Skip Navigation
Skip to contents

PHRP : Osong Public Health and Research Perspectives

OPEN ACCESS
SEARCH
Search

Search

Page Path
HOME > Search
8 "type 2 diabetes"
Filter
Filter
Article category
Keywords
Publication year
Authors
Original Articles
Estimation of the onset time of diabetic complications in type 2 diabetes patients in Thailand: a survival analysis
Natthanicha Sauenram, Jutatip Sillabutra, Chukiat Viwatwongkasem, Pratana Satitvipawee
Osong Public Health Res Perspect. 2023;14(6):508-519.   Published online November 23, 2023
DOI: https://doi.org/10.24171/j.phrp.2023.0084
  • 1,402 View
  • 70 Download
Graphical AbstractGraphical Abstract AbstractAbstract PDF
Objectives
This study aimed to identify factors associated with the onset time of diabetic complications in patients with type 2 diabetes mellitus (T2DM) and determine the best-fitted survival model. Methods: A retrospective cohort study was conducted among T2DM patients enrolled from October 1, 2016 to July 15, 2020 at the National Health Security Office (NHSO). In total, 388 T2DM patients were included. Cox proportional-hazard and parametric models were used to identify factors related to the onset time of diabetic complications. The Akaike information criterion, Bayesian information criterion, and Cox-Snell residual were compared to determine the best-fitted survival model. Results: Thirty diabetic complication events were detected among the 388 patients (7.7%). A 90% survival rate for the onset time of diabetic complications was found at 33 months after the first T2DM diagnosis. According to multivariate analysis, a duration of T2DM ≥42 months (time ratio [TR], 0.56; 95% confidence interval [CI], 0.33–0.96; p=0.034), comorbid hypertension (TR, 0.30; 95% CI, 0.15–0.60; p=0.001), mildly to moderately reduced levels of the estimated glomerular filtration rate (eGFR) (TR, 0.43; 95% CI, 0.24–0.75; p=0.003) and an eGFR that was severely reduced or indicative of kidney failure (TR, 0.38; 95% CI, 0.16–0.88; p=0.025) were significantly associated with the onset time of diabetic complications (p<0.05). Conclusion: Patients with T2DM durations of more than 42 months, comorbid hypertension, and decreased eGFR were at risk of developing diabetic complications. The NHSO should be aware of these factors to establish a policy to prevent diabetic complications after the diagnosis of T2DM.
Educational Needs Associated with the Level of Complication and Comparative Risk Perceptions in People with Type 2 Diabetes
Youngji Hwang, Dongsuk Lee, Yeon Sook Kim
Osong Public Health Res Perspect. 2020;11(4):170-176.   Published online August 31, 2020
DOI: https://doi.org/10.24171/j.phrp.2020.11.4.05
  • 5,267 View
  • 160 Download
AbstractAbstract PDF
Objectives

This study aimed to identify the educational needs of people with type 2 diabetes according to risk perceptions and the level of severity of complications.

Methods

There were 177 study participants who were outpatients of the internal medicine department at a university hospital located in the Republic of Korea, who consented to participate in the survey from December 10, 2016 to February 10, 2017. The data were analyzed using descriptive statistics, Pearson correlation, ANOVA with post-hoc comparison, and multiple regression analysis. Type 2 diabetes complications were classified into 3 groups: no complications, common complications, and severe complications.

Results

There were statistically significant positive correlations between educational needs and comparative risk perceptions, and the level of complication and comparative risk perception. Multiple regression analysis revealed that the factor predicting educational needs of type 2 diabetes people was their comparative risk perceptions, rather than the severity of diabetes complications or sociodemographic variables.

Conclusion

Since risk perception is the factor that indicates the educational needs of people with type 2 diabetes, there is a need to explore factors which increase risk perception, in order to meet educational needs. The findings suggest that a more specific and individualized educational program, which focuses on each person's risk perceptions, should be developed.

The Association Between Lung Function and Type 2 Diabetes in Koreans
Do-Youn Lee, Seung-min Nam
Osong Public Health Res Perspect. 2020;11(1):27-33.   Published online February 28, 2020
DOI: https://doi.org/10.24171/j.phrp.2020.11.1.05
  • 5,747 View
  • 132 Download
  • 2 Web of Science
  • 2 Crossref
AbstractAbstract PDF
Objectives

This study was performed to test the association between lung function and type 2 diabetes mellitus (T2DM) in Korean patients.

Methods

Data from the 6th Korea National Health and Nutrition Examination Survey (2013 to 2015) was used in this study. There were 3,466 individuals aged between 40 and 80 years, with T2DM, who had a smoking and alcohol status listed, and blood analysis (including blood pressure), were included in this study. Lung function, measured by spirometer ventilatory dysfunction was categorized into 3 patterns: normal, restrictive ventilatory dysfunction, and obstructive ventilatory dysfunction (OVD).

Results

Based on multivariate logistic regression analysis, individuals with restrictive ventilatory dysfunction had an increased odds ratio (OR 1.615, 95% CI 1.137–2.294) for T2DM compared with individuals with normal ventilatory function, whereas OVD had no increase in the odds ratio (OR 1.169, 95 % CI 0.857–1.594). Model 1, which adjusted for age and gender, showed that the probability of having restrictive disorder was 1.559 times (95% CI 1.617–2.082) higher for prediabetes patients, and 2.320 times (95% CI 1.611–3.343) higher for T2DM patients, compared to normal individuals. For Model 4, which was fully adjusted for variables, the probability of having a restrictive disorder was 1.837 times higher for T2DM patients (95% CI 1.260–2.679).

Conclusion

Restrictive ventilatory dysfunction, but not OVD, was associated independently with T2DM.

Citations

Citations to this article as recorded by  
  • Hidden chronic metabolic acidosis of diabetes type 2 (CMAD): Clues, causes and consequences
    Hayder A. Giha
    Reviews in Endocrine and Metabolic Disorders.2023; 24(4): 735.     CrossRef
  • Association of Pulmonary Function Decline over Time with Longitudinal Change of Glycated Hemoglobin in Participants without Diabetes Mellitus
    Wen-Hsien Lee, Da-Wei Wu, Ying-Chih Chen, Yi-Hsueh Liu, Wei-Sheng Liao, Szu-Chia Chen, Chih-Hsing Hung, Chao-Hung Kuo, Ho-Ming Su
    Journal of Personalized Medicine.2021; 11(10): 994.     CrossRef
Obesity, Hypertension, and Type-2 Diabetes Mellitus: The Interrelationships and the Determinants among Adults in Gaza City, Palestine
Mohammed S. Ellulu
Osong Public Health Res Perspect. 2018;9(6):289-298.   Published online December 31, 2018
DOI: https://doi.org/10.24171/j.phrp.2018.9.6.02
  • 6,185 View
  • 128 Download
  • 8 Crossref
AbstractAbstract PDF
Objectives

To describe the distribution of social factors, lifestyle habits and anthropometric measurements according to hypertension and Type-2 diabetes.

Methods

A cross-sectional study was conducted in Gaza City, Palestine that included 379 patients (20–60 years) who had hypertension and/or diabetes. Three groups of patients were involved; 106 hypertensive (HT), 109 diabetic (T2DM) and 164 hypertensive diabetics (HT + T2DM).

Results

The HT + T2DM group were older and had a higher body mass index compared to HT and T2DM groups. There were 62.3% patients who were female, 49.2% were highly educated HT patients, and 49.3% patients had a low level of education and were HT + T2DM. There were 55.8% patients who lived in large families. Patients who were passive smokers or never smoked before were mostly HT + T2DM, while active smokers and past smokers had T2DM. There were 48.2% patients who were highly physically active who had HT, 40.9% whom were moderately active had T2DM, and 53.8% of patients who had a low level of activity were HT + T2DM. Multivariate linear regression showed that having a diseased mother, living in a large family, being a past or passive smoker, or never having smoked, having a low or moderate level of activity, and having HT or HT + T2DM, were significantly associated with an increased body mass index.

Conclusion

Parental health/disease conditions and environmental factors (social network and lifestyle habits) played the greatest role in the development of obesity and disease.

Citations

Citations to this article as recorded by  
  • Factors associated with physical inactivity among Palestinians with type 2 diabetes mellitus treated in resource-limited settings
    Ramzi Shawahna, Mohammad Jaber, Arob Zmiro, Sewar Kashkoush
    Scientific Reports.2024;[Epub]     CrossRef
  • Analysis of the nutritional status in the Palestinian territory: a review study
    Enas A. Assaf, Haleama Al Sabbah, Ayoub Al-Jawadleh
    Frontiers in Nutrition.2023;[Epub]     CrossRef
  • The hypertension cascade of care in the midst of conflict: the case of the Gaza Strip
    Bassam A. Abu Hamad, Zeina Jamaluddine, Gloria Safadi, Marie-Elizabeth Ragi, Raeda El Sayed Ahmad, Eszter P. Vamos, Sanjay Basu, John S. Yudkin, Mohammed Jawad, Christopher Millett, Hala Ghattas
    Journal of Human Hypertension.2022; 37(10): 957.     CrossRef
  • Association between endothelial nitric oxide synthase and the renin-angiotensin-aldosterone system polymorphisms, blood pressure and training status in normotensive/pre-hypertension and hypertensive older adults: a pilot study
    Roberta Fernanda da Silva, Riccardo Lacchini, Lucas Cezar Pinheiro, Letícia Perticarrara Ferezin, José Eduardo Tanus-Santos, Marcelo Rizzatti Luizon, Thiago José Dionísio, Carlos Ferreira Santos, Thaís Amanda Reia, André Mourão Jacomini, Ana Maria Guilmo
    Clinical and Experimental Hypertension.2021; 43(7): 661.     CrossRef
  • Breakfast characteristics, perception, and reasons of skipping among 8th and 9th-grade students at governmental schools, Jenin governance, West Bank
    Manal Badrasawi, Ola Anabtawi, Yaqout Al-Zain
    BMC Nutrition.2021;[Epub]     CrossRef
  • Advanced Molecular Imaging (MRI/MRS/1H NMR) for Metabolic Information in Young Adults with Health Risk Obesity
    Khin Thandar Htun, Jie Pan, Duanghathai Pasanta, Montree Tungjai, Chatchanok Udomtanakunchai, Thanaporn Petcharoen, Nattacha Chamta, Supak Kosicharoen, Kiattisak Chukua, Christopher Lai, Suchart Kothan
    Life.2021; 11(10): 1035.     CrossRef
  • Prevalence and Predictors of Co-occurring Hypertension and Depression Among Community-Dwelling Older Adults
    Cicily A. Gray, Omar T. Sims, Hyejung Oh
    Journal of Racial and Ethnic Health Disparities.2020; 7(2): 365.     CrossRef
  • Prevalence of Type 2 Diabetes and Its Association with Added Sugar Intake in Citizens and Refugees Aged 40 or Older in the Gaza Strip, Palestine
    Majed Jebril, Xin Liu, Zumin Shi, Mohsen Mazidi, Akram Altaher, Youfa Wang
    International Journal of Environmental Research an.2020; 17(22): 8594.     CrossRef
Public Awareness of Early and Late Complications of Type 2 Diabetes - Application of Latent Profile Analysis in Determining Questionnaire Cut-Off Points
Nasrin Shirmohammadi, Ali Reza Soltanian, Shiva Borzouei
Osong Public Health Res Perspect. 2018;9(5):261-268.   Published online October 31, 2018
DOI: https://doi.org/10.24171/j.phrp.2018.9.5.08
  • 5,343 View
  • 122 Download
  • 4 Crossref
AbstractAbstract PDF
Objectives

A questionnaire was designed to determine public understanding of early and late complications of Type 2 diabetes mellitus (T2DM).

Methods

A cross-sectional study was performed in participants who were selected using a multi-stage sampling method and a standard questionnaire of 67 questions was proposed. An expert panel selected 53 closed-ended questions for content validity to be included in the questionnaire. The reliability of the questionnaire was tested using Cronbach’s alpha coefficient giving a score of 0.84.

Results

Of the 825 participants, 443 (57.6%) were male, and 322 (41.87%) were 40 years or more. The proportion of low-, moderate- and high- awareness about T2DM and its complications was 29.26%, 62.68%, and 8.06%, respectively. Friends (56.31%) and internet and social networks (20.55%) were the 2 major sources of awareness, respectively. The medical staff (e.g., physicians) had the lowest share in the level of public awareness (3.64%) compared to other sources.

Conclusion

These results present data that shows the general population awareness of T2DM is low. Healthcare policymakers need to be effective at raising awarenes of diabetes and it should be through improved education.

Citations

Citations to this article as recorded by  
  • Polycytosine RNA-binding protein 1 regulates osteoblast function via a ferroptosis pathway in type 2 diabetic osteoporosis
    Hong-Dong Ma, Lei Shi, Hai-Tian Li, Xin-Dong Wang, Mao-Wei Yang
    World Journal of Diabetes.2024; 15(5): 977.     CrossRef
  • Healthcare Practitioners' Perspective of Prevailing Awareness on Diabetes Complications: A Questionnaire-Based Pan-India Study
    Nikhil Tandon, Piya Ballani Thakkar, Jubbin Jacob, Pramila Kalra, Nanditha Arun, Alok Kanungo, Ashish Birla, Ashish Prasad, Mayuri Talathi
    Cureus.2023;[Epub]     CrossRef
  • What Is the Optimal Cut-Off Point of the 10-Item Center for Epidemiologic Studies Depression Scale for Screening Depression Among Chinese Individuals Aged 45 and Over? An Exploration Using Latent Profile Analysis
    Hanlin Fu, Lulu Si, Ruixia Guo
    Frontiers in Psychiatry.2022;[Epub]     CrossRef
  • Classification of probable online social networking addiction: A latent profile analysis from a large-scale survey among Chinese adolescents
    Ji-Bin Li, Anise M.S. Wu, Li-Fen Feng, Yang Deng, Jing-Hua Li, Yu-Xia Chen, Jin-Chen Mai, Phoenix K.H. Mo, Joseph T.F. Lau
    Journal of Behavioral Addictions.2020; 9(3): 698.     CrossRef
Depression among Korean Adults with Type 2 Diabetes Mellitus: Ansan-Community-Based Epidemiological Study
Chan Young Park, So Young Kim, Jong Won Gil, Min Hee Park, Jong-Hyock Park, Yeonjung Kim
Osong Public Health Res Perspect. 2015;6(4):224-232.   Published online August 31, 2015
DOI: https://doi.org/10.1016/j.phrp.2015.05.004
  • 3,189 View
  • 14 Download
  • 23 Crossref
AbstractAbstract PDF
Objectives
There are an increasing number of studies being carried out on depression in patients with diabetes. Individuals with diabetes have been reported as having a higher prevalence of depression compared to those without diabetes. However, only a few studies involving Korean patients have been conducted. The aims of this study were to examine the prevalence of depression and to find various risk factors according to the degree of depression among Korean patients with Type 2 diabetes mellitus (T2DM).
Methods
An Ansan-community-based epidemiological study was conducted from 2005 to 2012. The total number of participants in this study was 3,540, from which patients with diabetes (n = 753) have been selected. The presence of depression was evaluated using the Beck Depression Inventory total score.
Results
The prevalence of depression was 28.8%. The mean age of participants was 55.5 ± 8.2 years. We divided the participants into three groups (without-depression, moderate-depression, and severe-depression groups) to examine the depression prevalence among Korean T2DM patients. The unemployed participants had 2.40 [95% confidence interval (CI) 1.21–4.76], the low-income participants had 2.57 (95% CI 1.52–4.35), the participants using an oral diabetes medicine or insulin had 2.03 (95% CI 1.25–3.32), the participants who are currently smoking had 2.03 (95% CI 1.10–3.73), and those without regular exercise had 1.91 (95% CI 1.17–3.14) times higher odds of depression in the severe-depression group, compared with the without-depression group.
Conclusion
There was a significant association between depression prevalence and diabetes, and we found various risk factors according to the degree of depression in Korean patients with T2DM.

Citations

Citations to this article as recorded by  
  • Risk of Depression according to Cumulative Exposure to a Low-Household Income Status in Individuals with Type 2 Diabetes Mellitus: A Nationwide Population- Based Study
    So Hee Park, You-Bin Lee, Kyu-na Lee, Bongsung Kim, So Hyun Cho, So Yoon Kwon, Jiyun Park, Gyuri Kim, Sang-Man Jin, Kyu Yeon Hur, Kyungdo Han, Jae Hyeon Kim
    Diabetes & Metabolism Journal.2024; 48(2): 290.     CrossRef
  • Psychological Health and Diabetes Self-Management among Patients with Type 2 Diabetes during COVID-19 in the Southwest of Saudi Arabia
    Abdulrhman H. Alkhormi, Mohamed Salih Mahfouz, Najim Z. Alshahrani, Abdulrahman Hummadi, Wali A. Hakami, Doha H. Alattas, Hassan Q. Alhafaf, Leena E. Kardly, Mulook A. Mashhoor
    Medicina.2022; 58(5): 675.     CrossRef
  • Higher risk of depression in individuals with type 2 diabetes and obesity: Results of a meta-analysis
    Thelma Beatriz González-Castro, Yudy Merady Escobar-Chan, Ana Fresan, María Lilia López-Narváez, Carlos Alfonso Tovilla-Zárate, Isela Esther Juárez-Rojop, Jorge L Ble-Castillo, Alma Delia Genis-Mendoza, Pedro Iván Arias-Vázquez
    Journal of Health Psychology.2021; 26(9): 1404.     CrossRef
  • The Effects of Meditation with a Biofeedback Program on Stress and Depression Levels among People with Mild Depression Diabetes
    Ormanee Patarathipakorn, Manyat Ruchiwit, Marlaine Smith
    The Open Public Health Journal.2021; 14(1): 104.     CrossRef
  • Association between the level of adherence to dietary guidelines and depression among Korean patients with type 2 diabetes mellitus
    Seonghee Park, Kyong Park
    Journal of Psychosomatic Research.2021; 145: 110463.     CrossRef
  • Depression Among Patients with Type 2 Diabetes Mellitus: Prevalence and Associated Factors in Hue City, Vietnam
    Nhu Minh Hang Tran, Quang Ngoc Linh Nguyen, Thi Han Vo, Tran Tuan Anh Le, Ngoc Ha Ngo
    Diabetes, Metabolic Syndrome and Obesity: Targets .2021; Volume 14: 505.     CrossRef
  • Factors Associated with Depressive Symptoms in Korean Adults with Diabetes Mellitus: A Cross-Sectional Study
    Mihyun Jeong
    Healthcare.2021; 9(8): 1049.     CrossRef
  • Spiritual intelligence, mindfulness, emotional dysregulation, depression relationship with mental well-being among persons with diabetes during COVID-19 pandemic
    Wojujutari Kenni Ajele, Teslim Alabi Oladejo, Abimbola A. Akanni, Oyeyemi Bukola Babalola
    Journal of Diabetes & Metabolic Disorders.2021; 20(2): 1705.     CrossRef
  • Depression and Its Predictors among Diabetes Mellitus Patients Attending Treatment in Hawassa University Comprehensive Specialized Hospital, Southern Ethiopia
    Bereket Beyene Gebre, Suzan Anand, Zebene Mekonnen Assefa
    Journal of Diabetes Research.2020; 2020: 1.     CrossRef
  • Effect of Study Design and Survey Instrument to Identify the Association Between Depressive Symptoms and Physical Activity in Type 2 Diabetes, 2000-2018: A Systematic Review
    Jusung Lee, Timothy Callaghan, Marcia Ory, Hongwei Zhao, Margaret Foster, Jane N. Bolin
    The Diabetes Educator.2020; 46(1): 28.     CrossRef
  • Genetic Overlap Between Type 2 Diabetes and Depression in a Sri Lankan Population Twin Sample
    Carol Kan, Kaushalya Jayaweera, Anushka Adikari, Sisira Siribaddana, Helena M.S. Zavos, Lisa Harber-Aschan, Athula Sumathipala, Matthew Hotopf, Khalida Ismail, Frühling Rijsdijk
    Psychosomatic Medicine.2020; 82(2): 247.     CrossRef
  • Depression in Iranian Children with Diabetes and Related Factors
    Azadeh Sayarifard, Fatemeh Sayarifard, Maryam Nazari, Morteza Nikzadian, Mona Amrollahinia, Javad Mahmoudi-Gharaei
    Iranian Journal of Pediatrics.2020;[Epub]     CrossRef
  • Prevalence of Undiagnosed Depression in Patients With Type 2 Diabetes
    Dina Siddiq Abdulhadi Alajmani, Amna Mohamad Alkaabi, Mariam Waleed Alhosani, Ayesha Abdulaziz Folad, Fawzia Ahmed Abdouli, Frederick Robert Carrick, Mahera Abdulrahman
    Frontiers in Endocrinology.2019;[Epub]     CrossRef
  • Risk and protective factors of co-morbid depression in patients with type 2 diabetes mellitus: a meta analysis
    Aidibai Simayi, Patamu Mohemaiti
    Endocrine Journal.2019; 66(9): 793.     CrossRef
  • The prevalence of comorbid depression in patients with type 2 diabetes: an updated systematic review and meta-analysis on huge number of observational studies
    Mohammad Khaledi, Fahimeh Haghighatdoost, Awat Feizi, Ashraf Aminorroaya
    Acta Diabetologica.2019; 56(6): 631.     CrossRef
  • Effect of walking and aerobic exercise on physical performance and depression in cases of type 2 diabetes mellitus
    Manal K. Youssef
    The Egyptian Journal of Internal Medicine.2019; 31(2): 142.     CrossRef
  • Premorbid risk perception, lifestyle, adherence and coping strategies of people with diabetes mellitus: A phenomenological study in the Brong Ahafo Region of Ghana
    Philip Teg-Nefaah Tabong, Vitalis Bawontuo, Doris Ningwiebe Dumah, Joseph Maaminu Kyilleh, Tolgou Yempabe, Noël C. Barengo
    PLOS ONE.2018; 13(6): e0198915.     CrossRef
  • Past and Current Status of Adult Type 2 Diabetes Mellitus Management in Korea: A National Health Insurance Service Database Analysis
    Seung-Hyun Ko, Kyungdo Han, Yong-ho Lee, Junghyun Noh, Cheol-Young Park, Dae-Jung Kim, Chang Hee Jung, Ki-Up Lee, Kyung-Soo Ko
    Diabetes & Metabolism Journal.2018; 42(2): 93.     CrossRef
  • Why Early Psychological Attention for Type 2 Diabetics Could Contribute to Metabolic Control
    Alfredo Briones-Aranda, Manuela Castellanos-Pérez, Raquel Gómez-Pliego
    Romanian Journal of Diabetes Nutrition and Metabol.2018; 25(3): 329.     CrossRef
  • Depression and Mortality in People with Type 2 Diabetes Mellitus, 2003 to 2013: A Nationwide Population-Based Cohort Study
    Jong-Hyun Jeong, Yoo Hyun Um, Seung-Hyun Ko, Jong-Heon Park, Joong-Yeol Park, Kyungdo Han, Kyung-Soo Ko
    Diabetes & Metabolism Journal.2017; 41(4): 296.     CrossRef
  • Diabetes-related distress and its associated factors among patients with type 2 diabetes mellitus in China
    Huanhuan Zhou, Junya Zhu, Lin Liu, Fan Li, Anne F. Fish, Tao Chen, Qingqing Lou
    Psychiatry Research.2017; 252: 45.     CrossRef
  • Comorbidity of depression and diabetes: an application of biopsychosocial model
    Tesfa Dejenie Habtewold, Md. Atiqul Islam, Yosef Tsige Radie, Balewgizie Sileshi Tegegne
    International Journal of Mental Health Systems.2016;[Epub]     CrossRef
  • Differences in depression between unknown diabetes and known diabetes: results from China health and retirement longitudinal study
    Huaqing Liu, Xiaoyue Xu, John J. Hall, Xuesen Wu, Min Zhang
    International Psychogeriatrics.2016; 28(7): 1191.     CrossRef
Association of TNF-α 308 G/A Polymorphism With Type 2 Diabetes: A Case–Control Study in the Iranian Kurdish Ethnic Group
Hasan Golshani, Karimeh Haghani, Majid Dousti, Salar Bakhtiyari
Osong Public Health Res Perspect. 2015;6(2):94-99.   Published online April 30, 2015
DOI: https://doi.org/10.1016/j.phrp.2015.01.003
  • 3,018 View
  • 23 Download
  • 20 Crossref
AbstractAbstract PDF
Objectives
Tumor necrosis factor-α (TNF-α) plays roles in the development of obesity, insulin resistance, and possibility of Type 2 diabetes mellitus (T2DM). The objective of the current study was to evaluate the association of TNF-α promoter−308 G/A polymorphism with T2DM.
Methods
In all, 1038 patients with T2DM and 1023 normoglycemic controls were included in this study. All participants were genotyped using the polymerase chain reaction-restriction fragment length polymorphism method. Genotypic and allelic frequencies were then analyzed in each group. Serum lipids, fasting glucose, fasting serum insulin, homeostatic model assessment of insulin resistance, and hemoglogin A1c levels were determined by conventional methods.
Results
The allelic frequency of the A allele was significantly different between case and control participants (p = 0.006). Genotypes GA and AA were found to be significantly associated with 2.24- and 3.18-fold increased risk for T2DM, respectively. Similarly, the dominant model of -308 G/A polymorphism was found to have a higher risk for T2DM (odds ratio = 2.34, p = 0.001). Individuals with T2DM carrying the GA + AA genotypes of -308 G/A variation had significantly lower fasting plasma insulin than those carrying GG genotype.
Conclusion
Our findings revealed that there is an association between the TNF-α promoter -308 G/A polymorphism and T2DM in this population.

Citations

Citations to this article as recorded by  
  • Effects of tumor necrosis factor-α rs1800629 and interleukin-10 rs1800872 genetic variants on type 2 diabetes mellitus susceptibility and metabolic parameters among Jordanians
    Lana Nasrallah Mousa, Yazun Jarrar, Munir Gharaibeh, Hussam Alhawari
    Drug Metabolism and Personalized Therapy.2024;[Epub]     CrossRef
  • Estimating the role of single-nucleotide polymorphism (rs1800629)-308 G/A of TNF-alpha gene as genetic marker associated with angina pectoris in a sample of Iraqi patients
    Shaimaa Y. Abdulfattah, Farah Thamer Samawi
    Journal of Genetic Engineering and Biotechnology.2023; 21(1): 2.     CrossRef
  • Oral Supplementation with Three Vegetable Oils Differing in Fatty Acid Composition Alleviates High-Fat Diet-Induced Obesity in Mice by Regulating Inflammation and Lipid Metabolism
    Waleed Aldamarany, Huang Taocui, Deng Liling, Yang Wanfu, Geng Zhong
    Polish Journal of Food and Nutrition Sciences.2023; : 80.     CrossRef
  • Diabetes as one of the long-term COVID-19 complications: from the potential reason of more diabetic patients’ susceptibility to COVID-19 to the possible caution of future global diabetes tsunami
    Yasamin Sharbatdar, Ronak Mousavian, Seyed Mostafa Noorbakhsh Varnosfaderani, Fatemeh Aziziyan, Mahsa Liaghat, Payam Baziyar, Ali Yousefi Rad, Chanour Tavakol, Amir Mansour Moeini, Mohsen Nabi-Afjadi, Hamidreza Zalpoor, Fatemeh Kazemi-Lomedasht
    Inflammopharmacology.2023; 31(3): 1029.     CrossRef
  • Single-nucleotide polymorphisms as important risk factors of diabetes among Middle East population
    Iman Akhlaghipour, Amir Reza Bina, Mohammad Reza Mogharrabi, Ali Fanoodi, Amir Reza Ebrahimian, Soroush Khojasteh Kaffash, Atefeh Babazadeh Baghan, Mohammad Erfan Khorashadizadeh, Negin Taghehchian, Meysam Moghbeli
    Human Genomics.2022;[Epub]     CrossRef
  • Glucose-Related Traits and Risk of Migraine—A Potential Mechanism and Treatment Consideration
    Md Rafiqul Islam, Dale R. Nyholt
    Genes.2022; 13(5): 730.     CrossRef
  • A meta-analysis on the association of the -308 G/A polymorphism of the TNF-alpha gene with the development of malaria
    Raphael Enrique Tiongco, Ivy Cayabyab, Benjie Clemente, Chastene Christopher Flake, Dianne Dizon, Joey Kyle Mallari, Maria Ruth Pineda-Cortel
    Gene Reports.2022; 27: 101626.     CrossRef
  • Exploring the Relationship between TNF-α Gene Expression in Non Diabetic Nephropathy Type 2 Diabetes Patients
    Muhammad Roman, Samra Anees, Saima Sharif, Shah Jahan
    Futuristic Biotechnology.2022; : 07.     CrossRef
  • Association between ABCC8 Ala1369Ser Polymorphism (rs757110 T/G) and Type 2 Diabetes Risk in an Iranian Population: A Case-Control Study
    Amin Bakhtiyari, Karimeh Haghani, Salar Bakhtiyari, Mohammad A. Zaimy, Ali Noori-Zadeh, Ali Gheysarzadeh, Shahram Darabi, Ali Seidkhani-Nahal, Mansour Amraei, Iraj Alipourfard
    Endocrine, Metabolic & Immune Disorders - Drug Tar.2021; 21(3): 441.     CrossRef
  • Association of TNF-α 308G/A and LEPR Gln223Arg Polymorphisms with the Risk of Type 2 Diabetes Mellitus
    Maria Trapali, Dimitra Houhoula, Anthimia Batrinou, Anastasia Kanellou, Irini F. Strati, Argyris Siatelis, Panagiotis Halvatsiotis
    Genes.2021; 13(1): 59.     CrossRef
  • Biological and Clinical Implications of TNF-α Promoter and CYP1B1 Gene Variations in Coronary Artery Disease Susceptibility
    Rashid Mir, Imadeldin Elfaki, Chandan K. Jha, Jamsheed Javid, Abdullatif T. Babakr, Shaheena Banu, Mohammad M. Mir, Dheeraj Jamwal, Naina Khullar, Khalid J Alzahrani, Sukh M.S. Chahal
    Cardiovascular & Hematological Disorders-Drug Targ.2021; 21(4): 266.     CrossRef
  • TNF-α gene polymorphism in Iranian Azeri population
    Mohammad Asgharzadeh, Manouchehr Fadaee, Hamed Ebrahimzadeh Leylabadlo, Behroz Mahdavi Poor, Jalil Rashedi, Vahid Asgharzadeh, Ali Vegari, Behrooz Shokouhi, Nima Najafi Ghalelou, Hossein Samadi Kafil
    Gene Reports.2020; 19: 100651.     CrossRef
  • The association of TNF-α −308G/A and −238G/A polymorphisms with type 2 diabetes mellitus: a meta-analysis
    Xiaoliang Guo, Chenxi Li, Jiawei Wu, Qingbu Mei, Chang Liu, Wenjing Sun, Lidan Xu, Songbin Fu
    Bioscience Reports.2019;[Epub]     CrossRef
  • Association of polymorphisms at −174 in IL-6, and −308 and −238 in TNF-α, in the development of tuberculosis and type 2 diabetes mellitus in the Mexican population
    Ruth Elizabeth Lara-Gómez, María Luisa Moreno-Cortes, Raquel Muñiz-Salazar, Roberto Zenteno-Cuevas
    Gene.2019; 702: 1.     CrossRef
  • A Review of Type 2 Diabetes Mellitus Predisposing Genes
    Tajudeen O. Yahaya, Titilola F. Salisu
    Current Diabetes Reviews.2019; 16(1): 52.     CrossRef
  • TNF-alpha G/A308 polymorphism association with nasal polyposis in North part of Iran
    Masoumeh Faghani, Parvaneh Keshavars, Alireza Sharafshah, Babak Pourgholamali, Farshad Moharami, Shadman Nemati
    European Archives of Oto-Rhino-Laryngology.2018; 275(9): 2253.     CrossRef
  • TNF-α -308G/A gene polymorphism in bullous pemphigoid and alopecia areata
    Hamideh Moravvej, Pardis-Sadat Tabatabaei-Panah, Elaheh Ebrahimi, Nafiseh Esmaeili, Sayyed Mohammad Hossein Ghaderian, Ralf J. Ludwig, Reza Akbarzadeh
    Human Antibodies.2018; 26(4): 201.     CrossRef
  • Infliximab ameliorates tumor necrosis factor‐alpha‐induced insulin resistance by attenuating PTP1B activation in 3T3L1 adipocytes in vitro
    Lucia A. Méndez‐García, Fernanda Trejo‐Millán, Camilo P. Martínez‐Reyes, Aarón N. Manjarrez‐Reyna, Marcela Esquivel‐Velázquez, Guillermo Melendez‐Mier, Sergio Islas‐Andrade, Araceli Rojas‐Bernabé, Julia Kzhyshkowska, Galileo Escobedo
    Scandinavian Journal of Immunology.2018;[Epub]     CrossRef
  • Study of the associations between polymorphic markers rs1800629 TNFa, rs909253 Lta, rs767455 TNFR1, rs1061624 TNFR2 and the development of type 2 diabetes
    Mikhail I. Churnosov, Oksana N. Belousova, Svetlana Sergeyevna Sirotina
    Diabetes mellitus.2017; 20(3): 166.     CrossRef
  • IL-6, TNF-α, and IL-10 levels/polymorphisms and their association with type 2 diabetes mellitus and obesity in Brazilian individuals
    Kathryna Fontana Rodrigues, Nathalia Teixeira Pietrani, Adriana Aparecida Bosco, Fernanda Magalhães Freire Campos, Valéria Cristina Sandrim, Karina Braga Gomes
    Archives of Endocrinology and Metabolism.2017; 61(5): 438.     CrossRef
Development of a Predictive Model for Type 2 Diabetes Mellitus Using Genetic and Clinical Data
Juyoung Lee, Bhumsuk Keam, Eun Jung Jang, Mi Sun Park, Ji Young Lee, Dan Bi Kim, Chang-Hoon Lee, Tak Kim, Bermseok Oh, Heon Jin Park, Kyu-Bum Kwack, Chaeshin Chu, Hyung-Lae Kim
Osong Public Health Res Perspect. 2011;2(2):75-82.   Published online June 30, 2011
DOI: https://doi.org/10.1016/j.phrp.2011.07.005
  • 2,887 View
  • 18 Download
  • 11 Crossref
AbstractAbstract PDFSupplementary Material
Objectives
Recent genetic association studies have provided convincing evidence that several novel loci and single nucleotide polymorphisms (SNPs) are associated with the risk of developing type 2 diabetes mellitus (T2DM). The aims of this study were: 1) to develop a predictive model of T2DM using genetic and clinical data; and 2) to compare misclassification rates of different models.
Methods
We selected 212 individuals with newly diagnosed T2DM and 472 controls aged in their 60s from the Korean Genome and Epidemiology Study. A total of 499 known SNPs from 87 T2DM-related genes were genotyped using germline DNA. SNPs were analyzed for significant association with T2DM using various classification algorithms including Quest (Quick, Unbiased, Efficient, Statistical tree), Support Vector Machine, C4.5, logistic regression, and K-nearest neighbor.
Results
We tested these models using the complete Korean Genome and Epidemiology Study cohort (n = 10,038) and computed the T2DM misclassification rates for each model. Average misclassification rates ranged at 28.2–52.7%. The misclassification rates for the logistic and machine-learning algorithms were lower than the statistical tree algorithms. Using 1-to-1 matched data, the misclassification rate of the statistical tree QUEST algorithm using body mass index and SNP variables was the lowest, but overall the logistic regression performed best.
Conclusions
The K-nearest neighbor method exhibited more robust results than other algorithms. For clinical and genetic data, our “multistage adjustment” model outperformed other models in yielding lower rates of misclassification. To improve the performance of these models, further studies using warranted, strategies to estimate better classifiers for the quantification of SNPs need to be developed.

Citations

Citations to this article as recorded by  
  • Population stratification in type 2 diabetes mellitus: A systematic review
    Sam Hodgson, Sukhmani Cheema, Zareena Rana, Doyinsola Olaniyan, Ellen O’Leary, Hermione Price, Hajira Dambha‐Miller
    Diabetic Medicine.2022;[Epub]     CrossRef
  • The Prediction of Diabetes
    Lalit Kumar, Prashant Johri
    International Journal of Reliable and Quality E-He.2022; 11(1): 1.     CrossRef
  • Hypertension: Constraining the Expression of ACE-II by Adopting Optimal Macronutrients Diet Predicted via Support Vector Machine
    Mohammad Farhan Khan, Gazal Kalyan, Sohom Chakrabarty, M. Mursaleen
    Nutrients.2022; 14(14): 2794.     CrossRef
  • Supervised and unsupervised algorithms for bioinformatics and data science
    Ayesha Sohail, Fatima Arif
    Progress in Biophysics and Molecular Biology.2020; 151: 14.     CrossRef
  • Medical Internet of things using machine learning algorithms for lung cancer detection
    Kanchan Pradhan, Priyanka Chawla
    Journal of Management Analytics.2020; 7(4): 591.     CrossRef
  • Perspective: Advancing Understanding of Population Nutrient–Health Relations via Metabolomics and Precision Phenotypes
    Stephanie Andraos, Melissa Wake, Richard Saffery, David Burgner, Martin Kussmann, Justin O'Sullivan
    Advances in Nutrition.2019; 10(6): 944.     CrossRef
  • Stacked classifiers for individualized prediction of glycemic control following initiation of metformin therapy in type 2 diabetes
    Dennis H. Murphree, Elaheh Arabmakki, Che Ngufor, Curtis B. Storlie, Rozalina G. McCoy
    Computers in Biology and Medicine.2018; 103: 109.     CrossRef
  • Machine Learning and Data Mining Methods in Diabetes Research
    Ioannis Kavakiotis, Olga Tsave, Athanasios Salifoglou, Nicos Maglaveras, Ioannis Vlahavas, Ioanna Chouvarda
    Computational and Structural Biotechnology Journal.2017; 15: 104.     CrossRef
  • Survey on clinical prediction models for diabetes prediction
    N. Jayanthi, B. Vijaya Babu, N. Sambasiva Rao
    Journal of Big Data.2017;[Epub]     CrossRef
  • Rule Extraction From Support Vector Machines Using Ensemble Learning Approach: An Application for Diagnosis of Diabetes
    Longfei Han, Senlin Luo, Jianmin Yu, Limin Pan, Songjing Chen
    IEEE Journal of Biomedical and Health Informatics.2015; 19(2): 728.     CrossRef
  • Depression among Korean Adults with Type 2 Diabetes Mellitus: Ansan-Community-Based Epidemiological Study
    Chan Young Park, So Young Kim, Jong Won Gil, Min Hee Park, Jong-Hyock Park, Yeonjung Kim
    Osong Public Health and Research Perspectives.2015; 6(4): 224.     CrossRef

PHRP : Osong Public Health and Research Perspectives