Population Attributable Fraction of abnormal body mass index for diabetes mellitus in Yazd during a 10-Year Longitudinal Cohort Study: Yazd Healthy Heart cohort (YHHC) in Yazd, Iran

Population Attributable Fraction of abnormal body mass index for diabetes mellitus in Yazd during a 10-Year Longitudinal Cohort Study: Yazd Healthy Heart cohort (YHHC) in Yazd, Iran | 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 Article Population Attributable Fraction of abnormal body mass index for diabetes mellitus in Yazd during a 10-Year Longitudinal Cohort Study: Yazd Healthy Heart cohort (YHHC) in Yazd, Iran Seyedeh Mahdieh Namayandeh, 2. Maryam Askari, Masoud Mohammadi, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4461723/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 Type 2 diabetes is one of the public health problems. And its prevalence is growing. Obesity and overweight are preventable risk factors for diabetes. The objective of this study was to calculate the Population Attributable Fraction (PAF) of abnormal body mass index (BMI) for diabetes mellitus (DM). In the present study, 1641 non-diabetic Participants were followed up for ten years (2006 - 2016). The prevalence of the outcome in the reference group and the relative risk of abnormal BMI were calculated, and then the PAF formula was used. Data were analyzed using SPSS version 16 software (Chicago, SPSS Inc.). The prevalence of DM in people with normal BMI was 18.7%. The crud RR, adjusted RR by age, and adjusted RR by age and sex were calculated as 1.66 (1.25-2.14), 1.74 (1.29-2.26), and 1.84 (1.36-2.37), respectively. Suppose we can reduce the prevalence of obesity to the ideal value (zero) without adjustment, age adjustment, or sex adjustment. In that case, the incidence of diabetes will be reduced by 27%, 29%, and 32%, respectively. Diabetes attributed to obesity, about 30% decreases; If we can reduce the prevalence of obesity to the ideal value (zero). Health sciences/Diseases Health sciences/Diseases/Metabolic disorders Population Attributable Fraction Diabetes Mellitus Body Mass Index Highlights The prevalence of diabetes is high in obese and overweight people. Diabetes attributed to obesity by about 30% decreases If we can reduce the prevalence of obesity to the ideal value (zero). 1. Introduction Noncommunicable diseases (NCDs) are responsible for the death of 41 million people each year, equivalent to 71% of all deaths globally. The most common causes of death are cardiovascular diseases (17.9 million), cancer (9 million), respiratory diseases (3.9 million), and diabetes (DM) (1.6 million) in the world. Diabetes is associated with a high rate of early death in some countries (1, 2). Diabetes Mellitus (T2DM) is ranked third or fourth in disability-adjusted life years; And therefore, it's essential from the aspect of public health (3, 4). T2DM is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both (5). Part of the increasing prevalence of diabetes is factors such as rapid industrialization and urbanization and changes in lifestyle factors (6, 7). The most important risk factors for type 2 diabetes are Race, family history of diabetes, previous gestational diabetes with increasing age, overweight and obesity, high-calorie intake, unhealthy diet, physical inactivity, and smoking. Overweight and obesity, along with physical inactivity, form a large part of the global burden of diabetes. (8, 9). The prevalence of type 2 diabetes has increased over the past decade. And often associated with obesity (10). According to the International Diabetes Federation (IDF), the number of people with diabetes, from 382 million people worldwide in 2013, will rise to 592 million in 2035. Eighty percent of people with diabetes live in low-income and middle-income countries, and more than 60 percent live in Asia (11). In 2011, impaired fasting glucose and total DM prevalence rates were 14.60% and 11.37% among 25–70 years, respectively (12). There were 4,985.5 cases of diabetes in Iran in 2017and the prevalence of diabetes in adults was 8.9 % (13). In 2012, the prevalence of type 2 diabetes mellitus in Yazd City was 16.3%, which is twice more than the overall prevalence of diabetes in Iranian adults. One percent of the urban population over the age of 20 years old is suffering from type 2 diabetes .( 14 ) Risk factors for diabetes, including Race, family history of diabetes, previous gestational diabetes with increasing age, overweight and obesity, unhealthy diet, physical inability, and smoking increase the risk of developing diabetes. Overweight, obesity, and physical inactivity are a big part of the enormous global burden of diabetes (9) Obesity increases the incidence of various diseases such as cardiovascular disease, diabetes, certain cancers, hypertension, and lipid disorders and is associated with their risk factors (15). Because the prevalence of diabetes is increasing in Iran, And Since the health system, politicians need to be aware of the impact of a risk factor on the burden of disease. The results of this study can be used as a basis for preventing diabetes interventions. The definition of Population Attributable Fraction (PAF) is that disease or mortality is reduced if exposure to the risk factor is ideal (16). The objective of this study was to calculate PAF of abnormal body mass index (BMI) for diabetes mellitus in the 20-74-year-old Population of Yazd during a 10-year Longitudinal Cohort 2. Materials and methods 2.1. Study population : We included data from the research project of the healthy heart cohort in Yazd. In brief, the baseline investigation was conducted in 2006. A second to fifth follow-up examination was conducted in 2007, 2008, 2009, and 2010, respectively. The eighth follow-up was conducted in 2013. The study ended in 2016. At the study's baseline, 2000 people aged 20-74 were selected through Multi-Stage Cluster random sampling. Inclusion criteria include living in Yazd for at least one year before the start of the study. Exclusion criteria include Participants with a history of diabetes or those Diagnosed with Diabetes. Finally, the study population comprised 1641 individuals, 48 % women. I confirm that all methods were performed in accordance with the relevant guidelines and regulations to this effect. 2.2. Definition of incident T2DM and prevalence abnormal BMI : The definition of diabetes was based on the American Diabetes Association; a history of taking anti-diabetes drugs or fasting plasma glucose (FPG) levels ≥ 126 mg/dl were the criteria for diabetic patients (17). WHO classified BMI for adults as overweight as having a BMI greater than or equal to 25 and obesity as having a BMI greater than or equal to 30. In our study, BMI was classified into two groups: normal and abnormal BMI (18). Abnormal BMI was a BMI greater than or equal to 25. 2.3. Ethical approval : This study was approved by the Shahid Sadoughi University of Medical Sciences ethics committee (code of ethics: IR.SSU.SPH.REC.1396.37). All participants provided written informed consent before participating in the study. 2.4. Statistical analysis method Mean and standard deviation (M±SD), frequency, and percentage were used to describe the data using SPSS 16 statistical software. Attributable Risk (AR) should be interpreted as a proper etiologic fraction. Attributable Risk In Exposed Individuals (AR exp ) indicates the number of disease cases among exposed individuals attributed to that exposure. AR exp shows the difference between the risk estimates of different exposure levels and a reference exposure level (19) . And the PAF formula is as follows: prevalence of abnormal BMI × (20). Binary logistic regression was used to calculate Odds Ratio. And then, the formula (RR=OR/(1−P ref )+(P ref *OR)) was used to reverse OR to RR. (RR = risk ratio; OR = odds ratio and P ref = Prevalence of the outcome in the reference group) According to the PAF definition, the risk of diabetes mellitus associated with obesity suggests that lifestyle changes to prevent overweight and obesity can substantially reduce the incidence of diabetes in the community. 3. Results The M ± SD age of the participant at the baseline of this study was 54.5 ± 1.55. 88.3 percent of the subjects were married. The mean ± SD BMI and FPG were also 26.24 ± 7.50 and 87.94 ± 16.77, respectively (Table 1). 3.2% of Non-DM and abnormal BMI people have taken diabetes after ten years. In comparison, 10.8% of Non-DM and abnormal BMI people developed diabetes. At the end of 10 years, the prevalence of abnormal BMI in all subjects was estimated at 70.4%. The incidence of DM was calculated to be 29.2%. The prevalence of DM in people with normal and abnormal BMI was 18.7% and 31.2%, respectively. The prevalence of abnormal BMI in non-diabetics was 67.8%. The crud OR, adjusted OR by age, and adjusted OR by age and sex were calculated as 1.97, 2.11, and 2.28, respectively (Table 2). According to the following formula, the OR converts to RR. (RR=OR/(1−P ref )+(P ref *OR)) OR crud = 1.97 OR * adjusted by age = 2.11 OR * adjusted by age and sex = 2.28 P ref = Prevalence of outcome in the non-exposed group = 18.7% According to the above numbers, the RR was calculated in Table 3. The crud RR, adjusted RR by age, and adjusted RR by age and sex were calculated as 1.66, 1.74, and 1.84 respectively (Table 3). To calculate PAF: prevalence of abnormal BMI × RR crud = 1.66 RR * adjusted by age = 1.74 RR * adjusted by age and sex = 1.84 PAF 1 = 70.4% × 1.66-1/1.66=0.27* PAF 2 = 70.4% × 1.74-1/1.74= 0.29** PAF 3 = 70.4% × 1.84 -1/1.84= 0.32*** * If we can reduce the prevalence of obesity to the ideal value (zero), the incidence of diabetes will be reduced by 27. ** If we can reduce the prevalence of obesity to the ideal value (zero), with the adjustment of age, the incidence of diabetes will be reduced by 29%. *** If we can reduce the prevalence of obesity to the ideal value (zero), with the adjustment of age and sex, the incidence of diabetes will be reduced by 32%. 4. Discussion & conclusion Obesity and overweight are prevalent conditions in the risk factors of diabetes. The term ‘attributable’ is related to causation. The results of our study showed that the prevalence of DM in people with normal BMI was 18.7%. The results of our study showed that abnormal BMI (obesity and overweight) increases the risk of diabetes by 66%. Even after the age effect was removed, the risk of diabetes reached 74%. And after removing the effects of age and sex, this risk of diabetes increased to 84%. The results of our study showed that suppose we can reduce the prevalence of obesity to the ideal value (zero), Without adjustment, with the adjustment of age, with the adjustment of age and sex. In that case, the incidence of diabetes will be reduced by 27%, 29%, and 32%, respectively. In the study of Wang et al., the population-attributable risk of obesity-related to diabetes in men and women was calculated at 10.1% and 16.8%, respectively (21). In a study by Anjana et al., abdominal obesity contributed the most to the incidence of diabetes; this study stated that abdominal obesity increased the risk of diabetes by 63 % (22). A study by Xue et al. estimated that 2.4 million diabetes events were attributable to abdominal obesity in 2010, more attributable to abdominal obesity in men (23). Diabetes prevalence risk in South Asian women with a BMI of 22.0 kg/m2, black women with a BMI of 26.0 kg/m2, and Chinese women with a BMI of 24.0 kg/m2 was equivalent to white women with a BMI of 30 kg/m2 (24). Obesity and diabetes represent a concern worldwide. Weight gain is central to the growth and rising incidence of DM. Overweight and obesity create some insulin resistance. Insulin has a significant role in the metabolism of glucose, and a lack of Insulin leads to Increased blood glucose (25). In the ethnicity-specific public health obesity guidance has been suggested that obesity interventions are required to Decrease diabetes in black and South-Asian populations (26). The strengths of the present study include its large sample of non-DM participants. BMI was objectively assessed using validated measures, trained staff, and standard measuring devices. Diabetes was determined by laboratory/clinical diagnosis. The present study was a cohort. Therefore, it is possible to determine causality in the direction of the relationship between abnormal BMI and DM from the data. Therefore, there was no potential contribution of reverse causality in the data—this study population base and then broadly generalizable to the Yazd population. We also adjusted our models for confounders. One of the limitations of our study was that we did not specify the difference between Type 1 and Type 2 diabetes. The present results suggest that Obesity and overweight control programs should be considered a Policy for policymakers. Identifying and eliminating modifiable risk factors in high-risk groups, such as diabetes, is essential in keeping the increasing prevalence. In conclusion, the present study showed that diabetes might decrease in the future with the increase of educational programs for weight loss in the community. Declarations Conflict of interest There are no conflicts of interest. Funding The funding of this study was provided by the Shahid Sadoughi University of Medical Sciences Author Contribution A. Maryam Askari B. Seyedeh Mahdieh NamayandehC. Masoud MohammadiD. Leila HadianiE. Mohammadhosein SoltaniF. Mohammadtaghi SarebanhassanabadiG. Mojtaba mohammadhoseiniH. Fatemeh majidpourI. Ahmad KarimiA wrote the main manuscript text.B has analyzed the data.C has participated in the study design.D, E, F have participated in the specialized consultation.G, H, I have participated in data extraction.All authors reviewed the manuscript. Acknowledgment The authors thank the participants in this study, the staff of Yazd Cardiovascular Research Center and Afshar Hospital, Yazd. Data Availability Researchers can access the data at the request of the corresponding author. References WHO. Noncommunicable diseases 2015. 2018 [updated 2018]. Available from: http://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases Wild S, Roglic G, Green A, Sicree R, King H. Global prevalence of diabetes: estimates for the year 200 and projections for 2030. Diabetes care. 2004;27(5):1047-53 Hewitt J, Smeeth L, Bulpitt C, Fletcher A. The prevalence of Type 2 diabetes and its associated health problems in a community-dwelling elderly population. Diabetic medicine. 2009;26(4):370 -6. Q R. Diabetes mellitus: a global health problem. . Rev Port Cardiol. 2010;29(4):539-43 MR B. A review on the latest criteria for laboratory diagnosis of diabetes mellitus, pre-diabetes and gestational diabetes. . Laboratory & Diagnosis. 2014;6 (24):4-8. Chan JC, Malik V, Jia W, Kadowaki T, Yajnik CS, Yoon K-H, et al. Diabetes in Asia: epidemiology, risk factors, and pathophysiology. Jama. 2009;301(20):2129-40 Ramachandran A, Ma RCW, Snehalatha C. Diabetes in Asia. The Lancet. 2010;375(971 ):408-18. Kumar H. Relation between Type 2 Diabetes and obesity: A review. International Journal of Biomedical Research. 2013;4(8):367-72 Forouzanfar Mh Fau - Alexander L, Alexander L Fau - Anderson HR, Anderson Hr Fau - Bachman VF, Bachman Vf Fau - Biryukov S, Biryukov S Fau - Brauer M, Brauer M Fau - Burnett R, et al. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990-2013 : a systematic analysis for the Global Burden of Disease Study 2013. Lancet. p. 2287-323. doi: 10.1016/S0140-6736(15)00128-2. Epub 2015 Sep 11 Menke A, Casagrande S, Geiss L, Cowie CC. Prevalence of and trends in diabetes among adults in the United States, 1988-2012. Jama. 2015;314(10):1021-9 Guariguata L, Whiting DR, Hambleton I, Beagley J, Linnenkamp U, Shaw JE. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes research and clinical practice. 2014;103(2):137-49 Esteghamati A, Etemad K, Koohpayehzadeh J, Abbasi M, Meysamie A, Noshad S, et al. Trends in the prevalence of diabetes and impaired fasting glucose in association with obesity in Iran: 2005–2011. Diabetes research and clinical practice. 2014;103(2):3 -27. International Diabetes Federation. IDF MENA Members. 2017. Available from: https://www.idf.org/our-network/regions-members/middle-east-and-north-africa/members/35-iran.html Harati H, Hadaegh F, Saadat N, Azizi F. Population-based incidence of Type 2 diabetes and its associated risk factors: results from a six-year cohort study in Iran. BMC public health. 2009;9(1):186 Flegal KM, Panagiotou OA, Graubard BI. Estimating population attributable fractions to quantify the health burden of obesity. Annals of epidemiology. 2015;25(3):201-7 Askari M, Namayandeh SM. The Difference between the Population Attributable Risk (PAR) and the Potentioal Impact Fraction (PIF). Iranian Journal of Public Health. 2020;49(10):2018-9 AD A. Diagnosing Diabetes and Learning About Prediabetes 2015 [updated June 1, 2015; cited 2018 march 30, 2018 ]. Organization WH. Obesity and overweight. Fact sheet No. 311; 2011. Google Scholar. 2015 Szklo M, Nieto FJ, Miller D. Epidemiology: beyond the basics. Oxford Univ Press; 2001. p. 84-9 Benichou J. A review of adjusted estimators of attributable risk. Statistical methods in medical research. 2001;10(3):195-216 Wang C, Li J, Xue H, Li Y, Huang J, Mai J, et al. Type 2 diabetes mellitus incidence in Chinese: contributions of overweight and obesity. Diabetes research and clinical practice. 2015;107(3):424-32 Anjana RM, Sudha V, Nair DH, Lakshmipriya N, Deepa M, Pradeepa R, et al. Diabetes in Asian Indians—how much is preventable? Ten-year follow-up of the Chennai Urban Rural Epidemiology Study (CURES-142). Diabetes research and clinical practice. 2015;109(2):253-61 Xue H, Wang C, Li Y, Chen J, Yu L, Liu X, et al. Incidence of type 2 diabetes and number of events attributable to abdominal obesity in C hina: A cohort study: . Journal of diabetes. 2016;8(2):190-8 Ntuk UE, Gill JM, Mackay DF, Sattar N, Pell JP. Ethnic-specific obesity cutoffs for diabetes risk: cross-sectional study of 490,288 UK biobank participants. Diabetes care. 20DC_132966 Al-Goblan AS, Al-Alfi MA, Khan MZ. Mechanism linking diabetes mellitus and obesity. Diabetes, metabolic syndrome and obesity: targets and therapy. 2014;7:587 Nice P. Assessing body mass index and waist circumference thresholds for intervening to prevent ill health and premature death among adults from black, Asian and other minority ethnic groups in the UK. NICE July. 2013;46 Tables Table 1 : Demographic characteristics of the participant at the baseline of the study frequency percent Age 20-34 390 23.8 35-49 540 32.9 50-64 418 25.5 65 269 16.4 sex Male 831 50.6 female 787 48 Marital status Married 1449 88.3 single 86 5.2 other 83 5.1 Education uneducated 304 18.5 Under diploma 839 51.1 Diploma 277 16.9 High diploma 152 9.3 BMI Normal 667 40.6 Abnormal 911 55.5 BMI : Body Mass Index Table 2 . The odds ratio of abnormal BMI in the incidence of diabetes Variable OR crud OR * adjusted OR** adjusted Abnormal BMI 1.97(1.33-2.92) 2.11(1.39-3.19) 2.28(1.49-3.47) Binary logistic regression * Adjusted by age ** Adjusted by age and sex Table 3 . The Relative risk of abnormal BMI in the incidence of diabetes Variable RR crud RR * adjusted RR** adjusted Abnormal BMI 1.53(1.1-2.14) 1.74(1.29-2.26) 1.84(1.36-2.37) Calculated using formulas (RR=OR/(1−Pref)+(Pref*OR)) * Adjusted by age ** Adjusted by age and sex Additional Declarations No competing interests reported. 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-4461723","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":310143036,"identity":"0ea8261f-37bb-4bb5-9468-524af7fee50d","order_by":0,"name":"Seyedeh Mahdieh Namayandeh","email":"","orcid":"","institution":"Yazd Cardiovascular Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.","correspondingAuthor":false,"prefix":"","firstName":"Seyedeh","middleName":"Mahdieh","lastName":"Namayandeh","suffix":""},{"id":310143037,"identity":"be8f61d6-630a-4f98-9c60-587cc7ac792a","order_by":1,"name":"2.\tMaryam 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Introduction","content":"\u003cp\u003eNoncommunicable diseases (NCDs) are responsible for the death of 41 million people each year, equivalent to 71% of all deaths globally. The most common causes of death are cardiovascular diseases (17.9 million), cancer (9 million), respiratory diseases (3.9 million), and diabetes (DM) (1.6 million) in the world. Diabetes is associated with a high rate of early death in some countries (1, 2). Diabetes Mellitus (T2DM) is ranked third or fourth in disability-adjusted life years; And therefore, it\u0026apos;s essential from the aspect of public health (3, 4).\u003c/p\u003e\n\u003cp\u003eT2DM\u0026nbsp;is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both (5). Part of the increasing prevalence of diabetes is factors such as rapid industrialization and urbanization and changes in lifestyle factors (6, 7). The most important risk factors for type 2 diabetes are Race, family history of diabetes, previous gestational diabetes with increasing age, overweight and obesity, high-calorie intake, unhealthy diet, physical inactivity, and smoking. Overweight and obesity, along with physical inactivity, form a large part of the global burden of diabetes. (8, 9).\u003c/p\u003e\n\u003cp\u003eThe prevalence of type 2 diabetes has increased over the past decade. And often associated with obesity (10). According to the International Diabetes Federation (IDF), the number of people with diabetes, from 382 million people worldwide in 2013, will rise to 592 million in 2035. Eighty percent of people with diabetes live in low-income and middle-income countries, and more than 60 percent live in Asia \u0026nbsp;(11). In 2011, impaired fasting glucose and total DM prevalence rates were 14.60% \u0026nbsp;and 11.37% among 25\u0026ndash;70 years, respectively (12). There were 4,985.5 cases of diabetes in Iran in 2017and the prevalence of diabetes in adults was 8.9 % (13).\u003c/p\u003e\n\u003cp\u003eIn 2012, the prevalence of type 2 diabetes mellitus in Yazd City was 16.3%, which is twice more than the overall prevalence of diabetes in Iranian adults. One percent of the urban population over the age of 20 years old is suffering from type 2 diabetes\u0026nbsp;\u003cspan dir=\"RTL\"\u003e.(\u003c/span\u003e\u003cspan dir=\"RTL\"\u003e14\u003c/span\u003e\u003cspan dir=\"RTL\"\u003e)\u0026nbsp;\u003c/span\u003e Risk factors for diabetes, including Race, family history of diabetes, previous gestational diabetes with increasing age, overweight and obesity, unhealthy diet, physical inability, and smoking increase the risk of developing diabetes. Overweight, obesity, and physical inactivity are a big part of the enormous global burden of diabetes\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e(9)\u0026nbsp;\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eObesity increases the incidence of various diseases such as cardiovascular disease, diabetes, certain cancers, hypertension, and lipid disorders and is associated with their risk factors (15).\u003c/p\u003e\n\u003cp\u003eBecause the prevalence of diabetes is increasing in Iran, And Since the health system, politicians need to be aware of the impact of a risk factor on the burden of disease. The results of this study can be used as a basis for preventing diabetes interventions. The definition of Population Attributable Fraction (PAF) is that disease or mortality is reduced if exposure to the risk factor is ideal (16).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe objective of this study was to calculate PAF of abnormal body mass index (BMI) for diabetes mellitus in the 20-74-year-old Population of Yazd during a 10-year Longitudinal Cohort \u0026nbsp;\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e2.1.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eStudy population :\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe included data from the research project of the healthy heart cohort in Yazd. In brief, the baseline investigation was conducted in 2006. A second to fifth follow-up examination was conducted in 2007, 2008, 2009, and 2010, respectively. The eighth follow-up was conducted in 2013. The study ended in 2016. At the study\u0026apos;s baseline, 2000 people aged 20-74 were selected through Multi-Stage Cluster random sampling.\u003c/p\u003e\n\u003cp\u003eInclusion criteria include living in Yazd for at least one year before the start of the study. Exclusion criteria include Participants with a history of diabetes or those Diagnosed with Diabetes. Finally, the study population comprised 1641 individuals, 48 % women. I confirm that all methods were performed in accordance with the relevant guidelines and regulations to this effect.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e2.2.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eDefinition of incident T2DM and prevalence abnormal\u003c/span\u003e\u003c/strong\u003e\u003cspan\u003e\u0026nbsp;\u003cstrong\u003eBMI :\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe definition of diabetes was based on the American Diabetes Association; a history of taking anti-diabetes drugs or fasting plasma glucose (FPG) levels \u0026ge; 126 mg/dl were the criteria for diabetic patients (17). WHO classified BMI for adults as overweight as having a BMI greater than or equal to 25 and obesity as having a BMI greater than or equal to 30. In our study, BMI was classified into two groups: normal and abnormal BMI (18). Abnormal BMI was a BMI greater than or equal to 25.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e2.3.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eEthical approval :\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Shahid Sadoughi University of Medical Sciences ethics committee (code of ethics: IR.SSU.SPH.REC.1396.37). All participants provided written informed consent before participating in the study.\u003c/p\u003e\n\u003cp\u003e2.4.\u003cstrong\u003e\u0026nbsp;Statistical analysis method\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMean and standard deviation (M\u0026plusmn;SD), frequency, and percentage were used to describe the data using SPSS 16 statistical software. Attributable Risk (AR) \u0026nbsp;should be interpreted as a proper etiologic fraction. Attributable Risk In Exposed Individuals (AR \u003csub\u003eexp\u003c/sub\u003e) indicates the number of disease cases among exposed individuals attributed to that exposure.\u003c/p\u003e\n\u003cp\u003eAR\u003csub\u003e\u0026nbsp;exp\u003c/sub\u003e shows the difference between the risk estimates of different exposure levels and a reference exposure level\u0026nbsp;\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1717569144.png\"\u003e\u003c/span\u003e \u003cspan dir=\"RTL\"\u003e\u0026nbsp;(19)\u0026nbsp;\u003c/span\u003e. And the PAF formula is as follows: prevalence of abnormal BMI\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u0026times;\u0026nbsp;\u0026nbsp;\u003cimg src=\"data:image/png;base64,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\"\u003e (20). Binary logistic regression was used to calculate Odds Ratio. And then, the formula (RR=OR/(1\u0026minus;P\u003csub\u003eref\u003c/sub\u003e)+(P\u003csub\u003eref\u003c/sub\u003e*OR)) was used to reverse OR to RR. (RR = risk ratio; OR = odds ratio and P\u003csub\u003eref\u003c/sub\u003e = Prevalence of the outcome in the reference group)\u003c/p\u003e\n\u003cp\u003eAccording to the PAF definition, the risk of diabetes mellitus associated with obesity suggests that lifestyle changes to prevent overweight and obesity can substantially reduce the incidence of diabetes in the community.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe M \u0026plusmn; SD age of the participant at the baseline of this study was 54.5 \u0026plusmn; 1.55. 88.3 percent of the subjects were married. The mean \u0026plusmn; SD BMI and FPG were also 26.24 \u0026plusmn; 7.50 and 87.94 \u0026plusmn; 16.77, respectively (Table 1). 3.2% of Non-DM and abnormal BMI people have taken diabetes after ten years. In comparison, 10.8% of Non-DM and abnormal BMI people developed diabetes.\u003c/p\u003e\n\u003cp\u003eAt the end of 10 years, the prevalence of abnormal BMI in all subjects was estimated at 70.4%. The incidence of DM was calculated to be 29.2%. The prevalence of DM in people with normal and abnormal BMI was 18.7% and 31.2%, respectively. The prevalence of abnormal BMI in non-diabetics was 67.8%.\u003c/p\u003e\n\u003cp\u003eThe crud OR, adjusted OR by age, and adjusted OR by age and sex were calculated as 1.97, 2.11, and 2.28, respectively (Table 2).\u003c/p\u003e\n\u003cp\u003eAccording to the following formula, the OR converts to RR. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(RR=OR/(1\u0026minus;P\u003csub\u003eref\u003c/sub\u003e)+(P\u003csub\u003eref\u003c/sub\u003e*OR))\u003c/p\u003e\n\u003cp\u003eOR\u003csub\u003ecrud\u003c/sub\u003e= 1.97\u003c/p\u003e\n\u003cp\u003eOR\u003csup\u003e*\u003c/sup\u003e \u003csub\u003eadjusted by age\u0026nbsp;\u003c/sub\u003e= 2.11\u003c/p\u003e\n\u003cp\u003eOR\u003csup\u003e*\u003c/sup\u003e \u003csub\u003eadjusted by age and sex\u003c/sub\u003e = 2.28\u003c/p\u003e\n\u003cp\u003eP\u003csub\u003eref\u0026nbsp;\u003c/sub\u003e=\u0026nbsp;Prevalence of outcome in the non-exposed group = 18.7%\u003c/p\u003e\n\u003cp\u003eAccording to the above numbers, the RR was calculated in Table 3. The crud RR, adjusted RR by age, and adjusted RR by age and sex were calculated as 1.66, 1.74, and 1.84 respectively (Table 3).\u003c/p\u003e\n\u003cp\u003eTo calculate PAF: prevalence of abnormal BMI\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u0026times;\u0026nbsp;\u0026nbsp;\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eRR\u003csub\u003ecrud\u003c/sub\u003e= 1.66\u003c/p\u003e\n\u003cp\u003eRR\u003csup\u003e*\u003c/sup\u003e \u003csub\u003eadjusted by age\u0026nbsp;\u003c/sub\u003e= 1.74\u003c/p\u003e\n\u003cp\u003eRR\u003csup\u003e*\u003c/sup\u003e \u003csub\u003eadjusted by age and sex\u003c/sub\u003e = 1.84\u003c/p\u003e\n\u003cp\u003ePAF\u003csub\u003e1\u003c/sub\u003e= 70.4%\u0026nbsp;\u0026times; 1.66-1/1.66=0.27*\u003c/p\u003e\n\u003cp\u003ePAF\u003csub\u003e2\u003c/sub\u003e= 70.4%\u0026nbsp;\u0026times; 1.74-1/1.74= 0.29**\u003c/p\u003e\n\u003cp\u003ePAF\u003csub\u003e3\u003c/sub\u003e= 70.4%\u0026nbsp;\u0026times; 1.84 -1/1.84= 0.32***\u003c/p\u003e\n\u003cp\u003e* If we can reduce the prevalence of obesity to the ideal value (zero), the incidence of diabetes will be reduced by 27.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e** If we can reduce the prevalence of obesity to the ideal value (zero), with the adjustment of age, the incidence of diabetes will be reduced by 29%.\u003c/p\u003e\n\u003cp\u003e*** If we can reduce the prevalence of obesity to the ideal value (zero), with the adjustment of age and sex, the incidence of diabetes will be reduced by 32%.\u003c/p\u003e"},{"header":"4.\tDiscussion \u0026 conclusion","content":"\u003cp\u003eObesity and overweight are prevalent conditions in the risk factors of diabetes. The term \u0026lsquo;attributable\u0026rsquo; is related to causation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results of our study showed that the prevalence of DM in people with normal BMI was 18.7%.\u003c/p\u003e\n\u003cp\u003eThe results of our study showed that abnormal BMI (obesity and overweight) increases the risk of diabetes by 66%. Even after the age effect was removed, the risk of diabetes reached 74%. And after removing the effects of age and sex, this risk of diabetes increased to 84%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results of our study showed that suppose we can reduce the prevalence of obesity to the ideal value (zero), Without adjustment, with the adjustment of age, with the adjustment of age and sex. In that case, the incidence of diabetes will be reduced by 27%, 29%, and 32%, respectively.\u003c/p\u003e\n\u003cp\u003eIn the study of Wang et al., the population-attributable risk of obesity-related to diabetes in men and women was calculated at 10.1% and 16.8%, respectively (21).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn a study by Anjana et al., abdominal obesity contributed the most to the incidence of diabetes; this study stated that abdominal obesity increased the risk of diabetes by 63 % (22).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA study by Xue et al. estimated that 2.4 million diabetes events were attributable to abdominal obesity in 2010, more attributable to abdominal obesity in men (23).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDiabetes prevalence risk in South Asian women with a BMI of 22.0 kg/m2, black women with a BMI of 26.0 kg/m2, and Chinese women with a BMI of 24.0 kg/m2 was equivalent to white women with a BMI of 30 kg/m2 (24).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eObesity and diabetes represent a concern worldwide. Weight gain is central to the growth and rising incidence of DM. Overweight and obesity create some insulin resistance. Insulin has a significant role in the metabolism of glucose, and a lack of Insulin leads to Increased blood glucose (25).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the ethnicity-specific public health obesity guidance has been suggested that obesity interventions are required to Decrease diabetes in black and South-Asian populations (26).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe strengths of the present study include its large sample of non-DM participants. BMI was objectively assessed using validated measures, trained staff, and standard measuring devices. Diabetes was determined by laboratory/clinical diagnosis. The present study was a cohort. Therefore, it is possible to determine causality in the direction of the relationship between abnormal BMI and DM from the data. Therefore, there was no potential contribution of reverse causality in the data\u0026mdash;this study population base and then broadly generalizable to the Yazd population. We also adjusted our models for confounders.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOne of the limitations of our study was that we did not specify the difference between Type 1 and Type 2 diabetes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe present results suggest that Obesity and overweight control programs should be considered a Policy for policymakers. Identifying and eliminating modifiable risk factors in high-risk groups, such as diabetes, is essential in keeping the increasing prevalence. In conclusion, the present study showed that diabetes might decrease in the future with the increase of educational programs for weight loss in the community.\u003c/p\u003e"},{"header":"Declarations","content":" \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThere are no conflicts of interest.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe funding of this study was provided by the Shahid Sadoughi University of Medical Sciences\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA. Maryam Askari B. Seyedeh Mahdieh NamayandehC. Masoud MohammadiD. Leila HadianiE. Mohammadhosein SoltaniF. Mohammadtaghi SarebanhassanabadiG. Mojtaba mohammadhoseiniH. Fatemeh majidpourI. Ahmad KarimiA wrote the main manuscript text.B has analyzed the data.C has participated in the study design.D, E, F have participated in the specialized consultation.G, H, I have participated in data extraction.All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgment\u003c/h2\u003e \u003cp\u003e The authors thank the participants in this study, the staff of Yazd Cardiovascular Research Center and Afshar Hospital, Yazd.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eResearchers can access the data at the request of the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWHO. Noncommunicable diseases 2015. 2018 [updated 2018]. Available from: http://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases\u003c/li\u003e\n \u003cli\u003eWild S, Roglic G, Green A, Sicree R, King H. Global prevalence of diabetes: estimates for the year 200\u003cspan\u003e\u0026nbsp;\u003c/span\u003eand projections for 2030. Diabetes care. 2004;27(5):1047-53\u003c/li\u003e\n \u003cli\u003eHewitt J, Smeeth L, Bulpitt C, Fletcher A. The prevalence of Type 2 diabetes and its associated health problems in a community-dwelling elderly population. Diabetic medicine. 2009;26(4):370\u003cspan\u003e-6.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eQ R. Diabetes mellitus: a global health problem. . Rev Port Cardiol. 2010;29(4):539-43\u003c/li\u003e\n \u003cli\u003eMR B. A review on the latest criteria for laboratory diagnosis of diabetes mellitus, pre-diabetes and gestational diabetes. . Laboratory \u0026amp; Diagnosis. 2014;6\u003cspan\u003e(24):4-8.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eChan JC, Malik V, Jia W, Kadowaki T, Yajnik CS, Yoon K-H, et al. Diabetes in Asia: epidemiology, risk factors, and pathophysiology. Jama. 2009;301(20):2129-40\u003c/li\u003e\n \u003cli\u003eRamachandran A, Ma RCW, Snehalatha C. Diabetes in Asia. The Lancet. 2010;375(971\u003cspan\u003e):408-18.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eKumar H. Relation between Type 2 Diabetes and obesity: A review. International Journal of Biomedical Research. 2013;4(8):367-72\u003c/li\u003e\n \u003cli\u003eForouzanfar Mh Fau - Alexander L, Alexander L Fau - Anderson HR, Anderson Hr Fau - Bachman VF, Bachman Vf Fau\u003cspan\u003e\u0026nbsp;-\u0026nbsp;\u003c/span\u003eBiryukov S, Biryukov S Fau - Brauer M, Brauer M Fau - Burnett R, et al. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990-2013\u003cspan\u003e:\u0026nbsp;\u003c/span\u003ea systematic analysis for the Global Burden of Disease Study 2013. Lancet. p. 2287-323. doi: 10.1016/S0140-6736(15)00128-2. Epub 2015 Sep 11\u003c/li\u003e\n \u003cli\u003eMenke A, Casagrande S, Geiss L, Cowie CC. Prevalence of and trends in diabetes among adults in the United States, 1988-2012. Jama. 2015;314(10):1021-9\u003c/li\u003e\n \u003cli\u003eGuariguata L, Whiting DR, Hambleton I, Beagley J, Linnenkamp U, Shaw JE. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes research and clinical practice. 2014;103(2):137-49\u003c/li\u003e\n \u003cli\u003eEsteghamati A, Etemad K, Koohpayehzadeh J, Abbasi M, Meysamie A, Noshad S, et al. Trends in the prevalence of diabetes and impaired fasting glucose in association with obesity in Iran: 2005\u0026ndash;2011. Diabetes research and clinical practice. 2014;103(2):3\u003cspan\u003e-27.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eInternational Diabetes Federation. IDF MENA Members. 2017. Available from: https://www.idf.org/our-network/regions-members/middle-east-and-north-africa/members/35-iran.html\u003c/li\u003e\n \u003cli\u003eHarati H, Hadaegh F, Saadat N, Azizi F. Population-based incidence\u003cspan\u003e\u0026nbsp;\u003c/span\u003eof Type 2 diabetes and its associated risk factors: results from a six-year cohort study in Iran. BMC public health. 2009;9(1):186\u003c/li\u003e\n \u003cli\u003eFlegal KM, Panagiotou OA, Graubard BI. Estimating population attributable fractions to quantify the health burden of obesity. Annals of epidemiology. 2015;25(3):201-7\u003c/li\u003e\n \u003cli\u003eAskari M, Namayandeh SM. The Difference between the Population Attributable Risk (PAR) and the Potentioal Impact Fraction (PIF). Iranian Journal of Public Health. 2020;49(10):2018-9\u003c/li\u003e\n \u003cli\u003eAD A. Diagnosing Diabetes and Learning About Prediabetes 2015 [updated June 1, 2015; cited 2018 march 30, 2018\u003cspan\u003e].\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eOrganization WH. Obesity and overweight. Fact sheet No. 311; 2011. Google Scholar. 2015\u003c/li\u003e\n \u003cli\u003eSzklo M, Nieto FJ, Miller D. Epidemiology: beyond the basics. Oxford Univ Press; 2001. p. 84-9\u003c/li\u003e\n \u003cli\u003eBenichou J. A review of adjusted estimators of attributable risk. Statistical methods in medical research. 2001;10(3):195-216\u003c/li\u003e\n \u003cli\u003eWang C, Li J, Xue H, Li Y, Huang J, Mai J, et al. Type 2 diabetes mellitus incidence in Chinese: contributions of overweight and obesity. Diabetes research and clinical practice. 2015;107(3):424-32\u003c/li\u003e\n \u003cli\u003eAnjana RM, Sudha V, Nair DH, Lakshmipriya N, Deepa M, Pradeepa R, et al. Diabetes in Asian Indians\u0026mdash;how much is preventable? Ten-year follow-up of the Chennai Urban Rural Epidemiology Study (CURES-142). Diabetes research and clinical practice. 2015;109(2):253-61\u003c/li\u003e\n \u003cli\u003eXue H, Wang C, Li Y, Chen J, Yu L, Liu X, et al. Incidence of type 2 diabetes and number of events attributable to abdominal obesity in C hina: A cohort study: . Journal of diabetes. 2016;8(2):190-8\u003c/li\u003e\n \u003cli\u003eNtuk UE, Gill JM, Mackay DF, Sattar N, Pell JP. Ethnic-specific obesity cutoffs for diabetes risk: cross-sectional study of 490,288 UK biobank participants. Diabetes care. 20DC_132966\u003c/li\u003e\n \u003cli\u003eAl-Goblan AS, Al-Alfi MA, Khan MZ. Mechanism linking diabetes mellitus and obesity. Diabetes, metabolic syndrome and obesity: targets and therapy. 2014;7:587\u003c/li\u003e\n \u003cli\u003eNice P. Assessing body mass index and waist circumference thresholds for intervening to prevent ill health and premature death among adults from black, Asian and other minority ethnic groups in the UK. NICE July. 2013;46\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 : Demographic characteristics of the participant at the baseline of the study\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"596\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.644295302013422%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.624161073825505%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.530201342281877%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003efrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.201342281879196%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003epercent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.644295302013422%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.624161073825505%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e20-34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.530201342281877%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.201342281879196%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e23.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.768736616702355%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e35-49\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.687366167023555%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.54389721627409%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e32.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.768736616702355%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e50-64\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.687366167023555%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.54389721627409%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e25.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.768736616702355%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003cstrong\u003e65\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.687366167023555%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.54389721627409%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.644295302013422%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003esex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.624161073825505%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.530201342281877%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.201342281879196%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e50.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.768736616702355%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003efemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.687366167023555%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.54389721627409%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.644295302013422%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.624161073825505%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eMarried\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.530201342281877%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e1449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.201342281879196%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e88.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.768736616702355%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003esingle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.687366167023555%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.54389721627409%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.768736616702355%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eother\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.687366167023555%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.54389721627409%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.644295302013422%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.624161073825505%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003euneducated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.530201342281877%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.201342281879196%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.768736616702355%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eUnder diploma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.687366167023555%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.54389721627409%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e51.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.768736616702355%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eDiploma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.687366167023555%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.54389721627409%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e16.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.768736616702355%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eHigh diploma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.687366167023555%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.54389721627409%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.644295302013422%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.624161073825505%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eNormal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.530201342281877%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.201342281879196%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e40.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.768736616702355%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eAbnormal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.687366167023555%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.54389721627409%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e55.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp dir=\"LTR\"\u003eBMI : Body Mass Index\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003eTable 2 .\u003c/strong\u003e The odds ratio of abnormal BMI in the incidence of diabetes\u003c/p\u003e\n\u003cdiv align=\"left\" dir=\"ltr\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eVariable\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eOR \u003csub\u003ecrud\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eOR\u003csup\u003e*\u003c/sup\u003e \u003csub\u003eadjusted\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eOR** \u003csub\u003eadjusted\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eAbnormal BMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e1.97(1.33-2.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e2.11(1.39-3.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e2.28(1.49-3.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp dir=\"LTR\"\u003eBinary logistic regression\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e*\u0026nbsp;Adjusted by age\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e** Adjusted by age and sex\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003eTable 3 .\u003c/strong\u003e The Relative risk of abnormal BMI in the incidence of diabetes\u003c/p\u003e\n\u003cdiv align=\"left\" dir=\"ltr\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eVariable\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eRR \u003csub\u003ecrud\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eRR\u003csup\u003e*\u003c/sup\u003e \u003csub\u003eadjusted\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eRR** \u003csub\u003eadjusted\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eAbnormal BMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e1.53(1.1-2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e1.74(1.29-2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp dir=\"LTR\"\u003e1.84(1.36-2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp dir=\"LTR\"\u003eCalculated using formulas (RR=OR/(1\u0026minus;Pref)+(Pref*OR))\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e*\u0026nbsp;Adjusted by age\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e** Adjusted by age and sex\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Population Attributable Fraction, Diabetes Mellitus, Body Mass Index","lastPublishedDoi":"10.21203/rs.3.rs-4461723/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4461723/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eType 2 diabetes is one of the public health problems. And its prevalence is growing. Obesity and overweight are preventable risk factors for diabetes. The objective of this study was to calculate the Population Attributable Fraction (PAF) of abnormal body mass index (BMI) for diabetes mellitus (DM). In the present study, 1641 non-diabetic Participants were followed up for ten years (2006 - 2016). The prevalence of the outcome in the reference group and the relative risk of abnormal BMI were calculated, and then the PAF formula was used. Data were analyzed using SPSS version 16 software (Chicago, SPSS Inc.). The prevalence of DM in people with normal BMI was 18.7%. The crud RR, adjusted RR by age, and adjusted RR by age and sex were calculated as 1.66 (1.25-2.14), 1.74 (1.29-2.26), and 1.84 (1.36-2.37), respectively. Suppose we can reduce the prevalence of obesity to the ideal value (zero) without adjustment, age adjustment, or sex adjustment. In that case, the incidence of diabetes will be reduced by 27%, 29%, and 32%, respectively. Diabetes attributed to obesity, about 30% decreases; If we can reduce the prevalence of obesity to the ideal value (zero).\u003c/p\u003e","manuscriptTitle":"Population Attributable Fraction of abnormal body mass index for diabetes mellitus in Yazd during a 10-Year Longitudinal Cohort Study: Yazd Healthy Heart cohort (YHHC) in Yazd, Iran","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-05 06:49:47","doi":"10.21203/rs.3.rs-4461723/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":"cb101d8a-9fd7-44da-be4e-6d43261ab26b","owner":[],"postedDate":"June 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32773570,"name":"Health sciences/Diseases"},{"id":32773571,"name":"Health sciences/Diseases/Metabolic disorders"}],"tags":[],"updatedAt":"2024-06-18T07:57:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-05 06:49:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4461723","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4461723","identity":"rs-4461723","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}