data available from http:// ghdx.healt hdata.org/ gbd-resul ts-tool).

Figure
Figure Numbers of deaths and death rate (A) and number of DALYs and the DALY rate (B) of diseases attributable to excess body weight in the Middle East and North Africa region, by age and cause in 2019.The right axis corresponds to the line chart and the left axis corresponds to the bar chart.DALY = disability adjusted life years.(Generated from data available from http:// ghdx.healt hdata.org/ gbd-resul ts-tool).

Figure 4 .
Figure 4. Age-standardized DALY rates of diseases attributable to excess body weight for 21 countries from 1990 to 2019, by SDI; Expected values based on the Socio-demographic Index and disease rates in all locations are shown as the black line.Each point shows the observed age-standardized DALY rate for each country.DALY = disability adjusted life years.SDI = Socio-demographic Index (Generated from data available from http:// ghdx.healt hdata.org/ gbd-resul ts-tool).