Validation of Ethnicity-Specific anthropometric measurements thresholds for Obesity Based on Diabetes Risk in Abu Dhabi-United Arab Emirates: A Population-Based Cohort Study

Validation of Ethnicity-Specific anthropometric measurements thresholds for Obesity Based on Diabetes Risk in Abu Dhabi-United Arab Emirates: A Population-Based Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Validation of Ethnicity-Specific anthropometric measurements thresholds for Obesity Based on Diabetes Risk in Abu Dhabi-United Arab Emirates: A Population-Based Cohort Study Latifa Baynouna AlKetbi, Khadija Doucoure, Zinab Al Ansari, Nico Nagelkerke, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9305155/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective This study validates updated ethnicity-specific anthropometric measurements thresholds for the Arab population. Methodology : Community-based retrospective cohort study conducted in 2011 and 2013 in Abu Dhabi-UAE. The average follow-up was 9.4 years. Results: Most (37.5 %) participants were obese and 77.3% with Waist-Height ratio (WHtR) ≥ 0.5. risk factors of end of higher follow-up BMI were higher baseline BMI, WHtR ≥ 0. 5, hypertension, and current smoking, were predictors of end of follow-up BMI; while female sex, older age, baseline diabetes, and higher random glucose levels were independently associated with lower. end of follow-up BMI. The overall incidence of diabetes was higher (12. 0%) among those with WHtR > 0.5 versus in those with WHtR < 0.52 (.8%). Cox regression identified WHtR ≥ 0.5 as a significant independent predictor of incident diabetes while BMI categories were not (overall p = 0. 903). Conclusion: This study validated indicates an underestimated metabolic risk in the Abu Dhabi population, which support more effective public health planning in the UAE and across the Middle East. obesity prediction risk retrospective study Figures Figure 1 Figure 2 Figure 3 Introduction In 1997, the World Health Organization (WHO) declared Obesity a global epidemic. Across various nations of the world, being overweight and Obese are perceived as significant public health issues. [1] [2] Overweight is prevalent in 1.9 billion adults, and more than half a billion are clinically obese. [3] Reportedly, no nation has been able to switch this rising weight trend. [4] It is estimated that more than 1 billion people will be living with Obesity by 2030 [5] , which will impact the burden of other health conditions, such as type 2 diabetes and cardiovascular disease. [6] Several studies have shown the adverse effect of Obesity on an individual's overall health [7] such as type 2 diabetes, hypertension, dyslipidemia, coronary heart disease, stroke, obstructive sleep apnea, cancers, and more. In addition to its association with a low quality of life. [1, 2] Over the past few decades, the United Arab Emirates (UAE) has experienced significant economic and social change driven by rapid growth. The UAE has a high per capita income, among the highest in the world. As a result, the population has shifted from a simple tribal lifestyle to a modern, business-oriented society. The high per capita income, strong purchasing power, and availability of a wide variety of foods in the local market have led to changes in dietary habits, a move toward a Western lifestyle, and a greater tendency to gain weight. [8] Obesity in the United Arab Emirates (UAE) has emerged as a critical public health issue, negatively impacting the well-being of its population. A study conducted to investigate the prevalence and associated risk factors of overweight and obesity among the adult population in Dubai concluded that the highest obesity rates were reported among women (21.6%) and UAE nationals (39.6%). [9] Another study in 2009 revealed that the prevalence of overweight and obesity was 27% and 16%, respectively. [12] Hajat et al. reported a high prevalence of obesity in 2011 at 35%, with 31.6% among males and 38.3% among females, and an overweight prevalence of 32%, with 36.1% among males and 28.8% among females. [10] The definition and classification of obesity is based on the Body Mass Index (BMI), which was adopted by the WHO in 1998. [11]. Nevertheless, recent scientific publications and bodies have called for ethnicity-based classifications of obesity. The AMA issued a policy highlighting BMI's limitations due to its history of harm and racist exclusion, because BMI is based primarily on data collected from previous generations of non-Hispanic white populations. The AMA recommends using BMI alongside other measures, such as visceral fat, body adiposity, and genetic factors, as its accuracy as a measure of obesity varies among individuals and across races, sexes, and ages [12]. In a United Kingdom study that aimed to prospectively identify ethnicity-specific BMI cutoffs for obesity based on the risk of type 2 diabetes, BMI cutoffs equivalent to the obesity cutoff for White populations (≥30 kg/m2) were identified. In the Arab population, probably similar to the UAE population, the obesity cutoff point was 26.6 kg/m2. Thus, they suggested revisions of ethnicity-specific BMI cutoffs for the different ethnic populations to provide appropriate clinical surveillance and optimize the prevention, early diagnosis, and timely management of type 2 diabetes. To our knowledge, no study has explored the epidemiology of obesity among adult Emiratis in Abu Dhabi since then, nor has it been used in a longitudinal study design. This community-based cohort study explores this escalating issue of obesity by investigating secular trends in its epidemiology among UAE nationals over a decade of follow-up and validates the suggested ethnicity-specific Arab cut-off points for obesity and overweight. These insights are crucial for developing effective strategies to address and mitigate the impact of obesity in the UAE. Methodology Study design and setting This retrospective cohort study involves 8699 subjects enrolled in the Emirate of Abu Dhabi’s cardiovascular disease screening program, Weqaya, representing nearly 2% of the total Abu Dhabi population in the United Arab Emirates in 2011. Abu Dhabi is the largest emirate in the UAE and Abu Dhabi city serves as the capital of the country (UAE), with a population of 455,065, consisting of 220,651 men and 234,414 women, which accounts for approximately 46% of the national population of the United Arab Emirates. The Weqaya program was described in a previous publication [10]. The methodology for this retrospective cohort study was also described in another publication[13] . Participants The cohort of UAE nationals was enrolled from 2011 to 2013, and follow-up data were collected in 2023. Of the 8,699 participants aged 18 years and older, 311 were excluded for having no recorded BMI at baseline, an extreme BMI of 50 or more kg/m², or being pregnant at baseline. The final analytic sample included 8388 participants, divided into 4061 (48.4%) women and 4327 (51.6%) men among the included subjects. Variables At baseline, physical measurements, including Height, Weight, waist circumference, and waist-hip ratio, were measured, -to assess central Obesity. Investigations such as laboratory and radiography were also included in the present analysis. Individuals' sociodemographic information, medical health history, and psychological history were included with other risk factors. In the initial selection step, the World Health Organization's (WHO) diagnostic criteria for obesity were used, which included a body mass index (BMI) of 30 or greater. kg/m2 for both genders. Overweight was defined as an individual having a Body Mass Index (BMI) of 25 to 29.99 [11]. Outcome data At follow-up, the last BMI recorded in the participants’ EMR (Electronic Medical Record) was obtained through an EMR chart review conducted by physicians and nurses. The availability of participants' records in their EMR was facilitated by several factors. First, Ambulatory Health Care Services is the largest network of primary care centers serving most of the UAE national population. Second, due to the COVID-19 pandemic, AHS provided COVID-19 vaccinations for the public, and BMI was recorded for all vaccinated participants. Additionally, the BMI was regularly updated with each visit due to Department of Health requirements. Therefore, the latest BMI is recorded within the follow-up period for each participant, noting that the follow-up in this cohort was excellent, with an average of 9.2 years (ranging from a minimum of 1 year to a maximum of 12 years), with 22.8% followed for 8 years or less and 16% for 5 years or less. Statistical analysis A predicted change in BMI was estimated for each gender. Subsequently, for each subject, a deviation from this average estimate of BMI change was calculated. A paired t-test was conducted to assess the significance of BMI increase over the follow-up years among males and females, investigating any observed trends. Data were analyzed using statistical software (IBM SPSS, version 29), employing descriptive statistics to characterize the study population and inferential statistics (logistic regression and linear regression analysis) to identify associations between variables. Ethical approval Ethical approval was sought from the relevant AHS institutional review boards (IRB) and Al Ain HREC. According to SEHA, each patient visiting the family medicine clinic signed a general informed consent form. Results The prevalence of obesity among adults in Abu Dhabi, United Arab Emirates, is high. At baseline, the prevalence of obesity, defined as ≥30 kg/m² based on WHO BMI criteria, was 37.5% overall, 41.3% among females, and 33.7% among males. Among those with obesity, 17.1% of females and 11.1% of males were classified as Class II or III (BMI ≥35 kg/m²). Approximately 29% of females and 27% of males had a BMI classified as normal (BMI <25 kg/m²) at baseline. In 2023, the prevalence of obesity increased to 39.2% overall; Class II and III obesity accounted for 18.2% of females and 10.8% of males, consistent with the baseline pattern (Supplement 1). Using the Arab cutoff values suggested by Caleyachetty et al. [14] (≥26.6 kg/m²), the baseline obesity prevalence was 60.9% overall, 61.9% among females, and 60.0% among males. By the end of follow-up in 2023, overall obesity prevalence had increased to 64.7%, with a notable rise among females to 69.8%, while males prevalence remained nearly the same at 59.3%, highlighting a particularly concerning trend in women (Figure 1A). Furthermore, using the suggested Arab cutoff, 89.0% of females and 78.5% of males who were obese at baseline remained obese at the end of follow-up (Figure 1B). Among those who were non-obese at baseline, 39.0% of females and 30.3% of males had transitioned into the obese category by 2023, while further underscoring the progressive nature of obesity in this Arab population. Those who were obese and transitioned to <22.1 kg/m2 were 5% among males and 9.4% among females. To explore the trend in BMI and obesity over time we had to adjust for the fact that the cohort had aged between the two measurements. The change in BMI during the follow-up period due to aging was estimated by calculating the predicted change from baseline to follow-up. An average change was predicted based the relationship between BMI and sex and age at baseline; and for each subject, the deviation from this average BMI change estimate was calculated. A paired-sample t-test was used to assess the significance of BMI change over the follow-up years in males and females. A positive trend in obesity was observed, especially among females. On average, males in this cohort had an increase of 0.4 kg/m2(C.I. 0.23-0.57) in BMI, while females had an increase of 0.57 kg/m2 (C.I. 0.39-0.76). Supplement 2 Abdominal obesity, as measured by the waist-to-height ratio (WHtR), represents a possible more accurate classification of obesity than BMI. Among subjects without diabetes at baseline (n = 6,739), 72.3% had WHtR ≥ 0.5, with the proportion increasing progressively across BMI categories. Notably, 37.6% of those classified as normal weight by standard WHO BMI were already WHtR ≥ 0.5, rising to 84.3% in the overweight category. Under Arab-specific classification, 60.1% of the overweight stratum (BMI 22.1–26.59 kg/m²) and 94.3% of the obese stratum (≥ 26.6 kg/m²) had WHtR ≥ 0.5 — underscoring early central fat accumulation not adequately captured by the lower BMI thresholds proposed by Caleyachetty. WHtR ≥ 0.5 consistently identified a higher-risk group for incident diabetes. DM cumulative incidence among those with WHtR ≥ 0.5 ranged from 9.6% in WHO normal-weight subjects to 17.2% in Class II obesity, compared to 2.0–6.5% in those with WHtR < 0.5 (with no events in Class II or III due to small sample sizes, n = 7 and n = 3, respectively). Overall DM incidence was 9.4%: 2.8% in the WHtR < 0.5 group versus 12.0% in the WHtR ≥ 0.5 group. Under Arab BMI classification, DM incidence in the WHtR ≥ 0.5 group was 8.8%, 10.2%, and 12.6% across the three strata, versus 1.5%, 4.2%, and 3.2% in the WHtR < 0.5 group, confirming the independent predictive value of central adiposity across all obesity definitions. Sex differences were most pronounced at lower adiposity: males with BMI < 22.1 and WHtR ≥ 0.5 had nearly double the DM incidence of females (13.1% vs 5.3%), despite comparable WHtR prevalence (15.1% vs 12.5%). This disparity attenuated at higher adiposity, with near-identical DM rates in the Arab BMI ≥ 26.6 stratum (12.5% males vs 12.7% females). In the full cohort including subjects with prevalent diabetes (N = 8,652), 77.5% had WHtR ≥ 0.5 — 5.2 percentage points higher than in the DM-free subcohort, with the gap most pronounced at lighter BMI categories (normal weight: 42.2% vs 37.6%; overweight: 87.1% vs 84.3%). Under Arab classification, proportions were similarly elevated (95.6%, 65.3%, and 15.5% vs 94.3%, 60.1%, and 13.5% in the DM-free cohort). Sex differences were most striking in the normal weight stratum (50.3% males vs 34.4% females, p < 0.0001) and the Arab overweight category (72.5% vs 56.3%, p < 0.0001), attenuating toward saturation at higher obesity classes. Collectively, these findings demonstrate that central adiposity, already prevalent well below conventional obesity thresholds, is further concentrated among those with DM, and that standard BMI substantially underestimates this risk, particularly in males and those classified as normal weight or overweight. (Figure 2) Table 1 shows the baseline characteristics of the population, with a mean age of 35±10 years. Notably, the DM rate is lowest among those with BMI < 22.1 kg/m2. Only 6.3% of subjects with BMI =26.6 or >=40, respectively. With regards to pre-DM, 16.7% of subjects with BMI =26.6 or >=40, respectively. Similarly, dyslipidemia, hypertension, and CKD all have the lowest prevalence in BMI class <= 22.1. In both genders, the overweight and obesity categories tend to have above-normal waist-hip ratios (more than 0.85, more than 0,90), respectively. Table 1 Using linear regression to identify predictors of BMI at the end of follow-up, baseline BMI was the strongest predictor (β = 0.663; 95% CI, 0.636–0.690; p < 0.001), indicating strong tracking of adiposity over time. Among modifiable and clinical predictors, central adiposity measured by WHtR ≥ 0.5 was linked to a 2.6-unit increase in follow-up BMI (β = 2.639; 95% CI, 0.679–4.598; p = 0.008), the largest effect among independently significant predictors, compared to a 0.4-unit increase associated with current smoking (β = 0.431; 95% CI, 0.048–0.815; p = 0.028) and a 0.4-unit increase with baseline hypertension (β = 0.448; 95% CI, 0.087–0.809; p = 0.015). Conversely, prevalent diabetes at baseline (β = −0.472; 95% CI, −0.923 to −0.020; p = 0.041) and higher random glucose levels (β = −0.146; 95% CI, −0.245 to −0.047; p = 0.004) were linked to lower follow-up BMI, perhaps reflecting weight loss due to established metabolic disease or its treatment. Male sex (β = −0.915; 95% CI, −1.150 to −0.680; p < 0.001) and older age (β = −0.049; 95% CI, −0.059 to −0.040; p < 0.001) were also independently associated with lower follow-up BMI. Pre-diabetes status, HbA1c, total cholesterol, and HDL cholesterol did not reach statistical significance. Table 2. Cox proportional hazards regression was used to identify independent predictors of incident diabetes mellitus over the follow-up period across four models: Arab-specific BMI classification with (Model A) and without (Model B) glycaemic markers, and standard WHO BMI classification with (Model C) and without (Model D) glycaemic markers. Table 3. WHtR ≥ 0.5 was a significant independent predictor of incident DM in all four models, with the effect size increasing when glycaemic markers were excluded, rising from HR = 1.563 (95% CI, 1.116–2.190; p = 0.009) in Model A to HR = 1.791 (95% CI, 1.281–2.504; p < 0.001) in Model B, and from HR = 1.657 (95% CI, 1.183–2.321; p = 0.003) in Model C to HR = 1.894 (95% CI, 1.355–2.649; p < 0.001) in Model D. In contrast, standard WHO BMI classes were not independently associated (i.e. after adjusting for glycaemic markers) with incident DM in Model C (overall p = 0.903), with no individual category reaching significance. Although the overall BMI reached marginal significance in Model D (p = 0.008), no individual BMI class was significant once WHtR was added to the model. Arab-specific BMI ≥ 26.6 kg/m² reached independent significance only in Model B (HR = 1.756; 95% CI, 1.108–2.784; p = 0.017), becoming non-significant when glycaemic markers were included (Model A: p = 0.137). With regards to the predictive performance of all four, models A and C achieved identical AUC values (0.818; 95% CI, 0.801–0.835), confirming that Arab-specific and WHO BMI cutoffs contribute equally when glycaemic markers are present. Without glycaemic markers, Arab (Model B: AUC = 0.766; 95% CI, 0.748–0.785) and WHO (Model D: AUC = 0.769; 95% CI, 0.751–0.787) models performed identically. In the fully adjusted Arab model with glycaemic markers (Model A), HbA1c was the dominant predictor of incident DM (HR = 6.698; 95% CI, 5.241–8.560; p < 0.001), followed by random glucose (HR = 1.202; 95% CI, 1.137–1.270; p < 0.001). Beyond glycaemia, WHtR ≥ 0.5 remained independently significant (HR = 1.563; 95% CI, 1.116–2.190; p = 0.009), as did older age (HR per year = 1.027; 95% CI, 1.021–1.033; p < 0.001), current smoking (HR = 1.402; 95% CI, 1.084–1.814; p = 0.010), mean blood pressure (HR per mm Hg= 1.009; 95% CI, 1.001–1.017; p = 0.021), and eGFR percentile category (HR = 1.081; 95% CI, 1.034–1.130; p < 0.001). Higher HDL cholesterol was protective (HR = 0.453; 95% CI, 0.343–0.597; p < 0.001), and female sex was associated with higher DM risk (HR = 0.809; 95% CI, 0.675–0.969; p = 0.022). Hypertension did not reach significance in this model (p = 0.055). While Arab-specific obesity (BMI ≥ 26.6 kg/m²) reached independent significance at the obese threshold in the glycaemia-free model, standard WHO BMI classes failed to achieve significance at any individual category level across all models that included WHtR. These findings indicate that Arab-specific BMI thresholds capture a degree of cardiometabolic risk that WHO categories do not, but that even this advantage is contingent on the absence of a central adiposity measure. When WHtR ≥ 0.5 is present in the model, it consistently absorbs the explanatory variance previously attributable to BMI category, whether WHO or Arab-specific, indicating that the risk associated with elevated BMI in this population operates primarily through central fat distribution rather than total body mass. Kaplan–Meier survival analysis demonstrated graded, shorter DM-free survival associated with WHtR ≥ 0.5 across all strata of both standard WHO BMI and Arab-specific BMI classification over a median follow-up of 9.4 years (log-rank χ² = 57.33 and χ² = 50.53, respectively; both p < 0.001). Figure 3. Discussion The prevalence of obesity in the UAE in this study (37.5%) is higher than previously reported. [15, 16], although it aligns with the higher rates observed specifically in UAE nationals. A 2023 Dubai population study reported an overall obesity rate of 17.8% across all nationalities, increasing to 39.6% among UAE nationals alone. [17] [10]. Of concern is our finding that 72.3% of the DM-free cohort and 77.5% of the full cohort already had WHtR ≥ 0.5. Importantly, there is also evidence of normal weight and overweight misclassification. Firstly, 37.6% of WHO normal-weight individuals already had WHtR ≥ 0.5, rising to 84.3% in the overweight category. Secondly, evidence of missclassification is further provided by the distribution of DM incidence across WHtR groups: 12.0% in WHtR ≥ 0.5 vs 2.8% in WHtR < 0.5, with rates ranging from 9.6% in normal-weight subjects to 17.2% in Class II obesity among those with WHtR ≥ 0.5. WHtR is therefore emerging as an important risk stratification tool. Thirdly, linear regression showed that baseline WHtR was the largest modifiable BMI predictor. The finding that WHtR ≥ 0.5 was associated with a 2.6 kg/m² higher follow-up BMI, the largest effect among all modifiable predictors, strengthens the argument that central adiposity drives future adiposity progression. Finally, this is further supported by the performance of our prediction models and the AUC equivalence between the Arab and WHO models. The finding that Arab-specific and WHO BMI models achieved identical AUC (0.818) when WHtR was included is a critical argument and directly challenges the added value of Arab-specific BMI cutoffs or current WHO BMI criteria. The independent association between higher eGFR percentile and incident DM, first reported in this cohort by AlKetbi et al. [18], is further corroborated in the current analysis, where it remained significant after full adjustment for WHtR, BMI classification, glycaemic markers, blood pressure, and lipid profile (Model D: HR = 1.695 at the highest significant level; 95% CI, 1.158–2.481; p = 0.007). The persistence of this association after inclusion of WHtR ≥ 0.5 is particularly noteworthy, as it suggests that renal hyperfiltration constitutes an independent pathway to diabetes risk that is not explained by central adiposity alone. As discussed in our prior publication, this likely reflects the haemodynamic consequences of early insulin resistance and hyperinsulinaemia, increased glomerular capillary pressure and sodium reabsorption, that precede overt hyperglycaemia by years [19]. The current finding that this effect persists after adjustment for the full cardiometabolic risk profile, including the central adiposity marker absent from the original model, strengthens the case for incorporating age- and sex-specific eGFR percentile monitoring into pre-diabetes risk stratification protocols in this population. Sex differences in central adiposity distribution and its metabolic consequences were evident. Among normal-weight subjects by the standard WHO classification, males had a significantly higher prevalence of WHtR ≥ 0.5 than females (50.3% vs 34.4%, p < 0.0001; full cohort, N = 8,652), confirming that central fat accumulation at lower total body mass is more prevalent in males in this population. This is clinically important in the unadjusted analysis: among subjects in the leanest Arab BMI stratum (< 22.1 kg/m²) with WHtR ≥ 0.5, male DM incidence was nearly double that of females (13.1% vs 5.3%), despite similar WHtR prevalence in that stratum (15.1% vs 12.5%), suggesting that male sex amplifies the metabolic consequence of central adiposity at low total body mass. However, in the fully adjusted Cox models, male sex was associated with lower DM risk (HR = 0.788–0.825), meaning that after accounting for WHtR, blood pressure, HDL, and smoking, risk factors disproportionately concentrated in males, female sex was associated with a higher independent DM hazard. This reflects the pattern whereby females accumulate proportionally more subcutaneous fat, which is metabolically less active, yet remain at elevated risk of DM through hormonal and reproductive pathways not fully captured by anthropometric measures alone [20, 21]. Taken together, these findings suggest that a single WHtR threshold of 0.5 may differ by sex, and that sex-stratified screening thresholds merit further investigation, particularly for identifying lean males with central adiposity who carry a high unadjusted DM risk despite appearing low-risk by BMI. The finding that established diabetics had lower follow-up BMI likely reflects treatment or disease-related weight loss, which may also explain the associations with glycemic markers and HDL . Although bariatric surgery in Abu Dhabi, as in other countries, is free for those who meet international selection criteria, only around 5,000 bariatric procedures are performed annually in the UAE [22]. Unfortunately, there are no prescription data available for the new pharmacological interventions, but these drugs, free for UAE nationals, seem to enjoy great popularity. Despite these interventions, the burden of obesity is increasing, as the 2023 figures are significantly higher than the cohort baseline. These trends [23] strengthen the case for additional preventive strategies calibrated to Arab-specific risk strata to avoid disadvantaging people at risk due to improper cut-off points, as shown in this study. WHtR ≥ 0.5, a single inexpensive measurement requiring only a tape measure, may be preferable to BMI-based obesity classification as a frontline screening tool for diabetes risk stratification in Arab populations, offering independent predictive value that persists even after full adjustment for glycaemic markers, blood pressure, lipid profile, renal function, and smoking status. Challenging the obesity definition in this population is not new; Caleyachetty et al. identified a lower BMI cutoff of 26.6 kg/m² for Arab populations as risk-equivalent to the White BMI ≥30 threshold for type 2 diabetes [14]. This is in line with a, systematic review and meta-analysis of 55 studies involving over 677,000 participants that found optimal BMI thresholds for cardiometabolic risk in Arab and Middle Eastern populations to be ranging from 26.22 to 27.45 kg/m² [24]. Another study from Qatar biobank reported that the optimal body mass index (BMI) cut points are 25.2 kg/m² for males and 24.8 kg/m² for females. Similarly, WC cut-points were 84.3 cm for males and 74.5 cm for females, also lower than global standards [25]. This is supported by other regional studies [26]. Settling the definition of obesity in the Arab population and in other ethnicities is a priority and needs to be included into international guidelines. Research investigating the ethnicity factor found that Arab individuals were more at risk of obesity and overweight. [15]. A population-level study from Kuwait [27]and from young Emirati men in the UAE [28], found that over 60% of men aged 18–29 already had at least one cardiometabolic risk factor, supporting the case for lower intervention thresholds. Research on the multifactorial nature of obesity is a priority. Metabolic, genetic, behavioral, environmental, and sociodemographic factors interact in complex ways [29], and accurate classification is necessary. This study identifies modifiable risk factors such as smoking and high blood pressure, but the environmental, genetic, and behavioral factors mentioned are also essential. A current study is now being conducted to investigate the American Heart Association AHA's eight essentials in this population. Understanding these factors in the context of the UAE population will enable the design of targeted, personalized preventive and management strategies for obesity. This study supports recent suggestions for alternative anthropometric thresholds, such as based on waist circumference (WC) and waist-to-hip ratio (WHR), to improve obesity and CVD risk classification in Arabs. [30] [25]. The use of BMI to assess weight status and identify potential health risks associated with excess body weight poorly distinguishes between muscle and fat mass. The use of WC and WHR reflects body fat content and distribution, as well as the presence of abdominal obesity, which is associated with increased metabolic risk factors and health complications. Waist-to-height ratio (WHtR), has been proposed as a more ethnicity-neutral alternative, with a universal cutoff of 0.5 validated across 78 studies in 14 countries, including Caucasian, Asian, and Central American subjects. [31] Finally, the study identified risk factors of obesity from a 9.4-year follow-up, and higher BMI at baseline plays a significant role. Additionally, being female is a risk factor for becoming obese, which can be attributed partly to multiple pregnancies, hormonal differences, and a sedentary lifestyle. A notable finding is the association of higher BMI with lower RBS and HbA1c and higher HDL. This is counterintuitive, as higher rates of metabolic disorders are found amongst overweight and obese individuals compared to normal individuals [20] [21] . Perhaps it reflects greater effort at lifestyle change due to obesity, with possible access to health counseling following identification of obesity. Despite its large sample size and a 9.4-year follow-up, our results should be interpreted cautiously as we did not account for lifestyle (diet, exercise, and medication use) or genetic predispositions. These may affect conclusions as the usage of certain medications can alter the body mass index, and thus may be potential confounders. Conclusion The study provides important insights into the high burden of overweight and obesity, as well as indicating diabetes risk at lower BMI in the Emirate of Abu Dhabi population, calling for preventive strategies and community awareness programs encouraging lifestyle modifications. Declarations Ethical approval and consent to participate: The study was approved by the Alain Human Ethics Committee, approval number 13/58, and Ambulatory Healthcare Services IRB 19-2022. All methods were carried out under relevant guidelines and regulations. The authors confirm that the study was conducted in accordance with the Helsinki Declaration. Consent statement in the Ethics approval and consent to participate: Informed consent was waived by the IRBs as the study was designed for retrospective data gathered as part of patient care and anonymized at analysis. Competing interests: None. Funding: None. Authors' contributions: LBK and NN conceptualized and analyzed data. LBK wrote the manuscript; all other co-authors collected data and reviewed the manuscript. All authors have read and approved the final manuscript. Consent to publish: Not Applicable. Availability of data and materials: The author elects not to share data. References Bhaskaran K, Douglas I, Forbes H, dos-Santos-Silva I, Leon DA, Smeeth L. Body-mass index and risk of 22 specific cancers: a population-based cohort study of 5·24 million UK adults. Lancet. 2014;384:755-765. Yao TC, Tsai HJ, Chang SW, Chung RH, Hsu JY, Tsai MH et al. Obesity disproportionately impacts lung volumes, airflow and exhaled nitric oxide in children. PLoS One. 2017;12:e0174691. Organisaiton WH. Obesity and overweight. 2025. Available from: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight Roberto CA, Swinburn B, Hawkes C, Huang TT, Costa SA, Ashe M et al. 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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:e062053. Hajat C, Harrison O, Al Siksek Z. Weqaya: a population-wide cardiovascular screening program in Abu Dhabi, United Arab Emirates. Am J Public Health. 2012;102:909-914. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. 2000;894:i-xii, 1. Association AM. AMA adopts new policy clarifying role of BMI as a measure in medicine. 2023. Available from: https://www.ama-assn.org/press-center/ama-press-releases/ama-adopts-new-policy-clarifying-role-bmi-measure-medicine#:~:text=Jun%2014%2C%202023&text=The%20report%20also%20outlined %20the,genders%2C%20and%20a ge%2Dspan. AlKetbi LB, Nagelkerke N, AlAlawi N, Humaid A, AlKetbi R, Aleissaee H et al. Disease Risk Score Derivation and Validation in Abu Dhabi, United Arab Emirates: A Retrospective Cohort Study. J Am Heart Assoc. 2024;13:e035930. Caleyachetty R, Barber TM, Mohammed NI, Cappuccio FP, Hardy R, Mathur R et al. Ethnicity-specific BMI cutoffs for obesity based on type 2 diabetes risk in England: a population-based cohort study. Lancet Diabetes Endocrinol. 2021;9:419-426. Abdelgadir E, Rashid F, Bashier A, Zidan M, McGowan B, Alawadi F. Prevalence of overweight and obesity in adults from the Middle East: A large‐scale population‐based study. Diabetes, Obesity and Metabolism. 2025;27:3676-3685. Al Hajeri OM, Al Hammadi O. Obesity in the UAE. Healthcare in the United Arab Emirates. Springer; 2025. p. 277-291. Observatory GO. Obesity Prevalence, 2017-2018. 2026. Available from: https://data.worldobesity.org/country/united-arab-emirates-225/#data_prevalence Baynouna AlKetbi LM, AlKetbi R, AlShamsi MS, Nagelkerke N, Afandi B, AlDobaee M et al. Incidence and predictors of type 2 diabetes mellitus in a population-based cohort study in Abu Dhabi. Sci Rep. 2025;15:23639. Tonneijck L, Muskiet MH, Smits MM, van Bommel EJ, Heerspink HJ, van Raalte DH et al. Glomerular Hyperfiltration in Diabetes: Mechanisms, Clinical Significance, and Treatment. J Am Soc Nephrol. 2017;28:1023-1039. Geer EB, Shen W. Gender differences in insulin resistance, body composition, and energy balance. Gend Med. 2009;6 Suppl 1:60-75. Hamman RF, Wing RR, Edelstein SL, Lachin JM, Bray GA, Delahanty L et al. Effect of weight loss with lifestyle intervention on risk of diabetes. Diabetes Care. 2006;29:2102-2107. owenhaskins. The status of obesity and bariatric surgery in the United Arab Emirates. 2022. Available from: https://www.bariatricnews.net/post/the-status-of-obesity-and-bariatric-surgery-in-the-united-arab-emirates Malekpour MR, Abbasi-Kangevari M, Ghamari SH, Khanali J, Heidari-Foroozan M, Moghaddam SS et al. The burden of metabolic risk factors in North Africa and the Middle East, 1990-2019: findings from the Global Burden of Disease Study. EClinicalMedicine. 2023;60:102022. Al Busaidi S, Al-Maqbali JS, Al Nou’mani J, Al Harthi T, Al Alawi AM, Al Kharusi A. Optimal BMI cut-offs associated with cardiometabolic risks in Arab and Middle Eastern populations: a systematic review and meta-analysis. Int J Obes (Lond). 2026;50:23-32. Ajeen R, Turk-Adawi KI, Ammerman AS, Batsis JA, Ng SW, Adair LS. Deriving sex-specific anthropometric cut-points for obesity and cardiovascular disease risk in Qatari adults. Int J Obes (Lond). 2026;50:397-406. Khader YS, Batieha A, Jaddou H, Batieha Z, El-Khateeb M, Ajlouni K. Anthropometric cutoff values for detecting metabolic abnormalities in Jordanian adults. Diabetes Metab Syndr Obes. 2010;3:395-402. Oguoma VM, Coffee NT, Alsharrah S, Abu-Farha M, Al-Refaei FH, Alkandari A et al. Anthropometric cut-points for discriminating diabetes and the metabolic syndrome among Arabs and Asians: the Kuwait Diabetes Epidemiology Program. Br J Nutr. 2022;127:92-102. Alzaabi A, Al-Kaabi J, Al-Maskari F, Farhood AF, Ahmed LA. Prevalence of diabetes and cardio-metabolic risk factors in young men in the United Arab Emirates: A cross-sectional national survey. Endocrinol Diabetes Metab. 2019;2:e00081. Vourdoumpa A, Paltoglou G, Charmandari E. The Genetic Basis of Childhood Obesity: A Systematic Review. Nutrients. 2023;15:1416. Al-Lawati JA, Jousilahti P. Body mass index, waist circumference and waist-to-hip ratio cut-off points for categorisation of obesity among Omani Arabs. Public Health Nutr. 2008;11:102-108. Browning LM, Hsieh SD, Ashwell M. A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0·5 could be a suitable global boundary value. Nutr Res Rev. 2010;23:247-269. Tables Tables are available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files Table1.doc Table2.docx Table3.docx supplement1.png supplement2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9305155","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616734467,"identity":"08fb5c47-5a2f-46a3-b005-34a7071fecf6","order_by":0,"name":"Latifa Baynouna 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Prevalence of obesity based on WHO criteria and proposed Arab BMI criteria\u003c/p\u003e\n\u003cp\u003eB. BMI Class Transition from baseline to End of Follow-up – By Gender\u003c/p\u003e\n\u003cp\u003eBody mass index (BMI)\u003c/p\u003e\n\u003cp\u003eWorld health organization (WHO)\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9305155/v1/01903afc8054d364fefcb11d.png"},{"id":106096040,"identity":"0d82ec21-9ed9-4a4e-916d-a6d5e5f4ca9d","added_by":"auto","created_at":"2026-04-03 11:52:26","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3767299,"visible":true,"origin":"","legend":"\u003cp\u003eProportion with central adiposity (WHtR ≥0.5) and incidence of diabetes mellitus by standard and Arab-specific obesity categories, stratified by sex.\u003c/p\u003e\n\u003cp\u003eWaist to height Ratio (WHtR)\u003c/p\u003e","description":"","filename":"Figure2jpg.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9305155/v1/589231edb05ab1ba0ce01ef7.jpg"},{"id":106096042,"identity":"a8447206-49ba-40f4-b928-25cd2c6de5c0","added_by":"auto","created_at":"2026-04-03 11:52:26","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1760256,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier DM-free survival curves by WHtR status stratified by Arab-specific and standard BMI classification.\u003c/p\u003e\n\u003cp\u003eWaist to height Ratio (WHtR)\u003c/p\u003e\n\u003cp\u003eBody mass index (BMI)\u003c/p\u003e\n\u003cp\u003eDiabetes Mellitus (DM)\u003c/p\u003e","description":"","filename":"Figure3jpg.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9305155/v1/e22854129bd9e2a2afd066ca.jpg"},{"id":106724184,"identity":"bdda261c-8d97-438e-893e-119b0eddc339","added_by":"auto","created_at":"2026-04-12 18:26:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6752688,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9305155/v1/c54f944e-ff70-4bb4-bde8-6e317eb7610e.pdf"},{"id":106096086,"identity":"ffcc0ae4-7f82-4a30-a7e5-9ac64183aac9","added_by":"auto","created_at":"2026-04-03 11:52:46","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":142336,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.doc","url":"https://assets-eu.researchsquare.com/files/rs-9305155/v1/23ea30e4c53cc0e857f56f04.doc"},{"id":106096110,"identity":"85b74e39-cbe8-4d8b-b3cd-5bc9301b84fe","added_by":"auto","created_at":"2026-04-03 11:52:51","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16695,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9305155/v1/064dca66686022de2eb3bdb9.docx"},{"id":106096039,"identity":"927dc606-5b28-4cf5-8cae-3e560b14477a","added_by":"auto","created_at":"2026-04-03 11:52:25","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1743885,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-9305155/v1/683379af66e34249c8b662ae.docx"},{"id":106096036,"identity":"c5430f30-9b7d-4c4c-a1cf-0a783bfd87dd","added_by":"auto","created_at":"2026-04-03 11:52:25","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":477225,"visible":true,"origin":"","legend":"","description":"","filename":"supplement1.png","url":"https://assets-eu.researchsquare.com/files/rs-9305155/v1/44c38fd7e22926a89f673e3d.png"},{"id":106096041,"identity":"b940cdd8-a426-4840-ba60-e2d1f6958a79","added_by":"auto","created_at":"2026-04-03 11:52:26","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":3582042,"visible":true,"origin":"","legend":"","description":"","filename":"supplement2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9305155/v1/24f52a2ca9c4d0edb6e4b183.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eValidation of Ethnicity-Specific anthropometric measurements thresholds for Obesity Based on Diabetes Risk in Abu Dhabi-United Arab Emirates: A Population-Based Cohort Study\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn 1997, the World Health Organization (WHO) declared Obesity a global epidemic. Across various nations of the world, being overweight and Obese are perceived as significant public health issues. [1] [2] Overweight is prevalent in 1.9 billion adults, and more than half a billion are clinically obese. \u003csup\u003e[3]\u0026nbsp;\u003c/sup\u003eReportedly, no nation has been able to switch this rising weight trend. \u003csup\u003e[4] \u0026nbsp;\u003c/sup\u003eIt is estimated that more than 1 billion people will be living with Obesity by 2030 \u003csup\u003e[5]\u003c/sup\u003e, which will impact the burden of other health conditions, such as type 2 diabetes and cardiovascular disease. \u003csup\u003e[6]\u0026nbsp;\u003c/sup\u003eSeveral studies have shown the adverse effect of Obesity on an individual\u0026apos;s overall health\u0026nbsp;[7] such as type 2 diabetes, hypertension, dyslipidemia, coronary heart disease, stroke, obstructive sleep apnea, cancers, and more. In addition to its association with a low quality of life.\u0026nbsp;[1, 2]\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eOver the past few decades, the United Arab Emirates (UAE) has experienced significant economic and social change driven by rapid growth. The UAE has a high per capita income, among the highest in the world. As a result, the population has shifted from a simple tribal lifestyle to a modern, business-oriented society. The high per capita income, strong purchasing power, and availability of a wide variety of foods in the local market have led to changes in dietary habits, a move toward a Western lifestyle, and a greater tendency to gain weight. [8]\u003c/p\u003e\n\u003cp\u003eObesity in the United Arab Emirates (UAE) has emerged as a critical public health issue, negatively impacting the well-being of its population. A study conducted to investigate the prevalence and associated risk factors of overweight and obesity among the adult population in Dubai concluded that the highest obesity rates were reported among women (21.6%) and UAE nationals (39.6%). [9] Another study in 2009 revealed that the prevalence of overweight and obesity was 27% and 16%, respectively. [12] Hajat et al. reported a high prevalence of obesity in 2011 at 35%, with 31.6% among males and 38.3% among females, and an overweight prevalence of 32%, with 36.1% among males and 28.8% among females.\u003csup\u003e\u0026nbsp;\u003c/sup\u003e[10]\u003c/p\u003e\n\u003cp\u003eThe definition and classification of obesity is based on the Body Mass Index (BMI), which was adopted by the WHO in 1998. [11]. Nevertheless, recent scientific publications and bodies have called for ethnicity-based classifications of obesity. The AMA issued a policy highlighting BMI\u0026apos;s limitations due to its history of harm and racist exclusion, because BMI is based primarily on data collected from previous generations of non-Hispanic white populations. The AMA recommends using BMI alongside other measures, such as visceral fat, body adiposity, and genetic factors, as its accuracy as a measure of obesity varies among individuals and across races, sexes, and ages [12]. In a United Kingdom study that aimed to prospectively identify ethnicity-specific BMI cutoffs for obesity based on the risk of type 2 diabetes, BMI cutoffs equivalent to the obesity cutoff for White populations (\u0026ge;30 kg/m2) were identified. In the Arab population, probably similar to the UAE population, the obesity cutoff point was 26.6 kg/m2. Thus, they suggested revisions of ethnicity-specific BMI cutoffs for the different ethnic populations to provide appropriate clinical surveillance and optimize the prevention, early diagnosis, and timely management of type 2 diabetes. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo our knowledge, no study has explored the epidemiology of obesity among adult Emiratis in Abu Dhabi since then, nor has it been used in a longitudinal study design. This community-based cohort study explores this escalating issue of obesity by investigating secular trends in its epidemiology among UAE nationals over a decade of follow-up and validates the suggested ethnicity-specific Arab cut-off points for obesity and overweight. These insights are crucial for developing effective strategies to address and mitigate the impact of obesity in the UAE.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eStudy design and setting\u003c/p\u003e\n\u003cp\u003eThis retrospective cohort study involves 8699 subjects enrolled in the Emirate of Abu Dhabi\u0026rsquo;s cardiovascular disease screening program, Weqaya, representing nearly 2% of the total Abu Dhabi population in the United Arab Emirates in 2011. Abu Dhabi is the largest emirate in the UAE and Abu Dhabi city serves as the capital of the country (UAE), with a population of 455,065, consisting of 220,651 men and 234,414 women, which accounts for approximately 46% of the national population of the United Arab Emirates. The Weqaya program was described in a previous publication [10]. The methodology for this retrospective cohort study was also described in another publication[13] .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe cohort of UAE nationals was enrolled from 2011 to 2013, and follow-up data were collected in 2023. Of the 8,699 participants aged 18 years and older, 311 were excluded for having no recorded BMI at baseline, an extreme BMI of 50 or more kg/m\u0026sup2;, or being pregnant at baseline. The final analytic sample included 8388 participants, divided into 4061 (48.4%) women and 4327 (51.6%) men among the included subjects.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVariables\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt baseline, physical measurements, including Height, Weight, waist circumference, and waist-hip ratio, were measured, -to assess central Obesity. Investigations such as laboratory and radiography were also included in the present analysis. Individuals\u0026apos; sociodemographic information, medical health history, and psychological history were included with other risk factors. In the initial selection step, the World Health Organization\u0026apos;s (WHO) diagnostic criteria for obesity were used, which included a body mass index (BMI) of 30 or greater. kg/m2 for both genders. Overweight was defined as an individual having a Body Mass Index (BMI) of 25 to 29.99 [11].\u003c/p\u003e\n\u003cp\u003eOutcome data\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt follow-up, the last BMI recorded in the participants\u0026rsquo; EMR (Electronic Medical Record) was obtained through an EMR chart review conducted by physicians and nurses. The availability of participants\u0026apos; records in their EMR was facilitated by several factors. First, Ambulatory Health Care Services is the largest network of primary care centers serving most of the UAE national population. Second, due to the COVID-19 pandemic, AHS provided COVID-19 vaccinations for the public, and BMI was recorded for all vaccinated participants. Additionally, the BMI was regularly updated with each visit due to Department of Health requirements. Therefore, the latest BMI is recorded within the follow-up period for each participant, noting that the follow-up in this cohort was excellent, with an average of 9.2 years (ranging from a minimum of 1 year to a maximum of 12 years), with 22.8% followed for 8 years or less and 16% for 5 years or less.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u003c/p\u003e\n\u003cp\u003eA predicted change in BMI was estimated for each gender. Subsequently, for each subject, a deviation from this average estimate of BMI change was calculated. A paired t-test was conducted to assess the significance of BMI increase over the follow-up years among males and females, investigating any observed trends. Data were analyzed using statistical software (IBM SPSS, version 29), employing descriptive statistics to characterize the study population and inferential statistics (logistic regression and linear regression analysis) to identify associations between variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthical approval\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthical approval was sought from the relevant AHS institutional review boards (IRB) and Al Ain HREC. According to SEHA, each patient visiting the family medicine clinic signed a general informed consent form.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe prevalence of obesity among adults in Abu Dhabi, United Arab Emirates, is high. At baseline, the prevalence of obesity, defined as \u0026ge;30 kg/m\u0026sup2; based on WHO BMI criteria, was 37.5% overall, 41.3% among females, and 33.7% among males. Among those with obesity, 17.1% of females and 11.1% of males were classified as Class II or III (BMI \u0026ge;35 kg/m\u0026sup2;). Approximately 29% of females and 27% of males had a BMI classified as normal (BMI \u0026lt;25 kg/m\u0026sup2;) at baseline. In 2023, the prevalence of obesity increased to 39.2% overall; Class II and III obesity accounted for 18.2% of females and 10.8% of males, consistent with the baseline pattern (Supplement 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing the Arab cutoff values suggested by Caleyachetty et al. [14] (\u0026ge;26.6 kg/m\u0026sup2;), the baseline obesity prevalence was 60.9% overall, 61.9% among females, and 60.0% among males. By the end of follow-up in 2023, overall obesity prevalence had increased to 64.7%, with a notable rise among females to 69.8%, while males prevalence remained nearly the same at 59.3%, highlighting a particularly concerning trend in women (Figure 1A).\u003c/p\u003e\n\u003cp\u003eFurthermore, using the suggested Arab cutoff, 89.0% of females and 78.5% of males who were obese at baseline remained obese at the end of follow-up (Figure 1B). Among those who were non-obese at baseline, 39.0% of females and 30.3% of males had transitioned into the obese category by 2023, while further underscoring the progressive nature of obesity in this Arab population. Those who were obese and transitioned to \u0026lt;22.1 kg/m2 were 5% among males and 9.4% among females.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo explore the trend in BMI and obesity over time we had to adjust for the fact that the cohort had aged between the two measurements. The change in BMI during the follow-up period due to aging was estimated by calculating the predicted change from baseline to follow-up. An average change was predicted based the relationship between BMI and sex and age at baseline; and for each subject, the deviation from this average BMI change estimate was calculated. A paired-sample t-test was used to assess the significance of BMI change over the follow-up years in males and females. A positive trend in obesity was observed, especially among females. On average, males in this cohort had an increase of 0.4 kg/m2(C.I. 0.23-0.57) in BMI, while females had an increase of 0.57 kg/m2 (C.I. 0.39-0.76). Supplement 2 \u0026nbsp; Abdominal obesity, as measured by the waist-to-height ratio (WHtR), represents a possible more accurate classification of obesity than BMI. Among subjects without diabetes at baseline (n = 6,739), 72.3% had WHtR \u0026ge; 0.5, with the proportion increasing progressively across BMI categories. Notably, 37.6% of those classified as normal weight by standard WHO BMI were already WHtR \u0026ge; 0.5, rising to 84.3% in the overweight category. Under Arab-specific classification, 60.1% of the overweight stratum (BMI 22.1\u0026ndash;26.59 kg/m\u0026sup2;) and 94.3% of the obese stratum (\u0026ge; 26.6 kg/m\u0026sup2;) had WHtR \u0026ge; 0.5 \u0026mdash; underscoring early central fat accumulation not adequately captured by the lower BMI thresholds proposed by Caleyachetty.\u003c/p\u003e\n\u003cp\u003eWHtR \u0026ge; 0.5 consistently identified a higher-risk group for incident diabetes. DM cumulative incidence among those with WHtR \u0026ge; 0.5 ranged from 9.6% in WHO normal-weight subjects to 17.2% in Class II obesity, compared to 2.0\u0026ndash;6.5% in those with WHtR \u0026lt; 0.5 (with no events in Class II or III due to small sample sizes, n = 7 and n = 3, respectively). Overall DM incidence was 9.4%: 2.8% in the WHtR \u0026lt; 0.5 group versus 12.0% in the WHtR \u0026ge; 0.5 group. Under Arab BMI classification, DM incidence in the WHtR \u0026ge; 0.5 group was 8.8%, 10.2%, and 12.6% across the three strata, versus 1.5%, 4.2%, and 3.2% in the WHtR \u0026lt; 0.5 group, confirming the independent predictive value of central adiposity across all obesity definitions. Sex differences were most pronounced at lower adiposity: males with \u0026nbsp;BMI \u0026lt; 22.1 and WHtR \u0026ge; 0.5 had nearly double the DM incidence of females (13.1% vs 5.3%), despite comparable WHtR prevalence (15.1% vs 12.5%). This disparity attenuated at higher adiposity, with near-identical DM rates in the Arab BMI \u0026ge; 26.6 stratum (12.5% males vs 12.7% females).\u003c/p\u003e\n\u003cp\u003eIn the full cohort including subjects with prevalent diabetes (N = 8,652), 77.5% had WHtR \u0026ge; 0.5 \u0026mdash; 5.2 percentage points higher than in the DM-free subcohort, with the gap most pronounced at lighter BMI categories (normal weight: 42.2% vs 37.6%; overweight: 87.1% vs 84.3%). Under Arab classification, proportions were similarly elevated (95.6%, 65.3%, and 15.5% vs 94.3%, 60.1%, and 13.5% in the DM-free cohort). Sex differences were most striking in the normal weight stratum (50.3% males vs 34.4% females, p \u0026lt; 0.0001) and the Arab overweight category (72.5% vs 56.3%, p \u0026lt; 0.0001), attenuating toward saturation at higher obesity classes. Collectively, these findings demonstrate that central adiposity, already prevalent well below conventional obesity thresholds, is further concentrated among those with DM, and that standard BMI substantially underestimates this risk, particularly in males and those classified as normal weight or overweight. (Figure 2)\u003c/p\u003e\n\u003cp\u003eTable 1 shows the baseline characteristics of the population, with a mean age of 35\u0026plusmn;10 years. Notably, the DM rate is lowest among those with BMI \u0026nbsp;\u0026lt; 22.1 kg/m2. Only 6.3% of subjects with BMI \u0026lt;=22.1 have diabetes, compared with 22.3% to 31.8% among subjects with BMI \u0026gt;=26.6 or \u0026gt;=40, respectively. With regards to pre-DM, 16.7% of subjects with BMI \u0026lt;=22.1 had prediabetes at baseline, compared with 35.9% to 53.1% among subjects with BMI \u0026gt;=26.6 or \u0026gt;=40, respectively. Similarly, dyslipidemia, hypertension, and CKD all have the lowest prevalence in BMI class \u0026lt;= 22.1. In both genders, the overweight and obesity categories tend to have above-normal waist-hip ratios (more than 0.85, more than 0,90), respectively. Table 1\u003c/p\u003e\n\u003cp\u003eUsing linear regression to identify predictors of BMI at the end of follow-up, baseline BMI was the strongest predictor (\u0026beta; = 0.663; 95% CI, 0.636\u0026ndash;0.690; p \u0026lt; 0.001), indicating strong tracking of adiposity over time. Among modifiable and clinical predictors, central adiposity measured by WHtR \u0026ge; 0.5 was linked to a 2.6-unit increase in follow-up BMI (\u0026beta; = 2.639; 95% CI, 0.679\u0026ndash;4.598; p = 0.008), the largest effect among independently significant predictors, compared to a 0.4-unit increase associated with current smoking (\u0026beta; = 0.431; 95% CI, 0.048\u0026ndash;0.815; p = 0.028) and a 0.4-unit increase with baseline hypertension (\u0026beta; = 0.448; 95% CI, 0.087\u0026ndash;0.809; p = 0.015). Conversely, prevalent diabetes at baseline (\u0026beta; = \u0026minus;0.472; 95% CI, \u0026minus;0.923 to \u0026minus;0.020; p = 0.041) and higher random glucose levels (\u0026beta; = \u0026minus;0.146; 95% CI, \u0026minus;0.245 to \u0026minus;0.047; p = 0.004) were linked to lower follow-up BMI, perhaps reflecting weight loss due to established metabolic disease or its treatment. Male sex (\u0026beta; = \u0026minus;0.915; 95% CI, \u0026minus;1.150 to \u0026minus;0.680; p \u0026lt; 0.001) and older age (\u0026beta; = \u0026minus;0.049; 95% CI, \u0026minus;0.059 to \u0026minus;0.040; p \u0026lt; 0.001) were also independently associated with lower follow-up BMI. Pre-diabetes status, HbA1c, total cholesterol, and HDL cholesterol did not reach statistical significance. Table 2.\u003c/p\u003e\n\u003cp\u003eCox proportional hazards regression was used to identify independent predictors of incident diabetes mellitus over the follow-up period across four models: Arab-specific BMI classification with (Model A) and without (Model B) glycaemic markers, and standard WHO BMI classification with (Model C) and without (Model D) glycaemic markers. Table 3.\u003c/p\u003e\n\u003cp\u003eWHtR \u0026ge; 0.5 was a significant independent predictor of incident DM in all four models, with the effect size increasing when glycaemic markers were excluded, rising from HR = 1.563 (95% CI, 1.116\u0026ndash;2.190; p = 0.009) in Model A to HR = 1.791 (95% CI, 1.281\u0026ndash;2.504; p \u0026lt; 0.001) in Model B, and from HR = 1.657 (95% CI, 1.183\u0026ndash;2.321; p = 0.003) in Model C to HR = 1.894 (95% CI, 1.355\u0026ndash;2.649; p \u0026lt; 0.001) in Model D. In contrast, standard WHO BMI classes were not independently associated (i.e. after adjusting for glycaemic markers) with incident DM in Model C (overall p = 0.903), with no individual category reaching significance. Although the overall BMI reached marginal significance in Model D (p = 0.008), no individual BMI class was significant once WHtR was added to the model. Arab-specific BMI \u0026ge; 26.6 kg/m\u0026sup2; reached independent significance only in Model B (HR = 1.756; 95% CI, 1.108\u0026ndash;2.784; p = 0.017), becoming non-significant when glycaemic markers were included (Model A: p = 0.137). With regards to the predictive performance of all four, models A and C achieved identical AUC values (0.818; 95% CI, 0.801\u0026ndash;0.835), confirming that Arab-specific and WHO BMI cutoffs contribute equally when glycaemic markers are present. Without glycaemic markers, Arab (Model B: AUC = 0.766; 95% CI, 0.748\u0026ndash;0.785) and WHO (Model D: AUC = 0.769; 95% CI, 0.751\u0026ndash;0.787) models performed identically.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the fully adjusted Arab model with glycaemic markers (Model A), HbA1c was the dominant predictor of incident DM (HR = 6.698; 95% CI, 5.241\u0026ndash;8.560; p \u0026lt; 0.001), followed by random glucose (HR = 1.202; 95% CI, 1.137\u0026ndash;1.270; p \u0026lt; 0.001). Beyond glycaemia, WHtR \u0026ge; 0.5 remained independently significant (HR = 1.563; 95% CI, 1.116\u0026ndash;2.190; p = 0.009), as did older age (HR per year = 1.027; 95% CI, 1.021\u0026ndash;1.033; p \u0026lt; 0.001), current smoking (HR = 1.402; 95% CI, 1.084\u0026ndash;1.814; p = 0.010), mean blood pressure (HR per mm Hg= 1.009; 95% CI, 1.001\u0026ndash;1.017; p = 0.021), and eGFR percentile category (HR = 1.081; 95% CI, 1.034\u0026ndash;1.130; p \u0026lt; 0.001). Higher HDL cholesterol was protective (HR = 0.453; 95% CI, 0.343\u0026ndash;0.597; p \u0026lt; 0.001), and female sex was associated with higher DM risk (HR = 0.809; 95% CI, 0.675\u0026ndash;0.969; p = 0.022). Hypertension did not reach significance in this model (p = 0.055).\u003c/p\u003e\n\u003cp\u003eWhile Arab-specific obesity (BMI \u0026ge; 26.6 kg/m\u0026sup2;) reached independent significance at the obese threshold in the glycaemia-free model, standard WHO BMI classes failed to achieve significance at any individual category level across all models that included WHtR. These findings indicate that Arab-specific BMI thresholds capture a degree of cardiometabolic risk that WHO categories do not, but that even this advantage is contingent on the absence of a central adiposity measure. When WHtR \u0026ge; 0.5 is present in the model, it consistently absorbs the explanatory variance previously attributable to BMI category, whether WHO or Arab-specific, indicating that the risk associated with elevated BMI in this population operates primarily through central fat distribution rather than total body mass.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKaplan\u0026ndash;Meier survival analysis demonstrated graded, shorter DM-free survival associated with WHtR \u0026ge; 0.5 across all strata of both standard WHO BMI and Arab-specific BMI classification over a median follow-up of 9.4 years (log-rank \u0026chi;\u0026sup2; = 57.33 and \u0026chi;\u0026sup2; = 50.53, respectively; both p \u0026lt; 0.001). Figure 3.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe prevalence of obesity in the UAE in this study (37.5%) is higher than previously reported. [15, 16], although it aligns with the higher rates observed specifically in UAE nationals. A 2023 Dubai population study reported an overall obesity rate of 17.8% across all nationalities, increasing to 39.6% among UAE nationals alone. [17] [10].\u003c/p\u003e\n\u003cp\u003eOf concern is our finding that\u0026nbsp;72.3% of the DM-free cohort\u0026nbsp;and\u0026nbsp;77.5% of the full cohort already had WHtR \u0026ge; 0.5. Importantly, there is also evidence of normal weight and overweight misclassification. Firstly, 37.6% of WHO normal-weight individuals\u0026nbsp;already had WHtR \u0026ge; 0.5, rising to\u0026nbsp;84.3% in the overweight category. Secondly, evidence of missclassification is further provided by the distribution of DM incidence across WHtR groups: 12.0% in WHtR \u0026ge; 0.5 vs 2.8% in WHtR \u0026lt; 0.5, with rates ranging from 9.6% in normal-weight subjects to 17.2% in Class II obesity among those with WHtR \u0026ge; 0.5. WHtR is therefore emerging as an important risk stratification tool. Thirdly, linear regression showed that baseline WHtR was the largest modifiable BMI predictor. The finding that WHtR \u0026ge; 0.5 was associated with a\u0026nbsp;2.6 kg/m\u0026sup2; higher follow-up BMI, the largest effect among all modifiable predictors, strengthens the argument that central adiposity drives future adiposity progression. Finally, this is further supported by the performance of our prediction models and the AUC equivalence between the Arab and WHO models.\u0026nbsp;The finding that Arab-specific and WHO BMI models achieved\u0026nbsp;identical AUC (0.818)\u0026nbsp;when WHtR was included is a critical argument and directly challenges the added value of Arab-specific BMI cutoffs or current WHO BMI criteria.\u003c/p\u003e\n\u003cp\u003eThe independent association between higher eGFR percentile and incident DM, first reported in this cohort by AlKetbi et al. [18], is further corroborated in the current analysis, where it remained significant after full adjustment for WHtR, BMI classification, glycaemic markers, blood pressure, and lipid profile (Model D: HR = 1.695 at the highest significant level; 95% CI, 1.158\u0026ndash;2.481; p = 0.007). The persistence of this association after inclusion of WHtR \u0026ge; 0.5 is particularly noteworthy, as it suggests that renal hyperfiltration constitutes an independent pathway to diabetes risk that is not explained by central adiposity alone. As discussed in our prior publication, this likely reflects the haemodynamic consequences of early insulin resistance and hyperinsulinaemia, increased glomerular capillary pressure and sodium reabsorption, that precede overt hyperglycaemia by years [19]. The current finding that this effect persists after adjustment for the full cardiometabolic risk profile, including the central adiposity marker absent from the original model, strengthens the case for incorporating age- and sex-specific eGFR percentile monitoring into pre-diabetes risk stratification protocols in this population.\u003c/p\u003e\n\u003cp\u003eSex differences in central adiposity distribution and its metabolic consequences were evident. Among normal-weight subjects by the standard WHO classification, males had a significantly higher prevalence of WHtR \u0026ge; 0.5 than females (50.3% vs 34.4%, p \u0026lt; 0.0001; full cohort, N = 8,652), confirming that central fat accumulation at lower total body mass is more prevalent in males in this population. This is clinically important in the unadjusted analysis: among subjects in the leanest Arab BMI stratum (\u0026lt; 22.1 kg/m\u0026sup2;) with WHtR \u0026ge; 0.5, male DM incidence was nearly double that of females (13.1% vs 5.3%), despite similar WHtR prevalence in that stratum (15.1% vs 12.5%), suggesting that male sex amplifies the metabolic consequence of central adiposity at low total body mass. However, in the fully adjusted Cox models, male sex was associated with lower DM risk (HR = 0.788\u0026ndash;0.825), meaning that after accounting for WHtR, blood pressure, HDL, and smoking, risk factors disproportionately concentrated in males, female sex was associated with a higher independent DM hazard. This reflects the pattern whereby females accumulate proportionally more subcutaneous fat, which is metabolically less active, yet remain at elevated risk of DM through hormonal and reproductive pathways not fully captured by anthropometric measures alone [20, 21]. Taken together, these findings suggest that a single WHtR threshold of 0.5 may differ by sex, and that sex-stratified screening thresholds merit further investigation, particularly for identifying lean males with central adiposity who carry a high unadjusted DM risk despite appearing low-risk by BMI. The finding that established diabetics had lower follow-up BMI likely reflects treatment or disease-related weight loss, which may also explain the associations with glycemic markers and HDL .\u003c/p\u003e\n\u003cp\u003eAlthough bariatric surgery in Abu Dhabi, as in other countries, is free for those who meet international selection criteria, only around 5,000 bariatric procedures are performed annually in the UAE [22]. Unfortunately, there are no prescription data available for the new pharmacological interventions, but these drugs, free for UAE nationals, seem to enjoy great popularity. Despite these interventions, the burden of obesity is increasing, as the 2023 figures are significantly higher than the cohort baseline. These trends [23] strengthen the case for additional preventive strategies calibrated to Arab-specific risk strata to avoid disadvantaging people at risk due to improper cut-off points, as shown in this study. WHtR \u0026ge; 0.5, a single inexpensive measurement requiring only a tape measure, may be preferable to BMI-based obesity classification as a frontline screening tool for diabetes risk stratification in Arab populations, offering independent predictive value that persists even after full adjustment for glycaemic markers, blood pressure, lipid profile, renal function, and smoking status. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChallenging the obesity definition in this population is not new; Caleyachetty et al. identified a lower BMI cutoff of 26.6 kg/m\u0026sup2; for Arab populations as risk-equivalent to the White BMI \u0026ge;30 threshold for type 2 diabetes [14]. This is in line with a, systematic review and meta-analysis of 55 studies involving over 677,000 participants that found optimal BMI thresholds for cardiometabolic risk in Arab and Middle Eastern populations to be ranging from 26.22 to 27.45 kg/m\u0026sup2; [24]. Another study from Qatar biobank reported that the optimal body mass index (BMI) cut points are 25.2\u0026thinsp;kg/m\u0026sup2; for males and 24.8\u0026thinsp;kg/m\u0026sup2; for females. Similarly, WC cut-points were 84.3\u0026thinsp;cm for males and 74.5\u0026thinsp;cm for females, also lower than global standards [25]. This is supported by other regional studies [26].\u003c/p\u003e\n\u003cp\u003eSettling the definition of obesity in the Arab population and in other ethnicities is a priority and needs to be included into international guidelines. Research investigating the ethnicity factor found that Arab individuals were more at risk of obesity and overweight. [15]. A population-level study from Kuwait [27]and from young Emirati men in the UAE [28], found that over 60% of men aged 18\u0026ndash;29 already had at least one cardiometabolic risk factor, supporting the case for lower intervention thresholds. \u0026nbsp;Research on the multifactorial nature of obesity is a priority. Metabolic, genetic, behavioral, environmental, and sociodemographic factors interact in complex ways [29], and accurate classification is necessary. This study identifies modifiable risk factors such as smoking and high blood pressure, but the environmental, genetic, and behavioral factors mentioned are also essential. A current study is now being conducted to investigate the American Heart Association AHA\u0026apos;s eight essentials in this population. Understanding these factors in the context of the UAE population will enable the design of targeted, personalized preventive and management strategies for obesity.\u003c/p\u003e\n\u003cp\u003eThis study supports recent suggestions for alternative anthropometric thresholds, such as based on waist circumference (WC) and waist-to-hip ratio (WHR), to improve obesity and CVD risk classification in Arabs. [30] [25]. The use of BMI to assess weight status and identify potential health risks associated with excess body weight poorly distinguishes between muscle and fat mass. The use of WC and WHR reflects body fat content and distribution, as well as the presence of abdominal obesity, which is associated with increased metabolic risk factors and health complications.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWaist-to-height ratio (WHtR), has been proposed as a more ethnicity-neutral alternative, with a universal cutoff of 0.5 validated across 78 studies in 14 countries, including Caucasian, Asian, and Central American subjects. [31]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, the study identified risk factors of obesity from a 9.4-year follow-up, and higher BMI at baseline plays a significant role. Additionally, being female is a risk factor for becoming obese, which can be attributed partly to multiple pregnancies, hormonal differences, and a sedentary lifestyle. A notable finding is the association of higher BMI with lower RBS and HbA1c and higher HDL. This is counterintuitive, as higher rates of metabolic disorders are found amongst overweight and obese individuals compared to normal individuals\u003csup\u003e\u0026nbsp;[20]\u003c/sup\u003e \u003csup\u003e[21]\u003c/sup\u003e. Perhaps it reflects greater effort at lifestyle change due to obesity, with possible access to health counseling following identification of obesity. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite its large sample size and a 9.4-year follow-up, our results should be interpreted cautiously as we did not account for lifestyle (diet, exercise, and medication use) or genetic predispositions. These may affect conclusions as the usage of certain medications can alter the body mass index, and thus may be potential confounders.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study provides important insights into the high burden of overweight and obesity, as well as indicating diabetes risk at lower BMI in the Emirate of Abu Dhabi population, calling for preventive strategies and community awareness programs encouraging lifestyle modifications.\u0026nbsp;\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Alain Human Ethics Committee, approval number 13/58, and Ambulatory Healthcare Services IRB 19-2022. All methods were carried out under relevant guidelines and regulations. The authors confirm that the study was conducted in accordance with the Helsinki \u003cstrong\u003eDeclaration.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent statement in the Ethics approval and consent to participate:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was waived by the IRBs as the study was designed for retrospective data gathered as part of patient care and anonymized at analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eNone.\u003cbr\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNone.\u003cbr\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eLBK and NN conceptualized and analyzed data. LBK wrote the manuscript; all other co-authors collected data and reviewed the manuscript. All authors have read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish:\u0026nbsp;\u003c/strong\u003eNot Applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe author elects not to share data.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBhaskaran K, Douglas I, Forbes H, dos-Santos-Silva I, Leon DA, Smeeth L. Body-mass index and risk of 22 specific cancers: a population-based cohort study of 5\u0026middot;24 million UK adults. Lancet. 2014;384:755-765.\u003c/li\u003e\n\u003cli\u003eYao TC, Tsai HJ, Chang SW, Chung RH, Hsu JY, Tsai MH et al. Obesity disproportionately impacts lung volumes, airflow and exhaled nitric oxide in children. PLoS One. 2017;12:e0174691.\u003c/li\u003e\n\u003cli\u003eOrganisaiton WH. Obesity and overweight. 2025. Available from: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight\u003c/li\u003e\n\u003cli\u003eRoberto CA, Swinburn B, Hawkes C, Huang TT, Costa SA, Ashe M et al. Patchy progress on obesity prevention: emerging examples, entrenched barriers, and new thinking. Lancet. 2015;385:2400-2409.\u003c/li\u003e\n\u003cli\u003eRassy N, Van Straaten A, Carette C, Hamer M, Rives-Lange C, Czernichow S. Association of Healthy Lifestyle Factors and Obesity-Related Diseases in Adults in the UK. JAMA Netw Open. 2023;6:e2314741.\u003c/li\u003e\n\u003cli\u003eHe F, Rodriguez-Colon S, Fernandez-Mendoza J, Vgontzas AN, Bixler EO, Berg A et al. Abdominal obesity and metabolic syndrome burden in adolescents--Penn State Children Cohort study. J Clin Densitom. 2015;18:30-36.\u003c/li\u003e\n\u003cli\u003eSumińska M, Podg\u0026oacute;rski R, Bogusz-G\u0026oacute;rna K, Skowrońska B, Mazur A, Fichna M. Historical and cultural aspects of obesity: From a symbol of wealth and prosperity to the epidemic of the 21st century. Obes Rev. 2022;23:e13440.\u003c/li\u003e\n\u003cli\u003eAmine EK, Samy M. Obesity among female university students in the United Arab Emirates. J R Soc Health. 1996;116:91-96.\u003c/li\u003e\n\u003cli\u003eMamdouh H, Hussain HY, Ibrahim GM, Alawadi F, Hassanein M, Zarooni AA et al. 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:e062053.\u003c/li\u003e\n\u003cli\u003eHajat C, Harrison O, Al Siksek Z. Weqaya: a population-wide cardiovascular screening program in Abu Dhabi, United Arab Emirates. Am J Public Health. 2012;102:909-914.\u003c/li\u003e\n\u003cli\u003eObesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. 2000;894:i-xii, 1.\u003c/li\u003e\n\u003cli\u003eAssociation AM. AMA adopts new policy clarifying role of BMI as a measure in medicine. 2023. Available from: https://www.ama-assn.org/press-center/ama-press-releases/ama-adopts-new-policy-clarifying-role-bmi-measure-medicine#:~:text=Jun%2014%2C%202023\u0026amp;text=The%20report%20also%20outlined\n%20the,genders%2C%20and%20a\nge%2Dspan.\u003c/li\u003e\n\u003cli\u003eAlKetbi LB, Nagelkerke N, AlAlawi N, Humaid A, AlKetbi R, Aleissaee H et al. Disease Risk Score Derivation and Validation in Abu Dhabi, United Arab Emirates: A Retrospective Cohort Study. J Am Heart Assoc. 2024;13:e035930.\u003c/li\u003e\n\u003cli\u003eCaleyachetty R, Barber TM, Mohammed NI, Cappuccio FP, Hardy R, Mathur R et al. Ethnicity-specific BMI cutoffs for obesity based on type 2 diabetes risk in England: a population-based cohort study. Lancet Diabetes Endocrinol. 2021;9:419-426.\u003c/li\u003e\n\u003cli\u003eAbdelgadir E, Rashid F, Bashier A, Zidan M, McGowan B, Alawadi F. Prevalence of overweight and obesity in adults from the Middle East: A large‐scale population‐based study. Diabetes, Obesity and Metabolism. 2025;27:3676-3685.\u003c/li\u003e\n\u003cli\u003eAl Hajeri OM, Al Hammadi O. Obesity in the UAE. Healthcare in the United Arab Emirates. Springer; 2025. p. 277-291.\u003c/li\u003e\n\u003cli\u003eObservatory GO. Obesity Prevalence, 2017-2018. 2026. Available from: https://data.worldobesity.org/country/united-arab-emirates-225/#data_prevalence\u003c/li\u003e\n\u003cli\u003eBaynouna AlKetbi LM, AlKetbi R, AlShamsi MS, Nagelkerke N, Afandi B, AlDobaee M et al. Incidence and predictors of type 2 diabetes mellitus in a population-based cohort study in Abu Dhabi. Sci Rep. 2025;15:23639.\u003c/li\u003e\n\u003cli\u003eTonneijck L, Muskiet MH, Smits MM, van Bommel EJ, Heerspink HJ, van Raalte DH et al. Glomerular Hyperfiltration in Diabetes: Mechanisms, Clinical Significance, and Treatment. J Am Soc Nephrol. 2017;28:1023-1039.\u003c/li\u003e\n\u003cli\u003eGeer EB, Shen W. Gender differences in insulin resistance, body composition, and energy balance. Gend Med. 2009;6 Suppl 1:60-75.\u003c/li\u003e\n\u003cli\u003eHamman RF, Wing RR, Edelstein SL, Lachin JM, Bray GA, Delahanty L et al. Effect of weight loss with lifestyle intervention on risk of diabetes. Diabetes Care. 2006;29:2102-2107.\u003c/li\u003e\n\u003cli\u003eowenhaskins. The status of obesity and bariatric surgery in the United Arab Emirates. 2022. Available from: https://www.bariatricnews.net/post/the-status-of-obesity-and-bariatric-surgery-in-the-united-arab-emirates\u003c/li\u003e\n\u003cli\u003eMalekpour MR, Abbasi-Kangevari M, Ghamari SH, Khanali J, Heidari-Foroozan M, Moghaddam SS et al. The burden of metabolic risk factors in North Africa and the Middle East, 1990-2019: findings from the Global Burden of Disease Study. EClinicalMedicine. 2023;60:102022.\u003c/li\u003e\n\u003cli\u003eAl Busaidi S, Al-Maqbali JS, Al Nou\u0026rsquo;mani J, Al Harthi T, Al Alawi AM, Al Kharusi A. Optimal BMI cut-offs associated with cardiometabolic risks in Arab and Middle Eastern populations: a systematic review and meta-analysis. Int J Obes (Lond). 2026;50:23-32.\u003c/li\u003e\n\u003cli\u003eAjeen R, Turk-Adawi KI, Ammerman AS, Batsis JA, Ng SW, Adair LS. Deriving sex-specific anthropometric cut-points for obesity and cardiovascular disease risk in Qatari adults. Int J Obes (Lond). 2026;50:397-406.\u003c/li\u003e\n\u003cli\u003eKhader YS, Batieha A, Jaddou H, Batieha Z, El-Khateeb M, Ajlouni K. Anthropometric cutoff values for detecting metabolic abnormalities in Jordanian adults. Diabetes Metab Syndr Obes. 2010;3:395-402.\u003c/li\u003e\n\u003cli\u003eOguoma VM, Coffee NT, Alsharrah S, Abu-Farha M, Al-Refaei FH, Alkandari A et al. Anthropometric cut-points for discriminating diabetes and the metabolic syndrome among Arabs and Asians: the Kuwait Diabetes Epidemiology Program. Br J Nutr. 2022;127:92-102.\u003c/li\u003e\n\u003cli\u003eAlzaabi A, Al-Kaabi J, Al-Maskari F, Farhood AF, Ahmed LA. Prevalence of diabetes and cardio-metabolic risk factors in young men in the United Arab Emirates: A cross-sectional national survey. Endocrinol Diabetes Metab. 2019;2:e00081.\u003c/li\u003e\n\u003cli\u003eVourdoumpa A, Paltoglou G, Charmandari E. The Genetic Basis of Childhood Obesity: A Systematic Review. Nutrients. 2023;15:1416.\u003c/li\u003e\n\u003cli\u003eAl-Lawati JA, Jousilahti P. Body mass index, waist circumference and waist-to-hip ratio cut-off points for categorisation of obesity among Omani Arabs. Public Health Nutr. 2008;11:102-108.\u003c/li\u003e\n\u003cli\u003eBrowning LM, Hsieh SD, Ashwell M. A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0\u0026middot;5 could be a suitable global boundary value. Nutr Res Rev. 2010;23:247-269.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"obesity, prediction, risk, retrospective study","lastPublishedDoi":"10.21203/rs.3.rs-9305155/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9305155/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study validates updated ethnicity-specific anthropometric measurements thresholds for the Arab population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eCommunity-based retrospective cohort study conducted in 2011 and 2013 in Abu Dhabi-UAE. The average follow-up was 9.4 years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMost (37.5 %) participants were obese and 77.3% with Waist-Height ratio (WHtR) ≥ 0.5. risk factors of end of higher follow-up BMI were higher baseline BMI, WHtR ≥ 0. 5, hypertension, and current smoking, were predictors of end of follow-up BMI; while female sex, older age, baseline diabetes, and higher random glucose levels were independently associated with lower. end of follow-up BMI. The overall incidence of diabetes was higher (12. 0%) among those with WHtR \u0026gt; 0.5 versus in those with WHtR \u0026lt; 0.52 (.8%). Cox regression identified WHtR ≥ 0.5 as a significant independent predictor of incident diabetes while BMI categories were not (overall p = 0. 903).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study validated indicates an underestimated metabolic risk in the Abu Dhabi population, which support more effective public health planning in the UAE and across the Middle East.\u003c/p\u003e","manuscriptTitle":"Validation of Ethnicity-Specific anthropometric measurements thresholds for Obesity Based on Diabetes Risk in Abu Dhabi-United Arab Emirates: A Population-Based Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-03 11:25:22","doi":"10.21203/rs.3.rs-9305155/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aec50005-a683-4632-b4a7-7cfb6fe299d1","owner":[],"postedDate":"April 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-03T11:25:22+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-03 11:25:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9305155","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9305155","identity":"rs-9305155","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}