شناسایی عوامل تعیین کننده ی وقوع پیش دیابت با استفاده از مدل رگرسیون لجستیک در مشهد

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناسی ارشد آمار زیستی، گروه پزشکی اجتماعی، دانشکده پزشکی، دانشگاه علوم پزشکی سبزوار

2 گروه آمار زیستی و اپید میولوژی، دانشکدة بهداشت، دانشگاه علوم پزشکی مشهد، مشهد، ایران

3 استاد گروه زیست فناوری پزشکی، دانشکده پزشکی، دانشگاه علوم پزشکی مشهد

چکیده

مقدمه: ﻋﺪم ﮐﻨﺘﺮل ﺑﻪ ﻣﻮﻗﻊ دﯾﺎﺑﺖ ﻣﻨﺠﺮ ﺑﻪ ﻋﻮارض ﺟﺒﺮان ﻧﺎﭘﺬﯾﺮی در ﺳﺎﯾﺮ اندام ﻫﺎی ﺑﺪن از ﺟﻤﻠﻪ ﻗﻠﺐ، ﮐﻠﯿﻪ و ﭼﺸﻢ ﻣﯽﮔﺮدد. هدف از این مطالعه بررسی ﻋﻮاﻣﻞ ﺗﻌﯿﯿﻦ ﮐﻨﻨﺪه اﺑﺘﻼ ﺑﻪ پیش دﯾﺎﺑﺖ ﺑﺎ اﺳﺘﻔﺎده از مدل رگرسیون لجستیک می‌باشد.
روش ها: این مطالعه از نوع مقطعی- تحلیلی بوده و داده ها مربوط به آن از مطالعه ی مشهد می‌باشد. جمعیت مورد مطالعه با استفاده از روش نمونه گیری طبقه ای- خوشه ای انتخاب شدند. نمونه‌ها شامل 8810 فرد بین 64-35 سال بودند. متغیرهای مستقل شامل: اطلاعات دموگرافیک، شاخص تن سنجی، فشارخون، اضطراب، افسردگی، سطح فعالیت فیزیکی، الگوهای غذایی سالم و ناسالم، فاکتورهای التهابی، بیوشیمی و لیپیدی بودند. برای تحلیل داده ها از نرم افزار SPSS22 استفاده گردید و سطح معنی داری 05/0 در نظر گرفته شد. مدل رگرسیون لجستیک به منظور شناسایی عوامل تعیین کننده بر داده ها برازش داده شد.
یافته ها: بر اساس نتایج شیوع پیش دیابت، 2/10%(885 نفر) بود، نتایج همچنین نشان داد بین متغیرهای سن، شاخص توده ی بدنی، دور کمر، دور ران، دور بازو، فشارخون، اضطراب، افسردگی، الگوی غذایی سالم و ناسالم، hs-CRP، اوریک اسید، کلسترول، تری گلیسرید و ابتلا به پیش دیابت از لحاظ آماری اختلاف معنی داری وجود دارد(p

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Identify Determinative Factors the Occurrence of Pre-Diabetes Using Logistic Regression Model in Mashhad

نویسندگان [English]

  • elham navipour 1
  • habibollah esmaily 2
  • majid ghayourmobarhan 3
1 Department of Social Medicine, Faculty of Medicine, Sabzevar University of Medical Sciences, Khorasan razavi, Iran
2 Dept. of Epidemiology and Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
3 Biochemistry & Nutrition Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
چکیده [English]

Introduction: The lack of timely control of diabetes leads to irreparable complications in other organs of the body, including the heart, kidney and eye. The aim of this study was to, Identify determinative Factors the Occurrence of Pre-Diabetes Using Logistic Regression Model in Mashhad by using logistic regression model.
Material and Methods: This is an analytical- cross sectional study. The data are related to MASHAD study. The population was selected by using stratified-cluster sampling. The samples included 8810 individuals aged 35-64 years. Independent variables included: demographic information, anthropometric index, blood pressure, anxiety, depression, physical activity level, healthy and unhealthy diet patterns, inflammatory, biochemical and lipid factors. SPSS-22 software was used to analyze the data and a significant level of 0.05 was considered. The Logistic regression model was fitted to identify the determinant factors on the data.
Results The prevalence of pre-diabetes was, 10.2% (885 cases). The results showed statistically significant association between age, anthropometric index, blood pressure, anxiety, depression, pattern Healthy and unhealthy diet, hs-CRP, uric acid, cholesterol, triglyceride and pre-diabetes (p

کلیدواژه‌ها [English]

  • determinative factors
  • Pre-diabetes
  • Logistic regression
[1] Azizy F, Janghorbani M, Hatami H. Epidemiology and control of common disease in Iran. Tehran: Khosravi Publications. 2011; 342.
[2] Alexandria. Diabetes 1996 : vital statistics. American Diabetes Association. 1996.
[3] Bethesda M. National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health (NIH). US Department of Health and Human Services. 2009.
[4] Dunstan DW, Zimmet PZ, Welborn TA, De Courten MP, Cameron AJ, Sicree RA, et al. The rising prevalence of diabetes and impaired glucose tolerance. Diabetes care. 2002; 25(5):829-34.
[5] Bansal N. Prediabetes diagnosis and treatment: A review. World journal of diabetes. 2015; 6(2):296.
[6] Mainous AG, Tanner RJ, Baker R, Zayas CE, Harle CA. Prevalence of prediabetes in England from 2003 to 2011: population-based, cross-sectional study. BMJ open. 2014; 4(6):e005002.
[7] Azizi F, Hadaegh F. The Raising diabetes and pre-diabetes in Iran. Iranian Journal of Endocrinology and Metabolism. 2015; 17(1):1-3.
[8] Esteghamati A, Gouya MM, Abbasi M, Delavari A, Alikhani S, Alaedini F, et al. Prevalence of diabetes and impaired fasting glucose in the adult population of Iran: National Survey of Risk Factors for Non-Communicable Diseases of Iran. Diabetes care. 2008; 31(1):96-8.
[9] Wikner C, Gigante B, Hellénius M-L, de Faire U, Leander K. The risk of type 2 diabetes in men is synergistically affected by parental history of diabetes and overweight. PloS one. 2013;8(4):e61763.
[10] Rahmati-Najarkolaei F, Pakpour AH, Saffari M, Hosseini MS, Hajizadeh F, Chen H, et al. Determinants of Lifestyle Behavior in Iranian Adults with Prediabetes: Applying the Theory of Planned Behavior. Archives of Iranian medicine. 2017; 20(4).
[11] Screening for type 2 diabetes. Diabetes care- American Diabetes Association. 2004; 27:s11-s4.
[12] Association AD. Follow-up report on the diagnosis of diabetes mellitus. Diabetes Spectrum. 2004; 17(1):51-9.
[13] Garber A, Handelsman Y, Einhorn D, Bergman D, Bloomgarden Z, Fonseca V, et al. Diagnosis and management of prediabetes in the continuum of hyperglycemia—when do the risks of diabetes begin? A consensus statement from the American College of Endocrinology and the American Association of Clinical Endocrinologists. Endocrine practice. 2008; 14(7):933-46.
[14] Lango D, FauciA KD, Hauser S. Harrison's Principles of Internal Medicine 18th ed. Vol. 1. 2. McGraw-Hill Professional; 2011.
[15] Hightower JD, Hightower CM, Vázquez BYS, Intaglietta M. Incident prediabetes/diabetes and blood pressure in urban and rural communities in the Democratic Republic of Congo. Vascular health and risk management. 2011; 7:483.
[16] Zandkarimi E, Safavi AA, Rezaei M, Rajabi G. Comparison logistic regression and discriminant analysis in identifying the determinants of type 2 diabetes among prediabetes of Kermanshah rural areas. Journal of Kermanshah University of Medical Sciences (J Kermanshah Univ Med Sci). 2013; 17(5):300-8.
[17] Xin Z, Yuan J, Hua L, Ma Y-H, Zhao L, Lu Y, et al. A simple tool detected diabetes and prediabetes in rural Chinese. Journal of clinical epidemiology. 2010; 63(9):1030-5.
[18] Zhao X, Zhu X, Zhang H, Zhao W, Li J, Shu Y, et al. Prevalence of diabetes and predictions of its risks using anthropometric measures in southwest rural areas of China. BMC public health. 2012; 12(1):821.
[19] Najafipour H, Sanjari M, Shokoohi M, Haghdoost AA, Afshari M, Shadkam M, et al. Epidemiology of diabetes mellitus, pre‐diabetes, undiagnosed and uncontrolled diabetes and its predictors in general population aged 15 to 75 years: A community‐based study (KERCADRS) in southeastern Iran. Journal of diabetes. 2015; 7(5):613-21.
[20] Latifi SM, Karandish M, Shahbazian H, Hardani Pasand L. Incidence of prediabetes and type 2 diabetes among people aged over 20 years in ahvaz: a 5-year perspective study (2009–2014). Journal of diabetes research. 2016.
[21] Dorsey R, Songer T. Lifestyle behaviors and physician advice for change among overweight and obese adults with prediabetes and diabetes in the United States, 2006. Prev Chronic Dis. 2011; 8(6):A132.
[22] Man RE, Charumathi S, Gan ATL, Fenwick EK, Tey CS, Chua J, et al. Cumulative incidence and risk factors of prediabetes and type 2 diabetes in a Singaporean Malay cohort. Diabetes Research and Clinical Practice. 2017; 127:163-71.
[23] Barber SR, Davies MJ, Khunti K, Gray LJ. Risk assessment tools for detecting those with pre-diabetes: a systematic review. Diabetes research and clinical practice. 2014; 105(1):1-13.
[24] Choi SB, Kim WJ, Yoo TK, Park JS, Chung JW, Lee Y-h, et al. Screening for prediabetes using machine learning models. Computational and mathematical methods in medicine. 2014; 2014.
[25] Bardenheier BH, Bullard KM, Caspersen CJ, Cheng YJ, Gregg EW, Geiss LS. A novel use of structural equation models to examine factors associated with prediabetes among adults aged 50 years and older. Diabetes Care. 2013; 36(9):2655-62.
[26] Noorshahi N, Sotoudeh G, Djalali M, Eshraghian M, Karimi Z, Mirzaei K. Healthy and Unhealthy Dietary Patterns are related to Lipid Parameters in Patients with Type 2 Diabetes Mellitus. Journal of Nutrition and Health Sciences. 2016; 3(1).
[27] Rezazadeh A, Rashidkhani B, Omidvar N. Evaluation of major dietary patterns and general and central obesity in adult women of north Tehran in 2007. Research in Medicine. 2010; 33(4):246-58.
[28] Rezazadeh A, Rashidkhani B. The association of general and central obesity with major dietary patterns of adult women living in Tehran, Iran. Journal of nutritional science and vitaminology. 2010; 56(2):132-8.
[29] Moslehi N, Hosseini-Esfahani F, Hosseinpanah F, Mirmiran P, Hojjat P, Azizi F. Adherence to a whole grin and legumes based dietedary pattern and risk of type 2 diabets.. ijdld. 2016; 15(2):120-9.
[30] EsmaeilZade A, kimiagar M, Mehrabi Y, AzadBakht L. Relationship between dietary patterns with insulin resistance and metabolic syndrome in women. Journal of Diabetes and Metabolism. 2008; 7(3):325-42.
[31] Fallahi E, Anbari K. Identify dietary patterns in Iranian adults. Lorestan University of Medical Sciences. 2012; 14(5):29-39.
[32] Montonen J, Knekt P, Härkänen T, Järvinen R, Heliövaara M, Aromaa A, et al. Dietary patterns and the incidence of type 2 diabetes. American journal of epidemiology. 2005; 161(3):219-27.
[33] Odegaard AO, Koh W-P, Butler LM, Duval S, Gross MD, Mimi CY, et al. Dietary patterns and incident type 2 diabetes in Chinese men and women. Diabetes care. 2011; 34(4):880-5.