• Perspectives in Nursing Science
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• v.13 no.1
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• pp.17-28
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• 2016

Purpose: This study aimed to identify patterns of diagnosis and to explore risk factors for type 2 diabetes beyond the postpartum period in women with a previous history of gestational diabetes, and to identify differences in such risk factors between early and late-onset (aged <45 and ${\geq}45$). Methods: Using epidemiological data from the Korean Genome and Epidemiology Study, a retrospective analysis of 175 women with various timings of type 2 diabetes diagnosis was performed. Results: The average age ($42.6{\pm}10.6$) at type 2 diabetes diagnosis was earlier than the general population, and obesity was prevalent with marked weight gains around 35 years old. Longer duration of breastfeeding was observed in women with late-onset of type 2 diabetes. Conclusion: For prevention of type 2 diabetes, early intervention is required, and modifiable factors such as weight control and breastfeeding should be taken into consideration for intervention strategies.

• Journal of Preventive Medicine and Public Health
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• v.43 no.5
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• pp.451-454
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• 2010

Objectives: The American Diabetes Association (ADA) has recently recommended the HbA1c assay as one of four options for making the diagnosis of diabetes mellitus, with a cut-point of $\geq$ 6.5%. We compared the HbA1c assay and the fasting plasma glucose level for making the diagnosis of diabetes among Korean adults. Methods: We analyzed 8710 adults (age 45-74 years), who were not diagnosed as having diabetes mellitus, from the Namwon study population. A fasting plasma glucose level of $\geq$126 mg/dL and an A1c of $\geq$ 6.5% were used for the diagnosis of diabetes. The kappa index of agreement was calculated to measure the agreement between the diagnosis based on the fasting plasma glucose level and the HbA1c. Results: The kappa index of agreement between the fasting plasma glucose level and HbA1c was 0.50. Conclusions: The agreement between the fasting plasma glucose and HbA1c for the diagnosis of diabetes was moderate for Korean adults.

• Journal of Yeungnam Medical Science
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• v.37 no.1
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• pp.13-21
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• 2020

Maturity-onset diabetes of the young (MODY) is a clinically heterogeneous group of monogenic disorders characterized by ß-cell dysfunction. MODY accounts for between 2% and 5% of all diabetes cases, and distinguishing it from type 1 or type 2 diabetes is a diagnostic challenge. Recently, MODY-causing mutations have been identified in 14 different genes. Sanger DNA sequencing is the gold standard for identifying the mutations in MODY-related genes, and may facilitate the diagnosis. Despite the lower frequency among diabetes mellitus cases, a correct genetic diagnosis of MODY is important for optimizing treatment strategies. There is a discrepancy in the disease-causing locus between the Asian and Caucasian patients with MODY. Furthermore, the prevalence of the disease in Asian populations remains to be studied. In this review, the current understanding of MODY is summarized and the Asian studies of MODY are discussed in detail.

• Journal of Nutrition and Health
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• v.32 no.4
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• pp.401-406
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• 1999

The purpose of this study was to identify some anthropometric characteristics related to the incidence of diabetes mellitus in Korea. The subjects were 165 male and female patients aged 30 to 70 years who had been diagnosed with diabetes mellitus for less than five year, recruied from eight different hospitals in Seoul, Korea. Weight, height, waist circumference, hip circumference and triceps skinfold thickness were measured. Weight before diagnosis of diabetes was also surveyed. The body mass index(BMI) of diabetic patients before diabetic diagnosis was significantly higher than that of reference values. Fifty percent of patients had BMI values greater than 25kg/$m^2$, and female patients were somewhat fatter than male patients. Since many subjects were overweight before diagnosis, obestty could be regarded as a risk factor for the incidence of diabetes mellitus. However, waist-hip ratios(WHR) fell within the normal range, so WHR may not be regarded as an important risk factor for NIDDM in Korea. This study suggests that the risk factors of onset of diabetes in Western populations may not be applicable to the Korea population. More study is needed to clarify the risk factors of Korean diabetes.

• Journal of Preventive Medicine and Public Health
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• v.49 no.2
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• pp.108-117
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• 2016

Objectives: Smoking is known to be associated with nephropathy in patients with diabetes. The distinct effects of smoking before and after diabetes has been diagnosed, however, are not well characterized. We evaluated the association of cigarette smoking before and after a diagnosis of diabetes with the presence of diabetic nephropathy. Methods: We analyzed data from the 2011-2013 editions of the Korea National Health and Nutrition Examination Survey. A total of 629 male patients diagnosed with diabetes were classified as non-smokers (90 patients), former smokers (225 patients), or continuing smokers (314 patients). A "former smoker" was a patient who smoked only before receiving his diagnosis of diabetes. A "continuing smoker" was a patient who smoked at any time after his diabetes had been diagnosed. Diabetic nephropathy was defined as the presence of albuminuria (spot urine albumin/creatinine ratio ${\geq}30mg/g$) or low estimated glomerular filtration rate ($<60mL/min/1.73m^2$). Multiple logistic regression models were used to assess the independent association after adjusting for age, duration of diabetes, hemoglobin A1c, body mass index, systolic blood pressure, medication for hypertension, and medication for dyslipidemia. Female patients were excluded from the study due to the small proportion of females in the survey who smoked. Results: Compared to non-smokers, continuing smokers had significantly higher odds ratio ([OR], 2.17; 95% confidence interval [CI], 1.23 to 3.83) of suffering from diabetic nephropathy. The corresponding OR (95% CI) for former smokers was 1.26 (0.70 to 2.29). Conclusions: Smoking after diagnosis of diabetes is significantly associated with the presence of diabetic nephropathy in the Korean male population.

• Perspectives in Nursing Science
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• v.9 no.2
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• pp.94-101
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• 2012

Purpose: The purpose of this study was to examine the relationship between glucose control, diabetes self-care and depression in community dwelling older adults with type 2 diabetes mellitus. Methods: The cross-sectional survey data of 148 older adults at a senior center were analyzed in this study. We collected data on diabetes self-care, depression, and demographics by face-to-face interviews. Blood samples for HbA1C were obtained from the participants. Results: The average duration of diabetes for the participants was $10.6{\pm}9.31$ years. Fifty percent of the participants had HbA1c higher than 7.0% (mean 7.179%). The level of diabetes self-care was related to depression (r=-.225, p<.01). HbA1c was positively related with the duration of diabetes diagnosis (r=.224, p<.01). The only sub-dimension of diabetes self-care that was related to depression was exercise (r=-.307, p<.01). Conclusion: Only half of the community dwelling older adults with type 2 diabetes had an optimal level of diabetes control. Supported by the evidence, the longer the duration of diabetes since the initial diagnosis, the poorer the glucose control was. Identification and intervention for depression in people with diabetes should be considered to improve diabetes self-care, especially to perform more exercise.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.61-68
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• 2024

In the quest for advancing diabetes diagnosis, this study introduces a novel two-step machine learning approach that synergizes the probabilistic predictions of Logistic Regression with the classification prowess of Random Forest. Diabetes, a pervasive chronic disease impacting millions globally, necessitates precise and early detection to mitigate long-term complications. Traditional diagnostic methods, while effective, often entail invasive testing and may not fully leverage the patterns hidden in patient data. Addressing this gap, our research harnesses the predictive capability of Logistic Regression to estimate the likelihood of diabetes presence, followed by employing Random Forest to classify individuals into diabetic, pre-diabetic or nondiabetic categories based on the computed probabilities. This methodology not only capitalizes on the strengths of both algorithms-Logistic Regression's proficiency in estimating nuanced probabilities and Random Forest's robustness in classification-but also introduces a refined mechanism to enhance diagnostic accuracy. Through the application of this model to a comprehensive diabetes dataset, we demonstrate a marked improvement in diagnostic precision, as evidenced by superior performance metrics when compared to other machine learning approaches. Our findings underscore the potential of integrating diverse machine learning models to improve clinical decision-making processes, offering a promising avenue for the early and accurate diagnosis of diabetes and potentially other complex diseases.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.105-110
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• 2024

Among those who participated in the 8th 1st year of the National Health and Nutrition Examination Survey (2019), diabetes patients were divided into 4 types and the prevalence and diagnosis rates of diabetes were investigated by age group. In addition, we analyzed the correlation between glycated hemoglobin levels and body weight, waist circumference, cholesterol, triglyceride levels, weight-adjusted waist circumference, and body mass index in patients already diagnosed with diabetes. As a result of the study, the prevalence of diabetes in 2019 was 16.03%, and male The prevalence rate for men continued to increase after the 30s, and that for women was lower than that for men until the 40s, but increased rapidly after the 50s and became similar to that of men after the 70s. In the diabetes diagnosis group, the glycated hemoglobin level had a low and non-significant correlation with weight, waist circumference, BMI, and WWI levels, but showed a correlation coefficient of 0.178 with the triglyceride level, and the p value was less than 0.001, which was statistically very significant.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2904-2926
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• 2022

Currently, diabetes is the most common chronic disease in the world, affecting 23.7% of the population in the Kingdom of Saudi Arabia. Diabetes may be the cause of lower-limb amputations, kidney failure and blindness among adults. Therefore, diagnosing the disease in its early stages is essential in order to save human lives. With the revolution in technology, Artificial Intelligence (AI) could play a central role in the early prediction of diabetes by employing Machine Learning (ML) technology. In this paper, we developed a diagnosis system using machine learning models for the detection of type 2 diabetes among adults, through the adoption of two different diabetes datasets: one for training and the other for the testing, to analyze and enhance the prediction accuracy. This work offers an enhanced classification accuracy as a result of employing several pre-processing methods before applying the ML models. According to the obtained results, the implemented Random Forest (RF) classifier offers the best classification accuracy with a classification score of 98.95%.

• Journal of Preventive Medicine and Public Health
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• v.46 no.5
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• pp.249-260
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• 2013

Objectives: Previous observations propose that risk-taking behaviors such as cigarette smoking are prevailing among young people with chronic conditions including diabetes. The purpose of this study was to examine whether cigarette smoking is more prevalent among diabetics than non-diabetics and whether it differs by age at the time of diagnosis with diabetes from young adulthood (YAH) to adulthood (AH). Methods: We used US panel data from the National Longitudinal Study of Adolescent Health (Add Health Study) during the years 2001 to 2002 (Wave III, YAH) and 2007 to 2008 (Wave IV, AH). Multivariate logistic regression models were applied to estimate odds ratios (ORs) and 95% confidence intervals (CIs) of cigarette use behaviors according to age at the time of diagnosis with diabetes, after adjusting for demographic and selected behavioral factors. Results: Of 12 175 study participants, 2.6% reported having been diagnosed with diabetes up to AH. Early-onset diabetics (age at diagnosis <13 years) were more likely than non-diabetics to report frequent cigarette smoking (smoking on ${\geq}20$ days during the previous 30 days) in YAH (OR, 3.34; 95% CI, 1.27 to 8.79). On the other hand, late-onset diabetics (age at diagnosis ${\geq}13$ years) were more likely than non-diabetics to report heavy cigarette smoking (smoking ${\geq}10$ cigarettes per day during the previous 30 days) in AH (OR, 1.54; 95% CI, 1.03 to 2.30). Conclusions: The current study indicated that diabetics are more likely than non-diabetics to smoke cigarettes frequently and heavily in YAH and AH. Effective smoking prevention and cessation programs uniquely focused on diabetics need to be designed and implemented.