Osong Public Health and Research Perspectives

Original Articles

Periodontitis and non-communicable diseases in a Brazilian population, a cross-sectional study, Vila Velha-ES, Brazil: Gustavo Vital de Mendonça, Crispim Cerutti Junior, Alfredo Carlos Rodrigues Feitosa, Brígida Franco Sampaio de Mendonça, Lucia Helena Sagrillo Pimassoni; Osong Public Health Res Perspect. 2024;15(3):212-220. Published online June 27, 2024; DOI: https://doi.org/10.24171/j.phrp.2024.0021

1,124 View
29 Download

Graphical Abstract Abstract PDF: Objectives
The objective of this study was to examine the hypothesis that periodontal disease is associated with chronic non-communicable diseases. Methods: In this cross-sectional study, we evaluated the periodontal health condition of the population, based on the community periodontal index, as well as the number of missing teeth and the presence of systemic health conditions. We quantified the association between oral health and the presence of chronic diseases using simple logistic regression, adjusting for confounding factors including age, smoking, and overweight. Results: The study population consisted of 334 volunteers, aged between 19 and 81 years. In patients over 45 years old, periodontal disease was found to be significantly associated with hypertension and diabetes. Furthermore, in female patients, periodontal disease was significantly associated with hypertension, diabetes, and cancer. Conclusion: Our findings indicate that periodontal disease is positively and significantly associated with both arterial hypertension and diabetes, independent of potential confounding factors.

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

2,128 View
101 Download

Graphical Abstract Abstract 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.

The risk associated with psychiatric disturbances in patients with diabetes in Indonesia (2018): a cross-sectional observational study: Siti Isfandari, Betty Roosihermiatie, Sulistyowati Tuminah, Laurentia Konadi Mihardja; Osong Public Health Res Perspect. 2023;14(5):368-378. Published online October 18, 2023; DOI: https://doi.org/10.24171/j.phrp.2023.0144; Correction in: Osong Public Health Res Perspect 2023;14(6):530

2,204 View
83 Download
2 Web of Science
1 Crossref

Graphical Abstract Abstract PDF

Objectives
The global prevalence of psychiatric disturbances is rising, detrimentally affecting the quality of care and treatment outcomes for individuals, particularly those with diabetes.This study investigated the association of risk factors for psychiatric disturbances among productive-age patients with diabetes (ages 30−59 years), considering sociodemographic characteristics and co-existing diseases. The risk factors considered included sociodemographic factors (e.g., residence, age, sex, marital status, education, and occupation) and co-existing diseases (e.g., hypertension, heart disease, stroke, renal failure, rheumatism, asthma, and cancer). Methods: This cross-sectional study utilized data from the 2018 Indonesian National Health Survey (Riskesdas). The study population comprised respondents aged between 30 and 59 years who had diabetes and had completed the 20-question self-reporting questionnaire (SRQ-20). After the exclusion of incomplete SRQ-20 data, the sample included 8,917 respondents. Data were analyzed using logistic regression. Results: Approximately 18.29% of individuals with diabetes displayed symptoms indicative of psychiatric disturbances. After adjusting for sociodemographic factors such as age, sex, education level, occupation, marital status, and place of residence, patients with diabetes who had co-existing conditions such as hypertension, heart diseases, rheumatic disorders, asthma, or cancer had a higher risk for developing psychiatric disturbances than those with diabetes alone (adjusted odds ratio, 6.67; 95% confidence interval, 4.481−9.928; p<0.001). Conclusion: The elevated risk of psychiatric disturbances among patients with diabetes who had comorbidities underscores the importance of addressing mental health issues in the management of diabetes, especially in patients with concurrent disease conditions.

Citations

Citations to this article as recorded by

Correction to “The risk associated with psychiatric disturbances in patients with diabetes in Indonesia (2018): a cross-sectional observational study” [Osong Public Health Res Perspect 2023;14(5):368–78]
Siti Isfandari, Betty Roosihermiatie, Sulistyowati Tuminah, Laurentia Konadi Mihardja
Osong Public Health and Research Perspectives.2023; 14(6): 530. CrossRef

Chronic kidney disease in Indonesia: evidence from a national health survey: Puti Sari Hidayangsih, Dwi Hapsari Tjandrarini, Noor Edi Widya Sukoco, Nikson Sitorus, Ika Dharmayanti, Feri Ahmadi; Osong Public Health Res Perspect. 2023;14(1):23-30. Published online February 14, 2023; DOI: https://doi.org/10.24171/j.phrp.2022.0290

4,736 View
324 Download

Graphical Abstract Abstract PDF: Objectives
Several previous studies have stated that consuming certain foods and beverages might increase the risk of chronic kidney disease (CKD). This study aimed to examine the relationships of food and beverage consumption with other risk factors for CKD. Methods: Data sources included the 2018 Basic Health Research (Riskesdas) and the National Socio-Economic Survey (Susenas), which were analyzed using a cross-sectional design. The study samples were households from 34 provinces in Indonesia, and the analysis was performed with provincial aggregates. Data were analyzed using risk factor analysis followed by linear regression to identify relationships with CKD. Results: The prevalence of CKD in Indonesia was 0.38%. The province with the highest prevalence was North Kalimantan (0.64%), while the lowest was found in West Sulawesi (0.18%). Five major groups were formed from 15 identified risk factors using factor analysis. A linear regression model presented 1 significant selected factor (p=0.006, R2 =31%). The final model of risk factors included water quality, consumption of fatty foods, and a history of diabetes. Conclusion: Drinking water quality, fatty food consumption, and diabetes are associated with CKD. There is a need to monitor drinking water, as well as to promote health education and provide comprehensive services for people with diabetes, to prevent CKD.

Insufficient weight management in pregnant women with gestational diabetes mellitus: Kyunghee Han, Dong Wook Kwak, Hyun Mee Ryu, Hyun-Young Park; Osong Public Health Res Perspect. 2022;13(4):242-251. Published online August 31, 2022; DOI: https://doi.org/10.24171/j.phrp.2022.0182

2,900 View
98 Download

Graphical Abstract Abstract PDF: Objectives
This study investigated whether weight was managed appropriately in pregnant women with gestational diabetes mellitus (GDM) and examined the association between insufficient gestational weight gain (GWG) and adverse pregnancy outcomes.
Methods
The study included 235 pregnant women with GDM from the Korean Pregnancy Outcome Study. GWG from the second to the third trimester (kg/wk) and total GWG (kg) were classified as insufficient, appropriate, or excessive according to the 2009 Institute of Medicine guidelines. Adverse pregnancy outcomes included maternal (hypertensive disorders of pregnancy, preterm birth, cesarean delivery, and delivery complications) and infant (low birth weight, high birth weight, neonatal intensive care unit admission, and congenital anomalies) outcomes.
Results
The proportion of pregnant women with GDM who had insufficient GWG from the second to the third trimester was 52.3%, and that of participants with total insufficient GWG was 48.1%. There were no significant associations between insufficient GWG from the second to the third trimester and adverse pregnancy outcomes. Participants with total insufficient GWG had a significantly lower risk of preterm birth (odds ratio [OR], 0.17; 95% confidence interval [CI], 0.05–0.60) and high birth weight (OR, 0.23; 95% CI, 0.07–0.80).
Conclusion
Our findings suggest the importance of appropriate weight management and the need for GWG guidelines for pregnant women with GDM.

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

5,813 View
165 Download

Abstract 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.

Enrolment Phase Results of the Tabari Cohort Study: Comparing Family History, Lipids and Anthropometric Profiles Among Diabetic Patients: Mahmood Moosazadeh, Mahdi Afshari, Kaveh Jafari, Motahareh Kheradmand, Zahra Kashi, Mohsen Aarabi, Adeleh Bahar, Mohammad Khademloo; Osong Public Health Res Perspect. 2019;10(5):289-294. Published online October 31, 2019; DOI: https://doi.org/10.24171/j.phrp.2019.10.5.05

5,854 View
68 Download
4 Crossref

Abstract PDF

Objectives

Different factors are responsible for the silent epidemic of diabetes mellitus in developing and developed countries. This study aimed to determine the role of demographic factors, lipid profile, family history (the estimation of genetic association) and anthropometric factors on diabetes onset.

Methods

Data from the enrolment phase of the Tabari Cohort study was applied for this study and included 10,255 participants aged between 35–70 years. Anthropometric variables were measured by trained staff using standard tools. Blood specimens were collected for lipid profile and blood glucose measurements. Data analyses were performed using SPSS version 24, with univariate and multivariate logistic regression.

Results

The prevalence of diabetes mellitus was estimated to be 17.2% in the cohort population, 15.6% in men, and 18.3% in women. The adjusted odds ratios (95% confidence intervals) for age groups 40–49, 50–59 and over 60 were 2.58 (2.20–3.69), 5.80 (4.51–7.48) and 8.72 (6.67–11.39), respectively. In addition, the odds ratios (95% confidence intervals) for 2 (or more), and 1 affected family member were 4.12 (3.55–4.90) and 2.34 (2.07–2.65), respectively. Triglyceride concentrations more than 500, and abnormal high-density lipoprotein levels increased the odds of diabetes mellitus by 3.29- and 1.18-fold, respectively.

Conclusion

The current study showed that old age and a family history were strong predictors for diabetes mellitus.

Citations

Citations to this article as recorded by

The prevalence and determinants of diabetes mellitus and thyroid disorder comorbidity in Tabari cohort population
Mahmood Moosazadeh, Saeedeh Khakhki, Adele Bahar, Akbar Hedayatizadeh-Omran, Motahareh Kheradmand, Reza Alizadeh-Navaei, Erfan Ghadirzadeh
Scientific Reports.2024;[Epub] CrossRef
Free-fatty acid receptor-4 gene polymorphism (rs61866610) and colorectal cancer risk
Ramin Shekarriz, Maryam Hasanian, Mohadeseh Ahmadi, Versa Omrani-Nava, Reza Alizadeh-Navaei
Nucleosides, Nucleotides & Nucleic Acids.2024; : 1. CrossRef
The relationship between spiritual intelligence and self-management in patients with diabetes type 1
Sima Rafiei, Saber Souri, Zahra Nejatifar, Mohammad Amerzadeh
BMC Endocrine Disorders.2023;[Epub] CrossRef
Prevalence and determinants of diabetes and prediabetes in southwestern Iran: the Khuzestan comprehensive health study (KCHS)
Sanam Hariri, Zahra Rahimi, Nahid Hashemi-Madani, Seyyed Ali Mard, Farnaz Hashemi, Zahra Mohammadi, Leila Danehchin, Farhad Abolnezhadian, Aliasghar Valipour, Yousef Paridar, Mohammad Mahdi Mir-Nasseri, Alireza Khajavi, Sahar Masoudi, Saba Alvand, Bahman
BMC Endocrine Disorders.2021;[Epub] CrossRef

Factors that Correlate with Poor Glycemic Control in Type 2 Diabetes Mellitus Patients with Complications: Mohammad Haghighatpanah, Amir Sasan Mozaffari Nejad, Maryam Haghighatpanah, Girish Thunga, Surulivelrajan Mallayasamy; Osong Public Health Res Perspect. 2018;9(4):167-174. Published online August 31, 2018; DOI: https://doi.org/10.24171/j.phrp.2018.9.4.05

10,002 View
176 Download
63 Crossref

Abstract PDF

Objectives

Inadequate glycemic control amongst patients with Type 2 diabetes mellitus (T2DM) indicates a major public health problem and a significant risk factor for the progression and complications caused by diabetes. Glycemic control is the main therapeutic objective for the prevention of organ damage and other complications arising from diabetes.

Methods

This was a retrospective observational study of T2DM patients with complications, who were aged 40 years and older. The study was conducted retrospectively on medical records (in-patient and out-patient) obtained from a South Indian teaching hospital, Manipal, India. The patients included in the study had fasting blood sugar, postprandial blood sugar and HbA1c measured at least twice during follow-ups the previous year. Patients’ HbA1c levels were categorized into good control ≤7% (≤53mmol/mol), and poor control >7% (>53mmol/mol), and patients’ characteristics were analyzed.

Results

A total of 657 patients were included in the study. The mean age was 59.67 (SD = 9.617) years, with 152 (23.1%) females and 505 (76.9%) males, and 514 (78.2%) patients had poor glycemic control. Most of the patients were on insulin mono-therapy [n = 271 (42.1%)], about a third of the patients were on combination therapy that included an oral hypoglycemic agent and insulin [n = 236 (36.6%)]. Patients with a history of more than 10 years of diabetes [n = 293 (44.6%)], had a family history of diabetes [n = 256 (39%)] and obesity [n = 95 (14.5%)], all had poor glycemic control.

Conclusion

This present study indicated a significant association of gender (female), age, high-density lipoprotein level, duration of diabetes and type of medication, with poor glycemic control in T2DM patients that had secondary medical complications.

Citations

Citations to this article as recorded by

Effectiveness of Family-Based Diabetes Management Intervention on Glycated Haemoglobin Among Adults With Type 2 Diabetes: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Margareta Teli, Ratsiri Thato, Faizul Hasan, Yohanes Andy Rias
Biological Research For Nursing.2024; 26(2): 315. CrossRef
Factors linked to poor glycemic control in an outpatient diabetic clinic: a cross-sectional study in Saint-Nicolas Hospital, Haiti
Ludentz Dorcélus, Emmanuel R. Alexandre, Charnee M. Villemenay, Scaïde U. Benjaminel, Eddie Charles
Journal of Global Health Reports.2024;[Epub] CrossRef
The prevalence of hypogonadism in male patients with type 2 diabetes mellitus and clinically relevant factors
Hakan Düğer
Journal of Health Sciences and Medicine.2024; 7(1): 53. CrossRef
Real-world effectiveness of iGlarLixi in individuals with T2D sub-optimally controlled on oral anti-diabetic drugs with or without basal insulin in daily practice in Saudi Arabia (EMPOWER study)
Anwar Jammah, Nagwa Roushdy, Mohamed Gamil, Nidal Abu Diab, Naglaa Abdelmonaem, Saher Safarini, Mohamed Gadallah, Nedal Abu Zaid, Yahya Shihadeh, Mohamed Saeed, Jamaa Sadik, Yasser Akil
Endocrine and Metabolic Science.2024; 15: 100164. CrossRef
Rejuvenating Mobility: Impact of Concurrent Exercise on Functional Claudication Distance and Vascular Health among Patients with T2DM-Associated PAD
Uroosa Amin, Qurat-ul-Ain Adnan, Dr. Tauseef Ahmad
Allied Medical Research Journal.2024; : 138. CrossRef
Exploring the Interplay of Socioeconomic and Behavioral Factors: Unraveling Gender Disparities in Glycemic Control Among Adult Type 2 Diabetic Patients in Outpatient Care
Amar Mankar, Umesh Kawalkar, Nilesh Jadhao, Umesh Joge, Ashutosh Paldiwal, Manoj Talapalliwar, Manoj S Patil
Cureus.2024;[Epub] CrossRef
Landscape of pharmacogenetic variants associated with non-insulin antidiabetic drugs in the Indian population
Ambily Sivadas, S Sahana, Bani Jolly, Rahul C Bhoyar, Abhinav Jain, Disha Sharma, Mohamed Imran, Vigneshwar Senthivel, Mohit Kumar Divakar, Anushree Mishra, Arpita Mukhopadhyay, Greg Gibson, KM Venkat Narayan, Sridhar Sivasubbu, Vinod Scaria, Anura V Kurp
BMJ Open Diabetes Research & Care.2024; 12(2): e003769. CrossRef
Prevalence of and factors associated with suboptimal glycemic control among patients with type 2 diabetes mellitus attending public hospitals in the Greater Male’ Region, Maldives: a hospital-based cross-sectional study
Jeehana Shareef, Tawatchai Apidechkul, Peeradone Srichan
BMC Public Health.2024;[Epub] CrossRef
Exploring the self-efficacy of patients with diabetes: its role as a predictor of diabetes management and well-being
Ayoub Ali Alshaikh, Faisal Saeed Al-Qahtani, Saif Aboud M. Alqahtani, Ahmad Ali AlFarhan, Ali Mushabbab Al Nuwayhidh, Ayman Mohammed Madkhali, Riyad Saeed AlQahtani, Ali Fayez AlAsmari, Abdulaziz Saeed Alserhani, Hatim Ahmed Alqubaisi, Ziyad Saad Saeed Al
Frontiers in Endocrinology.2024;[Epub] CrossRef
Dietary glycemic index and glycemic load predict longitudinal change in glycemic and cardio-metabolic biomarkers among old diabetic adults living in a resource-poor country
Yen Nhi Hoang, Trong Hung Nguyen, Dang Khanh Ngan Ho, Chyi-Huey Bai, Wen-Ling Lin, Huong Duong Phan, Hoang Hiep Phan, Ngoc Luong Tran, Jung-Su Chang
International Journal of Food Sciences and Nutriti.2024; 75(6): 550. CrossRef
Impact of Gender and Age in HbA1c Levels among Libyan Adults Without Known Diabetes in Zeletin City, Libya: A Cross-Sectional Study
Aisha Zaidi
AlQalam Journal of Medical and Applied Sciences.2024; : 464. CrossRef
The association between serum high-density lipoprotein and hemoglobin A1c in T2DM: Evidence from a nationwide cross-sectional study in diabetic patients
Methavee Poochanasri, Sethapong Lertsakulbunlue, Chutawat Kookanok, Ram Rangsin, Wisit Kaewput, Boonsub Sakboonyarat, Mathirut Mungthin, Parinya Samakkarnthai
Diabetes Epidemiology and Management.2024; 16: 100232. CrossRef
Enhancing Type 2 Diabetes Treatment Decisions With Interpretable Machine Learning Models for Predicting Hemoglobin A1c Changes: Machine Learning Model Development
Hisashi Kurasawa, Kayo Waki, Tomohisa Seki, Akihiro Chiba, Akinori Fujino, Katsuyoshi Hayashi, Eri Nakahara, Tsuneyuki Haga, Takashi Noguchi, Kazuhiko Ohe
JMIR AI.2024; 3: e56700. CrossRef
Patterns of glycaemic control and associated factors among adult patients with diabetes attending medical referral clinics in two public hospitals in North-West Ethiopia: a cross-sectional study
Hailemariam Kassahun, Abere Genetu, Taye Abuhay, Hiwot Tesfa
BMJ Public Health.2024; 2(2): e000828. CrossRef
Prevalence and predictors of hypoglycemia in older outpatients with type 2 diabetes mellitus
Ahmad Al-Azayzih, Roaa J. Kanaan, Shoroq M. Altawalbeh, Karem H. Alzoubi, Zelal Kharaba, Anan Jarab, Miquel Vall-llosera Camps
PLOS ONE.2024; 19(8): e0309618. CrossRef
Determinants of poor glycemic control among type 2 diabetes mellitus patients at University of Gondar Comprehensive Specialized Hospital, Northwest Ethiopia: Unmatched case-control study
Gebrehiwot Lema Legese, Getahun Asres, Shitaye Alemu, Tesfaye Yesuf, Yeabsira Aklilu Tesfaye, Tsegaw Amare
Frontiers in Endocrinology.2023;[Epub] CrossRef
Prevalence of Glycemic Control and Factors Associated With Poor Glycemic Control: A Systematic Review and Meta-analysis
Zebenay Workneh Bitew, Ayinalem Alemu, Desalegn Abebaw Jember, Erkihun Tadesse, Fekadeselassie Belege Getaneh, Awole Seid, Misrak Weldeyonnes
INQUIRY: The Journal of Health Care Organization, .2023; 60: 004695802311557. CrossRef
Glycemic control and diabetes complications among adult type 2 diabetic patients at public hospitals in Hadiya zone, Southern Ethiopia
Abraham Lomboro Dimore, Zerihun Kura Edosa, Asmelash Abera Mitiku, Suman S. Thakur
PLOS ONE.2023; 18(3): e0282962. CrossRef
Using Machine Learning for the Risk Factors Classification of Glycemic Control in Type 2 Diabetes Mellitus
Yi-Ling Cheng, Ying-Ru Wu, Kun-Der Lin, Chun-Hung Lin, I-Mei Lin
Healthcare.2023; 11(8): 1141. CrossRef
Knowledge and Awareness About Diabetes Mellitus Among Urban and Rural Population Attending a Tertiary Care Hospital in Haryana
Dr.Lalit Kumar, Rahul Mittal, Akhil Bhalla, Ashwani Kumar, Hritik Madan , Kushagra Pandhi, Yukta Garg, Kamaldeep Singh, Arpit Jain, Surya Rana
Cureus.2023;[Epub] CrossRef
Glycemic Control and Its associated Determinants among Type II Diabetic Patients at Tertiary Care Hospital in North India
Soorvir singh Gurger, Anshu Mittal, Gauri shankar Goel, Anuj Mittal, Deepmala Kamboj
Healthline.2023; 14(1): 17. CrossRef
Prevalence of medication adherence and glycemic control among patients with type 2 diabetes and influencing factors: A cross-sectional study
Budi Suprapti, Zamrotul Izzah, Ade Giriayu Anjani, Mareta Rindang Andarsari, Wenny Putri Nilamsari, Cahyo Wibisono Nugroho
Global Epidemiology.2023; 5: 100113. CrossRef
The impact of pharmacist interventions, follow-up frequency and default on glycemic control in Diabetes Medication Therapy Adherence Clinic program: a multicenter study in Malaysia
Phei Ching Lim, Hooi Hoon Tan, Nurul Ain Mohd Noor, Chee Tao Chang, Te Ying Wong, Ee Linn Tan, Chiou Ting Ong, Kalyhani Nagapa, Lee Shyong Tai, Wei Ping Chan, Yong Boey Sin, Yin Shan Tan, Shanty Velaiutham, Rohaizan Mohd Hanafiah
Journal of Pharmaceutical Policy and Practice.2023;[Epub] CrossRef
Early detection system of risk factors for diabetes mellitus type 2 utilization of machine learning-random forest
Johannes B. Ginting, Tri Suci, Chrismis N. Ginting, Ermi Girsang
Journal of Family and Community Medicine.2023; 30(3): 171. CrossRef
Identifying Profiles of Patients With Uncontrolled Type 2 Diabetes Who Would Benefit From Referral to an Endocrinologist
Eden Avnat, Gabriel Chodick, Varda Shalev
Endocrine Practice.2023; 29(11): 855. CrossRef
Glycemic control and associated factors among type 2 diabetes mellitus patients: a cross-sectional study of Azar cohort population
Masoud Faghieh Dinavari, Sarvin Sanaie, Kimia Rasouli, Elnaz Faramarzi, Roghayeh Molani-Gol
BMC Endocrine Disorders.2023;[Epub] CrossRef
Metabolic Biomarkers in Adults with Type 2 Diabetes: The Role of PPAR-γ2 and PPAR-β/δ Polymorphisms
Sandra A. Reza-López, Susana González-Gurrola, Oscar O. Morales-Morales, Janette G. Moreno-González, Ana M. Rivas-Gómez, Everardo González-Rodríguez, Verónica Moreno-Brito, Angel Licón-Trillo, Irene Leal-Berumen
Biomolecules.2023; 13(12): 1791. CrossRef
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 and Research Perspectives.2023; 14(6): 508. CrossRef
What drives glycemic control in a person living with diabetes?
Rajiv Singla, Geetu Gupta, Yashdeep Gupta
International Journal of Diabetes in Developing Co.2022; 42(2): 369. CrossRef
Prevalence and predictors of suboptimal glycemic control among patients with type 2 diabetes mellitus in northern Thailand: A hospital-based cross-sectional control study
Fartima Yeemard, Peeradone Srichan, Tawatchai Apidechkul, Naphat Luerueang, Ratipark Tamornpark, Suphaphorn Utsaha, Sompop Bencharit
PLOS ONE.2022; 17(1): e0262714. CrossRef
Exploring of Determinants Factors of Anti-Diabetic Medication Adherence in Several Regions of Asia – A Systematic Review
Much Ilham Novalisa Aji Wibowo, Nanang Munif Yasin, Susi Ari Kristina, Yayi Suryo Prabandari
Patient Preference and Adherence.2022; Volume 16: 197. CrossRef
Visual impairment and its predictors among people living with type 2 diabetes mellitus at Dessie town hospitals, Northeast Ethiopia: institution-based cross-sectional study
Mohammed Abdu Seid, Adugnaw Ambelu, Mengistie Diress, Yigizie Yeshaw, Yonas Akalu, Baye Dagnew
BMC Ophthalmology.2022;[Epub] CrossRef
Relations of Well-Being, Coping Styles, Perception of Self-Influence on the Diabetes Course and Sociodemographic Characteristics with HbA1c and BMI Among People with Advanced Type 2 Diabetes Mellitus
Agnieszka Łukasiewicz, Andrzej Kiejna, Ewelina Cichoń, Aleksandra Jodko-Modlińska, Marcin Obrębski, Andrzej Kokoszka
Diabetes, Metabolic Syndrome and Obesity: Targets .2022; Volume 15: 407. CrossRef
A bibliometric analysis of highly cited insulin resistance publications in Science Citation Index Expanded
Yuh-Shan Ho, Priyanga Ranasinghe
Obesity Medicine.2022; 31: 100399. CrossRef
Alternate-day add-on therapy with dapagliflozin in patients with type 2 diabetes mellitus: potential benefits and concerns
Harmanjit Singh, Dinesh Joshi, Seerat Narula, Mandeep Singla, Ravi Rohilla, Jagjit Singh
Expert Review of Clinical Pharmacology.2022; 15(2): 197. CrossRef
Factors associated with Glycemic control among Syrian patients with Type 2 Diabetes Mellitus
Khadija Khalil, Afraa Zrieki`
Research Journal of Pharmacy and Technology.2022; : 1701. CrossRef
A Comparative analysis of type 2 diabetes management quality indicators in cancer survivors
Eun J. Ko, Su J. Lee
Asia-Pacific Journal of Oncology Nursing.2022; 9(11): 100116. CrossRef
Analysis of the Association between Metabolic Syndrome and Renal Function in Middle-Aged Patients with Diabetes
Yoonjin Park, Su Jung Lee
International Journal of Environmental Research an.2022; 19(18): 11832. CrossRef
Effective data-driven precision medicine by cluster-applied deep reinforcement learning
Sang Ho Oh, Su Jin Lee, Jongyoul Park
Knowledge-Based Systems.2022; 256: 109877. CrossRef
Management goal achievements of diabetes care in Iran: study profile and main findings of DiaCare survey
Gita Shafiee, Safoora Gharibzadeh, Nekoo Panahi, Farideh Razi, Seyed Masoud Arzaghi, Vahid Haghpanah, Afshin Ostovar, Alireza Raeisi, Alireza Mahdavi-Hezareh, Bagher Larijani, Ensieh Nasli Esfahani, Ramin Heshmat
Journal of Diabetes & Metabolic Disorders.2022; 22(1): 355. CrossRef
Glycemic control and its determinants among people with type 2 diabetes mellitus in Ernakulam district, Kerala
ShanaShirin Najeeb, TeenaMary Joy, Aswathy Sreedevi, K Vijayakumar, Syama, . Glycaemic Control and Determinants Team
Indian Journal of Public Health.2022; 66(5): 80. CrossRef
Sex Differences in the Effects of CDKAL1 Variants on Glycemic Control in Diabetic Patients: Findings from the Korean Genome and Epidemiology Study
Hye Ah Lee, Hyesook Park, Young Sun Hong
Diabetes & Metabolism Journal.2022; 46(6): 879. CrossRef
Relationship and influences of behavioral and psychological factors on metabolic control of patients with type 2 diabetes mellitus
Vojislav Stanojevic, Marija Jevtic, Milena Mitrovic, Marko Panajotovic, Aleksandar Aleksic, Cedomirka Stanojevic
Vojnosanitetski pregled.2022; 79(12): 1177. CrossRef
Lipid Profile and Glycemic Control in Type 2 Diabetic Patients
Sarah Maan AL-Bahrani, Batool A.Gh. Yassin
Arab Board Medical Journal.2022; 23(1): 21. CrossRef
Glycemic Control of Diabetes Mellitus Patients in Referral Hospitals of Amhara Region, Ethiopia: A Cross-Sectional Study
Berhanu Elfu Feleke, Teferi Elfu Feleke, Melkamu Beyene Kassahun, Wondemu Gebrekirose Adane, Netsanet Fentahun, Abel Girma, Alamirew Alebachew, Eyaya Misgan, Hanna Demelash Desyibelew, Mulat Tirfie Bayih, Omer Seid, Daniel Diaz
BioMed Research International.2021; 2021: 1. CrossRef
Association of glycemic control and anthropometric measurement among type 2 diabetes mellitus: a cross-sectional study
Mitku Mammo Taderegew, Mamo Solomon Emeria, Betregiorgis Zegeye
Diabetology International.2021; 12(4): 356. CrossRef
Impact of Pharmacist-led Educational Intervention on Predictors of Diabetic Foot at Two Different Hospitals of Malaysia
Amer Hayat Khan, Muhammad Zahid Iqbal, Syed Azhar Syed Sulaiman, Aznita Ibrahim, Nor Shaffinaz Binti Yusoff Azmi, Muhammad Shahid Iqbal, Ahmed A. Albassam
Journal of Pharmacy and Bioallied Sciences.2021; 13(1): 108. CrossRef
Factors Associated with Glycaemic Control among Diabetic Patients Managed at an Urban Hospital in Hanoi, Vietnam
Luu Quang Thuy, Hoang Thi Phuong Nam, Tran Thi Ha An, Bui Van San, Tran Nguyen Ngoc, Le Hong Trung, Pham Huy Tan, Nguyen Hoang Thanh, Everson A Nunes
BioMed Research International.2021; 2021: 1. CrossRef
Predictors of Poor Plasma Glucose Maintenance in Type II Diabetic People with Ophthalmic Complication: The Case of Dessie Hospitals in Ethiopia
Mohammed Abdu Seid, Baye Dagnew
Diabetes, Metabolic Syndrome and Obesity: Targets .2021; Volume 14: 2317. CrossRef
Oral health and longitudinal changes in fasting glucose levels: A nationwide cohort study
Tae-Jin Song, Yoonkyung Chang, Jimin Jeon, Jinkwon Kim, David M. Ojcius
PLOS ONE.2021; 16(6): e0253769. CrossRef
Poor Glycemic Control and Its Contributing Factors Among Type 2 Diabetes Patients at Adama Hospital Medical College in East Ethiopia
Tewodros Yosef, Dejen Nureye, Eyob Tekalign
Diabetes, Metabolic Syndrome and Obesity: Targets .2021; Volume 14: 3273. CrossRef
Probucol Pharmacological and Bio-Nanotechnological Effects on Surgically Transplanted Graft Due to Powerful Anti-Inflammatory, Anti-Fibrotic and Potential Bile Acid Modulatory Actions
Armin Mooranian, Corina Mihaela Ionescu, Susbin Raj Wagle, Bozica Kovacevic, Daniel Walker, Melissa Jones, Jacqueline Chester, Thomas Foster, Edan Johnston, Momir Mikov, Marcus D. Atlas, Hani Al-Salami
Pharmaceutics.2021; 13(8): 1304. CrossRef
Clinical pharmacists education and counselling in patients with co-morbid hypertension and diabetes in a Municipal hospital in Ghana
A. O. Kwakye, K. O. Buabeng, N. A. M. Opare-Addo, E. Owusu-Dabo
African Journal of Pharmacy and Pharmacology.2021; 15(10): 183. CrossRef
Effect of educational interventions on knowledge of the disease and glycaemic control in patients with type 2 diabetes mellitus: a systematic review and meta-analysis of randomised controlled trials
Wondimeneh Shibabaw Shiferaw, Tadesse Yirga Akalu, Melaku Desta, Ayelign Mengesha Kassie, Pammla Margaret Petrucka, Yared Asmare Aynalem
BMJ Open.2021; 11(12): e049806. CrossRef
Persistent Cardiometabolic Health Gaps: Can Therapeutic Care Gaps Be Precisely Identified from Electronic Health Records
Xiaowei Yan, Walter F. Stewart, Hannah Husby, Jake Delatorre-Reimer, Satish Mudiganti, Farah Refai, Andrew Hudnut, Kevin Knobel, Karen MacDonald, Frangiscos Sifakis, James B. Jones
Healthcare.2021; 10(1): 70. CrossRef
Glycemic control and awareness of foot care indiabetic foot syndrome
Ayten Guner Atayoglu, Ali Timucin Atayoglu, Rahime Ozgur, Hammad Khan
INTERNATIONAL JOURNAL OF ENDOCRINOLOGY (Ukraine).2021; 17(3): 200. CrossRef
Раннє призначення інсуліну при цукровому діабеті 2-го типу: плюси і мінуси
S.V. Jargin
INTERNATIONAL JOURNAL OF ENDOCRINOLOGY (Ukraine).2021; 17(2): 169. CrossRef
Salutogenic model of health to identify turning points and coping styles for eating practices in type 2 diabetes mellitus
C. M. M. Polhuis, L. Vaandrager, S. S. Soedamah-Muthu, M. A. Koelen
International Journal for Equity in Health.2020;[Epub] CrossRef
Self-Care in Adults with Type 2 Diabetes Mellitus: A Systematic Review
Rebeca Barbosa da Rocha, Cristiano Sales Silva, Vinícius Saura Cardoso
Current Diabetes Reviews.2020; 16(6): 598. CrossRef
Medication adherence assessment among patients with type 2 diabetes mellitus treated polytherapy in indonesian community health center: A cross sectional-study
Nora Wulandari, Maifitrianti Maifitrianti, Faridlatul Hasanah, Sri Atika, Risa Dini Putri
Journal of Pharmacy And Bioallied Sciences.2020; 12(6): 758. CrossRef
Glycemic Control Among People Living with Diabetes and Human Immunodeficiency Virus in Ethiopia: Leveraging Clinical Care for the Looming Co-Epidemics

Tsegaye Melaku, Legese Chelkeba, Zeleke Mekonnen, Kabaye Kumela
Diabetes, Metabolic Syndrome and Obesity: Targets .2020; Volume 13: 4379. CrossRef
Electronic medical records-based comparison of glycemic control efficacy between sulfonylureas and dipeptidyl peptidase-4 inhibitors added on to metformin monotherapy in patients with type 2 diabetes
Suhrin Lee, SeungHwan Lee, In-Jin Jang, Kyung-Sang Yu, Su-jin Rhee
Translational and Clinical Pharmacology.2020; 28(4): 199. CrossRef
Poor-Glycaemic-Control Prevalence and Determinants among Type 2 Diabetes Mellitus Patients Attending a Primary Health Care Setting in Central Kerala
Sajith Kumar Soman, Binu Areekal, Sudhiraj Thiruthara Sukumaran, Safa Puliyakkadi, Rajesh Koothupalakkal Ravi
Journal of Evidence Based Medicine and Healthcare.2020; 7(49): 2892. CrossRef

Effects of Physical Activity on Depression in Adults with Diabetes: Deok-Ju Kim; Osong Public Health Res Perspect. 2018;9(4):143-149. Published online August 31, 2018; DOI: https://doi.org/10.24171/j.phrp.2018.9.4.02

6,498 View
150 Download
8 Crossref

Abstract PDF

Objectives

The purpose of this study was to identify the current state of physical activity in adults with diabetes and to investigate the effect of physical activity on depression.

Methods

The present study was conducted using data from the 2^nd year of the 6^th Korea National Health and Nutritional Examination Survey. From the total of 7,550 individuals, 418 adults diagnosed with diabetes were selected as participants, and their physical activity and depression levels were examined.

Results

The physical activity status of the participants showed that they did not usually engage in physical activities at work, and only a few participants were involved in moderate intensity physical leisure activity. Apart from walking for 10 minutes each day, which accounted for 1/3 of the participants, most of the participants did not engage in specific forms of exercise. An examination of the effects of physical activity on depression revealed that moderate intensity physical activity at work and leisure influenced depression. In terms of demographic characteristics, gender, occupation, income quintile, and subjective health status were all found to affect depression.

Conclusion

For elderly (60 years or older) patients with diabetes, which accounted for the majority of the diabetic population, a systematic leisure program and professional education are necessary to help them to manage stress and depression in daily life. Additionally, provision of community and family support should encourage regular, moderate intensity exercise and promote lifestyle changes to encourage increased physical activity.

Citations

Citations to this article as recorded by

Association of Physical Activity and Sleep Metrics with Depression in People with Type 1 Diabetes
Abdullah Al-Ozairi, Mohammad Irshad, Husain Alsaraf, Jumana AlKandari, Ebaa Al-Ozairi, Stuart Gray
Psychology Research and Behavior Management.2024; Volume 17: 2717. CrossRef
Prevalence of comorbid depression and associated factors among hospitalized patients with type 2 diabetes mellitus in Hunan, China
Rehanguli Maimaitituerxun, Wenhang Chen, Jingsha Xiang, Atipatsa C. Kaminga, Xin Yin Wu, Letao Chen, Jianzhou Yang, Aizhong Liu, Wenjie Dai
BMC Psychiatry.2023;[Epub] CrossRef
Barriers & facilitators to physical activity in people with depression and type 2 diabetes mellitus in Pakistan: A qualitative study to explore perspectives of patient participants, carers and healthcare staff
Aatik Arsh, Saima Afaq, Claire Carswell, Karen Coales, Najma Siddiqi
Mental Health and Physical Activity.2023; 25: 100542. CrossRef
Moderating Effect of Grip Strength in the Association between Diabetes Mellitus and Depressive Symptomatology
Diogo Veiga, Miguel Peralta, Élvio R. Gouveia, Laura Carvalho, Jorge Encantado, Pedro J. Teixeira, Adilson Marques
Sports.2023; 12(1): 3. CrossRef
Modeling the effects of physical activity, education, health, and subjective wealth on happiness based on Indonesian national survey data
Bhina Patria
BMC Public Health.2022;[Epub] CrossRef
Triad of impairment in older people with diabetes-reciprocal relations and clinical implications
A.H. Abdelhafiz, P.C. Davies, A.J. Sinclair
Diabetes Research and Clinical Practice.2020; 161: 108065. CrossRef
Association between exercise and health-related quality of life and medical resource use in elderly people with diabetes: a cross-sectional population-based study
Chien-Cheng Huang, Chien-Chin Hsu, Chong-Chi Chiu, Hung-Jung Lin, Jhi-Joung Wang, Shih-Feng Weng
BMC Geriatrics.2020;[Epub] CrossRef
Challenges and Strategies for Diabetes Management in Community-Living Older Adults
Alan J. Sinclair, Ahmed H. Abdelhafiz
Diabetes Spectrum.2020; 33(3): 217. 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

3,582 View
15 Download
24 Crossref

Abstract 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
Assessment of Depression Risk Factors in Type 2 Diabetics at an Outpatient Clinic of a Tertiary Hospital in North Central, Nigeria
Godwin Abah Akor, Nndunno Ashaku Akwaras, David Aondona Daniel, Laadi Swuende, Onuh Friday, Aganyi Paul
International Journal of Innovative Science and Re.2024; : 1972. 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

Evidence Gap on the Prevalence of Non-conventional Risk Factors for Type 2 Diabetes in Iran: Abdolreza Shaghaghi, Ali Ahmadi; Osong Public Health Res Perspect. 2014;5(5):292-297. Published online October 31, 2014; DOI: https://doi.org/10.1016/j.phrp.2014.08.002

3,128 View
20 Download
3 Crossref

Abstract PDF

Objectives
Robust scientific evidence exists about the role of non-conventional risk factors in type 2 diabetes worldwide. The current epidemiological pattern of the disease in Iran suggests a precipitating role for these non-conventional risk factors. This review was performed to examine the research evidence suggesting a higher prevalence of non-conventional type 2 diabetes risk factors in Iran.
Methods
MeSH keywords were applied to search several databases, including PUBMED, MEDLINE, AMED, EMBASE, Iran DOC, and the Scientific Information Database without a time limit from inception to September 2011. The quality of the non-interventional and population-based studies on Iranians included in these databases was assessed by the authors and any disagreement was resolved with consensus.
Results
The literature search yielded 1847 publications, of which 62 were included in this study after eliminating non-relevant and overlapping papers. No study was found that verified a higher prevalence of the non-conventional type 2 diabetes risk factors in the Iranian population.
Conclusion
The identified evidence gap about the role of prominent non-conventional risk factors of type 2 diabetes in the Iranian population could be a major caveat in the application of an evidence-based approach to endorse or reject existing hypothesis about these risk factors. Studies on the prevalence of non-conventional biomarkers of type 2 diabetes among Iranians could be a promising area of research.

Citations

Citations to this article as recorded by

Application of Pender’s health promotion model for type 2 diabetes treatment adherence: protocol for a mixed methods study in southern Iran
Nahid Shahabi, Zahra Hosseini, Teamur Aghamolaei, Amin Ghanbarnejad, Ahmad Behzad
Trials.2022;[Epub] CrossRef
Prevalence and determinants of diabetes and prediabetes in southwestern Iran: the Khuzestan comprehensive health study (KCHS)
Sanam Hariri, Zahra Rahimi, Nahid Hashemi-Madani, Seyyed Ali Mard, Farnaz Hashemi, Zahra Mohammadi, Leila Danehchin, Farhad Abolnezhadian, Aliasghar Valipour, Yousef Paridar, Mohammad Mahdi Mir-Nasseri, Alireza Khajavi, Sahar Masoudi, Saba Alvand, Bahman
BMC Endocrine Disorders.2021;[Epub] CrossRef
Association of modified Nordic diet with cardiovascular risk factors among type 2 diabetes patients: a cross-sectional study
Elnaz Daneshzad, Shaghayegh Emami, Manije Darooghegi Mofrad, Sahar Saraf-Bank, Pamela J. Surkan, Leila Azadbakht
Journal of Cardiovascular and Thoracic Research.2018; 10(3): 153. CrossRef

Knowledge of Diabetes Mellitus: Does Gender Make a Difference?: Patrício Fernando Lemes dos Santos, Poliana Rodrigues dos Santos, Graziele Souza Lira Ferrari, Gisele Almeida Amaral Fonseca, Carlos Kusano Bucalen Ferrari; Osong Public Health Res Perspect. 2014;5(4):199-203. Published online August 31, 2014; DOI: https://doi.org/10.1016/j.phrp.2014.06.004

3,637 View
25 Download
18 Crossref

Abstract PDF

Objective Diabetes mellitus (DM) is a chronic disease considered an important public health problem. In recent years, its prevalence has been exponentially rising in many developing countries. Chronic complications of DM are important causes of morbidity and mortality among patients, which impair their health and quality of life. Knowledge on disease prevention, etiology, and management is essential to deal with parents, patients, and caregivers. The aim of this study was to evaluate the knowledge regarding DM in an adult population from a Middle-western Brazilian city.
Methods
This was a cross-sectional study covering 178 adults, aged 18–64 years, who answered a diabetes knowledge questionnaire. In order to identify the difference between groups, analysis of variance was used.
Results
Higher knowledge scores were found regarding the role of sugars on DM causality, diabetic foot care, and the effects of DM on patients (blindness, impaired wound healing, and male sexual dysfunction). However, lower scores were found amongst types of DM, hyperglycemic symptoms, and normal blood glucose levels. Females tended to achieve better knowledge scores than males.
Conclusion
Women had better knowledge regarding types of DM, normal blood glucose values, and consequences of hyperglycemia revealed that diabetes education should be improved.

Citations

Citations to this article as recorded by

Health-related quality of life and influencing factors of patients with paroxysmal nocturnal hemoglobinuria in China
Huaxin Yu, Shengnan Duan, Pei Wang, Rong Fu, Zixuan Lv, Yuchi Yu, Pu Miao, Junwei Shi, Niekun Zhuang, Huiying Hu, Ni Yuan, Sijia Che
Orphanet Journal of Rare Diseases.2024;[Epub] CrossRef
The interplay of social support and education on diabetes knowledge: a focus on Korean American women
Young Ji Yoon, Soonok An, Y. Joon Choi, Hee Yun Lee
Ethnicity & Health.2024; : 1. CrossRef
The Relationship Between Diabetes Knowledge Level, Physical Activity, and Quality of Life in Older Adults
Burçin AKÇAY, Tuğba KURU ÇOLAK, Sultan İĞREK, Bahar ÖZGÜL, Adnan APTI
Bandırma Onyedi Eylül Üniversitesi Sağlık Bilimler.2023; 5(2): 162. CrossRef
Assessment of prediabetes knowledge among adults in Al-Madinah, Saudi Arabia
Ameerah Almaski, Manal Almughamisi
Nutrition and Health.2023; : 026010602311557. CrossRef
Knowledge, Attitude, and Practices Related to Foot Care Among Diabetic Patients in Tabuk City, Saudi Arabia
Tariq M Shaqran, Saud N Alqahtani, Abdullah F Alhalafi , Norah M Alsabeelah, Rafaa A Algethmi, Ammar S Azhari, Abdulrahman Y Alhashmi, Abdullah N Almaghrabi, Hibah A Alshammari, Mohammed Saeed Alshahrani
Cureus.2023;[Epub] CrossRef
Psychometric properties of the revised Diabetes Knowledge Test using Rasch analysis
Eun-Hyun Lee, Young Whee Lee, Hyun-Jung Kang
Patient Education and Counseling.2022; 105(4): 851. CrossRef
Recognition of diabetes and sociodemographic predictors: results of a cross-sectional nationwide population-based survey in Singapore
Kumarasan Roystonn, Jue Hua Lau, PV AshaRani, Fiona Devi Siva Kumar, Peizhi Wang, Chee Fang Sum, Eng Sing Lee, Siow Ann Chong, Mythily Subramaniam
BMJ Open.2022; 12(3): e050425. CrossRef
Public knowledge and awareness of diabetes mellitus, its risk factors, complications, and prevention methods among adults in Poland—A 2022 nationwide cross-sectional survey
Kuba Sękowski, Justyna Grudziąż-Sękowska, Jarosław Pinkas, Mateusz Jankowski
Frontiers in Public Health.2022;[Epub] CrossRef
Physical Comorbidity According to Diagnoses and Sex among Psychiatric Inpatients in South Korea
Suin Park, Go-Un Kim, Hyunlye Kim
International Journal of Environmental Research an.2021; 18(8): 4187. CrossRef
Diabetes knowledge, risk perception, and quality of life among South Asian caregivers in young adulthood
Angela Koipuram, Sandra Carroll, Zubin Punthakee, Diana Sherifali
BMJ Open Diabetes Research & Care.2020; 8(2): e001268. CrossRef
Small molecule IVQ, as a prodrug of gluconeogenesis inhibitor QVO, efficiently ameliorates glucose homeostasis in type 2 diabetic mice
Ting-ting Zhou, Tong Zhao, Fei Ma, Yi-nan Zhang, Jing Jiang, Yuan Ruan, Qiu-ying Yan, Gai-hong Wang, Jin Ren, Xiao-wei Guan, Jun Guo, Yong-hua Zhao, Ji-ming Ye, Li-hong Hu, Jing Chen, Xu Shen
Acta Pharmacologica Sinica.2019; 40(9): 1193. CrossRef
Problematic drinking in the old and its association with muscle mass and muscle function in type II diabetes
Nikolaus Buchmann, Dominik Spira, Maximilian König, Kristina Norman, Ilja Demuth, Elisabeth Steinhagen-Thiessen
Scientific Reports.2019;[Epub] CrossRef
A survey to validate the traditional Siddha perception of diabetes mellitus
Amulya Vijay, Priyadharshan Ranganathan, Balachandar Vellingiri
Journal of Public Health.2019; 27(5): 581. CrossRef
Knowledge and self-care management of the uncontrolled diabetes patients
Somsak Thojampa
International Journal of Africa Nursing Sciences.2019; 10: 1. CrossRef
Acculturation and Dietary Intakes by Gender Among Mongolians in South Korea: Nutrition Education Implication for Multicultural Families
Hae Ryun Park, Zuunnast Tserendejid, Joung Hee Lee, Young Suk Lim
Asia Pacific Journal of Public Health.2017; 29(7): 608. CrossRef
Knowledge of type 2 diabetic patients about their condition in Kimpese Hospital diabetic clinic, Democratic Republic of the Congo
Patrick N. Ntontolo, Philippe N. Lukanu, Gboyega A. Ogunbanjo, Jean-Pierre L. Fina, Léon N.M. Kintaudi
African Journal of Primary Health Care & Family Me.2017;[Epub] CrossRef
Assessment of Knowledge of Diabetes Mellitus in the Urban Areas of Klang District, Malaysia
Sasikala Chinnappan, Palanisamy Sivanandy, Rajenthina Sagaran, Nagashekhara Molugulu
Pharmacy.2017; 5(1): 11. CrossRef
Affective Bond, Loneliness and Socioeconomic Aspects of an Elderly Population in Midwest, Brazil
CKB Ferrari, GSL Ferrari, LD Nery, DF dos Santos, NS Pereira
Archives of Nursing Practice and Care.2016; 2(1): 024. CrossRef

Prevalence of Dyslipidemia and Hypertension in Indian Type 2 Diabetic Patients with Metabolic Syndrome and its Clinical Significance: Dhananjay Yadav, Meerambika Mishra, Arvind Tiwari, Prakash Singh Bisen, Hari Mohan Goswamy, G.B.K.S. Prasad; Osong Public Health Res Perspect. 2014;5(3):169-175. Published online June 30, 2014; DOI: https://doi.org/10.1016/j.phrp.2014.04.009

3,614 View
29 Download
19 Crossref

Abstract PDF

Objectives
The present study was designed to estimate the prevalence of dyslipidemia and hypertension based on the National Cholesterol Educational Programme Adult Treatment Panel III definition of metabolic syndrome (MetS). The study also focuses on prevalence for MetS with respect to the duration of disease in Gwalior–Chambal region of Madhya Pradesh, India.
Methods
Type 2 diabetic patients (n = 700) were selected from a cross-sectional study that is regularly being conducted in the School of Studies in Biochemistry, Jiwaji University Gwalior, India. The period of our study was from January 2007 to October 2009. Dyslipidemia and hypertension were determined in type 2 diabetic patients with MetS as per National Cholesterol Educational Programme Adult Treatment Panel III criteria.
Results
The mean age of the study population was 54 ± 9.3 years with 504 (72%) males and 196 (28%) females. The prevalence of MetS increased with increased duration of diabetes in females; however, almost constant prevalence was seen in the males. Notable increase in the dyslipidemia (64.1%) and hypertension (49%) in type 2 diabetic patients were seen. The steep increase in dyslipidemia and hypertension could be the reason for the growing prevalence of diabetes worldwide. The study also noted a close association between age and occurrence of MetS.
Conclusion
Individual variable of MetS appears to be highly rampant in diabetic population. Despite treatment, almost half of patients still met the criteria for MetS. Effective treatment of MetS components is required to reduce cardiovascular risk in diabetes mellitus hence accurate and early diagnosis to induce effective treatment of MetS in Indian population will be pivotal in the prevention of cardiovascular disease and type 2 diabetes.

Citations

Citations to this article as recorded by

Complete blood count inflammation derived indexes as predictors of metabolic syndrome in type 2 diabetes mellitus
Almir Fajkić, Rijad Jahić, Edin Begić, Amela Dervišević, Avdo Kurtović, Orhan Lepara
Technology and Health Care.2024; 32(4): 2321. CrossRef
Mapping multimorbidity from diabetes mellitus and its association with depressive symptoms among older people of India: a cross-sectional study from a nationally representative survey
Gayatri Khanal, Y. Selvamani, J. Kezia Angeline
International Journal of Diabetes in Developing Co.2024;[Epub] CrossRef
Metabolic Syndrome Frequency in Type 2 Diabetics Using International Diabetes Federation (IDF) Criteria Analysis
Sheena Kumari, Disha K Kataria, Sona Kumari, Riya Rani, Neha Ahuja, FNU Partab, Sooraj Raja, Hafsa Asif, FNU Sanam, Mohsin Ali
Cureus.2024;[Epub] CrossRef
Prevalence of metabolic syndrome and its risk factors among newly diagnosed type 2 diabetes mellitus patients – A hospital-based cross-sectional study
S Teja Rama Krishna, Yogesh Bahurupi, Ravi Kant, Pradeep Aggarwal, Athulya V. Ajith
Journal of Family Medicine and Primary Care.2024; 13(8): 3325. CrossRef
Metabolic and genetic risk factors associated with pre-diabetes and type 2 diabetes in Thai healthcare employees: A long-term study from the Siriraj Health (SIH) cohort study
Pichanun Mongkolsucharitkul, Apinya Surawit, Thamonwan Manosan, Suphawan Ophakas, Sophida Suta, Bonggochpass Pinsawas, Tanyaporn Pongkunakorn, Sureeporn Pumeiam, Winai Ratanasuwan, Mayuree Homsanit, Keerati Charoencholvanich, Yuthana Udomphorn, Bhoom Sukt
PLOS ONE.2024; 19(6): e0303085. CrossRef
Utilization of Hypolipidemic Drugs, Patterns, and Factors Affecting Dyslipidemia Among Type 2 Diabetes Mellitus at a Tertiary Care Teaching Hospital in South India
Sandeep Khot, Ananya Chakraborty, Savitha Vijaykumar
Cureus.2023;[Epub] CrossRef
Real-World Observational Study on Vildagliptin With Insulin (VIL-INS) or Vildagliptin and Metformin With Insulin (VIL-MET-INS) Therapy in Indian Patients With Type 2 Diabetes Mellitus
P Panneerselvam, Dibakar Biswas, Hema Singh, K Dilip Kumar, P Ravi Kumar, Pramila Kalra, Santosh Revankar, Sona Warrier
Cureus.2023;[Epub] CrossRef
Magnitude and Determinants of Diabetic Retinopathy Among Indian Diabetic Patients Undergoing Telescreening in India
Rajiv Khandekar, Tamilarasan Senthil, Malathi Nainappan, Deepak P. Edward
Telemedicine and e-Health.2022; 28(2): 176. CrossRef
An experimental study of rosuvastatin’s analgesic effect and its interaction with etoricoxib, tramadol, amlodipine, and amitriptytline in albino mice
Prafull Mohan, Ashok Kumar Sharma, Sharmila Sinha, R. Sabarad
Medical Journal Armed Forces India.2022; 78: S61. CrossRef
Receptor for Advanced Glycation End Product, Organ Crosstalk, and Pathomechanism Targets for Comprehensive Molecular Therapeutics in Diabetic Ischemic Stroke
Nivedita L. Rao, Greeshma B. Kotian, Jeevan K. Shetty, Bhaskara P. Shelley, Mackwin Kenwood Dmello, Eric C. Lobo, Suchetha Padar Shankar, Shellette D. Almeida, Saiqa R. Shah
Biomolecules.2022; 12(11): 1712. CrossRef
Metabolic and Energy Imbalance in Dysglycemia-Based Chronic Disease
Sanjay Kalra, Ambika Gopalakrishnan Unnikrishnan, Manash P Baruah, Rakesh Sahay, Ganapathi Bantwal
Diabetes, Metabolic Syndrome and Obesity: Targets .2021; Volume 14: 165. CrossRef
Association between diet quality scores, adiposity, glycemic status and nutritional biomarkers among Indian population with type 2 diabetes mellitus: A cross-sectional study
Aamir Bashir, Krishna Pandey, Md Azharuddin, Anjali Kumari, Ishfaq Rashid, N.A. Siddiqui, Chandra Shekhar Lal, Krishna Murti
Clinical Epidemiology and Global Health.2020; 8(1): 53. CrossRef
The Impact of BMI Categories on Metabolic Abnormality Development in Chinese Adults Who are Metabolically Healthy: A 7-Year Prospective Study

Xiangtong Liu, Jingbo Zhang, Jingwei Wu, Xiaolin Xu, Lixin Tao, Yue Sun, Shuo Chen, Yumei Han, Yanxia Luo, Xinghua Yang, Xiuhua Guo
Diabetes, Metabolic Syndrome and Obesity: Targets .2020; Volume 13: 819. CrossRef
Metabolic syndrome in north Indian type 2 diabetes mellitus patients: A comparison of four different diagnostic criteria of metabolic syndrome
Deepak Gahlan, Rajesh Rajput, Vandana Singh
Diabetes & Metabolic Syndrome: Clinical Research &.2019; 13(1): 356. CrossRef
Prevalence of Type 2 Diabetes and Prediabetes in the Gwalior-Chambal Region of Central India
Senthil Kumar Subramani, Dhananjay Yadav, Meerambika Mishra, Umamaheswari Pakkirisamy, Prakesh Mathiyalagen, GBKS Prasad
International Journal of Environmental Research an.2019; 16(23): 4708. CrossRef
A PROSPECTIVE STUDY OF DYSLIPIDAEMIA AND OBESITY IN HYPERTENSION PATIENTS
Ponnana Raja Kumar, Siripurapu Sasikala
Journal of Evidence Based Medicine and Healthcare.2018; 5(1): 43. CrossRef
Prevalence and pattern of co morbidity among type2 diabetics attending urban primary healthcare centers at Bhubaneswar (India)
Sandipana Pati, F. G. Schellevis, Alessandra Marengoni
PLOS ONE.2017; 12(8): e0181661. CrossRef
Dyslipidemia Prevalence in Iranian Adult Men: The Impact of Population-Based Screening on the Detection of Undiagnosed Patients
Abolfazl Mohammadbeigi, Esamil Moshiri, Narges Mohammadsalehi, Hossein Ansari, Ali Ahmadi
The World Journal of Men's Health.2015; 33(3): 167. CrossRef
Association of high-density lipoprotein with development of metabolic syndrome components: a five-year follow-up in adults
Xiangtong Liu, Lixin Tao, Kai Cao, Zhaoping Wang, Dongning Chen, Jin Guo, Huiping Zhu, Xinghua Yang, Youxin Wang, Jingjing Wang, Chao Wang, Long Liu, Xiuhua Guo
BMC Public Health.2015;[Epub] 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

3,260 View
19 Download
11 Crossref

Abstract PDF Supplementary 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

First
Prev
Page of 1
Next
Last

Search