Examining the impact of dietary diversity and sugar-rich food intake on diabetes prevalence: a cross-sectional study in Asir region of Saudi Arabia

Examining the impact of dietary diversity and sugar-rich food intake on diabetes prevalence: a cross-sectional study in Asir region of Saudi Arabia | 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 Examining the impact of dietary diversity and sugar-rich food intake on diabetes prevalence: a cross-sectional study in Asir region of Saudi Arabia Ali Mohieldin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7284929/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background: type 2 diabetes mellitus (T2DM) represents a growing public health challenge in Saudi Arabia, with increasing prevalence linked to lifestyle and dietary shifts. While dietary diversity is often promoted for nutritional adequacy, its relationship with T2DM risk remains unclear, particularly in the Asir region. Objective: this study aimed to examine the associations between dietary diversity, frequency of sugar-rich food intake, and T2DM prevalence among adults in the Asir region of Saudi Arabia. Methods: a cross-sectional study was conducted among 430 adults recruited via online platforms. Dietary diversity and frequency of specific food group consumption including sugary food consumption were assessed using a validated food frequency questionnaire. Bivariate analyses and binary logistic regression were performed to explore associations between dietary habits and self-reported T2DM, adjusting for sociodemographic and lifestyle confounders. Results: T2DM prevalence in the study population was 10.5%. Frequent sugary food consumption was significantly associated with increased odds of diabetes. Participants consuming sugary foods daily had an adjusted odds ratio (OR) of 11.18 (95% CI: 1.18–105.69; p = 0.035), and those consuming more than once per daily had an OR of 14.58 (95% CI: 1.35–157.64; p = 0.027) compared to rare consumers. No significant associations were found between T2DM and dietary diversity, frequency of fruit, vegetables, grain, or fast-food consumption. Middle-aged adults (36–45 years) exhibited borderline increased odds of diabetes (OR = 2.88; p = 0.056). Conclusion: frequent consumption of sugar-rich foods is a strong, independent predictor of T2DM in the Asir region, while overall dietary diversity does not appear to significantly influence diabetes prevalence. These findings highlight the need for targeted public health interventions to reduce sugar intake as a primary strategy for prevention of diabetes in the population. dietary diversity sugar-rich foods diabetes prevalence Figures Figure 1 Introduction Type 2 diabetes mellitus (T2DM) continues to represent a major public health challenge globally, with the Middle East and North Africa (MENA) regions experiencing particularly high prevalence rates. According to the World Health Organization (WHO), more than 537 million adults worldwide had diabetes in 2021 a number projected to rise to 643 million by 2030 ( 1 ). In Saudi Arabia, the national adult diabetes prevalence is estimated at 24%, with around 18% in the general adult population ( 2 ). This rapid increase is linked to urbanization, sedentary lifestyles, and shifts towards unhealthy eating ( 3 ). The global rise in T2DM is closely linked to dietary patterns rich in added sugars. Specifically, sugar-rich foods and beverages such as sugar-sweetened beverages (SSBs), packaged sweets, and desserts drive excess caloric intake and rapid postprandial glucose spikes, contributing to weight gain and insulin resistance, central factors in T2D pathogenesis ( 4 , 5 ) Within the Asir region, recent cross-sectional research reported that approximately 78.7% of surveyed adults were ever-diagnosed with diabetes, while only 42% engaged in regular physical activity highlighting widespread poor lifestyle practices ( 6 ). Another King Khalid University (KKU) published cross-sectional study among diabetic patients in the Asir Diabetes Center found age, gender, education, income, physical activity, and Body Mass Index (BMI) to be significant predictors of diabetes self-care behaviors ( 7 ). Dietary diversity, typically expressed as a Dietary Diversity Score (DDS), captures the variety of food groups consumed. In global cohorts like the Multi Ethnic Study of Atherosclerosis (MESA), higher diet quality but not necessarily broader food variety was inversely associated with incident T2DM ( 8 ). Increased diversity may correlate with both healthier and less healthy food exposures, complicating associations with diabetes risk. In the U.S., a higher dietary inflammatory index (DII) reflecting pro-inflammatory eating patterns was positively associated with T2DM and obesity, with an OR of 1.56 (95% CI 1.16–2.10) comparing highest to lowest thirds of DII scores ( 8 ). Similar findings from National Health and Nutrition Examination Survey (NHANES) showed the impact of BMI, oral health, and income on DII T2DM associations ( 1 ). Gulf-region studies on diet and T2DM are fewer. A 2017 cross-sectional study in Almajmaah, Saudi Arabia, found that diabetic patients consumed less protein, carbohydrates, dairy, fruits, and vegetables and more fats and sweets than recommended ( 9 ). A recent systematic review on Saudi eating habits concluded that poor eating patterns such as high frequency of processed foods and low fruit/vegetable intake were strongly linked to higher T2DM prevalence ( 10 ). WHO recommends that free (added) sugars should contribute to less than 10% of total energy intake, with further benefits at levels < 5%, to reduce risks of obesity and metabolic disease. WHO’s meta‑analysis concluded that high intake of sugar-sweetened beverages significantly increases the risk of obesity and T2D ( 11 ) The CDC emphasizes limiting added sugars and Sugar-Sweetened Beverages (SSBs) to prevent chronic conditions, including diabetes. National data show that over 50% of U.S. children consume SSBs weekly, correlating with obesity trends and early metabolic risk factors ( 12 ) . High-fructose syrups in SSBs create rapid glycemic load, prompting repeated insulin surges, β-cell stress, insulin resistance, and chronic inflammation which is a key driver in the development of T2D, independent of body weight. A landmark cohort found women consuming ≥ 1 SSB/day had an 83% higher risk of T2D compared to infrequent consumers (> 1/month) ( 13 – 16 ) A recent SCIE meta-analysis of ultra-processed foods (including sugar-laden items) showed that each 10% increase in ultra-processed food intake is associated with a 12% higher risk of T2D ( 4 , 14 ). A global simulation revealed that in 2020 SSB consumption accounted for approximately 2.2 million new T2D cases a substantial proportion of the disease burden ( 17 ). The King Khalid University Hospital (KKUH) survey (n = 1,520 T2D patients, Riyadh, 2009) reported poor glycemic control in 60–70% of patients—highlighting prevalent dietary inadequacies, including high sugar consumption ( 4 ) Saudi Ministry of Health (MOH) nutritional guidelines echo global recommendations by advocating reduced free sugar intake, aligned with WHO goals (< 10% of total calories, ideally < 5%) key to curbing obesity and diabetes epidemics ( 18 ). Emerging evidence suggests that shifting towards plant-based, low-processed diets substantially lowers T2D risk; conversely, unhealthful plant-based patterns (high in added sugars) may increase it by ~ 16%, underlining the significance of food quality over simple dietary labels ( 19 ) Rationale: Despite the known influence of excessive sugar, fast food, and fat intake on diabetes, few studies in Asir have quantitatively explored how specific dietary diversity parameters interact with diabetes status, after controlling for socio-demographic and lifestyle confounders (physical activity, smoking, age, gender, education, BMI, income, etc.) and drawing on a validated DDS framework, our study investigates regular dietary diversity associations with diabetes prevalence to address local knowledge gaps and support more culturally relevant dietary guidelines in the Asir region. Methodology Study design: Study design and setting : A descriptive cross-sectional study was conducted to Examining the impact of dietary diversity and sugar-rich food intake on diabetes prevalence: a cross-sectional study in Asir region of Saudi Arabia. The study period extended from December 2024 to June 2025 ( 20 ) the area characterized by a rapidly urbanizing population undergoing significant lifestyle changes. This setting has an increasing burden of non-communicable diseases, particularly T2DM, which underscores its importance for exploring DD and T2DM. Study population The target population included adults aged 18 years and older residing in the Aseer region. Eligibility criteria required participants to be literate, able to provide informed consent, and willing to complete an online questionnaire. Exclusion criteria were individuals with severe chronic conditions affecting dietary intake (e.g., cancer, renal failure), pregnant women, and participants with incomplete survey responses. A total of 340 participants were recruited via social media platforms over a six-week period (( 21 , 22 ). A self-administered online questionnaire was developed in both Arabic and English using google forms. The questionnaire was reviewed by two public health academic experts, approved by The Research Ethics Committee (HAPO-06-B-001) at King Khalid University and pilot-tested to ensure clarity, face validity, and comprehensibility. Minor modifications were made based on pilot feedback prior to final distribution. Study variables: Independent variables DD factors encompassed various dietary habits and patterns, including the variety and frequency of food consumption. These included self-reported adherence to specific dietary patterns, (e.g., vegetarian, mediterranean), sugary foods, total number of daily meals, and weekly frequency of consumption of fruits, vegetables, grains, dairy products, proteins (e.g., meat), fats, and fast food. Confounding variables : Confounders included were physical activity classification (sedentary, moderate, active), smoking status, age, gender, marital status, educational, occupation, geographic residence (urban/rural), income level, and body mass index (BMI) categories (underweight, normal, overweight, obese). Dependent variable : T2DM status was the health outcome which was self-reported as physician-diagnosed T2DM with “Yes” or “No” response options ( 23 ). Dietary diversity was assessed using a food frequency questionnaire aligned with Food and Agriculture Organization (FAO) guidelines, with minor modifications to fit the local context of Aseer. The questionnaire evaluated the variety and frequency of key food groups consumption over the preceding week ( 24 ). Data analysis Data was analyzed using SPSS version 26. Descriptive statistics summarize demographic characteristics, dietary habits, and T2DM prevalence. Associations between T2DM and categorical variables such as dietary diversity, age groups, and T2DM were examined using chi-square tests. Binary logistic regression was employed to identify independent predictors of T2DM, including dietary DD and T2DM, while adjusting for confounders such as age, gender, and BMI. Sample size calculation The initial sample size was calculated using yamane’s formula, yielding 385 participants based on a 95% confidence level, an estimated proportion of 0.5, and a 5% margin of error. To account for potential biases related to self-reporting and online survey methodology, a 10% adjustment was applied, resulting in a final target sample size of 430 participants. Data quality and bias mitigation Seven incomplete responses were excluded via listwise deletion to ensure data quality, and only complete cases were included in the analysis. Sensitivity analysis was not performed due to the minimal extent of missing data. The use of self-reported T2DM and dietary intake data introduces potential recall and reporting biases. To mitigate these, validated questionnaires and standardized data collection procedures were used. Self-reported T2DM has been shown in previous studies to have moderate to high reliability , with sensitivity ranging from approximately 70–85% and specificities above 90% when compared to clinical measurements ( 25 ). Additionally, the questionnaire was pilot-tested, and participant anonymity was assured to reduce social desirability bias. Limitations The generalizability of the study findings is limited by using snowball sampling and the specific geographical focus on the Aseer region. To enhance external validity, future studies should consider broader sampling across diverse regions. Results This cross-sectional study aimed to evaluate the relationship between dietary patterns, specific dietary habits, and sociodemographic factors in relation to diabetes diagnosis among a sample of 430 participants. Out of the 430 individuals surveyed, 45 participants (10.5%) reported being diagnosed with diabetes, while the remaining 385 (89.5%) did not report such a diagnosis. In crosstab analyses, sugary food consumption showed a clear upward trend in diabetes prevalence with increased frequency of sugary food consumption, highlighting a dose-response relationship. Table 1 Frequency distribution and crude odds ratios Sugary food consumption Diabetes No diabetes Total Diabetes % Crude OR Rarely 4 87 91 4.4% 1.00 1–2 times/week 7 83 90 7.8% 1.83 3–5 times/week 8 75 83 9.6% 2.32 Daily 12 60 72 16.7% 4.35 More than daily 15 39 54 27.8% 8.37 Chi-square test: χ² = 21.46, df = 4, p = 0.00026 This analysis confirmed a statistically significant relationship between sugary food consumption and diabetes status (p < 0.001). Dietary Patterns show no statistically significant association between adherence to a specific dietary pattern and diabetes status (χ² = 0.753, p = 0.386). The number of meals consumed per day was not significantly related to diabetes (χ² = 2.196, p = 0.533). Consumption of fruits, vegetables, and grains (χ² = 2.693, p = 0.610), (χ² = 3.683, p = 0.451), (χ² = 3.122, p = 0.538) respectively showed no statistically significant association. Table 2 Logistic regression analysis and significant predictors of diabetes Predictor Category OR 95% CI p-value Sugary food consumption 1–2 times/week 6.35 0.74–54.33 0.091 Sugary food consumption 3–5 times/week 8.38 0.97–72.61 0.054 Sugary food consumption Daily 11.18 1.18–105.69 0.035* Sugary food consumption More than daily 14.58 1.35–157.64 0.027* Fast food consumption 3 times/week 4.30 0.74–24.99 0.105 (*Significant at p < 0.05) Table 3 Logistic regression model performance statistics for predicting diabetes Model Chi-Square p-value Hosmer-Lemeshow Test (p) Nagelkerke R² Accuracy Block 0 (Constant only) — — — — 89.5% Block 1 (Dietary variables only) 45.54 0.132 0.746 0.206 89.8% Block 2 (Full model) 88.91 0.009 0.827 0.382 90.2% The full model including all variables was statistically significant (p = 0.009), had good model fit (Hosmer-Lemeshow p = 0.827), and exhibited high classification accuracy of 90.2%. Table 4 Sociodemographic variables and their association with diabetes Predictor Category OR 95% CI p-value Age 36–45 years 2.88 0.97–8.50 0.056 Smoking Yes 7.59 0.18–322.26 0.289 Table 4 presents the results of logistic regression analysis evaluating the association between selected sociodemographic variables and the likelihood of having diabetes. Age (36–45 years) was associated with an increased odds of diabetes (OR = 2.88, 95% ci: 0.97–8.50), suggesting that individuals in this age group may be at elevated risk compared to younger age groups. Although the result did not reach conventional statistical significance (p = 0.056), it approached the threshold, indicating a potentially meaningful trend that warrants further investigation. Smoking status (Yes) showed a high odds ratio (OR = 7.59), indicating a possible strong association with diabetes. However, the confidence interval was very wide (95% CI: 0.18–322.26), and the p-value was not significant (p = 0.289). This imprecision suggests instability in the estimate, likely due to a small number of smokers in the sample making result unreliable. In summary, key findings of this study showed that frequent sugary food consumption (daily or more than once per day) was the most robust predictor of diabetes. Fast food intake showed an increasing risk trend, though not statistically significant. Middle-aged adults (36–45 years) had elevated but borderline-significant odds. Smoking had a high but imprecise odds ratio due to wide confidence intervals. Other dietary behaviors, such as fruit, vegetable, and grain consumption, as well as meal frequency, were not significantly associated. This analysis highlights high-frequency sugary food consumption as a potent and statistically significant risk factor for diabetes. These findings underscore the importance of public health interventions focused on reducing dietary sugar intake. Additionally, trends observed in fast food intake and middle-aged individuals warrant further research to explore their potential roles in diabetes risk. Discussion This cross-sectional study in the Asir region of Saudi Arabia found that frequent consumption of sugary foods especially daily or more than daily intake was significantly associated with increased odds of self-reported type 2 diabetes mellitus (T2DM). In contrast, the overall dietary diversity, including fruit, vegetables, grain, protein, and dairy intake, did not show statistically significant associations with diabetes status. These findings highlight that food quality and frequency of sugar intake may be more critical to diabetes risk than broader dietary diversity measures. Our findings corroborate a growing body of international evidence that highlights the significant role of sugar-rich diets in increasing the risk of developing type 2 diabetes mellitus (T2DM). For instance, Schulze et al.( 26 ) conducted a large-scale prospective cohort study which revealed that women who consumed one or more sugar-sweetened beverages (SSBs) daily exhibited an 83% higher risk of incident T2DM compared to those who consumed such beverages infrequently. This study underscores the impact of habitual SSB intake on glycemic control and insulin sensitivity, mechanisms known to contribute to diabetes pathogenesis. In a complementary study, Chen et al. ( 14 ) analyzed dietary patterns characterized by high consumption of ultra-processed foods many of which are rich in added sugars—and demonstrated that each 10% increment in the proportion of ultra-processed foods in the diet was linked with a 12% elevated risk of developing T2DM. This association further emphasizes the detrimental metabolic effects of excessive intake of sugar-laden and highly processed food items, which often contribute to chronic inflammation, insulin resistance, and weight gain, all key drivers of T2DM. Our own data aligns closely with these observations, revealing a clear dose-response relationship between sugar intake frequency and diabetes risk. Specifically, we observed that the odds of having diabetes increased progressively, with odds ratios ranging from 1.83 for moderate sugar consumption to as high as 14.58 for the highest frequency of sugar intake. This pronounced gradient supports the hypothesis that incremental increases in dietary sugar, particularly from sugary beverages and processed foods, substantially amplify the risk of developing T2DM. Such findings reinforce the importance of public health strategies aimed at reducing sugar consumption to mitigate the global diabetes burden. Furthermore, recent global estimates underscore the substantial public health impact of sugar-sweetened beverage (SSB) consumption on diabetes incidence. Lara-castor et al. ( 27 ) projected that in the year 2020 alone, SSB intake was responsible for approximately 2.2 million new cases of type 2 diabetes mellitus (t2dm) worldwide. This staggering figure reflects the scale at which excessive dietary sugar intake contributes to the global diabetes epidemic and reinforces the urgent need for effective policy interventions targeting sugar reduction. While dietary diversity is traditionally associated with improved micronutrient adequacy and overall nutritional status, its relationship with t2dm risk is nuanced and context dependent. Otto et al. ( 28 ), in their prospective cohort analysis, reported that higher dietary diversity did not necessarily correlate with reduced risk of central obesity or t2dm, particularly when the diversity included energy-dense, nutrient-poor food items such as sweets, refined grains, and processed snacks. This distinction underscores the critical role of food quality rather than mere variety. Our findings are consistent with this perspective, as we observed that greater diversity within healthy food categories including fruits, vegetables, and whole grains did not exhibit a statistically significant protective association against T2DM prevalence. This suggests that the inclusion of high-quality foods alone may be insufficient to mitigate diabetes risk if accompanied by frequent consumption of unhealthy, sugar-rich items. These findings align with current recommendations from the world health organization (who), which emphasizes the importance of dietary quality over quantity. Specifically, the who advises limiting free sugar intake to less than 10% of total daily energy intake, with additional benefits potentially achieved by reducing it to below 5% ( 29 ). Regionally, the Saudi ministry of health (MOH) has adopted a similar stance through its healthy food strategy 2021–2025, which promotes substantial reductions in sugar consumption across the population ( 30 ). The burden of poor dietary practices is further exemplified by institutional data from King Khalid University hospital (KKUH), where recent clinical audits revealed that 60–70% of diabetic patients experienced suboptimal glycemic control. These outcomes were primarily linked to unhealthy dietary behaviors, notably high intake of sugary foods and beverages ( 4 ). Complementing this, a systematic review by Almutairi et al. ( 31 ) highlighted that prevalent dietary patterns in Saudi Arabia marked by frequent consumption of sweets, fats, and limited intake of vegetables substantially contribute to the escalating prevalence of t2dm within the country. Collectively, these findings reinforce the need for targeted nutritional education and public health strategies focused not merely on increasing food variety but on enhancing dietary quality especially by reducing added sugar intake to effectively curb the growing diabetes burden. This study has some limitations that should be considered when interpreting the findings. Cross-sectional design inherently limits the ability to draw causal inferences, while the reliance on self-reported data for both diabetes diagnosis and dietary intake may introduce recall and reporting biases. Additionally, the use of snowball sampling restricts the generalizability of the results to the broader population. Some variables, such as smoking, yielded wide confidence intervals, likely due to small subgroup sizes. Despite these limitations, the study provides valuable region-specific insights into the dietary determinants of diabetes. Future research should prioritize longitudinal designs incorporating clinical biomarkers, such as HbA1c, to enhance causal inference. Qualitative assessments of dietary diversity, with a focus on nutrient density and food quality, are also recommended. Expanding survey coverage to include broader regional representation will improve external validity, while stratified analyses by gender, age group, and urban-rural residence could help inform more targeted public health interventions. The study concludes that frequent consumption of sugary foods is a strong, statistically significant predictor of diabetes in the adult population of the Aseer region. Public health strategies should prioritize sugar reduction in everyday diets, aligning with national and international dietary guidelines. This finding reinforces the notion that targeted dietary modifications particularly limiting sugar intake can significantly curb the diabetes epidemic in Saudi Arabia. Declarations Institutional review board statement The Research Ethics Committee (HAPO-06-B-001) at King Khalid University, Abha, Kingdom of Saudi Arabia, verified the study’s validated and ethical integrity and, after carefully reviewing the proposal, provided Institutional Ethical approval No (ECM#2024–3190), dated 30 December 2024. The questionnaire was subtitled by a clarification statement that informs the participants about the study objectives and the voluntary nature of participation in the study. The participants were also informed that any collected data will be treated with strict confidentiality, and their completion and submission of the online questionnaire is equivalent to their informed agreement to participate in the study. Conflicts of interest: The authors declare no conflicts of interest. Funding: The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Small Research Project under grant number RGP1/265/46 Author Contribution This is a single author Manuscript Data availability statement: The data are available from the corresponding author upon reasonable request due to privacy and ethical restrictions. References Mo T, Wei M, Fu J. Dietary inflammatory index and type 2 diabetes in US women: a cross-sectional analysis of the National Health and Nutrition Examination Survey, 2007–2018. Front Nutr. 2024;11:1455521. Dera AA. 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J Clin Epidemiol. 2004;57(10):1096–103. Schulze MB, Sugar-Sweetened Beverages W, Gain, and Incidence of Type 2 Diabetes in Young and Middle-Aged Women. JAMA [Internet]. 2004 Aug 25 [cited 2025 Jun 10];292(8):927. Available from: http://jama.jamanetwork.com/article.aspx?doi=10.1001/jama.292.8.927 Lara-Castor L, O’Hearn M, Cudhea F, Miller V, Shi P, Zhang J et al. Burdens of type 2 diabetes and cardiovascular disease attributable to sugar-sweetened beverages in 184 countries. Nat Med [Internet]. 2025 Feb [cited 2025 Jun 10];31(2):552–64. Available from: https://www.nature.com/articles/s41591-024-03345-4 De Oliveira Otto MC, Padhye NS, Bertoni AG, Jacobs DR, Mozaffarian D. Everything in Moderation - Dietary Diversity and Quality, Central Obesity and Risk of Diabetes. Portero-Otin M, editor. PLoS ONE [Internet]. 2015 Oct 30 [cited 2025 Jun 10];10(10):e0141341. Available from: https://dx.plos.org/10.1371/journal.pone.0141341 World Health Organization. Guideline: sugars intake for adults and children [Internet]. Geneva: World Health Organization. 2015 [cited 2025 Jun 10]. 49 p. Available from: https://iris.who.int/handle/10665/149782 Bin Sunaid FF, Al-Jawaldeh A, Almutairi MW, Alobaid RA, Alfuraih TM, Bensaidan FN et al. Saudi Arabia’s Healthy Food Strategy: Progress & Hurdles in the 2030 Road. Nutrients [Internet]. 2021 Jun 22 [cited 2025 Jun 10];13(7):2130. Available from: https://www.mdpi.com/ 2072-6643/13/7/2130. Almutairi OO, Alhomaid TA, Alshuaibi AM, Ahmad Alahmad RM, Al Mardhamah NH, Alamri T. The Influence of Eating Habits on Type 2 Diabetes in Saudi Arabia: A Systematic Review. Cureus [Internet]. 2023 Jul 29 [cited 2025 Jun 10]; Available from: https://www.cureus.com/articles/172994-the-influence-of-eating-habits-on-type-2-diabetes-in-saudi-arabia-a-systematic-review Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 21 Oct, 2025 Editor assigned by journal 06 Aug, 2025 Submission checks completed at journal 06 Aug, 2025 First submitted to journal 03 Aug, 2025 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-7284929","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":496542305,"identity":"bdb0c92e-9731-4b5b-8f1b-d8ad040c507b","order_by":0,"name":"Ali Mohieldin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYDACdiBObGBgMGBmYHzYcAAklEBACzNCC7Mh8VoYQVoYGNgkidLC38yd+ODhjnvy5uzcaZUzzhxm4GfPMWD8UYNbi8Rh3s0GiWeKDXc28267ueHGYQbJnjcGzDzH8FhzmHebRGJbAuMGIOPmgw+HGQxu5AD9xYZbhzxUiz1ISyFIi/0NkMP+4dZiANWSCNLCCHKYgUSOAQNvG24thhC/JCQDtWyWnHEmnUfizLOCw7x9uLXIHe/d+PDnjgTbDefPbvzYc8xajr89eePDH9/weB8d8ICIAyRoGAWjYBSMglGABQAA7ARZxQ1M0NsAAAAASUVORK5CYII=","orcid":"","institution":"King Khalid University","correspondingAuthor":true,"prefix":"","firstName":"Ali","middleName":"","lastName":"Mohieldin","suffix":""}],"badges":[],"createdAt":"2025-08-03 18:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7284929/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7284929/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88894269,"identity":"d455f60e-7120-4600-bb32-59e9ff2281e0","added_by":"auto","created_at":"2025-08-12 13:02:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":43586,"visible":true,"origin":"","legend":"\u003cp\u003eA bar and line chart show an increase in diabetes prevalence with more frequent sugary food consumption and rise in crude odds ratios, indicating progressively higher risk associated with increasing sugary food intake frequency\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7284929/v1/2e341b356b9c28b7a04c922d.png"},{"id":88894340,"identity":"5eaf5ea7-88ee-4bd9-8cd8-0f2c63602fb7","added_by":"auto","created_at":"2025-08-12 13:02:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":709147,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7284929/v1/bc852378-e34a-428e-8239-4d704737a998.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Examining the impact of dietary diversity and sugar-rich food intake on diabetes prevalence: a cross-sectional study in Asir region of Saudi Arabia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eType 2 diabetes mellitus (T2DM) continues to represent a major public health challenge globally, with the Middle East and North Africa (MENA) regions experiencing particularly high prevalence rates. According to the World Health Organization (WHO), more than 537\u0026nbsp;million adults worldwide had diabetes in 2021 a number projected to rise to 643\u0026nbsp;million by 2030 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In Saudi Arabia, the national adult diabetes prevalence is estimated at 24%, with around 18% in the general adult population (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). This rapid increase is linked to urbanization, sedentary lifestyles, and shifts towards unhealthy eating (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe global rise in T2DM is closely linked to dietary patterns rich in added sugars. Specifically, sugar-rich foods and beverages such as sugar-sweetened beverages (SSBs), packaged sweets, and desserts drive excess caloric intake and rapid postprandial glucose spikes, contributing to weight gain and insulin resistance, central factors in T2D pathogenesis (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eWithin the Asir region, recent cross-sectional research reported that approximately 78.7% of surveyed adults were ever-diagnosed with diabetes, while only 42% engaged in regular physical activity highlighting widespread poor lifestyle practices (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Another King Khalid University (KKU) published cross-sectional study among diabetic patients in the Asir Diabetes Center found age, gender, education, income, physical activity, and Body Mass Index (BMI) to be significant predictors of diabetes self-care behaviors (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDietary diversity, typically expressed as a Dietary Diversity Score (DDS), captures the variety of food groups consumed. In global cohorts like the Multi Ethnic Study of Atherosclerosis (MESA), higher diet quality but not necessarily broader food variety was inversely associated with incident T2DM (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Increased diversity may correlate with both healthier and less healthy food exposures, complicating associations with diabetes risk.\u003c/p\u003e\u003cp\u003eIn the U.S., a higher dietary inflammatory index (DII) reflecting pro-inflammatory eating patterns was positively associated with T2DM and obesity, with an OR of 1.56 (95% CI 1.16–2.10) comparing highest to lowest thirds of DII scores (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Similar findings from National Health and Nutrition Examination Survey (NHANES) showed the impact of BMI, oral health, and income on DII T2DM associations (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGulf-region studies on diet and T2DM are fewer. A 2017 cross-sectional study in Almajmaah, Saudi Arabia, found that diabetic patients consumed less protein, carbohydrates, dairy, fruits, and vegetables and more fats and sweets than recommended (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). A recent systematic review on Saudi eating habits concluded that poor eating patterns such as high frequency of processed foods and low fruit/vegetable intake were strongly linked to higher T2DM prevalence (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWHO recommends that free (added) sugars should contribute to less than 10% of total energy intake, with further benefits at levels \u0026lt; 5%, to reduce risks of obesity and metabolic disease. WHO’s meta‑analysis concluded that high intake of sugar-sweetened beverages significantly increases the risk of obesity and T2D (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThe CDC emphasizes limiting added sugars and Sugar-Sweetened Beverages (SSBs) to prevent chronic conditions, including diabetes. National data show that over 50% of U.S. children consume SSBs weekly, correlating with obesity trends and early metabolic risk factors (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) .\u003c/p\u003e\u003cp\u003eHigh-fructose syrups in SSBs create rapid glycemic load, prompting repeated insulin surges, β-cell stress, insulin resistance, and chronic inflammation which is a key driver in the development of T2D, independent of body weight. A landmark cohort found women consuming ≥ 1 SSB/day had an 83% higher risk of T2D compared to infrequent consumers (\u0026gt; 1/month) (\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e–\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eA recent SCIE meta-analysis of ultra-processed foods (including sugar-laden items) showed that each 10% increase in ultra-processed food intake is associated with a 12% higher risk of T2D (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA global simulation revealed that in 2020 SSB consumption accounted for approximately 2.2\u0026nbsp;million new T2D cases a substantial proportion of the disease burden (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe King Khalid University Hospital (KKUH) survey (n = 1,520 T2D patients, Riyadh, 2009) reported poor glycemic control in 60–70% of patients—highlighting prevalent dietary inadequacies, including high sugar consumption (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eSaudi Ministry of Health (MOH) nutritional guidelines echo global recommendations by advocating reduced free sugar intake, aligned with WHO goals (\u0026lt; 10% of total calories, ideally \u0026lt; 5%) key to curbing obesity and diabetes epidemics (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEmerging evidence suggests that shifting towards plant-based, low-processed diets substantially lowers T2D risk; conversely, unhealthful plant-based patterns (high in added sugars) may increase it by ~ 16%, underlining the significance of food quality over simple dietary labels (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eRationale: Despite the known influence of excessive sugar, fast food, and fat intake on diabetes, few studies in Asir have quantitatively explored how specific dietary diversity parameters interact with diabetes status, after controlling for socio-demographic and lifestyle confounders (physical activity, smoking, age, gender, education, BMI, income, etc.) and drawing on a validated DDS framework, our study investigates regular dietary diversity associations with diabetes prevalence to address local knowledge gaps and support more culturally relevant dietary guidelines in the Asir region.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cb\u003eStudy design: Study design and setting\u003c/b\u003e: A descriptive cross-sectional study was conducted to Examining the impact of dietary diversity and sugar-rich food intake on diabetes prevalence: a cross-sectional study in Asir region of Saudi Arabia. The study period extended from December 2024 to June 2025 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) the area characterized by a rapidly urbanizing population undergoing significant lifestyle changes. This setting has an increasing burden of non-communicable diseases, particularly T2DM, which underscores its importance for exploring DD and T2DM.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe target population included adults aged 18 years and older residing in the Aseer region. Eligibility criteria required participants to be literate, able to provide informed consent, and willing to complete an online questionnaire. Exclusion criteria were individuals with severe chronic conditions affecting dietary intake (e.g., cancer, renal failure), pregnant women, and participants with incomplete survey responses.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA total of 340 participants were recruited via social media platforms over a six-week period ((\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). A self-administered online questionnaire was developed in both Arabic and English using google forms. The questionnaire was reviewed by two public health academic experts, approved by The Research Ethics Committee (HAPO-06-B-001) at King Khalid University and pilot-tested to ensure clarity, face validity, and comprehensibility. Minor modifications were made based on pilot feedback prior to final distribution.\u003c/p\u003e\u003cp\u003eStudy variables: \u003cb\u003eIndependent variables\u003c/b\u003e DD factors encompassed various dietary habits and patterns, including the variety and frequency of food consumption. These included self-reported adherence to specific dietary patterns, (e.g., vegetarian, mediterranean), sugary foods, total number of daily meals, and weekly frequency of consumption of fruits, vegetables, grains, dairy products, proteins (e.g., meat), fats, and fast food. \u003cb\u003eConfounding variables\u003c/b\u003e: Confounders included were physical activity classification (sedentary, moderate, active), smoking status, age, gender, marital status, educational, occupation, geographic residence (urban/rural), income level, and body mass index (BMI) categories (underweight, normal, overweight, obese). \u003cb\u003eDependent variable\u003c/b\u003e: T2DM status was the health outcome which was self-reported as physician-diagnosed T2DM with “Yes” or “No” response options (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Dietary diversity was assessed using a food frequency questionnaire aligned with Food and Agriculture Organization (FAO) guidelines, with minor modifications to fit the local context of Aseer. The questionnaire evaluated the variety and frequency of key food groups consumption over the preceding week (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eData was analyzed using SPSS version 26. Descriptive statistics summarize demographic characteristics, dietary habits, and T2DM prevalence. Associations between T2DM and categorical variables such as dietary diversity, age groups, and T2DM were examined using chi-square tests. Binary logistic regression was employed to identify independent predictors of T2DM, including dietary DD and T2DM, while adjusting for confounders such as age, gender, and BMI.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSample size calculation\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe initial sample size was calculated using yamane’s formula, yielding 385 participants based on a 95% confidence level, an estimated proportion of 0.5, and a 5% margin of error. To account for potential biases related to self-reporting and online survey methodology, a 10% adjustment was applied, resulting in a final target sample size of 430 participants.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData quality and bias mitigation\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eSeven incomplete responses were excluded via listwise deletion to ensure data quality, and only complete cases were included in the analysis. Sensitivity analysis was not performed due to the minimal extent of missing data. The use of self-reported T2DM and dietary intake data introduces potential recall and reporting biases. To mitigate these, validated questionnaires and standardized data collection procedures were used. Self-reported T2DM has been shown in previous studies to have moderate to high \u003cb\u003ereliability\u003c/b\u003e, with \u003cb\u003esensitivity\u003c/b\u003e ranging from approximately 70–85% and specificities above 90% when compared to clinical measurements (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Additionally, the questionnaire was pilot-tested, and participant anonymity was assured to reduce social desirability bias.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe generalizability of the study findings is limited by using snowball sampling and the specific geographical focus on the Aseer region. To enhance external validity, future studies should consider broader sampling across diverse regions.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis cross-sectional study aimed to evaluate the relationship between dietary patterns, specific dietary habits, and sociodemographic factors in relation to diabetes diagnosis among a sample of 430 participants. Out of the 430 individuals surveyed, 45 participants (10.5%) reported being diagnosed with diabetes, while the remaining 385 (89.5%) did not report such a diagnosis.\u003c/p\u003e\u003cp\u003eIn crosstab analyses, sugary food consumption showed a clear upward trend in diabetes prevalence with increased frequency of sugary food consumption, highlighting a dose-response relationship.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFrequency distribution and crude odds ratios\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSugary food consumption\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo diabetes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDiabetes %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCrude OR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRarely\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;2 times/week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u0026ndash;5 times/week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaily\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e16.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMore than daily\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e27.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eChi-square test: χ\u0026sup2; = 21.46, df\u0026thinsp;=\u0026thinsp;4, p\u0026thinsp;=\u0026thinsp;0.00026\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThis analysis confirmed a statistically significant relationship between sugary food consumption and diabetes status (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eDietary Patterns show no statistically significant association between adherence to a specific dietary pattern and diabetes status (χ\u0026sup2; = 0.753, p\u0026thinsp;=\u0026thinsp;0.386). The number of meals consumed per day was not significantly related to diabetes (χ\u0026sup2; = 2.196, p\u0026thinsp;=\u0026thinsp;0.533). Consumption of fruits, vegetables, and grains (χ\u0026sup2; = 2.693, p\u0026thinsp;=\u0026thinsp;0.610), (χ\u0026sup2; = 3.683, p\u0026thinsp;=\u0026thinsp;0.451), (χ\u0026sup2; = 3.122, p\u0026thinsp;=\u0026thinsp;0.538) respectively showed no statistically significant association.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic regression analysis and significant predictors of diabetes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSugary food consumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;2 times/week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.74\u0026ndash;54.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSugary food consumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u0026ndash;5 times/week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.97\u0026ndash;72.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSugary food consumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDaily\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.18\u0026ndash;105.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.035*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSugary food consumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMore than daily\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.35\u0026ndash;157.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.027*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFast food consumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 times/week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.74\u0026ndash;24.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.105\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e(*Significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic regression model performance statistics for predicting diabetes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChi-Square\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHosmer-Lemeshow Test (p)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNagelkerke R\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlock 0 (Constant only)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e89.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlock 1 (Dietary variables only)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.746\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e89.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlock 2 (Full model)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.827\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.382\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e90.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eThe full model including all variables was statistically significant (p\u0026thinsp;=\u0026thinsp;0.009), had good model fit (Hosmer-Lemeshow p\u0026thinsp;=\u0026thinsp;0.827), and exhibited high classification accuracy of 90.2%.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSociodemographic variables and their association with diabetes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36\u0026ndash;45 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.97\u0026ndash;8.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.18\u0026ndash;322.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.289\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the results of logistic regression analysis evaluating the association between selected sociodemographic variables and the likelihood of having diabetes. Age (36\u0026ndash;45 years) was associated with an increased odds of diabetes (OR\u0026thinsp;=\u0026thinsp;2.88, 95% ci: 0.97\u0026ndash;8.50), suggesting that individuals in this age group may be at elevated risk compared to younger age groups. Although the result did not reach conventional statistical significance (p\u0026thinsp;=\u0026thinsp;0.056), it approached the threshold, indicating a potentially meaningful trend that warrants further investigation.\u003c/p\u003e\u003cp\u003eSmoking status (Yes) showed a high odds ratio (OR\u0026thinsp;=\u0026thinsp;7.59), indicating a possible strong association with diabetes. However, the confidence interval was very wide (95% CI: 0.18\u0026ndash;322.26), and the p-value was not significant (p\u0026thinsp;=\u0026thinsp;0.289). This imprecision suggests instability in the estimate, likely due to a small number of smokers in the sample making result unreliable.\u003c/p\u003e\u003cp\u003eIn summary, key findings of this study showed that frequent sugary food consumption (daily or more than once per day) was the most robust predictor of diabetes. Fast food intake showed an increasing risk trend, though not statistically significant. Middle-aged adults (36\u0026ndash;45 years) had elevated but borderline-significant odds. Smoking had a high but imprecise odds ratio due to wide confidence intervals. Other dietary behaviors, such as fruit, vegetable, and grain consumption, as well as meal frequency, were not significantly associated.\u003c/p\u003e\u003cp\u003eThis analysis highlights high-frequency sugary food consumption as a potent and statistically significant risk factor for diabetes. These findings underscore the importance of public health interventions focused on reducing dietary sugar intake. Additionally, trends observed in fast food intake and middle-aged individuals warrant further research to explore their potential roles in diabetes risk.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis cross-sectional study in the Asir region of Saudi Arabia found that frequent consumption of sugary foods especially daily or more than daily intake was significantly associated with increased odds of self-reported type 2 diabetes mellitus (T2DM). In contrast, the overall dietary diversity, including fruit, vegetables, grain, protein, and dairy intake, did not show statistically significant associations with diabetes status. These findings highlight that food quality and frequency of sugar intake may be more critical to diabetes risk than broader dietary diversity measures.\u003c/p\u003e\u003cp\u003eOur findings corroborate a growing body of international evidence that highlights the significant role of sugar-rich diets in increasing the risk of developing type 2 diabetes mellitus (T2DM). For instance, Schulze et al.(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) conducted a large-scale prospective cohort study which revealed that women who consumed one or more sugar-sweetened beverages (SSBs) daily exhibited an 83% higher risk of incident T2DM compared to those who consumed such beverages infrequently. This study underscores the impact of habitual SSB intake on glycemic control and insulin sensitivity, mechanisms known to contribute to diabetes pathogenesis.\u003c/p\u003e\u003cp\u003eIn a complementary study, Chen et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) analyzed dietary patterns characterized by high consumption of ultra-processed foods many of which are rich in added sugars\u0026mdash;and demonstrated that each 10% increment in the proportion of ultra-processed foods in the diet was linked with a 12% elevated risk of developing T2DM. This association further emphasizes the detrimental metabolic effects of excessive intake of sugar-laden and highly processed food items, which often contribute to chronic inflammation, insulin resistance, and weight gain, all key drivers of T2DM.\u003c/p\u003e\u003cp\u003eOur own data aligns closely with these observations, revealing a clear dose-response relationship between sugar intake frequency and diabetes risk. Specifically, we observed that the odds of having diabetes increased progressively, with odds ratios ranging from 1.83 for moderate sugar consumption to as high as 14.58 for the highest frequency of sugar intake. This pronounced gradient supports the hypothesis that incremental increases in dietary sugar, particularly from sugary beverages and processed foods, substantially amplify the risk of developing T2DM. Such findings reinforce the importance of public health strategies aimed at reducing sugar consumption to mitigate the global diabetes burden.\u003c/p\u003e\u003cp\u003eFurthermore, recent global estimates underscore the substantial public health impact of sugar-sweetened beverage (SSB) consumption on diabetes incidence. Lara-castor et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) projected that in the year 2020 alone, SSB intake was responsible for approximately 2.2\u0026nbsp;million new cases of type 2 diabetes mellitus (t2dm) worldwide. This staggering figure reflects the scale at which excessive dietary sugar intake contributes to the global diabetes epidemic and reinforces the urgent need for effective policy interventions targeting sugar reduction.\u003c/p\u003e\u003cp\u003eWhile dietary diversity is traditionally associated with improved micronutrient adequacy and overall nutritional status, its relationship with t2dm risk is nuanced and context dependent. Otto et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), in their prospective cohort analysis, reported that higher dietary diversity did not necessarily correlate with reduced risk of central obesity or t2dm, particularly when the diversity included energy-dense, nutrient-poor food items such as sweets, refined grains, and processed snacks. This distinction underscores the critical role of food quality rather than mere variety.\u003c/p\u003e\u003cp\u003eOur findings are consistent with this perspective, as we observed that greater diversity within healthy food categories including fruits, vegetables, and whole grains did not exhibit a statistically significant protective association against T2DM prevalence. This suggests that the inclusion of high-quality foods alone may be insufficient to mitigate diabetes risk if accompanied by frequent consumption of unhealthy, sugar-rich items.\u003c/p\u003e\u003cp\u003eThese findings align with current recommendations from the world health organization (who), which emphasizes the importance of dietary quality over quantity. Specifically, the who advises limiting free sugar intake to less than 10% of total daily energy intake, with additional benefits potentially achieved by reducing it to below 5% (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Regionally, the Saudi ministry of health (MOH) has adopted a similar stance through its healthy food strategy 2021\u0026ndash;2025, which promotes substantial reductions in sugar consumption across the population (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe burden of poor dietary practices is further exemplified by institutional data from King Khalid University hospital (KKUH), where recent clinical audits revealed that 60\u0026ndash;70% of diabetic patients experienced suboptimal glycemic control. These outcomes were primarily linked to unhealthy dietary behaviors, notably high intake of sugary foods and beverages (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Complementing this, a systematic review by Almutairi et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) highlighted that prevalent dietary patterns in Saudi Arabia marked by frequent consumption of sweets, fats, and limited intake of vegetables substantially contribute to the escalating prevalence of t2dm within the country.\u003c/p\u003e\u003cp\u003eCollectively, these findings reinforce the need for targeted nutritional education and public health strategies focused not merely on increasing food variety but on enhancing dietary quality especially by reducing added sugar intake to effectively curb the growing diabetes burden.\u003c/p\u003e\u003cp\u003eThis study has some limitations that should be considered when interpreting the findings. Cross-sectional design inherently limits the ability to draw causal inferences, while the reliance on self-reported data for both diabetes diagnosis and dietary intake may introduce recall and reporting biases. Additionally, the use of snowball sampling restricts the generalizability of the results to the broader population. Some variables, such as smoking, yielded wide confidence intervals, likely due to small subgroup sizes. Despite these limitations, the study provides valuable region-specific insights into the dietary determinants of diabetes. Future research should prioritize longitudinal designs incorporating clinical biomarkers, such as HbA1c, to enhance causal inference. Qualitative assessments of dietary diversity, with a focus on nutrient density and food quality, are also recommended. Expanding survey coverage to include broader regional representation will improve external validity, while stratified analyses by gender, age group, and urban-rural residence could help inform more targeted public health interventions.\u003c/p\u003e\u003cp\u003eThe study concludes that frequent consumption of sugary foods is a strong, statistically significant predictor of diabetes in the adult population of the Aseer region. Public health strategies should prioritize sugar reduction in everyday diets, aligning with national and international dietary guidelines. This finding reinforces the notion that targeted dietary modifications particularly limiting sugar intake can significantly curb the diabetes epidemic in Saudi Arabia.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eInstitutional review board statement\u003c/h2\u003e\u003cp\u003e The Research Ethics Committee (HAPO-06-B-001) at King Khalid University, Abha, Kingdom of Saudi Arabia, verified the study\u0026rsquo;s validated and ethical integrity and, after carefully reviewing the proposal, provided Institutional Ethical approval No (ECM#2024\u0026ndash;3190), dated 30 December 2024. The questionnaire was subtitled by a clarification statement that informs the participants about the study objectives and the voluntary nature of participation in the study. The participants were also informed that any collected data will be treated with strict confidentiality, and their completion and submission of the online questionnaire is equivalent to their informed agreement to participate in the study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u003c/strong\u003e\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThe authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Small Research Project under grant number RGP1/265/46\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThis is a single author Manuscript\u003c/p\u003e\u003ch2\u003eData availability statement:\u003c/h2\u003e\u003cp\u003eThe data are available from the corresponding author upon reasonable request due to privacy and ethical restrictions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMo T, Wei M, Fu J. 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Cureus [Internet]. 2023 Jul 29 [cited 2025 Jun 10]; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cureus.com/articles/172994-the-influence-of-eating-habits-on-type-2-diabetes-in-saudi-arabia-a-systematic-review\u003c/span\u003e\u003cspan address=\"https://www.cureus.com/articles/172994-the-influence-of-eating-habits-on-type-2-diabetes-in-saudi-arabia-a-systematic-review\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"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":"journal-of-health-population-and-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"johp","sideBox":"Learn more about [Journal of Health, Population and Nutrition](http://jhpn.biomedcentral.com/)","snPcode":"41043","submissionUrl":"https://submission.nature.com/new-submission/41043/3","title":"Journal of Health, Population and Nutrition","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"dietary diversity, sugar-rich foods, diabetes prevalence","lastPublishedDoi":"10.21203/rs.3.rs-7284929/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7284929/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003etype 2 diabetes mellitus (T2DM) represents a growing public health challenge in Saudi Arabia, with increasing prevalence linked to lifestyle and dietary shifts. While dietary diversity is often promoted for nutritional adequacy, its relationship with T2DM risk remains unclear, particularly in the Asir region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003ethis study aimed to examine the associations between dietary diversity, frequency of sugar-rich food intake, and T2DM prevalence among adults in the Asir region of Saudi Arabia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003ea cross-sectional study was conducted among 430 adults recruited via online platforms. Dietary diversity and frequency of specific food group consumption including sugary food consumption were assessed using a validated food frequency questionnaire. Bivariate analyses and binary logistic regression were performed to explore associations between dietary habits and self-reported T2DM, adjusting for sociodemographic and lifestyle confounders.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eT2DM prevalence in the study population was 10.5%. Frequent sugary food consumption was significantly associated with increased odds of diabetes. Participants consuming sugary foods daily had an adjusted odds ratio (OR) of 11.18 (95% CI: 1.18–105.69; p = 0.035), and those consuming more than once per daily had an OR of 14.58 (95% CI: 1.35–157.64; p = 0.027) compared to rare consumers. No significant associations were found between T2DM and dietary diversity, frequency of fruit, vegetables, grain, or fast-food consumption. Middle-aged adults (36–45 years) exhibited borderline increased odds of diabetes (OR = 2.88; p = 0.056).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003efrequent consumption of sugar-rich foods is a strong, independent predictor of T2DM in the Asir region, while overall dietary diversity does not appear to significantly influence diabetes prevalence. These findings highlight the need for targeted public health interventions to reduce sugar intake as a primary strategy for prevention of diabetes in the population.\u003c/p\u003e","manuscriptTitle":"Examining the impact of dietary diversity and sugar-rich food intake on diabetes prevalence: a cross-sectional study in Asir region of Saudi Arabia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 12:53:44","doi":"10.21203/rs.3.rs-7284929/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-10-22T00:23:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-06T10:38:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-06T10:38:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Health, Population and Nutrition","date":"2025-08-03T17:58:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-health-population-and-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"johp","sideBox":"Learn more about [Journal of Health, Population and Nutrition](http://jhpn.biomedcentral.com/)","snPcode":"41043","submissionUrl":"https://submission.nature.com/new-submission/41043/3","title":"Journal of Health, Population and Nutrition","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"389b3105-daaa-4c8e-9191-dca0868f6e00","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-22T00:38:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-12 12:53:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7284929","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7284929","identity":"rs-7284929","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}